'Word-of-Mouse' in China: In-Depth Interviews

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Insider Threat Risk Prediction based on Bayesian Network

Insider threat protection has received increasing attention in the last ten years due to the serious con-sequences of malicious insider threats. Moreover, data leaks and the sale of mass data have become much simpler to achieve, e.g., the dark web can allow malicious insiders to divulge confidential data whilst hiding their identities. In this paper, we propose a novel approach to predict the risk of malicious insider threats prior to a breach taking place. Firstly, we propose a new framework for insider threat risk prediction, drawing on technical, organisational and human factor perspectives. Secondly, we employ a Bayesian network to model and implement the proposed framework. Furthermore, this Bayesian network-based prediction model is evaluated in a range of challenging environments. The risk level predictions for each authorised users within the organisation are examined so that any in-sider threat risk can be identified. The proposed insider threat prediction model achieved better results when compared to the empirical judgements of security experts.</p

Study on curving performance of heavy haul train under braking condition

In order to study train curving performances, 1D and 3D train dynamics models were built by numerical method. The 1D model was composed of 210 simple wagons allowed of only longitudinal motions, while the 3D train model included three complicated wagons of which longitudinal, lateral and vertical DOF could be considered. Combined with calculated results under braking condition from 1D model, behaviours of draft gear and brake shoe could be added into 3D model. The assessment of train curving performance is more focused on making a comparison between idling and braking conditions. Some results indicate as follows: When train braking is operated on a curved track, wheel-rail lateral force and derailment factor are larger than those under idling condition. Because yawing movement of wheelset is limited by brake shoe, the zone of wheel contact along wheel tread is wider than that under idling condition. Besides, as curvature gets tighter, traction ratio shows a nonlinear increasing trend, whether under idling or braking condition.With brake shoe pressure increasing, train steering gets more difficult

Co-Design Secure Control Based on Image Attack Detection and Data Compensation for Networked Visual Control Systems

The incomplete and untrue data caused by cyberattacks (e.g., image information leakage and tampering) willaffect control performance and even lead to system instability.To address this problem, a novel co-design secure control methodbased on image attack detection and data compensation fornetworked visual control systems (NVCSs) is proposed. Firstly,the existing problems of NVCSs under image attacks are an-alyzed, and a co-design secure control method including imageencryption, watermarking-based attack detection and online datacompensation is presented. Then, a detector based on double-layer detection mechanism of timeout and digital watermarkingis designed for real-time, integrity and authenticity discriminationof the image. Furthermore, according to the detection results, anonline compensation scheme based on cubic spline interpolationand post-prediction update is proposed to reduce the effect ofcumulative errors and improve control performance. Finally,the online compensation scheme is optimized by consideringthe characters of networked inverted pendulum visual controlsystems, and experimental results demonstrate the feasibility andeffectiveness of the proposed detection and control method.</p

Co-Design Secure Control Based on Image Attack Detection and Data Compensation for Networked Visual Control Systems

The incomplete and untrue data caused by cyberattacks (e.g., image information leakage and tampering) willaffect control performance and even lead to system instability.To address this problem, a novel co-design secure control methodbased on image attack detection and data compensation fornetworked visual control systems (NVCSs) is proposed. Firstly,the existing problems of NVCSs under image attacks are an-alyzed, and a co-design secure control method including imageencryption, watermarking-based attack detection and online datacompensation is presented. Then, a detector based on double-layer detection mechanism of timeout and digital watermarkingis designed for real-time, integrity and authenticity discriminationof the image. Furthermore, according to the detection results, anonline compensation scheme based on cubic spline interpolationand post-prediction update is proposed to reduce the effect ofcumulative errors and improve control performance. Finally,the online compensation scheme is optimized by consideringthe characters of networked inverted pendulum visual controlsystems, and experimental results demonstrate the feasibility andeffectiveness of the proposed detection and control method.</p

Melt Flow-Induced Mechanical Deformation and Fracture Behaviour of Dendrites in Alloy Solidification

Cellular automaton-finite volume approach and finite element method are combined to study flow-induced dendritic deformation in alloy solidification. Simulation results reveal that dendrites can undergo mechanical fracture in Al–Cu alloy solidification. The root of primary dendrite is not the location of maximum stress due to secondary dendritic bridging and uneven radius of the primary dendritic trunk. Corresponding dendrite deformation and fracture mechanisms are suggested in the paper

Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences from Three fMRI Paradigms

