Maternal OGTT Glucose Levels at 26–30 Gestational Weeks with Offspring Growth and Development in Early Infancy

Aims. We aim to evaluate the association of maternal gestational oral glucose tolerance test (OGTT) glucose concentrations with anthropometry in the offspring from birth to 12 months in Tianjin, China. Methods:. A total of 27,157 pregnant women underwent OGTT during 26–30 weeks gestation, and their children had body weight/length measured from birth to 12 months old. Results:. Maternal OGTT glucose concentrations at 26–30 gestational weeks were positively associated with Z-scores for birth length-for-gestational age and birth weight-for-length. Compared with infants born to mothers with normal glucose tolerance, infants born to mothers with gestational diabetes mellitus (impaired glucose tolerance/new diabetes) had higher mean values of Z-scores for birth length-for-gestational age (0.07/0.23; normal group −0.08) and birth weight-for-length (0.27/0.57; normal group −0.001), smaller changes in mean values of Z-scores for length-for-age (0.75/0.62; normal group 0.94) and weight-for-length (0.18/−0.17; normal group 0.37) from birth to month 3, and bigger changes in mean values in Z-scores for weight-for-length (0.07/0.12; normal group 0.02) from month 9 to 12. Conclusions:. Abnormal maternal glucose tolerance during pregnancy was associated with higher birth weight and birth length, less weight and length gain in the first 3 months of life, and more weight gain in the months 9–12 of life

Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering

For the problem of easily losing track target when obstacles appear in intelligent robot target tracking, this paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm. After analyzing the defects of Camshift algorithm, compare the performance with the SIFT algorithm and Mean Shift algorithm, and Kalman filtering algorithm is used for fusion optimization aiming at the defects. Then aiming at the increasing amount of calculation in integrated algorithm, reduce dimension with the phase-path volume integral instead of the Gaussian integral in Kalman algorithm and reduce the number of sampling points in the filtering process without influencing the operational precision of the original algorithm. Finally set the target centroid position from the Camshift algorithm iteration as the observation value of the improved Kalman filtering algorithm to fix predictive value; thus to make optimal estimation of target centroid position and keep the target tracking so that the robot can understand the environmental scene and react in time correctly according to the changes. The experiments show that the improved algorithm proposed in this paper shows good performance in target tracking with obstructions and reduces the computational complexity of the algorithm through the dimension reduction

Stabilization of Discrete Nonlinear Systems with Continuous Feedback Law

The most powerful tool is the approach of control Lyapunov function which has been employed to address various issues to nonlinear control systems, and the stabilization problem of nonlinear control systems has attracted much attention. As we know, Artstein given an important theorem [1] which proved that the control system exists a control Lyapunov function if and only if there is a stabilizing relaxed feedback. Of course the existence of a smooth Lyapunov function fails for a nonlinear system in general. The key result used in most of the feedback stabilizers is a well known theorem due to Sontag[2] and Brockett [3]. The stabilization of discrete systems is studied by means of this paper. Cai Xiushan[4], Tang Fengjun [5] also studied the same systems , but the control Lyapunov function and control law which they given are not easy to get, we will give another way to get continuous control law

Securing Smart Grid In-Network Aggregation through False Data Detection

Existing prevention-based secure in-network data aggregation schemes for the smart grids cannot e ectively detect accidental errors and falsified data injected by malfunctioning or compromised meters. In this work, we develop a light-weight anomaly detector based on kernel density estimator to locate the smart meter from which the falsified data is injected. To reduce the overhead at the collector, we design a dynamic grouping scheme, which divides meters into multiple interconnected groups and distributes the verification and detection load among the root of the groups. To enable outlier detection at the root of the groups, we also design a novel data re-encryption scheme based on bilinear mapping so that data previously encrypted using the aggregation key is transformed in a form that can be recovered by the outlier detectors using a temporary re-encryption key. Therefore, our proposed detection scheme is compatible with existing in-network aggregation approaches based on additive homomorphic encryption. We analyze the security and eÿciency of our scheme in terms of storage, computation and communication overhead, and evaluate the performance of our outlier detector with experiments using real-world smart meter consumption data. The results show that the performance of the light-weight detector yield high precision and recall

Multi-Layer Dense Attention Decoder for Polyp Segmentation

Detecting and segmenting polyps is crucial for expediting the diagnosis of colon cancer. This is a challenging task due to the large variations of polyps in color, texture, and lighting conditions, along with subtle differences between the polyp and its surrounding area. Recently, vision Transformers have shown robust abilities in modeling global context for polyp segmentation. However, they face two major limitations: the inability to learn local relations among multi-level layers and inadequate feature aggregation in the decoder. To address these issues, we propose a novel decoder architecture aimed at hierarchically aggregating locally enhanced multi-level dense features. Specifically, we introduce a novel module named Dense Attention Gate (DAG), which adaptively fuses all previous layers' features to establish local feature relations among all layers. Furthermore, we propose a novel nested decoder architecture that hierarchically aggregates decoder features, thereby enhancing semantic features. We incorporate our novel dense decoder with the PVT backbone network and conduct evaluations on five polyp segmentation datasets: Kvasir, CVC-300, CVC-ColonDB, CVC-ClinicDB, and ETIS. Our experiments and comparisons with nine competing segmentation models demonstrate that the proposed architecture achieves state-of-the-art performance and outperforms the previous models on four datasets. The source code is available at: https://github.com/krushi1992/Dense-Decoder

How much do Large Language Models know about panel paintings preservation?

This study evaluates the capability of large language models (LLMs) in understanding the preservation of paintings on panel by comparison with the predictions obtained through the digital platform HERIe. The latter is specialized tool for heritage object risk assessment. Four large language models (LLMs) - ChatGPT 3.5, ChatGPT 4, Claude, and Gemini - were tested asking what are the levels of strain experienced by panel paintings under different conditions. The models were also tested on their ability to rank different environments conditions in order of suitability for storing panel paintings and were examined whether the languages of prompts affected results. The study concludes that while LLMs demonstrate a general understanding of wood panel preservation principles, they lack the specialized calculation abilities of purpose-built tools like HERIe for precise risk assessment in cultural heritage preservation