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IntroductionWith the continuous development of exploration and development, tight sandstone reservoirs have become an essential field of oil and gas exploration. The tight sandstone reservoirs are characterized by complex lithology, poor pore structure, and strong heterogeneity, which bring great difficulties to formation evaluation by well logs. Especially, the accuracy of the Archie formula for calculating the water saturation of tight sandstone reservoirs containing fractures is not high, saturation evaluation faces greater challenges. Therefore, how to calculate the saturation of tight sandstone reservoirs more accurately is an urgent problem to be solved.MethodsIn this paper, the tight sandstone reservoirs of the Jurassic Ahe Formation in the Tarim Basin were taken as the research area. A saturation model that combines the effects of shale and fractures was proposed. Specifically, if the shale content is more than 20%, the Indonesian formula is used to calculate the saturation. If the shale content is less than 20%, the dual porosity model is adopted. Based on the rock resistivity and nuclear magnetic resonance (NMR) experiment results, the porosity and T2 logarithmic mean values are selected as influencing factors to calculate the key parameters of the dual porosity model.Results and DiscussionThe saturation of tight sandstone reservoirs in the Jurassic Ahe Formation of the Tarim Basin is calculated through the method proposed in this paper. The case study shows that the accuracy of the proposed method is higher than that of the Archie model. The method proposed in this paper demonstrates excellent adaptability in the quantitative evaluation of saturation for tight sandstone reservoirs.

Abstract Auricularia heimuer, the third most frequently cultivated edible mushroom species worldwide, has high medicinal value. However, a shortage of molecular marker hinders the efficiency and accuracy of genetic breeding efforts for A. heimuer. High-throughput transcriptome sequencing data are essential for gene discovery and molecular markers development. This study aimed to clarify the distribution of SSR loci across the A. heimuer transcriptome and to develop highly informative EST-SSR markers. These tools can be used for phylogenetic analysis, functional gene mining, and molecular marker-assisted breeding of A. heimuer. This study used Illumina high-throughput sequencing technology to obtain A. heimuer transcriptome data. The results revealed 37,538 unigenes in the A. heimuer transcriptome. Of these unigenes, 24,777 (66.01%) were annotated via comparison with the COG, Pfam, and NR databases. Overall, 2510 SSRs were identified from the unigenes, including 6 types of SSRs. The most abundant type of repeats were trinucleotides (1425, 56.77%), followed by mononucleotides (391, 15.58%) and dinucleotides (456, 18.17%). Primer pairs for 102 SSR loci were randomly designed for validity confirmation and polymorphism identification; this process yielded 53 polymorphic EST-SSR markers. Finally, 13 pairs of highly polymorphic EST-SSR primers were used to analyze the genetic diversity and population structure of 52 wild A. heimuer germplasms, revealing that the 52 germplasms could be divided into three categories. These results indicated that SSR loci were abundant in types, numbers, and frequencies, providing a potential basis for germplasm resource identification, genetic diversity analysis, and molecular marker-assisted breeding of A. heimuer.

Fish stock assessment is crucial for sustainable marine fisheries management in rangeland ecosystems. To address the challenges posed by the overfishing of offshore fish species and facilitate comprehensive deep-sea resource evaluation, this paper introduces an improved fish sonar image detection algorithm based on the you only look once algorithm, version 5 (YOLOv5). Sonar image noise often results in blurred targets and indistinct features, thereby reducing the precision of object detection. Thus, a C3N module is incorporated into the neck component, where depth-separable convolution and an inverse bottleneck layer structure are integrated to lessen feature information loss during downsampling and forward propagation. Furthermore, lowercase shallow feature layer is introduced in the network prediction layer to enhance feature extraction for pixels larger than $ 4 \times 4 $. Additionally, normalized weighted distance based on a Gaussian distribution is combined with Intersection over Union (IoU) during gradient descent to improve small target detection and mitigate the IoU's scale sensitivity. Finally, traditional non-maximum suppression (NMS) is replaced with soft-NMS, reducing missed detections due to occlusion and overlapping fish targets that are common in sonar datasets. Experiments show that the improved model surpasses the original model and YOLOv3 with gains in precision, recall and mean average precision of 2.3%, 4.7% and 2.7%, respectively, and 2.5%, 6.3% and 6.7%, respectively. These findings confirm the method's effectiveness in raising sonar image detection accuracy, which is consistent with model comparisons. Given Unmanned Underwater Vehicle advancements, this method holds the potential to support fish culture decision-making and facilitate fish stock resource assessment.

