A Distributed Photovoltaics Ordering Grid-Connected Method for Analyzing Voltage Impact in Radial Distribution Networks

In recent years,distributed photovoltaics(DPV)has ushered in a good development situation due to the advantages of pollution-free power generation,full utilization of the ground or roof of the installation site,and balancing a large number of loads nearby.However,under the background of a large-scale DPV grid-connected to the county distribution network,an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV.Therefore,a DPV orderly grid-connected method based on photovoltaics grid-connected order degree(PGOD)is proposed.This method aims to orderly analyze the change of voltage in the distribution network when large-scale DPV will be connected.Firstly,based on the voltagemagnitude sensitivity(VMS)index of the photovoltaics permitted grid-connected node and the acceptance of grid-connected node(AoGCN)index of other nodes in the network,thePGODindex is constructed to determine the photovoltaics permitted grid-connected node of the current photovoltaics grid-connected state network.Secondly,a photovoltaics orderly grid-connected model with a continuous updating state is constructed to obtain an orderly DPV grid-connected order.The simulation results illustrate that the photovoltaics grid-connected order determined by this method based on PGOD can effectively analyze the voltage impact of large-scale photovoltaics grid-connected,and explore the internal factors and characteristics of the impact.

Comprehensive Evaluation of Distributed PV Grid-Connected Based on Combined Weighting Weights and TOPSIS-RSR Method

To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and objective combination of assignment and technique for order preference by similarity to an ideal solution(TOPSIS)—rank sum ratio(RSR)(TOPSIS-RSR)method.Based on the traditional distribution network evaluation system,a comprehensive evaluation system has been constructed.It fully considers the new development requirements of distributed PV access on the environmental friendliness and absorptive capacity of the distribution grid and comprehensively reflects the impact of distributed PV grid connection.The analytic hierarchy process(AHP)was used to determine the subjective weights of the primary indicators,and the Spearman consistency test was combined to determine the weights of the secondary indicators based on three objective assignment methods.The subjective and objective combination weights of each assessment indicator were calculated through the principle of minimum entropy.Calculate the distance between the indicators to be evaluated and the positive and negative ideal solutions,the relative closeness ranking,and qualitative binning by TOPSIS-RSR method to obtain the comprehensive evaluation results of different scenarios.By setting up different PV grid-connected scenarios and utilizing the IEEE33 node simulation algorithm,the correctness and effectiveness of the proposed subject-object combination assignment and integrated assessment method are verified.

On the Adaptability Range, Self-Selection, and Economic Nature of Biological Evolution

This article employs a combined approach of biology and economics to reveal that biological evolution has an economic nature, evolving towards improved energy efficiency. The orthodox Darwinian theory of evolution describes evolution as the random variation of organisms and their survival through natural selection. In fact, the natural environment itself is a constantly changing context, and the strategy to adapt to this change is to enhance behavioral capabilities, thereby expanding the range and dimensions of behavior. Therefore, the improvement of behavioral capabilities is an important aspect of evolution. The enhancement of behavioral capabilities expands the range of adaptation to the natural environment and increases the space for behavioral choices. Within this space of behavioral choices, some options are more effective and superior to others;thus, the ability to select is necessary to make the improved behavioral capabilities more beneficial to the organism itself. The birth and development of the brain serve the purpose of selection. By using the brain to make selections, at least the “better” behavior will be chosen between two alternatives. Once the better behavior yields better results, and the organism can associate these results with the corresponding behavior, it will persist in this behavior. The persistent repetition of a behavior over generations will form a habit. Habits passed down through generations constitute a new environment, causing the organism’s genes to activate or deactivate certain functions, ultimately leading to genetic changes that are beneficial to that habit. Since the brain’s selection represents the organism’s self-selection, it differs from random variation;it is also a rational selection, choosing behaviors that either obtain more energy or reduce energy consumption. Thus, this evolution possesses an economic nature.

