An evaluation tool for backbone extraction techniques in weighted complex networks

The combination of increasing demand for air transportation, high fuel prices and the growing environmental awareness motivates the need for fuel efficiency improvements and emissions reductions of the aviation sector. One potential approach to improve aviation’s fuel and CO2 emissions efficiency is to improve the structure of the air transportation network. This research focuses on alternative topologies for the US air transportation network, passenger routing and fleet composition. In order to identify and evaluate these alternatives, an optimization model was developed were the network topology, routing strategies and aircraft assignment are jointly optimized. It was found that the 2007 US air transportation network exhibits a topology that is close to its optimum and that further topological improvements may only yield a reduction in fuel burn of approximately 1%. Changes in aircraft assignment and alternative airline routing strategies between origin and destination markets appear to provide greater potential for efficiency improvements. It was found that improved passenger routing could decrease fuel burn by over 8% while the utilization of larger aircraft yields a reduction of 10%. These findings suggest that there is limited potential from structural network topological improvements for the US air transportation network. However, strategies and policies that would incentivize the use of larger aircraft and alternative aircraft and passenger routings would provide greater potential for fuel efficiency and climate change emission reductions.

Network industries include a multitude of organizations and companies delivering valuable services and products for modern life. Surprisingly, the impact of the structural complexity of their underlying physical infrastructure on the behavior of these organizations has not been explored yet. The aim of this paper is to study the role of an initial network structure on the throughput of these complex systems, and to provide evidence for the value of using agent-based modeling (ABM) in governing competition and collaboration in network industries. A two-stage multiround game provides the mathematical foundation to examine the behavior of players in the complex interconnected structure of a network industry. The United States Air Transportation Network is used as the case study. The real data on the different elements of the system are embedded into an agent-based model to provide a descriptive model of behaviors within it. The outcome of the model shows path dependence in the system and highlights the impact of initial network conditions on the market. Moreover, this model provides evidence on the usefulness of ABM for understanding the interrelationships between economic behavior and the physical structure of the system.

Modern infrastructures such as transportation and communication networks are large-scale systems with complex dependence structures between various sub-systems. Human-interpretable performance targets in such systems are often represented in terms of lower-dimensional projections of the high-dimensional state space. We consider the problem of designing control strategies for high-dimensional systems that lack a detailed model. To do so, we leverage the ability of copulas to represent dependant structures in high-dimensional data, and approximate the state space model through inverse sampling. We demonstrate the applicability of the control policies obtained from our methodology through a data-driven case study of controlling flight delays within the US air transportation network.

* † ‡† * *† § The Aviation Integrated Model is a policy assessment tool designed to simulate the operation and economic/environmental effects of local and world airline networks over the next 30-50 years within a modular framework. Feedback between demand, capacity, air traffic delays and policy measures is a key part of this model. For example, unconstrained model projections of US air transportation system growth with no passenger, airline or policy response included show average arrival delays of over two hours at major US airports in 2030, a condition that is unlikely to materialize. A more likely situation is that a combination of responses act to bring the air transport system to a new equilibrium at which higher fares, extra capacity or increased operational efficiency reduces the delays to a more acceptable level. Similarly, the application of policies designed to mitigate some of the environmental impacts of air transportation will also alter the system equilibrium state. In this paper we use the Aviation Integrated Model to systematically examine these feedback effects, concentrating specifically on the passenger response to increases in travel time, airfare, and policy responses to environmental concerns. We contrast the reference case in which the main feedback effect is passenger and airline response to air traffic delay with sample policy scenarios.

Airport infrastructure spillovers in a network system

The Open Data Kit (ODK) originated as a google.org initiative where research efforts were focused on applying technology to assist developing countries in the health and environment sectors. ODK is an open-source tool that helps organizations, writers / researchers, field workers, and manage mobile data collection solutions. Its goal is to create open-source and standards-based tools that are easy to try, easy to use, easy to modify and easy to measure.
 This research was conducted to help teachers and Korwil Operators to collect and manage teacher attendance data at each elementary school in Ciater sub-district by utilizing the Open Data Kit as a medium for collecting and managing primary school teacher attendance data. This research was conducted by conducting literature studies, interviews and observations, and studying the use of the Open Data Kit. Furthermore, the stages will be carried out to define the needs of analysis, design and implementation.
 The final result of this research is a presence data collection application that utilizes the Open Data Kit as a medium consisting of ODK Collect as a data collection tool and ODK Aggregate as a tool for managing data.

