Background: To address the problem of high energy consumption of self-driving electric vehicles when following a planned trajectory, constraints are added to path planning and speed planning respectively. Methods: Given the limitations of the existing path planning algorithms in terms of search efficiency and path length, this study introduces an innovative and improved strategy in the horizontal dimension. Based on the cost function of the distance between sampling points, this strategy aims to improve the search efficiency of the dynamic planning algorithm and reduce the search path length. Furthermore, the smoothness of the path is optimized to suit the actual driving conditions by applying a quadratic programming algorithm. An energy consumption model for pure electric vehicles is established in the vertical dimension, effectively constraining energy use during speed dynamic planning to reduce consumption while driving. Finally, the smoothness of speed planning is improved using a quadratic programming algorithm. Results: The results of simulation experiments show that compared with traditional methods, the proposed algorithm achieves a substantial improvement in path length reduction of 5.8%, average curvature reduction of 31.6%, and average energy consumption reduction of 2.04% in static and dynamic obstacle avoidance environments. Conclusion: The results show that the improved dynamic planning algorithm proposed in this study is significantly optimized in terms of mean path length, mean curvature, and energy consumption. Moreover, the proposed algorithm can meet the requirements of energy efficiency of vehicle driving.

Multi-mode dispersion compensation in Lamb wave time-reversal method for high damage sensitivity across frequencies.

Directed regulation of high-temperature Daqu microbiota and metabolites using synthetic communities.

Altered brain network topological properties in insomniacs with prolonged sleep onset latency: a graph-based resting-state fMRI study.

Network analysis of cattle movement among premises in Minas Gerais state, Brazil, from 2013 to 2023.

Abstract Accurately modelling of the exceptional low excess noise observed in AlxGa1-xAs0.56Sb0.44 Avalanche Photodiodes (APDs) is crucial for optimizing device performance. In this study, the Random Path Length (RPL) model, incorporating the Weibull-Fréchet (WF) distribution function, was used to simulate electron and hole impact ionisation in APDs with non-uniform electric fields. The model successfully reproduces the experimentally measured multiplication gain, 〈M〉 and excess noise factor, F, in electron-initiated APDs with compositions Al0.55Ga0.45As0.56Sb0.44, Al0.75Ga0.25As0.56Sb0.44, Al0.85Ga0.15As0.56Sb0.44, and AlAs0.56Sb0.44, while also predicting a steep increase in F for hole-initiated APDs. The results demonstrate that ionisation path length distributions are strongly influenced by electric field strength and alloy composition. The model effectively captures the probability density function (PDF) of ionisation path lengths, which is responsible for low excess noise. The results reveal that electron dead space increases as Al composition decreases, exhibiting an inverse trend compared to the reducing mean ionisation path length in these alloys. This behaviour is attributed to the alloy scattering effects, which become more pronounced in mid-composition alloys.

In post-disaster search and rescue scenarios, robotic path planning must operate in unpredictable, dynamic environments where conventional coverage path planning (CPP) algorithms often struggle to adapt. To address this challenge, we propose an intelligent path planning algorithm called PERM-QN (Q-learning with priority experience replay and memory network), designed for energy-aware, complete area coverage in uncertain terrains. PERM-QN integrates a dynamic weight reward function, priority experience replay, and a memory network to enable efficient, comprehensive exploration in complex, obstacle-laden environments. The dynamic weight reward function adaptively balances coverage, path length, and energy consumption across different phases of operation, and the priority experience replay mechanism accelerates learning convergence by focusing on high-value past experiences. Finally, the memory network expedites route planning in regions with similar terrain, reducing redundant exploration. Experiments in simulated post-disaster environments of varying complexity demonstrate that PERM-QN achieves more efficient and comprehensive exploration than traditional methods while maintaining robust performance. These findings highlight PERM-QN as an effective path planning solution for robotic search in complex, dynamic environments.

Intelligent algorithms continue to develop, and efficient path planning for mobile robots in complex environments relies heavily on such algorithms. This article proposes an ACO-SA path planning method that combines ant colony and simulated annealing to solve the problems of slow iteration and long computation in classical ant colony algorithms. The training base map is gridded and modeled, and the path is initially calculated and parameterized using traditional ant colony algorithms. The simulated annealing cooling mechanism is introduced to optimize the pheromone strategy, and the robustness of the algorithm is tested using a multi-modal large model random map. Simulation shows that under the map of the training base, the path length of the ACO-SA algorithm remains unchanged, and the convergence speed is improved by 88.9% and 58.3% respectively, while the running time is shortened by 1.5% and 3.5% respectively; In the worst results of the random map, the shortest path is shortened by 36.57% and 35.95% respectively compared to the traditional ant colony algorithm. This algorithm has better optimization effect and path stability, providing a practical solution for intelligent detection robot path planning.

