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Abstract To quickly assess slope stability based on field displacement monitoring data, this paper constructs a hybrid optimization model that predicts surface displacement during tunnel excavation in base-overburden slopes. The model combines Wavelet Decomposition (WD) with a Gated Recurrent Unit (GRU), and the GRU's hyperparameters are optimized using an Improved Particle Swarm Optimization algorithm (IPSO). The specific steps are as follows: First, the Wavelet Decomposition (WD) technique is applied to decompose the raw displacement data, extracting features at different time–frequency scales. Next, the Dropout technique is incorporated into the GRU model to prevent overfitting. Additionally, nonlinear inertia weight ω improved cognitive factor c 1 , and social factor c 2 are introduced. The PSO algorithm is improved by integrating crossover and mutation concepts from genetic algorithms. Finally, the IPSO is used to optimize the number of neural units h N , H N , L N and dropout rates D 1 and D 2 in the GRU network architecture. After constructing the WD-IPSO-GRU model, a comprehensive comparison is made with various swarm intelligence algorithms and state-of-the-art models. The experimental results demonstrate that the WD-IPSO-GRU model significantly improves the prediction accuracy of surface displacement in slopes during tunnel excavation. Compared to directly using raw data for prediction, the introduction of the WD preprocessing technique improved the prediction accuracy at measurement points 01 and 02 by 28% and 45.9%, respectively. Additionally, with the model optimized by IPSO, the prediction accuracy at measurement points 01 and 02 increased by 76% and 56.7%, respectively. The WD-IPSO-GRU model effectively addresses the challenges of extracting features from univariate displacement time-series data and determining the parameters of the GRU network. It improves the prediction accuracy of surface displacement in base-overburden type slopes and demonstrates excellent generalization ability and reliability. The research results validate the potential application of the model in geotechnical engineering and provide strong support for assessing slope stability during tunnel excavation.

In order to reduce the sawing noise generated by diamond circular saw blades during stone processing, five typical diamond circular saw blades were selected for modal analysis and traveling wave vibration analysis by using Workbench software. The study explored the impact of various saw blade designs, including conventional saw blades, perforated saw blades, slit saw blades, and saw blades with nose-shaped radial grooves, holes, and interlayers, on traveling wave vibrations. The results show that the traveling wave resonance occurs in the conventional saw blades with diameters of 180 mm and 230 mm, causing strong noise. Conventional saw blades with diameters of 115 mm, 350 mm and 105 mm do not show traveling wave resonance. The δ value for the 180 mm and 230 mm saw blades is too large, which effectively presents traveling wave resonance. The δ value for the 115 mm and 350 mm saw blade is too small, which does not reduce noise very well. The δ value for the 105 mm sandwich saw blade is too large and has a significant effect on noise reduction. To avoid traveling wave resonance and enhance noise reduction, five design schemes incorporating radial grooves, raindrop holes, and damping interlayers are proposed for various blade diameters. The resulting δ values are 8.13 %, 7.21 %, 6.01 %, 6.39 %, and 7.00 %, respectively.

ABSTRACTDietary fibers/probiotics may relieve constipation via optimizing gut microbiome, yet with limited trial-based evidences. We aimed to evaluate the effects of formulas with dietary fibers or probiotics on functional constipation symptoms, and to identify modulations of gut microbiota of relevance. We conducted a 4-week double-blinded randomized placebo-controlled trial in 250 adults with functional constipation. Intervention: A: polydextrose; B: psyllium husk; C: wheat bran + psyllium husk; D: Bifidobacterium animalis subsp. lactis HN019 + Lacticaseibacillus rhamnosus HN001; Placebo: maltodextrin. Oligosaccharides were also included in group A to D. 16S rRNA sequencing was used to assess the gut microbiota at weeks 0, 2, and 4. A total of 242 participants completed the study. No time-by-group effect was observed for bowel movement frequency (BMF), Bristol stool scale score (BSS), and degree of defecation straining (DDS), while BSS showed mean increases of 0.95–1.05 in group A to D (all P

