A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

The structure-directing role of heterologous seeds in the synthesis of zeolite

Zeolites have been widely used as catalysts,ion-exchangers,and adsorbents in chemical industries,detergent industry,steel industry,glass industry,ceramic industry,medical and healthfield,and environmentalfield,and recently applied in energy storage.Seed-assisted synthesis is a very effective approach in promoting the crystallization of zeolites.In some cases,the target zeolite cannot be formed in the absence of seed zeolite.In homologous seed-assisted synthesis,the structure of the seed zeolite is the same to that of the target zeolite,while the structure of the seed zeolite is different to that of the target zeolite in the heterologous seed-assisted synthesis.In this review,we briefly summarized the heterologous seed-assisted syntheses of zeolites and analyzed the structure-directing effect of heterologous seeds and surveyed the“common composite building units(CBUs)hypothesis”and the“common secondary building units(SBUs)hypothesis”.However,both hypotheses cannot explain all observations on the heterologous seed-assisted syntheses.Finally,we proposed that the formation of the target zeolite does need nuclei with the structure of target zeolite and the formation of the nuclei of the target zeolite can be promoted by either the undissolved seed crystals with the same CBUs or SBUs to the target zeolite or by the facilitated appropriate distribution of the specific building units due to the presence of the heterologous seed that does not have any common CBUs and SBUs with the target zeolite.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Detailed Analyses of Nymphea Seeds Consumed in the Senegal River Valley

Physicochemical investigations were performed on seeds of Nymphea lotus and N. micrantha consumed in the Senegal River valley. They revealed a composition similar to that of cereals. In order to estimate their intrinsic quality, the determination of their amino acid, fatty acid and monosaccharids profiles was done. The results indicate that monosaccharides are represented specifically by saccharose (7%) and glucose (0.67%);a predominance of stearic acid and linoleic acid as unsaturated acids (24.86%);arachidic and palmitic acids as the only saturated acid found (11.12%);a good ratio of unsaturated/saturated acid (2.23);a lack of oleic acid, linoleic, palmitoleic, myristic, caprylic acids;a poor-quality index protein due to low quantity amino acids. Nevertheless, all essential amino acids are present in the seeds. The Nymphea sp grains consumed by the populations around the Senegal River valley offer an interesting nutritional quality linked to fatty acids and carbohydrates.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Dormancy of Amaranthus retroflexus L. Seeds and Physiological Response Seedlings to Acifluorfen Sodium

Amaranthus retroflexus L. is a serious and widespread malignant weed in soybean fields in Heilongjiang Province. Exploring the dormancy characteristics of A. retroflexus L. seeds and the physiological response of its seedlings to acifluorfen sodium can provide a basis for further researches on its resistance mechanism. Using newly harvested and stored A. retroflexus L. seeds for one year as experimental materials, the effects of different concentrations of HCl, NaOH, water temperature, gibberellic acid(GA) and polyethylene glycol(PEG) on the dormancy and germination of A. retroflexus L. seeds were studied. The sensitivity of A. retroflexus L.to acifluorfen sodium was determined using bioassay. The effects on leaf chlorophyll content and target enzyme activity were studied at a normal dosage of 360 g a.i. hm^(-2) and a doubling dosage of 720 g a.i. hm^(-2) of acifluorfen sodium. Newly harvested seeds exhibiting dormancy were soaked in water of various temperatures and in different concentrations of NaOH and HCl, which were ineffective in breaking the seed dormancy. GA could break seed dormancy, and the highest seed germination rate reached 93.33% when they were soaked at 3 000 mg·L^(-1) for 72 h and 4 000 mg·L^(-1) for 48 h. The drought stress was simulated with a 15%-25% polyethylene glycol solution, which had no significant effect on the seed germination rate. The GR_(50) value of acifluorfen sodium for A. retroflexus L. was 705.7 g a.i. hm^(-2), which was 1.96 times the recommended dose in the field. After the application of different doses of acifluorfen sodium, the chlorophyll content of A. retroflexus L. reached its minimum value 3 days after treatment(DAT), and then gradually increased. The activity of the target enzyme protoporphyrinogen oxidase(PPO) reached the highest value at 7 DAT under different dosages, and gradually returned to normal levels thereafter. Soaking with gibberellin was an effective method to break seed dormancy. A. retroflexus L. seeds had certain drought resistance during the germination process. A. retroflexus L. was not sensitive to acifluorfen sodium and acifluorfen sodium ether, and could not effectively inhibit the PPO activity, indicating that A. retroflexus L. had target resistance to acifluorfen sodium.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

