Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of stratospheric sudden warming events : data analysis and modelling

The polar vortex is a large scale cyclone located in the middle atmosphere near to the planet’s geographic poles. These vortices form during the hemispheric winter and break down in the spring of the following year. They may also break down in mid winter, causing a sudden stratospheric warming event (SSW). The vortex is thought to be preconditioned leading up to these warming events, resulting in the breakdown of the vortex. Integral diagnostics are used to investigate the stripping of air from the vortex as part of this preconditioning. Contour diagnostics of mass and circulation are calculated using ERA-40 reanalysis data for the stratosphere. The edge of the vortex is easily identifiable in these diagnostics as a high gradient of Ertel’s potential vorticity (PV), and the warming events are also clearly visible. From these the amount of air removed from the vortex is determined from the balance equation of the mass integral. These terms show that there are significant amounts of air removed from the vortex, with several stripping events identifiable in them through the winter, especially in those during which a major sudden warming event occurred. These stripping events can be seen in corresponding PV maps, where tongues of PV can be seen to be stripped from the vortex and mixed into the surrounding surf zone of turbulent air. From the integral diagnostics a Lagrangian measure of the meridional circulation in the stratosphere is also calculated. In the final part of the thesis a shallow water model is used to investigate a quantitative link between forcing and the amount of stripping of the vortex. It is found that when the forcing is large enough there is significant stripping of mass from the vortex. This does not lead to SSWs in all cases, and the total amount of
Q-Learning for Sum-Throughput Optimization in Wireless Visible-Light UAV Networks

Unmanned aerial vehicles (UAVs) have been adopted as aerial base stations (ABSs) to provide wireless connectivity to ground users in events of increased network demand, and points-of-failure infrastructure (such as in disasters). However, with the existing crowded radio frequency (RF) spectrum, UAV ABSs cannot provide high-data-rate communication required in 5G and beyond. To address this challenge, visible light communication (VLC) is proposed to be equipped on UAVs to take advantage of the flexible and on-demand deployment feature of the UAV, and the high-data-rate communication of the VLC. However, VLC has strong alignment requirements between transceivers, therefore, how to determine the position and orientation of the UAV is critically important for sum-throughput improvement. In this paper, we propose two Q-learning based methods to maximize the sum throughput of the wireless visible-light UAV network by jointly controlling the position and orientation of the UAV. The results show that the proposed approaches can achieve a network-wide data rate very close to the optimal solution obtained by exhaustive search and outperform up to 18% compared with the intuitive centroid-based method. Computation complexity is also evaluated, where results showing that the proposed two Q-learning based methods can both consume less computational time, i.e., approximately 9 times and 210 times less on average than that of the exhaustive search approach.
Development HW/SW Controller for Power System Communication with Protocols Conversion to IEK-60870-5-104 and IEK-61850

The paper discusses the prospects of application and defines the requirements of the developing software and hardware Communication Controller for Power Electronic Devices integration into digital electrical networks with the conversion of data transmission protocols to IEC 60870-5-104 and IEC 61850 on a domestic microprocessor component base. The requirements for the Communication Controller for the integration of the Power Electronic Devices into digital electrical networks are defined. The Information Structure of the Communication Controller for data transmission according to IEC 61850 and IEC 60870-5-4 protocols has been developed. The Algorithm of operation of the program blocks of the Communication Controller has been developed. The hardware part of the device has been developed on information structure basis: selection of components that satisfies the described requirements and circuit solutions development. An IEC 61850 and IEC 60870-5-4 Information Model has been developed for a wide class of Power Electronic Devices in order to be integrated into the Communication Controller software.
Joint Task Assignment, Power Allocation and Node Grouping for Cooperative Computing in NOMA-mmWave Mobile Edge Computing

In this paper, we investigate the cooperation of idle computation resources of nearby mobile devices in mobile edge computing (MEC) systems, in which each mobile device jointly offloads computation tasks to a MEC node and a nearby mobile device by employing non-orthogonal multiple access (NOMA) in a millimeter-wave (mmWave) heterogeneous network. In this setup, the nearby device acts as a helper by performing local computation and offloading data simultaneously to the MEC system. We formulate an optimization problem for joint taSk assignmenT, poweR allOcation and Node Grouping (STRONG) aiming to minimize the energy consumption of devices (i.e., user and helper devices). To tackle this problem, we present a two-step solution. First, we adopt a low complexity search-based algorithm for both helper and MEC server selection. Next, considering the non-convex nature of the energy minimization problem, we develop algorithms that provide sub-optimal solutions for power allocation to the helper and MEC server, as well as the offloading task ratios between them. Numerical results are provided to validate the effectiveness of our proposed algorithms. The results not only validate the efficiency of our approach but also demonstrate the superiority of our cooperative NOMA-based MEC scenario compared to methods without cooperation and other cooperation-based scenarios.
Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning

