Existence and global attractivity of positive almost periodic solutions for a kind of fishing model with pure-delay

summary:By using some analytical techniques, modified inequalities and Mawhin's continuation theorem of coincidence degree theory, some sufficient conditions for the existence of at least one positive almost periodic solution of a kind of fishing model with delay are obtained. Further, the global attractivity of the positive almost periodic solution of this model is also considered. Finally, three examples are given to illustrate the main results of this paper

Optimal Eco-driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension

In this paper, we develop a model to plan energy-efficient speed trajectories of electric trucks in real-time by taking into account the information of topography and traffic ahead of the vehicle. In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge. We then formulate an energy minimization problem and solve it by an alternating direction method of multipliers (ADMM) method that exploits the structure of the problem. A model predictive control framework is then employed to deal with topographic and traffic uncertainties in real-time. An empirical study is conducted on the performance of the proposed eco-driving algorithm and its impact on battery degradation. The experimental results show that the energy consumption by using the developed method is reduced by up to 5.05%, and the battery life extended by as high as 35.35% compared to benchmarking solutions

Quadratic convergence of approximate solutions of two-point boundary value problems with impulse

The method of quasilinearization, coupled with the method of upper and lower solutions, is applied to a boundary value problem for an ordinary differential equation with impulse that has a unique solution. The method generates sequences of approximate solutions which converge monotonically and quadratically to the unique solution. In this work, we allow nonlinear terms with respect to velocity; in particular, Nagumo conditions are employed

Nutrition Diet of Grazing Sheep and Forage Supply on Natural Grassland

Forages are a major asset of any livestock operation and the foundation of most rations in a forage-based livestock system. The available nutrients in a forage influence individual animal production (e.g. gain per animal), while the amount of forage produced affects production per hectare. The relationship between voluntary food intake and animal productivity is well recognized. Many studies related to the regulation of food consumption by sheep and cattle have been reported (Provenza 1996). Willoughby (1958) dis-cussed a number of factors which might influence the herbage intake of grazing animals. By contrast, less attention has been given to the nutritional supply which influences the intake of herbage by grazing animals. It is necessary to know about animal daily nutrient requirements for production and forage supply in order to evaluate grazing capacity

Pavement roughness identification research in time domain based on neural network

A new simulation study method based on general regression neural network (GRNN) is proposed for identifying the pavement roughness in the time domain. First, a seven degree-of-freedoms vehicle vibration model is estbalished for the vehicle’s riding comfort analysis. The vertical acceleration and pitching angular acceleration of vehicle body centroid are calculated by simulation. The nonlinear mapping relations between the two above accelerations and pavement roughness in time domain are built by GRNN, and then the pavement roughness is identified by training the networks. Finally, the vertical acceleration and pitching angular acceleration of the vehicle body centriod are acquired by ADAMS/View virtual experiment simulation and the result are used to identify pavement roughness. In the end, the availability for identifying the pavement roughness by GRNN is confirmed

Targeted and Reversible Blood-Retinal Barrier Disruption via Focused Ultrasound and Microbubbles

The blood-retinal barrier (BRB) prevents most systemically-administered drugs from reaching the retina. This study investigated whether burst ultrasound applied with a circulating microbubble agent can disrupt the BRB, providing a noninvasive method for the targeted delivery of systemically administered drugs to the retina. To demonstrate the efficacy and reversibility of such a procedure, five overlapping targets around the optic nerve head were sonicated through the cornea and lens in 20 healthy male Sprague-Dawley rats using a 690 kHz focused ultrasound transducer. For BRB disruption, 10 ms bursts were applied at 1 Hz for 60 s with different peak rarefactional pressure amplitudes (0.81, 0.88 and 1.1 MPa). Each sonication was combined with an IV injection of a microbubble ultrasound contrast agent (Definity). To evaluate BRB disruption, an MRI contrast agent (Magnevist) was injected IV immediately after the last sonication, and serial T1-weighted MR images were acquired up to 30 minutes. MRI contrast enhancement into the vitreous humor near targeted area was observed for all tested pressure amplitudes, with more signal enhancement evident at the highest pressure amplitude. At 0.81 MPa, BRB disruption was not detected 3 h post sonication, after an additional MRI contrast injection. A day after sonication, the eyes were processed for histology of the retina. At the two lower exposure levels (0.81 and 0.88 MPa), most of the sonicated regions were indistinguishable from the control eyes, although a few tiny clusters of extravasated erythrocytes (petechaie) were observed. More severe retinal damage was observed at 1.1 MPa. These results demonstrate that focused ultrasound and microbubbles can offer a noninvasive and targeted means to transiently disrupt the BRB for ocular drug delivery