As delay-tolerant and large-size content, for example, software updates, TV series, and virtual reality related content, become more prevalent in mobile networks, the need for efficient content delivery mechanisms becomes increasingly important,the traffic that carries these contents is not suitable to be evaluated using traditional network performance metrics, e.g., delay, throughput, and jitter. Based on this insight, we propose the solution of content dissemination from opportunistic mobile social communications (CODOMOC)which utilizes energy cost as an alternative performance metric and exploits daily human activity mobility pattern to determine how, when, and where the contents should be disseminated. Then, we introduce two options in CODOMOC to achieve different network operators’ objectives. The two options are the Only Dense (OD) option which aims at minimizing energy consumption for network operators and the Broadcast Efficiency (BE) option to further reduce the total carbon footprint of network operators. CODOMOC is evaluated by comparing with a mobility-based broadcast method. The results show that CODOMOC reduces the average energy consumption by 51% and 60% in the OD and BE options respectively. The proposed solution equipped with the two modes is expected to provide a higher degree of flexibility and reduce energy consumption for mobile networks, while, admittedly, the application scope of the solution and the associated methodologies proposed in this paper is restricted to important but non-urgent traffic delivery.

The inversion of gravity and magnetic data can obtain the density and magnetic structure of underground space, which provide important information for resource exploration and geological structure division. The most commonly used inversion method is smooth inversion in which the objective function is built with L2-norm, which has good stability, but it produces non-focused results that make subsequent interpretation difficult. The power-type structural self-constrained inversion (PTSS) method with L2-norm is proposed to improve the resolution of smooth inversion. A self-constraint term based on the power gradient of the results is introduced, which takes advantage of the structural feature that the power gradient can better focus on the model boundary to improve the resolution. For the joint inversion of gravity and magnetic data, the power-type mutual-constrained term between different physical structures and the self-constrained term can be simultaneously used to obtain higher-resolution results. The modeling tests demonstrated that the PTSS method can produce converged high-resolution results with good noise immunity in both the respective inversions and the joint inversion. Then, the PTSS joint inversion was applied to the airborne gravity and magnetic data of the iron ore district in Shandong, revealing the shape and location of the mineralized rock mass, which are crucial information for subsequent detailed exploration.

The three-dimensional magnetic vector structure (magnetization intensity and direction) of the planet can be effectively used to analyze the characteristics of its formation and operation. However, the quick acquisition of a large region of the magnetic vector structure of the planet with bigger observation surfaces undulation is hard and indispensable. We firstly proposed a fast magnetization vector inversion method for the inversion of a magnetic anomaly with the undulating observation surfaces in the spherical coordinate system, which first transforms the data to a plane when the data are distributed on a surface. Then, it uses a block-Toeplitz-Toeplitz-block (BTTB)-FFT to achieve fast inversion with the constraint that the magnetization intensities of the grids between the transformed observation surfaces and the terrain are zero. In addition, Gramian constraint term is used to reduce the ambiguity of the magnetic vector inversion. The theoretical model tests show that the proposed method can effectively improve the computational efficiency by 23 times in the 60 × 60 × 10 grid division compared to the conventional inversion method, and the accuracy of the two computation methods is comparable. The root-mean-square error of the magnetization intensity is only 0.017, and the angle error is within 1°. The magnetization vector structure shows that the largest crater diameter does not exceed 340 km in the Mare Australe region, the amplitude of the magnetic anomaly is much higher than the current meteorite impact simulation results, and the depth of the magnetic source is less than 10 km, which cannot be explained by the impact simulation experiments. In addition, the magnetization directions of adjacent sources differ by 122° (or 238°), and the high-frequency dynamics of the Moon as well as the short-lived dynamics may be responsible for this phenomenon. The magnetization directions of the three adjacent sources in the Mare Crisium region are close to each other and differ in depth with different cooling times, making it difficult to record the transient fields produced by meteorite impacts. In addition to the above characteristics, the magnetization direction of the magnetic sources in both regions is uniformly distributed without reflecting the dispersion of the magnetization direction of the meteorite impact magnetic field. Therefore, it can be inferred that the magnetic anomalies in these two regions are related to the generator hypothesis.

