We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Relatively, recent work by Jeganathan (2008, Cowles Foundation Discussion Paper 1649) and Wang (2014, Econometric Theory, 30(3), 509–535) on generalized martingale central limit theorems (MCLTs) implicitly introduces a new class of instrument arrays that yield (mixed) Gaussian limit theory irrespective of the persistence level in the data. Motivated by these developments, we propose a new semiparametric method for estimation and inference in nonlinear predictive regressions with persistent predictors. The proposed method that we term chronologically trimmed least squares (CTLS) is comparable to the IVX method of Phillips and Magdalinos (2009, Econometric inference in the vicinity of unity. Mimeo, Singapore Management University) and yields conventional inference in regressions where the nature and extent of persistence in the data are uncertain. In terms of model generality, our contribution to the existing literature is twofold. First, our covariate model space allows for both nearly integrated (NI) and fractional processes (stationary and nonstationary) as a special case, while the vast majority of articles in this area only consider NI arrays. Second, we allow for nonlinear regression functions. The CTLS estimator is obtained by applying certain chronological trimming to the OLS instruments using appropriate kernel functions of time trend variables. In particular, the instruments under consideration are a generalized (averaged) version of those widely used for time-varying parameter (TVP) models. For the purposes of our analysis, we develop a novel asymptotic theory for sample averages of various processes weighted by such kernel functionals which is of independent interest and highly relevant to the TVP literature. Leveraging our nonlinear framework, we also provide an investigation on the effects of misbalancing on the predictability hypothesis. A new methodology is proposed to mitigate misbalancing effects. These methods are used for exploring the predictability of SP500 returns.
The exact mechanisms underlying dysfunction of the basal ganglia that lead to Parkinson’s disease (PD) remain unclear. According to the standard model of PD, motor symptoms result from abnormal neuronal activity in dysfunctional basal ganglia, which can be recorded in human basal ganglia structures as functional neurosurgery for PD provides a unique opportunity to record from these regions. Microelectrode and local field potential recordings studies show alterations exist in basal ganglia nuclei as well as in the motor thalamus. Lesioning or stimulation of the basal ganglia results in significant improvement of PD symptoms, supporting the view that basal ganglia–thalamocortical circuits abnormality is important in parkinsonism generation. Different patterns of oscillatory neuronal activity plus changes in firing rate are associated with different parkinsonian motor subtypes. We present recordings of basal ganglia activity obtained with microelectrode recordings in parkinsonian patients, providing pathophysiology insight.
Let X be a smooth projective variety of dimension $n\geq 2$ and $G\cong \mathbf {Z}^{n-1}$ a free abelian group of automorphisms of X over $\overline {\mathbf {Q}}$. Suppose that G is of positive entropy. We construct a canonical height function $\widehat {h}_G$ associated with G, corresponding to a nef and big $\mathbf {R}$-divisor, satisfying the Northcott property. By characterizing the zero locus of $\widehat {h}_G$, we prove the Kawaguchi–Silverman conjecture for each element of G. As for other applications, we determine the height counting function for non-periodic points and show that X satisfies potential density.
In this paper, on–off switching digitization of a W-band variable gain power amplifier (VGPA) with above 60 dB dynamic range is introduced for large-scale phased array. Digitization techniques of on–off switching modified stacking transistors with partition are proposed to optimize configuration of control sub-cells. By the proposed techniques, gain control of a radio frequency variable gain amplifier (VGA) could be highly customized for both coarse and fine switching requirements instead of using additional digital-to-analog converters to tune the overall amplifier bias. The designed VGA in 130 nm SiGe has achieved switchable gain range from −46.4 to 20.6 dB and power range from −25.0 to 15.7 dBm at W band. The chip size of the fabricated VGPA is about 0.31 mm × 0.1 mm.
