Study on Path Selection and Implementation Effect of Counselors in College Students’Career Planning Education

Under the background of a severe job market and fierce talent competition,the career planning education of college students is of great importance.In this study,a combination of quantitative and qualitative research methods was used;500 students in Shenzhen Polytechnic of Information Technology were surveyed by questionnaire,and 40 counselors were interviewed in depth to comprehensively analyze the role and practical effect of counselors in career planning education.The research showed that the role positioning of counselors includes the guide of students’career development,emotional support,and job-hunting strategy,and the specific path selection involves the strengthening of professional knowledge,the construction of interpersonal relationship networks,and personalized counseling.Based on the survey and interview data,this paper constructed a model for enhancing the role of counselors in career planning education and put forward a set of targeted work paths.

Dual-Path Vision Transformer用于急性缺血性脑卒中辅助诊断

急性缺血性脑卒中是由于脑组织血液供应障碍导致的脑功能障碍,数字减影脑血管造影(DSA)是诊断脑血管疾病的金标准。基于患者的正面和侧面DSA图像,对急性缺血性脑卒中的治疗效果进行分级评估,构建基于Vision Transformer的双路径图像分类智能模型DPVF。为了提高辅助诊断速度,基于EdgeViT的轻量化设计思想进行了模型的构建;为了使模型保持轻量化的同时具有较高的精度,提出空间-通道自注意力模块,促进Transformer模型捕获更全面的特征信息,提高模型的表达能力;此外,对于DPVF的两分支的特征融合,构建交叉注意力模块对两分支输出进行交叉融合,促使模型提取更丰富的特征,从而提高模型表现。实验结果显示DPVF在测试集上的准确率达98.5%,满足实际需求。

Enhancing Critical Path Problem in Neutrosophic Environment Using Python

In the real world,one of the most common problems in project management is the unpredictability of resources and timelines.An efficient way to resolve uncertainty problems and overcome such obstacles is through an extended fuzzy approach,often known as neutrosophic logic.Our rigorous proposed model has led to the creation of an advanced technique for computing the triangular single-valued neutrosophic number.This innovative approach evaluates the inherent uncertainty in project durations of the planning phase,which enhances the potential significance of the decision-making process in the project.Our proposed method,for the first time in the neutrosophic set literature,not only solves existing problems but also introduces a new set of problems not yet explored in previous research.A comparative study using Python programming was conducted to examine the effectiveness of responsive and adaptive planning,as well as their differences from other existing models such as the classical critical path problem and the fuzzy critical path problem.The study highlights the use of neutrosophic logic in handling complex projects by illustrating an innovative dynamic programming framework that is robust and flexible,according to the derived results,and sets the stage for future discussions on its scalability and application across different industries.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

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.

A Multi-Constraint Path Optimization Scheme Based on Information Fusion in Software Defined Network

The existingmultipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is alack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptationof service requirements and network resources. To address these issues, we propose a multi-constraint pathoptimization scheme based on information fusion in SDN. The proposed scheme collects network topology andnetwork state information on the network side and computes disjoint paths between end hosts. It uses the FuzzyAnalytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters andconstructs a composite quality evaluation function for the paths to determine the priority of the disjoint paths. TheSDN controller extracts the service attributes by analyzing the packet header and selects the optimal path for flowrule forwarding. Furthermore, the service attributes are fed back to the path composite quality evaluation function,and the path priority is dynamically adjusted to achieve dynamic adaptation between service requirements andnetwork status. By continuously monitoring and analyzing the service attributes, the scheme can ensure optimalrouting decisions in response to varying network conditions and evolving service demands. The experimentalresults demonstrated that the proposed scheme can effectively improve average throughput and link utilizationwhile meeting the Quality of Service (QoS) requirements of various applications.

Depth First:Optimal Path Discovery Between Designated Nodes in Random Ring-Based Graphs

This paper focuses on optimally determining the existence of connected paths between some given nodes in random ring-based graphs.Serving as a fundamental underlying structure in network modeling,ring topology appears as commonplace in many realistic scenarios.Regarding this,we consider graphs composed of rings,with some possible connected paths between them.Without prior knowledge of the exact node permutations on rings,the existence of each edge can be unraveled through edge testing at a unit cost in one step.The problem examined is that of determining whether the given nodes are connected by a path or separated by a cut,with the minimum expected costs involved.Dividing the problem into different cases based on different topologies of the ring-based networks,we propose the corresponding policies that aim to quickly seek the paths between nodes.A common feature shared by all those policies is that we stick to going in the same direction during edge searching,with edge testing in each step only involving the test between the source and the node that has been tested most.The simple searching rule,interestingly,can be interpreted as a delightful property stemming from the neat structure of ring-based networks,which makes the searching process not rely on any sophisticated behaviors.We prove the optimality of the proposed policies by calculating the expected cost incurred and making a comparison with the other class of strategies.The effectiveness of the proposed policies is also verified through extensive simulations,from which we even disclose three extra intriguing findings:i)in a onering network,the cost will grow drastically with the number of designated nodes when the number is small and will grow slightly when that number is large;ii)in ring-based network,Depth First is optimal in detecting the connectivity between designated nodes;iii)the problem of multi-ring networks shares large similarity with that of two-ring networks,and a larger number of ties between rings will not influence the expected cost.