Objective: The severity and prognosis of coronary artery disease are closely associated with treatment strategy choice. To achieve timely, accurate, early selection of a suitable treatment plan and assess patients’ prognosis, we developed an effective predictive model for early identification of high-risk patients according to lesion severity. Methods: Among the 510 patients with chest pain admitted to the Qinghai Red Cross Hospital between August 2018 and October 2019, 386 had coronary artery disease detected by coronary angiography. A total of 24 demographic characteristics and serum markers were analyzed in study participants. Least absolute shrinkage and selection operator regression was used to select variables, and multivariate logistic regression was used to build predictive models by using nominal plots. The discriminatory power of the models was evaluated with the area under the receiver operating characteristic curve (AUC). Predictive models were calibrated with calibration plots and the Hosmer–Lemeshow test. Their clinical validity was evaluated via decision curve analysis. Results: Data were randomly divided (7:3) into training (358 cases) and test (152 cases) sets. The predictive model included sex, age, smoking status, heart rate, systolic blood pressure, diastolic blood pressure, albumin, urea nitrogen, creatinine, uric acid, total cholesterol, and high-density lipoprotein cholesterol as predictors. The AUCs for the training and test sets were 0.793 and 0.732, respectively. The predictive model showed a good fit, and decision curve analysis indicated the clinical validity of the predictive model. Conclusions: We developed an effective risk predictive model with good clinical value for predicting multivessel disease. Smoking cessation, lowering creatinine, and increasing HDL cholesterol concentrations might decrease the risk of developing multivessel disease, thereby avoiding severe disease.

When submarine cable line fails or other lines need load transfer, it often suffers from emergency ampacity that exceeds the steady-state ampacity. The layout environment of the submarine cable is always complex and changeable, and the overload capacity of the submarine cable in different layout environments is also different. Therefore, it is necessary to analyze the overload capacity of the submarine cable. In this paper, a coupled multi-physical field model by finite element method is established for AC 500 kV XLPE extra high voltage submarine cable in landing section, which is the ampacity bottleneck section of the whole line. The overload capacity of submarine cable in two typical layout environments which are direct buried and within pipeline is analyzed. The results show that the overload capacity of submarine cable in the direct buried environment is much higher than that in the pipeline environment. The allowable emergency time in the direct buried environment is 2–3 times that of the pipeline environment under the same condition. In the two typical layout environments, when the emergency current are 2500 A and 3500 A, the ratio of the emergency time allowed to run in the direct buried environment to that in the pipeline environment is about 5 times under the same initial capacity. The proposed model can provide a reference for dynamic capacity control of the extra high voltage submarine cable.

In a sedimentary environment, the conventional one-dimensional (1D) inversion based on the horizontal layered model has difficulty restoring the resistivity distribution of the inclined strata when a coal seam has some dip angle or a small interval between layers. In such cases, the inversion resistivity exhibits horizontal discontinuities, which cannot accurately represent actual geological conditions. Therefore, in view of the good horizontal continuity of the underground electrical structure of sedimentary strata, we propose a high-resolution inversion method based on weighted horizontal and vertical constraints. As a quasi-two-dimensional (2D) inversion, this not only ensures the horizontal continuity of resistivity and recovers the inclined strata, but also improves the vertical resolution. Because the constrained factor has a significant influence on the inversion result, different constrained factors are applied in the horizontal and vertical directions to adjust the constraint strength on the model parameters of each layer and the continuity of the layer interface. In the numerical experiments, we design synthetic models with different tilt angles and layer spacings to test the inversion method and optimize the constrained factors used for coal seam detection. Finally, the transient electromagnetic (TEM) field data processing results in Inner Mongolia show that the resistivity distributions of sedimentary strata can be accurately restored by the new method, and the inversion results are consistent with known geological information.

Underground 3D density variation can be obtained via the inversion of gravity data, which is a very important basis for structural division, oil and gas structure definition, and mineral resource evaluation. A depth-weighting function is usually introduced as a structural constraint in density inversion to solve the skin effect. We propose an adaptive space–location-weighting (ASW) function for gravity field data to improve the resolution of the inversion, which adds the position and depth information provided by the DEXP method to form a new weighting function. The weighting function is partitioned according to the horizontal distribution of the source and can effectively improve the resolution of field sources with different positions and different depths. The results of model tests have shown that the ASW function method can significantly improve the precision and resolution of density inversion results and has good noise immunity. The ASW method was applied to interpret the real gravity data of a mining area in Shandong, and we speculated potential mineralization based on the inversion results, which corresponded well with the logging results.

