Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.e., latent variables) that change together over time. However, state-space modeling has typically relied on continuous measurements, whereas psychological data often come in the form of ordinal measurements such as Likert scale items. In this manuscript, we develop a general estimation approach for state-space models with ordinal measurements, specifically focusing on a graded response model for Likert scale items. We evaluate the performance of our model and estimator against that of the commonly used “linear approximation” model, which treats ordinal measurements as though they are continuous. We find that our model resulted in unbiased estimates of the state dynamics, while the linear approximation resulted in strongly biased estimates of the state dynamics. Finally, we develop an approximate standard error, termed slice standard errors and show that these approximate standard errors are more liberal than true standard errors (i.e., smaller) at a consistent bias.

This paper proposes a cooperative midcourse guidance law with target changing and topology switching for multiple interceptors intercepting targets in the case of target loss and communication topology switching. Firstly, a three-dimensional guidance model is established and a cooperative trajectory shaping guidance law is given. Secondly, the average position consistency protocol of virtual interception points is designed for communication topology switching, and the convergence of the average position of virtual interception points under communication topology switching is proved by Lyapunov stability theory. Then, in the case of the target changing, the target handover law and the handover phase guidance law are designed to ensure the acceleration smoothing, at last, the whole cooperative midcourse guidance law is given based on the combination of the above guidance laws. Finally, numerical simulation results show the effectiveness and the superiority of the proposed cooperative midcourse guidance law.

Young stellar objects (YSOs) are protostars that exhibit bipolar outflows fed by accretion disks. Theories of the transition between disk and outflow often involve a complex magnetic field structure thought to be created by the disk coiling field lines at the jet base; however, due to limited resolution, these theories cannot be confirmed with observation and thus may benefit from laboratory astrophysics studies. We create a dynamically similar laboratory system by driving a $\sim$1 MA current pulse with a 200 ns rise through a $\approx$2 mm-tall Al cylindrical wire array mounted to a three-dimensional (3-D)-printed, stainless steel scaffolding. This system creates a plasma that converges on the centre axis and ejects cm-scale bipolar outflows. Depending on the chosen 3-D-printed load path, the system may be designed to push the ablated plasma flow radially inwards or off-axis to make rotation. In this paper, we present results from the simplest iteration of the load which generates radially converging streams that launch non-rotating jets. The temperature, velocity and density of the radial inflows and axial outflows are characterized using interferometry, gated optical and ultraviolet imaging, and Thomson scattering diagnostics. We show that experimental measurements of the Reynolds number and sonic Mach number in three different stages of the experiment scale favourably to the observed properties of YSO jets with $Re\sim 10^5\unicode{x2013}10^9$ and $M\sim 1\unicode{x2013}10$, while our magnetic Reynolds number of $Re_M\sim 1\unicode{x2013}15$ indicates that the magnetic field diffuses out of our plasma over multiple hydrodynamical time scales. We compare our results with 3-D numerical simulations in the PERSEUS extended magnetohydrodynamics code.

Exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), an environmental endocrine disruptor and model AhR agonist, is linked to skeletal abnormalities, cardiac edema, stunted growth rate, altered metabolism, and neurobehavioral deficits. We have previously reported transgenerational reproductive outcomes of developmental TCDD exposure in adult zebrafish (Danio rerio), an NIH-validated model for developmental and generational toxicology. Using the same paradigm of sublethal TCDD exposure (50 pg/ml) at both 3 and 7 weeks post fertilization (wpf), we investigated several novel endpoints, including longitudinal morphometrics and anxiety-linked behavior, in fish exposed as juveniles. We also assessed developmental abnormalities and neurobehavior in their F1 larval offspring. TCDD exposure induced timepoint-dependent decreases in several craniofacial and trunk morphometrics across juvenile development. In early adulthood, however, only exposed males underwent a transient period of compensatory growth, ending between 7 and 12 months post fertilization (mpf). At 12 mpf, exposed adult fish of both sexes displayed increased exploratory behaviors in a novel tank test. The F1 offspring of parents exposed at both 3 and 7 wpf were hyperactive, but neurobehavioral outcomes diverged depending on parental exposure window. F1 exposure-lineage larvae had increased rates of edema and skeletal abnormalities, but fewer unhatched larvae compared to controls. Parent- and timepoint-specific effects of exposure on abnormality rate were also evaluated; these outcomes were considerably less severe. Our novel behavioral findings expand current knowledge of the long-term and intergenerational consequences of early-life TCDD exposure in a zebrafish model, in addition to delineating minor longitudinal morphometric changes in exposed fish and abnormalities in larval offspring.

