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This paper presents a strategy to autonomously explore unknown indoor environments, focusing on 3D mapping of the environment and performing grid level semantic labeling to identify all available objects. Unlike conventional exploration techniques that utilize geometric heuristics and information gain theory on an occupancy grid map, the work presented in this paper considers semantic information, such as the class of objects, in order to gear the exploration towards environmental segmentation and object labeling. The proposed approach utilizes deep learning to map 2D semantically segmented images into 3D semantic point clouds that encapsulate both occupancy and semantic annotations. A next-best-view exploration algorithm is employed to iteratively explore and label all the objects in the environment using a novel utility function that balances exploration and semantic object labeling. The proposed strategy was evaluated in a realistically simulated indoor environment, and results were benchmarked against other exploration strategies.

Fabrication of anisotropic piezoresistive materials is ultimately important for the realization of multi-loading recognization in robotics, auto-driving, artificial intelligence and health care, etc., whereas most of the existing production strategies are time-consuming and costly. Herein, we applied a simple structural design of carbonized cotton thread (CCT) to produce anisotropic piezoresistive CCT/silicone composites. Specifically, the rectangular wave shaped CCT/silicone composite (Re-CCT) and straight CCT/silicone composite (St-CCT), which exhibit distinct sensing performance to tension and compression, are fabricated. Their sensors’ gauge factor (GF) for tension and compression obeys: GFTen-St > GFCom-St ≈ GFCom-Re > GFTen-Re ≈ 0. Consequently, the flexible multifunctional anisotropic piezoresistive composite integrated with Re-CCT and St-CCT is able to distinguish multiple loading modes, including compression, tension and their combination, via monitoring the micro-deformation applied by a finger and the status of a bottle handled by a robotic hand. The current strategy shows a great promise in manufacturing flexible anisotropic piezoresistive composite sensors for robotics, auto-driving, artificial intelligence and health care applications, etc.

Angiosarcomas are highly aggressive tumors of endothelial origin, which carry a poor prognosis. Fenofibrate is a hypolipidemic drug, which acts by activating the transcription factor PPARα. It has also been widely reported to have ‘anti-cancer’ activity. The current study investigated its effect in a murine VEGF-dependent angiosarcoma cell-line, MS1 VEGF. The study utilised assays to monitor cell proliferation and viability, apoptosis, cell cycle progression, mitochondrial membrane potential, changes in protein expression, and changes in miRNA expression using microarrays. Fenofibrate showed potent anti-proliferative action in MS1 VEGF angiosarcoma cells, without inducing apoptosis. It enriched cells in G2/M cell cycle phase and hyperpolarised mitochondria. Other PPARα activators failed to mimic fenofibrate action. Inhibitors of PPARα and NFκB failed to reverse the inhibitory effect of fenofibrate and their combination with fenofibrate was cytotoxic. Fenofibrate downregulated the expression of key VEGF-effector proteins, including Akt, ERK, Bcl-2 and survivin, and a chemical inhibitor screen discovered relevance of these proteins to cell proliferation. A miRNA microarray revealed that fenofibrate differentially regulated cellular miRNAs with known roles in cancer and angiogenesis. The data raise the possibility that fenofibrate could be useful in angiosarcoma therapy, especially considering its well-established clinical safety and tolerability profile.

Edge devices often have to processdata at low power and would benefit from being adaptable. Given that the data coming into these devices is generally unlabeled, unsupervised training on these devices is beneficial. This paper examines a low power approach to implement the winner takes all algorithm, for self-organizing maps through a memristor crossbar based circuit. A novel neuron circuit is designed for the winning neuron detection and lateral inhibition operations. Our experimental results show that the proposed system can self-organize based on unlabeled training data. The proposed design was around 0.002mm$^{\mathbf{2\, $textbf{{in area and consumed about 0.2mW of power. When compared to a CPU, the design had a higher error rate, but was 100 times faster and consumed much lower area and power. Thus when area or power reduction are crucially important, this approach is quite viable.