Robust Quadratic Regression and Its Application to Energy-Growth Consumption Problem

We propose a robust quadratic regression model to handle the statistics inaccuracy. Unlike the traditional robust statistic approaches that mainly focus on eliminating the effect of outliers, the proposed model employs the recently developed robust optimization methodology and tries to minimize the worst-case residual errors. First, we give a solvable equivalent semidefinite programming for the robust least square model with ball uncertainty set. Then the result is generalized to robust models under l1- and l∞-norm critera with general ellipsoid uncertainty sets. In addition, we establish a robust regression model for per capital GDP and energy consumption in the energy-growth problem under the conservation hypothesis. Finally, numerical experiments are carried out to verify the effectiveness of the proposed models and demonstrate the effect of the uncertainty perturbation on the robust models

Low Skeletal Muscle Mass Is Associated With Inferior Preoperative and Postoperative Shoulder Function in Elderly Rotator Cuff Tear Patients

BACKGROUND: The age-related loss of skeletal muscle mass is an important characteristic of sarcopenia, an increasingly recognized condition with systemic implications. However, its association with shoulder function in elderly patients with rotator cuff tears (RCT) remains unknown. This study aimed to investigate the relationship between low skeletal muscle mass and shoulder function in elderly RCT patients. METHODS: A retrospective analysis was conducted on RCT patients who underwent chest computed tomography (CT) scans for clinical evaluation. Preoperative CT scan images of the chest were used to calculate the cross-sectional area (CSA) of thoracic muscle at the T4 level. The medical records were reviewed. Shoulder function was assessed using the ASES score and CMS score both preoperatively and at the final follow-up. Data on the preoperative range of motion (ROM) for the affected shoulder, were collected for analysis. Subgroup analyses by sex were also performed. RESULTS: A total of 283 RCT patients, consisting of 95 males and 188 females, with a mean age of 66.22 ± 4.89(range, 60-95 years) years were included in this retrospective study. The low muscle mass group showed significantly higher level of c-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) compared to the normal group(3.75 ± 6.64 mg/L vs. 2.17 ± 2.30 mg/L, p = 0.021; 19.08 ± 12.86 mm/H vs.15.95 ± 10.76 mm/H, p = 0.038; respectively). In the normal group, pre-operative passive ROM, including forward elevation, abduction, lateral rotation, and abductive external rotation, was significantly better than that in the low muscle mass group (127.18 ± 34.87° vs. 89.76 ± 50.61°; 119.83 ± 45.76° vs. 87.16 ± 53.32°; 37.96 ± 28.33° vs. 25.82 ± 27.82°; 47.71 ± 23.56° vs. 30.87 ± 27.76°, all p \u3c 0.01, respectively). Similar results were found in the active ROM of the shoulder. The female low muscle mass group exhibited significantly poorer passive and active ROM (p \u3c 0.05). The post-operative ASES scores and CMS scores of the female low muscle mass group were also statistically worse than those of the female normal group (p \u3c 0.05). CONCLUSIONS: The results of present study revealed that the low skeletal muscle mass is associated with inferior ROM of the shoulder and per- and post-operative shoulder function, especially for elderly female patients

U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds

In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target. Considering existing methods typically fail to handle noisy partial observations, U-RED is designed to address this issue from two aspects. First, since one partial shape may correspond to multiple potential full shapes, the retrieval method must allow such an ambiguous one-to-many relationship. Thereby U-RED learns to project all possible full shapes of a partial target onto the surface of a unit sphere. Then during inference, each sampling on the sphere will yield a feasible retrieval. Second, since real-world partial observations usually contain noticeable noise, a reliable learned metric that measures the similarity between shapes is necessary for stable retrieval. In U-RED, we design a novel point-wise residual-guided metric that allows noise-robust comparison. Extensive experiments on the synthetic datasets PartNet, ComplementMe and the real-world dataset Scan2CAD demonstrate that U-RED surpasses existing state-of-the-art approaches by 47.3%, 16.7% and 31.6% respectively under Chamfer Distance.Comment: ICCV202