Replication and Versioning of Partial RDF Graphs

Limited Network Connections and the Distribution of Wages

In big data epoch, one of the major challenges is the large volume of mixed structured and unstructured data, which comes in heterogeneous sources. Because of different form, structured and unstructured data are often considered apart from each other. However, they may speak about the same entities of the world. If a query involve both structured data and its unstructured counterpart, it is inefficient to execute it separately. The paper presents a novel index structure tailored towards the combinations of structured and unstructured data. The combined index is a joint index over structured database and unstructured document, based on entity co-occurrences. It is also a semantic index which describes the semantic relationships between entities and their multiple resources. We store the index as RDF graphs and queries are SPARQL-like. Experiments show that the associated index can not only provide apposite information but also execute queries efficiently.

While it is well known that mental fatigue impairs fine motor performance, the investigation into its neural basis remains scant. Here, we investigate the impact of mental fatigue on fine motor performance and explore its underlying neural network connectivity mechanisms. A total of 24 healthy male university students were recruited and randomly divided into two groups: a mental fatigue group (MF) and a control group (Control). Both groups completed 50 dart throws, while electroencephalography (EEG) data were collected. Following the Stroop intervention, participants in the MF group exhibited a decrease in Stroop task accuracy and throwing performance, and an increase in reaction time along with VAS and NASA scores. The EEG data during dart-throwing revealed that the network connectivity strength of theta oscillations in the frontal and left central regions was significantly higher in the MF group compared with the Control group, while the network connectivity strength of alpha oscillations in the left parietal region was significantly enhanced. The interregional connectivity within the theta and alpha rhythm bands, particularly in the frontal-central-parietal network connections, also showed a significant increase in the MF group. Mental fatigue impairs dart throwing performance and is accompanied by increased connectivity in alpha and theta.

As a national strategic development area, the Yellow River Basin (YRB) has seen progress in research on the synergy efficiency of pollution reduction and carbon reduction (SEPCR). However, there are still notable gaps. The theoretical framework for this area is lacking, leading to diverse and inconsistent conclusions. Additionally, difficulties in data collection and processing, along with incomplete and inconsistent data, negatively affect the accuracy of research findings. Current studies tend to focus on single aspects and lack a comprehensive and systematic analysis of the SEPCR across the entire basin. There is insufficient understanding of key network nodes, connections, and overall structural characteristics. A scientific assessment of its spatial correlation structure has far-reaching implications for the national battle against pollution and the realization of “dual carbon” goals. This study is based on panel data from 75 cities in the YRB from 2006 to 2022. It employs an ultra-efficiency SBM model to measure the SEPCR. Additionally, it utilizes a modified gravity model and social network analysis to explore the spatial network correlation structure in depth. Furthermore, the QAP model is used to clarify the mechanisms of various influencing factors. The research findings indicate that there is an imbalance in the spatial and temporal distribution of the SEPCR in the YRB. Although there is a fluctuating upward trend over time, significant internal spatial disparities exist. While the gaps between regions are gradually narrowing, there are still evident research disparities. Moreover, the spatial connectivity of the SEPCR in the YRB is gradually strengthening, with overall network connectivity also improving, yet there remains a considerable distance from an ideal state. The network density shows a decreasing trend from the downstream to the midstream and then to the upstream regions, with significant differences in spatial network centrality among these areas, particularly pronounced between the midstream and upstream regions. Differences in economic development levels, technological development levels, and industrial structure development levels promote the formation of spatial correlations in SEPCR, while disparities in energy utilization have a suppressive effect.

Linked Data refers to a set of guidelines and best practices for publishing and accessing structured data on the Web. It builds upon established Web technologies, in particular HTTP and URIs, extended with Semantic Web representation formats and protocols such as RDF, RDFS, OWL and SPARQL, by which data from different sources can be shared, interconnected and used beyond the application scenarios for which it was originally created. RDF is a central building block of the Linked Data technology stack. It is a graph-based data model based on the idea of making statements about (information and non-information) resources on the Web in terms of triples of the form subject predicate object. The object of any RDF triple may be used in the subject position in other triples, leading to a directed, labeled graph typically referred to as an 'RDF graph'. Both nodes and edges in such graphs are identified via URIs; nodes represent Web resources, while edges stand for attributes of such resources or properties connecting them. Schema information can be expressed using languages such as RDFS and OWL, by which resources can be typed as classes described in terms of domain-specific attributes, properties and constraints. RDF graphs can be natively queried using the query language SPARQL. A SPARQL query is composed of graph patterns and can be stored as RDF triples together with any RDF domain model using SPIN to facilitate the definition of constraints and inference rules in ontologies.

