Hybrid and Oriented Harmonic Potentials for Safe Task Execution in Unknown Environment

Harmonic potentials provide globally convergent potential fields that are provably free of local minima. Due to its analytical format, it is particularly suitable for generating safe and reliable robot navigation policies. However, for complex environments that consist of a large number of overlapping non-sphere obstacles, the computation of associated transformation functions can be tedious. This becomes more apparent when: (i) the workspace is initially unknown and the underlying potential fields are updated constantly as the robot explores it; (ii) the high-level mission consists of sequential navigation tasks among numerous regions, requiring the robot to switch between different potentials. Thus, this work proposes an efficient and automated scheme to construct harmonic potentials incrementally online as guided by the task automaton. A novel two-layer harmonic tree (HT) structure is introduced that facilitates the hybrid combination of oriented search algorithms for task planning and harmonic-based navigation controllers for non-holonomic robots. Both layers are adapted efficiently and jointly during online execution to reflect the actual feasibility and cost of navigation within the updated workspace. Global safety and convergence are ensured both for the high-level task plan and the low-level robot trajectory. Known issues such as oscillation or long-detours for purely potential-based methods and sharp-turns or high computation complexity for purely search-based methods are prevented. Extensive numerical simulation and hardware experiments are conducted against several strong baselines.Comment: 16 pages, 13 figure

Tensile strength of sea ice using splitting tests based on the digital image correlation method

The splitting test is a competitive alternative method to study the tensile strength of sea ice owing to its suitability for sampling. However, the approach was questioned to the neglect of local plastic deformation during the tests. In this study, splitting tests were performed on sea ice, with 32 samples subjected to the regular procedure and 8 samples subjected to the digital image correlation method. The salinity, density, and temperature were measured to determine the total porosity. With the advantage of the digital image correlation method, the full-field deformation of the ice samples could be determined. In the loading direction, the samples mainly deformed at the ice–platen contact area. In the direction vertical to the loading, deformation appears along the central line where the splitting crack occurs. Based on the distribution of the sample deformation, a modified solution was derived to calculate the tensile strength with the maximum load. Based on the modified solution, the tensile strength was further calculated together with the splitting test results. The results show that the tensile strength has a negative correlation with the total porosity, which agrees with previous studies based on uniaxial tension tests

Association of the gut microbiome with fecal short-chain fatty acids, lipopolysaccharides, and obesity in young Chinese college students

IntroductionObesity is a growing health problem among young people worldwide and is associated with gut conditions. This study aimed to explore the relationship between obesity, intestinal microbiota, fecal short-chain fatty acids (SCFAs), and lipopolysaccharide (LPS) in young college students.Methods16S rRNA gene sequences, SCFA and LPS contents, and obesity status were analyzed in 68 young college students (20-25 years old).ResultsThere were significant differences in intestinal microbial beta diversity among students with different body mass index (BMI). The abundance and proportion of Firmicutes and Bacteroides had no significant correlation with BMI. The contents of butyric acid and valeric acid in the feces of obese students were low, and the content of SCFAs had no significant correlation with BMI and LPS. The content of LPS in the feces of obese people was significantly higher than that in healthy people, and there was a significant positive correlation between LPS content and BMI.ConclusionIn general, there was a correlation between intestinal microbiota, SCFA, LPS, and BMI in young college students. Our results may enrich the understanding of the relationship between intestinal conditions and obesity and contribute to the study of obesity in young college student

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions

BackgroundWhole-lesion histogram analysis can provide comprehensive assessment of tissues by calculating additional quantitative metrics such as skewness and kurtosis; however, few studies have evaluated its value in the differential diagnosis of lung lesions.PurposeTo compare the diagnostic performance of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) in differentiating lung cancer from focal inflammatory lesions, based on whole-lesion volume histogram analysis.MethodsFifty-nine patients with solitary pulmonary lesions underwent multiple b-values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), apparent diffusional kurtosis (Kapp) and kurtosis-corrected diffusion coefficient (Dapp) were calculated and compared between the lung cancer and inflammatory lesion groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance.ResultsThe ADCmean, ADCmedian, Dmean and Dmedian values of lung cancer were significantly lower than those of inflammatory lesions, while the ADCskewness, Kappmean, Kappmedian, KappSD, Kappkurtosis and Dappskewness values of lung cancer were significantly higher than those of inflammatory lesions (all p < 0.05). ADCskewness (p = 0.019) and Dmedian (p = 0.031) were identified as independent predictors of lung cancer. Dmedian showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a Dmedian of 1.091 × 10-3 mm2/s as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value were 69.23%, 85.00%, 90.00% and 58.62%, respectively.ConclusionsWhole-lesion histogram analysis of DWI, IVIM and DKI parameters is a promising approach for differentiating lung cancer from inflammatory lesions, and Dmedian shows the best performance in the differential diagnosis of solitary pulmonary lesions

