Source Selection Strategy Research Articles

Recent Advances in Biomass Waste Derived Carbon Materials for Supercapacitors: A Review

As the core component of supercapacitors (SCs), the electrode material plays a decisive role in performance. Among electrode materials, carbon materials are the most widely used due to high porosity and specific surface area (SSA, 1000–3000 m2 g−1). Biomass carbon stands out in carbon materials because of its wide source, low cost, and less pollution. This work reviews the recent advances on carbon materials derived from biomass waste for SCs and explores the effects of different biomass sources including plant, animal, and aquatic organism waste on the performance of biomass waste‐derived carbon materials (BWDCs). Because of its high conductivity (1.25 × 103 ≈ 3 × 105 S m−1), power density (>10 kW kg−1), and extended cycle life (>10 000 cycles), BWDCs are advancing quickly in energy storage. It also discusses different methods and optimization pathways of BWDCs thoroughly. Finally, the challenges and future prospects for BWDCs are summarized. Overall, this critical will help researchers to gain a better understanding of source selection, structural classification, and research strategies for BWDCs, thereby promoting the application of BWDCs in electrochemistry and resource utilization.

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

In the existing medical knowledge graphs, there are problems concerning inadequate knowledge discovery strategies and the use of single sources of medical data. Therefore, this paper proposed a research method for multi-data-source medical knowledge graphs based on the data, information, knowledge, and wisdom (DIKW) system to address these issues. Firstly, a reliable data source selection strategy was used to assign priorities to the data sources. Secondly, a two-step data fusion strategy was developed to effectively fuse the processed medical data, which is conducive to improving the quality of medical knowledge graphs. The proposed research method is for the design of a multi-data-source medical knowledge graph based on the DIKW system. The method was used to design a set of DIK three-layer knowledge graph architectures according to the DIKW system in line with the medical knowledge discovery strategy, employing a scientific method for expanding and updating knowledge at each level of the knowledge graph. Finally, question and answer experiments were used to compare the two different ways of constructing knowledge graphs, validating the effectiveness of the two-step data fusion strategy and the DIK three-layer knowledge graph.

A three-stage transfer learning framework for multi-source cross-project software defect prediction

Transfer learning techniques have been proved to be effective in the field of Cross-project defect prediction (CPDP). However, some questions still remain. First, the conditional distribution difference between source and target projects has not been considered. Second, facing multiple source projects, most studies only rarely consider the issues of source selection and multi-source data utilization; instead, they use all available projects and merge multi-source data together to obtain one final dataset. To address these issues, in this paper, we propose a three-stage weighting framework for multi-source transfer learning (3SW-MSTL) in CPDP. In stage 1, a source selection strategy is needed to select a suitable number of source projects from all available projects. In stage 2, a transfer technique is applied to minimize marginal differences. In stage 3, a multi-source data utilization scheme that uses conditional distribution information is needed to help guide researchers in the use of multi-source transferred data. First, we have designed five source selection strategies and four multi-source utilization schemes and chosen the best one to be used in stage 1 and 3 in 3SW-MSTL by comparing their influences on prediction performance. Second, to validate the performance of 3SW-MSTL, we compared it with four multi-source and six single-source CPDP methods, a baseline within-project defect prediction (WPDP) method, and two unsupervised methods on the data from 30 widely used open-source projects. Through experiments, bellwether and weighted vote are separately chosen as a source selection strategy and a multi-source utilization scheme used in 3SW-MSTL. And, our results indicate that 3SW-MSTL outperforms four multi-source, six single-source CPDP methods and two unsupervised methods. And, 3SW-MSTL is comparable to the WPDP method. The proposed 3SW-MSTL model is more effective for considering the two issues mentioned before.

Mirror, Mirror on the Wall, Who is the Most Autonomous of Them All?

