AppTCP: The design and evaluation of application-based TCP for e-VLBI in fast long distance networks

Electric Very Long Baseline Interferometry (e-VLBI) is a typical astronomical interferometry used in radio astronomy. It allows observations of an object that are made simultaneously by many radio telescopes to be combined, emulating a telescope with the size equal to the maximum separation between the telescopes. The main requirements of transporting e-VLBI data are the high and constant transmission rate. However, the traditional TCP and its variants cannot meet these requirements. In an effort to solve the problem of transporting e-VLBI data in fast long distance networks, we propose an application-based TCP (AppTCP) congestion control algorithm, using Closed-Loop Control theory to keep the stable and constant transmission rate. AppTCP can swiftly reach the required transmission rate by increasing the congestion control window, and keep the transmission rate and allows the other TCP flows to share the remaining bandwidth. We further conduct extensive experiments in both fast long distance network test-bed and actual national networks (i.e., from Beijing to Shanghai in China) and international networks (i.e., from Hongkong in China to Chicago in USA) to evaluate and verify the performance and effectiveness of AppTCP. The results show that the AppTCP can effectively utilize the link capacity and maintain the constant rate during the data transmission, and its performance significantly outperforms that of the existing TCP variants

Wait time prediction for airport taxis using weighted nearest neighbor regression

In this paper, we address the neighborhood identification problem in the presence of a large number of heterogeneous contextual features. We formulate our research as a problem of queue wait time prediction for taxi drivers at airports and investigate heterogeneous factors related to time, weather, flight arrivals, and taxi trips. The neighborhood-based methods have been applied to this type of problem previously. However, the failure to capture the relevant heterogeneous contextual factors and their weights during the calculation of neighborhoods can make existing methods ineffective. Specifically, a driver intelligence-biased weighting scheme is introduced to estimate the importance of each contextual factor that utilizes taxi drivers' intelligent moves. We argue that the quality of the identified neighborhood is significantly improved by considering the relevant heterogeneous contextual factors, thus boosting the prediction performance (i.e., mean prediction error < 0.09 and median prediction error < 0.06). To support our claim, we generated an airport taxi wait time dataset for the John F. Kennedy International Airport by fusing three real-world contextual datasets, including taxi trip logs, passenger wait times, and weather conditions. Our experimental results demonstrate that the presence of heterogeneous contextual features and the drivers' intelligence-biased weighting scheme significantly outperform the baseline approaches for predicting taxi driver queue wait times

App usage on-the-move: Context- and commute-aware next app prediction

The proliferation of digital devices and connectivity enables people to work anywhere, anytime, even while they are on the move. While mobile applications have become pervasive, an excessive amount of mobile applications have been installed on mobile devices. Nowadays, commuting takes a large proportion of daily human life, but studies show that searching for the desired apps while commuting can decrease productivity significantly and sometimes even cause safety issues. Although app usage behaviour has been studied for general situations, little to no study considers the commuting context as vital information. Existing models for app usage prediction cannot be easily generalised across all commuting contexts due to: (1) continuous change in user locations; and (2) limitation of necessary contextual information (i.e., lack of knowledge to identify which contextual information is necessary for different commuting situations. We aim to address these challenges by extracting essential contextual information for on-commute app usage prediction. Using the extracted features, we propose AppUsageOTM, a practical statistical machine learning framework to predict both destination amenity and utilise the inferred destination to contextualise the app usage prediction with travelling purposes as crucial information. We evaluate our framework in terms of accuracy, which shows the feasibility of our work. Using a real-world mobile and app usage behaviour dataset with more than 12,495 trajectory records and more than 1046 mobile applications logged, AppUsageOTM significantly outperformed all baseline models, achieving Accuracy@k 46.4%@1, 66.4%@5, and 75.9%@10

Spatial variation of functional structure of fish communities in the Bohai Sea

Spatial variation of functional structure of fish communities in the Bohai Se

App usage on-the-move: Context- and commute-aware next app prediction

The proliferation of digital devices and connectivity enables people to work anywhere, anytime, even while they are on the move. While mobile applications have become pervasive, an excessive amount of mobile applications have been installed on mobile devices. Nowadays, commuting takes a large proportion of daily human life, but studies show that searching for the desired apps while commuting can decrease productivity significantly and sometimes even cause safety issues. Although app usage behaviour has been studied for general situations, little to no study considers the commuting context as vital information. Existing models for app usage prediction cannot be easily generalised across all commuting contexts due to: (1) continuous change in user locations; and (2) limitation of necessary contextual information (i.e., lack of knowledge to identify which contextual information is necessary for different commuting situations. We aim to address these challenges by extracting essential contextual information for on-commute app usage prediction. Using the extracted features, we propose AppUsageOTM, a practical statistical machine learning framework to predict both destination amenity and utilise the inferred destination to contextualise the app usage prediction with travelling purposes as crucial information. We evaluate our framework in terms of accuracy, which shows the feasibility of our work. Using a real-world mobile and app usage behaviour dataset with more than 12,495 trajectory records and more than 1046 mobile applications logged, AppUsageOTM significantly outperformed all baseline models, achieving Accuracy@k 46.4%@1, 66.4%@5, and 75.9%@10

