Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

Design Strategies for Aqueous Zinc Metal Batteries with High Zinc Utilization: From Metal Anodes to Anode-Free Structures

Aqueous zinc metal batteries(AZMBs)are promising candidates for next-generation energy storage due to the excellent safety, environmental friendliness, natural abundance, high theoretical specific capacity, and low redox potential of zinc(Zn) metal. However,several issues such as dendrite formation, hydrogen evolution, corrosion, and passivation of Zn metal anodes cause irreversible loss of the active materials. To solve these issues, researchers often use large amounts of excess Zn to ensure a continuous supply of active materials for Zn anodes. This leads to the ultralow utilization of Zn anodes and squanders the high energy density of AZMBs. Herein, the design strategies for AZMBs with high Zn utilization are discussed in depth, from utilizing thinner Zn foils to constructing anode-free structures with theoretical Zn utilization of 100%, which provides comprehensive guidelines for further research. Representative methods for calculating the depth of discharge of Zn anodes with different structures are first summarized. The reasonable modification strategies of Zn foil anodes, current collectors with pre-deposited Zn, and anode-free aqueous Zn metal batteries(AF-AZMBs) to improve Zn utilization are then detailed. In particular, the working mechanism of AF-AZMBs is systematically introduced. Finally, the challenges and perspectives for constructing high-utilization Zn anodes are presented.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Effects of the kinetic pattern of dietary glucose release on nitrogen utilization, the portal amino acid profile, and nutrient transporter expression in intestinal enterocytes in piglets

Background Promoting the synchronization of glucose and amino acid release in the digestive tract of pigs could effectively improve dietary nitrogen utilization.The rational allocation of dietary starch sources and the exploration of appropriate dietary glucose release kinetics may promote the dynamic balance of dietary glucose and amino acid supplies.However,research on the effects of diets with different glucose release kinetic profiles on amino acid absorption and portal amino acid appearance in piglets is limited.This study aimed to investigate the effects of the kinetic pattern of dietary glucose release on nitrogen utilization,the portal amino acid profile,and nutrient transporter expression in intestinal enterocytes in piglets.Methods Sixty-four barrows(15.00±1.12 kg)were randomly allotted to 4 groups and fed diets formulated with starch from corn,corn/barley,corn/sorghum,or corn/cassava combinations(diets were coded A,B,C,or D respectively).Protein retention,the concentrations of portal amino acid and glucose,and the relative expression of amino acid and glucose transporter m RNAs were investigated.In vitro digestion was used to compare the dietary glucose release profiles.Results Four piglet diets with different glucose release kinetics were constructed by adjusting starch sources.The in vivo appearance dynamics of portal glucose were consistent with those of in vitro dietary glucose release kinetics.Total nitrogen excretion was reduced in the piglets in group B,while apparent nitrogen digestibility and nitrogen retention increased(P<0.05).Regardless of the time(2 h or 4 h after morning feeding),the portal total free amino acids content and contents of some individual amino acids(Thr,Glu,Gly,Ala,and Ile)of the piglets in group B were significantly higher than those in groups A,C,and D(P<0.05).Cluster analysis showed that different glucose release kinetic patterns resulted in different portal amino acid patterns in piglets,which decreased gradually with the extension of feeding time.The portal His/Phe,Pro/Glu,Leu/Val,Lys/Met,Tyr/Ile and Ala/Gly appeared higher similarity among the diet treatments.In the anterior jejunum,the glucose transporter SGLT1 was significantly positively correlated with the amino acid transporters B0AT1,EAAC1,and CAT1.Conclusions Rational allocation of starch resources could regulate dietary glucose release kinetics.In the present study,group B(corn/barley)diet exhibited a better glucose release kinetic pattern than the other groups,which could affect the portal amino acid contents and patterns by regulating the expression of amino acid transporters in the small intestine,thereby promoting nitrogen deposition in the body,and improving the utilization efficiency of dietary nitrogen.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

OsNPF3.1,a nitrate,abscisic acid and gibberellin transporter gene,is essential for rice tillering and nitrogen utilization efficiency

