Flexible,thermal processable,self-healing,and fully bio-based starch plastics by constructing dynamic imine network

The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.

Technique of Error Concealment for Block-Based Image Coding Using Genetic Algorithm

Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.

On-line Detection of Gas Pipeline Based on the Real-Time Algorithm and Network Technology with Robot

The detection system integrates control technology, network technology, video encoding and decoding, video transmiss-ion, multi-single chip microcomputer communication, dat-abase technology, computer software and robot technology. The robot can adaptively adjust its status according to diameter (from 400 mm to 650 mm) of pipeline. The maximum detection distance is up to 1 000 m. The method of video coding in the system is based on fractal transformation. The experiments show that the coding scheme is fast and good PSNR. The precision of on-line detection is up to 3% thickness of pipeline wall. The robot can also have a high precision of location up to 0.03 m. The control method is based on network and characterized by on-line and real-time. The experiment in real gas pipeline shows that the performance of the detection system is good.

Kinetics and microstructural evolution during recrystallization of a Ni_3Al-based single crystal superalloy

The recrystallization kinetics and microstructural evolution of a Ni3Al-based single crystal superalloy were presented, especially the different recrystallization behaviors between the dendrite arm and the interdendritic region. The single crystal alloy was deformed by grit blasting. A succeeding annealing under inert atmosphere at 1280 ℃ for different time led to the formation of recrystallized grains close to the grit blasting surface. It was found that the recrystallization depth and velocity in the dendrite arm were respectively deeper and faster than those in the interdendritic region where the Y-NiMo phase existed. The recrystallization process in the interdendritic region was significantly inhibited by the Y-NiMo precipitates. However, the pinning effect gradually weakened with the annealing time due to the dissolution of the Y-NiMo phase, and the recrystallization depth in the dendrite arm was deeper than that in the interdendritic region.

Computer vision-based limestone rock-type classification using probabilistic neural network

Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.

Virtual population pharmacokinetic using physiologically based pharmacokinetic model for evaluating bioequivalence of oral lacidipine formulations in dogs

The aim of the present study was to investigate virtual population pharmacokinetic using physiologically based pharmacokinetic(PBPK) model for evaluating bioequivalence of oral lacidipine formulations in dogs. The dissolution behaviors of three lacidipine formulations including one commercial product and two self-made amorphous solid dispersions(ASDs)capsules were determined in 0.07% Tween 80 media. A randomized 3-period crossover design in 6 healthy beagle dogs after oral administration of the three formulations at a single dose of 4 mg was conducted. The PBPK modeling was utilized for the virtual bioequivalence study.In vitro dissolution experiment showed that the dissolution behaviors of lacidipine amorphous solid dispersions(ASDs) capsules, which was respectively prepared by HPMC-E5 or Soluplus, as polymer displayed similar curves compared with the reference formulation in 0.07% Tween 80 media. In vivo pharmacokinetics experiments showed that three formulations had comparable maximum plasma drug concentration(Cmax), and the time(Tmax) to reach Cmax of lacidipine tablet, which was prepared by Soluplus, as polymer was slower than other two formulations in consistency with the in vitro dissolution rate. The 90% confidence interval(CI) for the Cmax, AUC0–24 h and AUC0–∞ of the ratio of the test drug to the reference drug exceeded the acceptable bioequivalence(BE) limits(0.80–1.25). However, the 90% CI of the AUC0–24 h, AUC0–∞ and Cmax of the ratio of test to reference drug were within the BE limit,calculated using PBPK modeling when the virtual subjects reached 24 dogs. The results all demonstrated that virtual bioequivalence study can overcome the inequivalence caused by inter-subject variability of the 6 beagle dogs involved in in vivo experiments.

A long-term-based handover decision algorithm for dense macro-femto coexistence networks

For the dense macro-femto coexistence networks scenario, a long-term-based handover（LTBH） algorithm is proposed. The handover decision algorithm is jointly determined by the angle of handover（AHO） and the time-tostay（TTS） to reduce the unnecessary handover numbers.First, the proposed AHO parameter is used to decrease the computation complexity in multiple candidate base stations（CBSs） scenario. Then, two types of TTS parameters are given for the fixed base stations and mobile base stations to make handover decisions among multiple CBSs. The simulation results show that the proposed LTBH algorithm can not only maintain the required transmission rate of users, but also effectively reduce the unnecessary numbers of handover in the dense macro-femto networks with the coexisting mobile BSs.

