A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions

Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.

APPROXIMATE POWER OF HETEROSCEDASTICITY TEST IN NONLINEAR MODELS WITH ARIMA（0,1,0） ERRORS

This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power calculations for the score test of heteroscedasticity in European rabbit data （Ratkowsky, 1983）. Simulation studies are presented which indicate that the asymptotic approximation to the finite-sample situation is good over a wide range of parameter configurations.

Development and Application of a Power Law Constitutive Model for Eddy Current Dampers

Eddy current dampers (ECDs) have emerged as highly desirable solutions for vibration control due to theirexceptional damping performance and durability. However, the existing constitutive models present challenges tothe widespread implementation of ECD technology, and there is limited availability of finite element analysis (FEA)software capable of accurately modeling the behavior of ECDs. This study addresses these issues by developing anewconstitutivemodel that is both easily understandable and user-friendly for FEAsoftware. By utilizing numericalresults obtained from electromagnetic FEA, a novel power law constitutive model is proposed to capture thenonlinear behavior of ECDs. The effectiveness of the power law constitutive model is validated throughmechanicalproperty tests and numerical seismic analysis. Furthermore, a detailed description of the application process ofthe power law constitutive model in ANSYS FEA software is provided. To facilitate the preliminary design ofECDs, an analytical derivation of energy dissipation and parameter optimization for ECDs under harmonicmotionis performed. The results demonstrate that the power law constitutive model serves as a viable alternative forconducting dynamic analysis using FEA and optimizing parameters for ECDs.

Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy

The accurate prediction of photovoltaic(PV)power generation is an important basis for hybrid grid scheduling.With the expansion of the scale of PV power plants and the popularization of distributed PV,this study proposes a multilayer PV power generation prediction model based on transfer learning to solve the problems of the lack of data on new PV bases and the low accuracy of PV power generation prediction.The proposed model,called DRAM,concatenates a dilated convolutional neural network(DCNN)module with a bidirectional long short-term memory(BiLSTM)module,and integrates an attention mechanism.First,the processed data are input into the DCNN layer,and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data.Subsequently,the temporal characteristics between the features are extracted in the BiLSTM layer.Finally,an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables.In addition,the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model.In this study,the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed.

Stability of GM(1,1) power model on vector transformation

The morbidity problem of the GM（1,1） power model in parameter identification is discussed by using multiple and rotation transformation of vectors. Firstly we consider the morbidity problem of the special matrix and prove that the condition number of the coefficient matrix is determined by the ratio of lengths and the included angle of the column vector, which could be adjusted by multiple and rotation transformation to turn the matrix to a well-conditioned one. Then partition the corresponding matrix of the GM（1,1） power model in accordance with the column vector and regulate the matrix to a well-conditioned one by multiple and rotation transformation of vectors, which completely solve the instability problem of the GM（1,1） power model. Numerical results show that vector transformation is a new method in studying the stability problem of the GM（1,1） power model.

Establishment of a humanized ST6GAL1 mouse model for influenza research

Background:This study aimed to construct and characterize a humanized influenza mouse model expressing hST6GAL1.Methods:Humanized fragments,consisting of the endothelial cell-specific K18 promoter,human ST6GAL1-encoding gene,and luciferase gene,were microinjected into the fertilized eggs of mice.The manipulated embryos were transferred into the oviducts of pseudopregnant female mice.The offspring were identified using PCR.Mice exhibiting elevated expression of the hST6GAL1 gene were selectively bred for propagation,and in vivo analysis was performed for screening.Expression of the humanized gene was tested by performing immunohistochemical(IHC)analysis.Hematologic and biochemical analyses using the whole blood and serum of humanized hST6GAL1 mice were performed.Results:Successful integration of the human ST6GAL1 gene into the mouse genome led to the overexpression of human SiaT ST6GAL1.Seven mice were identified as carrying copies of the humanized gene,and the in vivo analysis indicated that hST6GAL1gene expression in positive mice mirrored influenza virus infection characteristics.The IHC results revealed that hST6GAL1 was expressed in the lungs of humanized mice.Moreover,the hematologic and biochemical parameters of the positive mice were within the normal range.Conclusion:A humanized influenza mouse model expressing the hST6GAL1 gene was successfully established and characterized.

