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The formation and evolution of laser-induced periodic structures (LIPSS) have attracted much attention due to their broad applications and rich physics. The literature has shown that excessive laser energy accumulation, such as increasing single pulse energy or cumulative pulse number on a sample, leads to a final fuzzy LIPSS period or even disappearance of the period. This article discovers a new phenomenon by increasing the laser fluence; the periodic structure appears blurred and disappears in the middle of the laser fluence region. Two contrary evolution tendencies of the period’s disappearance are observed for the first time. This phenomenon can be attributed to femtosecond ablation regimes in different fluence regions. The experimental results and discussion provide a powerful guarantee for the high-quality preparation of structures by controlling the experimental parameters for future practical applications. The findings of this study play a significant role in regulating the LIPSS period and provide ideas for avoiding the hidden danger of the cycle structure disappearing during the creation of the LIPSS structure, which has practical implications for future LIPSS applications.

The spatiotemporal mobility patterns and next location prediction of fake base stations (FBS) provide important technical support for the police to prevent spam messages from FBS. However, due to the difficulty in locating their real-time locations, our understanding of the mobility patterns and predictability of FBS is still limited. Based on the crowdsourced spam data, we extract the time and potential locations of FBS and propose a Tucker-MMC method that combines Tucker decomposition with a Mobility Markov Chain (MMC) model to investigate the mobility patterns and predictability of FBS sending spam messages. First, we utilize Tucker decomposition to reflect the spatial and temporal preferences during the movement of the corresponding FBS. Then the mobility regularity and the theoretical maximum predictability of the FBS trajectories with similar mobility preferences are analyzed by entropy and Fano's inequality. A Tucker-MMC is also established for the next location prediction. The results using the spam dataset in Beijing show that the accuracy of Tucker-MMC is more than double that of the MMC. The accuracy of the actual location prediction model is more likely to approach the theoretical maximum predictability when FBS send spam messages in a shorter time, shorter transfer distance, and smaller access range. [ABSTRACT FROM AUTHOR]

Spam messages affect the normal communication of mobile phone users and public security. Fake base station (FBS) is the main channel for sending spam messages. Accurate spatiotemporal prediction of spam messages from FBS is important for implementing short-term spam prevention measures by the police. However, existing models used for spam messages modeling often focus on capturing spatial dependencies based on distance, thus failing to fully exploit spatial dependencies hidden in the data. Meanwhile, they encounter difficulties in mitigating the impact of excessive zero data. Therefore, based on the crowdsourced spam message data, a temporal graph-convolutional-network model (FBS-TGCN) is developed for the spatiotemporal prediction of spam messages from FBS. This model includes graph convolutional network (GCN) and gated temporal convolutional network (TCN) to capture the spatial and temporal dependencies alternatively. We further combine a weighted adjacency matrix and a self-adaptive adjacency matrix to capture both distance-based spatial dependencies and hidden spatial dependencies. A weighted loss function is used to mitigate the influence of excessive zero data. Experimental results using the spam dataset in Beijing demonstrate the effectiveness of the proposed model when compared to the baseline models, especially in the prediction of spam messages quantity and high spamming areas. [ABSTRACT FROM AUTHOR]

Urban agglomerations are becoming increasingly important in driving economic development in China. Accurate representation of the economic development status and spillover effects of cities within an urban agglomeration is the foundation of and an effective approach for promoting the coordinated development of that agglomeration. However, current studies of economic spillovers tend to focus on urban agglomerations as a whole, and there is a lack of scrutiny and validation of research data. Therefore, this study proposes a framework for detecting economic spillover effects within an urban agglomeration based on a prolonged night-time light dataset. Firstly, we explored the most suitable night-time light index to characterize the economic status. Then, we used this index to construct the economic network and hierarchical structure of the urban agglomeration. Finally, we explored the heterogeneity of spillover effects under the hierarchical structure. The results of a case study in the Pearl River Delta (PRD) urban agglomeration show that (1) the total night-time light in built-up areas (BNTL) has the highest Pearson correlation coefficient with GDP, which is 0.82; (2) there is an obvious hierarchical structure within the PRD; (3) there are significant and sustained economic spillover effects among the core cities, with Guangzhou–Foshan and Shenzhen–Dongguan having more obvious spillover effects; and (4) the economic spillover effects within the three metropolitan areas have different characteristics. The Guangzhou–Foshan–Zhaoqing metropolitan area is closely linked, to apparent differences in the pace of spillover effects. The Shenzhen–Dongguan–Huizhou metropolitan area has strong close linkages, with strong synchronization of spillover effects. The Zhuhai–Zhongshan–Jiangmen metropolitan area has not yet formed a stable synergistic development relationship. Overall, the framework can effectively reveal the hierarchical structure and different characteristics of economic spillovers within urban agglomerations, which can provide a scientific reference for policy making related to the coordinated development of such agglomerations. [ABSTRACT FROM AUTHOR]

