Dense Space Research Articles

Fit for purpose? Just Energy Transition Partnerships and accountability in international climate governance

AbstractThis contribution examines whether just energy transition partnerships (JETPs), a new type of financial agreement between G7/G7‐allies and low‐to‐middle income states, can serve as accountability mechanisms for the United Nation Framework Convention on Climate Change (UNFCCC). It identifies important structural elements from the existing literature on climate governance for holding actors to account and then assesses the extent to which they are present within JETPs. While JETPs appear in theory to offer increased potential to shore up accountability in the UNFCCC, the case of the South African JETP demonstrates that the presence of these structural elements is likely insufficient to ensure accountability vis‐à‐vis the UNFCCC. This preliminary analysis therefore finds several practical limitations to accountability within JETPs and dense governance spaces in general. It thus points to the importance of identifying the underlying conditions needed for institutional accountability structures to function as designed.

SItomo – A toolbox for splitting intensity tomography and application in the Eastern Alps

The tomographic inversion of shear wave splitting data for upper mantle anisotropy has been a longstanding challenge. This is due to the ray-based approximation of classical approaches and the near-vertical incidence of the core-mantle converted phases such as SKS that are often used. Recent developments include the calculation of finite-frequency sensitivity kernels for SKS splitting intensity observations, which allows us to accurately take into account the sensitivity to anisotropic structure with depth. A requirement of this tomographic technique is a dense station spacing, which results in overlapping sensitivity kernels at depth and allows for the localization of anisotropic structure. This is satisfied by a growing number of temporary seismic deployments, which motivates the desire to image anisotropic complexities with depth. Here, we introduce and make available a toolbox for the MATLAB environment that facilitates the application of finite-frequency splitting intensity tomography to dense seismic arrays. Our implementation includes several key features, including: 1) A forward calculation of splitting intensities and sensitivity kernels for a complex anisotropic model space. 2) Consideration of the dominant period of the wave, allowing for multiple-frequency analysis, as well as the incoming wave’s non-vertical incidence. 3) The inversion can be based on a classical gradient descent, on a form of the conjugate gradient method known as the BFGS algorithm, or on a gradient-informed stochastic reversible jump algorithm, allowing for a data-driven parametrization of the model space. 4) Importing splitting intensity measurements from waveforms processed in SplitRacer allows for fast pre-processing of large data sets due to its fully automatic design. To illustrate our method, we present both synthetic tests and an application to real data. We apply our inversion procedure to data from the Swath-D network, which densely covers the transition of the Central to the Eastern Alps. Previous studies showed evidence for an abrupt lateral change of layered seismic anisotropy that had been attributed to an opening for channeled asthenospheric flow. Using an SKS splitting intensity tomography approach, we can confirm previous inferences while providing additional constraints on the distribution of anisotropy laterally and with depth.

Study on the infection control performance under interactive cascade ventilation: A focus on a conference room environment

With the rise of respiratory infectious diseases, many scholars focus on finding an appropriate ventilation method to effectively improve the indoor environmental protection effect, especially for crowded spaces, which can cause a widespread spread of the epidemic. Interactive cascade ventilation (ICV), as a recently proposed ventilation method, has presented advantages in thermal comfort, infection control and energy consumption. This study discusses the effect of the outlet/inlet locations on infection control performance under interactive cascade ventilation in a conference room with an experimentally verified model. Ninety-five simulations are carried out by considering changes in the location of the outlet/inlet opening and infection source. Particle removal efficiency (ε), predicted infection risk (Pi) and contaminant dispersion index (CDI) are introduced to evaluate the infection control performance comprehensively. The simulation results show that double-side supply air outlets coupled with double-side return air inlets can reduce Pi by 51.1 % compared with other ventilation strategies investigated in this paper. It indicates that ventilation zoning design is more effective for reducing cross-infection in dense spaces. The findings in this study can efficiently provide new insights for the actual application of ventilation strategies in conference rooms under epidemic normalization.

