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At present, the energy consumption and carbon emissions of maritime transportation have raised concerns about environmental issues. A potential way to reduce carbon emissions from vessels is the use of chemical-based carbon capture and storage (CCS) technology. However, this technology faces challenges such as high energy consumption, large space occupation, and high processing costs. Therefore, the development of a technology with low energy consumption and compact CO2 storage is crucial to promote the advancement of CCS technology. This paper introduces a magnesium CO2 battery system that converts CO2 into new energy, in the form of hydrogen, while storing CO2. By preparing highly efficient catalytic electrodes and testing the electrolyte and CO2 flow rate on the battery performance, the optimal process parameters were determined to be Pd/CeO2-oct for the electrodes, a 0.5 mol/L NaOH solution for the electrolyte, and a CO2 flow rate of 1 L/h. The battery system demonstrated high cycling stability and conversion efficiency at a current density of 8 mA·cm−2, with a stable cycling time of 600 min (20 cycles), a cathode hydrogen production of 10.135 mL, and a Faraday efficiency of 97.03%. [ABSTRACT FROM AUTHOR]

The excavation of a foundation pit will break the original stress balance and affect the surface settlement of the natural foundation of adjacent buildings, so deformation control is very important. In this paper, numerical simulation results of the additional stress of a natural foundation caused by a foundation pit excavation were compared with experimental results to verify the accuracy of the numerical model of a foundation pit excavation and obtain a reliable ratio of building width to the average particle size of the sand that was not affected by the particle size effect. Then, the distribution and variation law of the surface settlement of a natural foundation caused by excavation was studied by a model test, and the relationship between the deformation of a natural foundation and the depth of a foundation pit excavation and the additional load of building was obtained. [ABSTRACT FROM AUTHOR]

Up to now, a great number of catalysts have been reported that are active in the oxidation of volatile organic compounds (VOCs). However, supported noble-metal catalysts (especially Pt-based catalysts) are still the most excellent ones for this reaction. In this study, Pt species supported on γ-Al2O3 and ranging from single-atom sites to clusters (less than 1 nm) and 1–2 nm nanoparticles were prepared and investigated for oxidizing C2H4. The Pt-loaded γ-Al2O3 nanoflakes (PtAl-NF) and Pt-loaded γ-Al2O3 nanowires (PtAl-NW) were successfully prepared. The samples were characterized using XRD, TEM, XPS, HAADF-STEM, and in situ DRIFTS. Based on in situ DRIFTS, a simple surface reaction mechanism was developed. The stable intermediates CO on single-atom Pt, subnanometer Pt particles, and fully exposed Pt clusters could be explained by the strong binding of CO molecule poisoning Pt sites. Moreover, the oxidation of C2H4 was best achieved by Pt particles that were 1–2 nm in size and the catalytic activity of PtAl-NF was better when it had less Pt. Lastly, the most exposed (110) facets of γ-Al2O3 nanoflakes were more resistant to water than the majorly exposed (100) facets of γ-Al2O3 nanowires. [ABSTRACT FROM AUTHOR]

In recent years, thermal imaging cameras are widely used in the field of intelligent surveillance because of their special imaging characteristics and better privacy protection properties. However, due to the low resolution and fixed location for current thermal imaging cameras, it is difficult to effectively identify human behavior using a single detection method based on skeletal keypoints. Therefore, a self-update learning method is proposed for fixed thermal imaging camera scenes, called the behavioral parameter field (BPF). This method can express the regularity of human behavior patterns concisely and directly. Firstly, the detection accuracy of small targets under low-resolution video is improved by optimizing the YOLOv4 network to obtain a human detection model under thermal imaging video. Secondly, the BPF model is designed to learn the human normal behavior features at each position. Finally, based on the learned BPF model, we propose to use metric modules, such as cosine similarity and intersection over union matching, to accomplish the classification of human abnormal behaviors. In the experimental stage, the living scene of the indoor elderly living alone is applied as our experimental case, and a variety of detection models are compared to the proposed method for verifying the effectiveness and practicability of the proposed behavioral parameter field in the self-collected thermal imaging dataset for the indoor elderly living alone. [ABSTRACT FROM AUTHOR]

