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This is an article in the field of metallurgical engineering. Titanium gypsum is an industrial waste residue mainly composed of calcium sulfate dehydrate when the titanium dioxide is produced by the sulfuric acid method. At present, the utilization rate of titanium gypsum is relatively low due to its high impurity content , difficulty in dehydration and poor mechanical properties of the subsequent products. A large amount of titanium gypsum stored in the open air has caused great environmental and economic stress on the titanium dioxide enterprises, which is a serious waste of land resources. In this article, the current situation and research progress of global titanium gypsum in building materials, chemical industry, agriculture and climate change were summarized. The suggestions and prospects for the effective reduction and high-quality resource utilization of titanium gypsum from the aspects of structure adjustment, technological innovation, green development, standard formulation and policy support were provided.

The un-plasticized polyvinyl chloride (UPVC) is increasingly used in the construction industry because of its superior properties, particularly in greenhouses with UPVC as the skeleton. This study investigated the bearing capacities of UPVC pipes under axial compression. Eighteen UPVC pipe specimens with length-to-diameter ratios ranging from 24 to 45 were tested under axial compression. The failure modes and buckling resistances of the specimens were analysed. A finite element (FE) model was established to simulate the compressive behaviour of UPVC pipes, and the FE predictions were verified against the experimental results. Subsequently, extensive parametric studies were performed to analyse the effects of the diameter-to-thickness ratio, yield strength, and geometric imperfections on the overall stability of UPVC pipes under axial compression. The experimental and FE results were compared with the buckling curves obtained from the design codes for metallic material columns. Based on the test data and parametric analysis, a formulation of the buckling curves for UPVC pipes was proposed to predict the buckling performance of UPVC pipes better. These findings provide a basis for studying the mechanical performance of UPVC pipes subjected to axial compressive loading.

Abstract Event cameras or dynamic vision sensors (DVS) record asynchronous response to brightness changes instead of conventional intensity frames, and feature ultra-high sensitivity at low bandwidth. The new mechanism demonstrates great advantages in challenging scenarios with fast motion and large dynamic range. However, the recorded events might be highly sparse due to either limited hardware bandwidth or extreme photon starvation in harsh environments. To unlock the full potential of event cameras, we propose an inventive event sequence completion approach conforming to the unique characteristics of event data in both the processing stage and the output form. Specifically, we treat event streams as 3D event clouds in the spatiotemporal domain, develop a diffusion-based generative model to generate dense clouds in a coarse-to-fine manner, and recover exact timestamps to maintain the temporal resolution of raw data successfully. To validate the effectiveness of our method comprehensively, we perform extensive experiments on three widely used public datasets with different spatial resolutions, and additionally collect a novel event dataset covering diverse scenarios with highly dynamic motions and under harsh illumination. Besides generating high-quality dense events, our method can benefit downstream applications such as object classification and intensity frame reconstruction.

Recently, tracking with unmanned aerial vehicle (UAVs) platforms has played significant roles in Earth observation tasks. However, target occlusion remains a challenging factor during the continuous tracking procedure. In particular, incomplete local appearance features can mislead the tracking network to produce inaccurate size and position estimations when the target is occluded. Furthermore, the tracking network lacks sufficient occlusion supervision information, which may lead to template degradation during template updating. To address these challenges, in this article, we design an occlusion-aware tracker with local-global features modeling, which contains two key components, namely the feature intrinsic association module (FIAM) and the feature verification module (FVM). Specifically, the FIAM divides the local features into blocks and utilizes the transformer network to explore the relative relationships among each subblock, which supplements the damaged local target features and assists the modeling for global target features. In addition, the FVM establishes a correlation measurement network between the target and the template. To precisely evaluate the occlusion status, masked samples with occlusion exceeding 50% are selected as negative samples for independent training, which ensures the purity of the target template. Qualitative and quantitative experiments are conducted on publicly available datasets, including UAV20 L, UAV123, and LaSOT. Qualitative and quantitative experiments have demonstrated the effectiveness of the proposed tracking algorithm over the other state-of-the-art trackers in occlusion scenarios.

