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  Time-resolved crystallography using the Hadamard transform

  Yorke, Briony A; Beddard, G.S.; Owen, R.L.; Pearson, A.R. (2014-11)

  We describe a method for performing time-resolved X-ray crystallographic experiments based on the Hadamard transform, in which time resolution is defined by the underlying periodicity of the probe pulse sequence, and signal/noise is greatly improved over that for the fastest pump-probe experiments depending on a single pulse. This approach should be applicable on standard synchrotron beamlines and will enable high-resolution measurements of protein and small-molecule structural dynamics. It is also applicable to other time-resolved measurements where a probe can be encoded, such as pump-probe spectroscopy.
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  Tracking time evolving data streams for short-term traffic forecasting

  Abdullatif, Amr R.A.; Masulli, F.; Rovetta, S. (2017-09)

  Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.
• Clustering of nonstationary data streams: a survey of fuzzy partitional methods

  Abdullatif, Amr R.A.; Masulli, F.; Rovetta, S. (2018-07)

  Data streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift). Clustering is often one of the earliest and most important steps in the streaming data analysis workflow. A comprehensive literature is available about stream data clustering; however, less attention is devoted to the fuzzy clustering approach, even though the nonstationary nature of many data streams makes it especially appealing. This survey discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift.
• Ghana’s child panels: effective child protection and juvenile justice system or superfluous creation?

  Adu-Gyamfi, Jones (2019-12)

  In accordance with the United Nations’ requirements for dealing with juvenile offenders, Ghana’s Children Act 1998 mandated local authorities to establish child panels to mediate minor offences committed by children. However, to date there has not been any research that has examined the functioning and effectiveness of the child panels. This research examined the operationalisation and effectiveness of child panels in Ghana. The study involved the use of semi-structured interviews and focus group discussions with panel members of four local authorities. Findings showed that the child panels are not functioning effectively in Ghana. The relevance of the child panels has been questioned since it was found to be duplicating the roles of some other child welfare agencies. This article discusses the challenges impeding the effectiveness of the child panels and outlines recommendations to improve their effectiveness.
• Measuring the efficiency of two stage network processes: a satisficing DEA approach

  Mehdizadeh, S.; Amirteimoori, A.; Vincent, Charles; Behzadi, M.H.; Kordrostami, S. (2020)

  Regular Network Data Envelopment Analysis (NDEA) models deal with evaluating the performance of a set of decision-making units (DMUs) with a two-stage construction in the context of a deterministic data set. In the real world, however, observations may display a stochastic behavior. To the best of our knowledge, despite the existing research done with different data types, studies on two-stage processes with stochastic data are still very limited. This paper proposes a two-stage network DEA model with stochastic data. The stochastic two-stage network DEA model is formulated based on the satisficing DEA models of chance-constrained programming and the leader-follower concepts. According to the probability distribution properties and under the assumption of the single random factor of the data, the probabilistic form of the model is transformed into its equivalent deterministic linear programming model. In addition, the relationship between the two stages as the leader and the follower, respectively, at different confidence levels and under different aspiration levels, is discussed. The proposed model is further applied to a real case concerning 16 commercial banks in China in order to confirm the applicability of the proposed approach at different confidence levels and under different aspiration levels.

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