Millimeter-Wave Channel Modeling and Coverage Analysis for Indoor Dense Spaces

Millimeter-Wave Channel Modeling and Coverage Analysis for Indoor Dense Spaces

Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

Abstract Based on the formal definition of semantic language and specific natural language, the research adopts the Word2vec model to map words into a low-dimensional dense space and performs semantic modeling by unsupervised learning through a large amount of unlabeled data to realize the vectorized representation of translated text. For the vectorized text, the text features are combined and filtered based on the TextCNN network, and the TextCNN network is implemented under the Keras framework. Based on the TextCNN network of Keras, the stylistic features of Business English and Daily English are compared at three levels: lexical, syntactic and discourse. Business English was rated 23.6 points higher in terms of conciseness of diction compared to everyday English, and 28.7 points improved the modesty of diction compared to everyday English. This study furthers the improvement of various English translation types.

Event pattern analysis: the point density around the appearance and disappearance of points

ABSTRACT Point pattern analysis is a fundamental analytical tool in various academic fields related to spatial information science. Basic but important patterns discussed in existing studies are spatially clustered and dispersed points. Analysis of these static patterns aims to reveal their underlying structure, i.e. why and how they are formed. However, to answer this research question, the direct tracking of the pattern formation process is more effective, though we have not yet fully conducted such analysis along the temporal axis. To fill the research gap, this paper develops a new method of spatiotemporal analysis. We focus on the point density around the appearance and disappearance of points. A research question is whether points appear/disappear in dense or sparse space. Extending Ripley’s K-function, we develop four measures statistically evaluating the point density. We applied the proposed method to the analysis of the competition among convenience store chains in Shibuya-ku, Tokyo.

PS-Insar point cloud densification using Sentinel-1 and TerraSAR-X data

ABSTRACT The persistent scatterer interferometric SAR (PS-InSAR) was presented to overcome the temporal and spatial circumspection in the studied areas. The interferometric phase contains different phase components, with many researchers being particularly interested in the displacement component. In this article, contrary to the conventional use of this method in evaluating the displacement phase, we try to estimate the residual phase component of the topography contribution by changing master image selection criteria and spatial baseline maximization. By calculating the height error of the topography model utilized in PS-InSAR processing, also known as the height phase, it becomes possible to determine the height correction for each permanent scatterer. There is a challenge with generating 3D models of urban buildings due to the absence of acceptable point clouds. To overcome this obstacle, we used shape-completion learning methods. The training process directly transforms from discrete point cloud space to dense space by extracting the general geometric structure of the desired shape while preserving the details. The loss criterion of this network is based on the Chamfer Distance, which measures the distance between the non-dense input and dense output point clouds. We generated 30,000 individual building datasets for network training. We utilized two SAR image datasets consisting of 27 Sentinel-1 and 20 TerraSAR-X images. After evaluating the test dataset of 3D building models and comparing them with aerial LiDAR reference data, we calculated the Root Mean Square Error (RMSE) for the PS height point clouds. For Sentinel-1 VH and VV channels, the RMSE values were 6.369 m and 5.478 m, respectively. As for TerraSAR-X HH channel, the RMSE was 4.317 m.

Transformer based contextual text representation framework for intelligent information retrieval

With the advent of transformer-based architectures, the contextual representation of text data has leveraged the query and the document to be represented in low-dimensional dense vector space. These vectors are learned embeddings of fixed sizes, resulting in deeper text understanding. In this study, we designed a pipeline for effectively retrieving documents from a large search space by combining the deeper text understanding capabilities of the transformer-based BERT model and a phrase embedding-based query expansion model. To learn the contextual representations, we fine-tuned a deep semantic matching model by separately encoding the document and the query. The encoder model is based on the Sentence BERT (SBERT) architecture, which separately generates dense vector representations of documents and queries. The study has also addressed the maximum token length limitation of transformer-based models through the summarization of lengthy documents. In addition, to improve the clarity and completeness of short queries and reduce the semantic gap, a phrase embedding-based query expansion model is employed. The documents and their dense vectors are indexed using the Elasticsearch engine, and matched them with query vectors for retrieving query-specific documents. Finally, the BERT-based cross-encoder model is used to re-rank the relevant records for each query. It performs full self-attention over the inputs, and yields richer text interactions to produce the final results. To assess performance, experiments are conducted on two well-known datasets, TREC-CDS-2014 and OHSUMED. A comparative analysis is carried out, which clearly demonstrates that the proposed framework produced competitive retrieval results.

