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Bragg scattering of nonlinear surface waves over a wavy bottom is studied using two-dimensional fully nonlinear numerical wave tanks (NWTs). In particular, we consider cases of high nonlinearity which lead to complex wave generation and transformations, hence possible multiple Bragg resonances. The performance of the NWTs is well verified by benchmarking experiments. Classic Bragg resonances associated with second-order triad interactions among two surface (linear incident and reflected waves) and one bottom wave components (class I), and third-order quartet interactions among three surface (linear incident and reflected waves, and second-order reflected/transmitted waves) and one bottom wave components (class III) are observed. In addition, class I Bragg resonance occurring for the second-order (rather than linear) transmitted waves, and Bragg resonance arising from quintet interactions among three surface and two bottom wave components, are newly captured. The latter is denoted class IV Bragg resonance which magnifies bottom nonlinearity. It is also found that wave reflection and transmission at class III Bragg resonance have a quadratic rather than a linear relation with the bottom slope if the bottom size increases to a certain level. The surface wave and bottom nonlinearities are found to play opposite roles in shifting the Bragg resonance conditions. Finally, the results indicate that Bragg resonances are responsible for the phenomena of beating and parasitic beating, leading to a significantly large local free surface motion in front of the depth transition.
Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
In this paper, to address the cooperative localisation of a heterogeneous UAV swarm in the GNSS-denied environment, an adaptive simulated annealing-particle swarm optimisation (SA-PSO) cooperative localisation algorithm is proposed. Firstly, the forming principle of the communication and measurement framework is investigated in light of a heterogeneous UAV swarm composition. Secondly, a reasonably cooperative localisation function is established based on the proposed forming principle, which can minimise the relative localisation error with limited available information. Then, an adaptive weight principle is incorporated into the particle swarm optimisation (PSO) for better performance. Furthermore, in order to overcome the drawbacks of PSO algorithm easily falling into the local extreme point, an adaptive SA-PSO algorithm is improved to promote the convergence speed of cooperative localisation. Finally, comparative simulations are performed among the adaptive SA-PSO, adaptive PSO, and PSO algorithms to demonstrate the feasibility and superiority of the proposed adaptive SA-PSO algorithm. Simulation results show that the proposed algorithm has better performance in convergence speed, and the cooperative localisation precision can be guaranteed.
A shock-induced separation loss reduction method, using local blade suction surface shape modification (smooth ramp structure) with constant adverse pressure gradient with the consideration of radial equilibrium effect to split a single shock foot into multiple weaker shock wave configuration, is investigated on the NASA Rotor 37 for promoting aerodynamic performance of a transonic compressor rotor. Numerical investigation on baseline blade and improved one with blade modification on suction side has been conducted employing the Reynolds-averaged Navier–Stokes method to reveal flow physics of ramp structure. The results indicate that the passage shock foot of baseline is replaced with a family of compression waves and a weaker shock foot generating moderate adverse pressure gradient on ramp profile, which is beneficial for mitigating the shock foot and shrinking flow separation region as well. In addition, the radial secondary flow of low-momentum fluids within boundary layer is decreased significantly in the region of passage shock-wave/boundary-layer interaction on blade suction side, which mitigates the mass flow and mixing intensity of tip leakage flow. With the reduction of flow separation loss induced by passage shock, the adiabatic efficiency and total pressure ratio of improved rotor are superior to baseline model. This study herein implies a potential application of ramp profile in design method of transonic and supersonic compressors.
