Evolution of microstructure and texture of AZ80 magnesium alloy under hot torsion with constant decreasing temperature rate

Hot torsion tests for AZ80 magnesium alloy were carried out in the temperature range of 380℃-260℃,with a constant decreasing temperature rate of 10℃/s in order to weaken the basal texture and refine the grains.The results indicated that the average grain sizes were refined forming gradient structure with increasing specimen radial position from center(12.2-5.4μm),and that the initial basal texture intensity of the extruded magnesium alloy was weakened from 46.2 to 8.3.Furthermore,the extension twins(ETs)could be disintegrated from the twins forming separated twins with smaller sizes.Interestingly,ETs with the same twin variant intersecting with each other could be coalesced forming grains with similar orientation,while ETs with different twin variants were separated by twins boundaries contributing to grain refinement.Moreover,in addition to the conventional continuous dynamic recrystallized(CDRX)grains with 30˚orientation rotated around C-axis of the parent grains,CDRXed grains with 30˚rotation around a-axis and random rotation axis were also discerned.Besides,the CDRX evolution induced twins were also elaborated,exhibiting the complex competition between CDRX and twining.Hot torsion deformation with constant decreasing temperatures rate is an effective way of grain refinement and texture modification.

Multi-objective optimization design of anti-roll torsion bar using improved beluga whale optimization algorithm

Purpose – This study aims to reduce the redundant weight of the anti-roll torsion bar brought by thetraditional empirical design and improving its strength and stiffness.Design/methodology/approach – Based on the finite element approach coupled with the improved belugawhale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the designof the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar weredefined as random variables, and the torsion bar’s mass and strength were investigated using finite elements.Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whaleoptimization (BWO) algorithm and run case studies.Findings – The findings demonstrate that the IBWO has superior solution set distribution uniformity,convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimizethe anti-roll torsion bar design. The error between the optimization and finite element simulation results wasless than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress wasreduced by 35% and the stiffness was increased by 1.9%.Originality/value – The study provides a methodological reference for the simulation optimization process ofthe lateral anti-roll torsion bar.

Rupture of a giant jejunal mesenteric cystic lymphangioma misdiagnosed as ovarian torsion: A case report

BACKGROUND Cystic lymphangioma is a rare benign tumor that affects the lymphatic system.Mesenteric lymphangiomas in the small bowel are extremely uncommon.CASE SUMMARY We present a 21-year-old female patient who complained of abdominal pain.The diagnosis of ovarian torsion was suspected after abdominopelvic unenhanced computed tomography and ultrasound revealed a large cyst in contact with the bladder,ovary,and uterus.The patient underwent emergency laparotomy per-formed by gynecologists,but it was discovered that the cystic tumor originated from the jejunum.Gastrointestinal surgeons were then called in to perform a cystectomy.Pathological examination confirmed the diagnosis of cystic lymphangioma of the mesentery.The patient had an uneventful postoperative recovery.CONCLUSION Mesenteric lymphangiomas can cause abdominal pain,and imaging techniques can help determine their characteristics,location,and size.Complete surgical excision and pathological examination are considered the standard treatment and diagnostic method.

The Effects of the Longitudinal Axis of Loading upon Bending, Shear and Torsion of a Thin-Walled Cantilever Channel Beam

Three aluminium channel sections of US standard extruded dimension are mounted as cantilevers with x-axis symmetry. The flexural bending and shear that arise with applied axial torsion are each considered theoretically and numerically in terms of two longitudinal axes of loading not coincident with the shear centre. In particular, the warping displacements, stiffness and stress distributions are calculated for torsion applied to longitudinal axes passing through the section’s centroid and its web centre. The stress conversions derived from each action are superimposed to reveal a net sectional stress distribution. Therein, the influence of the axis position upon the net axial and shear stress distributions is established compared to previous results for each beam when loading is referred to a flexural axis through the shear centre. Within the net stress analysis is, it is shown how the constraint to free warping presented by the end fixing modifies the axial stress. The latter can be identified with the action of a ‘bimoment’ upon each thin-walled section.

Emergence of Multidrug-Resistant Proteus mirabilis Harboring both blaNDM-1 and cfr genes

Proteus mirabilis is abundant in soil and water,and although it is part of the normal human intestinal flora(along with Klebsiella species and Escherichia coli),it is known to cause serious infections in humans,with a fatality rate of 20%-50%[1].Proteus mirabilis is intrinsically resistant to tetracycline,tigecycline,and colistin.The widespread and irregular use of antimicrobial agents,P.mirabilis antimicrobial resistance has mirabilis.They acquire antimicrobial resistance(AMR)by capturing plasmids,transposons,or other mobile elements harboring antimicrobial resistance genes.This mechanism allows the rapid selection and transmission of numerous AMR genes in clinical,veterinary,food production,transportation,and environmental settings.