Cell migration in dense microenvironments.

The nucleus has been viewed as a passenger during cell migration that functions merely to protect the genome. However, increasing evidence shows that the nucleus is an active organelle, constantly sensing the surrounding environment and translating extracellular mechanical inputs into intracellular signaling. The nuclear envelope has a large membrane reservoir which serves as a buffer for mechanical inputs as it unfolds without increasing its tension. In contrast, when cells cope with mechanical strain, such as migration through solid tumors or dense interstitial spaces, the nuclear envelope folds stretch, increasing nuclear envelope tension and sometimes causing rupture. Different degrees of nuclear envelope tension regulate cellular behaviors and functions, especially in cells that move and grow within dense matrices. The crosstalk between extracellular mechanical inputs and the cell nucleus is a critical component in the modulation of cell function of cells that navigate within packed microenvironments. Moreover, there is a link between regimes of nuclear envelope unfolding and different cellular behaviors, from orchestrated signaling cascades to cellular perturbations and damage.

공동주택 단지 외부공간의 거주자 범죄불안감 저감을 위한 개선사항 분석

This study analyzed the actual condition of the external spaces of the apartment, and analyzed the residents' fear of crime, satisfaction, and utilization of these external spaces among residents to derive factors that need to be improved to reduce the residents’ fear of crime in external spaces of the apartment.
 The study found that fear of crime by external spaces is analyzed to be high in apartments where access control for outsiders was not actively controlled and where a large number of blocked or hidden spaces could occur. It is only necessary to improve overall satisfaction to reduce fear of crime by securing access control and territoriality and increasing natural surveillance in landscape dense spaces.
 In addition, it was analyzed that the higher the resident use rate of the external space of the apartment, the lower the fear of crime, so that the proper location and number of external spaces and facilities such as rest, exercise, and playgrounds are calculated and placed to strengthen accessibility and activity. It is necessary to come up with a plan to reduce the fear of crime about the external spaces of apartments.

Reconfigurable AWGR based on LNOI with a tunable central wavelength and bandwidth used in elastic optical networking.

Elastic optical networking introduces elasticity and adaptation into the optical domain, which highly depends on reconfigurable optical devices. In this paper, a tunable 4×4 arrayed waveguide grating router based on lithium niobate on insulator is designed. By using the electro-optic effect of lithium niobate, we design electrode regions with specific shapes in the array waveguide region to realize the tuning of the routing wavelength and bandwidth of the third output channel. The designed arrayed waveguide grating router (AWGR) has a dense channel spacing of 0.8nm, and the minimum insertion loss is 2.3dB. Experiments show that the tuning range of the central wavelength can reach 3.2nm, and the 3dB bandwidth can be expanded from 0.2 to 0.6nm.

Identifying influential nodes in complex networks based on network embedding and local structure entropy

The identification of influential nodes in complex networks remains a crucial research direction, as it paves the way for analyzing and controlling information diffusion. The currently presented network embedding algorithms are capable of representing high-dimensional and sparse networks with low-dimensional and dense vector spaces, which not only keeps the network structure but also has high accuracy. In this work, a novel centrality approach based on network embedding and local structure entropy, called the ELSEC, is proposed for capturing richer information to evaluate the importance of nodes from the view of local and global perspectives. In short, firstly, the local structure entropy is used to measure the self importance of nodes. Secondly, the network is mapped to a vector space to calculate the Manhattan distance between nodes by using the Node2vec network embedding algorithm, and the global importance of nodes is defined by combining the correlation coefficients. To reveal the effectiveness of the ELSEC, we select three types of algorithms for identifying key nodes as contrast approaches, including methods based on node centrality, optimal decycling based algorithms and graph partition based methods, and conduct experiments on ten real networks for correlation, ranking monotonicity, accuracy of high ranking nodes and the size of the giant connected component. Experimental results show that the ELSEC algorithm has excellent ability to identify influential nodes.