Abstract Background The COVID-19 pandemic had brought the increased levels of depression and anxiety on people. Our study investigated the levels of mental health and influencing factors among back-to-Wuhan university students. Methods A cross-sectional questionnaire survey was conducted from 31 August 2020, to 14 September 2020 by convenience sampling on the back-to-Wuhan university students, which included the Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire-9 (PHQ-9), the Insomnia Severity Index-7 (ISI-7), the revised Impact of Event Scale (IES-R) scales, and the basic demographic characteristics. Moreover, quantile regression analysis was used to identify the key factors related to the mental health variables of the back-to-Wuhan university students during the COVID-19 period. Results The results from 1017 participants suggested that the prevalence rates of the anxiety, depression, insomnia, and distress were 44%, 47.5%, 37.7%, 57.7%, respectively. Quantile regression showed that mental health scores were negatively associated to age, years from graduation, being discriminated against owing to the experience in Wuhan, and the attitude on the future of COVID-19, while was positively related to the education level (P

In order to explore the development and research trends of polypropylene (PP) in electrical engineering, the research literature is quantitatively analyzed using a bibliometric method with the VOSviewer and CiteSpace software. First, the research literature about PP material in electrical engineering applications is collected, from 1990 to 2022. Then, by analyzing the keyword co-occurrence, keyword co-occurrence timezone, author cooperation network, and national cooperation network, the research hotspots of the PP field and its time evolutionary path and development direction are introduced. It is found that the nano-modification, mechanical, and electrical properties are the most popular research hotspots in this field. Most research studies were completed by few specific researchers. A stable cooperative group has not been formed in this field yet, indicating the necessity of further integration. Most articles about PP were published in dielectric and material journals. It is suggested that more open access journals are required to popularize the existing research results among the public and to promote the development of PP. Although the most published country is China, the United States publishes the most cited papers on average.

To reduce the impact of wildfires on the operation of power systems, a back-propagation neural network (BPNN) model is used to evaluate the wildfire risk distribution after feature selection. Data from 14 types of wildfire-related features, including anthropogenic, geographical, and meteorological factors, were collected from public data websites and local departments. The weight ranking was calculated using filtering and wrapper methods to form five feature subsets. These are used as the input sets of the BPNN model training, and network parameters are optimized by genetic algorithm (GA). Finally, the optimal feature subset is chosen to establish the optimal BPNN model. With the optimal model, the prediction results are graded to draw a wildfire risk distribution map. Situated in medium-, high-, and very-high-risk zones are 90.26% of new fire incidents, indicating the applicability of the proposed BPNN model.

Distributed Artificial Intelligence (DAI) is one of the most promising techniques to provide intelligent services under strict privacy protection regulations for multiple clients. By applying DAI, training on raw data is carried out locally. At the same time, the trained outputs, e.g., model parameters from multiple local clients, are sent back to a central server for aggregation. DAI is recently studied in conjunction with wireless communication networks to achieve better practicality, incorporating various random effects brought by wireless channels. However, because of wireless channels’ complex and case-dependent nature, a generic simulator for applying DAI in wireless communication networks is still lacking. To accelerate the development of DAI in wireless communication networks, we propose a generic system design in this paper and an associated simulator that can be set according to wireless channels and system-level configurations. Details of the system design and analysis of the impacts of wireless environments are provided to facilitate further implementations and updates. We employ a series of experiments to verify the effectiveness and efficiency of the proposed system design and reveal its superior scalability.

Nanoparticles are found to have the capacity to improve the specific properties of polypropylene (PP) for recyclable cable insulation material. However, previous studies have focused on the modification effect at room temperature, and few studies, on the nano-modification effect under high temperature environments. This article aims to study the effects of nanoparticles on the electrical and dielectric properties of PP at different high temperatures. Both TiO2 nanoparticles and PP/TiO2 nanocomposite samples are prepared in the laboratory by the wet chemical method and surface modification. Then, the AC and DC breakdown strength, conductivity, permittivity, and dielectric loss of the PP/TiO2 nanocomposite are measured at different temperatures. The results show that TiO2 nanoparticles introduce a small amount of charge traps into PP to restrain the conductivity and improve the breakdown strength of PP. But due to the additional interfacial polarization between TiO2 nanoparticles and the PP matrix, the permittivity and dielectric loss of the PP/TiO2 nanocomposite are enhanced, especially at low frequency.

