With the guidance of advanced information technology, education in China is constantly evolving, and both the teaching methods of educators and the learning methods of students are constantly being ...updated and optimized. Constructing a dependent learning mode for college students under the intelligent learning environment not only enhances their dependent learning ability, but also breaks the limitations of time and space in traditional teaching, enabling various educational resources to be fully utilized and shared. This paper elaborates on the concept of intelligent learning environment and the core elements of dependent learning, analyzes the significance of dependent learning for college students, and proposes a strategy for constructing a dependent learning mode for college students based on intelligent learning environment for reference.
Recent clinical successes of cancer immunotherapy necessitate the investigation of the interaction between malignant cells and the host immune system. However, elucidation of complex tumor-immune ...interactions presents major computational and experimental challenges. Here, we present Tumor Immune Estimation Resource (TIMER; cistrome.shinyapps.io/timer) to comprehensively investigate molecular characterization of tumor-immune interactions. Levels of six tumor-infiltrating immune subsets are precalculated for 10,897 tumors from 32 cancer types. TIMER provides 6 major analytic modules that allow users to interactively explore the associations between immune infiltrates and a wide spectrum of factors, including gene expression, clinical outcomes, somatic mutations, and somatic copy number alterations. TIMER provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers.
.
The development of advanced luminescent materials is of great importance to the anticounterfeiting application and still confronts with lots of challenges. At present, most anticounterfeiting ...luminescent materials are based on a monotonous photoluminescence model, which is easily faked by substitutes. Therefore, in this work, a multimodal La4GeO8: Eu2+, Er3+ material is reported, which can emit red, purple, baby blue, and green light under the increased excitation wavelength from 250 to 380 nm. Meanwhile, the phosphor also shows green upconversion luminescence under the NIR (980 and 808 nm) laser irradiation. Moreover, the phosphor features excellent stability and humidity resistance against harsh conditions. Based on the integrated feature, a functional anticounterfeiting application is designed. Results demonstrate that the multimodal luminescent feature can be easily detected by using a portable ultraviolet lamp or NIR (808 or 980 nm) laser. The unique characteristic will be complicated to counterfeit and show high‐level security in the field of advanced anticounterfeiting.
An advanced Eu2+/Er3+ codoping La4GeO8 phosphor with excellent upconversion (UC) and downconversion performances, which can emit red, purple, baby blue, and green light under the increased excitation wavelength from 250 to 380 nm, shows UC photoluminescence under the irradiation NIR laser (980 and 808 nm). Based on the integrated feature, its excellent optical properties can be used in anticounterfeiting.
•H. serotina polysaccharides were successfully encapsulated in W1/O/W2 nanoemulsions.•The prepared conditions of H. serotina polysaccharides nanoemulsions were optimized.•Optimal encapsulation ...efficiency and particle size were respectively 75.42% and 410.1nm.•The release characteristic of H. serotina polysaccharides nanoemulsions was investigated.
The aim of this research was to develop novel W1/O/W2 nanoemulsions for encapsulating Hohenbuehelia serotina polysaccharides to resolve the low soluble and unstable problems. The prepared parameters (PVA content, polysaccharides concentration, stirring speed and stirring time) of H. serotina polysaccharides nanoemulsions were optimized based on the response surface methodology. Through systematic analysis of the model, the optimal conditions were chosen as PVA content of 0.60%, polysaccharides concentration of 9.7μg/mL, stirring speed of 11,000rpm, and stirring time of 2.4min. Under the optimal prepared conditions, the encapsulation efficiency and particle size were respectively 75.42±0.69% and 410.1±2.3nm, which were well consistent with the predicted values. The optimized nanoemulsions possessed the spherical multilayer structure with the zeta potential value of −52.34±5.62mV, and they could be stably stored at 25°C for 6days. Moreover, the nanoemulsions had the excellent sustained-release characteristics in the simulated gastric fluid. This study may provide a valuable contribution for the application of nanoemulsions in the functional food field.
Water-soluble carbon nanocrystals (CNCs) with electrochemiluminescence (ECL) activity were released into aqueous solution from a graphite rod by applying a scanning potential. ECL emission of CNCs ...observed during their preparation probably provides a useful method for monitoring and screening nanocrystal preparation. The ECL behavior and its mechanism in CNCs have been studied in detail for the first time. The results suggest promising applications of CNCs in the development of new types of biosensors and display devices in the future on the basis of their strong and stable ECL emission, good stability, low cytotoxicity, excellent water solubility, easy labeling, and environmental friendliness.
Reactive oxygen species (ROS) are produced by plants. Hydrogen peroxide (H2O2) is one important component of ROS and able to modulate plant growth and development at low level and damage plant cells ...at high concentrations. Ascorbate peroxidase (APX) shows high affinity towards H2O2 and plays vital roles in H2O2-scavenging. In order to explore the differences of APXs from selected plant species, bioinformatics methods and public databases were used to evaluate the physicochemical properties, conserved motifs, potential modifications and cis-elements in all the APXs, and protein-protein network and expression profiles of rice APXs. The results suggested that APXs in the selected plant species showed high evolutionary conservation and were able to divide into seven groups, group I to VII. Members in the groups contained abundant phosphorylation sites. Interestingly, group I and VII had only PKC site. Additionally, promoters of the APXs contained abundant stress-related cis-elements. APXs in rice plant were able to interact with dehydroascorbate reductase 2. The eight APXs expressed differently in root, leaf, panicle, anther, pistil and seed. Drought, Pi-free, Cd and Xanthomonas oryzae pv. oryzicola B8-12 treatments were able to significantly alter the expression profiles of rice APXs. This study increases our knowledge to further explore functions and mechanisms of APXs and also guides their applications.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•A holistic water quality assessment was conducted in the Erdao Songhua River Basin.•The IWPI showed “Good” water quality with spatiotemporal variations in studied basin.•PI, DO, CODcr, and BOD5 are ...the most important parameters in this basin.•The potential risks of point source and non-point source pollution in middle-downstream should be of concern.
