Search

Abnormal behavior of crew members is an important cause of frequent ship safety accidents. The existing abnormal crew recognition algorithms are affected by complex ship environments and have low performance in real and open shipborne environments. This paper proposes an abnormal crew detection network for complex ship scenarios (ACD-Net), which uses a two-stage algorithm to detect and identify abnormal crew members in real-time. An improved YOLOv5s model based on a transformer and CBAM mechanism (YOLO-TRCA) is proposed with a C3-TransformerBlock module to enhance the feature extraction ability of crew members in complex scenes. The CBAM attention mechanism is introduced to reduce the interference of background features and improve the accuracy of real-time detection of crew abnormal behavior. The crew identification algorithm (CFA) tracks and detects abnormal crew members' faces in real-time in an open environment (CenterFace), continuously conducts face quality assessment (Filter), and selects high-quality facial images for identity recognition (ArcFace). The CFA effectively reduces system computational overhead and improves the success rate of identity recognition. Experimental results indicate that ACD-Net achieves 92.3% accuracy in detecting abnormal behavior and a 69.6% matching rate for identity recognition, with a processing time of under 39.5 ms per frame at a 1080P resolution. [ABSTRACT FROM AUTHOR]

Understanding the dynamic spatial distribution and characteristics of fishing activities is crucial for fisheries management and sustainable development. In recent years, small pelagic fish and cephalopods in the Arabian Sea have become new targets for light purse seine fishing; however, there is a lack of publicly available reports. This study uses automatic identification system (AIS) data from January to May and October to December of 2021 to 2022 in the region between 58°–70° E and 10°–22° N to extract spatial distribution information through three methods. The results show that with a spatial resolution of 0.25° × 0.25°, the spatial similarity index between the fishing ground information extracted in 2022 and catch data was consistently above 0.60, reaching 0.76 in March 2021 and 0.79 in November 2022, while the spatial similarity index in March 2022 exceeded 0.71. The spatial distribution of fishing effort and kernel density was similar to that of the fishing grounds, and the fishing intensity information exhibited the highest spatiotemporal similarity with commercial catch data, making it more suitable as a substitute for fishery data. Therefore, effective international cooperation and efficient joint management mechanisms for fishing vessels are needed to enhance the regulatory oversight of fishing vessels in this region. Integrating AIS data with other technological methods is crucial for more effective monitoring and management of fishing vessels. The findings presented in this paper provide both quantitative and qualitative scientific support for resource conservation and sustainable development in the region. [ABSTRACT FROM AUTHOR]

Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced. [ABSTRACT FROM AUTHOR]

Soft marine soil exhibits unique mechanical properties that can lead to significant deformation and instability in the surrounding rock of urban subway tunnels. This presents a critical challenge for tunnel engineering researchers and designers. This thesis investigates the stability characteristics of surrounding rock in marine soft soil tunnels under cyclic loading conditions. Focusing on the shield tunnel segment between Left Fortress Station and Taiziwan Station of Shenzhen Urban Rail Transit Line 12, a discrete–continuous coupled numerical analysis method is employed to examine the deformation characteristics of the surrounding rock. This analysis takes into account the effects of dynamic loads resulting from train operations on the arch bottom's surrounding rock. The findings indicate that damage to the surrounding rock occurs gradually, with the marine soft soil layer, particularly at higher water content, being prone to substantial plastic deformation. Additionally, under the influence of train vibration loads, the degree of vertical fluctuation in the internal marine soft soil diminishes with increasing depth from the bottom of the tunnel arch. This coupled numerical analysis approach offers valuable insights and methodologies for assessing the structural safety of tunnel projects throughout their operational periods. [ABSTRACT FROM AUTHOR]

Globalization has contributed to the increasing complexity of supply chain structures. In this regard, precise demand forecasting for the intricate supply chain holds paramount importance in effective supply chain management. Traditional statistical models and deep learning methods often face challenges in efficiently discerning correlations within a myriad of interconnected demands. To tackle this issue, this paper proposes an intricate supply chain demand forecasting method based on graph convolution networks adept at handling non-Euclidean data. First, the companies within the supply chain are treated as nodes in the graph structure, and the relationships between them are treated as edges, with demand data serving as the features of these edges. Then, a graph convolutional network is constructed to aggregate node and edge information. Through a multi-layer network, the relationships among nodes, edges, and historical demand are established to facilitate the prediction of supply chain demands. In this process, the graph convolutional network incorporates supply chain connectivity information into demand time series analysis. This integration of surface-level topological features and deeper latent correlation attributes across the supply chain's nodes refines the demand forecasting precision across the entire supply chain. The validation experiment in this paper is grounded in sales data of a singular product from multiple sales nodes of an electronics company. The results demonstrate that the proposed method surpasses four other traditional demand forecasting algorithms significantly in terms of accuracy, providing substantial evidence for the superior performance of graph networks in the analysis of intricate supply chain relationships. [ABSTRACT FROM AUTHOR]

Cell printing is a promising technology in tissue engineering, with which the complex three-dimensional tissue constructs can be formed by sequentially printing the cells layer by layer. Though some cell printing experiments with commercial inkjet printers show the possibility of this idea, there are some problems, such as cell damage due the mechanical impact during cell direct writing, which include two processes of cell ejection and cell landing. Cell damage observed during the bioprinting process is often simply attributed to interactions between cells and substrate. However, in reality, cell damage can also arise from complex mechanical effects caused by collisions between cell droplets during continuous printing processes. The objective of this research is to numerically simulate the collision effects between continuously printed cell droplets within the bioprinting process, with a particular focus on analyzing the consequent cell droplet deformation and stress distribution. The influence of gravity force was ignored, cell droplet landing was divided into four phases, the first phase is cell droplet free falling at a certain velocity; the second phase is the collision between the descending cell droplet and the pre-existing cell droplets that have been previously printed onto the substrate. This collision results in significant deformation of the cell membranes of both cell droplets in contact; the third phase is the cell droplet hitting a rigid body substrate; the fourth phase is the cell droplet being bounced. We conducted a qualitative analysis of the stress and strain of cell droplets during the cell printing process to evaluate the influence of different parameters on the printing effect. The results indicate that an increase in jet velocity leads to an increase in stress on cell droplets, thereby increasing the probability of cell damage. Adding cell droplet layers on the substrate can effectively reduce the impact force caused by collisions. Smaller droplets are more susceptible to rupture at higher velocities. These findings provide a scientific basis for optimizing cell printing parameters. [ABSTRACT FROM AUTHOR]

