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Comprehensive Summary: The saline and buffered environment in actual wastewater imposes higher demands on Fenton and Fenton‐like catalytic systems. This study developed a MoS2 co‐catalytic Fe2O3 Fenton‐like system with controllable Lewis acid‐base sites, achieving efficient treatment of various model pollutants and actual industrial wastewater under neutral buffered environment. The acidic microenvironment structured by the edge S sites (Lewis basic sites) in the MoS2/Fe2O3 catalyst is susceptible to the influence of Lewis acidic sites constructed by Mo and Fe element, affecting catalytic performance. Optimizing the ratio of precursor amounts ensures the stable presence of the acidic microenvironment on the surface of catalyst, enabling the beneficial co‐catalytic effect of Mo sites to be realized. Furthermore, it transcends the rigid constraints imposed by the Fenton reaction on reaction environments, thereby expanding the applicability of commonplace oxides such as Fe2O3 in actual industrial water remediation. [ABSTRACT FROM AUTHOR]

Image style transfer is an important way to combine different styles and contents to generate new images, which plays an important role in computer vision tasks such as image reconstruction and image texture synthesis. In style transfer tasks, there are often long-distance dependencies between pixels of different styles and contents, and existing neural network-based work cannot handle this problem well. This paper constructs a generation model for style transfer based on the cycle-consistent network and the attention mechanism. The forward and backward learning process of the cycle-consistent mechanism could make the network complete the mismatch conversion between the input and output of the image. The attention mechanism enhances the model's ability to perceive the long-distance dependencies between pixels in process of learning feature representation from the target content and the target styles, and at the same time suppresses the style feature information of the non-target area. Finally, a large number of experiments were carried out in the monet2photo dataset, and the results show that the misjudgment rate of Amazon Mechanical Turk (AMT) perceptual studies achieves 45%, which verified that the cycle-consistent network model with attention mechanism has certain advantages in image style transfer. [ABSTRACT FROM AUTHOR]

Using steel slag in architectural three-dimensional (3D) printing not only enhances its utilization efficiency but also significantly reduces cement consumption, thereby mitigating carbon emissions. This study evaluated the substitution of steel slag for cement in 3D printing and examined its impact on rheological properties, fluidity, green strength, and early hydration microstructure based on alkalinity level, particle-size distribution, and blending quantity. The relationship between the early properties of 3D-printed steel slag cementitious materials and their pore microstructures was investigated using low-field nuclear magnetic resonance (NMR) tests. The results showed that high-alkalinity steel slag has superior rheological characteristics and generates a larger amount of gel water within the initial 30 min of hydration. Steel slag particles ranging from 5 to 20 μm had the most significant influence on enhancing the rheological properties of 3D-printed steel slag cementitious materials by facilitating the formation of a flocculated mesh structure during early hydration. Optimal rheological performance was achieved with a dosage level of 10% steel slag, effectively reducing porosity and improving compactness in the mortar. [ABSTRACT FROM AUTHOR]

Employees' innovative behavior is a crucial catalyst for public organizations' survival, development, and growth in service quality and problem-solving capabilities. Drawing on the broaden-and-build theory and self-determination theory, we proposed a conceptual model to delineate the dynamic within-person impact of the universal human experience flow on innovative behavior. We tried to disentangle the underlying mechanisms and boundary conditions by taking into consideration both job involvement and person-organization fit. We followed 106 public organizational employees' self-reports on daily flow, job involvement, and innovative behavior at work for two consecutive weeks. Data analyzed via multi-level structural equation modeling revealed that daily flow exerts a positive impact on innovative behavior through job involvement at the within-person level. Such impact is moderated by person-organization fit at the between-person level, indicating a more substantial indirect effect of flow on innovative behavior via job involvement for employees with higher (versus lower) person-organization fit. Taken together, our findings elucidate how and when flow predicts innovative behavior on a daily basis, providing empirical evidence for the role of daily flow at work in facilitating employees' daily innovative behavior. Theoretical and managerial implications for nurturing employees' innovative behavior in public organizations are put forward. [ABSTRACT FROM AUTHOR]

The bottle gourd [Lagenaria siceraria (Molina) Standl.] is often utilized as a rootstock for watermelon grafting. This practice effectively mitigates the challenges associated with continuous cropping obstacles in watermelon cultivation. The lower ground temperature has a direct impact on the rootstocks' root development and nutrient absorption, ultimately leading to slower growth and even the onset of yellowing. However, the mechanisms underlying the bottle gourd's regulation of root growth in response to low root zone temperature (LRT) remain elusive. Understanding the dynamic response of bottle gourd roots to LRT stress is crucial for advancing research regarding its tolerance to low temperatures. In this study, we compared the physiological traits of bottle gourd roots under control and LRT treatments; root sample transcriptomic profiles were monitored after 0 h, 48 h and 72 h of LRT treatment. LRT stress increased the malondialdehyde (MDA) content, relative electrolyte permeability and reactive oxygen species (ROS) levels, especially H2O2 and O2−. Concurrently, LRT treatment enhanced the activities of antioxidant enzymes like superoxide dismutase (SOD) and peroxidase (POD). RNA-Seq analysis revealed the presence of 2507 and 1326 differentially expressed genes (DEGs) after 48 h and 72 h of LRT treatment, respectively. Notably, 174 and 271 transcription factors (TFs) were identified as DEGs compared to the 0 h control. We utilized quantitative real-time polymerase chain reaction (qRT-PCR) to confirm the expression patterns of DEGs belonging to the WRKY, NAC, bHLH, AP2/ERF and MYB families. Collectively, our study provides a robust foundation for the functional characterization of LRT-responsive TFs in bottle gourd roots. Furthermore, these insights may contribute to the enhancement in cold tolerance in bottle gourd-type rootstocks, thereby advancing molecular breeding efforts. [ABSTRACT FROM AUTHOR]

