Crosstalk between degradation and bioenergetics: how autophagy and endolysosomal processes regulate energy production

Cells undergo metabolic reprogramming to adapt to changes in nutrient availability, cellular activity, and transitions in cell states. The balance between glycolysis and mitochondrial respiration is crucial for energy production, and metabolic reprogramming stipulates a shift in such balance to optimize both bioenergetic efficiency and anabolic requirements. Failure in switching bioenergetic dependence can lead to maladaptation and pathogenesis. While cellular degradation is known to recycle precursor molecules for anabolism, its potential role in regulating energy production remains less explored. The bioenergetic switch between glycolysis and mitochondrial respiration involves transcription factors and organelle homeostasis, which are both regulated by the cellular degradation pathways. A growing body of studies has demonstrated that both stem cells and differentiated cells exhibit bioenergetic switch upon perturbations of autophagic activity or endolysosomal processes. Here, we highlighted the current understanding of the interplay between degradation processes, specifically autophagy and endolysosomes, transcription factors, endolysosomal signaling, and mitochondrial homeostasis in shaping cellular bioenergetics. This review aims to summarize the relationship between degradation processes and bioenergetics, providing a foundation for future research to unveil deeper mechanistic insights into bioenergetic regulation.

Single-base editing in IGF2 improves meat production and intramuscular fat deposition in Liang Guang Small Spotted pigs

Background Chinese indigenous pigs are popular with consumers for their juiciness,flavour and meat quality,but they have lower meat production.Insulin-like growth factor 2(IGF2) is a maternally imprinted growth factor that promotes skeletal muscle growth by regulating cell proliferation and differentiation.A single nucleotide polymorphism(SNP) within intron 3 of porcine IGF2 disrupts a binding site for the repressor,zinc finger BED-type containing 6(ZBED6),leading to up-regulation of IGF2 and causing major effects on muscle growth,heart size,and backfat thickness.This favorable mutation is common in Western commercial pig populations,but absent in most Chinese indigenous pig breeds.To improve meat production of Chinese indigenous pigs,we used cytosine base editor 3(CBE3)to introduce IGF2 intron3-C3071T mutation into porcine embryonic fibroblasts(PEFs) isolated from a male Liang Guang Small Spotted pig(LGSS),and single-cell clones harboring the desired mutation were selected for somatic cell nuclear transfer(SCNT) to generate the founder line of IGF2^(T/T) pigs.Results We found the heterozygous progeny IGF2^(C/T) pigs exhibited enhanced expression of IGF2,increased lean meat by 18%-36%,enlarged loin muscle area by 3%-17%,improved intramuscular fat(IMF) content by 18%-39%,marbling score by 0.75-1,meat color score by 0.53-1.25,and reduced backfat thickness by 5%-16%.The enhanced accumulation of intramuscular fat in IGF2^(C/T) pigs was identified to be regulated by the PI3K-AKT/AMPK pathway,which activated SREBP1 to promote adipogenesis.Conclusions We demonstrated the introduction of IGF2-intron3-C3071T in Chinese LGSS can improve both meat production and quality,and first identified the regulation of IMF deposition by IGF2 through SREBP1 via the PI3KAKT/AMPK signaling pathways.Our study provides a further understanding of the biological functions of IGF2and an example for improving porcine economic traits through precise base editing.

Coagulation indices and fibrinogen degradation products as predictive biomarkers for tumor-node-metastasis staging and metastasis in gastric cancer