The accuracy of demand forecasting is critical for supply chain management and strategic business decisions. However, as data volumes grow and demand patterns become increasingly complex, traditional forecasting methods encounter significant challenges in processing intricate multi-dimensional data and achieving a satisfactory predictive accuracy. To address these challenges, this paper proposed an end-to-end multi-model demand forecasting framework based on attention mechanisms. The framework employs a dual attention mechanism to dynamically extract features from both the temporal and product dimensions, while integrating conditional information captured through convolutional neural networks, thereby enhancing its ability to model complex demand patterns. Additionally, a channel attention mechanism is introduced to perform the weighted fusion of outputs from multiple predictive models, thereby overcoming the limitations of single-model approaches and improving adaptability to varying demand patterns across diverse scenarios. The experimental results demonstrate that the proposed method outperforms conventional approaches across several evaluation metrics, achieving a 42% reduction in Mean Squared Error (MSE) compared to the baseline model. This notable improvement enhances both the accuracy and stability of demand forecasting. The framework offers valuable insights for addressing large-scale and complex demand patterns, providing guidance for precise decision-making and resource optimization within supply chain management. Future research will concentrate on further enhancing the model's generalization capability to manage missing data and demand fluctuations. Additionally, efforts will focus on integrating diverse heterogeneous data sources to assess its performance in various practical scenarios, ultimately improving the model's accuracy and flexibility. [ABSTRACT FROM AUTHOR]

This study proposes a two-level optimization scheduling method for multi-region integrated energy systems (IESs) that considers dynamic time intervals within the day, addressing the diverse energy characteristics of electricity, heat, and cooling. The day-ahead scheduling aims to minimize daily operating costs by optimally regulating controllable elements. For intra-day scheduling, a predictive control-based dynamic rolling optimization model is utilized, with the upper-level model handling slower thermal energy fluctuations and the lower-level model managing faster electrical energy fluctuations. Building on the day-ahead plan, different time intervals are used for fast and slow layers. The slow layer establishes a decision index for command cycle intervals, dynamically adjusting based on ultra-short-term forecasts and incremental balance corrections. Case studies demonstrate that this method effectively leverages energy network characteristics, optimizes scheduling intervals, reduces adjustment costs, and enhances system performance, achieving coordinated operation of the IES network and multi-energy equipment. [ABSTRACT FROM AUTHOR]

Point cloud registration is one of the fundamental tasks in computer vision, but faces challenges under low overlap conditions. Recent approaches use transformers and overlapping masks to improve perception, but mask learning only considers Euclidean distances between features, ignores mismatches caused by fuzzy geometric structures, and is often computationally inefficient. To address these issues, we introduce a novel matching framework. Firstly, we fuse adaptive graph convolution with PPF features to obtain rich feature perception. Subsequently, we construct a PGT framework that uses GeoTransformer and combines it with location information encoding to enhance the geometry perception between source and target clouds. In addition, we improve the visibility of overlapping regions through information exchange and the AIS module, aiming at subsequent keypoint extraction, preserving points with distinct geometrical structures while suppressing the influence of non-overlapping regions to improve computational efficiency. Finally, the mask is refined through contrast learning to preserve geometric and distance similarity, which helps to compute the transformation parameters more accurately. We have conducted comprehensive experiments on synthetic and real-world scene datasets, demonstrating superior registration performance compared to recent deep learning methods. Our approach shows remarkable improvements of 68.21% in R R M S E and 76.31% in t R M S E on synthetic data, while also excelling in real-world scenarios with enhancements of 76.46% in R R M S E and 45.16% in t R M S E . [ABSTRACT FROM AUTHOR]

This study investigated the effects of solid-state fermentation with Clostridium butyricum on the microstructure of wheat bran, the release of dietary fiber and phenolic compounds, and antioxidant capacity. Compared with unfermented wheat bran, insoluble dietary fiber and phytic acid content decreased, whereas soluble dietary fiber and water-extractable arabinoxylan content increased in C. butyricum culture. Because of the increased release of phenolic compounds, such as ferulic acid and apigenin, and organic acids, such as isobutyric acid, the antioxidant capacity of the culture was considerably improved. Furthermore, the culture of C. butyricum treated with dextran sulfate sodium-induced ulcerative colitis in mice enhanced the expression of intestinal mucus and tight-junction proteins, modulating the gut microbiota structure, increasing the levels of short-chain fatty acids in the intestine, and restoring the essential functions of the gut microbiota. These anti-inflammatory effects stemmed from the combined action of various effective components. [ABSTRACT FROM AUTHOR]

Socialized services are crucial for addressing the issue of "who will farm the land" and subsequently enhancing agricultural economic resilience (AER). However, few studies have examined the mechanisms and effects of socialized services on AER. Consequently, this study aims to elucidate the impact and mechanisms of socialized services on AER, with the objective of providing new policy recommendations for enhancing AER and ensuring food security. Based on provincial panel data from China spanning 2009 to 2021, this paper examines the impact and mechanisms of socialized services on AER using a two-way fixed effects model, a mediated effects model, and a panel threshold model. The findings reveal that socialized services significantly enhance AER. Mechanism analysis indicates that socialized services enhance AER by accelerating the substitution of machinery for manpower and promoting the efficiency of labor division. Heterogeneity analysis indicates that in regions with high grain cropping ratios and high internet penetration rates, the enhancement effect of socialized services on AER is stronger. Further analysis uncovers a significant nonlinear threshold effect of socialized services on AER. The impact becomes more pronounced when AER surpasses 0.4689. Consequently, this study argues that in the process of constructing a modern agricultural business system, it is essential to focus on improving the differentiated socialized service system and accelerating the development of rural digital infrastructure. [ABSTRACT FROM AUTHOR]