The effect of uniaxial and multiaxial mechanical strain on the electrical conductivity of particle filled polymer composites is investigated in the framework of concentration-driven percolation. For composites consisting of low aspect ratio, rigid conductive particles in a compliant polymer matrix, a simple argument leads to the conclusion that the effective volume fraction of conductive particles (the ratio of total particle volume to the total volume of the deformed composite) plays a dominant role, with conductivity remaining isotropic despite the directional bias of the strain state. As such, conductivity is expected to exhibit classical power, law-dependence on concentration, which in this case takes the form of a strain-dependent effective volume fraction. Consideration of deformation effects on particle agglomerates suggest, however, that particle-to-particle network connections are likely to be affected most significantly along directions experiencing the most severe strains, introducing a directional bias in network connectivity at a higher length scale. To assess the importance of this possible directional bias, random resistor network models are used to study the conductivity of uniaxially strained composites. For conservative assumptions on the severity of the bias in bond probabilities, network conductivities exhibit approximately isotropic, concentration-driven behavior for moderate strains, supporting the predictive utility of the simple percolation conduction-effective volume fraction approach. Further corroboration is provided by experiments in the literature on silicone-graphite composites subjected to uniaxial compressive strain, where good agreement is obtained through moderate strains for the theoretically correct value of the conduction exponent in concentration-driven percolation.

Dynamic Network Connectivity: A new form of neuroplasticity

Respiration and swallowing are motor behaviours that require the interaction of two brainstem central pattern generators (r‐CPG, sw‐CPG). Previously we studies the interaction of swallowing and breathing in the in situ perfused brainstem preparation that identified a pivotal role of the pontine Kolliker‐Fuse nuclei for the mediation of protective glottal closure. We recently developed a computational model to further aid and test network connectivity and neural interaction between sw‐ and r‐CPGs. The model is based on a stepwise integration of synaptic connectivity between central sensory relay, sw‐ and r‐CPG neuron populations. The model is validated by experimental data obtained in situ. In the model and experimental data, stimulation of the superior laryngeal nerve (SLN, 10s) reliably triggers sequential swallowing with concomitant swallowing‐related glottal closure and suppression of inspiratory activity. Short SLN stimulation (100ms) evokes single swallows and respiratory phase resetting in the model and in situ yielding comparable phase response curves. Consistent with experiments, numerical circuit‐busting simulations show that deletion of ponto‐medullary synaptic interactions triggers apneusis and eliminates swallowing‐related glottal closure during sequential swallowing. Additionally, systematic variations of the synaptic strengths of distinct network connections predict vulnerable network connections that can mediate clinically relevant breathing‐swallowing disorders observed in the elderly and patients with neurodegenerative disease. Thus, the present model provides novel insights that can guide future experiments and the development of efficient treatments for prevalent breathing‐swallowing disorders.

This paper describes a research and development program called Proactive Integrated Link Selection for Network Robustness (PILSNER) that is being executed at the Communications-Electronics Research Development and Engineering Center (CERDEC), Ft. Monmouth, NJ. The objective of PILSNER is to develop, mature and integrate networking technologies that will solve the issues of limited network reliability and connectivity, resulting from the inability to make optimal use of all available link types simultaneously. The PILSNER effort therefore focuses on the development of Pro-active Diverse Link Selection (PAD-LS) technologies, which enable automatic link selection based on network characteristics and mission objectives, over a heterogeneous multi-tiered, multi-link network, in order to bypass congestion and outages. The PAD-LS technologies are designed to proactively allocate and manage bandwidth over multiple transmission paths in order to provide a level of redundancy for assured network availability and connectivity. PAD-LS technologies will address issues related to optimal link selection, capturing link local information and neighbor link information across multi-hop connections, adaptive bandwidth management, IP Quality of Service, IP multicast, and functionality within a mobile tactical environment. Demonstration of these technologies shall be integrated and conducted with a variety of transmission media consistent with the projected Warfighter Information Network - Tactical (WIN-T) capabilities. PILSNER is part of the Tactical Mobile Networks (TMN) Advanced Technology Objective (Demonstration) (ATO(D)), and is scheduled to run from FY2006 through FY2009.

