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Renjian Kan,1 Haibo Xu2,3 1School of Nursing, Xuzhou Medical University, Xuzhou, People’s Republic of China; 2School of Management, Xuzhou Medical University, Xuzhou, People’s Republic of China; 3Research Center for Psychological Crisis Prevention and Intervention of College Students in Jiangsu Province, Xuzhou Medical University, Xuzhou, People’s Republic of ChinaCorrespondence: Haibo Xu, Email xhb@xzhmu.edu.cnPurpose: To examine the influence of learning adjustment dimensions (motivation, attitude, ability, teaching pattern, and environment) on depressive symptoms in Chinese nursing freshmen, and the mediating role of psychological capital.Methods: A cross-sectional survey was conducted among 471 nursing freshmen at a university in May 2024. The anonymous self-reported questionnaire included the Patient Health Questionnaire, the Learning Adjustment Questionnaire, and the Psychological Capital Questionnaire. Data were analyzed using SPSS v25.0 and PROCESS v4.1 macro, including correlation and mediation analysis.Results: Learning adjustment had a significant direct effect on depressive symptoms among nursing freshmen (β = − 0.565, p < 0.001), and psychological capital partially mediated this relationship (β= − 0.156, 95% Boot CI [− 0.107, − 0.135]), suggesting psychological capital explains part of the link between poor learning adjustment and depressive symptoms.Conclusion: More attention should be paid to the mental health issues of freshmen. Addressing learning adjustment issues and fostering psychological capital among nursing freshmen may alleviate depressive symptoms, which will require relevant educational interventions and mental health support from university educators and counselors.Keywords: depressive symptom, learning adjustment, psychological capital, nursing freshmen

Peng Yu,1– 4 Ranran Kan,1,3 Xiaoyu Meng,1,3 Zhihan Wang,1,3 Yuxi Xiang,1,3 Beibei Mao,1,3 Xuefeng Yu1,3 1Department of Endocrinology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2Department of Endocrinology, the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 3Branch of National Clinical Research Center for Metabolic Diseases, Hubei, People’s Republic of China; 4Key Laboratory for Molecular Diagnosis of Hubei Province, Wuhan, People’s Republic of ChinaCorrespondence: Xuefeng Yu, Department of Internal Medicine, Tongji Hospital, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan, 430030, People’s Republic of China, Tel +8602783663331, Email xfyu188@163.comBackground: In China, the spectrum of causes for CKD has been changing in recent years, and the proportion of CKD caused by cardiometabolic diseases, such as diabetes and hypertension continues to increase. Thus, predicting CKD based on cardiometabolic risk factors can to a large extent help identify those at increased risk and facilitate the prevention of CKD. In this study, we aimed to develop a nomogram for predicting CKD risk based on cardiometabolic risk factors.Methods: We developed a nomogram for predicting CKD risk by using a subcohort population of the 4C study, which was located in central China. The prediction model was designed by using a logistic regression model, and a backwards procedure based on the Akaike information criterion was applied for variable selection. The performance of the model was evaluated by the concordance index (C-index), and Hosmer‒Lemeshow goodness-of-fit test. The bootstrapping method was applied for internal validation.Results: During the 3-years follow-up, 167 cases of CKD developed. By using univariate and multivariate logistic regression models, the following factors were identified as predictors in the nomogram: age, sex, HbA1c, baseline eGFR, low HDL-C levels, high TC levels and SBP. The bootstrap-corrected C-index for the model was 0.84, which indicated good discrimination ability. The Hosmer‒Lemeshow goodness-of-fit tests yielded chi-square of 13.61 (P=0.192), and the calibration curves demonstrated good consistency between the predicted and observed probabilities, which indicated satisfactory calibration ability.Conclusion: We developed a convenient and practicable nomogram for the 3‑year risk of incident CKD among a population in central China, which may help to identify high-risk individuals for CKD and contribute to the prevention of CKD.Keywords: CKD, cardiometabolic risk factors, nomogram