Optical redox imaging of ANT1-de-cient muscles

Adenine nucleotide translocator(ANT)is a mitochondrial protein involved in the exchange of ADP and ATP across the mitochondrial inner membrane.It plays a crucial role in cellular energy metabolism by facilitating the transport of ATP synthesized within the mitochondria to the cytoplasm.The isoform ANT1 predominately expresses in cardiac and skeletal muscles.Mutations or dysregulation in ANT1 have been implicated in various mitochondrial disorders and neuromuscular diseases.We aimed to examine whether ANT1 deletion may affect mitochondrial redox state in our established ANT1-de-cient mice.Hearts and quadriceps resected from age-matched wild type(WT)and ANT1-de-cient mice were snap-frozen in liquid nitrogen.The Chance redox scanner was utilized to perform 3D optical redox imaging.Each sample underwent scanning across 3–5 sections.Global averaging analysis showed no signi-cant differences in the redox indices(NADH,flavin adenine dinucleotide containing-flavoproteins Fp,and the redox ratio Fp/(NADH+Fp)between WT and ANT1-de-cient groups.However,quadriceps had higher Fp than hearts in both groups(p¼0:0004 and 0.01,respectively).Furthermore,the quadriceps were also more oxidized(a higher redox ratio)than hearts in WT group(p¼0:004).NADH levels were similar in all cases.Our data suggest that under non-stressful physical condition,the ANT1-de-cient muscle cells were in the same mitochondrial state as WT ones and that the signi-cant difference in the mitochondrial redox state between quadriceps and hearts found in WT might be diminished in ANT1-de-cient ones.Redox imaging of muscles under physical stress can be conducted in future.

An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect.The contemporary methods of steganography are at best a compromise between these two.In this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic embedding.The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret data.This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images.The ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual quality.Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement.The levels of pheromone are modified to reinforce successful pixel choices.Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of 0.98.The approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.

Nano-scale Reinforcements and Properties of Al-Si-Cu Alloy Processed by High-Pressure Torsion

To improve the comprehensive mechanical properties of Al-Si-Cu alloy,it was treated by a high-pressure torsion process,and the effect of the deformation degree on the microstructure and properties of the Al-Si-Cu alloy was studied.The results show that the reinforcements(β-Si andθ-CuAl_(2)phases)of the Al-Si-Cu alloy are dispersed in theα-Al matrix phase with finer phase size after the treatment.The processed samples exhibit grain sizes in the submicron or even nanometer range,which effectively improves the mechanical properties of the material.The hardness and strength of the deformed alloy are both significantly raised to 268 HV and 390.04 MPa by 10 turns HPT process,and the fracture morphology shows that the material gradually transits from brittle to plastic before and after deformation.The elements interdiffusion at the interface between the phases has also been effectively enhanced.In addition,it is found that the severe plastic deformation at room temperature induces a ternary eutectic reaction,resulting in the formation of ternary Al+Si+CuAl_(2)eutectic.

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

Micro-Arthropods in the Surroundings of Nests of Messor arenarius Ants in the Negev Desert in Israel

The present research investigated a segment of the micro-arthropod populations residing within nests of Messor arenarius ants in the Negev Desert of Israel. The total frequencies of micro-arthropods in the chaff of those ants’ nests were found to be higher than in the surrounding soil of the same nests. Acari (mites) were observed to be more abundant during the spring season, whereas their presence decreased during the summer months. Springtails (Collembola) were found to follow the Acari pattern, commonly found within the nests of those ants during spring but were absent during summer. Psocoptera order inhabiting soil habitats were infrequently encountered during spring, but their prevalence increased significantly during summer, particularly within the chaff of the ants’ nests, suggesting that chaff is their primary food source in the Negev Desert. Our research suggests that shifts in seasonality have important consequences on the distribution of soil invertebrate communities with implications on nutrient cycling.

Clinical characteristics of testicular torsion and factors influencing testicular salvage in children:A 12-year study in tertiary center

BACKGROUND Testicular torsion is the most common acute scrotum worldwide and mainly occurs in children and adolescents.Studies have demonstrated that the duration of symptoms and torsion grade lead to different outcomes in children diagnosed with testicular torsion.AIM To predict the possibility of testicular salvage(TS)in patients with testicular torsion in a tertiary center.METHODS We reviewed the charts of 75 pediatric patients with acute testicular torsion during a 12-year period from November 2011 to July 2023 at the Suzhou Hospital of Anhui Medical University.Univariate and multivariate logistic regression analyses were used to determine independent predictors of testicular torsion.The data included clinical findings,physical examinations,laboratory data,color Doppler ultrasound findings,operating results,age,presenting institution status,and follow-up results.RESULTS Our study included 75 patients.TS was possible in 57.3%of all patients;testicular torsion occurred mostly in winter,and teenagers aged 11-15 years old accounted for 60%.Univariate logistic regression analyses revealed that younger age(P=0.09),body mass index(P=0.004),torsion angle(P=0.013),red blood cell count(P=0.03),neutrophil-to-lymphocyte ratio(P=0.009),and initial presenting institution(P<0.001)were associated with orchiectomy.In multivariate analysis,only the initial presenting institution predicted TS(P<0.05).CONCLUSION The initial presenting institution has a predictive value for predicting TS in patients with testicular torsion.Children with scrotal pain should be admitted to a tertiary hospital as soon as possible.

