Optimal Fit Research Articles

Prediction of Battery Life and Fault Inspection of New Energy Vehicles using Big Data

New energy vehicles have gradually become the preferred means of transportation for people to travel greenly. Lithium batteries, as batteries for new energy vehicles, its quality directly affects the safety of vehicles and mileage is also the core data that people consider when choosing vehicles one. Therefore, the research uses big data to predict and test the battery life and failure of new energy vehicles. When predicting the battery life, the improved P-GN model has a good prediction effect and the model reaches the convergence state only three iterations and converged to 0.83. The optimal fitness converges at the beginning of iteration, reaching the optimal value of 1.946 after only four iterations. The error between the predicted value and the actual value of the remaining cruising range is within an acceptable range. When the weather condition is good, the prediction effect of the remaining cruising range is excellent and the fluctuation of the prediction difference is small. When the weather conditions are severe, the model can still predict the cruising range of the battery pack normally. When performing fault detection on the battery pack, the fault detection system can accurately and quickly detect the type of fault and effectively analyse the inconsistency of the battery and be accurate to the single faulty battery. When analysing the single faulty battery, it is proposed that the fault detection system can accurately diagnose the fault in the test battery, which not only takes a short time but also has good feasibility.

Improved Zero-DCE for pig face image enhancement with low-light and high-noise

To solve the problem that individual visual features could not be accurately extracted from low-light and high-noise pig face images in intensive farming, the optimal fitting curve parameters of image brightness enhancement were defined, and the Zero-DCE model was improved and Denoise-Net was introduced to achieve brightness enhancement and high-noise suppression of a single low-light pig face image. The experimental results show that, compared with EnlightGAN, Zero-DCE, Retinex, and SSE, the algorithm in this paper (DCE-Denoise-Net) has good results on image quality metrics such as information entropy, Brisque, NIQE, and PIQE in the absence of reference images. The image quality is improved. On the basis of improving the low visibility of low-light images, denoising was achieved. It is more suitable for low-light pig face image enhancement in a real breeding environment.

Mass transfer and electrochemical behavior of nitrate reduction to ammonia in electrocatalytic flow cell reactor

AbstractThe electrochemical nitrate reduction reaction (e‐NITRR) process based on plasma has become a potential technology for commercial large‐scale green ammonia synthesis. This work emphasized the prominent influence of operating parameters on e‐NITRR through the combination of experiments and simulations in an electrocatalytic flow cell reactor. An integrated model was developed and the non‐linear functions between operating conditions and exchange current density were obtained by optimization fitting. The average relative error of experiments and simulations was <5%, which can provide solutions for the design of reactor structures and optimization of process parameters. Besides, the results also showed that the tandem steps of NO3− → NO2− and NO2− → NH3 were, respectively, controlled by mass transfer and electrochemical kinetics at low electrolyte flow rate, and this characteristic makes the increase in electrode dimensions have the opposite effect on the formation rate of NO2− and NH3.

Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT)

BackgroundFractures are rare events and can occur because of a fall. Fracture counts are distinct from other count data in that these data are positively skewed, inflated by excess zero counts, and events can recur over time. Analytical methods used to assess fracture data and account for these characteristics are limited in the literature.MethodsCommonly used models for count data include Poisson regression, negative binomial regression, hurdle regression, and zero-inflated regression models. In this paper, we compare four alternative statistical models to fit fracture counts using data from a large UK based clinical trial evaluating the clinical and cost-effectiveness of alternative falls prevention interventions in older people (Prevention of Falls Injury Trial; PreFIT).ResultsThe values of Akaike information criterion and Bayesian information criterion, the goodness-of-fit statistics, were the lowest for negative binomial model. The likelihood ratio test of no dispersion in the data showed strong evidence of dispersion (chi-square = 225.68, p-value < 0.001). This indicates that the negative binomial model fits the data better compared to the Poisson regression model. We also compared the standard negative binomial regression and mixed effects negative binomial models. The LR test showed no gain in fitting the data using mixed effects negative binomial model (chi-square = 1.67, p-value = 0.098) compared to standard negative binomial model.ConclusionsThe negative binomial regression model was the most appropriate and optimal fit model for fracture count analyses.Trial registrationThe PreFIT trial was registered as ISRCTN71002650.