In order to better analyze and design the fabric structure, a layering overlay method is proposed for the fabrics with the layering relationship of backed weave, sandwich and double layers. According to the spatial relationship between the upper and lower layers of the fabric, the warp and weft yarns of different fabric layers are represented by the dark and light color groups of different shade, and the layers are drawn successively from the lower layer, the interlayer to the upper layer. The drawing method of layering overlay method is introduced with fabric CAD and Photoshop (PS) as application conditions, and the application of this method in fabric structure analysis and design is illustrated. When fabric CAD is used, the inner yarn and its interweaving law, the connecting yarn, the surface yarn and its interweaving law are superimposed from bottom to top. When drawing with PS, each layer of yarn and its interweaving rule are drawn on different layers respectively, and the upper and lower positions of the layers are adjusted according to the relationship between the layers in the fabric surface. Layering and superposition method makes the process of fabric drawing more intuitive, clear and convenient. It is helpful for designers to analyze, understand and innovate fabrics. [ABSTRACT FROM AUTHOR]

Various types of micro-/nanostructures can be fabricated by femtosecond laser irradiation on metal surfaces. It has received extensive attention and research because of its potential application value. In this paper, the effect of laser focusing position on the micro-/nanostructure formation on the surface of a titanium alloy (TC4) was systematically researched. The experimental results show that, in the process of femtosecond laser focusing, a spatial distribution of laser energy with a narrower beam center and a ring around the beam is formed in front of the geometric focus and the distribution of laser energy changes along the laser transmission direction. This property can affect the morphology of the micro-/nanostructure on the surface of irradiated materials. It is believed that the new spatial distribution of laser fluence caused by focusing is due to the nonlinear process of plasma defocus and laser self-focusing caused by multi-photon or tunnel ionization in front of the target by focusing the femtosecond laser. The discovery of the nonlinear characteristics of the focused femtosecond laser and the application of the unique energy spatial distribution to the surface morphology of the fabricated surface put forward a new idea for the direct fabrication of micro-/nanostructures via femtosecond laser, which has certain significance for the development of micro-/nanofabrication technology of femtosecond lasers. [ABSTRACT FROM AUTHOR]

The dynamic population distributions by activity type (e.g. working, shopping or in-home) are vital for resource allocation, urban planning and epidemic containment. Although studies have incorporated individual-level human mobility data to map population distribution by activity type, access to such data is hindered due to privacy issues and they rely on auxiliary data to provide priori activity knowledge. This paper presents a method for generating the population dynamics by activity type. We first introduce more readily available sequential snapshot data to construct the population mixture model, then decompose the population mixture, and finally estimate the dynamic population size for each activity. We test the method in the central districts of Guangzhou city, China, based on real-time Tencent user density data. Correlation analysis and accuracy assessment prove that our method can accurately estimate hourly distributions for populations engaging in working, stay-at-home, and socializing activities. The temporal distribution of the working population reproduces the regular work scenarios and socializing population displays complex spatial patterns. We also find that there is an underlying relationship between a region's function and its dynamic population structure. The presented method has great potential for application and could provide new insight for studying urban dynamic functions. [ABSTRACT FROM AUTHOR]

As the COVID-19 vaccination has been quickly rolling out around the globe, the evaluation of the effects of vaccinating populations for the safe reopening of schools has become a focal point for educators, decision-makers, and the general public. Within this context, we develop a contact network agent-based model (CN-ABM) to simulate on-campus disease transmission scenarios. The CN-ABM establishes contact networks for agents based on their daily activity patterns, evaluates the agents' health status change in different activity environments, and then simulates the epidemic curve. By applying the model to a real-world campus environment, we identify how different community risk levels, teaching modalities, and vaccination rates would shape the epidemic curve. The results show that without vaccination, retaining under 50% of on-campus students can largely flatten the curve, and having 25% on-campus students can achieve the best result (peak value < 1%). With vaccination, having a maximum of 75% on-campus students and at least a 45% vaccination rate can suppress the curve, and a 65% vaccination rate can achieve the best result. The developed CN-ABM can be employed to assist local government and school officials with developing proactive intervention strategies to safely reopen schools. [ABSTRACT FROM AUTHOR]

