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Statistical models used to estimate the spatiotemporal pattern in disease risk from areal unit data represent the risk surface for each time period with known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterized by a spatially smooth evolution between some pairs of adjacent areal units whereas other pairs exhibit large step changes. This spatial heterogeneity is not consistent with existing global smoothing models, in which partial correlation exists between all pairs of adjacent spatial random effects. Therefore we propose a novel space-time disease model with an adaptive spatial smoothing specification that can identify step changes. The model is motivated by a new study of respiratory and circulatory disease risk across the set of local authorities in England and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk and exhibit some common step changes in the unmeasured component of risk between neighbouring local authorities.

The extraction and timely updating of land use /cover information is a key issue in remote sensing change detection. The change vector analysis (CVA) is a better method of change detection. However, the CVA method is the blindness of artificial choice of threshold. Moreover, the direction cosine of CVA cannot represent the unique point in change vector space and it can’t distinguish the change category effectively. In order to avoid this defect, the midline vector is added to CVA method. In this paper, we use the midline change vector analysis (MCVA) method to detect the land use /cover change in multi temporal remote sensing images. We proposed the two-step threshold method to get the optimal threshold and determine the change and the unchanged region of the difference remote sensing image. We chose Hefei city of Anhui Province as the study area, and adopted two Landsat5 TM images in 2000 and 2008 year as experiment data. We use the MCVA and two-step threshold method to achieve remote sensing change detection. In order to compare the detection accuracy between MCVA method and the traditional post classification comparison method, the paper choose the same area (178 pixels × 180 pixels) in the two images to analyse the accuracy, and compare the accuracy of MCVA method with that of the traditional post classification comparison method based on SVM. The experiment results show that the MCVA method has higher overall accuracy, lower allocation disagreement and quantity disagreement. What’s more, the overall accuracy of MCVA method can reach nearly 60%, much higher than the traditional post classification comparison method (less than 40%). And the MCVA method can effectively avoid the problem of change vector direction cosine values are not unique, and the result is much better than the traditional post classification (SVM) comparison method. It indicates that MCVA is a more effective method in land use / cover change detection for middle resolution multispectral images.

Understanding the mechanisms of instability can aid in reducing fall risk. As a sensitive measure of fall risk, the distance between the center of pressure (COP) and center of mass (COM) is currently assessed through discrete points assumed to represent physiological important fall mechanisms. However, it is unclear if these discrete points are appropriate measures of fall risk. Statistical parametric mapping (SPM) is a waveform analysis technique that removes this possibly biased a priori approach. Sixteen healthy young adults (8 males, 8 females; Age: 29 ± 3.6 years, Height: 1.7 ± 0.9 m, Mass: 75 ± 16 kg) performed two tasks that disturbed dynamic stability: voluntary stepping at different step lengths, and forward perturbations at different accelerations. COP-COM distance magnitudes were extracted during the first step in both tasks at discrete points typically assessed in previous research. Discrete point analysis (DPA) was performed on these discrete points and SPM analysis was completed on the COP-COM distance waveform. The results from the study found that SPM analysis identified equivalent significant differences to DPA and identified additional significant differences elsewhere in the COP-COM distance waveform that were not able to be detected by DPA. Two key advantages from using SPM: (1) reduction of possibly biased a priori selection, and (2) increased efficiency and reduced time-cost in data post-processing as inherent variability can limit the detection of discrete points resulting in identifying physiologically different discrete points across trials. This study suggests the use of SPM as a sensitive data analysis approach in detecting fall risk as an alternative to DPA.

We study the asymptotics for jump-penalized least squares regression aiming at approximating a regression function by piecewise constant functions. Besides conventional consistency and convergence rates of the estimates in L²([0, 1)) our results cover other metrics like Skorokhod metric on the space of càdlàg functions and uniform metrics on C([0, 1]). We will show that these estimators are in an adaptive sense rate optimal over certain classes of \"approximation spaces.\" Special cases are the class of functions of bounded variation (piecewise) Hölder continuous functions of order 0 < α ≤ 1 and the class of step functions with a finite but arbitrary number of jumps. In the latter setting, we will also deduce the rates known from change-point analysis for detecting the jumps. Finally, the issue of fully automatic selection of the smoothing parameter is addressed.

