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This study examines the shift in China’s seafood production, revealing that mariculture has surpassed marine capture as the primary source of marine food. The research aims to assess the roles of marine capture and mariculture in meeting rising food demand and ensuring nutrition security, given the limitations of land-based food production. Using data from 2003 to 2021, the study highlights several key trends: mariculture, focused on shellfish and algae, now outpaces marine capture, which remains centered on fish and crustaceans. Significant regional disparities are observed, with mariculture expanding rapidly in several coastal provinces as marine capture declines. Nutrient supply from marine capture has followed a three-phase pattern of growth, stagnation, and decline, while mariculture’s nutrient output has steadily increased, particularly in protein. By 2019-2020, mariculture surpassed marine capture in energy and protein supply, though fat supply remains lower. Regional differences in nutrient supply show mariculture leading in multiple provinces. The study concludes by recommending strategies to promote sustainable, diverse, and environmentally friendly practices for China’s marine food systems.

This paper investigates the influence of SiC dosage on the high-temperature mechanical properties (compressive strength, flexural strength, and bond flexural strength) of geopolymer mortar. The findings reveal that appropriate amount of SiC was found to improve the high-temperature mechanical properties of geopolymer mortar. Compared to the specimen without SiC, the addition of 3 wt. % SiC to the geopolymer mortar resulted in a noteworthy 14.59% increase in compressive strength at 300 °C, along with respective augmentations of 24.72% and 22.54% in flexural strength and bond flexural strength at 200 °C. Utilizing the experimental data as a foundation, a thermodynamic mathematical model was formulated, linking the strength and temperature parameters of SiC-modified geopolymer mortar. Furthermore, FTIR (Raman and Fourier transform infrared) analysis indicated that the polycondensation reaction of geopolymer mortar treated at 300 °C was promoted, and the products of displacement reaction increased, which provides a plausible explanation for the observed enhancement in compressive strength at 300 °C. Consequently, it is found that the incorporation of SiC can enhance the high-temperature mechanical properties of geopolymer mortar.

There are many tricky optimization problems in real life, and metaheuristic algorithms are the most effective way to solve optimization problems at a lower cost. The dung beetle optimization algorithm (DBO) is a more innovative algorithm proposed in 2022, which is affected by the action of dung beetles such as ball rolling, foraging, and reproduction. Therefore, A dung beetle optimization algorithm is proposed based on quasi-oppositional learning and Q-learning (QOLDBO). First, the quantum state update idea is cleverly integrated into quasi-oppositional learning to increase the randomness of the generated population. And the best behavior pattern is selected by adding Q-learning in the rolling stage to improve the search effect. In addition, the variable spiral local domain method is proposed to make up for the shortage of developing only around the neighborhood optimum. For the optimal solution of each iteration, the dimensional adaptive Gaussian variation is selected and the optimal solution is retained. Experimental performance tests show that QOLDBO performs well in both benchmark test functions and CEC 2017. Simultaneously, the validity of the algorithm is verified on several classical practical application engineering problems.

This study applies data envelopment analysis (DEA) to gauge technical efficiency and allocative efficiency in China’s beef cattle-fattening industry using survey data. The Tobit model considers the salient determinants that drive these efficiencies. The results indicate that (1) large-scale farms exhibit robust TE and pure technical efficiencies (PTE), whereas scale efficiencies (SE) diverge significantly between large and medium-sized operations. The cost efficiency (CE) of smaller farms lags behind their larger counterparts, with the latter displaying greater revenue efficiencies (RE) and profit efficiency (PE). (2) The influence of identical factors on the efficiency of beef cattle fattening production can vary, sometimes antithetically, across different scales. Local policy interventions must be differentiated according to farm type and size. (3) The unique context of China’s national conditions and the status quo of livestock farming render the dual implementation of environmental regulations and technological subsidies less viable for Chinese beef cattle farms. These entities should prioritize production over technological innovation and advancement. Policymakers should adopt strategies such as targeted skill/technological training for farm managers at particular scales of operation. This could represent a critical trajectory to augment the efficiency of beef cattle production and increase beef yield in China.