Validation of the Consumer Health Activation Index (CHAI) in general population samples of older Australians

ObjectiveTo validate the 10-item Consumer Health Activation Index (CHAI), developed in the United States, as an activation measure for interventions targeted at the Australian older general population. MethodsThe study was a cross sectional design. Exploratory factor analysis (EFA) was conducted on survey data from a community sample of participants (n = 250), aged 55–75 years. Confirmatory factor analysis (CFA) was used to evaluate dimensionality among a second sample of participants randomly sampled from the electoral roll (n = 571), aged 50–75 years. Associations between the CHAI and self-reported health behaviours were examined. ResultsEFA revealed a 7-item, two-factor structure (‘Health self-management’ and ‘Patient-provider engagement’). CFA indicated optimum model fit was obtained with this structure. Subscale reliability and validity were confirmed, with significant correlation to age, functional health literacy and health screening. ConclusionIn contrast to the original structure, optimum model fit was obtained with a two-factor solution and retention of seven items. The subscales have utility as a measure of health activation for tailoring of information in this group. InnovationA freely-available, unidimensional health activation measure has demonstrated an underlying two-scale structure that will enable tailored approaches toward the enhancement and maintenance of self- and externally-managed health behaviours in an Australian population.

Measurement invariance of the Youth Self-Report across youth who have committed sexual and nonsexual offenses.

Justice-involved youth experience high rates of mental health problems that require proper screening and assessment in order to effectively intervene. The Youth Self-Report (YSR) is a general psychopathology rating scale that measures several dimensions of psychopathology and is commonly used in clinical assessments, including with justice-involved youth. Yet, the underlying factor structure of the YSR has not been examined specifically in a sample of justice-involved youth. We examined the factor structure of the YSR using confirmatory factor analysis with a sample of 961 male youth involved with the justice system (12-18 years of age). Measurement invariance of the YSR was also examined across groups of youth who committed a sexual offence and those who committed a nonsexual offence. The eight-factor model presented with optimal fit to the data, consistent with previous research with nonjustice involved samples, and the model demonstrated strong measurement invariance across youth who committed both types of offenses (sexual and nonsexual). Youth who committed nonsexual offenses reported significantly higher degrees of rule-breaking behavior and lower degrees of social problems than youth who committed sexual offenses. The current findings provide strong psychometric evidence that supports the use of the YSR with justice-involved male youth. As such, clinicians and researchers can be confident in using the YSR as a mental health screening tool with male youth involved with the justice system who have committed various offenses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Complexity of Hearing Aid Fitting: Children with Congenital Hearing Loss and Middle Ear Dysfunction.

The early diagnosis of hearing loss (HL) and hearing rehabilitation facilitate language and communication development. Some children exhibit mixed HL due to middle ear effusion (MEE) or acute otitis media (AOM). Mixed HL can affect HL evaluation and hearing aid (HA) fitting. The present study retrospectively evaluated the prevalence of MEE/AOM among children with congenital sensorineural HL (SNHL) who were fitted with HAs and its effect on the HA fitting. Thirty-six HA fittings carried out between 2017 and 2020 at one rehabilitation center were examined. Medical and audiological information was retrieved for children between 6 and 32 months old. The number of appointments and HA fitting times were recorded. Twenty-eight children were included in the study. Eighteen children, in addition to SNHL, had a conductive component resulting from MEE/AOM. The children with these pathologies required significantly more HA fitting sessions and hearing tests, fewer real ear to coupler difference (RECD) measurements and longer HA fitting periods. The findings indicate that a large number of children fitted with HAs have an additional conductive component that makes the fitting process longer. Since early rehabilitation is necessary for language development, otolaryngologists should be aware of the adverse effects of MEE/AOE on the HA fitting process. It is important to inform parents that when there is a conductive component, the HA fitting process may take longer and that treatment by an otolaryngologist is vital. This study stresses the importance of multidisciplinary cooperation for optimal HA fitting.

Analysis of Corneo-scleral Measurements and Ocular Biometry as Predictors of Scleral Lens Parameters: A Review

Purpose. In the latest years, the interest in scleral lenses is progressively increasing among practitioners and patients from all around the world. An optimal lens fit is necessary to ensure patient comfort and visual quality. However, it is not straightforward to estimate the appropriate scleral lens parameters for a particular patient. This review paper aims to summarize the current state of knowledge in predictors of scleral lens parameters based on corneo-scleral shape. Material and Methods. Literature was reviewed from PubMed. A total of 33 articles were specifically selected for the current study. Results. Even though not all available corneo-scleral meas- urements may be helpful in the fitting experience, the re- fractive state of the cornea, corneal flattest and steepest keratometry, scleral toricity, and axial length have proven to influence the scleral lens parameters and, consequently, the quality of scleral lens fit. Conclusion. The usefulness of corneal, scleral, and corneo- scleral measures is reviewed and critically evaluated. A special effort was made to highlight the clinical implications of the findings. Keywords Corneal topography, scleral profile, profilometry, myopia progression, axial length, contact lens fitting

