Miracle-dz/UniFAR-FTUs huggingface dataset:
CLINICAL TRIALS IN NEW-ONSET EPILEPSY —

Although a number of new antiepileptic drugs (AEDs) have entered clinical practice during the last decade, only 2 first-line agents (lamotrigine and oxcarbazepine) have been specifically approved for epilepsy monotherapy, and a third agent (topiramate) has received preliminary monotherapy approval from the US Food and Drug Administration. Older agents are often used for epilepsy monotherapy, although these drugs were approved before rigorous monotherapy testing was required. Large randomized double-blind clinical trials have shown that the newer AEDs lamotrigine, oxcarbazepine, and topiramate are at least as effective as older agents such as carbamazepine and valproate for monotherapy in patients with newly diagnosed epilepsy, but with better tolerability and long-term adherence to therapy. Initial clinical trials have suggested that these agents are also well suited for special patient populations, including elderly or pediatric patients. The results of a recent large, randomized, double-blind clinical trial demonstrate that topiramate at a dose of 100 mg once daily is effective and well tolerated for the control of seizures in adult and pediatric patients with newly diagnosed epilepsy. (Adv Stud Med. 2005;5(3B):S180-S184)
Aboriginal healing practices and Australian bush medicine

Colonists who arrived in Australia from 1788 used the bush to alleviate shortages of basic supplies, such as building materials, foods and medicines. They experimented with types of material that they considered similar to European sources. On the frontier, explorers and settlers gained knowledge of the bush through observing Aboriginal hunter-gatherers. Europeans incorporated into their own ‘bush medicine’ a few remedies derived from an extensive Aboriginal pharmacopeia. Differences between European and Aboriginal notions of health, as well as colonial perceptions of ‘primitive’ Aboriginal culture, prevented a larger scale transfer of Indigenous healing knowledge to the settlers. Since British settlement there has been a blending of Indigenous and Western European health traditions within the Aboriginal community.
Sliding Mode Controller for Voltage Regulation in DC Micro grid

The variations in loads can be defined as either increment or decrement in the load. Variation in load causes a similar change in the amount of current which produces opposite variation in output voltage. Microgrids typically use renewable energy resources to meet varying load demands of the local area. Such sources need power electronic converters for controlling proper voltage level for efficient operation. Hence it becomes essential to design control scheme for the DC-DC converters to stabilize the voltage in DC micro grids under any type of load variations. This research work proposes a DC micro grid structure in which sliding mode controller based DC-DC converters are implemented. Microgrid structure is then simulated using MATLAB SIMULINK where output voltage regulation is achieved efficiently in case of rise as well as fall in the load connected on DC micro gird.
Cooperative-hierarchical based edge-computing approach for resources allocation of distributed mobile and IoT applications

Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied; therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput.
Building Efficient Software to Support Content Delivery Services

Many content delivery services use key components such as web servers, databases, and key-value stores to serve content over the Internet. These services, and their component systems, face unique modern challenges. Services now operate at massive scale, serving large files to wide user-bases. Additionally, resource contention is more prevalent than ever due to large file sizes, cloud-hosted and collocated services, and the use of resourceintensive features like content encryption. Existing systems have difficulty adapting to these challenges while still performing efficiently. For instance, streaming video web servers work well with small data, but struggle to service large, concurrent requests from disk. Our goal is to demonstrate how software can be augmented or replaced to help improve the performance and efficiency of select components of content delivery services. We first introduce Libception, a system designed to help improve disk throughput for web servers that process numerous concurrent disk requests for large content. By using serialization and aggressive prefetching, Libception improves the throughput of the Apache and nginx web servers by a factor of 2 on FreeBSD and 2.5 on Linux when serving HTTP streaming video content. Notably, this improvement is achieved without changing the source code of either web server. We additionally show that Libception’s benefits translate into performance gains for other workloads, reducing the runtime of a microbenchmark using the diff utility by 50 % (again without modifying the application’s source code). We next implement Nessie, a distributed, RDMA-based, in-memory key-value store. Nessie decouples data from indexing metadata, and its protocol only consumes CPU on servers that initiate operations. This design makes Nessie resilient against CPU interference, allows it to perform well with large data values, and conserves energy during periods of non-peak load. We find that Nessie doubles throughput versus other approaches when CPU contention is introduced, and has 70 % higher throughput when managing large data in write-oriented workloads. It also provides 41 % power savings (over idle power consumption) versus other approaches when system load is at 20 % of peak throughput. Finally, we develop RocketStreams, a framework which facilitates the dissemination of live streaming video. RocketStreams exposes an easy-to-use API to applications, obviating the need for services to manually implement complicated data management and networking code. RocketStreams’ TCP-based dissemination compares favourably to an alternative solution, reducing CPU utilization on delivery nodes by 54 % and increasing viewer throughput by 27 % versus the Redis data store. Additionally, when RDMA-enabled hardware is available, RocketStreams provides RDMA-based dissemination which further increases overall performance, decreasing CPU utilization by 95 % and increasing concurrent viewer throughput by 55 % versus Redis.
The Chicago Women's Health Risk Study