BACKGROUND Gastric cancer(GC)is a prevalent malignancy with a substantial health burden and high mortality rate,despite advances in prevention,early detection,and treatment.Compared with the global average,Asia,notably China,reports disproportionately high GC incidences.The disease often progresses asymptoma-tically in the early stages,leading to delayed diagnosis and compromised out-comes.Thus,it is crucial to identify early diagnostic biomarkers and enhance treatment strategies to improve patient outcomes and reduce mortality.METHODS Retrospectively analyzed the clinical data of 148 patients with GC treated at the Civil Aviation Shanghai Hospital between December 2022 and December 2023.The associations of coagulation indices-partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen,fibrinogen degradation products(FDP),fasting blood glucose,and D-dimer(D-D)with TNM stage and distant metastasis were examined.RESULTS Prolongation of APTT,PT,and TT was significantly correlated with the GC TNM stage.Hence,abnormal coagulation system activation was closely related to disease progression.Elevated FDP and D-D were significantly associated with distant metastasis in GC(P<0.05),suggesting that increased fibrinolytic activity contributes to increased metastatic risk.CONCLUSION Our Results reveal coagulation indices,FDPs as GC biomarkers,reflecting abnormal coagulation/fibrinolysis,aiding disease progression,metastasis prediction,and helping clinicians assess thrombotic risk for early intervention and personalized treatment plans.

High-rate electrochemical H_(2)O_(2) production over multimetallic atom catalysts under acidic–neutral conditions

Hydrogen peroxide(H_(2)O_(2))production by the electrochemical 2-electron oxygen reduction reaction(2e−ORR)is a promising alternative to the energy-intensive anthraquinone process,and single-atom electrocatalysts show the unique capability of high selectivity toward 2e−ORR against the 4e−one.The extremely low surface density of the single-atom sites and the inflexibility in manipulating their geometric/electronic configurations,however,compromise the H_(2)O_(2) yield and impede further performance enhancement.Herein,we construct a family of multiatom catalysts(MACs),on which two or three single atoms are closely coordinated to form high-density active sites that are versatile in their atomic configurations for optimal adsorption of essential*OOH species.Among them,the Cox–Ni MAC presents excellent electrocatalytic performance for 2e−ORR,in terms of its exceptionally high H_(2)O_(2) yield in acidic electrolytes(28.96 mol L^(−1) gcat.^(−1) h^(−1))and high selectivity under acidic to neutral conditions in a wide potential region(>80%,0–0.7 V).Operando X-ray absorption and density functional theory analyses jointly unveil its unique trimetallic Co2NiN8 configuration,which efficiently induces an appropriate Ni–d orbital filling and modulates the*OOH adsorption,together boosting the electrocatalytic 2e−ORR capability.This work thus provides a new MAC strategy for tuning the geometric/electronic structure of active sites for 2e−ORR and other potential electrochemical processes.

A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model

Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challenges due to the complex law of decline, nonlinear and non-stationary features in production data, which greatly repair the robustness of current models in predicting shale gas production time series. To address these challenges and improve accuracy in production forecasting, this paper introduces a novel and innovative approach: a hybrid proxy model that combines the bidirectional long short-term memory(BiLSTM) neural network and random forest(RF) through deep learning. The BiLSTM neural network is adept at capturing long-term dependencies, making it suitable for understanding the intricate relationships between input and output variables in shale gas production.On the other hand, RF serves a dual purpose: reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM. By integrating these two models, the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series, thereby reducing model uncertainty. Furthermore, the combination of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm(MPA) to fine-tune hyperparameters and enhance the overall performance of the proxy model. The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and demonstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series. Compared to other models such as LSTM, BiLSTM, and RF, the proposed model exhibits superior fitting and prediction performance, with an average improvement in performance indicators exceeding 20%. This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs, which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization.

Evolving patterns of agricultural production space in China:A network-based approach

The agricultural production space,as where and how much each agricultural product grows,plays a vital role in meeting the increasing and diverse food demands.Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types,using methods such as remote sensing or statistical metrological analysis.In this study,we characterize the agricultural production space(APS)by bipartite network connecting agricultural products and provinces,to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China.The results show that core products are cereal,pork,melon,and pome fruit;meanwhile the milk,grape,and fiber crop show an upward trend in centrality,which is in line with diet structure changes in China over the past decades.The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable.Additionally,identified provincial communities closely resemble China's agricultural natural zones.Furthermore,the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production.Despite the superior production capa-bilities of southern provinces,recent years have witnessed a notable decline,warranting special attentions.The findings provide a comprehensive perspective for understanding the complex relationship of agricultural prod-ucts'relatedness,production capabilities and production patterns,which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.

