The role of the auxin-response genes MdGH3.1 and MdSAUR36 in bitter pit formation in apple

Apples often exhibit bitter pits in response to metabolic disorders during ripening and storage;however, the mechanisms underlying the bitter pit(BP) development remain unclear. Here, metabolome and transcriptome analyses were performed to investigate BP pulp of 'Fuji'. Two auxin-response genes, MdGH3.1 and MdSAUR36, were screened. Their expression as well as the auxin content in BP pulp were found to be higher than those in healthy pulp(P < 0.01). In the field, excess CO(NH2)2increased the incidence of BP. Moreover, the auxin content and MdGH3.1 expression increased in apples after nitrogen fertilization. On Day 30 before harvest, the two genes were transiently transferred to the fruit, and 20.69% and 23.21% of BP fruits were harvested. After 10 μmol·L-1auxin was infiltrated at low pressure into postharvest fruit, the increase in MdGH3.1 expression occurred earlier than that in MdSAUR36. MdGH3.1 increased the expression of MdSAUR36, but MdSAUR36 did not increase expression of MdGH3.1. Therefore, we suggest that MdGH3.1 acts upstream of MdSAUR36 during BP formation and that these genes induce BP formation by regulating auxin and phenylpropanoid biosynthesis.

The Importance of Stakeholder Engagement in Progressive Rehabilitation of Large-Scale Open Pit Mines—A Case Study of Lumwana Mine

Mining globally contributes to the growth of many economies of the world. Since its inception, the Zambian mining industry has contributed largely to the country’s economy. The various developments both in technology and knowledge have contributed to the scale at which mining is being done. Challenges in such a setting arise due to the socio-economic and environmental impacts of mining, which create multidimensional problems. The study investigated the importance of engaging stakeholders in progressive rehabilitation programs for large-scale open pit mines, using a case study of the Lumwana Mine and its host community, Manyama. A qualitative approach was used, and data was collected through one-on-one interviews. A combination of convenient and quota sampling was used to engage with host community leaders, professionals and academicians from various fields and institutions. Results showed that most participants had agreed that stakeholder engagement is important for progressive rehabilitation, but the challenge was that the host community and municipal council representatives were not aware of any progressive rehabilitation efforts at Lumwana Mine. This was attributed to a lack of stakeholder engagement and communication of mitigation progress activities by the Lumwana Mine. Results also revealed that the lack of environmental impact assessment regulations to compel companies to involve stakeholders throughout the entire life of the mine other than just at the pre-mining stage led to a lack of compliance and accountability in rehabilitation.

Development of a DFN-based probabilistic block theory approach for bench face angle design in open pit mining

In open pit mining,uncontrolled block instabilities have serious social,economic and regulatory consequences,such as casualties,disruption of operation and increased regulation difficulties.For this reason,bench face angle,as one of the controlling parameters associated with block instabilities,should be carefully designed for sustainable mining.This study introduces a discrete fracture network(DFN)-based probabilistic block theory approach for the fast design of the bench face angle.A major advantage is the explicit incorporation of discontinuity size and spatial distribution in the procedure of key blocks testing.The proposed approach was applied to a granite mine in China.First,DFN models were generated from a multi-step modeling procedure to simulate the complex structural characteristics of pit slopes.Then,a modified key blocks searching method was applied to the slope faces modeled,and a cumulative probability of failure was obtained for each sector.Finally,a bench face angle was determined commensurate with an acceptable risk level of stability.The simulation results have shown that the number of hazardous traces exposed on the slope face can be significantly reduced when the suggested bench face angle is adopted,indicating an extremely low risk of uncontrolled block instabilities.

