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• This study employs the GDIM model and the ARDL method to examine several key drivers of agricultural carbon emissions in the main grain-producing areas of China. • Despite being a significant factor in agricultural carbon emissions, the impact of chemical fertilizer input shifted from a rising to a falling trend, confirming the existence of an agriculture-based CO2 EKC. • Agricultural mechanical application had a long-term inhibiting effect, while the aging of the agricultural labor force had a short-term driving effect. Low-carbon agricultural development is inseparable from the support of green finance. This study utilizes the GDIM model and the ARDL method to examine several key drivers of agricultural carbon emissions in China's main grain-producing areas from 2000 to 2020. The objective is to offer a valuable policy framework for green finance in promoting low-carbon agricultural production. The results indicated that despite being a driving factor in agricultural carbon emissions, the effect of the chemical fertilizer input shifted from rising to falling, and an agriculture-based CO 2 EKC was confirmed. Moreover, from the perspective of the causal links between agricultural carbon emissions and their drivers, this study found that chemical fertilizer input had both long- and short-term driving effects, agricultural mechanical application had a long-term inhibiting effect, and the aging of the agricultural labor force had a short-term driving effect. The findings of this paper will help policymakers develop green financial resources to guide capital towards low-carbon agricultural production. [ABSTRACT FROM AUTHOR]

Disposable aluminum cans and plastic bottles are common wastes found in modern societies. This article shows that they can be upcycled into functional materials, such as metal-organic frameworks and hierarchical porous carbon nanomaterials for high-value applications. Through a solvothermal method, used poly(ethylene terephthalate) bottles and aluminum cans are converted into MIL-53(Al). Subsequently, the as-prepared MIL-53(Al) can be further carbonized into a nitrogen-doped (4.52 at%) hierarchical porous carbon framework. With an optical amount of urea present during the carbonization process, the carbon nanomaterial of a high specific surface area of 1324 m2 g−1 with well-defined porosity can be achieved. These features allow the nitrogen-doped hierarchical porous carbon to perform impressively as the working electrode of supercapacitors, delivering a high specific capacitance of 355 F g−1 at 0.5 A g−1 in a three-electrode cell and exhibiting a high energy density of 20.1 Wh kg−1 at a power density of 225 W kg−1, while simultaneously maintaining 88.2% capacitance retention over 10,000 cycles in two-electrode system. This work demonstrates the possibility of upcycling wastes to obtain carbon-based high-performance supercapacitors. [Display omitted] • Upcycle used PET bottles and aluminum cans into the nitrogen-doped hierarchical porous carbons. • Prepared carbon as supercapacitor electrode delivers a high energy density of 20.1 Wh/kg. • Well-defined porous carbon framework is vital to the excellent supercapacitive performance. • This work paves an effective environmental-friendly way to prepare carbon-based supercapacitor. [ABSTRACT FROM AUTHOR]

• GF-1 and GF-6 remote sensing data have potential for wetland change detection. • Spatial-temporal attention method can identify information changes in cultivated land to wetland areas. • Sanjiang National Nature Reserve has large wetland restoration areas located near water sources. • The results of the study have important implications for the restoration and sustainable development of wetlands. Wetlands are important ecosystems, and detecting changes in wetlands plays a very important role in the management of wetland resources. Therefore, we selected Sanjiang National Nature Reserve as the study area and proposed a method in which bi-temporal GaoFen (GF) remote sensing data was combined with a spatial–temporal attention neural network (STANet) model, coupled with the selection of spectral variables. We used the bi-temporal images from GF-1 and GF-6 and compared three band selection methods, Relief F, principal component analysis, and RGB combination, and two STANet-based change detection methods (BAM: basic spatial–temporal attention module and PAM: pyramid spatial–temporal attention module) to improve the change detection of the wetland restoration. The results show that the accuracy of wetland change detection is related to different combinations of spectral variable selection and STANet models. The combination of Relief F feature selection and the BAM model led to the highest accuracy of the wetland change detection, 71.72 % for F1-Score and 76.92 % for MIou. The vector results of the change detection statistics show that the area of the Sanjiang National Nature Reserve cultivated lands converted to wetlands is 46.05 km2. This study implies that the STANet method has the capacity of detecting complex wetland restoration. Red, Bule, NIR and Red Edge1 bands of GaoFen data can provide effective information in the detection of changes in wetlands. The results of the study can be used as a reference for rational planning of wetland restoration, seeking a balance between cultivated lands and wetlands, which is important for the sustainable development of wetland. [ABSTRACT FROM AUTHOR]

