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Objective We aimed to compare the dosimetric characteristics of 6‐MV flattening filter mode (FF) and flattening filter‐free mode (FFF) volumetric‐modulated arc therapy (VMAT) plans in preoperative radiotherapy for rectal cancer using an Edge linear accelerator. Methods Two‐arc FF‐VMAT and FFF‐VMAT plans were generated for 15 patients with rectal cancer using the Philips Pinnacle treatment planning system (version 9.10). The homogeneity index, conformity index, planning target volume covered by 105% isodose lines, organ‐at‐risk dosimetry characteristics, and monitor units (MUs) were compared between the plans. The results were analyzed using paired t‐tests. Results The conformity index of the FF‐VMAT plan and FFF‐VMAT plan were (0.93 ± 0.01) and (0.92 ± 0.02), and the homogeneity index of the two groups were (0.07 ± 0.03) and (0.08 ± 0.01), the differences were not statistically significant (both P > 0.05). The volume covered by 105% isodose lines of the FFF‐VMAT plan was significantly higher than that of the FF‐VMAT plan. The FFF‐VMAT plan required a higher monitor unit (t = 11.49, P = 0.002) and shorter beam‐on time (t = 5.78, P = 0.001) compared with the FF‐VMAT plan. There were no significant differences in the absolute volume receiving 30 Gy and 40 Gy of the bladder between the plans. The absolute volume receiving 30 Gy and absolute volume receiving 40 Gy of the intestine and colon in the FFF‐VMAT plan increased by 5.5% and 2.1% compared with those in the FF‐VMAT plans that increased by 7.2% and 2.2%, respectively (both P > 0.05). The absolute volume receiving 20 Gy of the left and right femoral head in the FFF‐VMAT plan decreased by 3.7% and 4.9% compared with those in the FF‐VMAT plans (P > 0.05). Conclusion Both FF‐VMAT and FFF‐VMAT plans met the clinical requirements.

Background Collecting (Bellini) duct carcinoma (CDC) is a highly malignant and rare kidney tumor. We report our 12-year experience with CDC and the results of a retrospective analysis of patients and tumor characteristics, clinical manifestations, and imaging features by computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)/CT. Methods Retrospective examination of tumors between January 2007 and December 2019 identified 13 cases of CDC from three medical centers in northern China. All 13 patients underwent CT scan, among which eight underwent dynamic enhanced CT scan, two underwent PET/CT scan, and one underwent magnetic resonance cholangiopancreatography (MRCP) examination. The lesions were divided into nephritis type and mass type according to the morphology of the tumors. Results The study group included ten men and three women with an average age of 64.23 ± 10.74 years. The clinical manifestations were gross hematuria, flank pain, and waist discomfort. The mean tumor size was 8.48 ± 2.48 cm. Of the 13 cases, six (46.2%) were cortical-medullary involved type and seven (53.8%) were cortex–medullary–pelvis involved type. Eleven (84.6%) cases were nephritis type and two (15.4%) were mass type. The lesions appeared solid or complex solid and cystic on CT and MRI. The parenchymal area of the tumors showed isodensity or slightly higher density on unenhanced CT scan in the 13 cases. PET/CT in two cases showed increased radioactivity intake. Evidence of intra-abdominal metastatic disease was present on CT in nine (69.2%) cases. Conclusions The imaging characteristics of CDC differ from those of other renal cell carcinomas. In renal tumors located in the junction zone of the renal cortex and medulla that show unclear borders, slight enhancement, and metastases in the early stage, a diagnosis of CDC needs to be considered. PET/CT provides crucial information for the diagnosis of CDC, as well as for designing treatment strategies including surgery.

Underwater image processing is an intelligence research field that has great potential to help developers better explore the underwater environment. Underwater image processing has been used in a wide variety of fields, such as underwater microscopic detection, terrain scanning, mine detection, telecommunication cables, and autonomous underwater vehicles. However, underwater imagery suffers from strong absorption, scattering, color distortion, and noise from the artificial light sources, causing image blur, haziness, and a bluish or greenish tone. Therefore, the enhancement of underwater imagery can be divided into two methods: 1) underwater image dehazing and 2) underwater image color restoration. This paper presents the reason for underwater image degradation, surveys the state-of-the-art intelligence algorithms like deep learning methods in underwater image dehazing and restoration, demonstrates the performance of underwater image dehazing and color restoration with different methods, introduces an underwater image color evaluation metric, and provides an overview of the major underwater image applications. Finally, we summarize the application of underwater image processing.