Optimization of Gas Production from Hydrate-Bearing Sediments with Fluctuation Characteristics

As an important source of low-carbon,clean fossil energy,natural gas hydrate plays an important role in improving the global energy consumption structure.Developing the hydrate industry in the South China Sea is important to achieving‘carbon peak and carbon neutrality’goals as soon as possible.Deep-water areas subjected to the action of long-term stress and tectonic movement have developed complex and volatile terrains,and as such,the morphologies of hydrate-bearing sediments(HBSs)fluctuate correspondingly.The key to numerically simulating HBS morphologies is the establishment of the conceptual model,which represents the objective and real description of the actual geological body.However,current numerical simulation models have characterized HBSs into horizontal strata without considering the fluctuation characteristics.Simply representing the HBS as a horizontal element reduces simulation accuracy.Therefore,the commonly used horizontal HBS model and a model considering the HBS’s fluctuation characteristics with the data of the SH2 site in the Shenhu Sea area were first constructed in this paper.Then,their production behaviors were compared,and the huge impact of the fluctuation characteristics on HBS production was determined.On this basis,the key parameters affecting the depressurization production of the fluctuating HBSs were studied and optimized.The research results show that the fluctuation characteristics have an obvious influence on the hydrate production of HBSs by affecting their temperatures and pressure distributions,as well as the transmission of the pressure drop and methane gas discharge.Furthermore,the results show that the gas productivity of fluctuating HBSs was about 5%less than that of horizontal HBSs.By optimizing the depressurization amplitude,well length,and layout location of vertical wells,the productivity of fluctuating HBSs increased by about 56.6%.

Influence of Production Factors on the Physico-Chemical Characteristics of Fermented Cassava Dough and Sensory Evaluation of Attieke

Attieke is an Ivorian semolina which obtained by fermenting, pressing and steaming cassava dough. Attieke production remains a traditional activity carried out by less literate women. However, perceived differences in measurable factors and attieke qualities require an investigation of their influence on the characteristics of the pressed dough and attieke. The aim of this study is to improve the quality of the dough in relation to that of the attieke produced. The experiment was carried out on 4 production factors, namely the type of boiled or braised ferment, the incorporation rate of the ferment between 8 and 10%, the addition of oil from 0.1 to 1% and the fermentation time from 12 to 15 hours applied to the Improved African Cassava (IAC) variety. A complete experiment design of 16 samples of fermented dough and attieke was employed. These samples underwent physic-chemical analyses for the fermented dough and sensory evaluation for the attieke. It was found that, except for titratable acidity, reducing sugar content and ash content, the physico-chemical characteristics of the dough of IAC variety were significantly influenced by all production factors and their interaction. Fermentation time significantly influences 60% of the physico-chemical characteristics of the fermented dough. The type of ferment, the oil addition and the ferment rate have a significant influence at 40% of these characteristics. At the sensory level, color, acidity and grain binding with an explained variance of 34.60% were essential for the appreciation of the attieke samples. Thus, these production factors could be considered for the improvement of the fermented dough and attieke production process.

Optimized electrochemical ammonia production:From metal-N_(2)/NO_(x) batteries to aqueous metal-NO_(x)^(–) batteries

Ammonia plays a crucial role in contemporary society,impacting medicine,agriculture,and the chemical industry.The conventional industrial synthesis of NH_(3) through the Haber-Bosch technique,carried out under severe reaction conditions,leads to substantial energy consumption and environmental pollution.It is thus imperative for NH_(3) synthesis methods to be investigated under more favorable conditions.Synthesis of ammonia by electrocatalysis can effectively reduce the environmental damage and other urgent problems,which is a promising solution.Metal-nitrogen series batteries(M-N batteries),such as metal-nitrogen gas batteries,metal-nitrogen oxide batteries and metal-oxynitride batteries have been regarded recently as an exemplar of concurrent NH_(3) synthesis and energy production.Nonetheless,the large-scale application of these batteries is still limited by numerous challenges are currently existing in building high-efficiency M-N batteries,including poor Faradic efficiency and low NH_(3) yield.Therefore,a comprehensive overview of M-N batteries is offered,specifically focusing on advanced strategies for designing highly efficient cathode catalysts in anticipation of future developments.The metal anodes,cathodic electro-reduction reactions,and design principles are encompassed in the discussion,offering detailed insights to enhance understanding.Mechanisms,feasibility analyses,technoeconomic assessments,device combinations,and comparative evaluations are delved into in the review,contributing to a thorough comprehension of diverse systems and their application potential.Perspectives and opportunities for future research directions are also delineated.