Estimation of evapotranspiration from artificial forest in mountainous areas of western Loess Plateau based on HYDRUS-1D model

Evapotranspiration is the most important expenditure item in the water balance of terrestrial ecosystems,and accurate evapotranspiration modeling is of great significance for hydrological,ecological,agricultural,and water resource management.Artificial forests are an important means of vegetation restoration in the western Loess Plateau,and accurate estimates of their evapotranspiration are essential to the management and development of water use strategies for artificial forests.This study estimated the soil moisture and evapotranspiration based on the HYDRUS-1D model for the artificial Platycladus orientalis(L.)Franco forest in western mountains of Loess Plateau,China from 20 April to 31 October,2023.Moreover,the influence factors were identified by combining the correlation coefficient method and the principal component analysis(PCA)method.The results showed that HYDRUS-1D model had strong applicability in portraying hydrological processes in this area and revealed soil water surplus from 20 April to 31 October,2023.The soil water accumulation was 49.64 mm;the potential evapotranspiration(ET_(p))was 809.67 mm,which was divided into potential evaporation(E_(p);95.07 mm)and potential transpiration(T_(p);714.60 mm);and the actual evapotranspiration(ET_(a))was 580.27 mm,which was divided into actual evaporation(E_(a);68.27 mm)and actual transpiration(T_(a);512.00 mm).From April to October 2023,the ET_(p),E_(p),T_(p),ET_(a),E_(a),and T_(a) first increased and then decreased on both monthly and daily scales,exhibiting a single-peak type trend.The average ratio of T_(a)/ET_(a) was 0.88,signifying that evapotranspiration mainly stemmed from transpiration in this area.The ratio of ET_(a)/ET_(p) was 0.72,indicating that this artificial forest suffered from obvious drought stress.The ET_(p) was significantly positively correlated with ET_(a),and the R^(2) values on the monthly and daily scales were 0.9696 and 0.9635(P<0.05),respectively.Furthermore,ET_(a) was significantly positively correlated with temperature,solar radiation,and wind speed,and negatively correlated with relative humidity and precipitation(P<0.05);and temperature exhibited the highest correlation with ET_(a).Thus,ET_(p) and temperature were the decisive contributors to ET_(a) in this area.The findings provide an effective method for simulating regional evapotranspiration and theoretical reference for water management of artificial forests,and deepen understanding of effects of each influence factors on ET_(a) in arid areas.

Assessment of Axial Power Peaking Factors in GHARR-1 LEU Core: A Decadal Simulation Analysis

This study aims to thoroughly investigate the axial power peaking factors (PPF) within the low-enriched uranium (LEU) core of the Ghana Research Reactor-1 (GHARR-1). This study uses advanced simulation tools, like the MCNPX code for analysing neutron behavior and the PARET/ANL code for understanding power variations, to get a clearer picture of the reactor’s performance. The analysis covers the initial six years of GHARR-1’s operation and includes projections for its whole 60-year lifespan. We closely observed the patterns of both the highest and average PPFs at 21 axial nodes, with measurements taken every ten years. The findings of this study reveal important patterns in power distribution within the core, which are essential for improving the safety regulations and fuel management techniques of the reactor. We provide a meticulous approach, extensive data, and an analysis of the findings, highlighting the significance of continuous monitoring and analysis for proactive management of nuclear reactors. The findings of this study not only enhance our comprehension of nuclear reactor safety but also carry significant ramifications for sustainable energy progress in Ghana and the wider global context. Nuclear engineering is essential in tackling global concerns, such as the demand for clean and dependable energy sources. Research on optimising nuclear reactors, particularly in terms of safety and efficiency, is crucial for the ongoing advancement and acceptance of nuclear energy.

