Secure Layered Transmission in Multicast Systems with Wireless Information and Power Transfer

This paper considers downlink multicast transmit beamforming for secure layered transmission systems with wireless simultaneous information and power transfer. We study the power allocation algorithm design for minimizing the total transmit power in the presence of passive eavesdroppers and energy harvesting receivers. The algorithm design is formulated as a non-convex optimization problem. Our problem formulation promotes the dual use of energy signals in providing secure communication and facilitating efficient energy transfer. Besides, we take into account a minimum required power for energy harvesting at the idle receivers and heterogeneous quality of service (QoS) requirements for the multicast video receivers. In light of the intractability of the problem, we reformulate the considered problem by replacing a non-convex probabilistic constraint with a convex deterministic constraint. Then, a semidefinite programming relaxation (SDR) approach is adopted to obtain an upper solution for the reformulated problem. Subsequently, sufficient conditions for the global optimal solution of the reformulated problem are revealed. Furthermore, we propose two suboptimal power allocation schemes based on the upper bound solution. Simulation results demonstrate the excellent performance and significant transmit power savings achieved by the proposed schemes compared to isotropic energy signal generation.Comment: 7 pages, 3 figures, accepted for presentation at the IEEE International Conference on Communications (ICC), Sydney, Australia, 201

Power Efficient MISO Beamforming for Secure Layered Transmission

This paper studies secure layered video transmission in a multiuser multiple-input single-output (MISO) beamforming downlink communication system. The power allocation algorithm design is formulated as a non-convex optimization problem for minimizing the total transmit power while guaranteeing a minimum received signal-to-interference-plus-noise ratio (SINR) at the desired receiver. In particular, the proposed problem formulation takes into account the self-protecting architecture of layered transmission and artificial noise generation to prevent potential information eavesdropping. A semi-definite programming (SDP) relaxation based power allocation algorithm is proposed to obtain an upper bound solution. A sufficient condition for the global optimal solution is examined to reveal the tightness of the upper bound solution. Subsequently, two suboptimal power allocation schemes with low computational complexity are proposed for enabling secure layered video transmission. Simulation results demonstrate significant transmit power savings achieved by the proposed algorithms and layered transmission compared to the baseline schemes.Comment: Accepted for presentation at the IEEE Wireless Communications and Networking Conference (WCNC), Istanbul, Turkey, 201

Cognitive Restoration Design: A Psychological Intervention for Stress Mitigation in Neighbourhood Park

Numerous research studies have revealed the landscape’s positive effects on human health and well-being. While prior research underscores landscapes' positive impact on well-being, a gap still needs to be in comprehending their influence on psychological and cognitive aspects. This research focuses on how landscape attributes, specifically those seen in neighbourhood parks, may serve as a stress-relieving cognitive stimulus. In this study, we utilised qualitative research design by employing an in-depth expert interview method to explore what causes stress in urban communities and how specific landscape attributes can improve mental health and well-being. A total of 12 experts consisting of clinical psychologists, counsellors, neuropsychologists, therapists, landscape professionals and academia have consented to participate in this interview. The results illuminate a conceptual framework illustrating how psychological and cognitive landscape attributes can effectively promote cognitive restoration. The findings indicate that the design must be human centred as people are born with innate sense, intuition, and preference, all of which should be considered while designing for their psychological needs. Particularly to stimulate the cognitive part, providing landscape design elements that could inspire enthusiasm is important. This could encourage people to go to the park and interact with various stress-relieving landscape stimuli. Hence, designing for user comfort, safety, social interaction, and pleasurable experiences is critical for achieving cognitive restoration goals

Salutogenic Landscape Design with Cognitive Restoration Stimuli for Stress Intervention

This study explores the potential of neighbourhood parks in reducing stress and promoting mental well-being in urban settings. Through in-depth interviews with experts in clinical psychology, neuropsychology, and landscape architecture, landscape attributes influencing psychological well-being were identified. Thematic analysis revealed the importance of human-centred design and incorporating elements that inspire enthusiasm for cognitive restoration. The study resulted in a salutogenic and cognitive landscape framework that integrating cognitive behavioural therapy (CBT) into the park design. This framework offers valuable insights and practical guidance for creating healing spaces in neighbourhood parks, catering to physical and psychological needs in urban environments

A requirement engineering model for big data software

Most prevailing software engineering methodologies assume that software systems are developed from scratch to capture business data and subsequently generate reports. Nowadays, massive data may exist even before software systems are developed. These data may also be freely available on Internet or may present in silos in organizations. The advancement in artificial intelligence and computing power has also prompted the need for big data analytics to unleash more business values to support evidence-based decisions. Some business values are less evident than others, especially when data are analyzed in silos. These values could be potentially unleashed and augmented from the insights discovered by data scientists through data mining process. Data mining may involve overlaying and merging data from different sources to extract data patterns. Ideally, these values should be eventually incorporated into the information systems to be. To realize this, we propose that software engineers ought to elicit software requirements together with data scientists. However, in the traditional software engineering process, such collaboration and business values are usually neglected. In this paper, we present a new requirement engineering model that allows software engineers and data scientists to discover these values hand in hand as part of software requirement process. We also demonstrate how the proposed requirement model captures and expresses business values that unleashed through big data analytics using an adapted use case diagram