Low-affinity nitrate transporter genes have been identified in subfamilies 4-8 of the rice nitrate transporter 1(NRT1)/peptide transporter family(NPF),but the OsNPF3 subfamily responsible for nitrate and phytohormone transport and rice growth and development remains unknown.In this study,we described OsNPF3.1 as an essential nitrate and phytohormone transporter gene for rice tillering and nitrogen utilization efficiency(NUtE).OsNPF3.1 possesses four major haplotypes of its promoter sequence in 517 cultivars,and its expression is positively associated with tiller number.Its expression was higher in the basal part,culm,and leaf blade than in other parts of the plant,and was strongly induced by nitrate,abscisic acid(ABA)and gibberellin 3(GA_3)in the root and shoot of rice.Electrophysiological experiments demonstrated that OsNPF3.1 is a pH-dependent low-affinity nitrate transporter,with rice protoplast uptake assays showing it to be an ABA and GA_3 transporter.OsNPF3.1 overexpression significantly promoted ABA accumulation in the roots and GA accumulation in the basal part of the plant which inhibited axillary bud outgrowth and rice tillering,especially at high nitrate concentrations.The NUtE of OsNPF3.1-overexpressing plants was enhanced under low and medium nitrate concentrations,whereas the NUtE of OsNPF3.1 clustered regularly interspaced short palindromic repeats(CRISPR)plants was increased under high nitrate concentrations.The results indicate that OsNPF3.1 transports nitrate and phytohormones in different rice tissues under different nitrate concentrations.The altered OsNPF3.1 expression improves NUtE in the OsNPF3.1-overexpressing and CRISPR lines at low and high nitrate concentrations,respectively.

Boost the Utilization of Dense FeN_(4) Sites for High-Performance Proton Exchange Membrane Fuel Cells

Iron-nitrogen-carbon(Fe-N-C)catalysts for the oxygen reduction reaction(ORR)in proton exchange membrane fuel cells(PEMFCs)have seriously been hindered by their poor ORR performance of Fe-N-C due to the low active site density(SD)and site utilization.Herein,we reported a melamine-assisted vapor deposition approach to overcome these hindrances.The melamine not only compensates for the loss of nitrogen caused by high-temperature pyrolysis but also effectively etches the carbon substrate,increasing the external surface area and mesoporous porosity of the carbon substrate.These can provide more useful area for subsequent vapor deposition on active sites.The prepared 0.20Mela-FeNC catalyst shows a fourfold higher SD value and site utilization than the FeNC without the treatment of melamine.As a result,0.20Mela-FeNC catalyst exhibits a high ORR activity with a half-wave potential(E_(1/2))of 0.861 V and 12-fold higher ORR mass activity than the FeNC in acidic media.As the cathode in a H_(2)-O_(2)PEMFCs,0.20Mela-FeNC catalyst demonstrates a high peak power density of 1.30 W cm^(-2),outstripping most of the reported Fe-N-C catalysts.The developed melamine-assisted vapor deposition approach for boosting the SD and utilization of Fe-N-C catalysts offers a new insight into high-performance ORR electrocatalysts.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Amorphous Iridium Oxide-Integrated Anode Electrodes with Ultrahigh Material Utilization for Hydrogen Production at Industrial Current Densities

Herein,ionomer-free amorphous iridium oxide(IrO_(x))thin electrodes are first developed as highly active anodes for proton exchange membrane electrolyzer cells(PEMECs)via low-cost,environmentally friendly,and easily scalable electrodeposition at room temperature.Combined with a Nafion 117 membrane,the IrO_(x)-integrated electrode with an ultralow loading of 0.075 mg cm^(-2)delivers a high cell efficiency of about 90%,achieving more than 96%catalyst savings and 42-fold higher catalyst utilization compared to commercial catalyst-coated membrane(2 mg cm^(-2)).Additionally,the IrO_(x)electrode demonstrates superior performance,higher catalyst utilization and significantly simplified fabrication with easy scalability compared with the most previously reported anodes.Notably,the remarkable performance could be mainly due to the amorphous phase property,sufficient Ir^(3+)content,and rich surface hydroxide groups in catalysts.Overall,due to the high activity,high cell efficiency,an economical,greatly simplified and easily scalable fabrication process,and ultrahigh material utilization,the IrO_(x)electrode shows great potential to be applied in industry and accelerates the commercialization of PEMECs and renewable energy evolution.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

Residual fluoride self-activated effect enabling upgraded utilization of recycled graphite anode

Recycling graphite anode from spent lithium-ion batteries(SLIBs)is regarded as a crucial approach to promoting sustainable energy storage industry.However,the recycled graphite(RG)generally presents degraded structure and performance.Herein,the residual fluoride self-activated effect is proposed for the upgraded utilization of RG.Simple and low-energy water immersion treatment not only widens the interlayer spacing,but also retains appropriate fluoride on the surface of RG.Theoretical analysis and experiments demonstrate that the residual fluoride can optimize Li~+migration and deposition kinetics,resulting in better Li~+intercalation/deintercalation in the interlayer and more stable Li metal plating/stripping on the surface of RG,As a result,the designed LFP||RG full cells achieve ultrahigh reversibility(~100%Coulombic efficiency),high capacity retention(67%after 200 cycles,0.85 N/P ratio),and commendable adaptability(stable cycling without short-circuiting,0.15 N/P ratio).The energy density is improved from 334 Wh kg^(-1)of 1.1 N/P ratio to 367 Wh kg^(-1)of 0.85 N/P ratio(total mass based on cathode and anode).The exploration of RG by residual fluoride self-activated effect achieves upgraded utilization beyond fresh commercial graphite and highlights a new strategy for efficient reuse of SLIBs.

