Long, short, and medium terms wind speed prediction model based on LSTM optimized by improved moth flame optimization algorithm.

Time series prediction of wind speed has been widely used in wind power generation. The volatility and instability of wind speed have a large negative impact on wind turbines and power systems, which can lead to grid collapse in severe cases. Therefore, accurate wind speed prediction is crucial for wind power generation. In this paper, considering the influence of different parameters on algorithm training and prediction, an improved moth flame optimization algorithm is constructed to optimize the LSTM wind energy prediction system to obtain better performance. The system consists of three modules: data preprocessing, optimization, and prediction. The data preprocessing module uses fuzzy information granulation to blur the input data. On this basis, the combination of swarm intelligent optimization algorithm and prediction model can effectively predict wind speed time series. Taking the California wind farm as an example, the MAPE of the experiment in the short-term forecast is 3.15%, the MAPE of the medium-term forecast is 4.38%, and the MAPE of the long-term forecast is 18.28%. The experimental results show that the proposed model has obvious advantages over the previous model.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index.

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

The research on a novel multivariate grey model and its application in carbon dioxide emissions prediction.

Accurate small-sample prediction is an urgent, very difficult, and challenging task due to the quality of data storage restricted in most realistic situations, especially in developing countries. The grey model performs well in small-sample prediction. Therefore, a novel multivariate grey model is proposed in this study, called FBNGM (1, N, r), with a fractional order operator, which can increase the impact of new information and background value coefficient to achieve high prediction accuracy. The utilization of an intelligence optimization algorithm to tune the parameters of the multivariate grey model is an improvement over the conventional method, as it leads to superior accuracy. This study conducts two sets of numerical experiments on CO2 emissions to evaluate the effectiveness of the proposed FBNGM (1, N, r) model. The FBNGM (1, N, r) model has been shown through experiments to effectively leverage all available data and avoid the problem of overfitting. Moreover, it can not only obtain higher prediction accuracy than comparison models but also further confirm the indispensable importance of various influencing factors in CO2 emissions prediction. Additionally, the proposed FBNGM (1, N, r) model is employed to forecast CO2 emissions in the future, which can be taken as a reference for relevant departments to formulate policies.

Predicting dissolved oxygen level using Young's double-slit experiment optimizer-based weighting model.

Accurate prediction of the dissolved oxygen level (DOL) is important for enhancing environmental conditions and facilitating water resource management. However, the irregularity and volatility inherent in DOL pose significant challenges to achieving precise forecasts. A single model usually suffers from low prediction accuracy, narrow application range, and difficult data acquisition. This study proposes a new weighted model that avoids these problems, which could increase the prediction accuracy of the DOL. The weighting constructs of the proposed model (PWM) included eight neural networks and one statistical method and utilized Young's double-slit experimental optimizer as an intelligent weighting tool. To evaluate the effectiveness of PWM, simulations were conducted using real-world data acquired from the Tualatin River Basin in Oregon, United States. Empirical findings unequivocally demonstrated that PWM outperforms both the statistical model and the individual machine learning models, and has the lowest mean absolute percentage error among all the weighted models. Based on two real datasets, the PWM can averagely obtain the mean absolute percentage errors of 1.0216%, 1.4630%, and 1.7087% for one-, two-, and three-step predictions, respectively. This study shows that the PWM can effectively integrate the distinctive merits of deep learning methods, neural networks, and statistical models, thereby increasing forecasting accuracy and providing indispensable technical support for the sustainable development of regional water environments.

A novel grey Verhulst model with four parameters and its application to forecast the carbon dioxide emissions in China.

In the context of dual carbon targets, a reliable prediction of China's carbon dioxide emissions is of great significance to the design and formulation of emission reduction policies by Chinese government. To this end, a novel grey Verhulst model with four parameters is proposed in this paper according to the evolution law and the data characteristics of China's carbon dioxide emissions. The new model solves the defect of poor structural adaptability of the traditional grey Verhulst model by introducing a nonlinear correction term. Besides, the range of values for the order of the grey generation operator of the new model is expanded from a positive real number to any real number (r ∈ R+ â†’ r ∈ R) by expanding the value range of the Gamma function. The new model is used to simulate China's carbon dioxide emissions, and its comprehensive mean relative percentage error is only 0.65 %, which is better than that of the other three grey models (2.39 %, 2.34 %, 2.35 % respectively). It shows that the proposed new model has better modeling ability. Finally, the new model is applied to predict China's carbon dioxide emissions, and the results show that it will still increase year by year, reaching 13,687 million tons by 2028 (only 11,420 million tons in 2021). Therefore, some countermeasures and suggestions are proposed to control China's carbon dioxide emissions in this paper.