Learner Autonomy is a key element in the field of education, and the number of academic studies in the field has considerably increased recently. Educators have also attached importance to classroom implementations to raise their learners’ autonomy over time. In this classroom action research study at a university in Turkey, I aimed to present the findings to this question: What is my learners’ level of awareness regarding their own learning and study habits? This article will present the results of this classroom inquiry based on a portfolio task that asked learners to prepare an oral report about their study habits. Data were collected through student notes, my notes on the whiteboard, and observations throughout the task. The thematic analysis method was used to analyze patterns in the data. The results suggest that learners are good at accessing study materials according to their identified needs. Although they have some effective strategies for how to learn, these strategies are outnumbered by source selection strategies in the students’ suggestions list. This article may be useful to those who are interested in promoting learner autonomy through classroom activities.

Ghost Elimination via Multi-Component Collaboration for Unmanned Aerial Vehicle Remote Sensing Image Stitching

Ghosts are a common phenomenon widely present in unmanned aerial vehicle (UAV) remote sensing image stitching that seriously affect the naturalness of stitching results. In order to effectively remove ghosts and produce visually natural stitching results, we propose a novel image stitching method that can identify and eliminate ghosts through multi-component collaboration without object distortion, segmentation or repetition. Specifically, our main contributions are as follows: first, we propose a ghost identification component to locate a potential ghost in the stitching area; and detect significantly moving objects in the two stitched images. In particular, due to the characteristics of UAV shooting, the objects in UAV remote sensing images are small and the image quality is poor. We propose a mesh-based image difference comparison method to identify ghosts; and use an object tracking algorithm to accurately correspond to each ghost pair. Second, we design an image information source selection strategy to generate the ghost replacement region, which can replace the located ghost and avoid object distortion, segmentation and repetition. Third, we find that the process of ghost elimination can produce natural mosaic images by eliminating the ghost caused by initial blending with selected image information source. We validate the proposed method on VIVID data set and compare our method with Homo, ELA, SPW and APAP using the peak signal to noise ratio (PSNR) evaluation indicator.

Reducing Calibration Efforts in RSVP Tasks With Multi-Source Adversarial Domain Adaptation

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this article, we propose a multi-source conditional adversarial domain adaptation with the correlation metric learning (mCADA-C) framework that utilizes data from other subjects to reduce the data requirement from the new subject for training the model. This model utilizes adversarial training to enable a CNN-based feature extraction network to extract common features from different domains. A correlation metric learning (CML) loss is proposed to constrain the correlation of features based on class and domain to maximize the intra-class similarity and minimize inter-class similarity. Also, a multi-source framework with a source selection strategy is adopted to integrate the results of multiple domain adaptation. We constructed an RSVP-based dataset that includes 11 subjects each performing three RSVP experiments on three different days. The experimental results demonstrate that our proposed method can achieve 87.72% cross-subject balanced-accuracy under one block calibration. The results indicate our method can realize a higher performance with less calibration efforts.

A Transfer Learning Framework for RSVP-based Brain Computer Interface.

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we collect an RSVP-based electroencephalogram (EEG) dataset, which includes 11 subjects. The experimental task is image retrieval. Also, we propose a multi-source transfer learning framework by utilizing data from other subjects to reduce the data requirement on the new subject for training the model. A source-selection strategy is firstly adopted to avoid negative transfer. And then, we propose a transfer learning network based on domain adversarial training. The convolutional neural network (CNN)-based network is designed to extract common features of EEG data from different subjects, while the discriminator tries to distinguish features from different subjects. In addition, a classifier is added for learning semantic information. Also, conditional information and gradient penalty are added to enable stable training of the adversarial network and improve performance. The experimental results demonstrate that our proposed method outperforms a series of state-of-the-art and baseline approaches.

