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Abstract Background Digital health literacy (DHL) is pivotal for individuals to access trustworthy health information and make informed decisions, closely intertwined with health behaviors and psychological resilience—these factors collectively shape an individual’s approach to health management. Despite extensive research on digital health literacy in broader populations, the specific dynamics of digital health literacy in relation to health behaviors and psychological resilience among undergraduate nursing students in China remain understudied, highlighting a gap that this research endeavors to address. This study aims to assess the levels of digital health literacy among undergraduate nursing students in China, and to explore its association with health lifestyles and psychological resilience, which are considered influential factors. Methods Employing a descriptive cross-sectional research design, eligible Chinese nursing undergraduates were surveyed from June to October 2023. This study recruited a total of 418 undergraduate nursing students. After excluding 11 students on leave, 407 students participated in questionnaire completion and submission. The College Students’ Digital Health Literacy Scale, Healthy Lifestyle Assessment Scale and The Psychological Resilience Scale were used in this study. Results The mean scores for digital health literacy, health lifestyles, and psychological resilience were 74.78 ± 8.44, 127.14 ± 11.22, and 79.21 ± 11.02, respectively. Pearson correlation analysis results indicated that the correlations between digital health literacy and both health lifestyles (r = 0.707, P

Abstract Background Implicit absenteeism is very common among nurses. Poor perceived social support of intensive care unit nurses has a negative impact on their mental and physical health. There is evidence that lack of occupational coping self-efficacy can promote implicit absenteeism; however, the relationship between lack of occupational coping self-efficacy in perceived social support and implicit absenteeism of intensive care unit nurses is unclear. Therefore, this study aimed to evaluate the role of perceived social support between lack of occupational coping self-efficacy and implicit absenteeism of intensive care unit nurses, and to provide reliable evidence to the management of clinical nurses. Methods A cross-sectional study of 517 intensive care unit nurses in 10 tertiary hospitals in Sichuan province, China was conducted, of which 474 were valid questionnaires with a valid recovery rate of 91.6%. The survey tools included the Chinese version of Implicit Absenteeism Scale, the Chinese version of Perceived Social Support Scale, the Chinese version of Occupational Coping Self-Efficacy Scale and the Sociodemographic characteristics. Descriptive analysis and Pearson correlation analysis were performed using SPSS version 22.0, while the mediating effects were performed using AMOS version 24.0. Results The average of intensive care unit nurses had a total implicit absenteeism score of (16.87 ± 3.98), in this study, the median of intensive care unit nurses’ implicit absenteeism score was 17, there were 210 intensive care unit nurses with low implicit absenteeism (44.3%) and 264 ICU nurses with high implicit absenteeism (55.7%). A total perceived social support score of (62.87 ± 11.61), and a total lack of occupational coping self-efficacy score of (22.78 ± 5.98). The results of Pearson correlation analysis showed that implicit absenteeism was negatively correlated with perceived social support (r = -0.260, P

BackgroundOsteonecrosis of the femoral head (ONFH) is a severe complication of systemic lupus erythematosus (SLE) and occurs more frequently in SLE patients than in other autoimmune diseases, which can influence patients’ life quality. The objective of this research was to analyze risk factors for the occurrence of ONFH in female SLE patients, construct and validate a risk nomogram model.MethodsClinical records of SLE patients who fulfilled the 1997 American College of Rheumatology SLE classification criteria were retrospectively analyzed. The Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis were used to summarize the independent risk factors of ONFH in female SLE patients, which were used to develop a nomogram. The predictive performance of the nomogram was assessed using the receiver characteristic (ROC) curve, calibration curves and decision curve analysis (DCA).Results793 female SLE patients were ultimately included in this study, of which 87 patients (10.9%) developed ONFH. Ten independent risk factors including disease duration, respiratory involvement, menstrual abnormalities, Sjögren's syndrome, osteoporosis, anti-RNP, mycophenolate mofetil, cyclophosphamide, biologics, and the largest daily glucocorticoid (GC) were identified to construct the nomogram. The area under the ROC curve of the nomogram model was 0.826 (95% CI: 0.780–0.872) and its calibration for forecasting the occurrence of ONFH was good (χ2= 5.589, P = 0.693). DCA showed that the use of nomogram prediction model had certain application in clinical practice when the threshold was 0.05 to 0.95. In subgroup analysis, we found that the risk of ONFH was significantly increased in age at SLE onset of ≤ 50 years old, largest daily GC dose of ≥50 mg and the therapy of GC combined with immunosuppressant patients with menstrual abnormalities.ConclusionMenstrual abnormalities were the first time reported for the risk factors of ONFH in female SLE patients, which remind that clinicians should pay more attention on female SLE patients with menstrual abnormalities and take early interventions to prevent or slow the progression of ONFH. Besides, the nomogram prediction model could provide an insightful and applicable tool for physicians to predict the risk of ONFH.

