In recent years, renewable energy sources have been installed in large numbers. Wind power in particular, a technology with very high potential, has become a significant source of energy in most ...power grids. However, wind power generation forecasting and scheduling remain very difficult tasks due to the uncertainty and stochastic behaviour of wind speed. This work provides a novel, powerful tool for wind power forecasting based on neural expansion analysis for time series forecasting (N-BEATS), a deep neural network approach. N-BEATS was designed as an easy-to-implement approach to solving non-linear stochastic time series forecasting problems. Additionally, a loss function is tailored to wind power forecasting to confront the issue of forecast bias. The results are compared with established models, such as statistical and machine learning approaches as well as hybrid models, using the real-world wind power data from 15 different European countries as input. Comprehensive and accurate results are obtained during this work, showing that this methodology can easily compete with other approaches and even outperform them in terms of accuracy in most cases. Additionally, the tailored loss function reduces the error significantly. The N-BEATS architecture is further customized to deliver decomposed components such as trend and seasonality, yielding interpretable outputs. These findings constitute considerable progress and provide support for decision makers.
•Deep neural architecture for short-term wind power production.•Forecast bias addressed by a tailored loss function.•Decomposed and interpretable results to facilitate decision making.
The COVID-19 pandemic has considerably affected healthcare systems worldwide and is expected to influence cancer incidence, mortality, stage at diagnosis, and survival. This study aimed to assess ...COVID-19-related changes in cancer incidence observed in 2020 in the Greater Poland region.
Data from the Greater Poland Cancer Registry on cancer patients diagnosed between 2010 and 2020 were analysed. To quantify the change in the number of incident cancer cases during the COVID-19 pandemic, we calculated the standardized incidence ratio (SIR) and the incidence rate difference (IRD) to assume the pandemic-attributable gap in cancer incidence.
In 2020, in Greater Poland, the expected number of new cancer cases was 18 154 (9 226 among males and 8 927 among females), while the observed number was 14 770 (7 336 among males and 7 434 among females). The registered number of cancer cases decreased in 2020 by 20% (SIR 0·80, 95% CI 0·78 to 0·81) and 17% (SIR 0·83, 95% CI 0·81 to 0·85) in males and females, respectively. Among men, the most significant difference was reported for myeloma (SIR 0·59, 95% CI 0·45 to 0·77), among women for bone cancer (SIR 0·47, 95% CI 0·20 to 0·93). In females the observed incidence was higher than expected for cancer of an unspecified site (SIR 1·19, 95% CI 1·01 to 1·38). In our study, the decrease in new cancer cases was greater in males than in females.
The observed incidence was affected in most cancer sites, with the most significant deviation from the expected number in the case of myeloma. An increase in the observed incidence was reported only in women diagnosed with cancer of an unspecified site, which might reflect shortages in access to oncological diagnostics.
Coronavirus genomic infection-2019 (COVID-19) has been announced as a serious health emergency arising international awareness due to its spread to 201 countries at present. In the month of April of ...the year 2020, it has certainly taken the pandemic outbreak of approximately 11,16,643 infections confirmed leading to around 59,170 deaths have been recorded world-over. This article studies multiple countries-based pandemic spread for the development of the COVID-19 originated in the China. This paper focuses on forecasting via real-time responses data to inherit an idea about the increase and maximum number of virus-infected cases for the various regions. In addition, it will help to understand the panic that surrounds this nCoV-19 for some intensely affecting states possessing different important demographic characteristics that would be affecting the disease characteristics. This study aims at developing soft-computing hybrid models for calculating the transmissibility of this genome viral. The analysis aids the study of the outbreak of this virus towards the other parts of the continent and the world. A hybrid of wavelet decomposed data into approximations and details then trained & tested through neuronal-fuzzification approach. Wavelet-based forecasting model predicts for shorter time span such as five to ten days advanced number of confirmed, death and recovered cases of China, India and USA. While data-based prediction through interpolation applied through moving average predicts for longer time spans such as 50–60 days ahead with lesser accuracy as compared to that of wavelet-based hybrids. Based on the simulations, the significance level (alpha) ranges from 0.10 to 0.67, MASE varying from 0.06 to 5.76, sMAPE ranges from 0.15 to 1.97, MAE varies from 22.59 to 6024.76, RMSE shows a variation from 3.18 to 8360.29 & R2 varying through 0.0018 to 0.7149. MASE and sMAPE are relatively lesser applied and novel measures that aimed to achieve increase in accuracy. They eliminated skewness and made the model outlier-free. Estimates of the awaited outburst for regions in this study are India, China and the USA that will help in the improvement of apportionment of healthcare facilities as it can act as an early-warning system for government policy-makers. Thus, data-driven analysis will provide deep insights into the study of transmission of this viral genome estimation towards immensely affected countries. Also, the study with the help of transmission concern aims to eradicate the panic and stigma that has spread like wildfire and has become a significant part of this pandemic in these times.
