In recent years, researches on combining wavelet decomposition and convolutional neural network (CNN) together to classify hyperspectral images (HSI) have emerged, and some effective classification models have been proposed and achieved good classification results. However, there are two problems for most of the proposed models. One is the heavy training parameters and the other is that there is no distinction between the classification effectiveness of low frequency and high frequency features after wavelet decomposition. In this paper, a new light-weighted HSI classification model (LLFWCNN) is proposed, which performs multi-layer wavelet decomposition for HSI after dimensionality reduction, and only arranges the low frequency features in a specific stack mode, then classifies them through a well-designed convolutional neural network. Compared with other classification models, the number of parameters is only 2.5% of that of other models. And only the low frequency features after wavelet decomposition are used, while the high frequency features are abandoned. The results showed that compared with the state-of-the-art classification models, LLFWCNN could obtain the same or even better classification results with fewer network parameters and proved that the low frequency features after wavelet decomposition provide LLFWCNN with more favorable information for HSI classification as well.

In the field of engineering, tracked vehicles play a significant role in accessing challenging terrains. Maintaining trafficability is of the utmost importance when it comes to tracked vehicles traversing rugged terrains; it provides stability, traction, and adaptability for a wide range of applications, including deep-sea exploration. The terrain characteristics pertain to the interactions that determine the mobility of tracked vehicles. Traditional Terramechanics models are in-adequate in depicting the correlation between tracks and soil. An important issue is the lack of consideration for the moisture content in the soil and the rate at which sinking occurs in these models. Examining sinkage speed helps describe the ability of tracked vehicles to move at high speeds. The focus is on the critical factors influencing the mobility of tracked vehicles, precisely the sinkage speed and its relationship with pressure. Experimental tests in a soil bin, utilizing Bentonite/Diatom sediment soil, aim to measure these terrain factors. The study introduces a rubber-tracked vehicle, pressure, and moisture sensors to monitor pressure sinkage and moisture, evaluating cohesive soils (Bentonite/Diatom) in combination with sand and gravel mixtures. Findings reveal that higher moisture content in Bentonite soil leads to increased track slippage and sinkage compared to Diatom soil. Regarding soil behavior, diatom soil exhibited notable compaction and sinkage characteristics in contrast to bentonite soil. The distinct behaviors of these soils underscore the complexity of terrains, emphasizing the need for a meticulous understanding of soil properties before applying them in natural environments. Furthermore, this study contributes precision in assessing natural terrains, enhancing tracked vehicle design, and advancing terrain mechanics comprehension for off-road exploration. This knowledge contributes to better vehicle design and a deeper comprehension of terrain mechanics.

Exploring the spatio-temporal differentiation characteristics and coupling coordination relationship of the composite ecosystem of urban agglomerations is of great significance for promoting the synergistic development and integration construction of urban agglomerations. This paper is based on LUCC data, DEM, temperature, precipitation and other multi-source data, taking the central Yunnan urban agglomeration as an example. The entropy weight method is first used to determine the comprehensive weights to evaluate the regional economic and social development level. Then, the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs)model was used to quantitatively calculate and analyze the spatial and temporal evolution characteristics of the four ecosystem services, including water conservation, soil conservation, carbon sequestration services, and habitat quality. The coupling coordination model was used to quantitatively analyze the evolution of the coupling coordination of the composite ecosystems in the central Yunnan urban agglomeration during the period of 2010-2020, and to reveal its development law. The results show the following: ①During the study period, the socio-economic subsystems in central Yunnan urban agglomeration demonstrated an outward radiative growth from Kunming City, marked by underdeveloped sub-centers and prevalent low-level areas. ②Trends in ecosystem services varied, with water and soil conservation showing fluctuating increases, carbon sequestration remaining stable, and habitat quality declining. The integrated ecosystem services critically important zone is mainly located in the northeastern region and southwestern edge of the study area. ③Economic, social and ecological subsystems are highly coupled, with consistent overall development trends and strong interactions. ④The increase in the degree of harmonization amounted to 7.82%, with lagging subsystems varying in different degree of harmonization subregions. The study can provide a scientific basis for urban agglomerations to formulate scientific and reasonable development policies, promoting regional sustainable development and optimizing the spatial pattern of the national territory.

Crystalline silicon nanomembrane (Si NM) is promising in wearable and implantable bioelectronics due to its electrical and mechanical performances. However, the current explorations are not enough to firmly bridge ultrathin encapsulation strategies with high performance crystalline Si NM based transistors. In this work, we utilized Al2O3/alucone as the ultrathin encapsulation strategies aim to thoroughly investigate the effect of gate dielectric surface area on the electrical performance of crystalline Si NM based metal-oxide-semiconductor capacitors (MOSCAPs). The comparative analysis was implemented on the three types of diameters (Ø) MOSCAPs with and without Al2O3/alucone encapsulation under planar and concave/convex bending conditions. Benefited from the Al2O3/alucone ultrathin encapsulations, the stability of gate leakage current density, and interfacial characteristics were maintained and improved under mechanical bending deformations for MOSCAPs with predefined Ø of 160, 240, and 320 μm compared to the bare MOSCAPs. Combined with the Ø-related mechanical analysis on the maximum strain in the critical layers and the practical measurements, the encapsulated MOSCAPs with Ø 160 μm showed the most stable electro-mechanical performances. The variations for encapsulated MOSCAPs with three types of Ø emerged in the oxide layer and the interface at dielectric/Si NM that were carefully evaluated according to the bending deformations. These findings are significant for leveraging the practical applications in the ultrathin encapsulation strategies for reliable operations of crystalline Si NM based integrated circuits (ICs).

Pool boiling heat transfer is recognized as one of the most effective heat transfer methods and has extensive applications in industry. The application of non-azeotropic binary mixtures can not only meet the environmental requirements of modern industrial development, but also improve the coefficient of performance (COP) of many systems. Therefore, studying the boiling heat transfer characteristics of non-azeotropic mixtures is important on improving their potential industrial application. This article selected ethylene glycol/deionized water (EG/DW) at varies concentration as the research working fluids, and conducted experimental research on the pool boiling performance. At the same time, the thermophysical parameters of the mixtures were studied and analyzed. The results showed that the concentration of EG in the mixture had a significant impact on the thermal properties and temperature glide of the mixture, where both affected the heat transfer deterioration. Based on experimental data, six widely used heat transfer coefficient prediction correlations were used for calculation, and two of them were modified. The deviation between the modified correlations and the experimental results remained within 20%.

Tracked vehicles play a vital role in accessing challenging terrains, offering stability, traction, and versatility for various applications, including deep-sea exploration. However, the seafloor and sediment contain valuable but poorly understood resources, making it a significant challenge for tracked vehicles during exploration. Consequently, it is imperative to conduct comprehensive studies before considering these terrains for off-road purposes. In this study, we developed a rigid, flexible track vehicle equipped with various sensors, focusing on monitoring pressure sinkage. Cohesive soils like Bentonite and Diatom soil, particularly when combined with sand and gravel mixtures, were used for the investigation and assessed for ground pressure, bearing capacity, and sinkage in rigid-flexible tracked vehicles on these characterized terrains under varying moisture conditions. Our findings indicated that increased moisture content in bentonite soil resulted in significantly higher track slippage and sinkage compared to diatomaceous earth soil. Regarding soil behavior, diatom soil exhibited notable compaction and sinkage characteristics compared to bentonite soil. These significant variations between these two soil types under changing moisture conditions highlight the complexity of such terrains. Therefore, understanding the soil and its physical properties is crucial before applying them to natural terrains. Furthermore, this study emphasizes the importance of precision in assessing the physical properties of natural terrains for the development of tracked vehicles for off-road exploration. This knowledge contributes to better vehicle design and a deeper comprehension of terrain mechanics.

China is facing the development requirements of the dual carbon goal and the construction needs of resilient cities. Currently, high-strength composite structural systems are a feasible solution, but their research and application in the construction field are insufficient, especially for the combination forms of concrete above C100 and steel above Q550. In response to this issue, this paper proposes a new high-performance structural system, namely the composite frame - high-strength steel plate wall core tube resilient structure system, which includes high-strength steel plate (Q550) - ultra-high strength concrete (C100) shear walls, replaceable energy dissipation coupling beams, and composite frames. Taking a 200 meter building as an example, this paper designs and establishes calculation models for a conventional reinforced concrete frame-core tube structure and a high-performance structure based on PKPM software, and establishes elastoplastic analysis models for both based on SAUSAGE software, and then conducts dynamic elastoplastic time history analysis and seismic resilience assessment of structures under design basis earthquakes (DBEs), maximum considered earthquake (MCEs) and extremely rare earthquakes (EREs). Research has shown that compared to conventional structures, new high-performance structures can effectively reduce the size of shear walls, reduce the self-weight of the structure, and enhance the space for building use; The story drift ratio has higher redundancy than specification limits, lower plastic damage and overall stiffness degradation of the structure, and better seismic performance; The seismic resilience has significantly improved, especially in the case of casualties, which can better ensure the safety of people's lives and property.

For massive multiple-input multiple-output (MIMO) communications, the minimum mean square error (MMSE) scheme provides close to optimal recognition but involves inverting the high-dimensional matrix. A Gauss-Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing is introduced to address this problem. Firstly, the signal is initialized with the best of the three initialization regimes for faster algorithm convergence. Secondly, the signal is processed together with CJ. Finally, the pre-processed result is transferred to the GS detector. The simulation results indicate that the proposed iterative algorithm has a lower BER performance than the GS and improved iterative scheme based on GS in channels with different correlation levels.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

Background: Colorectal cancer (CRC) is one of the major causes of cancer-related death worldwide. Aerobic glycolysis precedes obtainment of oncogenic mutations and loss of tumor suppressors, promoting the progress of CRC. Although numerous biomarkers have been identified to be associated with prognosis and survival, a glycolysis-related gene signature in CRC has not been explored. Methods: mRNA expression profiling data in a group of CRC patients (n = 540) was extracted from the Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GESA) was performed to identify gene sets that were significantly different between CRC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan–Meier survival estimates and the log-rank test were used to validate the significance of risk parameters for prognosis prediction. Results: Five glycolysis-related genes (ENO3, GPC1, P4HA1, IDUA, ANKZF1) were identified to be significantly associated with overall survival (AUC=0.754). Based on the five‑gene signature, patients with CRC were divided into high and low‑risk subgroups. CRC patients with a low-risk score had better survival benefits than those with a high one (P < 0.001). Conclusions: A five-gene signature associated with glycolysis for predicting the outcome of CRC patients was generated, serving as a valuable prognosis model with high efficiency and potential targeted therapy of CRC patients.

Parental phubbing behavior is a significant factor in predicting adolescent smartphone dependence, but prior research has predominantly focused on a unidirectional perspective from the child's viewpoint, overlooking potential perceptual differences between parents and their children regarding parental phubbing. Therefore, are there differences in the perceptions of parental phubbing between parents and their children? Based on the "Discrepancy-Maladaptive" hypothesis, the presence of perceptual discrepancies in family factors can lead to adverse developmental outcomes in children. Does the parent-child perceptual discrepancy in parental phubbing influence adolescent smartphone dependence? And what role does parent-child relationship play in this context? This study selected 728 families from a middle school in Wuhan, and surveys were administered to both children and parents. The findings reveal: (1) There exist significant parent-child perceptual discrepancies in parental phubbing. (2) Parent-child perceptual discrepancies in parental phubbing significantly positively predict adolescent smartphone dependence and negatively predict parent-child relationships, while parent-child relationships significantly negatively predict adolescent smartphone dependence. (3) The parent-child relationship plays a mediating role between parent-child perceptual differences in parental phubbing behavior and adolescent smartphone dependence.