Drones have been used in a variety of scenarios, such as atmospheric monitoring, fire rescue, agricultural irrigation, etc., in which accurate environmental perception is of crucial importance for both decision making and control. Among drone sensors, the RGB camera is indispensable for capturing rich visual information for vehicle navigation but encounters a grand challenge in high-dynamic-range scenes, which frequently occur in real applications. Specifically, the recorded frames suffer from underexposure and overexposure simultaneously and degenerate the successive vision tasks. To solve the problem, we take object tracking as an example and leverage the superior response of event cameras over a large intensity range to propose an event-assisted object tracking algorithm that can achieve reliable tracking under large intensity variations. Specifically, we propose to pursue feature matching from dense event signals and, based on this, to (i) design a U-Net-based image enhancement algorithm to balance RGB intensity with the help of neighboring frames in the time domain and then (ii) construct a dual-input tracking model to track the moving objects from intensity-balanced RGB video and event sequences. The proposed approach is comprehensively validated in both simulation and real experiments.

Pre-harvest application of elicitors improves grape quality, specifically the phenolic compounds and color characteristics. Limited research has been conducted on the impact of elicitors on the C6 compounds found in grapes. This is due to lack of comprehensive studies examining the combined effects of bound aroma compounds, enzyme activity, and substrate availability. This study aimed to assess the impact of benzothiadiazole (BTH) on the physicochemical properties and C6 compounds of Cabernet Gernischt grapes during ripening. Compared with the control group (CK), BTH treatment significantly increased the 100-berry weight, skin/berry ratio, pH, total phenolic content, and total flavonoid content in ripe grapes. Additionally, BTH treatment led to significant reductions in reducing sugar, total soluble solids, titratable acidity, linoleic acid, linolenic acid, and free C6 aldehydes. Furthermore, BTH treatment significantly decreased the contents of free C6 alcohols and increased the levels of free and bound C6 esters. BTH treatment also increased the activities of lipoxygenase, alcohol dehydrogenase, and alcohol acetyltransferase enzymes, while it decreased the activity of hydroperoxide lyase enzyme. The application of BTH resulted in changes to the physicochemical properties and levels of C6 compounds in Cabernet Gernischt grapes by up-regulating enzyme activity and down-regulating precursors.

Solar energy is one of the main renewable energy sources and has rapidly developed in many countries. However, the photovoltaic (PV) output power will be different under various meteorological and geographical conditions. Therefore, this paper presents an optimization method for the deployment of PV panels in a centralized PV power plant considering multiple factors. Firstly, the whole planning area is divided into a certain amount of sub-areas according to a given area, and fuzzy C-means algorithm is used for terrain clustering according to the geographical characteristics of the sub-areas. Secondly, the correlation analysis between each meteorological factor and PV output power is carried out separately to select the main factors affecting PV output power, and then the expected annual PV output power under the joint action of several main meteorological factors in each terrain is calculated by dual-stage attention mechanism based long short-term memory algorithm. Finally, according to the expected annual PV output of each terrain, considering the constraints including cost, area and so on, the deployment optimization of PV panels is obtained to maximize the annual PV output of the whole PV power plant and minimize the construction cost. The results of case studies show that the proposed methods effectively improve the expected PV output power of the PV power plant and reduce the construction cost.

Unmanned aerial vehicles (UAVs) have been widely applied in military and civilian fields due to their flexibility and effectiveness. As a vital component of UAVs, the vision system has taken on great significance in different applications (e.g., autonomous landing, traffic surveillance, and disaster rescue) to attract widespread attention in recent years. Therefore, the automatic understanding of visual data collected from these air platforms becomes urgently needed in UAV systems. In this review, we revisit and summarize the recent techniques and developments for several typical UAV applications, including object detection, object tracking, and semantic segmentation. In addition, we also highlight the difficulties and subsequent orientations from different perspectives, which may stimulate future research and applications in the UAV vision era.

