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The InVEST model, with its ability to perform spatial visualization and quantification, is an important tool for mapping ecosystem services. However, the spatial accuracy and simulating performance of the model are deeply influenced by the land use parameter, which often relies on the accuracy of land use/cover data. To address this issue, we propose a novel method for optimizing the land use parameter of the InVEST model based on the vegetation–impervious surface–soil (V–I–S) model and a machine learning algorithm. The optimized model is called Sub-InVEST, and it improves the performance of assessing ecosystem services on a sub-pixel scale. The conceptual steps are (i) extracting the V–I–S fraction of remote sensing images based on the spectral unmixing method; (ii) determining the mapping relationship of the V–I–S fraction between land use/cover type using a machine learning algorithm and field observation data; (iii) inputting the V–I–S fraction into the original model instead of the land use/cover parameter of the InVEST model. To evaluate the performance and spatial accuracy of the Sub-InVEST model, we employed the habitat quality module of InVEST and multi-source remote sensing data, which were applied to acquire Sub-InVEST and estimate the habitat quality of central Guangzhou city from 2000 to 2020 with the help of the LSMA and ISODATA methods. The experimental results showed that the Sub-InVEST model is robust in assessing ecosystem services in sets of complex ground scenes. The spatial distribution of the habitat quality of both models revealed a consistent increasing trend from the southwest to the northeast. Meanwhile, linear regression analyses observed a robust correlation and consistent linear trends, with R2 values of 0.41, 0.35, 0.42, 0.39, and 0.47 for the years 2000, 2005, 2010, 2015, and 2020, respectively. Compared with the original model, Sub-InVEST had a more favorable performance in estimating habitat quality in central Guangzhou. The spatial depictions and numerical distribution of the results of the Sub-InVSET model manifest greater detail and better concordance with remote sensing imagery and show a more seamless density curve and a substantially enhanced probability distribution across interval ranges.

In this paper, we define the generalized Bertrand curves of non-light-like framed curves in Lorentz–Minkowski 3-space; their study is essential for understanding many classical and modern physics problems. Here, we consider two non-light-like framed curves as generalized Bertrand pairs. Our generalized Bertrand pairs can include Bertrand pairs with either singularities or not, and also include Mannheim pairs with singularities. In addition, we discuss their properties and prove the necessary and sufficient conditions for two non-light-like framed curves to be generalized Bertrand pairs.

Flavonoids are major active small-molecule compounds in bamboo leaves, which can be easily obtained from the bamboo leaves extraction residues (BLER) after the polysaccharides extraction. Six macroporous resins with different properties were screened to prepare and enrich isoorientin (IOR), orientin (OR), vitexin (VI), and isovitexin (IVI) from BLER, and the XAD-7HP resin with the best adsorption and desorption performance was selected for further evaluation. Based on the static adsorption experiments, the experimental results showed that the adsorption isotherm fitted well with the Langmuir isotherm model, and the adsorption process was better explained by the pseudo-second-order kinetic model. After the dynamic trial of resin column chromatography, 20 bed volume (BV) of upload sample and 60% ethanol as eluting solvent was used in a lab scale-up separation, and the results demonstrated that the content of four flavonoids could be increased by 4.5-fold, with recoveries between 72.86 and 88.21%. In addition, chlorogenic acid (CA) with purity of 95.1% was obtained in water-eluted parts during dynamic resin separation and further purified by high-speed countercurrent chromatography (HSCCC). In conclusion, this rapid and efficient method can provide a reference to utilize BLER to produce high-value-added food and pharmaceutical products.

Background: Xiaochaihu capsule is composed of seven traditional Chinese medicines. The pharmacopoeia only focuses on the quantitative detection of baicalin, which cannot fully reflect the quality of the preparation. Some medium polar components were used to establish the fingerprint of Xiaochaihu capsule, but there was no report on the strong polar components. Methods: A high performance liquid chromatography-corona charged aerosol detection technology was used to establish a fingerprint analysis method for Xiaochaihu capsules following an analytical quality by design approach. Definitive screening designed experiments were used to optimize the method parameters. A stepwise regression method was used to build quantitative models. The method operable design region was calculated using the experimental error simulation method. Plackett–Burman designed experiments were carried out to test robustness. Results: The contents of four components were simultaneously determined. There were seven common peaks in the fingerprint. The common peak area accounted for 91.72%. Both fingerprint and quantitative analysis methods were validated as applicable in the methodology study. The quantitative fingerprint analysis method for sugar components can fill the gap in the detection of strong polar components in the existing methods. It provides a new technology for the comprehensive overall evaluation of Xiaochaihu capsule.