EMS-Mediated Mutagenesis in Marigold Seeds and Its Effects on Seedling Growth and Physiology

Marigolds(Tagetes spp.)are popular horticultural plants worldwide.The current study aimed to investigate the optimal mutagenic conditions for marigold seeds using EMS(ethyl methanesulfonate)mutagenesis.Different con-centrations and treatment times of EMS were applied to investigate their effects on the marigold seed germination rate,growth traits,antioxidant enzyme activities(i.e.,SOD and POD),and malondialdehyde(MDA)contents.Results indicated that with increasing the EMS treatment duration and concentration,the seed germination rate and growth treatments were reduced,accompanied by elevated MDA content.In addition,SOD and POD activ-ities initially correlated positively with the growth tratis at the lowest concentrations and shortest durations of EMS,but such relationship diminished beyond certain thresholds.The comprehensive analysis identified the opti-mal mutagenic conditions as 1%EMS treatment for 12 h,achieving a semi-lethal dose and enhancing stress-resis-tant components in seedlings.Thesefindings are pivotal for advancing genetic enhancement and germplasm innovation in marigolds.

Highly ordered crystallization of α-FAPbl_(3) films via homogeneous seeds for efficient perovskite solar cells

Formamidine lead triiodide(FAPbI_(3))perovskites have become the most promising photovoltaic materials for perovskite solar cells with record power conversion efficiency(PCE).However,random nucleation,phase transition,and lattice defects are still the key challenges limiting the quality of FAPbI_(3) films.Previous studies show that the introduction or adding of seeds in the precursor is effective to promote the nucleation and crystallization of perovskite films.Nevertheless,the seed-assisted approach focuses on heterogeneous seeds or hetero-composites,which inevitably induce a lattice-mismatch,the genera-tion of strain or defects,and the phase segregation in the perovskite films.Herein,we first demonstrate that high-quality perovskite films are controllably prepared using α-and δ-phases mixed FAPbI_(3) micro-crystal as the homogeneous seeds with the one-step antisolvent method.The partially dissolved seeds with suitable sizes improve the crystallinity of the perovskite flm with preferable orientation,improved carrier lifetime,and increased carrier mobility.More importantly,the α-phase-containing seeds promote the formation of α-phase FAPbI_(3) films,leading to the reduction of residual lattice strain and the suppres-sion of I-ion migration.Besides,the adding of dimethyl 2,6-pyridine dicarboxylate(DPD)into the pre-cursor further suppresses the generation of defects,contributing to the PCE of devices prepared in air ambient being significantly improved to 23.75%,among the highest PCEs for fully air-processed FAPbI_(3) solar cells.The unpackaged target devices possess a high stability,maintaining 80%of the initial PCE under simulated solar illumination exceeding 800 h.

Large-Scale Carbon Dioxide Storage in Salt Caverns:Evaluation of Operation,Safety,and Potential in China

Underground salt cavern CO_(2) storage(SCCS)offers the dual benefits of enabling extensive CO_(2) storage and facilitating the utilization of CO_(2) resources while contributing the regulation of the carbon market.Its economic and operational advantages over traditional carbon capture,utilization,and storage(CCUS)projects make SCCS a more cost-effective and flexible option.Despite the widespread use of salt caverns for storing various substances,differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness,carbon injection,brine extraction control,long-term carbon storage stability,and site selection criteria.These distinctions stem from the unique phase change characteristics of CO_(2) and the application scenarios of SCCS.Therefore,targeted and forward-looking scientific research on SCCS is imperative.This paper introduces the implementation principles and application scenarios of SCCS,emphasizing its connections with carbon emissions,carbon utilization,and renewable energy peak shaving.It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods,and addresses associated scientific challenges.In this paper,we establish a pressure equation for carbon injection and brine extraction,that considers the phase change characteristics of CO_(2),and we analyze the pressure during carbon injection.By comparing the viscosities of CO_(2) and other gases,SCCS’s excellent sealing performance is demonstrated.Building on this,we develop a long-term stability evaluation model and associated indices,which analyze the impact of the injection speed and minimum operating pressure on stability.Field countermeasures to ensure stability are proposed.Site selection criteria for SCCS are established,preliminary salt mine sites suitable for SCCS are identified in China,and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons,utilizing only 20%-30%volume of abandoned salt caverns.This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters,such as the operating pressure,burial depth,and storage scale,and it offers essential guidance for implementing SCCS projects in China.