This study studies the deformation characteristics of the diversion tunnel of Jinping II Hydropower Station in order to guarantee the safety of the excavation of a large-section soft rock tunnel with a depth of 1000 m and increased ground stress. Using field data, theoretical computations, and numerical modeling, the proper reserved deformation of a deep soft rock tunnel is investigated, taking into consideration the size, in situ stress, and grade of the surrounding rock. The study reveals that (1) The diversion tunnel's incursion limit, which is typically between 20 and 60 cm, is serious; (2) The surrounding rock level > geostress > tunnel size are the influencing parameters of reserved deformation that remain unchanged while using the numerical simulation method, which is more accurate in simulating field conditions; (3) The west end of the Jinping diversion tunnel has a 30–60 cm reserved deformation range for the chlorite schist tunnel. The deformation law of a large-section, 1000 m-deep soft rock tunnel is better understood, and it also offers important references for high-stress soft rock tunnel engineering design, construction, and safety management. [ABSTRACT FROM AUTHOR]

Melting point depressants (MPDs) are required to lower the melting point of filler for brazing. In this study, Zr was used as the MPD, and powder filler was prepared by adjusting the Zr and Mo content referring to Thermo-Calc calculations. The prepared filler was used to braze a high-Mo Ni3Al-based single crystal superalloy, IC21, for 1200 °C/30 min. The effects of adjusting the Zr and Mo content on the microstructure and tensile properties of the joint were investigated. The increase in Zr content promotes the formation of Ni7Zr2 in the joint, leading to a decrease in the tensile strength of the joint. The increase in Mo content forms diffusion barriers between the BM and filler, resulting in an enhancement in the tensile strength of the joint. However, continued increases in Mo content leads to an increase in the P-topologically close packed phase, causing a decline in the tensile strength of the joint. When the Zr content was (11.8–12.2) wt.% and the Mo content was (7.3–7.7) wt.%, the tensile strength of the joint at 980 °C reached a maximum of 550 MPa. This study provides a potential direction for the design of brazing filler composition for high-Mo Ni3Al-based superalloys. [ABSTRACT FROM AUTHOR]

It is important to determine the ventilation required in the construction of deep and long tunnels and the variation law of tunnel temperature fields to reduce the numbers of high-temperature disasters and serious accidents. Based on a tunnel project with a high ground temperature, with the help of convection heat transfer theory and the theoretical analysis and calculation method, this paper clarifies the contribution of various heat sources to the air demand during tunnel construction, and reveals the important environmental parameters that determine the ventilation value by changing the construction conditions. The results show that increasing the fresh air temperature greatly increases the required air volume, and the closer the supply air temperature is to 28 °C, the more the air volume needs to be increased. The air temperature away from the palm face is not significantly affected by changes in the supply air temperature. Adjusting the wall temperature greatly accelerates the rate of temperature growth. The supply air temperature rose from 15 to 25 °C, while the tunnel temperature at 800 m only increased by 1.5 °C. Over a 50 m range, the wall temperature rose from 35 to 60 degrees Celsius at a rate of 0.0842 to 0.219 degrees Celsius per meter. The total air volume rises and the surface heat transfer coefficient decreases as the tunnel's cross-section increases. For every 10 m increase in the tunnel diameter, the temperature at 800 m from the tunnel face drops by about 0.5 °C. Changing the distance between the air duct and the tunnel face has little influence on the temperature distribution law. The general trend is that the farther the air duct outlet is from the tunnel face, the higher the temperature is, and the maximum difference is within the range of 50 m~250 m from the tunnel face. The maximum difference between the air temperatures at 12 m and 27 m is 0.79 °C. The geological structure and geothermal background have the greatest influence on the temperature prediction of high geothermal tunnels. The prediction results are of great significance for guiding tunnel construction, formulating cooling measures, and ensuring construction safety. [ABSTRACT FROM AUTHOR]

A Zonal Detached Eddy Simulation (ZDES) based on the SST turbulence model is implemented to the numerical investigation of the trailing edge stall of a LS-0417 airfoil, which includes multiple DES modes for different classifications of flow separation and adopts the subgrid scale definition of Δ ω . The entire stall process under a series of AOA is simulated according to the experiment condition. The performance of URANS and ZDES in the prediction of the stall flow field are compared. The results reveal that the stall point obtained through ZDES is consistent with the experiment; the deviation of the predicted maximum lift coefficient from the measured result is only 0.8%, while the maximum lift is overpredicted by both RANS and URANS. The high frequency fluctuations are observed in the time history of the lift in ZDES result during stall. With the increase in the AOA, a mild development of separation and a gradual decrease in leading edge peak suction are manifested in the ZDES result. The alternate shedding of shear layers and the interference between the leading edge and trailing edge vortices are illustrated through ZDES near the stall point; the corresponding turbulent fluctuations with high intensity are captured in the separation region, which indicates the essential difference in the prediction of stall process between URANS and ZDES. [ABSTRACT FROM AUTHOR]

Determining the criticality of ice shapes is a necessary condition for verifying compliance with icing airworthiness regulations. However, the clear, concise, and applicable criterion based on the geometric characteristics of ice shapes has not been clearly given out by current advisory circulars. To address this problem, this paper summarizes aerodynamic performance items and recommended ice shapes the latest version of CCAR-25 and corresponding advisory circulars for a variety of flight phases, including takeoff, holding, en route, DTO, etc., instead of the single phase of holding in the previous research. Based on the geometric classification of the ice shapes, the dominant parameters of various ice shapes are clarified by the correlation between the geometric parameters and aerodynamic effects. The geometric parameters to determine the criticality of specific ice shapes are defined as the roughness height and range for the roughness ice and the total projection height in the direction of lift for the horn ice. On this basis, the detailed determination criterion of critical ice shape geometries corresponding to different flight phases and aircraft components is formulated, which will provide an operational selection methodology for determining the geometries of critical ice shapes at the airworthiness certification stage. [ABSTRACT FROM AUTHOR]

