Cloud computing emerges as a new computing paradigm that aims to provide reliable, customized and quality of service guaranteed computation environments for cloud users. Applications and databases ...are moved to the large centralized data centers, called cloud. Due to resource virtualization, global replication and migration, the physical absence of data and machine in the cloud, the stored data in the cloud and the computation results may not be well managed and fully trusted by the cloud users. Most of the previous work on the cloud security focuses on the storage security rather than taking the computation security into consideration together. In this paper, we propose a privacy cheating discouragement and secure computation auditing protocol, or SecCloud, which is a first protocol bridging secure storage and secure computation auditing in cloud and achieving privacy cheating discouragement by designated verifier signature, batch verification and probabilistic sampling techniques. The detailed analysis is given to obtain an optimal sampling size to minimize the cost. Another major contribution of this paper is that we build a practical secure-aware cloud computing experimental environment, or SecHDFS, as a test bed to implement SecCloud. Further experimental results have demonstrated the effectiveness and efficiency of the proposed SecCloud.
A photosynthetic light-response (PLR) curve is a mathematical description of a single biochemical process and has been widely applied in many eco-physiological models. To date, many PLR measurement ...designs have been suggested, although their differences have rarely been explored, and the most effective design has not been determined. In this study, we measured three types of PLR curves (High, Middle and Low) from planted Larix olgensis trees by setting 31 photosynthetically active radiation (PAR) gradients. More than 530 million designs with different combinations of PAR gradients from 5 to 30 measured points were conducted to fit each of the three types of PLR curves. The influence of different PLR measurement designs on the goodness of fit of the PLR curves and the accuracy of the estimated photosynthetic indicators were analysed, and the optimal design was determined. The results showed that the measurement designs with fewer PAR gradients generally resulted in worse predicted accuracy for the photosynthetic indicators. However, the accuracy increased and remained stable when more than ten measurement points were used for the PAR gradients. The mean percent error (M%E) of the estimated maximum net photosynthetic rate (P
) and dark respiratory rate (R
) for the designs with less than ten measurement points were, on average, 16.4 times and 20.1 times greater than those for the designs with more than ten measurement points. For a single tree, a unique PLR curve design generally reduced the accuracy of the predicted photosynthetic indicators. Thus, three optimal measurement designs were provided for the three PLR curve types, in which the root mean square error (RMSE) values reduced by an average of 8.3% and the coefficient of determination (R
) values increased by 0.3%. The optimal design for the High PLR curve type should shift more towards high-intensity PAR values, which is in contrast to the optimal design for the Low PLR curve type, which should shift more towards low-intensity PAR values.
When conventional delivery vehicles are driven over complex terrain, large vibrations can seriously affect vehicle-loaded equipment and cargo. Semi-active vehicle-mounted vibration isolation control ...based on road preview can improve the stability of loaded cargo and instruments by enabling them to have lower vertical acceleration. A combined dynamic model including a vehicle and platform is developed first. In order to obtain a non-linear relationship between damping force and input current, a continuous damping control damper model is developed, and the corresponding external characteristic tests are carried out. Because some conventional control algorithms cannot handle complex constraints and preview information, a model predictive control algorithm based on forward road preview and input constraints is designed. Finally, simulations and real tests of the whole vehicle vibration environment are carried out. The results show that the proposed model predictive control based on road preview can effectively improve vibration isolation performance of the vehicle-mounted platform.
The natural forest ecosystem has been affected by wind storms for years, which have caused several down wood (DW) and dramatically modified the fabric and size. Therefore, it is very important to ...explain the forest system by quantifying the spatial relationship between DW and environmental parameters. However, the spatial non-stationary characteristics caused by the terrain and stand environmental changes with distinct gradients may lead to an incomplete description of DW, the local neural-network-weighted models of geographically neural-network-weighted (GNNWR) models are introduced here. To verify the validity of models, our DW and environmental factors were applied to investigate of occurrence of DW and number of DW to establish the generalized linear (logistic and Poisson) models, geographically weighted regression (GWLR and GWPR) models and GNNWR (GNNWLR and GNNWPR) models. The results show that the GNNWR models show great advantages in the model-fitting performance, prediction performance, and the spatial Moran's I of model residuals. In addition, GNNWR models can combine the geographic information system technology for accurately expressing the spatial distribution of DW relevant information to provide the key technology that can be used as the basis for human decision-making and management planning.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with clinical, biological, and pathological features occurring along a continuum from normal to end-stage disease. Currently, the ...diagnosis of AD depends on clinical assessments and post-mortem neuropathology, which is unbenefited early diagnosis and progressive monitoring. In recent years, clinical studies have reported that the level of cerebrospinal fluid (CSF) and blood neurogranin (Ng) are closely related to the occurrence and subsequent progression of AD. Therefore, the study used meta-analysis to identify the CSF and blood Ng levels for the development of diagnosis biomarker of patients with AD and mild cognitive impairment (MCI). We searched the Pubmed, Embase, Cochrane Library, and Web of Science databases. A total of 24 articles eligible for inclusion and exclusion criteria were assessed, including 4661 individuals, consisting of 1518 AD patients, 1501 MCI patients, and 1642 healthy control subjects. The level of CSF Ng significantly increased in patients with AD and MCI compared with healthy control subjects (SMD: 0.84 95% CI: 0.70-0.98, P < 0.001; SMD: 0.53 95% CI: 0.40-0.66, P = 0.008), and higher in AD patients than in MCI patients (SMD: 0.18 95% CI: 0.07-0.30, P = 0.002), and CSF Ng level of patients with MCI-AD who progressed from MCI to AD was significantly higher than that of patients with stable MCI (sMCI) (SMD: 0.71 95% CI: 0.25-1.16, P = 0.002). Moreover, the concentration of Ng in blood plasma exosomes of patients with AD and MCI was lower than that of healthy control subjects (SMD: -6.657 95% CI: -10.558 to -2.755, P = 0.001; and SMD: -3.64 95% CI: -6.50 to -0.78, P = 0.013), and which in patients with AD and MCI-AD were also lower than those in patients with sMCI (P < 0.001). Furthermore, regression analysis showed a negative relationship between MMSE scores and CSF Ng levels in MCI patients (slope = -0.249 95% CI: -0.003 to -0.495, P = 0.047). Therefore, the Ng levels increased in CSF, but decreased in blood plasma exosomes of patients with AD and MCI-AD, and highly associated with cognitive declines. These findings provide the clinical evidence that CSF and blood exosomes Ng can be used as a cognitive biomarker for AD and MCI-AD, and further studies are needed to define the specific range of Ng values for diagnosis at the different stages of AD.