Custom low power hardware for network intrusion detection is in great demand, as this would allow for efficient security in battery-powered network devices. This paper presents a memristor crossbar based Multilayer Perceptron (MLP) capable of performing network intrusion detection. The MLP is trained based on the Knowledge Discovery and Datamining (KDD) dataset. Once the network is trained, the weight values are programmed into a simulated memristor crossbar hardware system to show that custom circuitry can be designed to perform on-chip intrusion detection. We show the proposed memristor system is capable of achieving greater than 99% intrusion detection accuracy if the memristors utilized are able to store at least 4 unique values (essentially acting as 2-bit devices). Furthermore, we present a number of studies that quantify the amount of classification accuracy lost due to analog processing and amplification in the signal path. This circuit is capable of tolerating significant amplifier gain error (up to 30%) before classification accuracy drops below 99%.

Despite the positive impact of corporate governance on corporate performance has been widely recognized and many academic and practitioners considered it as a prerequisite for sustainable development and effective resources management, few works empirically addressed this phenomenon and academic literature has paid limited attention towards factors influencing corporate governance applications in the developing economics context. It focused mainly on governance regulation. In responding to this call, the current study aimed at helping to narrow the existing research gap in the literature and contribute to the accumulated knowledge through achieving the following interrelated objectives. (a) Provide further insights into the current state of relevant literature (B) investigate empirically the critical factors influencing applying corporate governance for achieving sustainable development(C) developing a structural equation model (SEM) for predicting these factors in the developing economics context and provide empirical evidence on its validity and relevance. A multi-stage research methodology was utilized, combining quantitative and qualitative methods to validate the research model and empirically test the hypothesized relationships. The developed questionnaire was pretested and feedback from the pre-test and the panel of experts used to revise the survey instrument. The reliability was assessed using Cronbach’s alpha coefficients and the results showed an acceptable level for each construct. The hypothesized relationships have been tested using structural equation modeling. The evidence of a good fit, reliability and validity indicated that the measurement model deemed appropriate for testing the structural model that demonstrated highly explanatory power. The current study provided empirical evidences and validation for the existing specialized literature concerning corporate governance and added empirical weight to research addressing developing countries.

Nowadays, multi-rotor Unmanned Aerial Vehicles (UAVs) are deployed widely in different fields to serve multiple purposes. Safety and reliability of multi-rotor system are rapidly growing research area, and enhancing the control design to account for system failure. In this paper, the Fault-Tolerance Control (FTC) is introduced as a method for recovery from failure in quad-rotor UAV actuator in hovering mode. The ultimate aim is to land safely with minimal damage. This research focuses on designing a FTC method that is capable of handling one and/or two rotors failures. Two different controllers are designed and implemented in simulation for controlling the UAV which are Proportional-Integral-Derivative (PID) control and Linear Quadratic Regulator (LQR). LQR shows its effectiveness after comparing both methods responses. Furthermore, a state observer is designed using Linear Quadratic Estimator (LQE). Gain scheduling (GS) method is developed to switch between the controllers of the system in the event of failure. The GS-PID control design is validated in ROS/Gazebo using the new Robotics System Toolbox showing the effectiveness of the proposed method.

In this paper we introduce a novel rewarding scheme for the classical POMDP formulation. The proposed scheme aims to reinforce preference of objectives. It ensures that a high-priority preferences get high accumulative rewards, solve ambiguities and it can be conducted before the low-priority-preference. In order to show conflicting of objectives, context of search and rescue has been selected for this paper. It involves heterogeneous team with potential conflicting multiobjective situations. Our rewarding scheme has been tested in simulated scenarios using multiple POMDP solvers. Results obtained from the simulated experiments show that the system is able to represent an impact on the human (first responder) through enforcing priority of objectives and iteratively optimize policies to better suit the first responder and the search and rescue mission goals.

Motivated by the need to regulate and regularly inspect construction sites, we developed a real-time robust drone system that performs site inspection and violation detection in construction sites. The proposed system uses the drone to inspect and monitor numerous types of construction sites remotely from any control station or a nearby locations. Our drone system was developed as an entry to participate in “Drones For Good” 2015 competition, and qualified to the finals. Our proposed technological solution presented in this paper introduces the possible utilization of drone technology in the construction industry. We will first introduce the motivation and benefits of using this system, and how it's usage will increase the ability to control the health and safety, efficiency, and cost of the site inspection process. We will then detail the technical components of the drone, and present the results of experiments and evaluations conducted in realistic scenarios.