Multicast traffic is growing rapidly due to the development of multimedia streaming. Lately, stateless multicast protocols, such as BIER, have been proposed to solve the excessive routing states problem of traditional multicast protocols. However, the high complexity of multicast tree computation and the limited scalability for concurrent requests still pose daunting challenges, especially under dynamic group membership. In this paper, we propose Hawkeye, a dynamic and stateless multicast mechanism with deep reinforcement learning (DRL) approach. For real-time responses to multicast requests, we leverage DRL enhanced by a temporal convolutional network (TCN) to model the sequential feature of dynamic group membership and thus is able to build multicast trees proactively for upcoming requests. Moreover, an innovative source aggregation mechanism is designed to help the DRL agent converge when faced with a large amount of multicast requests, and relieve ingress routers from excessive routing states. Evaluation with real-world topologies and multicast requests demonstrates that Hawkeye adapts well to dynamic multicast: it reduces the variation of path latency by up to 89.5% with less than 12% additional bandwidth consumption compared with the theoretical optimum.
Use of a new proximity assay (NanoBRET) to investigate the ligand binding characteristics of three fluorescent ligands to the human β1-adrenoceptor expressed in HEK-293 cells. and

Previous research has indicated that allosteric interactions across the dimer interface of b 1 -adrenoceptors may be responsible for a secondary low afﬁnity binding conformation. Here we have investigated the potential for probe dependence, in the determination of antagonist pK i values at the human b 1 -adenoceptor, which may result from such allosterism interactions. Three ﬂuorescent b 1 -adrenoceptor ligands were used to investigate this using bioluminescence energy transfer (BRET) between the receptor-bound ﬂuorescent ligand and the N-terminal NanoLuc tag of a human b 1 -adrenoceptor expressed in HEK 293 cells (NanoBRET). This proximity assay showed high-afﬁnity-speciﬁc binding to the NanoLuc- b 1 -adrenoceptor with each of the three ﬂuorescent ligands yielding K D values of 87.1 (cid:1) 10 nmol/L ( n = 8), 38.1 (cid:1) 12 nmol/L ( n = 7), 13.4 (cid:1) 2 nmol/L ( n = 14) for propranolol-Peg8-BY630, propranolol-b (Ala-Ala)-BY630 and CGP-12177-TMR, respectively. Parallel radioligand-binding studies with 3 H-CGP12177 and TIRF microscopy, to monitor NanoLuc bioluminescence, conﬁrmed a high cell surface expression of the NanoLuc- b 1 -adrenoceptor in HEK 293 cells (circa 1500 fmol.mg protein (cid:3) 1 ). Following a 1 h incubation with ﬂuorescent ligands and b 1 -adrenoceptor competing antagonists, there were signiﬁcant differences ( P < 0.001) in the pK i values obtained for CGP20712a and CGP 12177 with the different ﬂuorescent ligands and 3 H-CGP 12177. However, increasing the incubation time to 2 h removed these signiﬁcant differences. The data obtained show that the NanoBRET assay can be applied successfully to study ligand-receptor interactions at the human b 1 -adrenoceptor. However, the study also emphasizes the importance of ensuring that both the ﬂuorescent and competing ligands are in true equilibrium before interpretations regarding probe dependence can be made. N-terminal tagged NanoLuc human b 1 -adrenoceptor expressed in HEK 293 cells.
Applications of Deep Neural Networks in Exploration Seismology: a technical survey

Exploration seismology uses reflected and refracted seismic waves, emitted from a controlled (active) source into the ground, and recorded by an array of seismic sensors (receivers) to image the subsurface geological structures. These seismic images are the main resources for energy and resource exploration and scientific investigation of the crust and upper mantle. The exponential growth of data volumes from seismic surveys using modern instruments drives the need for algorithmic developments to process large-volume high-dimensional data. This is where artificial intelligence and modern machine learning techniques could play a key role. We survey recent advances in applications of machine learning (ML) methods, more specifically deep neural networks (DNNs), in the exploration seismology. We provide comprehensive and technically oriented review of DNNs applications for seismic data acquisition, data pre-processing tasks such as interpolation/extrapolation, denoising, first break picking, velocity picking, seismic migration, data processing tasks such as geological and structural interpretations, and data modeling tasks such as the inference of sub-surface structures and lithologic and petrophysical properties. Our goal is to document the technological trends, progress, and challenges for DNN approaches in the exploration seismology. This could benefit other sectors of the seismological community such as earthquake and engineering seismology by sharing how progress was achieved. data pre-processing tasks such as interpolation/extrapolation, denoising, first break picking, velocity picking, seismic migration, data processing tasks such as geological and structural interpretations, and data modeling tasks such as the inference of sub-surface structures and lithologic and petrophysical properties. Our goal is to document the technological trends, progress, and challenges for DNN approaches in the exploration seismology. This could benefit other sectors of the seismological com-#xD;munity such as earthquake and engineering seismology by sharing how progress was achieved.
Aggregate Model of Parallel Distributed Energy Resources Controlled using Virtual Admittance