This paper presents a novel X-ray Computed Tomography (CT) image analysis method to characterize the Fracture Process Zone (FPZ) in scaled centre-notched quasi-isotropic carbon/epoxy laminates. A total of 61 CT images of a small specimen were used to fine-tune a pre-trained Convolutional Neural Network (CNN) (i.e., VGG16) to classify fibre orientations. The proposed CNN model achieves a 100% accuracy when tested on the CT images of the same scale as the training set. However, the accuracy drops to a maximum of 84% when tested on unlabelled images of the specimens having larger scales potentially due to their lower resolutions. Another code was developed to automatically measure the size of the FPZ based on the CNN identified 0 °plies in the largest specimen which agrees well with the manual measurement (on average within 3.3%). The whole classification and measurement process can be automated without human intervention. [Display omitted] • A novel method was proposed to characterise Fracture Process Zone (FPZ). • A Convolutional Neural Network (CNN) was developed to identify fibre orientations. • The CNN model showed a test accuracy of 100% for classification. • The CNN was applied to unlabelled CT images achieving an accuracy of up to 84%. • The estimated FPZ size of the largest scale was within 3.3% from actual measurement. [ABSTRACT FROM AUTHOR]

Crack migration into the composite in adhesively bonded joints can cause a major degradation of strength. This usually occurs via cracks in the surface plies, but this paper demonstrates that crack migration can occur in joints even with 0° surface plies. The role of surface treatment is investigated by comparing grit blasted and plasma surface treatment before the secondary bonding process. Unidirectional Double Cantilever Beam specimens and quasi-isotropic Double Lap Joints with surface 0° plies were tested. It was found that with grit blasted specimens, crack migration occurred due to broken fibres present on the composite surface. Plasma treated specimens with no broken fibres on the surface failed in the adhesive with substantial increases in the Mode I fracture toughness and the strength of the double lap joints. [ABSTRACT FROM AUTHOR]

In this paper, a set of initial R-curves were measured for three different in-plane sizes using scaled Over-height Compact Tension (OCT) specimens and two different stacking sequences. The data reduction scheme was based on a Virtual Crack Closure Technique which is informed by the measured failure load and effective crack length. The R-curve effects were studied by using X-ray Computed Tomography to measure the crack length throughout fracture propagation. It was found that the initial R-curves measured from all three specimen sizes follow the same linearly increasing trend, with similar results for both stacking sequences. However, no plateau was seen on any of the measured R-curves, implying that even the largest specimens are not large enough to generate a full R-curve. a) Scaled OCT tests with two quasi-isotropic stacking sequences b) Initial R-curves showing the same linearly increasing trend but no plateau yet. [Display omitted] [ABSTRACT FROM AUTHOR]

The sintering process is the main process that affects the performance of ternary cathode materials. Due to the closed, continuous, and long-term characteristics of the sintering process carried out in a roller kiln, the reaction state including reaction degree, particle size, and etc., of the raw materials cannot be directly obtained through sampling detection, resulting in ineffective temperature control. This article mainly focuses on modeling the dehydration reaction stage of the sintering process to obtain the reaction state of the raw materials. Firstly, a two-sphere model is established based on Newton's second law and the particle volume conservation equation to describe the relationship between the particle radius and sintering temperature of lithium hydroxide and ternary precursor; Secondly, the chemical kinetic parameters of the dehydration reaction in the two-sphere model are obtained by combining the single heating rate scanning method with the multiple heating rate scanning method; Then, in order to improve the accuracy of the two-sphere model and reduce the impact of external environmental temperature changes, a physics-informed neural network model is constructed by combining the two-sphere model with the neural ordinary differential equation model. Finally, the effectiveness of the modeling method is verified through numerical simulation and empirical data. [ABSTRACT FROM AUTHOR]

