Radius Constraints Research Articles

Ant colony optimization for the identification of dysregulated gene subnetworks from expression data

BackgroundHigh-throughput experimental technologies can provide deeper insights into pathway perturbations in biomedical studies. Accordingly, their usage is central to the identification of molecular targets and the subsequent development of suitable treatments for various diseases. Classical interpretations of generated data, such as differential gene expression and pathway analyses, disregard interconnections between studied genes when looking for gene-disease associations. Given that these interconnections are central to cellular processes, there has been a recent interest in incorporating them in such studies. The latter allows the detection of gene modules that underlie complex phenotypes in gene interaction networks. Existing methods either impose radius-based restrictions or freely grow modules at the expense of a statistical bias towards large modules. We propose a heuristic method, inspired by Ant Colony Optimization, to apply gene-level scoring and module identification with distance-based search constraints and penalties, rather than radius-based constraints.ResultsWe test and compare our results to other approaches using three datasets of different neurodegenerative diseases, namely Alzheimer’s, Parkinson’s, and Huntington’s, over three independent experiments. We report the outcomes of enrichment analyses and concordance of gene-level scores for each disease. Results indicate that the proposed approach generally shows superior stability in comparison to existing methods. It produces stable and meaningful enrichment results in all three datasets which have different case to control proportions and sample sizes.ConclusionThe presented network-based gene expression analysis approach successfully identifies dysregulated gene modules associated with a certain disease. Using a heuristic based on Ant Colony Optimization, we perform a distance-based search with no radius constraints. Experimental results support the effectiveness and stability of our method in prioritizing modules of high relevance. Our tool is publicly available at github.com/GhadiElHasbani/ACOxGS.git.

Influence of Orbit and Mass Constraints on Reflected Light Characterization of Directly Imaged Rocky Exoplanets

Survey strategies for upcoming exoplanet direct imaging missions have considered varying assumptions of prior knowledge. Precursor radial velocity surveys could have detected nearby exo-Earths and provided prior orbit and mass constraints. Alternatively, a direct imaging mission performing astrometry could yield constraints on the orbit and phase angle of target planets. Understanding the impact of prior mass and orbit information on planetary characterization is crucial for efficiently recognizing habitable exoplanets. To address this question, we use a reflected-light retrieval tool to infer the atmospheric and bulk properties of directly imaged Earth-analogs while considering varying levels of prior information and signal-to-noise ratio (S/N). Because of the strong correlation between the orbit-related parameters and the planetary radius, prior information on the orbital distance and planetary phase angle yield much tighter constraints on the planetary radius: from Rp=2.95−1.95+2.69R⊕ without prior knowledge, to Rp=1.01−0.19+0.33R⊕ with prior determination of the orbit for S/N = 20 in the visible/near-infrared spectral range, thus allowing size determination from reflected light observations. However, additional knowledge of planet mass does not notably enhance radius ( Rp=0.98−0.14+0.17R⊕ ) or atmospheric characterization. Also, prior knowledge of the mass alone does not yield a tight radius constraint ( Rp=1.64−0.80+1.29R⊕ ) nor improves atmospheric composition inference. By contrast, because of its sensitivity to gas column abundance, detecting a Rayleigh scattering slope or bounding Rayleigh opacity helps to refine gas mixing ratio inferences without requiring prior mass knowledge. Overall, apart from radius determination, increasing the S/N is more beneficial than additional prior observations.

