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To address the issues of sluggish response and inadequate precision in traditional gate opening control systems, this study presents a novel approach for direct current (DC) motor control utilizing an enhanced beetle antennae search (BAS) algorithm to fine-tune the parameters of a fuzzy proportional integral derivative (PID) controller. Initially, the mathematical model of the DC motor drive system is formulated. Subsequently, employing a search algorithm, the three parameters of the PID controller are optimized in accordance with the control requirements. Next, software simulation is employed to analyze the system's response time and overshoot. Furthermore, a comparative analysis is conducted between fuzzy PID control based on the improved beetle antennae search algorithm, and conventional approaches such as the traditional beetle antennae search algorithm, the traditional particle swarm algorithm, and the enhanced particle swarm algorithm. The findings indicate the superior performance of the proposed method, characterized by reduced oscillations and accelerated convergence compared to the alternative methods. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments. [ABSTRACT FROM AUTHOR]

Despite mounting evidence for dietary protease benefits, the mechanisms beyond enhanced protein degradation are poorly understood. This study aims to thoroughly investigate the impact of protease addition on the growth performance, intestinal function, and microbial composition of weaned piglets. Ninety 28-day-old weaned pigs were randomly assigned to the following three experimental diets based on their initial body weight for a 28-day experiment: (1) control (CC), a basic diet with composite enzymes without protease; (2) negative control (NC), a diet with no enzymes; and (3) dietary protease (PR), a control diet with protease. The results show that dietary proteases significantly enhanced growth performance and boosted antioxidant capacity, increasing the total antioxidant capacity (T-AOC) levels (p < 0.05) while reducing malonaldehyde levels (p < 0.05). Additionally, protease addition reduced serum levels of inflammatory markers TNF-α, IL-1β, and IL-6 (p < 0.05), suppressed mRNA expression of pro-inflammatory factors in the jejunum (p < 0.01), and inhibited MAPK and NF-κB signaling pathways. Moreover, protease-supplemented diets improved intestinal morphology and barrier integrity, including zonula occludens protein 1(ZO-1), Occludin, and Claudin-1 (p < 0.05). Microbiota compositions were also significantly altered by protease addition with increased abundance of beneficial bacteria (Lachnospiraceae_AC2044_group and Prevotellaceae_UCG-001) (p < 0.05) and reduced harmful Terrisporobacter (p < 0.05). Further correlation analysis revealed a positive link between beneficial bacteria and growth performance and a negative association with inflammatory factors and intestinal permeability. In summary, dietary protease addition enhanced growth performance in weaned piglets, beneficial effects which were associated with improved intestinal barrier integrity, immunological response, and microbiota composition. [ABSTRACT FROM AUTHOR]

Inductive power transfer (IPT) systems are pivotal in various applications, relying heavily on the accurate estimation of mutual inductance to enable system interoperability discrimination and optimal efficiency tracking control. This paper introduces a novel mutual inductance estimation method for Series-Series IPT (SS-IPT) systems, utilizing time-domain modeling combined with nonlinear least squares. Initially, the time-domain model of SS-IPT systems is developed by deriving its ordinary differential equations (ODEs). Subsequently, the mutual inductance is estimated directly from these ODEs using a nonlinear least-squares approach. This approach necessitates only primary-side information, eliminating the need for communication, supplementary equipment, or frequency scanning. The simplicity and directness of using collected real-time data enhance the practical applicability of our approach. The effectiveness of the proposed method is substantiated through simulations and experimental data. Results demonstrate that the estimation accuracy of our method remains more than 95.0% in simulations and more than 92.5% in experimental data. [ABSTRACT FROM AUTHOR]

This article presents a vibration model of neighboring rolling parts that takes into account non-smooth symmetric collisions. This model was used to examine the motion state of the rolling element and the collision force between nearby rolling elements. It also determined the motion posture and overall collision form of the rolling element after setting the functional slot. Afterwards, the level of disorderly movement and the structure of the moving object were examined and confirmed through the use of a phase diagram of the motion system in relation to zero symmetry, the Lyapunov exponent, and a platform for measuring irregular vibrations in the bearing. This work aims to clarify the factors that contribute to the persistent chaotic state of rolling elements in bearing vibration. [ABSTRACT FROM AUTHOR]

As the producers of environmental pollution, it is urgent for enterprises to make up for their lack of environmental responsibility and to realize green transformation and development. At the same time, resource dependence is promoted from the single level of economic growth to the field of green development, which is a field of research and development on resource dependence and broadens the perspective of related research in the academic world. In this paper, we select panel data from 30 regions in China from 2009 to 2022 to validate the research on the impact of resource dependence and corporate social responsibility on green innovation performance. The conclusions are as follows: (1) From 2009 to 2022, the average industrial green innovation performance of the 30 provinces in China was 0.553, with the efficiency values of the eastern, central, and western regions showing a gradual decreasing trend. (2) We found a consistently negative correlation between resource dependency and green innovation performance, confirming the existence of a "resource curse" linking the two. Meanwhile, the regression coefficient of CSR for green innovation performance was positive, confirming the driving effect of the former on the latter. (3) The "resource curse" does not manifest conditionally or have a threshold effect. Instead, we found that it has long-term and persistent characteristics. Meanwhile, the impact of CSR on green innovation performance shows a "reverse N-shaped" double-threshold effect, where CSR can improve green innovation performance only when it reaches a certain threshold value. This paper provides insights to support Chinese enterprises in enhancing their green innovation performance and lays a theoretical foundation for enterprises to fulfill their social responsibility. [ABSTRACT FROM AUTHOR]

Aquaculture plays a vital role in global food production, with fish pond water quality directly impacting aquatic product quality. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a key producer of aquatic products in South China. Monitoring environmental changes in fish ponds serves as an indicator of their health. This study employed the extreme gradient boosting tree (BST) model of machine learning, utilizing Landsat imagery data, to assess Chlorophyll-a (Chl-a) concentration in GBA fish ponds from 2013 to 2022. The study also examined the corresponding spatiotemporal variations in Chl-a concentration. Key findings include: (1) clear seasonal fluctuations in Chl-a concentration, peaking in summer (56.7 μg·L−1) and reaching lows in winter (43.5 μg·L−1); (2) a slight overall increase in Chl-a concentration over the study period, notably in regions with rapid economic development, posing a heightened risk of eutrophication; (3) influence from both human activities and natural factors such as water cycle and climate, with water temperature notably impacting summer Chl-a levels; (4) elevated Chl-a levels in fish ponds compared to surrounding natural water bodies, primarily attributed to human activities, indicating an urgent need to revise breeding practices and address eutrophication. These findings offer a quantitative assessment of fish pond water quality and contribute to sustainable aquaculture management in the GBA. [ABSTRACT FROM AUTHOR]