Background: This study investigated the effect of carbodiimide (EDC) combined with Clearfil SE self-etch adhesive on the shear bond strength (SBS), crosslinking degree, denaturation temperature, and enzyme activity of dentin in vitro. Materials and methods: Collected human sound third molars were randomly divided into different groups with or without EDC treatment (0.01–1 M). The specimens (n = 16)were stored for 24 h (immediate) or 12 months (aging) before testing the SBS. Fine dentin powder was obtained and treated with the same solutions. Then the crosslinking degree, denaturation temperature (Td), and enzyme activity were tested. Statistical analysis was performed using a one-way analysis of variance (ANOVA) to compare the differences of data between groups (α = 0.05). Results: There was a significant drop in immediate SBS and more adhesive fracture of 1.0 M EDC group, while there were no significant differences among the other groups. SEM showed a homogeneous interface under all treatments. After 12 months of aging, the SBS significantly decreased. Less decreases of SBS in the 0.3 and 0.5 M groups were found. Due to thermal and enzymatical properties consideration, the 0.3 and 0.5 M treatments also showed higher cross-link degree and Td with lower enzyme activity. Conclusion: 0.3 and 0.5 M EDC may be favorable for delaying the aging of self-etch bond strength for 12 months. But it is still needed thoroughly study. [ABSTRACT FROM AUTHOR]

GeoAI combines artificial intelligence (AI) with geospatial data, science, and technologies. In this paper, a successful case in utilizing GeoAI to improve the data quality in the national topographic database (TDB) of Finland was introduced. The project employed a GeoAI model to identify buildings from the input data of true orthophotos, digital elevation model (DTM), and digital surface model (DSM). The GeoAI-derived buildings served as reference data, enabling a comparison with building polygons from the topographic database (TDB) to reveal TDB building location deviations, missing structures, and demolished buildings. Throughout the project, algorithms were developed to match the TDB building vectors to the GeoAI-derived building polygons. The challenges include i) the differences between the GeoAI-derived building outlines and the TDB build footprints; ii) the reliability of the GeoAI model across data over different environments: urban, suburban, rural, and forest areas. Throughout the project, the GeoAI model was continuously improved by training massive new datasets: corrected vectors from the model prediction. Testing was conducted on datasets from twelve areas of Finland, covering 2204 km2 and including hundreds of thousands of buildings. The test areas covered urban, suburban, rural, and forest areas. The evaluation was conducted by the mapping team in the organization. The results showed that our methods greatly enhanced the quality of the TDB building footprints. The challenges and lessons of the project were addressed in the paper. [ABSTRACT FROM AUTHOR]

Background: Health Information-Seeking Behaviour (HISB) is necessary for self-management and medical decision-making among patients with inflammatory bowel disease (IBD). With the advancement of information technology, health information needs and seeking are reshaped among patients with IBD. This scoping review aims to gain a comprehensive understanding of HISB of people with IBD in the digital age. Methods: This scoping review adhered to Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews frameworks (PRISMA-ScR). A comprehensive literature search was conducted in PubMed, Embase, Web of Science, PsycINFO, CINAHL, and three Chinese databases from January 1, 2010 to April 10, 2023. Employing both deductive and inductive content analysis, we scrutinized studies using Wilson's model. Results: In total, 56 articles were selected. Within the information dimension of HISB among patients with IBD, treatment-related information, particularly medication-related information, was identified as the most critical information need. Other information requirements included basic IBD-related information, daily life and self-management, sexual and reproductive health, and other needs. In the sources dimension, of the eight common sources of information, the internet was the most frequently mentioned source of information, while face-to-face communication with healthcare professionals was the preferred source. Associated factors were categorized into six categories: demographic characteristics, psychological aspects, role-related or interpersonal traits, environmental aspects, source-related characteristics, and disease-related factors. Moreover, the results showed five types of HISB among people with IBD, including active searching, ongoing searching, passive attention, passive searching, and avoid seeking. Notably, active searching, especially social information seeking, appeared to be the predominant common type of HISB among people with IBD in the digital era. Conclusion: Information needs and sources for patients with IBD exhibit variability, and their health information-seeking behaviour is influenced by a combination of diverse factors, including resource-related and individual factors. Future research should focus on the longitudinal changes in HISB among patients with IBD. Moreover, efforts should be made to develop information resources that are both convenient and provide credible information services, although the development of such resources requires further investigation and evaluation. [ABSTRACT FROM AUTHOR]

Automatizing the extraction of different objects from remote sensing data with deep learning methods has been a popular research topic. Buildings have been one of those popular objects to be extracted. Not only does the selection of neural network affect the results and accuracy of extracted buildings, but also the selection of different types of data for the task. Digital surface models (DSMs) are increasingly used in remote sensing and their demand has increased. Retrieving height information from surface models has proved helpful for accurate extraction of buildings. In this study was investigated, if the use of light detection and ranging (LiDAR) DSMs and DEMs with 25 cm pixel resolution will lead to more accurate building extraction results in comparison to the use of aerial DSMs. Results with UNet models trained with building vector labels, DSMs, DEMs and true orthophotos from multiple areas of Finland, were produced with different data combinations from two Finnish cities, Savonlinna and Pudasj¨arvi, to see, which combination would lead to the most accurate building detection results. Results were evaluated partly by visual inspection, and partly by quantitative assessment. Based on the tests carried out, combining the information from true orthophotos with LiDAR DSMs and 25 cm DEMs provided the most accurate results. In forest area, using LiDAR data increased the accuracy of building detection. However, in urban area, due to missing buildings from LiDAR data, its advantages were compromised. We suggest that the use of both imagery and LiDAR data should be the optimal solution. [ABSTRACT FROM AUTHOR]

Precise road segmentation is an essential part of many applications related to road information extraction from remote sensing data. The effect of color space on road detection has rarely been studied. In this paper, the effects of different color spaces of aerial images and multitask learning methods were experimented on road segmentation using three deep convolutional neural networks, UNet, DenseU-Net, and RoadVecNet. The color spaces included RGB, HSV, LAB, YCbCr, and YUV. The multitask learning methods adopted in this study involved utilizing multiple inputs, and multiple outputs. Multiple inputs were aerial images from the same area with different color spaces, and multiple outputs were road segmentation and road outline segmentation. As remote sensing data, National Land Survey of Finland's true orthophotos (from 2020), Massachusetts road imagery dataset, and Ottawa dataset were applied. Segmentation masks for National Land Survey of Finland's true orthophotos were extracted from Digiroad vectors with road width information. Road outline masks were generated from the segmentation masks. The studied neural networks were trained with the same data, learning rate, loss function, and optimizer for each color space, and pairs of color spaces. Multiple outputs were experimented with RGB color space. The comparative analysis assessed the performance of various neural networks across different color spaces using the F1-score metric. The experimental findings indicate that the choice of color space has little influence on the results of neural networks Deep learning methods can adapt to different color spaces well. In addition, the use of sharpening and edge enhancement augmentations had a slight effect on the results. [ABSTRACT FROM AUTHOR]