Abstract We have developed an optical simulator to test real-time optical two-way timefrequency transfer (O-TWTFT) at the femtosecond level capable of simulating relative motion between two linked optical clock nodes up to Mach 1.8 with no moving parts. The technique is enabled by artificially Doppler shifting femtosecond pulses from auxiliary stabilized optical frequency combs. These pulses are exchanged between the nodes to simulate the Doppler shifts observed from a changing optical path length. We can continuously scan the simulated velocity from 14 to 620 m/s while simultaneously measuring velocity-dependent clock shifts at much higher velocities than previously recorded. This system provides an effective testbed that allows us to explore issues and solutions to enable femtosecond-level optical time transfer between optical clocks moving at high relative velocities.

To assess whether 3D photoacoustic tomography (PAT) using the Fabry-Perot (FP) scanner can (i) reliably detect synovitis in participants with rheumatoid arthritis (RA), and (ii) assess the severity of active inflammation. A total of 247 3D-PAT images of the finger and wrist joint from 11 healthy volunteers and 9 patients with RA were obtained using the FP scanner. Patients underwent power doppler ultrasound (PDUS) assessment of RA disease activity. 3D-PAT images were acquired over a 15x15x10mm3 volume in < 15 s. The images were assessed quantitatively by segmentation of the vasculature using k-means clustering followed by skeletonisation to measure vascular path length (PAT-VPL). Patient and volunteer PAT-VPL were compared using t-tests and area under the receiver operating characteristic curve (ROC-AUC). 3D-PAT differentiates between unaffected and affected joints in patients with RA, showing a statistically significant difference in PAT-VPL (unaffected joint: 11.88 mm, affected joints: 45.7 mm, p < 0.001). The ROC-AUC was 0.91 (95% confidence interval (CI) 0.81-0.96), with sensitivity and specificity of 86% (95%CI 72%-95%) and 78% (95%CI 60%-90%), respectively. PAT-VPL was associated with PDUS-based clinical severity grade, with a correlation coefficient of 0.74 (95% CI: 0.67-0.8). Significant differences in PAT-VPL were observed between each clinical severity group. The FP scanner generates rapid high-resolution quantitative 3D-PAT images of the synovial microvasculature that can be used to distinguish between inflamed and non-inflamed joints, and assess the severity of active inflammation. The 3D-PAT FP scanner reliably distinguishes between affected and unaffected finger and wrist joints in patients with rheumatoid arthritis. Compared to previous 2D PAT scanners used to image joint inflammation, it offers superior image quality and the prospect of mitigating operator-dependent variations in probe positioning by virtue of its 3D imaging capability.

High-resolution mobility-based ion separations in Structures for Lossless Ion Manipulations (SLIM) have been useful for ion mobility separations for a variety of molecular classes in the gas phase. Here, we present multipass SLIM separations for gas-phase proteins in their near-native state exhibiting charge-state-dependent arrival time distributions using carbonic anhydrase (29 kDa), alcohol dehydrogenase (148 kDa), and apo-transferrin (79 kDa). The experimental CCS values were obtained from calibration curves for the arrival times of Agilent Tune Mix ions. For multipass separations, the ATDs were converted to CCS values by deconvoluting the multipass arrival times into accurate single-pass values amenable to the single-pass calibration curves. Mass spectra of carbonic anhydrase (CA) showed three different charge states (z = 9+ to 11+). Their corresponding mobility peaks were baseline-separated by using 8-m single-pass separations. When compared to the corresponding drift tube ion mobility (DTIMS) measurements, the CCS values obtained from DTIMS and SLIM were in agreement within experimental error. Single-pass analysis of alcohol dehydrogenase (ADH) exhibits three predominant charge states (z = 23+ to 25+) with mobility overlap between adjacent charge states. The mobility peak resolution for ADH improved with multipass separations (up to 24-m path length). In addition, CCS distributions obtained for charge states z = 16+ to 18+ of apo-transferrin reveal a transition from a compact unimodal form (z = 18+ and 19+) to broader multimodal CCS distributions for z = 16+. For apo-transferrin, 40-m multipass separations were performed allowing for complete isolation of the selected mobility range corresponding to z = 17+, leading to selective isolation of a narrow arrival time window. The extended mobility separations provided minimal alterations to the structure of the proteins, and the experimentally derived CCS values showed minimal change as a function of the separation time or number of passes. Mobility-based ion separations for native-like proteins, using SLIM, open opportunities for native-IMS applications as well as other manipulations enabled by SLIM-like mobility-selective isolation and collection.