Objective : Multimodal-based methods show great potential for neuroscience studies by integrating complementary information. There has been less multimodal work focussed on brain developmental changes. Methods : We propose an explainable multimodal deep dictionary learning method to uncover both the commonality and specificity of different modalities, which learns the shared dictionary and the modality-specific sparse representations based on the multimodal data and their encodings of a sparse deep autoencoder. Results : By regarding three fMRI paradigms collected during two tasks and resting state as modalities, we apply the proposed method on multimodal data to identify the brain developmental differences. The results show that the proposed model can not only achieve better performance in reconstruction, but also yield age-related differences in reoccurring patterns. Specifically, both children and young adults prefer to switch among states during two tasks while staying within a particular state during rest, but the difference is that children possess more diffuse functional connectivity patterns while young adults have more focused functional connectivity patterns. Conclusion and Significance : To uncover the commonality and specificity of three fMRI paradigms to developmental differences, multimodal data and their encodings are used to train the shared dictionary and the modality-specific sparse representations. Identifying brain network differences helps to understand how the neural circuits and brain networks form and develop with age. </p

Melt flow-induced mechanical deformation of dendrites in alloy solidification: A coupled thermal fluid - solid mechanics approach

Melt flow during alloy solidification promotes more dendrite fragments or equiaxed grains due to dendrite fragmentation, while the complex interactions between melt flow and dendritic growth causing flow-induced dendrite deformation and dendrite fragmentations remain obscure. In this work, cellular automaton-finite volume approach and the displacement-based finite element method have been combined to simulate dendrite growth, fluid flow and flow-induced mechanical deformation in Al-4.5 wt%Cu alloy, and to reveal the stress evolution during dendritic growth under melt convection. It is found that dendrites can undergo visible mechanical bending under fluid flow. The stress increases with the enhancement of fluid flow. The primary dendritic trunk is the location of the dominant deformation under a parallel fluid flow. Though the secondary dendrite does not suffer the large stress, it significantly affects the stress concentration of the primary dendritic trunk. As flow velocity increases, the secondary dendrite arm spacing gradually decreases and bridging occurs due to the contact of tertiary dendrites. The bridging of secondary dendrites impedes the development of stress concentrations. Especially, as inflow velocity exceeds 0.05 m/s, the stress does not get larger than that under the velocity of 0.01 m/s under the complication interactions between the dendritic growth and flow patterns. Unlike previous understanding, the maximum stresses induced by melt flow are mainly located at the position where primary dendritic trunk intersects with unconnected secondary dendrites or the thin dendrite trunk.</p

A phase I, multicenter, open-label, first-in-human, dose-escalation study of the oral smoothened inhibitor Sonidegib (LDE225) in patients with advanced solid tumors

PURPOSE: This phase I trial was undertaken to determine the maximum tolerated dose (MTD), dose-limiting toxicities (DLT), safety, tolerability, pharmacokinetics, pharmacodynamics, and preliminary antitumor activity of the novel smoothened inhibitor sonidegib (LDE225), a potent inhibitor of hedgehog signaling, in patients with advanced solid tumors. EXPERIMENTAL DESIGN: Oral sonidegib was administered to 103 patients with advanced solid tumors, including medulloblastoma and basal cell carcinoma (BCC), at doses ranging from 100 to 3,000 mg daily and 250 to 750 mg twice daily, continuously, with a single-dose pharmacokinetics run-in period. Dose escalations were guided by a Bayesian logistic regression model. Safety, tolerability, efficacy, pharmacokinetics, and biomarkers in skin and tumor biopsies were assessed. RESULTS: The MTDs of sonidegib were 800 mg daily and 250 mg twice daily. The main DLT of reversible grade 3/4 elevated serum creatine kinase (18% of patients) was observed at doses ≥ the MTD in an exposure-dependent manner. Common grade 1/2 adverse events included muscle spasm, myalgia, gastrointestinal toxicities, increased liver enzymes, fatigue, dysgeusia, and alopecia. Sonidegib exposure increased dose proportionally up to 400 mg daily, and displayed nonlinear pharmacokinetics at higher doses. Sonidegib exhibited exposure-dependent reduction in GLI1 mRNA expression. Tumor responses observed in patients with medulloblastoma and BCC were associated with evidence of hedgehog pathway activation. CONCLUSIONS: Sonidegib has an acceptable safety profile in patients with advanced solid tumors and exhibits antitumor activity in advanced BCC and relapsed medulloblastoma, both of which are strongly associated with activated hedgehog pathway, as determined by gene expression