Auxin response factors (ARFs) are pivotal transcription factors involved in many aspects of auxin-dependent developmental processes. While functions of ARFs have been extensively studied in Arabidopsis, their distinct role in cucumber remains unclear. In this study, a cucumber auxin response factor homolog, CsARF10a, was cloned and overexpressed in tomato plants. RT-qPCR analysis indicated that the expression abundance of CsARF10a was significantly decreased in cucumber leaves and female flowers, and the expression level of CsARF10a was relatively low in pollinated fruits and hormone-treated fruits compared with that in unpollinated fruits. Moreover, the overexpression of CsARF10a in tomato resulted in multiple phenotypic changes, including a wider leaf blade, delayed fruit ripening, and parthenocarpic fruit set in CsARF10a-OE lines. Taken together, our research shed light on the regulatory importance of CsARF10a in regulating various phenotype alterations and laid a solid foundation for further functional studies. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the association between patient clinical characteristics and technique failure in peritoneal dialysis-related peritonitis (PDRP). The effect of peritonitis-associated technique failure on patient survival was also assessed. Patients diagnosed with PDRP from January 1, 2010 to June 30, 2022 were retrospectively reviewed and analyzed. Relevant demographic, biochemical, and clinical data were collected. Univariate and multivariate logistic regression analyses were used to determine the predictors of peritonitis-associated technique failure in PD. Patients were divided into technique failure (F group) and nontechnique failure (NF group) groups. Patients were followed until death or until the date of Oct 1, 2022. Kaplan–Meier survival curves and landmark analysis were used to assess the survival of the PDRP cohort. Cox regression models were used to assess the association between potential risk factors and mortality. A total of 376 patients with 648 cases of PDRP were included in this study. Multivariate logistic regression analysis demonstrated that peritoneal dialysis (PD) duration (OR = 1.12 [1.03, 1.21], p = 0.005), dialysate WBC count on Day 3 after antibiotic therapy (OR = 1.41 [1.22, 1.64], p = 0.001), blood neutrophil-to-lymphocyte ratio (NLR) (OR = 1.83 [1.25, 2.70], p = 0.002), and serum lactate dehydrogenase (LDH) (OR = 4.13 [1.69, 10.11], p = 0.002) were independent predictors for technique failure in PDRP. Furthermore, serum high-density lipoprotein (HDL) (OR = 0.28 [0.13, 0.64], p = 0.002) was a protective factor against technique failure. According to the Kaplan–Meier analysis, patients experiencing peritonitis-associated technique failure had lower postperitonitis survival (log-rank = 4.326, p = 0.038). According to the landmark analysis, patients with a history of peritonitis-associated technical failures had a higher 8-year mortality after peritoneal dialysis. A Cox model adjusted for plausible predetermined confounders showed that technique failure was independently associated with all-cause mortality. Dialysate WBC count on Day 3, PD duration, NLR, and LDH were independent risk factors for technique failure, whereas HDL was a protective factor. Peritonitis-associated technique failure had a higher risk of mortality and adverse effects on postperitonitis survival. [ABSTRACT FROM AUTHOR]

The aim of this paper is the Ore extension of group-cograded Hopf coquasigroups. This paper first shows a categorical interpretation and some examples of group-cograded Hopf coquasigroups, and then provides a necessary and sufficient condition for the Ore extension of group-cograded Hopf coquasigroups to be group-cograded Hopf coquasigroups. Finally, a certain isomorphism between Ore extensions are considered. [ABSTRACT FROM AUTHOR]

The number of people living in cities is continuously growing, and the buildings in topographic maps are in need of frequent updates, which are costly to perform manually. This makes automatic building extraction a significant research subject. Transfer learning, on the other hand, offers solutions in situations where the data of a target area are scarce, making it a profitable research subject. Moreover, in previous studies, there was a lack of metrics in quantifying the accuracy improvement with transfer learning techniques. This paper investigated various transfer learning techniques and their combinations with U-Net for the semantic segmentation of buildings from true orthophotos. The results were analyzed using quantitative methods. Open-source remote sensing data from Poland were used for pretraining a model for building segmentation. The fine-tuning techniques including a fine-tuning contracting path, a fine-tuning expanding path, a retraining contracting path, and a retraining expanding path were studied. These fine-tuning techniques and their combinations were tested with three local datasets from the diverse environment in Finland: urban, suburban, and rural areas. Knowledge from the pretrained model was transferred to the local datasets from Helsinki (urban), Kajaani (suburban), and selected areas in Finland (rural area). Three models with no transfer learning were trained from scratch with three sets of local data to compare the fine-tuning results. Our experiment focused on how various transfer learning techniques perform on datasets from different environments (urban, suburban, and rural areas) and multiple locations (southern, northern, and across Finland). A quantitative assessment of performance improvement by using transfer learning techniques was conducted. Despite the differences in datasets, the results showed that using transfer learning techniques could achieve at least 5% better accuracy than a model trained from scratch with several different transfer learning techniques. In addition, the effect of the sizes of training datasets was also studied. [ABSTRACT FROM AUTHOR]