Planning for civic amenities in a fast-changing urban setting such as Singapore is never an easy task. And as urban planners look toward more data-driven approaches toward urban planning, so grows the demand for more flexible geospatial analytics tools to facilitate a more iterative and granular approach toward urban planning [1]. Such specific tools however, are not always readily available as plugins for traditional desktop GIS software, as numerous customizations must be made to model specific temporal planning scenarios for quick analysis, which could prove both costly and time-consuming. Hence, to address this need, open-source tools such as R Shiny could be used to rapidly prototype and test urban planning models, in an iterative fashion. To demonstrate how this could be done, we developed a proof-of-concept that aims to provide urban planners with an open-source, interactive geospatial analytics tool to help optimize the placement of amenities and services through K-means clustering, making them as accessible as possible to the city residents they serve. The platform also allows planners to compare the accessibility of the existing amenities and services, against a suggested set of optimized amenity locations, using the Hansen Accessibility Score as a measure of accessibility. This allows planners a tangible grasp on how much of an impact and improvement a relocation of amenities could make for the residents served. This paper details our research and development efforts to design and implement an open-source web-based geospatial tool for supporting the analysis of the accessibility of amenities and services.

Abstract. The process-based model 4C (FORESEE) has been developed over the past 20 years to study climate impacts on forests and is now freely available as an open-source tool. The objective of this paper is to provide a comprehensive description of this 4C version (v2.2) for scientific users of the model and to present an evaluation of 4C at four different forest sites across Europe. The evaluation focuses on forest growth as well as carbon (net ecosystem exchange, gross primary production), water (actual evapotranspiration, soil water content), and heat fluxes (soil temperature) using data from the PROFOUND database. We applied different evaluation metrics and compared the daily, monthly, and annual variability of observed and simulated values. The ability to reproduce forest growth (stem diameter and biomass) differs from site to site and is best for a pine stand in Germany (Peitz, model efficiency ME=0.98). 4C is able to reproduce soil temperature at different depths in Sorø and Hyytiälä with good accuracy (for all soil depths ME > 0.8). The dynamics in simulating carbon and water fluxes are well captured on daily and monthly timescales (0.51 < ME < 0.983) but less so on an annual timescale (ME < 0). This model–data mismatch is possibly due to the accumulation of errors because of processes that are missing or represented in a very general way in 4C but not with enough specific detail to cover strong, site-specific dependencies such as ground vegetation growth. These processes need to be further elaborated to improve the projections of climate change on forests. We conclude that, despite shortcomings, 4C is widely applicable, reliable, and therefore ready to be released to the scientific community to use and further develop the model.

Biomedical knowledge graphs (KGs), which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods and non-uniform evaluation metrics. In this work, we established a comprehensive KG system for the biomedical field in an attempt to bridge the gap. Here, we introduced PharmKG, a multi-relational, attributed biomedical KG, composed of more than 500000 individual interconnections between genes, drugs and diseases, with 29 relation types over a vocabulary of ~8000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure and disease word embedding, while preserving the semantic and biomedical features. For baselines, we offered nine state-of-the-art KG embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a KG in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical KG construction, embedding and application.

This work aims to assess standard evaluation practices used by the research community for evaluating medical imaging classifiers, with a specific focus on the implications of class imbalance. The analysis is performed on chest X-rays as a case study and encompasses a comprehensive model performance definition, considering both discriminative capabilities and model calibration. We conduct a concise literature review to examine prevailing scientific practices used when evaluating X-ray classifiers. Then, we perform a systematic experiment on two major chest X-ray datasets to showcase a didactic example of the behavior of several performance metrics under different class ratios and highlight how widely adopted metrics can conceal performance in the minority class. Our literature study confirms that: (1) even when dealing with highly imbalanced datasets, the community tends to use metrics that are dominated by the majority class; and (2) it is still uncommon to include calibration studies for chest X-ray classifiers, albeit its importance in the context of healthcare. Moreover, our systematic experiments confirm that current evaluation practices may not reflect model performance in real clinical scenarios and suggest complementary metrics to better reflect the performance of the system in such scenarios. Our analysis underscores the need for enhanced evaluation practices, particularly in the context of class-imbalanced chest X-ray classifiers. We recommend the inclusion of complementary metrics such as the area under the precision-recall curve (AUC-PR), adjusted AUC-PR, and balanced Brier score, to offer a more accurate depiction of system performance in real clinical scenarios, considering metrics that reflect both, discrimination and calibration performance. This study underscores the critical need for refined evaluation metrics in medical imaging classifiers, emphasizing that prevalent metrics may mask poor performance in minority classes, potentially impacting clinical diagnoses and healthcare outcomes. Common scientific practices in papers dealing with X-ray computer-assisted diagnosis (CAD) systems may be misleading. We highlight limitations in reporting of evaluation metrics for X-ray CAD systems in highly imbalanced scenarios. We propose adopting alternative metrics based on experimental evaluation on large-scale datasets.