Tidal modeling based on satellite altimetry observations of TOPEX/ Poseidon, Jason1, Jason2, and Jason3 with high prediction capability: A case study of the Baltic Sea

This research aims to optimize the utilization of long-term sea level data from the TOPEX/Poseidon,Jason1,Jason2,and Jason3 altimetry missions for tidal modeling.We generate a time series of along-track observations and apply a developed method to produce tidal models with specific tidal constituents for each location.Our tidal modeling methodology follows an iterative process:partitioning sea surface height(SSH)observations into analysis/training and prediction/validation parts and ultimately identi-fying the set of tidal constituents that provide the best predictions at each time series location.The study focuses on developing 1256 time series along the altimetry tracks over the Baltic Sea,each with its own set of tidal constituents.Verification of the developed tidal models against the sSH observations within the prediction/validation part reveals mean absolute error(MAE)values ranging from 0.0334 m to 0.1349 m,with an average MAE of 0.089 m.The same validation process is conducted on the FES2014 and EOT20 global tidal models,demonstrating that our tidal model,referred to as BT23(short for Baltic Tide 2023),outperforms both models with an average MAE improvement of 0.0417 m and 0.0346 m,respectively.In addition to providing details on the development of the time series and the tidal modeling procedure,we offer the 1256 along-track time series and their associated tidal models as supplementary materials.We encourage the satellite altimetry community to utilize these resources for further research and applications.

Choice of the Best Production Prediction Model for the Zagtouli Solar Power Plant in Burkina-Faso

In this paper, we present a study on the prediction of the power produced by the 33 MWp photovoltaic power plant at Zagtouli in Burkina-Faso, as a function of climatic factors. We identified models in the literature, namely the Benchmark, input/output, Marion, Cristo-fri, Kroposki, Jones-Underwood and Hatziargyriou prediction models, which depend exclusively on environmental parameters. We then compared our linear model with these seven mathematical models in order to determine the most optimal prediction model. Our results show that the Hatziargyriou model is better in terms of accuracy for power prediction.

An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

Numerical Models and Methods of Atmospheric Parameters Originating in the Formation of the Earth’s Climatic Cycle

Atmospheric models are physical equations based on the ideal gas law. Applied to the atmosphere, this law yields equations for water, vapor (gas), ice, air, humidity, dryness, fire, and heat, thus defining the model of key atmospheric parameters. The distribution of these parameters across the entire planet Earth is the origin of the formation of the climatic cycle, which is a normal climatic variation. To do this, the Earth is divided into eight (8) parts according to the number of key parameters to be defined in a physical representation of the model. Following this distribution, numerical models calculate the constants for the formation of water, vapor, ice, dryness, thermal energy (fire), heat, air, and humidity. These models vary in complexity depending on the indirect trigonometric direction and simplicity in the sum of neighboring models. Note that the constants obtained from the equations yield 275.156˚K (2.006˚C) for water, 273.1596˚K (0.00963˚C) for vapor, 273.1633˚K (0.0133˚C) for ice, 0.00365 in/s for atmospheric dryness, 1.996 in2/s for humidity, 2.993 in2/s for air, 1 J for thermal energy of fire, and 0.9963 J for heat. In summary, this study aims to define the main parameters and natural phenomena contributing to the modification of planetary climate. .

成纤维细胞生长因子受体1 抑制剂对胶原诱导关节炎模型大鼠骨破坏的影响

背景:课题组前期的研究表明靶向成纤维细胞生长因子受体1(fibroblast growth factor receptor 1,FGFR1)可能是治疗类风湿性关节炎的有效靶点。目的:探讨FGFR1抑制剂(PD173074)对胶原诱导关节炎模型大鼠骨破坏的影响。方法:将25只雌性SD大鼠随机分为5组,正常对照组、模型组、甲氨蝶呤组、PD173074低剂量组、PD173074高剂量组。除正常对照组外,其余各组大鼠建立Ⅱ型胶原诱导关节炎模型。造模成功后正常组及模型组大鼠腹腔注射无菌PBS,甲氨蝶呤组药物注射剂量为1.04 mg/kg,PD173074低剂量组和高剂量组药物注射剂量分别为5,20 mg/kg,1次/周。给药4周后取材,观察大鼠临床症状以及关节肿胀情况,踝关节Micro-CT三维重建及分析,观察踝关节病理变化,检测关节周围血管生成情况及核因子κB受体活化因子配体的表达,检测关节滑膜中p-FGFR1、血管内皮生长因子A、抗酒石酸酸性磷酸酶的表达,观察肝、脾、肾病理变化并计算肝、脾、肾指数。结果与结论:①PD173074能够减轻模型大鼠踝关节临床症状及关节肿胀,延缓骨质丢失,改善骨结构,减轻关节滑膜侵袭以及软骨骨侵蚀,降低关节周围破骨细胞数量,抑制关节滑膜组织中的血管生成,降低核因子κB受体活化因子配体的表达,抑制FGFR1磷酸化蛋白、抗酒石酸酸性磷酸酶和血管内皮生长因子A的蛋白表达。②大鼠肝、脾、肾病理观察表明经过PD173074治疗后无明显的毒副作用。③研究证明了FGFR1抑制剂能够延缓Ⅱ型胶原诱导关节炎模型大鼠关节炎症及骨破坏的进展,并抑制血管的生成。初步验证了PD173074在Ⅱ型胶原诱导关节炎模型中的治疗作用,其可能是通过抑制FGFR1磷酸化发挥作用,为寻找类风湿性关节炎新的治疗靶点提供了方向。

