The selection strategy of ammonium-group organic salts in vapor deposited perovskites: From dimension regulation to passivation

Nano Energy ◽  
2021 ◽  
Vol 84 ◽  
pp. 105893
Author(s):  
Dongxu Lin ◽  
Xin Xu ◽  
Tiankai Zhang ◽  
Nana Pang ◽  
Jiming Wang ◽  
...  
Keyword(s):  
Selection Strategy ◽  
Organic Salts ◽  
Ammonium Group

On the reliability of volume-based thermodynamics for inorganic-organic salts and coordination compounds with uncharged ligands

2017 ◽  
Author(s):  
Robson de Farias
Keyword(s):  
Coordination Compound ◽  
Coordination Compounds ◽  
Formation Enthalpy ◽  
Lattice Energy ◽  
Chemical Hardness ◽  
Acid Anion ◽  
Organic Salts ◽  
Density Values ◽  
Lattice Energies

In the present work, the reliability of the volume-based thermodynamics (VBT) methods in the calculation of lattice energies is investigated by applying the “traditional” Kapustinskii equation [8], as well as Glasser-Jenkins [3] and Kaya [5] equations to calculate the lattice energies for Na, K and Rb pyruvates [9-11] as well as for the coordination compound [Bi(C<sub>7</sub>H<sub>5</sub>O<sub>3</sub>)<sub>3</sub>C<sub>12</sub>H<sub>8</sub>N<sub>2</sub>] [17] (in which C<sub>12</sub>H<sub>8</sub>N<sub>2</sub> = 1,10 phenathroline and C<sub>7</sub>H<sub>5</sub>O<sub>3</sub><sup>-</sup>= <i>o</i>-hyddroxybenzoic acid anion). As comparison, the lattice energies are also calculated using formation enthalpy values for sodium pyrivate and [Bi(C<sub>7</sub>H<sub>5</sub>O<sub>3</sub>)<sub>3</sub>C<sub>12</sub>H<sub>8</sub>N<sub>2</sub>]. For the pyruvates, is verified that none of the considered approach, Kapustinskii, Glasser, Kaya or density, provides values that agrees in an acceptable % difference, with the lattice energy values calculated from the formation enthalpy values. However, it must be pointed out that Kaya approach, with deals with a chemical hardness approach is the better one for such kind of inorganic-organic salts. Based on data obtained for [Bi(C<sub>7</sub>H<sub>5</sub>O<sub>3</sub>)<sub>3</sub>C<sub>12</sub>H<sub>8</sub>N<sub>2</sub>] is concluded that the only one VBT method that provides reliable lattice energies for compounds with bulky uncharged ligands is that one based on density values (derived by Glasser-Jenkins).

Optimal Route Selection Strategy in Wireless Mesh Networks

2018 ◽  
Vol 6 (1) ◽  
pp. 238-243
Author(s):  
Pushpender Sarao ◽  
◽  
T. Raghavendra Gupta ◽  
S. Suresh ◽  
◽  
...  
Keyword(s):  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Selection Strategy ◽  
Route Selection ◽  
Optimal Route ◽  
Wireless Mesh

Identifying the Predictors of Self-Management Behaviors in Patients with Diabetes Based on Ecological Approach: A Systematic Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahnaz Davari ◽  
Hamed Rezakhani Moghaddam ◽  
Aghil Habibi Soola
Keyword(s):  
Scientific Information ◽  
The Self ◽  
Self Management ◽  
Ecological Approach ◽  
Selection Strategy ◽  
Diabetic Patients ◽  
Personal Factors ◽  
Management Interventions ◽  
Number Of Patients ◽  
Management Behaviors

Background: Recognizing and promoting the factors that affect the self-management behaviors of diabetes leads to a reduction in the number of patients and an improvement in the quality of care. The ecological approach focuses on the nature of people's interactions with their physical and socio-cultural environments. Objective: The purpose of this study was to identify the predictors of self-management behaviors with a comprehensive approach in these patients. Methods: The Keywords were investigated in the relevant national and international databases, including PubMed, Google Scholar, Science Direct, Scopus, and Scientific Information Database, Magiran, and Iran Medex to obtain the articles published from 2009 to 2019. The search and article selection strategy was developed based on the Prisma checklist and was carried out in three steps. Results: Most studies have shown that personal factors had the highest prediction power for the self-management of diabetes. Then, the interpersonal factors, society and policy-making factors, and group and organization factors were most frequently reported predictors of self-management behaviors in diabetic patients. Conclusion: Self-management of diabetes is necessary for controlling it because 95% of care is done by the patient. When designing self-management interventions, factors based on the individual level that increasing self-management behaviors should be taken into account.

User-Driven Network Selection during Vertical Handover in 4G Networks

2017 ◽  
Vol 10 ◽  
pp. 1
Author(s):  
Abubakar Muhammad Miyim ◽  
Mahamod Ismail ◽  
Rosdiadee Nordin
Keyword(s):  
Blocking Probability ◽  
Service Selection ◽  
Vertical Handover ◽  
Network Selection ◽  
Selection Strategy ◽  
Intelligent Network ◽  
Decision Algorithm ◽  
Radio Access ◽  
Service Application ◽  
Multi Access

The importance of network selection for wireless networks, is to facilitate users with various personal wireless devices to access their desired services via a range of available radio access networks. The inability of these networks to provide broadband data service applications to users poses a serious challenge in the wireless environment. Network Optimization has therefore become necessary, so as to accommodate the increasing number of users’ service application demands while maintaining the required quality of services. To achieve that, the need to incorporate intelligent and fast mechanism as a solution to select the best value network for the user arises. This paper provides an intelligent network selection strategy based on the user- and network-valued metrics to suit their preferences when communicating in multi-access environment. A user-driven network selection strategy that employs Multi-Access Service Selection Vertical Handover Decision Algorithm (MASS-VHDA) via three interfaces; Wi-Fi, WiMAX and LTE-A is proposed, numerically evaluated and simulated. The results from the performance analysis demonstrate some improvement in the QoS and network blocking probability to satisfy user application requests for multiple simultaneous services.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

scholarly journals Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments

2021 ◽  
Vol 11 (13) ◽  
pp. 5963
Author(s):  
Phuc Thanh-Thien Nguyen ◽  
Shao-Wei Yan ◽  
Jia-Fu Liao ◽  
Chung-Hsien Kuo
Keyword(s):  
Robot Navigation ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Mobile Robot Navigation ◽  
Selection Strategy ◽  
Localization And Mapping ◽  
Pure Pursuit ◽  
Stable Performance ◽  
Large Curve ◽  
Moving Speed

In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV’s moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

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