The parallel realization of adaptive finite element methods (FEM) has to deal with several irregular and dynamic algorithmic properties caused by adaptive mesh refinement (AMR). For an implementation on distributed memory machines irregular communication behavior results from dynamically growing data structures and statically unknown communication partners. An efficient parallel implementation has to provide appropriate mechanisms to cope with the flexibility of the adaptive finite element approach at runtime. We have implemented a data management and communication layer for an adaptive, 3-dimensional, hexahedral FEM on distributed memory machines and use it to parallelize an existing sequential code. The data management and communication layer realizes duplicated data structures for boundaries of distributed data, additional hierarchical data structures to deal with uneven refinement, and coherence protocols to guarantee correctness of communication partners and messages. An easy to use interface provides access to the functionality of the layer.

The Government of India proposed "Clean India Mission" in 2014 that has led to the establishment of many public restrooms across the country. The purpose of this mission is to provide hygienic toilets to prevent open defecation and eradicate water-borne diseases. These restrooms are assigned human resources. However, the majority of them are in bad condition for usage. For increasing the availability of toilets, there is a requirement of a smart sanitation system for its automated monitoring. Moreover, rural and semi-urban restroom locations suffer from limited network connectivity. Thus, restroom state analysis needs to be performed near the site for seamless monitoring. This paper proposes an edge analytics framework based on the Internet of Things (IoT) and fog computing to analyze restroom data at the network edge and provide intelligent decisions. The machine learning model runs over Docker containers for analyzing data for the resource-constraint fog nodes. The choice of a machine learning model for the analysis depends on the model's performance in classification and on Docker containers over Raspberry Pi. Results show Adaptive Neuro-Fuzzy Inference System (ANFIS) is suitable over Docker containers for handling restroom data at the edge of the network.

Self - localization in Wireless Sensor Networks (WSN) should be precise and reliable. Alternative Least-Square Scaling Algorithm (ALESSA) is a recently proposed centralized Multidimensional Scaling (MDS)-based localization algorithm, which uses an iterative approach to solve for the coordinates of discrete points. While ALESSA converges most of the time, like most iterative algorithm, it can be trapped in local minima causing large errors in the location estimates. In this paper, we propose the reseeding of the initial random estimates to improve the convergence of the algorithm. Performance of the proposed algorithm is evaluated under different network topologies with limited connectivity. We also analyzed the effects of low Signal to Noise Ratio (SNR) and the population of the nodes deployed in the network to the algorithm's localization precision. Simulation results show that at 26 dB SNR reseeding always results in convergence with the estimation errors within 5 % of the reference communication range. Analysis and test runs also verified that our algorithm provides accurate and consistent localization estimates under range-based localization with limited network connectivity, even with a low SNR. The algorithm also performs well with limited number of nodes.

In manufacturing environments, the proper use of Personal Protective Equipment (PPE) is essential to prevent workplace accidents. Despite this need, existing PPE monitoring methods remain largely manual and suffer from limited coverage, significant errors, and inefficiencies. This article focuses on addressing this deficiency by designing a computer vision desktop application for automated monitoring of PPE use. This system uses lightweight YOLOv8 models, developed to run on the local system and operate even in industrial locations with limited network connectivity. Using a Lean UX approach, the development of the system involved creating empathy maps, assumptions, product backlog, followed by high-fidelity prototype interface components. C4 and physical diagrams helped define the system architecture to facilitate modifiability, scalability, and maintainability. Usability was verified using the System Usability Scale (SUS), with a score of 87.6/100 indicating “excellent” usability. The findings demonstrate that a user-centered design approach, considering user experience and technical flexibility, can significantly advance the utility and adoption of AI-based safety tools, especially in small- and medium-sized manufacturing operations. This article delivers a validated and user-centered design solution for implementing machine vision systems into manufacturing safety processes, simplifying the complexities of utilizing advanced AI technologies and their practical application in resource-limited environments.

Mobile devices such as PDAs and cellular phones that offer Internet access are proliferating. Due to the many unique characteristics of these devices, such as their small screen size and limited network connectivity, existing Web sites may have to undergo significant evolution to support this type of mobile client. The paper outlines some of the issues in accessing Web sites from mobile devices, such as the Apple Newton, the Compaq iPAQ, and WAP-enabled cell phones. These issues are based on our experiences as both users of mobile devices and as content providers.

Block-HRG: Block-based differentially private IoT networks release