Reduction In Path Length Research Articles

Sawtooth scanning strategy for additive manufacturing

PurposeThis study aims to improve the acceleration in the additive manufacturing (AM) process. AM tools, such as extrusion heads, jets, electric arcs, lasers and electron beams (EB), experience negligible forces. However, their speeds are limited by the positioning systems. In addition, a thin tool must travel several kilometers in tiny motions with several turns while realizing the AM part. Hence, acceleration is a more significant limiting factor than the velocity or precision for all except EB.Design/methodology/approachThe sawtooth (ST) scanning strategy presented in this paper minimizes the time by combining three motion features: zigzag scan, 45º or 135º rotation for successive layers in G00 to avoid the CNC interpolation, and modifying these movements along 45º or 135º into sawtooth to halve the turns.FindingsSawtooth effectiveness is tested using an in-house developed Sand AM (SaAM) apparatus based on the laser–powder bed fusion AM technique. For a simple rectangle layer, the sawtooth achieved a path length reduction of 0.19%–1.49% and reduced the overall time by 3.508–4.889 times, proving that sawtooth uses increased acceleration more effectively than the other three scans. The complex layer study reduced calculated time by 69.80%–139.96% and manufacturing time by 47.35%–86.85%. Sawtooth samples also exhibited less dimensional variation (0.88%) than zigzag 45° (12.94%) along the build direction.Research limitations/implicationsSawtooth is limited to flying optics AM process.Originality/valueDevelopment of scanning strategy for flying optics AM process to reduce the warpage by improving the acceleration.

A compression-based routing strategy for energy saving in wireless sensor networks

Data compression and optimal path discovery for data communication are vital for enhancing energy efficiency in wireless sensor networks (WSNs), crucial for their sustainability due to limited power resources. This study proposes a strategy that combines data compression and routing to optimize energy in WSNs. It is based on a Bio-Inspired Ant-Cuckoo optimized Relay-based Energy Efficient Data Aggregation (BACREED) algorithm which leverages both Ant Colony Optimization (ACO) and Cuckoo Search (CS) algorithms to enhance communication efficiency between cluster leads (CLs) and the destination node through a forwarding node. Comparative evaluation against Low Energy Adaptive Clustering Hierarchy (LEACH) and its variants, Genetic Algorithm Data Aggregation, Ant optimized using Energy Efficient Data aggregation, and Bio-inspired Ant Cuckoo Energy Efficient Data aggregation demonstrate superior performance in energy efficiency, throughput, and network longevity. This work integrates ‘Fast and Efficient Lossless Adaptive Compression Scheme with Outlier Detection and Replacement (FELACS-ODR) data compression algorithm with CS to improve network performance in terms of energy efficiency and optimal path discovery. Simulation results using MATLAB exhibit a path length reduction of 84 % for the proposed algorithm compared to a 73 % rate for the baseline algorithm with an optimal cluster-lead count between 40 to 80. Energy consumption increases slowly with data compression, significantly outperforming rapid increase scenarios without compression, particularly evident as the node count increases from 1100 and 200 nodes respectively. This research underscores the potential of leveraging FELACS-ODR and CS techniques for substantial enhancements in WSN energy efficiency.

Study on the influence of design parameters of porous asphalt pavement on drainage performance

To investigate the design factors influencing the drainage performance of porous asphalt pavement and its performance degradation, a three-dimensional infiltration model of porous asphalt pavement was established. The influence of permeability coefficient, drainage layer thickness, drainage path length, and pavement cross slope for drainage performance was analyzed. The drainage performance was evaluated with the multiple influence factors, and the drainage performance degradation was explored with the porosity decay. The results showed that the design parameters including permeability coefficient, drainage layer thickness, drainage path length, and pavement cross slope exhibit strong correlations with pavement drainage performance, with correlations greater than 0.6. The increase in the permeability coefficient, drainage layer thickness, pavement cross slope, and the reduction in drainage path length are all benefits to improved drainage performance. It is effective to enhance drainage performance by increasing the permeability coefficient combined with a higher pavement cross slope in high drainage requirement areas. The porosity decay caused by road load and porosity reduction leads to a 20%-45% degradation in drainage performance. Therefore, during the road surface design process, it is necessary to comprehensively consider the synergistic effects of design parameters, while also evaluating both the initial and residual drainage performance with the combined influence of load and dust.