To help a broad array of practitioners identify women at greatest risk, the Chicago Women's Health Risk Study explored factors indicating significant danger of death or life-threatening injury in intimate violence situations. The study compared longitudinal interviews with physically abused women sampled at health centers with similar interviews of people who knew intimate partner homicide victims. The many agencies and individuals who collaborated to accomplish this complex project feel that the collaboration was successful because it evolved, developed a collaborative culture, had permeable role definitions, and agreed on a few central research and practice standards.
Performance Analysis for 5G cellular networks: Millimeter Wave and UAV Assisted Communications

Recent years have witnessed exponential growth in mobile data and traffic. Limited available spectrum in microwave (μWave) bands does not seem to be capable of meeting this demand in the near future, motivating the move to new frequency bands. Therefore, operating with large available bandwidth at millimeter wave (mmWave) frequency bands, between 30 and 300 GHz, has become an appealing choice for the fifth generation (5G) cellular networks. In addition to mmWave cellular networks, the deployment of unmanned aerial vehicle (UAV) base stations (BSs), also known as drone BSs, has attracted considerable attention recently as a possible solution to meet the increasing data demand. UAV BSs are expected to be deployed in a variety of scenarios including public safety communications, data collection in Internet of Things (IoT) applications, disasters, accidents, and other emergencies and also temporary events requiring substantial network resources in the short-term. In these scenarios, UAVs can provide wireless connectivity rapidly. In this thesis, analytical frameworks are developed to analyze and evaluate the performance of mmWave cellular networks and UAV assisted cellular networks. First, the analysis of average symbol error probability (ASEP) in mmWave cellular networks with Poisson Point Process (PPP) distributed BSs is conducted using tools from stochastic geometry. Secondly, we analyze the energy efficiency of relay-assisted downlink mmWave cellular networks. Then, we provide an stochastic geometry framework to study heterogeneous downlink mmWave cellular networks consisting ofK tiers of randomly located BSs, assuming that each tier operates in a mmWave frequency band. We further study the uplink performance of the mmWave cellular networks by considering the coexistence of cellular and potential D2D user equipments (UEs) in the same band. In addition to mmWave cellular networks, the performance of UAV assisted cellular networks is also studied. Signal-to-interference-plus-noise ratio (SINR) coverage performance analysis for UAV assisted networks with clustered users is provided. Finally, we study the energy coverage performance of UAV energy harvesting networks with clustered users. performance analysis for 5g cellular networks: millimeter wave and uav assisted communications
Extensible Conditional Privacy Protection Authentication Scheme for Secure Vehicular Networks in a Multi-Cloud Environment

With an increasing number of cloud service providers (CSPs), research works on multi-cloud environments to provide solutions to avoid vendor lock-in and deal with the single-point failure problem have expanded considerably. However, a few schemes focus on the conditional privacy protection authentication of vehicular networks under a multi-cloud environment. In this regard, we propose a robust and extensible authentication scheme for vehicular networks to fulfil the ever-growing diversified service demands from users. According to our solution, the vehicles need to register with the trusted authority (TA) only once to achieve a fast and efficient authentication with CSPs. Additionally, as long as the new CSP is successfully registered in TA, it can participate in vehicular service. A cloud broker, which is managed by the TA, is responsible for connecting all the cloud services; consequently, the complexity involved in the selection of CSPs is hidden from the users’ view. A detailed security analysis establishes that our scheme can fulfil conditional privacy protection and achieve the security objectives of vehicular networks. Our scheme is based on elliptic curve cryptography and does not employ the complex bilinear pairing operation. An evaluation of performance of the proposed scheme indicates that it is suitable for applications involving vehicular networks.
Linear Programming Based Online Policies for Real-Time Assortment of Reusable Resources