Short-term load forecasting system based on sliding fuzzy granulation and equilibrium optimizer.

Short-term electricity load forecasting is critical and challenging for scheduling operations and production planning in modern power management systems due to stochastic characteristics of electricity load data. Current forecasting models mainly focus on adapting to various load data to improve the accuracy of the forecasting. However, these models ignore the noise and nonstationarity of the load data, resulting in forecasting uncertainty. To address this issue, a short-term load forecasting system is proposed by combining a modified information processing technique, an advanced meta-heuristics algorithm and deep neural networks. The information processing technique utilizes a sliding fuzzy granulation method to remove noise and obtain uncertainty information from load data. Deep neural networks can capture the nonlinear characteristics of load data to obtain forecasting performance gains due to the powerful mapping capability. A novel meta-heuristics algorithm is used to optimize the weighting coefficients to reduce the contingency and improve the stability of the forecasting. Both point forecasting and interval forecasting are utilized for comprehensive forecasting evaluation of future electricity load. Several experiments demonstrate the superiority, effectiveness and stability of the proposed system by comprehensively considering multiple evaluation metrics.

Research of a combination system based on fuzzy sets and multi-objective marine predator algorithm for point and interval prediction of wind speed.

Short-term wind speed forecasting is fundamental to improving the stability of power grid operation and enhancing its transmission efficiency; thus, it has long been a research hotspot. Nonetheless, quantities of literature in this field only used the single prediction model and overemphasized deterministic prediction, which resulted in deficient forecasting performance. To address these issues, a novel point and interval combination prediction system was developed in this paper. Specifically, wind speed time series were reconstructed by dividing windows and fuzzification to input highly effective data; next, four single prediction models and a multi-objective weight-determining mechanism were integrated to obtain the point prediction results; and their distributions were assessed to implement interval prediction under distinct confidence levels. In the meantime, this study demonstrated that the proposed system reached the Pareto optimal by the theoretical proof, and empirical research was conducted based on 10-min real wind speed data from the wind farm in China. Judging from the experimental results, the combination prediction system was always capable of providing the most satisfactory forecasting performance by contrast with the comparative models. Consequently, it has broad application prospects in guiding the operation of wind farms and optimizing the power grid dispatching.

Uncertainty quantification of PM2.5 concentrations using a hybrid model based on characteristic decomposition and fuzzy granulation.

The prediction of air pollution plays an important role in reducing the emission of air pollutants and guiding people to carry out early warning and control, so it attracts many scholars to conduct modeling and research on it. However, most of the current researches fail to quantify the uncertainty in prediction and only use traditional fuzzy information granulation to process data, resulting in the loss of much detail information. Therefore, this paper proposes a hybrid model based on decomposition and granular fuzzy information to solve these problems. The trend item and the Granulation fluctuation item are respectively predicted and the results are combined to obtain the change trend and fluctuation range of the sequence. This paper selects PM2.5 concentrations of 3 cities. The experimental results show that the evaluation index of the prediction model is significantly lower than other benchmark models, and a variety of statistical methods are used to further verify the effectiveness of the prediction model.

New genetic mutations in a Chinese child with Ehlers-Danlos syndrome-like spondyloepimetaphyseal dysplasia: A case report.