Smoke is more observable than open fires. Optical satellite video has the advantages of a wide monitoring range, fast response speed, and good economy in large-scale surface smoke monitoring tasks. It can be used in wide-area forest wildfire monitoring, battlefield dynamic monitoring, disaster relief decision-making. The smoke segmentation method based on traditional handcrafted features is easily limited by the scene and data. This article introduces the deep learning method to the optical satellite video smoke target segmentation. However, due to the lack of real smoke images and the blurred edges of smoke, there are currently few labeled datasets for smoke segmentation in high-resolution optical satellite imagery scenes, which cannot provide sufficient training data for deep learning models. The smoke image from the satellite perspective also has the characteristics of multiscale features and ground object background interference. To solve the abovementioned problems, we construct a set of high-resolution optical satellite imagery smoke synthesis datasets based on the optical imaging process of smoke targets, which saves the cost of manual labeling. In addition, we design an attention-guided optical satellite video smoke segmentation network model, which can effectively suppress the ground object background's false alarm and extract the smoke's multiscale features. Synthetic data faces the transferability problem in real-world applications, so the physical constraints of the smoke imaging process are introduced into the loss function to improve the generalization of the model in real smoke data. The comprehensive evaluation results show that the method outperforms representative semantic segmentation networks.

Recently, the improvement of detection performance always relies on deeper convolutional layers and complex convolutional structures in remote sensing images, which significantly increases the storage space and computational complexity of the detector. Although previous work has designed various novel lightweight convolutions, when these convolutional structures are applied to remote sensing detection tasks, the inconsistency between features and targets as well as between features and tasks in the detection architecture is often ignored: (1) The features extracted by convolution sliding in a fixed direction make it difficult to effectively model targets with arbitrary direction distribution, which leads to the detector needing more parameters to encode direction information and the network parameters being highly redundant; (2) The detector shares features from the backbone, but the classification task requires rotation-invariant features while the regression task requires rotation-sensitive features. This inconsistency in the task can lead to inefficient convolutional structures. Therefore, this paper proposed a detector that uses the Feature Decoupling for Lightweight Oriented Object Detection (FDLO-Det). Specifically, we constructed a rotational separable convolution that extracts rotational equivariant features while significantly compressing network parameters and computational complexity through highly shared parameters. Next, we introduced an orthogonal polarization transformation module that decomposes rotational equivariant features in both horizontal and vertical orthogonal directions, and used polarization functions to filter out the required features for classification and regression tasks, effectively improving detector performance. Extensive experiments on DOTA, HRSC2016, and UCAS-AOD show that the proposed detector can achieve the best performance and achieve an effective balance between computational complexity and detection accuracy.

Aircraft recognition in high-resolution remote sensing images has rapidly progressed with the advance of convolutional neural networks (CNNs). However, the previous CNN-based methods may not work well for recognizing aircraft in low-resolution remote sensing images because the blurred aircraft in these images offer insufficient details to distinguish them from similar types of targets. An intuitive solution is to introduce superresolution preprocessing. However, conventional superresolution methods mainly focus on reconstructing natural images with detailed texture rather than constructing a high-resolution object with strong discriminative information for the recognition task. To address these problems, we propose a unified framework for joint superresolution and aircraft recognition (Joint-SRARNet) that tries to improve the recognition performance by generating discriminative, high-resolution aircraft from low-resolution remote sensing images. Technically, this network integrates superresolution and recognition tasks into the generative adversarial network (GAN) framework through a joint loss function. The generator is constructed as a joint superresolution and refining subnetwork that can upsample small blurred images into high-resolution ones and restore high-frequency information. In the discriminator, we introduce a new classification loss function that forces the discriminator to distinguish between real and fake images while recognizing the type of aircraft. In addition, the classification loss function is back-propagated to the generator to obtain high-resolution images with discriminative information for easier recognition. Extensive experiments on the challenging multitype aircraft of remote sensing images (MTARSI) dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a small blurred image and significant improvement in the recognition performance. To our knowledge, this is the first work on joint superresolution and aircraft recognition tasks.

