Up-regulation of serum ephrinA2 is common in various malignancies and has been suggested as a potential biomarker for the diagnosis prognosis prostate cancer (PCa).However, type expressed PCa patients remains elusive.Furthermore, level exosomal derived from increased with osteoporosis, complication undergoing androgen deprivation therapy.It unknown whether exosomes patient contains ephrinA2.In this study, we explored expression whole tissues identified circulating PCa.Exosomes were isolated...

In contemporary agricultural practices, greenhouses serve as a critical component of infrastructure, where soil temperature plays vital role in enhancing pest management and regulating crop growth. However, achieving precise greenhouse environmental control continues to pose significant challenge. this context, the present study proposes ReSSA-iTransformer, an advanced predictive model engineered accurately forecast temperatures within across diverse temporal scales, encompassing both...

Abstract Visual affordance grounding enables a computer system to comprehend and recognize an object function potential uses from image. This requires not only recognizing objects by their shape appearance, but also understanding interactions with the environment users. paper introduces SEHD-Afford, weakly supervised framework designed enhance proficiency of intelligent agents in utilizing complex environments. SEHD-Afford achieves weakly-supervised regions using shallow-deep-semantic...

To solve the problem of low accuracy remaining useful life (RUL) prediction caused by insufficient sample data equipment under complex operating conditions, an RUL method small based on a deep convolutional neural network—bidirectional long short-term memory network (DCNN-BiLSTM) and domain adaptation is proposed. Firstly, in order to extract common features condition sufficient samples, model that combines (DCNN) bidirectional (BiLSTM) was used train source target simultaneously. The...

Abstract To solve the problem of local minima and unreachable destination traditional artificial potential field method in mobile robot path planning, chaos optimization is introduced to improve method. The function was adopted as a target optimization, kind “two-stage” used. corresponding movement step direction were achieved by search. Comparison improved proposed this paper performed simulation. simulation results show that gets rid drawbacks, such goal. Furthermore, also verified...

Deep dictionary learning (DDL) shows good performance in visual classification tasks. However, almost all existing DDL methods ignore the locality relationships between input data representations and learned atoms, learn sub-optimal feature coding stage, which are less conducive to classification. To this end, we propose a hierarchical locality-aware deep (HILADLE) framework for classification, can locality-constrained dictionaries at different abstract levels through learning. The...

Learning the accurate dynamics of robotic systems directly from trajectory data is currently a prominent research focus. Recent physics-enforced networks, exemplified by Hamiltonian neural networks and Lagrangian demonstrate proficiency in modeling ideal physical systems, but face limitations when applied to with uncertain non-conservative due inherent constraints conservation laws foundation. In this paper, we present novel augmented deep network, which seamlessly integrates network...

Multi-scale object detection is critically important in complex driving environments within the field of autonomous driving. To enhance accuracy both small-scale and large-scale targets environments, this paper proposes an improved YOLOv5-AFAM algorithm. Firstly, Adaptive Fusion Attention Module (AFAM) Down-sampling (DownC) are introduced to increase precision small targets. Secondly, Efficient (EMA) incorporated, enabling model simultaneously recognize Finally, a Minimum Point Distance...

The rapid development of communication transmission, including 6G technology, is creating increasing challenges for real-world object recognition tasks in transportation, which now must operate within complex external environments and the requirement time efficiency. Although machine learning-based hybrid intelligence has attracted significant attention achieved much success recent years, current models are often ineffective have poor generalization extreme weather. This because training a...

Feature analysis and selection are highly considered topics in deep learning (DL) real-world applications. However, most existing methods manual lack of insights training mechanisms. This is because DL often viewed as a "black box" the mechanisms providing output hidden from user difficult to understand. Some scientists have utilized visualization, sensitivity analysis, adversary attack machine increase transparency demonstrated successful understanding DL, especially related convolutional...

Abstract Remaining useful life (RUL) estimation is fundamental to prediction and health management technology. Traditional machine learning generally assumes that the training testing sets are independent identically distributed. As distribution differences exist in real scenarios, this assumption hinders effectiveness of traditional methods. Aiming at these problems, we propose a CNN-LSTM-based domain adaptation framework for RUL work. A shared encoding network mechanism introduced decrease...

Operational optimal control (OOC) is an essential component of wastewater treatment process (WWTP). The variables usually are high-dimensional, nonlinear, and strongly coupled, which can easily fail traditional optimization methods. Mathematically, these operational in the unknown low-dimensional space embedded high-dimensional space. Therefore, OOC problem WWTP be resolved as challenge involving space, presented form a set controlled normal real-world industries. Here, dimension-reducible...

Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised are powerless in case of inadequate labeled samples. Therefore, end-to-end detection with overcritical efficiency and endoscope detection, an ensemble-learning-based model a semi-supervised mechanism is developed this work. To gain more accurate result multiple models, we propose new...