Background: Ehlers-Danlos syndrome (EDS) spinal deformity type 2 has clinical features similar to those of spondyloepimetaphyseal dysplasia with joint laxity, type 1 (SEMDJL1). They have similar clinical manifestations and a similar genetic basis, both of which can be caused by mutations in the B3GALT6 gene. Hence, genetic screening and careful clinical examination are key to the differential diagnosis of these two diseases. Case presentation: A 4-month-old boy was admitted to our hospital in order to find the causes of developmental delay. The clinical examination revealed that the child was delayed, with an excessive range of motion of joints, patent foramen ovale, and was accompanied by skin aging; the child was suspected to have EDS. However, unlike EDS, the child had normal muscle tension, and at the same time had a spinal deformity, mild kyphosis, widened right hip joint space, as well as a special face, joint laxity, and slender fingers, which were typical characteristics of SEMDJL1. A gene analysis showed two suspicious mutations in the B3GALT6 gene: c.808G > A(p.(G270S)) and c.942G > C(p.(W314C)), which were verified to be compound heterozygous mutations by analyzing genes in his parents. This mutation was not included in the HGMD, ClinVar, and other mutation databases, and thus was a newly discovered mutation. Conclusion: Using the clinical and genetic analyses, this study reported a Chinese case with EDS-like SEMDJL1 for the first time. Two pathogenic mutations were discovered in the B3GALT6 gene: c.808G > A(p.(G270S)) and c.942G > C(p.(W314C)).

Ensemble system for short term carbon dioxide emissions forecasting based on multi-objective tangent search algorithm.

Carbon emissions play a crucial role in inducing global warming and climate change. Accurate and stable carbon emissions forecasting is beneficial for formulating emissions reduction schemes and achieving carbon neutrality as early as possible. Although previous studies have concentrated on employing one or several methods for carbon emissions forecasting, the improvement in forecasting performance is limited because they ignore the importance of objectively selecting the models and the necessity of interval forecasting. In this paper, a novel ensemble prediction system, composed of data decomposition, model selection, phase space reconstruction, ensemble point prediction, and interval prediction, is proposed to conduct both point and interval predictions, which has been proven to be effective in prompting carbon emissions forecasting accuracy and stability. According to the empirical results, the mean MAPE results of our proposed forecasting strategy in point prediction are 1.1102% (in Dataset A) and 1.1382% (in Dataset B), and the mean CWC values in the interval forecasting are 0.3512 and 0.1572, respectively. Thus, the proposed forecasting system improves the forecasting performance relative to other models considerably, which can provide meaningful references for policymakers.

Evaluation of the long-term effect of foldable capsular vitreous bodies in severe ocular rupture.

AIM: To evaluate the long-term effect of foldable capsular vitreous body (FCVB) in the treatment of severe ocular rupture to provide a practical basis for clinical selection. METHODS: A total of 26 patients (26 eyes), 23 men and 3 women, with severe ocular rupture who underwent FCVB implantation between March 2018 and September 2018 were retrospectively analysed. All open ocular wounds located in zone III, with preoperative visual acuity grade IV and above (Snellen less than 4/200). The best corrected visual acuity (BCVA), intraocular pressure (IOP), cornea, anterior chamber, iris, lens, choroid, and retina were evaluated before and after the surgery. The subjective feeling and the location of FCVB were also assessed. RESULTS: The average age of the 26 patients was 36y (20-60y). Postoperative follow-up was from 10 to 14mo. At the end of follow up, BCVA was light perception (LP) in 10 cases, no light perception (NLP) in 13 cases, hand motions (HM) in 3 cases. IOP was 11±5 mm Hg. Corneal degeneration was in 3 cases and corneal endothelial dystrophy was in 7 cases. Shallow anterior chamber was in 8 cases and hyphema was in 8 cases. Organized membrane in the pupil was in 14 cases. Epiphora occurred in 3 cases. FCVB drainage tube exposed in 3 cases. All FCVBs were in their normal location and no rejection occurred. CONCLUSION: FCVB implantation is a long-term effective treatment and may provide a practical selection for severe ocular rupture.

Forecasting the Number of New Coronavirus Infections Using an Improved Grey Prediction Model.

BACKGROUND: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. METHODS: The number of new coronavirus infections was characterized by "small data, poor information" in the short term. The grey prediction model provides an effective method to study the prediction problem of "small data, poor information". Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). RESULTS: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. CONCLUSION: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts forward specific suggestions.

iTRAQ-based proteomic analysis of sperm reveals candidate proteins that affect the quality of spermatozoa from boars on plateaus.