Geothermal resources have been recognized as important sources of clean renewable energy. The exploration, development, and utilization of geothermal resources, especially hot dry rock (HDR) resources, are highly important for achieving peak carbon and carbon neutrality. However, there is no comprehensive evaluation method for determining HDR target areas, and the evaluation scale and application disciplines are relatively simplistic. In this paper, we sought to optimize the identification of HDR target areas through a multiscale and multidisciplinary method and formed a set of generalized and demonstrative processes to guide the exploration of HDR resources. Through practical application to the Gonghe Basin and the Zhangzhou Basin, it was found that the comprehensive geothermal conditions of the Gonghe Basin are superior to those of the Zhangzhou Basin, and the geothermal reservoir depth, geothermal reservoir temperature, geothermal gradient, and heat flow are the four most important factors affecting hot dry rock geothermal resources. Using this method, the prioritization of target areas changes from a qualitative study to a quantitative and semiquantitative study, increasing the specificity and reliability of the decision-making process. [ABSTRACT FROM AUTHOR]

Based on the energy consumption data of the Changjiang River Administration of Navigation Affairs public building located in Wuhan, China, this paper analyzes the energy consumption characteristics of public buildings. Using the DesignBuilder energy simulation software (v7.0.0.096), various factors influencing building energy consumption are investigated. Furthermore, SPSS (v26.0.0.0) is employed to conduct correlation analyses between measured and simulated energy consumption data. The results indicate that the most significant factors affecting energy consumption are outdoor temperature, office equipment usage time, anticipated room temperature settings, boiler usage time, and per capita daily water consumption. By exploring the factors influencing energy consumption in public buildings, this study contributes novel insights into setting targets, adjusting schemes, and achieving energy-saving effects. [ABSTRACT FROM AUTHOR]

Cultivated grasslands are an important part of grassland ecosystems and have been proven to be major carbon sinks, then playing an important role in the global carbon balance. The effect of cultivated grassland type (Medicago sativa, Triticum aestivum, Secale cereale, and Vicia villosa grasslands) on carbon flux (including net ecosystem CO2 exchange (NEE), ecosystem respiration (ER), and gross ecosystem productivity (GEP)) downstream of the Yellow River was studied via the static chamber technique and a portable photosynthetic system. Bare land was used as a control. The results showed that the four cultivated grassland types were mainly carbon sinks, and bare land was a carbon source. The cultivated grassland types significantly affected carbon flux. The average NEE and GEP of the grassland types were in the following order from high to low: Medicago sativa, Secale cereale, Triticum aestivum, and Vicia villosa grassland. Stepwise regression analysis showed that among all measured environmental factors, soil pH, soil bulk density (BD), soil organic carbon (SOC), and soil microbial carbon (MBC) were the main factors affecting CO2 flux. The combined influence of soil BD, SOC, and pH accounted for 77.6% of the variations in NEE, while soil BD, SOC, and MBC collectively explained 79.8% of changes in ER and 72.9% of the changes in GEP. This finding indicates that Medicago sativa grassland is a cultivated grassland with a high carbon sink level. The changes in carbon flux were dominated by the effects of soil physicochemical properties. [ABSTRACT FROM AUTHOR]

In marine engineering and water conservancy projects, dredging often occurs due to silt accumulation, which can impede the long-term development of water ecosystems and water storage systems. To enhance dredging efficiency and the performance of dredging machines, a novel type of winch suction underwater dredging robot was designed. Computational fluid dynamics software was utilized to establish a fluid model of the robot's winch suction dredging device and conduct model simulation experiments. The simulation test results were used to investigate the factors influencing dredging performance and their impact laws. Five key factors—namely, the reamer rotational speed, reamer arrangement angle, water flow rate, inlet pipe diameter, and outlet pipe diameter—were selected for consideration. By setting up various sets of factor levels, the significant influence of different factors on dredging efficiency was examined. Analysis of variance was employed to analyse the results of the orthogonal experimental design, leading to the identification of optimal factor levels and the establishment of an optimal scheme group. The results of the optimal scheme were verified, demonstrating a 13.049% increase in dredging efficiency and a 19.23% decrease in power consumption of the sludge pump compared to the initial experimental setup. The performance of the optimal program surpassed that of all the experimental designs and met the design requirements. [ABSTRACT FROM AUTHOR]

Large-scale wind farms incorporating doubly-fed induction generators (DFIGs) are considered a promising direction for modern energy supply systems due to their role in reducing dependence on fossil energy sources. However, the dynamic interactions between DFIGs and AC grids sometimes lead to sub-synchronous oscillation (SSO) that threatens the safe and stable operation of wind power systems. Therefore, it is essential to develop a mathematical model and design an algorithm to quantitatively design the control parameters. Such algorithms are helpful in preventing or mitigating system stability problems coming from wind power connected to the grid and reducing damage to power equipment. The traditional state-space model is mainly established to determine the stable operating point and analyze the influence of parameters on the system operating mode. However, this method does not provide the selection area for the system parameters. To address this shortcoming, this paper introduces a modular state-space model for DFIGs containing series compensation lines and proposes an algorithm for calculating the parameter selection area based on the Guardian map method. First, a detailed modular state-space model based on the virtual synchronous generator (VSG) control is established. The modular model helps to reflect the relationship between state variables and focuses on describing the operating state of DFIGs in wind farms. Second, this paper focuses on the influence of VSG control parameters and compensation capacitance on SSO. It aims to clarify the role of the series compensation level and control parameters on SSO based on VSG control. Then, an algorithm for the parameter selection area based on the Guardian map is proposed and the area of the VSG-controlled DFIG is obtained. Finally, the accuracy and validity of the algorithm are verified by time domain simulation in MATLAB/Simulink and HIL experiment. [ABSTRACT FROM AUTHOR]