Genetic algorithm based LQR vibration wireless control of laminated plate using photostrictive actuators

A photostrictive type of opto-electromechanical actuator activated by high-energy lights can introduce actuation and control effects without hard-wired connections. This paper addresses the controllability aspect in wireless vibration control of plate structures via photostrictive actuators. A modal force index, which has taken into account the mode number, the spatial distribution, and the dimension of the actuator, is chosen as an objective function to determine the optimal locations of photostrictive actuators. A linear methodology is proposed in this paper and the vibration equation is written in the standard state-space form. A binary-coded GA based combined optimal placement and LQR （linear quadratic regulator） control scheme has been incorporated, which maximizes the modal force index, the closed loop damping and minimizes input light intensity to the actuators. In the present method only three weighting factors have been used to search optimal Q and R matrices using GA, which reduces chromosome length and hence minimizes computational time. Numerical results demonstrate that the use of strategically positioned actuator patches can effectively control the fundamental modes that dominate the structural vibration.

Elastic wave insulation and propagation control based on the programmable curved-beam periodic structure

Curved-beams can be used to design modular multistable metamaterials(MMMs)with reprogrammable material properties,i.e.,programmable curved-beam periodic structure(PCBPS),which is promising for controlling the elastic wave propagation.The PCBPS is theoretically equivalent to a spring-oscillator system to investigate the mechanism of bandgap,analyze the wave propagation mechanisms,and further form its geometrical and physical criteria for tuning the elastic wave propagation.With the equivalent model,we calculate the analytical solutions of the dispersion relations to demonstrate its adjustability,and investigate the wave propagation characteristics through the PCBPS.To validate the equivalent system,the finite element method(FEM)is employed.It is revealed that the bandgaps of the PCBPS can be turned on-and-off and shifted by varying its physical and geometrical characteristics.The findings are highly promising for advancing the practical application of periodic structures in wave insulation and propagation control.

The effect of salt anion in ether‐based electrolyte for electrochemical performance of sodium‐ion batteries:A case study of hard carbon

Compared with the extensively used ester‐based electrolyte,the hard carbon(HC)electrode is more compatible with the ether‐based counterpart in sodium‐ion batteries,which can lead to improved cycling stability and robust rate capability.However,the impact of salt anion on the electrochemical performance of HC electrodes has yet to be fully understood.In this study,the anionic chemistry in regulating the stability of electrolytes and the performance of sodium‐ion batteries have been systematically investigated.This work shows discrepancies in the reductive stability of the anionic group,redox kinetics,and component/structure of solid electrolyte interface(SEI)with different salts(NaBF_(4),NaPF_(6),and NaSO_(3)CF_(3))in the typical ether solvent(diglyme).Particularly,the density functional theory calculation manifests the preferred decomposition of PF_(6)−due to the reduced reductive stability of anions in the solvation structure,thus leading to the formation of NaF‐rich SEI.Further investigation on redox kinetics reveals that the NaPF_(6)/diglyme can induce the fast ionic diffusion dynamic and low charge transfer barrier for HC electrode,thus resulting in superior sodium storage performance in terms of rate capability and cycling life,which outperforms those of NaBF_(4)/diglyme and NaSO_(3)CF_(3)/diglyme.Importantly,this work offers valuable insights for optimizing the electrochemical behaviors of electrode materials by regulating the anionic group in the electrolyte.

Elevating Localization Accuracy in Wireless Sensor Networks:A Refined DV-Hop Approach

Localization is crucial in wireless sensor networks for various applications,such as tracking objects in outdoor environments where GPS(Global Positioning System)or prior installed infrastructure is unavailable.However,traditional techniques involve many anchor nodes,increasing costs and reducing accuracy.Existing solutions do not address the selection of appropriate anchor nodes and selecting localized nodes as assistant anchor nodes for the localization process,which is a critical element in the localization process.Furthermore,an inaccurate average hop distance significantly affects localization accuracy.We propose an improved DV-Hop algorithm based on anchor sets(AS-IDV-Hop)to improve the localization accuracy.Through simulation analysis,we validated that the ASIDV-Hop proposed algorithm is more efficient in minimizing localization errors than existing studies.The ASIDV-Hop algorithm provides an efficient and cost-effective solution for localization in Wireless Sensor Networks.By strategically selecting anchor and assistant anchor nodes and rectifying the average hop distance,AS-IDV-Hop demonstrated superior performance,achieving a mean accuracy of approximately 1.59,which represents about 25.44%,38.28%,and 73.00%improvement over other algorithms,respectively.The estimated localization error is approximately 0.345,highlighting AS-IDV-Hop’s effectiveness.This substantial reduction in localization error underscores the advantages of implementing AS-IDV-Hop,particularly in complex scenarios requiring precise node localization.