Abstract Diabetes has long been considered a risk factor in implant therapy and impaired wound healing in soft and hard oral tissues. Magnesium has been proved to promote bone healing under normal conditions. Here, we elucidate the mechanism by which Mg2+ promotes angiogenesis and osseointegration in diabetic status. We generated a diabetic mice model and demonstrated the alveolar bone healing was compromised, with significantly decreased angiogenesis. We then developed Mg-coating implants with hydrothermal synthesis. These implants successfully improved the vascularization and osseointegration in diabetic status. Mechanically, Mg2+ promoted the degradation of Kelch-like ECH-associated protein 1 (Keap1) and the nucleation of nuclear factor erythroid 2-related factor 2 (Nrf2) by up-regulating the expression of sestrin 2 (SESN2) in endothelial cells, thus reducing the elevated levels of oxidative stress in mitochondria and relieving endothelial cell dysfunction under hyperglycemia. Altogether, our data suggested that Mg2+ promoted angiogenesis and osseointegration in diabetic mice by regulating endothelial mitochondrial metabolism.

Background: Mounting evidence has demonstrated that high temperature was associated with adverse health outcomes, especially morbidity and mortality. Nonetheless, the impact of extreme high temperature on cognitive performance, which is the fundamental capacity for interpreting one's surroundings, decision-making, and acquiring new abilities, has not been thoroughly investigated. Methods: We aimed to assess associations between extreme high temperature at different time scales and poor cognitive function. We used longitudinal survey data from the three waves of data from China Family Panel Study, providing an 8-year follow-up of 53,008 participants from China. We assessed temperature and extreme high temperature exposure for each participant based on the residential area and date of cognitive test. We defined the proportion of days/hours above 32 °C as the metric of the exposure to extreme high temperature. Then we used generalized additive model and difference-in-differences approach to explore the associations between extreme high temperature and cognitive function. Results: Our results demonstrated that either acute exposure or long-term exposure to extreme high temperature was associated with cognitive decline. At hourly level, 0–1 hour acute exposure to extreme high temperature would induce −0.93 % (95 % CI: −1.46 %, −0.39 %) cognitive change. At annual level, 10 percentage point increase in the hours proportion exceeding 32 °C in the past two years induced −9.87 % (95 % CI: −13.99 %, −5.75 %) cognitive change. Furthermore, subgroup analyses indicated adaptation effect: for the same 10 percentage increase in hours proportion exceeding 32 °C, people in warmer areas had cognitive change of −6.41 % (-11.22 %, −1.61 %), compared with −15.30 % (-21.07 %, −9.53 %) for people in cool areas. Conclusion: Our results demonstrated that extreme high temperature was associated with reduced cognitive function at hourly, daily and annual levels, warning that people should take better measures to protect the cognitive function in the context of climate change.

Abstract Purpose The aim of this study is to examine and compare the accuracy of implant placement using implant positional guide and freehand. Methods 48 implants were placed in patients with single tooth loss with implant positional guide and freehand, respectively. The accuracy of implant placement was assessed by comparing the actual and planned position, including four parameters: coronal deviation, apical deviation, angular deviation, and vertical deviation. Results Comparing all the variables, it has been found that the implant positional guide is more accurate than the freehand. All parameters describing in the deviation were significantly lower in the implant positional guide group than the freehand. Conclusions The implant positional guide can act as a practicable tool for dental implant surgery. It is a promising technology that guarantees low cost and high precision in implant surgery. However, based on the restricted evidence from clinical studies, longer follow-up periods, larger population studies, and standardized experimental studies are required. Trial registration CHICTR, ChiCTR2300071024. Registered 28 April 2023—CHICTR, ChiCTR2300071024. Registered 28 April 2023—Retrospectively registered, https://www.chictr.org.cn/showproj.html?proj=195424 .