As a typical hazard threat for buried pipelines, an active fault can induce large plastic deformation in a pipe, leading to rupture failure. The mechanical behavior of high-strength X80 pipeline subjected to strike-slip fault displacements was investigated in detail in the presented study with parametric analysis performed by the finite element model, which simulates pipe and soil constraints on pipe by shell and nonlinear spring elements respectively. Accuracy of the numerical model was validated by previous full-scale experimental results. Insight of local buckling response of high-strength pipe under compressive strike-slip fault was revealed. Effects of the pipe-fault intersection angle, pipe operation pressure, pipe wall thickness, soil parameters and pipe buried depth on critical section axial force in buckled area, critical fault displacement, critical compressive strain and post buckling response were elucidated comprehensively. In addition, feasibility of some common buckling failure criteria (i.e., the CSA Z662 model proposed by Canadian Standard association, the UOA model proposed by University of Alberta and the CRES-GB50470 model proposed by Center of Reliable Energy System) was discussed by comparing with numerical results. This study can be referenced for performance-based design and assessment of buried high-strength pipe in geo-hazard areas. [ABSTRACT FROM AUTHOR]

High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error. [ABSTRACT FROM AUTHOR]

With continued urbanization in China, the construction of urban gas pipelines is increasing, and the safety of gas pipelines are also increasingly affected by urban development and the increased scope of buildings and roads. Pipes with defects are more likely to fail under the surface loads. In this study, uniaxial tensile tests of high-density polyethylene (HDPE) pipes were carried out to obtain the real material parameters of pipe. A pipeline-soil interaction finite element model of HDPE pipeline with defects under surface load was established. The failure mechanism of the urban gas pipeline was studied and the influence of parameters such as internal pressure, defect position, defect depth on the mechanical behavior, and failure of pipelines were analyzed. A failure criterion for HDPE pipes with defects under surface load was proposed based on the limit-state curves obtained under different working conditions. Furthermore, an accurate and efficient fitness-for-service assessment procedure of pipes with defects under surface load was proposed. The results showed that maximum Mises stress of the pipeline gradually increased with increasing surface load and the position of maximum stress changed from the top and bottom of the pipe to the defect position and both sides of the pipe. Finally, when Mises stress of the HDPE pipe exceeds the yield limit, failure will occur. Internal pressure, defect location, and defect depth were found to influence the failure process and critical surface load of the pipeline. Safety evaluation curves of the gas pipeline with defects under surface load were obtained by calculating the critical failure load of the pipeline under various working conditions. Finally, a nonlinear fitting method was used to derive a formula for calculating the critical surface load under different defect parameters. The proposed method provides a useful reference for urban gas pipeline safety management. [ABSTRACT FROM AUTHOR]

In this study, a wear-resistant ultra-thin wear layer was fabricated with polyurethane as an adhesive to investigate its durability for pavement applications. Its road performance was investigated based on indoor tests. First, the abrasion test was performed using a tire–pavement dynamic friction analyzer (TDFA), and the surface elevation information of the wear layer was obtained by laser profile scanning. The relationship between the anti-skid properties of the wear layer and the macro-texture was analyzed. Second, a Fourier infrared spectrometer and scanning electron microscope were employed to analyze the evolution of polyurethane aging properties in the pull-out test and accelerated ultraviolet (UV) aging test. The results showed that the mean profile depth (MPD), arithmetic mean wavelength of contour (λa), surface wear index (SBI), stage mass loss rate (σ), and total stage mass loss rate (ω) of the abrasive layer aggregate had significant multivariate quadratic polynomial relationships with the skidding performance of the abrasive layer. The tensile strength of the polyurethane ultra-thin abrasive layer decreased by only 2.59% after 16 days of UV aging, indicating a minimal effect of UV action on the aggregate and structural spalling of the polyurethane abrasive layer. [ABSTRACT FROM AUTHOR]

China is the world's largest importer of logs, possessing the scale to exert significant influence in the international market. This paper uses a fixed-effect variable coefficient Pricing-to-Market panel model to measure China's market power in log import trade. It also utilizes the Almost Ideal Demand System model from an elasticity perspective to explore the market behavior characteristics of various source countries in China's log import trade, thereby validating the mechanism of market power. The results indicate that: ① China's main trading partners can be categorized into four groups according to their market power in the log import trade. Specifically, China holds superlative market power in log imports from Indonesia, Malaysia, and Myanmar; holds strong market power in log imports from Russia, the Democratic Republic of the Congo, and Mozambique; holds weak market power in log imports from Papua New Guinea, Equatorial Guinea, France, Germany, Australia, and New Zealand; and holds no market power in Japan, Cameroon, and the United States. ② As China's expenditure on log imports increases, there is a tendency to purchase high-quality precious wood and a greater concern for the legality of market transactions. Consequently, China is anticipated to augment its imports from source countries with no or weak market power. ③ The simple price elasticity of log imports from each source country is negative. Source countries with stronger market power tend to increase prices to achieve higher total revenue, while those with weaker market power are more inclined to lower prices to achieve the same. ④ Log products from various source countries are complementary in the Chinese market, indicating that China's substantial demand for logs relies on the simultaneous supply from multiple countries and diverse wood types. Based on the existence or absence of market power in China's log import trade, this paper provides targeted insights into enhancing international market power and reducing trade losses. [ABSTRACT FROM AUTHOR]