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
Weapon target allocation (WTA) is an effective method to solve the battlefield fire optimisation problem, which plays an important role in intelligent automated decision-making. We researched the multitarget allocation problem to maximise the attack effectiveness when multiple interceptors cooperatively attack multiple ground targets. Firstly, an effective and reasonable fitness function is established, based on the situation between the interceptors and targets, by comprehensively considering the relative range, relative angle, speed, capture probability and radiation source matching performance and thoroughly evaluating them based on the advantage of the attack effectiveness. Secondly, the optimisation performance of the particle swarm optimisation (PSO) algorithm is adaptively improved. We propose an adaptive simulated annealing-particle swarm optimisation (SA-PSO) algorithm by introducing the simulated annealing algorithm into the adaptive PSO algorithm. The proposed algorithm can enhance the convergence speed and overcome the disadvantage of the PSO algorithm easily falling into a local extreme point. Finally, a simulation example is performed in a scenario where ten interceptors cooperate to attack eight ground targets; comparative experiments are conducted between the adaptive SA-PSO algorithm and PSO algorithm. The simulation results indicate that the proposed adaptive SA-PSO algorithm demonstrates great performance in convergence speed and global optimisation capabilities, and a maximised attack effectiveness can be guaranteed.
Wisdom is a personality trait comprising seven components: self-reflection, pro-social behaviors, emotional regulation, acceptance of diverse perspectives, decisiveness, social advising, and spirituality. Wisdom, a potentially modifiable trait, is strongly associated with well-being. We have published a validated 28-item San Diego Wisdom Scale, the SD-WISE-28. Brief scales are necessary for use in large population-based studies and in clinical practice. The present study aimed to create an abbreviated 7-item version of the SD-WISE.
Participants included 2093 people, aged 20-82 years, recruited and surveyed through the online crowdsourcing platform Amazon Mechanical Turk. The participants’ mean age was 46 years, with 55% women. Participants completed the SD-WISE-28 as well as validation scales for various positive and negative constructs. Psychometric analyses (factor analysis and item response theory) were used to select one item from each of the seven SD-WISE-28 subscales.
We selected a combination of items that produced acceptable unidimensional model fit and good reliability (ω = 0.74). Item statistics suggested that all seven items were strong indicators of wisdom, although the association was weakest for spirituality. Analyses indicated that the 28-item and 7-item SD-WISE are both very highly correlated (r = 0.92) and produce a nearly identical pattern of correlations with demographic and validity variables.
The SD-WISE-7, and its derived Jeste-Thomas Wisdom Index (JTWI) score, balances reliability and brevity for research applications.
Evolution of multiple herbicide–resistant Palmer amaranth warrants the development of integrated strategies for its control in the southcentral Great Plains (SGP). To develop effective control strategies, a better understanding of the emergence biology of Palmer amaranth populations from the SGP region is needed. A common garden study was conducted in a no-till (NT) fallow field at the Kansas State University Agricultural Research Center near Hays, KS, during the 2018 and 2019 growing seasons, to determine the emergence pattern and periodicity of Palmer amaranth populations collected from the SGP region. Nine Palmer amaranth populations collected from five states were included: Colorado (CO1, CO2), Oklahoma (OK), Kansas (KS1, KS2), Texas (TX), and Nebraska (NE1, NE2, NE3). During the 2018 growing season, the CO1 and KS1 populations displayed more rapid emergence rates, with greater parameter b values (−5.4, and −5.3, respectively), whereas the TX and NE3 populations had the highest emergence rates (b = −12.2) in the 2019 growing season. The cumulative growing degree days (cGDD) required to achieve 10%, 50%, and 90% cumulative emergence ranged from 125 to 144, 190 to 254, and 285 to 445 in 2018; and 54 to 74, 88 to 160, and 105 to 420 in the 2019 growing season across all tested populations, respectively. The OK population exhibited the longest emergence duration (301 and 359 cGDD) in both growing seasons. All tested Palmer amaranth populations had a peak emergence period between May 11 and June 8 in 2018, and April 30 and June 1 in the 2019 growing season. Altogether, these results indicate the existence of differential emergence pattern and peak emergence periods of geographically distant Palmer amaranth populations from the SGP region. This information will help in developing prediction models for decision-making tools to manage Palmer amaranth in the region.