Distal-scanning common path probe for optical coherence tomography

In this paper,we present a distal-scanning common path probe for optical coherence tomography(OCT)equipped with a hollow ultrasonic motor and a simple and specially designed beam-splitter.This novel probe proves to be able to effectively circumvent polarization and dispersion mismatch caused by fiber motion and is more robust to a variety of interfering factors during the imaging process,experimentally compared to a conventional noncommon path probe.Furthermore,our design counteracts the attenuation of backscattering with depth and the fall-off of the signal,resulting in a more balanced signal range and greater imaging depth.Spectral-domain OCT imaging of phantom and biological tissue is also demonstrated with a sensitivity of∼100dB and a lateral resolution of∼3μm.This low-cost probe offers simplified system configuration and excellent robustness,and is therefore particularly suitable for clinical diagnosis as one-off medical apparatus.

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Mechanical response and dilatancy characteristics of deep marble under different stress paths:A sight from energy dissipation

Dilatancy is a fundamental volumetric growth behavior observed during loading and serves as a key index to comprehending the intricate nonlinear behavior and constitutive equation structure of rock.This study focuses on Jinping marble obtained from the Jinping Underground Laboratory in China at a depth of 2400 m.Various uniaxial and triaxial tests at different strain rates,along with constant confining pressure tests and reduced confining pressure tests under different confining pressures were conducted to analyze the mechanical response and dilatancy characteristics of the marble under four stress paths.Subsequently,a new empirical dilatancy coefficient is proposed based on the energy dissipation method.The results show that brittle failure characteristics of marble under uniaxial compression are more obvious with the strain rate increasing,and plastic failure characteristics of marble under triaxial compression are gradually strengthened.Furthermore,compared to the constant confining pressure,the volume expansion is relatively lower under unloading condition.The energy dissipation is closely linked to the process of dilatancy,with a rapid increase of dissipated energy coinciding with the beginning of dilatancy.A new empirical dilatancy coefficient is defined according to the change trend of energy dissipation rate curve,of which change trend is consistent with the actual dilatancy response in marble under different stress paths.The existing empirical and theoretical dilatancy models are analyzed,which shows that the empirical dilatancy coefficient based on the energy background is more universal.

Multi-circular formation control with reinforced transient profiles for nonholonomic vehicles:A path-following framework

This article investigates a multi-circular path-following formation control with reinforced transient profiles for nonholonomic vehicles connected by a digraph.A multi-circular formation controller endowed with the feature of spatial-temporal decoupling is devised for a group of vehicles guided by a virtual leader evolving along an implicit path,which allows for a circumnavigation on multiple circles with an anticipant angular spacing.In addition,notice that it typically imposes a stringent time constraint on time-sensitive enclosing scenarios,hence an improved prescribed performance control(IPPC)using novel tighter behavior boundaries is presented to enhance transient capabilities with an ensured appointed-time convergence free from any overshoots.The significant merits are that coordinated circumnavigation along different circles can be realized via executing geometric and dynamic assignments independently with modified transient profiles.Furthermore,all variables existing in the entire system are analyzed to be convergent.Simulation and experimental results are provided to validate the utility of suggested solution.

On traceable iterated line graph and hamiltonian path index

Xiong and Liu[21]gave a characterization of the graphs G for which the n-iterated line graph L^(n)(G)is hamiltonian,for n≥2.In this paper,we study the existence of a hamiltonian path in L^(n)(G),and give a characterization of G for which L^(n)(G)has a hamiltonian path.As applications,we use this characterization to give several upper bounds on the hamiltonian path index of a graph.

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Correlation and Pathway Analysis of the Carbon,Nitrogen,and Phosphorus in Soil-Microorganism-Plant with Main Quality Components of Tea(Camellia sinensis)

The contents of carbon(C),nitrogen(N),and phosphorus(P)in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea,such as tea polyphenols,amino acids,and caffeine.However,few studies have quantified the effects of these factors on the main quality components of tea.The study aimed to explore the interactions of C,N,and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method.The results indicated that(1)The contents of C,N,and P in soil,microorganisms,and tea plants were highly correlated and collinear,and showed significant correlations with the main quality components of tea.(2)Optimal regression equations were established to esti-mate tea polyphenol,amino acid,catechin,caffeine,and water extract content based on C,N,and P contents in soil,microorganisms,and tea plants(R^(2)=0.923,0.726,0.954,0.848,and 0.883,respectively).(3)Pathway analysis showed that microbial biomass phosphorus(MBP),root phosphorus,branch nitrogen,and microbial biomass carbon(MBC)were the largest direct impact factors on tea polyphenol,catechin,water extracts,amino acid,and caffeine content,respectively.Leaf carbon,root phosphorus,and leaf nitrogen were the largest indirect impact factors on tea polyphenol,catechin,and water extract content,respectively.Leaf carbon indirectly affected tea polyphenol content mainly by altering MBP content.Root phosphorus indirectly affected catechin content mainly by altering soil organic carbon content.Leaf nitrogen indirectly affected water extract content mainly by altering branch nitrogen content.The research results provide the scientific basis for reasonable fertilization in tea gardens and tea quality improvement.