In multivariate chaotic time series prediction, correlation analysis is important for reducing input dimensions and improving prediction performance. Grey relational analysis (GRA) has proved to be an effective method for data correlation analysis, especially for inexact data and incomplete data. In GRA, points are usually regarded as objects, and the distance between points or the concave and convex degree are mostly used to measure the correlations. However, with discrete variables, correlation analysis results always tend to have some deviations when using prior GRA methods. Furthermore, GRA methods cannot directly use vector datasets. Therefore, in this paper, an improved GRA method is proposed based on vector projections. The input and output variables are expressed as vectors by linking two adjacent points. The vectors, instants of the points, are regarded as the objects, and the projection length of input variables to output variables is used to measure the correlations. The smaller the difference between the projection length and the input variables, the higher the correlation. Then, a hybrid variable selection and prediction model is proposed based on the improved GRA method for multivariate chaotic time series predictions, in order to overcome the negative effects of irrelevant and redundant variables caused by phase-space reconstruction. The experimental results based on the gas furnace dataset and San Francisco river runoff dataset demonstrate that the improved GRA method is effective for data correlation analysis, and the prediction accuracy is better than prior GRA-based methods.

Visual saliency (VS) is an important mechanism for defining which areas of an image will attract more attention of the HVS. Thus, VS can be employed to weight the just noticeable difference (JND) with different attention levels. Some VS-based JND profiles have been proposed in the DCT domain, which used the bottom-up features, such as luminance and texture only. Recently, the research about saliency detection has shown that a better saliency model considering both bottom-up features and top-down features will lead to a significant improvement for the overall saliency detection performance. In this paper, we propose a novel two-layer VS-induced JND profile, which is composed of the bottom-up features and the top-down feature extracted from DCT blocks in the transformed domain. In this model, the luminance and texture features are adapted to calculate the bottom-up features maps, while the top-down feature of focus is used to guide the generation of final salient regions since the camerapersons are used to facilitate the attention regions in focus. The proposed two-layer saliency-induced JND model is further applied to modulate the quantization step in the watermarking framework, which can make full use of its individual merits to achieve a better tradeoff between fidelity and robustness. The experimental results show that the proposed scheme has superior performance than the previous watermarking schemes.

To explore the feasibility of using Volumetric-Modulated Arc Therapy (VMAT) to protect left anterior descending branch (LAD) after breast-conserving surgery for left breast cancer. 15 left breast cancer patients after breast-conserving surgery were selected. 7F-IMRT and 2A-VMAT treatment plans were designed with Varian Eclipse TPS (13.6version). The prescriptions of PTV and PTV Boost were 43.5Gy and 49.5Gy in 15 fractions. The dosimetric parameters, OARs dose sparing and second cancer risk (SCR) were compared between the two plans using a paired t-test. The VMAT plans obtain better PTV conformity and higher mean dose. VMAT plans show a better dose distribution in high dose areas and better sparing of OARs, including left lung, heart, and LAD. The Dmax and Dmean of LAD decreased significantly in VMAT plans. The SCRs of the contralateral lung and breast significantly increased with a higher mean dose. We recommend that contouring and evaluating the dose of LAD and LAD helping structures in left breast cancer radiotherapy. SCR should be evaluated for younger patients.

Background. The prevalence of coronary artery anomalies (CAAs) is rare and varies among different countries or areas. More importantly, the symptoms exhibited by some CAAs make the diagnosis of coronary artery disease (CAD) difficult and hamper the physician from making the right intervention for CAD patients. Objective. To investigate the prevalence of CAAs in 11,267 patients from three hospitals in Southwest China. Methods. 11,267 patients who have undergone coronary angiography from three Southwest China hospitals were investigated retrospectively. Dominance patterns, prevalence, and the location of each CAA were recorded and analyzed. Results. The presence of a dominant right coronary artery (RCA) was found in 60.58% of patients. CAAs were found in 11.12% (1258) patients, and 87.66% anomalies were located in the left anterior descending (LAD) artery and its branches. Most of CAAs were found to be myocardial bridges (MBs, 1060 cases, 9.41%). Other CAAs included anomalous coronary origin (43 cases, 0.38%), coronary artery fistulas (CAFs, 36 cases, 0.32%), and coronary artery aneurysm or ectasia (119 cases, 1.06%). It also noted that most anomalies were found with RCA originating from the left coronary sinus (79.07%), most CAFs were located in the LAD and its branches (58.33%), and most coronary artery ectasias were located in the RCA (43.25%). Conclusions. CAAs in patients from Southwest China were unique compared to other studies. Recognition of these CAAs is important for accurate diagnosis and treatment choice of patients with chest pain.