(1) Background: The social robot interaction design is critical in determining acceptance and user experience. However, few studies have systematically discussed the current focus and future research directions of social robot interaction design from a bibliometric perspective. Therefore, in order to identify the latest research progress and evolution trajectory of research hotspots in social robot interaction design over the last decade. (2) Methods: based on 2416 papers related to social robot interaction design collected in the Web of Science (WOS) database, a comprehensive review was conducted by using bibliometrics to draw a knowledge map using VOSviewer and CiteSpace in an integrated manner. (3) Conclusions: The current research hotspots of social robot interaction design mainly focus on #1the study of human-robot relationships in social robot, #2Research on the Emotional Design of Social Robot, #3research on social robot for children's psychotherapy, #4research on companion robot for elderly rehabilitation, and #5research on educational social robot. The reference co-citation identifies the classic literature that forms the basis of the current research, which provides theoretical guidance and methods for the current research. Finally, we discuss several future research directions and challenges in this field.

Revealing the multidimensional value of cultivated land resources, improving calculation methods, and exploring their spatial distribution characteristics and influencing factors are crucial for the enhancement of natural resource accounting system and the protection of cultivated land resources. This study take Guangxi, China as an example and construct a comprehensive multidimensional value evaluation system for cultivated land resources at the county scale. The income capitalization method, substitution market method, and value equivalent correction method were applied to calculate the economic, social, and ecological values of cultivated land resources in 111 counties. Furthermore, spatial autocorrelation analysis was employed to study their distribution characteristics and influencing factors. The results show that: (1) The total value of cultivated land resources in Guangxi reach 19,729.2 billion yuan, and the average cultivated land value is 5.3511 million yuan/hm2, of which the ratio of economic, social and ecological values is about 6:1:2; (2) The value of cultivated land has a certain distribution pattern in the global space, and the average economic and social value show the spatial distribution patterns of "low in the west and high in the east" and "low in the northwest and high in the southeast" respectively."(3) There is a significant spatial clustering effect of all values of cultivated land resources in local space, with "high-high" and "low-low" clustering. (4) Economic development level is significantly negatively correlated with the ecological value of cultivated land, while has a significant positive correlation with social value; (5) Natural conditions, economic development and agricultural development approaches are the key factors affecting the value of cultivated land resources.

The endosomal sorting complex required for transport (ESCRT) is an essential molecular machinery in eukaryotic cells that facilitates the invagination of endosomal membranes, leading to the formation of multivesicular bodies (MVBs). It participates in various cellular processes, including lipid bilayer remodeling, cytoplasmic separation, autophagy, membrane fission and remodeling, plasma membrane repair, as well as the invasion, budding, and release of certain enveloped viruses. The ESCRT complex consists of five complexes, ESCRT-0 to ESCRT-III and VPS4, along with several accessory proteins. ESCRT-0 to ESCRT-II form soluble complexes that shuttle between the cytoplasm and membranes, mainly responsible for recruiting and transporting membrane proteins and viral particles, as well as recruiting ESCRT-III for membrane neck scission. ESCRT-III, a soluble monomer, directly participates in vesicle scission and release, while VPS4 hydrolyzes ATP to provide energy for ESCRT-III complex disassembly, enabling recycling. Studies have confirmed the hijacking of ESCRT complexes by enveloped viruses to facilitate their entry, replication, and budding. Recent research has focused on the interaction between various components of the ESCRT complex and different viruses. In this review, we discuss how different viruses hijack specific ESCRT regulatory proteins to impact the viral life cycle, aiming to explore commonalities in the interaction between viruses and the ESCRT system.

A blockchain-based continuous micropayment is a crucial component of the digital economy as it facilitates transactions and promotes small purchases. However, due to the throughput limitations of blockchain, payment channels (PC) are often used to process a large volume of transactions through an off-chain mode. Nevertheless, the introduction of PC requires a trusted third party to ensure transaction security, which creates an additional security assumption since only the first and last transactions can be witnessed by other system users. To address this issue, we propose a novel micropayment scheme based on lockable signatures. All transactions in the PC form a continuous microtransaction hash-chain (CMHC) to prevent malicious users from obtaining transaction information during the process. Furthermore, all locks in the CMHC are invisible during the entire transaction process, and all information is transferred in a tamper-resistant manner. We provide corresponding security analysis and conduct a series of evaluations. The results show that the proposed scheme performs better than the state-of-the-art solutions in terms of transaction time and verification costs. This lockable signature-based micropayment scheme not only guarantees security but also improves transaction speed and efficiency, thereby promoting the development of the digital economy.

Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields. In this paper, we propose a hypergraph regularized Lp smooth nonnegative matrix factorization (HGSNMF), by incorporating hypergraph regularization and Lp smoothing constraint terms into the standard NMF. The hypergraph regularization term can capture the intrinsic geometry structure of the high dimension space data more comprehensively than simple graph, the Lp smoothing constraint may yield a smooth and more accurate solution to the optimization problem. The updating rules are given using multiplicative update techniques, and the convergence of HGSNMF is theoretically investigated. The experimental results on four different data sets show that the proposed method has better clustering effect than the related state-of-the-art methods in the vast majority of cases.

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and dorsal pathway of HVS, a dual-pathway convolutional neural network is proposed for BIQA task. The proposed method consists of two pathways: “what” pathway, which mimics the ventral pathway of HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, the gradient images weighted by the contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. The experiments conducted on six databases show that the proposed method achieves the state-of-the-art performance.

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on context information and large effective receptive field (ERF) is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements on it. First, the CAA-Net, which has a large ERF, is proposed to better merge the context and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.

Based on the 2013–2017 Chinese Family Database and China Household Finance Survey, the study examines the impact of farmland transfer on the income structure of agricultural households. We observe a significant rise in the income of transferred-out households, while that of transferred-in households is unchanged. A further empirical decomposition of income shows a significant increase in both the wage and asset generated incomes of transferred-out households. Although the income generated by transferred-in households through agricultural production and management increases significantly, the decrease in wage income is larger. The study’s empirical designs are robustly tested with propensity score matching and instrumental variables. Moreover, we conduct a comparative analysis of the external constraints faced by the two groups to explore the reasons for the divergence of the income effect. The results show that transferred-in households face highly pronounced external constraints in terms of capital, technology, and farmland size, while transferred-out households face relatively fewer impediments to non-agricultural employment. Our empirical results imply that external constraints can limit the social welfare improvements of agricultural land reform, suggesting that other complementary measures would be required for future policy improvements to work better.

When the image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information of the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. To enable the model to focus on both global and local features, a multi-scale feature fusion module is proposed. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.

Seniors safety is a compelling need, which necessitates 24/7 real-time monitoring and timely dangerous action recognition. Being able to mirror characteristics of physical objects (PO) to corresponding logical objects (LO) and seamlessly monitor their footprints thus detect anomaly parameters, Digital Twins (DT) has been considered a practical way to provide virtual health services for seniors safety. Meanwhile, widely adopted Internet of Medical Things (IoMT) consisting of wearable sensors and non-contact optical cameras for self and remote health data monitoring also raises concerns on information security and privacy violation. Therefore, security of POs, LOs and reliable data sharing among healthcare professionals are challenging as constructing trust and privacy-preserving virtual health services. Thanks to characteristics of decentralization, traceability and unalterability, Blockchain is promising to enhance security and privacy properties in many areas like data analysis, finance and healthcare. This paper envisions a lightweight authentication framework (LAF) to enable secure and privacy-preserving virtual healthcare services. Leveraging Non-Fungible Token (NFT) technology to tokenize LOs and data streams on blockchain, anyone can certify the authenticity of a digital LO along with its synchronized data between PO without relying on a third-party agency. In addition, the NFT-based tokenization not only allows owners fully control their IoMT devices and data, but it also enables verifiable ownership and traceable transferability during data sharing process. Moreover, NFT only contains references to encrypted raw data that are saved on off-chain storage like local files or distributed databases, such a hybrid storage strategy ensures privacy-preservation for sensitive information. A proof-of-concept prototype is implemented and tests are conducted on a case study of seniors safety. The experimental evaluation shows the feasibility and effectiveness of the proposed LAF solution.

Using the 2011-2020 digital inclusive finance data released by Peking University and the index system constructed by the Blue Book of China Rural Revitalization and Development Index (2018), this paper analyzes the dynamic evolution of the digital inclusive finance and the rural revitaliza-tion from time and space dimensions with the help of kernel density estimation, Markov chain and Moran index. Using The Gini coefficient decomposition method to analyze the source of differ-ences in digital inclusive finance, and then using the spatial autoregressive (SAR) model and ge-ographically weighted regression (GWR) model to study the boosting effect of digital inclusive finance on rural revitalization from the perspective of spatial aggregation and spatial differentia-tion, respectively. The results show that: (1) The growth rate of China's digital inclusive finance slows down year by year, and the inter-provincial differences increase year by year and show gradient characteristics, indicating that there may be a trend of multipolar differentiation. The overall level of Rural Revitalization shows an increasing trend, and the gap between provinces is still apparent. (2) The evolution of digital inclusive finance and rural revitalization is a slow ad-justment process, and there has been no cross-level jump in the past ten years. In digital inclusive finance, the liquidity from the highest and lowest levels to the medium level is high. However, the liquidity in the states of rural revitalization development level is not high. (3) Rural revitalization has a positive spatial spillover effect. The level of rural revitalization in the western area is signif-icantly lower than in the eastern area. At the same time, there is no significant difference between the east and central areas. The depth of the use of digital inclusive finance has a significant positive impact on the revitalization and development of rural areas, indicating that the further promotion of digital inclusive finance business in rural areas can substantially boost the revitalization of rural areas. (4) The boosting effect of digital inclusive finance on rural revitalization shows prominent spatial differentiation characteristics. The depth of use and the degree of digitization generally show a positive impact. The central and eastern coastal cities have the highest impact, decreasing toward the southwest and northeast. The areas with the lowest usage depth impact are clustered in the northeast, and the areas with the lowest digitization impact are clustered in the southwest.

Thrombosis has threatened human health in past decades. Bacillus nattokinase is a potential thrombolytic drug without side-effect and low cost and has been introduced into the consumer market as a functional food or dietary supplement. This review firstly summarizes the biodiversity sources and fermentation process of nattokinase, and systematically expounds the structure, cata-lytic mechanism and enzymatic properties of nattokinase; In view of the problems of low fermen-tation yield, insufficient activity and stability of nattokinase, this review discusses the heterologous expression of nattokinase in different microbial hosts, and summarizes the protein and genetic en-gineering progress of nattokinase-producing strains; Finally, this review summarizes the clinical application of nattokinase.

Purpose: Pitch plays an important role in auditory perception of music and language. This study provides a systematic review with meta-analysis to investigate whether individuals with autism spectrum disorder (ASD) have enhanced pitch processing ability and identify the potential factors associated with processing differences between ASD and neurotypicals. Method: We conducted a systematic search through six major electronic databases focusing on the studies that used nonspeech stimuli to provide a qualitative and quantitative assessment across existing studies on pitch perception in autism. We identified potential participant- and methodology-related moderators and conducted meta-regression analyses using mixed-effects models. Results: On the basis of 22 studies with a total of 464 participants with ASD, we obtained a small-to-medium positive effect size (0.26) in support of enhanced pitch perception in ASD.Moreover, the mean age and non-verbal IQ of participants were found to significantly moderate the between-studies heterogeneity. Conclusion: Our study provides the first meta-analysis on auditory pitch perception in ASD and demonstrates the existence of different developmental trajectories between individuals with ASD and neurotypicals. Non-verbal ability can be a significant contributor to the lower-level/ local processing bias in ASD. We highlight the need for further investigation of pitch perception in ASD under challenging listening conditions. Future neurophysiological and brain imaging research studies with a longitudinal design are also needed to better understand the nature of the atypical processing in ASD to obtain new insights into the underlying neural mechanisms and to help guide auditory-based interventions for improving language and social functioning.