Digital microfluidic biochips (DMFBs) find extensive applications in biochemical experiments, medical diagnostics, and safety-critical domains, with their reliability dependent on efficient online testing technologies. However, traditional random search algorithms suffer from slow convergence and susceptibility to local optima under complex fluidic constraints. This paper proposes a hybrid optimization method based on priority strategy and an improved sparrow search algorithm for DMFB online test path planning. At the algorithmic level, the improved sparrow search algorithm incorporates three main components: tent chaotic mapping for population initialization, cosine adaptive weights together with Elite Opposition-based Learning (EOBL) to balance global exploration and local exploitation, and a Gaussian perturbation mechanism for fine-grained refinement of promising solutions. Concurrently, this paper proposes an intelligent rescue strategy that integrates global graph-theoretic pathfinding, local greedy heuristics, and space–time constraint verification to establish a closed-loop decision-making system. The experimental results show that the proposed algorithm is efficient. On the standard 7 × 7–15 × 15 DMFB benchmark chips, the shortest offline test path length obtained by the algorithm is equal to the length of the Euler path, indicating that, for these regular layouts, the shortest test path has reached the known optimal value. In both offline and online testing, the shortest paths found by the proposed method are better than or equal to those of existing mainstream algorithms. In particular, for the 15 × 15 chip under online testing, the proposed method reduces the path length from 543 and 471 to 446 compared with the IPSO and IACA algorithms, respectively, and reduces the standard deviation by 53.14% and 39.4% compared with IGWO in offline and online testing.

Compromised balance is implicated with increased injury risk in athletes. Although multiple modalities are available to improve balance, investigation into slackline balance training (SLT) has remained limited, especially in regard to elite athletes. The primary aim of this study was to determine the effects of SLT, using a novel portable slacklining board, on static and dynamic balance. Thirty male NCAA Division I American football athletes were randomized into one of three groups: a control group (CON) who received no balance training, a group undergoing conventional balance training (CBT), or SLT. Each group trained thrice weekly for eight weeks. Body mass, in addition to sway path length on three surfaces and the modified Star Excursion Balance Test (mSEBT), was assessed before and after the training period. Improvements in static and dynamic balance were observed in CBT and SLT compared to CON (p < 0.05). Notably, superior balance enhancements were observed in SLT relative to CBT in wobble board sway path length (p < 0.031), posteromedial mSEBT performance (p < 0.05), and composite mSEBT scores (p = 0.033). These results are the first to suggest that SLT may confer balance benefits in elite American football players that are comparable and, in some conditions, superior to those associated with CBT.

Network survivability is a crucial requirement in high-speed optical networks. Typical approaches of providing survivability have considered the failure of a single component such as a link or a node. In this paper, we consider a failure model in which any two links in the network may fail in an arbitrary order. Three loopback methods of recovering from double-link failures are presented. The first two methods require the identification of the failed links, while the third one does not. However, pre computing the backup paths for the third method is more difficult than for the first two. A heuristic algorithm that pre-computes backup paths for links is presented. Numerical results comparing the performance of our algorithm with other approaches suggests that it is possible to achieve recovery from double-link failures with a modest increase in backup capacity. Current means of providing loop-back recovery, which is widely used in SONET, rely on ring topologies, or on overlaying logical ring topologies upon physical meshes. Loop-back is desirable to provide rapid preplanned recovery of link or node failures in a bandwidth-efficient distributed manner. We introduce generalized loop-back, a novel scheme for performing loop-back in optical mesh networks. We present an algorithm to perform recovery for link failure and one to perform generalized loop-back recovery for node failure. We illustrate the operation of both algorithms, prove their validity, and present a network management protocol algorithm, which enables distributed operation for link or node failure. We present three different applications of generalized loop-back. First, we present heuristic algorithms for selecting recovery graphs, which maintain short maximum and average lengths of recovery paths. Second, we present WDM-based loop-back recovery for optical networks where wavelengths are used to back up other wavelengths. We compare, for WDM-based loop-back, the operation of generalized loop-back operation with known ring-based ways of providing loop- back recovery over mesh networks. Finally, we introduce the use of generalized loop-back to provide recovery in a way that allows dynamic choice of routes over preplanned directions.

