586 research outputs found
Switching of excited states in cyclometalated platinum complexes incorporating pyridyl-acetylide ligands (Pt-C[triple bond, length as m-dash]C-py): a combined experimental and theoretical study
International audienceThis article presents the design of cyclometalated platinum(II) complexes incorporating pyridyl-appended acetylide ligands of the form Pt-C[triple bond, length as m-dash]C-py, acting either as sites for protonation or methylation reactions or as a host receptor for binding metal cations. The complexes studied are Pt(t-Bu2phbpy)(-C[triple bond, length as m-dash]C-py), 2, which can undergo protonation at the pyridyl N; its cationic N-methylated derivative [Pt(t-Bu2phbpy)(-C[triple bond, length as m-dash]C-pyMe)]+, 4, which serves as a model of the N-protonated species; and a derivative in which the pyridyl ring is incorporated into a macrocyclic diamide-crown ether ligand (3). The co-ligand t-Bu2phbpy is a cyclometalated, N[caret]N[caret]C-coordinated phenylbipyridine ligand carrying tert-butyl groups at the 4-positions of the pyridyl rings. The photophysical properties of the neutral compounds 2 and 3 have been compared to those of the pyridinium, methyl-pyridinium or metal-complexed species (namely 2-H+, 4 and 3-Pb2+). Detailed TD-DFT calculations provide a theoretical basis to account for the experimentally-observed changes upon protonation/methylation/complexation. The joint TD-DFT and experimental studies provide evidence for an unprecedented molecular switch in the nature of the excited state (from mixed L′LCT/MLCT to ML′CT) in which the acceptor ligand in the CT process switches from being the N[caret]N[caret]C ligand to the pyridyl acetylide
A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
This paper presents a novel spectral algorithm with additive clustering
designed to identify overlapping communities in networks. The algorithm is
based on geometric properties of the spectrum of the expected adjacency matrix
in a random graph model that we call stochastic blockmodel with overlap (SBMO).
An adaptive version of the algorithm, that does not require the knowledge of
the number of hidden communities, is proved to be consistent under the SBMO
when the degrees in the graph are (slightly more than) logarithmic. The
algorithm is shown to perform well on simulated data and on real-world graphs
with known overlapping communities.Comment: Journal of Theoretical Computer Science (TCS), Elsevier, A Para\^itr
Analysis and design of randomised clinical trials involving competing risks endpoints
<p>Abstract</p> <p>Background</p> <p>In randomised clinical trials involving time-to-event outcomes, the failures concerned may be events of an entirely different nature and as such define a classical competing risks framework. In designing and analysing clinical trials involving such endpoints, it is important to account for the competing events, and evaluate how each contributes to the overall failure. An appropriate choice of statistical model is important for adequate determination of sample size.</p> <p>Methods</p> <p>We describe how competing events may be summarised in such trials using cumulative incidence functions and Gray's test. The statistical modelling of competing events using proportional cause-specific and subdistribution hazard functions, and the corresponding procedures for sample size estimation are outlined. These are illustrated using data from a randomised clinical trial (SQNP01) of patients with advanced (non-metastatic) nasopharyngeal cancer.</p> <p>Results</p> <p>In this trial, treatment has no effect on the competing event of loco-regional recurrence. Thus the effects of treatment on the hazard of distant metastasis were similar via both the cause-specific (unadjusted <it>csHR </it>= 0.43, 95% CI 0.25 - 0.72) and subdistribution (unadjusted <it>subHR </it>0.43; 95% CI 0.25 - 0.76) hazard analyses, in favour of concurrent chemo-radiotherapy followed by adjuvant chemotherapy. Adjusting for nodal status and tumour size did not alter the results. The results of the logrank test (<it>p </it>= 0.002) comparing the cause-specific hazards and the Gray's test (<it>p </it>= 0.003) comparing the cumulative incidences also led to the same conclusion. However, the subdistribution hazard analysis requires many more subjects than the cause-specific hazard analysis to detect the same magnitude of effect.</p> <p>Conclusions</p> <p>The cause-specific hazard analysis is appropriate for analysing competing risks outcomes when treatment has no effect on the cause-specific hazard of the competing event. It requires fewer subjects than the subdistribution hazard analysis for a similar effect size. However, if the main and competing events are influenced in opposing directions by an intervention, a subdistribution hazard analysis may be warranted.</p
Minimising pain in farm animals: the 3S approach - ‘Suppress, Substitute, Soothe'
