The importance of awareness of nonverbal communication in leadership success: focus on American culture

Objectives This study aims to explore relevant leadership models that factor in the impact of nonverbal cues on perception of good leadership, to find which leadership qualities are perceived from which nonverbal behaviors, to and to propose what changes should be done to make recommendations for leadership development practice. Summary This empirical study was conducted with qualitative interview. The interview was administered to five participants who are U.S employees working in different industries, and who answered all of fifteen questions, which are mostly open ended questions. All fifteen questions are developed based on theories in literature review, and formulated with the structure of the theoretical framework in mind. Conclusions Nonverbal behaviors influence the way employees judge their leaders’ leadership qualities. Specifically, employees actively look for top qualities, which can be shown through several specific nonverbal cues. Overall, leaders’ ability to decode nonverbal signals is to some extent important for both their performance and ratings from employees, and it is suggested that there seems to be a connection between that ability and leaders’ chance of being promoted. Regarding current situation of firms in the interview, there seems to be a lack of training in nonverbal communication for leaders

Medical Image Segmentation Combining Level Set Method and Deep Belief Networks

Medical image segmentation is an important step in medical image analysis, where the main goal is the precise delineation of organs and tumours from medical images. For instance there is evidence in the field that shows a positive correlation between the precision of these segmentations and the accuracy observed in classification systems that use these segmentations as their inputs. Over the last decades, a vast number of medical image segmentation models have been introduced, where these models can be divided into five main groups: 1) image-based approaches, 2) active contour methods, 3) machine learning techniques, 4) atlas-guided segmentation and registration and 5) hybrid models. Image-based approaches use only intensity value or texture for segmenting (i.e., thresholding technique) and they usually do not produce precise segmentation. Active contour methods can use an explicit representation (i.e., snakes) with the goal of minimizing an energy function that forces the contour to move towards strong edges and maintains the contour smoothness. The use of implicit representation in active contour methods (i.e., level set method) embeds the contour as zero level set of a higher dimensional surface (i.e., the curve representing the contour does not need to be parameterized as in the Snakes model). Although successful, the main issue with active contour methods is the fact that the energy function must contain terms describing all possible shape and appearance variations, which is a complicated task given that it is hard to design by hand all these terms. Also, this type of active contour methods may get stuck at image regions that do not belong to the object of interest. Machine learning techniques address this issue by automatically learning shape and appearance models using annotated training images. Nevertheless, in order to meet the high accuracy requirements of medical image analysis applications, machine learning methods usually need large and rich training sets and also face the complexity of the inference process. Atlas-guided segmentation and registration use an atlas image, which is constructed based on manually segmentation images. The new image is segmented by registering it with the atlas image. These techniques have been applied successfully in many applications, but they still face some issues, such as their ability to represent the variability of anatomical structure and scale in medical image, and the complexity of the registration algorithms. In this work, we propose a new hybrid segmentation approach by combining a level set method with a machine learning approach (deep belief network). Our main objective with this approach is to achieve segmentation accuracy results that are either comparable or better than the ones produced with machine learning methods, but using relatively smaller training sets. These weaker requirements on the size of training sets is compensated by the hand designed segmentation terms present in typical level set methods, that are used as prior information on the anatomy to be segmented (e.g., smooth contours, strong edges, etc.). In addition, we choose a machine learning methodology that typically requires smaller annotated training sets, compared to other methods proposed in this field. Specifically, we use deep belief networks, with training sets consisting to a large extent of un-annotated training images. In general, our hybrid segmentation approach uses the result produced by the deep belief network as a prior in the level set evolution. We validate this method on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 left ventricle segmentation challenge database and on the Japanese Society of Radiological Technology (JSRT) lung segmentation dataset. The experiments show that our approach produces competitive results in the field in terms of segmentation accuracy. More specifically, we show that the use of our proposed methodology in a semi-automated segmentation system (i.e., using a manual initialization) produces the best result in the field in both databases above, and in the case of a fully automated system, our method shows results competitive with the current state of the art.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

