The space–time CESE scheme for shallow water equations incorporating variable bottom topography and horizontal temperature gradients

The effects of bottom topography and horizontal temperature gradients on the shallow water flows are theoretically investigated. The considered systems of partial differential equations (PDEs) are non-strictly hyperbolic and non-conservative due to the presence of non-conservative differential terms on the right hand side. The solutions of these model equations are very challenging for a numerical scheme. Thus, our primary goal is to introduce an improved numerical scheme which can handle the non-conservative differential terms efficiently and accurately. In this paper, the space–time conservation element and solution element (CESE) method is extended to approximate these model equations. The proposed scheme has capability to overcome all difficulties posed by this nonlinear system of PDEs. The performance of the scheme is analyzed by considering several case studies of practical interest and the results of suggested scheme are compared with those of central NT scheme. The accuracy of the scheme is verified qualitatively and quantitatively.     

Toward analyzing impact of disjoint axioms for merging heterogeneous ontologies

In recent years, the question on Automatic Ontology Merging (AOM) become challenging to address for the researchers. Our research and development for the Disjoint Knowledge Perservation based Automatic Ontology Merging (DKP-AOM) is a milestone in the same direction. This paper provides a more specific discussion about disjoint knowledge axioms in DKP-AOM and makes an assessment of our merge algorithm that looks-up within disjoint partitions of concept hierarchies of ontologies. The significant use of disjoint knowledge is corroborated by testing conference and vertebrate ontologies. The results reveal that disjoint knowledge axioms help identifying initial inaccurate mappings and remove ambiguity when the concept with same symbolic identifier has a different meaning in different local ontologies in the process of ontology merging. Disjoint axioms separate the knowledge in distinct chunks and enable concept matching within the boundaries of sub-hierarchies of the entire ontology concept hierarchy. While finding matches between concepts of ontologies, disjoint partitions with the disjoint knowledge about concepts in source ontologies minimize the search space and reduce the runtime complexity of ontology merging. We also discuss encouraging results obtained by our DKP-AOM system within the OAEI 2015 campaign.     

Scale coding bag of deep features for human attribute and action recognition

Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.     

Translating the impact of knowledge management into knowledge‐based innovation: The neglected and mediating role of knowledge‐worker satisfaction

The purpose of knowledge management is innovation. However, this study proposes that the existence of knowledge management in the knowledge‐worker's work environment can nurture the overall satisfaction of knowledge worker. As the ultimate purpose of knowledge management is innovation performance, therefore, the satisfaction of knowledge worker as an outcome of knowledge management should be greater innovation performance. Consequently, the purpose of this study is to test the mediating role of satisfaction of knowledge worker between knowledge management and innovation. The study collected the data from 306 knowledge workers (engineers and managers) of software houses from Pakistan. The SmartPLS 3 Version 2.7 software that uses the PLS‐SEM (Partial Least Square‐Structural Equation Modelling) technique was used. The results indicate that satisfaction of knowledge worker mediates between two knowledge management processes (knowledge creation and knowledge sharing) and innovation significantly. However, it does not mediate between knowledge utilization and innovation significantly.     

Adaptive Auction Framework for Spectrum Market in Cognitive Radio Networks

For the recent decade, cognitive radio networks have received much attention as an alternative to the traditional static spectrum allocation policy since the licensed spectrum channels are not being used efficiently. The most critical issue of the cognitive radio networks is how to distribute the idle spectrum channels to the secondary users opportunistically. The auction-based market is desirable for the trade of idle spectrum channels since the secondary users can purchase a channel in timely manner and the licensed primary users can earn the additional profit while not using the channels. Among the auction algorithms proposed for the spectrum market, we focus on the TASG framework, which consists of two nested auction algorithms, because it enables the group-buying of spectrum channels for the secondary users with limited budgets, and possesses many positive properties such as budget-balance, individual rationality and truthfulness. However, the TASG framework is not very attractive to the market participants since the seller earns the small revenue and the buyer has the low utility. In this paper, we propose a new auction framework for the spectrum markets, called aDaptive and Economically robust Auction-based Leasing (DEAL), that keeps all the benefits of TASG while improving the utility (or revenue) of the participants. To this end, we develop an enhanced inner-auction algorithm, called the Global Auction algorithm in our DEAL framework, and adapt the involved parameters dynamically based on the previous bids from the potential buyers. Simulation results demonstrate that our framework significantly outperforms the previous TASG.     