High oxygen vacancy concentration produced by bismuth volatilization during the sintering process has become one of the most important obstacles for the practical applications of Bi4Ti3O12 (BIT)-based high temperature piezoelectric ceramics. In this work, Cu and Sb were selected to modify the oxygen vacancy concentration and the electrical properties of the BIT ceramic. The crystal structure, microstructure, electrical conduction behaviors, and piezoelectric properties of Bi4Ti3−x(Cu1/3Sb2/3)xO12 (BTCS) (x = 0, 0.01, 0.02, 0.03, 0.035, 0.04, and 0.05) ceramics were investigated in detail. Codoping significantly reduced the oxygen vacancy concentration and resulted in enhanced electrical resistivity along with improved piezoelectric properties. The BTCS ceramic with x = 0.035 exhibited optimized comprehensive performances such as a remarkable DC resistivity of 1.40 × 107 Ω cm at 500 °C and a relatively large piezoelectric coefficient (24 pC/N). Furthermore, the ceramic also showed good thermal stability of the piezoelectric coefficient and an excellent fatigue behavior. These results demonstrate the great potentials of Cu/Sb codoped BIT ceramics for piezoelectric device applications at high temperatures up to 500 °C. [ABSTRACT FROM AUTHOR]

Background: Awake prone positioning (APP) is a recommended therapy for non-intubated ARDS patients, but adherence can be challenging. Understanding the barriers and facilitators of adherence to APP is essential to increase the adherence of therapy and improve patient outcomes. The objective of this study was to explore the barriers and facilitators of adherence to awake prone ventilation using a qualitative approach and the Capability, Opportunity, Motivation-Behavior (COM-B) model. Methods: Semi-structured, in-depth interviews were conducted with patients involved in awake prone ventilation. Data were analyzed using an adapted inductive thematical approach and mapped onto the COM-B model to identify barriers and facilitators to adherence of APP. Results: Nineteen patients were interviewed (aged 55–92 years). Fifteen themes were identified and mapped directly on to the six COM-B constructs, with "physical challenges" related to physical capability being the primary barrier. These COM-B sub-items reflected five other barriers, including low self-efficacy(M), treatment environment(O), availability of time(O), misconceptions about the treatment(C), and insufficient knowledge(C). Key facilitators in adhering to APP were ability to identify and overcome obstacles(C), availability and affordability of treatment(O), family influences(O), beliefs and trust in treatment(M), fear about the disease(M), and perceived benefits(M). In addition, three factors played the role of both facilitator and barrier, such as media influences(O), healthcare influences(O), and behavioral habits(M). Conclusion: The COM-B model was proved to be a useful framework for identifying the barriers and facilitators of adherence to awake prone ventilation. The findings suggest that adherence behavior is a dynamic and balanced process and interventions aimed at improving adherence to APP should address the barriers related to capability, opportunity, and motivation. Healthcare providers should focus on providing proper guidance and training, creating a comfortable environment, and offering social support to improve patients' capability and opportunity. Additionally, promoting patients' positive beliefs and attitudes towards the treatment and addressing misconceptions and fears can further enhance patients' motivation to adhere to the treatment plan. [ABSTRACT FROM AUTHOR]

Vector datasets of small watercourses, such as rivulets, streams, and ditches, are important for many visualization and analysis use cases. Mapping small watercourses with traditional methods is laborious and costly. Convolutional neural networks (CNNs) are state-of-the-art computer vision methods that have been shown to be effective for extracting geospatial features, including small watercourses, from LiDAR point clouds, digital elevation models (DEMs), and aerial images. However, the cause of the false predictions by machine-learning models is often not thoroughly explored, and thus the impact of the results on the process of producing accurate datasets is not well understood. We digitized a highly accurate and complete dataset of small watercourses from a study area in Finland. We then developed a process based on a CNN that can be used to extract small watercourses from DEMs. We tested and validated the performance of the network with different input data layers, and their combinations to determine the best-performing layer. We analyzed the false predictions to gain an understanding of their nature. We also trained models where watercourses with high levels of uncertainty were removed from the training sets and compared the results to training models with all watercourses in the training set. The results show that the DEM was the best-performing layer and that combinations of layers provided worse results. Major causes of false predictions were shown to be boundary errors with an offset between the prediction and labeled data, as well as errors of omission by watercourses with high levels of uncertainty. Removing features with the highest level of uncertainty from the labeled dataset increased the overall f1-score but reduced the recall of the remaining features. Additional research is required to determine if the results remain similar to other CNN methods. [ABSTRACT FROM AUTHOR]

Background: Hemorrhagic fever with renal syndrome (HFRS) is a widespread zoonotic disease seriously threatening Chinese residents' health. HFRS of Weihe Basin remains highly prevalent in recent years and attracts wide attention. With the acceleration of urbanization and related environmental changes, the interaction among anthropogenic activities, environmental factors, and host animals becomes more complicated in this area, which posed increasingly complex challenges for implementing effective prevention measures. Identifying the potential influencing factors of continuous HFRS epidemics in this typical area is critical to make targeted prevention and control strategies. Methods: Spatiotemporal characteristics of HFRS epidemic were analyzed based on HFRS case point data in Weihe Basin from 2005 to 2020. MaxEnt models were constructed to explore the main influencing factors of HFRS epidemic based on HFRS data, natural environment factors and socioeconomic factors. Results: Results showed that the HFRS epidemics in Weihe Basin were temporally divided into three periods (the relatively stable period, the rapid rising period, and the fluctuating rising period) and were spatially featured by relatively concentrated in the plains alongside the Weihe River. Landscape played controlling effect in this area while land use, vegetation and population in the area interacted with each other and drove the change of HFRS epidemic. The potential high-risk area for HFRS epidemic was 419 km2, where the HFRS case density reached 12.48 cases/km2, especially in the northern plains of Xi'an City. Conclusion: We suggested that the temporal and spatial variations in the HFRS epidemics, as well as their dominant influencing factors should be adequately considered for making and/or adjusting the targeted prevention and control strategies on this disease in Weihe Basin. Author summary: In recent years, the incidence rate of hemorrhagic fever with renal syndrome (HFRS) has declined in most area of China, although the acceleration of urbanization caused increasingly complicated interactions among anthropogenic activities, environmental factors, and host animals during the past decades. However, there were still some typical regions with serious HFRS epidemics in this country like the Weihe Basin in Shaanxi Province, in which more efforts should be paid for identifying the potential influencing factors and their driving mechanisms. Our study employed the MaxEnt modeling to reveal the spatio-temporal variations in the influencing factors on the epidemics of this disease from 2005 to 2020 in the Weihe Basin. We found that the landscape played controlling effect in this area while land use, vegetation and population in the area interacted with each other and drove the change of HFRS epidemic. The underling influencing factors and the stimulation of HFRS epidemic would provide detailed information for targeted strategy formulation, and the also provide reference for similar studies. [ABSTRACT FROM AUTHOR]