Assessment of cheese sign and its association with vascular risk factors: Data from PUMCH dementia cohort.

In the clinic, practitioners encounter many patients with an abnormal pattern of dense punctate magnetic resonance imaging (MRI) signal in the basal ganglia, a phenomenon known as "cheese sign". This sign is reported as common in cerebrovascular diseases, dementia, and old age. Recently, cheese sign has been speculated to consist of dense perivascular space (PVS). This study aimed to assess the lesion types of cheese sign and analyze the correlation between this sign and vascular disease risk factors. A total of 812 patients from Peking Union Medical College Hospital (PUMCH) dementia cohort were enrolled. We analyzed the relationship between cheese sign and vascular risk. For assessing cheese sign and defining its degree, the abnormal punctate signals were classified into basal ganglia hyperintensity (BGH), PVS, lacunae/infarctions and microbleeds, and counted separately. Each type of lesion was rated on a four-level scale, and then the sum was calculated; this total was defined as the cheese sign score. Fazekas and Age-Related White Matter Changes (ARWMC) scores were used to evaluate the paraventricular, deep, and subcortical gray/white matter hyperintensities. A total of 118 patients (14.5%) in this dementia cohort were found to have cheese sign. Age (odds ratio [OR]: 1.090, 95% confidence interval [CI]: 1.064-1.120, P <0.001), hypertension (OR: 1.828, 95% CI: 1.123-2.983, P = 0.014), and stroke (OR: 1.901, 95% CI: 1.092-3.259, P = 0.025) were risk factors for cheese sign. There was no significant relationship between diabetes, hyperlipidemia, and cheese sign. The main components of cheese sign were BGH, PVS, and lacunae/infarction. The proportion of PVS increased with cheese sign severity. The risk factors for cheese sign were hypertension, age, and stroke. Cheese sign consists of BGH, PVS, and lacunae/infarction.

Evaluating normalization accounts against the dense vowel space of Central Swedish.

Talkers vary in the phonetic realization of their vowels. One influential hypothesis holds that listeners overcome this inter-talker variability through pre-linguistic auditory mechanisms that normalize the acoustic or phonetic cues that form the input to speech recognition. Dozens of competing normalization accounts exist-including both accounts specific to vowel perception and general purpose accounts that can be applied to any type of cue. We add to the cross-linguistic literature on this matter by comparing normalization accounts against a new phonetically annotated vowel database of Swedish, a language with a particularly dense vowel inventory of 21 vowels differing in quality and quantity. We evaluate normalization accounts on how they differ in predicted consequences for perception. The results indicate that the best performing accounts either center or standardize formants by talker. The study also suggests that general purpose accounts perform as well as vowel-specific accounts, and that vowel normalization operates in both temporal and spectral domains.