Transmission electron microscopy (TEM) imaging relies on specific orientations of the incident electron beam relative to the sample in both conventional TEM and high-resolution TEM/scanning transmission electron microscopy (STEM). In conventional TEM, contrast arises from diffraction, where elastically scattered electrons form diffracted beams at angles defined by the Bragg law. In high-resolution TEM/STEM, contrast results from phase interference between the transmitted and diffracted waves, each acquiring a distinct phase at the exit surface due to their different path lengths. This interference can be constructive, destructive or intermediate between the two. The visibility of these contrasts depends critically on sample orientation. Traditionally, achieving optimal alignment has relied on empirical trial and error, requiring user expertise and considerable time. To overcome this limitation, we developed a new method supported by a specially written module in the ATEX software. This method leverages the determined crystal orientation, expressed by Euler angles with respect to the sample holder. It establishes the geometric relations between the incident beam, the desired diffraction vector g (for the two-beam condition) or a zone axis (for on-axis imaging), and the tilt/rotation axes of the holder. Using this information, the software provides precise tilt and rotation instructions to reach the desired beam condition efficiently. Unlike conventional methods, this approach significantly reduces the alignment effort, typically requiring no more than two tilts of the sample holder.

Safe and energy-aware navigation for unmanned aerial vehicles (UAVs) requires the simultaneous optimization of path length, curvature, obstacle clearance, altitude, energy expenditure, and mission time—within the tight computational limits of on-board processors. This study proposes a two-phase hybrid optimizer that couples the global search capability of Differential Evolution (DE) with an Enhanced Whale Optimization Algorithm (E-WOA) specialized for local refinement. E-WOA improves on the canonical WOA through three principled modifications: real-time boundary repair to ensure path feasibility, quasi-oppositional learning to restore population diversity, and an adaptive stagnation trigger that re-initiates exploration when progress stalls. When the population’s improvement plateaus, control transfers from DE to E-WOA, combining broad exploration with focused exploitation. Comparative experiments conducted in 3D environments with static obstacles that block direct line-of-sight routes demonstrate that the hybrid achieves lower composite cost—normalized over path length, curvature, risk, altitude, energy and time—shorter and smoother trajectories, and faster convergence than standard metaheuristics while preserving obstacle clearances and curvature limits. Averaged over 30 independent trials, our hybrid framework reduced the normalized composite cost by 14.5% relative to the next-best algorithm (Grey Wolf Optimizer) and produced feasible paths in an average of 2.35 seconds on commodity hardware—adequate for strategic re-planning, though further optimization is needed for sub-second control loops. Blending DE’s global reach with a diversity-aware, adaptively stalled WOA provides a practical foundation for strategic, near-real-time replanning in 3D airspaces.

Abstract Recent advancements in Large Language Models (LLMs) offer powerful capabilities in common sense reasoning and planning, making them a promising tool for Object Goal Navigation (ObjectNav). However, existing LLM-based approaches face two significant challenges: the high computational cost of LLM inference, which limits real-time decision making, and a domain gap between the LLMs’ general-purpose knowledge and the specific demands of navigation scenarios. To overcome these challenges, we propose a Knowledge-Enhanced navigation framework with an Intuitive-Deliberate mechanism (KEID). KEID employs an Intuitive-Deliberate mechanism that mimics human cognition, using a lightweight intuition module to strategically invoke the LLM, which reduces computational overhead. Meanwhile, KEID enhances the LLM with two specialized knowledge bases: a Scene Description Tree that describes the complex spatial and semantic relationships of indoor environments within a hierarchical framework and a Navigation Example database for in-context learning adaptation. Evaluations on the HM3D dataset within the Habitat simulator validate our method’s efficacy, demonstrating that KEID achieves a 47.1% success rate and a competitive 18.8% success weighted by path length, significantly outperforming existing baselines. Our work not only improves navigation performance but also enhances decision-making efficiency, establishing an effective framework for developing practical, real-time LLM-based robotic agents.