Introduction Radiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could mitigate this risk. While previous studies have suggested this technology has accuracy comparable to radiologists most have been limited by using ‘enriched’ datasets and/or not considering the interaction between the algorithm and human readers. This study will address these limitations by comparing the accuracy of a workflow using AI alongside radiologists on a large consecutive cohort of examinations from a breast cancer screening programme. The study will combine the strengths of a large retrospective design with the benefit of prospective data collection. It will test this technology without risk to screening programme participants nor the need to wait for follow-up data. With a sample of 2 years of consecutive screening examinations, it is likely the largest test of this technology to date. The study will help determine whether this technology can safely be introduced into the BreastScreen New South Wales (NSW) population-based screening programme to address radiology workforce risks without compromising cancer detection rates or increasing false-positive recalls.Methods and analysis A retrospective, consecutive cohort of digital mammography screens from 658 207 examinations from BreastScreen NSW will be reinterpreted by the Lunit Insight MMG AI product. The cohort includes 4383 screen-detected and 1171 interval cancers. The results will be compared with radiologist single reading and the AI results will also be used to replace the second reader in a double-reading model. New adjudication reading will be performed where the AI disagrees with the first reader. Recall rates and cancer detection rates of combined AI–radiologist reading will be compared with the rates obtained at the time of screening.Ethics and dissemination This study has ethical approval from the NSW Health Population Health Services Research Ethics Committee (2022/ETH02397). Findings will be published in peer-reviewed journals and presented at conferences. The findings of this evaluation will be provided to programme managers, governance bodies and other stakeholders in Australian breast cancer screening programmes.

Change detection in remote sensing imagery is vital for Earth monitoring but faces challenges such as background complexity and pseudo-changes. Effective interaction between bitemporal images is crucial for accurate change information extraction. This paper presents a multistage interaction network designed for effective change detection, incorporating interaction at the image, feature, and decision levels. At the image level, change information is directly extracted from intensity changes, mitigating potential change information loss during feature extraction. Instead of separately extracting features from bitemporal images, the feature-level interaction jointly extracts features from bitemporal images. By enhancing relevance to spatial variant information and shared semantic channels, the network excels in overcoming background complexity and pseudo-changes. The decision-level interaction combines image-level and feature-level interactions, producing multiscale feature differences for precise change prediction. Extensive experiments demonstrate the superior performance of our method compared to existing approaches, establishing it as a robust solution for remote sensing image change detection.

This article primarily focuses on the localization and extraction of multiple moving objects in images taken from a moving camera platform, such as image sequences captured by drones. The positions of moving objects in the images are influenced by both the camera’s motion and the movement of the objects themselves, while the background position in the images is related to the camera’s motion. The main objective of this article was to extract all moving objects from the background in an image. We first constructed a motion feature space containing motion distance and direction, to map the trajectories of feature points. Subsequently, we employed a clustering algorithm based on trajectory distinctiveness to differentiate between moving objects and the background, as well as feature points corresponding to different moving objects. The pixels between the feature points were then designated as source points. Within local regions, complete moving objects were segmented by identifying these pixels. We validated the algorithm on some sequences in the Video Verification of Identity (VIVID) program database and compared it with relevant algorithms. The experimental results demonstrated that, in the test sequences when the feature point trajectories exceed 10 frames, there was a significant difference in the feature space between the feature points on the moving objects and those on the background. Correctly classified frames with feature points accounted for 67% of the total frames.The positions of the moving objects in the images were accurately localized, with an average IOU value of 0.76 and an average contour accuracy of 0.57. This indicated that our algorithm effectively localized and segmented the moving objects in images captured by moving cameras.

Infrared small target detection plays a crucial role in maritime security. However, detecting small targets within heavy sea clutter environments remains challenging. Existing methods often fail to deliver satisfactory performance in the presence of substantial clutter interference. This paper analyzes the spatial–temporal appearance characteristics of small targets and sea clutter. Based on this analysis, we propose a novel detection method based on the appearance stable isotropy measure (ASIM). First, the original images are processed using the Top-Hat transformation to obtain the salient regions. Next, a preliminary threshold operation is employed to extract the candidate targets from these salient regions, forming a candidate target array image. Third, to distinguish between small targets and sea clutter, we introduce two characteristics: the gradient histogram equalization measure (GHEM) and the local optical flow consistency measure (LOFCM). GHEM evaluates the isotropy of the candidate targets by examining their gradient histogram equalization, while LOFCM assesses their appearance stability based on local optical flow consistency. To effectively combine the complementary information provided by GHEM and LOFCM, we propose ASIM as a fusion characteristic, which can effectively enhance the real target. Finally, a threshold operation is applied to determine the final targets. Experimental results demonstrate that our proposed method exhibits superior comprehensive performance compared to baseline methods.