Acute myeloid leukemia (AML) is a hematological malignancy that is the culmination of genetic and epigenetic alterations in the hematopoietic progenitor cells, leading to uncontrolled proliferation at the expense of normal hematopoiesis and bone marrow exhaustion. Although the outcomes for pediatric AML have improved in recent decades, at least one third of children still have relapses. Recent studies have notably highlighted the important role of dysregulated epi-genetic mechanisms in myeloid leukemogenesis. Epigenetic modifications are frequently reversible compared to genetic alterations, thus providing opportunities for targeted epigenetic therapy. In this review, we summarize the landscape of epigenetic alterations and the progress to date in epigenetic targeted therapy, and focus on the future role of epigenetic abnormalities in predicting relapse and the precision therapy in pediatric AML.

Terzaghi proposed two-dimensional (2D) arching theory through trapdoor tests based on the assumption of a vertical slip surfaces. However, the original 2D assumption is different from a real three-dimensional (3D) excavation condition and an actual slip surfaces caused by downward movement of a trapdoor is an inclined surface. Therefore, a 3D theoretical model was proposed in this study considering inclined slip surfaces. Horizontal thin layer differential element method was used to obtain the loosening soil pressure. Using the 3D theoretical model, the effects of buried depth ratio of loose area, length of loose area, soil parameters, and lateral earth pressure coefficient were investigated. The loosening earth pressure was highly affected by inclination angle of slip surface, buried depth ratio and length of loose area. Neglecting the inclined slip surface will underestimate the value of the loosening earth pressure, which will lead to the insecurity of the design. Loosening earth pressure calculated by this study was also compared with trapdoor tests. Results from this study was in good agreement with the experimental results. Compared with the traditional 2D solution, results from this study can more accurately analyze the soil arching effect in 3D excavation cases.

Alternating current field measurement (ACFM) testing is one of promising techniques in the field of non-destructive testing with advantages of the non-contact capability and the reduction of lift-off effects. In this paper, a novel crack detection approach is proposed to reduce the effect of the angled crack (cack orientation) by using rotated ACFM techniques. The sensor probe is composed of an excitation coil and two receiving coils. Two receiving coils are orthogonally placed in the centre of the excitation coil where the magnetic field is measured. It is found that the change of the x component and the peak value of the z component of the magnetic field when the sensor probe rotates around a crack follows a sine wave shape. A customised accelerated finite element method solver programmed in MATLAB is adopted to simulate the performance of the designed sensor probe which can significantly improve the computation efficiency due to the small crack perturbation. The experiments have also been carried out to validate the simulations. It is found that the ratio between the z and x components of the magnetic field remains stable under various rotation angles. It shows the potential to estimate the depth of the crack from the ratio detected by combining the magnetic fields from both receiving coils (i.e., the x and z components of the magnetic field) using the rotated ACFM technique.

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are deployed around the world. Proliferation of artificial intelligence (AI) and machine learning (ML) technologies has gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference scale as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capture a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.

(1) Purpose: This work aims at developing a generalizable MRI reconstruction model in the meta-learning framework. The standard benchmarks in meta-learning are challenged by learning on diverse task distributions. The proposed network learns the regularization function in a variational model and reconstructs MR images with various under-sampling ratios or patterns that may or may not be seen in the training data by leveraging a heterogeneous dataset. (2) Methods: We propose an unrolling network induced by learnable optimization algorithms (LOA) for solving our nonconvex nonsmooth variational model for MRI reconstruction. In this model, the learnable regularization function contains a task-invariant common feature encoder and task-specific learner represented by a shallow network. To train the network we split the training data into two parts: training and validation, and introduce a bilevel optimization algorithm. The lower-level optimization trains task-invariant parameters for the feature encoder with fixed parameters of the task-specific learner on the training dataset, and the upper-level optimizes the parameters of the task-specific learner on the validation dataset. (3) Results: The PSNR increases 1.5 dB on average compared to the network trained through conventional supervised learning on the seen CS ratios. We test the result of quick adaption on the unseen tasks after meta-training, the average PSNR arises 1.22 dB compared to the conventional learning procedure that is directly trained on the unseen CS ratios in the meanwhile saving half of the training time. The average PSNR arises 1.87 dB for unseen sampling patterns comparing to conventional learning; (4) Conclusion: We proposed a meta-learning framework consisting of the base network architecture, design of regularization, and bi-level optimization-based training. The network inherits the convergence property of the LOA and interpretation of the variational model. The generalization ability is improved by the designated regularization and bilevel optimization-based training algorithm.

In this paper, the problem of observer-based adaptive sliding mode control is discussed for nonlinear systems with sensor and actuator faults. The time-varying actuator degradation factor and external disturbance are considered in the system simultaneously. In this study, the original system is described as a new normal system by combining the state vector, sensor faults and external disturbance into a new state vector. For the augmented system, a new sliding mode observer is designed, where a discontinuous term is introduced such that the effects of sensor and actuator faults and external disturbance will be eliminated. In addition, based on a tricky design of the observer, the time-varying actuator degradation factor term is developed in the error system. On the basis of the state estimation, an integral-type adaptive fuzzy sliding mode controller is constructed to ensure the stability of the closed-loop system. Finally, the effectiveness of the proposed control methods can be illustrated with a numerical example.

Highly-correlated electrons – electrons that engage in strong electron-electron interactions – have been observed in transition metal oxides and quantum dots and can create unusual material behavior that is difficult to model, such as switching between a low resistance metal state and a high resistance Mott insulator state. Tests of devices using a layer-by-layer deposition process for forming multilayer arrays of ferritin (a transition metal (iron) oxide storage protein) have been previously reported that indicate that highly-correlated electron transport is occurring, consistent with models of electron transport in quantum dots. This paper reports the results of the effect of various degrees of structural homogeneity on the electrical characteristics of these ferritin arrays, as well as demonstrating that these structures can provide a switching function associated with the circuit that they are contained within, consistent with the observed behavior of highly-correlated electrons.

Humankind has yearned for hundreds of thousands of years to explore and truly understand the dynamics of our enigmatic universe. However, we have often been limited by our hindsight here on Earth, or the limitations of human travel to further destinations. Having the ability to send humans to deep space would arguably set us millions of years ahead in our scientific understanding of various physical processes, including insight on some of the key mysteries that we still face. Perhaps a settlement further away from the Sun, Earth, and limiting optical resolution could provide us with greater ability to collect more meaningful data through our telescopes or other instruments. Moreover, one of the most significant abilities of such a settlement could be slightly easier access to sites of research within our own solar system that could be essential in the forthcoming years, such as Jupiter, Titan, the Kuiper Belt, and some Oort Cloud objects. Here we describe a unique option and set of goals to pursue and achieve just that via a mission to an L5 Trojan asteroid of Neptune, 2011 HM102. In this paper, we propose a number of possible challenges presented by traveling to the base, modeling its layout, maintaining the presence of life, and opportunities for research on the asteroid as well as possible solutions to these challenges. We begin our journey by outlining some motivations for creating a settlement on the asteroid, then outline a few key technological goals that must be achieved before we are capable of efficiently sending a colony of people to the settlement. We describe materials necessary for its construction as well as a novel mechanism for artificial gravity utilizing the Roche limit of the asteroid and multiple applications of quark-gluon plasma. From there we explore more aspects of settlement design, including key considerations for the layout of our settlement, sources of power, and protection from high-energy radiation from sources such as galactic cosmic rays. Then, we embark on a detailed discussion on the measures needed to maintain life on the settlement, such as hydroponic crop growth, a cycle for water recovery and oxygen generation, temperature maintenance, and air purification. Some remarks on the sustainability of life on the settlement follow, as well as some recommendations for entertainment, social life, economics, and governmental systems on the settlement.

Digital microfluidics (DMF) devices enable precise manipulation of small liquid volumes in point-of-care testing. Printed circuit board (PCB) substrate is commonly utilized to build DMF devices. However, inkjet printing can be used to fabricate DMF circuits, providing a less expensive alternative to PCB-based DMF designs while enabling more rapid design iteration cycles. We demonstrate the fabrication process of the inkjet-printed DMF circuit. We compare Kapton and polymethyl methacrylate (PMMA) as dielectric coatings by measuring the minimal droplet actuation voltage for a range of actuation frequencies. The minimum actuation voltage of 5.6 V was required for droplet movement with the PMMA layer thickness of 0.2 μm and a hydrophobic layer of 0.17 μm. Significant issues with PMMA dielectric breakdown were observed at actuation voltages above 10 V. In comparison, devices that utilized Kapton were found to be more robust even at the actuation voltage up to 100 V.

The conversion of CO2 into more synthetically flexible CO is an effective and potential method for CO2 remediation, utilization and carbon emission reduction. In this paper, the reaction of carbon-carbon dioxide (Boudouard reaction) was performed in a microwave fixed bed reactor using semi-coke (SC) as both the microwave absorber and reactant and was systematically compared with that heated in a conventional thermal field. The effects of the heating source, SC particle size, CO2 flow rate and additives on CO2 conversion and CO output were investigated. By microwave heating (MWH), CO2 conversion reached more than 99 %, while by conventional heating (CH), the maximum conversion of CO2 was approximately 29% at 900 °C. Meanwhile, for the reaction with 5 wt% Barium Carbonate added as a promoter, the reaction temperature was significantly reduced to 750 °C with almost quantitative conversion of CO2. Further kinetic calculations showed that the apparent activation energy of the reaction under microwave heating was 46.3 kJ/mol, which was only one-third of that observed under conventional heating. The microwave-assisted Boudouard reaction with catalytic barium carbonate is a promising method for carbon dioxide utilization.

In recent years, the consumption of over-the-counter probiotics used to promote health has grown rapidly worldwide and become an industry. In medicine, various studies have proven that probiotics can help improve the immune system and intestinal health. They are usually safe, but in some rare cases, they may cause concerning adverse reactions. Although the use of probiotics has been widely popularized in the public, the results of many probiotics clinical trials are contradictory. Especially for the cancer patients, the feasibility of probiotics management to provide benefits by targeting cancer and lessening anti-cancer side effects requires further investigations. And this review summarizes the interactions between probiotics and the host and current pros and cons of applying probiotics in the cancer patients.

The corrected mechanism model of battery voltammetric function is helpful to guide the development and application of battery. There are two scientific issues that need to be answered: First, how many mechanisms do batteries have; Second, how to establish the mechanism model separately under the overlapping of these mechanisms. Volt-ampere characteristics of both linear state and nonlinear state exist; the monotonic decreasing of volt-ampere characteristics indicates that the battery have only three kinds of mechanisms. Without changing the basic form of the function and under the principle of the mechanism function’s working region considered, we propose a mechanism function which satisfies the monotonic decreasing characteristic of the voltammetric curve of battery, via the derivative law of each mechanism function in the voltammetric function of battery. By using the voltammetric data, the obtained cell mechanism function can accurately predict the potential (current or voltage) when the independent variable of the cell is zero, and provide the theoretical basis for the internal working mechanism of the cell, which can guide the practice.

This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety.

There always exist steel cuttings, holes and cracks at the interfaces in the explosive composite plate. Multi-pass friction stir processing (M-FSP) is proposed in this research to optimize the interface microstructure and the interface connection for 1060Al/Q235 explosive composite plate. Results show that the microstructures of 1060Al after M-FSP are fine and uniform owing to the strong stirring effect and recrystallization. Micro-defects formed by the explosive welding can be repaired by the M-FSP. However, M-FSP can also form tunnel defects in the aluminum, especially when the passes are one and two. The melting block and the melting lump in the composite plates are easy to become source of crack. The shear strengths and the bending properties for the 1060Al/Q235 explosive composite plate after M-FSP are the best when the passes are three, with the tool rotation speed of 1200rpm and the forward speed of 60mm/min. The optimized interfaces for the explosive composite plate after M-FSP are mainly by the metallurgical bondings, with a certain thickness and are discontinuous. Therefore, the crack extension stress is the largest and the mechanical properties are the best.