LHS 1140 b Is a Potentially Habitable Water World

LHS 1140 b is a small planet orbiting in the habitable zone of its M4.5V dwarf host. Recent mass and radius constraints have indicated that it has either a thick H2-rich atmosphere or substantial water by mass. Here we present a transmission spectrum of LHS 1140 b between 1.7 and 5.2 μm, obtained using the NIRSpec instrument on JWST. By combining spectral retrievals and self-consistent atmospheric models, we show that the transmission spectrum is inconsistent with H2-rich atmospheres with varied size and metallicity, leaving a water world as the remaining scenario to explain the planet’s low density. Specifically, a H2-rich atmosphere would result in prominent spectral features of CH4 or CO2 on this planet, but they are not seen in the transmission spectrum. Instead, the data favor a high mean molecular weight atmosphere (possibly N2 dominated with H2O and CO2) with a modest confidence. Forming the planet by accreting C- and N-bearing ices could naturally give rise to a CO2- or N2-dominated atmosphere, and if the planet evolves to or has the climate-stabilizing mechanism to maintain a moderate-size CO2/N2-dominated atmosphere, the planet could have liquid-water oceans. Our models suggest CO2 absorption features with an expected signal of 20 ppm at 4.2 μm. As the existence of an atmosphere on TRAPPIST-1 planets is uncertain, LHS 1140 b may well present the best current opportunity to detect and characterize a habitable world.

Quark star matter in the color-flavor-locked state with a density-dependent quark mass model

The properties of strange quark matter (SQM) and color-flavor-locked (CFL) quark matter are investigated in quark stars (QSs) at zero temperature case within confined-isospin-density-dependent-mass (CIDDM) model. The mass–radius relation of QSs are also studied by considering newly proposed mass–radius constraints in CFL phase. Our results indicate that we can obtain more stable and stiffer equation of state (EOS) by considering CFL phase within CIDDM model at zero temperature. While the GW190814's secondary component with a mass around 2.6 M ⊙ cannot be QSs within CIDDM model in SQM case, it can be well described as QSs by considering CFL phase within CIDDM model in this work. In particular, we further construct a density-dependent pairing energy gap to connect the EOS of SQM and CFL quark matter with constant pairing energy gap Δ, and the results indicate that by extending the paring energy gap to include density dependence, the mass–radius lines within CIDDM model can satisfy most of the mass–radius region constraints in recent pulsar observations.

Coverage Path Planning of UAV Based on Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.

A Method for Tomato Plant Stem and Leaf Segmentation and Phenotypic Extraction Based on Skeleton Extraction and Supervoxel Clustering

To address the current problem of the difficulty of extracting the phenotypic parameters of tomato plants in a non-destructive and accurate way, we proposed a method of stem and leaf segmentation and phenotypic extraction of tomato plants based on skeleton extraction and supervoxel clustering. To carry out growth and cultivation experiments on tomato plants in a solar greenhouse, we obtained multi-view image sequences of the tomato plants to construct three-dimensional models of the plant. We used Laplace’s skeleton extraction algorithm to extract the skeleton of the point cloud after removing the noise points using a multi-filtering algorithm, and, based on the plant skeleton, searched for the highest point path, height constraints, and radius constraints to separate the stem from the leaf. At the same time, a supervoxel segmentation method based on Euclidean distance was used to segment each leaf. We extracted a total of six phenotypic parameters of the plant: height, stem diameter, leaf angle, leaf length, leaf width and leaf area, using the segmented organs, which are important for the phenotype. The results showed that the average accuracy, average recall and average F1 scores of the stem and leaf segmentation were 0.88, 0.80 and 0.84, and the segmentation indexes were better than the other four segmentation algorithms; the coefficients of determination between the measurement values of the phenotypic parameters and the real values were 0.97, 0.84, 0.88, 0.94, 0.92 and 0.93; and the root-mean-square errors were 2.17 cm, 0.346 cm, 5.65°, 3.18 cm, 2.99 cm and 8.79 cm2. The measurement values of the proposed method had a strong correlation with the actual values, which could satisfy the requirements of daily production and provide technical support for the extraction of high-throughput phenotypic parameters of tomato plants in solar greenhouses.

A Finite Topological Type Theorem for Open Manifolds with Non-negative Ricci Curvature and Almost Maximal Local Rewinding Volume

Abstract In this paper, we present finite topological type theorems for open manifolds with non-negative Ricci curvature, under almost maximal local rewinding volume. Unlike previous related research, our theorems remove the constraints of sectional curvature or conjugate radius, which were crucial additional assumptions on metric regularity in prior results. Notably, our settings do not necessarily satisfy a triangle comparison of Toponogov type. In fact, the method we adopt also extends to many previous related studies.