The thermal structure of the lithosphere is key to understanding its thickness, properties, evolution, and geothermal resources. Cratons are known for their low heat flow and deep lithospheric roots. However, present-day cratons in East China have geothermal characteristics that are highly complex, with variable heat flow values, diverging from the typical thermal state of cratons. In this study, we conducted a detailed analysis of the geothermal geological background of the cratons in East China, summarizing the thermal state and tectono-thermal processes of different tectonic units, calculating the temperature at various depths, and discussing differences in temperature and thermal reservoirs at different depths. The observed lithospheric thermal thickness within the North Jiangsu Basin and the Bohai Bay Basin is notably reduced in comparison to that of the Jianghan Basin and the Southern North China Basin. The phenomenon of craton destruction during the Late Mesozoic emerges as a pivotal determinant, enhancing the geothermal resource prospects of both the Bohai Bay Basin and the North Jiangsu Basin. Our findings contribute significantly to the augmentation of theoretical frameworks concerning the origins of heat sources in global cratons. Furthermore, they offer invaluable insights for the methodical exploration, evaluation, advancement, and exploitation of geothermal resources. [ABSTRACT FROM AUTHOR]

The construction sector is one of the principal contributors to carbon dioxide emissions (CDEs) and has a vital role to play in responding to the issue of long-term environmental sustainability. This research proposes a process-based hybrid life-cycle assessment (LCA) method depending on a process-based LCA and an input–output LCA. The process-based hybrid LCA model provides a supplementary method to quickly estimate carbon emissions that are not considered in the system boundary due to the limitation of inventory data. The proposed hybrid method was applied to a carbon emissions assessment in a slurry shield tunnel. The results suggest that 93.88% of emissions are from materials. Of the materials contribution, 55.9% comes from steel and 34.55% arises from concrete. It has also been found that emissions during the tunneling stage are negatively correlated with the efficiency of tunnel construction. Recommendations for carbon emissions reductions in tunnel construction are provided for promoting sustainable transportation and management. [ABSTRACT FROM AUTHOR]

With the promotion of the dual carbon target, the scale of the wind power grid connection will significantly increase. However, wind power has characteristics such as randomness and volatility, and its grid connection challenges the pressure of system peak shaving, making it increasingly difficult to regulate the power system. To solve the problem of wind power abandonment, the positive and negative peak shaving characteristics of wind power were first analyzed. Based on this, it is proposed that demand response resources and energy storage with adjustable characteristics are used as the new means of wind power consumption. Together with the thermal power units, they participate in the optimization and scheduling of the power grid, forming a coordinated and optimized operation mode of source load storage. With the goal of minimizing system operating costs, a two-stage economic scheduling model was formed for the day-ahead and intra-day periods. Finally, optimization software was used to solve the problem, and the simulation results showed the effectiveness of the proposed economic scheduling model, which can improve the system's new energy consumption and reduce the system's operating costs. [ABSTRACT FROM AUTHOR]

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen's slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (>84%) and F-score value (>82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed. [ABSTRACT FROM AUTHOR]

The underground pressure disaster caused by the exploitation of deep mineral resources has become a major hidden danger restricting the safe production of mines. Microseismic monitoring technology is a universally recognized means of underground pressure monitoring and early warning. In this paper, the wavelet coefficient threshold denoising method in the time–frequency domain, STA/LTA method, AIC method, and skew and kurtosis method are studied, and the automatic P-phase-onset-time-picking model based on noise reduction and multiple detection indexes is established. Through the effect analysis of microseismic signals collected by microseismic monitoring system of coral Tungsten Mine in Guangxi, automatic P-phase onset time picking is realized, the reliability of the P-phase-onset-time-picking method proposed in this paper based on noise reduction and multiple detection indexes is verified. The picking accuracy can still be guaranteed under the severe signal interference of background noise, power frequency interference and manual activity in the underground mine, which is of great significance to the data processing and analysis of microseismic monitoring. [ABSTRACT FROM AUTHOR]

Human-caused nitrogen (N) deposition is a global environmental issue that can change community composition, functions, and ecosystem services. N deposition affects plants, soil, and microorganisms regionally and is linked to ecosystem, soil, and climate factors. We examined the effects of six N addition levels (0, 2.34 g, 4.67, 9.34,18.68, and 37.35 g N m−2 yr−1) on aboveground vegetation, surface soil properties, and microbial community. Alterations in microbial communities in response to N addition were monitored using 16S rRNA (16S ribosomal ribonucleic acid, where S donates a sedimentation coefficient) and ITS (internal transcribed spacer) regions for bacterial and fungal communities, respectively. N addition positively affected aboveground vegetation traits, such as biomass and community weighted mean of leaf nitrogen. N addition also limited phosphorus (P) availability and altered the microbial community assembly process from random processes to deterministic processes. The microbial community diversity and composition, however, were not sensitive to N addition. Partial least squares structural equation models showed that the composition of bacterial communities was mainly driven by the composition of plant communities and total nitrogen, while the composition of fungal communities was driven by soil pH and community weighted mean of leaf nitrogen. Taken together, the results of this research improved our understanding of the response of grassland ecosystems to N deposition and provided a theoretical basis for grassland utilization and management under N deposition. [ABSTRACT FROM AUTHOR]

Under the premise of continuously increasing the grid-connected capacity of new energy, the fluctuation and anti-peak shaving characteristics of wind power have always constrained the development of green power systems. Considering the characteristics of power system flexibility resources, this paper introduces a two-stage regulation approach for power systems with enhanced source–load flexibility. In the day-ahead stage, an advanced peak regulation transformation is employed, leveraging the combined heat storage device of conventional thermal power units to enhance their peak regulation capability. Additionally, the Energy Intensive Load (EIL) is integrated into the regulation system. A two-level coordinated optimization model is developed, incorporating wind power integration and dispatching power allocation, with the aim of optimizing wind power integration and achieving the optimal allocation of dispatching power. In the intra-day stage, the connection of wind plants and energy storage devices is utilized to minimize the wind power fluctuations and improve the control ability over wind power variations. Compared with traditional methods, the wind power consumption in Scenario 1 and Scenario 2 increases by 2741.1 MW/h and 2478.5 MW/h respectively. Furthermore, the inclusion of an energy storage device in the intra-day stage significantly reduces the wind power fluctuations, maintaining a stable fluctuation rate within ±1%. Therefore, this method can effectively improve the level of wind power consumption and reduce the impact of real-time fluctuations on the power system. [ABSTRACT FROM AUTHOR]

Geothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST readings are influenced by various factors such as sun radiation, cyclical variations, and precipitation, which can mask the temperature anomalies caused by geothermal heat. To address these issues and highlight the LST anomalies caused by geothermal heat, this paper proposes a methodology to efficiently and quickly calculate the multi-temporal LST leveraging of the Google Earth Engine (GEE) in the Damxung–Yangbajain basin, Qinghai–Tibet Plateau. This method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The existing geothermal manifestations are used as a benchmark to further refine the methodology. The results indicate that the annual mean winter LST is a sensitive indicator of geothermal anomaly signals. The annual mean winter LST between 2015 and 2020 varied from −14.7 °C to 26.7 °C, with an average of 8.6 °C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from −22.1 °C to 23.3 °C, with an average of 6.2 °C. When combining the distribution of faults with the results of the annual mean winter LST, this study delineated the geothermal potential areas that are located predominantly around the fault zone at the southern foot of the Nyainqentanglha Mountains. Geothermal potential areas exhibited a higher LST, ranging from 12.6 °C to 23.3 °C. These potential areas extend to the northeast, and the thermal anomaly range reaches as high as 19.6%. The geothermal potential area makes up 8.2% of the entire study area. The results demonstrate that the approach successfully identified parts of known geothermal fields and indicates sweet spots for future research. This study highlights that utilizing the multi-temporal winter LST is an efficient and cost-effective method for prospecting geothermal resources in plateau environments. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is an advanced visualization detection technique with non-invasive, radiationless, and fast-response characteristics. As an important means in dredging engineering, EIT-based measurement can realize the estimate and computation of key flow parameters such as the flow velocity and solid phase fraction of solid-liquid two-phase flow in a pipe. Despite progress, both the data acquisition rate and the signal-to-noise ratio (SNR) of the existing EIT system are too low to meet many practical requirements. In this study, efforts are made concerning the EIT acquisition system by optimizing the sensor array, data acquisition system, and data communication module. Experimental results show that (1) the optimized system has an SNR of 73 dB, which is about 40% higher than the original system, and (2) the optimization of data transmission methods can achieve a maximum allowable transmission rate of 316.4 Mbps, sufficient to support data transmission over 1000 frames per second. Consequently, the key problems of the existing EIT acquisition system are substantially overcome. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive, irreversible neurodegenerative disorder that requires early diagnosis for timely treatment. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique for detecting brain activity. To improve the accuracy of Alzheimer's disease diagnosis, we propose a new network architecture called Dynamic Multi-Task Graph Isomorphism Network (DMT-GIN). This approach uses fMRI images transformed into brain network structures to classify Alzheimer's disease more effectively. In the DMT-GIN architecture, we integrate an attention mechanism with the Graph Isomorphism Network (GIN) to capture node features and topological structure information. To further enhance AD classification performance, we incorporate auxiliary tasks of gender and age classification prediction alongside the primary AD classification task in the network. This is achieved through sharing network parameters and adaptive weight adjustments for simultaneous task optimization. Additionally, we introduce a method called GradNorm for dynamically balancing gradient updates between tasks. Evaluation results demonstrate that the DMT-GIN model outperforms existing baseline methods on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, leading in various metrics with a prediction accuracy of 90.44%. This indicates that our DMT-GIN model effectively captures brain network features, providing a powerful auxiliary means for the early diagnosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has been extensively utilized in deep-space exploration. However, GPR modeling commonly employs simplified antenna models and carrier-free impulse signals, resulting in reduced accuracy and interpretability. In this paper, we addressed these limitations by combining a tilted monopole antenna and linear frequency modulation continuous wave (LFMCW) to simulate real conditions. Additionally, a radiation-pattern-compensation back-propagation (RPC-BP) algorithm was developed to improve the illumination of the right-inclined structure. We first introduced the LFMCW used by the Mars Rover Penetrating Radar (RoPeR) onboard the Zhurong rover, where frequencies range from 15 to 95 MHz. Although the LFMCW signal improves radiation efficiency, it increases data processing complexity. Then, the radiation patterns and response of the tilted monopole antenna were analyzed, where the radiated signal amplitude varies with frequency. Finally, a series of numerical and laboratory experiments were conducted to interpret the real RoPeR data. The results indicate that hyperbolic echoes tilt in the opposite direction of the survey direction. This study demonstrates that forward modeling considering real transmit signals and complex antenna models can improve modeling accuracy and prevent misleading interpretations on deep-space exploration missions. Moreover, the migration process can improve imaging quality by considering radiation pattern compensation. [ABSTRACT FROM AUTHOR]

Near-surface structure investigation plays an important role in studying shallow active faults and has various engineering applications. Therefore, we developed a near-surface structure investigation method using ambient noise in a water environment. This newly developed seismic acquisition technology, fiber-optic distributed acoustic sensing (DAS), was used to acquire ambient noise from the Yangtze River. The recorded data were processed to reconstruct surface waves based on the theory of seismic interferometry. The fundamental-mode dispersion curves were extracted and inverted to obtain a shear-wave velocity model below the DAS line. We compared the inverted velocity model with the subsurface geological information from near the study area. The results from the inverted model were consistent with the prior geological information. Therefore, ambient noise in the water environment can be combined with DAS technology to effectively investigate near-surface structures. [ABSTRACT FROM AUTHOR]

Satellite hyperspectral imagery is an important data source for large-scale refined land cover classification and mapping, but the high spatial heterogeneity and spectral variability at low spatial resolution and the high computation cost for massive data remain challenges in the research community. In recent years, convolutional neural network (CNN) models with the capability for feature extraction have been widely used in hyperspectral image classification. However, incomplete feature extraction, inappropriate feature fusion, and high time consumption are still the major problems for CNN applications in large-scale fine land cover mapping. In this study, a Spatial-Convolution Spectral-Transformer Interactive Network (SCSTIN) was proposed to integrate 2D-CNN and Transformer into a dual-branch network to enhance feature extraction capabilities by exploring spatial context information and spectral sequence signatures in a targeted manner. In addition, spatial-spectral interactive fusion (SSIF) units and category-adaptive weighting (CAW) as two feature fusion modules were also adopted between and after the two feature extraction branches to improve efficiency in feature fusion. The ZY1-02D hyperspectral imagery was collected to conduct the experiments in the study area of the eastern foothills of the Helan Mountains (EFHLM), covering an area of about 8800 km2, which is the largest hyperspectral dataset as far as we know. To explore the potential of the proposed network in terms of accuracy and efficiency, SCSTIN models with different depths (SCSTIN-4 and SCSTIN-2) were performed. The results suggest that compared with the previous eight advanced hyperspectral image classifiers, both SCSTIN models achieved satisfactory performance in accuracy and efficiency aspects with low complexity, where SCSTIN-4 achieved the highest accuracy and SCSTIN-2 obtained higher efficiency. Accordingly, the SCSTIN models are reliable for large-scale fast refined land cover classification and mapping. In addition, the spatial distribution pattern of diverse ground objects in EFHLM is also analyzed. [ABSTRACT FROM AUTHOR]