A Time-Varying Connectivity Analysis from Distributed EEG Sources: A Simulation Study

Time-varying connectivity analysis based on sources reconstructed using inverse modeling of electroencephalographic (EEG) data is important to understand the dynamic behaviour of the brain. We simulated cortical data from a visual spatial attention network with a time-varying connectivity structure, and then simulated the propagation to the scalp to obtain EEG data. Distributed EEG source modeling using sLORETA was applied. We compared different dipole (representing a source) selection strategies based on their time series in a region of interest. Next, we estimated multivariate autoregressive (MVAR) parameters using classical Kalman filter and general linear Kalman filter approaches followed by the calculation of partial directed coherence (PDC). MVAR parameters and PDC values for the selected sources were compared with the ground-truth. We found that the best strategy to extract the time series of a region of interest was to select a dipole with time series showing the highest correlation with the average time series in the region of interest. Dipole selection based on power or based on the largest singular value offer comparable alternatives. Among the different Kalman filter approaches, the use of a general linear Kalman filter was preferred to estimate PDC based connectivity except when only a small number of trials are available. In the latter case, the classical Kalman filter can be an alternative.

Archives and Inference: Documentary Evidence in Case Study Research and the Debate over U.S. Entry into World War II

Did Franklin Delano Roosevelt escalate confliict with Japan and Germany before Pearl Harbor, or did he attempt to avoid war? To what extent did U.S. public opinion influence these decisions? And, crucially, how do we know? Scholars offer diametrically opposed analyses of this historical case, bearing directly on international relations theories regarding the effects of democracy on war and foreign policy. In this debate and the broader security studies field, scholars increasingly employ published and archival primary sources. Because researchers lack a clear template for descriptive and causal inference with documentary evidence, though, such work is indeterminate and ultimately unpersuasive. How can political scientists approach archives and primary documents more effectively and efficiently? Above all, case studies need stronger research designs and clearer source selection strategies, not just more authoritative documents. A critical review of the sources cited in recent scholarship in the debate leading to the United States' entry into World War II, and a replication analysis of a key portion of the documentary record, underscores this need for improved research design and buttresses eight guidelines for the selection and analysis of textual evidence in case study research.

Dynamic data source selection for humanistic culture integrated information retrieval

One of the core technologies of data source selection is to construct summary. When the data source content updates, it will inevitably lead to changes in the data source summary. The calculation work load to completely update the summary is huge, so a partial update strategy is used to improve the efficiency. In order to enhance the efficiency of tourism cultural information integration, we propose a new dynamic data source selection strategy. Firstly, we build a summary based on the relationship between people and characteristic words. Then, we design a local summary updating algorithm based on sampling technique. Finally, we propose a scoring method of data source base on the gain of cultural content of a single or synthetic character for integration purposes. We conduct a number of experiments based on field data collection and the results showed that compared with the existing update algorithms, our method has higher accuracy.

Dynamic data source selection for humanistic culture integrated information retrieval

One of the core technologies of data source selection is to construct summary. When the data source content updates, it will inevitably lead to changes in the data source summary. The calculation work load to completely update the summary is huge, so a partial update strategy is used to improve the efficiency. In order to enhance the efficiency of tourism cultural information integration, we propose a new dynamic data source selection strategy. Firstly, we build a summary based on the relationship between people and characteristic words. Then, we design a local summary updating algorithm based on sampling technique. Finally, we propose a scoring method of data source base on the gain of cultural content of a single or synthetic character for integration purposes. We conduct a number of experiments based on field data collection and the results showed that compared with the existing update algorithms, our method has higher accuracy.

An Opportunistic Source Selection Spectrum Sharing Protocol for Overlay Cognitive Two-Way Relaying System

In this paper, an overlay two-way relaying spectrum sharing protocol is proposed by employing an opportunistic source selection strategy. In this protocol, a secondary user acts as a relay and allocates a fraction of its transmit power to assist two-way communication between two primary users (PUs), thus, gaining spectrum sharing as long as without degrading the primary system performance. Moreover, to exploit the multiuser diversity inherent in the multiuser primary systems, the two PUs transmit their own messages in an opportunistic manner, depending on the instantaneous channel conditions. Specifically, at each transaction, only one of the PUs is selected for transmission such that the instantaneous mutual information on the whole primary network is maximized and as a result, the multiuser diversity gain is achieved by the primary system. Then, we derive the exact closed-form outage probabilities for both the primary and secondary systems. To gain more insights, closed-form asymptotic outage probability expressions are also derived. Based on the approximations, we further obtain analytical results on the diversity order of the primary system and the power-allocation threshold above which the spectrum sharing is allowed for secondary transmission. Numerical results are presented to verify our analysis and highlight the advantages of our proposed scheme compared to another two-phase spectrum sharing protocol based on analog network coding.