The measurement process of ground shock wave overpressure is influenced by complex field conditions, leading to notable errors in peak measurements. This study introduces a novel pressure measurement model that utilizes the Rankine−Hugoniot relation and an equilateral ternary array. The research delves into examining the influence of three key parameters (array size, shock wave incidence angle, and velocity) on the precision of pressure measurement through detailed simulations. The accuracy is compared with that of a dual-sensor array under the same conditions. Static explosion tests were conducted using bare charges of 0.3 kg and 3 kg TNT to verify the numerical simulation results. The findings indicate that the equilateral ternary array shock wave pressure measurement method demonstrates a strong anti-interference capability. It effectively reduces the peak overpressure error measured directly by the shock wave pressure sensor from 17.73% to 1.25% in the test environment. Furthermore, this method allows for velocity-based measurement of shock wave overpressure peaks in all propagation direction, with a maximum measurement error of 3.59% for shock wave overpressure peaks ≤ 9.08 MPa.

Based on the Integrated Land–Sea Management, this study established a theoretical framework for the sustainability of coastal regions by combining sustainable development with coupling coordination theory. The improved coupling coordination model was used to analyze the sustainable development of the Bohai Rim and its coastal provinces and cities from 2006 to 2020. Our implications were as follows: (1) The theoretical framework showed an S-shaped spiral trend, and the empirical results on the Bohai Rim were consistent with the trajectory conclusions. (2) The economic subsystem played a crucial role in the system’s evolution toward sustainable development. (3) The region and city models demonstrated consistent coupling and coordination development degrees. However, the consistency was not completely synchronous. Conscious eco-environmental governance activities can promote benign interactions among systems and improve this relationship. (4) The sustainable development of coastal cities is different from that of the provinces in which they are located. It merely demonstrates their relative status among all coastal cities and does not fully represent the wider region in which they are located. The findings suggest that adaptive policies, whether economic, social or environmental, can promote sustainable development. Economic stimulus policies can promote a transition of sustainable development; in the economic downturn, the adaptive environmental policy is realized by adjusting the relationship between subsystems to promote the coordination of regional systems, preparing for the next sustainable system transition. The established theoretical model and improvised mathematical method can be extended to study various coastal regions

Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.

The multi-beam array stitching test system (MASTS) based on the Hartmann principle is employed to measure the aberrations in large-aperture optical systems. As each small-aperture and ideal parallel beam traverses the optical system, it is converged into a spot at the focal plane of the optical system. The centroid position of the spot contains the information about the wavefront slope of the sub-aperture at that specific location in the optical system. Scanning the optical system with this small-aperture beam across the entire aperture of the optical system, we can yield the aberration information to be tested. To mitigate pointing errors induced by scanning motion and accurately obtain the aberration signals of the optical system, nine beams are integrated into a 3 × 3 multi-beam array system, and their directions are aligned to be identical. However, achieving complete alignment in the same direction for all nine beams is a challenging task, resulting in errors due to their pointing differences within the array. This paper introduces an iterative algorithm designed to obtain high-precision multi-beam pointing errors and to reconstruct the wavefront of the optical system under test. This enables a more accurate measurement of wavefront aberrations in the optical system to be tested. Firstly, simulation models were implemented to validate the algorithm’s feasibility. Additionally, a scanning optical measurement system with a multi-beam array was developed in our lab, and the iterative algorithm was applied to process our experimental data. The results were then compared with interferometer data, demonstrating that our algorithm is feasible for MASTS to measure aberrations in large-aperture optical systems with high accuracy.