Purpose: Localization and mapping with LiDAR data is a fundamental building block for autonomous vehicles. Though LiDAR point clouds can often encode the scene depth more accurate and steadier compared with visual information, laser-based Simultaneous Localization And Mapping (SLAM) remains challengeable as the data is usually sparse, density variable and less discriminative. The purpose of this paper is to propose an accurate and reliable laser-based SLAM solution. Design/methodology/approach: The method starts with constructing voxel grids based on the 3D input point cloud. These voxels are then classified into three types to indicate different physical objects according to the spatial distribution of the points contained in each voxel. A global environment model with Partition of Unity (POU) implicit surface is maintained along the process and located voxels are merged into it from stage to stage, through scan-to-model matching implemented by Levenberg-Marquardt method. Findings: We find a laser-based SLAM method. The method uses POU implicit surface representation to build the model and is evaluated on the KITTI odometry benchmark without loop closure. Experimental results indicate that the method achieves accuracy comparable to the state-of-the-art methods. Originality/value: We propose a novel, low-drift SLAM method, which falls into a scan-to-model matching paradigm, operates on point clouds obtained from Velodyne HDL64. The method is of value to researchers developing SLAM systems for autonomous vehicles.

Estimating evapotranspiration (ET) has been one of the most important research in agriculture recently because of water scarcity, growing population, and climate change. ET is the sum of evaporation from the soil and transpiration from the crops to the atmosphere. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, people use weighing lysimeters, Bowen ratio, eddy covariance and many other methods to estimate ET. However, these ET methods are points or location-specific measurements and cannot be extended to a large scale of ET estimation. With the advent of satellites technology, remote sensing images can provide spatially distributed measurements. The satellites multispectral images spatial resolution, however, is in the range of meters, which is often not enough for crops with clumped canopy structure such as trees and vines. And, the timing or frequency of satellites overpass is not always enough to meet the research or water management needs. The Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, can help solve these spatial and temporal challenges. Lightweight cameras and sensors can be mounted on drones and take high-resolution images on a large scale of field. Compared with satellites images, the spatial resolution of UAVs’ images can be as high as 1 cm per pixel. And, people can fly a drone at any time if the weather condition is good. Cloud cover is less of a concern than satellite remote sensing. Both temporal and spatial resolution is highly improved by drones. In this paper, a review of different UAVs based approaches of ET estimations are presented. Different modified models used by UAVs, such as Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), Two-source energy balance (TSEB) model, etc, are also discussed.

The landscape image is the bridge of communication between people and forests, and the cut point of the supply-side reform of forest tourism products. The research collected 140 copies in total of forest landscape image drawings from non-art-major graduate students by randomly sampling during April and May, 2018, and constructed the landscape image conceptual model of forest by utilizing the landscape image sketching technique. The results showed that (1) In regard to linguistic knowledge, the natural landscape elements for instance, herbaceous plants, terrains, creatures, water and sky, and the broad-leaf forest objectively reflected not only the real forest landscape and the local native vegetation, but the variation of forest species with little attention. (2) On the perspective of spatial view, the sideways view indicated that graduate students preferred to watch forests at a moderate distance externally and few looked at forests internally. (3) In the view of self-orientation, the objective landscape indicated that graduate students preferred to demonstrate forest landscapes, they did not realize to interact with the environment. (4) On the aspect of social meaning, the scenic view and forest structure stated that graduate students preferred rural forest landscapes, not significantly for other special interests for forest. In conclusions, (1) the forest is thought to be a feature of people's life world and of rural scenes around homes, not an objective perception of the forest. (2) The forest is regarded as an important habitat for animals and a limited resource for people's life, production and recreation needs, into which people will go only to meet such needs. (3) The natural values of forests, like the ecology and aesthetics, etc. get more attention, while the social values of forests, like the life, production and culture receives rather low attention.

Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. By analyzing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images using literature from the Science Citation Index reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, geological remote sensing research needs to be truly multidisciplinary or interdisciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved automatic processing and data analysis capabilities.

Ferrite body is the origin of crack and corrosion initiation of steels. Distribution and density of ferrite in seven steel ingots were examined by light optical microscopy and computational modeling in the study to explore the correlation of ferrite formation to chemical composition and mushy zone temperature in ingot forming. The central segregation phenomenon in ferrite distribution was observed in all the examined steel specimens except 0Cr17Ni4Cu4Nb stainless steel. No significant difference was found in distribution and density of ferrite amongst zones of the surface, ½ radius and core in neither risers nor tails of 0Cr17Ni4Cu4Nb ingots. Additionally, fewer ferrite was found in 0Cr17Ni4Cu4Nb compared to other examined steels. The difference of ferrite formation in 0Cr17Ni4Cu4Nb elicited a debate on the traditional models explicating ferrite formation. Considering the compelling advantages in mechanical strength, plasticity and corrosion resistance, further investigation on the unusual ferrite formation in 0Cr17Ni4Cu4Nb would help understand the mechanism to improve steel quality. In summary, we observed that ferrite formation in steel was correlated with mushy zone temperature. The advantages of 0Crl7Ni4Cu4Nb in corrosion resistance and mechanical stability could be resulted from that fewer ferrites formed and distributed in a scattered manner in microstructure of the steel.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modeling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using the Neo4j graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an extended place graph. Then three experimental tasks, namely georeferencing, reasoning, and querying, are selected to demonstrate the superiority of the extended model.

A novel plasma source of pulsed electrolyte cathode atmospheric pressure discharge (pulsed-ECAD) driven by an alternating current (AC) power supply coupled with a high voltage diode was generated, and the discharge was generated in the open-to-air atmosphere between a metal electrode and a small-sized flowing liquid cathode. The spatial distribution of plasma temperatures (excitation, vibrational and rotational) of the pulsed-ECAD were investigated. The electron excitation temperature of H T_exc(H), vibrational temperature of N₂ T_vib(N₂), and rotational temperature of OH T_rot(OH) were measured as 4900±36-6800±108 K, 4600±86-5800±100 K and 1050±20-1140±10 K, respectively. Meanwhile, the temperature characteristics of dc solution cathode glow discharge (dc-SCGD) were also studied for comparison with pulsed-ECAD. The effects of operating parameters, including discharge voltage and discharge frequency, on the plasma temperatures were investigated. The electron number density determined in the discharge system and dc-SCGD were within the range (3.8–18.9) ×10¹⁴ cm^-3 and 2.6×10¹⁴-17.2×10¹⁴ cm^-3, respectively.

With the increasing of copper consumption, the in-use stock of copper tends to increase. This paper used the “average use life method” to quantify the amount of copper in-use stock, and calculated the average age of in-use stock. It was indicated that the total in-use stock had an overall smooth trends, and reached its peak in 2007 was about 68.9 Mt (million tons), in addition, in-use stock per capita reached its peak ,234 kg/capita in 2001. The results demonstrated that during the period 1992-2002, the average age of copper in-use stock was continually decreased, but gradually increased since the year 2003. The fixed assets depreciation method used in this paper is applied to analyze depreciated copper in-use stock, and to analyze the relationship with economic indicator (GDP). It is demonstrated that it was inconsistent with the theory of Environmental Kuznets Curve (EKC) before 2000, this might be the demand for copper in the service sector is greater than the reduced strength of alternatives. Finally, scenario analysis of future copper in-use stock and depreciated copper in-use stock per capita in the U.S. were presented. The corresponding average age of the in-use stock will have a slight rise in the next decade.

In this study, a new structure is proposed based on the body-centered cubic (BCC) lattice structure: adding a cubic truss in the center of the BCC structure, which is denoted by TLC (Truss-lattice-cube). The different dimensions of the central cube can affect the mechanical properties of the lattice structure notably. With a fixed length (15mm) of a unit cell, the optimal size for the central cube is determined to be 5 mm. Quasi-static compressive tests are performed on specimens made of Polylactic Acid (PLA) using additive manufacturing technology. The deformation characteristics of the new structure is analyzed in detail by experiments and numerical simulations. Compared to the BCC structure, the mechanical properties of the TLC structure are significantly improved. The initial flow stress of the TLC has been increased by 122% at a strain of 0.1; the specific strength has been enhanced by 293% at a strain of 0.5; and the specific energy absorption has been improved by 312% at the strain of 0.6. Printing defects in the lattice structure could deteriorate the mechanical property remarkably. In this work, incorporation of microcracks into the finite element model makes the simulation capture the influence of printing defects and improve the predictive accuracy of simulation significantly.

Pedestrian object detection plays a significant role in intelligent systems such as intelligent traffic and monitoring. Traditional machine learning methods on pedestrian detection have shown various drawbacks, \eg{} low accuracy, slow speed, \etc The Convolutional Neural Network (CNN) based object detection algorithms have demonstrated remarkable advantages in the field of pedestrian detection. However, the mainstream CNNs still face the problems of slow speed and low detection accuracy, especially on small and occluded targets from aerial perspective. In this paper, we propose Multi-Scale Attention YOLO (MSA-YOLO) detection algorithm to address the above issues. MSA-YOLO includes a Squeeze, Excitation and Cross Stage Partial (SECSP) channel attention module for CNNs to extract richer pedestrian features with a small number of extra parameters. It also contains a multi-scale prediction module to capture the information among different pedestrian scales, which can recognize the small objects with higher accuracy and significantly reduce the missed detection. To sufficiently evaluate our proposed model, we manually collect and annotate a new benchmark dataset, Luoyang Pedestrian Dataset \footnote{The dataset can be downloaded through this anonymous link: \url{https://drive.google.com/drive/folders/13po1FX7Qk5RDgb60-dzOi74cq_kqDJtM?usp=sharing} }, which has much more sample annotations, features, scenes and image view angles than the existing benchmark datasets. In addition, the images in our dataset have higher resolution than most of benchmark pedestrian detection datasets, which can provide more detailed features of pedestrians and thus improve the model performance. When tested on Luoyang Pedestrian Dataset, our proposed MSA-YOLO algorithm significantly outperform the most commonly used baseline models with almost the same model size. This shows the efficiency of our proposed model. (The code and new dataset will be released to the public later.)

This research paper presents a comprehensive study on the gasification of wheat straw pellets in a 10-kW fixed bed gasifier through a combination of Computational Fluid Dynamics (CFD) simulation and experimental validation. The developed 2D CFD model in ANSYS meshing simulates the gasification process in ANSYS Fluent software. The investigation evaluates key parameters such as equivalence ratio, higher heating value, and temperature distribution within the gasifier to enhance syngas production efficiency. The simulated results exhibit a noteworthy agreement with experimental data obtained from gasification. The impact of the equivalence ratio on gas production and lower heating value (LHV) is systematically explored, revealing that an equivalence ratio of 0.35 is optimal for syngas production. The resulting producer gas composition at this optimum condition includes CO (~27.67%), CH4 (~3.29%), CO2 (~11.09%), H2 (~11.09%), and N2 (~51%). The study further investigates the positive influence of reactor temperature on increasing syngas quantity while considering the uneven shape of the reactor and various thermal reactions affecting temperature, static pressure, velocity, and density. The findings contribute valuable insights to improve the efficiency of fixed-bed gasifiers, offering essential information on performance parameters for sustainable and optimized syngas production.

