Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction

Oral diet management for carcinoma at the base of tongue with radiotherapy and chemotherapy associated dysphagia: a case report

IntroductionTongue cancer is one of the common malignancy of the head and neck, and directly impacts chewing, swallowing, and other eating activities. Based on the evidence-based guidelines and clinical management, this paper presents nutrition management experience of a patient with tongue cancer who had a dysphagia and feeding reflux while undergoing radiotherapy and chemotherapy.MethodsNutritional risk screening and comprehensive nutritional assessment were performed based on the patient’s medical history, and personalized nutritional programs were developed under the guidance of the clinical pharmaceutical consensus of parenteral nutrition and nutritional treatment guidelines for patients with tumors during radiotherapy. For the management of oral feeding, the patient’s swallowing function was evaluated to manage oral feeding. Thickening powders were used to improve the consistency of the patient’s food, which successfully achieved oral feeding of the patient.ResultsThe patient finally ate five meals a day by mouth, and energy requirements were met using industrialized nutritional supplements, and homogenized food was added in between the meals. The energy provided by enteral nutrition can reached approximately 60–75%. The patient’s weight and albumin levels had increased significantly at the time of discharge.DiscussionThe nutritional management of patients with dysphagia should be jointly managed by clinicians, nurses, nutritionists, and family members to effectively improve the quality of life (QOL) and nutritional status of patients. To ensure adequate nutritional supply, appropriate swallowing training may delay the deterioration of the chewing function and improve the eating experience of such patients

On

A (k,r)-coloring of a graph G is a proper coloring with k colors such that for every vertex v with degree d(v) in G, the color number of the neighbors of v is at least min{d(v),r}. The smallest integer k such that G has a (k,r)-coloring is called the r-hued chromatic number and denoted by χr(G). In Kaliraj et al. [Taibah Univ. Sci. 14 (2020) 168–171], it is determined the 2-hued chromatic numbers of Cartesian product of complete graph and star graph. In this paper, we extend its result and determine the r-hued chromatic number of Cartesian product of complete graph and star graph

SGT: Session-based Recommendation with GRU and Transformer

Session-based recommendation aims to predict user preferences based on anonymous behavior sequences. Recent research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns, which has achieved significant results. However, most existing studies only consider individual items in a session and do not extract information from continuous items, which can easily lead to the loss of information on item transition relationships. Therefore, this paper proposes a session-based recommendation algorithm (SGT) based on Gated Recurrent Unit (GRU) and Transformer, which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined way. By combining short-term sessions and long-term behavior, user dynamic preferences are captured. Extensive experiments were conducted on three session-based recommendation datasets, and compared to the baseline methods, both the recall rate Recall@20 and the mean reciprocal rank MRR@20 of the SGT algorithm were improved, demonstrating the effectiveness of the SGT method

Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI

Launched on 15 November 2017, China’s FengYun-3D (FY-3D) has taken over prime operational weather service from the aging FengYun-3B (FY-3B). Rather than directly implementing an FY-3B operational snow depth retrieval algorithm on FY-3D, we investigated this and four other well-known snow depth algorithms with respect to regional uncertainties in China. Applicable to various passive microwave sensors, these four snow depth algorithms are the Environmental and Ecological Science Data Centre of Western China (WESTDC) algorithm, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) algorithm, the Chang algorithm, and the Foster algorithm. Among these algorithms, validation results indicate that FY-3B and WESTDC perform better than the others. However, these two algorithms often result in considerable underestimation for deep snowpack (greater than 20 cm), while the other three persistently overestimate snow depth, probably because of their poor representation of snowpack characteristics in China. To overcome the retrieval errors that occur under deep snowpack conditions without sacrificing performance under relatively thin snowpack conditions, we developed an empirical snow depth retrieval algorithm suite for the FY-3D satellite. Independent evaluation using weather station observations in 2014 and 2015 demonstrates that the FY-3D snow depth algorithm’s root mean square error (RMSE) and bias are 6.6 cm and 0.2 cm, respectively, and it has advantages over other similar algorithms