Shark vertebrae and their centra (vertebral bodies) are high-performance structures able to survive millions of cycles of high amplitude strain despite lacking a repair mechanism for accumulating damage. Shark centra consist of mineralized cartilage, a biocomposite of bioapatite (bAp), and collagen, and the nanocrystalline bAp's contribution to functionality remains largely uninvestigated. Using the multiple detector energy-dispersive diffraction (EDD) system at 6-BM-B, the Advanced Photon Source, and 3D tomographic sampling, the 3D functionality of entire centra were probed. Immersion in ethanol vs phosphate-buffered saline produces only small changes in bAp d-spacing within a great hammerhead centrum. EDD mapping under in situ loading was performed an entire blue shark centrum, and 3D maps of bAp strain showed the two structural zones of the centrum, the corpus calcareum and intermedialia, contained opposite-signed strains approaching 0.5%, and application of ~8% nominal strain did not alter these strain magnitudes and their spatial distribution.

Empirical reaction progress diagrams showing the trends of major element oxide concentrations (in g/cm3) as functions of bulk density for a diabase saprolite reveal discontinuities in the trends of Al2O3, MgO, H2O+, and nonextractable Fe2O3. The discontinuities coincide with discontinuities in the trends of (1) the kaolinite-smectite 001 peak-intensity ratio, (2) the smectite 002-001 peak intensity ratio, and (3) the smectite basal spacing as functions of bulk density. The discontinuities are apparently related to redox conditions in the weathering profile because they occur at a depth where siderite veins first appear in the saprolite. Oxidizing conditions in the upper part of the profile appear to have favored the formation of Ferich smectite over kaolinite, whereas reducing conditions deeper in the profile favored the formation of kaolinite over Al-rich smectite. These results indicate that where geochemical conditions favor retention of Fe over Al, smectites can form in preference to kaolinite or gibbsite even under conditions of strong leaching.

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor information, taking deep reinforcement learning (DRL) as the starting point. The DRL performance is significantly improved by lifting the sensor signals to dynamic features (DFs), which predict future flow states. The resulting DF-based DRL (DF-DRL) automatically learns a feedback control in the plant without a dynamic model. Results show that the drag coefficient of the DF-DRL model is 25 % less than the vanilla model based on direct sensor feedback. More importantly, using only one surface pressure sensor, DF-DRL can reduce the drag coefficient to a state-of-the-art performance of approximately 8 % at Reynolds number $(Re) = 100$ and significantly mitigates lift coefficient fluctuations. Hence, DF-DRL allows the deployment of sparse sensing of the flow without degrading the control performance. This method also exhibits strong robustness in flow control under more complex flow scenarios, reducing the drag coefficient by 32.2 % and 46.55 % at $Re =500$ and 1000, respectively. Additionally, the drag coefficient decreases by 28.6 % in a three-dimensional turbulent flow at $Re =10\,000$. Since surface pressure information is more straightforward to measure in realistic scenarios than flow velocity information, this study provides a valuable reference for experimentally designing the active flow control of a circular cylinder based on wall pressure signals, which is an essential step toward further developing intelligent control in a realistic multi-input multi-output system.

Background: Neurological complications following vaccinations have been described before, but the rates of neurological complications, and their variation by ethnicity, following COVID-19 vaccine are not well-known. Methods: We conducted a population-based cohort study of Ontarians aged 18 years and over who received their first COVID-19 vaccine, and followed them for six weeks to estimate the incidence of neurological events, ascertained using validated case definitions based on ICD-10 codes. Ethnicity was defined using last name surname algorithm. We used multivariable logistic regression models, adjusting for age, sex, and vaccine-type to evaluate ethnic differences. Results: In the included 10,063,466 Ontario residents, incidence of GBS (n=72), CVST (n=52) and transverse myelitis (n=25) after first COVID-19 vaccine was rare. The crude rate of ischemic stroke (240/1,000,000 people) was the highest followed by Bell’s palsy (54/1,000,000). Compared to the general population, the adjusted odds of ischemic stroke and Bell’s palsy were lower in Chinese (aORBell’s 0.62; 0.39-0.98 and a ORischemic stroke 0.74; 0.59-0.91) and South Asians (aORBell’s 0.83; 0.52-1.31 and aORischemic stroke 0.84; 0.65-1.08). Conclusions: The incidence of neurological events following COVID-19 vaccine is low, and it varies by ethnicity. Our findings should encourage vaccination against COVID-19 in all ethnic groups.

Background: Electrophysiological tests such as the tapping test are used to distinguish functional and organic tremors, in which patients with functional tremor commonly show entrainment and amplitude reduction (>50% decrease relative to baseline) of contralateral tremor during tapping. While these features are suggested to be specific to functional tremor, the tapping test in Parkinson’s disease (PD) tremor has not been tested. Methods: We evaluated 18 PD patients (2F, age 64.17±7.30 [mean±SD] years) with rest and postural tremors using surface electromyography and triaxial accelerometry. Patients were recorded while tapping at 1, 3 and 5 Hz with the contralateral arm at rest or outstretched. Tremor amplitude and frequency were calculated using power spectrum analysis from accelerometer recordings. Results: Reduction of rest tremor amplitude was observed in 3/18 patients during 1 and 3 Hz tapping. Reduction was seen in 3/16 and 1/16 patients with postural tremors at 1 and 3 Hz tapping, respectively. Frequency shifts (>1.5 Hz) were observed in 3/18 rest tremors and 6/16 postural tremors. Seven patients exhibited rest and/or postural tremor entrainment during 3 or 5 Hz tapping. Conclusions: Distractibility and entrainment can be found in PD tremor. The tapping test may not reliably distinguish between PD tremor and functional tremor.