The ‘Abstainer Question’: Relationships between alcohol use and suicidal ideation in Australian online help-seekers

BackgroundWhen compared to social drinkers, high levels of suicidal ideation have been observed in both heavy alcohol consumers and abstainers. Heavy alcohol use or abstention may indicate different risk pathways to the development of suicidal ideation (SI). MethodsVisitors to a mental health website (N = 1,561) completed a survey, and latent profile analysis (LPA) was used to explore differences in risk factor patterns. Risk factors explored included psychological distress, help-seeking intent, financial wellbeing, thwarted belongingness and perceived burdensomeness. ResultsMost participants (75.1%) reported SI in the past four weeks. A three-class LPA model emerged as the optimal fit: (1) low SI/lower alcohol; (2) high SI/lower alcohol; (3) high SI/high alcohol. Members of the high SI/lower alcohol profile displayed significantly higher psychological distress, thwarted belongingness, and perceived burdensomeness; lower financial wellbeing and help-seeking intentions than both other profiles. Members of the high SI/high alcohol profile were more likely to be male and already receiving help for their psychological distress. Members of the high SI/lower alcohol profile were more likely to be younger and report lower help-seeking intent. LimitationsOur study design was cross-sectional, utilising a largely young, female, English-speaking, help-seeking sample that had chosen to visit a mental health website. ConclusionsWhile the links between heavy alcohol use and suicide risk are well documented, these findings suggest that practitioners should also be alert for abstention patterns, as they may be indicators of underlying psychosocial concerns that a client could be reluctant to disclose.

Stock price index prediction based on SSA-BiGRU-GSCV model from the perspective of long memory

PurposeBecause the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.Design/methodology/approachThe authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.FindingsThree critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.Practical implicationsThe empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.Social implicationsIf the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.Originality/valueAlthough long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

Optimization Method for Hot Air Reflow Soldering Process Based on Robust Design

The process design of hot air reflow soldering is one of the key factors affecting the quality of PCBA (Printed Circuit Board Assembly) component products. In order to improve the product quality during the design process, this paper proposes a robust optimization-based finite element simulation analysis method including significant influencing factor screening, robustness evaluation, robust optimization, and reliability verification for the reflow soldering process. The simulation model of the reflow soldering process temperature field based on experiments is constructed and validated. Sensitivity analysis is used to select important influencing factors, such as the last five set temperature zones (T5 to T9) in the reflow oven and the thermal properties of materials such as PCBs (printed circuit boards), BGAs (ball grid arrays), and solder paste, as well as noise factors like the heating environment during the soldering process. Several surrogate models are used to construct the response surface, and the optimal fitting scheme is selected to effectively avoid poor fitting caused by inappropriate surrogate models. The 6σ robust optimization approach is introduced to evaluate and optimize the robustness of the process design parameter where the heating factor is chosen as the optimization target. The reliability analysis method is employed to validate the product quality. This paper establishes a comprehensive robustness analysis method for hot air reflow soldering, effectively reducing design costs and addressing the lack of robustness analysis in the current hot air reflow soldering process design.

Non-unit ultra-high-speed DC line protection method for HVDC grids using dynamic fitting algorithm with data reconstruction

Existing non-unit protection schemes are inevitably influenced by the high fault-resistance in MMC-HVDC grid, which is a serious problem affecting the rapidity and reliability of fault recognition. This study proposes a dynamic fitting of data reconstruction-based protection method to improve the accuracy and rapidity of HVDC grid protection. Due to the propagation characteristics of the initial voltage traveling wave being unaffected by the transition resistance, the self-adapting exponential fitting algorithm is used to recognize the fault waveform. Then, sliding data window with the voltage traveling wave fit by the optimal basis exponential fitting algorithm could avoid the influence of initial value selection on fitting convergence. Meanwhile, the original fault traveling wave is reconstructed by “Bayesian compressed sensing algorithm (BCSA)” in a low sampling rate. This method could reduce the operation response time of the proposed method to satisfy the ultra-high-speed requirement of MMC-HVDC grid and also adapt the limitation of sampling rate in existing protection devices. Practical case studies in PSCAD/EMTDC and protection prototype testing have demonstrated that the proposed method is effective under different fault locations, fault types, and high fault impedance.