The concept of entrepreneurial leadership in any challenging industry involves fusing the concepts of a strategic approach of the management change, enhancing capabilities for continuously, creating and appropriating support, and development of value and competitive advantages in the company and technological growth. The challenge for shipping and transportation industry is to build a compatibility to continuously explore and reduce the threats of secure and safety and enhance new successful and competitive opportunities. Shipping Entrepreneurial Leadership style that scanned The twenty one attributes of outstanding leadership dimensions in the GLOBE project scales to identify nineteen attributes of their extraordinary performance, The chapter categorizes ten shipping behavior likely to be expected in loading on the two roles that impede the shipping entrepreneurial leaders' performance enactment scenario and Eight leadership behavior likely to be facilitated in their roles towered encouraging the team performance.

Background & objective: The gliptins, dipeptidyl peptidase inhibitors (DPP-4 inhibitors) are a relatively new class of antidiabetic drugs that, via their inhibition of DPP-4, a cell membrane–associated serine-type protease enzyme, promote the effects of the endogenous incretins such as glucagon-like peptide 1 (GLP-1) and enhance glucose disposal. The advantage of the gliptins over GLP-1 analogues is that they are orally effective whereas the clinically used incretins, exenatide and liraglutide, have to be administered via sub-cutaneous injection. The first gliptin, sitagliptin, was approved by the FDA in 2006 and six other gliptins have subsequently been approved – vildagliptin (2007 in Europe); saxagliptin (2009, FDA); linagliptin (2011, FDA); anagliptin (2012, Japan); teneligliptin (2012, Japan); alogliptin (2013, FDA). There is a close association between diabetes and cardiovascular disease (CVD) and vascular complications associated with diabetes are responsible for 75% of the deaths of diabetics. Therefore it is important that for any new class of antidiabetic drugs introduced for clinical use that we determine whether such drugs not only show therapeutic efficacy as antidiabetic drugs, but also that they are vasculoprotective and reduce both cardiovascular morbidity and mortality. Endothelial dysfunction that can be functionally defined as a reduced vasorelaxation response to an endothelium-dependent vasodilator, such as acetylcholine, or, at the molecular level, a reduction in the bioavailability of nitric oxide (NO) and/or reduced activity of the enzyme responsible for the generation of NO, namely, endothelial nitric oxide synthase (eNOS). Endothelial dysfunction is a very early indicator of the onset of vascular disease and thus determining whether the gliptins also reduce endothelial dysfunction is very important. The literature concerning the vasculoprotective effects of the gliptins is contradictory with some of the clinical data suggesting a negative effect of the gliptins on vascular function. Thus, the objective of this study was to determine whether the gliptins possess positive or negative effects on endothelial function. Materials & methods: In the present study we used a cell culture protocol with mouse vascular endothelial endothelial cells (MS1-VEGF; CRL-2460, from ATCC, USA) of micro-vascular endothelial origin. The endothelial cells, MMECs, were either cultured under normoglycaemic (NG) conditions for a mouse, 11 mM, or high glucose 40 mM (HG) – a level that equates to the plasma glucose levels that are seen in mouse models of type 2 diabetes, such as the db/db leptin receptor mutant model of type 2 diabetes. The gliptin, alogliptin was chosen for this study and the protocols were designed to determine whether this gliptin reduced, or prevented, the high glucose induced reduction in eNOS phosphorylation at serine 1177 (p-eNOSser1177) as determined by western immunoblot densitometry quantification. A reduction in p-eNOSser1177 will result in a reduced activity of eNOS and hence a reduction in the generation of NO. Thus, the quantification of p-eNOSser1177 serves as a measure of endothelial function. The band densities of the western blot images for eNOS and p-eNOSser1177 were quantified using the basic Quantity One software (Biorad, Inc. CA, USA). Statistical analysis was performed using one-way analysis of variance (ANOVA) and post-hoc comparisons between groups were performed by Tukey's multiple comparison tests. ‘p’ values less than 0.05 were considered to be statistically significant. Results: Our data indicates that a 24-hour exposure to HG reduced p-eNOSser1177 phosphorylation, but the presence of alogliptin reverses the effects of HG and significantly increased the phosphorylation of eNOS, suggesting that this gliptin does protect the microvascular endothelium against hyperglycaemia-induced endothelial dysfunction. Furthermore, the effects of alogliptin were concentration-dependent and were significant with 50 or 100 μM, but not 10 μM alogliptin. Conclusion: Our findings indicate that, in a concentration-dependent manner, alogliptin protects endothelial cells against the negative effects that hyperglycaemia (high glucose) has on endothelial function as measured by alogliptin-induced changes in the phosphorylation of eNOS at serine 1177. Further studies are underway, using a functional myograph assay, to determine whether alogliptin can also prevent hyperglycaemia-induced endothelial dysfunction in mouse aortic vessels.AcknowledgementThis work was supported by a Summer Student Research Fellowship (SSRF) from Weill Cornell Medicine-Qatar and an Undergraduate Research Experience Program grant, UREP 18-055-3-012 from the Qatar National Research Fund (QNRF). The statements made herein are solely the responsibility of the authors.