To improve the utilization efficiency of renewable biomass into high-value carbon materials, two types of carbon quantum dots (G-CQDs, B-CQDs) and two types of porous carbon (G-PCs, B-PCs) were synergistically prepared from lignin by a hydrothermal method. The physicochemical properties of the carbon materials were systematically studied by high-resolution transmission electron microscopy, Raman spectrosco-py, Fourier infrared spectroscopy, X-ray photoelectron spectroscopy, UV-vis absorption spectroscopy, fluorescence spectroscopy, scanning electron microscopy, specific surface, and porosity analysis. Experimental results show that G-CQDs and B-CQDs emit bright green and blue fluorescence under 340 nm light excitation with the optimal relative fluorescence quantum yields of 23.7% and 5.2%, respectively. G-CQDs have more functional groups on the surface, while B-CQDs have a higher degree of graphitization. The specific surface areas of G-PCs and B-PCs are 1 505. 8 and 1 082.6 m²/g, respectively, and the total pore volumes are as high as 0.74 and 0.51 cm³/g. The synergistic conversion of lignin waste into carbon quantum dots and porous carbon is an effective strategy to realize the comprehensive utilization of biomass, which provides a new idea for the green and large-scale production of multifunctional carbon materials. [ABSTRACT FROM AUTHOR]

The transformation of lignin with natural aromatic structure into value-added carbon dots (CDs) achieves a win-win situation for low-cost production of novel nanomaterials and reasonable disposal of biomass waste. However, it remains challenging to produce multi-emission CDs from biomass for advanced applications. Herein, a green and facile approach to preparing multi-emission CDs from alkali lignin via N and B co-doping is developed. The obtained N and B co-doped CDs (NB-CDs) show multi-emission fluorescence centers at 346, 428 and 514 nm under different excitations. As the doping amount of N and B increases, the fluorescence emission band gradually shifts to 428 and 514 nm, while that at 346 nm decreases. The fluorescence mechanism is explored through the research of the structure, composition and optical performance of NB-CDs in combination with density functional theory (DFT) calculations. It demonstrates that the effect of doping with B-containing functional groups on the fluorescence emission behavior is multivariate, which may be the crucial contribution to the unique multi-emission fluorescence of CDs. The multi-emission NB-CDs with prominent stability are applied for multilevel anti-counterfeiting printing. It provides a promising direction for the sustainable and advanced application of biomass-derived CDs, and the theoretical results highlight a new insight into the deep understanding of the multi-emission fluorescence mechanism. [ABSTRACT FROM AUTHOR]

Let H be a crossed group-cograded Hopf quasigroup. We first introduce the notion of p-Yetter–Drinfeld quasimodule over H. If the antipode of H is bijective, we show that the category Y D Q (H) of Yetter–Drinfeld quasimodules over H is a crossed category, and the subcategory Y D (H) of Yetter–Drinfeld modules is a braided crossed category. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of renal cancer. Here we conduct a comprehensive proteogenomic analysis of 232 tumor and adjacent non-tumor tissue pairs from Chinese ccRCC patients. By comparing with tumor adjacent tissues, we find that ccRCC shows extensive metabolic dysregulation and an enhanced immune response. Molecular subtyping classifies ccRCC tumors into three subtypes (GP1–3), among which the most aggressive GP1 exhibits the strongest immune phenotype, increased metastasis, and metabolic imbalance, linking the multi-omics-derived phenotypes to clinical outcomes of ccRCC. Nicotinamide N-methyltransferase (NNMT), a one-carbon metabolic enzyme, is identified as a potential marker of ccRCC and a drug target for GP1. We demonstrate that NNMT induces DNA-dependent protein kinase catalytic subunit (DNA-PKcs) homocysteinylation, increases DNA repair, and promotes ccRCC tumor growth. This study provides insights into the biological underpinnings and prognosis assessment of ccRCC, revealing targetable metabolic vulnerabilities. Clear cell renal cell carcinoma is an aggressive form of renal cancer, with differences in genomic mutations reported between Western and Eastern populations. In this study, the authors have compiled proteogenomic analysis of Chinese ccRCC to reveal genomic alterations and dysregulation of immune and metabolic responses. [ABSTRACT FROM AUTHOR]

Repetitive transients are usually generated in the monitoring data when a fault occurs on the machinery. As a result, many methods such as kurtogram and optimized Morlet wavelet and kurtosis method are proposed to extract the repetitive transients for fault diagnosis. However, one shortcoming of these methods is that they are constructed based on the index of kurtosis and are sensitive to the impulsive noise, leading to failure in accurately diagnosing the fault of the machinery operating under harsh environment. To address this issue, an optimized SES entropy wavelet method is proposed. In the proposed method, the optimized parameters including bandwidth and central frequency of Morlet wavelets are selected. Then, based on the wavelet coefficients decomposed using the optimized Morlet wavelet, the SES entropy is calculated to select the scales of wavelet coefficients. Finally, the repetitive transients are reconstructed based on the denoising wavelet coefficients of the selected scales. One simulation case and vibration data collected from the experimental setup are used to verify the effectiveness of the proposed method. The simulated and experimental analyses showed that the signal-to-noise ratio (SNR) of the proposed method has the largest value. Specifically, the SNR in the experimental analysis of the proposed method is 0.6, while that of the other three methods is 0.043, 0.0065, and 0.0045, respectively. Therefore, the result shows that the proposed method is superior to the traditional methods for repetitive transient extraction from the vibration data suffered from impulsive noise. [ABSTRACT FROM AUTHOR]