Properties of Cement Thermal Insulation Materials Containing Tailing Waste for Connecting Mines Assessed Using the Orthogonal Method with the Response Surface Method

High temperature in deep mines has become the main concern to limit the production capacity of mines. To mitigate the adverse impact of the underground high-temperature environment on mine production, an orthogonal test was used to design an experimental scheme, i.e., the contents of tailings, glass beads, and ordinary Portland cement PC32.5 (PC) were set as the main horizontal factors to prepare thermal insulation material samples with different combination ratios. Compressive strength and thermal conductivity were investigated, as well as the microstructure. Based on the response surface method (RSM), the interaction between the horizontal factors was explored and analyzed using Design Expert 12 software, and thus non-linear fitting equations were established with the volume fractions of the main horizontal factors as independent variables and compressive strength and thermal conductivity as dependent variables in the analysis parameters of thermal insulation materials. The results showed that errors between the fitted calculated values of compressive strength and thermal conductivity and the experimental results were only 0.42% and 1.2%, respectively, indicating that the process parameters obtained with the optimum fitting ratio under the established fitting equation are highly reliable and have excellent compressive strength and thermal insulation properties. The optimized results obtained by combining the orthogonal method and the RSM show accurate prediction and applicability in the field of thermal insulation materials in mines.

Genome-wide fitness profiling reveals molecular mechanisms that bacteria use to interact with Trichoderma atroviride exometabolites.

Trichoderma spp. are ubiquitous rhizosphere fungi capable of producing several classes of secondary metabolites that can modify the dynamics of the plant-associated microbiome. However, the bacterial-fungal mechanisms that mediate these interactions have not been fully characterized. Here, a random barcode transposon-site sequencing (RB-TnSeq) approach was employed to identify bacterial genes important for fitness in the presence of Trichoderma atroviride exudates. We selected three rhizosphere bacteria with RB-TnSeq mutant libraries that can promote plant growth: the nitrogen fixers Klebsiella michiganensis M5aI and Herbaspirillum seropedicae SmR1, and Pseudomonas simiae WCS417. As a non-rhizosphere species, Pseudomonas putida KT2440 was also included. From the RB-TnSeq data, nitrogen-fixing bacteria competed mainly for iron and required the siderophore transport system TonB/ExbB for optimal fitness in the presence of T. atroviride exudates. In contrast, P. simiae and P. putida were highly dependent on mechanisms associated with membrane lipid modification that are required for resistance to cationic antimicrobial peptides (CAMPs). A mutant in the Hog1-MAP kinase (Δtmk3) gene of T. atroviride showed altered expression patterns of many nonribosomal peptide synthetase (NRPS) biosynthetic gene clusters with potential antibiotic activity. In contrast to exudates from wild-type T. atroviride, bacterial mutants containing lesions in genes associated with resistance to antibiotics did not show fitness defects when RB-TnSeq libraries were exposed to exudates from the Δtmk3 mutant. Unexpectedly, exudates from wild-type T. atroviride and the Δtmk3 mutant rescued purine auxotrophic mutants of H. seropedicae, K. michiganensis and P. simiae. Metabolomic analysis on exudates from wild-type T. atroviride and the Δtmk3 mutant showed that both strains excrete purines and complex metabolites; functional Tmk3 is required to produce some of these metabolites. This study highlights the complex interplay between Trichoderma-metabolites and soil bacteria, revealing both beneficial and antagonistic effects, and underscoring the intricate and multifaceted nature of this relationship.

Cell-Type Composition Affects Adipose Gene Expression Associations With Cardiometabolic Traits.

Understanding differences in adipose gene expression between individuals with different levels of clinical traits may reveal the genes and mechanisms leading to cardiometabolic diseases. However, adipose is a heterogeneous tissue. To account for cell-type heterogeneity, we estimated cell-type proportions in 859 subcutaneous adipose tissue samples with bulk RNA-sequencing (RNA-seq) using a reference single nuclear RNA-seq dataset. Cell-type proportions were associated with cardiometabolic traits; for example, higher macrophage and adipocyte proportions were associated with higher and lower body mass index (BMI), respectively. We evaluated cell-type proportions and BMI as covariates in tests of association between >25,000 gene expression levels and 22 cardiometabolic traits. For >95% of genes, the optimal, or best fit, models included BMI as a covariate, and for 79% of associations, the optimal models also included cell type. After adjusting for the optimal covariates, we identified 2,664 significant associations (P ≤ 2e-6) for 1,252 genes and 14 traits. Among genes proposed to affect cardiometabolic traits based on colocalized genome-wide association study and adipose expression quantitative trait locus signals, 25 showed a corresponding association between trait and gene expression levels. Overall, these results suggest the importance of modeling cell-type proportion when identifying gene expression associations with cardiometabolic traits.