Background: Collective duct carcinoma (CDC) is a highly malignant kidney tumor which is rare in clinical. We report our 12-year experience of Collecting (Bellini) duct carcinoma (CDC) and retrospectively analyzed patients and tumour characteristics, clinical manifestations, different imaging characteristics including CT, MRI and PET/CT. Methods: From January 2007 to December 2019, we retrospectively examined all renal tumors and identifed 13 cases of CDC from 3 medical centers in the northern China. All 13 patients underwent CT scan, 8 of whom underwent dynamic enhanced CT scan, 2 underwent PET/CT scan and 1 underwent MRCP examination. The lesions were divided into nephritis type and mass type according to the morphology of the tumors.Results: The study group included 10 men and 3 women, with an average age of 64.23±10.74 years old. Of these 13 patients, the main clinical manifestations include gross hematuria, flank pain or waist discomfort. The mean tumour size was 8.48 ± 2.48 cm. In this group of cases, 6 (46.2%) cases are cortical-medullary involved type, 7 (53.8%) cases are cortex-medullary-pelvis involved type. 11 (84.6%) cases were nephritis type and 2 (15.4%) cases were mass type. The lesions appeared solid or complex solid and cystic on CT and MRI. The parenchymal part of 13 CDC tumors showed isodensity or slightly higher density on unenhanced CT scan. the parenchymal part of 13 CDC tumors showed isodensity or slightly higher density on unenhanced CT scan. Two patients with PET/CT showed increased radioactivity intake. Evidence of intra-abdominal metastatic disease was present on CT in 9 ( 69.2% ) cases. The incidence of metastasis reached 69%.Conclusions: The Collecting (Bellini) duct carcinoma has its certain imaging characteristics which were different from the other renal cell carcinoma. A renal tumor should be considered as CDC when it locates in the junction zone of the renal cortex and medulla, with unclear border, slight enhancement and metastases in early stage. PET/CT can greatly enrich the key information of diagnosis, surgery and treatment options, which provide significant help to the clinic.

Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU).

Just noticeable distortion (JND) is widely employed to describe the perception redundancy in the quantization-based watermarking framework. However, the existing JND models are generally constructed to treat every region of the photograph with an equal focus level, whereas the defocus effect has never been considered. In this paper, the defocus feature, which can portray the aesthetic emphasis in the photograph, is provided to improve the perceptual JND model. Firstly, two indicators which consider the block energy in the defocus measurement (DM) are proposed. Then, the defocus feature map (DFM) is obtained by integrating the influence of the circumambient blocks, and it is applied to the proposed JND contrast masking (CM) processing. In this way, a new blind photograph watermarking method, with emphasis on defocus-JND estimation combined with the proposed CM, is presented. Simulations show that the proposed JND is more suitable for watermarking framework than some exiting JND models, and the proposed watermarking scheme with the improved defocus-based JND model has superior robustness compared with some watermarking schemes.

Crystalline silicon is dominating the current solar cell market due to the significant efficiency improvement and cost reduction in last decades. However, its indirect band gap nature leads to inefficient visible-light absorption, which seriously impedes further performance enhancement in silicon-based photovoltaic devices. Thus, it is highly desirable to develop direct band gap silicon materials. Herein, by means of ab initio swarm-intelligence structure-searching method, we predicted a quasi-direct gap semiconducting tri-layer silicene structure consisting of alternating arrays of six-membered Si rings, which can be converted into a direct gap semiconductor of 0.86 eV by applying a low tensile strain (~ 2.5%). Our calculations revealed that the photovoltaic efficiency of the tri-layer silicene reaches 29% at 1.0 µm, which is comparable to that of bulk GaAs with the highest conversion efficiency among thin-film solar cell absorbers.

With the applications of Internet of Things (IoT) for smart cities, the real-time performance for a large number of network packets is facing serious challenge. Thus, how to improve the emergency response has become a critical issue. However, traditional packet scheduling algorithms cannot meet the requirements of the large-scale IoT system for smart cities. To address this shortcoming, this paper proposes EARS, an efficient data-emergency-aware packet scheduling scheme for smart cities. EARS describes the packet emergency information with the packet priority and deadline. Each source node informs the destination node of the packet emergency information before sending the packets. The destination node determines the packet scheduling sequence and processing sequence according to emergency information. Moreover, this paper compares EARS with a first-come, first-served, multilevel queue algorithm and a dynamic multilevel priority packet scheduling algorithm. Simulation results show that EARS outperforms these previous scheduling algorithms in terms of packet loss rate, average packet waiting time, and average packet end-to-end delay.