We use the extended Huygens–Fresnel integral to analyze the propagation properties of a class of partially coherent beams with Laguerre non-uniform coherence properties (called Laguerre non-uniformly correlated beams) in free space and in a turbulent atmosphere. We focus on how different initial beam orders and coherence lengths affect the propagation behavior of the beams, such as the evolution of intensity, degree of coherence, propagation factor, and beam wander. Our results show that non-uniform coherence properties play a role in resisting the degrading effects of turbulence. Furthermore, adjusting the initial beam parameter of the non-uniform coherence structure, i.e., increasing the beam order and decreasing the coherence, can further improve the turbulence resistance of the beams. Our results have potential applications in free-space optical communications. [ABSTRACT FROM AUTHOR]

In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The R 2 , MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm's goodness-of-fit value will greatly improve the accuracy of stock price predictions. [ABSTRACT FROM AUTHOR]

This paper proposes a modified process analysis method that combines with the input–output method for carbon emissions assessment in slurry shield tunnel construction. The method was applied to analyze the carbon emissions generated during the construction procedures of a slurry shield tunnel. The results indicate that the carbon emissions from building materials account for the majority of the total emissions, while those from the shield machine and construction procedure are relatively small. In addition, BP and CNN-LSTM neural network models were established to validate the accuracy of the calculation results with model error of 0.1031. Finally, recommendations for reducing carbon emissions in the construction course of slurry shield tunnels are provided. [ABSTRACT FROM AUTHOR]

As the manufacturing process level of semiconductor devices continues to improve, the device size gradually decreases, and the devices are affected by the single event effect more and more severely. In this paper, the physical process of single particle incident N-channel Metal-Oxide-Semiconductor Field-Effect Transistor (NMOSFET) is simulated. By changing the particle incidence position, incidence angle, LET value, and temperature, the transient current variation with time is obtained, and the susceptibility of the device to single event effect under the action of four factors is analyzed, which provides the basis for the next research of the device against single event effect and ideas for the radiation hardening of semiconductor devices. In addition, in these experiments, there were bimodal-shaped current pulses that were different from single peak transient current pulses. Therefore, a new model describing the bimodal single event transient (SET) current waveform was proposed, which can be used as a current source model in circuit simulation and help to predict the ability of the circuit to resist the single event effect. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of state estimation for nonlinear multi-sensor systems with cross-correlated noise and packet loss compensation. In this case, the cross-correlated noise is modeled by the synchronous correlation of the observation noise of each sensor, and the observation noise of each sensor is correlated with the process noise at the previous moment. Meanwhile, in the process of state estimation, since the measurement data may be transmitted in an unreliable network, data packet dropout will inevitably occur, leading to a reduction in estimation accuracy. To address this undesirable situation, this paper proposes a state estimation method for nonlinear multi-sensor systems with cross-correlated noise and packet dropout compensation based on a sequential fusion framework. Firstly, a prediction compensation mechanism and a strategy based on observation noise estimation are used to update the measurement data while avoiding the noise decorrelation step. Secondly, a design step for a sequential fusion state estimation filter is derived based on an innovation analysis method. Then, a numerical implementation of the sequential fusion state estimator is given based on the third-degree spherical-radial cubature rule. Finally, the univariate nonstationary growth model (UNGM) is combined with simulation to verify the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Photocatalytic degradation technology has developed rapidly in the treatment of organic pollutants due to its high efficiency, mild reaction conditions and easy control. In this paper, a series of heterogeneous photocatalysts, BWZ-en-R (BWZ = [BW11Z(H2O)O39]7−, Z = Zn, Cd, Mn, en = ethylenediamine, R = Merrifield resin), were prepared by using ethanediamine as a linker to immobilize Keggin-type transition elements substituting tungstoborates on Merrifield resin and characterized by Fourier transform infrared spectroscopy, X-ray powder diffraction, scanning electron microscopy and energy-dispersive X-ray spectroscopy. The photocatalytic properties of BWZ-en-R (Z = Zn, Cd, Mn) for the degradation of methyl red (MR) were investigated. The results show that the BWZ-en-R (Z = Zn, Cd, Mn) photocatalysts exhibited high photodegradation ability for MR under the irradiation of ultraviolet light, and were easily separated from the reaction media. The maximum degradation rate (%) of MR (40 mL, 25 μM, pH = 2) reached 96.4% for the BWMn-en-R photocatalyst (40 mg) after being irradiated for 30 min, making this a promising photocatalyst candidate for dye degradation. Moreover, the influences of some factors, such as the Z-substituted elements in the BWZ, the BWZ-en-R dosage and the MR initial concentration, on the photocatalytic degradation rate of MR were also examined. [ABSTRACT FROM AUTHOR]

Aiming at the problems of power flow fluctuation and voltage exceeding standard caused by high permeability distributed power supply access, this paper proposes a load state perception early warning method for distribution networks. Firstly, the random behavior characteristics and voltage early warning mechanisms of power supply and load in distribution networks are analyzed, the dynamic model of distribution networks based on complex network theory is established, and the risk index of voltage exceeding limits under the conditions of high permeability distributed power supply access is put forward. Secondly, the random power flow of distribution networks based on the Monte Carlo method is studied by sampling and analyzing the dynamic model of distribution networks. Then, the risk calculation and safety assessment of voltage exceeding limits are carried out on the currently extracted model, and the risk control strategy of distribution network operation is put forward. Finally, an improved IEEE30-node distribution network topology is proposed. Through simulation analysis, it is proven that the load situation awareness early warning method of distribution networks can effectively predict, improve the security of distribution networks, and provide timely early warning information for maintenance personnel. [ABSTRACT FROM AUTHOR]