The technology for object detection in remote sensing images finds extensive applications in production and people's lives, and improving the accuracy of image detection is a pressing need. With that goal, this paper proposes a range of improvements, rooted in the widely used YOLOv7 algorithm, after analyzing the requirements and difficulties in the detection of remote sensing images. Specifically, we strategically remove some standard convolution and pooling modules from the bottom of the network, adopting stride-free convolution to minimize the loss of information for small objects in the transmission. Simultaneously, we introduce a new, more efficient attention mechanism module for feature extraction, significantly enhancing the network's semantic extraction capabilities. Furthermore, by adding multiple cross-layer connections in the network, we more effectively utilize the feature information of each layer in the backbone network, thereby enhancing the network's overall feature extraction capability. During the training phase, we introduce an auxiliary network to intensify the training of the underlying network and adopt a new activation function and a more efficient loss function to ensure more effective gradient feedback, thereby elevating the network performance. In the experimental results, our improved network achieves impressive mAP scores of 91.2% and 80.8% on the DIOR and DOTA version 1.0 remote sensing datasets, respectively. These represent notable improvements of 4.5% and 7.0% over the original YOLOv7 network, significantly enhancing the efficiency of detecting small objects in particular. [ABSTRACT FROM AUTHOR]

Bistatic and multistatic SAR technology, with its multi-dimensional, ultra-wide swath, and high-resolution advantages, is widely used in earth observation, military reconnaissance, deep space exploration, and other fields. The LuTan-1 (LT-1) mission employs two full-polarimetric L-band SAR satellites for the BiSAR system. The bistatic mode introduces phase errors in echo reception paths due to different transmission links, making echo compensation a key factor in ensuring BiSAR performance. This paper proposes a novel bistatic internal calibration strategy that combines ground temperature compensation, in-orbit internal calibration, and pulsed alternate synchronization to achieve echo compensation. Prior to launch, temperature compensation data for the internal calibration system are obtained via temperature experiments. During in-orbit operation, calibration data are acquired by executing the internal calibration pulse sequence and noninterrupted pulsed alternate synchronization. In ground processing, echo compensation is completed based on the above two parts of calibration data. A comprehensive analysis of the entire calibration chain reveals a temperature compensation accuracy of 0.10 dB/1.38°. Additionally, a ground verification system is established to conduct BiSAR experiments, achieving a phase synchronization accuracy of 0.16°. Furthermore, the in-orbit test obtained DSM products with an average error of 1.3 m. This strategy provides a valuable reference for future spaceborne bistatic and multistatic SAR systems. [ABSTRACT FROM AUTHOR]

This study advances our understanding of the Jiangnan Orogenic Belt by integrating high-precision geochronological data with interpretations of sedimentary and tectonic environments. Specifically, it addresses the controversy over the geological significance, origins, and tectonic significance of the Shengong Unconfomity: at the base of the Luojiamen Formation. This paper shows that the formation developed over four stages with the primary source of detritus lying in a volcanic arc to the south. The study also reassesses the "unconformity" between the Luojiamen Formation of the Heshangzhen Group and the Zhangcun Formation of the Shuangxiwu Group, concluding that it does not demarcate the end of the orogenic collision between the Cathaysia and Yangtze blocks. [ABSTRACT FROM AUTHOR]

The rational design of magnetic carbon composites, encompassing both their composition and microstructure, holds significant potential for achieving exceptional electromagnetic wave-absorbing materials (EAMs). In this study, FeCo@CM composites were efficiently fabricated through an advanced microwave plasma-assisted reduction chemical vapor deposition (MPARCVD) technique, offering high efficiency, low cost, and energy-saving benefits. By depositing graphitized carbon microspheres, the dielectric properties were significantly enhanced, resulting in improved electromagnetic wave absorption performances through optimized impedance matching and a synergistic effect with magnetic loss. A systematic investigation revealed that the laminar-stacked structure of FeCo exhibited superior properties compared to its spherical counterpart, supplying a higher number of exposed edges and enhanced catalytic activity, which facilitated the deposition of uniform and low-defect graphitized carbon microspheres. Consequently, the dielectric loss performance of the FeCo@CM composites was dramatically improved due to increased electrical conductivity and the formation of abundant heterogeneous interfaces. At a 40 wt% filling amount and a frequency of 7.84 GHz, the FeCo@CM composites achieved a minimum reflection loss value of −58.2 dB with an effective absorption bandwidth (fE) of 5.13 GHz. This study presents an effective strategy for developing high-performance EAMs. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power fluctuates with weather changes, and traditional forecasting methods typically decompose the power itself to study its characteristics, ignoring the impact of multidimensional weather conditions on the power decomposition. Therefore, this paper proposes a short-term PV power generation method based on MVMD (multivariate variational mode decomposition) feature extraction and the Informer model. First, MIC correlation analysis is used to extract weather features most related to PV power. Next, to more comprehensively describe the relationship between PV power and environmental conditions, MVMD is used for time–frequency synchronous analysis of the PV power time series combined with the highest MIC correlation weather data, obtaining frequency-aligned multivariate intrinsic modes. These modes incorporate multidimensional weather factors into the data-decomposition-based forecasting method. Finally, to enhance the model's learning capability, the Informer neural network model is employed in the prediction phase. Based on the input PV IMF time series and associated weather mode components, the Informer prediction model is constructed for training and forecasting. The predicted results of different PV IMF modes are then superimposed to obtain the total PV power generation. Experiments show that this method improves PV power generation accuracy, with an MAPE value of 4.31%, demonstrating good robustness. In terms of computational efficiency, the Informer model's ability to handle long sequences with sparse attention mechanisms reduces training and prediction times by approximately 15%, making it faster than conventional deep learning models. [ABSTRACT FROM AUTHOR]

Through an activation reaction sintering method, porous (Mo2/3Y1/3)2AlC ceramics were prepared by Mo, Y, Al, and graphite powders as raw materials. The phase composition, microstructure, element distribution, and pore structure characteristics were comprehensively studied using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), Archimedes method, and bubble point method. A detailed investigation was conducted on the influence of sintering temperature on the phase composition. Possible routes of phase transition and pore formation mechanisms during the sintering process were provided. The experimental results reveal that at 650–850 °C, transition metals react with aluminum, forming aluminum-containing intermetallics and a small amount of carbides. At 850–1250 °C, transition metals collaborate with graphite, producing transition metal carbides. Then, at 1250–1450 °C, these aluminum intermetallics interact with transition metal carbides and remaining unreacted Y, Al, and C, yielding the final product (Mo2/3Y1/3) 2AlC. Simultaneously, the pore structure alters correspondingly with the solid-phase reaction at different reaction temperatures. [ABSTRACT FROM AUTHOR]