Human activity recognition (HAR) is a vital component of human–robot collaboration. Recognizing the operational elements involved in an operator’s task is essential for realizing this vision, and HAR plays a key role in achieving this. However, recognizing human activity in an industrial setting differs from recognizing daily living activities. An operator’s activity must be divided into fine elements to ensure efficient task completion. Despite this, there is relatively little related research in the literature. This study aims to develop machine learning models to classify the sequential movement elements of a task. To illustrate this, three logistic operations in an integrated circuit (IC) design house were studied, with participants wearing 13 inertial measurement units manufactured by XSENS to mimic the tasks. The kinematics data were collected to develop the machine learning models. The time series data preprocessing involved applying two normalization methods and three different window lengths. Eleven features were extracted from the processed data to train the classification models. Model validation was carried out using the subject-independent method, with data from three participants excluded from the training dataset. The results indicate that the developed model can efficiently classify operational elements when the operator performs the activity accurately. However, incorrect classifications occurred when the operator missed an operation or awkwardly performed the task. RGB video clips helped identify these misclassifications, which can be used by supervisors for training purposes or by industrial engineers for work improvement.
Aiming at the problems of poor coordination effect and low positioning accuracy of unmanned aerial vehicle (UAV) formation cooperative navigation in complex environments, an adaptive time-varying factor graph framework UAV formation cooperative navigation algorithm is proposed. The proposed algorithm uses the factor graph to describe the relationship between the navigation state of the UAV fleet and its own measurement information as well as the relative navigation information, and detects the relative navigation information at each moment by the double-threshold detection method to update the factor graph model at the current moment. And the robust estimation is combined with the factor graph, and the weight function measurements are used in the construction of the factor nodes for adaptive adjustment to make the system highly robust. The simulation results show that the proposed method realises the effective fusion of airborne multi-source sensing information and relative navigation information, which effectively improves the UAV formation cooperative navigation accuracy.
Psychostimulants and nonstimulants have partially overlapping pharmacological targets on attention-deficit/hyperactivity disorder (ADHD), but whether their neuroimaging underpinnings differ is elusive. We aimed to identify overlapping and medication-specific brain functional mechanisms of psychostimulants and nonstimulants on ADHD.
Methods
After a systematic literature search and database construction, the imputed maps of separate and pooled neuropharmacological mechanisms were meta-analyzed by Seed-based d Mapping toolbox, followed by large-scale network analysis to uncover potential coactivation patterns and meta-regression analysis to examine the modulatory effects of age and sex.
Results
Twenty-eight whole-brain task-based functional MRI studies (396 cases in the medication group and 459 cases in the control group) were included. Possible normalization effects of stimulant and nonstimulant administration converged on increased activation patterns of the left supplementary motor area (Z = 1.21, p < 0.0001, central executive network). Stimulants, relative to nonstimulants, increased brain activations in the left amygdala (Z = 1.30, p = 0.0006), middle cingulate gyrus (Z = 1.22, p = 0.0008), and superior frontal gyrus (Z = 1.27, p = 0.0006), which are within the ventral attention network. Neurodevelopmental trajectories emerged in activation patterns of the right supplementary motor area and left amygdala, with the left amygdala also presenting a sex-related difference.
Conclusions
Convergence in the left supplementary motor area may delineate novel therapeutic targets for effective interventions, and distinct neural substrates could account for different therapeutic responses to stimulants and nonstimulants.
The Automatic Identification System (AIS) is extensively used in monitoring vessel traffic, and ship navigation related information can be obtained from the AIS data. However, AIS data contain extensive redundant information, which leads to the general need to compress the data when applying it in practice or conducting research. In this paper, a three-dimensional compression of ship trajectories using the Dynamic Programming algorithm has been proposed. The AIS data near the ports of Long Beach and San Francisco in the United States were used to test and compare the Dynamic Programming algorithm with the Top-down Time-ratio algorithms. The experimental results show that the proposed algorithm can better retain the position and time information at low compression ratio such as 1%, 20% and 40%. Moreover, the algorithm is applicable to ship trajectories with different motion modes such as steering, mooring and straight ahead. The results show that the proposed algorithm can reasonably solve the problem of AIS data redundancy and ensure the quality of data, which is of practical significance for water transport, transport planning and other related research.