Prime period sequences can serve as the fundamental tool to construct arbitrary composite period sequences. This is a review study of prime period perfect Gaussian integer sequence (PGIS). When cyclic group {1,2,…,N−1} can be partitioned into k cosets, where N=kf+1 is an odd prime number, the construction of a degree-(k+1) PGIS can be derived from either matching the flat magnitude spectrum criterion or making the sequence with ideal periodic autocorrelation function (PACF). This is a systematic approach of prime period N=kf+1 PGIS construction, and is applied to construct PGISs with degrees 1, 2, 3 and 5. However, for degrees larger than 3, matching either the flat magnitude spectrum or achieving the ideal PACF encounters a great challenge of solving a system of nonlinear constraint equations. To deal with this problem, the correlation and convolution operations can be applied upon PGIS of lower degrees to generate new PGIS with degree-4 and other higher degrees, e.g., 6, 7, 10, 11, 12, 14, 20 and 21 in this paper. In this convolution based scheme, both degree and pattern of a PGIS vary and can be indeterminant, which is rather nonsystematic compared with the systematic approach. The combination of systematic and nonsystematic schemes contributes the great efficiency for constructing abundant PGISs with various degrees and patterns for the associated applications.

The combustion of fuel oil releases sulfur compounds, which react to form sulfur oxides that are harmful to the environment and pose a threat to human health. To mitigate these negative effects, it is crucial to pretreat fuel oil through desulfurization processes. One promising approach involves the use of deep eutectic solvents (DESs), carbon nanotubes (CNTs), and their composites (DESs/CNTs) to effectively reduce sulfur content in fuel oil.However, the conventional methods of DES-based extraction desulfurization and CNT-based adsorption desulfurization suffer from low desulfurization rates. Additionally, the composite material DESs/CNTs is in a semi-solidified form, resulting in high viscosity, complex operation procedures, and increased waste and environmental pollution. To address these challenges, this study focused on the preparation of CNTs/PEG-PVA hydrogel. This hydrogel not only maintains the high desulfurization rate observed in the DESs/CNTs composite material but also possesses several advantages. Firstly, it is convenient to use, offering ease of handling and application. Secondly, it is biodegradable, meaning it can break down naturally over time, reducing its impact on the environment. Lastly, the hydrogel is reusable, allowing for repeated use, thereby minimizing material waste. The desulfurization rate achieved with the CNTs/PEG-PVA hydrogel is remarkably high, reaching up to 99.8%. Moreover, the material utilization rate is close to 100%, meaning that almost all of the hydrogel is utilized in the desulfurization process, reducing unnecessary material consumption. In summary, the innovative CNTs/PEG-PVA hydrogel presents a highly efficient and environmentally friendly solution for desulfurization in fuel oil. Its superior desulfurization rate, alongside its convenience, biodegradability, and reusability, positions it as a promising alternative that not only ensures effective desulfurization but also contributes to sustainable practices in the energy industry.

Colleges and universities play a crucial role in fostering innovation, making it essential to explore effective strategies for promoting innovation at the institutional policy level. This paper focuses on the establishment of intellectual property (IP) model cities as a starting point and conducts an empirical analysis using innovation data from 234 cities and 942 colleges and universities between 2007 and 2017. By constructing a multi-temporal double-difference model, this study reveals that the establishment of IP model cities effectively fosters innovation in colleges and universities. Further analysis demonstrates that this promotion effect is particularly significant in the western region, key cities, key colleges and universities, as well as in the fields of invention and utility model patents. These conclusions withstand a series of robustness tests, confirming their validity. In terms of the underlying mechanisms, it is found that the national IP pilot city policy has a substantial impact on university innovation by promoting research and development (R&D) investment and strengthening innovation cooperation. The insights provided by this study offer valuable policy recommendations for leveraging the innovation potential of the IP model city policy, thereby driving economic transformation, upgrading, and fostering innovation development in China.

As the control brain of the petrochemical plant, blast resistant performance requirements of the petrochemical control room should be guaranteed when the vapor cloud explosion occurs in the petrochemical production process. 3D-Kagome truss core sandwich structure is a kind of blast resistant material with high energy absorption. Considering the influence factors of the radius of the truss core rod and thickness of the upper and lower panels, in this paper, blast resistant performance of a real steel petrochemical control room with 3D-Kagome truss core sandwich wall is analyzed. With the optimization goal of plastic deformation energy and panel displacement, the optimal wall thickness and radius of the truss core rode are obtained. The optimized blast resistant wall is assembled, and the dynamic response of the steel petrochemical control room with the 3D-Kagome truss core sandwich blast resistant wall is analyzed. The simulation results show that the truss core layer does not dissipate the blast energy effectively when the radius ratio of the truss core rod is larger than 2.7% of the total thickness of the wall; with the increase of the thickness of the upper and lower panels, the proportion of plastic deformation energy of the truss core layer increases gradually and then tends to be a stable proportion value, that is about 90%; When the thickness ratio of the panel is 6.7% of the total one of the wall and the radius ratio of the truss core rod is 2.7% of the total thickness of the wall, the optimal blast resistant 3D-Kagome sandwich wall is determined.

The utilization of titanium dioxide (TiO2) as a photocatalyst for the treatment of wastewater has attracted significant attention in the environmental field. Herein, we have prepared a NH2-MIL-125-derived TiO2@N-doped C Visible Light Catalyst through an in-situ calcination method. The nitrogen element in the organic connector is released through calcination, simul-taneously doping into the sample, thereby enhancing its spectral response to cover the visible region. The as-prepared TiO2@N-doped C catalyst exhibited preserved cage structure even after calcination, thereby alleviating the optical shielding effect and further augmenting its photo-catalytic performance by increasing the reaction sites between the catalyst and pollutants. The calcination time of the TiO2@N-doped C-450 °C catalyst was optimized to achieve a balance be-tween the TiO2 content and nitrogen doping level, ensuring efficient degradation rates for basic fuchsin (99.7%), Rhodamine B (89.9%), and tetracycline hydrochloride (93%) within 90 minutes.

We successfully fabricated Ag@Cu2O core-shell decorated on reduced graphene oxide (rGO) nanocomposites (ACRN) by a simple and convenient in situ substitution method. The properties of these ACRN with heterostructure layers were characterized by scanning and transmission electron microscopies and absorption spectroscopy. We used p-nitrophenol (4-NP) as a probe molecule to determine the chemical catalytic activity of the ACRN. Upon introduction of rGO, a high electron transfer efficiency was achieved; thus, the catalytic activity was improved significantly. Therefore, the ACRN exhibited significantly improved catalytic activity for the reduction of 4-NP and showed the high application value in the removal of toxic and harmful substances from water. In addition, the fabricated ACRN was used for the reduction of organic dyes and explosive pollutants to generate nontoxic products. Furthermore, the high charge redistribution and transfer among Ag, Cu2O and rGO in the ACRN induced the high catalytic reduction of organic pollutants, indicating the excellent potential of these materials for applications in water pollution treatment.

Aim at a better understanding of the flow field around a fully appended Joubert BB2 submarine model and complementing the experimental investigations of the wake of the hydroplanes and sail, the Large eddy simulation (LES) with the dynamic Smagorinsky model is conducted. Three sets of grids with a maximum grid number of up to 228 million are designed, to perform the LES simulation for the Joubert BB2 at 10° yaw conditions, with a freestream Reynolds (Re) number based on the local freestream velocity and the hull length of ReL=2.2×107. And comparison of the wake of the cruciform appendage is made with experiments, to verify the computational accuracy and examine the influence of the spatial resolution. A satisfactory result shows more favorable agreement with experimental measurements as the improvement of spatial resolution, with the relative error of the vortice centers well within 7.2% in the most refined grid arrangement. And under this grid arrangement, the evolution characteristics of three co-rotating vortices originating from the cruciform appendage are further described in detail at straight ahead and 10° yaw conditions.

This study examines how the design of museum archaeological boxes and the overall museum image affect behavioral intention. This research used partial least squares-structural equation modeling, indicating a relationship between aesthetics, symbolism, affective museum image, perceived value, and behavioral intention. This study found that symbolism predicted behavioral intention. Moreover, aesthetics, symbolism, and affective museum image affected perceived value, in turn triggering behavioral intention. This indicates theoretical and practical implications for museum products.

Multi-board electronic cases with high density and high power modules are widely used in industrial power supply management. With the improvement of case performance and miniaturization requirements, heat dissipation becomes one of the important factors to be considered in the design process. First,The existing small electronic thermal design methods focus on heat dissipation structure or heat source layout optimization,and ignores on-load test for modeling analysis. Second,The selected power module has on-load power consumption, resulting in relatively low calorific value and the effectiveness of thermal design cannot be verified. Third, The thermal lacks Intelligent monitoring and feedback control mechanism. In order to solve these problems, this paper designs a kind of heat dissipation case with intelligent temperature control based on high-power and high-density power supply array. Based on the extremely miniaturization design principle, we adopt the composite heat dissipation mode based on conduction and supplemented by forced air cooling . The case is made of magnesium and aluminum alloy with a perforated design. Finally, we compare and analyze with the existing cooling design. The results show that the case is smaller in volume, and the cooling performance parameters are slightly better than the existing case. Under the condition of high-density and high-power design, The output power of the whole system is not less than 10kw and the lowest packing-level density is not less than 47w/cm2 with high reliability, portability and practicability. It also provides technical support and prototype support for the standardized design of similar power arrays.

Due to the non-contact measurement characteristics of trackside acoustic technology, it is now utilized for train bearing fault diagnosis. However, the relative motion between the train and the trackside acoustic detection device introduces Doppler aberration in the collected acoustic signal, which affects bearing fault diagnosis. Moreover, when a fault occurs in the train bearing, its acoustic signal exhibits cyclic smoothness characteristics that can be effectively analyzed using the cyclic smoothness method for more accurate judgment. In order to minimize diagnostic errors and enhance accuracy in bearing fault diagnosis, this study integrates bearing fault characteristics with Doppler aberration correction methods and cyclic smoothness techniques for current-stage bearings diagnostics. The overall time-domain graph becomes more compact with an approximately 50% increase in amplitude after correction compared to pre-correction values; other parameters experience enhancements ranging from 20-60%. These results validate the feasibility of our proposed approach and establish a framework for conducting bearing fault diagnosis based on cyclically smoothed Doppler aberration correction.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

Understanding park events and their categorization offer pivotal insights into urban parks and their integral roles in cities. This study utilized images and event category data from the New York City Parks Events Listing database to train a Convolutional Neural Network (CNN) for image-based park event categorization. Different CNN models were tuned to complete this multi-label classification task, their performances compared. Preliminary results underscore the efficacy of deep learning in automating the event classification process, revealing the multifaceted activities within urban green spaces. The CNN showcased proficiency in discerning various event nuances, emphasizing the diverse recreational and cultural offerings of urban parks. Such categorization has potential applications in urban planning, aiding decision-making processes related to resource distribution, event coordination, and infrastructure enhancements tailored to specific park activities.

The application of melatonin (MT) has been shown to improve the quality during the storage of fruits and vegetables. The primary objective of this study is to investigate the effects of MT on the quality of fresh-cut Gastrodia elata during low temperature (4°C) storage. The results indicate that MT treatment could suppress the respiratory rate and malondialdehyde content of fresh-cut G. elata, while also slowing down the decline in total acidity and total soluble solids. The treatment with MT reduced the superoxide anions and hydrogen peroxide production, inhibited the activity and expression of peroxidase and polyphenol oxidase. Additionally, it led to increased activity and expression of antioxidant-related enzymes, including superoxide dismutase, catalase, ascorbate peroxidase, glutathione reductase, monodehydroascorbate reductase, and dehydroascorbate reductase, while also resulted in elevated levels of ascorbic acid and glutathione. Furthermore, the treatment with MT induced an increase in total phenolic and flavonoid content of fresh-cut G. elata and enhanced the activity and expression of key enzymes involved in phenylpropanoid pathway (phenylalanine ammonia-lyase, cinnamate-4-hydroxylase, 4-coumarate: CoA ligase). In summary, MT enhances the antioxidant capacity by activating both ROS metabolism and phenylpropanoid pathway, thus maintaining the quality of fresh cut G. elata.