Diabetic retinopathy is not just the most common complication of diabetes but also the leading cause of adult blindness. Currently, doctors determine the cause of diabetic retinopathy primarily by diagnosing fundus images. Large-scale manual screening is difficult to achieve for retinal health screen. In this paper, we proposed an improved U-net network for segmenting retinal vessels. Firstly, due to the lack of retinal data, pre-processing of the raw data is required. The data processed by grayscale transformation, normalization, CLAHE, gamma transformation. Data augmentation can prevent overfitting in the training process. Secondly, the basic network structure model U-net is built, and the Bi-FPN network is fused based on U-net. Datasets from a public challenge are used to evaluate the performance of the proposed method, which is able to detect vessel SP of 0.8604, SE of 0.9767, ACC of 0.9651, and AUC of 0.9787.

Unsupervised learning is applicable to classification that does not know the number of specific categories in advance, and sparse auto-encoders (SAE) are widely used for feature extraction of unsupervised learning. Therefore, this paper proposes an electromagnetic signal classification system based on SAE which is combined with the machine learning clustering algorithm. In particular, we propose to perform feature preprocessing on signals using STFT. Then, the features extracted by SAE training are clustered by t-SNE and DBSCAN to obtain clustering results. Finally, we prove the feasibility of this method classification by comparing with traditional clustering methods. Because of the feature extraction, SAE not only learns the key feature information but also effectively compresses the data content, which greatly reduces the data dimension that the clustering algorithm needs to deal with and improves the clustering accuracy. As the experimental results show, the evaluation indicators of the result obtained by our method are significantly improved compared with the traditional clustering algorithms, the compactness (CP) index decreases by 73.76%; the Davies-Bouldin Index (DB) decreases by 18.50%; the Dunn Validity Index (DVI) increases by 6.24%; and the Rand Index (RI) increases by 43.14%.

Infrared ocean image simulation has been widely used in water-pollution prevention, meteorological observation and melting-ice monitoring. However, in actual remote sensing observation scenes, the simulation images provided by conventional algorithms are lacking sufficient wave details because the viewing angle and the scale of simulation images are simplex. In this paper, an infrared ocean image simulation algorithm based on the Pierson–Moskowitz spectrum and a bidirectional reflectance distribution function is proposed. First, a 3D model of ocean surface is set up based on Pierson–Moskowitz spectrum. Then, the imaging position is calculated by the pinhole camera imaging method, which describes how each point of the 3D model is mapping to the 2D image. Next, by using a bidirectional reflectance distribution function, the radiation intensity from every point of the ocean model to the camera is computed. Finally, we figure up the sum of the radiation intensity received by every point of the detector and obtain the infrared simulation ocean image by quantizing the radiation intensity sum to grayscale. The entropy of the simulation images is 2.725, which is, respectively, improved by 71.86% and 16.83% compared with two other algorithms. The Kullback–Leibler divergence of the simulation images is 11.446, which is improved by 0.54% and 0.59% compared with other algorithms. The quantitative experimental results prove that the authenticity and clarity of the presented simulation images have remarkable advantages over conventional algorithms.

Biodiesel is an alternative and renewable biofuel for blending with fossil-based diesel. One of the challenges is to design the supply-chain network for biohydrodeoxygenated diesel (BHD) under uncertainty from both raw materials availability and the demand. The supply chain with four echelons; suppliers, factories, inventories and customers has been studied. The biodiesel factory has been simulated by commercial simulation program Pro/II to find the utility cost and biodiesel production capacity. In this study, the stochastic optimization is used to solve mathematical model of the BHD supply chain network. The proposed model is based on stochastic mixed-integer programming with chance constraints. The chance constrained optimization has been applied to design supply chain network under the uncertainty at different levels of confidence. Then optimized supply chain network has been investigated on stability of the supply chain network under the uncertainties, accuracy of the profit and the effect of penalty. This study shows profit and trade-off between profit and penalty cost with different levels of confidence of BHD supply chain under uncertainties in raw-material supply and biodiesel demand. The results show that the chance constrained optimization can be used to design optimum supply chain network under uncertainties within levels of confidence.