Enhanced Design Automation for Hydraulic Manifolds Produced Using Additive Manufacturing

Complex, highly functional components such as hydraulic manifolds can be produced using metal additive manufacturing (AM). The design process is a major challenge in the development of AM components that exploit this potential. Since traditional CAD systems are not made to efficiently create highly complex designs, new approaches to the design process are required. Design Automation (DA) and Knowledge-Based Engineering (KBE) are approaches that can be used to create designs and implement design changes faster and improve functionality. For the application of fluid components, KBE design workflows were developed which enable the automated generation of AM manifolds.However, these workflows have some limitations that need to be improved to provide a flexible design tool that enables users to automatically create geometries that meet all requirements. This paper shows how the workflows have been improved by introducing two new features. First, the object-oriented input format presents a new approach to how the information of a hydraulic circuit diagram can be processed to start the workflows. This is done through a component library that contains a selection of flow components providing all information and geometries of one component, a 3D layout assembly to orient the relevant components in space, and a connectivity matrix that defines the connection through flow channels for the manifold. Second, the bending radius constraint allows users to define a radius that the centerline of a flow channel should not undergo along the whole path. The new features are presented in detail in this paper. Further, the advantages are shown and compared to the previously published workflows in a case study. This work lays the foundation for future work that has the aim to fully automate the design process by only taking a hydraulic circuit diagram as an input and providing all required manufacturing files as an output.

On Hierarchical Multi-UAV Dubins Traveling Salesman Problem Paths in a Complex Obstacle Environment.

This article aims to solve a hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP). Optimal hierarchical coverage and multi-UAV collaboration are achieved by the proposed approaches in a 3-D complex obstacle environment. A multi-UAV multilayer projection clustering (MMPC) algorithm is presented to reduce the cumulative distance from multilayer targets to corresponding cluster centers. A straight-line flight judgment (SFJ) was developed to reduce the calculation of obstacle avoidance. An improved adaptive window probabilistic roadmap (AWPRM) algorithm is addressed to plan obstacle-avoidance paths. The AWPRM improves the feasibility of finding the optimal sequence based on the proposed SFJ compared with a traditional probabilistic roadmap. To solve the solution to TSP with obstacles constraints, the proposed sequencing-bundling-bridging (SBB) framework combines the bundling ant colony system (BACS) and homotopic AWPRM. An obstacle-avoidance optimal curved path is constructed with a turning radius constraint based on the Dubins method and followed up by solving the TSP sequence. The results of simulation experiments indicated that the proposed strategies can provide a set of feasible solutions for HMDTSPs in a complex obstacle environment.

Noise-aware and correlation analysis-based for fuzzy-rough feature selection

Feature selection has gained significant attention, with a focus on removing redundant or irrelevant features to improve subsequent machine learning tasks. The fuzzy rough set is extensively utilized for feature selection because of its ability to manage uncertainty. The fuzzy dependency-based approach represents an effective branch within the domain of fuzzy rough set models. However, it has been revealed that fuzzy dependency function is not robust to noise data while the noise objects often occur. Furthermore, as the size of dataset increases, the computational complexity of evaluating metrics also tends to increase due to potentially unnecessary computational effort. Hence, to address both of these research issues simultaneously, we propose a fuzzy rough correlation-based feature selection algorithm, denoted as FRCB. The introduction of neighborhood radius constraints and granularity conditions aims to interpret and mitigate the impact of noisy data by partitioning objects into a Core-Boundary-Outlier structure. A more robust hybrid fuzzy relation and correlation-based fuzzy rough dependency is proposed by considering the representativeness and attractiveness of the core to other objects within the internal class, as well as the impacts on other objects in external classes. To minimize processing overhead and time complexity, redundant features are identified through correlation analysis, which involves evaluating, ranking, and grouping before proceeding to the next iteration. The experiments were conducted using eleven datasets. The analysis of the noise curves and the raw data curve indicates that the proposed correlation-based fuzzy rough dependency is more robust than other benchmarking methods. Moreover, the results of the classification and running time comparisons indicate that the FRCB algorithm proposed in this study exhibits superior performance compared to the baseline methods.