Although extensive research shows that CNNs achieve good classification results in HSI classification, they still struggle to effectively extract spectral sequence information from HSIs. Additionally, the high-dimensional features of HSIs, the limited number of labeled samples, and the common sample imbalance significantly restrict classification performance improvement. To address these issues, this article proposes a double-branch multi-scale dual-attention (DBMSDA) network that fully extracts spectral and spatial information from HSIs and fuses them for classification. The designed multi-scale spectral residual self-attention (MSeRA), as a fundamental component of dense connections, can fully extract high-dimensional and intricate spectral information from HSIs, even with limited labeled samples and imbalanced distributions. Additionally, this article adopts a dataset partitioning strategy to prevent information leakage. Finally, this article introduces a hyperspectral geological lithology dataset to evaluate the accuracy and applicability of deep learning methods in geology. Experimental results on the geological lithology hyperspectral dataset and three other public datasets demonstrate that the DBMSDA method exhibits superior classification performance and robust generalization ability compared to existing methods. [ABSTRACT FROM AUTHOR]

Slow-moving landslides often occur in areas of high relief, which are significantly affected by tropospheric delay. In general, tropospheric delay correction methods in the synthetic-aperture radar interferometry (InSAR) field can be broadly divided into those based on external auxiliary information and those based on traditional empirical models. External auxiliary information is hindered by the low spatial–temporal resolution. Traditional empirical models can be adaptable for the spatial heterogeneity of tropospheric delay, but are limited by preset window sizes and models. In this regard, this paper proposes an improved tropospheric delay correction method based on the multivariable move-window variation model (MMVM) to adaptively determine the window size and the empirical model. Considering topography and surface deformation, the MMVM uses multivariate variogram models with iterative weight to determine the window size and model, and uses the Levenberg–Marquardt (LM) algorithm to enhance convergence speed and robustness. The high-precision surface deformation is then derived. Combined with hotspot analysis (HSA), wide-area potential landslides can be automatically identified. The reservoir area of the Baihetan hydropower station in the lower reaches of the Jinsha River was selected as the study area, using 118 Sentinel-1A images to compare with four methods in three aspects: corrected interferograms, derived deformation rate, and stability of time-series deformation. In terms of mean standard deviation, the MMVM achieved the lowest value for the unwrapped phase in the non-deformed areas, representing a reduction of 56.4% compared to the original value. Finally, 32 landslides were identified, 16 of which posed a threat to nearby villages. The experimental results demonstrate the superiority of the proposed method and provide support to disaster investigation departments. [ABSTRACT FROM AUTHOR]

Excessive application of nitrogen fertilizer in the swollen-stem mustard cultivation leads to a series of environmental and quality issues. It was reported that reducing nitrogen fertilizer with biochar could increase crop yield and reduce environmental risks. However, the effect of nitrogen reduction combined with biochar application on the flavor substances was rarely reported. Thus, two genetic stem mustard varieties (Yx: Yong'an xiaoye and Fz: Fuza No. 2), and four N treatments (control: 0 N kg/ha with biochar; N150: 150 N kg/ha with biochar, N300: 300 N kg/ha with biochar, and N450: 450 N kg/ha) were chosen to study the effects of nitrogen reduction combined with biochar on the flavor substance content of mustard stem, and N balance. The results showed that the residual soil inorganic N in N300 was lower by 37% than that in N450 (156.5 kg/ha) in Fz mustard soil, and lower by 33% in N150 than in N450 (163.1 kg/ha) in Yx mustard. The highest biomass of stem mustard tumors of Fz (35.4 × 103 kg/ha) and Yx (35.7 × 103 kg/ha) was in N300. The content of umami amino acids, sweet amino acids, and bitter amino acids of Yx and Fz stem was the highest in N450, and N300, respectively. After comprehensive consideration, the Fz was recommended to be planted in the Three Gorges Reservoir Area with N300. [ABSTRACT FROM AUTHOR]

High-temperature smoke generated by tunnel fires is the most important factor causing casualties. To explore the influence of natural wind on fire smoke movement in an extra-long highway tunnel based on the Taihang Mountain Tunnel, the distribution law of natural wind in the tunnel was obtained by on-site monitoring of the meteorological conditions at the tunnel site. A three-dimensional fire dynamics tunnel model considering an inclined shaft smoke exhaust was established, and the influence of natural wind on tunnel temperature distribution, smoke spread and smoke exhaust efficiency was studied. The results show that the natural wind speed of the Taihang Mountain Tunnel is mainly concentrated at 0~3 m/s. The main wind direction of the natural wind on the left tunnel is opposite to the driving direction, and the distribution probability of the main wind direction in each section is 81.27% and 72.15%, respectively. The main wind direction of the right tunnel is the same as the driving direction, and the distribution probability of the main wind direction in each section is 56.78%, 69.73%, 67.32% and 64.65%, respectively. The negative natural wind can inhibit the smoke spread downstream of the smoke exhaust port, but it is not conducive to the smoke exhaust. The positive natural wind promotes the smoke spread to the downstream of the smoke exhaust port, and the larger the natural wind speed, the longer the spread length. Natural wind reduces the smoke exhaust efficiency. For positive or negative natural wind with a guaranteed rate of 70%, the smoke exhaust efficiency is reduced by 27.76% and 15.59%, respectively, compared with the condition without natural wind. The research results can provide a useful reference for the design of fire smoke exhausts and smoke control schemes in extra-long highway tunnels. [ABSTRACT FROM AUTHOR]