Our study is based on 68 continuous GNSS observation data time series in Sichuan-Yunnan covering 4 years. We detected and deleted the outliers according to the IQR law of skewness, and the world’s first Heaviside step function model of crustal strain sequence was built. In the meantime, the sequence of crustal strain was calculated, and a correlation analysis of the strain sequence of micro dynamic information was made. It was found that strain sequence is consistent with the linear trend of the tectonic movement, yet its steady state is damaged after geophysical events, e.g., earthquakes. Finally, a method of linear fitting way for a precise strain rate field based on strain time series was proposed, and the correctness of this method was verified from the algorithm and the experiment. Compared with other current commonly used methods, it was found that detecting changes of the strain slope is more sensitive than the method of only using the GNSS time series to detect changes in the slope. It was shown that the method is more accurate than the other current methods for micro dynamic strain rate field.

This study introduces a novel high-accuracy fruit fly detection model based on the Transformer structure, specifically aimed at addressing the unique challenges in fruit fly detection such as identification of small targets and accurate localization against complex backgrounds. By integrating a step attention mechanism and a cross-loss function, this model significantly enhances the recognition and localization of fruit flies within complex backgrounds, particularly improving the model’s effectiveness in handling small-sized targets and its adaptability under varying environmental conditions. Experimental results demonstrate that the model achieves a precision of 0.96, a recall rate of 0.95, an accuracy of 0.95, and an F1-score of 0.95 on the fruit fly detection task, significantly outperforming leading object detection models such as YOLOv8 and DETR. Specifically, this research delves into and optimizes for challenges faced in fruit fly detection, such as recognition issues under significant light variation, small target size, and complex backgrounds. Through ablation experiments comparing different data augmentation techniques and model configurations, the critical contributions of the step attention mechanism and cross-loss function to enhancing model performance under these complex conditions are further validated. These achievements not only highlight the innovativeness and effectiveness of the proposed method, but also provide robust technical support for solving practical fruit fly detection problems in real-world applications, paving new paths for future research in object detection technology.

Older adults with a history of falling often encounter challenges in participating in group exercise programs. Recent technological advances, such as activity trackers, can potentially enhance home-based exercise programs by providing continuous physical activity monitoring and feedback. The aim of the study is to explore whether integrating wearable activity trackers with a home-based exercise intervention is effective in reducing fear of falling and improving physical function in older adults. This was a 12-week, parallel-group, randomized controlled trial involving 30 older adults (≥60 years) with a history of falling. Participants were randomly assigned in a 1:1 ratio to either a group combining an activity tracker with a home-based multicomponent exercise intervention, which included in-person exercise sessions, exercise videos, and objective feedback via phone calls (AT+EX group) or to a group using the activity tracker only for self-monitoring (AT-only group). The primary and secondary outcomes included fall-related parameters (fear of falling assessed by the Activities-Specific Balance Confidence [ABC] and the Falls Efficacy Scale-International [FES-I] scales), depression (Short Geriatric Depression Scale), cognition (Montreal Cognitive Assessment), physical function (grip strength, Short Physical Performance Battery, Timed Up and Go [TUG] test, and 2-Minute Step Test), and body composition. Changes in the average daily step count were monitored and analyzed. Overall, 28 (mean age 74.0, SD 6.4 years; n=23, 77% female) participants completed the 12-week follow-up period (28/30, 93%). In the activity tracker and exercise group (AT+EX group), significant improvements were observed in fear of falling (15.5 points of ABC: P=.002; -5.1 points of FES-I: P=.01). The activity tracker alone group (AT-only group) also showed a significant improvement in FES-I score (-5.5 points: P=.01). Physical function significantly improved in the AT+EX group (1.1 points of Short Physical Performance Battery: P=.004; -1.4 seconds of TUG; P=.008; and 26.7 steps of 2-Minute Step Test: P=.001), whereas the AT-only group showed significant improvement only in the TUG test (-1.3 seconds: P=.002). However, no significant between-group differences were observed in the ABC score, FES-I score, or physical function. Despite no significant increase in daily step counts, both groups maintained close to 10,000 steps per day throughout the 12 weeks. Both groups showed improvements in the FES-I and TUG test scores without significant between-group differences. Wearable technology, with or without an exercise intervention, seems to be an effective tool in reducing the fear of falling and improving physical function in older adults susceptible to falls.