Gas-Water Production of a Continental Tight-Sandstone Gas Reservoir under Different Fracturing Conditions

A numerical model of hydraulic fracture propagation is introduced for a representative reservoir(Yuanba continental tight sandstone gas reservoir in Northeast Sichuan).Different parameters are considered,i.e.,the interlayer stress difference,the fracturing discharge rate and the fracturing fluid viscosity.The results show that these factors affect the gas and water production by influencing the fracture size.The interlayer stress difference can effectively control the fracture height.The greater the stress difference,the smaller the dimensionless reconstruction volume of the reservoir,while the flowback rate and gas production are lower.A large displacement fracturing construction increases the fracture-forming efficiency and expands the fracture size.The larger the displacement of fracturing construction,the larger the dimensionless reconstruction volume of the reservoir,and the higher the fracture-forming efficiency of fracturing fluid,the flowback rate,and the gas production.Low viscosity fracturing fluid is suitable for long fractures,while high viscosity fracturing fluid is suitable for wide fractures.With an increase in the fracturing fluid viscosity,the dimensionless reconstruction volume and flowback rate of the reservoir display a non-monotonic behavior,however,their changes are relatively small.

Socio-Economic Impact Evaluation of Bovine Tuberculosis on Primary Meat Production at the Bobo-Dioulasso Slaughterhouse in Burkina Faso

Bovine tuberculosis is one of the zoonoses which has a very significant socio-economic importance due to the losses in agribusiness and hampers commercial exchange of animals and products. The present study highlights the risk of considerable potential economic and health impacts of this major zoonosis. It was carried out at the Bobo-Dioulasso slaughterhouse, in Burkina Faso over 3 years. A retrospective study was conducted based on bovine tuberculosis suspected carcass seizures during primary meat production between January 1st, 2020 and December 31st, 2022. The diagnosis and the criteria for suspecting bovine tuberculosis were addressed by post-mortem inspection. All carcasses were examined for tuberculosis lesions detection. All cattle slaughtered in the abattoir for primary meat production during the study period were included. Economic losses were determined from recorded seizure data and we included all the cattle slaughtered during the study period. Three thousand two hundred ten (3210) bovine carcasses were seized on a total of 180,827 cattle slaughteredwith a prevalence of 19.48%. Economic loss was estimated to be 53,505,000 F CFA, while the average quantity of animal protein lost was 4746 kg, 435 kg, and 13,445 kg for the carcass, livers, and lungs, respectively. The various results show a real health issue linked to exposure to M. bovis for agents and stakeholders in the primary meat production chain, processors and consumers. In addition, the survey conducted over the study period, reveals important material seized and destroyed. This leads to significant loss in rural agriculture and also in the primary meat production industry for the population. The figures are enormous and impact both the nutritional intake linked to animal protein consumption and the livelihood of the beef industry. The Burkinabe administration should invest in biosecurity and biosafety measures to minimize the risks of the disease and also provide compensation for losses recorded among breeders and butchers.

Better use of experience from other reservoirs for accurate production forecasting by learn-to-learn method

To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods.