Bayesian learning of coupled biogeochemical–physical models

Predictive dynamical models for marine ecosystems are used for a variety of needs. Due to the sparse measurements and limited understanding of the myriad of ocean processes, there is however significant uncertainty. There is model uncertainty in the parameter values, functional forms with diverse parameterizations, and level of complexity needed, and thus in the state variable fields. We develop a Bayesian model learning methodology that allows interpolation in the space of candidate dynamical models and discovery of new models from noisy, sparse, and indirect observations, all while estimating state variable fields and parameter values, as well as the joint probability distributions of all learned quantities. We address the challenges of high-dimensional and multidisciplinary dynamics governed by partial differential equations (PDEs) by using state augmentation and the computationally efficient Gaussian Mixture Model — Dynamically Orthogonal filter. Our innovations include stochastic formulation parameters and stochastic complexity parameters to unify candidate models into a single general model as well as stochastic expansion parameters within piecewise function approximations to generate dense candidate model spaces. These innovations allow handling many compatible and embedded candidate models, possibly none of which are accurate, and learning elusive unknown functional forms that augment these models. Our new Bayesian methodology is generalizable and interpretable. It seamlessly and rigorously discriminates among existing models, but also extrapolates out of the space of models to discover new ones. We perform a series of twin experiments based on flows past a ridge coupled with three-to-five component ecosystem models, including flows with chaotic advection. We quantify the learning skill, and evaluate convergence and the sensitivity to hyper-parameters. Our PDE framework successfully discriminates among functional forms and model complexities, and learns in the absence of prior knowledge by searching in dense function spaces. The probabilities of known, uncertain, and unknown model formulations, and of biogeochemical–physical fields and parameters, are updated jointly using Bayes’ law. Non-Gaussian statistics, ambiguity, and biases are captured. The parameter values and the model formulations that best explain the noisy, sparse, and indirect data are identified. When observations are sufficiently informative, model complexity and model functions are discovered.

Environmental assessment of design choices of green walls based for materials combination and plants

PurposeGreen infrastructures have been considered as a sustainable solution to deal with the problems in dense urban spaces. This acceptance has led to the use of various materials and methods in designing and producing green infrastructures. The purpose of this study is to emphasize the components and materials used in the green walls system. By determining the environmental impact of each element, it can be replaced with another component with the same function. This approach ultimately leads to the sustainable design of systems.MethodsAccordingly, two green wall systems were selected for the environmental assessment using the life cycle assessment method. Data related to all processes and components involved in each system are reviewed for a 10-year lifespan. For each system, eight different scenarios of component composition are considered. The functional unit is 1 square meter and the ILCD method is used to evaluate the impact of the life cycle. OpenLCA software and Ecoinvent® v3.7 database are used to model and analyze all 16 scenarios.ResultsThe results clearly show that the components and materials used in the production and construction of the green wall system have a very high impact on the environmental performance of these systems. A better design can be achieved by doing an environmental assessment, identifying the undesirable components in the system, and replacing them. Different scenarios' results show the potential to improve the environmental performance of systems in all impact categories. This highlights the great importance of the green wall system's design, material selection and maintenance methods.ConclusionsConsidering the ability of the LCA method to identify the weak and strong points of the systems, the lack of using this method in the design phase is strongly sensed. With this method's help, it is possible to produce more sustainable systems. On the other hand, the studies conducted in the field of green walls are limited compared to other green infrastructures. At the same time, the design of the green wall system can be changed based on geographical location, weather conditions, access to raw materials and consumer needs. As the number of studies on these systems increases in different places and weather conditions, it can help the stability of these systems.

Green walls and health: An umbrella review

Current societal challenges like climate change led to a general agreement that our cities need to become greener and our lifestyles more sustainable. This transformation of our daily living environments can also impact the prevalence of non-communicable diseases as a global disease burden of our time. These positive impacts of horizontal green spaces on human health are widely recognized. However, it is still unclear whether the same is true for green walls, as a promising nature-based solution for dense urban spaces which is increasingly applied. To date, the available research on green walls has not been systematically synthesized along the potential impact pathways of reducing environmental stressors (Mitigation), restoring capacities (Restoration), and promoting healthier behavior (Instoration). We conducted a systematic review of 30 reviews to synthesize available evidence on all three pathways and direct health outcomes, following the established strategies of PICOS and PRISMA. We assessed the review quality through AMSTAR. We found strong consistent evidence that green walls can mitigate urban heat island effects (daylight surface temperature: -0.3 °C to -31.9°, daylight air temperature: -0 °C to -8.7 °C), air pollution (PM2.5: -25% to -99%, PM10: -23% to -60%), and noise pollution (sound pressure level: -1dBA to -5dBA). We found some evidence for disaster risk reduction and restoration effects. There were no reviews on the instoration pathway or direct health outcomes. The underlying reviews rated low according to the AMSTAR checklist, which might limit our findings. We recognize a generally young research field and conclude that more in-field studies are needed in all pathways to better understand the relationship between green walls and health.