Background: Older individuals have a high risk of morbidity and mortality due to COVID-19, and one of the most effective ways to prevent COVID-19 is vaccination. Little is known about older people’s willingness to receive a COVID-19 vaccine. Therefore, the objective of this study was to assess the acceptance of and factors influencing the intention to receive a COVID-19 vaccination among older adults in Shanghai, China. Methods: A cross-sectional study was conducted among older adults (≥60 years old) in Shanghai. Face-to-face interviews with a questionnaire were conducted in community health service centers, recording several parameters: demographic information, health-related factors; perceived likelihood, severity, and burden of COVID-19; perceived safety, effectiveness, necessity, and benefit of the COVID-19 vaccine, as well as their trust in the vaccine delivery system and doctors; willingness to receive a COVID-19 vaccination. Bivariate analysis between several survey items and the willingness to receive a COVID-19 vaccination was conducted using a chi-square test. Logistic regression was used to assess to what degree each variable affected the willingness to receive a COVID-19 vaccination. Results: Of the 1067 participants, 90.91% (970/1067) confirmed that they were willing to receive a COVID-19 vaccination. The participants were more likely to be willing to be vaccinated if they were immigrants (OR = 1.988, 95%CI = 1.062–3.717), had an education level of junior high school (OR = 2.724, 95%CI = 1.000–7.423) or high school or above (OR = 3.993, 95%CI = 1.576–10.119), and had a monthly income of CNY 3000–5000 (OR = 32.770, 95%CI = 1.144–6.711) or CNY >5000 (OR = 2.309, 95%CI = 1.003–5.319). The participants were also more likely to be willing to be vaccinated if they had received a pneumonia vaccination previously (OR = 2.138, 95%CI = 1.016–4.500), perceived the safety of the COVID-19 vaccine (OR = 1.508, 95%CI = 1.073–2.119), perceived the necessity of the COVID-19 vaccine (OR = 2.604, 95%CI = 1.946–3.484), or trusted the suggestions of doctors (OR = 1.706, 95%CI = 1.115–2.618). The participants were less likely to be willing to be vaccinated if they were aged ≥76 years (OR = 0.498, 95%CI = 0.264–0.939), reported a physical health condition of bad or below (OR = 0.229, 95% CI = 0.095–0.549), or were worried about the adverse effects of a COVID-19 vaccination (OR = 0.503, 95%CI = 0.364–0.695). Conclusions: Under the free vaccination policy for COVID-19, older adults have a high intention to be vaccinated to prevent COVID-19 in Shanghai, China. Widely publicizing the safety and necessity of COVID-19 vaccination is necessary.

The intensity of human activity, habitat loss and habitat degradation have significant impacts on biodiversity. Habitat quality plays an important role in spatial dynamics when evaluating fragmented landscapes and the effectiveness of biodiversity conservation. This study aimed to evaluate the status and characteristic variation in habitat quality to analyze the underlying factors affecting habitat quality in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Here, we applied Kendall’s rank correlation method to calculate the sensitivity of habitat types to threat factors for the Integrated Valuation of Ecosystem Services and Tradeoffs habitat quality (InVEST-HQ) model. The spatiotemporal variation in habitat quality of the GBA in the period 1995–2015 was estimated based on the InVEST-HQ model. We analyzed the characteristic habitat quality using different ecosystem classifications and at different elevation gradients. Fractional vegetation cover, the proportion of impervious surface, population distribution and gross domestic product were included as the effect factors for habitat quality. The correlation between the effect factors and habitat quality was analyzed using Pearson’s correlation tests. The results showed that the spatial pattern of habitat quality decreased from fringe areas to central areas in the GBA, that the forest ecosystem had the highest value of habitat quality, and that habitat quality increased with elevation. In the period from 1995 to 2015, habitat quality declined markedly and this could be related to vegetation loss, land use change and intensity of human activity. Built-up land expansion and forest land fragmentation were clear markers of land use change. This study has great significance as an operational approach to mitigating the tradeoff between natural environment conservation and rapid economic development.