Trajectory Planning Research Based on Rapidly-exploring Random Trees (RRT) Algorithm and Modified Forms

With the rapid advancement of computer technology, robots have become indispensable in various fields. Trajectory planning plays a crucial role in optimizing robot performance by enhancing work efficiency and reducing operational costs. While the widely used Rapidly-exploring Random Trees (RRT) algorithm is effective in path planning, it often generates non-optimal paths with poor smoothness. By incorporating elements from RRT-Connect and reverse optimization, the algorithm can efficiently navigate through intricate and dynamic surroundings. Furthermore, the utilization of cubic polynomial interpolation in optimizing path curves adds a layer of sophistication to the trajectory planning process. This interpolation technique allows for the creation of smoother and more natural path trajectories, reducing jerky movements and enhancing overall motion control precision. The paths produced by the enhanced RRT algorithm demonstrate increased continuity and visual attractiveness, crucial for tasks necessitating accurate and elegant robotic motions. The improved algorithm showcases reduced path length and time consumption, as well as enhanced path smoothness. Particularly, relative to the traditional RRT approach, the time consumption has dropped by 69.15% and the median duration of the paths has fallen by 21.11%. These improvements signify a substantial advancement in trajectory planning for robots, offering more efficient and smoother paths for robotic operations across various applications.

A novel global path planning method for robot based on dual-source light continuous reflection

Aiming to address the problem of robot path planning in environments containing narrow passages, this paper proposes a novel global path planning method: the DSR (Dual-source Light Continuous Reflection Exploration) algorithm. This algorithm, inspired by the natural reflection of light, employs the concept of continuous reflection for path planning. It can efficiently generate an asymptotically optimal path on the map containing narrow passages. The DSR algorithm has been evaluated on different maps with narrow passages and compared with other algorithms. In comparison with the bidirectional Rapidly-exploring Random Tree algorithm, the DSR algorithm achieves a significant reduction in both path length (by 27.08% and 34.35%) and time consumption (by 98.47% and 91.03%). Numerical simulations and experimental analysis have demonstrated the excellent performance of the DSR algorithm.

Trajectory Planning for Autonomous Formation of Wheeled Mobile Robots via Modified Artificial Potential Field and Improved PSO Algorithm

This paper addresses two substantial aspects in the field of robotics: trajectory planning and trajectory tracking for multi-robot systems. The collective movement of the multi-wheeled mobile robots is based on a formation control with a leader–follower strategy. To ensure safe navigation toward the destination within a workspace containing multiple obstacles, we skillfully combine the Modified Artificial Potential Field (MAPF) method and the Improved Particle Swarm Optimization (IPSO) algorithm for a group of nonholonomic mobile robots. A global planner based on IPSO algorithm is assigned to the leader robot, to find the optimal collision-free path. Compared to recent nature-inspired methodologies, the improvement suggested enhance the search capabilities of the algorithm, resulting in a 2% reduction in path length and a 29% decrease in computation time. Simultaneously, the follower robot has the ability to employ either a formation policy or a local planner using MAPF. The proposed modifications address the primary limitations of the conventional method, notably the susceptibility to local minima and the challenge of reaching goals near obstacles. Furthermore, this local planner is adept at evading both static and dynamic obstacles. Extensive real hardware experiments are conducted within multiple scenarios using the mobile robot Qbot-2e to corroborate the obtained simulation results and validate the effectiveness of the proposed approaches both in tracking the desired trajectories, finding the optimal feasible collision-free path and avoiding static and dynamic obstacles for multi-robot systems.

Hand Motion Analysis Using Accelerometer-Based Sensors and Sheep's Head Model for Basic Training in Functional Endoscopic Sinus Surgery.

