Multiple Data Streams Research Articles

Mapping fine-resolution nested social-ecological system archetypes to reveal archetypical human-environmental interactions

Scaling up case-based knowledge to understand human-environmental interactions is key to addressing the sustainability challenges we face in the Anthropocene. Mapping social-ecological system (SES) archetypes has emerged as a promising tool in identifying these interactions in a spatially explicit manner and developing transferrable sustainability solutions based on context-specific challenges. However, state-of-the-art SES archetype mapping is mainly bound to administrative borders. Lack of spatial details in SES maps does not always capture the panorama of SES configurations and hidden mechanisms. Empowered by multiple open-access geospatial big data streams, this study mapped two-tier nested SES archetypes at 1 km fine resolution in the Beijing-Tianjin-Hebei (BTH) urban agglomeration with high validation accuracy. Global SES archetypes derived from top-down rule-based classification revealed broad social-ecological configurations and social-ecological gradients across SESs. Meanwhile, regional SES sub-archetypes generated by bottom-up hierarchical clustering presented detailed SES characteristics that helped to identify location-specific sustainability challenges. Additionally, by scrutinizing the relationships among SES variables, we identified empirical evidence supporting the existence of four archetypical human-environmental interactions in an urban agglomeration area, manifesting as telecoupling between human and environment, human dependence on the environment, human adaption to the environment, and human modification and conservation of the environment. Generally, fine-resolution SES archetypes demonstrate great potential in supporting the recognition of local needs and gaps in existing policies, and further promoting sustainable landscape management in the BTH region and similar regions.

A massively parallel implicit 3D unstructured grid solver for computing turbulent flows on latest distributed memory computational architectures

An implicit unstructured grid density-based solver–PRAVAHA based on a parallel variant of the lower upper symmetric Gauss-Seidel (LUSGS) method is developed to compute large-scale engineering problems. A four-layered parallel algorithm is designed to efficiently compute three-dimensional turbulent flows on massively parallel modern multiple instruction multiple data-stream (MIMD) computational hardware. This data parallel approach achieves multiple layers of parallelism including continuity of flow solution, transfer of solution gradients, and calculation of drag/lift/solution residuals, right up to the innermost implicit LUSGS solver sub-routine, which is relatively less explored in the literature. Domain decomposition is performed using the METIS software based on multi-level graph partitioning algorithms. Non-blocking message-passing interface library functions are used to manage inter-processor communication through explicit message passing, efficiently. Super-linear scalability of the parallel solver is established on the current state-of-the-art supercomputing facility, the 838 teraflops PARAM seva on up to 6144 cores. Linear or even super-linear speedup on problems of significant size is observed even on ad-hoc parallel computing platforms like workstations and multi-node clusters, for turbulent flow simulations.

Reduced-Space Multistream Classification Based on Multiobjective Evolutionary Optimization

In traditional data stream mining, classification models are typically trained on labeled samples from a single source. However, in real-world scenarios, obtaining accurate labels is very hard and expensive, especially when multiple data streams are concurrently sampled from an environment or the same process. To address this issue, multistream classification is proposed, in which a data stream with biased labels (called the source stream) is leveraged to train a suitable model for prediction over another stream with unlabeled samples (called the target stream). Despite the growing research in this field, previous multistream classification methods are mostly designed for single source stream scenarios. However, various source streams contain diverse data distributions, providing more valuable information for building a more accurate model. In addition, previous works construct classification models in the original shared feature space, ignoring the effect of redundant or low-quality features on the classification performance. This may produce inefficient knowledge transfer across streams. In view of this, a reduced-space multistream classification based on multi-objective evolutionary optimization is proposed in this paper. First, a multi-objective evolutionary optimization is employed to seek the most valuable feature subset shared in the source and target domains, with the purpose of narrowing the distribution difference between source and target streams. Following that, a Gaussian Mixture Model-based weighting mechanism for source samples is presented. More especially, two drift adaptation methods are proposed to address asynchronous drift. Experimental results on benchmark datasets show that the proposed method outperforms other comparative methods on classification accuracy and G-mean.