Assessment of Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in China

Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow cover area (SCA) mapping algorithms have rarely been reported, especially in China. In this study, the performances of seven PMW SCA mapping algorithms were tested using in situ snow depth measurements and a one-kilometer Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product over China. The selected algorithms are the FY3 algorithm, Grody’s algorithm, the South China algorithm, Kelly’s algorithm, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm. During the test period, most algorithms performed reasonably well. The overall accuracy of all algorithms is higher than 0.895 against in situ observations and higher than 0.713 against the IMS product. In general, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm had poor performance during the test. Their misclassification errors were larger than those of the remaining algorithms. Grody’s algorithm, the South China algorithm and Kelly’s algorithm had higher positive predictive values and lower omission errors than those of the others. The errors of these three algorithms were mainly caused by variations in commission errors. Comparing to Grody’s algorithm, the South China algorithm and Kelly’s algorithm, the FY3 algorithm presented a conservative snow cover estimation to balance the problem between snow identification and overestimation. As a result, the overall accuracy of the FY3 algorithm was the highest of all the tested algorithms. The accuracy of all algorithms tended to decline with a decreased snow cover fraction as well as SD < 5 cm. All tested algorithms have severe omission errors over barren land and grasslands. The results shown in this study contribute to ongoing efforts to improve the performance and applicability of PMW SCA algorithms

Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China

Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R2 values ranging from 0.46 to 0.80

Photovoltaic Performance of Pin Junction Nanocone Array Solar Cells with Enhanced Effective Optical Absorption

Abstract The photovoltaic performance of axial and radial pin junction GaAs nanocone array solar cells is investigated. Compared with the cylinder nanowire arrays, the nanocone arrays not only improve the whole optical absorption but more importantly enhance the effective absorption (absorption in the depletion region). The enhanced effective absorption is attributed to the downward shift and extension of the absorption region induced by the shrinking top, which dramatically suppresses the absorption loss in the high-doped top region and enhances the absorption in the depletion region. The highest conversion efficiencies for axial and radial GaAs nanocone solar cells are 20.1% and 17.4%, obtained at a slope angle of 5° and 6°, respectively, both of which are much higher than their cylinder nanowire counterparts. The nanocone structures are promising candidates for high-efficiency solar cells

High concentration of vitamin E decreases thermosensation and thermotaxis learning and the underlying mechanisms in the nematode Caenorhabditis elegans.

α-tocopherol is a powerful liposoluble antioxidant and the most abundant isoform of vitamin E in the body. Under normal physiological conditions, adverse effects of relatively high concentration of vitamin E on organisms and the underlying mechanisms are still largely unclear. In the present study, we used the nematode Caenorhabditis elegans as an in vivo assay system to investigate the possible adverse effects of high concentration of vitamin E on thermosensation and thermotaxis learning and the underlying mechanisms. Our data show that treatment with 100-200 µg/mL of vitamin E did not noticeably influence both thermosensation and thermotaxis learning; however, treatment with 400 µg/mL of vitamin E altered both thermosensation and thermotaxis learning. The observed decrease in thermotaxis learning in 400 µg/mL of vitamin E treated nematodes might be partially due to the moderate but significant deficits in thermosensation, but not due to deficits in locomotion behavior or perception to food and starvation. Treatment with 400 µg/mL of vitamin E did not noticeably influence the morphology of GABAergic neurons, but significantly decreased fluorescent intensities of the cell bodies in AFD sensory neurons and AIY interneurons, required for thermosensation and thermotaxis learning control. Treatment with 400 µg/mL of vitamin E affected presynaptic function of neurons, but had no remarkable effects on postsynaptic function. Moreover, promotion of synaptic transmission by activating PKC-1 effectively retrieved deficits in both thermosensation and thermotaxis learning induced by 400 µg/mL of vitamin E. Therefore, relatively high concentrations of vitamin E administration may cause adverse effects on thermosensation and thermotaxis learning by inducing damage on the development of specific neurons and presynaptic function under normal physiological conditions in C. elegans