Numerical investigation of the effects of soil-structure and granular material-structure interaction on the seismic response of a flat-bottom reinforced concrete silo

In this work,a numerical study of the effects of soil-structure interaction(SSI)and granular material-structure interaction(GSI)on the nonlinear response and seismic capacity of flat-bottomed storage silos is conducted.A series of incremental dynamic analyses(IDA)are performed on a case of large reinforced concrete silo using 10 seismic recordings.The IDA results are given by two average IDA capacity curves,which are represented,as well as the seismic capacity of the studied structure,with and without a consideration of the SSI while accounting for the effect of GSI.These curves are used to quantify and evaluate the damage of the studied silo by utilizing two damage indices,one based on dissipated energy and the other on displacement and dissipated energy.The cumulative energy dissipation curves obtained by the average IDA capacity curves with and without SSI are presented as a function of the base shear,and these curves allow one to obtain the two critical points and the different limit states of the structure.It is observed that the SSI and GSI significantly influence the seismic response and capacity of the studied structure,particularly at higher levels of PGA.Moreover,the effect of the SSI reduces the damage index of the studied structure by 4%.

A Comparative Study on the Post-Buckling Behavior of Reinforced Thermoplastic Pipes(RTPs)Under External Pressure Considering Progressive Failure

The collapse pressure is a key parameter when RTPs are applied in harsh deep-water environments.To investigate the collapse of RTPs,numerical simulations and hydrostatic pressure tests are conducted.For the numerical simulations,the eigenvalue analysis and Riks analysis are combined,in which the Hashin failure criterion and fracture energy stiffness degradation model are used to simulate the progressive failure of composites,and the“infinite”boundary conditions are applied to eliminate the boundary effects.As for the hydrostatic pressure tests,RTP specimens were placed in a hydrostatic chamber after filled with water.It has been observed that the cross-section of the middle part collapses when it reaches the maximum pressure.The collapse pressure obtained from the numerical simulations agrees well with that in the experiment.Meanwhile,the applicability of NASA SP-8007 formula on the collapse pressure prediction was also discussed.It has a relatively greater difference because of the ignorance of the progressive failure of composites.For the parametric study,it is found that RTPs have much higher first-ply-failure pressure when the winding angles are between 50°and 70°.Besides,the effect of debonding and initial ovality,and the contribution of the liner and coating are also discussed.

Reinforced tissue matrix to strengthen the abdominal wall following reversal of temporary ostomies or to treat incisional hernias

BACKGROUND Abdominal wall deficiencies or weakness are a common complication of tem-porary ostomies,and incisional hernias frequently develop after colostomy or ileostomy takedown.The use of synthetic meshes to reinforce the abdominal wall has reduced hernia occurrence.Biologic meshes have also been used to enhance healing,particularly in contaminated conditions.Reinforced tissue matrices(R-TMs),which include a biologic scaffold of native extracellular matrix and a syn-thetic component for added strength/durability,are designed to take advantage of aspects of both synthetic and biologic materials.To date,RTMs have not been reported to reinforce the abdominal wall following stoma reversal.METHODS Twenty-eight patients were selected with a parastomal and/or incisional hernia who had received a temporary ileostomy or colostomy for fecal diversion after rectal cancer treatment or trauma.Following hernia repair and proximal stoma closure,RTM(OviTex®1S permanent or OviTex®LPR)was placed to reinforce the abdominal wall using a laparoscopic,robotic,or open surgical approach.Post-operative follow-up was performed at 1 month and 1 year.Hernia recurrence was determined by physical examination and,when necessary,via computed tomo-graphy scan.Secondary endpoints included length of hospital stay,time to return to work,and hospital readmissions.Evaluated complications of the wound/repair site included presence of surgical site infection,seroma,hematoma,wound dehiscence,or fistula formation.RESULTS The observational study cohort included 16 male and 12 female patients with average age of 58.5 years±16.3 years and average body mass index of 26.2 kg/m^(2)±4.1 kg/m^(2).Patients presented with a parastomal hernia(75.0%),in-cisional hernia(14.3%),or combined parastomal/incisional hernia(10.7%).Using a laparoscopic(53.6%),robotic(35.7%),or open(10.7%)technique,RTMs(OviTex®LPR:82.1%,OviTex®1S:17.9%)were placed using sublay(82.1%)or intraperitoneal onlay(IPOM;17.9%)mesh positioning.At 1-month and 1-year follow-ups,there were no hernia recurrences(0%).Average hospital stays were 2.1 d±1.2 d and return to work occurred at 8.3 post-operative days±3.0 post-operative days.Three patients(10.7%)were readmitted before the 1-month follow up due to mesh infection and/or gastrointestinal issues.Fistula and mesh infection were observed in two patients each(7.1%),leading to partial mesh removal in one patient(3.6%).There were no complications between 1 month and 1 year(0%).CONCLUSION RTMs were used successfully to treat parastomal and incisional hernias at ileostomy reversal,with no hernia recurrences and favorable outcomes after 1-month and 1-year.

Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

Electrochemical Study of the Corrosion Inhibitory Capacity of Calcined Attapulgite in Reinforced Concrete Medium

The durability of reinforced concrete structures is greatly influenced by the corrosion of the reinforcement. In addition to air pollution related to the repair of corroded structures, chloride ions are the main factors of corrosion of reinforced concrete structures. This study aims to valorize a clay inhibitor against reinforcement corrosion in reinforced concrete. This clay (Attapulgite) was incorporated into reinforced concretes at different percentages of substitution of calcined attapulgite (0%, 5% and 10%) to cement in the formulation. The corrosion inhibitory power of attapulgite is evaluated in reinforced concretes subjected to the action of chloride ions at different intervals in the NaCl solution (1 day, 21 days and 45 days) by electrochemical methods (zero current chronopotentiometry, polarization curves and electrochemical impedance spectroscopy). This study showed that in the presence of chloride ions, the composition based on 10% attapulgite has an appreciable inhibitory effect with an average inhibitory efficiency of 82%.

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Stability behavior of the Lanxi ancient flood control levee after reinforcement with upside-down hanging wells and grouting curtain

The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the evolution laws of its seepage, displacement and stability before and after reinforcement with the upside-down hanging wells and grouting curtain through numerical simulation methods combined with experiments and observations. The study results indicate that the filled soil is less affected by water level fluctuations and groundwater concentration after reinforcement. A high groundwater level is detrimental to the levee's long-term stability, and the drainage issues need to be fully considered. The deformation of the reinforced levee is effectively controlled since the fill deformation is mainly borne by the upside-down hanging wells. The safety factors of the levee before reinforcement vary significantly with the water level. The minimum value of the safety factors is 0.886 during the water level decreasing period, indicating a very high risk of the instability. While it reached 1.478 after reinforcement, the stability of the ancient levee is improved by a large margin.

Interfacial reinforcement of core-shell HMX@energetic polymer composites featuring enhanced thermal and safety performance

The weak interface interaction and solid-solid phase transition have long been a conundrum for 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane(HMX)-based polymer-bonded explosives(PBX).A two-step strategy that involves the pretreatment of HMX to endow—OH groups on the surface via polyalcohol bonding agent modification and in situ coating with nitrate ester-containing polymer,was proposed to address the problem.Two types of energetic polyether—glycidyl azide polymer(GAP)and nitrate modified GAP(GNP)were grafted onto HMX crystal based on isocyanate addition reaction bridged through neutral polymeric bonding agent(NPBA)layer.The morphology and structure of the HMX-based composites were characterized in detail and the core-shell structure was validated.The grafted polymers obviously enhanced the adhesion force between HMX crystals and fluoropolymer(F2314)binder.Due to the interfacial reinforcement among the components,the two HMX-based composites exhibited a remarkable increment of phase transition peak temperature by 10.2°C and 19.6°C with no more than 1.5%shell content,respectively.Furthermore,the impact and friction sensitivity of the composites decreased significantly as a result of the barrier produced by the grafted polymers.These findings will enhance the future prospects for the interface design of energetic composites aiming to solve the weak interface and safety concerns.