Recently, the French National Institute for Agricultural Research appointed an expert committee to review the issue of pain in food-producing farm animals. To minimise pain, the authors developed a ‘3S' approach accounting for ‘Suppress, Substitute and Soothe' by analogy with the ‘3Rs' approach of ‘Reduction, Refinement and Replacement' applied in the context of animal experimentation. Thus, when addressing the matter of pain, the following steps and solutions could be assessed, in the light of their feasibility (technical constraints, logistics and regulations), acceptability (societal and financial aspects) and availability. The first solution is to suppress any source of pain that brings no obvious advantage to the animals or the producers, as well as sources of pain for which potential benefits are largely exceeded by the negative effects. For instance, tail docking of cattle has recently been eliminated. Genetic selection on the basis of resistance criteria (as e.g. for lameness in cattle and poultry) or reduction of undesirable traits (e.g. boar taint in pigs) may also reduce painful conditions or procedures. The second solution is to substitute a technique causing pain by another less-painful method. For example, if dehorning cattle is unavoidable, it is preferable to perform it at a very young age, cauterising the horn bud. Animal management and constraint systems should be designed to reduce the risk for injury and bruising. Lastly, in situations where pain is known to be present, because of animal management procedures such as dehorning or castration, or because of pathology, for example lameness, systemic or local pharmacological treatments should be used to soothe pain. These treatments should take into account the duration of pain, which, in the case of some management procedures or diseases, may persist for longer periods. The administration of pain medication may require the intervention of veterinarians, but exemptions exist where breeders are allowed to use local anaesthesia (e.g. castration and dehorning in Switzerland). Extension of such exemptions, national or European legislation on pain management, or the introduction of animal welfare codes by retailers into their meat products may help further developments. In addition, veterinarians and farmers should be given the necessary tools and information to take into account animal pain in their management decision
Agroecology as an Alternative Vision to Conventional Development and Climate Smart Agriculture
Mukaiyama addition of (trimethylsilyl) acetonitrile to dimethyl acetals mediated by trimethylsilyl trifluoromethanesulfonate
(Trimethylsilyl) acetonitrile reacts smoothly with dimethyl acetals in the presence of stoichiometric trimethylsilyl trifluoromethanesulfonate (TMSOTf) to yield β-methoxynitriles. The ideal substrates for this reaction are acetals derived from aromatic aldehydes. Elimination to the corresponding α,β-unsaturated nitriles is observed as the major product in the case of electron-rich acetals. A mechanistic hypothesis that includes isomerization of the silylnitrile to a nucleophilic N-silyl ketene imine is presented
Modelling the time-varying cell capacity in LTE networks
In wireless orthogonal frequency-division multiple access (OFDMA) based networks like Long Term Evolution (LTE) or Worldwide Interoperability for Microwave Access (WiMAX) a technique called adaptive modulation and coding (AMC) is applied. With AMC, different modulation and coding schemes (MCSs) are used to serve different users in order to maximise the throughput and range. The used MCS depends on the quality of the radio link between the base station and the user. Data is sent towards users with a good radio link with a high MCS in order to utilise the radio resources more efficiently while a low MCS is used for users with a bad radio link. Using AMC however has an impact on the cell capacity as the quality of a radio link varies when users move around; this can even lead to situations where the cell capacity drops to a point where there are too little radio resources to serve all users. AMC and the resulting varying cell capacity notably has an influence on admission control (AC). AC is the algorithm that decides whether new sessions are allowed to a cell or not and bases its decisions on, amongst others, the cell capacity. The analytical model that is developed in this paper models a cell with varying capacity caused by user mobility using a continuous -time Markov chain (CTMC). The cell is divided into multiple zones, each corresponding to the area in which data is sent towards users using a certain MCS and transitions of users between these zones are considered. The accuracy of the analytical model is verified by comparing the results obtained with it to results obtained from simulations that model the user mobility more realistically. This comparison shows that the analytical model models the varying cell capacity very accurately; only under extreme conditions differences between the results are noticed.