Goal: This study aims at a systematic assessment of five computational models of a birdcage coil for magnetic resonance imaging (MRI) with respect to accuracy and computational cost. Methods: The ...models were implemented using the same geometrical model and numerical algorithm, but different driving methods (i.e., coil "defeaturing"). The defeatured models were labeled as: specific (S2), generic (G32, G16), and hybrid (H16, H16 fr-forced ). The accuracy of the models was evaluated using the "symmetric mean absolute percentage error" ("SMAPE"), by comparison with measurements in terms of frequency response, as well as electric (||E⃗||) and magnetic (||B⃗||) field magnitude. Results: All the models computed the ||B⃗|| within 35% of the measurements, only the S2, G32, and H16 were able to accurately model the ||E⃗|| inside the phantom with a maximum SMAPE of 16%. Outside the phantom, only the S2 showed a SMAPE lower than 11%. Conclusions: Results showed that assessing the accuracy of ||B⃗|| based only on comparison along the central longitudinal line of the coil can be misleading. Generic or hybrid coils - when properly modeling the currents along the rings/rungs - were sufficient to accurately reproduce the fields inside a phantom while a specific model was needed to accurately model ||E⃗|| in the space between coil and phantom. Significance: Computational modeling of birdcage body coils is extensively used in the evaluation of radiofrequency-induced heating during MRI. Experimental validation of numerical models is needed to determine if a model is an accurate representation of a physical coil.
Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam ...and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011–2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011–2019 period observed, and
Plasmodium falciparum
(74.5%) was the most predominant parasite in Assam, whereas
Plasmodium vivax
(66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.
Residential energy consumption is rapidly increasing every year due to demographic and behavioral changes, such as the rising population and the adoption of work-from-home post-COVID-19. High energy ...consumption emits a substantial amount of carbon dioxide and other Greenhouse Gases, contributing to global warming. It becomes crucial to accurately predict residential load. To enable smart home electricity consumption control, as well as efficient generation, planning, and usage, we predict household energy consumption at very short-term, short-term, and medium-term forecast levels using univariate and multivariate time series data. This study assesses the impact of different household units (water heater and air conditioning), areas (kitchen, laundry, office, living room, bathroom, ironing room, teenager room, and parents' room), and time (i.e., hour, day, and month) on energy consumption. Comparative analysis and numerical experimental results between the most used approaches, Support Vector Regression and Long Short-Term Memory, reveal that the former outperforms the latter across all forecast levels using different datasets. The findings of this paper will be useful to energy companies and household owners in enhancing energy efficiency and earning carbon credits by reducing the emission of carbon dioxide and other Greenhouse Gases.
This article presents the long term wind speed and power output of a 40kW wind turbine based on a layer recurrent neural network as the predictor. The forecast model utilized the levenberg marquardt ...back propagation (BP) algorithm with a tap delay for prediction of the wind speed and power generation at 5-min steps of up to 5days ahead at station A. In addition, the BP algorithm was considered for prediction of the wind potential at station B using 10-min samples at the same tower height. For accuracy comparisons, the 10-min synthetic samples were generated from the sampled 5-min measurements at station A; and the wind predictions were compared with the 5-min predictions. To prepare the forecast model, a one month weather samples were obtained at the 20m tower height on both wind stations. The first day data was used to train the model and forecast began at the second day for maximum period of 5days. A usable total electricity generation of 1322.61kWh using the sampled 5-min measurements, and 4485.56kWh using the sampled 10-min measurements were predicted for the period of 30days for the stations A and B, respectively. Using the generated synthetic samples at station A, a usable total electricity generation of 1320.55kWh was predicted. The wind forecast shows a very small deviation between the use of the 5-min measurements, and the 10-min synthetic samples at station A. Furthermore, the forecast model was assessed to test how well the LRNN performed with the selected network parameters. A new weather sample was obtained from a remote station at a 20m tower height to test the forecast model accuracy. The estimated errors were used to determine the closeness of the wind predictions to its acceptable or actual value at both stations. Accuracy test results using independent samples show close relationship with the validation results using the weather samples at station A.
Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution ...soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones.
Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAP/Sentinel-1 Active-Passive Soil Moisture Product in April 2017.
This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9km grid instead of a 36km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correction/calibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015–October 30, 2016. It was found that the performance of the enhanced 9km L2_SM_P_E is equivalent to that of the standard 36km L2_SM_P, attaining a retrieval uncertainty below 0.040m3/m3 unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.
•SMAP enhanced passive product validation covered diversified spatial scales.•Product retrieval accuracy is found to be below 0.040m3/m3 with good correlation.•Single channel algorithm using v-polarized TB channel showed the best performance.•Descending 6:00am retrieval is more accurate than ascending 6:00pm retrieval.•First project-level publication on this new product
PDF