BACKGROUND: Tibetan pigs (TP) exhibit heritable adaptations to their hypoxic environments as a result of natural selection. However, candidate proteins that affect the sperm quality of boars on plateaus have not yet been clearly investigated. METHODS: In this study, to reveal the candidate proteins that affect the quality of spermatozoa of boars on plateaus, we analyzed the sperm quality using computer-assisted semen analysis (CASA) system and reactive oxygen species (ROS) levels. We also compared the proteomes of sperm proteomes between TP and Yorkshire pigs (YP) raised at high altitudes using the isobaric tags for relative and absolute quantitation (iTRAQ) in combination with the liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic method, and confirmed the relative expression levels of the four proteins by western blotting. RESULTS: The sperm quality of the TP was superior to that of the YP on plateaus. Of the 1,555 quantified proteins, 318 differentially expressed proteins (DEPs) were identified. Gene ontology (GO) analysis revealed that the DEPs were predominantly associated with the sorbitol metabolic process, removal of superoxide radicals, cellular response to superoxide, response to superoxide and regulation of the mitotic spindle assembly. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were mainly enriched in pathways involved in the regulation of the actin cytoskeleton, glutathione metabolism, oxidative phosphorylation, and estrogen signaling. Based on the protein-protein interaction (PPI) network analysis, we identified 8 candidate proteins (FN1, EGF, HSP90B1, CFL1, GPX4, NDUFA6, VDAC2, and CP) that might play important roles and affect the sperm quality of boars on plateaus. Moreover, the relative expression levels of four proteins (CFL1, EGF, FN1, and GPX4) were confirmed by western blot analysis. CONCLUSIONS: Our study revealed 8 candidate proteins (FN1, EGF, HSP90B1, CFL1, GPX4, NDUFA6, VDAC2, and CP) that affect the sperm quality of boar on plateaus and provide a reference for further studies on improving sperm quality and the molecular breeding of boars on plateaus.

Impacts of haze pollution on China's tourism industry: A system of economic loss analysis.

Haze pollution not only negatively influences public health but also causes great economic losses. However, most previous studies have mainly focused on health-related economic losses, while the negative effects of haze pollution on the tourism industry are often ignored, leading to the unsustainable development of tourism. In this context, contrasting with previous research perspectives, this article selected several representative tourist cities from East China, South China, Central China, North China, Northwest China, Southwest China, and Northeast China as research objects in an empirical study, developing an economic loss analysis system to quantitatively evaluate the losses in the tourism industry caused by haze pollution. This system uses the satin bower bird optimization-based distribution estimation method to identify the optimal distribution of haze pollution, demonstrating superior performance to the traditional estimation method. Meanwhile, the optimal distribution of haze pollution is employed to measure the probability of different concentration limits in each area. Furthermore, the economic loss formula of the tourism industry is proposed in the devised system, calculating the economic loss caused by haze pollution at different degrees. The results show that haze pollution in different degrees has caused varying degrees of losses to China's tourism industry. In terms of seasonality and regionality, different seasons and different regions produce different results. Compared with summer, autumn and winter haze pollution is more severe, creating obvious seasonal differences. There is also a regional agglomeration effect, whereby the regional distribution of haze pollution is consistent with each region's economic development.

A novel assessment and forecasting system for traffic accident economic loss caused by air pollution.

Air pollution greatly reduces the visibility of the air, leading to frequent traffic accidents (TA), and the resulting economic losses cannot be ignored. In order to better control and mitigate the traffic accident economic losses of air pollution, this paper proposes a novel assessment and forecasting system for TA economic loss of air pollution, which contains assessment module and forecasting module. In the assessment module, a reasonable assessment of TA economic loss is provided which also analyzes the efficiency of air pollution control based on data envelope analysis directional distance function. In the forecasting module, this system develops a rolling nonlinear optimized initial self-adapting grey model based on multi-objective optimization algorithm to forecast the TA economic loss of air pollution. The results from the proposed system indicate that the proposed system has outstanding performance which can provide great information assistant for a decision-maker.