Mathematical Modeling and Control Algorithm Development for Bidirectional Power Flow in CCS-CNT System

As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.

Application of Modelica Based Multi- Domain Modeling and Simulation for Gravity-1

In the R&D phase of Gravity-1(YL-1), a multi-domain modeling and simulation technology based on Modelica language was introduced, which was a recent attempt in the practice of modeling and simulation method for launch vehicles in China. It realizes a complex coupling model within a unified model for different domains, so that technologists can work on one model. It ensured the success of YL-1 first launch mission, supports rapid iteration, full validation, and tight design collaboration.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

Power V和机械式随钻测斜仪在黑池1井的应用

详细介绍了Power V垂直钻井系统和机械式无线随钻测斜仪的组成、工作原理及其在黑池1井的应用情况。现场应用表明,Power V系统可以解决高陡地层和自然造斜率大地层的垂直钻井问题,改善井身质量,提高钻井速度;机械式无线随钻测斜仪具有操作使用方便、抗高温高压、测量结果稳定等优点,既能满足应用Power V系统垂直钻井过程中随钻监测井斜的需要,又可降低钻井成本。

基于波长选择方法Modeling Power的黑木耳产地判别研究

应用近红外光谱技术结合波长选择方法(modeling power,MP)实现了黑木耳产地的快速准确判别。共收集4个产地240个黑木耳样本,通过光谱扫描,建立了最优的偏最小二乘(PLS)判别模型。同时应用MP选择对黑木耳产地判别的有效波长,作为输入变量,建立最小二乘-支持向量机(MP-LS-SVM)模型。比较了3种MP选择波长的阈值方法,分别为MP值大于0.95,0.90和(0.90+Peak),并建立了相应的MP-LS-SVM模型。以预测集样本的准确判别率作为模型评价标准,分别设定预测的残差绝对值标准0.1,0.2和0.5。预测结果表明,MP-LS-SVM(0.90+Peak)模型在残差标准为0.1,0.2和0.5时的判别效果均为最优,正确判别率分别为98.3%,100%和100%。说明ModelingPower是一种非常有效的波长选择方法,应用近红外光谱技术结合MP-LS-SVM进行黑木耳产地判别是可行的,并获得了满意的判别精度。

Variable-based Ramberg-Osgood constitutive model of power spinning bushing

The influences of power spinning process parameters on the mechanical properties of spinning parts were analyzed with an SXD100/3-CNC numerical control power spinning machine.The unidirectional tensile tests were carried out.Based on the experimental data,a ternary quadratic regression equation was established by orthogonal experiment.The Ramberg-Osgood constitutive model of tin-bronze connecting rod bushing was obtained.Referred to the constitutive relation of macroscopic incremental,the incremental elastoplastic constitutive relation of spinning parts was deduced based on the Mises yield criterion and kinematic hardening model.The results can be applied to the elastoplastic analysis in finite element numerical simulation.

A Thermal-Conscious Integrated Circuit Power Model and Its Impact on Dynamic Voltage Scaling Techniques

We propose a novel thermal-conscious power model for integrated circuits that can accurately predict power and temperature under voltage scaling. Experimental results show that the leakage power consumption is underestimated by 52 % if thermal effects are omitted. Furthermore, an inconsistency arises when energy and temperature are simultaneously optimized by dynamic voltage scaling. Temperature is a limiting factor for future integrated circuits,and the thermal optimization approach can attain a temperature reduction of up to 12℃ with less than 1.8% energy penalty compared with the energy optimization one.