The photosensitive resins for 3D printing technology have been widely applied throughout the advanced communication field due to their merits of high molding accuracy and fast processing speed. Regardless, they, in particular, should have better mechanical properties, heat resistance, and dielectric properties. Herein, photocurable fluorinated poly (phthalazinone ether) (FSt-FPPE) was utilized as a prepolymer to improve the performance of photosensitive resin. A series of UV-curable inks named FST/DPGs were prepared with FSt-FPPE and acrylic diluents of different mass fractions. The FST/DPGs were cured into films by UV curing and post-treatment. After curing, their properties were characterized in detail. In terms of heat resistance, glass transition temperature (Tg) could reach 233 °C and the 5% thermal decomposition temperature (Td5%) was 371 °C. The tensile strength surprisingly reached 61.5 MPa, and the dielectric constant (Dk) could be significantly reduced to 2.75. Additionally, FST/DPGs were successfully employed in UV-assisted direct writing (DIW) to print 3D objects that benefited from their commendable fluidity and rapid curing speed. A stiff cylinder sample with a smooth surface and distinct pattern was ultimately obtained, indicating their remarkable 3D printing adaptation. Such photosensitive resin for UV-assisted DIW exhibits tremendous potential in the electronic industry. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is the top priority for mankind for dealing with climate issues. The Chinese government selected 30 demonstration cities in three batches and provided these cities green special transfer payment funds for three years to build green projects and achieve energy saving and emission reduction (ESER). This policy provides a great opportunity to study whether green transfer payments play an important role in carbon reduction, which has received little attention before. Additionally, the central government set a series of fiscal performance assessment indicators, including the ESER effect, the completion of green projects, and long-term mechanism construction in order to evaluate the effectiveness of the use of funds. This article creatively conducts theoretical analysis from the perspective of performance assessment, takes ESER special transfer payment policy as a quasi-natural experiment, and uses the panel data of 284 cities in China from 2007 to 2017 and 2019 to verify the impact of the green transfer payment on carbon emissions in and after demonstration periods and its mechanisms with the staggered DID method and the new DID Multiplegt (DIDM) model. This article found that green transfer payments could reduce carbon emissions in demonstration cities, and this effect still existed even after policy withdrawal. Mechanism analysis further corroborates that the carbon-reduction effect of green transfer payments could be achieved by increasing the urban green area and improving energy efficiency. Heterogeneity analysis reveals that green transfers have a greater carbon reduction effect on demonstration cities, which receives more green transfer payment funds and has a higher level of financial development. Recommendations related to setting proper performance assessment ESER indicators include improving the management of ESER transfer funds and maximizing the cost–benefit ratio of fiscal funds, which are proposed according to the research conclusions. [ABSTRACT FROM AUTHOR]

Ordered porous polyimide films were fabricated from (AB)m type of diblock copolymer polyimide-b-polystyrene (PI-b-PS). An increase in PS volume fraction (f VPS) is beneficial to decrease the relative dielectric constants and water contact angles of the obtained porous PI films: the relative dielectric constant decreases to 1.89 and water contact angle decreases to 43° when f VPS is 0.36, indicating porous PI films can be used as low-κ material and have good wettability. The solvent uptake of the porous PI films increases with increasing f VPS. A net-like morphology appears when f VPS reaches 0.36. The net-like porous PI film reaches equilibrium uptake of water within approximately 2.5 h, with a final equilibrium uptake ratio of 17.5%, and reaches equilibrium uptake of toluene within approximately 8 h, with a final equilibrium uptake ratio of 15.4%, displaying the highest and fastest solvent uptake compared with other microstructured porous PI films, which is ascribed to the specific characteristic of the interconnecting porous channels derived from the self-assembly of PI-b-PS with f VPS 0.36. Introducing thermally degradable segments to PI-based block copolymer is an effective method to prepare porous PI films and can enhance some of their properties. [ABSTRACT FROM AUTHOR]

In this paper, an improved back-to-back converter is proposed, and the converter is used as a test power source for vector inspection of relay protection in an active distribution network, which effectively solves the problem that the output voltage and current of the test power source cannot be continuously and stably adjusted. Firstly, a three-phase back-to-back cascade converter is established to analyze the impedance characteristics of its DC terminal. Then a feedforward voltage is added to the inverter to improve the input impedance characteristics of the inverter. Secondly, the system stability and parameter stability of the improved back-to-back converter are analyzed. Finally, the improved converter is used as the test power source for vector inspection of relay protection in the active distribution network. The simulation results show that the stability of the improved back-to-back converter system is greatly improved. The experiment shows that the vector check technology based on an improved back-to-back converter can effectively check the vector of relay protection in an active distribution network and find various installation problems. [ABSTRACT FROM AUTHOR]

For the frequent occurrence of harmful algal blooms (HABs) in the Qinhuangdao coastal sea (QCS) of the Bohai Sea in summer, we tested the hypothesis that high-biodiversity HAB species exist in the area, and a series of censuses of HAB species were conducted in the QCS in the summers of 2014–2019. Through morphological identification, we found 100 algae species representing 42 genera in 3 phytoplankton phyla in this study, among which Bacillariophyta was the most dominant phylum. We also found that the population density of Dinoflagellata increased from 2016 to 2019. In total, 59 HAB species were annotated in this study, including 39 of Bacillariophyta, 18 of Dinoflagellata and 2 of Ochrophyta, of which 13 HAB species were reported in the Bohai Sea for the first time, and most HAB species were widely distributed in the QCS in summer. Notably, four dominant HAB species displayed unique temporal and spatial distribution characteristics, while their distribution ranges and population densities increased from 2014 to 2019. The distributions of five environmental factors were different in the QCS, while the temperature, salinity, and dissolved inorganic nitrogen might be the key environmental factors influencing the distribution of dominant HAB species in the summer. In conclusion, this study provides a detailed evaluation of phytoplankton diversity and interannual variation in the QCS. The existence of a high level of biodiversity of algal bloom species suggests the need for long-term monitoring in order to further study and prevent potential HABs. [ABSTRACT FROM AUTHOR]