Spatial evolution and spatial production of traditional villages from "backward poverty villages" to "ecologically well-off villages": Experiences from the hinterland of national nature reserves in China

With the rapid urbanization process,the space of traditional villages in China is undergoing significant changes.Studying the spatial evolution of traditional villages is significant in promoting rural spatial transformation and realizing rural revitalization and sustainable rural development.Based on the traceability analysis of spatial production theory,this paper constructed an analytical framework for the spatial production evolution of traditional villages,analyzed the spatial evolution process and characteristics of traditional villages by using buffer analysis,spatial syntax,and other research methods,and revealed the characteristics of the spatial production evolution of traditional villages and the driving mechanism.The results show that:(1)The village spatial formation and development follow the village life cycle theory and usually develop from embryonic villages to diversified and integrated villages;(2)The evolution of village spatial production is characterized by the diversity of material space,the sublimation of daily life space,and the integration of social system space and generalization of emotional space;(3)The evolution of village spatial production from backward and poor village to ecologically well-off village is influenced by a combination of factors;(4)The village has formed a spatial structure of"people-land-scape-culture-industry",realized comprehensive reconstruction and spatial reproduction.The study results reflect the spatial evolution characteristics of traditional villages in mountainous areas in a more comprehensive way,which helps to promote the protection and development of traditional villages in mountainous areas and,to a certain extent,provides a reference for the development of rural revitalization.

Impacts of information about COVID-19 on pig farmers'production willingness and behavior:Evidence from China

This paper examines the impacts of information about COVID-19 on pig farmers'production willingness by using endorsement experiments and follow-up surveys conducted in 2020 and 2021 in China.Our results show that,first,farmers were less willing to scale up production when they received information about COVID-19.The information in 2020 that the second wave of COVID-19 might occur without a vaccine reduced farmers'willingness to scale up by 13.4%,while the information in 2021 that COVID-19 might continue to spread despite the introduction of vaccine reduced farmers'willingness by 4.4%.Second,farmers whose production was affected by COVID-19 were considerably less willing to scale up,given the access to COVID-19 information.Third,farmers'production willingness can predict their actual production behavior.

High glucose reduces Nrf2-dependent cRAGE release and enhances inflammasome-dependent IL-1βproduction in monocytes:the modulatory effects of EGCG

Soluble receptor for advanced glycation end products(sRAGE)acts as a decoy sequestering of RAGE ligands,thus preventing the activation of the ligand-RAGE axis linking human diseases.However,the molecular mechanisms underlying sRAGE remain unclear.In this study,THP-1 monocytes were cultured in normal glucose(NG,5.5 mmol/L)and high glucose(HG,15 mmol/L)to investigate the effects of diabetesrelevant glucose concentrations on sRAGE and interleukin-1β(IL-1β)secretion.The modulatory effects of epigallocatechin gallate(EGCG)in response to HG challenge were also evaluated.HG enhanced intracellular reactive oxygen species(ROS)generation and RAGE expression.The secretion of sRAGE,including esRAGE and cRAGE,was reduced under HG conditions,together with the downregulation of a disintegrin and metallopeptidase 10(ADAM10)and nuclear factor erythroid 2-related factor 2(Nrf2)nuclear translocation.Mechanistically,the HG effects were counteracted by siRAGE and exacerbated by siNrf2.Chromatin immunoprecipitation results showed that Nrf2 binding to the ADAM10 promoter and HG interfered with this binding.Our data reinforce the notion that RAGE and Nrf2 might be sRAGE-regulating factors.Under HG conditions,the treatment of EGCG reduced ROS generation and RAGE activation.EGCG-stimulated cRAGE release was likely caused by the upregulation of the Nrf2-ADAM10 pathway.EGCG inhibited HG-mediated NLRP3 inflammasome activation at least partly by stimulating sRAGE,thereby reducing IL-1βrelease.