Improved length of calcium sulfate crystal seeds and whiskers via ball milling and hydration treatment

Elucidating the effect of growth periods on the quality of calcium sulfate whiskers(CSWs)prepared from calcium sulfate dihydrate(DH)is imperative.Herein,crystal seeds and whiskers were prepared from DH in a water–glycerol system.Longer whiskers were obtained from crystal seeds prepared via hydration of DH for 30 s than via ball milling for 5 min followed by hydration for 20 s.The attachment of cetyltrimethyl ammonium bromide and glycerol additives to the whisker tops promoted whisker growth.The whisker sponges exhibited good thermal barrier properties and compression cycle stability.

Pumpkin seeds as nutraceutical and functional food ingredient for future:A review

Pumpkin belongs to the family of Cucurbitaceae,which comprises several species that has economical as well as agronomical importance.All parts of pumpkin are edible and laden with beneficial neutraceutical compounds.Pumpkin seeds are valuable source protein which can help in eradicating protein malnutrition and lipids(rich in PUFAs)contains essential as well as non essential fatty acids which prevents from various ailments like cancer and other cardiovascular diseases.Since,seeds of pumpkin are abundant in macro(magnesium,phosphorous,potassium,sodium and calcium)and micro minerals(iron,copper,manganese,zinc and selenium),they can be used as an incredible dietary supplement which in turn helps in curbing various deficiency disorders.This review enlightens the characteristics of pumpkin seeds,process of valorization of pumpkin seeds and the effect of processing on their nutritional composition which have been studied currently with the aim to use this wonder seeds for human wellbeing.Pumpkin seeds possess many bioactive compounds like polyphenols,flavonoids,phytosterols and squalene which makes it a lucrative raw material for pharmacological and food industries.Pumpkin seeds work as anti-depressant and helps majorly in the treatment of benign prostate hyperplasia(BHP).Daily consumption of pumpkin seeds can reduce the chances of Alzheimer's and Parkinson's disease.Pumpkin seeds are rich in tocopherols and can be used for oil extraction for edible purposes and utilized in other food formulations for future use.

Factorized Smith Method for A Class of High-Ranked Large-Scale T-Stein Equations

We introduce a factorized Smith method(FSM)for solving large-scale highranked T-Stein equations within the banded-plus-low-rank structure framework.To effectively reduce both computational complexity and storage requirements,we develop techniques including deflation and shift,partial truncation and compression,as well as redesign the residual computation and termination condition.Numerical examples demonstrate that the FSM outperforms the Smith method implemented with a hierarchical HODLR structured toolkit in terms of CPU time.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Soaking Rice Seeds in High Concentration Salt Solution Improved Salt Tolerance at the Seedling Stage

[Objectives]This study was conducted to enhance the salt tolerance of current rice varieties at the seedling stage and fulfill the urgent requirement for salt-tolerant rice varieties in coastal tidal flats.[Methods]Four high-generation stable rice lines with diverse salt tolerance were employed as test materials,and four NaCl concentration gradients were established for seed soaking treatment.[Results]The seedling survival rate of line 151465 underwent significant alterations after soaking with four different salt concentrations,and the survival rate was the highest after treatment with 1.8%NaCl for 1 d,reaching 65.2%.The average survival rate of other three lines with different salt tolerance reached 62%after soaking with 1.8%NaCl for 1 d,which was significantly higher than those of the 2.2%NaCl and 0%NaCl treatments.[Conclusions]This study provides a basis for reducing the effect of abiotic stress on rice growth and development and improving the utilization rate of saline-alkali land.