The developed analytical and simulation models are then used to investigate the effects of a varying cell capacity on AC. Also, an optimisation algorithm that adapts the parameter of the AC algorithm which determines the amount of resources that are reserved in order to mitigate the effects of the varying cell capacity is studied using the models. Updating the parameter of the AC algorithm is done by reacting to certain triggers that indicate good or bad performance and adapt the parameters of the AC algorithm accordingly. Results show that using this optimisation algorithm improves the quality of service (QoS) that is experienced by the users.This work was partially supported by the Spanish Government through project TIN2010-21378-C02-02 and contract BES-2007-15030.Sas, B.; Bernal Mor, E.; Spaey, K.; Pla, V.; Blondia, C.; Martínez Bauset, J. (2014). Modelling the time-varying cell capacity in LTE networks. Telecommunication Systems. 55(2):299-313. https://doi.org/10.1007/s11235-013-9782-2S2993135523GPP (2010). 3GPP TR 36.213: Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Physical layer procedures, June 2010.3GPP (2010). 3GPP TR 36.942: Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Radio Frequency (RF) system scenarios, September 2010.Al-Rawi, M., & Jäntti, R. (2009). Call admission control with active link protection for opportunistic wireless networks. Telecommunications Systems, 41(1), 13–23.Bhatnagar, S., & Reddy, B.B.I. (2005). Optimal threshold policies for admission control in communication networks via discrete parameter stochastic approximation. Telecommunications Systems, 29(1), 9–31.Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2(5), 483–502.E3. ict-e3.eu.Elayoubi, S.-E., & Chahed, T. (2005). Admission control in the downlink of WCDMA/UMTS. In LNCS: Vol. 3427. Mobile and wireless systems (pp. 136–151).Garcia, D., Martinez, J., & Pla, V. (2005). Admission control policies in multiservice cellular networks: optimum configuration and sensitivity. In G. Kotsis, & O. Spaniol (Eds.), Lecture notes in computer science: Vol. 3427. Wireless systems and mobility in next generation Internet (pp. 121–135).Guo, J., Liu, F., & Zhu, Z. (2007). Estimate the call duration distribution parameters in GSM system based on K-L divergence method. In International conference on wireless communications, networking and mobile computing (pp. 2988–2991), Shanghai, China, September 2007.Hossain, M., Hassan, M., & Sirisena, H. R. (2004). Adaptive resource management in mobile wireless networks using feedback control theory. Telecommunications Systems, 24(3–4), 401–415.Jeong, S.S., Han, J.A., & Jeon, W.S. (2005). Adaptive connection admission control scheme for high data rate mobile networks. In IEEE 62nd Vehicular technology conference, 2005. VTC-2005-Fall (Vol. 4, pp. 2607–2611).Kim, D.K., Griffith, D., & Golmie, N. (2010). A novel ring-based performance analysis for call admission control in wireless networks. IEEE Communications Letters, 14(4), 324–326.Latouche, G., & Ramaswami, V. (1999). Introduction to matrix analytic methods in stochastic modeling. ASA-SIAM. Baltimore: Philadelphia.MONOTAS. http://www.macltd.com/monotas .Neuts, M. (1981). Matrix-geometric solutions in stochastic models: an algorithmic approach. Baltimore: The Johns Hopkins University Press.NGMN. NGMN Radio Access Performance Evaluation Methodology, January 2008.NGMN. www.ngmn.org .Prehofer, C., & Bettstetter, C. (2005). Self-organization in communication networks: principles and design paradigms. IEEE Communications Magazine, 43(7), 78–85.Ramjee, R., Nagarajan, R., & Towsley, D. (1997). On optimal call admission control in cellular networks. Wireless Networks, 3(1), 29–41.Siwko, J., & Rubin, I. (2001). Call admission control for capacity-varying networks. Telecommunications Systems, 16(1–2), 15–40.SOCRATES. www.fp7-socrates.eu .Spaey, K., Sas, B., & Blondia, C. (2010). Self-optimising call admission control for LTE downlink. In COST 2100 TD(10)10056, Joint Workshop COST 2100 SWG 3.1 & FP7-ICT-SOCRATES, Athens, Greece.Spilling, A. G., Nix, A. R., Beach, M. A., & Harrold, T. J. (2000). Self-organisation in future mobile communications. Electronics & Communication Engineering Journal, 3, 133
- …