In recent years, due to the growing complexity of real-world problems, researchers have been favoring stochastic search algorithms as their preferred method for problem solving. The slime mould algorithm is a high-performance, stochastic search algorithm inspired by the foraging behavior of slime moulds. However, it faces challenges such as low population diversity, high randomness, and susceptibility to falling into local optima. Therefore, this paper presents an enhanced slime mould algorithm that combines multiple strategies, called the ESMA. The incorporation of selective average position and Lévy flights with jumps in the global exploration phase improves the flexibility of the search approach. A dynamic lens learning approach is employed to adjust the position of the optimal slime mould individual, guiding the entire population to move towards the correct position within the given search space. In the updating method, an improved crisscross strategy is adopted to reorganize the slime mould individuals, which makes the search method of the slime mould population more refined. Finally, the performance of the ESMA is evaluated using 40 well-known benchmark functions, including those from CEC2017 and CEC2013 test suites. It is also recognized by Friedman’s test as statistically significant. The analysis of the results on two real-world engineering problems demonstrates that the ESMA presents a substantial advantage in terms of search capability.

Marine fishery is an important part of China’s maritime power strategy. Improving the ecological efficiency of marine fishery is the inevitable way to achieve the sustainable development of fishery. Based on the perspective of industrial sustainable development, this study used the Super-SBM model to evaluate the ecological efficiency of marine fishery in 11 coastal provinces of China from 2011 to 2020. Combined with Malmquist index, Moran index and other methods, the spatial and temporal evolution characteristics were analyzed. On this basis, the Tobit panel model was used to explore the influencing factors of marine fishery eco-efficiency. The results show that: (1) From 2011 to 2020, the marine fishery eco-efficiency in the 10 coastal provinces and cities of China shows a clear trend of improvement, and the efficiency values in high-efficiency areas remain basically stable. The relative gap between efficient and inefficient regions remains significant. (2) From the perspective of spatial distribution characteristics, the ecological efficiency of marine fishery in coastal provinces and cities in China had no obvious spatial correlation, and showed a trend of cross-distribution between high-efficiency regions and low-efficiency regions. (3) The change of marine fishery eco-efficiency is the result of a variety of influencing factors. Fishery industrial structure, scientific and technological support levels and environmental regulation play a role in promoting the improvement of marine fishery eco-efficiency. Therefore, optimizing the structure of the fishery industry, improving environmental regulation and increasing investment in science and technology are all effective measures for local governments to improve the eco-efficiency of marine fisheries.

Over the past 20 years, as the depth and diameter of shaft lines increased in China, the cracking and water leakage of the inner walls of frozen shafts have become increasingly severe, resulting in significant safety threats and economic losses. Understanding the stress variation patterns of cast-in-place inner walls under the combined effects of temperature and constraint during construction is a prerequisite for evaluating the crack resistance performance of inner walls and preventing water leakage in frozen shafts. The temperature stress testing machine is an important instrument for studying the early-age crack resistance performance of concrete materials under the combined effects of temperature and constraint. However, existing testing machines have shortcomings in terms of applicable specimen cross-sectional shapes, temperature control methods for concrete structures, and axial loading capacity. In this paper, a novel temperature stress testing machine suitable for the inner wall structure shape, capable of simulating the hydration heat of the inner walls, was developed. Then, a reduced-scale model of the inner wall according to similarity criteria was manufactured indoors. Finally, preliminary investigations of the temperature, strain, and stress variations of the inner wall under 100% end constraint conditions were conducted by simulating the actual hydration heating and cooling process of the inner walls. Results show that the hydration heating and cooling process of the inner wall can be accurately simulated. After approximately 69 h of concrete casting, the accumulated relative displacement and strain of the end-constrained inner wall model were −244.2 mm and 187.8 με, respectively. The end constraint force of the model increased to a maximum value of 1.7 MPa and then rapidly unloaded, causing the model concrete to crack in tension. The temperature stress testing method presented in this paper provides a reference for scientifically formulating technical approaches to prevent cracking in cast-in-place concrete inner walls.

Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve the detection accuracy and performance, we proposed an intrusion detection model based on improved deep belief network (DBN). Traditional neural network training methods, like Back Propagation (BP), start to train a model with preset parameters such as the randomly initialized weights and thresholds, which may bring some issues, e.g., attracting the model to the local optimal solutions, or requiring a long training period. We use the Kernel-based Extreme Learning Machine (KELM) with the supervised learning ability to replace the BP algorithm in DBN in a bid to ameliorate the situation. Considering the problem of poor classification performance usually caused by randomly initializing kernel parameters with KELM, an enhanced grey wolf optimizer (EGWO) is designed to optimize the parameters of KELM. In order to improve the search ability and optimization ability of the traditional grey wolf optimizer algorithm, a novel optimization strategy combining the inner and outer hunting is introduced. Experiments on KDDCup99, NSL-KDD, UNSW-NB15 and CICIDS2017 datasets show that the proposed DBN-EGWO-KELM algorithm has greater advantages in terms of its accuracy, precision, true positive rate, false positive rate and other evaluation indices compared with BP, RBF, SVM, KELM, LIBSVM, CNN, DBN-KELM and other intrusion detection models, and can effectively meet the requirements of intrusion detection of complex networks.

We, the Publisher, have retracted the following article: Rui Yang, Dahai Li (2022). Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173165 The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany. From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission. Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.

This paper presents a nonlinear error feedback controller for a pneumatic soft bionic fin based on an extended state observer. Manufacturing process is shown for a bionic stingray with the pneumatic soft bionic fin. A test experiment is carried out for a driving part in the pneumatic soft bionic fin by upper chamber inflated and lower chamber inflated with different internal pressures. A dynamic nonlinear system is established for the pneumatic soft bionic fin by test experiment results and kinematic modeling. Total disturbances of the dynamic nonlinear system are estimated and compensated by the extended state observer and the nonlinear error feedback controller, respectively. Moreover, both the convergence of the extended state observer and stability of the dynamic nonlinear system are proved by Lyapunov approach. Finally, simulation results are shown to verify the effectiveness of the proposed control method for the pneumatic soft bionic fin.

This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173792. Attention mechanism is widely used in fine-grained image classification. Most of the existing methods are to construct an attention weight map for simple weighted processing of features, but there are problems of low efficiency and slow convergence. Therefore, this paper proposes a multi-channel attention fusion mechanism based on the deep neural network model which can be trained end-to-end. Firstly, the different regions corresponding to the object are described by the attention diagram. Then the corresponding higher order statistical characteristics are extracted to obtain the corresponding representation. In many standard fine-grained image classification test tasks, the proposed method works best compared with other methods.

There exists a defect of the narrow viewing angle in the conventional integral imaging system. One reason for this is that only partial pixels of each elemental image contribute to the viewing angle and the others cause image flips. In this paper, a wide-viewing-angle integral imaging system with a full-effective-pixels elemental image array (FEP-EIA) was proposed. The correspondence between viewpoints and pixel coordinates within the elemental image array was built up, and effective pixel blocks and pixels leading to flipping images were deduced. Then, a pixel replacement method was proposed to generate the FEP-EIAs, which adapt to different viewing distances. As a result, the viewing angle of the proposed integral imaging system was effectively extended through the replacement of the pixels, which caused the image flips. Experiment results demonstrated that wide viewing angles are available for the proposed integral imaging system regardless of the viewing distances.

Sampled points on a measured surface are not evenly spaced on rectangular grids but, rather, have a more general quadrilateral geometry due to the perspective effect of the off-axis configuration, which is extensively utilized in phase measuring deflectometry (PMD). The current procedures for slope-to-wavefront integration results in reconstruction errors, which impact PMD to achieve a higher measurement accuracy. Due to the advantageous properties of the radial basis function (RBF) for irregular sampling and incomplete data, the quadrilateral geometry acquired by a CCD is first realized in this paper using the ray tracing method, which is used to obtain the actual measurement area and sampling interval in off-axis PMD instead of the area defined by the mathematical formula in the past when RBF was used. The RBF integration method is researched in this paper. The simulation and experiment show that RBF is a robust and higher accuracy algorithm for off-axis PMD. We further implement the RBF integration method to measure a crystal transparent element where an off-axis configuration is required.