It is estimated that one-quarter of the world’s population has Metabolic Syndrome (MS)(1), a key driver of growth in healthcare expenditure. Traditional approaches to treating MS through the application of standard dietary recommendations and caloric restriction have had limited success. More recent evidence suggests that novel, anti-inflammatory approaches such as replacing refined carbohydrates and ultra-processed food with unprocessed or minimally processed, lower carbohydrate foods and adapting meal timing and frequency may be more effective(2). The aim of the study was twofold: 1) To determine the effectiveness of anti-inflammatory dietary strategies for long-term weight loss and improvement in metabolic health and 2) To examine the relationships between eating behaviours and long-term weight loss. Twelve-month audit data from a UK based 12-week lifestyle program that focuses the principles of consuming an anti-inflammatory diet was analysed using repeated-measures ANOVA to examine the effects of the program on changes in weight and waist circumference. A quantitative, survey-based research design was used to retrospectively identify relationships between eating behaviours and both anti-inflammatory and pro-inflammatory dietary patterns. Multivariate regression using stepwise method was used to examine differences in weight change based on eating patterns and behaviours. Six hundred and forty-two (N = 642) participants (age = 50.4 ± 12.5 years, female 63.6%, weight = 96.1 kg ± 22.1, BMI 35.2 kg/m2 ± 7.5) demonstrated a weight loss average of 4.49 kg ± 3.78 post-lifestyle program (12 weeks). Survey respondents (N = 64) reported a maximum long term weight loss of 13.9 kg ± 11.9. Weight loss and percentage weight loss after the program was significantly predicted by daily consumption of sweet drinks and grain-based foods. The model predicted one unit increase in daily serving consumption of these foods resulted in less weight lost [2.3 kg (4.5%)]. Seventy one percent of survey respondents had maintained most or all their weight loss for more than 6 months. The model predicted change in consumption of grain-based foods, TFEQ-emotional eating score, consumption of savoury ultra-processed foods, and following an alternative dietary approach after the program were statistically significant in predicting weight loss maintenance (R2 = 0.803, F(4, 20) = 20.376, p < 0.001). The preliminary findings suggest that anti-inflammatory dietary approaches are effective and sustainable for weight loss. Eating behaviour may both support and hinder long term changes in eating patterns and whilst there are significant relationships between eating behaviour and eating patterns, the extent to which dietary patterns drive eating behaviour remains unclear.

Elicited imitation (EI) tasks are a practical tool for measuring second language (L2) knowledge and skills. In this study, we implemented a web-based EI task that measures English morphosyntactic knowledge and compared its measurement properties to a traditional laboratory-based EI. A cohort of 149 L2 English learners engaged in the web-based EI task, and 151 participants completed a traditional lab-based EI counterpart. Correlation analyses revealed a significant, comparable relationship between English proficiency and the two EI versions, with the ungrammatical items showing less consistency that neither improved nor harmed the overall EI effectiveness. Factor analyses corroborated the validity of web-programmed EI, with both EI versions relating similarly to time-pressured, implicit knowledge and untimed, explicit knowledge measures. Our results suggest the potential for utilizing web-based EI to substitute lab-based tasks, enabling larger-scale, more diverse sampling. We end with implications for future web-based EI task users and include a coding guideline for customized web-based EI use.

The traditional ground collision avoidance system (GCAS) makes avoidance decisions based on predicted collision time, without considering the impact of terrain environment and dynamic changes in load factor on avoidance decisions. This increases the risk of ground collisions for the aircraft. To solve the problem, a GCAS with multi-trajectory risk assessment and decision function is proposed. Firstly, a variety of predicted flight avoidance trajectories are established within the final manoeuvering capability of the aircraft. Secondly, for each predicted trajectory, the uncertain length between adjacent prediction points is used to construct a rectangular distance bin, and the terrain data below the avoided trajectory is extracted. Finally, the regret theory is used to establish a multi-attribute avoidance decision model to evaluate and prioritise the risk of collision avoidance trajectories, to provide effective collision avoidance decision for pilots. The algorithm is tested and verified with real digital elevation model and simulated flight data, and compared with traditional GCAS. Simulation results show that the proposed algorithm can comprehensively consider manoeuvering performance and threatening terrain, and provide safe and effective avoidance decisions for pilots.