Background Meningeal carcinomatosis (MC) is characterized by diffuse infiltration of tumor cells in meninges. There is no tumor mass in the brain and parenchyma of the spinal cord. MC is divided into primary and metastatic types. MC cases were previously diagnosed postoperatively or at autopsy. Recent advances in spinal abbreviation cytology and imaging have led to increase in number of reported cases. In this study, we discuss the manifestations of MC patients based on magnetic resonance imaging (MRI) findings, as well as the correlation between the manifestations and pathology. Case presentation MC was confirmed in all three cases by lumbar puncture and gadopentetate dimeglumine-enhanced magnetic resonance imaging. Due to different primary diseases, the patients had specific imaging manifestations. Conclusion Enhanced MRI examination is extremely sensitive for detecting abnormalities in meninges, which plays a very important role in the diagnosis of MC. Since meninges of some MC patients cannot be enhanced, the enhanced MRI examination cannot be replaced by conventional cerebrospinal abbreviation examination. Attribute to the diversity of MR contrast agents, which could provide higher lesion conspicuity and enhances lesion detection, there may be some more choices to improve the detection rate of MC patients and prolong their survival lifetime.

Monolayer MoS2 has long been considered as the most promising candidate for wearable photovoltaic devices. However, its photovoltaic efficiency is restricted by its large band gap (2.0 eV). Though the band gap can be reduced by increasing the number of layers, the indirect band gap nature of the resulting multilayer MoS2 is unfavorable. Herein, we report a theoretical discovery of the hitherto unknown symmetry-broken phase (denoted as 1Td) of monolayer MoS2 through a swarm structure search. The 1Td phase has a distorted octahedral coordinated pattern of Mo, and its direct band gap of 1.27 eV approaches the optimal value of 1.34 eV that gives the Shockley–Queisser limit for photovoltaic efficiency. Importantly, the direct band gap nature persists in thin films with multilayers owing to extremely weak vdW forces between adjacent 1Td layers. The theoretical photovoltaic efficiency at 30 nm thickness reaches ∼33.3%, which is the highest conversion efficiency among all the thin-film solar cell absorbers known thus far. Furthermore, several feasible strategies including appropriate electron injection and annealing methods were proposed to synthesize the 1Td phase. Once synthesized, the superior photovoltaic properties of the 1Td phase may lead to the development of an entirely new line of research for transition metal dichalcogenide solar cells.

Blending is a promising method to modify the ash-fusion characteristics of coal to meet gasification requirements. To investigate the variation in ash-fusion behavior for high ash-fusion-temperature (AFT) coal through coal blending and its regulating mechanism, the ash-chemistry characteristics of four coals (Nantun, Pengzhuang, Biaodian, and Xiangyuan) were analyzed. Mineral-transformation behaviors in the mixed ashes were investigated by X-ray powder diffractometry using normalized reference intensity ratio software, scanning electron microscopy analyzer equipped with energy-dispersive X-ray spectrometry, and FactSage software. Pengzhuang and Biaodian coals, AFTs meet entrained-flow gasifier requirements when the Xiangyuan mass ratio reaches 20%. The Nantun requires a Xiangyuan mass ratio of XY > 30% because of its high aluminum oxide content. The required Xiangyuan optimal blending mass ratio is 40%–70% for the three high AFT coals to meet gasification requirements in the entrained-flow gasifier. An increased Xiangyuan mass ratio leads to decreasing contents of high melting-point mullite and silica and increasing contents of low melting-point anorthite, hercynite, gehlenite, and hedenbergite. Amorphous matter in three mixed ash types resulted in a decrease in their AFTs. The variation in liquid phase under certain conditions reflects the change in AFT.

The influences of two red mud (RM) samples (ZZ and ZM) on fusion behaviors of three high ash fusion temperature (AFT) coals (FZ, JC and JZ) were investigated. It was evaluated by determining AFTs of coals, RMs, and the mixtures of coal ash with RM, as well as their compositions of ashes and mineral. The results showed that the contents of CaO, Fe2O3, and Na2O in RMs were higher than those in coal ashes, which caused RMs’ AFTs lower than those of coals. The formation of high melting-point mullite might be the main factor causing coal AFTs higher. With the increase in RM mass ratio, the increasing amount of low-melting eutectic (anorthite (CaAl2Si2O8), fayalite (Fe2SiO4), and anorthite (Na-rich) (Ca,Na)(Si,Al)4O8)) and amorphous matter made AFTs lower. ZZ reduced AFTs obviously than ZM because the content of Na2O in ZZ was higher than that in ZM. Owing to lower ion potential of Na+ than those of Ca2+ and Fe2+, Na+ is easier to enter into the lattice of mullite and results in the reduction of AFTs.

The ash adhesion and deposition (AD)characteristics of three low-rank coal (LRC) were investigated by self-made AD analyzer, X-ray fluorescence spectrometry, scanning electron microscopy equipped with energy dispersive X-ray detector, and X-ray diffraction. Under a given condition, the deposition mass ratios of different LRC ashes are different, which mainly due to the difference in iron content. The mass ratios of ash deposition under oxidizing atmospheres are higher than those under reducing atmospheres because of the effects of different valence of iron. The mass ratio of AD increases with the increase of temperature, while it decreases with gas velocity increases. The possible formation mechanism of AD is proposed by adhesive amorphous matter formation, iron enrichment and the generation of ferro-aluminate and ferro-alumino silicate, substitution of Fe 2 + by Ca 2 + , and mullite adhesion.

