Having a strong professional network is important. It’s “…an investment that pays off in the future” (Wolf & Moser, 2009), predicting positive changes in career attainment. However, networking is ...often considered more of a soft-skill, where many lack formal training. With that in mind, panelists from a diverse set of fields and career levels will share their personal experiences and lessons learned on networking. Audience members will be given the opportunity to ask panelists follow up questions. The last third of the alternative format panel will be spent engaged in an activity to foster networking and a debrief of the activity.
Presentation time: 40 minutes, Q/A time: 20 minutes, Networking Activity: 15 minutes, Activity Debrief: 15 minutes.
Wolf-Hirschhorn syndrome: A review and update Battaglia, Agatino; Carey, John C.; South, Sarah T.
American journal of medical genetics. Part C, Seminars in medical genetics,
September 2015, Volume:
169C, Issue:
3
Journal Article
ABSTRACT
Large wind kinetic power of Wolf-Rayet (WR) stars make them ideal targets in low radio frequencies to search for non-thermal emission due to relativistic particle acceleration. In this ...paper, we present observations of two WR stars, WR 114 and WR 142, in Band 4 (550–950 MHz) and Band 5 (1050–1450 MHz) using the upgraded Giant Meterwave Radio Telescope. Neither star is detected in the observed frequency bands, nor extended emission associated with them. The upper limit to the free–free radio emission from the stellar wind enables us to constrain the mass-loss rate of WR 114 to $\lesssim \rm 10^{-5}\, M_{ \odot }\, yr^{-1}$; this is a factor three smaller than previously estimated using spectroscopic modelling. If we further assume that the WR stars are binaries, the non-detection of synchrotron emission from the putative wind collision region implies that the stars are either in very wide binary systems away from periastron, or that the stars are in close binary systems with an orbital separation <70 AU for WR 114 and <20 AU for WR 142. The non-detection of low-frequency radio emission from these two systems thus provides evidence that narrows their nature, though it does not rule them out as bona fide particle-accelerating colliding-wind binaries.
In this work, a novel binary version of the grey wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the ...latest bio-inspired optimization techniques, which simulate the hunting process of grey wolves in nature. The binary version introduced here is performed using two different approaches. In the first approach, individual steps toward the first three best solutions are binarized and then stochastic crossover is performed among the three basic moves to find the updated binary grey wolf position. In the second approach, sigmoidal function is used to squash the continuous updated position, then stochastically threshold these values to find the updated binary grey wolf position. The two approach for binary grey wolf optimization (bGWO) are hired in the feature selection domain for finding feature subset maximizing the classification accuracy while minimizing the number of selected features. The proposed binary versions were compared to two of the common optimizers used in this domain namely particle swarm optimizer and genetic algorithms. A set of assessment indicators are used to evaluate and compared the different methods over 18 different datasets from the UCI repository. Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.
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The evolutionary history of the wolf-like canids of the genus Canis has been heavily debated, especially regarding the number of distinct species and their relationships at the population and species ...level 1–6. We assembled a dataset of 48 resequenced genomes spanning all members of the genus Canis except the black-backed and side-striped jackals, encompassing the global diversity of seven extant canid lineages. This includes eight new genomes, including the first resequenced Ethiopian wolf (Canis simensis), one dhole (Cuon alpinus), two East African hunting dogs (Lycaon pictus), two Eurasian golden jackals (Canis aureus), and two Middle Eastern gray wolves (Canis lupus). The relationships between the Ethiopian wolf, African golden wolf, and golden jackal were resolved. We highlight the role of interspecific hybridization in the evolution of this charismatic group. Specifically, we find gene flow between the ancestors of the dhole and African hunting dog and admixture between the gray wolf, coyote (Canis latrans), golden jackal, and African golden wolf. Additionally, we report gene flow from gray and Ethiopian wolves to the African golden wolf, suggesting that the African golden wolf originated through hybridization between these species. Finally, we hypothesize that coyotes and gray wolves carry genetic material derived from a “ghost” basal canid lineage.