We propose a method of detecting the period in which a burst of information diffusion took place from an observed diffusion sequence data over a social network and report the results obtained by applying it to the real Twitter data. We assume a generic information diffusion model in which time delay associated with the diffusion follows the exponential distribution and the burst is directly reflected to the changes in the time delay parameter of the distribution. The shape of the parameter’s change is approximated by a step function and the problem of detecting the change points and finding the values of the parameter is formulated as an optimization problem of maximizing the likelihood of generating the observed diffusion sequence. Time complexity of the search is almost proportional to the number of observed data points and has been shown to be very efficient. We first demonstrated that the proposed method can detect the burst using a synthetic data and showed that it performs better than one of the representative state-of-the-art methods, confirming that the proposed method covers a wider range of change patterns. Then, we extended our evaluation on synthetic data to show that it is efficient and effective comparing it with a naive exhaustive search and a simple greedy method. We then apply the method to the real Twitter data of the 2011 To-hoku earthquake and tsunami, and reconfirmed its efficiency and effectiveness. Two interesting discoveries are that a burst period detected by the proposed method tends to contain massive homogeneous tweets on a specific topic even if the observed diffusion sequence consists of heterogeneous tweets on various topics, and that assuming the information diffusion path to be a line shape tree can give a good approximation of the maximum likelihood estimator when the actual diffusion path is not known.

Accelerometers have the potential to provide objective, non-invasive methods for detecting changes in animal behavior and health. Our objectives were to: (1) determine the effects of micro-acceleration data loggers (accelerometers) and habituation to accelerometers on turkey gait and health status, (2) determine age-related changes in gait and health status, and (3) assess the validity and reliability of the accelerometers. Thirty-six male commercial turkeys were randomly assigned to one of five groups: accelerometer and habituation period (AH), accelerometer and no habituation period (AN), VetRap bandage (no accelerometer) and habituation period (VH), bandage (no accelerometer) and no habituation period (VN), and nothing on either leg (C). Health status and body condition were assessed prior to video-recording birds as they walked across a Tekscan® pressure pad at 8, 12, and 16 weeks to determine effects of treatment on number of steps, cadence, gait time, gait distance, gait velocity, impulse, gait cycle time, maximum force, peak vertical pressure, single support time, contact time, step length, step time, step velocity, stride length, total double support time, and duty factor. Accelerometer validity and reliability were determined by comparing the number of steps detected by the accelerometer to the number of steps determined from video recordings. Several age-related changes in turkey gait were found regardless of habituation including a slower cadence at 16 weeks, shorter gait distance at 8 weeks, and slower gait velocity at 16 weeks. When comparing bandaged vs. unbandaged limbs, both treatment and age-treatment interactions were found depending on the gait parameter. Accelerometer validity and reliability were affected by both age and treatment. False discovery rate increased, while accuracy and specificity decreased with age. Validity and reliability were lowest for non-habituated birds (AN and VN). Results demonstrated that micro-data loggers do not adversely affect turkey health status, but habituation to wearing accelerometers greatly affects accelerometer reliability and validity. Accelerometer validity and turkey gait are also greatly affected by the age of the turkeys.

A new algorithm of automatic extraction of multi-size lunar craters has been proposed. The proposed algorithm could detect multi-size craters by using different filters in multi-steps, which is a loop to detect multi-size craters by different size filters. In the first step, the new algorithm will use a larger filter to reduce noise and detect larger craters by using circle fitting at de-noised image. After marked these areas in the original image, it will remove those detected crater areas from the original image and do the noise reduction with a smaller filter again. The new algorithm will repeat the second step several times to finish the detection of whole image. Finally the new algorithm will merge results of these steps and output a final result. The new algorithm has been tested based on the Chang’E Data in the Matlab environment. The result has shown that this new algorithm does have the ability to detect different sized craters on the lunar surface, which has pointed a way to detect secondary craters automatically.