Ribosomal protein S3 mediates drug resistance of proteasome inhibitor: potential therapeutic application in multiple myeloma

Multiple myeloma (MM) remains incurable due to drug resistance. Ribosomal protein S3 (RPS3) has been identified as a non-Rel subunit of NF-κB. However, the detailed biological roles of RPS3 remain unclear. Here, we report for the first time that RPS3 is necessary for MM survival and drug resistance. RPS3 was highly expressed in MM, and knockout of RPS3 in MM inhibited cell growth and induced cell apoptosis both in vitro and in vivo. Overexpression of RPS3 mediated the proteasome inhibitor resistance of MM and shortened the survival of MM tumor-bearing animals. Moreover, our present study found an interaction between RPS3 and the thyroid hormone receptor interactor 13 (TRIP13), an oncogene related to MM tumorigenesis and drug resistance. We demonstrated that the phosphorylation of RPS3 was mediated by TRIP13 via PKCδ, which played an important role in activating the canonical NF-κB signaling and inducing cell survival and drug resistance in MM. Notably, the inhibition of NF-κB signaling by the small-molecule inhibitor targeting TRIP13, DCZ0415, was capable of triggering synergistic cytotoxicity when combined with bortezomib in drug-resistant MM. This study identifies RPS3 as a novel biomarker and therapeutic target in MM

Incoherent Interference Detection and Mitigation for Millimeter-Wave FMCW Radars

Current automotive radar technology is almost exclusively implemented using frequency modulated continuous wave (FMCW) radar in the millimeter wave bands. Unfortunately, incoherent interference is becoming a serious problem due to the increasing number of automotive radars in dense traffic situations. To address this issue, this article presents a sparsity-based technique for mitigating the incoherent interference between FMCW radars. First, a low-pass filter-based technique is developed to detect the envelope of the interference. Next, the labeled regions where interference is present are considered as missing data. In this way, the problem of mitigating interference is further formulated as the restoration of the echo using L1 norm-regularized least squares. Finally, the alternating direction method of the multipliers-based technique is applied to restore the radar echoes. Extensive experimental results demonstrate the effective performance of the proposed approach. Compared to state-of-the-art interference mitigation methods, the proposed method remarkably improves the quality of radar targets

Machine Learning-Based Driving Style Identification of Truck Drivers in Open-Pit Mines

The significance in constructing a driving style identification model for open-pit mine truck drivers is to reduce diesel consumption and improve training. First, we developed a driving behavior and mining truck condition monitoring system for an open-pit mine. Under heavy-load and no-load conditions of a mining truck, based on the same experimental truck and haulage road, the data of driving behavior and truck status of different drivers were collected. The driving style characteristic parameters of mining trucks under heavy-load and no-load conditions were constructed through Pearson correlation analysis. Using a k-means clustering algorithm, driving style can be divided into three types: normal type, soft type, and aggressive type, and we verified the validity of this driving style classification with a box plot. On this basis, the parameters of random forest, k-nearest neighbor, support vector machine, and neural network models were optimized and the accuracy was compared through a cross-validation grid search, and then a driving style identification model based on the random forest method was finally proposed. Driving style parameter weight values were obtained based on the Gini coefficient. Last, the fuel consumption characteristics of different driving styles were calculated. The results show that the driving style identification models based on random forest can effectively identify different driving styles when the mining truck is operating under heavy load and no load, and the overall accuracy of the model is 95.39% and 90.74% respectively. The fuel consumption of the aggressive driving style was the largest and was 10% higher than the average fuel consumption. The research results provide data support and new ideas for operation training and fuel-saving driving of mining trucks in open-pit mines