2 at.% Tm, xat.% Dy:Y 2 O 3 (x = 0, 0.1, 0.5 and 1) transparent ceramics were fabricated via vacuum sintering. The microstructural properties of the prepared ceramics were determined using XRD and SEM. The absorption cross-section of 2 at.% Tm, 1 at.% Dy:Y 2 O 3 ceramic was 0.53 × 10−20 cm2 with the FWHM of 43.59 nm. The increased cross-section originates from a large overlapping range appearing in the absorption spectrum of the Dy3+:6H 15/2 → 6F 5/2 and Tm3+:3H 6 →6H 4 transitions. The J-O intensity parameters Ω 2 , Ω 4 and Ω 6 and the fluorescence characteristics of the pivotal luminescent level of the Dy3+ ions were investigated. Under 793 nm excitation, the emission cross section of the Tm,Dy:Y 2 O 3 ceramic at 3094 nm was 3.63 × 10−21 cm2 with the FWHM of 355 nm. The fluorescence lifetimes of Dy3+:6H 13/2 level of 2 at.% Tm, xat.% Dy:Y 2 O 3 (x = 0.1, 0.5 and 1) ceramics were fitted to be 357 μs, 282 μs and 149 μs, respectively. In order to explore the quenching mechanism of Tm3+:3F 4 level, the fluorescence lifetimes of Tm3+:3F 4 of the 2 at.% Tm, xat.% Dy:Y 2 O 3 ceramics (x = 0, 0.1, 0.5 and 1) were measured to be 4.878 ms, 462 μs, 104 μs and 61 μs, respectively. The possible energy transfer mechanisms between Tm3+ and Dy3+ ions are discussed. The results show that adding Tm3+ ions to Dy:Y 2 O 3 ceramics can effectively enhance the 2.9 μm MIR through energy transfer. [ABSTRACT FROM AUTHOR]

• An analytical parameters estimation results by the MLE method for the Wiener process with random effects is obtained. • The comparative study between different MLE methods is implemented. • An unbiased parameters estimation method is proposed and applied to other types of Wiener process-based degradation models. • An empirical unbiased parameters estimation results when the degradation data is measured at different times is developed. • Mis-specification issue about whether considering the random effects is analyzed in theory. Accurate parameters estimation is one of the most crucial components in remaining useful life (RUL) prediction. This paper proposes an unbiased parameters estimation method and analyzes the impact of model mis-specification of Wiener process-based degradation model with random effects. First, we obtain an analytical expression of parameters estimation for the linear Wiener process. Then, by analyzing the natures of parameters estimation, an unbiased parameters estimation method is proposed and applied to other types of Wiener process-based degradation models. This unbiased estimation method is very similar to the sample variance of normal distribution. Since the sample size of degradation data is typically small, hence the proposed unbiased estimation method is suggested. After that, we present an empirical unbiased parameters estimation method with a closed-form solution when the degradation data is measured at different times. The proposed unbiased estimation method could increase computational speed and avoid falling into local minimum. In addition, based on parameters estimation results, the mis-specification issue about whether the random effects should be considered in the degradation modeling is analyzed in theory. Finally, several numerical examples and case studies are used for experimental verification. The experimental results demonstrate that the proposed unbiased parameters estimation method is superior to other parameters estimation methods and the random effects are suggested to be considered. [ABSTRACT FROM AUTHOR]