International communication and global cooperation have greatly accelerated the worldwide spread of dengue fever, increasing the impact of imported cases on dengue outbreaks in non-naturally endemic areas. Existing studies mostly focus on describing the quantitative relationship between imported cases and local transmission but ignore the space-time diffusion mode of imported cases under the influence of individual mobility. In this paper, we propose a comprehensive framework at a fine scale to establish the disease transmission network and a mathematical model, which constructs 'source-sink' links between the imported and indigenous cases on a regular grid with a spatial resolution of 1 km to explore the diffusion pattern and spatiotemporal heterogeneity of imported cases. An application to Guangzhou, China, reveals the main flow and transmission path of imported cases under the influence of human movement and identifies the spatiotemporal distribution of transmission speed according to the time lag of each source-sink link. In addition, we demonstrate that using individual-based movement data and socio-economic factors to study human mobility and imported cases can help to understand the driving forces of dengue spread. Our research provides a comprehensive framework for the analysis of early dengue transmission patterns with benefits to similar urban applications. [ABSTRACT FROM AUTHOR]

Urban system is shaped by the interactions between different regions and regions planned by the government, then reshaped by human activities and residents' needs. Understanding the changes of regional structure and dynamics of city function based on the residents' movement demand are important to evaluate and adjust the planning and management of urban services and internal structures. This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition, for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes. First, a four-dimensional tensor of time (T) × week (W) × origin (O) × destination (D) was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern. Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor. Finally, we further analysed the space-time interaction on different time scales to deduce the actual function and connection strength of each region. Our research shows that the application of individual-based spatial-temporal data in human mobility and space-time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective. [ABSTRACT FROM AUTHOR]

With rapid economic growth since the 21st century, cities in China have experienced considerable economic and social reconstruction. Driven by rapid industrialization, urban spatial structures are undergoing evolution and change. Therefore, this paper analyzes the processes and mechanisms associated with the evolution of the commercial spatial structure in Guangzhou after the financial crisis in 2008 based on both theoretical and empirical analyses. We use a Dirichlet multinomial regression (DMR) model to extract latent semantic information and determine urban functional areas from global positioning system (GPS) and point-of-interest (POI) data collected in Guangzhou in 2009 and 2013. In addition, we use movement patterns and POI data to identify the evolution of Guangzhou's commercial zones from 2009 to 2013. The results show that the urban commercial structure in Guangzhou gradually changed from a single-center model to a multi-center model with dispersed clusters and that the distribution of the entire spatial structure changed. Meanwhile, Guangzhou’s commercial structure not only varied over time but also exhibited specific geographical features. This paper demonstrates that the proposed method can clearly identify the boundary of the commercial area in Guangzhou and provides a valid spatial-temporal model of change in the city. Moreover, this study not only expounds the future development trends of the urban spatial structure in Guangzhou from a microcosmic perspective but also provides a scientific basis for clarifying the spatial locations and development advantages of urban functions within the city. [ABSTRACT FROM AUTHOR]

Multitask learning (MTL) has recently yielded impressive results for classification of remotely sensed data due to its ability to incorporate shared information across multiple tasks. However, it remains a challenging issue to achieve robust classification results in the case that the data are from nonlinear subspaces. In this paper, we propose a kernel low-rank MTL (KL-MTL) method to handle multiple features from the 2-D variational mode decomposition (2-D-VMD) domain for multi-/hyperspectral classification. On the one hand, a nonrecursive 2-D-VMD method is applied to extract various features [i.e., intrinsic mode functions (IMFs)] of the original data concurrently. Compared with the existing 2-D empirical mode decomposition, 2-D-VMD has much stronger mathematical foundation and does not need any recursive sifting process. On the other hand, KL-MTL is proposed for classification by taking the extracted IMFs as features of multiple tasks. In KL-MTL, the low-rank representation formulated by nuclear norm can capture global structure of multiple tasks, while the kernel tricks are utilized for nonlinear extension of the low-rank MTL. Moreover, the optimization problem in KL-MTL is solved by the inexact augmented Lagrangian method. Compared with several state-of-the-art feature extraction and classification methods, the experimental results using both multi-/hyperspectral images demonstrate that the proposed method has satisfactory classification performance. [ABSTRACT FROM AUTHOR]

Retinoid-related orphan receptor gamma ( RORγ) directly controls the differentiation of Th17 cell and the production of interleukin-17, which plays an integral role in autoimmune diseases. To obtain insight into RORγ, we have determined the first crystal structure of a ternary complex containing RORγ ligand-binding domain ( LBD) bound with a novel synthetic inhibitor and a repressor peptide, 22-mer peptide from silencing mediator of retinoic acid and thyroid hormone receptor ( SMRT). Comparison of a binary complex of nonliganded (apo) RORγ- LBD with a nuclear receptor co-activator ( NCoA-1) peptide has shown that our inhibitor displays a unique mechanism different from those caused by natural inhibitor, ursolic acid ( UA). The compound unprecedentedly induces indirect disruption of a hydrogen bond between His479 on helix 11 (H11) and Tyr502 on H12, which is crucial for active conformation. This crystallographic study will allow us to develop novel synthetic compounds for autoimmune disease therapy. [ABSTRACT FROM AUTHOR]