Motion analysis, the study of movement patterns to evaluate performance, plays a crucial role in surgical training. It provides objective data that canbe usedto assess and improve trainee's precision, efficiency, and overall surgical technique. The primary aim of this study is to employ accelerometer-based sensors placed on the wrist to analyze hand motions during endoscopic sinus surgery training using the sheep's head. By capturing detailed movement data, the study seeks to quantify the motion characteristics that distinguish different levels of surgical expertise. This approach seeks to quantify motion characteristics indicative of surgical expertise and enhance the objectivity and effectiveness of surgical training feedback mechanisms. Twenty-four participantswere dividedinto three groups based on their experience with endoscopic endonasal surgery. Each participantwas tasked with performingspecified procedures on an individual sheep's head, concentrating on exploring both nasal passages. A single Bluetooth Accelerometer WitMotion sensorwas mountedon the dorsal surface of each hand.Thisfacilitates the evaluation of efficiency parameters such as time, path length, and acceleration during the training procedures. Accelerometer data were collected and imported in CSV format (comma-separated values) for each group of surgeons-senior, specialist, and resident-mean values and standard deviationswere computed. The Shapiro-Wilk Test assessed the normality of the distribution. The Kruskal-Wallis test was employed to compare procedural time, acceleration, and path length differences across the three surgeon experience levels. For the procedural time, statistical significance appears in all surgical steps (p<0.001), with thebiggestdifference in the septoplasty group in favor of the senior group. A clear difference can be observed between the resulting acceleration of the dominant hands (instrument hand) and the non-dominant hand (endoscopic hand) and between the study groups. The difference between groups reaches statistical significance with a p-value <0.001. A statistically significant difference canbe seenbetween the paths covered by each hand of every participant (p<0.001). Also, senior doctors covered significantly less movement with both hands than the specialists and the resident doctors (p<0.001). The data showa clearlearning curve from resident to senior,with residents taking more time and using more hand movementsto complete the same tasks. Specialists are in the intermediate phase, showing signs of honing their technique towards efficiency. This comprehensive data set can help tailor training programs to focus onbothefficiency (quicker procedures) and economy of motion (reduced path length and acceleration), especially in more complex procedures where the difference in performance ismore pronounced.

Route planning of mobile robot based on improved RRT star and TEB algorithm

This paper presents a fusion algorithm based on the enhanced RRT* TEB algorithm. The enhanced RRT* algorithm is utilized for generating an optimal global path. Firstly, proposing an adaptive sampling function and extending node bias to accelerate global path generation and mitigate local optimality. Secondly, eliminating path redundancy to minimize path length. Thirdly, imposing constraints on the turning angle of the path to enhance path smoothness. Conducting kinematic modeling of the mobile robot and optimizing the TEB algorithm to align the trajectory with the mobile robot's kinematics. The integration of these two algorithms culminates in the development of a fusion algorithm. Simulation and experimental results demonstrate that, in contrast to the traditional RRT* algorithm, the enhanced RRT* algorithm achieves a 5.8% reduction in path length and a 62.5% decrease in the number of turning points. Utilizing the fusion algorithm for path planning, the mobile robot generates a superior, seamlessly smooth global path, adept at circumventing obstacles. Furthermore, the local trajectory meticulously conforms to the kinematic constraints of the mobile robot.

Enhanced path planning algorithm via hybrid WOA-PSO for differential wheeled mobile robots

This study introduces an enhanced algorithm for global path planning of Differential Wheeled Mobile Robots (DWMRs) that merges the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). This hybrid strategy, termed HWPSO, is designed to leverage WOA's exploration strength with PSO's efficient exploitation, specifically targeting the challenges of non-holonomic constraints in complex terrains. To validate the effectiveness of the proposed algorithm, its performance is evaluated across five diverse environments and compared against PSO, WOA, and Grey Wolf Optimization which is widely used for mobile robot path planning. Moreover, the comparison broadens to encompass four established environments from the literature where algorithms based on firefly, ant colony, A*, and other PSO variants have previously exhibited optimal performance. Additionally, a new environment is introduced to analyze the efficacy of the proposed approach for path planning for two DWMRs. Simulation results consistently demonstrate the superiority of the proposed HWPSO, manifesting performance improvements of up to 19.3% for path length reduction and up to 12.7% for DWMR travel duration reduction when compared to other methods. This underscores the efficacy of the proposed hybrid approach in achieving enhanced path planning outcomes for DWMRs in diverse scenarios.