Low frequency ac transmission for power systems

Conventional transmission systems utilize either high voltage ac (HVAC), operating at 50 or 60 Hz, or high voltage dc (HVDC) to transfer bulk power. The HVAC system is designed to operate at a high voltage level to reduce losses and increase bulk power transfer. This method is limited, however, by the constraints of installed transmission overhead lines, such as voltage level and power transfer capability. In contrast, the HVDC system can handle a large amount of power on the transmission line by utilizing dc current instead of ac. While the HVDC system has no limitation in transmission line length for power transfer, it does however, require a high initial cost for converter stations and specialized protection systems. Additionally, the HVDC system is a point-to-point connection, and thus not flexible for multi-terminal connection. The low frequency ac (LFAC) transmission system, first introduced in 2000, is another solution for bulk power transmission that inherits advantages from both high voltage ac and high voltage dc systems. The primary advantage of LFAC transmission is that by operating the system at a frequency lower than 50 or 60 Hz, the transmission line reactance can be significantly reduced, thus extending power capacity. Further advantages include multi-terminal connections, distance protection using alternating-current based circuit breakers, and improving power transfer capability close to that of an HVDC system. This dissertation investigates the benefits of an LFAC system in terms of power transfer capability and voltage stability. First, the steady-state performances of the transmission line under low frequency range are investigated, including the skin effect to see any possible changes in transmission line model. More importantly, the power transfer capability, and voltage profiles during no-load and full-load conditions are examined. Next, the system voltage stability under low frequency operations is studied in detail to analyze how the low frequency operation helps improve a power system stability. Furthermore, the system modeling, protection, and hardware platform to test the low frequency ac transmission are studied deeply. A new grid synchronization configuration to interface a low frequency system with a 60 Hz ac system is also proposed. Moreover, a fast and robust fault detection mechanism based on the second-order generalized-integrator (SOGI) grid synchronization technique is to develop and work properly in a low frequency system. Last but not least, a small-scaled transmission and distribution system is built to verify the basic performances of a power system at a low frequency operations.Electrical and Computer Engineerin

Effect of blue light on the photosynthesis and flavonoid accumulation in leaves of Hedyotis corymbosa (L.) Lam.

In plants, blue light with a short wavelength can promote light reaction in photosynthesis and increase dry mass. Photosynthesis plays an important role in supporting carbohydrates for primary and secondary metabolism processes. Flavonoids are phenolic compounds, a class of plant secondary metabolites, that can be obtained from many medicinal herbs. These phenolic compounds are involved in the reactive oxygen species scavenging system, inhibit lipid peroxidation by free-radical, chelate redox-active metals resulting in their antioxidant ability and cardioprotective effects. In this study, H. corymbosa (L.) Lam., one of the common medicinal herbs, was cultured for 4 weeks under conditions of 450 nm blue LED (light-emitting diode) lights at the different light intensity as treatments and fluorescent lamp light as a control to investigate the effects of blue light on photosynthesis and flavonoid accumulation in leaves. The results show that blue light at 450 nm promoted photosynthetic rate by enhancing stomatal opening, electron transport rate in light reaction. Blue light also enhanced photoprotection by decrease the quantum yield of non-photochemical losses, increase the quantum yield of non-photochemical quenching and gained 24% more in dry mass. The accumulation of flavonoid and total phenolic compounds in leaves was followed by a decrease in sucrose. These events proved that blue light enhances photosynthesis and increase carbohydrate and flavonoid accumulation in leaves

Population-level approaches to universal health coverage in resource-poor settings: Lessons from tobacco control policy in Vietnam

Population-based health promotion and disease prevention approaches are essential elements in achieving universal health coverage; yet they frequently do not appear on national policy agendas. This paper suggests that resource-poor countries should take greater advantage of such approaches to reach all segments of the population to positively affect health outcomes and equity, especially considering the epidemic of chronic non-communicable diseases and associated modifiable risk factors. Tobacco control policy development and implementation in Vietnam provides a case study to discuss opportunities and challenges associated with such strategies

The development of Tobacco Harm Prevention Law in Vietnam: stakeholder tensions over tobacco control legislation in a state owned industry

Background: Building on its National Tobacco Control Policy initiated in 2000, Vietnam is currently considering introducing a comprehensive law to strengthen the implementation of tobacco control policy. This study analyses the positions of key stakeholders in the development of tobacco control legislation in the context of a largely state-owned industry, and discusses their implications for the policy process

Deep Learning-Aided Multicarrier Systems

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity

Trajectory Tracking Control Design for Dual-Arm Robots Using Dynamic Surface Controller

This paper presents a dynamic surface controller (DSC) for dual-arm robots (DAR) tracking desired trajectories. The DSC algorithm is based on backstepping technique and multiple sliding surface control principle, but with an important addition. In the design of DSC, low-pass filters are included which prevent the complexity in computing due to the “explosion of terms”, i.e. the number of terms in the control law rapidly gets out of hand. Therefore, a controller constructed from this algorithm is simulated on a four degrees of freedom (DOF) dual-arm robot with a complex kinetic dynamic model. Moreover, the stability of the control system is proved by using Lyapunov theory. The simulation results show the effectiveness of the controller which provide precise tracking performance of the manipulator

Optimal operation of Hoa Binh reservoir for flood control on Hong-Thai Binh river system

In the paper the optimal operation of Hoabinh reservoir for flood control on Hong–Thai Binh river system is presented. The findings show that in the flood season in 1996 if the operation of gates and outlets of Hoa Binh reservoir was made base on the calculated release, the water level at Hanoi would be 12.3 m and the water level of Hoa Binh reservoir would be 98 m. So the calculated release from Hoabinh reservoir in August 1996 can be considered as optimal in the mean that the water level at Hanoi can be controlled and the Hoabinh reservoir still have necessary pool for controlling the next floods