Application and Adaptability of Ornamental Grasses in Road Flower Borders in Hefei, Anhui Province

In-depth research was conducted on the types,seasonal patterns,spatial levels,overwintering,summering,drought resistance,and disease and pest resistance of ornamental grasses in the road flower borders of Hefei,Anhui Province.Through field investigations,a total of 36 ornamental grasses were identified,with Liliaceae having the most species and the Lamiaceae the least.The results show that there are more warm season ornamental grasses,of 23 species;Acorus gramineus and Melinis minutiflora have poor overwintering ability;Arrhenatherum elatius,Festuca glauca,and Stipa tenuissima have poor summer tolerance;most ornamental grasses have strong drought resistance and resistance to pests and diseases.The comprehensive investigation shows that ornamental grasses have good application prospects and adaptability in the road flower borders in Hefei city.

Change in self-image pressure level before and after autologous fat breast augmentation and its effect on social adaptability

BACKGROUND There is an increasingly strong demand for appearance and physical beauty in social life,marriage,and other aspects with the development of society and the improvement of material living standards.An increasing number of people have improved their appearance and physical shape through aesthetic plastic surgery.The female breast plays a significant role in physical beauty,and droopy or atrophied breasts can frequently lead to psychological inferiority and lack of confidence in women.This,in turn,can affect their mental health and quality of life.AIM To analyze preoperative and postoperative self-image pressure-level changes of autologous fat breast augmentation patients and their impact on social adaptability.METHODS We selected 160 patients who underwent autologous fat breast augmentation at the First Affiliated Hospital of Xinxiang Medical University from January 2020 to December 2022 using random sampling method.The general information,selfimage pressure level,and social adaptability of the patients were investigated using a basic information survey,body image self-assessment scale,and social adaptability scale.The self-image pressure-level changes and their effects on the social adaptability of patients before and after autologous fat breast augmentation were analyzed.RESULTS We collected 142 valid questionnaires.The single-factor analysis results showed no statistically significant difference in the self-image pressure level and social adaptability score of patients with different ages,marital status,and monthly income.However,there were significant differences in social adaptability among patients with different education levels and employment statuses.The correlation analysis results revealed a significant correlation between the self-image pressure level and social adaptability score before and after surgery.Multiple factors analysis results showed that the degree of concern caused by appearance in selfimage pressure,the degree of possible behavioral intervention,the related distress caused by body image,and the influence of body image on social life influenced the social adaptability of autologous fat breast augmentation patients.CONCLUSION The self-image pressure on autologous fat breast augmentation patients is inversely proportional to their social adaptability.

Dynamic phasor-based hybrid simulation for multi-inverter grid-connected system

To realize the efficient transient simulation of a grid-connected power generation system based on multiple inverters, this paper proposes a hybrid simulation method integrating the models of electromagnetic transient and dynamic phasors. Based on a demonstration of the concepts and properties of dynamic phasors, the models of single-phase and three-phase inverters described by dynamic phasors are established first. Considering the numerical compatibility problem between dynamic phasors and instantaneous values, an interface scheme between dynamic phasors and instantaneous values is designed, and the efficiency and precision differences of various transformation methods are compared in detail.Finally, by utilizing MATLAB/Simulink, a hybrid simulation platform of a multi-inverter grid-connected system is built, and the efficiency and accuracy of the hybrid simulation are validated via comparison with the full electromagnetic transient simulation.

Contraction of Heat Shock Protein 70 Genes Uncovers Heat Adaptability of Ostrea denselamellosa

The milin oyster Ostrea denselamellosa is a live-bearing species with a sharp decline in the natural population.Unlike other oysters,O.denselamellosa lives in the subtidal zone and its adaptability to heat,salinity,etc.is different from most other oys-ters.Heat shock proteins 70(HSP70)are a family of conserved ubiquitously expressed heat shock proteins which are produced in re-sponse to stressful conditions,especially heat.In this study,we identified Hsp70 genes through bioinformatic analysis in five species of oyster.Among them,O.denselamellosa holds the fewest number of Hsp70 genes,which may be one of the reasons why O.den-selamellosa cannot tolerate high temperatures.The conserved motifs and gene structures of Hsp70B2 sub-family and other types of Hsp70 in O.denselamellosa were different from that of Hspa12 sub-family,which may be due to performing necessary multiple phy-siological functions.Transcription profile analysis for Hsp70 genes of O.denselamellosa indicated that gills play an important role in responding to multiple external challenges.In addition,synteny analysis of Hsp70 genes among O.denselamellosa,O.edulis and Cras-sostrea ariakensis showed that Hsp70 genes in genus of Ostrea genome might have evolved from a common ancestor with genus of Crassostrea.In short,our results lay the foundation for further investigation of the evolution of O.denselamellosa Hsp70 genes and heat adaptability.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Relationship between family cohesion/adaptability and postpartum depressive symptoms:A single-center retrospective study