Little is known about the preferences of US at-home gardeners for potting mix characteristics. This study uses a Best-Worst Scaling approach to evaluate consumer preferences for eleven characteristics of potting mix. The most important characteristics identified are formulated for specific plant or garden types, pre-mixed ingredients, and price. The least important are the brand, packaging, and home delivery. There is some variation in the relative importance of these potting mix characteristics depending on consumer demographics. This study guides Industry stakeholders and policymakers on product development while enhancing environmental sustainability.
In this paper, we prove that the third near-infrared (NIR-III) window high-power laser with wavelength in the range of 1600–1800 nm can be obtained by the coherent Raman fiber amplification technique through theoretical and experimental study. Detailed numerical simulation reveals that the nonlinear dynamics of the Raman fiber amplification in the polarization-maintaining double-clad erbium-ytterbium co-doped fiber is similar to that of the Mamyshev oscillator. Through the spectral filtering effect induced by finite Raman gain, we can obtain a high-quality Raman pulse. According to the theoretical results, we design a simple Raman fiber amplification laser and finally obtain a high-quality watt-level NIR-III window laser pulse in which the central wavelength is about 1650 nm and the pulse width can reach 85 fs. The experimental results correspond to the simulation results. Such nonlinear effect is universal in all kinds of fibers, and we think this technology can provide a great contribution to the development of ultrafast fiber lasers.
Mastitis in dairy cows is an important factor restricting the healthy development of dairy industry. Natural extracts have become a research hotspot to alleviate and prevent diseases because of their unique properties. The purpose of this study was to investigate the effects of resveratrol (RES) on the mitochondrial biosynthesis, antioxidation, and anti-inflammatory in bovine mammary epithelial cells (BMECs) and its mechanism involved. Blood samples were collected from six healthy cows and six mastitis affected cows, respectively, and lipopolysaccharide (LPS) was used to treat BMECs to construct inflammation models, gene interference is achieved by transfection. The results showed that messenger RNA (mRNA) expression of peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α) was down-regulated and mitochondrial biogenesis-related gene expression was disrupted in the blood of mastitis cows and LPS-induced BMECs. RES is the best active substance to activate PGC-1α. The addition of RES can effectively alleviate the production of BMECs reactive oxygen species (ROS) and mitochondrial damage induced by LPS, and improve the antioxidation and anti-inflammatory ability, while the alleviation effect of RES is inhibited after interfering with protein kinase AMP-activated catalytic subunit α 1 (PRKAA1). In summary, our study emphasizes that PRKAA1 is a key gene mediating the activation of PGC-1α by RES, which regulates mitochondrial biosynthesis, inhibits ROS release, attenuates mitochondrial damage, and improves mitochondrial antioxidant capacity through the activation of PGC-1α by PRKAA1, thus attenuating the inflammatory response in BMECs.
Multinomial processing tree models assume that an observed behavior category can arise from one or more processing sequences represented as branches in a tree. These models form a subclass of parametric, multinomial models, and they provide a substantively motivated alternative to loglinear models. We consider the usual case where branch probabilities are products of nonnegative integer powers in the parameters, 0≤θs≤1, and their complements, 1 - θs. A version of the EM algorithm is constructed that has very strong properties. First, the E-step and the M-step are both analytic and computationally easy; therefore, a fast PC program can be constructed for obtaining MLEs for large numbers of parameters. Second, a closed form expression for the observed Fisher information matrix is obtained for the entire class. Third, it is proved that the algorithm necessarily converges to a local maximum, and this is a stronger result than for the exponential family as a whole. Fourth, we show how the algorithm can handle quite general hypothesis tests concerning restrictions on the model parameters. Fifth, we extend the algorithm to handle the Read and Cressie power divergence family of goodness-of-fit statistics. The paper includes an example to illustrate some of these results.