With the increasing adoption of composite materials in aircraft construction, traditional anti-icing technologies face significant challenges due to the low thermal conductivity and heat resistance of composite resins. These limitations have spurred the development of lightweight, efficient, durable, and cost-effective integrated anti-icing technologies as a critical area of research. This paper begins with an overview of advancements in electrothermal anti-icing and de-icing technologies for aircraft. It then explores the configurations and applications of functional-structural integration technology for anti-icing and de-icing, emphasizing pivotal technologies and current challenges in this field. Finally, the study forecasts the development trends in the multifunctional integration of thermal conductivity/insulation, anti-icing, and electromagnetic wave transparency/wave-absorbing properties. These advancements are driven by the evolution of composite materialization in aircraft and the progression of multi-electrical/all-electrical technologies. The objective is to provide a comprehensive guide for technological development in anti-icing, aiding researchers and relevant departments to further enhance the application of anti-icing technology in composite material aircraft.

Abstract Sclerotinia sclerotiorum is a necrotrophic plant pathogenic fungus with broad distribution and host range. Bioactive compounds derived from plant extracts have been proven to be effective in controlling S. sclerotiorum. In this study, the mycelial growth of S. sclerotiorum was effectively inhibited by maleic acid, malonic acid, and their combination at a concentration of 2 mg/mL, with respective inhibition rates of 32.5%, 9.98%, and 67.6%. The treatment of detached leaves with the two acids resulted in a decrease in lesion diameters. Interestingly, maleic acid and malonic acid decreased the number of sclerotia while simultaneously increasing their weight. The two acids also disrupted the cell structure of sclerotia, leading to sheet-like electron-thin regions. On a molecular level, maleic acid reduced oxalic acid secretion, upregulated the expression of Ss-Odc2 and downregulated CWDE10, Ss-Bi1 and Ss-Ggt1. Differently, malonic acid downregulated CWDE2 and Ss-Odc1. These findings verified that maleic acid and malonic acid could effectively inhibit S. sclerotiorum, providing promising evidence for the development of an environmentally friendly biocontrol agent.

Fish object detection and counting in pelagic fisheries face many challenges in complex environments. Sonar imaging technology offers a solution because it generates high-resolution images underwater. In this paper, we propose a sonar-based fish object detection and counting method using an improved YOLOv8 combined with BoT-SORT to address issues such as missed detection, false detection, and low accuracy caused by complex factors such as equipment motion, light changes, and background noise in pelagic environments. The algorithm utilizes the techniques of lightweight upsampling operator CARAFE, generalized feature pyramid network GFPN, and partial convolution. It integrates with the BoT-SORT tracking algorithm to propose a new region detection method that detects and tracks the schools of fish, providing stable real-time fish counts in the designated area. The experimental results indicate that while focusing on maintaining a lightweight design, the improved algorithm achieved a 3.8% increase in recall and a 2.4% increase in mAP0.5 compared to the original algorithm. This significantly impacts scientific and rational fishery planning, marine resource protection, and improved productivity. At the same time, it provides important data support for marine ecological monitoring, environmental protection, and fishery management, contributing to sustainable fishery development and marine ecology preservation.

Introduction: Permeability is one of the most important parameters for reservoir evaluation. It is commonly measured in laboratories using underground core samples. However, it cannot describe the entire reservoir because of the limited number of cores. Therefore, petrophysicists use well logs to establish empirical equations to estimate permeability. This method has been widely used in conventional sandstone reservoirs, but it is not applicable to tight sandstone reservoirs with low porosity, extremely low permeability, and complex pore structures.Methods: Machine learning models can identify potential relationships between input features and sample labels, making them a good choice for establishing permeability prediction models. A stacking model is an ensemble learning method that aims to train a meta-learner to learn an optimal combination of expert models. However, the meta-learner does not evaluate or control the experts, making it difficult to interpret the contribution of each model. In this study, we design a gate network stacking (GNS) model, which is an algorithm that combines data and model-driven methods. First, an input log combination is selected for each expert model to ensure the best performance of the expert model and selfoptimization of the hyperparameters. Petrophysical constraints are then added to the inputs of the expert model and meta-learner, and weights are dynamically assigned to the output of the expert model. Finally, the overall performance of the model is evaluated iteratively to enhance its interpretability and robustness.Results and discussion: The GNS model is then used to predict the permeability of a tight sandstone reservoir in the Jurassic Ahe Formation in the Tarim Basin. The case study shows that the permeability predicted by the GNS model is more accurate than that of other ensemble models. This study provides a new approach for predicting the parameters of tight sandstone reservoirs.