Proton exchange membrane fuel cell (PEMFC) powered propulsion has gained increasing attention in urban air mobility applications in recent years. Due to its high power density, ultra-thin heat pipe technology has great potential for cooling PEMFCs, but optimizing the limited internal cavity of the heat pipe remains a significant challenge. In this study, a three-dimensional multiphase model of the heat pipe cooled PEMFC is built to evaluate the impact of three internal structures, layered, spaced, and composite, of ultra-thin heat pipes on system performance. The results show that the heat pipe cooling with the composite structure yields a lower thermal resistance and a larger operating range for the PEMFC system compared to other internal structures because of more rational layout of the internal cavity. In addition, the relationship between land to channel width ratio (LCWR) and local transport property is analyzed and discussed based on composite structural heat pipes. The heat pipe cooled PEMFC with a LCWR of 0.75 has a significant advantage in limiting current density and maximum power density compared to the LCWRs of 1 and 1.33 as a result of more uniform in-plane distributions of temperature and liquid water within its cathode catalyst layer.

In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure, resulting in a significant increase in the storage space and computational complexity. Although previous works have designed a variety of new lightweight convolution and compression algorithms, these works often require complex manual design and cause the detector to be greatly modified, which makes it difficult to directly apply the algorithms to different detectors and general hardware. Therefore, this paper proposes an iterative pruning framework based on assistant distillation. Specifically, a structured sparse pruning strategy for detectors is proposed. By taking the channel scaling factor as a representation of the weight importance, the channels of the network are pruned and the detector is greatly slimmed. Then, a teacher assistant distillation model is proposed to recover the network performance after compression. The intermediate models retained in the pruning process are used as assistant models. By way of the teachers distilling the assistants and the assistants distilling the students, the students’ underfitting caused by the difference in capacity between teachers and students is eliminated, thus effectively restoring the network performance. By using this compression framework, we can greatly compress the network without changing the network structure and can obtain the support of any hardware platform and deep learning library. Extensive experiments show that compared with existing detection networks, our method can achieve an effective balance between speed and accuracy on three commonly used remote sensing target datasets (i.e., NWPU VHR-10, RSOD, and DOTA).

BackgroundDifferentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.PurposeTo differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses.Methods and MaterialsA total of 350 patients were collected from two institutions, including 182 patients with GBM and 168 patients with MET, which were all proven by pathology. The ROI of the tumor was obtained on axial postcontrast MRI which was performed before operation. Seven radiomic feature selection methods and four classification algorithms constituted 28 classifiers in two classification strategies, with the best classifier serving as the final radiomics model. The clinical and combination models were constructed using the nomograms developed. The performance of the nomograms was evaluated in terms of calibration, discrimination, and clinical usefulness. Student’s t-test or the chi-square test was used to assess the differences in the clinical and radiological characteristics between the training and internal validation cohorts. Receiver operating characteristic curve analysis was performed to assess the performance of developed models with the area under the curve (AUC).ResultsThe classifier fisher_decision tree (fisher_DT) showed the best performance (AUC: 0.696, 95% CI:0.608-0.783) for distinguishing between GBM and MET in internal validation cohorts; the classifier reliefF_random forest (reliefF_RF) showed the best performance (AUC: 0.759, 95% CI: 0.613-0.904) for distinguishing between MET-lung and MET-other in internal validation cohorts. The combination models incorporating the radiomics signature and clinical-radiological characteristics were superior to the clinical-radiological models in the two classification strategies (AUC: 0.764 for differentiation between GBM in internal validation cohorts and MET and 0.759 or differentiation between MET-lung and MET-other in internal validation cohorts). The nomograms showed satisfactory performance and calibration and were considered clinically useful, as revealed in the decision curve analysis.Data ConclusionThe combination of radiomic and non-radiomic features is helpful for the differentiation among GBM, MET-lung, and MET-other.