Fluorinated Al2O3/siloxane modified PI films towards vastly enhanced corona resistance performance

Polyimide (PI) films with excellent corona-resistant performance are vital in multitudinous electrical equipment. Here we employ a largely unexplored strategy to effectively restrain phase separation behavior of siloxane modified PI through chain extension copolymerization (PI-e-Si), thereby generating dense SiO2-like storage space under corona that shows powerful ability of encircling and restraining electron/ion, which restricts their direct and persistent attack for PI segment. Therefore, PI-e-Si film owns obviously longer corona-resistant lifetime (30.8 min) than that of pristine PI film (6.4 min) based on accelerated corona-aged test. Furthermore, fluorinated Al2O3 (F-Al2O3) with strong corona-resistant ability is applied to construct a solid barrier for dense SiO2-like storage space, which synergistically strengthens restraint effect for electron/ion, and it also optimizes heat-conducting and dielectric properties of PI film. Finally, fabricated composite film (PI-e-Si/F-Al2O3) further improves corona-resistant lifetime up to 66.8 min, superior to that of Kapton 100 CR film (a famous corona-resistant PI film).

Distributed non-negative RESCAL with automatic model selection for exascale data

With the boom in the development of computer hardware and software, social media, IoT platforms, and communications, there has been exponential growth in the volume of data produced worldwide. Among these data, relational datasets are growing in popularity as they provide unique insights regarding the evolution of communities and their interactions. Relational datasets are naturally non-negative, sparse, and extra-large. Relational data usually contain triples (subject, relation, object) and are represented as graphs/multigraphs, called knowledge graphs, which need to be embedded into a low-dimensional dense vector space. Among various embedding models, RESCAL allows the learning of relational data to extract the posterior distributions over the latent variables and to make predictions of missing relations. However, RESCAL is computationally demanding and requires a fast and distributed implementation to analyze extra-large real-world datasets. Here we introduce a distributed non-negative RESCAL algorithm for heterogeneous CPU/GPU architectures with automatic selection of the number of latent communities (model selection), called pyDRESCALk. We demonstrate the correctness of pyDRESCALk with real-world and large synthetic tensors and the efficacy showing near-linear scaling that concurs with the theoretical complexities. Finally, pyDRESCALk determines the number of latent communities in an 11-terabyte dense and 9-exabyte sparse synthetic tensor.

Dual-channel embedding learning model for partially labeled attributed networks

Network embedding is an important fundamental work in many network application tasks, which encodes the input network from the high-dimensional and sparse topological space into a low-dimensional and dense vector space. Recently, there has been a growing interest in embedding learning on Partially Labeled Attributed Networks (PLANs) due to the increasing occurrence of node attributes and partially available category labels in real-world networks. Semi-supervised embedding learning is a standard approach employed in PLANs, utilizing category labels to supervise the learning process. However, the semi-supervised learning procedure can fail when labels are scarce, noisy, or unreliable. Additionally, most existing embedding algorithms have not successfully integrated heterogeneous information, such as labels, attributes, and structure. To address these issues, we develop a new model, the Dual-Channel Network Embedding (DcNE), which integrates different types of network information into embeddings from a mutual information (MI) perspective. Specifically, we construct a dual-channel information propagation framework to encode the input network in semi-supervised and self-supervised learning paradigms in parallel. Furthermore, a redundancy elimination module is implemented to capture and eliminate the redundant information between the two encoders. Finally, we propose a unified optimization model that integrates the two learning paradigms to collaborate effectively. In the experiments, we demonstrate the effectiveness of DcNE in various network analysis tasks using real-world datasets, establishing its superiority over state-of-the-art baselines.