The increased number of distributed generation sources in distribution networks in recent years has emphasized the importance of their accurate and computationally efficient modelling. The development of efficient models becomes more difficult and complex with the introduction of novel controllers that aim to support the grid, often emulating the operation of passive circuit elements. One of such controllers has been recently proposed that emulates the operation of an admittance to support the voltage at the point of the converter grid connection. In this paper, an aggregate model of distributed energy resources controlled using virtual admittance approach and connected in parallel is derived. The resemblance of the controller to classical circuit elements is used to obtain an equivalent circuit for each converter including its controller part and the distribution line connecting it to the main grid. Then, circuit analysis techniques are applied to synthesize the aggregate model of all the parallel converters in the network, simplifying the assessment of their combined effect on the rest of the network and reducing the computational burden. Simulation results from Matlab and its SimPowerSystems toolbox are used to validate the accuracy of the proposed model.
Ketamine-Induced Apoptosis in Normal Human Urothelial Cells A Direct, N-Methyl-D-Aspartate ReceptoreIndependent Pathway Characterized by Mitochondrial Stress

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"Myth" or "construct"?: What students are learning about race in the South African history classroom

History education in post-apartheid South Africa addresses topics that are highly salient to the concept of race. To make sense of colonialism, slavery, the Holocaust, and most notably apartheid, students require an understanding of what race is, and how it has been used to justify discriminatory and unjust behaviour. The South African Curriculum and Policy Statements for Grade 9 History therefore devotes two hours to a topic on "the definition of race" (Department of Basic Education [DBE], 2011a: 43). However, what are students learning about 'the definition of race' from their history education? In this article, we draw on our experience as a history educator and history education researcher to argue that students often develop inaccurate and unhelpful understandings of race. This is partially since both the South African history curricula and textbooks describe race as a "myth" (DBE, 2011a, 43; Bottaro, Cohen, Dilley, Duffett, & Visser, 2013) with no scientific or evolutionary basis. Hence, students who learn that race is a 'myth' understandably struggle to understand discourses and policies that refer to racial identity and are at risk of misunderstanding theories of evolution. While we agree that the concept of race has no legitimate scientific basis, we nonetheless argue that students require an historical understanding of race; one that demonstrates how racial identities have been constructed in different ways and for different purposes over time. Such an approach would introduce students to the extensive historiography of the construction of race (e.g. DuBois, 1940; Dubow, 1995). By understanding race as a construct rather than a myth, we suggest that students will be better able to engage with the legacies of racialised violence as well as the ways in which racial identity is a legitimate source of meaning for many South Africans.
CryoEM structures of membrane pore and prepore complex reveal cytolytic mechanism of Pneumolysin

Many pathogenic bacteria produce pore-forming toxins to attack and kill human cells. We have determined the 4.5 Å structure of the ~2.2 MDa pore complex of pneumolysin, the main virulence factor of Streptococcus pneumoniae, by cryoEM. The pneumolysin pore is a 400 Å ring of 42 membrane-inserted monomers. Domain 3 of the soluble toxin refolds into two ~85 Å β-hairpins that traverse the lipid bilayer and assemble into a 168-strand β-barrel. The pore complex is stabilized by salt bridges between β-hairpins of adjacent subunits and an internal α-barrel. The apolar outer barrel surface with large sidechains is immersed in the lipid bilayer, while the inner barrel surface is highly charged. Comparison of the cryoEM pore complex to the prepore structure obtained by electron cryo-tomography and the x-ray structure of the soluble form reveals the detailed mechanisms by which the toxin monomers insert into the lipid bilayer to perforate the target membrane. DOI: http://dx.doi. org/10.7554/eLife.23644. 001
Optimizing the Structural Parameters of the Mechanical Sensing Element of a Microoptoelectromechanical Micro-g Accelerometer

