Global Antifungal Profile Optimization of Chlorophenyl Derivatives against Botrytis cinerea and Colletotrichum gloeosporioides

Twenty-two aromatic derivatives bearing a chlorine atom and a different chain in the para or meta position were prepared and evaluated for their in vitro antifungal activity against the phytopathogenic fungi Botrytis cinerea and Colletotrichum gloeosporioides. The results showed that maximum inhibition of the growth of these fungi was exhibited for enantiomers S and R of 1-(40-chlorophenyl)- 2-phenylethanol (3 and 4). Furthermore, their antifungal activity showed a clear structure-activity relationship (SAR) trend confirming the importance of the benzyl hydroxyl group in the inhibitory mechanism of the compounds studied. Additionally, a multiobjective optimization study of the global antifungal profile of chlorophenyl derivatives was conducted in order to establish a rational strategy for the filtering of new fungicide candidates from combinatorial libraries. The MOOPDESIRE methodology was used for this purpose providing reliable ranking models that can be used later

Medicare Gaps and Widow Poverty

Several categories of medical expenditures are not covered by Medicare, including prescription drugs, most nursing home stays, and extended hospital visits. Out-of-pocket costs for these items can be substantial, and what’s more, they are likely to be concentrated at the end of life. At the same time, it is well documented that poverty is 3-4 times more common among widows than among similarly aged married women. This study examines the potential link between these two phenomena, asking the question: to what extent do out-of-pocket health care costs of a dying spouse affect the financial position of the survivor? We find that out-of-pocket medical spending increases substantially just prior to death, and that these expenditures are large relative to income for a large share of elderly couples. Simulations investigate the extent to which expansions in insurance coverage to include nursing home care or prescription drug coverage could improve the financial well-being of the surviving spouse.

Multi-Objective Optimization for Power Efficient Full-Duplex Wireless Communication Systems

In this paper, we investigate power efficient resource allocation algorithm design for multiuser wireless communication systems employing a full-duplex (FD) radio base station for serving multiple half-duplex (HD) downlink and uplink users simultaneously. We propose a multi-objective optimization framework for achieving two conflicting yet desirable system design objectives, i.e., total downlink transmit power minimization and total uplink transmit power minimization, while guaranteeing the quality-of-service of all users. To this end, the weighted Tchebycheff method is adopted to formulate a multi-objective optimization problem (MOOP). Although the considered MOOP is non-convex, we solve it optimally by semidefinite programming relaxation. Simulation results not only unveil the trade-off between the total downlink and the total uplink transmit power, but also confirm that the proposed FD system provides substantial power savings over traditional HD systems.Comment: Accepted for presentation at the IEEE Globecom 2015, San Diego, CA, USA, Dec. 201

Constraining generalisation in artificial language learning : children are rational too

Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Examples occur throughout language. For example, English native speakers know that certain noun-adjective combinations are impermissible (e.g. strong winds, high winds, strong breezes, *high breezes). Another example is the restrictions imposed by verb subcategorization, (e.g. I gave/sent/threw the ball to him; I gave/sent/threw him the ball; donated/carried/pushed the ball to him; * I donated/carried/pushed him the ball). Such lexical exceptions have been considered problematic for acquisition: if learners generalize abstract patterns to new words, how do they learn that certain specific combinations are restricted? (Baker, 1979). Certain researchers have proposed domain-specific procedures (e.g. Pinker, 1989 resolves verb subcategorization in terms of subtle semantic distinctions). An alternative approach is that learners are sensitive to distributional statistics and use this information to make inferences about when generalization is appropriate (Braine, 1971). A series of Artificial Language Learning experiments have demonstrated that adult learners can utilize statistical information in a rational manner when determining constraints on verb argument-structure generalization (Wonnacott, Newport & Tanenhaus, 2008). The current work extends these findings to children in a different linguistic domain (learning relationships between nouns and particles). We also demonstrate computationally that these results are consistent with the predictions of domain-general hierarchical Bayesian model (cf. Kemp, Perfors & Tenebaum, 2007)

Modeling Profit of Sliced 5G Networks for Advanced Network Resource Management and Slice Implementation

The core innovation in future 5G cellular networksnetwork slicing, aims at providing a flexible and efficient framework of network organization and resource management. The revolutionary network architecture based on slices, makes most of the current network cost models obsolete, as they estimate the expenditures in a static manner. In this paper, a novel methodology is proposed, in which a value chain in sliced networks is presented. Based on the proposed value chain, the profits generated by different slices are analyzed, and the task of network resource management is modeled as a multiobjective optimization problem. Setting strong assumptions, this optimization problem is analyzed starting from a simple ideal scenario. By removing the assumptions step-by-step, realistic but complex use cases are approached. Through this progressive analysis, technical challenges in slice implementation and network optimization are investigated under different scenarios. For each challenge, some potentially available solutions are suggested, and likely applications are also discussed

Comparing generalisation in children and adults learning an artificial language

Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Examples occur throughout language. For example, English native speakers know that certain noun-adjective combinations are impermissible (e.g., strong winds, high winds, strong breezes, *high breezes). Another example is the restrictions imposed by verb sub-categorization (e.g., I gave/sent/threw the ball to him; I gave/sent/threw him the ball; I donated/carried/pushed the ball to him; * I donated/carried/pushed him the ball; Baker, 1979). A central debate has been the extent to which learning such patterns depends on semantic cues (Pinker, 1989) and/or distributional statistics (Braine et al., 1990). The current experiments extend previous work which used Artificial Language learning to demonstrate that adults (Wonnacott et al., 2008) and 6 year olds (Wonnacott, 2011) are able to learn lexically based restrictions on generalization using distributional statistics. Here we directly compare the two age groups learning the same artificial language, with a view to exploring maturational differences in language learning. In addition to manipulating frequency (across high and low frequency items) and quantity of exposure (across days), languages were constructed such that a word’s semantic class was helpful for learning the restrictions for some types of lexical items, but potentially misleading for others

Official Poverty Statistics Mask the Economic Vulnerability of Seniors A Comparison of Maine to the Nation An

In this brief, authors Andrew Schaefer and Beth Mattingly compare Maine, one of the oldest states in the nation, to the United States as a whole. Historically, both children and the elderly were regarded as vulnerable groups in need of support from government programs. Traditional poverty estimates suggest that at least since the late 1960s, senior poverty has been on the decline, whereas poverty among children has increased. Declines among seniors are largely attributable to the advent of programs such as Social Security. Similar to the nation, about half of Maine seniors (51.0 percent) would be poor without Social Security benefits. However, traditional poverty measurement masks the role rising medical costs play in pushing seniors into poverty. The newer Supplemental Poverty Measure (SPM), which accounts for these costs, reveals that more than one in ten Maine seniors over age 55 were living below the poverty line in 2009–2013. This is 2.3 percentage points higher than official estimates suggest. Without medical expenses, the SPM indicates that poverty among Maine seniors would be roughly cut in half, from 10.2 percent to 5.2 percent. A similar reduction is evident across the United States (from 14.2 percent to 9.0 percent), though this represents a smaller relative reduction in poverty (by just over one-third)