Abstract Island studies play an important role in the development of anthropology. It is of academic value and practical significance to understand the island world as the field where multiple modernization forces and globalization interwine. This paper explores the intricate and diverse connections between continental and marine culture from a perspective of “viewing the world through the island”. In terms of overall diversity and exoteric mobility, this paper reviews the various aspects of island studies, examines the internal and external transformation of islands within land-sea interaction, and analyzes the dynamic historical process of the island world’s involvement in the global network, which blends and integrates various cultural elements of the external world. In the context of globalization, the island world is undergoing dramatic changes and in coping with them generating its new features.

The Shuangjianzishan deposit in Inner Mongolia is a typical Ag-Pb-Zn deposit of the southern Great Xing’an Range. Proven reserves of Ag, Pb, and Zn in this deposit have reached the scale of super-large deposits, with favorable metallogenic conditions, strong prospecting signs, and high metallogenic potential. This paper reports a study involving integrated geophysical methods, including controlled-source audio-frequency magnetotelluric, gravity, magnetic, and shallow-seismic-reflection methods, to determine the spatial distribution of ore-controlling structures and subsurface intrusive rock for a depth range of

Deep reinforcement learning is the technology of artificial neural networks in the field of decision-making and control. The traditional model-free reinforcement learning algorithm requires a large amount of environment interactive data to iterate the algorithm. This model’s performance also suffers due to low utilization of training data, while the model-based reinforcement learning (MBRL) algorithm improves the efficiency of the data, MBRL locks into low prediction accuracy. Although MBRL can utilize the additional data generated by the dynamic model, a system dynamics model with low prediction accuracy will provide low-quality data and affect the algorithm’s final result. In this paper, based on the A3C (Asynchronous Advantage Actor-Critic) algorithm, an improved model-based deep reinforcement learning algorithm using a learning degree network (MBRL-LDN) is presented. By comparing the differences between the predicted states outputted by the proposed multidynamic model and the original predicted states, the learning degree of the system dynamics model is calculated. The learning degree represents the quality of the data generated by the dynamic model and is used to decide whether to continue to interact with the dynamic model during a particular episode. Thus, low-quality data will be discarded. The superiority of the proposed method is verified by conducting extensive contrast experiments.

Aeromagnetic gradient data have higher horizontal resolution on shallow sources, a magnetic anomaly that can better reflect the features of deeper sources. Therefore, we used a joint survey of aeromagnetic and gradient data to obtain the distribution of sources with different depths. In this paper, we propose an intersection constraint weighting (ICW) method based on aeromagnetic and gradient data for original and gradient anomalies with inconsistent high-low variation characteristics. The ICW method can effectively improve the resolution of inversion results and can more accurately obtain the distribution of magnetic bodies via cross-gradient by gradually adding a gradient component and applying a normalized property weighting function. Our theoretical model tests indicate that the distribution of the recovered magnetic susceptibility model of the ICW method was similar to that of the true model. In addition, the anomaly containing noise with different signal-to-noise ratios verified that the ICW method had a stronger anti-noise ability than other methods. We also inverted real data in the Zhurihe area of the Inner Mongolia Autonomous Region in northern China. The inversion result showed that the main trend of high magnetic bodies was in the northeast direction, that the shallowest depth of high magnetic bodies was 100 m, and that the greatest depth was 960 m.