A dynamic algorithm for trust inference based on double DQN in the internet of things

The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge to choose reliable users with which to interact in the IoT.Therefore,trust plays a crucial role in the IoT because trust may avoid some risks.Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning.However,trust propagation is time-consuming,and trust changes with the interaction process in social networks.To track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each other.The proposed algorithm simulates the interactions among users by double DQN.Firstly,CurrentNet and TargetNet networks are used to select users for interaction.The users with high trust are chosen to interact in future iterations.Secondly,the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction.Finally,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation strategy.Experiments are carried out on the FilmTrust and the Epinions datasets.Compared with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.

Numerical investigation of geostress influence on the grouting reinforcement effectiveness of tunnel surrounding rock mass in fault fracture zones

Grouting is a widely used approach to reinforce broken surrounding rock mass during the construction of underground tunnels in fault fracture zones,and its reinforcement effectiveness is highly affected by geostress.In this study,a numerical manifold method(NMM)based simulator has been developed to examine the impact of geostress conditions on grouting reinforcement during tunnel excavation.To develop this simulator,a detection technique for identifying slurry migration channels and an improved fluid-solid coupling(FeS)framework,which considers the influence of fracture properties and geostress states,is developed and incorporated into a zero-thickness cohesive element(ZE)based NMM(Co-NMM)for simulating tunnel excavation.Additionally,to simulate coagulation of injected slurry,a bonding repair algorithm is further proposed based on the ZE model.To verify the accuracy of the proposed simulator,a series of simulations about slurry migration in single fractures and fracture networks are numerically reproduced,and the results align well with analytical and laboratory test results.Furthermore,these numerical results show that neglecting the influence of geostress condition can lead to a serious over-estimation of slurry migration range and reinforcement effectiveness.After validations,a series of simulations about tunnel grouting reinforcement and tunnel excavation in fault fracture zones with varying fracture densities under different geostress conditions are conducted.Based on these simula-tions,the influence of geostress conditions and the optimization of grouting schemes are discussed.

Evaluation of the Inhibitory Gel Aloe vera against Corrosion of Reinforcement Concrete in NaCl Medium

Most reinforced concrete structures in seaside locations suffer from corrosion damage to the reinforcement, limiting their durability and necessitating costly repairs. To improve their performance and durability, we have investigated in this paper Aloe vera extracts as a green corrosion inhibitor for reinforcing steel in NaCl environments. Using electrochemical methods (zero-intensity chronopotentiometry, Tafel lines and electrochemical impedance spectroscopy), this experimental work investigated the effect of these Aloe vera (AV) extracts on corrosion inhibition of concrete reinforcing bar (HA, diameter 12mm) immersed in a 0.5M NaCl solution. The results show that Aloe vera extracts have an average corrosion-inhibiting efficacy of around 86% at an optimum concentration of 20%.

Effect of Shrinkage Reducing Agent and Steel Fiber on the Fluidity and Cracking Performance of Ultra-High Performance Concrete

Due to the low water-cement ratio of ultra-high-performance concrete(UHPC),fluidity and shrinkage cracking are key aspects determining the performance and durability of this type of concrete.In this study,the effects of different types of cementitious materials,chemical shrinkage-reducing agents(SRA)and steel fiber(SF)were assessed.Compared with M2-UHPC and M3-UHPC,M1-UHPC was found to have better fluidity and shrinkage cracking performance.Moreover,different SRA incorporation methods,dosage and different SF types and aspect ratios were implemented.The incorporation of SRA and SF led to a decrease in the fluidity of UHPC.SRA internal content of 1%(NSRA-1%),SRA external content of 1%(WSRA-1%),STS-0.22 and STE-0.7 decreased the fluidity of UHPC by 3.3%,8.3%,9.2%and 25%,respectively.However,SRA and SF improved the UHPC shrinkage cracking performance.NSRA-1%and STE-0.7 reduced the shrinkage value of UHPC by 40%and 60%,respectively,and increased the crack resistance by 338%and 175%,respectively.In addition,the addition of SF was observed to make the microstructure of UHPC more compact,and the compressive strength and flexural strength of 28 d were increased by 26.9%and 19.9%,respectively.