High oleic acid peanut (HOAP) is extensively embraced in China because of its high nutritional value and enhanced oxidative stability. However, its dissemination has been severely constrained in high altitude and high latitude areas due to chilling stress during sowing, resulting in significant yield loss in these regions. Despite the lack of understanding of the molecular mechanisms underlying low temperature germination (LTG) in HOAP, discovering the quantitative trait loci (QTL) that confer this trait will undoubtedly benefit breeding efforts. In the present study, we identified putative genomic regions and single nucleotide polymorphisms (SNPs) that govern LTG tolerance of HOAP in an F2 population derived from the cross of chilling-tolerant YH65 and chilling-sensitive FL14 using bulk segregant analysis (BSA). Analysis of ΔSNP-index and Euclidean distance (ED) value association pinpointed the overlapped region to a 2.29 Mb interval on chromosome A05. The candidate interval showed that 122 genes were significantly related to response to abiotic stress and plant–pathogen interaction. Furthermore, an SNP site associated with LTG tolerance was discovered. The SNP site was employed as a Kompetitive Allele Specific PCR (KASP) marker and validated in a universal peanut panel. These findings may provide valuable insight into the molecular mechanism underpinning LTG tolerance and facilitate marker-assisted selective breeding in HOAP. [ABSTRACT FROM AUTHOR]

The global issue of climate change has accelerated the international commitment to net-zero carbon emission development. Decarbonizing the building sector has been put on several governments' sustainable development agendas. To provide a reference for decarbonizing the building sector, this paper summarizes the U.S. experience in zero-carbon buildings (ZCBs) from the aspects of policies, codes, and standards at the federal and local levels and those of professional societies. Based on the definition and boundaries of ZCBs, this paper introduces policies on building energy efficiency, electrification, on-site renewable energy deployment, and "buy clean", illustrating highlights in building phases, energy systems, materials production, and fiscal incentives. The synergic efforts and coordination between federal and local levels and with professional societies are also introduced. Successful experiences in policy and standard implementation are summarized, including the systemic work of multilevel governance, clearly defined goals and stringent policies, constant upgrades of codes and standards, transparency in reporting and information sharing, and increased financial and investment opportunities. This paper provides concrete recommendations for developing zero-carbon building policies. [ABSTRACT FROM AUTHOR]

The ideal wind turbine power curve provided by the manufacturer cannot monitor the practical performance of wind turbines accurately in the engineering stage; in this paper, a modified approach of the wind turbine power curve is proposed based on improved Bins and K-means++ clustering. By analyzing the wind speed-power data collected by the supervisory control and data acquisition system (SCADA), the relationship between wind speed and output is compared and elaborated on. On the basis of data preprocessing, an improved Bins method for equal frequency division of data is proposed, and the results are clustered through K-means++. Then, the wind turbine power curve correction is realized by data weighting and regression analysis. Finally, an example is given to show that the power curve of the same type of wind turbines, which, installed in different locations, are discrepant and different from the MPC, and the wind turbine power curve obtained by using this method can reflect the output characteristics of the wind turbine operating more effectively in a complex environment. [ABSTRACT FROM AUTHOR]

In order to reduce the infrared emissivity to meet the requirements of modern warfare for infrared stealth materials, we prepared the polymers containing Schiff base moieties using polyetheramine and 2,6-pyridinedicarboxaldehyde by solution polycondensation and coordinated with Ni2+, Cu2+, and Sm3+ ions to prepare organic coatings. The structure and the thermal and mechanical properties of the coatings were studied in detail. Meanwhile, the effect of the conductivity change of coordination polymers on infrared emissivity was studied systematically. The results showed the polymer coordinated with Sm3+ ions had the lowest energy band gap, which was 2.99 eV, and the best electrical conductivity of 3.54 × 10−4 S/cm compared with Ni2+ and Cu2+ coordination polymers. The infrared emissivity was the lowest in the 2–22 μm infrared waveband range, which reached 0.58, suggesting the polymers containing Schiff base moieties and their coordination polymers may have a great potential to be applied as infrared stealth materials in military applications. [ABSTRACT FROM AUTHOR]

The high-resolution seismic characterization of gas hydrate reservoirs plays an important role in the detection and exploration of gas hydrate. The conventional AVO (amplitude variation with offset) method is based on a linearized Zoeppritz equation and utilizes only the reflected wave for inversion. This reduces the accuracy and resolution of the inversion properties and results in incorrect reservoir interpretation. We have studied a high-resolution wave-equation-based inversion method for gas hydrate reservoirs. The inversion depends on the scattering integral wave equation that describes a nonlinear relationship between the seismic wavefield and the elastic properties of the subsurface medium. In addition to the reflected wave, it considers more wavefields including the multiple scattering and transmission during inversion to improve the subsurface illumination, so as to enhance the accuracy and resolution of the inversion properties. The results of synthetic data from Pearl River Mouth Basin, South China Sea, demonstrate the validity and advantages of the wave-equation-based inversion method. It can effectively improve the resolution of inversion results compared to the conventional AVO method. In addition, it has good performance in the presence of noise, which makes it a promising method for field data. [ABSTRACT FROM AUTHOR]

Immediately and accurately assessing the risk of coal spontaneous combustion and taking targeted action are crucial steps in coal spontaneous combustion prevention and control. A new model, AEM-AHP-LSTM, was proposed to solve the weight calculation problem of multiobjective evaluation in the process of coal spontaneous combustion. Firstly, the key indicators of coal spontaneous combustion were analyzed and used as risk factors to establish an evaluation system. Next, the objective and subjective weights were calculated using AEM and AHP, respectively. The objective and subjective weights were then combined, and TOPSIS was used to calculate the score of the evaluation sample. Finally, the obtained evaluation samples were trained with the BP, RBF, and LSTM model to resolve the problem of model overdependence on historical data and achieve the auto-adapt adjustment of weight with data change. Additionally, data from 15 typical Chinese coal mines were applied to the model. The results indicate that, compared with the BP and RBF neural networks, the LSTM model has higher prediction accuracy, stronger generalization ability, and stronger practicability. The modeling and application findings show that the AEM-AHP-LSTM model was better appropriate for the risk assessment of coal spontaneous combustion. This method can potentially be further applied as reliable approach for the assessment of mine disaster risk. [ABSTRACT FROM AUTHOR]