Background: The aging population has become a serious challenge for health care service and will lead to an increasing demand for nurses to work with older people. However, working with older people has always been an unpopular career choice among nursing students. This study aimed to further explore the willingness and associated factors of undergraduate nursing students to work with older people in China. Methods: A convenience sampling method was employed from May to July 2017 among undergraduate nursing students from a nursing school in Nantong China. Demographic data, the Chinese version of the Kogan's Old Person's Scale, the Chinese version of the Facts of Aging Quiz and the motivation questionnaire were used to collect data. A series of Mann-Whitney U test, Kruskal-Wallis H test, Spearman correlation test and Ordinal logistic regression analysis were applied to analyze the data. Results: Of the 853 students surveyed, 38.1 % were willing to work with older people after graduation. Expectancy, interest, attainment value, cost, prejudice, whether they like nursing profession and whether they participated in elderly-related activities were the most significant predictors of the students' willingness to work with older people. Conclusions: Nursing students expressed a low level of willingness to work with older people upon graduation. Nursing educators have an important part in challenging students' stereotype of older people and inspire their career choice in caring for older people through both well-designed curriculum and elderly-related activities, and relevant education departments should actively optimize aged-related courses, strengthen professional ethics and gratitude education, and improve nursing students' sense of identity and mission in caring for older people, so as to improve their willingness to work with older people. [ABSTRACT FROM AUTHOR]

In order to investigate interactions among biomass components on pyrolysis kinetics, pyrolysis experiments of individual components, synthetic biomass (designed by Design-Export software) and natural biomass (rice husk and corn straw) were conducted on a thermogravimetric analyzer (TGA). The results revealed that the pyrolysis behavior of cellulose is sharp, which is with low pyrolysis reaction order (1.38), high activation energy (168.61 kJ/mol) and high pre-exponential factor (3.50E+12/s). The pyrolysis behavior of hemicellulose and lignin is slower but more complicated, both with high pyrolysis reaction order (2.30, 1.51), low activation energy (126.31, 87.21 kJ/mol), and low pre-exponential factor (9.67E+09, 2.59E+05 /s). Comparison of the experimental and calculated kinetics of synthetic samples confirmed that interactive effects on pyrolysis kinetics exist in the co-pyrolysis process. In particular, the presence of lignin inhibited the pyrolysis reaction rate of synthetic biomass, and cellulose played the dominant role in the activation energy and frequency factor. The pyrolysis reaction order was strongly influenced by hemicellulose owing to its abundant and complex branched chains. The predicted model was also established for calculating kinetic parameters of natural biomass with known proportions of three components. The predicted results were consistent with the experimental ones, validating the effectiveness of the prediction model. [ABSTRACT FROM AUTHOR]

Nb self‐doped Bi3Ti1‐xNb1+xO9 (x = 0, 0.02, 0.04, 0.06, 0.08, and 0.1) high‐temperature piezoelectric ceramics were fabricated through the conventional solid‐state sintering method. The effects of different Nb self‐doping levels on the microstructure, piezoelectric activities, and electrical conduction behaviors of these Nb self‐doped Bi3Ti1‐xNb1+xO9 ceramics were studied in detail. Large doping level effects on piezoelectric activity and resistivity were confirmed, which might be ascribed to the evolution of the crystal structure and the variations of the oxygen vacancy concentration and the grain anisotropy induced by Nb doping. An optimized piezoelectric coefficient (d33) of 11.6 pC/N was achieved at x = 0.04 with a Curie temperature of 906°C. Additionally, an improved DC resistivity of 6.18 × 105 Ω·cm at 600°C was acquired in this ceramic. Furthermore, the ceramic exhibited excellent thermal stability with the d33 value maintaining 95% of its initial value after being annealed at 850°C for 2 hours. These results showed that Nb self‐doped Bi3Ti1‐xNb1+xO9 ceramics might have great potentials for high‐temperature piezoelectric applications. [ABSTRACT FROM AUTHOR]

3D‐MoS2 can adsorb organic molecules and provide multidimensional electron transport pathways, implying a potential application for environment remediation. Here, we study the degradation of aromatic organics in advanced oxidation processes (AOPs) by a 3D‐MoS2 sponge loaded with MoS2 nanospheres and graphene oxide (GO). Exposed Mo4+ active sites on 3D‐MoS2 can significantly improve the concentration and stability of Fe2+ in AOPs and keep the Fe3+/Fe2+ in a stable dynamic cycle, thus effectively promoting the activation of H2O2/peroxymonosulfate (PMS). The degradation rate of organic pollutants in the 3D‐MoS2 system is about 50 times higher than without cocatalyst. After a 140 L pilot‐scale experiment, it still maintains high efficiency and stable AOPs activity. After 16 days of continuous reaction, the 3D‐MoS2 achieves a degradation rate of 120 mg L−1 antibiotic wastewater up to 97.87 %. The operating cost of treating a ton of wastewater is only US$ 0.33, suggesting huge industrial applications. [ABSTRACT FROM AUTHOR]

3D‐MoS2 can adsorb organic molecules and provide multidimensional electron transport pathways, implying a potential application for environment remediation. Here, we study the degradation of aromatic organics in advanced oxidation processes (AOPs) by a 3D‐MoS2 sponge loaded with MoS2 nanospheres and graphene oxide (GO). Exposed Mo4+ active sites on 3D‐MoS2 can significantly improve the concentration and stability of Fe2+ in AOPs and keep the Fe3+/Fe2+ in a stable dynamic cycle, thus effectively promoting the activation of H2O2/peroxymonosulfate (PMS). The degradation rate of organic pollutants in the 3D‐MoS2 system is about 50 times higher than without cocatalyst. After a 140 L pilot‐scale experiment, it still maintains high efficiency and stable AOPs activity. After 16 days of continuous reaction, the 3D‐MoS2 achieves a degradation rate of 120 mg L−1 antibiotic wastewater up to 97.87 %. The operating cost of treating a ton of wastewater is only US$ 0.33, suggesting huge industrial applications. [ABSTRACT FROM AUTHOR]

Two base‐promoted quadruple domino reactions between thioaurones and allylic phosphonium salts have been developed to synthesize benzothiophene‐fused oxa[6.6.5]tricyclic skeletons in moderate to good yields with excellent stereoselectivity and broad functional‐group tolerance. This is a simple and useful protocol for the rapid construction of the umbrella‐like oxa[6.6.5]tricyclic skeleton. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory. [ABSTRACT FROM PUBLISHER]