This paper studies the finite-time synchronization problem of fractional-order stochastic memristive bidirectional associative memory neural networks (MBAMNNs) with discontinuous jumps. A novel criterion for finite-time synchronization is obtained by utilizing the properties of quadratic fractional-order Gronwall inequality with time delay and the comparison principle. This criterion provides a new approach to analyze the finite-time synchronization problem of neural networks with stochasticity. Finally, numerical simulations are provided to demonstrate the effectiveness and superiority of the obtained results.

The sudden onset of natural hazards such as a flash flood events can be devastating to a country or region, often affecting numerous communities, and humanitarian supply chains play a major role in enabling the timely recovery of economic and social activities. However, the uncertainty and suddenness of natural disasters such as flash floods, as well as the potential for aftershocks, require humanitarian supply chains to be resilient during the relief process. This research first adopts the Delphi and literature review methods to identify the key indicators in the humanitarian supply chain to form an evaluation index system. Then, taking the 2021 mega-flash flood event which affected four cities in the mountainous region under the jurisdiction of the Zhengzhou municipal government, the ANP method is used to calculate the weights of each indicator and combines the weighted results with the Multi-styling and stereotyping method under Pythagorean fuzzy (PFs-VIKOR) to make an evaluation of the resilience of the humanitarian supply chain in the relief process. The findings suggest that policy makers and decision makers should pay close attention to the coordination of the parties involved in the humanitarian supply chain and improve the level of resilience of the entire supply chain by enhancing the resource scheduling capacity and responsiveness. At the same time, the VIKOR evaluation of the 4 cities highlighted the humanitarian supply chain in Dengfeng City to be most effective in this event due to the close cooperation and positive response of all parties involved in the humanitarian supply chain. The findings of this research provide some useful suggestions and guidance to the various practitioners involved in the humanitarian supply chain. Furthermore, the evaluation of the performance of the four cities in this mega-flash flood event provides some helpful indication of the importance of the various emergency measures which can help to inform policy recommendations for the Zhengzhou municipal government.

In order to effectively utilize red mud, an aluminum industrial waste, the stabilized soil material was developed using nano-SiO2 synergistically modified red mud, and the strength characteristics of the stabilized soil were analyzed to clarify the feasibility of such stabilized soil serves as a road base material. Through different combination schemes, the effects of different nano-SiO2 and cement contents on the strength of the stabilized materials were explored. The test results show that: in the synergistic modification of nano-SiO2 and cement, nano-SiO2 can significantly improve the early unconfined compressive strength of red mud-based stabilized soil; in the synergistic modification of nano-SiO2, gypsum and cement, the 7-d unconfined compressive strength of red mud-based stabilized soil is greater than 2 MPa, which meets the strength requirements of road base materials and shows the superiority of synergism. The nominal stress-strain curves are divided into five stages: compressed and compacted stage, elastic deformation stage, plastic deformation stage, damage deformation stage and residual deformation stage. The macroscopic compressive damage pattern of the specimens shows that the modified red mud-based stabilized soil mostly exhibits brittle damage.

Some biologically active compounds isolated from sea cucumbers stimulate the body’s immune response through activating immune cells. Immune function is closed related to the integrity intestinal barrier and balanced gut microbiota. However, it is unknown whether daily administration of holothurian wall hydrolysates (HWH) ameliorated intestinal dysbiosis and barrier injury induced by immunodeficiency. This study aimed to investigate the immunomodulatory effect and the underlying mechanism of HWH in cyclophosphamide (CTX)-induced immunocompromised mice. BALB/c mice received CTX (80 mg/kg, intraperitoneally) once a day for 3 days to induce immunodeficiency, and then oral administration of HWH (80 or 240 mg/kg), levamisole hydrochloride (LH, 40 mg/kg, positive control) respectively once a day for 7 days. We utilized 16S rRNA sequencing for microbial composition alterations, histopathological analysis for splenic and colonic morphology, Western blotting for expressions of tight junction proteins (TJs), and quantitative real-time (qRT-PCR) for measurements of pro-inflammatory cytokines. HWH attenuated the immune organ damage induced by CTX, increased the secretions of interleukin (IL)-6, IL-1β and tumor necrosis factor (TNF)-α, and promoted the recovery of goblet cells and the productions of TJs (claudin-1, occludin, and ZO-1) in the colon of the immunocompromised mice. Moreover, HWH promoted the growth of beneficial microorganisms such as Lactobacillus, Lachnospiraceae, Christensenellaceae, and Bifidobacterium, while suppressed the populations of Ruminococcus, Staphylococcus, and Streptococcus. These results demonstrate that HWH elicits intestinal mucosal immunity, repairs the damage to intestinal mucosal integrity, and normalizes the imbalanced intestinal microbial profiles in immunocompromised mice. It may be helpful to identify the biological activities of HWH, and supports the potential as new prebiotics, immunomodulatory agents, and medical additive for intestinal repair.

In order to effectively utilize red mud and reduce its occupation of land resources as well as its impact on the environment, experiments were conducted to develop stabilized soil materials using nano-SiO2 synergistically modified red mud and to investigate the curing mechanism of stabilized soil. The unconfined compressive strength, microscopic morphology and curing mechanism of the red mud-based stabilized soil materials with different amounts of modified materials were investigated. The test results show that after 7-days curing, the unconfined compressive strength of red mud-based stabilized soil meets the compressive strength requirement of road base material when nano-SiO2, gypsum and cement are synergistically modified. In such case, the soil structure has the lowest fracture rate and the best structural compactness when the amount of nano-SiO2 is 1%; It is found that the needle-like and columnar calcium alumina in the modified red mud-based stabilized soil increases, and the binding energy of hydration product ions in the modified material is improved; The chemical curing mechanism of modified red mud-based stabilized soil includes hydration reaction, volcanic ash reaction, promotion effect of nano-SiO2, and enhancement effect of gypsum. On this base, a model of early start hydration process of red mud-based stabilized soil promoted by nano-SiO2 is established.

In order to reveal the impact of boundary-spanning activities of cooperative innovation teams on team innovation performance, this paper takes the panel data of 71 cooperative innovation teams from January to November 2022 as the research sample. It introduces intermediary variables (teamwork crafting and individual work crafting) to analyze the impact mechanism of boundary-spanning activities of teams on innovation performance, 71 teams were divided into 41 experimental groups and 30 control groups, and a quasi-natural experiment was conducted on the innovation performance of team boundary-spanning activities using the Double Difference Model (DID).Research has shown that boundary-spanning activities of collaborative innovation teams can promote team innovation performance. Team job crafting has a mediating effect on team innovation performance in boundary-spanning activities of collaborative innovation teams. Team job crafting and individual job crafting mediate between the boundary-spanning activities of collaborative innovation teams and team innovation performance. Further analysis using the double difference model found that compared to teams without boundary-spanning activities, teams with boundary-spanning activities can directly improve team innovation performance. When team reflection is vital, and task interdependence is high, it will promote team innovation performance. This research enriches the research on the effects of boundary-spanning activities of collaborative innovation teams, explores solutions based on quasi-nature, and provides a reference for improving the team innovation performance of collaborative innovation teams.

Tree growth is under the combined influence of abiotic and biotic factors. Trees with different sizes may respond differently to these factors, implying that tree heterogeneity in tree size may also modulate the overall growth trend. To test this hypothesis, we focused on the radial growth trends of natural subalpine forests on the Tibetan Plateau. We first extended the iterative growth model (IGM) to the tree-ring scale (IGMR) to test the applicability of the generalized metabolic growth theory to tree growth. As predicted by the IGMR, the radial growth of trees at the aggregate scale is constrained by a unimodal pattern. Using the IGMR, we reconstructed the historical best growth trajectory (HBGT) of trees within the same community based on the tree with the largest radius and/or longest age in the community. From the average difference between the HBGT and the current radial growth rate of trees with different sizes, we constructed an indicator that can measure the overall variation in tree radial growth. Based on this indicator, we found a negative effect of tree size heterogeneity on the overall variability of tree growth across elevations. Further analysis also revealed that the radial growth rate of trees on the Tibetan Plateau has increased significantly compared to the past, where growing season average temperature and annual minimum temperature were negatively and positively correlated with tree growth below and above the treeline, respectively. Our study not only confirmed that the overall variability of tree growth depends on tree size heterogeneity but also proposed an indicator that reveals net changes in tree radial growth rate relative to the past. These theoretical advances facilitate the understanding of changes in the extent of subalpine forests.

This study was conducted to identify soil cadmium (Cd) removal pathways and their contribution rates during phytoremediation by Pennisetum hybridum, as well as to comprehensively assess its phytoremediation potential. Multilayered soil column tests and farmland simulating lysimeter tests were conducted to investigate the Cd phytoextraction and migration patterns in topsoil and subsoil simultaneously. The aboveground annual yield of P. hybridum grown in the lysimeter was 206 ton·ha–1. The total amount of Cd extracted in P. hybridum shoots was 234 g·ha–1, which was similar to that of other typical phytoremediation plants. After the test, the topsoil Cd removal rate was 21.50%–35.81%, whereas the extraction efficiency in P. hybridum shoots was only 4.17%–8.53%. These findings indicate that extraction by plant shoots was not the most important contributor to the decrease of Cd in the topsoil. The proportions of Cd retained by root cell wall was approximately 50% of the total Cd in root. Based on column test results, P. hybridum treatment led to a significant decrease in soil pH and considerably enhanced Cd migration to subsoil and groundwater. P. hybridum decreases Cd in the topsoil through multiple pathways and provides a relatively ideal material for phytoremediation of Cd-contaminated soils.

To improve the wear resistance of polyimide (PI), surface modification was developed. In this study, the tribological properties of graphene (GN), graphene oxide (GO), and KH550-grafted graphene oxide (K5-GO) modified PI were evaluated by molecular dynamics (MD) at the atomic level. The findings indicated that the addition of nanomaterials can significantly enhance the friction performance of PI. The friction coefficient of PI composites decreased from 0.253 to 0.232, 0.136, and 0.079 after coating GN, GO and K5-GO, respectively. Among them, the K5-GO/PI exhibited the best surface wear resistance. Importantly, the mechanism behind the modification of PI was thoroughly revealed by observing the wear state, analyzing the changes of interfacial interactions, interfacial temperature, and relative concentration.

The Chinese government has expressed great confidence in the role of green finance in fulfilling its carbon neutrality commitment. However, the effectiveness of green finance, especially under the impact of emergencies such as the COVID-19 pandemic scenario, requires further examination. Using data from 2000 to 2020 in China, the correlation between green finance and carbon dioxide (CO2) emissions has been analyzed, both in BaU scenario and the COVID-19 scenario. The following conclusions were drawn: (1) In BaU scenario, green finance can effectively reduce CO2 emissions, especially through government green expenditure and green credit; (2) In COVID-19 scenario, although there is no direct relationship between the pandemic and carbon emissions, the data shows that the pandemic has hindered the progress of green finance, weakened its ability to curb carbon emissions, and indirectly led to an increase in carbon emissions. This study not only clarifies the mechanism by which the COVID-19 pandemic affects carbon emissions through the green finance system but also addresses the common problem of data scarcity in green finance research.

