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.

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.

Frost deformation and microstructure evolution of porous rock under uniform and unidirectional freeze-thaw conditions

The frost deterioration and deformation of porous rock are commonly investigated under uniform freeze-thaw(FT)conditions.However,the unidirectional FT condition,which is also prevalent in engineering practice,has received limited attention.Therefore,a comparative study on frost deformation and microstructure evolution of porous rock under both uniform and unidirectional FT conditions was performed.Firstly,frost deformation experiments of rock were conducted under cyclic uniform and unidirectional FT action,respectively.Results illustrate that frost deformation of saturated rock exhibits isotropic characteristics under uniform FT cycles,while it shows anisotropic characteristics under unidirectional FT condition with both the frost heaving strain and residual strain along FT direction much higher than those perpendicular to FT direction.Moreover,the peak value and residual value of cumulative frost strain vary as logarithmic functions with cycle number under both uniform and unidirectional FT conditions.Subsequently,the microstructure evolution of rock suffered cyclic uniform and unidirectional FT action were measured.Under uniform FT cycles,newly generated pores uniformly distribute in rock and pore structure of rock remains isotropic in micro scale,and thus the frost deformation shows isotropic characteristics in macro scale.Under unidirectional FT cycles,micro-cracks or pore belts generate with their orientation nearly perpendicular to the FT direction,and rock structure gradually becomes anisotropic in micro scale,resulting in the anisotropic characteristics of frost deformation in macro scale.

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.

Fractal Study on the Evolution of Micro-Pores in Concrete Under Freeze-Thaw

After exposure to freeze-thaw cycles, scanning electron microscopy(SEM) and nuclear magnetic resonance(NMR) were used to test the four mixtures. The microstructure is qualitatively analyzed from the 2D SEM image and the 3D pore distribution curve before and after freezing and thawing. The fractal dimension is utilized to characterize the two-dimensional topography image and the three-dimensional pore distribution, quantitatively. The results reveal that the surface porosity and volume porosity increase as the freeze-thaw action increases. Self-similarity characteristics exist in micro-damage inside the concrete. In the fractal dimension, it is possible to characterize pore evolution quantitatively. The fractal dimension correlates with pore damage evolution. The fractal dimension effectively quantitatively characterizes micro-damage features at various scales from the local to the global level.

Freeze-thaw cycles and associated geomorphology in a post-glacial environment:current glacial,paraglacial,periglacial and proglacial scenarios at Pico de Orizaba volcano,Mexico

The glacial history of Pico de Orizaba indicates that during the Last Glacial Maximum,its icecap covered up to~3000 m asl;due to the air temperature increasing,its main glacier has retreated to 5050 m asl.The retraction of the glacier has left behind an intense climatic instability that causes a high frequency of freeze-thaw cycles of great intensity;the resulting geomorphological processes are represented by the fragmentation of the bedrock that occupies the upper parts of the mountain.There is a notable lack of studies regarding the fragmentation and erosion occurring in tropical high mountains,and the associated geomorphological risks;for this reason,as a first stage of future continuous research,this study analyzes the freezing and thawing cycles that occur above 4000 m asl,through continuous monitoring of surface ground temperature.The results allow us to identify and characterize four zones:glacial,paraglacial,periglacial and proglacial.It was found that the paraglacial zone presents an intense drop of temperature,of up to~9℃ in only sixty minutes.The rock fatigue and intense freeze-thaw cycles that occur in this area are responsible for the high rate of rock disintegration and represent the main factor of the constant slope dynamics that occur at the site.This activity decreases,both in frequency and intensity,according to the distance to the glacier,which is where the temperature presents a certain degree of stability,until reaching the proglacial zone,where cycles are almost non-existent,and therefore there is no gelifraction activity.The geomorphological processes have resulted in significant alterations to the mountain slopes,which can have severe consequences in terms of risk and water.

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.

Predicting uniaxial compressive strength of tuff after accelerated freeze-thaw testing: Comparative analysis of regression models and artificial neural networks

Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.

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.

Effect of sodium starch octenyl succinate-based Pickering emulsion on the physicochemical properties of hairtail myofibrillar protein gel subjected to multiple freeze-thaw cycles

A Pickering emulsion based on sodium starch octenyl succinate(SSOS)was prepared and its effects on the physicochemical properties of hairtail myofibrillar protein gels(MPGs)subjected to multiple freeze-thaw(F-T)cycles were investigated.The whiteness,water-holding capacity,storage modulus(G')and texture properties of the MPGs were significantly improved by adding 1%-2%Pickering emulsion(P<0.05).Meanwhile,Raman spectral analysis demonstrated that Pickering emulsion promoted the transformation of secondary structure,enhanced hydrogen bonds and hydrophobic interactions,and promoted the transition of disulfide bond conformation from g-g-g to g-g-t and t-g-t.At an emulsion concentration of 2%,theα-helix content decreased by 10.37%,while theβ-sheet content increased by 7.94%,compared to the control.After F-T cycles,the structure of the MPGs was destroyed,with an increase in hardness and a decrease in whiteness and water-holding capacity,however,the quality degradation of MPGs was reduced with 1%-2%Pickering emulsion.These findings demonstrated that SSOS-Pickering emulsions,as potential fat substitutes,can enhance the gel properties and the F-T stability of MPGs.