In order to mitigate the negative effects of global climate change, the Chinese government has committed to achieving peak carbon emissions by 2030 and carbon neutrality by 2060. Since municipal cities are the bottom administrative level for drawing up development plans, it is necessary and important to conduct decarbonization pathway research on municipal energy systems (MESs). However, there is little research on decarbonization at the municipal level, and the impact of development paths in each forecast scenario is mostly based on expert evaluation and qualitative assessment. Therefore, this study established a complete decarbonization framework for MESs, including general research procedures, models, and a sustainable evaluation method. The models of energy consumption and carbon emission were adapted and improved for MESs. In order to quantitatively evaluate the energy system development for each scenario, we proposed an energy–economy–environment–security (3E–S) evaluation method, in which principal component analysis (PCA) was adopted for multi-criterion decision making. According to the analysis results of the case city in Guangdong, this evaluation method was proved to be an effective way to identify the factors that may influence coordinated development. By adjusting the relevant parameters and factors in the model, the optimal decarbonization pathway can be found to promote sustainable and coordinated development, thus helping government decision makers to quantitatively evaluate planning paths. [ABSTRACT FROM AUTHOR]

Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study. [ABSTRACT FROM AUTHOR]

In near-neutral solutions, the effects of NH4HF2, H3PO4, phytic acid (IP6), and EDTA-ZnNa2 concentration on corrosion resistance and the Zn amount of micro-arc oxidation (MAO) coatings were revealed by an orthogonal experiment. The influencing order of four factors on coating corrosion resistance is EDTA-ZnNa2 > NH4HF2 > IP6 > H3PO4, while the sequence on the Zn amount is ranked as EDTA-ZnNa2 > NH4HF2 > H3PO4 > IP6. The fabricated Zn-containing coatings exhibit excellent corrosion resistance, and their icorr values are two orders of magnitude lower than that of the WE43 substrate, while the highest Zn amount achieves 4.12 wt.%. P and F compete to take part in coating formation, and Zn ions enter into anodic coatings by diffusion. Coating corrosion resistance is jointly determined by surface characteristics, which will provide the important theoretical foundation for fabricating Zn-containing coating with high corrosion resistance. [ABSTRACT FROM AUTHOR]

Due to the limited service life of new energy vehicle power batteries, a large number of waste power batteries are facing "retirement", so it will soon be important to effectively improve the recycling and reprocessing of waste power batteries. Consumer environmental protection responsibility awareness affects the recycling of waste power batteries directly. Therefore, under the two recycling modes of new energy vehicle manufacturers and third-party recycling enterprises, this study analyzes the impact of consumer environmental protection responsibility awareness on the recycling price of waste power batteries and profit in the supply chain. The influence of factors such as recycling income, recycling input cost, and black-market recycling prices on consumer awareness of responsibility is also analyzed. Through theoretical research, it was found that: Under the model that third-party recycling enterprises are responsible for recycling, it can obtain better overall supply chain benefits; consumer environmental protection responsibility awareness and recycling benefits are positively correlated with supply chain benefits overall; and recycling benefits have a certain role in promoting consumer awareness of responsibility, while the increase in informal recycling prices inhibits consumer awareness of responsibility. [ABSTRACT FROM AUTHOR]

In recent years, image segmentation based on deep learning has been widely used in medical imaging, automatic driving, monitoring and security. In the fields of monitoring and security, the specific location of a person is detected by image segmentation, and it is segmented from the background to analyze the specific actions of the person. However, in low-illumination conditions, it is a great challenge to the traditional image-segmentation algorithms. Unfortunately, a scene with low light or even no light at night is often encountered in monitoring and security. Given this background, this paper proposes a multi-modal fusion network based on the encoder and decoder structure. The encoder, which contains a two-branch swin-transformer backbone instead of the traditional convolutional neural network, fuses the RGB and depth features with a multiscale fusion attention block. The decoder is also made up of the swin-transformer backbone and is finally connected via the encoder with several residual connections, which are proven to be beneficial in improving the accuracy of the network. Furthermore, this paper first proposes the low light–human segmentation (LLHS) dataset of portrait segmentation, with aligned depth and RGB images with fine annotation under low illuminance, by combining the traditional monocular camera and a depth camera with active structured light. The network is also tested in different levels of illumination. Experimental results show that the proposed network has good robustness in the scene of human segmentation in a low-light environment with varying illumination. The mean Intersection over Union (mIoU), which is often used to evaluate the performance of image segmentation model, of the Swin-MFA in the LLHS dataset is 81.0, is better than those of ACNet, 3DGNN, ESANet, RedNet and RFNet at the same level of depth in a mixed multi-modal network and is far ahead of the segmentation algorithm that only uses RGB features, so it has important practical significance. [ABSTRACT FROM AUTHOR]

A fuzzy soft set is a mathematical tool used to deal with vagueness and uncertainty. Parameter reduction is an important issue when applying a fuzzy soft set to handle decision making. However, existing methods neglect newly added parameters and have higher computational complexities. In this paper, we propose a new S-Score table-based parameter-reduction approach for fuzzy soft sets. Compared with two existing methods of parameter reduction for a fuzzy soft set, our method takes newly added parameters into account, which brings about greater flexibility and is beneficial to the extension of fuzzy soft sets and a combination of multiple fuzzy soft sets. Additionally, our method accesses fewer elements from the dataset, which results in lower computation compared with the two existing approaches. The experimental results from two applications show the availability and feasibility of our approach. [ABSTRACT FROM AUTHOR]

For large-scale and sustainable water electrolysis, it is of great significance to develop cheap and efficient electrocatalysts that can replace platinum. Currently, it is difficult for most catalysts to combine high activity and stability. To solve this problem, we use cobalt to regulate the electronic structure of ruthenium to achieve high activity, and use carbon matrix to protect alloy nanoparticles to achieve high stability. Herein, based on the zeolitic imidazolate frameworks (ZIFs), a novel hybrid composed of RuCo alloy nano-particles and N-doped carbon was prepared via a facile pyrolysis-displacement-sintering strategy. Due to the unique porous structure and multi-component synergy, the optimal RuCo500@NC750 material in both acidic and alkaline media exhibited eminent HER catalytic activity. Notably, the 3-RuCo500@NC750 obtained a current density of 10 mA cm−2 at 22 mV and 31 mV in 0.5 M H2SO4 and 1.0 M KOH, respectively, comparable to that of the reference Pt/C catalyst. Furthermore, the Tafel slopes of the catalyst are 52 mV Dec−1 and 47 mV Dec−1, respectively, under acid and alkali conditions, and the catalyst has good stability, indicating that it has broad application prospects in practical electrolytic systems. This work contributes to understanding the role of carbon-supported polymetallic alloy in the electrocatalytic hydrogen evolution process, and provides some inspiration for the development of a high efficiency hydrogen evolution catalyst. [ABSTRACT FROM AUTHOR]