Threshing device is the core component of grain combine harvester, and its working performance determines the working quality and production efficiency of the harvester to a great extent. The straw guide plate plays an important role in the threshing device to dredge the threshing matter and control the threshing time of the combine harvester matter in the threshing chamber. In the past, the guiding structure of threshing device could not optimize the working performance of the machine by adjusting the spiral angle, that is, most cereal combine harvester may not be fed smoothly or even clogged when operating under different crop types, yield differences and operating environments, a kind of threshing device with adjustable guide straw board was studied. In this study, the movement model of the straw on the straw guide plate was analyzed, and it was found that the spiral angle “α”of the straw guide was 0<α<58°.Three-factor and three-level response surface method was used to carry out field experiment on drum speed, working speed and guide grass plate spiral angle, and the experimental data were analyzed by quadratic polynomial regression.The results show that the drum speed, working speed and the spiral angle of the guide grass plate have significant effects on the loss rate (PLR) , impurity content (PIR) and breakage rate (PBR) , the drum speed and working speed have an interactive effect on the impurity ratio (PIR) , and the working speed and the helical angle of the guide grass plate have an interactive effect on the loss ratio (PLR) and breakage ratio (PBR) , there is an interaction between the rotary speed of the roller and the helical angle of the guide grass plate on the crushing rate (PBR) .Further optimization analysis showed that the predicted value was PLR = 1.18% , PIR = 0.72% , PBR = 0.53% , the experimental verification value was PLR = 1.26% , PIR = 0.73% , PBR = 0.61% . The absolute error between the experimental value and the predicted value was very small, however, the optimized field test verified value decreased by 8.31% , 50.04% and 60.30% respectively, which indicated that the optimized harvester had better operation quality, in particular, PIR, PBR and other two indicators have a greater increase in the effect.

A number of challenging combinatorial optimization problems in logistics, transportations, aeronautics, and astronautics can be modeled as orienteering problems (OPs). To address the classic OP and its real-world variants, a parallel adaptive local-search algorithm based on competition and evolution (Palace) is proposed in this paper. In this algorithm, the parallelism runs proper local-search metaheuristics and operators to obtain the population per generation; then the competition grades those metaheuristics and operators to highlight the outperforming and eliminate the underperforming; also, the evolution explores large solution space and reproduces the best solutions for next generation. In this manner, the parallelism, competition, and evolution are organized in an easy-to-use algorithm and enable the expansibility, adaptivity, and exploration abilities, respectively. The Palace is examined on the classic and real-world Benchmarks about the OP, the time-dependent/independent OP with time windows, and the unmanned aerial vehicle and agile earth observation satellite planning. As a result, the Palace shows good performance in applicability and effectiveness in comparison with the state-of-the-art algorithms.

Imaging of the ocular vasculature can provide new insights into the pathophysiology of ocular diseases. This study proposes a novel high-frequency super-resolution ultrasound localization microscopy (SRULM) technique and evaluates its ability to measure in vivo perfusion changes in the rat eye at elevated intraocular pressure (IOP). A 38.4 MHz center frequency linear array transducer on a VisualSonics Vevo F2 imaging platform was used to collect high frame rate (1 kHz) radiofrequency data of the posterior rat eye following systemic microbubble contrast injection. Following clutter and spatiotemporal non-local means filtering, individual microbubbles were localized and tracked. The microbubble tracks were accumulated over 10,000 frames to generate vascular images quantifying perfusion velocity and direction. Experiments were performed using physiologic relevant controlled flow states for algorithm validation and subsequently performed in vivo on the rat eye at 10 mm Hg IOP increments from 10 to 60 mm Hg. The posterior vasculature of the rat eye, including the ophthalmic artery, long posterior ciliary arteries and their branches, central retinal artery and retinal arterioles and venules were successfully visualized, and velocities quantified at each IOP level. Significant reductions in arterial flow were measured as IOP was elevated. High-frequency SRULM can be used to visualize and quantify perfusion velocity of the rat eye in both the retrobulbar and intraocular vasculature simultaneously. The ability to detect ocular perfusion changes throughout the depth of the eye may help elucidate the role ischemia has in the pathophysiology of ocular diseases such as glaucoma.

Co-free Li-rich oxide is a promising cathode for low cost high energy density Li-ion battery but presents poor cyclic stability. To address this issue, CeO2 is coated on Co-free Li-rich oxide through a low-temperature aged process, resulting in a novel CeO2-coated Li-rich oxide compo-site. Due to the low-temperature aged process, a uniform CeO2 coating layer of 6 nm in thickness is successfully coated on Co-free Li-rich oxide. With this uniform coating, the resulting CeO2-coated Li-rich oxide composite presents improved cyclic stability as well as rate capability as the cathode of Li-ion battery. The capacity retention of the resulting CeO2-coated Li-rich oxide is enhanced from 67% to 85% after 100 cycles and its capacity retention of 5C/0.05C is enhanced from 10% to 23%, compared with the uncoated sample. Such significant improvements enable this low-temperature aged process to be helpful for the practical application of Li-rich oxide materials, not limited to Co-free Li-rich oxide.

As one of the critical state parameters of the battery management system, lithium battery state of charge (SOC) can provide an essential reference for battery safety management, charge/discharge control, and energy management of electric vehicles. To analyze the application of deep learning in electric vehicle power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, structural characteristics, advantages and disadvantages of lithium battery SOC estimation in deep learning method. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Secondly, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of four types of deep learning methods, were concluded using the structure of neural network used for training as the classification criterion. Finally, the challenges and future development directions of lithium battery SOC estimation in deep learning method were explained.

Research Highlights: This study identified the cell cycle genes in birch that likely play important roles during plant growth and development. This analysis provides a basis for understanding the regulatory mechanism of various cell cycles in Betula pendula. Background and Objectives: The cell cycle factors not only influence cell cycle progression together, but also regulate accretion, division and differentiation of cells, and then regulate growth and development of plant. In this study, we identified the putative cell cycle genes in B. pendula genome, based on the annotated cell cycle genes in A. thaliana. It could serve as a foundation for further functional studies. Materials and Methods: The transcript abundance was determined for all the cell cycle genes in xylem, root, leaf and flower tissues using RNA-seq technology. Results: We identified 59 cell cycle gene models in the genome of B. pendula, 17 highly expression genes among them. These genes were BpCDKA.1, BpCDKB1.1, BpCDKB2.1, BpCKS1.2, BpCYCB1.1, BpCYCB1.2, BpCYCB2.1, BpCYCD3.1, BpCYCD3.5, BpDEL1, BpDpa2, BpE2Fa, BpE2Fb, BpKRP1, BpKRP2, BpRb1 and BpWEE1. Conclusions: We identified 17 core cell cycle genes in the genome of birch by combining phylogenetic analysis and tissue specific expression data.

The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiogram (ECG) in research and advancement in sensors technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, posing a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas that these indices have shown to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.

Biosensors have shown great potential in realizing rapid, low cost and portable on-site detection for diseases. This work reports the development of a new bioelectronic sensor called AC electrokinetics-based capacitive (ABC) biosensor, for the detection of genomic DNA (gDNA) of methicillin-resistant Staphylococcus aureus (MRSA). The ABC sensor is based on interdigitated microelectrodes biofunctionalized with oligonucleotide probes. It uses a special AC signal for direct capacitive monitoring of topological change on nanostructured sensor surface, which simultaneously induce dieletrophoretic enrichment of target gDNAs. As a result, rapid and specific detection of gDNA/probe hybridization can be realized with high sensitivity. It requires no signal amplification such as labelling, hybridization chain reaction, or nucleic acid sequence-based amplification. This method involves only simple sample preparation. After optimization of nano-structured sensor surface and signal processing, the ABC sensor demonstrated fast turnaround of results (~10 s detection), excellent sensitivity (a detection limit of 4.7 DNA copies /µL MRSA gDNA) and high specificity, suitable for point of care diagnosis. As a bioelectronic sensor, the developed ABC sensors can be easily adapted for detection of other infectious agents.

Recent neuroscience research results show that the nerve information in the brain is not only encoded by the spatial information. Spiking neural network based on pulse frequency coding plays a very important role in dealing with the problem of brain signal, especially complicated space-time information. In this paper, an unsupervised learning algorithm for bilayer feedforward spiking neural networks based on spike-timing dependent plasticity (STDP) competitiveness is proposed and applied to SAR image classification on MSTAR for the first time. The SNN learns autonomously from the input value without any labeled signal and the overall classification accuracy of SAR targets reached 80.8%. The experimental results show that the algorithm adopts the synaptic neurons and network structure with stronger biological rationality, and has the ability to classify targets on SAR image. Meanwhile, the feature map extraction ability of neurons is visualized by the generative property of SNN, which is a beneficial attempt to apply the brain-like neural network into SAR image interpretation.

Recently, the application of deep reinforcement learning in recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns and sparse data issues in recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This paper proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in recommender system. Our experiment is carried out on MovieLens dataset. The result shows that, DRL based recommender system is superior than traditional algorithms in minimum error and the application of tags has little effect on accuracy when making up for interpretability. In addition, DRL based recommender system has excellent performance on user cold start problems.

In recent times, BERT based transformer models have become an inseparable part of the 'tech stack' of text processing models. Similar progress is being observed in the speech domain with a multitude of models observing state-of-the-art results by using audio transformer models to encode speech. This begs the question of what are these audio transformer models learning. Moreover, although the standard methodology is to choose the last layer embedding for any downstream task, but is it the optimal choice? We try to answer these questions for the two recent audio transformer models, Mockingjay and wave2vec2.0. We compare them on a comprehensive set of language delivery and structure features including audio, fluency and pronunciation features. Additionally, we probe the audio models' understanding of textual surface, syntax, and semantic features and compare them to BERT. We do this over exhaustive settings for native, non-native, synthetic, read and spontaneous speech datasets

The COVID-19 pandemic caused by SARS-CoV-2 poses a devastating threat to human society in terms of health, economy and lifestyle. Establishing accurate and real-time models to predict and assess the impact of the epidemic on the economy is instructive. We have designed a new model to quantitatively assess the impact of the COVID-19 on the economy of China’s mainland. The nominal GDP in the Q1 of 2020 that we predicted for China’s mainland with the Baidu Mi-gration Data is RMB 20,785.7 billion, which is less by 3.59% than that in 2019. The estimated val-ue is confirmed roughly by the official report released in April 17, 2020 (RMB 20,650 billion, 6.8% year-on-year declined). Strict control measures during the epidemic have greatly reduced Chi-na's economic activity and had a serious impact on the country's economy. Orderly promotion of population mobility plays a decisive role in economic recovery.

Castleman disease (CD) rarely presents with obstructive jaundice, which poses a diagnostic and therapeutic challenge to the management of the disease. A 40-year-old man was referred to our hospital for emergent management of upper abdominal pain. An abdominal mass was removed, and the postoperative pathology showed retroperitoneum CD, which was subsequently managed by adjuvant therapy of combination chemotherapy and steroids. One month later, a biliary metal stent was placed due to the presentation of obstructive jaundice. After approximately 3 months, the patient experienced another episode of obstructive jaundice, and SpyGlass DS cholangioscopy (Boston Scientific, Natick, Mass, USA) was performed via the biliary track for biopsy, which pathologically showed biliary malignancies. Radiofrequency ablation was performed with a probe (EMcision, Montreal, Canada), and another uncovered metal stent was placed within the existing metal stent. No stent occlusion occurred during a 6-month follow-up period. In conclusion, CD rarely presents with obstructive jaundice, and a combination of radiofrequency ablation with metal stent implantation under cholangioscopy can prolong the stent patency time and the survival time of patients.