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.

Assessment and solutions for vulnerability of urban rail transit network based on complex network theory: A case study of Chongqing

As a typical complex network system, the operating environment of rail transit network (RTN) is complex and demanding. This study aims to accurate assess the weaknesses and vulnerability of RTN, which is crucial for ensuring its smooth operation. Taking Chongqing Rail Transit (CRT) as an example, this study developed a network topology model using the spatial L method and analyzed the network structure characteristics, along with the importance of key nodes under different indicators, based on complex network theory. Additionally, this study analyzed the geographical spatial distribution characteristics of nodes based on the topography and urban spatial structure of Chongqing. Then, this study classified the nodes in the RTN according to basic topological indicators, namely degree, betweenness centrality, network efficiency, and passenger flow volume (PFV). The results indicated six cluster of nodes, reflecting the variability in node vulnerability concerning overall influence (providing alternative paths, reducing path length), regional aggregation capacity, and transportation capacity. Finally, this study proposed targeted management strategies for different clusters of nodes and their respective geographical locations, providing necessary references for rational planning, safety protection, and sustainable construction of RTN.

Intelligent path planning by an improved RRT algorithm with dual grid map

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.

U*: GA-based path planning algorithm for surface floating garbage cleaning robot

Marine debris is a serious global problem that is not limited to areas where humans live but also drifts around the world with wind and currents. More than 10 million tons of plastic waste flow into the ocean every year, posing a major threat to humanity. This study designs a path planning algorithm for surface garbage-cleaning robots called U*, which aims to improve the efficiency of salvaging marine debris and reduce labor and time costs. The U* algorithm consists of two procedures: exploration and path-planning. The exploration procedure searches for marine debris, while the path-planning procedure predicts the possible location of marine debris using the velocity and direction of ocean currents and finds the shortest path by using a genetic algorithm (GA) to collect the found marine debris. According to the experimental results, the U* method is more efficient in terms of reducing path length and time costs.

Workshop AGV path planning based on improved A* algorithm.

This article proposes an improved A* algorithm aimed at improving the logistics path quality of automated guided vehicles (AGVs) in digital production workshops, solving the problems of excessive path turns and long transportation time. The traditional A* algorithm is improved internally and externally. In the internal improvement process, we propose an improved node search method within the A* algorithm to avoid generating invalid paths; offer a heuristic function which uses diagonal distance instead of traditional heuristic functions to reduce the number of turns in the path; and add turning weights in the A* algorithm formula, further reducing the number of turns in the path and reducing the number of node searches. In the process of external improvement, the output path of the internally improved A* algorithm is further optimized externally by the improved forward search optimization algorithm and the Bessel curve method, which reduces path length and turns and creates a path with fewer turns and a shorter distance. The experimental results demonstrate that the internally modified A* algorithm suggested in this research performs better when compared to six conventional path planning methods. Based on the internally improved A* algorithm path, the full improved A* algorithm reduces the turning angle by approximately 69% and shortens the path by approximately 10%; based on the simulation results, the improved A* algorithm in this paper can reduce the running time of AGV and improve the logistics efficiency in the workshop. Specifically, the walking time of AGV on the improved A* algorithm path is reduced by 12s compared to the traditional A* algorithm.

Comparative Analysis of Interactive Modalities for Intuitive Endovascular Interventions.

