Temperature-hydrostatic pressure cross-sensitivity effect in elliptical-core, highly birefringent fibers

We determine the cross-sensitivity coefficient that represents the interaction between temperature and hydrostatic pressure in Corning elliptical-core, highly birefringent (HB) fiber. The measurement method we propose is especially useful to determine the cross-sensitivity effects in weakly sensitive HB fibers. The method involves registration of the residual temperature drift of thermally compensated polarimetric sensors at specially chosen values of pressure applied to the sensing fibers.     

System innovations in open WDM DCI networks

Photonic Network Communications - We review the most important WDM system innovations motivated by the evolution of DCI transport. State-of-the-art coherent transmission has already exceeded 6...     

Impact of Morphology on Charge Carrier Transport and Thermoelectric Properties of N‐Type FBDOPV‐Based Polymers

The impact of the chemical structure and molecular order on the charge transport properties of two donor–acceptor copolymers in their neutral and doped states is investigated. Both polymers comprise 3,7‐bis((E)‐7‐fluoro‐1‐(2‐octyl‐dodecyl)‐2‐oxoindolin‐3‐ylidene)‐3,7‐dihydrobenzo[1,2‐b:4,5‐b′]difuran‐2,6‐dione (FBDOPV) as electron‐accepting unit, copolymerized with 9,9‐dioctyl‐fluorene (P(FBDOPV‐F)) or with 3‐dodecyl‐2,2′‐bithiophene (P(FBDOPV‐2T‐C₁₂)). These copolymers possess an amorphous and semi‐crystalline nature, respectively, and exhibit remarkable electron mobilities of 0.065 and 0.25 cm² V^–1 s^–1 in field effect transistors. However, after chemical n‐doping with 4‐(1,3‐dimethyl‐2,3‐dihydro‐1H‐benzoimidazol‐2‐yl)phenyl)dimethylamine (N‐DMBI), electrical conductivities four orders of magnitude higher can be achieved for P(FBDOPV‐2T‐C₁₂) (σ = 0.042 S cm^?1). More charge‐transfer complexes are formed between P(FBDOPV‐F) and N‐DMBI, but the highly localized polaronic states poorly contribute to the charge transport. Doped P(FBDOPV‐2T‐C₁₂) exhibits a negative Seebeck coefficient of –265 µV K^?1 and a thermoelectric power factor (PF) of 0.30 µW m^?1 K^?2 at 303 K which increases to 0.72 µW m^?1 K^?2 at 388 K. The in‐plane thermal conductivity (κ_|| = 0.53 W m^?1 K^?1) on the same micrometer‐thick solution‐processed film is measured, resulting in a figure of merit (ZT) of 5.0 × 10^?4 at 388 K. The results provide important design guidelines to improve the doping efficiency and thermoelectric properties of n‐type organic semiconductors.     

Tunable Dopants with Intrinsic Counterion Separation Reveal the Effects of Electron Affinity on Dopant Intercalation and Free Carrier Production in Sequentially Doped Conjugated Polymer Films

Carrier mobility in doped conjugated polymers is limited by Coulomb interactions with dopant counterions. This complicates studying the effect of the dopant's oxidation potential on carrier generation because different dopants have different Coulomb interactions with polarons on the polymer backbone. Here, dodecaborane (DDB)‐based dopants are used, which electrostatically shield counterions from carriers and have tunable redox potentials at constant size and shape. DDB dopants produce mobile carriers due to spatial separation of the counterion, and those with greater energetic offsets produce more carriers. Neutron reflectometry indicates that dopant infiltration into conjugated polymer films is redox‐potential‐driven. Remarkably, X‐ray scattering shows that despite their large 2‐nm size, DDBs intercalate into the crystalline polymer lamellae like small molecules, indicating that this is the preferred location for dopants of any size. These findings elucidate why doping conjugated polymers usually produces integer, rather than partial charge transfer: dopant counterions effectively intercalate into the lamellae, far from the polarons on the polymer backbone. Finally, it is shown that the IR spectrum provides a simple way to determine polaron mobility. Overall, higher oxidation potentials lead to higher doping efficiencies, with values reaching 100% for driving forces sufficient to dope poorly crystalline regions of the film.     