Effort-aware cross-project just-in-time defect prediction framework for mobile apps

As the boom of mobile devices, Android mobile apps play an irreplaceable roles in people’s daily life, which have the characteristics of frequent updates involving in many code commits to meet new requirements. Just-in-Time (JIT) defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers, which is more suitable to mobile apps. As the within-app defect prediction needs sufficient historical data to label the commit instances, which is inadequate in practice, one alternative method is to use the cross-project model. In this work, we propose a novel method, called KAL, for cross-project JIT defect prediction task in the context of Android mobile apps. More specifically, KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative features. Then, the adversarial learning technique is used to extract the common feature embedding for the model building. We conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance evaluation. The results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.     

On accounting for screen resolution in adaptive video streaming: QoE-driven bandwidth sharing framework

Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link mean opinion score (MOS) to video bitrate for different screen types (e.g., Common Intermediate Format [CIF], Quarter Common Intermediate Format [QCIF], and High Definition [HD]). We leverage such studies and formulate a Quality of Experience (QoE)-driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and Karush Kuhn Tucker (KKT) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look-alike strategy implementing max-min fairness.     

3D Printed Enzyme-Functionalized Scaffold Facilitates Diabetic Bone Regeneration

Patients with diabetes mellitus (DM) suffer from a high risk of fractures and poor bone healing ability. Surprisingly, no effective therapy is available to treat diabetic bone defect in clinic. Here, a 3D printed enzyme-functionalized scaffold with multiple bioactivities including osteogenesis, angiogenesis, and anti-inflammation in diabetic conditions is proposed. The as-prepared multifunctional scaffold is constituted with alginate, glucose oxidase (GOx), and catalase-assisted biomineralized calcium phosphate nanosheets (CaP@CAT NSs). The GOx inside scaffolds can alleviate the hyperglycemia environment by catalyzing glucose and oxygen into gluconic acid and hydrogen peroxide (H₂O₂). Both the generated H₂O₂ as well as the overproduced H₂O₂ in DM can be scavenged by CaP@CAT NSs, while the initiated hypoxic microenvironment stimulates neovascularization. Moreover, the incorporation of CaP@CAT NSs not only enhance the mechanical property of the scaffolds, but also facilitate bone regeneration by the degraded Ca²⁺ and PO₄³⁻ ions. The remarkable in vitro and in vivo outcomes demonstrate that enzymes functionalized scaffolds can be an effective strategy for enhancing bone tissue regeneration in diabetic conditions, underpinning the potential of multifunctional scaffolds for diabetic bone regeneration.     

A Robust Wearable Point-of-Care CNT-Based Strain Sensor for Wirelessly Monitoring Throat-Related Illnesses

Point-of-care testing (POC) has the ability to detect chronic and infectious diseases early or at the time of occurrence and provide a state-of-the-art personalized healthcare system. Recently, wearable and flexible sensors have been employed to analyze sweat, glucose, blood, and human skin conditions. However, a flexible sensing system that allows for the real-time monitoring of throat-related illnesses, such as salivary parotid gland swelling caused by flu and mumps, is necessary. Here, for the first time, a wearable, highly flexible, and stretchable piezoresistive sensing patch based on carbon nanotubes (CNTs) is reported, which can record muscle expansion or relaxation in real-time, and thus act as a next-generation POC sensor. The patch offers an excellent gauge factor for in-plane stretching and spatial expansion with low hysteresis. The actual extent of muscle expansion is calculated and the gauge factor for applications entailing volumetric deformations is redefined. Additionally, a bluetooth-low-energy system that tracks muscle activity in real-time and transmits the output signals wirelessly to a smartphone app is utilized. Numerical calculations verify that the low stress and strain lead to excellent mechanical reliability and repeatability. Finally, a dummy muscle is inflated using a pneumatic-based actuator to demonstrate the application of the affixed wearable next-generation POC sensor.     

A closed-loop healthcare processing approach based on deep reinforcement learning

Multimedia Tools and Applications - In healthcare, the human body is a controlled input-output system, which generates different observations with the variations of external interventions. The...