Neutron stars in the Witten-Sakai-Sugimoto model

We utilize the top-down holographic QCD model, the Witten-Sakai-Sugimoto model, in a hybrid setting with the SLy4, soft chiral EFT and stiff chiral EFT equations of state to describe neutron stars with high precision. In particular, we employ a calibration that bootstraps the nuclear matter by fitting the Kaluza-Klein scale and the ’t Hooft coupling such that the physical saturation density and physical symmetry energy are achieved. We obtain static stable neutron star mass-radius data via the Tolman-Oppenheimer-Volkov equations that yield sufficiently large maximal masses of neutron stars to be compatible with the recently observed PSR-J0952-0607 data as well as all other known radius and tidal deformation constraints.

What Is the Nature of the HESS J1731-347 Compact Object?

Once further confirmed in future analyses, the radius and mass measurement of HESS J1731-347 with and will be among the lightest and smallest compact objects ever detected. This raises many questions about its nature and opens up the window for different theories to explain such a measurement. In this article, we use the information from Doroshenko et al. on the mass, radius, and surface temperature together with the multimessenger observations of neutron stars to investigate the possibility that HESS J1731-347 is one of the lightest observed neutron star, a strange quark star, a hybrid star with an early deconfinement phase transition, or a dark matter–admixed neutron star. The nucleonic and quark matter are modeled within realistic equation of states (EOSs) with a self-consistent calculation of the pairing gaps in quark matter. By performing the joint analysis of the thermal evolution and mass–radius constraint, we find evidence that within a 1σ confidence level, HESS J1731-347 is consistent with the neutron star scenario with the soft EOS as well as with a strange and hybrid star with the early deconfinement phase transition with a strong quark pairing and neutron star admixed with dark matter.

UAV 3D Trajectory Planning and Optimization Using A* Algorithm Based on Free Space Method

Due to the complexity of three-dimensional (3D) map modeling, the A* algorithm is inefficient in planning 3D trajectories for unmanned aerial vehicles (UAVs). This paper designs a method for planning 3D trajectories using A* algorithm based on free space method to address this issue. The method translates the 3D trajectory planning problem into a two-dimensional (2D) trajectory planning problem through flight surface extraction, thereby enhancing the efficiency of trajectory planning. In addition, a trajectory optimization method considering safety parameters and offset costs is designed for the waypoints that fail to meet UAV restrictions. The optimization method consists of longitudinal trajectory altitude adjustment and horizontal trajectory smoothing to ensure the trajectory aligns with the UAV climb rate constraint, normal overload constraint and minimum turning radius constraint. Simulation results show that the designed method delivers higher efficiency in 3D trajectory planning and optimization than the conventional method, and the optimized trajectory adheres to the above constraints.

Research on Automatic Vertical Parking Path-Planning Algorithms for Narrow Parking Spaces

Narrow parking spaces pose difficulties in path planning and spot turning caused by sudden changes and discontinuities in path curvature. To address these problems, this paper investigates the performance of three path-planning algorithms and proposes a path-optimization algorithm. First, a narrow parking space is defined based on single-step parking using the arc-line combination parking algorithm. Second, to compare the arc-line combination algorithm, Hybrid A* algorithm, and particle swarm optimization parking algorithm with respect to different narrow parking spaces, a multi-objective evaluation function is proposed, including three evaluation indicators, namely, the path length, the number of positive and negative conversions of vehicle speed, and the smoothness of the path. Their performance is compared using a simulation conducted in MATLAB. With the same starting point and different parking space widths, the three algorithms are simulated to generate different planned paths. Then, the evaluation indices are obtained to compare the performance of the algorithms based on the multi-objective function, the values of which indicate the fitness of the algorithm in a narrow parking environment. The results show that the Hybrid A* algorithm is better than the others for narrow parking spaces. Third, to smooth the planning path, a path optimization algorithm based on the cubic B-spline curve and gradient descent is proposed. Finally, the results of a simulation conducted on the proposed algorithm and the Hybrid A* algorithm are provided: the average minimum curvature of the path was reduced by 0.005 m−1, and the path meets the requirements of the minimum turning radius constraint of the analyzed vehicle. The results show that the proposed algorithm can effectively eliminate the curvature mutation point and constrain the path curvature to meet the requirements of a smooth path.