Differential evolution for multi-objective optimization is a useful and straightforward evolutionary optimization algorithm. Since the algorithm randomly searches in problem space, it may get into trouble when we adapt it to solve multi-objective problems. With regard to the limitation of DE, we propose a new algorithm PSDE to solve multi-objective functions based on the PSO operator and DE operator. In this algorithm, we utilize DE operator to promote the uniform spread of solutions and use PSO operator to obtain good local search ability by retaining the best solutions obtained so far. Experimental results on a set of benchmark test functions sustain the effectiveness of the proposed algorithm.

Titanium dioxide has been widely used in modern industrial applications, especially as an effective photocatalyst. Recently, freestanding TiO2 films with a markedly reduced bandgap of ∼1.8 eV have been synthesized, indicating that the dimension has a considerable influence on the bulk band gap (>∼3 eV) and enhances the adsorption range of visible light. Titanium oxide compounds have various stoichiometries and versatile properties. Therefore, it is very necessary to explore the electronic properties and functionalities of other titanium oxide films with different stoichiometries. Here, we combined structure searches with first-principle calculations to explore candidate Ti–O films with different stoichiometries. In addition to the experimentally synthesized TiO2 film, the structure searches identified three new energetically and dynamically stable Ti–O films with stoichiometries of Ti3O5, Ti3O2, and Ti2O. Calculations show that the Ti–O films undergo several interesting electronic transformations as the Ti fraction increases, namely, from a wide-gap semiconductor (TiO2, 3.2 eV) to a narrow-gap semiconductor (Ti3O5, 1.80 eV) and then to metals (Ti3O2 and Ti2O) due to the abundance of unpaired Ti_d electrons. In addition to the electronic transformations, we observed nonmagnetic (TiO2) to ferromagnetic (Ti3O5, Ti3O2, and Ti2O) transformations. Notably, the Ti3O5 film possesses both narrow-gap semiconductive and ferromagnetic properties, with a large magnetic moment of 2.0 µB per unit cell; therefore, this film has high potential for use in applications such as spintronic devices. The results highlight metal fraction-induced electronic and magnetic transformations in transition metal oxide films and provide an alternative route for the design of new, functional thin-film materials.Titanium dioxide has been widely used in modern industrial applications, especially as an effective photocatalyst. Recently, freestanding TiO2 films with a markedly reduced bandgap of ∼1.8 eV have been synthesized, indicating that the dimension has a considerable influence on the bulk band gap (>∼3 eV) and enhances the adsorption range of visible light. Titanium oxide compounds have various stoichiometries and versatile properties. Therefore, it is very necessary to explore the electronic properties and functionalities of other titanium oxide films with different stoichiometries. Here, we combined structure searches with first-principle calculations to explore candidate Ti–O films with different stoichiometries. In addition to the experimentally synthesized TiO2 film, the structure searches identified three new energetically and dynamically stable Ti–O films with stoichiometries of Ti3O5, Ti3O2, and Ti2O. Calculations show that the Ti–O films undergo several interesting electronic transformations as the T...

A better tradeoff between robustness and invisibility will be realized by using the just noticeable (JND) model into the quantization-based watermarking scheme. The JND model is usually used to describe the perception characteristics of human visual systems (HVS). According to the research of cognitive science, HVS can adaptively extract the structure features of an image. However, the existing JND models in the watermarking scheme do not consider the structure features. Therefore, a novel JND model is proposed , which includes three aspects: contrast sensitivity function, luminance adaptation, and contrast masking (CM). In this model, the CM effect is modeled by analyzing the direction features and texture complexity, which meets the human visual perception characteristics and matches well with the spread transform dither modulation (STDM) watermarking framework by employing a new method to measure edge intensity. Compared with the other existing JND models, the proposed JND model based on structural regularity is more efficient and applicable in the STDM watermarking scheme. In terms of the experimental results, the proposed scheme performs better than the other watermarking scheme based on the existing JND models.