With the advantages of levitation/guidance self-stability, large levitation gap, and high lift-to-drag ratios, superconducting electrodynamic suspension (SC-EDS) train is becoming a viable candidate for the high-speed and ultra-high-speed rail transportation. In order to provide the basis for designing the optimization and control strategy, this paper establishes a comprehensive model for the SC-EDS train, which considers the dynamics of the bogie and car body in all directions. The obtained model reveals the complex coupling and feedback relationships among the variables, which cannot be described by the existing local models of the SC-EDS train. Simulation examples under different parameters and initial conditions are presented and discussed to demonstrate the potential use of the model given in this paper.

Abstract Delay tolerant networks (DTNs) refer to a kind of novel wireless mobile network, where there is no constant end‐to‐end connection between network nodes due to frequent movement, sparse distribution and limited communication range of nodes. Instead of the traditional store‐forward routing strategy, in DTNs, the new store‐carry‐forward routing strategy is adopted for data transmission. Therefore, how to select the best next‐hop node among network nodes is the main challenge of the routing in DTNs. To this end, here, a k‐step double Q‐learning routing (K‐DQLR) algorithm is proposed, which integrates the multi‐step and double Q‐learning algorithms to make an unbiased, accurate and efficient routing decision in DTNs. Besides, a new dynamic reward mechanism is proposed, which combines the number of routing hops and the node centrality to adopt the dynamic network environment of DTNs. The simulation results show that K‐DQLR can significantly increase the delivery ratio while reducing the delivery delay and overhead compared with the related state‐of‐the‐art routing protocols of DTNs.

Background: Climate change has led to the frequent occurrence of high-temperature weather, which has various adverse effects on health, ranging from blood metabolism to systemic organ function. In particular, the sequelae of heat stress injury in most people are related to the nervous system. However, the mechanisms between heat stress and mental health conditions, especially heat stress and anxiety, remain unclear. Objective: We attempted to elucidate the effect of heat exposure intervention on anxiety levels in the population and its mechanism. Methods: We first carried out a randomized controlled trial in 20 college students in Beijing, China, to observe the results of the effects of heat exposure intervention on human anxiety. Then, we collected blood samples before and after heat exposure experiment and used metabolomic and transcriptomic approaches to quantify serum metabolites and ELISA measurements to explore the underlying mechanisms. Results: We found that even 1.5-hour heat exposure intervention significantly increased anxiety levels. Heat stress-induced anxiety was mediated by the activation of the HPA axis, inflammation, oxidative stress, and subsequently unbalanced neurotransmitters. Metabolites such as BDNF, GABA, and glucocorticoids released by the adrenal glands are biomarkers of heat stress-induced anxiety. Conclusions: We have demonstrated a causal link between heat stress and anxiety, explored possible biological pathway between heat stress and anxiety. Heat stress can cause the activation of the HPA axis and lead to changes in the body's metabolism, resulting in a series of changes such as inflammation and oxidative stress, leading to anxiety. This study reveals hidden health cost of climate change that has been underexplored, and also reminds us the importance of immediate climate actions.

Abstract Periodontium supports teeth in a mechanically stimulated tissue environment, where heterogenous stem/progenitor populations contribute to periodontal homeostasis. In this study, Leptin receptor+ (Lepr+) cells are identified as a distinct periodontal ligament stem cell (PDLSC) population by single‐cell RNA sequencing and lineage tracing. These Lepr+ PDLSCs are located in the peri‐vascular niche, possessing multilineage potential and contributing to tissue repair in response to injury. Ablation of Lepr+ PDLSCs disrupts periodontal homeostasis. Hyper‐loading and unloading of occlusal forces modulate Lepr+ PDLSCs activation. Piezo1 is demonstrated that mediates the mechanosensing of Lepr+ PDLSCs by conditional Piezo1‐deficient mice. Meanwhile, Yoda1, a selective activator of Piezo1, significantly accelerates periodontal tissue growth via the induction of Lepr+ cells. In summary, Lepr marks a unique multipotent PDLSC population in vivo, to contribute toward periodontal homeostasis via Piezo1‐mediated mechanosensing.

AbstractRecent research has proven that deep learning models are effective at mining the rich spectral-spatial information of hyperspectral images and attaining high classification performance. In this study, an end-to-end two-branch multi-feature deep fusion network framework (MFDFN) is proposed, combining object-oriented features of hyperspectral images after multi-scale super-pixel segmentation and original image point-image element neighbourhood block features, and designing different network modules in the upper and lower branches for different features in order to extract more distinguishable and advantageous features for classification. For the second part, a series spectral-spatial feature learning modules and augmentation modules for spatial feature learning are being developed to learn these features in a continuous manner while also effectively fusing features from various depth layers. It also uses a variety of techniques (BN, Dropout, residual join, etc.) to speed up the model’s convergence, avoid overfitting, and improve the model’s generalization performance. In addition, experiment results on the Indian Pines, University of Pavia, Tea, and Salinas datasets reveal that the proposed MFDFN framework outperforms current deep-learning-based methods in terms of comprehensive classification effectiveness evaluation.

Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using multi-modal data, outperforming single-modality models when classifying diseases such as Alzheimer’s Disease (AD). However, those models are usually not flexible enough to handle missing modalities. Currently, the most common workaround is discarding samples with missing modalities which leads to considerable data under-utilisation. Adding to the fact that labelled medical images are already scarce, the performance of data-driven methods like deep learning can be severely hampered. Therefore, a multi-modal method that can handle missing data in various clinical settings is highly desirable. In this paper, we present Multi-Modal Mixing Transformer (3MT), a disease classification transformer that not only leverages multi-modal data but also handles missing data scenarios. In this work, we test 3MT for AD and Cognitively normal (CN) classification and mild cognitive impairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI (sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded Modality Transformers architecture with cross-attention to incorporate multi-modal information for more informed predictions. We propose a novel modality dropout mechanism to ensure an unprecedented level of modality independence and robustness to handle missing data scenarios. The result is a versatile network that enables the mixing of arbitrary numbers of modalities with different feature types and also ensures full data utilization in missing data scenarios. The model is trained and evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with the state-of-the-art performance and further evaluated with The Australian Imaging Biomarker & Lifestyle Flagship Study of Ageing (AIBL) dataset with missing data.

Background: This study aims to evaluate the efficacy and safety of Danggui Niantong Decoction (DGNT) systematically on gout treating.Methods: This study was registered in PROSPERO, and the registration number was CRD42021271607. By the end of December, 2022, literature research was conducted among eight electronic databases. Main results of this study were blood uric acid (BUA) and Creactive protein (CRP). Secondary outcomes were erythrocyte sedimentation rate (ESR), serum creatinine (Scr), urinary protein quantified at 24 h (Upro), and interleukin-8 (IL-8). Study screening, data collection, as well as quality assessment were performed by two reviewers independently, and analysis was completed using Stata (SE15.0) and Review Manager (5.4).Results: A total number of 13 studies were included in our meta-analysis (n = 1,094 participants). Results showed DGNT combined with conventional western medicine (CWM) was more effective than WM alone in BUA (weighted mean differences (WMD) = −3.49, 95% confidence interval (CI) [−50.36, −32.59], p = 0.000), CRP (WMD = −41.48, 95% CI [−4.32, −2.66], p = 0.017), ESR (WMD = −6.23, 95% CI [−9.28, −3.17], p = 0.019), Scr (WMD = −18.64, 95% CI [−23.09, −14.19], p = 0.001), Upro (WMD = −0.72, 95% CI [−0.91, −0.53], p = 0.000), and IL-8 (WMD = −4.77, 95% CI [−11.48, 1.94], p = 0.000). None of the adverse effects noted were severe, and no life-threatening event was reported.Conclusion: This study shows that DGNT combined with CWM seems to have an effective clinical therapeutic potential. In addition, it also provides a scientific basis for better clinical application of DGNT in the future.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021271607; Identifier: PROSPERO, CRD42021271607.

Objectives: Alpha-ketoglutarate (αKG) is an essential metabolite that plays a crucial role in skeletal homeostasis. Here we aim to investigate the effect of αKG on alveolar socket healing and reveal the underlying mechanism in the view of macrophage polarization. Methods: In a murine model pretreated with or without αKG, mandibular first molars were extracted. Mandibular tissues were harvested for microCT and histological analyses. Immunofluorescence was used to evaluate macrophage polarization during healing process. Macrophages with αKG/vehicle supplementation in vitro were proceeded to quantitative real-time PCR and flow cytometry to further elucidate the mechanism. Results: MicroCT and histological analyses showed accelerated healing and enhanced bone regeneration of extraction sockets in experimental group. αKG increased new bone volume in alveolar sockets and promoted the activity of both osteoblastogenesis and osteoclastogenesis. αKG administration reduced M1 pro-inflammatory macrophages in an early phase and promoted anti-inflammatory M2 macrophage polarization in a later phase. Consistently, the expressions of M2 marker genes were augmented in αKG group, while M1 marker genes were downregulated. Flow cytometry revealed the increased ratio of M2/M1 macrophages in cells treated with αKG. Conclusions: αKG accelerates the healing process of extraction sockets via orchestrating macrophage activation, with promising therapeutic potential in oral clinics.