Atmospheric Effects on Neutron Star Parameter Constraints with NICER

We present an analysis of the effects of uncertainties in the atmosphere models on the radius, mass, and other neutron star parameter constraints for the NICER observations of rotation-powered millisecond pulsars. To date, NICER has applied the X-ray pulse profile modeling technique to two millisecond-period pulsars: PSR J0030+0451 and the high-mass pulsar PSR J0740+6620. These studies have commonly assumed a deep-heated, fully ionized hydrogen atmosphere model, although they have explored the effects of partial ionization and helium composition in some cases. Here, we extend that exploration and also include new models with partially ionized carbon composition, externally heated hydrogen, and an empirical atmospheric beaming parameterization to explore deviations in the expected anisotropy of the emitted radiation. None of the studied atmosphere cases have any significant influence on the inferred radius of PSR J0740+6620, possibly due to its X-ray faintness, tighter external constraints, and/or viewing geometry. In the case of PSR J0030+0451, both the composition and ionization state could significantly alter the inferred radius. However, based on the evidence (prior predictive probability of the data), partially ionized hydrogen and carbon atmospheres are disfavored. The difference in the evidence for ionized hydrogen and helium atmospheres is too small to be decisive for most cases, but the inferred radius for helium models trends to larger sizes around or above 14–15 km. External heating or deviations in the beaming that are less than 5% at emission angles smaller than 60°, on the other hand, have no significant effect on the inferred radius.

Jamming-Enhanced Secure UAV Communications With Propulsion Energy and Curvature Radius Constraints

A multi-unmanned aerial vehicles (UAVs)-aided secure communication network is studied, where multiple information UAVs carrying temporary aerial base stations transmit confidential information to multiple authorized receivers (ARs), and a jammer UAV is employed to send artificial noise to multiple unauthorized receivers (URs) for mitigating information leakage. Some practical constrains including the inter-UAV interference among the information UAVs and the jammer UAV, the maximal available propulsion energy at UAVs, and the curvature radius limitations of UAVs' trajectories are concurrently taken into account. In order to maximize the minimum secrecy rate among the ARs, the AR scheduling, the UAVs' power allocation and the trajectories of UAVs are jointly optimized by formulating a multi-variable optimization problem. To solve the non-convex problem efficiently, we present a block coordinate descent (BCD)-based approach, where the penalty dual decomposition (PDD)-based algorithm is designed to optimize the AR scheduling, and successive convex approximation (SCA)-based algorithm is proposed to optimize the UAVs' power allocation and the trajectories of UAVs. The complexity of the presented BCD-based approach is analyzed, which achieves polynomial complexity. Simulation results show that imposing curvature radius limitations on the UAVs' trajectory design is effective to avoid sharp turning of UAVs. Moreover, as curvature radius increases, the system max-min secrecy rate decreases. Besides, the secrecy rate performance of the system can be enhanced by exploiting one jammer UAV to interfere with the URs.

Neutron star mass–radius constraints using the high-frequency quasi-periodic oscillations of GRB 200415A

Context. Quasi-periodic oscillations (QPOs) observed in a giant flare of a strongly magnetized neutron star (magnetar) carry crucial information for extracting the properties od neutron stars. Aims. The aim of this study is to constrain the mass and radius of the neutron star model for GRB 200415A by identifying the observed QPOs with crustal torsional oscillations and comparing these with experimental constraints on the nuclear matter properties. The frequencies of the crustal torsional oscillations are determined by solving the eigenvalue problem with the Cowling approximation, assuming a magnetic field of about 1015 G. Methods. We find that the observed QPOs can be identified with several overtones of crustal oscillations for carefully selected combinations of the nuclear saturation parameters. Thus, we can inversely constrain the neutron star mass and radius for GRB 200415A by comparing them to the values of nuclear saturation parameters obtained from terrestrial experiments. Results. We impose further constraints on the neutron star mass and radius while the candidate neutron star models examined here are consistent with the constraints obtained from other available astronomical and experimental observations.