Improving modelled albedo over the Greenland ice sheet through parameter optimisation and MODIS snow albedo retrievals

Abstract. Greenland ice sheet mass loss continues to accelerate as global temperatures increase. The surface albedo of the ice sheet determines the amount of absorbed solar energy, which is a key factor in driving surface snow and ice melting. Satellite-retrieved snow albedo allows us to compare and optimise modelled albedo over the entirety of the ice sheet. We optimise the parameters of the albedo scheme in the ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) land surface model for 3 random years taken over the 2000–2017 period and validate over the remaining years. In particular, we want to improve the albedo at the edges of the ice sheet, since they correspond to ablation areas and show the greatest variations in runoff and surface mass balance. By giving a larger weight to points at the ice sheet's edge, we improve the model–data fit by reducing the root-mean-square deviation by over 25 % for the whole ice sheet for the summer months. This improvement is consistent for all years, even those not used in the calibration step. We also show the optimisation successfully improves the model–data fit at 87.5 % of in situ sites from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) network. We conclude by showing which additional model outputs are impacted by changes to the albedo parameters, encouraging future work using multiple data streams when optimising these parameters.

The State of the Art of Diagnostic Multiparty Eye Tracking in Synchronous Computer-Mediated Collaboration.

In recent years, innovative multiparty eye tracking setups have been introduced to synchronously capture eye movements of multiple individuals engaged in computer-mediated collaboration. Despite its great potential for studying cognitive processes within groups, the method was primarily used as an interactive tool to enable and evaluate shared gaze visualizations in remote interaction. We conducted a systematic literature review to provide a comprehensive overview of what to consider when using multiparty eye tracking as a diagnostic method in experiments and how to process the collected data to compute and analyze group-level metrics. By synthesizing our findings in an integrative conceptual framework, we identified fundamental requirements for a meaningful implementation. In addition, we derived several implications for future research, as multiparty eye tracking was mainly used to study the correlation between joint attention and task performance in dyadic interaction. We found multidimensional recurrence quantification analysis, a novel method to quantify group-level dynamics in physiological data, to be a promising procedure for addressing some of the highlighted research gaps. In particular, the computation method enables scholars to investigate more complex cognitive processes within larger groups, as it scales up to multiple data streams.

Generation of Photon Orbital Angular Momentum and Its Application in Space Division Multiplexing

In the last three decades, light’s orbital angular momentum (OAM) has been of great interest because it has unique characteristics that make it sought after in many research fields, especially in optical communications. To address the exponentially increasing demands for higher data rates and capacity in optical communication systems, OAM has emerged as an additional degree of freedom for multiplexing and transmitting multiple independent data streams within a single spatial mode using the spatial division multiplexing (SDM) technology. Innumerable research findings have proven to scale up the channel capacity of communication links by a very high order of magnitude, allowing it to circumvent the reaching of optical fiber’s non-linear Shannon limit. This review paper provides a background and overview of OAM beams, covering the fundamental concepts, the various OAM generators, and the recent experimental and commercial applications of the OAM-SDM multiplexing technique in optical communications.

Selective interference alignment and neutralization in coordinated multipoint using multiuser MIMO

SummaryIn wireless communication, the concept of coordinated multipoint (CoMP) transmission is more attractive, and it transpired the notion based on interference management techniques. Interference alignment (IA) and interference neutralization (IN) methods can substantially align and neutralize the interference signals. The existing work in the CoMP transmission using multiuser multi‐input and multi‐output (MU‐MIMO) had long‐held problems such as specific limit of intercell (ICI) and cochannel interference (CCI) cancelation that contain low performance in cell‐edge users. The proposed framework of the transmission signal in selective interference alignment and neutralization (SIAN) CoMP MU‐MIMO system transmits multiple data streams in multipath by using downlink coordination between base stations and receiver side. This work contributes the individual perspectives to implement the IA and IN to align and cancel the interfered signals at the receiver side. Once the perspectives mentioned above are executed, zero‐forcing (ZF) and rechanneling filter are applied on the receiver side to eliminate residual ICI. In addition, spectral efficiency significantly improves the achievable data rate and enhances the performance of cell‐edge users and reduces the CCI at the receiver side. Furthermore, the antenna configuration signals are decoded to get the exact version of interference‐free signals with null path loss of signal transmission. The effectiveness of the proposed framework analyzes and verifies the numerical evaluation of the achievable degree of freedom. Finally, the simulation demonstrates the comparison of the proposed CoMP scheme with MIMO ZF and non‐CoMP schemes, which significantly improve the performance of spectral efficiency for cell‐edge users.