Airborne magnetic and gradient measurements are commonly used geophysical remote sensing tools to obtain the distribution features of ore mineral bodies. It is known that ore mineral bodies generally contain remanent magnetization, and magnetization vector inversion (MVI) can produce the magnetization intensity and direction of the source, which is more suitably used to interpret measured airborne magnetic and gradient data. To accurately reveal the underground magnetization vector distribution, we proposed a high-precision method with double constraints on the data and physical structure, and we used the cross-gradient inversion of airborne magnetic anomalies and the combination matrix of airborne magnetic and gradient (CMG) data to recover the physical parameters of the sources with different depths. We used the combination matrix to produce the different component data constraints and the cross-gradient function to finish the inversion to provide structural constraints. For anomaly sources at similar depths, joint inversion based on the cross-gradient of magnetic gradient data and CMG data is more suitably used. The superiority of the double constraints method is proven by theoretical model tests. We apply the proposed method to interpret airborne magnetic and gradient data in Shandong Province to detect iron mineral resources, and we select the cross-gradient inversion of airborne magnetic anomalies and CMG data depending on the nonlinear features of the power spectrum. The main ore bodies have a northeast distribution with a depth range of 1048–1800 m, successfully giving the distribution range of the high-magnetic bodies; a better mineral potential is in the northern part of the survey area.

The aeromagnetic survey is a common remote sensing tool for detecting iron deposits. The local wavenumber of a magnetic anomaly is used to interpret the edges or positions of the sources, and can involve first- or second-order local wavenumbers. In this paper, we derived a linear equation between the second-order local wavenumber and the source location; therefore, we propose a constraint of first and second-order local wavenumbers. Tests on synthetic data show that the source parameters, computed using a combination of equations that involved different-order local wavenumbers, are closer to the true values and show a smaller spread in estimated values. For gridded data, we proved that the different-order combination allowed us to accurately estimate the source position. When applied to the aeromagnetic data from Hebei province, China, we refined the location of most magnetic features, which we interpreted as possible iron deposits.

Airborne (or satellite) gravity measurement is a commonly used remote sensing method to obtain the underground density distribution. Airborne gravity gradiometry data have a higher horizontal resolution to shallower causative sources than airborne gravity anomaly, so joint exploration of airborne gravity and its gradient data can simultaneously obtain the anomaly feature of sources with different depths. The most commonly used joint inversion method of gravity and its gradient data is the data combined method, which is to combine all the components into a data matrix as mutual constraints to reduce ambiguity and non-uniqueness. In order to obtain higher resolution results, we proposed a cooperate density-integrated inversion method of airborne gravity and its gradient data, which firstly carried out the joint inversion using cross-gradient constraints to obtain two density structures, and then fused two recovered models into a result through Fourier transform; finally, data combined joint inversion of airborne gravity, and gradient data were reperformed to achieve high-resolution density result using fused density results as a reference model. Compared to the data combined joint inversion method, the proposed cooperate density-integrated inversion method can obtain higher resolution and more accurate density distribution of shallow and deep bodies meanwhile. We also applied it to real data in the mining area of western Liaoning Province, China. The results showed that the depth of the skarn-type iron mine in the region is about 900–1300 m and gives a more specific distribution compared to the geological results, which provided reliable data for the next exploration plan.

Abstract The South China Sea Rim is a core area of the Maritime Silk Road, ranging from South and Southwest China to Southeast Asia. It has developed a cultural landscape in which all parties are interdependent through very complex processes of ethnic communication and social interaction. In this temporal-spatial process, the cultural exchanges occurring in this area are not only shaping the cultural ecologies of those involved but also integrating their societies. The networked inter/inter-regional social systems lay a methodological foundation on which the South China Sea Rim is dealt with as a whole. These social systems can be defined as a social unit, transcending the lower levels of family, community, ethnic group, nation and transnational region and making up a significant component of the global society. This concept presents a new research pathway to the interpretation of “community of a shared future for mankind” and the “Belt and Road Initiative”.

Gravity and magnetic measurements are common remote sensing strategies to obtain the property change of observed targets. Nowadays, the characteristic value of the derivatives of gravity and magnetic anomalies is commonly used to detect the source horizontal edge. We found that the horizontal coordinates of the characteristic value of different-order derivatives are not directly corresponding to the edge of the source, which varies with the depth and size of the source. The spatial different-order derivative (SDD) method of gravity and magnetic anomalies was developed, and we proved that the spatial intersection of different-order derivatives is corresponding to the location of the source, and used this feature to obtain horizontal location and depth of the source simultaneously. The model tests proved that the SDD method has high accuracy and strong anti-noisy. According to the corresponding relationship between the potential field data and lithology, we used the SDD method to delineate the potential metalorganic area in the survey region, which provides the basis for subsequent exploration.

Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to the influence of SAR speckle noise and nonlinear radiometric differences. This study proposes a robust algorithm based on phase congruency to register optical and SAR images (ROS-PC). It consists of a uniform Harris feature detection method based on multi-moment of the phase congruency map (UMPC-Harris) and a local feature descriptor based on the histogram of phase congruency orientation on multi-scale max amplitude index maps (HOSMI). The UMPC-Harris detects corners and edge points based on a voting strategy, the multi-moment of phase congruency maps, and an overlapping block strategy, which is used to detect stable and uniformly distributed keypoints. Subsequently, HOSMI is derived for a keypoint by utilizing the histogram of phase congruency orientation on multi-scale max amplitude index maps, which effectively increases the discriminability and robustness of the final descriptor. Finally, experimental results obtained using simulated images show that the UMPC-Harris detector has a superior repeatability rate. The image registration results obtained on test images show that the ROS-PC is robust against SAR speckle noise and nonlinear radiometric differences. The ROS-PC can tolerate some rotational and scale changes.

Since polymetallic ores show higher anomalies in gravity exploration methods, we usually obtain the position and range of ore bodies by density inversion of gravity data. The three-dimensional (3D) gravity focusing density inversion is a common interpretation method in mineral exploration, which can directly and quantitatively obtain the density distribution of subsurface targets. However, in actual cases, it is computation inefficient. We proposed the preconditioned Jacobian-free Newton-Krylov (JFNK) method to accomplish the focusing inversion. The JFNK method is an efficient algorithm in solving large sparse systems of nonlinear equations, and we further accelerate the inversion process by the preconditioned technique. In the actual area, the gravity anomalies are distributed on the naturally undulating surface. Nowadays, the gravity inversion under undulating terrain was mainly achieved by discretizing the ground into unstructured meshes, but it is complicated and time-consuming. To improve the practicality, we presented an equivalent-dimensional method that incorporates unstructured meshes with structured meshes in gravity inversion, and the horizontal size is determined by the gradient of observed gravity and terrain data. The small size meshes are adopted at the position where the terrain or gravity gradient is large. We used synthetic data with undulating-terrain to test our new method. The results indicated that the recovered model obtained by this method was similar to the inversion method of unstructured meshes, and the new method computes faster. We also applied the method to field data in Huayangchuan, Shaanxi Province. The survey area has complicated terrain conditions and contains multiple polymetallic ores. Based on the high-density characteristics of polymetallic ore bodies in the area, we calculate the field data into 3D density models of the subsurface by the preconditioned JFNK method and infer six polymetallic ores.

Gravity migration is a fast imaging technique based on the migration concept to obtain subsurface density distribution. For higher resolution of migration imaging results, we propose a 3D regularized focusing migration method that implements migration imaging of an entire gravity survey with a focusing stabilizer based on regularization theory. When determining the model parameters, the iterative direction is chosen as the conjugate migration direction, and the step size is selected on the basis of the Wolfe–Powell conditions. The model tests demonstrate that the proposed method can improve the resolution and precision of imaging results, especially for blocky structures. At the same time, the method has high computational efficiency, which allows rapid imaging for large-scale gravity data. It also has high stability in noisy conditions. The developed novel method is applied to interpret gravity data collected from the skarn-type iron deposits in Yucheng, Shandong province. Migration results show that the depth of the buried iron ore in this area is 750–1500 m, which is consistent with the drilling data. We also provide recommendations for further mineral exploration in the survey area. This method can be used to complete rapid global imaging of large mining areas and it provides important technical support for exploration of deep, concealed deposits.