Developing a smart cancer drug delivery carrier with enhanced cancer-targeting and effective drug release in tumors is critical for efficient cancer chemotherapy. Herein, we designed a pH-responsive copolymer (PEG-ELP[VH4-70]) that entraps DOX into a hydrophobic core and self-assembles into smart DOX-loaded nanoparticles. The DOX-loaded nanoparticles were stabilized by Zn2+ and disrupted as the pH drops from 7.4 to 5.6. An in vitro study demonstrated that the DOX-loaded nanoparticle system exhibited efficient internalization, triggered the release of DOX into the cytoplasm and enabled the inhibition of tumors effectively. When used to deliver chemotherapeutics to a murine cancer model, PEG-ELP[VH4-70]/DOX accomplished a 4.8-fold suppressed effect relative to the free drug after intravenous injection. This simple strategy can promote preeminent stability for targeting hydrophobic drugs to tumor tissues. [ABSTRACT FROM AUTHOR]

Hyaluronan is a widely distributed glycosaminoglycan which has multiple functions. Hyaluronic acid (HA) accumulation has been reported in many human diseases. Understanding the role of hyaluronan and its binding proteins in the pathobiology of disease will facilitate the development of novel therapeutics for many critical diseases. Current techniques described for the analysis of HA are mainly for HA quantification in solutions, not for the direct detection of HA in tissues or on cell surfaces. In our study, a fusion protein, named C-terminal domain of RHAMM–enhanced green fluorescence protein (RHC–EGFP), combined the HA-binding domain, C-terminal of receptor for hyaluronan-mediated motility, with EGFP, a widely used enhanced green fluorescence protein, was expressed and purified fromEscherichia coliwith high purity. Based on the sensitivity and convenience of fluorescence detection, methods for direct assay of HA in solutions, on cell surface or in tissues were established using RHC–EGFP. The binding specificity was also confirmed by competitive binding experiment and hyaluronidase degradation experiment. Our results provide an alternative choice for the specific and convenient assay of HA in various samples, and maybe helpful for further understanding of the fundamental and comprehensive functions of HA. [ABSTRACT FROM PUBLISHER]

An improved numerical model was proposed to solve the problem that the traditional DEM-CFD (Discrete element method-computational fluid dynamics) method was not suitable for the flow simulation of large particles. In the improved model, the large particle was regarded as an agglomerate of many small fictitious spheres. Herein, the drag force between gas and large pellets was assumed as a combined effect of that between gas and fictitious spheres by volume penalty method. Based on the proposed model, the flow of the mixtures of large biomass pellets and quartz sands in fluidized bed was simulated. It shows that the existence of the biomass pellets has a great impact on the flow field. The flow patterns and pressure drops under different working conditions in simulation results have a good agreement with that in experimental results partially, which also tests the proposed model. [ABSTRACT FROM AUTHOR]

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data. [ABSTRACT FROM AUTHOR]

This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution. [ABSTRACT FROM AUTHOR]

National mapping authorities aim to increase automation in their mapping processes. Automation increases the rate of updating features in topographic databases (TDBs), improves the quality in terms of regional homogeneity and logical consistency between the mapped features, and lowers costs. Recently, the National Land Survey of Finland (NLS) has achieved countrywide coverage for its digital elevation model with a 2-meter resolution (NLS-DEM2m). During the production of the NLS-DEM2m, the NLS has initiated several pilot projects to find automatic methods for extraction of topographic features from the DEM, including, contour lines (Kettunen, Koski and Oksanen 2017), cliffs, and hydrographic features. While producing encouraging results, these projects have highlighted the inconsistency between automatically generated features from the NLS-DEM2m and manually mapped features in the NLS TDB. For example, this is apparent when overlaying automatically generated contour lines and the current manually mapped hydrographic features of the TDB (Figure 1). Increased computing power, more data, and better models have made machine learning (ML) popular for automation. In May 2021, NLS started piloting automated mapping of hydrographic features from the NLS-DEM2m with the use of ML. Using the NLS-DEM2m to map hydrographic features to the NLS TDB can improve their positional accuracy and completeness and make them consistent with automatically generated contour lines. Previous piloting of automated mapping of hydrographic features with non-ML methods from high-resolution DEMs has not yet achieved a high enough quality for them to be viable options for fully automated production of hydrographic features (Eraña-Beitia 2013, Oksanen 2014). The main challenges have been to correctly identify and map small streams and ditches. Therefore, in this project, we will focus mainly on ML-based extraction of hydrographic features. The objectives of automated mapping of hydrographic features in the project are as follows: 1. To assess the quality of the ditch and stream vectors that are derived from the results of ML models. 2. To identify best practices for producing test and validation data of ML models that detect ditches and streams from the NLS-DEM2m. 3. To identify the most critical areas of the stream and ditch extraction process that need to be improved. 4. To improve the used methods by developing new and innovative solutions for automated mapping of hydrographic features from DEMs. Recently, convolutional neural networks (CNNs) have been used to identify both streams (Xu et al. 2021) and ditches (Paul, Ågren, and Lidberg 2021) from DEMs. These CNN approaches have displayed promising results using DEMs with resolutions as high as 1 and 0.5 meters as input data. Results of the CNN models have been assessed based on recall and precision on the pixel level. We will apply these CNN approaches and assess results achieved with them, using the NLSDEM2m as input data as this is the highest resolution country-wide DEM that is available. The results will then be used to generate ditch and stream vectors, which will be assessed based on their completeness and positional accuracy. The quality of results produced by CNN models is dependent on high-quality training and validation data, as well as input data. The positional accuracy and completeness of the data are critical for producing high quality results. However, mapping ditches and streams, especially those in forested areas can be difficult, even when done manually from the available source datasets (Figure 2). Apart from the positional accuracy and completeness of the training data, we need to assess how much training data is needed, what types of test areas are needed considering different types of terrain in Finland, and how to produce the most accurate data possible considering available resources and source data. In the final steps of the project, we aim to identify areas that need improvements and develop methods to improve the outcome. Possible areas of improvement include quality of training data, resolution of input DEM, CNN model architecture, and automated vectorization of the CNN results. We will identify the most promising areas of improvement by testing higher resolution DEMs produced from denser point clouds, by assessing the impact of the quality of training and validation data on the results, by testing different CNN models, and by comparing different methods for vectorization of the CNN model results. [ABSTRACT FROM AUTHOR]