Defining and testing the value proposition of outside-in planning processes

Traditional planning processes are inside-out. The focus is on mining the patterns of orders and shipments and synchronising demand and supply based on enterprise data. The problem with this approach is with product proliferation and item complexity, demand latency increases, making the order signal out of step with the market by weeks and months. In addition, as supply chain leaders attempt to better align supply chains with markets in the face of months after months of disruption, historical patterns of orders and shipments are irrelevant. The reason is simple. In a volatile world, history is not a good signal to drive decisions. To find a solution, o9 Solutions and Supply Chain Insights partnered with a group of business leaders and academics to test the use of multiple streams of demand data and determine the value of using market, or channel, data versus driving demand off of order or shipment patterns. In this paper, we share insights and the journey. While the opportunity is tremendous and the value clear, the biggest issue for technologists and business leaders is unlearning conventional paradigms. As the use of market data makes many of the traditional planning assumptions obsolete. Here the reader will learn the lessons from the testing and gain insights to adapt their planning processes to be more outside-in.

The opportunities of using AI for early disease detection

AI is a pattern recognition engine with many potential applications in early disease detection The hope is that AI can be trained to eliminate variability and subjectivity AI can potentially spot more complex, subtle and multifactorial patterns than a human can AI may be able to successfully integrate multiple streams of data into an integrated assessment There are important ethical issues in this subject.

Integrated Three‐Dimensional Geological and Numerical Groundwater Model Development

AbstractConceptual site model (CSM) development is a foundational step for numerical groundwater model construction. Tools for reviewing and combining disparate data to develop a CSM have been furthered through software that efficiently integrates multiple data streams, especially when a large amount of information is available at complex sites. Three‐dimensional (3D) interfaces for data visualization and geological modeling are becoming commonly used to compile data, support development of a robust CSM, and create effective visuals that enable project stakeholders to understand site complexity. This article describes a 3D interface workflow applied to a geological and numerical groundwater modeling case study where numerical modeling (i.e., MODFLOW family of codes) was used to assess remedy effectiveness. Specifically, the numerical model was used to overview an in situ solidification (ISS) application, where soil mixing will be conducted with cement, for possible adverse effects to an existing groundwater treatment system. 3D geological modeling allowed for the existing numerical groundwater flow model to be reconstructed to include previously overlooked data on buried infrastructure, which led to a detailed depiction of high‐permeability channels and a more accurate representation of site conditions. Modeling showed that the ISS remedy would not adversely impact the existing groundwater treatment system. Integration of existing data within a 3D interface led to hydrogeological insights that would have been difficult to obtain from two‐dimensional visualization of information, improved numerical modeling efficiency, and informed site remedy decisions. Additionally, the 3D interface created effective visuals that increased internal and external project team conceptual understanding.