Motivated by applications of rental services in e-commerce, we consider real-time assortment of reusable products. In our model, arriving consumers with heterogeneous types choose rental products from the offered assortment, pay the rental fees, and return the product to the platform after a rental time. Consumers' types specify their choice models, rental fees and rental time distributions for various products. Our goal is to design competitive online policies against an appropriate benchmark for both the prior-free setting, in which types are arbitrary (or adversarial), and the Bayesian setting, in which types are drawn independently from known distributions. Our contribution is threefold. We first introduce offline linear programming benchmarks in both settings, that use time-varying inventory constraints to capture feasibility of a policy under rentals, and are required to satisfy these constraints only in expectation. Second, in the prior-free setting, we develop a randomized primal-dual framework based on our introduced LP to settle that inventory balancing policies of Golrezaei et al. (2014) obtain same (asymptotically optimal) competitive ratios as with non-reusable resources when product rental times are fixed over time. As a corollary, we obtain the optimal competitive ratio of (1 − 1/e) when inventories are large. We also show this family of policies are constant competitive under i.i. d. (over time) stochastic rental times. Third, we change gear to the Bayesian setting by introducing simulation-based policies that use the expected LP solution as guidance. By using primal-dual analysis, we obtain a (1/2)-competitive simple and static simulation-based policy against the expected LP for general type-varying rental time distributions and rental fees. We further show optimal (1 − 1/ (cmin+3)0.5)-competitive adaptive policies against the same benchmark when rental times are infinite, where cmin is the smallest product inventory. Our analysis extends tools in the literature on prophet inequalities to design discarding threshold rules that maintain feasibility of a simulation-based policy in the Bayesian real-time assortment. We further justify the revenue performance of our proposed policies using numerical simulations.
Thoughts on spatio-temporal uncertainty metrics motivated by input sensitivity in the Spark bushfire spread model

Bushfires are highly complex events to both measure and predict. We want our models to predict certain observable features of fires, but which features to use is a question with many possible answers. It is important to understand both the skill of our bushfire spread prediction models, and the level of uncertainty inherent in their results. However, this can be difficult to quantify even when the desired features are known. One challenge is obtaining complete observed data. A second is knowing how to determine appropriate comparative metrics for predictions and observations that evolve over space and time. Further to this, we need to consider the intended use case of the metrics. Viability of a metric may depend on whether it is needed to inform: machine decision-making, researchers familiar with the subject area, or public stakeholders and decision makers. Here, we illustrate some problems and solutions that we have encountered in determining metrics appropriate to bushfire modelling, along with our proposed approach to measuring model skill. In particular we focus on metrics evaluating predicted bushfire perimeters, as opposed to other aspects of fire behaviour such as fire intensity, flame depth and height, etc. We model the evolution of a bushfire perimeter through time, given sparse data and incompletely observed perimeters. Considering the intended use cases of our comparison metrics, we have to communicate our model skill with human stakeholders and also use these metrics to improve our models in a relatively automated way. This adds an extra layer of complexity in selecting and applying metrics. Rather than focusing on a single metric that will likely never meet all our needs, we instead propose a standard approach to the development of metric sets appropriate to a problem. Our work utilises ideas from goodness-of-fit testing in the context of posterior and prior predictive approaches. We use this approach to simultaneously develop metrics for both human stakeholder and computational model development purposes. In this paper, we demonstrate both visual and computational two-dimensional metric solutions, as well as a scalar metric appropriate for different computational purposes. The scalar metric is one originally proposed in Baddeley (1992) which has been previously applied to weather forecasting (Gilleland et al., 2008). We also provide details on our standard approach, which could be extended to other spatio-temporal and complex models.
Communication for Generating Correlation: A Unifying Survey

The task of manipulating correlated random variables in a distributed setting has received attention in the fields of both Information Theory and Computer Science. Often shared correlations can be converted, using a little amount of communication, into perfectly shared uniform random variables. Such perfect shared randomness, in turn, enables the solutions of many tasks. Even the reverse conversion of perfectly shared uniform randomness into variables with a desired form of correlation turns out to be insightful and technically useful. In this article, we describe progress-to-date on such problems and lay out pertinent measures, achievability results, limits of performance, and point to new directions.
Energy-Efficient D2D Communications With Dynamic Time-Resource Allocation