Study on Damage Effectiveness of Single Projectile of Small Caliber Anti-aircraft Gun Under Multi-error Constraints

In this paper, the damage effectiveness of a single projectile of small caliber (30-35 mm) anti-aircraft gun under multi-error constraints is studied. Based on the known prior information of target motion and the dispersion mathematical model of prefabricated fragment warhead, under the constraints of fragment effective killing radius and target equivalent size, the fuze starting characteristic factor is introduced to establish the integrated model of fuze and warhead. Based on the Monte-Carlo method, the variation law of the damage effectiveness of a single projectile under the joint constraints of the gun firing angle error and the fuze fixed range error is analyzed. The simulated annealing (SA) algorithm is used to find the global optimal solution of the model. The optimal damage effectiveness of a single projectile under multi-error constraints is found. The results show that the damage effectiveness of single projectile is increased by 26.91% without changing the state of existing weapon and equipment.

Updated Planetary Mass Constraints of the Young V1298 Tau System Using MAROON-X

The early K-type T-Tauri star, V1298 Tau (V = 10 mag, age ≈ 20–30 Myr) hosts four transiting planets with radii ranging from 4.9 to 9.6 R ⊕. The three inner planets have orbital periods of ≈8–24 days while the outer planet’s period is poorly constrained by single transits observed with K2 and the Transiting Exoplanet Survey Satellite (TESS). Planets b, c, and d are proto–sub-Neptunes that may be undergoing significant mass loss. Depending on the stellar activity and planet masses, they are expected to evolve into super-Earths/sub-Neptunes that bound the radius valley. Here we present results of a joint transit and radial velocity (RV) modeling analysis, which includes recently obtained TESS photometry and MAROON-X RV measurements. Assuming circular orbits, we obtain a low-significance (≈2σ) RV detection of planet c, implying a mass of and a conservative 2σ upper limit of <39 M ⊕. For planets b and d, we derive 2σ upper limits of M b < 159 M ⊕ and M d < 41 M ⊕, respectively. For planet e, plausible discrete periods of P e > 55.4 days are ruled out at the 3σ level while seven solutions with 43.3 < P e/d < 55.4 are consistent with the most probable 46.768131 ± 000076 days solution within 3σ. Adopting the most probable solution yields a 2.6σ RV detection with a mass of 0.66 ± 0.26 M Jup. Comparing the updated mass and radius constraints with planetary evolution and interior structure models shows that planets b, d, and e are consistent with predictions for young gas-rich planets and that planet c is consistent with having a water-rich core with a substantial (∼5% by mass) H2 envelope.

Nuclear Physics with Gravitational Waves from Neutron Stars Disrupted by Black Holes

Gravitational waves from neutron star–black hole (NSBH) mergers that undergo tidal disruption provide a potential avenue to study the equation of state of neutron stars and hence the behavior of matter at its most extreme densities. We present a phenomenological model for the gravitational-wave signature of tidal disruption, which allows us to measure the disruption time. We carry out a study with mock data, assuming an optimistically nearby NSBH event with parameters tuned for measuring the tidal disruption. We show that a two-detector network of 40 km Cosmic Explorer instruments can measure the time of disruption with a precision of ≈0.5 ms, which corresponds to a constraint on the neutron star radius of ≈0.7 km (90% credibility). This radius constraint is wider than the constraint obtained by measuring the tidal deformability of the neutron star of the same system during the inspiral. Moreover, the neutron star radius is likely to be more tightly constrained using binary neutron star mergers. While NSBH mergers are important for the information they provide about stellar and binary astrophysics, they are unlikely to provide insights into nuclear physics beyond what we will already know from binary neutron star mergers.