The problem of poor real-time capability, a large number of traversals and the long operation time of the MUSIC algorithm bring great difficulties to the application of DOA estimation. For the above problems, this paper fuses MUSIC algorithm and genetic algorithm and proposes an improved genetic MUSIC algorithm (GA-MUSIC). Also, improvement strategies are proposed for the three operators of selection, crossover, and variation of the genetic algorithm. The GA-MUSIC was simulated for DOA estimation to verify the effectiveness of this algorithm. The research shows that the GA-MUSIC proposed in this paper has the best operational performance at different precisions and SNRs. This algorithm can effectively reduce the iterative computation of the algorithm with better real-time performance, and the search success rate is above 95% while ensuring accuracy. Furthermore, this paper corroborates that the proposed improved genetic MUSIC algorithm can achieve better performance in single signals and incoherent multi-signal sources. It can be seen that the algorithm in this paper can effectively solve the problems of low success rates of DOA estimation searches and poor real-time performance and has a significant reference value for practical engineering applications.

Abstract A sparse reconstruction algorithm for the localisation of mixed near‐field and far‐field sources (MFNS) based on four‐order statistics is proposed in this study. First, utilising the structural characteristics of a uniform symmetric linear array, a fourth‐order cumulant (FOC) matrix is constructed, which decouples the angular information from the range parameters. Based on the sparse representation framework, a weighted l1‐norm minimisation algorithm is developed to obtain the direction of arrivals (DOAs) of the MFNS. However, the existing selection strategy of the tuning factor is not adaptive to different observation scenarios. So a closed‐form expression of the tuning factor based on the FOC estimation error is presented. Then, another FOC matrix is constructed, which includes both the DOA and range information of the MFNS. With the DOA estimates, the two‐dimensional spatial dictionary can be reduced into a one‐dimensional dictionary, which only depends on the range parameters. Using the similar sparse reconstruction method, the range estimates of the MFNS can be obtained, and the types of the sources can be distinguished according to their range parameters. According to numerical simulations, the estimation performance of the proposed algorithm approaches the CRB in the high signal‐to‐noise ratio region, which successfully circumvents the saturation problem due to the fixed tuning factor.

Abstract In high‐speed mobile scenarios, due to the high‐speed relative motion between transmitter and receiver, the high Doppler frequency shift interferes with the inter‐subcarrier orthogonality in orthogonal frequency‐division multiplexing (OFDM) systems. Therefore its performance is significantly degraded. Recently, orthogonal time–frequency space (OTFS) is considered as an effective alternative scheme to OFDM for time‐varying channels. As with OFDM‐massive multiple input multiple output (MIMO) systems, downlink channel estimation is necessary for OTFS‐massive MIMO systems to improve the spectral efficiency in frequency‐division duplex (FDD) mode without channel reciprocity. Here, first the cyclic prefix ‐OFDM‐based massive MIMO OTFS system channel with antenna directivity pattern is analyzed, and transform the burst sparsity in the angle domain into block sparsity by using non‐uniform Fourier transform (NUFT). Furthermore, to solve the problem that the pilot overhead grows linearly with the number of antennas, we propose a three‐dimensional (3D) dynamic support detect (DSD) algorithm. Compared with the traditional OMP algorithm, and the 3D‐structured orthogonal matching pursuit algorithm, simulation results demonstrate the proposed DSD algorithm has higher channel estimation accuracy, and lower pilot overhead.

The field of homogeneous metal- and photocatalysis typically uses one-photon-absorbing photosensitizers, which are highly functional, but require higher-energy light. Here the authors report a group of ruthenium polypyridyl complexes possessing two-photon-absorption capabilities, active with irradiation with lower-energy (740 nm) light.

The direction estimation of the coherent source in a uniform circular array is an essential part of the signal processing area of the array, but the traditional uniform circular array algorithm has a low localization accuracy and a poor localization effect on the coherent source. To solve this problem, this paper proposes a two-dimensional direction of arrival (DOA) estimation for the coherent source in broadband. Firstly, the central frequency of the coherent sound source is estimated using the frequency estimation method of the delayed data, and a real-valued beamformer is constructed using the concept of the multiloop phase mode. Then, the cost function in the beam space is obtained. Finally, the cost function is searched in two dimensions to locate the sound source. In this paper, we simulate the DOA of the sound source at different frequencies and signal-to-noise ratios and analyze the resolution of the circular array. The simulation results show that the proposed algorithm can estimate the direction of arrival with high precision and achieve the desired results.