Qinghai Lake is the largest inland saline lake on the Tibetan Plateau. Climate change and catchment modifications induced by human activities are the main drivers playing a significant role in the dramatic variation of water levels in the lake (Δh); hence, it is crucial to provide a better understanding of the impacts caused by these phenomena. However, their respective contribution to and influence on water level variations in Qinghai Lake are still unclear and without characterizing them, targeted measures for a more efficient conservation and management of the lake cannot be implemented. In this paper, data monitored during the period 1960−2016 (e.g., meteorological and land use data) have been analyzed by applying multiple techniques to fill this gap and estimate the contribution of each parameter recorded to water level variations (Δh). Results obtained have demonstrated that the water level of Qinghai Lake declined between 1960 and 2004, and since then has risen continuously and gradually, due to the changes in evaporation rates, precipitation and consequently surface runoff associated with climate change effects and catchment modifications. The authors have also pinpointed that climate change is the main leading cause impacting the water level in Qinghai Lake because results demonstrated that 93.13% of water level variations can be attributable to it, while the catchment modifications are responsible for 6.87%. This is a very important outcome in the view of the fact that global warming clearly had a profound impact in this sensitive and responsive region, affecting hydrological processes in the largest inland lake of the Tibetan Plateau.

When marginal groups face social transformation, they risk being unable to adapt and acquire equal developmental opportunities, slipping into ‘further marginalisation’. This paper explores the case of the Dan fishing community of Sanya City, Hainan, China. Efforts to transform Sanya City into an international island tourism destination involve plans to relocate Sanya fishing port and to clear the adjacent neighbourhood inhabited by the Dan people, traditionally a boat-dwelling people, who have long been marginalised relative to China’s land-oriented society. As their natural and social resources dwindle, the Dan of Sanya City must cope with the loss of their homes and livelihoods, as they are forced into the city’s suburbs and as the port relocation complicates the economics and practicalities of making a living from the fishing industry. This paper argues for greater attention to be given to local needs in the formulation of urban development strategies in island cities.

Gastric cancer is one of the most common and aggressive malignancies. Both bacterial virulence factors and host chronic inflammation are thought to promote gastric cancer development. In this study, we investigated the potential involvement of follicular helper T cells in gastric cancer. Functions of follicular helper T subsets were examined in Helicobacter pylori –infected gastric cancer patients and H. pylori –infected but asymptomatic individuals. We found that the follicular helper T cells in gastric cancer individuals were skewed toward the Th1 and Th17 subsets compared to those in H. pylori –infected but asymptomatic individuals. In a naive B cell–follicular helper T cell coculture, the Th1–follicular helper T cells by themselves were ineffective at stimulating a robust antibody response, unlike the Th2–follicular helper T and Th17–follicular helper T cells. However, Th1–follicular helper T cells significantly promoted the immunoglobulin G response in collaboration with other follicular helper T subsets, through the secretion of interferon gamma. We also found that Th1–follicular helper T cells suppressed the development of interleukin-10 + regulatory B cells, a cell type previously thought to protect H. pylori –infected individuals from tissue damage. In addition, the frequency of Th1–follicular helper T cells in gastric cancer patients was negatively correlated with the disease-free survival of gastric cancer patients after tumor resection. These results suggested that dysregulation of follicular helper T subsets in gastric cancer patients, characterized by increased Th1–follicular helper T cells, contributed to inflammation and tumor development.

Hybrid organic–inorganic perovskites have emerged as very attractive light absorbers for the fabrication of low-cost and high-efficiency solar cells. However, their practical applications could be hindered due to their poor environmental stability and chemical incompatibility with charge transport layers and relevant electrodes in the devices. Herein, an ultrathin aluminum oxide (Al2O3) interface layer is constructed on the surfaces of perovskite films through in situ hydrolysis and condensation of aluminum triisopropoxide (ATIP) processed by a solution-processing method under ambient conditions. The Al2O3 layer is uniform and robust and can stabilize the perovskite film without affecting the charge transfer in the devices. Consequently, the constructed PSC exhibits a power conversion efficiency (PCE) of 23.52%, which presents the highest record reported so far for the Al2O3-based PSCs with the same configuration. More importantly, the Al2O3 incorporation leads to a significant improvement in the device stability: the PSC without encapsulation presents high operational stability with 90% of the initial efficiency after continuous maximum power point tracking over 1000 hours, and good thermal stability with 94% of the initial efficiency after aging at 60 °C in a N2 atmosphere for 300 hours. [ABSTRACT FROM AUTHOR]