The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance for HSI classification. Moreover, dictionary-based methods have low discriminative capability, which leads to less accurate classification. To solve the above problems, we propose a deep learning-based structure dictionary for HSI classification in this paper. The core ideas are threefold, as follows: (1) To extract the abundant spectral information, we incorporate deep residual neural networks in dictionary learning and represent input signals in the deep feature domain. (2) To enhance the discriminative ability of the proposed model, we optimize the structure of the dictionary and design sharing constraint in terms of sub-dictionaries. Thus, the general and specific feature of HSI samples can be learned separately. (3) To further enhance classification performance, we design two kinds of loss functions, including coding loss and discriminating loss. The coding loss is used to realize the group sparsity of code coefficients, in which within-class spectral samples can be represented intensively and effectively. The Fisher discriminating loss is used to enforce the sparse representation coefficients with large between-class scatter. Extensive tests performed on hyperspectral dataset with bright prospects prove the developed method to be effective and outperform other existing methods.

Multi-scale object detection within Synthetic Aperture Radar (SAR) images has become a research hotspot in SAR image interpretation. Over the past few years, CNN-based detectors have advanced sharply in SAR object detection. However, the state-of-the-art detection methods are continuously limited in Feature Pyramid Network (FPN) designing and detection anchor setting aspects due to feature misalignment and targets’ appearance variation (i.e., scale change, aspect ratio change). To address the mentioned limitations, a scale-aware feature pyramid network (SARFNet) is proposed in this study, which comprises a scale-adaptive feature extraction module and a learnable anchor assignment strategy. To be specific, an enhanced feature pyramid sub-network is developed by introducing a feature alignment module to estimate the pixel offset and contextually align the high-level features. Moreover, a scale-equalizing pyramid convolution is built through 3-D convolution within the feature pyramid to improve inter-scale correlation at different feature levels. Furthermore, a self-learning anchor assignment is set to update hand-crafted anchor assignments to learnable anchor/feature configuration. By using the dynamic anchors, the detector of this study is capable of flexibly matching the target with different appearance changes. According to extensive experiments on public SAR image data sets (SSDD and HRSID), our algorithm is demonstrated to outperform existing boat detectors.

Purpose: Developing an MRI-based radiomics model to effectively and accurately predict the predominant histopathologic growth patterns (HGPs) of colorectal liver metastases (CRLMs).Materials and Methods: In this study, 182 resected and histopathological proven CRLMs of chemotherapy-naive patients from two institutions, including 123 replacement CRLMs and 59 desmoplastic CRLMs, were retrospectively analyzed. Radiomics analysis was performed on two regions of interest (ROI), the tumor zone and the tumor-liver interface (TLI) zone. Decision tree (DT) algorithm was used for radiomics modeling on each MR sequence, and fused radiomics model was constructed by combining the radiomics signature of each sequence. The clinical and combination models were developed through multivariate logistic regression method. The performance of the developed models was assessed by receiver operating characteristic (ROC) curves with indicators of area under curve (AUC), accuracy, sensitivity, and specificity. A nomogram was constructed to evaluate the discrimination, calibration, and usefulness.Results: The fused radiomicstumor and radiomicsTLI models showed better performance than any single sequence and clinical model. In addition, the radiomicsTLI model exhibited better performance than radiomicstumor model (AUC of 0.912 vs. 0.879) in internal validation cohort. The combination model showed good discrimination, and the AUC of nomogram was 0.971, 0.909, and 0.905 in the training, internal validation, and external validation cohorts, respectively.Conclusion: MRI-based radiomics method has high potential in predicting the predominant HGPs of CRLM. Preoperative non-invasive identification of predominant HGPs could further explore the ability of HGPs as a potential biomarker for clinical treatment strategy, reflecting different biological pathways.