Simple Summary: Dumeng sheep is a novel, specialized meat sheep breed created through crossbreeding innovation, cross-fixation, and herd expansion, using Dorper sheep as the father and Mongolian sheep as the mother. Given the critical role of early growth and development stages in determining the meat production potential of livestock animals, estimating the genetic parameters of early growth traits is crucial for the genetic advancement of meat sheep populations. This study aimed to investigate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. It was found that the growth traits of Dumeng sheep could be improved indirectly by selecting for weaning weight. This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits. [ABSTRACT FROM AUTHOR]

Marine algal toxins, highly toxic secondary metabolites, have significant influences on coastal ecosystem health and mariculture safety. The occurrence and environmental control factors of lipophilic marine algal toxins (LMATs) in the surface seawater of the Changjiang estuary (CJE) and the adjacent East China Sea (ECS) were investigated. Pectenotoxin-2 (PTX2), okadaic acid (OA), dinophysistoxin-1(DTX1), and gymnodimine (GYM) were detected in the CJE surface seawater in summer, with concentration ranges of not detected (ND)–105.54 ng/L, ND–13.24 ng/L, ND–5.48 ng/L, and ND–12.95 ng/L, respectively. DTX1 (ND–316.15 ng/L), OA (ND–16.13 ng/L), and PTX2 (ND–4.97 ng/L) were detected in the ECS during spring. LMATs formed a unique low-concentration band in the Changjiang diluted water (CJDW) coverage area in the typical large river estuary. PTX2, OA, and DTX1 in seawater were mainly derived from Dinophysis caudate and Dinophysis rotundata, while GYM was suspected to be from Karenia selliformis. Correlation analyses showed that LMAT levels in seawater were positively correlated with dissolved oxygen and salinity, but negatively correlated with temperature and nutrients, indicating that the hydrological condition and nutritional status of seawater and climatic factors exert significant effects on the distribution of LMATs. [ABSTRACT FROM AUTHOR]

Polyoxometalates have emerged as promising bactericidal agents. In the current study, the bactericidal activity of polyoxometalate K6[P2Mo18O62] against Escherichia coli (E. coli) O157:H7 and its possible underlying mechanisms were explored. The obtained results demonstrated that K6[P2Mo18O62] could effectively kill E. coli O157:H7 at millimolar levels. Moreover, K6[P2Mo18O62] treatment also induced significant increases in recA protein expression and further triggered characteristic apoptosis-like bacterial death events such as DNA fragmentation and phosphatidylserine exposure. In conclusion, polyoxometalate K6[P2Mo18O62] possesses a desirable antibacterial activity, and induction of bacterial apoptosis-like death might be involved in its underlying bactericidal mechanisms. [ABSTRACT FROM AUTHOR]

Drought is an event of shortages in the water supply, whether atmospheric, surface water or ground water. Prolonged droughts have negative impacts on ecosystems, agriculture, society, and the economy. Although existing drought index products are widely utilized in drought monitoring, the coarse spatial resolution greatly limits their applications on regional or local scales. Machine learning driven by remote sensing observations offers an opportunity to monitor regional scale droughts. However, the limited time range of remote sensing observations such as vegetation index (VI) resulted in a substantial gap in generating high resolution drought index products before 2000. This study generated spatiotemporally continuous Standardized Precipitation Evapotranspiration Index (SPEI) data spanning from 1901–2018 in southwestern China by machine learning. It indicated that four Classification and Regression Tree (CART) approaches, decision trees (DT), random forest (RF), gradient boosted regression trees (GBRT) and extra trees (ET), can provide valid local drought information by downscaling the Estación Experimental de Aula Dei (EEAD) data. The in-situ SPEI dataset produced by the Penman–Monteith method was used as a benchmark to evaluate the temporal and spatial performance of the downscaled SPEI. In addition, the necessity of VI in SPEI downscaling was also assessed. The results showed that: (1) the ET-based product has the best performance (R2 = 0.889, MAE = 0.232, RMSE = 0.432); (2) the VI provides no significant improvement for SPEI re-construction; (3) topography exerts an obvious influence on the downscaling process, and (4) the downscaled SPEI shows more consistency with the in-situ SPEI compared with EEAD SPEI. The proposed method can be easily extended to other areas without in-situ data and enhance the ability of long-term drought monitoring. [ABSTRACT FROM AUTHOR]