Semantic segmentation is an important and challenging task in the aerial image community since it can extract the target level information for understanding the aerial image. As a practical application of aerial image semantic segmentation, building extraction always attracts researchers’ attention as the building is the specific land cover in the aerial images. There are two key points for building extraction from aerial images. One is learning the global and local features to fully describe the buildings with diverse shapes. The other one is mining the multi-scale information to discover the buildings with different resolutions. Taking these two key points into account, we propose a new method named global multi-scale encoder-decoder network (GMEDN) in this paper. Based on the encoder-decoder framework, GMEDN is developed with a local and global encoder and a distilling decoder. The local and global encoder aims at learning the representative features from the aerial images for describing the buildings, while the distilling decoder focuses on exploring the multi-scale information for the final segmentation masks. Combining them together, the building extraction is accomplished in an end-to-end manner. The effectiveness of our method is validated by the experiments counted on two public aerial image datasets. Compared with some existing methods, our model can achieve better performance.

An optimal control design for the uncertain Delta robot is proposed in the paper. The uncertain factors of the Delta robot include the unknown dynamic parameters, the residual vibration disturbances and the nonlinear joints friction, which are (possibly fast) time-varying and bounded. A fuzzy set theoretic approach is creatively used to describe the system uncertainty. With the fuzzily depicted uncertainty, an adaptive robust control, based on the fuzzy dynamic model, is established. It designs an adaptation mechanism, consisting of the leakage term and the dead-zone, to estimate the uncertainty information. An optimal design is constructed for the Delta robot and solved by minimizing a fuzzy set-based performance index. Unlike the traditional fuzzy control methods (if-then rules-based), the proposed control scheme is deterministic and fuzzily optimized. It is proven that the global solution in the closed form for this optimal design always exists and is unique. This research provides the Delta parallel robot a novel optimal control to guarantee the system performance regardless of the uncertainty. The effectiveness of the proposed control is illustrated by a series of simulation experiments. The results reveal that the further applications in other robots are feasible.

Microfluidic system, as a kind of miniature integrated operating platform, has been applied to solid-state nanopore sensors after many years of experimental study. In the process of introducing microfluidic into solid-state nanopore sensors, many novel device structures are designed due to the abundance of analytes and the diversity of detection methods. Here we review the fundamental setup of nanopore-based microfluidic systems and the developments and advancements that have been taking place in the field. The microfluidic systems with a multichannel strategy to elevate the throughput and efficiency of nanopore sensors are then presented. Multifunctional detection represented by optical-electrical detection, which is realized by microfluidic integration, is also described. A high integration microfluidic system with nanopore is further discussed, which shows the prototype of commercialization.

Over the past couple of decades, global positioning system (GPS) technology has been utilized to collect large-scale data from travel surveys. As the precise spatiotemporal characteristics of travel could be provided by GPS devices, the issues of traditional travel survey, such as misreporting and non-response, could be addressed. Considering the defects of dedicated GPS devices (e.g., the need for a large sum of money to buy devices, forgetfulness in the taking of devices to collect data, limiting of the sample size because of the number of devices, etc.), and the fact that the smartphone is becoming one of the necessities of life, there is an opportunity for smartphones to replace dedicated GPS devices. Although several general reviews of GPS travel survey and GPS data-processing methods have been written, a systematic review of smartphone-based GPS data collection and travel mode detection has not be made.. The studies were collected from six databases. The purpose of this review is to critically evaluate the current literature on the existing methodologies of travel mode detection based on GPS raw data collected by smartphones. Meanwhile, according to a systematic comparison of the different methods of data preprocessing for travel mode detection, this paper details the strengths and weaknesses of the existing methods. Furthermore, it is a very important step towards developing methodologies and applications for GPS raw data collected by smartphones.