Background: Intracranial hemangiopericytoma (IHPC) and meningioma are both meningeal neoplasms, but they have extremely different malignancy and outcomes. Because of their similar radiological characteristics, they are difficult to distinguish prior to surgery, leading to a high rate of misdiagnosis.Methods: We enrolled 292 patients (IHPC, 155; meningiomas, 137) with complete clinic-radiological and histopathological data, from a 10-year database established at Tiantan hospital. Radiomics analysis of tumor and peritumoral edema was performed on multisequence magnetic resonance images, and a fusion radiomics signature was generated using a machine-learning strategy. By combining clinic-radiological data with the fusion radiomics signature, we developed an integrated diagnostic approach that we named the IHPC and Meningioma Diagnostic Tool (HMDT).Results: The HMDT displayed remarkable diagnostic ability, with areas under the curve (AUCs) of 0.985 and 0.917 in the training and validation cohorts, respectively. The calibration curve showed excellent agreement between the diagnosis predicted by HMDT and the histological outcome, with p-values of 0.801 and 0.622 for the training and the validation cohorts, respectively. Cross-validation showed no statistical difference across three divisions of the cohort, with average AUCs of 0.980 and 0.941 for the training and validation cohorts, respectively. Stratification analysis showed consistent performance of the HMDT in distinguishing IHPC from highly misdiagnosed subgroups of grade I meningioma and angiomatous meningioma (AM) with AUCs of 0.913 and 0.914 in the validation cohorts for the two subgroups.Conclusions: By integrating clinic-radiological information with radiomics signature, the proposed HMDT could assist in preoperative diagnosis to distinguish IHPC from meningioma, providing the basis for strategic decisions regarding surgery.

Abstract This paper presents a model of cascading failures in cyber-physical power systems (CPPSs) based on an improved percolation theory, and then proposes failure mitigation strategies. In this model, the dynamic development of cascading failures is divided into several iteration stages. The power flow in the power grid, along with the data transmission and delay in the cyber layer, is considered in the improved percolation theory. The interaction mechanism between two layers is interpreted as the observability and controllability analysis and data update analysis influencing the node state transformation and security command execution. The resilience indices of the failures reflect the influence of cascading failures on both topological integrity and operational state. The efficacy of the proposed mitigation strategies is validated, including strategies to convert some cyber layer nodes into autonomous nodes and embed unified power flow controller (UPFC) into the physical layer. The results obtained from simulations of cascading failures in a CPPS with increasing initial failure sizes are compared for various scenarios. Dynamic cascading failures can be separated into rapid and slow processes. The interdependencies and gap between the observable and controllable parts of the physical layer with the actual physical network are two fundamental reasons for first-order transition failures. Due to the complexity of the coupled topological and operational relations between the two layers, mitigation strategies should be simultaneously applied in both layers.

Based on the anchoring effect due to the self-assembling behavior of the phospholipid molecules at the interface between the liquid crystal and water phases on the orientation of liquid crystals, the optical response associated with the orientation and structure of liquid crystals with respect to the concentration of 1,2-didodecanoyl-sn-glycero-3-phosphocholine solution has been investigated. The optical response owing to changes in the orientation and structure of the mixed cholesteric liquid crystals with respect to the change in the concentration of phosphatidylcholine has been obtained. Moreover, the feasibility of using as-prepared mixed cholesteric liquid crystals to measure the phosphatidylcholine concentration has been verified. A methodology to measure the reflectance spectrum by using mixed cholesteric liquid crystals to sensitize the phosphatidylcholine concentration has been further realized. The sensitization effect of the mixed cholesteric liquid crystals on the measurement of phosphatidylcholine concentration was also verified.

Recently, correlation filter-based trackers have received extensive researching interest because of their simplicity and superior running speed. However, such trackers perform poorly when the target undergoes occlusion, viewpoint change, or other challenging attributes due to pre-defined sampling strategy. To tackle these issues, the authors propose an adaptive region proposal strategy to facilitate object tracking. To be more specific, a novel tracking monitoring indicator is advocated to forecast tracking failure. Afterwards, detection and scale proposals were incorporated, respectively, to recover from model drift as well as handle aspect ratio variation. The algorithm was tested on several challenging video sequences, which demonstrates that the proposed algorithm outperforms other state-of-the-art trackers.

