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To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a large convolution kernel, which allows the detection network to precisely capture contextual information more comprehensively. Furthermore, a common convolution layer is added to the stem layer, to extract more valuable information from the images efficiently. Then, the BoT3 module with the multi-head self-attention (MHSA) mechanism is added into the neck module of YOLOv5, such that the detection network has a better effect in scenes with dense targets and the detection accuracy is further improved. The introduction of the BoT3 module represents a key innovation of this paper. Finally, union dataset augmentation (UDA) is performed on the training set using the Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) image augmentation method, and the result is used as the input to the improved YOLOv5 framework. Experiments on the underwater datasets URPC2019 and URPC2020 show that the proposed framework not only alleviates the interference of underwater image degradation, but also makes the mAP@0.5 reach 79.8% and 79.4% and improves the mAP@0.5 by 3.8% and 1.1%, respectively, when compared with the original YOLOv8 on URPC2019 and URPC2020, demonstrating that the proposed framework presents superior performance for the high-precision detection of marine organisms.

The influence of environmental attitudes on environmental behaviors has long been discussed. However, few studies have addressed the foundation of such attitudes. In the present study, we explored primitive belief underlying environmental attitudes, i.e., connections with nature, and its relationship with pro-environmental behaviors. Specifically, we used scales, a computerized Implicit Association Test, and a situational simulation experiment to examine both explicit and implicit connections with nature, both deliberate and spontaneous environmental behaviors, and to find correlations between environmental connectedness and environmental behaviors. Results showed that explicit connectedness was positively correlated with deliberate environmental behaviors, while implicit connectedness was positively correlated with spontaneous environmental behaviors. Additionally, explicit and implicit connectedness was independent of each other. In conclusion, the current study confirms the positive role played by connections with nature in promoting environmental behavior, and accordingly suggests means to encourage pro-environmental behavior by enhancing people's connectedness to nature.

The study of subjective well-being (SWB) has attracted considerable attention from scholars globally. This has stimulated numerous studies that have identified regional and individual factors associated with SWB, but the extant research lacks multi-level studies that simultaneously examine their influence on SWB. Environmental pollution is one of such factors, but few studies have investigated its effect on SWB in China particularly. The current study addressed these problems by conducting hierarchical linear regressions to explore the effects of regional and individual factors on Chinese people’s SWB. Three major environmental pollutions (wastewater pollution, domestic waste pollution, and air pollution) were studied using data from the Chinese General Social Survey 2013 and China Statistical Yearbook 2014. The results indicated that wastewater pollution and domestic waste pollution had significant negative influence on SWB. Moreover, gross domestic product (GDP) per capita might contribute more to the improvement of SWB than income inequality. This implies that individuals’ SWB might be enhanced by improving absolute income, which is consistent with the micro-level proposition of the Easterlin paradox. Overall, these findings signal that effective management of environmental pollution is essential for promoting the SWB of the people in China.

Bi2Sr2CaCu2O8+x (Bi2212) high-temperature superconductor exhibits broad application prospects in strong magnetic fields, superconducting magnets, and power transmission due to its exceptional electrical properties. However, during practical applications, Bi2212 superconducting round wires are prone to mechanical loading effects, leading to crack propagation and degradation of superconducting performance, which severely compromises their reliability and service life. To elucidate the damage mechanisms under mechanical loading and their impact on critical current, this study establishes a two-dimensional model with existing cracks based on phase field fracture theory, simulating crack propagation behaviors under varying conditions. The results demonstrate that crack nucleation and propagation paths are predominantly governed by stress concentration zones. The transition zone width of cracks is controlled by the phase field length scale parameter. By incorporating electric fields into the phase field model, coupled mechanical-electrical simulations reveal that post-crack penetration causes significant current shunting, resulting in a marked decline in current density. The research quantitatively explains the mechanism of critical current degradation in Bi2212 round wires under tensile strain from a mechanical perspective.

China’s ongoing urbanization, expanding land transfer, has reshaped rural land use and generational consumption patterns. Using three waves of China Family Panel Studies data, this study applies a two-way fixed effect model to examine the impact of farmland transfer-out on generational consumption structure and explores the mediating role of household income, the moderating role of non-agricultural income share, and regional and income heterogeneity. Findings show the following: (1) Farmland transfer-out significantly increases subsistence, developmental, and hedonic consumption among middle-aged and young farmers, with the greatest rise in hedonic consumption. For elderly farmers, only subsistence consumption increases, and to a lesser extent. (2) Among middle-aged and young farmers, transfer-out raises household income, boosting all consumption types; a higher share of non-farm income further strengthens subsistence and hedonic consumption. For elderly farmers, while income increases, a higher non-farm income share weakens the income effect on subsistence consumption. (3) Regionally, land transfer-out significantly boosts subsistence and hedonic consumption in the eastern region for younger farmers, and all three types—especially subsistence—in the central and western regions. Elderly farmers in the east also see a rise in subsistence consumption. (4) An income heterogeneity analysis shows stronger effects for low-income younger farmers and high-income elderly farmers. Based on these findings, this study proposes targeted policies to promote farmland transfer-out, offering insights for optimizing land use and enhancing rural consumption, with implications for other countries’ land management.