Background The majority of ruptured intracranial aneurysms are combined with subarachnoid hemorrhage, but patients with only intracerebral hematoma without any subarachnoid hemorrhage are extremely rare. Case presentation The patient was hospitalized due to sudden dizziness combined with slurred speech. The patient showed considerable decreased physical activity without any nuchal rigidity. Head CT showed hematoma in the left temporal lobe, and the shape of hematoma was extremely irregular. MRI indicated the absence of any vascular malformations. The patient was diagnosed with middle cerebral artery bifurcation aneurysm in the left by head CTA. Intracranial aneurysm clip and removal of hematoma in the left temporal lobe were performed under general anesthesia. The patient did not show any significant neurological dysfunction after the surgery and was followed up for 4 months after discharge with GOS score of 5 points. Conclusions Intracranial hematoma with irregular morphology around the lateral fissure of the brain should be considered critical in order to avoid misdiagnosis and any possibility of missed diagnosis of vascular lesions, so as to ensure an exact therapeutic strategy with good prognosis for the patients.

The fusion characteristics of three low-rank coals and cornstalk (CS) mixtures were investigated by ash fusion temperature (AFT) analysis, X-ray fluorescence spectrometry, scanning electron microscopy, and X-ray diffractometry. The increasing trend of CS ash fusion temperature (AFT) is not the same with the additions of different coals at the same mass ratio, due to the difference of the ash content and component of coal samples. Variations in AFTs depended on the mineral composition, which derived from mineral interaction. As the coal mass ratio increased at high temperature, Ca 2+ replaced K + in semi-molten alumino-silicate in CS ashes and led to formation of higher-melting-point anorthite, which also increased the AFT of the ash mixture. The AFT fluctuation of CS/Husheng lignite (HS) mixture during addition of HS resulted from formation of higher-MP mullite.

The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series. A sparse autoencoder is used to extract the features from the input instead of the randomly initialized weights. In this way, the RBLS retains the merit of fast computing and fits for processing sequential data. Motivated by the idea of “fine-tuning” in deep learning, the weights in the RBLS can be updated by conjugate gradient methods if the prediction errors are large. We exhibit the merits of our proposed model on several chaotic time series. Experimental results substantiate the effectiveness of the RBLS. For chaotic benchmark datasets, the RBLS achieves very small errors, and for the real-world dataset, the performance is satisfactory.

To deal with “curse of dimensionality” caused by high-dimensional data and the underutilization of spatial information in the classification of hyperspectral imagery (HSI), a new spectral-spatial classification method based on probabilistic support vector machine (SVM) and guided filter is proposed. The proposed method utilizes spatial information in two aspects. Firstly, the extended morphological profiles (EMP), differential morphological profiles (DMP) and Gabor textural features of hyperspectral image are extracted, which can preserve the texture, outline and shape of the original image. The spectral and extracted spatial features are joint classified by probabilistic SVM to obtain the initial classification probabilistic maps. Secondly, the guided filter is used to regularize the initial classification probabilistic maps, which can enhance the smoothness of local neighbourhood and preserve the edge information. The proposed algorithm is examined by the Indian Pines and University of Pavia hyperspectral datasets. On the same experimental conditions, the proposed method achieves the highest classification accuracy and the most reasonable classification map, demonstrating the proposed algorithm has obvious advantages in hyperspectral remote image classification.

It has been known that human visual systems (HVSs) can be applied to describe the underlying masking properties for the image processing. In general, HVS can only perceive small changes in a scene when they are greater than the just noticeable distortion (JND) threshold. Recently, the cognitive resources of huma visual attention mechanisms are limited, which can not concentrate on all stimuli. To be specific, only more important stimuli will react from the mechanisms. When it comes to visual attention mechanisms, we need to introduce the visual saliency to model the human perception more accurately. In this paper, we presents a new wavelet-based JND estimation method that takes into account the interrelationship between visual saliency and JND threshold. In the experimental part, we verify it from both subjective and objective aspects. In addition, the experimental results show that extracting the saliency map of the image in the discrete wavelet transform (DWT) domain and then modulating its JND threshold is better than the non-modulated JND effect.

Recently, the scale of observed time series becomes bigger and bigger, and the learning ability of conventional models is limited to depict the characteristics of large-scale time series. We put forward a deep model that uses the sparse autoencoder to extract the characteristics of the time series layer by layer. Sparse autoencoder can provide a simple explanation of the data by means of a reduced dimensionality of parts and extract the hidden architecture in the data. By this method, on the one hand, the dimension of the matrix that describes the problem is reduced, on the other hand, a large number of data can be compressed and summarized. Then we calculate the output weights by the method of pseudo inverse. Experiment results on Lorenz series and PM2.5 concentration in Shanghai dataset demonstrate the effectiveness of the proposed method.

In this research, for online modeling and prediction of multivariate time series, we propose a novel approach termed squared root cubature Kalman filter-γ echo state network (SCKF-γESN). First, multivariate time series are modeled by using γ echo state network (γESN). Then, by using squared root cubature Kalman filter (SCKF), we update parameters of γESN and predict future observations online. Furthermore, we add a robust outlier detection algorithm to SCKF to protect SCKF-γESN from divergence caused by outliers. Finally, two numerical examples, by using a multivariate benchmark dataset and a real-world dataset, are conducted to substantiate the effectiveness and characteristics of the proposed SCKF-γESN.