This paper proposes a novel approach to analyze the impacts of cyber layer failures (i.e., protection failures and monitoring failures) on the reliability evaluation of composite power systems. The reliability and availability of the cyber layer and its protection and monitoring functions with various topologies are derived based on a reliability block diagram method. The availability of the physical layer components are modified via a multi-state Markov chain model, in which the component protection and monitoring strategies, as well as the cyber layer topology, are simultaneously considered. Reliability indices of composite power systems are calculated through non-sequential Monte-Carlo simulation. Case studies demonstrate that operational reliability downgrades in cyber layer function failure situations. Moreover, protection function failures have more significant impact on the downgraded reliability than monitoring function failures do, and the reliability indices are especially sensitive to the change of the cyber layer function availability in the range from 0.95 to 1.

Cholesteric liquid crystals (CLCs) are sensitive to environmental temperature changes, and have been employed as a specific intermediary for biosensors. Considering the temperature-dependent structural changes of CLCs, this study aimed to determine the sensing properties of side-polished fibers (SPFs) after coating with CLCs. The experimental results demonstrated that, with regard to the transmitted spectrum, the loss peak of CLC-coated SPFs exhibited a positive linear relationship with temperature changes over a range of 20 to 50 °C. The linear correlation coefficient achieved 97.8% when the temperature increased by 10 °C, and the loss peak drifted by 12.72 nm. The reflectance spectrum of CLCs coated on the polished surface were obtained using optical fiber sensors. The feasibility of measuring the helical structure of CLCs was further verified using SPF transmission spectroscopy. The findings indicated that the transmitted spectrum of SPFs could be adopted to characterize the helical structure of CLCs, which lays a solid foundation for further study on SPF-based biosensors.

By using ginkgo leaves (GL) as template and Zn(CH3COO)2∙2H2O as Zn source, a series of ZnO samples with special morphology were prepared via a template-assisted two-steps method without adding any other additives. The degradation of the dye MB was used to evaluate the photocatalytic property of the as-prepared samples. The results showed that when a proper amount of the template was used, a 3D nanoarchitectural ZnO hollow sphere with porous sphere shell assembled by well-distributed nanoparticles was obtained and its photocatalytic activity was much higher than that of ZnO nanoparticles. The special morphology of the sample was herein considered to be very helpful for highly efficient adsorption and activation of reactant molecules by multi-times adsorption-desorption-adsorption, efficient absorption of irradiation light by repeated absorption-reflection-absorption, and efficient separation of the photogenerated e−-h+ pairs. In addition, the formation of 3D structure of sample ZnO was also discussed.

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

Occlusion is a challenging problem in visual tracking. Therefore, in recent years, many trackers have been explored to solve this problem, but most of them cannot track the target in real time because of the heavy computational cost. A spatio-temporal context (STC) tracker was proposed to accelerate the task by calculating context information in the Fourier domain, alleviating the performance in handling occlusion. In this paper, we take advantage of the high efficiency of the STC tracker and employ salient prior model information based on color distribution to improve the robustness. Furthermore, we exploit a scale pyramid for accurate scale estimation. In particular, a new high-confidence update strategy and a re-searching mechanism are used to avoid the model corruption and handle occlusion. Extensive experimental results demonstrate our algorithm outperforms several state-of-the-art algorithms on the OTB2015 dataset.

Conventional power systems are developing into cyber-physical power systems (CPPS) with wide applications of communication, computer and control technologies. However, multiple practical cases show that the failure of cyber layers is a major factor leading to blackouts. Therefore, it is necessary to discuss the cascading failure process considering cyber layer failures and analyze the vulnerability of CPPS. In this paper, a CPPS model, which consists of cyber layer, physical layer and cyber-physical interface, is presented using complex network theory. Considering power flow properties, the impacts of cyber node failures on the cascading failure propagation process are studied. Moreover, two vulnerability indices are established from the perspective of both network structure and power flow properties. A vulnerability analysis method is proposed, and the CPPS performance before and after cascading failures is analyzed by the proposed method to calculate vulnerability indices. In the case study, three typical scenarios are analyzed to illustrate the method, and vulnerabilities under different interface strategies and attack strategies are compared. Two thresholds are proposed to value the CPPS vulnerability roughly. The results show that CPPS is more vulnerable under malicious attacks and cyber nodes with high indices are vulnerable points which should be reinforced.