The Erdao Songhua River provides drinking water for 16.82 million people; therefore, ensuring that the water quality adequate is of particular concern. In this study, an improved water pollution index (IWPI) and multiple statistical methods were employed to assess the overall water pollution situation and investigate spatiotemporal variations of seven physicochemical parameters such as the permanganate index (PI), dissolved oxygen (DO), chemical oxygen demand (CODcr), five-day biochemical oxygen demand (BOD5), total nitrogen (TN), total phosphorus (TP) and ammonia nitrogen (NH3-N), which were collected monthly at 20 sites within the mainstream and major tributaries of the Erdao Songhua River Basin (ESRB) from 2015 to 2020. Stepwise regression analysis was conducted to build a minimum improved water pollution index (IWPImin) model consisted of four key elements (PI, DO, CODcr, and BOD5) proposed from seven parameters. The results demonstrated water quality within the ESRB was considered to be “Good” with the mean IWPI values <40. However, the water quality deteriorated from the upstream to the downstream within the basin, manifested as the mean value of IWPI of the downstream is 1.7 times of upstream. Seasonally, an improving trend of water quality was observed during the monitoring period and the mean IWPI value decreased by 23%. Furthermore, Seasonal variation in the IWPI value was evident, and the best water quality was found in winter (lowest IWPI value of 14.1) and the worst in summer (highest IWPI value of 21.7). The proposed IWPImin model uses the selected four crucial parameters and the weights of those parameters has exhibited excellent performance in the water quality assessment, with the highest coefficient of determination (R2) and lowest Root Mean Square Error (RMSE) values of 0.996 and 0.51, respectively, which can be used to optimize water quality assessment strategies at a lower cost. For future management, the water quality of middle and downstream should be carefully inspected, and strictly control the effects of point source and non-point source pollution in the ESRB.
•Lake evaporation modeling without wind speed data.•CRLE model implicitly considers the wind effect via vapor transfer coefficient.•Evaporation decreasing was responsible for 4% of recent rapid Nam ...Co Lake expansion.
Previous studies have shown that the majority of the lakes in the Tibetan Plateau (TP) started to expand rapidly since the late 1990s. However, the causes are still not well known. For Nam Co, being a closed lake with no outflow, evaporation (EL) over the lake surface is the only way water may leave the lake. Therefore, quantifying EL is key for investigating the mechanism of lake expansion in the TP. EL can be quantified by Penman- and/or bulk-transfer-type models, requiring only net radiation, temperature, humidity and wind speed for inputs. However, interpolation of wind speed data may be laden with great uncertainty due to extremely sparse ground meteorological observations, the highly heterogeneous landscape and lake-land breeze effects. Here, evaporation of Nam Co Lake was investigated within the 1979–2012 period at a monthly time-scale using the complementary relationship lake evaporation (CRLE) model which does not require wind speed data. Validations by in-situ observations of E601B pan evaporation rates at the shore of Nam Co Lake as well as measured EL over an adjacent small lake using eddy covariance technique suggest that CRLE is capable of simulating EL well since it implicitly considers wind effects on evaporation via its vapor transfer coefficient. The multi-year average of annual evaporation of Nam Co Lake is 635mm. From 1979 to 2012, annual evaporation of Nam Co Lake expressed a very slight decreasing trend. However, a more significant decrease in EL occurred during 1998–2008 at a rate of −12mmyr−1. Based on water-level readings, this significant decrease in lake evaporation was found to be responsible for approximately 4% of the reported rapid water level increase and areal expansion of Nam Co Lake during the same period.
Electric load forecasting has always been a key component of power grids. Many countries have opened up electricity markets and facilitated the participation of multiple agents, which create a ...competitive environment and reduce costs to consumers. In the electricity market, multi-step short-term load forecasting becomes increasingly significant for electricity market bidding and spot price calculation, but the performances of traditional algorithms are not robust and unacceptable enough. In recent years, the rise of deep learning gives us the opportunity to improve the accuracy of multi-step forecasting further. In this paper, we propose a novel model multi-scale convolutional neural network with time-cognition (TCMS-CNN). At first, a deep convolutional neural network model based on multi-scale convolutions (MS-CNN) extracts different level features that are fused into our network. In addition, we design an innovative time coding strategy called the periodic coding strengthening the ability of the sequential model for time cognition effectively. At last, we integrate MS-CNN and periodic coding into the proposed TCMS-CNN model with an end-to-end training and inference process. With ablation experiments, the MS-CNN and periodic coding methods had better performances obviously than the most popular methods at present. Specifically, for 48-step point load forecasting, the TCMS-CNN had been improved by 34.73%, 14.22%, and 19.05% on MAPE than the state-of-the-art methods recursive multi-step LSTM (RM-LSTM), direct multi-step MS-CNN (DM-MS-CNN), and the direct multi-step GCNN (DM-GCNN), respectively. For 48-step probabilistic load forecasting, the TCMS-CNN had been improved by 3.54% and 6.77% on average pinball score than the DM-MS-CNN and the DM-GCNN. These results show a great promising potential applied in practice.
PDF
