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Partial SOI as a HV platform technology for Power Integrated Circuits
No full textPartial SOI (PSOI) is revisited as a suitable High Voltage (HV) architecture for Power Integrated Circuits (PICs). The added process complexity compared to SOI RESURF is offset by the better heat conduction due to thinner BOX, the wider voltage range capability and the reduced parasitic capacitance to the Handle Wafer (HW). The new proposed platform technology is therefore particularly relevant to the manufacturing of high voltage integrated circuits (HVICs) where low Ron, fast switching and reduced self-heating are essential. This work reports on the extension of a 200V PSOI process to 400V while providing competitive Ron and low HCI degradation
Physical approaches for drug delivery: An overview. An overview.
No full textDelivery of exogenous materials or cargo such as drugs, proteins, peptides, and nucleic acids into cells is a vital segment in molecular and cellular biology for potential cellular therapy and drug-discovery applications contributing toward personalization of medicine. Over the years, drug-delivery techniques have been developed in order to gain more control over the drug dosage, targeted delivery, and to minimize side effects. The major drug-delivery techniques can be classified as viral, chemical, and physical methods. Viral vectors are prominently used for gene therapy; however, they are cell-specific and have an immune response with high toxicity. Chemical methods are often limited by the low efficiency of plasmid delivery into different cell types due to plasmid degradation and toxicity. Considering these limitations, different physical methods such as photoporation, gene gun, hydrodynamic injection, electroporation, and mechanoporation, etc., are being widely developed for highly efficient cargo delivery with low toxicity. These methods are able to create transient hydrophilic membrane pores to deliver cargos into cells using different physical energies. Currently, ex vivo cargo delivery is widely studied while few in vivo applications have been developed. Concerning several obstacles to cargo delivery into cells, this chapter mainly focuses on different physical drug-delivery techniques such as electroporation, optoporation, mechanoporation, magnetoporation, and hybrid techniques along with their working mechanisms, advantages, disadvantages, and limitations. An insight into the future prospects and real-time applications of these techniques is also discussed
Fabrication of gold-nanorings for massively parallel intracellular delivery
No full textWe propose a gold-nanorings (GNRs) based optoporation platform for flexible and highly efficient intracellular delivery, which is possibly applicable for cellular therapy. The prominent features of this device have high throughput, parallel, and controlled uniform delivery with high efficiency and cell viability
Bond behaviour of austenitic stainless steel reinforced concrete
No full textStainless steel reinforced concrete has seen a large increase in usage in recent years, in response to the ever-increasing demands for structures and infrastructure to be more durable, efficient and sustainable. Currently, existing design standards advise using the same design rules for stainless steel reinforced concrete as traditional carbon steel reinforced concrete, owing to a lack of alternative information. However, this is not based on test or performance data. As such, there is a real need to develop a full and fundamental understanding of the bond behaviour of stainless steel reinforced concrete, to achieve more sustainable and reliable design methods for reinforced concrete structures. This paper investigates the bond behavior of stainless steel reinforced concrete and compares the performance to traditional carbon steel reinforced concrete, through experimental testing and analysis. It also compares the results to existing design rules in terms of bond strength, anchorage length and lap length. It is shown that stainless steel rebar generally develops lower bond strength with the surrounding concrete compared with equivalent carbon steel reinforcement. Moreover, it is shown that existing design codes are very conservative and generally underestimate the actual bond strength by a significant margin. Therefore, following detailed analysis, it is concluded that current design rules can be safely applied for stainless steel rebar, although more accurate and efficient methods can be achieved. Hence, new design parameters are proposed reflecting the bond behaviour of stainless steel rebar, so that more efficient designs can be achieved. Moreover, a summary of recommendations for the codes of practice is provided
Ensemble approaches for uncertainty in spoken language assessment
No full textDeep learning has dramatically improved the performance of automated systems on a range of tasks including spoken language assessment. One of the issues with these deep learning approaches is that they tend to be overconfident in the decisions that they make, with potentially serious implications for deployment of systems for high-stakes examinations. This paper examines the use of ensemble approaches to improve both the reliability of the scores that are generated, and the ability to detect where the system has made predictions beyond acceptable errors. In this work assessment is treated as a regression problem. Deep density networks, and ensembles of these models, are used as the predictive models. Given an ensemble of models measures of uncertainty, for example the variance of the predicted distributions, can be obtained and used for detecting outlier predictions. However, these ensemble approaches increase the computational and memory requirements of the system. To address this problem the ensemble is distilled into a single mixture density network. The performance of the systems is evaluated on a free speaking prompt-response style spoken language assessment test. Experiments show that the ensembles and the distilled model yield performance gains over a single model, and have the ability to detect outliers
Spoken language 'grammatical error correction'