Lenses play an important role in imaging systems. Having an effective way to test the aberrations of imaging lenses is important. However, the existing methods cannot satisfy the requirements in some conditions. To overcome these difficulties, wavefront aberration measurement deflectometry (WAMD) is proposed in this paper, which can reconstruct the wavefront aberrations of imaging lenses by measuring the angular aberrations. The principle of WAMD is analyzed in detail, and the correctness and feasibility of the proposed method are verified by both a simulation and an experiment. A telephoto lens and a single imaging lens were tested in an experiment, and the RMS errors were 166.8 nm (5.71%) and 58.9 nm (4.74%), respectively, as compared with the interferometer’s results. This method is widely applicable with relatively reasonable accuracy. It has potential to be applied in the lens manufacturing and alignment process.

The Lubei Ni–Cu–Co deposit situated in western segment of the Huangshan-Jing’erquan mafic–ultramafic rock belt in eastern Tianshan of the Central Asian Orogenic Belt (CAOB). The estimated reserve is approximately 9.11 million tons of ore resources with average grades of 0.82 wt% Ni, 0.52 wt% Cu, and 0.03 wt% Co. The Lubei intrusion is mainly composed of gabbro (phase I), peridotite (phase II), pyroxene peridotite (phase III), olivine pyroxenite (phase IV), and diorite (phase V), which intruded into the early Carboniferous tuffaceous clastic rocks. Zircon Laser Ablation–Inductively Coupled Plasma–Mass Spectrometry (LA–ICP–MS) U–Pb age of the diorite (phase V) from the edge of the intrusion is interpreted as the top-limit metallogenic age, which is consistent with the formation ages of the Huangshan and Xiangshan Ni–Cu deposits in eastern Tianshan. The roughly parallel rare earth element (REE) curves of the Lubei intrusion indicate the magma originated from a homologous source. The slightly enriched large ion lithophile elements (LILE) are compared to high field strength elements (HFSE) with negative Nb and Ta anomalies show that the Lubei intrusion has arc-affiliate geochemical characteristics. The Sr–Nd–Hf isotopes show that the magma was derived from depleted lithospheric mantle, while suffering 4–10% lower crustal contamination with slight contamination of the upper crust. Based on a comprehensive conservation of regional geological, geochemical, and geochronological evidence, the primary magma of the Lubei intrusion was identified that it was derived from the partial melting of metasomatized lithospheric mantle previously modified by subduction events. The Lubei nickel–copper–cobalt sulfide deposit was formed after the primary magma experienced fractional crystallization, crustal contamination, and sulfide segregation in a post-collisional extensional geodynamic setting after the closure of the Kanggur ocean basin in the early Permian.

Based on the assumption that the long-term value of a venture capital satisfies the algebraic Brownian motion, we develop a continuous-time exit model of venture capital under different exit modes, namely, initial public offering (IPO) and mergers and acquisitions (M&A). The employee incentive problem is analyzed jointly with the exit decision of the firm in terms of the exit timing and the exit mode. Further, the problem of capital exit is considered from two perspectives, namely, optimal venture capital and social welfare maximization, and the differences between these exit decisions are compared. Our model predicts that the timing of an IPO, the purpose of which is to maximize the utility of the capitalists, lags behind the exit timing, whose purpose is to maximize social welfare. Using a numerical analysis, this paper also proves that increasing the production efficiency, lowering the interest rates, and improving risk management can make the exit decision of venture capitalists converge with that of maximizing social welfare.