Order and dynamics of intrinsic nanoscale inhomogeneities in manganites

Neutron elastic, inelastic, and high-energy x-ray scattering techniques are used to explore the nature of the polaron order and dynamics in the colossal magnetoresistive (CMR) system La0.7 Ca0.3 Mn O3. Polaron correlations are known to develop within a narrow temperature regime as the Curie temperature is approached from low temperatures, with a nanoscale correlation length that is only weakly temperature dependent. The static nature of these short-range polaron correlations indicates the presence of a glasslike state, very similar to the observations for the bilayer manganite in the metallic-ferromagnetic doping region. In addition to this elastic component, inelastic scattering measurements reveal dynamic correlations with a comparable correlation length and with an energy distribution that is quasielastic. The elastic component disappears at a higher temperature T*, above which the correlations are purely dynamic. These observations are identical to the polaron dynamics found in the bilayer manganite system in the CMR regime, demonstrating that they are a general phenomenon in the manganites. © 2007 The American Physical Society

A system of monitoring and analyzing human indoor mobility and air quality

Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO2, PM2.5, and PM10). We establish a system to monitor indoor human mobility with air quality and assess the interrelationship between these two types of time series data. More specifically, a sensor network was designed in indoor environments to observe air quality parameters continuously. Simultaneously, another sensing module detected participants' movements around the study areas. In this module, modern data analysis and machine learning techniques have been applied to reconstruct the trajectories of participants with relevant sensor information. Finally, a further study revealed the correlation between human indoor mobility patterns and indoor air quality parameters. Our experimental results demonstrate that human movements in different environments can significantly impact air quality during busy hours. With the results, we propose recommendations for future studies

A system of monitoring and analyzing human indoor mobility and air quality

Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO2, PM2.5, and PM10). We establish a system to monitor indoor human mobility with air quality and assess the interrelationship between these two types of time series data. More specifically, a sensor network was designed in indoor environments to observe air quality parameters continuously. Simultaneously, another sensing module detected participants' movements around the study areas. In this module, modern data analysis and machine learning techniques have been applied to reconstruct the trajectories of participants with relevant sensor information. Finally, a further study revealed the correlation between human indoor mobility patterns and indoor air quality parameters. Our experimental results demonstrate that human movements in different environments can significantly impact air quality during busy hours. With the results, we propose recommendations for future studies

Nutrient loading weakens seagrass blue carbon potential by stimulating seagrass detritus carbon emission

Coastal nutrient loading has been linked to a decline in the capacity of seagrass ecosystems to sequester carbon (‘blue carbon’); however, the mechanisms are unclear. Here we investigated how nutrient loading can affect the contribution that seagrass plant material makes to blue carbon stocks by investigating plant quality-decomposition dynamics. Specifically, we used a combination of laboratory and field experiments to account for various changes in biogeochemical cycling from seagrass meadows, ranging from changes in leaf quality to CO2 fluxes. It was found that nutrient loading increased the ‘labile’ content of seagrass (i.e. increased levels of leaf nitrogen, phosphorus and soluble organic carbon (amino acid and soluble sugar content), and at the same time decreased levels of ‘recalcitrant’ carbon (i.e. materials that are harder for microbes to break down, such hemicellulose, cellulose and lignin contents). Nutrient-enriched leaves decomposed ∼ 18 % faster than non-enriched leaves (i.e. greater biomass loss from nutrient-affected seagrass), resulting in ∼ 80 % more CO2 emissions from nutrient-enriched seagrass. We also found that seagrass that naturally contained high levels of labile carbon at the start of the experiment were affected to a greater degree (i.e. higher CO2 emissions) by nutrients addition than seagrass that had high proportions of recalcitrant carbon to begin with. Overall, these findings suggest that nutrient loading can weaken the capacity of seagrass ecosystems to act as blue carbon sinks through its effect on seagrass leaf decomposability

An ambient-physical system to infer concentration in open-plan workplace

One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks. Hence, being able to infer concentration levels of workers will allow building designers, managers, and workers to estimate what effect different open-plan layouts will have and to find an optimal one. In this article, we present an ambient-physical system to investigate the concentration inference problem. Specifically, we deploy a series of pervasive sensors to capture various ambient and physical signals related to perceived concentration at work. The practicality of our system has been tested on two large open-plan workplaces with different designs and layouts. The empirical results highlight promising applications of pervasive sensing in occupational concentration inference, which can be adopted to enhance the capabilities of modern workplaces