Macamides are a class of bioactive natural products obtained from Lepidium meyenii (maca), which have been reported to exert inhibitory activity in cancer. However, their role in lung cancer is currently unknown. In the present study, macamide B was shown to inhibit the proliferation and invasion of lung cancer cells, as determined by Cell Counting Kit-8 and Transwell assays, respectively. By contrast, macamide B induced cell apoptosis, as determined by Annexin V-FITC assay. Moreover, combined treatment with macamide B and olaparib, an inhibitor of poly (ADP-ribose) polymerase, further suppressed the proliferation of lung cancer cells. At the molecular level, the expression of ataxia-telangiectasia mutated (ATM), RAD51, p53 and cleaved caspase-3 were significantly increased by macamide B, as determined by western blotting, whereas the expression levels of Bcl-2 were decreased. By contrast, when ATM expression was knocked down by small interfering RNA technology in A549 cells treated with macamide B, the expression levels of ATM, RAD51, p53 and cleaved caspase-3 were reduced, whereas those of Bcl-2 were increased. Consistently, cell proliferation and invasive ability were partially rescued by ATM knockdown. In conclusion, macamide B inhibits lung cancer progression by inhibiting cell proliferation and invasion, and inducing apoptosis. Furthermore, macamide B may participate in regulating the ATM signaling pathway. The present study provides a potential new natural drug for treating patients with lung cancer. [ABSTRACT FROM AUTHOR]

An unprecedented copper(II)-catalyzed enantioselective 1,3-dipolar [3+4] cycloaddition of azomethine imines with in situ formed azoalkenes has been realized. This strategy provides a facile access to biologically important 1,2,4,5-tetrazepine derivatives in high yield with exclusive regioselectivity and high stereoselectivity. Moreover, enantioenriched azomethine imines could be obtained via an efficient kinetic resolution using the same approach. [ABSTRACT FROM AUTHOR]

The process of hydrogen production through water electrolysis involves ionic interactions and the dynamic process of hydrogen bubble formation on the electrode surface. In this study, we employed a combination of femtosecond laser and hydrothermal synthesis to fabricate a novel MoS 2 electrode. The double-scale structure of this substrate increases its specific surface area, facilitating the presence of active sites for the hydrogen evolution reaction. The growth of nano-scale catalyst further enhances wettability, promoting rapid detachment of gas bubbles and replenishment of water. This method not only facilitates ionic interactions but also accelerates the formation of macroscopic bubbles. Under alkaline conditions, electrode achieved an impressive current density of 10 mA cm−2 with a low overpotential of only 99 mV. This novel MoS 2 hydrogen evolution catalyst holds practical value, paving the way for the design of more efficient and stable electrocatalysts. • Electrode showed superior hydrogen producing performance and objective durability. • The surface of MoS 2 /fs-Mo electrode has superhydrophilic and superaerophobic. • The wettability properties can be improved by growing nanostructures on substrates. • The forming process of bubbles was observed and analyzed by high-speed camera. [ABSTRACT FROM AUTHOR]

Spectral unmixing is an important and challenging task in the field of hyperspectral data analysis. The existing methods of blind unmixing have certain limitations. In this paper, we present a new blind unmixing method, namely the ATGP-NMF algorithm , for hyperspectral imagery. The method is based on the improved target endmember acquired through integration of the Automatic Target Generation Process (ATGP) algorithm and the Non-negative Matrix Factorization (NMF). The Harsanyi-Farrand-Chang (HFC) algorithm was introduced firstly to determine the number of target endmembers. Then the ATGP algorithm and NonNegative East Squares (NNLS) were used to obtain the spectra and abundances of the target endmembers, which were then used as initial values for the NMF algorithm to obtain the refined endmembers. Finally, an improved cross correlogram spectral matching method was introduced to match the corresponding land cover type of each endmember. Three different sets of data, namely simulated data, laboratory-controlled spectral experimental data and remote sensing imagery, were used in this study to test the effectiveness and robustness of the proposed method, in comparison with the original NMF algorithm. Results from these experiments show that the ATGP-NMF algorithm can obtain endmembers with high accuracy and it is more robust and efficient than the original NMF algorithm in different situations, regardless of the existence of pure pixels, inter-class diversity, or correlation among the endmembers i spectra. The ATGP-NMF algorithm thus has great potential of application in blind unmixing for hyperspectral imagery. [ABSTRACT FROM AUTHOR]