Unconventional tight oil and gas resources, including shale oil and gas, have become the main focus for increasing reserves and production. The safe and efficient development of unconventional oil and gas is a crucial demand for the energy development strategy. Deep tight oil and gas resource development generally adopts horizontal well drilling methods. During drilling, especially in long horizontal sections, the high temperature frequently causes failures of downhole drilling tools and rotary steering tools. The temperature rises sharply during rock breaking with the drill bit. Existing wellbore heat transfer models do not fully consider the impact of heat generated by the drill bit on the wellbore temperature field. This paper aims to experimentally study the temperature rise law of the cutting tooth of the bottom polycrystalline diamond compact (PDC) bit during rock breaking. A set of evaluation devices was developed to study the temperature field distribution characteristics at the bottom of the PDC bit during rock breaking under different experimental conditions. The results indicate that the flow rate of drilling fluid, bit rotation speed, and weight on bit (WOB) significantly affect the distribution of the temperature field at the well bottom. This experimental research on the temperature field distribution characteristics at the bottom of the PDC bit during rock breaking helps reveal the heat transfer characteristics of the long horizontal section wellbore, guide the optimization of drilling parameters, and develop temperature control methods. It is of great significance for the advancement of efficient development technologies for unconventional resources in long horizontal wells. [ABSTRACT FROM AUTHOR]

Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits. [ABSTRACT FROM AUTHOR]

Simple Summary: Against the background of frequent threats to the cooling water intake systems of nuclear power plants by marine organisms, this study utilized a Generalized Additive Model to investigate the correlation between jellyfish aggregations and environmental factors in the South China Sea region of the Shandong Peninsula. The results indicate that key variables affecting jellyfish resource density include year, longitude, latitude, sea surface temperature (SST), and sea surface salinity (SSS). Subsequently, the study also examined the impact of sea winds and currents on jellyfish resource density. The results suggest that the environmental conditions around marine nuclear power plants (SST, SSS, and ocean current) provide a favorable environment for jellyfish survival. In recent years, there have been frequent jellyfish outbreaks in Chinese coastal waters, significantly impacting the structure, functionality, safety, and economy of nuclear power plant cooling water intake and nearby ecosystems. Therefore, this study focuses on jellyfish outbreaks in Chinese coastal waters, particularly near the Shandong Peninsula. By analyzing jellyfish abundance data, a Generalized Additive Model integrating environmental factors reveals that temperature and salinity greatly influence jellyfish density. The results show variations in jellyfish density among years, with higher densities in coastal areas. The model explains 42.2% of the variance, highlighting the positive correlation between temperature (20–26 °C) and jellyfish density, as well as the impact of salinity (27.5–29‰). Additionally, ocean currents play a significant role in nearshore jellyfish aggregation, with a correlation between ocean currents and site coordinates. This study aims to investigate the relationship between jellyfish blooms and environmental factors. The results obtained from the study provide data support for the prevention and control of blockages in nuclear power plant cooling systems, and provide a data basis for the implementation of monitoring measures in nuclear power plants. [ABSTRACT FROM AUTHOR]

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI > 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations. [ABSTRACT FROM AUTHOR]

The present paper introduces an innovative strain energy function (SEF) for incompressible anisotropic fiber-reinforced materials. This SEF is specifically designed to understand the mechanical behavior of carbon fiber-woven fabric. The considered model combines polyconvex invariants forming an integrity basisin polynomial form, which is inspired by the application of Noether's theorem. A single solution can be obtained during the identification because of the relationship between the SEF we have constructed and the material parameters, which are linearly dependent. The six material parameters were precisely determined through a comparison between the closed-form solutions from our model and the corresponding tensile experimental data with different stretching ratios, with determination coefficients consistently reaching a remarkable value of 0.99. When considering only uniaxial tensile tests, our model can be simplified from a quadratic polynomial to a linear polynomial, thereby reducing the number of material parameters required from six to four, while the fidelity of the model's predictive accuracy remains unaltered. The comparison between the results of numerical calculations and experiments proves the efficiency and accuracy of the method. [ABSTRACT FROM AUTHOR]

Oil–paper insulation is the critical insulation element in the modern power system. Under a harsh operating environment, oil–paper insulation will deteriorate gradually, resulting in electrical accidents. Thus, it is important to evaluate and monitor the insulation state of oil–paper insulation. Firstly, this paper introduces the geometric structure and physical components of oil–paper insulation and shows the main reasons and forms of oil–paper insulation's degradation. Then, this paper reviews the existing condition assessment techniques for oil–paper insulation, such as the dissolved gas ratio analysis, aging kinetic model, cellulose–water adsorption isotherm, oil–paper moisture balance curve, and dielectric response technique. Additionally, the advantages and limitations of the above condition assessment techniques are discussed. In particular, this paper highlights the dielectric response technique and introduces its evaluation principle in detail: (1) collecting the dielectric response data, (2) extracting the feature parameters from the collected dielectric response data, and (3) establishing the condition assessment models based on the extracted feature parameters and the machine learning techniques. Finally, two full potential studies are proposed, which research hotspots' oil–paper insulation and the electrical–chemical joint evaluation technique. In summary, this paper concludes the principles, advantages and limitation of the existing condition assessment techniques for oil–paper insulation, and we put forward two potential research avenues. [ABSTRACT FROM AUTHOR]

Large-scale management is the key to realizing long-term agricultural growth in smallholder countries. Land-scale management and service-scale management are two forms of agricultural large-scale management. The former is committed to changing the small-scale management pattern, but the latter tends to maintain it. There has been a lack of discussion and controversy about the relationship between the two. From the perspective of market maturity, this paper explores whether the two are complementary or mutually exclusive and how their complementary or mutually exclusive relationship affects agricultural green productivity. The results show the following: Land-scale management and service-scale management are complementary, not superficially contradictory. The benign interaction between the two has a consistent improvement effect on green productivity in both the short and long term, which has spatial spillovers appearing in the long term. The reasons are as follows: The farmland rental market can reverse the inhibitory effect of the current low-maturity outsourcing services market on green productivity. The outsourcing services market can delay the arrival of the inflection point beyond which expansion of farmland rental transactions reduces green productivity, and amplify the positive effect of farmland rental on it. Although the degree of benign interaction between the two forms of large-scale management has gradually increased in recent years, it is still low overall. Agricultural large-scale management in China is still in the stage driven by land-scale management. Smallholder countries such as China need not worry prematurely about which large-scale management path to take, and they should treat both forms of large-scale management with an equal perspective to accelerate the high-level interaction between them. [ABSTRACT FROM AUTHOR]