Engineering gain-of-function mutants of a WW domain by dynamics and structural analysis.

Proteins gain optimal fitness such as foldability and function through evolutionary selection. However, classical studies have found that evolutionarily designed protein sequences alone cannot guarantee foldability, or at least not without considering local contacts associated with the initial folding steps. We previously showed that foldability and function can be restored by removing frustration in the folding energy landscape of a model WW domain protein, CC16, which was designed based on Statistical Coupling Analysis (SCA). Substitutions ensuring the formation of five local contacts identified as "on-path" were selected using the closest homolog native folded sequence, N21. Surprisingly, the resulting sequence, CC16-N21, bound to Group I peptides, while N21 did not. Here, we identified single-point mutations that enable N21 to bind a Group I peptide ligand through structure and dynamic-based computational design. Comparison of the docked position of the CC16-N21/ligand complex with the N21 structure showed that residues at positions 9 and 19 are important for peptide binding, whereas the dynamic profiles identified position 10 as allosterically coupled to the binding site and exhibiting different dynamics between N21 and CC16-N21. We found that swapping these positions in N21 with matched residues from CC16-N21 recovers nature-like binding affinity to N21. This study validates the use of dynamic profiles as guiding principles for affecting the binding affinity of small proteins.

Method for solving atmospheric dispersion parameters of radioactive aerosols based on wind-tunnel laser measurement experiments

Certain fraction of radionuclides was inevitably discharged into the atmosphere during the normal operation of nuclear power plants. Therefore, the atmospheric dispersion of these airborne radioactive aerosols is an essential part of radiation environmental assessment during the siting, construction, and operation of nuclear power plants. Gaussian plume model is the most widely used method for this assessment. Evaluation uncertainty of this method mainly depends on the selection of atmospheric dispersion parameters. In this paper, the laser measurement method was carried out in the atmospheric wind-tunnel dispersion experiments of aerosols, and a method for solving atmospheric dispersion parameters based on high-resolution laser scattering images of plumes was proposed. Firstly, a full-frame statistical image of aerosol atmospheric plume trajectories was obtained through high-resolution laser measurement. Secondly, image processing was performed to remove noise, then to acquire light intensity of image pixels, and to make 95.45% (2σ) of the total light intensity fall within the plume trajectory to determine the plume boundary line at last. Finally, the atmospheric dispersion parameters were determined by optimal fitting, using the Gaussian plume model as a benchmark. Compared with the commonly used method for solving atmospheric dispersion parameters based on single-point densitometer detection experiments, the method based on laser measurement proposed in this work has advantages in reflecting directly realistic plume trajectory of both aerosols and particles with large sizes. This study can provide technical support for the accurate evaluation of aerosols or particles atmospheric dispersion.

A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port

Wind energy is an effective way to reduce emissions in ports. However, port wind power generation exhibits strong intermittency and randomness. Predicting port wind speed enables timely scheduling of port operations and improves wind energy utilization efficiency. To achieve high accuracy and rapid prediction of port wind speed, this paper proposes a wind speed prediction model based on the Sparrow Search Algorithm (SSA) optimized Wavelet Neural Network (WNN). Firstly, the SSA is used to optimize the Mean Squared Error (MSE) as the fitness function during the training process of the WNN model, obtaining the optimal fitness value corresponding to the network parameters. Then, the obtained parameters are used as the network model parameters of WNN for wind speed prediction. To validate the effectiveness of the proposed method, the model is validated using the measured wind speed data from the Chuanshan Port Area of Ningbo-Zhoushan Port throughout 2022, and its performance is compared with three other models: SSA–BP, SSA–LSTM, and WNN. The results demonstrate that the proposed prediction model exhibits good performance in port wind speed prediction and outperforms the other comparative models in terms of prediction accuracy and convergence speed.