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.

Experimental investigation of engineered geopolymer composite for structural strengthening against blast loads

The recent increase in blast/bombing incidents all over the world has pushed the development of effective strengthening approaches to enhance the blast resistance of existing civil infrastructures.Engineered geopolymer composite(EGC)is a promising material featured by eco-friendly,fast-setting and strain-hardening characteristics for emergent strengthening and construction.However,the fiber optimization for preparing EGC and its protective effect on structural elements under blast scenarios are uncertain.In this study,laboratory tests were firstly conducted to evaluate the effects of fiber types on the properties of EGC in terms of workability,dry shrinkage,and mechanical properties in compression,tension and flexure.The experimental results showed that EGC containing PE fiber exhibited suitable workability,acceptable dry shrinkage and superior mechanical properties compared with other types of fibers.After that,a series of field tests were carried out to evaluate the effectiveness of EGC retrofitting layer on the enhancement of blast performance of typical elements.The tests include autoclaved aerated concrete(AAC)masonry walls subjected to vented gas explosion,reinforced AAC panels subjected to TNT explosion and plain concrete slabs subjected to contact explosion.It was found that EGC could effectively enhance the blast resistance of structural elements in different scenarios.For AAC masonry walls and panels,with the existence of EGC,the integrity of specimens could be maintained,and their deflections and damage were significantly reduced.For plain concrete slabs,the EGC overlay could reduce the diameter and depth of the crater and spallation of specimens.

Optimal Design of Drying Process of the Potatoes withMulti-Agent Reinforced Deep Learning

Heat and mass transport through evaporation or drying processes occur in many applications such as food processing,pharmaceutical products,solar-driven vapor generation,textile design,and electronic cigarettes.In this paper,the transport of water from a fresh potato considered as a wet porous media with laminar convective dry air fluid flow governed by Darcy’s law in two-dimensional is highlighted.Governing equations of mass conservation,momentumconservation,multiphase fluid flowin porousmedia,heat transfer,and transport of participating fluids and gases through evaporation from liquid to gaseous phase are solved simultaneously.In this model,the variable is block locations,the object function is changing water saturation inside the porous medium and the constraint is the constant mass of porous material.It shows that there is an optimal configuration for the purpose of water removal from the specimen.The results are compared with experimental and analyticalmethods Benchmark.Then for the purpose of configuration optimization,multi-agent reinforcement learning(MARL)is used while multiple porous blocks are considered as agents that transfer their moisture content with the environment in a real-world scenario.MARL has been tested and validated with previous conventional effective optimization simulations and its superiority proved.Our study examines and proposes an effective method for validating and testing multiagent reinforcement learning models and methods using a multiagent simulation.

Design,Development and Testing of the JTech Bolt for Use in Static,Quasi-Static and Dynamic Domains

Designing a rock reinforcement element requires knowledge of:geomechanical behaviour,interaction of the reinforcement element with rock mass and the element’s mechanistic response in static and dynamic environments.Using this knowledge the JTech bolt was developed and subjected to a thorough program to test,gather data and validate the bolt performance in varying domains.By conducting FE(finite element)modeling,the simulation reviews the JTech bolt design evaluating the effects of threadbar geometric variation,threadbar and nut engagement results under high stress,coating friction response and effects of thread tolerance extremes on the failure mode.These results determine safety factors,tolerances and quality management criteria.Once manufactured,in-situ system testing,laboratory and underground short encapsulation testing,resin mixing testing,double shear testing and dynamic testing at varying velocity and mass,determine the system’s capacity and effectiveness in static,quasi-static and dynamic mining environments.In this paper,the process and results are described.