Porous suspended particles are hazardous to human health due to their strong absorption capacity for toxic substances. A fast, accurate, in situ and high-throughput method to characterize the microporous structure of porous particles has extensive application value. The polarization changes during the light scattering of aerosol particles are highly sensitive to their microstructural properties, such as pore size and porosity. In this study, we propose an overlapping sphere model based on the discrete dipole approximation (DDA) to calculate the polarization scattering characteristics of porous particles. By combining scattering calculations with multi-dimensional polarization indexes measured by a multi-angle polarized scattering vector detection system, we achieve the identification and classification of pore-type components in suspended particles. The maximum deviation based on multiple indexes is less than 0.16% for the proportion analysis of mixed particles. Simultaneously, we develop a quantitative inversion algorithm on pore size and porosity. The inversion results of the three porous polymer particles support the validity and feasibility of our method, where the inversion error of partial particles is less than 4% for pore size and less than 6% for porosity. The study demonstrates the potential of polarization measurements and index systems applied in characterizing the micropore structure of suspended particles. [ABSTRACT FROM AUTHOR]

The combination of Al nanoparticles (ANPs) as fuel and H2O2 as oxidizer is a potential green space propellant. In this research, reactive force field molecular dynamics (ReaxFF-MD) simulations were used to study the influence of water addition on the combustion of Al/H2O2. The MD results showed that as the percentage of H2O increased from 0 to 30%, the number of Al-O bonds on the ANPs decreased, the number of Al-H bonds increased, and the adiabatic flame temperature of the system decreased from 4612 K to 4380 K. Since the Al-O bond is more stable, as the simulation proceeds, the number of Al-O bonds will be significantly higher than that of Al-H and Al-OH bonds, and the Al oxides (Al[O]x) will be transformed from low to high coordination. Subsequently, the combustion mechanism of the Al/H2O2/H2O system was elaborated from an atomic perspective. Both H2O2 and H2O were adsorbed and chemically activated on the surface of ANPs, resulting in molecular decomposition into free radicals, which were then captured by ANPs. H2 molecules could be released from the ANPs, while O2 could not be released through this pathway. Finally, it was found that the coverage of the oxide layer reduced the rate of H2O2 consumption and H2 production significantly, simultaneously preventing the deformation of the Al clusters' morphology. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the effects of different strains and wheat bran on the fermentation quality, antioxidant activity, and bacterial community of mulberry leaf silage. Mulberry leaves were ensiled with Pediococcus acidilactici and Pediococcus pentosaceus (A), Bacillus subtilis and Bacillus licheniformi (DK), and Pediococcus acidilactici, Pediococcus pentosaceus, Bacillus subtilis, and Bacillus licheniformi (AK). Each treatment was supplemented with 10% wheat bran (fresh matter basis), and the strains were added in equal proportions for 7 days. The results indicated that the DK and AK groups exhibited higher dry matter (DM) content compared to the A group (p < 0.05). The A group (37.25 mg/g DM) and AK group (34.47 mg/g DM) demonstrated higher lactic acid content and lower pH (<4.40). Furthermore, the DK group had a significantly higher acetic acid content compared to the AK group (p < 0.05). Additionally, both the A and AK groups exhibited lower levels of ammonia-N content than the DK group (p < 0.05). The number of yeasts, molds, and coliform bacteria were low in mulberry leaf silage. Moreover, the antioxidant activity in the fermentation groups increased, with higher relative abundance of beneficial bacteria, Lactococcus and Lactobacillus, in the AK group. In summary, the AK group was observed to enhance fermentation quality and antioxidant capacity, leading to the establishment of a favorable microbial community composition. [ABSTRACT FROM AUTHOR]

Carbon fiber powder (CFP) was first applied to conductive asphalt concrete as a conductive phase material, but its action mechanism has not been clarified. In this paper, atomic force microscopy (AFM) and molecular dynamics (MDs) simulation are used to study the carbon fiber powder mechanism of action, guide the preparation of conductive asphalt concrete, and study the electrothermal properties of conductive asphalt concrete. The results show that carbon fiber powder weakens the adhesion property of asphalt mastic, and this weakening further strengthens in the water–temperature coupling, so water stability and conductivity are used as evaluation indicators to determine that the optimal content of carbon fiber powder is 2.0% and that the optimal content of carbon fibers (CFs) is 0.4%. Carbon fiber–carbon fiber powder conductive asphalt concrete with a resistivity of 0.98 Ω · m was finally prepared. In the temperature rise test of the Marshall specimen and rutting slab, its warming effect is obvious, and the heat transformation rate is more than 75%, so it has a very good ability to melt snow and ice. [ABSTRACT FROM AUTHOR]

Due to the broad usage and widespread popularity of drones, the demand for a more accurate object detection algorithm for images captured by drone platforms has become increasingly urgent. This article addresses this issue by first analyzing the unique characteristics of datasets related to drones. We then select the widely used YOLOv7 algorithm as the foundation and conduct a comprehensive analysis of its limitations, proposing a targeted solution. In order to enhance the network's ability to extract features from small objects, we introduce non-strided convolution modules and integrate modules that utilize attention mechanism principles into the baseline network. Additionally, we improve the semantic information expression for small targets by optimizing the feature fusion process in the network. During training, we adopt the latest Lion optimizer and MPDIoU loss to further boost the overall performance of the network. The improved network achieves impressive results, with mAP50 scores of 56.8% and 94.6% on the VisDrone2019 and NWPU VHR-10 datasets, respectively, particularly in detecting small objects. [ABSTRACT FROM AUTHOR]