The infected and fatal cases of Coronavirus Disease 2019 (COVID-19) keep increasing around the world, to explore the infection routes and pathogenesis of 2019-nCoV could be meaningful for prevention and treatment of COVID-19. Previous studies showed that the oral cavity is at potentially high risk of 2019-nCoV infection. The ACE2 receptor of 2019-nCoV was reported could express on oral epithelium and salivary glands, and 2019-nCoV could be detected in patients’ saliva. Recently, the amblygeustia were found to widely exist in the COVID-19 patients. To explore the potential mechanism of amblygeustia, we performed further analysis via independent in-house single-cell profiles. Our results showed that ACE2 was inclined to express in taste cells, which indicated that 2019-nCoV may invade into taste cells at the early stage of COVID-19, and lead to the amblygeustia of patients. Above findings about the 2019-nCoV and COVID-19 in oral cavity are valuable and enlightening for future epidemic prevention strategy.

3CL^pro is the main protease of the novel coronavirus (SARS-CoV-2) responsible for their intracellular duplication. Based on virtual screening technology, we found 23 approved clinical drugs such as Carfilzomib, Saquinavir, Thymopentin and etc., which showed high affinity with the 3CL^pro active sites. These findings suggest that 3CL^pro inhibitors might be potential candidates for further activity detection and molecular modification.

Postharvest 1-MCP treatment can inhibit lignification of fruit and vegetables. It has been suggested that the mode of action of 1-MCP is through inhibiting ethylene production, but the effect of 1-MCP and ethylene on lignification of common bean remain unknown. This work compared the effect of 0.5 μL L-1 1-MCP and 100 μL L-1 ethylene on lignification of common bean during storage. Postharvest 1-MCP significantly inhibited the increase of lignified cell group, sclerenchyma became thicker, vascular bundles thickened and lignified cells grew during storage, while ethylene is the opposite. 1-MCP inhibited the increase in respiration rate, sucrose phosphate synthase (SPS), sucrose synthase (SuSy), phenylalanine ammonialyase (PAL), cinnamyl-alcohol dehydrogenase (CAD), and peroxidase (POD) activities, whereas ethylene hastened all. Ethylene treatment can be stimulated and 1-MCP inhibited the decline of reducing sugar and cellulose content. Expression of genes, including PvACO1, PvAOG1, PvSuSy, PvPAL3, and Pv4CL1, PvCOMT1 together with lignin content were significantly increased in common bean during storage. 1-MCP treatment markedly inhibited expression of PvACO1, PvSuSy2, PvPAL3, Pv4CL1 and PvCOMT1 genes, while strengthened expression of PvETR1 and PvAOG1, while ethylene was opposite. This work provides evidence that ethylene plays a key role in regulating the lignin biosynthesis of common bean, and also provides strategies for the maintenance of fruit quality during storage.

In this work, cobalt doped ZnO nanorod arrays with anticorrosion function were successfully prepared on FTO substrate by a simple aqueous solution method. XRD and EDS results indicate the doped Co2+ has successfully incorporated into the ZnO crystal lattice. Photocurrent density and open circuit potential (OCP) results indicate the photocathodic protection performance for 316 stainless steel (316 SS) and Q235 carbon steel in 3.5 wt.% NaCl solution under 300 W Xe lamp enhanced with the increase of cobalt concentration, and the photoanode with 15% Co/Zn ratio has the optimal photocathodic protection effect. The mechanism of enhancement may be result from the narrowed band gap, the lower recombination rate of photogenerated electron-holes, the intermediate impurity level and the split of the hypo-outer shell of cobalt ions.

Directional protective relaying based on amplitude comparison of traveling wave on continuous co-phase auto-transformer(AT) power transmission system was proposed. Phase-model transformation is used for decoupling transient fault signals and get aerial mode component. The forward and backward traveling wave are obtained by calculating aerial mode component, then the intrinsic mode function(IMF) components can be obtained through Variational Mode Decomposition(VMD), the module maxima are obtained through calculating the IMF components. The fault direction is determined by the ratio of fault components. If two faults relays at the ends of electric traction networks detect a fault to be in the forward direction, the fault occurred in the internal area, the protection would operate properly. The simulation tests indicate that the protection scheme is feasible, and the proposed protection method can discriminate internal faults from external faults under various fault types, and its performances are immune to fault initial angle, ground resistance, etc.

Electric vehicles (EVs) are being endorsed as the uppermost successor to fuel-powered cars, with timetables for banning the sale of petrol-fueled vehicles announced in many countries. However, the range and charging times of EVs are still considerable concerns. Fast charging could be a solution to consumers' range anxiety and the acceptance of EVs. Nevertheless, it is a complicated and systematized challenge to realize the fast charging of EVs because it includes the coordinated development of battery cells, including electrode materials, EV battery power systems, charging piles, electric grids, etc. This paper aims to serve as an analysis for the development of fast-charging technology, with a discussion of the current situation, constraints and development direction of EV fast-charging technologies from the macroscale and microscale perspectives of fast-charging challenges. It is emphasized that to essentially solve the problem of fast charging, the development of new battery materials, especially anode materials with improved lithium ion diffusion coefficients, is the key. It is highlighted that red phosphorus is the most promising anode that can simultaneously satisfy the double standards of high-energy density and fast-charging performance to a maximum degree.

To realize high response and selectivity gas sensor in detecting dissolved gases in transformer oil, in this study, the adsorption of four kinds of gases (H2, CO, C2H2 and CH4) on Pd-graphyne as well as the gas sensing properties evaluation were investigated. The energetically favorable structure of Pd doped γ-Graphyne was first studied, including the comparison of different adsorption sites and discussion of electronic properties. Then, the adsorption of these four molecules on Pd-graphyne was explored. The adsorption structure, adsorption energy, electron transfer and electron distribution, the band structure and density of states were calculated and analyzed. The results show that the Pd atom prefers to be adsorbed on the middle of three C≡C bonds and the band gap is smaller. The CO adsorption exhibits the largest adsorption energy and electron transfer and brings obvious change to the structure and electron properties to Pd-graphyne. Because of the conductance decrease after adsorption CO and acceptable recovery time at high temperature, the Pd-graphyne can be promising gas sensing materials to detect CO with high selectivity. This work offers theoretical support for the design of nanomaterials based gas sensor using novel structure for industrial application.

Traditional phase change composites usually suffer poor mechanical property and easy collapsing in the phase changing process. Herein, a highly flexible phase change composite is fabricated using thermoplastic elastomer as the basic gel and the expanded graphite/paraffin as the filler. This new phase change composite shows a tensile strength of 2.1 MPa and a breaking elongation of 220%. It has a melting enthalpy of 145.4 J•g^-1 and a thermal conductivity of 2.2 W•m^-1•K^-1 with 70% of expanded graphite/paraffin. The thermoplastic elastomer based phase change composite exhibits great reversible property after 200 heating/cooling cycles. This flexible phase change composite demonstrates good photo-thermal energy charging/discharging property and shows great potential to be applied in the solar thermal energy systems.

Vanadium titano-magnetite is a significant resource in China, and in this study, we characterize its isothermal reduction mechanisms in the mixture of H₂, CO, and N₂ where the variables considered here include reduction time, reduction temperature, gas composition, and pellet size. The kinetics of the reduction process are mainly studied, which follows a shrinking core model. The results indicate that the reduction degree of oxidized VTM pellets increases with the increase of reduction time, reduction temperature but decreases with the increase of pellet size. Moreover, we found that an increase of H₂/(H₂+CO) ratio induces an increase of the reduction degree. Then the transformation of main Ti-bearing mineral phases is discussed, and the most probable reaction mechanism is revealed. In the whole reduction process, the kinetic results confirm the existence of an early stage and a latter stage, which are controlled by interface chemical reaction and diffusion, respectively. Furthermore, the results show that the diffusion-control step can be observably shortened with the decrease of pellet size because a thinner product layer is formed during the reduction process. Our study thus provides a valuable technical basis on the VTM industrial application.

A continuous Deep Belief Network (cDBN) with two hidden layers is proposed in this paper, focusing on the problem of weak feature learning ability when dealing with continuous data. In cDBN, the input data is trained in an unsupervised way by using continuous version of transfer functions, the contrastive divergence is designed in hidden layer training process to raise convergence speed, an improved dropout strategy is then implemented in unsupervised training to realize features learning by de-cooperating between the units, and then the network is fine-tuned using back propagation algorithm. Besides, hyper-parameters are analysed through stability analysis to assure the network can find the optimal. Finally, the experiments on Lorenz chaos series, CATS benchmark and other real world like CO2 and waste water parameters forecasting show that cDBN has the advantage of higher accuracy, simpler structure and faster convergence speed than other methods.

Porous asphalt (PA) pavement systems with and without a geotextile layer were investigated in laboratory experiments to determine the impacts of the geotextile layer on processes leading to lead ion (Pb2+) removal from stormwater runoff. Two types of geotextile membranes placed separately at upper and lower levels within the PA systems were tested in an artificial rainfall experiment using synthetic rainwater. The effect of storage capacity within the system on Pb2+ removal was also investigated. Results indicated that the use of a geotextile layer resulted in a longer delay to the onset of effluent. The non-woven geotextile membrane placed below the reservoir course improved the Pb2+ removal rate by 20% over removal efficiency of the system using a woven geotextile placed just below the surface but before the choker course. Pb2+ ions were reduced by over 98% in the effluent after being held for 24 hours in reservoir storage. Results suggest that temporary storage of stormwater in the reservoir course of a PA system is essential to improving Pb2+ ion removal capability.

In order to improve the strength of PVA/xylan composite films and endow them with ultraviolet (UV) shielding ability, TiO2-KH550 nanoparticle was synthesized and added into the PVA/xylan matrix. The TiO2-KH550 nanoparticle dispersed well in the 0.04% sodium hexametaphosphate (SHMP) solution under ultrasonic and stirring treatments. Investigations on the properties of films showed that TiO2-KH550 had the positive impact on improving the strength, moisture and oxygen barrier properties of the composite films. The maximum tensile strength (27.3 MPa), the minimum water vapor permeability (2.75×10-11 g•m-1•s-1•Pa-1) and oxygen permeability (4.013 cm3•m-2•24h-1•0.1MPa-1) were obtained under the addition of 1.5% TiO2-KH550. The tensile strength of TiO2-KH550 reinforced composite film was increased by 70% than that of the pure PVA/xylan composite film, and the water vapor and oxygen permeability were decreased by 31% and 41%, respectively. Moreover, the UV transmittance of film at the wavelength of 400 nm was almost zero when adding 1.5~2.5% of TiO2-KH550, which indicated the PVA/xylan composite films were endowed with excellent UV light shielding ability.

Increasing distributed generation and intermittency, along with the increasing frequency of extreme weather events, impose a serious challenge for the electric power supply security. Understanding the costs of interruption is vital in terms of enhancing the power system infrastructure and planning the distribution grid. On the other hand, customer rights and demand response techniques are further reasons to study the worth of power reliability. In this paper, the authors make use of directional distance function and shadow pricing method for a case study from Finland. The aim is to calculate the cost of one minute of power interruption from the distribution network operator perspective. The sample consists of 78 distribution network operators from Finland with cost and network information between 2013 and 2015.

A methodology for both accurate and smooth contour line generation from Light Detection and Ranging (LiDAR) point clouds is proposed in this paper. In order to improve the accuracy of contour lines in the area of forests, constrained triangulation networks with break lines are then constructed to generate contour lines. In break line extraction, a bi-threshold method for edge line detection is used to extract both complete and reliable break lines. A point clouds elevation adjustment with constrain of break lines and an interpolator considering a contour interval is proposed to improve the smoothness of contour lines. The proposed interpotator is also can avoid contour line intersection when contour lines are interpolated. Statistical parameters and shape index are then used to evaluate quantitatively the accuracy and smoothness of the resultant contour lines, which fill in the blank of contour lines evaluation in theory. The experiments show that high-quality contours in terms of smoothness and accuracy can be generated from LiDAR point clouds.