Abstract Deuterium (D) labeling is of great value in organic synthesis, pharmaceutical industry, and materials science. However, the state‐of‐the‐art deuteration methods generally require noble metal catalysts, expensive deuterium sources, or harsh reaction conditions. Herein, noble metal‐free and ultrathin ZnIn2S4 (ZIS) is reported as an effective photocatalyst for visible light‐driven reductive deuteration of carbonyls to produce deuterated alcohols using heavy water (D2O) as the sole deuterium source. Defective two‐dimensional ZIS nanosheets (D‐ZIS) are prepared in a surfactant assisted bottom‐up route exhibited much enhanced performance than the pristine ZIS counterpart. A systematic study is carried out to elucidate the contributing factors and it is found that the in situ surfactant modification enabled D‐ZIS to expose more defect sites for charge carrier separation and active D‐species generation, as well as high specific surface area, all of which are beneficial for the desirable deuteration reaction. This work highlights the great potential in developing low‐cost semiconductor‐based photocatalysts for organic deuteration in D2O, circumventing expensive deuterium reagents and harsh conditions.

Sclerotinia sclerotiorum is a broad-spectrum necrotrophic phytopathogen that can infect many plant species worldwide. The application of fungicides is a common measure for controlling Sclerotinia sclerotiorum. Due to the risk of developing resistance to fungicides, it is imperative to find ways to be environmentally friendly and even effective. Using bioactive compounds in plants to reduce the amounts of fungicides has become a clean and sustainable strategy of controlling Sclerotinia sclerotiorum. Our study found that selenium in soil mediated the phenylacetic acid-related metabolic pathway in oilseed rape and reduced the incidence rate of Sclerotinia sclerotiorum. The growth-inhibition rates of Sclerotinia sclerotiorum were observed at 25.82%, 19.67%, and 52.61% for treatments of 0.8 mg·L−1 dimethachlon, 0.1 mg·mL−1 phenylacetic acid, and dimethachlon (0.8 mg·L−1) + phenylacetic acid (0.1 mg·mL−1), respectively. Phenylacetic acid reduced the application amount of dimethachlon and enhanced the inhibition effect for Sclerotinia sclerotiorum. Results also suggested that phenylacetic acid severely damaged the morphological structure, changed the electrical conductivity, and reduced the capacity of acid production and oxalic acid secretion of Sclerotinia sclerotiorum mycelium. Further studies revealed that phenylacetic acid increased the gene-expression level of Ssodc1, Ssodc2, CWDE2 and CWDE10 in mycelium while decreasing the expression level of SsGgt1, and phenylacetic acid + dimethachlon reduced the relative expression level of SsBil. These findings verified that phenylacetic acid could partially replace the amount of dimethachlon, as well as enhance the prevention of Sclerotinia sclerotiorum by dimethachlon, which provides evidence for developing an environment-friendly method for Sclerotinia sclerotiorum control.

(1) Background: Tuberculosis (TB) is an infectious disease that seriously endangers health and restricts economic and social development. Shandong Province has the second largest population in China with a high TB burden. This study aimed to detect the epidemic characteristics and spatio-temporal pattern of reported TB incidence in Shandong Province and provide a scientific basis to develop more effective strategies for TB prevention and control. (2) Methods: The age, gender, and occupational distribution characteristics of the cases were described. The Seasonal-Trend LOESS decomposition method, global spatial autocorrelation statistic, local spatial autocorrelation statistics, and spatial-temporal scanning were used to decompose time series, analyze the spatial aggregation, detect cold and hot spots, and analyze the spatio-temporal aggregation of reported incidence. (3) Results: A total of 135,185 TB cases were reported in Shandong Province during the five years 2016–2020. Men and farmers are the main populations of TB patients. The time-series of reported tuberculosis incidence had a long-term decreasing trend with clear seasonality. There was aggregation in the spatial distribution, and the areas with a high reported incidence of TB were mainly clustered in the northwest and southeast of Shandong. The temporal scan also yielded similar results. (4) Conclusions: Health policy authorities should develop targeted prevention and control measures based on epidemiological characteristics to prevent and control TB more effectively.