While active back-support exoskeletons can reduce mechanical loading of the spine, current designs include only one pair of actuated hip joints combined with a rigid structure between the pelvis and trunk attachments, restricting lumbar flexion and consequently intended lifting behavior. This study presents a novel active exoskeleton including actuated lumbar and hip joints as well as subject-specific exoskeleton control based on a real-time active low-back moment estimation. We evaluated the effect of exoskeleton support with different lumbar-to-hip (L/H) support ratios on spine loading, lumbar kinematics, and back muscle electromyography (EMG). Eight healthy males lifted 15 kg loads using three techniques without exoskeleton (NOEXO) and with exoskeleton: minimal impedance mode (MINIMP), L/H support ratio in line with a typical L/H net moment ratio (R0.8), lower (R0.5) and higher (R2.0) L/H support ratio than R0.8, and a mechanically fixed lumbar joint (LF; simulating hip joint-only exoskeleton designs).
EMG-driven musculoskeletal model results indicated that R0.8 and R0.5 yielded significant reductions in spinal loading (4–11%, p < .004) across techniques when compared to MINIMP, through reducing active moments (14–30%) while not affecting lumbar flexion and passive moments. R2.0 and LF significantly reduced spinal loading (8–17%, p < .001; 22–26%, p < .001, respectively), however significantly restricted lumbar flexion (3–18%, 24–27%, respectively) and the associated passive moments.
An L/H support ratio in line with a typical L/H net moment ratio reduces spinal loading, while allowing normal lifting behavior. High L/H support ratios (e.g., in hip joint-only exoskeleton designs) yield reductions in spinal loading, however, restrict lifting behavior, typically perceived as hindrance.
A scheme for generating high-flux angularly uniform proton beams with high laser-to-proton energy conversion efficiency is proposed. Three laser beams are focused on a microwire array attached to a solid-density hemispheric target. The laser-driven hot electrons from the front of the microwire hemisphere generate a hot-electron sheath in the hollow behind it, so that the protons on its back are accelerated by target normal sheath acceleration. The accelerated protons are of high flux, as well as angularly and energetically uniform. The scheme should be useful for applications involving warm dense matter, such as isochoric heating and modification of materials, as well as for proton therapy and inertial confinement fusion.
We propose a novel and unified sampling scheme, called the accelerated group sequential sampling scheme, which incorporates four different types of sampling scheme: (i) the classic Anscombe–Chow–Robbins purely sequential sampling scheme; (ii) the accelerated sequential sampling scheme; (iii) the relatively new k-at-a-time group sequential sampling scheme; and (iv) the new k-at-a-time accelerated group sequential sampling scheme. The first-order and second-order properties of this unified sequential sampling scheme are fully investigated with two illustrations on minimum risk point estimation for the mean of a normal distribution and on bounded variance point estimation for the location parameter of a negative exponential distribution. We also provide extensive Monte Carlo simulation studies and real data analyses for each illustration.
Bridge engineering design drawings basic elements contain a large amount of important information such as structural dimensions and material indexes. Basic element detection is seen as the basis for digitizing drawings. Aiming at the problem of low detection accuracy of existing drawing basic elements, an improved basic elements detection algorithm for bridge engineering design drawings based on YOLOv5 is proposed. Firstly, coordinate attention is introduced into the feature extraction network to enhance the feature extraction capability of the algorithm and alleviate the problem of difficult recognition of texture features inside grayscale images. Then, targeting objectives across different scales, the standard 3 × 3 convolution in the feature pyramid network is replaced with switchable atrous convolution, and the atrous rate is adaptively selected for convolution computation to expand the sensory field. Finally, experiments are conducted on the bridge engineering design drawings basic elements detection dataset, and the experimental results show that when the Intersection over Union is 0.5, the proposed algorithm achieves a mean average precision of 93.6%, which is 3.4% higher compared to the original YOLOv5 algorithm, and it can satisfy the accuracy requirement of bridge engineering design drawings basic elements detection.