Polarimetric imaging techniques exploit the polarization characteristics of objects and, thus, can achieve a better discriminative performance. In this paper, an adaptive discrimination method based on the classification of depolarization Mueller matrix is proposed. According to the Mueller-Jones theory, the general formula of Mueller matrix for depolarization optical system is analyzed. In addition, a two-channel imaging platform is constructed to obtain a polarization-difference image with certain states of polarization. Under this methodology, every pixel of the image can be expanded as a combination of independent entries of the Mueller matrix, with polarization states as the representative coefficients. Thus, the optimal polarization states can be obtained via support vector machine (SVM), and a high contrast image is achieved. Finally, experiments on two groups of different materials are conducted to demonstrate the applicability and performance of the proposed method. The related criteria (e.g., Fisher ratio) are introduced to quantitatively evaluate the results. Experimental results indicate that the proposed method shows advantages for image discriminations.

Infrared image segmentation plays a significant role in many burgeoning applications of remote sensing, such as environmental monitoring, traffic surveillance, air navigation and so on. However, the precision is limited due to the blurred edge, low contrast and intensity inhomogeneity caused by infrared imaging. To overcome these challenges, a level set method using global and local information is proposed in this paper. In our method, a hybrid signed pressure function is constructed by fusing a global term and a local term adaptively. The global term is represented by the global average intensity, which effectively accelerates the evolution when the evolving curve is far away from the object. The local term is represented by a multi-feature-based signed driving force, which accurately guides the curve to approach the real boundary when it is near the object. Then, the two terms are integrated via an adaptive weight matrix calculated based on the range value of each pixel. Under the framework of geodesic active contour model, a new level set formula is obtained by substituting the proposed signed pressure function for the edge stopping function. In addition, a Gaussian convolution is applied to regularize the level set function for the purpose of avoiding the computationally expensive re-initialization. By iteration, the object of interest can be segmented when the level set function converges. Both qualitative and quantitative experiments verify that our method outperforms other state-of-the-art level set methods in terms of accuracy and robustness with the initial contour being set randomly.

Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods.

Infrared moving target tracking plays a fundamental role in many burgeoning research areas of Smart City. Challenges in developing a suitable tracker for infrared images are particularly caused by pose variation, occlusion, and noise. In order to overcome these adverse interferences, a total variation regularization term-based low-rank and sparse matrix representation (TV-LRSMR) model is designed in order to exploit a robust infrared moving target tracker in this paper. First of all, the observation matrix that is derived from the infrared sequence is decomposed into a low-rank target matrix and a sparse occlusion matrix. For the purpose of preventing the noise pixel from being separated into the occlusion term, a total variation regularization term is proposed to further constrain the occlusion matrix. Then an alternating algorithm combing principal component analysis and accelerated proximal gradient methods is employed to separately optimize the two matrices. For long-term tracking, the presented algorithm is implemented using a Bayesien state inference under the particle filtering framework along with a dynamic model update mechanism. Both qualitative and quantitative experiments that were examined on real infrared video sequences verify that our algorithm outperforms other state-of-the-art methods in terms of precision rate and success rate.

Inclinometer assembly error is one of the key factors affecting the measurement accuracy of photoelectric measurement systems. In order to solve the problem of the lack of complete attitude information in the measurement system, this paper proposes a new inclinometer assembly error calibration and horizontal image correction method utilizing plumb lines in the scenario. Based on the principle that the plumb line in the scenario should be a vertical line on the image plane when the camera is placed horizontally in the photoelectric system, the direction cosine matrix between the geodetic coordinate system and the inclinometer coordinate system is calculated firstly by three-dimensional coordinate transformation. Then, the homography matrix required for horizontal image correction is obtained, along with the constraint equation satisfying the inclinometer-camera system requirements. Finally, the assembly error of the inclinometer is calibrated by the optimization function. Experimental results show that the inclinometer assembly error can be calibrated only by using the inclination angle information in conjunction with plumb lines in the scenario. Perturbation simulation and practical experiments using MATLAB indicate the feasibility of the proposed method. The inclined image can be horizontally corrected by the homography matrix obtained during the calculation of the inclinometer assembly error, as well.