• Dongting Lake has experienced land covers changes due to extreme hydrologic events. • We use qualitative and quantitative methods for change detection in Dongting Lake. • A new change detection method Bi-SRUNet++ is proposed for change detection. • This method has the great potential to improve the accuracy of wetland change detection. • The obtained information is important for wetland management and sustainable development. The utility of semantic change detection in myriad change scenarios has garnered considerable attention in contemporary research; however, its applicability in monitoring alterations in wetland ecosystems remains incompletely elucidated. To surmount the constraints associated with binary change detection methodologies—chiefly their insufficiency in the extraction of bi-temporal attributes—we introduced the Bi-Temporal Semantic Reasoning UNet++ (Bi-SRUNet++) algorithm. This algorithm leverages the architectural strengths of UNet++ as its foundational network to precisely delineate features pertinent to multi-class change detection. As a preliminary step, the study focused on the Dongting Lake wetland in China and conducted an analysis of feature trends predicated upon the monthly Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), as derived from Landsat 8 data spanning 2021–2022. Subsequently, the optimal temporal phases for change detection were ascertained through differential analyses between NDWI and NDVI metrics. Implementing the Bi-SRUNet++ algorithm on a pair of Sentinel-2 images, captured during the optimal phases, yielded augmented change information. Comparative evaluations reveal that the Bi-SRUNet++ algorithm, conceptualized on the framework of Bi-Temporal Semantic Reasoning Network (Bi-SRNet), surpasses the performance indices of its counterpart Semantic Segmentation and Change Detection Late Fusion (SSCD-l). Furthermore, the incorporation of the UNet++ backbone network amplifies the algorithm's capacity for semantic feature extraction, thereby enhancing the efficacy of Bi-SRUNet++ in wetland change detection. The analysis divulges that the total altered area of Dongting Lake during the 2021–2022 period amounts to 1187.97 km2, comprising a water loss of 1186.16 km2, a 715.34 km2 transformation into vegetation, and a conversion of 469.96 km2 into mudflats. The codes and partial dataset in this paper are available at: https://github.com/vivianmiumiu/Bi-SRUNetplusplus-for-SCD. [ABSTRACT FROM AUTHOR]

• Comprehensive failure analysis of X-10 brake disc was investigated. • The fatigue crack occurred at the root of the connecting claw. • The crack source was attributed to the graphite sheet exposed on the brake disc matrix. • The truth braking power and fatigue tests and finite element simulation were performed. • Suggestions to prevent fatigue failure of X-10 brake disc were provided. Some X-10 type brake discs for urban rail vehicles were examined by fluorescent magnetic particle inspection test, and the magnetic trace accumulation was observed at the root of the connecting claw of the brake discs. In this work, the causes of failure for the brake discs are analyzed and discussed. The chemical composition, metallographic structure, and mechanical property of the failure samples were characterized by means of X-ray energy dispersive spectrometry, metallographic microscope, scanning electron microscopy, and an electronic universal material-testing machine. At the same time, the stress and crack propagation states were analyzed by numerical simulation, braking dynamic and fatigue tests, respectively. Based on the fracture mechanics theory, the residual fatigue life was also predicted. The failure reason of the inner wall of brake disc was that the fatigue crack occurred at the root of the connecting claw of the brake disc body, and the crack source was taken from graphite sheets exposed on the surface of the grey cast iron matrix. The results of the numerical simulation showed that the larger thermal stress acted on the graphite sheets at the root of the connecting claw, and promoted the crack to expand along the path of the graphite sheets. The results of the braking force and fatigue tests showed that there was stability propagation of the crack. Finally, some suggestions and prevention for brake discs are provided. [ABSTRACT FROM AUTHOR]

In this work, electron irradiation as a novel and simple method was used to fabricate wearable strain sensors with high comprehensive performance. Hydroxylated multi-walled carbon nanotubes (hydroxylated MWCNTs) were uniformly adsorbed on ecoflex etched by hydrofluoric acid (HF). After electron irradiation, a large number of defects and functional groups were successfully introduced into the hydroxylated MWCNTs/ecoflex, leading to a flexible strain sensor with high comprehensive performance such as ultra-low detection limit, fast response, and wide sensing range. Compared with the unirradiated sensors, the irradiated sensors have an ultra-low detection limit of 0.01% and a wide strain range (the maximum strain of 800%), the maximum gauge factor is increased by 344%, and the response time and recovery time are reduced by 50% and 89.2% respectively. The sensors can be used for monitoring the weak physiological movements and large-scale human movements, which has great potential in health monitoring. The electron irradiation as an activate method can provide a way for mass fabrication of flexible sensors with high comprehensive performance such as ultra-low detection limit and wide sensing range. [Display omitted] [ABSTRACT FROM AUTHOR]