Introduction: Angiosarcomas are aggressive and malignant endothelial tumors. Several risk factors have been identified (eg. BRCA1/2 mutations, radiation, toxins and lymphedema), but pathological mechanisms remain unclear and the prognosis is poor. Fenofibrate is a hypolipidemic drug widely prescribed to treat dyslipidemia. It reduces LDL, vLDL, and triglycerides and increases HDL levels. The primary mechanism of fenofibrate action involves activation of the transcription factor PPARα, but other PPARα-independent mechanisms also exist. More recently, its efficacy as an anti-tumor drug has emerged. However, little is known about the endothelial actions of fenofibrate and whether these contribute to its anti-tumor properties. Here, we explore the actions of fenofibrate in endothelial cells that are capable of forming angiosarcomas in vivo. Methods: Mouse endothelial cells (MS1 VEGF; # CRL-2460) capable of inducing angiosarcoma were purchased from ATCC. These are primary mouse pancreatic islet cells that were transformed with temperature-sensitive SV40 large T antigen and screened for resistance with G418. The G418-selected immortalized cells were then retro-virally infected with a vector encoding primate VEGF. These cells have been shown to induce angiosarcomas in vivo in mice and are a good model system for studies of cancers, particularly angiosarcomas. Results: Incubation of MS1 VEGF cells with clinically relevant concentrations of fenofibrate (25-100 μM) strongly reduced their viability, with cell numbers reduced by approximately 10-fold after a 48h incubation with 50 μM fenofibrate {Cells/ml: ~1.8 million (control) versus ~0.2 million (50 μM fenofibrate)}. Morphologically, the cells were characterized by the appearance of perinuclear cytoplasmic vacuoles. In contrast, fenofibrate had a smaller effect on cell viability in primary human endothelial cells {Cells/ml: ~0.6 million (control) versus ~0.4 million (50 μM fenofibrate)}, thereby suggesting a relatively selective effect on MS1 VEGF cells. Hypothesizing that the effect of fenofibrate in these cells occurred through the induction of apoptosis, the effect of fenofibrate on the expression of proteins critical for cell survival and apoptosis was investigated biochemically. The data revealed down-regulation of the anti-apoptotic Bcl family protein Bcl-2 and the inhibitor of apoptosis protein survivin after treatment with fenofibrate. Fenofibrate treatment also decreased the expression of Akt and Erk proteins. In contrast, expression of Bcl-xl was relatively unaffected. Expression of the anti-diabetic hormone FGF-21 was detected in MS1 VEGF cells and this was also decreased after fenofibrate incubation. Conclusions: The data revealed a strong anti-proliferative action of fenofibrate in angiosarcoma-inducing endothelial cells but not primary human endothelial cells. These effects potentially occurred through regulation of proteins critical for cell survival and apoptosis. Future studies: Future investigations will focus on: (a) delineating the mechanisms involved (PPARα-dependent or -independent) (b) direct measurements of apoptosis by flow cytometry (c) the effect of fenofibrate on endothelial migration, invasion, proliferation, wound healing and tubule formation and (d) testing the possibility that fenofibrate acts by triggering changes in cellular calcium handling and (e) testing fenofibrate efficacy in angiosarcomas in vivo in mice. Key words: Apoptosis, Bcl-2, cancer, fenofibrate, FGF-21, survivin. This work was supported by a National Priorities Research Program grant (NPRP 6-428-3-113).

This paper presents a framework for modelling the interaction between a human operator and a robotic device, that enables the robot to collaborate with the human to jointly accomplish tasks. States of the system are captured in a model based on a partially observable Markov decision process (POMDP). States representing the human operator are motivated by behaviours from the psychology of the human action cycle. Hierarchical nature of these states allows the exploitation of data structures based on algebraic decision diagrams (ADD) to efficiently solve the resulting POMDP. The proposed framework is illustrated using two examples from as-sistive robotics; a robotic wheel chair and an intelligent walking device. Experimental results from trials conducted in an office environment with the wheelchair is used to demonstrate the proposed technique. © 2011 IEEE.