Short‐term wind power prediction based on the combination of firefly optimization and LSTM

AbstractWith the development of social resources, people's consumption of energy is huge, so renewable energy, such as wind energy, has been widely concerned and developed. Although there has been sufficient development of wind power generation, its output has some problems such as uncertainty, which leads to insufficient utilization of wind energy resources and uneven power output quality level, which brings great challenges to the grid connection. To solve this problem, a short‐term wind power prediction model combining firefly algorithm and long term memory network is proposed. The main motivation of the research is to improve the accuracy of wind power prediction and thus improve the utilization of wind energy resources. Compared with the existing methods, the innovation of FA‐LSTM model lies in the integration of the two algorithms, making full use of the advantages of FA in global search optimization and LSTM in time series data processing, and improving the accuracy and stability of prediction. During the experiment, we used different wind farm data to train and test the model. The results show that the FA‐LSTM model can improve the optimal fitness by more than 50% compared with other algorithms, and the iterative prediction error is smaller. Standard root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the model. The accuracy of RMSE and MAE reached over 97% and 98% respectively. When the test data is highly volatile, the data accuracy of FA‐LSTM model reaches 92% and 94%, and the FA‐LSTM model drops to the stable value faster. FA‐LSTM model has the best fitting degree with the true value curve, and the fitting degree reaches more than 90%. Comparing the actual power and predicted power of different units, the actual power of Unit 1 is 34.875, and the predicted power obtained by FA‐LSTM model is 34.935, with an error of only 0.06. The key finding of this study is that the prediction model combining FA and LSTM has high accuracy and stability in wind power prediction, and can effectively deal with the uncertainty and volatility of wind energy resource utilization. FA‐LSTM model provides an effective solution for wind power prediction, which is helpful to improve the utilization rate of wind energy resources.

COVID-19 transmission driven by age-group mathematical model in Shijiazhuang City of China

BackgroundA COVID-19 outbreak in the rural areas of Shijiazhuang City was attributed to the complex interactions among vaccination, host, and non-pharmaceutical interventions (NPIs). Herein, we investigated the epidemiological characteristics of all reported symptomatic cases by picking Shijiazhuang City, Hebei Province in Northern China as research objective. In addition, we established an age-group mathematical model to perform the optimal fitting and to investigate the dynamical profiles under three scenarios. MethodsAll reported symptomatic cases of Shijiazhuang epidemic (January 2-February 3, 2021) were investigated in our study. The cases were classified by gender, age group and location, the distributions were analyzed by epidemiological characteristics. Furthermore, the reported data from Health Commission of Hebei Province was also analyzed by using an age-group mathematical model by two phases and three scenarios. ResultsShijiazhuang epidemic caused by SARS-CoV-2 wild strain was recorded with the peak 84 cases out of 868 reported symptomatic cases on January 11, 2021, which was implemented with strong NPIs by local government and referred as baseline situation in this study. The research results showed that R0 under baseline situation ranged from 4.47 to 7.72, and Rt of Gaocheng Distinct took 3.72 with 95% confidence interval from 3.23 to 4.35 on January 9, the declining tendencies of Rt under baseline situation were kept till February 3, the value of Rt reached below 1 on January 19 and remained low value up to February 3 for Gaocheng District and Shijiazhuang City during Shijiazhuang epidemic. This indicated Shijiazhuang epidemic was under control on January 19. However, if the strong NPIs were kept, but remote isolation operated on January 11 was not implemented as of February 9, then the scale of Shijiazhuang epidemic reached 9,482 cases from age group who were 60 years old and over out of 31,017 symptomatic cases. The investigation also revealed that Shijiazhuang epidemic reached 132,648 symptomatic cases for age group who were 60 years old and over (short for G2) under risk-based strategies (Scenario A), 58,048 symptomatic cases for G2 under late quarantine strategies (Scenario B) and 207,124 symptomatic cases for G2 under late quarantine double risk strategies (Scenario C), and that the corresponding transmission tendencies of Rt for three scenarios were consistently controlled on Jan 29, 2021. Compared with baseline situation, the dates for controlling Rt below 1 under three scenarios were delayed 10 days. ConclusionsShijiazhuang epidemic was the first COVID-19 outbreak in the rural areas in Hebei Province of Northern China. The targeted interventions adopted in early 2021 were effective to halt the transmission due to the implementation of a strict and village-wide closure. However we found that age group profile and NPIs played critical rules to successfully contain Shijiazhuang epidemic, which should be considered by public health policies in rural areas of mainland China during the dynamic zero-COVID policy.