Double-Anonymous Sketch: Achieving Top-K-fairness for Finding Global Top-K Frequent Items

Finding top-K frequent items has been a hot topic in data stream processing in recent years, which has a wide range of applications. However, most of existing sketch algorithms focuses on finding local top-K in a single data stream. In this paper, we work on finding global top-K in multiple disjoint data streams. We find that directly deploying prior sketch algorithms is often unfair under global scenarios, which will degrade the accuracy of global top-K. We define top-K-fairness and show that it is important for finding global top-K. To achieve top-K-fairness, we propose a new sketch framework, called the Double-Anonymous sketch. The process of finding global top-K items is similar to that of paper reviewing and democratic elections. In these scenarios, double-anonymity is often an effective strategy to achieve top-K-fairness. We also propose two techniques, hot panning, and early freezing, to further improve the accuracy. We theoretically prove that the Double-Anonymous sketch achieves top-K-fairnesswhile keeping high accuracy. We perform extensive experiments to verify top-K-fairness in the scenario of disjoint data streams. The experimental results show that the Double-Anonymous sketch's error is up to 129 times (60 times on average) smaller than the state-of-the-art. All the related source code is open-sourced and available at Github.

The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology

AbstractSpecies distribution models (SDMs) are widely applied to understand the processes governing spatial and temporal variation in species abundance and distribution but often do not account for measurement errors such as false negatives and false positives.We describeunmarked, a package for the freely available and open‐source R software that provides a complete workflow for modelling species distribution and abundance while explicitly accounting for measurement errors. Here we focus on recent advances inunmarkedfunctionality to support multi‐species, multi‐state, and multi‐season data, as well as support for fitting models with random effects.For illustration, we present an analysis of Acadian FlycatcherEmpidonax virescensabundance on Roanoke River National Wildlife Refuge, North Carolina, USA, over 18 years. We found that Acadian Flycatcher abundance was initially greater in hardwood plantation habitat relative to bottomland hardwood forest along river levees but that abundance declined over time in both habitats.We plan forunmarkeddevelopment to keep pace with advances in hierarchical modelling in ecology, including better handling of continuous‐time data from camera trap and automated recording units and integrated models for multiple data streams.

Breathe Easy, an automated respiratory data pipeline for waveform characteristic analysis

Whole body barometric flow through plethysmography is used to study respiratory output in adult rodent genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams for a reasonable estimation of tidal volume and key metabolic parameters. Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic, and degenerative diseases. However, such studies produce large amounts of data that are time-consuming and difficult to analyze. The investigator may use cumbersome hand annotation subject to observer bias or a series of software products to manage raw data, calculated data, statistical analysis, and graphing. Commercial software, however, is prohibitively expensive or bundled with turn-key systems limiting options for bespoke respiratory measurement systems. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge.Here, we present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs, and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds, and defining experimental variables. Behind the scenes we have a fine-tuned, python-based program called BASSPRO that takes the user-settings and selects all quantitatively defined, quality breaths from each period of interest in the signal files. After BASSPRO, STAGG takes those breaths and performs linear mixed effects modeling based on user selections for each dependent variable of interest. Furthermore, our software can integrate non-respiratory experimental outcomes including quantified behavioral or expression data to test for breathing related associations.To validate breath selection and generate optimized default values, results from Breathe Easy were compared to manual selection by experts, which represents the current standard in the field. We show high quality breath selection and a significant reduction in quality of breaths that were rejected by the software. We also demonstrate Breathe Easy’s utility and capacity by examining a 2-year longitudinal study of an Alzheimer’s Disease mouse model to assess contributions of forebrain pathology in disordered breathing (600+ files; 1TB data). We show that, at least with this model of AD, there does not seem to be an association between forebrain pathology and respiratory outcomes.Breathe Easy offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. NIH: 1F32HL160073-01A1, R01HL130249 44617-S4. BCM McNair Scholar Program, March of Dimes Basil O'Connor Research Award, Parker B. Francis Fellowship, CJ Foundation for SIDS. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A Novel Approach to Increase Data Transfer Rate in WIFI Network

Due to the proliferation of devices and the rise of applications that rely heavily on data, the demand for high-speed data transfer in WiFi networks has been rising rapidly in recent years. However, due to their limitations in terms of data transfer rate and capacity, conventional WiFi networks have prompted the investigation of novel strategies to address these issues. By combining beamforming techniques with Multiple Input Multiple Output (MIMO) technology, this study proposes a novel strategy for increasing the data transfer rate in WiFi networks. Multiple data streams can be transmitted simultaneously using MIMO technology, effectively increasing bandwidth and data transfer rate. The proposed approach explains the potential of using a hybrid LiFi-WiFi network was also explored to further increase the data transfer rate in WiFi networks. LiFi technology uses light waves instead of radio waves to transmit data and has the potential to provide faster data transfer rates than WiFi networks. The research was motivated by the limitations of conventional approaches and the rising demand for high-speed data transfer in WiFi networks. The new solution proposed in this approach has the potential to address these limitations and significantly increase the rate of data transfer.