Unmanned aerial vehicles (UAVs) are being equipped with high definition cameras to survey a wide range of low-contrast and diverse environments. From data captured by UAVs, image analysts can determine adversarial threats proficiently. However, there is simply too much data and not enough analysts to do this processing efficiently. Enabling computing systems to mimic the processes in the human brain to process sych data would be of significant benefit. CSRNs (cellular simultaneous recurrent networks) are capable of solving several spatial processing tasks that are carried out by human. In particular, they have been shown to be capable of pose invariant face recognition. Given the highly recurrent nature of CSRNs (a property also seen in the human cortex), the computational demands of these algorithms grow with input size. Therefore the acceleration of CSRNs would be highly beneficial. In this paper we examine the acceleration of CSRNs applied to face recognition. We develop optimized implementations of the algorithm on an Intel Xeon 2.67 GHz processor and an NVIDIA Tesla C2050 GPGPU (general purpose graphical processing unit). Our results show that the GPGPU is 22.9 times faster than the CPU implementation.

A novel method which combines an optimised global path planner with a real-time sensor-based collision avoidance mechanism to accommodate for dynamic changes in the environment (e.g., people) is presented. The basic concept is to generate a continually changing parameterised family of virtual force fields for the robot based on characteristics such as location, travelling speed and dimension of the objects in the vicinity, static and dynamic. The interactions among the repulsive forces associated with the various obstacles provide a natural way for local collision avoidance in a partially known cluttered environment. This is harnessed by locally modifying the planned behaviour of the moving platform in real-time, whilst preserving the optimised nature of the global path. Furthermore, path traversability is continually monitored by the global planner to trigger a complete path re-planning from the current location in case of major changes, most notably when the path is completely blocked by obstacles. Copyright © 2008, Inderscience Publishers.

This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. Several navigational techniques have been successfully developed in the past to assist with specific behaviours such as "door passing" or "corridor following". These shared control strategies normally require the user to manually select the level of assistance required during use. Recent research has seen a move towards more intelligent systems that focus on forecasting users' intentions based on current and past actions. However, these predictions have been typically limited to locations immediately surrounding the wheelchair. The key contribution of the work presented here is the ability to predict the users' intended destination at a larger scale, that of a typical office arena. The systems relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently drive the user to his destination. The projection is constantly being updated, allowing for true user-platform integration. This shifts users' focus from fine motor-skilled control to coarse control broadly intended to convey intention. Successful simulation and experimental results on a real wheelchair robot demonstrate the validity of the approach. ©2008 IEEE.

Electric wheelchairs give otherwise immobile people the free-dom of movement, they significantly increase independence and dramatically increase quality of life. However the physical control systems of such wheelchair can be prohibitive for some users; for example, people with severe tremors. Several assisted wheelchair platforms have been developed in the past to assist such users. Algorithms that assist specific behaviors such as door - passing, follow - corridor, or avoid - obstacles have been successful. Recent research has seen a move towards systems that predict the users intentions, based on the users input. These predictions have been typically limited to locations immediately surrounding the wheelchair. This paper presents a new assisted wheelchair driving system with large scale intelligent intention recognition based on POMDPs (Partially Observable Markov Decision Processes). The systems acts as an intelligent agent/decision-maker, it relies on minimal user input; to predict the users intention and then autonomously drives the user to his destination. The prediction is constantly being updated as new user input is received allowing for true user/system integration. This shifts the users focus from fine motor-skilled control to coarse control intended to convey intention. © 2007 IEEE.

This paper presents a one step smooth and efficient path planning algorithm for navigating a large robotic platform in known cluttered environments. The proposed strategy, based on the generation of a novel search space, relies on non-uniform density sampling of the free areas to direct the computational resources to troubled and difficult regions, such as narrow passages, leaving the larger open spaces sparsely populated. A smoothing penalty is also associated to the nodes to encourage the generation of gentle paths along the middle of the empty spaces. Collision detection is carried out off-line during the creation of the configuration space to speed up the actual search for the path, which is done on-line. Results prove that the proposed approach considerably reduces the search space in a meaningful and practical manner, improving the computational cost of generating a path optimised for fine and smooth motion. © 2006 IEEE.