Buildings are one of the most important energy use sectors in cities, and forecasting the abnormal increase in building energy demand in certain climatic conditions is necessary to adjust building energy operations and implement energy policy. Accordingly, this research proposes a data-driven abnormally high energy demand detection framework in urban buildings based on their design parameters and local weather data, with the support of machine learning techniques. In this study, 71 public buildings with energy records in Jianhu city, Jiangsu province, China, were selected to abstract urban morphologies at community scale. The weather profile for the city was obtained from year 2015–2018 to create weather characteristics. Three machine learning algorithms—random forest, support vector machine, and artificial neural network—were applied to identify the months of abnormally high electricity consumption in different building types. This framework also explores key variables in the data and provides the basis for a system that prioritizes the acquisition of variables when complete data is unavailable. The results show that, with complete data, the accuracy score of the system in this study can reach 0.854 with the SVM algorithm, and the model returned an accuracy of 0.865 with the RF model after the key variable selection. Based on those results, the framework in this study can generate preemptive warnings for months with an expected abnormally high energy consumption in target buildings as a prerequisite of energy policy. • A data-driven abnormally high electricity use detection system is proposed. • Different morphologies under different weather conditions were tested. • Key factors of urban morphologies and weather conditions were identified. • Three machine learning techniques were applied for the prediction in the system. • This study can be applied for energy management in building and urban scales. [ABSTRACT FROM AUTHOR]

• A series of 0.6 at% Pr:CaF 2 -RF 3 (R= Y3+, Gd3+, Lu3+) crystals were successfully grown by the temperature gradient technique(TGT) method. • By co-doping R3+ ion (R=Y3+, Gd3+, Lu3+), the spectral performance of the crystals could be significantly improved and the function effect is also different. • The spectral performance and spectral quality factor of the crystals were systematically described and compared for the first time. • The spectral modulation effect caused by Y3+, Gd3+ and Lu3+ co-doping ion was discussed. A series of 0.6 at% Pr:CaF 2 -RF 3 (R = Y3+, Gd3+, Lu3+) crystals have been successfully grown by the temperature gradient technique(TGT). The absorption spectra, fluorescence spectra, and fluorescence decay curves were measured and analyzed at room temperature. When the R3+ concentration is higher than a certain value, the diffraction peaks of Pr:CaF 2 -RF 3 crystals were well indexed to the (Ca,Y)F 2 phase. By co-doping R3+ ion (R=Y3+, Gd3+, Lu3+), the spectral performance of the crystals could be significantly improved and the function effect is also different. For 0.6 at% Pr, 28.5 at% Y:CaF 2 crystal, the fluorescence lifetime of 3P 0 energy level was fitted to be 45.46 µs, and the emission cross sections of 3P 0 →3H 6 (605 nm), and 3P 0 →3F 2 (641 nm) were calculated to be 1.78 × 10−20 cm2, and 4.61 × 10−20 cm2 with the full width at half (FWHM) of 20.1 nm and 6.8 nm, respectively. The 0.6 at% Pr, 28.5 at% Y:CaF 2 crystal could be favorble laser medium for ultra-fast laser opreration at 605 nm and 641 nm. For 0.6 at% Pr, 18 at% Gd:CaF 2 and 0.6 at% Pr, 10 at% Lu:CaF 2 crystals, the spectral quality factors σ em• τ f of 3P 0 →3F 2 transition (642 nm) were calculated to be 614.18 × 10−20 cm2•µs and 506.36 × 10−20 cm2•µs with the FWHM of 3.7 nm and 5.3 nm. All the results show that 0.6 at% Pr, 18 at% Gd:CaF 2 and 0.6 at% Pr, 10 at% Lu:CaF 2 crystals are promising gain media for high-efficiency visible laser output at 642 nm, which would compete with commercial Pr: YLF crystal in the future. [ABSTRACT FROM AUTHOR]