Abstract Small unmanned aerial vehicles (UAVs) have developed rapidly and are widely used for disaster relief, traffic monitoring and military surveillance. To perform these tasks better, it is necessary to improve the environmental perception ability of UAVs in a dynamic environment, including their static and dynamic perception ability. Specifically, both three‐dimensional reconstruction for a static scene and localization for moving objects are required. Simultaneous Localization And Mapping technology has made great progress in static scene structure reconstruction and UAV self‐motion estimation. However, accurate real‐time localization of moving objects is still challenging. In this article, a global averaging based localization method is proposed to locate moving objects for a small UAV platform. Inspired by global structure from motion, this idea is applied to the localization of moving objects. To solve moving object localization, the relative motion estimation and global position optimisation methods are proposed. The proposed method was tested in various scenarios with a several trajectories. The extensive experimental results demonstrate the robustness and effectiveness of the proposed method.

COVID-19’s impact on children and adolescents’ mental health has heightened global focus, leading to analysis with bibliometric and visualization tools of related studies in the Web of Science™ database from 2020 to 2023. In total, there are 5,189 studies authored by 27,102 researchers from 147 countries and significantly involving 6,926 institutions. These studies are covered by 1,246 journals. Depression, anxiety, and stress are currently well-developed and important research areas and will continue to be a priority and hot topic for further exploration and discussion in the academic research field in the future. However, certain topics, such as child maltreatment, need to be reassessed in terms of their importance. Additionally, emerging areas like telemedicine and vaccine hesitancy have emerged. Fundamental aspects such as COVID-19, family, and parenting reflect the far-reaching impact of the pandemic. Given that public health emergencies may affect the mental health of children and adolescents, it is particularly important to establish specialized response mechanisms. In addition, strengthened interdisciplinary and intersectoral cooperation focusing on the mental health of children and adolescents from vulnerable groups is crucial to ensure their healthy development and promote long-term social stability and scientific and technological progress.

Aiming at the instability of communication link caused by the high-speed movement of vehicle nodes in vehicular delay tolerant network （VDTN）， considering the characteristics of regularity and periodicity of vehicle nodes movement， a VDTN routing algorithm based on optimized multi-period Bayesian network （BN） was proposed.Firstly， a new multi-period BN model and its dynamic reward mechanism for node classification were proposed to describe the movement pattern of vehicle nodes with higher accuracy.Then， two novel time-optimal-partition algorithms of multi-period BN were proposed including binary search K2GA （BS-K2GA） algorithm and simulated annealing K2GA （SA-K2GA） algorithm， where BS-K2GA algorithm had the advantages of simplicity and efficiency， while SA-K2GA could effectively avoid falling into the local optimal solution and further optimize the performance.The simulation results show that the proposed VDTN routing algorithm based on optimized multi-period BN model can significantly improve the message delivery ratio and reduce the delivery delay.Thus， the effectiveness of the approach is validated.

Abstract As the basis of image processing, single image deraining has always been a significant and challenging issue. Due to the lack of real rainy images and corresponding clean images, most deraining networks are trained by synthetic datasets, which makes the output images unsatisfactory in real applications. Besides, note that a heavy rainfall is typically accompanied with some fog. Although some deraining networks have been proposed to remove the rain streaks in the rainy images, the output images may still be blurred due to the accompanied fog. In this paper, these problems existing in single image deraining is comprehensively considered, and propose a Cycle‐Derain network based on an unsupervised attention‐guided mechanism. Specifically, the Cycle‐Derain network takes advantage of generative adversarial networks with two mappings and the cycle consistency loss to train both unpaired rainy images and rain‐free images. Moreover, it introduces an unsupervised attention‐guided mechanism and exploits the loop‐search positioning algorithm to deal with the details of rain and fog in images. Extensive experiments have been carried out, and the results show that the proposed Cycle‐Derain network is preferable compared with other deraining networks, especially in term of rainy image restoration.

Herein, we have presented the clinical features of meningioangiomatosis associated with meningioma, which is considered to be a rare neoplastic lesion. Magnetic resonance spectroscopy (MRS) demonstrated a remarkably decreased N-acetylaspartate peak and an increase in the choline peak of the lesion, suggesting neuronal injury and active cell proliferation. These findings substantially differed from those observed in the case of pure meningioangiomatosis.