Material identification is playing an increasingly important role in various sectors such as industry, petrochemical, mining, and in our daily lives. In recent years, material identification has been utilized for security checks, waste sorting, etc. However, current methods for identifying materials require direct contact with the target and specialized equipment that can be costly, bulky, and not easily portable. Past proposals for addressing this limitation relied on non-contact material identification methods, such as Wi-Fi-based and radar-based material identification methods, which can identify materials with high accuracy without physical contact; however, they are not easily integrated into portable devices. This paper introduces a novel non-contact material identification based on acoustic signals. Different from previous work, our design leverages the built-in microphone and speaker of smartphones as the transceiver to identify target materials. The fundamental idea of our design is that acoustic signals, when propagated through different materials, reach the receiver via multiple paths, producing distinct multipath profiles. These profiles can serve as fingerprints for material identification. We captured and extracted them using acoustic signals, calculated channel impulse response (CIR) measurements, and then extracted image features from the time–frequency domain feature graphs, including histogram of oriented gradient (HOG) and gray-level co-occurrence matrix (GLCM) image features. Furthermore, we adopted the error-correcting output code (ECOC) learning method combined with the majority voting method to identify target materials. We built a prototype for this paper using three mobile phones based on the Android platform. The results from three different solid and liquid materials in varied multipath environments reveal that our design can achieve average identification accuracies of 90% and 97%.

Fine particulate matter (PM2.5) is of widespread concern, as it poses a serious impact on economic development and human health. Although the influence of socioeconomic factors on PM2.5 has been studied, the constitution and the effect analysis of social vulnerability to PM2.5 remain unclear. In this study, a comprehensive theoretical framework with appropriate indicators for social vulnerability to PM2.5 was constructed. Using spatial autocorrelation analysis, a positive global spatial autocorrelation and notable local spatial cluster relationships were identified. Spatial econometric modeling and geographically weighted regression modeling were performed to explore the cause-effect relationship of social vulnerability to PM2.5. The spatial error model indicated that population and education inequality in the sensitivity dimension caused a significant positive impact on PM2.5, and biocapacity and social governance in the capacity dimension strongly contributed to the decrease of PM2.5 globally. The geographically weighted regression model revealed spatial heterogeneity in the effects of the social vulnerability variables on PM2.5 among countries. These empirical results can provide policymakers with a new perspective on social vulnerability as it relates to PM2.5 governance and targeted environmental pollution management.

Laser cladding technology is a reliable and efficient surface modification technology, which has been widely used in surface alloying and composite processing of steel materials. Firstly, the characteristics of laser cladding technology were introduced, and the effects of process control and the material system on the geometric shape, size, microstructure, and properties of cladding coating were analyzed by summarizing the research results of laser cladding on steel surfaces. The results show that with the increase of laser power, the dilution rate and width of the cladding coating increase, and the grain becomes coarse. Thus, the wear resistance deteriorates. Compared with alloy cladding coating, composite cladding coating exhibits better wear and corrosion resistance, but the plastic toughness is worse than alloy cladding coating. The research progress of surface alloying and composite processing of steel worldwide was analyzed from various aspects. Current results suggest that laser cladding alloying and compounding can enhance the wear resistance and corrosion resistance of steel materials. Based on the summary of the current research results, the development prospect and planning of laser cladding technology in the field of surface alloying and composite processing of steel are further pointed out.

The Huangshui River Basin is located in the transition zone between the Loess Plateau and the Qinghai–Tibet Plateau, characterized by a fragile hydrological and ecological environment. Groundwater serves as a vital water source for local economic development and human livelihood. With the acceleration of urbanisation and climate change, groundwater resources face challenges such as pollution and over-exploitation. This study employs an improved DRASTIC model, tailored to the characteristics of the groundwater system in the Huangshui River Valley of the upper Yellow River, to integrate groundwater resources, groundwater environment, and ecological environment systems. Improving the DRASTIC model for groundwater vulnerability assessment. A two-tiered evaluation system with nine indicator parameters was proposed, including six groundwater quality vulnerability indicators and five groundwater quantity vulnerability indicators. Fuzzy analytic hierarchy process and entropy weight method were used to determine the weights, and Geographic Information System (GIS) spatial analysis was employed to evaluate groundwater vulnerability in the Huangshui River basin in 2006 and 2021. The results indicate that the proportion of areas with high groundwater quality vulnerability increased from 10.7% in 2006 to 31.57% in 2021, while the proportion of areas with high groundwater quantity vulnerability decreased from 22.33% to 14.02%. Overall, groundwater quality vulnerability in the Huangshui River basin is increasing, while groundwater quantity vulnerability is decreasing. Based on the evaluation results of water quality and quantity vulnerability, protection zoning maps for water quality and quantity were compiled, and preventive measures and recommendations for water quality and quantity protection zones were proposed. Human activities have a significant impact on groundwater vulnerability, with land use types and groundwater extraction coefficients having the highest weights. This study provides a scientific basis for the protection and sustainable use of groundwater in the Huangshui River basin.

Tandem alkali-catalyzed hydrolysis and alkaline electrolysis have gradually become appealing avenues for the reformation of polyester plastics into high-value-added chemicals and green hydrogen with remarkable environmental and economic benefits. In this study, an electrochemical upcycling strategy was developed for the electrocatalytic oxidation of polylactic acid (PLA) hydrolysate into valued C2 chemicals (i.e., acetate) and hydrogen fuel using N, P-doped CuOx nanowires (NW) supported on nickel foam (NF) as the electrocatalyst. This 3D well-integrated catalyst was easily prepared from a Cu(OH)2 NW/NF precursor with Saccharomycetes as a green and safe P and N source. The electrocatalyst can efficiently catalyze the lactate monomer derived from the hydrolysis of PLA waste to acetate with high selectivity and exhibits a lower onset potential for the lactate oxidation reaction (LOR) than for water oxidation, saving 224 mV to deliver a current density of 30 mA/cm2. The experimental results reveal that the plausible pathway of the LOR on these CuOx NW involves oxidation and subsequent decarboxylation. Divalent copper species have been verified to be active sites for LOR via in situ Raman spectroscopy.