Endovascular intervention is a minimally invasive method for treating cardiovascular diseases. Although fluoroscopy, known for real-time catheter visualization, is commonly used, it exposes patients and physicians to ionizing radiation and lacks depth perception due to its 2D nature. To address these limitations, a study was conducted using teleoperation and 3D visualization techniques. This in-vitro study involved the use of a robotic catheter system and aimed to evaluate user performance through both subjective and objective measures. The focus was on determining the most effective modes of interaction. Three interactive modes for guiding robotic catheters were compared in the study: 1) Mode GM, using a gamepad for control and a standard 2D monitor for visual feedback; 2) Mode GH, with a gamepad for control and HoloLens providing 3D visualization; and 3) Mode HH, where HoloLens serves as both control input and visualization device. Mode GH outperformed other modalities in subjective metrics, except for mental demand. It exhibited a median tracking error of 4.72 mm, a median targeting error of 1.01 mm, a median duration of 82.34 s, and a median natural logarithm of dimensionless squared jerk of 40.38 in the in-vitro study. Mode GH showed 8.5%, 4.7%, 6.5%, and 3.9% improvements over Mode GM and 1.5%, 33.6%, 34.9%, and 8.1% over Mode HH for tracking error, targeting error, duration, and dimensionless squared jerk, respectively. To sum up, the user study emphasizes the potential benefits of employing HoloLens for enhanced 3D visualization in catheterization. The user study also illustrates the advantages of using a gamepad for catheter teleoperation, including user-friendliness and passive haptic feedback, compared to HoloLens. To further gauge the potential of using a more traditional joystick as a control input device, an additional study utilizing the Haption VirtuoseTM robot was conducted. It reveals the potential for achieving smoother trajectories, with a 38.9% reduction in total path length compared to a gamepad, potentially due to its larger range of motion and single-handed control.

Characteristics of visual perception of patients’ faces and the formation of “observation” in the process of training cosmetologists

Professional experience assists specialists in processing information more quickly and effectively making precise decisions. Visual expertise, as a crucial component of experience, refers to the capability to rapidly perceive visual information within a specific professional domain, acquired through systematic education, professional development, and practice. Numerous studies utilize eye-tracking methods to analyze the relationship between professional experience and gaze patterns. For instance, experienced dermatologists have developed optimal scanning patterns, allowing them to efficiently allocate attention and accurately identify key diagnostic features within images amidst numerous secondary details. The purpose of this study is to analyze changes in oculomotor patterns after completing the author’s training program for doctors, aimed at versatile aesthetic analytics of the face, building individual treatment protocols and increasing the patient’s adherence to aesthetic medicine in order to assess the possibility of developing observation in the conditions of short-term training. Thirty cosmetic doctors participated in the study, which utilized eye-tracking methods with the Neuroburo program-hardware complex. The study was conducted in two phases – before and after training – with 15 patient facial images exhibiting various dermatological issues presented on a computer screen to the cosmetic doctors. Participants were tasked with identifying signs of dermatological disorders of the patients. Following training, a decrease in the number of fixations, an increase in saccadic amplitude, and a reduction in the overall scanning path length were observed among the cosmetic doctors. These findings indicate a more optimized perception strategy and suggest a more systematic and directed approach to diagnostic image analysis. The results suggest an enhanced global perception ability among the doctors, which may aid in recognizing broader patterns and structures and understanding the wider context and relationships between details.

A Safe Heuristic Path-Planning Method Based on a Search Strategy.

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the search node, then calculates the node state based on the search strategy, including whether it is a normal or dangerous state, and calculates the danger coefficient of the corresponding point to select the path with a lower danger coefficient. At the same time, the optimal boundary is obtained by incorporating the environmental facilities, and the optimal path between the starting point, the optimal boundary point and the end point is obtained. Compared to the traditional A-star algorithm, this method achieved significant improvements in various aspects such as path length, execution time, and path smoothness. Specifically, it reduced path length by 2.89%, decreased execution time by 13.98%, and enhanced path smoothness by 93.17%. The resulting paths are more secure and reliable, enabling robots to complete their respective tasks with reduced power consumption, thereby mitigating the drain on robot batteries.