The just noticeable distortion (JND) model plays an important role in measuring the visual visibility for spread transform dither modulation (STDM) watermarking. However, the existing JND model characterizes the suprathreshold distortions with an equal saliency level. Visual saliency (VS) has been widely studied by psychologists and computer scientists during the last decade, where the distortions are more likely to be noticeable to any viewer. With this consideration, we proposed a novel STDM watermarking method for a monochrome image by exploiting a visual saliency-based JND model. In our proposed JND model, a simple VS model is employed as a feature to reflect the importance of a local region and compute the final JND map. Extensive experiments performed on the classic image databases demonstrate that the proposed watermarking scheme works better in terms of the robustness than other related methods.

For extreme learning machine (ELM), it is difficult to select an appropriate number of hidden layer nodes, which has a great influence on the prediction performance of ELM. Therefore, in order to overcome the influence of a large number of hidden layer nodes to prediction and enhance the prediction performance of ELM, an improved ELM is proposed based on orthogonal projections to latent structures (O-PLS) method. The improved ELM contains two steps. Firstly, the hidden layer of ELM is used to build the nonlinear mapping for input variables and get the output matrix H, where the number of bidden layer nodes can be random assigned. Then O-PLS is used to build the linear regression. Compared with least squares regression (LSR), O-PLS can effectively reduce the high dimensions, overcome the multicollinearity among H, which are caused by an improper number of hidden layer nodes. The simulation experiment results based on Lorenz time series and sunspots-runoff of Yellow river time series demonstrate that the improved ELM can effectively overcome the influence of hidden layer nodes to prediction performance and improve the stability performance and prediction performance of ELM.

The perceptual just noticeable distortion (JND) model has attracted increasing attention in the field of the quantization-based watermarking framework. The JND model can provide a superior tradeoff between robustness and fidelity. However, the conventional JND models are not fit for the quantization-based watermarking, as the image has been altered by watermarking embedding. In this paper, we present an improved spread transform dither modulation (STDM) watermarking scheme, which is based on the image primitive features produced according to JND mechanism. The procedures include the contrast masking effect by utilizing a new measurement of edge strength which represent semantic information. What’s more, the proposed semantic information-based JND model can be theoretically invariant to the changes in the watermark-embedding processing. The newly proposed JND model is very simple but more effective in the STDM watermarking. Experiments results demonstrate that the proposed watermarking scheme can bring about better performance compared with previously proposed perceptual STDM schemes.

Rationale: Multiple primary central nervous system lymphoma (MPCNSL) is a rare disease with differential diagnosis and treatment. As the underlying pathogenesis is not yet clarified, the early-stage clinical manifestations are occult and atypical. Also, the imaging manifestations are not specific, which is challenging for the clinical diagnosis and treatment. Therefore, additional clinical research is essential to understand the etiology of the disease. Patient concerns: A 63-year-old male patient suffered from MPCNSLs but without typical clinical manifestations. The findings of the imaging examination were as follows. Magnetic resonance imaging (MRI) showed long T1 and T2 signal shadows in the right frontal lobe, right hippocampus, right cerebellar hemisphere, and the left occipital lobe. In addition, patchy T1-enhanced signal shadows were observed in the right frontal lobe and around the midline. Frontal lesions were detected in the magnetic resonance spectroscopy (MRS), Cho peak increased, and N-acetylaspartate (NAA) peak decreased. On the other hand, in the diffusion weighted imaging (DWI), apparent dispersion coefficient (ADC) showed low-value changes and high signal changes. The positron emission tomography-computed tomography (PET-CT) displayed radioactive accumulation in the right frontal lobe. Diagnosis: Multiple primary central nervous system lymphoma. Interventions: The patient received some conservative medical treatment, but his condition continued to worsen. Finally, he received a pathological biopsy, and refused further treatment after the result was reported. Outcomes: The patient died 1 week after biopsy, and the course of disease was about 100 days. Lessons: PCNSL is a primary intracranial malignancy with low incidence and a high degree of malignancy and specificity in clinical manifestations. To facilitate early clinical treatment and improve the long-term survival of patients, it is necessary to master the imaging diagnostic methods and its features. The comprehensive application of multiple imaging examinations, such as CT, MRI, PET/CT, and PET/MRI, as well as, cerebrospinal fluid cytology can greatly improve the diagnosis of PCNSL.