In order to detect both bright and dark small moving targets effectively in infrared (IR) video sequences, a saliency histogram and geometrical invariability based method is presented in this paper. First, a saliency map that roughly highlights the salient regions of the original image is obtained by tuning its amplitude spectrum in the frequency domain. Then, a saliency histogram is constructed by means of averaging the accumulated saliency value of each gray level in the map, through which bins corresponding to bright target and dark target are assigned with large values in the histogram. Next, single-frame detection of candidate targets is accomplished by a binarized segmentation using an adaptive threshold, and their centroid coordinates with sub-pixel accuracy are calculated through a connected components labeling method as well as a gray-weighted criterion. Finally, considering the motion characteristics in consecutive frames, an inter-frame false alarm suppression method based on geometrical invariability is developed to improve the precision rate further. Quantitative analyses demonstrate the detecting precision of this proposed approach can be up to 97% and Receiver Operating Characteristic (ROC) curves further verify our method outperforms other state-of-the-arts methods in both detection rate and false alarm rate.

Abstract Background Exposure to the nerve agent soman (GD) causes neuronal cell death and impaired behavioral function dependent on the induction of status epilepticus (SE). Little is known about the maturation of this pathological process, though neuroinflammation and infiltration of neutrophils are prominent features. The purpose of this study is to quantify the regional and temporal progression of early chemotactic signals, describe the cellular expression of these factors and the relationship between expression and neutrophil infiltration in damaged brain using a rat GD seizure model. Methods Protein levels of 4 chemokines responsible for neutrophil infiltration and activation were quantified up to 72 hours in multiple brain regions (i.e. piriform cortex, hippocampus and thalamus) following SE onset using multiplex bead immunoassays. Chemokines with significantly increased protein levels were localized to resident brain cells (i.e. neurons, astrocytes, microglia and endothelial cells). Lastly, neutrophil infiltration into these brain regions was quantified and correlated to the expression of these chemokines. Results We observed significant concentration increases for CXCL1 and MIP-1α after seizure onset. CXCL1 expression originated from neurons and endothelial cells while MIP-1α was expressed by neurons and microglia. Lastly, the expression of these chemokines directly preceded and positively correlated with significant neutrophil infiltration in the brain. These data suggest that following GD-induced SE, a strong chemotactic response originating from various brain cells, recruits circulating neutrophils to the injured brain. Conclusions A strong induction of neutrophil attractant chemokines occurs following GD-induced SE resulting in neutrophil influx into injured brain tissues. This process may play a key role in the progressive secondary brain pathology observed in this model though further study is warranted.

Abstract Background Neuroinflammation occurs following brain injury, including soman (GD) induced status epilepticus (SE), and may contribute to loss of neural tissue and declined behavioral function. However, little is known about this important pathological process following GD exposure. Limited transcriptional information on a small number of brain-expressed inflammatory mediators has been shown following GD-induced SE and even less information on protein upregulation has been elucidated. The purpose of this study is to further characterize the regional and temporal progression of the neuroinflammatory process following acute GD-induced SE. Methods The protein levels of 10 cytokines was quantified using bead multiplex immunoassays in damaged brain regions (i.e., piriform cortex, hippocampus and thalamus) up to 72 hours following seizure onset. Those factors showing significant changes were then localized to neural cells using fluorescent IHC. Results A significant concentration increase was observed in all injured brain regions for four acute phase response (APR) induction cytokines: interleukin (IL)-1α, IL-1β, IL-6, and tumor necrosis factor (TNF)-α. Increases in these APR cytokines corresponded both temporally and regionally to areas of known seizure damage and neuronal death. Neurotoxic cytokines IL-1α and IL-1β were primarily expressed by activated microglia whereas the potentially neuroprotective cytokine IL-6 was expressed by neurons and hypertrophic astrocytes. Conclusions Increases in neurotoxic cytokines likely play an active role in the progression of GD-induced SE neuropathology though the exact role that these and other cytokines play in this process require further study.