Path Planning of Deep-Sea Landing Vehicle Based on the Safety Energy-Dynamic Window Approach Algorithm

To ensure the safety and energy efficiency of autonomous sampling operations for a deep-sea landing vehicle (DSLV), the Safety Energy-Dynamic Window Approach (SE-DWA) algorithm was proposed. The safety assessment sub-function formed from the warning obstacle zone and safety factor addresses the safety issue arising from the excessive range measurement error of forward-looking sonar. The trajectory comparison evaluation sub-function with the effect of reducing energy consumption achieves a reduction in path length by causing the predicted trajectory to deviate from the historical trajectory when encountering “U”-shaped obstacles. The pseudo-power evaluation sub-function with further energy consumption reduction ensures optimal linear and angular velocities by minimizing variables when encountering unknown obstacles. The simulation results demonstrate that compared with the Minimum Energy Consumption-DWA algorithm, the SE-DWA algorithm improves the minimum distance to an actual obstacle zone by 68% while reducing energy consumption by 11%. Both the SE-DWA algorithm and the Maximum Safety-DWA (MS-DWA) algorithm ensure operational safety with minimal distance to the actual obstacle zone, yet the SE-DWA algorithm achieves a 24% decrease in energy consumption. In conclusion, the path planned by the SE-DWA algorithm ensures not only safety but also energy consumption reduction during autonomous sampling operations by a DSLV in the deep sea.

Poster Session: After up to a year of hyperglycemia Ins2Akita mice show minimal capillary change.

Diabetic retinopathy (DR) is a leading cause of blindness and is detected clinically via retinal vascular changes. Here we measure the impact of hyperglycemia in a mouse model to evaluate changes in retinal capillary density, path length and pericyte coverage. We crossbred mice with fluorescent pericytes (NG2DSRed) with hyperglycemic mice (Ins2Akita) to produce offspring with labeled pericytes and sustained hyperglycemia. Retinas from male mice (7 to 53 weeks) were fixed, flat mounted, then imaged with confocal microscopy (Nikon eclipse Ti2e). Pericytes and vessel path length were manually measured with ImageJ. Custom MATLAB code rendered path length as a function of depth. Two-tailed t-tests were used for statistics. Regardless of hyperglycemic duration, Akita+ mice (n=7) had a pericyte density of 307.03 ± 69.09 cells/mm2 which was less than, but not statistically different from Akita- mice (n=5; 348.51 ± 29.78 cells/mm2; p=0.12). We did not find a difference in total path length in any of the trilaminar layers: superficial (p=0.08), intermediate (p=0.95) or deep (p=0.52). Akita+ mice had increased blood glucose, decreased body weight, and polyuria. Despite a systemic phenotype, we saw no reduction in pericytes or vessel path length, even after ~1 year of hyperglycemic insult. These findings indicate that Ins2Akita vascular changes may be more subtle than previously reported and neural/behavioral deficits may not correlate with microvascular path length or pericyte density.

Design of an improved intelligent optimization logistics path planning model for the background of the new industrial era

ABSTRACT In the context of the new industrial era, intelligent manufacturing, intelligent factories, and intelligent logistics have become hot topics during the industrial revolution. In order to meet the flexibility, flexibility, and efficiency of factory logistics in the era of Industry 4.0, and improve the punctuality of logistics distribution, a factory logistics path planning model is designed based on grid environment and Ant colony optimization algorithms. The algorithm optimizes and improves the search ability and adaptability of Ant colony optimization algorithms. The test results show that the Pheromone volatile number is 0.3, which is a moderate value. When the importance of Pheromone is 3 and the heuristic factor is 6, the average optimal cost and the average number of iterations are the minimum. As the variable increases, both evaluation indicators show a trend of decreasing first and then increasing. The improvement of the initial Pheromone can speed up the Rate of convergence of the algorithm, while the quality of the planned path results is better than that of the traditional planned path, and the shortest path length is reduced from 26.46 to 24.38. The path smoothed by B-spline curve is smooth and continuous. The algorithm proposed by the research institute converges quickly, with the shortest path value being 59.43, and the maximum reduction in path length reaching 18.44%. The optimized path length converges quickly after fluctuations, and the algorithm has strong adaptability to the environment. This model can achieve accurate logistics path planning, provide competitive solutions for the implementation of intelligent logistics in factories, and promote the 4.0 factory to build a truly intelligent factory.