Similarity measures,penalty functions,and fuzzy entropy from new fuzzy subsethood measures

In this study, we discuss a new class of fuzzy subsethood measures between fuzzy sets. We propose a new definition of fuzzy subsethood measure as an intersection of other axiomatizations and provide two construction methods to obtain them. The advantage of this new approach is that we can construct fuzzy subsethood measures by aggregating fuzzy implication operators which may satisfy some properties widely studied in literature. We also obtain some of the classical measures such as the one defined by Goguen. The relationships with fuzzy distances, penalty functions, and similarity measures are also investigated. Finally, we provide an illustrative example which makes use of a fuzzy entropy defined by means of our fuzzy subsethood measures for choosing the best fuzzy technique for a specific problem.     

Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces

Graphs are a flexible and general formalism providing rich models in various important domains, such as distributed computing, intelligent tutoring systems or social network analysis. In many cases, such models need to take changes in the graph structure into account, that is, changes in the number of nodes or in the graph connectivity. Predicting such changes within graphs can be expected to yield important insight with respect to the underlying dynamics, e.g. with respect to user behaviour. However, predictive techniques in the past have almost exclusively focused on single edges or nodes. In this contribution, we attempt to predict the future state of a graph as a whole. We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels. The output of the regression is a point embedded in a pseudo-Euclidean space, which can be analyzed using subsequent dissimilarity- or kernel-based processing methods. We discuss strategies to speed up Gaussian processes regression from cubic to linear time and evaluate our approach on two well-established theoretical models of graph evolution as well as two real data sets from the domain of intelligent tutoring systems. We find that simple regression methods, such as kernel regression, are sufficient to capture the dynamics in the theoretical models, but that Gaussian process regression significantly improves the prediction error for real-world data.     

A Visualization‐Based Analysis System for Urban Search & Rescue Mission Planning Support

We propose a visualization system for incident commanders (ICs) in urban search and rescue scenarios that supports path planning in post‐disaster structures. Utilizing point cloud data acquired from unmanned robots, we provide methods for the assessment of automatically generated paths. As data uncertainty and a priori unknown information make fully automated systems impractical, we present the IC with a set of viable access paths, based on varying risk factors, in a 3D environment combined with visual analysis tools enabling informed decision making and trade‐offs. Based on these decisions, a responder is guided along the path by the IC, who can interactively annotate and reevaluate the acquired point cloud and generated paths to react to the dynamics of the situation. We describe visualization design considerations for our system and decision support systems in general, technical realizations of the visualization components, and discuss the results of two qualitative expert evaluation; one online study with nine search and rescue experts and an eye‐tracking study in which four experts used the system on an application case.     

Examining the intended and unintended consequences of organisational privacy safeguards

Research shows that despite organisational efforts to achieve privacy compliance, privacy breaches continue to rise. The extant studies on organisational privacy compliance concentrate on the extent to which privacy threats can be alleviated through a combination of technical and human controls and the positive (and often intended) influences of these controls. This focus inadvertently neglects unintended consequences such as impeded workflow in medical practices. To address this research conflict, this study uses an interpretive grounded theory research approach to investigate the consequences of privacy safeguard enactment in medical practices, including whether it influences their ability to meet privacy requirements and whether workflows are impeded. Our central contribution is a theoretical framework, the unintended consequences of privacy safeguard enactment (UCPSE) framework, which explicates the process by which privacy safeguards are evaluated and subsequently bypassed and the resulting influence on organisational compliance. The UCPSE highlights the importance of the imbalance challenge, which is the result of unintended consequences outweighing the intended consequences of privacy safeguard enactment. Failure to address the imbalance challenge leads to the adoption of workarounds that may ultimately harm the organisation’s privacy compliance. Despite several research calls, the consequences and effectiveness of organisational privacy efforts are largely missing from both information systems and health informatics research. This study is one of the first attempts to both systematically identify the impacts of privacy safeguard enactment and to examine its implications for privacy compliance in the healthcare domain. The findings also have practical implications for healthcare executives on the UCPSE and how they could alleviate the imbalance challenge to thwart workarounds and the subsequent negative effects on privacy compliance.     

Calibration of Stochastic Computer Simulators Using Likelihood Emulation

We calibrate a stochastic computer simulation model of “moderate” computational expense. The simulator is an imperfect representation of reality, and we recognize this discrepancy to ensure a reliable calibration. The calibration model combines a Gaussian process emulator of the likelihood surface with importance sampling. Changing the discrepancy specification changes only the importance weights, which lets us investigate sensitivity to different discrepancy specifications at little computational cost. We present a case study of a natural history model that has been used to characterize UK bowel cancer incidence. Datasets and computer code are provided as supplementary material.