BACKGROUND Depression is the most common mental illness in postpartum mothers,and the etiology of postpartum depression remains poorly understood.Over the past several decades,studies have reported that postpartum depression is caused by multiple factors,such as genetic,psychological,pregnancy,and environmental factors,with the family environment being an important environmental factor.The theory of family cohesion and adaptability put forward by Olson is a classic model that describes the level of family function.However,to date,this model has not been examined regarding its applicability to patients with postpartum depression.AIM To investigate the relationship between family cohesion and adaptability and the risk of postpartum depressive symptoms.METHODS We retrospectively analyzed 1446 patients admitted to the postpartum healthcare clinic of the Affiliated Foshan Maternity and Child Healthcare Hospital from April 2021 to December 2021.Patients were grouped according to whether postpartum depression symptoms were reported(symptoms,n=454;no symptoms,n=992).All patients completed the Edinburgh Postpartum Depression Scale and the Chinese version of the Family Cohesion and Adaptability Assessment Scale II.Baseline and clinical data were compared between groups.Univariate regression analysis was used to investigate the association between different types of family cohesion and postpartum depressive symptoms and the association between different family adaptability types and postpartum depressive symptoms.RESULTS After adjusting for age,education,occupation,gravidity,parity,and mode of delivery,disengaged[adjusted odds ratio(AOR)=3.36,95%CI:1.91-5.91],and separated(AOR=1.97,95%CI:1.34-2.90)family cohesion types showed a higher risk of postpartum depression than the connection type,whereas the enmeshed type(AOR=0.38,95%CI:0.28-0.51)protected against postpartum depressive symptoms.Rigid(AOR=4.41,95%CI:3.02-6.43)and structured families(AOR=1.88,95%CI:1.34-2.63)had a higher risk of postpartum depressive symptoms than flexible families,whereas chaotic families(AOR=0.35,95%CI:0.24-0.51)protected against postpartum depressive symptoms.CONCLUSION Family cohesion and adaptability are influencing factors for postpartum depressive symptoms,with higher family cohesion and adaptability being associated with a lower risk of postpartum depressive symptoms.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Study on Adaptability of Introduced Pear Varieties

[Objectives]To study the adaptability of introduced pear.[Methods]Five pear varieties,"Aidang"pear,"Taiwan Zaomi"pear,"Cuiguan"pear,"Tianjin Yali"pear and"Zaosheng Xinshui"pear,were introduced.Then,using these five varieties,the phenology of pear trees,various characters of fruit,stress resistance(heat tolerance and cold tolerance)of varieties were studied.[Results]The plants of 5 varieties of pear trees grew fast and were robust;in late March,it went into the flowering period;"Aidang"pear fruit had a certain number of stone cells;"Taiwan Zaomi"pear had the highest sweetness;"Cuiguan"pear had the largest fruit;these five varieties of pear trees had good water resistance,heat resistance and cold resistance.[Conclusions]This study can provide a reference for the introduction of pear trees,and can also provide a practical basis for the large-scale planting of pear trees.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

Effects of sports on school adaptability, resilience and cell phone addiction tendency of high school students

BACKGROUND Sport help promote healthy physical and mental development of high school students.To date,there have been few studies on the effect of sport on school adaptability,resilience and cell phone addiction tendency of high school students.AIM To explore the effects of sports on school adaptability,resilience and cell phone addiction of high school students.METHODS A stratified random sampling method was used to select 600 students from two high schools in Lhasa,and Physical Activity Rating Scale,School Adjustment Scale for High School Students,Chinese Adolescents Resilience Scale,and Mobile Phone Addiction Index were used for field questionnaire survey.RESULTS Sport was positively correlated with school adaptability and resilience(P<0.01),school adaptability was positively correlated with resilience(P<0.01)and cell phone addiction was negatively correlated with sports,school adaptability and resilience(P<0.01).The direct effect of sport on cell phone addiction was significant(P<0.001),accounting for 20.51%of the total effect.The mediating effect of school adaptability on sport and cell phone addiction was significant(P<0.001),accounting for 17.38%and 35.36%of the total effect value,respectively.School adjustment and resilience had a significant chain-mediated effect on sport and cell phone addiction(P<0.001),accounting for 26.75%of the total effect.CONCLUSION Sport affected the cell phone addiction tendency of senior high school students through the mediating effect of school adaptability and resilience,and through the chain mediation effect of both.