Structural and kinetic studies of a novel nerol dehydrogenase from Persicaria minor, a nerol-specific enzyme for citral biosynthesis

Geraniol degradation pathway has long been elucidated in microorganisms through bioconversion studies, yet weakly characterised in plants; enzyme with specific nerol-oxidising activity has not been reported. A novel cDNA encodes nerol dehydrogenase (PmNeDH) was isolated from Persicaria minor. The recombinant PmNeDH (rPmNeDH) is a homodimeric enzyme that belongs to MDR (medium-chain dehydrogenases/reductases) superfamily that catalyses the first oxidative step of geraniol degradation pathway in citral biosynthesis. Kinetic analysis revealed that rPmNeDH has a high specificity for allylic primary alcohols with backbone ≤10 carbons. rPmNeDH has ∼3 fold higher affinity towards nerol (cis-3,7-dimethyl-2,6-octadien-1-ol) than its trans-isomer, geraniol. To our knowledge, this is the first alcohol dehydrogenase with higher preference towards nerol, suggesting that nerol can be effective substrate for citral biosynthesis in P. minor. The rPmNeDH crystal structure (1.54 Å) showed high similarity with enzyme structures from MDR superfamily. Structure guided mutation was conducted to describe the relationships between substrate specificity and residue substitutions in the active site. Kinetics analyses of wild-type rPmNeDH and several active site mutants demonstrated that the substrate specificity of rPmNeDH can be altered by changing any selected active site residues (Asp280, Leu294 and Ala303). Interestingly, the L294F, A303F and A303G mutants were able to revamp the substrate preference towards geraniol. Furthermore, mutant that exhibited a broader substrate range was also obtained. This study demonstrates that P. minor may have evolved to contain enzyme that optimally recognise cis-configured nerol as substrate. rPmNeDH structure provides new insights into the substrate specificity and active site plasticity in MDR superfamily

Deep imitation learning for 3D navigation tasks

Deep learning techniques have shown success in learning from raw high dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: Deep-Q-networks (DQN) and Asynchronous actor critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an e�ective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples

Awareness of glycosylated haemoglobin (HbA1c) among type 2 diabetes mellitus patients in Hospital Putrajaya

The glycosylated haemoglobin (HbA1c) test is the most widely accepted laboratory test for evaluating long term glycaemic control. Patient’s understanding of HbA1c can lead to better glycaemic control. This study is aimed to determine the awareness and level of understanding of HbA1c among type 2 DM patients and its association with glycaemic control. A cross-sectional descriptive study among Type 2 DM patients undergoing routine follow up in an endocrine clinic of a tertiary centre in Malaysia. Patients were invited to answer a validated questionnaire which assessed their awareness and understanding of HbA1c. Their last HbA1c results were retrieved from the laboratory information system. A total of 92 participants were recruited. Fifty-six (60.9%) were aware of the term HbA1c. Fifty percent were categorised as having good HbA1c understanding, with age, monthly income and level of education between HbA1c understanding and glycaemic control, although more patients with good HbA1c understanding had achieved the target glycaemic control compared to those with poor understanding. The level of HbA1c awareness and understanding was acceptable. Factors associated with understanding were age, income and level of education. Continuing efforts however, must be made to improve patients understanding of their disease and clinical disease biomarkers

Is family history still underutilised? exploring the views and experiences of primary care doctors in Malaysia

Family history has long been recognised as a non-invasive and inexpensive tool to identify individuals at risk of genetic conditions. Even in the era of evolving genetic and genomic technology, the role of family history in predicting individual risk for genetic testing and guiding in preventive interventions is still relevant, especially in low-resource countries. The aim of this study was to explore primary care doctors' views and experiences in family history taking and how they utilised family history in day-to-day clinical consultations in Malaysia. Four focus group discussions and six in-depth interviews involving 25 primary care doctors were conducted. Three themes emerged from the analysis: (1) primary care doctors considered family history as an important part of clinical assessment, (2) proactive versus reactive approach in collecting family history and (3) family history collection was variable and challenging. Family history was documented in either free text or pedigree depending on the perception of its appropriateness during the consultation. This study highlighted the need to improve the approach, documentation and the implementation of family history in the Malaysian primary care settings. Integrating family filing concept with built-in clinical decision support into electronic medical records is a potential solution in ensuring effective family history taking in primary care