Norcantharidin (NCTD), a cantharidin derivative, induces ROS generation and is widely used to treat CRC. In this study, we clarified the role and mechanism of action of norcantharidin in increasing CRC sensitivity to radiotherapy. We treated the CRC cell lines LoVo and DLD-1 with NCTD (10 or 50 μmol/L), ionizing radiation (IR, 6 Gy), and a combination of the two and found that NCTD significantly inhibited the proliferation of CRC cells and enhanced their sensitivity to radiotherapy. NCTD induced ROS generation by decreasing the mitochondrial membrane potential, increasing mitochondrial membrane permeability, and promoting cytochrome C release from mitochondria into the cytoplasm. IR combined with NCTD induced ROS production, which activated the mitochondrial fission protein DRP1, leading to increased mitochondrial fission and CRC sensitivity to radiotherapy. NCTD also reduced CRC cell resistance to radiotherapy by blocking the cell cycle at the G2/M phase and decreasing p-CHK2, cyclin B1, and p-CDC2 expression. NCTD and IR also inhibited radiation resistance by causing DNA damage. Our findings provide evidence for the potential therapeutic use of NCTD and IR against CRC. Moreover, this study elucidates whether NCTD can overcome CRC radiation tolerance and provides insights into the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Kazakhstan has abundant resources of low-permeability oil reservoirs, among which the KKM low-permeability oil reservoir has geological reserves of 3844 × 104 t and a determined recoverable reserve of 1670 × 104 t. However, the water flooding efficiency is only 68%, and the recovery efficiency is as low as 32%. The development of the reservoir faces challenges such as water injection difficulties and low oil production from wells. In order to further improve the oil recovery rate of this reservoir, our team developed micro-pressure-driven development technology based on pressure-driven techniques by integrating theories of fluid mechanics and artificial intelligence. We also combined this with subsequent artificial lift schemes, resulting in a complete set of micro-pressure-driven process technology. The predicted results indicate that after implementing micro-pressure-driven techniques, a single well group in the KKM oilfield can achieve a daily oil production increase of 32.08 t, demonstrating a good development effect. [ABSTRACT FROM AUTHOR]

Metamaterial-based coded aperture imaging (MCAI) is a forward-looking radar imaging technique based on wavefront modulation. The scattering coefficients of the target can resolve as an ill-posed inverse problem. Data-based deep-learning methods provide an efficient, but expensive, way for target reconstruction. To address the difficulty in collecting paired training data, an untrained deep radar-echo-prior-based MCAI (DMCAI) optimization model is proposed. DMCAI combines the MCAI model with a modified U-Net for predicting radar echo. A joint loss function based on deep-radar echo prior and total variation is utilized to optimize network weights through back-propagation. A target reconstruction strategy by alternatively using the imaginary and real part of the radar echo signal (STAIR) is proposed to solve the DMCAI. It makes the target reconstruction task turn into an estimation from an input image by the U-Net. Then, the optimized weights serve as a parametrization that bridges the input image and the target. The simulation and experimental results demonstrate the effectiveness of the proposed approach under different SNRs or compression measurements. [ABSTRACT FROM AUTHOR]

Data from the fishery of S. oualaniensis in the northern Indian Ocean from January to March and October to December 2017 to 2019 were modeled with sea surface temperature (SST), wind speed (WS), and photosynthetically active radiation (PAR). In this study, the fishing effort was used to evaluate the suitability index (SI) at SST, WS, and PAR. An integrated habitat suitability model (HSI) was developed with different weighting scenarios and weighting schemes. The optimal case was selected by calculation and comparison with the proportion of catch, effort, and catch per unit effort (CPUE) in the HSI interval (0~0.2, 0.2~0.6, 0.6~1); validation was performed using data from 2019. The weight of the optimal HSI model was 0.25 for sea surface temperature and photosynthetically active radiation, and 0.5 for wind speed. This model yielded the best performance and could accurately predict the fishing ground of S. oualaniensis in the northern Indian Ocean. The findings suggest that the integrated HSI model can predict the distribution of S. oualaniensis commendably, with wind speed as the most important factor affecting the spatial distribution of S. oualaniensis' habitat in the northern Indian Ocean. By analyzing habitat selection by S. oualaniensis, this study verified and predicted the distribution of squid in the northern Indian Ocean, which allows the distribution of squid resources and fishing grounds to be modeled, and for the sustainable use of squid fishery resources. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges in achieving accurate stereo SAR image matching. To address these aforementioned challenges, this paper proposes a radargrammetric method based on the composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged using the proposed radargrammetric 3D imaging method, resulting in the 3D imaging of target scenes based on multi-aspect SAR images. For validation purposes, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency, while enhancing the imaging accuracy of crucial areas with limited data. [ABSTRACT FROM AUTHOR]

Secondary development is a key biological characteristic of woody plants and the basis of wood formation. Exogenous nitrogen can affect the secondary growth of poplar, and some regulatory mechanisms have been found in the secondary xylem. However, the effect of nitrogen on cambium has not been reported. Herein, we investigated the effects of different nitrogen concentrations on cambium development using combined transcriptome and metabolome analysis. The results show that, compared with 1 mM NH4NO3 (M), the layers of hybrid poplar cambium cells decreased under the 0.15 mM NH4NO3 (L) and 0.3 mM NH4NO3 (LM) treatments. However, there was no difference in the layers of hybrid poplar cambium cells under the 3 mM NH4NO3 (HM) and 5 mM NH4NO3 (H) treatments. Totals of 2365, 824, 649 and 398 DEGs were identified in the M versus (vs.) L, M vs. LM, M vs. HM and M vs. H groups, respectively. Expression profile analysis of the DEGs showed that exogenous nitrogen affected the gene expression involved in plant hormone signal transduction, phenylpropanoid biosynthesis, the starch and sucrose metabolism pathway and the ubiquitin-mediated proteolysis pathway. In M vs. L, M vs. LM, M vs. HM and M vs. H, differential metabolites were enriched in flavonoids, lignans, coumarins and saccharides. The combined analysis of the transcriptome and metabolome showed that some genes and metabolites in plant hormone signal transduction, phenylpropanoid biosynthesis and starch and sucrose metabolism pathways may be involved in nitrogen regulation in cambium development, whose functions need to be verified. In this study, from the point of view that nitrogen influences cambium development to regulate wood formation, the network analysis of the transcriptome and metabolomics of cambium under different nitrogen supply levels was studied for the first time, revealing the potential regulatory and metabolic mechanisms involved in this process and providing new insights into the effects of nitrogen on wood development. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34 × 10 7 . In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications. [ABSTRACT FROM AUTHOR]