Abstract The technique of vehicle license plate recognition can recognize and count the vehicles automatically, and thus many applications regarding the vehicles are greatly facilitated. However, the recognitions of vehicle license plates are extremely difficult especially in some fog‐haze environments because the fog and haze blur the boundaries and characters of license plates significantly, which makes the license plates hard to be detected or recognised. To this end, this paper proposes a vehicle License Plate Recognition method for Fog‐Haze environments (LPRFH). In LPRFH, a dark channel prior algorithm based on the local estimation of atmospheric light value is applied to dehaze the blurred images preliminarily. Then, the images are further dehazed, and the license plate regions are detected through a Joint Further‐dehazing and Region‐extracting Model on basis of an object detection convolution neural network. Finally, the image super‐resolution is accomplished with a convolution‐enhanced super‐resolution convolutional neural network, and hence the characters of license plates can be recognised successfully. Extensive experiments have been conducted, and the results indicate that LPRFH can recognise the license plates accurately even in some severe fog‐haze environments.

A charging pile recommendation method for idle electric taxis (CPRM-IET) based on recursive neural network was proposed to recommend the optimal charging piles for idle electric taxis.Usually,the movement of each idle electric taxi depends on the subconscious movement tendency and driving habits of the driver.Therefore,it is necessary to predict the future movement based on its historical movement trajectories,so as to find the charging piles with the least extra movements.In CPRM-IET,a dual-stage attention-based recurrent neural network (DA-RNN) model was provided to predict the future trajectories of electric taxis.DA-RNN model includes two types of attention mechanisms which are input attention mechanism and temporal attention mechanism.The input attention mechanism assigns different weights to the input driving sequence at each time slot,and the temporal attention mechanism assigns weights to the hidden state of the encoder.Based on the predicted future trajectories,several charging piles with the least extra movements were selected and recommended for the idle electric taxis.The simulation results show that CPRM-IET can achieve preferable results in terms of charging extra movement and root mean square error,which reflects that CPRM-IET can accurately predict the future trajectories of idle electric taxis and recommend optimal charging piles for these electric taxis.

Aiming at the problem of distortion of dark channel algorithm in defogging the sky region,an improved image defogging algorithm based on the guided filtering and adaptive tolerance mechanism was proposed.Firstly,the fitted transmissivity graphs were calculated for the windows with different sizes.Then,the transmissivity was further refined by the guided filtering technique.After that,the transmissivity in sky area was revised by an adaptive tolerance mechanism.Finally,the restored image was converted from RGB space to HSV space,and thus the brightness and contrast of the images could be color compensated.Experimental results show that the proposed algorithm restores the images effectively and obtain preferable defogging results with regard to the processing of bright areas such as the sky areas.

Delay Tolerant Networks (DTNs) are novel wireless mobile networks, which suffer from frequent disruption, high latency, and the lack of a complete path from source to destination. Vehicular Delay Tolerant Network (VDTN) is a special type of DTNs with vehicles as nodes. In VDTN, most nodes have specific movement patterns, however, traditional routing algorithms in DTNs do not take this characteristic into considerations very well. In this paper, a new routing algorithm based on Bayesian Network (BN) is proposed to construct the prediction model, which intends to predict the movement patterns of nodes in the real VDTN scenarios. Firstly, a comprehensive BN model is established, where more attributes of nodes are selected to improve the accuracy of the model prediction. Then, considering the complexity of the structure learning problem of BN, a novel structure learning algorithm, K2 algorithm based on Genetic Algorithm (K2-GA), is proposed to search the optimal BN structure efficiently. At last, Junction Tree Algorithm (JTA) is adopted in the inference of BN, which can accelerate the inference process through variable elimination and calculation sharing for large scale BN. The simulation results show that the proposed VDTN routing algorithm based on the BN model can improve the delivery ratio with a minor forwarding overhead.