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•Extensive gene flow in the genus Canis, especially among the crown group•Genetic contribution from an unknown canid into the ancestor of the gray wolf and coyote•The African golden wolf possibly a hybrid species, from the gray wolf and Ethiopian wolf•Possible ancient admixture between the dhole and African hunting dog
Gopalakrishnan et al. present evidence of pervasive gene flow among species of the genus Canis. In addition to previously known admixture events, they find evidence of gene flow from a “ghost” canid, related to the dhole, into the ancestor of the gray wolf and coyote. Further, they suggest that the African golden wolf is a species of hybrid origin.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Proposing an improved Grey Wolf Optimizer (I-GWO) for solving engineering problems.•Introducing a new search strategy named dimension learning-based hunting (DLH).•DLH is to enhance ...balance between local and global search and maintain diversity.•Performance of I-GWO is evaluated on the CEC2018 and three engineering problems.•I-GWO algorithm is very competitive and superior to the compared algorithms.
In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Recently, electricity generation from solar photovoltaic (PV) has gained popularity throughout the world due to its profuse availability and eco-friendly nature. Consequently, extraction of maximum ...power from solar PV energy systems was the point of interest in the current researches. Various techniques have been proposed to track the maximum power point (MPP) from solar PV energy systems under variable environmental conditions. Conventional maximum power point tracking (MPPT) techniques have demonstrated the ability to track MPP with uniform solar irradiance. However, the ability of these techniques to track the accurate MPP with the condition of partial shading (PS) is not guaranteed. Hence, this paper intended to present novel optimization techniques to mitigate the PS effect and proficiently track the global maximum power point (GMPP). Grey Wolf Optimization (GWO), Moth-Flame Optimization (MFO), Salp Swarm Algorithm (SSA) and Hybrid Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) techniques have been proposed to handle this dilemma. The proposed techniques have been simulated and analyzed using MATLAB/SIMULINK. Furthermore, these techniques have been compared with the conventional PSO algorithm for validation. Statistical and sensitivity analysis have been established to compare the performance, check the stability, and determine the best technique out of the proposed techniques. Results showed the superiority of GWO in the speed of convergence and the time to catch GMPP. Moreover, the sensitivity analysis demonstrated the stability, successfully rate, and tracking efficiency of PSO-GSA technique. Finally, this paper gives an open reference to these optimizers to attempt mass research works in PV systems under PS.
•Implementation of distinctive meta-heuristic optimization algorithms for increasing the PV system efficiency under PSC.•The proposed algorithms are GWO, MFO, PSO-GSA, and SSA.•Determination of the GMPP from the multiple local peaks caused by different irradiances.•Comparing the proposed algorithms with PSO algorithm for approval.•Introducing statistical and sensitivity analysis to compare the performance and check the stability of proposed algorithms.
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Stars stripped of their hydrogen-rich envelope through interaction with a binary companion are generally not considered when accounting for ionizing radiation from stellar populations, despite the ...expectation that stripped stars emit hard ionizing radiation, form frequently, and live 10–100 times longer than single massive stars. We compute the first grid of evolutionary and spectral models specially made for stars stripped in binaries for a range of progenitor masses (2–20 M⊙) and metallicities ranging from solar to values representative for pop II stars. For stripped stars with masses in the range 0.3–7 M⊙, we find consistently high effective temperatures (20 000–100 000 K, increasing with mass), small radii (0.2–1 R⊙), and high bolometric luminosities, comparable to that of their progenitor before stripping. The spectra show a continuous sequence that naturally bridges subdwarf-type stars at the low-mass end and Wolf-Rayet-like spectra at the high-mass end. For intermediate masses we find hybrid spectral classes showing a mixture of absorption and emission lines. These appear for stars with mass-loss rates of 10−8−10−6 M⊙ yr−1, which have semi-transparent atmospheres. At low metallicity, substantial hydrogen-rich layers are left at the surface and we predict spectra that resemble O-type stars instead. We obtain spectra undistinguishable from subdwarfs for stripped stars with masses up to 1.7 M⊙, which questions whether the widely adopted canonical value of 0.47 M⊙ is uniformly valid. Only a handful of stripped stars of intermediate mass have currently been identified observationally. Increasing this sample will provide necessary tests for the physics of interaction, internal mixing, and stellar winds. We use our model spectra to investigate the feasibility to detect stripped stars next to an optically bright companion and recommend systematic searches for their UV excess and possible emission lines, most notably HeII λ4686 in the optical and HeII λ1640 in the UV. Our models are publicly available for further investigations or inclusion in spectral synthesis simulations.
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