FHC-NDS: Fuzzy Hierarchical Clustering of Multiple Nominal Data Streams

The need of fuzzy clustering arises in many real-world applications such as clumping the users based on their web browsing behavior where the behavior of a user can be similar to two different sets of users at the same instance. The aptness of fuzzy clustering for data streams is further intensified given their concept evolving nature. Data streams can be clustered either by following clustering-by-variable approach or clustering-by-example approach. Most of the existing fuzzy clustering-by-variable methods are applicable to numeric data streams only. In this article, a fuzzy hierarchical clustering method is proposed for clustering multiple nominal data streams using clustering-by-variable approach. The fuzzy affinity of data streams to different clusters is calculated using normalized cosine similarity to the cluster centroids. It handles the concept evolution by updating the hierarchical clustering structure by either merging and/or splitting the nodes depending on the extent to which the node entropy changes. The performance of the proposed method is analyzed and compared to hierarchical clustering for multiple nominal data streams (HCND), semifuzzy online divisive-agglomerative clustering, and nTreeClus on synthetic as well as real-world web-browsing dataset where it has outperformed all three in terms of cluster quality as quantified by Dunn index, modified Hubert <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Gamma$</tex-math></inline-formula> statistic, and adjusted rand index. Furthermore, the experimental results show that the proposed method is highly promising with regard to capturing fuzzy clusters as indicated by Xie-Beni index, partition coefficient, and partition entropy.

Space-Time- and Frequency- Spreading for Interference Minimization in Dense IoT

In this article, we propose a space spreading-assisted framework that leverages either {time or frequency diversity or both to reduce interference and signal loss} owing to channel impairments and facilitate the efficient operation of large-scale dense Internet-of-Things (IoT). Our approach employs dispersion of data-streams transmitted from individual IoT devices over indexed space-time (ST), space-frequency (SF) or space-time-frequency (STF) blocks. As a result, no two devices transmit on the same block; only one is activated while the rest of the devices in the network is silent, thereby minimizing possibility of interference on the transmit side. On the receive side, multiple-antenna array ameliorates performance in presence of channel impairments while exploiting array-processing gain. As interference due to superposition of multiple data-streams is killed at its root, no extra energy is wasted in fighting interference and other impairments, thereby enabling energy-efficient transmission from multiple devices over multiple access channel (MAC). To validate the proposed concept, we simulate the performance of the framework against dense IoT networks deployed in generalized indoor and outdoor scenarios in terms of probability of signal outage. Results demonstrate that our conceptualized framework benefits from interference-free transmission as well as enhancement in overall system performance.

Generation of Mixed-OAM-Carrying Waves Using Huygens’ Metasurface for Mm-Wave Applications

Antennas that generate orbital angular momentum (OAM) have the potential to significantly enhance the channel capacity of upcoming wireless systems. This is because different OAM modes that are excited from a shared aperture are orthogonal, which means that each mode can carry a distinct stream of data. As a result, it is possible to transmit multiple data streams at the same time and frequency using a single OAM antenna system. To achieve this, there is a need to develop antennas that can create several OAM modes. This study employs an ultrathin dual-polarized Huygens' metasurface to design a transmit array (TA) that can generate mixed-OAM modes. Two concentrically-embedded TAs are used to excite the desired modes by achieving the required phase difference according to the coordinate position of each unit cell. The prototype of the TA, which operates at 28 GHz and has a size of 11 × 11 cm 2, generates mixed OAM modes of -1 and -2 using dual-band Huygens' metasurfaces. To the best of the authors' knowledge, this is the first time that such a low-profile and dual-polarized OAM carrying mixed vortex beams has been designed using TAs. The maximum gain of the structure is 16 dBi.