In recent years, unmanned aerial vehicles (UAVs) have rapidly developed, but the illegal use of UAVs by civilians has resulted in disorder and security risks and has increasingly triggered the community concern and worry. Therefore, the monitoring and recycling of UAVs in key regions are of great significance. This paper presents novel panoramic UAV surveillance and autonomous recycling system that is based on the unique structure-free fisheye camera array and has the capability of real-time UAV detection, 3D localization, tracking, and recycling capacity over a very wide field of view. The main characteristics of this paper include the following: 1) constructing a structure-free camera-array-based panoramic UAV surveillance and recycling system; 2) designing a robust dynamic near-infrared laser-source-based self-calibration algorithm for large-scale arbitrary layout of the camera array; and 3) presenting a set of UAV detection, 3D localization, tracking, and autonomous recycling algorithms based on a fish-eye camera array. The system has been tested in various challenging scenarios, including multiple UAVs with significant appearance and scale changes and even poor weather conditions. The extensive experimental results analyzed both qualitatively and quantitatively, and the analysis of the time performance demonstrate the robustness and effectiveness of the proposed system. In addition, we successfully conduct a recycling and landing experiment with the Parrot Beobop.

Gaussian Mixture Model (GMM) is a widely used approach for the background subtraction and the moving objects detection. However, the classical GMM probably detects incorrectly and cannot deal with the shadows with a pixel-level and time-domain classification, and thus it cannot monitor the water surface floats effectively. To solve this problem, an improved GMM-based automatic segmentation method (IGASM) is proposed to detect the water surface floats in this paper, where the background updating strategy is improved to segment the water surface floats more effectively. Besides, the GMM results are mapped into an HSV color space, and a light-shadow discriminant function is applied to solve the problems of light and shadow. Then, a morphological method is used to smooth the extracted foregrounds. Finally, Graph Cuts algorithm is applied to optimized the segmentation results according to the spatial information of video images. Experimental results demonstrate that IGASM can detect the water surface floats quickly and accurately, and the influences of light, shadows and ripples of water surface can be eliminated as much as possible.

Water quality monitoring and prediction are important parts of Cyber Physical Systems. Considering the complexity, diversity, and strong non-linearity of water quality data, a single water quality prediction model is difficult to have a significant effect on different data. To solve this problem, a new water quality prediction method based on the preferred classification is proposed in this study. A preferred classifier is established to integrate back propagation neural network, support vector machines for regression and long short-term memory due to the fact that these three prediction models can take into account the different characteristics of water quality data. When new data input, the proposed method preferentially selects the prediction model that is most suitable for the data, and then uses the selected model for prediction. Finally, the proposed method is applied in two actual datasets: Songhua River and Victoria Bay. Experimental results demonstrate that the water quality prediction method based on preferred classification achieves better performance than any of the three single prediction models.

The commercialization of solar cells based on hybrid perovskites requires challenges of device stability and scalable production to be addressed. Ronget al. report ambient-processed printable mesoscopic perovskite solar cells with a lifetime of over 130 days in ambient air with 30% relative humidity.

With the increasing concern over marine applications in recent years, the technology of underwater wireless sensor networks has received considerable attention. In underwater wireless sensor networks, the gathered data are sent to terrestrial control center through multi-hops for further processing. Underwater wireless sensor networks usually consist of three types of nodes: ordinary nodes, anchor nodes, and sink nodes. The data messages are transferred from an ordinary node or an anchored node to one of the sink nodes by discrete hops. Data forwarding algorithms are at the core position of underwater wireless sensor networks, which determines data in what way to forward. However, the existing data forwarding algorithms all have problems that transmission delay is too high and delivery ratio is low. Thus, we propose a data forwarding algorithm based on estimated Hungarian method to improve delivery ratio and reduce transmission delay. The estimated Hungarian method is applied to solve the assignment problem in data forwarding process, where the anchor nodes receive the forwarding requests from ordinary nodes and optimize the waiting queue. By applying this method in underwater wireless sensor networks, data forwarding has great advantages in success rate and transmission delay, which has been validated by both analysis and simulation results.

Delay-tolerant networks are novel wireless mobile networks, which are characterized with high latency and frequent disconnectivity. Besides, people carrying mobile devices form a lot of communities because of similar interests and social relationships. How to improve the routing efficiency in multi-community scenarios has become one of the research hot spots in delay-tolerant networks. In this article, we present a network model of the multi-community delay-tolerant networks and formulate a dynamic quota-controlled routing problem of minimizing the average number of copies of a message that satisfies the required delivery probability under the given time-to-live of the message as a nonlinear optimization problem. To solve this problem, we propose an improved genetic algorithm called genetic algorithm for delivery probability and time-to-live optimization for the dynamic quota-controlled routing scheme to reduce the routing cost further. In addition, a cost-efficient dynamic quota-controlled routing protocol based on genetic algorithm for delivery probability and time-to-live optimization is proposed, which can dynamically adjust message copies according to its assigned delivery probability and time-to-live in different communities on the shortest path. Both the numerical and simulation results show that our routing with the proposed algorithm is more cost efficient.