Two-stage dynamic recrystallization and texture evolution in Al-7Mg alloy during hot torsion

Hot torsion tests were performed on the Al-7Mg alloy at the temperature ranging from 300 to 500℃ and strain rates between 0.05 and 5 s^(-1) to explore the progressive dynamic recrystallization(DRX)and texture behaviors.The DRX behavior of the alloy manifested two distinct stages:Stage 1 at strain of≤2 and Stage 2 at strains of≥2.In Stage 1,there was a slight increase in the DRXed grain fraction(X_(DRX))with predominance of discontinuous DRX(DDRX),followed by a modest change in X_(DRX) until the transition to Stage 2.Stage 2 was marked by an accelerated rate of DRX,culminating in a substantial final X_(DRX) of~0.9.Electron backscattered diffraction(EBSD)analysis on a sample in Stage 2 revealed that continuous DRX(CDRX)predominantly occurred within the(121)[001]grains,whereas the(111)[110]grains underwent a geometric DRX(GDRX)evolution without a noticeable sub-grain structure.Furthermore,a modified Avrami’s DRX kinetics model was utilized to predict the microstructural refinement in the Al-7Mg alloy during the DRX evolution.Although this kinetics model did not accurately capture the DDRX behavior in Stage 1,it effectively simulated the DRX rate in Stage 2.The texture index was employed to assess the evolution of the texture isotropy during hot-torsion test,demonstrating significant improvement(>75%)in texture randomness before the commencement of Stage 2.This initial texture evolution is attributed to the rotation of parent grains and the substructure evolution,rather than to an increase in X_(DRX).

基于Ant Design Pro的物流系统前端开发与用户体验优化研究

本文探讨了Ant Design Pro在物流系统开发中的应用,特别是其对用户体验和数据可视化的影响。通过采用案例研究方法,分析Ant Design Pro在构建企业级物流系统中的应用。研究发现,Ant Design Pro的一致性设计和丰富的组件库显著提升了开发效率和用户交互体验。此外,其数据可视化组件有效支持了复杂物流数据的呈现和分析。然而,其在性能优化和定制化需求方面面临着挑战。最终,本文提出了一系列基于Ant Design Pro的物流系统开发最佳实践,并对其潜在的改进方向进行了讨论。研究结果为物流行业的信息系统开发提供了实用的指导,并为Ant Design Pro社区贡献了宝贵的实践经验。

Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research value.However,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this difficulty.This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing orders.Additionally,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its convergence.The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in total.The effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

Analysis of strength characteristics of loess before and after freezing using a hollow cylinder torsional shear apparatus

This paper aims to comprehensively analyze the influence of the principal stress angle rotation and intermediate principal stress on loess's strength and deformation characteristics. A hollow cylinder torsional shear apparatus was utilized to conduct tests on remolded samples under both normal and frozen conditions to investigate the mechanical properties and deformation behavior of loess under complex stress conditions. The results indicate significant differences in the internal changes of soil particles, unfrozen water, and relative positions in soil samples under normal and frozen conditions, leading to noticeable variations in strength and strain development.In frozen state, loess experiences primarily compressive failure with a slow growth of cracks, while at normal temperature, it predominantly exhibits shear failure. With the increase in the principal stress angle, the deformation patterns of the soil samples under different conditions become essentially consistent, gradually transitioning from compression to extension, accompanied by a reduction in axial strength. The gradual increase in the principal stress axis angle(α) reduces the strength of the generalized shear stress and shear strain curves.Under an increasing α, frozen soil exhibits strain-hardening characteristics, with the maximum shear strength occurring at α = 45°. The intermediate principal stress coefficient(b) also significantly impacts the strength of frozen soil, with an increasing b resulting in a gradual decrease in generalized shear stress strength. This study provides a reference for comprehensively exploring the mechanical properties of soil under traffic load and a reliable theoretical basis for the design and maintenance of roadbeds.

A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

An Effective Optimization Algorithm for Ant Colony Vehicular Congestion Management

Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being.

A Distributed Ant Colony Optimization Applied in Edge Detection

With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.