[Health Benefit Assessment of PM2.5 Pollution Control in Beijing].

The assessment of residents' health and economic benefits of PM2.5 pollution control is of great significance for the promotion of regional environmental air pollution monitoring, warning, and prevention. This paper utilized Poisson regression relative risk models and environmental value assessment methods to assess the health risks and economic benefits of PM2.5 pollution control in the 16 municipal districts of Beijing from 2016 to 2019 after reaching the secondary standard limit of 35 µg·m-3. The results showed that PM2.5 concentrations, various health effects, economic benefits, and per capita economic health benefits in all 16 districts showed a downward trend. Specifically, PM2.5 concentrations dropped from 73 µg·m-3 in 2016 to 42 µg·m-3 in 2019 at an average annual rate of 16.75%. The total number of healthy beneficiaries from PM2.5 pollution control dropped from 439985 cases in 2016 (95% confidence interval (CI):183987, 653476) to 77288 cases in 2019 (95% confidence interval (CI):30483, 120905) at an average annual rate of approximately 42.67%. The share of health economic benefits in GDP dropped from 3.16% (95% confidence interval (CI):1.10%, 4.73%) to 0.55% (95% confidence interval (CI):0.18%, 0.88%), and the per capita health economic benefit dropped from 3727.61 yuan (95% confidence interval (CI):1303.24, 5592.18) to 906.58 yuan (95% confidence interval (CI):295.14, 1438.27). Due to differences in PM2.5 concentrations, population number and density, and economic values of unit health endpoints, the results of the health economic benefit analysis, proportion of GDP, and per capita benefits varied between the 16 districts. Among these, Fengtai, Tongzhou, and Daxing show much higher values than others, indicating relatively high health and economic benefits from pollution control.

Osseous Rosai-Dorfman disease of tibia in children: A case report.

BACKGROUND: Rosai-Dorfman disease (RDD), or sinus histiocytosis with massive lymphadenopathy, is a benign histiocytic disorder. Extranodal involvement is common, occurring in > 40% of patients, but bone involvement occurs in < 10% of cases. In addition, primary bone RDD is extremely rare. The majority of patients are adolescents and young adults, and the mean age at onset is 20-years-old. CASE SUMMARY: We report an 8-year-old Chinese girl who presented to our hospital with an insidious onset of swelling and pain in the middle shaft of her right tibia for 4 mo. We performed total surgical resection of the right tibia lesion and allograft transplantation. A good prognosis was confirmed at the 6 mo follow-up. Pain and swelling symptoms were totally relieved, range of motion of her right knee and ankle returned to normal, and there was no clinical evidence of lesion recurrence at last follow up. Our case is the second reported case of osseous RDD without lymphadenopathy in the shaft of the tibia of a child. CONCLUSION: Extranodal RDD is a rare disease and can be misdiagnosed easily. Lesion resection and allograft transplantation are an option to treat extranodal RDD in children with good short term result. Pediatric orthopedist should be aware of this rare disease, especially extranodal involvement.

Intelligent multivariable air-quality forecasting system based on feature selection and modified evolving interval type-2 quantum fuzzy neural network.

Owing to the high nonlinearity and noise in the air quality index (AQI), tackling the uncertainties and fuzziness in the forecasting process is still a prevalent problem. Therefore, this study developed an intelligent hybrid air-quality forecasting system based on feature selection and a modified evolving interval type-2 quantum fuzzy neural network (eIT2QFNN), which provides accurate air-quality forecasting information by considering climate influencing factors. The main contributions of this study are as follows. The optimal input structure of the model is determined by the proposed second-stage feature-selection model, which can better extract the influencing variables and remove redundant information. Moreover, a novel multi-objective chaotic Bonobo optimizer algorithm is proposed to improve the eIT2QFNN. The modified eIT2QFNN implements AQI prediction by considering the importance of influencing variables that can cope with the uncertainties and fuzziness in the forecasting process. Finally, the Diebold-Mariano and modified Diebold-Mariano tests are employed to evaluate the performance of the proposed system. The experimental results demonstrate that our proposed system significantly improves the modeling performance in terms of high accuracy and compact structure, and can thus serve as an effective tool for air-quality management.