A Fe-34.5 wt % Mn-0.04 wt % C ultra-high Mn steel with a fully recrystallised fine-grained structure was produced by cold rolling and subsequent annealing. The steel exhibited excellent cryogenic temperature properties with enhanced work hardening rate, high tensile strength, and high uniform elongation. In order to capture the unique mechanical behaviour, a constitutive model within finite strain plasticity framework based on Hill-type yield function was established with standard Armstrong-Frederick type isotropic hardening. In particular, the evolution of isotropic hardening was determined by the content of martensite; thus, a relationship between model parameters and martensite content is built explicitly. [ABSTRACT FROM AUTHOR]

The polylactic acid (PLA) resin Ingeo 4032D was selected as the research object. Epoxy soybean oil (ESO) and zeolite (3A molecular sieve) were used as plasticizer and reinforcing filler, respectively, for PLA blend modification. The mixture was granulated in an extruder and then blown to obtain films under different conditions to determine the optimum processing temperatures and screw rotation. Then, the thermal behaviour, crystallinity, optical transparency, micro phase structure and physical properties of the film were investigated. The results showed that with increasing zeolite content, the crystallization behaviour of PLA changed, and the haze of the film increased from 5% to 40% compared to the pure PLA film. Zeolite and ESO dispersed in the PLA matrix played a role in toughening and strengthening. The PLA/8 wt% zeolite/3 wt% ESO film had the highest longitudinal tensile strength at 77 MPa. The PLA/2 wt% zeolite/3 wt% ESO film had the highest longitudinal elongation at 13%. The physical properties depended heavily on the dispersion of zeolite and ESO in the matrix. [ABSTRACT FROM AUTHOR]

Changes in customer needs are unavoidable during the design process of complex mechanical products, and may bring severely negative impacts on product design, such as extra costs and delays. One of the effective ways to prevent and reduce these negative impacts is to evaluate and manage the core parts of the product. Therefore, in this paper, a modified Dempster-Shafer (D-S) evidential approach is proposed for identifying the core parts. Firstly, an undirected weighted network model is constructed to systematically describe the product structure. Secondly, a modified D-S evidential approach is proposed to systematically and scientifically evaluate the core parts, which takes into account the degree of the nodes, the weights of the nodes, the positions of the nodes, and the global information of the network. Finally, the evaluation of the core parts of a wind turbine is carried out to illustrate the effectiveness of the proposed method in the paper. The results show that the modified D-S evidential approach achieves better performance regarding the evaluation of core parts than the node degree centrality measure, node betweenness centrality measure, and node closeness centrality measure. [ABSTRACT FROM AUTHOR]

Promoting the rise of Central China is one of the most important national strategies regarding the promotion of China’s economic development. However, the environmental issues in the central regions have become remarkably severe. It is therefore worthwhile exploring how economic development and environmental protection can be coordinated. Focusing on the 29 prefecture-level cities in the Central Plains Urban Agglomeration, the authors empirically analyze the relationship between the economy and the environment from 2004 to 2014. The combined methods of the spatial autocorrelation model, the environmental Kuznets curve, and the global spatial correlation test are systematically employed. The results show that: (1) a strong spatial correlation exists between industrial wastewater discharge, industrial sulfur dioxide, and dust emissions in the Central Plains Urban Agglomeration; (2) the relationship between the economy and the environment of this urban agglomeration reveals an inverted “U” curve, which confirms the classical environmental Kuznets curve hypothesis. Industrial dust emissions have surpassed the inflection point of the Kuznets curve, but its spatial spillover effect still remains strong. This is caused by an accumulation effect and a lag effect; (3) the proportion of the secondary industry and population has a strong positive effect on pollution discharge; investments in science and technology have a certain inhibitory effect on industrial sulfur dioxide emission. Moreover, an increase in the number of industrial enterprises has a negative effect on industrial wastewater emission. At the end, the authors put forward policy recommendations regarding the establishment of a joint supervisory department and unified environmental standards at the regional level to deal with the spillover effects of pollution. [ABSTRACT FROM AUTHOR]