Ecosystem degradation and decline are central issues that urgently require resolution within global environmental protection efforts. Accordingly, accurately analyzing the spatiotemporal evolution of ...regional ecological environmental quality and exploring its natural and anthropogenic driving factors of ecological environmental quality are crucial for protecting regional ecological environments and advancing sustainable development strategies. Therefore, this study created a new remote sensing ecological index to investigate the patterns of ecological quality change in vegetation-covered areas over a long time series and identified the intensity and local response relationships of various driving factors, including climate, topography, soil, and urbanization. The results revealed an upward trend in the ecological quality of the Heilongjiang region, with a high degree of spatial autocorrelation in ecological quality. The ecological grades in forest-covered areas significantly surpassed those in other vegetated, urban, and desert regions. Through a collaborative analysis of various geographical statistical methods, the intensities of the driving factors and their local response relationships were determined. This study provides a method for accurately and rapidly assessing regional ecological environmental quality and exploring the complex interactions of driving factors, thus offering a theoretical basis for monitoring regional-scale ecological conditions, balancing ecological and economic development, and informing environmental protection policies.
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•GPP and kNDVI introduction enhances ecological quality assessment for accuracy and comprehensiveness.•Counted the area percentage in various ecological classes and plotted trends in ecological quality.•Identified clustered distribution patterns and prepared maps for ecological quality in diverse regions.•Forest-covered areas have the highest ecological level.•Drivers interactions exert greater ecological impact than individual factors.
On 1 December 2016, an Mw 6.2 earthquake characterized by normal faulting occurred in the highlands of the central Andes in southern Peru, marking the region’s largest shallow event. The occurrence ...of the earthquake provides a significant chance to gain insight into the regional tectonic deformation and the seismogenic mechanism of the shallow normal-faulting earthquake, as well as the regional potential seismic risk. Here, we first utilize Sentinel-1A interferometric synthetic aperture radar (InSAR) data to extract the coseismic and postseismic deformation associated with this earthquake and then determine the detailed coseismic slip and postseismic afterslip distribution of this event. Coseismic modeling results indicate that the coseismic rupture is mainly characterized by normal faulting with some dextral strike-slip components. Most coseismic slip is confined to a depth range of 2–12 km, indicating an obvious slip deficit area in the shallow fault part. Further postseismic modeling reveals that the majority of afterslip is concentrated at depths of 0 to 5.4 km. The relatively shallow postseismic afterslip makes up for the coseismic slip deficit area to some extent. Through a joint analysis of the inversions, seismic data, and regional geology and geomorphology, we infer that the occurrence of this 2016 normal-faulting event is a result of regional gravitational collapse. In addition, we investigate the relationship between the 2016 earthquake and great historical earthquakes near the subduction zone of the central Andes and find that the 2016 event is likely promoted in advance by these events through our calculations of the coseismic and postseismic Coulomb stress changes. Finally, we should pay more attention to the nearby Falla Huaytacucho-Condoroma fault and the western segment of the Vilcanota Fault because of their relatively high stress loading.
This study focuses on the development of fully biodegradable poly (lactic acid) (PLA) fibre reinforced PLA and poly (butylene succinate) (PBS) matrix composites in order to reduce the impact on the ...environment by long-lasting plastics based composites. PLA self-reinforced composite (SRC) and the PLA reinforced PBS composite (PLA–PBS) composites were produced by film-stacking method. With the increasing of the loading direction fibre content, the composite moduli significantly increase by between 70% up to 6 times higher than those of the matrix films. Higher tensile strength and Young’s modulus have been found in PLA-SRC than in PLA–PBS composite. This is due to better tensile properties of the PLA film and better interfacial adhesion in PLA-SRC.
We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data ...combined with ground survey data. Taking
,
, and
plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with RLoo2 values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an RLoo2 reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.
Privacy-preserving metering aggregation is regarded as an important research topic in securing a smart grid. In this paper, we first identify and formalize a new attack, in which the attacker could ...exploit the information about the presence or absence of a specific person to infer his meter readings. This attack, coined as human-factor-aware differential aggregation (HDA) attack, cannot be addressed in existing privacy-preserving aggregation protocols proposed for smart grids. We give a formal definition on it and propose two novel protocols, including basic scheme and advanced scheme, to achieve privacy-preserving smart metering data aggregation and to resist the HDA attack. Our protocol ensures that smart meters periodically upload encrypted measurements to a (electricity) supplier/aggregator such that the aggregator is able to derive the aggregated statistics of all meter measurements but is unable to learn any information about the human activities. We present the formal security analysis for the proposed protocol to guarantee the strong privacy. Moreover, we evaluate the performance of our protocol in a Java-based implementation under different parameters. The performance and utility analysis shows that our protocol is simple, efficient, and practical.
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