Assessment of Sustainable Development

The objective of this paper is to introduce fuzzy set theory and develop fuzzy mathematical models to assess sustainable development based on context-dependent economic, ecological, and societal sustainability indicators. Membership functions are at the core of fuzzy models, and define the degree to which indicators contribute to development. Although a decision-making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic propositions, to numerical data, expressed in measurements of sustainability indicators. In the future, practical implementation of such models will be based on elicitation of expert knowledge to construct a membership function. The fuzzy models developed in this paper provide a novel approach to support decisions regarding sustainable development.agriculture;assessment;fuzzy set theory;sustainable development

Stable divisorial gonality is in NP

Divisorial gonality and stable divisorial gonality are graph parameters, which have an origin in algebraic geometry. Divisorial gonality of a connected graph $G$ can be defined with help of a chip firing game on $G$. The stable divisorial gonality of $G$ is the minimum divisorial gonality over all subdivisions of edges of $G$. In this paper we prove that deciding whether a given connected graph has stable divisorial gonality at most a given integer $k$ belongs to the class NP. Combined with the result that (stable) divisorial gonality is NP-hard by Gijswijt, we obtain that stable divisorial gonality is NP-complete. The proof consist of a partial certificate that can be verified by solving an Integer Linear Programming instance. As a corollary, we have that the number of subdivisions needed for minimum stable divisorial gonality of a graph with $n$ vertices is bounded by $2^{p(n)}$ for a polynomial $p$

Quantized spin wave modes in magnetic tunnel junction nanopillars

We present an experimental and theoretical study of the magnetic field dependence of the mode frequency of thermally excited spin waves in rectangular shaped nanopillars of lateral sizes 60x100, 75x150, and 105x190 nm2, patterned from MgO-based magnetic tunnel junctions. The spin wave frequencies were measured using spectrally resolved electrical noise measurements. In all spectra, several independent quantized spin wave modes have been observed and could be identified as eigenexcitations of the free layer and of the synthetic antiferromagnet of the junction. Using a theoretical approach based on the diagonalization of the dynamical matrix of a system of three coupled, spatially confined magnetic layers, we have modeled the spectra for the smallest pillar and have extracted its material parameters. The magnetization and exchange stiffness constant of the CoFeB free layer are thereby found to be substantially reduced compared to the corresponding thin film values. Moreover, we could infer that the pinning of the magnetization at the lateral boundaries must be weak. Finally, the interlayer dipolar coupling between the free layer and the synthetic antiferromagnet causes mode anticrossings with gap openings up to 2 GHz. At low fields and in the larger pillars, there is clear evidence for strong non-uniformities of the layer magnetizations. In particular, at zero field the lowest mode is not the fundamental mode, but a mode most likely localized near the layer edges.Comment: 16 pages, 4 figures, (re)submitted to PR

The formation of organizational reputation

In this article, we review four decades of research on the formation of organizational reputation. Our review reveals six perspectives that have informed past studies: a game theoretic, a strategic, a macro-cognitive, a micro-cognitive, a cultural-sociological, and communicative one. We compare and contrast the different assumptions about what reputation is and how it forms that characterize these perspectives, and we discuss the implications of these differences for our theoretical understanding of stability and change, control and contestation, and the micro-macro relationship in the complex process of reputation formation

Audio-visual interaction in emotion perception for communication

Information from multiple modalities contributes to recognizing emotions. While it is known interactions occur between modalities, it is unclear what characterizes these. These interactions, and changes in these interactions due to sensory impairments, are the main subject of this PhD project. This extended abstract for the Doctoral Symposium of ETRA 2018 describes the project; its background, what I hope to achieve, and some preliminary results.</p

Deep Learning-Based Natural Language Processing in Radiology:The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance

In radiology, natural language processing (NLP) allows the extraction of valuable information from radiology reports. It can be used for various downstream tasks such as quality improvement, epidemiological research, and monitoring guideline adherence. Class imbalance, variation in dataset size, variation in report complexity, and algorithm type all influence NLP performance but have not yet been systematically and interrelatedly evaluated. In this study, we investigate these factors on the performance of four types [a fully connected neural network (Dense), a long short-term memory recurrent neural network (LSTM), a convolutional neural network (CNN), and a Bidirectional Encoder Representations from Transformers (BERT)] of deep learning-based NLP. Two datasets consisting of radiologist-annotated reports of both trauma radiographs (n = 2469) and chest radiographs and computer tomography (CT) studies (n = 2255) were split into training sets (80%) and testing sets (20%). The training data was used as a source to train all four model types in 84 experiments (Fracture-data) and 45 experiments (Chest-data) with variation in size and prevalence. The performance was evaluated on sensitivity, specificity, positive predictive value, negative predictive value, area under the curve, and F score. After the NLP of radiology reports, all four model-architectures demonstrated high performance with metrics up to > 0.90. CNN, LSTM, and Dense were outperformed by the BERT algorithm because of its stable results despite variation in training size and prevalence. Awareness of variation in prevalence is warranted because it impacts sensitivity and specificity in opposite directions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10916-021-01761-4

Quantum dot encapsulation in virus-like particles with tuneable structural properties and low toxicity

A simple method for the encapsulation of quantum dots (QDs) in virus-like particle (VLP) nanoassemblies with tuneable structural properties and enhanced biocompatibility is presented. Cowpea chlorotic mottle virus-based capsid proteins assemble around the carboxylated QDs to form QD/VLP nanoassemblies of different capsid size as a function of pH and ionic strength. Detailed structural characterizations verify that nanoassemblies with probably native capsid icosahedral symmetry (T = 3) are obtained at low pH and high ionic strength (pH 5.0, 1.0 M NaCl), whereas high pH and low ionic strength conditions (pH 7.5, 0.3 M NaCl) result in the formation of smaller assembly sizes similar to T = 1 symmetry. In vitro studies reveal that QD/VLP nanoassemblies are efficiently internalized by RAW 264.7 macrophages and HeLa cells with no signs of toxicity at QD concentrations exceeding the potentially-toxic levels. The presented route holds great promise for preparation of size-tuneable, robust, non-toxic luminescent probes for long term cellular imaging applications. Furthermore, thanks to the possibility of chemical and genetic manipulation of the viral protein shell encaging the QDs, the nanoassemblies have potential for in vivo targeting applications