USEtox characterisation factors for textile chemicals based on a transparent data source selection strategy

PurposeLife cycle assessments (LCAs) of textile products which do not include the use and emission of textile chemicals, such as dyes, softeners and water-repellent agents, will give non-comprehensive results for the toxicity impact potential. The purpose of this paper is twofold: (1) to provide a set of characterisation factors (CFs) for some of the most common textile chemicals and (2) to propose a data source selection strategy in order to increase transparency when calculating new CFs.MethodsA set of 72 common textile-related substances was matched with the USEtox 2.01, USEtox 1.01 and the COSMEDE databases in order to investigate coverage and coherence. For the 25 chemicals that did not already have established CFs in any of these databases, new CFs were calculated. A data source selection strategy was developed and followed in order to ensure consistency and transparency, and USEtox 2.01 was used for calculations. The parameters that caused the most uncertainty were identified during the modelling and strategies for handling them were developed.Results and discussionOf the 72 textile-related substances, 48 already had calculated recommended or indicative CFs in existing databases, which showed good coherence. The main uncertainty identified during the calculation of 25 new CFs was the selection of input data regarding toxicity and degradation in water. However, for substances such as per- and polyfluoroalkyl substances (PFAS), the acid dissociation constant (pKa) and partitioning coefficients (Kow and KOC) also require special considerations. Other input parameters had less than one order of magnitude impact on the CF result for essentially all substances.ConclusionsThe paper presents a strategy for how to provide a complete set of toxicity CFs for a given list of substances. In addition, such a set of CFs for common textile-related substances is presented. The data source selection strategy provides a structured and transparent way of calculating additional CFs for textile chemicals with USEtox. Consequently, this study can help future LCA studies to provide relevant guidance towards environmentally benign chemical management in the textile industry.

Combining heterogeneous sources in an interactive multimedia content retrieval model

Interactive multimodal information retrieval systems (IMIR) increase the capabilities of traditional search systems, by adding the ability to retrieve information of different types (modes) and from different sources. This article describes a formal model for interactive multimodal information retrieval. This model includes formal and widespread definitions of each component of an IMIR system. A use case that focuses on information retrieval regarding sports validates the model, by developing a prototype that implements a subset of the features of the model. Adaptive techniques applied to the retrieval functionality of IMIR systems have been defined by analysing past interactions using decision trees, neural networks, and clustering techniques. This model includes a strategy for selecting sources and combining the results obtained from every source. After modifying the strategy of the prototype for selecting sources, the system is re-evaluated using classification techniques. This evaluation compares the normalised discounted cumulative gain (NDCG) measure obtained using two different approaches: the multimodal system using a baseline strategy based on predefined rules as a source selection strategy, and the same multimodal system with the functionality adapted by past user interactions. In the adapted system, a final value of 81,54% was obtained for the NDCG.

Maximizing Network Capacity with Optimal Source Selection: A Network Science Perspective

How to enhance the network capacity is one of the most important issues. To achieve this, the existing works have focused on improving either the network structure or routing strategies with a common assumption of uniformly distributing the replicas of information among the nodes in the network. The nodes associated with information replicas are considered as source nodes (or server). However, for many networks such as the Internet, some nodes have much more traffics than the others, exhibiting an asymmetric phenomenon. In this letter, we study the optimal source selection strategy to enhance the network capacity, where an optimization model is proposed to find the optimal source selection probability distribution. Simulation results show that in homogeneous networks, most of the nodes can be the sources. While in heterogeneous networks such as the scale-free networks, only a small number of the nodes can be the sources. Moreover, an interesting phenomenon is observed that the optimal proportion of source nodes in Erdős-Renyi random network and Barabasi-Albert scale-free network exhibits a power law relationship with the network size.