Phase measuring deflectometry (PMD) is a competitive method for specular surface measurement that offers the advantages of a high dynamic range, non-contact process, and full field measurement; furthermore, it can also achieve high accuracy. Camera calibration is a crucial step for PMD. As a result, a method based on the calibration of the entrance pupil center is introduced in this paper. Then, our proposed approach is compared with the most popular photogrammetric method based on Zhang’s technique (PM) and Huang’s modal phase measuring deflectometry (MPMD). The calibration procedures of these three methods are described, and the measurement errors introduced by the perturbations of degrees of freedom in the PMD system are analyzed using a ray tracing technique. In the experiment, a planar window glass and an optical planar element are separately measured, and the measurement results of the use of the three methods are compared. The experimental results for the optical planar element (removing the first 6 terms of the Zernike polynomial) show that our method’s measurement accuracy reached 13.71 nm RMS and 80.50 nm PV, which is comparable to accuracy values for the interferometer.

Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf’s boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

This article presents the design modeling of a novel 3-RRS spherical parallel mechanism for ankle rehabilitation applications. The kinematics of the 3-RRS spherical parallel mechanism is established. The degree of freedom of 3-RRS spherical parallel mechanism is calculated using screw theory. The inverse kinematics of 3-RRS spherical parallel mechanism is solved. Eight groups of inverse solutions of 3-RRS spherical parallel mechanism are obtained. A method for forward position analysis is developed with variation and iteration approaches, which is suitable for motor position control. The ankle rehabilitation robot can be widely used in clinical treatment and can also be used at home, hotels, and fitness centers for ankle muscle relaxation.

We propose a dual-view integral imaging 3D display based on a multiplexed lens-array holographic optical element (MHOE). A MHOE is a volume holographic optical element obtained by multiplexing technology, which can be used for dual-view integral imaging 3D display due to the angle selectivity of the volume HOE. In the fabrication of the MHOE, two spherical wavefront arrays with different incident angles are recorded using photopolymer material. In the reconstruction, two projectors are used to project the elemental image arrays (EIA) with corresponding angles for two viewing zones. We have developed a prototype of the dual-view integral imaging display. The experimental results demonstrate the correctness of the theory.

Halo artifact, edge detail loss and noise amplification are the main problems in low illumination image enhancement, an image enhancement algorithm combining Retinex and illumination map weighted guided filtering is proposed. The traditional defogging physical models only enhance the images based on dark channels prior, resulting in different depths of field in local areas, and it can lead to some problems such as image overexposure and halo artifacts. To solve this problem, the method of combining light and dark channels is adopted to calculate the atmospheric light value and transmittance. For the problem that edge information is easily lost, the illumination gradient domain weighted guided filtering is utilized to improve the thinning transmittance. Experimental results with the proposed method have obvious improvement in denoising, halo elimination, brightness adjustment and edge preservation in the low-illumination image under different conditions. [ABSTRACT FROM AUTHOR]

The industrial smoke scene is complex and diverse, and the cost of labeling a large number of smoke data is too high. Under the existing conditions, it is very challenging to efficiently use a large number of existing scene annotation data and network models to complete the image classification and recognition task in the industrial smoke scene. Traditional deep learn-based networks can be directly and efficiently applied to normal scene classification, but there will be a large loss of accuracy in industrial smoke scene. Therefore, we propose a novel generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition. In this paper, a low-cost data enhancement method is used to effectively reduce the difference in the pixel field of the image. The smoke image is input into the LSTM in generator and encoded as a hidden layer vector. This hidden layer vector is then entered into the discriminator. Meanwhile, a convolutional block attention module is integrated into the discriminator to improve the feature self-extraction ability of the discriminator model, so as to improve the performance of the whole smoke image recognition network. Experiments are carried out on real diversified industrial smoke scene data, and the results show that the proposed method achieves better image classification and recognition effect. In particular, the F scores are all above 89%, which is the best among all the results. [ABSTRACT FROM AUTHOR]