A Fast Topological Parallel Algorithm for Traversing Large Datasets

This work presents a parallel implementation of a graph-generating algorithm designed to be straightforwardly adapted to traverse large datasets. This new approach has been validated in a correlated scenario known as the word ladder problem. The new parallel algorithm induces the same topological structure proposed by its serial version and also builds the shortest path between any pair of words to be connected by a ladder of words. The implemented parallelism paradigm is the Multiple Instruction Stream - Multiple Data Stream (MIMD) and the test suite embraces 23-word ladder instances whose intermediate words were extracted from a dictionary of 183,719 words (dataset). The word morph quality (the shortest path between two input words) and the word morph performance (CPU time) were evaluated against a serial implementation of the original algorithm. The proposed parallel algorithm generated the optimal solution for each pair of words tested, that is, the minimum word ladder connecting an initial word to a final word was found. Thus, there was no negative impact on the quality of the solutions comparing them with those obtained through the serial ANG algorithm. However, there was an outstanding improvement considering the CPU time required to build the word ladder solutions. In fact, the time improvement was up to 99.85%, and speedups greater than 2.0X were achieved with the parallel algorithm.

Feasibility of a Smartwatch Platform to Assess Ecological Mobility: Real-Time Online Assessment and MobilityMonitor.

Early detection of mobility decline is critical to prevent subsequent reductions in quality of life, disability, and mortality. However, traditional approaches to mobility assessment are limited in their ability to capture daily fluctuations that align with sporadic health events. We aim to describe findings from a pilot study of our Real-time Online Assessment and Mobility Monitor (ROAMM) smartwatch application, which uniquely captures multiple streams of data in real time in ecological settings. Data come from a sample of 31 participants (Mage=74.7, 51.6% female) who used ROAMM for approximately 2 weeks. We describe the usability and feasibility of ROAMM, summarize prompt data using descriptive metrics, and compare prompt data with traditional survey-based questionnaires or other established measures. Participants were satisfied with ROAMM's function (87.1%) and ranked the usability as "above average." Most were highly engaged (average adjusted compliance=70.7%) and the majority reported being "likely" to enroll in a 2-year study (77.4%). Some smartwatch features were correlated with their respective traditional measurements (eg, certain GPS-derived life-space mobility features (r=0.50-0.51, p < .05) and ecologically measured pain (r=0.72, p=.01), but others were not (eg, ecologically measured fatigue). ROAMM was usable, acceptable, and effective at measuring mobility and risk factors for mobility decline in our pilot sample. Additional work with a larger and more diverse sample is necessary to confirm associations between smartwatch-measured features and traditional measures. By monitoring multiple data streams simultaneously in ecological settings, this technology could uniquely contribute to the evolution of mobility measurement and risk factors for mobility loss.

IMPROVING PARALLEL SOLUTIONS FOR METHOD OF LINES TO 1-D HEAT EQUATION BY USING FIVE POINT FINITE DIFFERENE

The aim of the study is to explain the numerical solutions of the one dimensional (1-D) of heat by using method of lines (MOLs). In the (MOLs) the derivative is firstly transformed to equivalent 5 point central finite differences methods (FDM) that is also transformed to the ordinary differential equations (ODEs). The produced (ODEs) systems are solved by the well-known techniques method of ODEs such as the 4th Runge - Kutta method and Runge - Kutta Fehlberg. And since of the conversion of the second derivative to the equivalent of the 5 points FDM which led to an increase in the size of the system equations ODEs, and thus increased we have improved the performance of these (MOLs) techniques by introduce parallel processing to speed up the solution of the produced ODE systems. The developed parallel technique, are suitable for running on MIMD (Multiple Instruction Stream, Multiple Data Stream) computers.