Radioimmunotherapy (RIT) with α-emitters represents an attractive alternative for the treatment of refractory Non-Hodgkin lymphoma (NHL) due to the high linear energy transfer and short path length of α-radiation in tissues. We have previously shown that α-RIT with [212Pb]Pb-TCMC-rituximab is potentially useful for treatment of NHL. In this study, we performed radiation dosimetry and evaluated the long-term toxicity in mice to determine safety of [212Pb]Pb-TCMC-rituximab,. Biodistribution data obtained after intravenous administration of [212Pb]Pb-TCMC-rituximab (185kBq) in healthy mice were used to calculate the absorbed radiation doses from [212Pb]Pb-TCMC-rituximab. Analyses show that the alveolar-interstitial, kidneys, and spleen receive the highest dose. In order to evaluate the toxicity of RIT for up to 9 months, [212Pb]Pb-TCMC-rituximab was administered intravenously in healthy C57BL/6 mice (277.5 and 555kBq) and in a B-NHL immunocompetent mouse model (277.5kBq, specific activity of 37 or 370 MBq/mg). Our previous study revealed a high efficacy of [212Pb]Pb-TCMC-rituximab at 277.5kBq and that activities of 185-370kBq of [212Pb]Pb-TCMC-rituximab were well-tolerated. However, in this long-term study, toxicity emerged in healthy mice after four months. The median survival for the 277.5 and 555kBq groups were 189 and 161 days, respectively. There was no significant hepatic toxicity, but there was a significant increase in urea and creatinine levels at 6 months, indicating long-term renal toxicity (p < 0.001). These results were supported by histopathological data. Long-term renal toxicity is also observed in the toxicity study performed on tumor model with two specific activities of [212Pb]Pb-TCMC-rituximab. Nevertheless, this toxicity was reduced at 370 MBq/mg compared to 37 MBq/mg. This study shows that long-term toxicity is induced by [212Pb]Pb-TCMC-rituximab, particularly affecting the kidneys. However, it highlights that this renal toxicity can be reduced through optimization, possibly by modifying the specific activity of the treatment.

We numerically investigate the robustness of networks with degree-degree correlations between nodes separated by distance l = 2 in terms of shortest path length. The degree-degree correlation between the l-th nearest neighbors can be quantified by Pearson’s correlation coefficient rl for the degrees of two nodes at distance l. We introduce l-th nearest-neighbor correlated random networks (l-NNCRNs) that are degree-degree correlated at less than or equal to the l-th nearest neighbor scale and maximally random at farther scales. We generate 2-NNCRNs with various r1 and r2 using two steps of random edge rewiring based on the Metropolis-Hastings algorithm and compare their robustness against failures of nodes and edges. As typical cases of homogeneous and heterogeneous degree distributions, we adopted Poisson and power law distributions. Our results show that the range of r2 differs depending on the degree distribution and the value of r1. Moreover, comparing 2-NNCRNs sharing the same degree distribution and r1, we demonstrate that a higher r2 makes a network more robust against random node/edge failures as well as degree-based targeted attacks. This behavior was observed in nearly all simulated cases, except for highly assortative power-law networks, where the relationship is more complex.

Background: Biomechanical disorders may result from joint deformities. The purpose of this prospective research was to assess total load distribution over the lower limbs and balance in individuals before and after an hemiepiphysiodesis procedure performed due to valgus or varus knee deformity. Methods: Thirty-five patients, mean age 12 years, who underwent hemiphysiodesis for valgus or varus deformity of the knee were evaluated in comparison to a healthy control group. In patients, the percentage distribution of weight-bearing capacity between the operated and unoperated limbs was analyzed before and after surgery. Balance was assessed based on CoG (center of gravity) sway area and the CoG path length. Results were collected using the FreeMED MAXI pedobarographic platform. Results: Before surgery, statistically significant lower load on the entire affected limb was noted compared to unaffected limb. The values of path of center of gravity improved statistically significantly after surgery, compared to the values before surgery. There were no differences in the load on the treated lower limb in the study group and the non-dominant limb in the control group. There were no differences between the load on the non-operated limb in the study group and the load on the dominant limb in the control group. In the hemiepiphysiodesis group there were no significant differences between the mean total load on the treated and untreated limb after surgery. The median CoG sway area and path length in the group of patients after hemiphysiodesis and in the healthy control group did not differ. Conclusions: After hemiphysiodesis, the percentage load distribution did not differ between the operated and non-operated lower limb. Hemiepiphysiodesis allows for achieving balance similar to the healthy control group. Performing hemiepiphysiodesis allows for the improvement of balance parameters and load distribution in the lower limbs.