The resistivity index is an important parameter for determining the rock saturation index. However, the saturation index changes greatly in unconventional reservoirs, which leads to oil saturation estimation with great difficulty. Hence, we try to establish the relationship between the resistivity index and log data. Firstly, a novel model of estimating the resistivity index with T2 time was derived based on fractal theory, the relationship between nuclear magnetic resonance (NMR) T2 spectrum and capillary pressure curve (T2-Pc), and Archie formula. It regards the logarithm of the resistivity index as the dependent variable, with T2 time and T2 time when water saturation is 100% as the independent variables. Second, 17 cores were drilled, and T2 spectrum and the relationship between the resistivity index and water saturation (Ir-Sw) were jointly measured. Next, the experimental results were substituted into the established model to get the model parameters via the multivariate statistics regression method. Then, the experimental data engaged and not engaged in modeling were used to test the established model. The average relative errors of estimated resistivity indices and experimental results are smaller than 8%, and those of the regressed saturation index are smaller than 5%. Finally, the established model was applied in log data processing and interpretation with good effects. It thus proves that the method of the estimating resistivity index with T2 time is reliable, which provides a novel solution for determining rock electrical parameter of unconventional reservoirs.

Considering the diversity of uniform distribution for the solutions of multi-objective optimization problems, we propose the multi-objective genetic algorithm based on fitting (MOGA/F) and interpolation (MOGA/I). The selected operator is based on the optimal reference points uniformly distributed in the objective space, which is calculated by applying a fitting function or interpolation method from a finite set of objective values. After sorting the ranks of the population, the objective space for the last front can be easily calculated by using fitting and interpolation functions, and the uniformly distributed points can be obtained without parameter setting. The individuals with the shortest Euclidean distance to the reference points are chosen according to the error matrix. This method can maintain the diversity and spread of the solutions without destroying the convergence. In this paper, MOGA/F and MOGA/I are compared with the traditional methods, non-dominated sorting genetic algorithm-II and multi-objective evolutionary algorithm based on decomposition, by optimizing the mathematical problems. The numerical examples show that MOGA/F and MOGA/I have a much higher performance in terms of diversity and convergence of the final solutions.

Opportunistic beamforming (OBF) is a potential technique in the fifth generation (5G) and beyond 5G (B5G) that can boost the performance of communication systems and encourage high user quality of service (QoS) through multi-user selection gain. However, the achievable rate tends to be saturated with the increased number of users, when the number of users is large. To further improve the achievable rate, we proposed a multi-antenna opportunistic beamforming-based relay (MOBR) system, which can achieve both multi-user and multi-relay selection gains. Then, an optimization problem is formulated to maximize the achievable rate. Nevertheless, the optimization problem is a non-deterministic polynomial (NP)-hard problem, and it is difficult to obtain an optimal solution. In order to solve the proposed optimization problem, we divide it into two suboptimal issues and apply a joint iterative algorithm to consider both the suboptimal issues. Our simulation results indicate that the proposed system achieved a higher achievable rate than the conventional OBF systems and outperformed other beamforming schemes with low feedback information.

Based on the existing classification research of rockburst dynamic disasters, a new rockburst model is introduced. The model specimens with inclusion rock are designed and produced, and the indoor test of inclusion rockburst with or without cementation, different inclusion angles, and prefabricated cracks of different lengths is carried out. The failure process and displacement variation of rock specimens are recorded and measured by the high-speed camera system and digital image correlation method, and based on this, the characteristics and laws of rockburst are analyzed. The results show that the failure process of inclusion rock can be divided into several periods, such as crushing and extrusion of the cement body, and the cracks in the upper left tip, the lower right tip, the lower left tip, and the upper tip are expanded and broken in different orders. The dynamic ejection failure of the rock fragments occurs when the degree of failure is severe, and the static failure occurs when the degree of failure is weak. The inclusions have cementation, and the better the cementation, the greater the stress loading required in the failure, the greater the energy accumulated, the more severe the failure, and the higher the fractal dimension of the fragments. The larger the angle of the inclusion tip, the greater the stress loading in failure; the greater the change of the u and v displacements, the more the displacement of the inclusions changed; and the more the energy accumulated, the higher the fractal dimension of the fragment. The smaller the crack length, the greater the stress loading in failure, and the more the energy accumulated, the higher the fractal dimension of the fragments in failure. With more number of cracks and the more severe expansion before failure, the failure of rock mass can be transformed from the dynamic to the static.

A Feedback Particle Swarm Optimization (FPSO) with a family of fitness functions is proposed to minimize sidelobe level (SLL) and control null. In order to search in a large initial space and converge fast in local space to a refined solution, a FPSO with nonlinear inertia weight algorithm is developed, which is determined by a subtriplicate function with feedback taken from the fitness of the best previous position. The optimized objectives in the fitness function can obtain an accurate null level independently. The directly constrained SLL range reveals the capability to reduce SLL. Considering both element positions and complex weight coefficients, a low-level SLL, accurate null at specific directions, and constrained main beam are achieved. Numerical examples using a uniform linear array of isotropic elements are simulated, which demonstrate the effectiveness of the proposed array pattern synthesis approach.