High-throughput microfluidic production of carbon capture microcapsules:fundamentals,applications,and perspectives

In the last three decades,carbon dioxide(CO_(2)) emissions have shown a significant increase from various sources.To address this pressing issue,the importance of reducing CO_(2) emissions has grown,leading to increased attention toward carbon capture,utilization,and storage strategies.Among these strategies,monodisperse microcapsules,produced by using droplet microfluidics,have emerged as promising tools for carbon capture,offering a potential solution to mitigate CO_(2) emissions.However,the limited yield of microcapsules due to the inherent low flow rate in droplet microfluidics remains a challenge.In this comprehensive review,the high-throughput production of carbon capture microcapsules using droplet microfluidics is focused on.Specifically,the detailed insights into microfluidic chip fabrication technologies,the microfluidic generation of emulsion droplets,along with the associated hydrodynamic considerations,and the generation of carbon capture microcapsules through droplet microfluidics are provided.This review highlights the substantial potential of droplet microfluidics as a promising technique for large-scale carbon capture microcapsule production,which could play a significant role in achieving carbon neutralization and emission reduction goals.

Shale oil production predication based on an empirical modelconstrained CNN-LSTM

Accurately predicting the production rate and estimated ultimate recovery(EUR)of shale oil wells is vital for efficient shale oil development.Although numerical simulations provide accurate predictions,their high time,data,and labor demands call for a swifter,yet precise,method.This study introduces the DuongeCNNeLSTM(D-C-L)model,which integrates a convolutional neural network(CNN)with a long short-term memory(LSTM)network and is grounded on the empirical Duong model for physical constraints.Compared to traditional approaches,the D-C-L model demonstrates superior precision,efficiency,and cost-effectiveness in predicting shale oil production.

Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system

The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal.However,subsea sensors degrade rapidly due to harsh working environments and long service time.This leads to frequent false alarm incidents.A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed.A combinatorial algorithm is proposed to group sensors.The long short-term memory network(LSTM)is used to establish a single inference model.A counting-based judging method is proposed to identify abnormal sensors.Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method.The results show that the proposed method can identify the abnormal sensors effectively.

Direct Regeneration of Spent Lithium-Ion Battery Cathodes:From Theoretical Study to Production Practice

Direct regeneration method has been widely concerned by researchers in the field of battery recycling because of its advantages of in situ regeneration,short process and less pollutant emission.In this review,we firstly analyze the primary causes for the failure of three representative battery cathodes(lithium iron phosphate,layered lithium transition metal oxide and lithium cobalt oxide),targeting at illustrating their underlying regeneration mecha-nism and applicability.Efficient stripping of material from the collector to obtain pure cathode material has become a first challenge in recycling,for which we report several pretreatment methods currently available for subsequent regeneration processes.We review and discuss emphatically the research progress of five direct regeneration methods,including solid-state sintering,hydrothermal,eutectic molten salt,electrochemical and chemical lithiation methods.Finally,the application of direct regeneration technology in production practice is introduced,the problems exposed at the early stage of the industrialization of direct regeneration technol-ogy are revealed,and the prospect of future large-scale commercial production is proposed.It is hoped that this review will give readers a comprehensive and basic understanding of direct regeneration methods for used lithium-ion batteries and promote the industrial application of direct regeneration technology.

Multi-surrogate framework with an adaptive selection mechanism for production optimization

Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem.A surrogate model that has demonstrated success in one scenario may not perform as well in others.In the absence of prior knowledge,finding a promising surrogate model that performs well for an unknown reservoir is challenging.Moreover,the optimization process often relies on a single evolutionary algorithm,which can yield varying results across different cases.To address these limitations,this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism(MSFASM)to tackle production optimization problems.MSFASM consists of two stages.In the first stage,a reduced-dimensional broad learning system(BLS)is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period.In the second stage,the multi-objective algorithm,non-dominated sorting genetic algorithm II(NSGA-II),is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models.A novel optimal point criterion is utilized in this stage to select the Pareto solutions,thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator.The two stages are combined using sequential transfer learning.From the two most important perspectives of an evolutionary algorithm and a surrogate model,the proposed method improves adaptability to optimization problems of various reservoir types.To verify the effectiveness of the proposed method,four 100-dimensional benchmark functions and two reservoir models are tested,and the results are compared with those obtained by six other surrogate-model-based methods.The results demonstrate that our approach can obtain the maximum net present value(NPV)of the target production optimization problems.