No full textSpoken language 'grammatical error correction' (GEC) is an important mechanism to help learners of a foreign language, here English, improve their spoken grammar. GEC is challenging for non-native spoken language due to interruptions from disfluent speech events such as repetitions and false starts and issues in strictly defining what is acceptable in spoken language. Furthermore there is little labelled data to train models. One way to mitigate the impact of speech events is to use a disfluency detection (DD) model. Removing the detected disfluencies converts the speech transcript to be closer to written language, which has significantly more labelled training data. This paper considers two types of approaches to leveraging DD models to boost spoken GEC performance. One is sequential, a separately trained DD model acts as a pre-processing module providing a more structured input to the GEC model. The second approach is to train DD and GEC models in an end-to-end fashion, simultaneously optimising both modules. Embeddings enable end-to-end models to have a richer information flow. Experimental results show that DD effectively regulates GEC input; end-to-end training works well when fine-tuned on limited labelled in-domain data; and improving DD by incorporating acoustic information helps improve spoken GEC
Anesthesia guidelines for COVID-19 patients: A narrative review and appraisal
No full textThe coronavirus disease 2019 (COVID-19) pandemic has challenged health systems glob-ally and prompted the publication of several guidelines. The experiences of our international colleagues should be utilized to protect patients and healthcare workers. The prima-ry aim of this article is to appraise national guidelines for the perioperative anesthetic management of patients with COVID-19 so that they can be enhanced for the management of any resurgence of the epidemic. PubMed and EMBASE databases were systemati-cally searched for guidelines related to SARS-CoV and SARS-CoV-2. Additionally, the World Federation Society of Anesthesiologists COVID-19 resource webpage was searched for national guidelines; the search was expanded to include countries with a high inci-dence of SARS-CoV. The guidelines were evaluated using the Appraisal of Guidelines for Research and Evaluation II tool. Guidelines from Australia, Canada, China, India, Italy, South Africa, South Korea, Taiwan, the United Kingdom, and the United States of Ameri-ca were evaluated. All the guidelines focused predominantly on intubation and infection control. The scope and purpose of guidelines from China were the most comprehensive. The UK and South Africa provided the best clarity. Editorial independence, the rigor of development, and applicability scored poorly. Heterogeneity and gaps pertaining to preop-erative screening, anesthesia technique, subspecialty anesthesia, and the lack of auditing of guidelines were identified. Evidence supporting the recommendations was weak. Early guidelines for the anesthetic management of COVID-19 patients lacked quality and a ro-bust reporting framework. As new evidence emerges, national guidelines should be updated to enhance rigor, clarity, and applicability
MiND: Mind Networked Device Architecture for Attention-Gated Ambient Assisted Living Systems
No full textThis paper proposes and evaluates an online architecture for network brain-computer interfaced smart home systems for enabling ambient assisted living. This paper uses a number of portable, low-cost, single-channel electroencephalograph systems to achieve Mind Networked Devices (MiND) for home-appliance control, to enable better accessibility for the infirm and people with mobility disability. We tested our approach on real-life hardware toward the control of devices such as lights, fans, and room-temperature conditioning systems, furthermore, we emulated the behavior of the networked hardware for a more substantial number of users on conceptualized scenarios, which will be encountered in implementing this architecture. The Network Simulator 2 simulation results of the large-scale implementation show promising results and give a precise idea about various essential features such as routing protocols, routing configuration, mode of transmission, attenuation losses, and delays incurred during the in-house operation of this architecture. The real-life evaluation of the system also supports the proposed MiND architecture
ECoR: Energy-Aware Collaborative Routing for Task Offload in Sustainable UAV Swarms
No full textIn this work, we propose an Energy-aware Collaborative Routing (ECoR) scheme for optimally handling task offloading between source and destination UAVs in a grid-locked UAV swarm. We divide the proposed scheme into two parts -- routing path discovery and routing path selection. The scheme selects the most optimal path between a source and destination from a massive set of all possible paths, based on the maximization of residual energy of UAVs along a selected path. This routing path selection ensures balanced energy utilization between members of the UAV swarm and enhances the overall path lifetime without incurring additional delays in doing so. Actual readings from our small-scale UAV swarm testbed are utilized to emulate a large-scale scenario and analyze the behavior of our proposed scheme. Upon comparison of the ECoR scheme with broadcast-based routing and the shortest path based routing, we observe better sustainability regarding the longevity of the UAV lifetimes in the swarm, optimized individual UAV, as well as reduced collective path-based energy consumption, all the while having comparable transmission delays to the shortest path based scheme
Blockchain-enabled SDN for securing fog-based resource-constrained IoT
No full textSoftware-Defined Network (SDN) is vital in simplifying the dynamic network characteristics and device management. However, the centralized architecture of SDN opens the scope for malicious attacks on the controllers. To mitigate such attacks in real-time, we propose an SDN architecture for resource-constrained devices in a fog-enabled IoT environment using a private blockchain (pBC) network. We exploit the decentralized nature of pBC for enabling resource-constrained SDN controllers towards transparently setting flow rules for fog nodes and other devices in the network. In case the miners identify faulty flow rules, pBC allows the SDN devices/fog nodes to retract back to an earlier flow rule while raising a flag against the alleged controller. Additionally, since data in pBC are accessible by all the candidates having the same genesis file, they are readily available to malicious users. Towards this, we further propose encrypting the data before inserting them into the blocks, which helps in securing the data from undesired users. Through the extensive deployment of our proposed fusion, we observe CPU usage of 30% among the devices and latencies in the range of milliseconds, which presents the feasibility of our system with minimum delay. We also observe a reduction in energy consumption by more than 90%, compared to traditional SDN