In this paper, we investigate resource allocation schemes for Device-to-Device (D2D) communications, which aim to minimize the energy consumption of cellular users (CUs) and D2D pairs. Different from existing works where the resource allocation is performed in the premise that the sizes of orthogonal channels/resource blocks are given, we consider the case that the time resource can be dynamically adjusted according to the rate requirements of CUs and D2D pairs during the resource allocation. We first formulate the resource allocation as a mixed integer non-linear programming (MINLP). We then demonstrate that, given the selections of CUs for D2D pairs, the formulated energy minimization problem is conditionally convex, and the convexity condition is derived accordingly. If the convexity condition is not satisfied, we propose an iterative algorithm to minimize the energy consumption. Based on these results, we further develop a random-switch-based iterative (RSBI) algorithm to find the solution to the MINLP by improving the CU-selection for D2D pairs. Simulation results show that, compared with the equipotent and proportional-fair time allocation schemes, our approach can achieve an energy saving ratio of 17%–81% under various network settings.
Engineered Inks for Environment-Friendly Additive Manufacturing of Hierarchical Microstructures

Engineered Inks for Environment-Friendly Additive Manufacturing of Hierarchical Microstructures Maria Alejandra Torres Arango Novel material synthesis and green engineering innovations in manufacturing are important enablers of new technologies. Here, a range of functional materials systems and their processing through additive manufacturing are investigated. The materials studied consist of Ag, ZnO and TiO2 based inks for additive manufacturing with potential application across multiple technological fields. In particular, the synthesis of multi-functional emulsion inks, and their deposition through continuous-flow direct writing are investigated. Highlights of this work include the study of the curing/sintering conditions of such ink systems aiming for fundamental insights in their synthesis at low-temperatures (below 150°C), compatible with flexible polymeric substrates. Also, the investigation of texturing and microstructural control via ink composition, direct writing conditions and curing/sintering treatments is reported. In addition, the design and investigation of hybrid (organic/inorganic) multiphase composite ink systems for the realization of planar and 3D printable cellular structures, is discussed. Furthermore, the nucleation or decoration of the latter, via encapsulation of the decorative material and/or their precursors in one of the phases of the synthesized ink is investigated. Special emphasis is placed on an environmentally-friendly approach for the realization of sustainable inks, aiming for industry-transferable developments. Such approach provides the potential to further additive manufacturing and its utilization in a plethora of technological applications; ranging from energy, to waste management and water cleaning, to biomedical.
Dengue Diagnostics: Current Scenario

There is an urgent requirement for specific, sensitive and inexpensive dengue diagnostic tools that can be used for clinical management, surveillance and outbreak investigations would permit early intervention to treat patients and prevent or control epidemics. Additionally, new techniques for the early detection of severe disease such as the use of biomarkers have the potential to decrease morbidity and mortality. Recent developments in rapid detection technologies offer the promise of improved diagnostics for case management and the early detection of dengue outbreaks. This short review summarizes the various diagnostics tests currently pursued.
Seamless infrastructure programming and control for quality-critical cloud applications

By providing elastic infrastructure capacity and flexible pay-as-you-go business model, Cloud environments can significantly reduce the operational cost for resource-intensive applications like big data, deep learning, and the Internet of Things (IoT). In the application lifecycle, Clouds can not only automate the provisioning of application infrastructure, the deployment of the software components, but also provide advanced features, e.g., dynamic migration, scaling, and programmable virtual networking, for adapting the complex application to be continuously operational. However, these advanced Cloud features are so far only used at runtime phase of the application and have not yet been effectively included in the application programming model, which makes the Cloud application optimisation difficult across the entire software development and operation (DevOps) lifecycle. For applications with high-quality constraints, e.g., when processing IoT data queries within a given time window, or transmitting the data from the source to the computing resource within required latency, only runtime adaption will not be sufficient if infrastructures are not correctly designed. We thus identify our key research question as: how to seamlessly program and control the virtual infrastructure in the Cloud application DevOps lifecycle? To tackle the problem, we leveraged "Infrastructure as Code" to represent infrastructure specifications and model infrastructure operations. By decoupling the infrastructure abstraction from the application DevOps, we proposed the Cloud programming model and control mechanism. Overlay network mechanisms were further explored to provision a networked infrastructure. Finally, we investigated blockchain to improve Cloud Service Level Agreement (SLA) for enhancing the infrastructure service quality assurance at runtime.