High-order compact difference methods for solving two-dimensional nonlinear wave equations

Nonlinear wave equations are widely used in many areas of science and engineering. This paper proposes two high-order compact (HOC) difference schemes with convergence orders of $O\left({{\tau ^4} + h_x^4 + h_y^4} \right)$ that can be used to solve nonlinear wave equations. The first scheme is a nonlinear compact difference scheme with three temporal levels. After calculating the second-order spatial derivatives of the previous time level using the Padé scheme, numerical solutions of the next time level are obtained through repeated iterations. The second scheme is a three-level linearized compact difference scheme. Unlike the first scheme, iterations are not required and it obtains numerical solutions through an explicit calculation. The two proposed schemes are applied to solutions of the coupled sine-Gordon equations. Finally, some numerical experiments are presented to confirm the effectiveness and accuracy of the proposed schemes

Design of and Experiment on a Film Removal Device of an Arc-Toothed Residual Film Recovery Machine before Sowing

In view of the serious film wrapping phenomenon and poor film removal effect of the film removal devices of residual film recovery machines, a combined “mechanical + air flow” film removal device is designed. It is mainly composed of vane-type film removal rollers and diversion shells and can complete film removal and film transportation in turn. The analysis and parameter design of the key working parts, named film stripping blades, are carried out. The condition of film removal is calculated by force analysis, and the internal flow field of the device is simulated based on the Fluent software. Taking rotating speed of the vane-type film removal roller, the inclination angle of the film stripping blade, and the diameter of the roller as test factors, and the area ratio of the vortex region to the effective region as the evaluation index, a three-factor three-level orthogonal simulation test is designed. The response surface model of each test factor is established, and the significance of each test factor on the evaluation index is analyzed. Through optimization, the optimal parameter combination suitable for the film removal flow field is obtained as follows: the rotating speed of the vane-type film removal roller is 283 r/min, the inclination angle of the film stripping blade is 25° and the diameter of the roller is 219 mm. Under the optimal combination of parameters, the device is manufactured, and the effect of the device is verified by a field test. The results show that the film removal rate of the device is 98.04%, and there is no film wrapping phenomenon in the operation process, which can meet the needs of residual film recovery before sowing

The Impact of Perioperative Multimodal Pain Management on Postoperative Outcomes in Patients (Aged 75 and Older) Undergoing Short-Segment Lumbar Fusion Surgery

Background. Due to the presence of multimorbidity and polypharmacy, patients aged 75 and older are at a higher risk for postoperative adverse events after lumbar fusion surgery. More effective enhanced recovery pathway is needed for these patients. Pain control is a crucial part of perioperative management. The objective of this study is to determine the impact of multimodal pain management on pain control, opioid consumption, and other outcomes. Methods. This is a retrospective review of a prospective collected database. Consecutive patients who underwent elective posterior lumbar fusion surgery (PLF) from October 2017 to April 2021 in our hospital were reviewed. Perioperative multimodal pain management (PMPM) group (from January 2019 to April 2021) in which patients received multimodal analgesia was case-matched to the control group (from October 2017 to December 2018) in which patients were treated under the conventional patient-controlled analgesia (PCA) method. Postoperative visual analogue scale (VAS), opioid consumption, complications within 3 months, and other outcomes were collected and compared between groups. Results. A total of 122 consecutive patients (aged 75 and older) were included in the PMPM group and compared with previous 122 patients. The PMPM group had a lower maximal VAS score (3.0 ± 1.7 vs. 3.7 ± 2.0, p<0.001) and frequency of additional opioid consumption (6.6% vs. 19.7%, p=0.001) on POD3 than the control group. The rates of postoperative complications were lower in the PMPM group compared with the control group (25% vs. 49%, p=0.006) during a 3-month follow-up period. Conclusions. This study demonstrates that the PMPM protocol is effective in pain control and reducing additional opioid consumption when compared with conventional analgesia, even for patients aged 75 and older. Moreover, these improvements occur with a lower incidence of postoperative complications within three months after PLF surgery