Experiences of Mothers on the Utilization of Elimination of Mother to Child Transmission of HIV Services at Mtendere Clinic, Lusaka

Introduction: Acquired immunodeficiency syndrome is one of the leading causes of mortality among women of reproductive age and Mother to Child Transmission of Human immunodeficiency virus is still a challenge affecting many countries. Globally, an estimation of 180,000 children under 15 years acquire the Human immunodeficiency virus every day, and more than 90% of those infections are due to Mother to Child Transmission. The study sought to explore the experiences of mothers on the Elimination of Mother to Child Transmission-HIV services at Mtendere Clinic, Lusaka. Materials and Methods: Qualitative interpretive phenomenology study design was employed using in-depth interviews to collect data from a sample that was selected using purposive sampling technique. Thirteen participants were recruited, and these were HIV-positive mothers at least between the ages of 15 and 49 years and enrolled in the Elimination of Mother To Child Transmission-HIV program. The in-depth interviews were audiotape recorded and transcribed verbatim. Data was analyzed using thematic method. Findings: Three main themes that emerged are;living with HIV, support system and barriers to utilization of Elimination of Mother To Child Transmission-HIV services. Most of the participants expressed having acquired knowledge from the program, and received counselling and support from spouses, family as well as health personnel at Mtendere health facility which culminated into a positive experience and enhanced their uptake of the Elimination of Mother to Child Transmission-HIV services. However, barriers to service utilization were identified and these included fear of stigma, negative attitudes from health workers, long waiting times, lack of support and lack of transport to the health facility. Conclusion: Interventions such as community awareness campaigns on Elimination of Mother To Child Transmission-HIV, male involvement and implementing mother-to-mother peer support strategies in Elimination of Mother To Child Transmission-HIV service utilization should be prioritized so as to alleviate stigma and enhancing a positive experience for these mothers thus reducing on the Mother to Child HIV Transmission burden and mortality rates.

"Three-in-One" Ecological Development and Efficient Utilization Model of Aggregates

Green mining and the formation of an effective and efficient development model have become key issues that aggregates enterprises around the world need to solve urgently.On the basis of analyzing the development status of aggregates industry in Xiluodu area,the paper studied the main problems faced in the construction of green aggregates mines at present,and proposed a"three-in-one"ecological,intelligent and efficient green mine construction model for"ecological development","green logistics"and"solid waste recycling"of aggregates.The study has certain theoretical value and practical significance for the construction of green aggregates mine in Xiluodu area.

Comprehensive reutilization of herbal waste:Coproduction of magnolol,honokiol,and β-amyrin from Magnolia officinalis residue

Herbal extraction residues(HERs)cause serious environmental pollution and resource waste.In this study,a novel green route was designed for the comprehensive reutilization of all components in HERs,taking Magnolia officinalis residues(MOR)as an example.The reluctant structure of MOR was first destroyed by alkali pretreatment to release the functional ingredients(magnolol and honokiol)originally remaining in MOR and to make MOR more accessible for hydrolysis.A metal–organic frame material MIL-101(Cr)with a maximum absorption capacity of 255.64 mg g^(-1)was synthesized to absorb the released honokiol and magnolol from the pretreated MOR solutions,and 40 g L^(-1)reducing sugars were obtained with 81.8%enzymatic hydrolysis rate at 10%MOR solid loading.Finally,382 mg L-1β-amyrin was produced from MOR hydrolysates by an engineered yeast strain.In total,1 kg honokiol,8 kg magnolol,and 7.64 kg β-amyrin could produce from 1 ton MOR by this cleaner process with a total economic output of 170,700 RMB.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Technological advancement and industrialization path of Sinopec in carbon capture,utilization and storage,China

Carbon capture,utilization and storage(CCUS)technology is an important means to effectively reduce carbon emissions from fossil energy combustion and industrial processes.With the crisis of climate change,CCUS has attracted increasing attention in the world.CCUS technology as developed rapidly in China is technically feasible for large-scale application in various industries.The R&D and demonstration of CCUS in China Petroleum&Chemical Corporation(Sinopec)are summarized,including carbon capture,carbon transport,CO_(2)enhanced energy recovery(including oil,gas,and water,etc.),and comprehensive utilization of CO_(2).Based on the source-sink matching characteristics in China,two CCUS industrialization scenarios are proposed,namely,CO_(2)-EOR,CO_(2)-driven enhanced oil recovery using centralized carbon sinks in East China and CO_(2)-EWR,CO_(2)-driven enhanced water recovery(EWR)using centralized carbon sources from the coal chemical industry in West China.Finally,a CCUS industrialization path from Sinopec's perspective is suggested,using CO_(2)-EOR as the major means and CO_(2)-EWR,CO_(2)-driven enhanced gas recovery(CO_(2)-EGR)and other utilization methods as important supplementary means.