This paper first uses a low-speed stereoscopic particle image velocimetry (SPIV) system to measure the convergent statistical quantities of the flow field and then simultaneously measure the time-resolved flow field and the wall mass transfer rate by a high-speed SPIV system and an electrochemical system, respectively. We measure the flow field and wall mass transfer rate under upstream pipe Reynolds numbers between 25 000 and 55 000 at three specific locations behind the orifice plate. Moreover, we apply proper orthogonal decomposition (POD), stochastic estimation and spectral analysis to study the properties of the flow field and the wall mass transfer rate. More importantly, we investigate the large-scale coherent structures’ effects on the wall mass transfer rate. The collapse of the wall mass transfer rates’ spectra by the corresponding time scales at the three specific positions of orifice flow suggest that the physics of low-frequency wall mass transfer rates are probably the same, although the flow fields away from the wall are quite different. Furthermore, the spectra of the velocity reconstructed by the most energetic eigenmodes agree well with the wall mass transfer rate in the low-frequency region, suggesting that the first several energetic eigenmodes capture the flow dynamics relevant to the low-frequency variation of the wall mass transfer. Stochastic estimation results of the velocity field associated with large wall mass transfer rate at all three specific locations further reveal that the most energetic coherent structures are correlated with the wall mass transfer rate.
We report on a theoretical and experimental study on the anisotropic diffusion of isolated prolate spheroidal particles in the presence of an aligning potential field. By analysing the microscopic stochastic equations of motion, we obtained the coarse-grained Fokker–Planck equations that govern the evolution of the probability distributions of particle orientation in various configurational spaces. In particular, we found explicit formulae for the diffusion coefficients parallel ($D_x$) and perpendicular ($D_y$) to the field direction in the long-time limit. The predicted results were experimentally validated by measuring the Brownian motions of fluid-suspended carbon nanotubes in an electric field. Good agreement was observed between theoretical and experimental results, both of which showed increasing $D_x$ and decreasing $D_y$ with increasing field strength up to a critical field strength beyond which both curves start to flatten. Our theory and experimental results provide a framework for understanding the coupling between rotation and translation in a diffusion process, and for controlling the diffusion of particles with aligning potential fields.
In this case–case control study, we identified receipt of β-lactam antibiotics and older age as independently associated with increased infection risk with ESBL-producing Escherichia coli among residents aged 20–88 years in a rural Maine hospital system where the infection prevalence of antibiotic-resistant E. coli is low.
Due to lack of data on the epidemiology, cardiac, and neurological complications among Ontario visible minorities (Chinese and South Asians) affected by coronavirus disease (COVID-19), this population-based retrospective study was undertaken to study them systematically.
From January 1, 2020 to September 30, 2020 using the last name algorithm to identify Ontario Chinese and South Asians who were tested positive by PCR for COVID-19, their demographics, cardiac, and neurological complications including hospitalization and emergency visit rates were analyzed compared to the general population.
Chinese (N = 1,186) with COVID-19 were found to be older (mean age 50.7 years) compared to the general population (N = 42,547) (mean age 47.6 years) (p < 0.001), while South Asians (N = 3,459) were younger (age of 42.1 years) (p < 0.001). The 30-day crude rate for cardiac complications among Chinese was 169/10,000 (p = 0.069), while for South Asians, it was 64/10,000 (p = 0.008) and, for the general population, it was 112/10,000. For neurological complications, the 30-day crude rate for Chinese was 160/10,000 (p < 0.001); South Asians was 40/10,000 (p = 0.526), and general population was 48/10,000. The 30-day all-cause mortality rate was significantly higher for Chinese at 8.1% vs 5.0% for the general population (p < 0.001), while it was lower in South Asians at 2.1% (p < 0.001).
Chinese and South Asians in Ontario affected by COVID-19 during the first wave of the pandemic were found to have a significant difference in their demographics, cardiac, and neurological outcomes.