A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.

A Path Planning Algorithm Based on Improved RRT Sampling Region

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree(RRT)algorithm,a feedback-biased sampling RRT,called FS-RRT,is proposedbasedon RRT.Firstly,toimprove the samplingefficiency of RRT to shorten the search time,the search area of the randomtree is restricted to improve the sampling efficiency.Secondly,to obtain better information about obstacles to shorten the path length,a feedback-biased sampling strategy is used instead of the traditional random sampling,the collision of the expanding node with an obstacle generates feedback information so that the next expanding node avoids expanding within a specific angle range.Thirdly,this paper proposes using the inverse optimization strategy to remove redundancy points from the initial path,making the path shorter and more accurate.Finally,to satisfy the smooth operation of the robot in practice,auxiliary points are used to optimize the cubic Bezier curve to avoid path-crossing obstacles when using the Bezier curve optimization.The experimental results demonstrate that,compared to the traditional RRT algorithm,the proposed FS-RRT algorithm performs favorably against mainstream algorithms regarding running time,number of search iterations,and path length.Moreover,the improved algorithm also performs well in a narrow obstacle environment,and its effectiveness is further confirmed by experimental verification.

Effects of cold rolling path on recrystallization behavior and mechanical properties of pure copper during annealing

The recrystallization behavior,grain boundary characteristic distribution,and mechanical properties of pure Cu sheets that were subjected to different cold rolling paths,and then annealed at 400°C for 10,30,60,and 420 min,were investigated.Different rolling paths changed the grain boundary orientations of cold-rolled copper,causing recrystallized grains to nucleate and grow in an oriented manner.However,the evolution of the texture indicated that cold-rolled copper with different rolling paths did not show an obvious preferred orientation after annealing.The RD-60 specimen exhibited the smallest grain size(6.6μm).The results indicated that the grain size and low-ΣCSL grain boundaries worked together to provide RD-60 samples with appropriate mechanical properties and high plasticity.The yield strength,ultimate tensile strength,and elongation of RD-60 sample were 81 MPa,230 MPa,and 49%,respectively.These results could provide guidance for tuning the microstructures and properties of pure Cu foils,as well as designing fabrication routes for pure Cu foils through processes such as rolling and drawing.

Heuristic Expanding Disconnected Graph:A Rapid Path Planning Method for Mobile Robots

Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of the neighborhood search strategy,the robots could hardly obtain the most optimal global path.A global path planning algorithm,denoted as EDG*,is proposed by expanding nodes using a well-designed expanding disconnected graph operator(EDG)in this paper.Firstly,all obstacles are marked and their corners are located through the map pre-processing.Then,the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes.Finally,the EDG*heuristic iterative algorithm is proposed.It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset.Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM.The result shows that the proposed EDG*reduced the planning time by more than 90%and total length of paths reduced by more than 4.6%.Compared to A*,Dijkstra and JPS,EDG*does not show an exponential explosion effect in map size.The EDG*showed better performance in terms of path smoothness,and collision avoidance.This shows that the EDG*algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.

Analysis of environmental selection pressure of superoxide dismutase in deep-sea sea cucumber

Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is essential for the survival of sea cucumbers.Six MnSODs were identified from the transcriptomes of deep and shallow-sea sea cucumbers.To explore their environmental adaptation mechanism,we conducted environmental selection pressure analysis through the branching site model of PAML software.We obtained night positive selection sites,and two of them were significant(97F→H,134K→V):97F→H located in a highly conservative characteristic sequence,and its polarity c hange might have a great impact on the function of MnSOD;134K→V had a change in piezophilic a bility,which might help MnSOD adapt to the environment of high hydrostatic pressure in the deepsea.To further study the effect of these two positive selection sites on MnSOD,we predicted the point mutations of F97H and K134V on shallow-sea sea cucumber by using MAESTROweb and PyMOL.Results show that 97F→H,134K→V might improve MnSOD’s efficiency of scavenging superoxide a nion and its ability to resist high hydrostatic pressure by moderately reducing its stability.The above results indicated that MnSODs of deep-sea sea cucumber adapted to deep-sea environments through their amino acid changes in polarity,piezophilic behavior,and local stability.This study revealed the correlation between MnSOD and extreme environment,and will help improve our understanding of the organism’s adaptation mechanisms in deep sea.

A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.