Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

Integrated circuit and system design for renewable energy inverters

This study proposes that a novel integrated circuit (IC) and system design for renewable energy inverters can harvest renewable energy to power direct current (DC) and alternating current (AC) loads. In addition, an intelligent synthesis and management tool is developed to design the proposed system and to judge the system’s operational maintenance decisions. Finally, a renewable energy inverter’s information is posted to an online system. Users can obtain the proposed system’s information at any time and place. The accurate and superior performance of the proposed IC and system is confirmed by computer simulations and experimental results.     

Public-sector reform: Lessons from the privatisation experiment in Greece

This paper explores the process of public sector reform in Greece through the lens of privatisation. It is suggested that the transformation of the domestic post-war interventionist state into the current market-oriented paradigm has been disrupted and dependent on changing reform rationales. Along with the effect of historical dynamics on the direction and scale of change, the explanatory power of external determinants of reform is stressed. Findings reflect a direct linkage between the intensity of external pressure and the pace of reforms. The sustainability of gradual privatisation reforms seems more certain than the outcomes of fast-track privatisations under policy conditionality.     

Remarkably enhanced energy storage properties of lead-free Ba0.53Sr0.47TiO3 thin films capacitors by optimizing bottom electrode thickness

The lead-free Ba_0.53Sr_0.47TiO₃ (BST) thin films buffered with La_0.67Sr_0.33MnO₃ (LSMO) bottom electrode of different thicknesses were fabricated by pulsed laser deposition method on a (001) SrTiO₃ substrate. It was found that the roughness of electrode decreases and substrate stress relaxes gradually with the increase of LSMO thickness, which is beneficial for weakening local high electric field and achieving higher E_b. Therefore, the recoverable energy density (W_rec) of BST films can be greatly improved up to 67.3 %, that is, from 30.6 J/cm³ for the LSMO thickness of 30 nm up to 51.2 J/cm³ for the LSMO thickness of 140 nm after optimizing the LSMO thickness. Furthermore, the thin film capacitor with a 140 nm LSMO bottom electrode shows an outstanding thermal stability from 20 °C to 160 °C and superior fatigue resistance after 10⁸ electrical cycles with only a slightly decrease of W_rec below 1.6 % and 3.7 %, respectively. Our work demonstrates that optimizing bottom electrodes thickness is a promising way for enhancing energy storage properties of thin-film capacitors.     

Novel organic dyes containing N-bridged oligothiophene coplanar cores for dye-sensitized solar cells

Three novel organic dyes adopting fully-fused coplanar heteroarene as the donor moieties end-capped with two cyanoacrylic acids as acceptors and anchoring groups have been synthesized, characterized, and used as the sensitizers for dye-sensitized solar cells (DSSCs). The photophysical and electrochemical properties of the novel dyes and the characteristics of the DSSCs based on the novel organic dyes were investigated. The incorporation of the coplanar cores with electron-donating N-bridges are beneficial for the better intramolecular charge transfer (ICT), giving these new dyes good light-harvesting capability. The LUMO energy levels of these coplanar heteroacene-based dyes are sufficiently high for the efficient electron injection to TiO₂ upon photo-excitation, while the suitable HOMOs allow the regeneration of oxidized dyes with the electrolyte redox (I⁻/I₃⁻). The structural features of the coplanar cores (penta vs. hexa heteroarene) as well as the alkyl substitutions play crucial roles in governing the physical properties and device performance. Among these three novel organic sensitizers, the EHTt dye composed of a fully fused hexa-arene core and less bulky N-alkyl groups caused the DSSC to show the best photovoltaic performance with an open-circuit voltage (V_OC) of 0.58 V, a short-circuit photocurrent density (J_SC) of 13.72 mA/cm², and a fill factor (FF) of 0.69, yielding an overall power conversion efficiency (PCE) of 5.52% under AM 1.5G solar irradiation.     

Heterometallic sulfide cluster [Ag6Sn6S20]10 −: Solvothermal syntheses and characterizations of silver thiostannates with lanthanide complex counter cations

Novel organic hybrid silver thiostannates [Hen]₄[Ln(en)₄]₂[Ag₆Sn₆S₂₀]·3en (Ln = Er, 1; Tm, 2; Yb, 3) were prepared by the reactions of Ln₂O₃, Ag, Sn and S in ethylenediamine (en) under solvothermal conditions. Six SnS₄ tetrahedra and six AgS₃ triangles are connected into the heterometallic sulfide cluster [Ag₆Sn₆S₂₀]^10 − via edge-sharing. In the [Ag₆Sn₆S₂₀]^10 − cluster, a hexanuclear Ag₆S₆ core is enclosed by two Sn₃S₁₀ fragments. The Ag₆S₆ core is the first As–S cluster stabilized by inorganic SnS₄ ligands. In 1–3, all Ln^3 + ions are in 8-fold coordination environments that involved four bidentate en ligands, forming bicapped trigonal prisms. Compounds 1–3 show well-defined absorption edges with band gaps in the range of 2.18–2.47 eV.     

Mathematical representation of radiality constraint in distribution system reconfiguration problem

Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study.     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.