The selection of the proper feature extraction method is an essential issue for any Automatic Speech Recognition system. This has been conducted in order to choose the suitable feature extraction method for Arabic phoneme recognition system based Malay speakers. In this paper, the implementation of three feature extraction methods involves Frequency Cepstral Coefficients (MFCC), Linear Predictive Coefficients (LPC) and Perceptual Linear Prediction (PLP) has been done. And each feature extraction method is applied on Arabic phonemes in two cases the first case is; the signal is noisy and the second case is the signal is enhanced. The phoneme signals enhancement is achieved using wiener filter. Feed-Forward Neural Network is implemented as a recognizer. The outcome of this study shows that proposed system can give the highest recognition rate with MFCC. The recognition rate is 95.3% and 98.12% in the case of noisy phoneme signal and enhanced phoneme signal respectively. The evaluation and testing the feature extraction methods were based on Arabic phonemes corpus has collected from Malay speakers.

Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction. In this study, we search optimal feature extraction algorithm by evaluating the performance of different feature extraction algorithms namely Principle Component Analysis (PCA), Sparse PCA, Kernel PCA and Incremental PCA. These algorithms are integrated with machine learning models to improve HF prediction. The performance of all these integrated models are evaluated by analyzing Cleveland heart failure database. Experimental results pointed out that Kernel PCA algorithm integrated with linear discriminant analysis model and Sparse PCA integrated with Gaussian Naive Bayes (GNB) model offers 91.11% of HF classification accuracy. Hence, based on the experimental results it is shown that Kernel PCA and Sparse PCA are suitable feature extraction methods for HF data.

To find a suitable water extraction method from Mapping satellite-1 imagery in urban areas, this paper compared the results of different water extraction methods and studied the effect of building shadow on water extraction. Taken parts of Xinjiang (Beitun of Irtysh river Basin) as the study area, Mapping satellite-1 imagery as data sources, single-band threshold method, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and spectral relationship method based on spectral area were applied respectively to extract information of water body. The results of information extraction by use of the four methods were compared to determine the optimal method for water body extraction. The results showed that, for Mapping satellite-1 imagery, single-band threshold method and spectral relationship method which based on spectral area were effective to eliminate classification errors cased by shadows from buildings, fast, easy and accurate to extract information of water body from urban areas. In addition, the spectral relationship method based on spectral area also had the character of extracting small tributary rivers.

This paper is a survey work for designing a Vision based Simultaneous Localization and Mapping (VSLAM) humanoid robot to generate a map of an unknown environment. A lot of factors have to be considered while designing a VSLAM robot. Vision Sensors are very attractive for application in SLAM because of their rich sensory output and cost effectiveness. Different issues are involved in the problem of vision based SLAM and many different approaches exist in order to solve these issues. Similarly the type of environment determines the suitable feature extraction method. The main objective of this survey is to conduct a comparative study among the current vision sensing methods in terms of imaging systems used for performing VSLAM, feature extraction algorithms used in some recently published papers, and initialization of landmarks, and to figure out the best for our work.

This research proposes the graphic system based on the computer vision technology using fingertips. It is the integrated system of graphic to the part that tracked the fingertips after reading the images of hands combined with artistic concept. This work of art being introduced is a graphic expression based on the screen and interactive media art for modern people who experience absence of communication and personal relationships that became estranged socially.1)2) Upon putting hand on the manufacture interactive table, it extracts the fingertips as feature points through adaptively suitable oval extraction method considering geometric features of hands. To project the graphic in the input image of camera, precise extraction and tracking technology of feature points of fingertips are important, and this thesis is about the explanation of that technology.BR Although technology and art has been separated for some time, the intimate communication between the two will take on a leading role for this academic field during the current fusion era, just as the word