Considering the problem that for multivariate time series prediction, the adaptation of a single reservoir may not be sufficient to improve the prediction accuracy, we propose a novel hierarchical neural network herein. In the first hierarchy, several simplified echo state networks - simple cycle reservoirs (SCRs) are used to extract the dynamical features of the multivariate time series. Particle swarm optimization method is conducted in the pre-training stage to optimize the free parameters of SCRs. The reservoir states of SCRs are collected as dynamical features. In the second hierarchy, a feature selection method based on mutual information is used to select a compact feature set as the input for the extreme learning machine (ELM). In order to further improve the prediction accuracy, the optimal number of hidden nodes of the ELM is chosen by a modified recursive algorithm. Simulation results on monthly average temperature and rainfall series in Dalian China sustain that the proposed model is effective for multivariate time series.

The crowdsourcing and internet of things (IoT) have played a significant role in revolutionizing the information age. There is significant literature which has attempted to investigate the role of crowdsourcing and IoT in improving disaster response. However there is hardly any literature which attempted to reflect upon integration of crowdsourcing and IoT to explain better coordination among disaster relief workers to improve disaster response. In response to pressing need, we have attempted to develop a theoretical framework which can help disaster relief workers to improve their coordination using valuable information derived using comprehensive crowdsourcing framework. In this study we have used two-prong research strategies. First we have conducted extensive review of articles published in reputable journals, magazines and blogs by eminent practitioners and policy makers followed by case studies: stampede in Godavari River at Rajahmundry (2015), earthquake in Nepal (2015), flood in Uttarakhand (2013). Finally we have concluded our research findings with further research directions.

This paper presents a method based on partial least squares regression (PLSR) and multiple kernel extreme learning machine (MKLEM) for multivariate chaotic time series prediction. At first, singular spectrum analysis (SSA) is applied for the time series extraction of complex trends and eliminating the influence of noise. Then, partial least squares regression is used to capture the essential structure of the data and extract the compositions, in order to overcome the multicollinearity problem among time series and reduce the input dimension of neural networks. Finally, multiple kernel extreme learning machine is used to predict the time series. Multiple kernel extreme learning machine overcomes the problem that single extreme learning machine with kernels (KELM) doesn't present an effective generalization performance. Root mean square error (RMSE) is used to measure the performance of the proposed prediction model. The simulation experiment results based on Lorenz chaotic time series and Dalian monthly average temperature-rainfall time series demonstrate that the proposed model is effective for time series prediction, and the prediction accuracy is higher than other models.

This paper presents a multivariate time series modeling and prediction method based on reservoir independent components. As a new type of recurrent neural networks (RNNs), reservoir computing methods have become a new hot topic and attracted wide attention from researchers in the field of time series prediction. It has overcome the problems that traditional gradient descent training algorithms present, for example, the process is computationally expensive, and easy to end in a local minimum. However, there are ill-posed solutions when least square estimation methods are used to calculate the output weights because of the collinear columns or rows in the state matrix. Therefore, we use independent component analysis (ICA) to extract the independent components of the state matrix. In addition, this paper proposes an iterative prediction model based on local error compensation to solve the problem of accumulated errors in multiple-step prediction, in order to realize medium-term prediction. The models have been simulated on benchmark dataset of Lorenz time series and a real-world application of Dalian monthly average temperature-rainfall time series. Simulation results substantiate the proposed methods' effectiveness and characteristics.

Fan blade loss is the most serious condition of typical extreme working conditions in turbofan engines. The transient huge unbalance force leads to the complicated and destructive vibration response of the rotor system. The paper proposed a set of safety design method for the rotor system, which ensured the engine to stop safely after the fan blade lost. The variable stiffness supporting structure at the rear of the fan was presented and its parameters were optimized through dynamic characteristics calculation. The results revealed that the vibration response of the rotor system could be suppressed during the speed reduction process. The spherical matching surface between the bearing ring and the rotation shaft could reduce the impact of the large shaft deformation, and enhanced the bearing's safety.

This is an educational art by designing teaching situations cleverly to improve teaching effectiveness. It has many advantages in politics class. Politics teachers can use a variety of materials cleverly to create teaching situation. In order to create a valuable teaching situation, teachers must master the principles of the creation of teaching situation.

In this paper, we present an echo state network model based on sparse Gaussian process regression, which has been successfully applied to multivariate time series prediction. While combining the Gaussian process with Echo State Network, the computational complexity of the model is very high. We consider using a group of limited basis functions instead of the original covariance function, which reduces the computational complexity and maintains the prediction performance of the model. In the framework of Bayesian inference, the model can combine prior knowledge and observation data perfectly and provide prediction confidence. The model realizes adaptive estimation of the hyper-parameters by using maximum likelihood approach and avoids complex computation process. Two simulation results show the effectiveness and practicality of the proposed method.