We introduce and test the performance of two sampling methods that utilize distance distributions of laser point clouds in terrestrial and mobile laser scanning geometries. The methods are leveled histogram sampling and inversely weighted distance sampling. The methods aim to reduce a significant portion of the laser point cloud data while retaining most characteristics of the full point cloud. We test the methods in three case studies in which data were collected using a different terrestrial or mobile laser scanning system in each. Two reference methods, uniform sampling and linear point picking, were used for result comparison. The results demonstrate that correctly selected distance-sensitive sampling techniques allow higher point removal than the references in all the tested case studies. [ABSTRACT FROM AUTHOR]

Serious vibration or wear with large friction usually appear when faults occur, which leads to more serious faults such as the destruction of the oil film, bringing great damages to both the society and economic sector. Therefore, the accurate diagnosis of a fault in the early stage is important for the safety operation of machinery. To effectively extract the fault features for diagnosis, EMD-based methods are widely used. However, these methods spend lots of efforts diagnosing faults and require plenty of professional knowledge of diagnosis. Although many intelligent classifiers can be used to automatically diagnose faults such as wear, a broken tooth and imbalance, the combing EMD-based method, the scarcity of samplings with labels hinder the application of these methods to engineering. It is because the model of the intelligent classifier must be constructed based on sufficient samplings with a label. To solve this problem, we propose a novel fault diagnosis method, which is performed based on the EEMD and statistical distance analysis. In this method, the EEMD is used to decompose one original signal into several IMFs and then the probability density distribution of each IMF is calculated. To diagnose the fault of the machinery, the Euclidean distance between the signal acquired under an unknown fault with the other referenced signals acquired previously under various fault types is calculated. At last, the fault of the signal is the same with the referenced signal when the distance is the smallest. To verify the effectiveness of our proposed method, a dataset of bearings with different faults, and a dataset of 2009 Prognostics and Health Management (PHM) data challenge, including gear, bearing and shaft faults are used. The result shows that the proposed method can not only automatically diagnose faults effectively, but also fewer samplings with a label are used compared with the intelligent methods. [ABSTRACT FROM AUTHOR]

Pump as turbine (PAT) is one of the micro-hydro system components. It can be used alone as a generator in remote areas without power supply, and in hydraulic networks instead of pressure-reducing valves. However, its hydraulic optimization still remains an open research problem. One of the optimization techniques is the rounding of the sharp edges at the blade periphery. Existing studies are mostly based on prototype experiments to obtain the optimization effect. In order to more intuitively analyze the influence of this structural optimization on the internal flow of PAT, this paper uses the CFD method to study the influence of the leading edge of the centrifugal pump blade and the fillet of the impeller on the turbine performance. By simulating the PAT performance at different flow rates and speeds, the internal hydraulic performance changes caused by the inverted circular blade are analyzed. Simulation results show that, under various operating conditions, the impeller inverted circle improves the efficiency of PAT to different degrees. At the speed of 1500 rpm, the efficiency is most obviously improved, which can reach 8.09%. Internal flow results show that the efficiency increases along with the decrease in impeller inlet resistance and the flow separation region in the impeller. This paper provides an effective method for studying the PAT hydraulic optimization problem. [ABSTRACT FROM AUTHOR]

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision. [ABSTRACT FROM AUTHOR]

As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for surface reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for surface reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for surface reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known surface reconstruction methods, i.e., Alpha shapes, Screened Poisson reconstruction (SPR), the Crust, and Algebraic point set surfaces (APSS Marching Cubes), were utilized for object reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for surface reconstruction. These reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that (i) the capacity of surface reconstruction in dealing with diverse objects needs to be improved; (ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the reconstruction methods might fail; (iii) for some reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; (iv) all reconstruction methods are beneficial from the reduction of running time; and (v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics. [ABSTRACT FROM AUTHOR]

The polysaccharide is the main active substance contained in Hericium erinaceus and is commonly used in the treatment of neurasthenia, tumors, and digestive diseases. Six intracellular polysaccharide components were obtained from H. erinaceus fruiting bodies cultivated by ARTP (atmospheric and room temperature plasma) mutagenic strain (321) and the original strain (0605), respectively. This study was designed to investigate the physicochemical characteristics of these polysaccharide components and their potential immunomodulatory activities on RAW264.7 macrophages. The results showed that the yield of fruiting body cultivated by mutated strain increased by 22% and the polysaccharide content improved by 16% compared with the original one owing to ARTP mutagenesis. The molecular weight distribution and the monosaccharide compositions of polysaccharide components from H. erinaceus induced by ARTP mutagenesis were significantly different from that of the original one. The NO, IL-6, IL-10, IL-1β, and TNF-α production activities of macrophages were enhanced by stimulation of 20% ethanol precipitated polysaccharides from H. erinaceus induced by ARTP mutagenesis. These results indicated that ARTP is an efficient and practical method for high polysaccharide content breeding of the H. erinaceus strain and this provided a reference for obtaining high quality resources and healthy product development from H. erinaceus. [ABSTRACT FROM AUTHOR]

The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can't meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS. [ABSTRACT FROM AUTHOR]

Based on simple mixing and polymerization of a hydroxyl-containing derivative of perylene bisimide (PBI) and l-malic acid, here, we demonstrate a new type of dye-polymer conjugate, PBI-poly(α,β-malic acid) (PBI–PMA). Benefiting from the excellent water-solubility of weak polyanionic PMA structure and the high fluorescence of PBI, the PBI-PMA conjugates readily dissolve in water, displaying strong pH-dependent fluorescence with the highest intensity at pH 6. Due to the excellent biocompatibility of PMA, those conjugates showed low cytotoxicity on L929 cells. Using L929 and HeLa cells, we also confirmed that the PBI-PMA-labeled cells display intense fluorescence. Overall, the PBI-PMA conjugate demonstrates high potential as a cell labeling agent with its synthesis ease, good solubility in aqueous medium, low cytotoxicity, and high fluorescence. [ABSTRACT FROM AUTHOR]