The underwater wireless sensor networks composed of sensor nodes are deployed underwater for monitoring and gathering submarine data. Since the underwater environment is usually unpredictable, making the nodes move or be damaged easily, such that there are several vital objectives in the data forwarding issue, such as the delivery success rate, the error rate, and the energy consumption. To this end, we propose a data forwarding algorithm based on Markov thought, which logically transforms the underwater three-dimensional deployment model into a two-dimensional model, and thus the nodes are considered to be hierarchically deployed. The data delivery is then achieved through a “bottom to top” forwarding mode, where the delivery success rate is improved and the energy consumption is reduced because the established paths are more stable, and the proposed algorithm is self-adaptive to the dynamic routing loads.

Underwater wireless sensor networks are the enabling technology for the aquatic environmental monitoring and exploring and have attracted much attention recently. Due to the highly hostile and unpredictable underwater environments, some beacon nodes tend to move or be damaged. Therefore, the unknown nodes will be positioned with larger error, which abases the value of data collected by sensor nodes. In order to solve the beacon error problem, this article proposes an error beacon filtering algorithm based on K -means clustering. First, the coordinate of each beacon is calculated through an improved trilateration method, and then the beacon with the maximum positioning error is filtered out via the K -means clustering algorithm. The remaining beacons repeat the above processes until the distance error of each beacon does not exceed a preset threshold. The analysis of simulation results indicates that the error beacons can be accurately found and filter out through our proposed error beacon filtering algorithm (based on K -means clustering), and thus the localization accuracy is enhanced. Besides, error beacon filtering algorithm also has a provable low complexity.

Nodes in underwater wireless sensor networks (UWSNs) keep moving and dispersing due to force of water flow and aquatic creatures touching, and thus some isolated unknown nodes emerge. This type of isolated unknown nodes cannot directly communicate with enough beacons in their neighborhoods, which makes localizations for them disabled or the localization error unbearable. To this end, a multihops fitting localization approach is proposed in this paper. Firstly, some intermediate nodes between beacons and unknown nodes are set as routers to construct paths via a greedy method; then, the multihop paths are approximately fitted into straight lines; finally, the positions of unknown nodes can be estimated by trilateration. The proposed algorithm is analyzed and simulated in terms of localization error and error variance, and the results are proven preferable.

A multisensor data fusion approach for water quality evaluation using Dempster-Shafer evidence theory is presented. To evaluate water quality, each sensor measurement is considered as a piece of evidence. Based on the water quality parameters measured by sensor node, the mass function of water quality class is calculated. Evidence from each sensor is given a reliability discounting and then combined with the others by D-S rule. According to the decision rule which uses the fusion mass function values, the class of water quality can be determined. Finally, experiments are given to demonstrate that the proposed approach can evaluate water quality from uncertain sensor data and improve evaluation performance.

In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM) was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt’s method, Softmax regression and Naive Bayes). Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt’s method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine) was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples.

The traditional multiview distributed video coding scheme using regional unified coding may lead to distortion problem of decoding estimation of the intense motion region. This paper presents a novel multiview distributed video coding algorithm. In the main perspective, gaining the intense motion regions of Wyner-Ziv frame according to the criteria of ROI, the algorithm extracts their DCT low-frequency coefficients for the entropy coding, in order to generate the best temporal side information. For the nonintense motion regions, the algorithm utilizes motion compensation interpolation (MCI) to generate side information. Finally, side information based on fusion of temporal and spatial side information will be gained. Experimental results show that our proposed algorithm can gain more accurate motion estimation in the intense motion region. Quality of decoded image is improved with the same transmit rate; thus, energy consumption of sensor nodes will be decreased ultimately.

The topology control techniques of underwater wireless sensor networks and terrestrial wireless sensor networks are significantly different because of the particularity of underwater environments and acoustic communication. In this paper, an underwater wireless sensor network model was constructed, and six universal topology control objectives were concluded. The QoS topology control problem was mapped into an ordinal potential game model, and a distributed strategy adjustment algorithm for nodes was designed accordingly. The strategy vector resulting from the algorithm converges to the Nash equilibrium; minor complexity and preferable approximate ratios can be represented by the algorithm as well. The performance of the algorithm was analyzed through simulation experiments which indicate a well-constructed topology. Every objective was upgraded when model parameters were set suitable.