A novel spin-FET based on 2D antiferromagnet

Engineering the electronic band structure of material systems enables the unprecedented exploration of new physical properties that are absent in natural or as-synthetic materials.Electronic structures of bilayer two-dimensional(2D)systems can be flexibly engineered by the external electric field.For example.

Development and experimental validation of kinetic models for the hydrogenation/dehydrogenation of Mg/Al based metal waste for energy storage

With the increased use of renewable energy sources,the need to store large amounts of energy will become increasingly important in the near future.A cost efficient possibility is to use the reaction of recycled Mg waste with hydrogen as thermo-chemical energy storage.Owing to the high reaction enthalpy,the moderate pressure and appropriate temperature conditions,the broad abundance and the recyclability,the Mg/Al alloy is perfectly suitable for this purpose.As further development of a previous work,in which the performance of recycled Mg/Al waste was presented,a kinetic model for hydro-and dehydrogenation is derived in this study.Temperature and pressure dependencies are determined,as well as the rate limiting step of the reaction.First experiments are carried out in an autoclave with a scaled-up powder mass,which is also used to validate the model by simulating the geometry with the scaled-up experiments at different conditions.

Genome-wide association with transcriptomics reveals a shade-tolerance gene network in soybean

Shade tolerance is essential for soybeans in inter/relay cropping systems.A genome-wide association study(GWAS)integrated with transcriptome sequencing was performed to identify genes and construct a genetic network governing the trait in a set of recombinant inbred lines derived from two soybean parents with contrasting shade tolerance.An improved GWAS procedure,restricted two-stage multi-locus genome-wide association study based on gene/allele sequence markers(GASM-RTM-GWAS),identified 140 genes and their alleles associated with shade-tolerance index(STI),146 with relative pith cell length(RCL),and nine with both.Annotation of these genes by biological categories allowed the construction of a protein–protein interaction network by 187 genes,of which half were differentially expressed under shading and non-shading conditions as well as at different growth stages.From the identified genes,three ones jointly identified for both traits by both GWAS and transcriptome and two genes with maximum links were chosen as beginners for entrance into the network.Altogether,both STI and RCL gene systems worked for shade-tolerance with genes interacted each other,this confirmed that shadetolerance is regulated by more than single group of interacted genes,involving multiple biological functions as a gene network.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

Buckling Optimization of Curved Grid Stiffeners through the Level Set Based Density Method

Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.

Research progress on electronic and active site engineering of cobalt‐based electrocatalysts for oxygen evolution reaction

Electrocatalytic water splitting has been identified as a potential candidate for producing clean hydrogen energy with zero carbon emission.However,the sluggish kinetics of oxygen evolution reaction on the anode side of the watersplitting device significantly hinders its practical applications.Generally,the efficiency of oxygen evolution processes depends greatly on the availability of cost‐effective catalysts with high activity and selectivity.In recent years,extensive theoretical and experimental studies have demonstrated that cobalt(Co)‐based nanomaterials,especially low‐dimensional Co‐based nanomaterials with a huge specific surface area and abundant unsaturated active sites,have emerged as versatile electrocatalysts for oxygen evolution reactions,and thus,great progress has been made in the rational design and synthesis of Co‐based nanomaterials for electrocatalytic oxygen evolution reactions.Considering the remarkable progress in this area,in this timely review,we highlight the most recent developments in Co‐based nanomaterials relating to their dimensional control,defect regulation(conductivity),electronic structure regulation,and so forth.Furthermore,a brief conclusion about recent progress achieved in oxygen evolution on Co‐based nanomaterials,as well as an outlook on future research challenges,is given.

A Trajectory Prediction Based Intelligent Handover Control Method in UAV Cellular Networks

The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular networks.When mobile users move among adjacent ABSs,the measurement information reported by a single mobile user is used to trigger the handover mechanism.This handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users,which may lead to handover misjudgments and even communication interrupts.In this paper,we propose an intelligent handover control method in UAV cellular networks.Firstly,we introduce a deep learning model to predict the user trajectories.This prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow pedestrians.Secondly,we propose a handover decision method,which can calculate the users' corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel,to make handover decisions accurately.Finally,we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover controlmethod.The simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.

Application of PID Controller Based on BP Neural Network in Export Steam’s Temperature Control System

By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.