The paper investigates a microoptoelectromechanical accelerometer that implements optical measuring transducer built on the optical tunneling effect. The article discusses the issues of micromechanical sensing element (MSE) optimization for an MOEM- accelerometer fabricated by silicon on insulator (SOI) technology. The results represent the requirements to the design to achieve a sensitivity of 1 μg m/s2at a calculated eigenvalue of the MSE. The main parameters of the mechanical part of a MOEM-accelerometer affecting its sensitivity are dimensions, mass, resonance frequency and corresponding stiffness of the spring suspension. Following the Brownian noise that characterizes the accelerometer's sensitivity, the relations between the parameters are determined. Two designs of the MSE are considered according to the fabrication process. One design contains only a device layer; the second one implements a SOI-wafer basic layer as a weighting additive. The latter design occupies less area. To prevent MSE deflection stipulated by its larger dimensions and to increase the number of sensors fabricated from a single SOI wafer, MSE with additional mass are more reasonable to use. The MSE mass at an eigenfrequency of 10, 20, 50 and 100 Hz should exceed 0.01. 10−6 kg, while at a frequency of 200 Hz, the mass should exceed 0.023. 10−6 kg to comply with the requirement of minimal measurable acceleration of 1 μg m/s2. Subsequent increase of the MSE mass at the same eigenfrequency decreases the thermal mechanical noise.
Effect of land use on estimates of evapotranspiration and recharge in the Daly River catchment

Mounting development pressures in the Daly River region of the Northern Territory require an assessment of environmental conditions to be made, with particular attention to water issues. Increased agricultural development would involve widespread clearing of native deep rooted trees. The impact of this on the water balance, in particular on groundwater recharge and subsequent groundwater flow into the Daly River, is unknown. This project measures components of the water balance under native savanna vegetation and cleared land within the Daly catchment. In particular, the project provides preliminary estimates of evapotranspiration and groundwater recharge beneath these different land uses.
Privacy-preserving Stable Crowdsensing Data Trading for Unknown Market

As a new paradigm of data trading, Crowdsensing Data Trading (CDT) has attracted widespread attention in recent years, where data collection tasks of buyers are crowdsourced to a group of mobile users as sellers through a platform as a broker for long-term data trading. The stability of the matching between buyers and sellers in the data trading market is one of the most important CDT issues. In this paper, we focus on the privacy-preserving stable CDT issue with unknown preference sequences of buyers. Our goal is to maximize the accumulative data quality for each task while protecting the data qualities of sellers and ensuring the stability of the CDT market. We model such privacy-preserving stable CDT issue with unknown preference sequences as a differentially private competing multi-player multi-armed bandit problem. We define a novel metric δ-stability and propose a privacy-preserving stable CDT mechanism based on differential privacy, stable matching theory, and competing bandit strategy, called DPS-CB, to solve this problem. Finally, we prove the security and the stability of the CDT market under the effect of privacy concerns and analyze the regret performance of DPS-CB. Also, the performance is demonstrated on a real-world dataset.
The FOMC versus the Staff : Where Can Monetary Policymakers Add Value ?

A key issue in monetary policymaking is the appropriate division of labor between the profes­ sional staff of the central bank and the appointed policymakers. Lars E. O. Svensson (1999) argues that the appropriate role of a policymaking group, such as the Federal Open Market Com­ mittee (FOMC) in the United States, is to make judgments about social welfare, taking as given the likely outcomes of different policies as esti­ mated by the staff. In this division, the staff is relied upon to assess current and prospective economic conditions and to forecast the effects of different policies. Policymakers’ only role is to decide which of the various options should be chosen. The obvious alternative is for policymakers to also play a role in forecasting and in predict­ ing the consequences of policy actions. In this division, policymakers supplement the staff’s analysis with their own information about likely economic developments and the effects of policy. Their choice of a particular policy then reflects their views not only of desired outcomes, but also of prospective developments and of the working of the economy. Which of these divisions of labor is best clearly cannot be determined from first princi­ ples. It depends on the relative skill of the staff and the policymakers in forecasting and under­ standing the economy. As a result, the answer may vary across times and places. In the United States, policymakers certainly appear to believe they have useful information to add to the staff’s forecasts and estimated policy The FOMC versus the Staff: Where Can Monetary Policymakers Add Value?
Marten: A Built-in Security DRL-Based Congestion Control Framework by Polishing the Expert

Deep reinforcement learning (DRL) has been proved to be an effective method to improve the congestion control algorithms (CCAs). However, the lack of training data and training scale affect the effectiveness of DRL model. Combining rule-based CCAs (such as BBR) as a guide for DRL is an effective way to improve learning-based CCAs. By experiment measurement, we find that the rule-based CCAs limit the action exploration and even cause DRL’s excessive dependence to gain higher DRL’s reward gain. To overcome the constraints, we propose Marten, a framework which improves the effectiveness of rule-based CCAs for DRL. Marten uses entropy as the degree of exploration and uses it to expand the exploration of DRL. Furthermore, Marten introduces the shielding mechanism to avoid wrong DRL actions. We have implemented Marten in both simulation platform OpenAI Gym and deployment platform QUIC. The experimental results in production network demonstrate Marten can improve throughput by 0.36% and reduce latency by 14.89% on average compared with Eagle, and improve throughput by 2.79% and reduce latency by 11.73% on average compared with BBR.