Optimization Strategy for Passive Form Design of Architectural Grey Space under the Background of Climate Adaptability

Taking the bottom grey space with great influence on outdoor thermal comfort as the research object,this paper summarizes the morphological characteristics and climate response methods of two types of bottom grey space:overhead grey space and canopy grey space.The spatial form indexes that greatly affect the ecological performance of architectural grey space such as ventilation,shading,etc.are discussed,and two passive spatial form indexes of spatial scale and location orientation are studied.According to the research of related scholars and literature summary,the optimization strategies for passive form design of architectural grey space based on climate adaptability are put forward,which will provide a reference for the climate adaptive design of architectural grey space,and helps to improve the outdoor thermal environment from the micro scale and create a better living environment.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

Adaptability analysis and optimization of log suite for uncased wellbores of shale gas reservoirs:A case study of Sichuan Basin,China

Significant progress has been made in the exploration and development of unconventional gas resources in China since the beginning of the 21st century.With a rapid increase in yield,the exploration and development of shale gas have been upgraded to a level of national strategy.Logging is one of the core technologies in gas exploration and development.However,logging technologies face the challenges of complex geological conditions and well casing environment,high temperature,high pressure,and strong heterogeneity.Despite the rich experience accumulated,unified understanding and implementation specifications are yet to be established for shale gas well logging.Given the analysis and comparison of the effectiveness and adaptability of logging technologies at different stages of shale gas exploration and development for the Sichuan Basin,this study optimizes the log suite to meet the demand for highefficiency exploration and development of shale gas.According to the adaptability analysis of shale gas log suite,the mandatory logging items of exploratory wells should include caliper log(CAL),natural gamma ray spectrometry(NGS)log,spontaneous potential(SP)log,directional survey,borehole compensated sonic log,litho-density log(LDL),compensated neutron log(CNL),dual laterologmicrospherically focused log/dual induction-laterolog log/array induction log,temperature log,elemental capture spectroscopy(ECS)log,formation microimager(FMI)log,and array sonic log.The log suite of appraisal wells is almost the same as that for exploratory wells,excluding the ECS log and microresistivity scanning imaging.Meanwhile,for the logging of horizontal/highly-deviated holes,the log suite of vertical wells should be referred to according to specific well conditions.

Operations Improvement Function and Organisational Adaptability of Petroleum Tank Farms in South-South, Nigeria

The study examined the nexus between operations improvement function (dimensioned by contingency planning, benchmarking and continuous improvement processes) and organisational adaptability of Petroleum tank farms in South-South, Nigeria. The contingency theory and the theory of routine dynamics underpinned the study, and positivism was the underlying philosophy. The study adopted the cross-sectional survey through the use of questionnaire. 820 middle and top-level managers constituted the elements of the population, and the Krejcie & Morgan’s formula was used to determine the sample size of 262 respondents. Structural Equation Modeling was deployed to test the hypotheses at a 0.05 significance level. The results showed that contingency planning;benchmarking and continuous improvement processes all have a significant positive relationship with organisational adaptability of Petroleum tank farms in South-South, Nigeria. The study concludes that Petroleum tank farms’ operations should focus on the adoption of contingency planning, benchmarking and continuous improvement processes to enhance organisational adaptability. Therefore, it is recommended that the management of Petroleum tank farms should put in place mechanisms to advance continuous improvement processes by allocating the necessary amount of resources, such as energy, time and money, in order to promote the continuous development of the continuous improvement systems. Furthermore, managers of Petroleum tank farms should make better the adoption of contingency planning, ensuring that there is as much necessary training and information for employees on how to act during a crises situation, in order to evaluate safety and prepare in advance for recovery from disasters.

An Adaptive Program Recommendation System for Multi-User Sharing Environment

More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.