A model to take advantage of Physical Internet for vendor inventory management

This paper investigates inventory management problems for fast-moving consumer goods (FMCG) with the Physical Internet (interconnected logistic services) where goods are stored and distributed in an interconnected and open network of hubs. Unlike current hierarchical inventory model where the source assignment is predetermined for each replenishment order either from the retailers or from the warehouses, the PI-inventory model enables multiple source selection options to each order and transshipments of inventories among hubs, resulting in complete pooling of inventories within the network. We adopt a continuous review (Q, R) ordering policy for each facility and propose four dynamic source selection strategies related to the supplying nodes’ inventory levels, the distance and lead time between the supplying and the ordering nodes. The objective is to determine the optimal replenishment policies for hubs in order to minimize the total logistic costs of the distribution system. A nonlinear optimization model is proposed and a heuristic using simulated annealing is applied. A simulation study based on two categories of FMCG is taken to evaluate the performance of the proposed inventory control policies. Our results suggest that compared to current centralized inventory control policy, the PI-inventory control model with proposed source selection strategies can significantly reduce the total costs as well as the average inventory level of the network of hubs while reaching the same end customer service level.

Enhancing topology adaptation in information-sharing social networks

The advent of the Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. It has thus become important to address issues like who gets followed and how to allow people to discover new and better information sources. In this paper we conduct an empirical analysis of different online social networking sites and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. These rules create networks which are effective in information diffusion and resemble structures resulting from real social systems.

Outage performance of fixed-gain bidirectional opportunistic relaying in Nakagami-m fading

Assuming an opportunistic source selection strategy, a tight closed-form approximation for the outage probability of fixed-gain bidirectional cooperative networks is derived. The presented analysis allows for independent, reciprocal Nakagami-m fading channels with distinct fading parameters among the links. Simulation results are presented to corroborate the tightness of the proposed approximations.

SCALABLE CATALOG INFRASTRUCTURE FOR MANAGING ACCESS COSTS AND SOURCE SELECTION IN WIDE AREA NETWORKS

A WAN environment, such as the Internet, connects a federation of hundreds of servers with tens of thousands of clients, which poses a substantial scalability challenge. Clients may choose among sources that vary in both their content and quality as well as in their access latencies. At the same time, Internet accessible data sources exhibit transient behavior; the unpredictable behavior of a dynamic WAN results in a wide variability in access cost (end-to-end latency). This motivates a need for a source selection strategy that requires maintaining access cost distributions (latency profiles) for each client/server pair. However, in the presence of hundreds of servers and thousands of clients, managing latency profiles cannot scale. We present a scalable methodology to manage latency profiles that use non-random associations between client/server pairs. Such non-random associations may be identified by topology-independent measures such as correlation and mutual information. We propose a Catalog infrastructure that implements our methodology and utilize non-randomly associated latency profiles to estimate access cost distribution for client/server pairs. We perform an extensive experimental study demonstrating feasibility and efficiency of our approach.

Estimation of multiple fiber orientations from diffusion tensor MRI using independent component analysis

Determination of fiber orientation from diffusion tensor is problematic when the tensor is not linear anisotropic. Particularly planar anisotropy is often an indicator of multiple fibers in a voxel. A novel method has been developed to identify the orientations of multiple fiber tracts in a voxel of diffusion tensor magnetic resonance imaging (MRI) using independent component analysis (ICA). A computationally efficient algorithm to estimate the independent sources has been derived by introducing a new adaptive nonlinear function to model the cumulative distribution of the sources. Monte Carlo simulation was used to evaluate the method. Simulations suggest that the orientations of two tensors in a voxel can be estimated with mean error of less than 10/spl deg/ for most interfiber angles when the signal-to-noise ratio is higher than 30. A processing and source selection strategy has been proposed and successfully tested with simulated tensor fields incorporating fiber crossing and with human data. Qualitative assessment of the result from human data analysis demonstrated that the ICA method reasonably estimated multiple fiber orientations corresponding to anatomically known white matter tracts.