Abstract Background Human cytomegalovirus (HCMV) is a herpesvirus that can infect various cell types and modulate host gene expression and immune response. It has been associated with the pathogenesis of various cancers, but its molecular mechanisms remain elusive. Methods We comprehensively analyzed the expression of HCMV pathway genes across 26 cancer types using the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We also used bioinformatics tools to study immune invasion and tumor microenvironment in pan-cancer. Cox regression and machine learning were used to analyze prognostic genes and their relationship with drug sensitivity. Results We found that HCMV pathway genes are widely expressed in various cancers. Immune infiltration and the tumor microenvironment revealed that HCMV is involved in complex immune processes. We obtained prognostic genes for 25 cancers and significantly found 23 key genes in the HCMV pathway, which are significantly enriched in cellular chemotaxis and synaptic function and may be involved in disease progression. Notably, CaM family genes were up-regulated and AC family genes were down-regulated in most tumors. These hub genes correlate with sensitivity or resistance to various drugs, suggesting their potential as therapeutic targets. Conclusions Our study has revealed the role of the HCMV pathway in various cancers and provided insights into its molecular mechanism and therapeutic significance. It is worth noting that the key genes of the HCMV pathway may open up new doors for cancer prevention and treatment.

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Marine Spatial Planning (MSP) is essential for optimizing marine resources allocation and resolving conflicts in sea use, but it is challenged by the complex and multi-functional nature of maritime resources. This study utilizes participatory methods, inductive reasoning, and overlay analysis to explore both theoretical and practical aspects of compatible marine utilization models. The paper begins by clarifying the logic of compatible sea use through marine functional zoning and identifying its core characteristics: quantity, spatial conflict, and impacts on natural attributes. Building on China’s national territorial spatial planning reforms and practical experiences, the paper introduces three major categories of compatible marine use models: development sequencing compatibility, spatial coexistence compatibility, and functional synergy compatibility. A compatibility discrimination matrix is developed to assess these models across different marine functional areas. Combined with the compatibility discrimination results, taking the marine and coastal spatial planning of Yantai City in China as a case study, the paper analyzes the compatibility demands and planning strategies within various marine functional areas. Finally, it evaluates the risks associated with each compatibility model and proposes targeted control strategies tailored to the specific features of each model and characteristics of sea use activities. The research findings highlight the positive role of compatible sea use models in promoting marine economic development, enhancing spatial efficiency, and mitigating sea use conflicts. However, these models also present varying potential risks, necessitating differentiated control strategies aligned with legal frameworks and specific sea use activities. The study offers valuable insights for MSP and contributes to the efficient utilization of marine resources and the advancement of marine spatial governance.

4-1BB agonists for cancer immunotherapy have shown good preliminary efficacy in clinical trials, but several of the first-generation 4-1BB agonistic antibodies entering the clinic have failed due to safety issues. Selenium nanoparticles (SeNPs) exhibit anti-inflammatory, anti-tumor, antioxidant, and immune-modulating properties. In addition, they have been shown to have detoxifying effects and prevent oxidative liver damage. In this study, we used an anti-4-1BB antibody in combination with SeNPs to evaluate the anti-lung cancer effects in in vitro and in vivo experiments and explore the underlying mechanisms by pathological analyses, quantitative PCR, and enzyme-linked immunoassay. We found that 5 μmol·L–1 anti-4-1BB antibody combined with 1 μmol·L–1 SeNPs increased the expression of IFN-γ and promoted the killing effects of peripheral blood mononuclear cells on Lewis lung carcinoma cells, with a lethality rate up to 56.88 %. Experiments in tumor-bearing mice showed that the tumor inhibition rate was 58.61 % after treatment with 3.5 mg/kg anti-4-1BB antibody combined with 0.25 mg/kg SeNPs, and the liver function index returned to normal. When the combined treatment was compared with the antibody treatment alone, detection of immune relevant factors demonstrated that the expression of FOXP3, IL-2, IL-12, and TNF-α in the spleen was downregulated, whereas the expression of IFN-γ in the spleen, serum, and tumor was upregulated, accompanied by increased Fas ligand expression in the tumor tissues. Based on these findings, we get the conclusion that anti-4-1BB antibody combined with SeNPs may alleviate the immunosuppression of regulatory T cells, promote the immune cell proliferation and metastasis to synergistically kill tumor cells. This combination also reduces the inflammatory damage to normal tissues and slows overstimulation of the splenic immune response.