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Mechanical metamaterial is a synthetic material that can possess extraordinary physical characteristics, such as abnormal elasticity, stiffness, and stability, by carefully designing its internal structure. To make metamaterials contain delicate local structures with unique mechanical properties, it is a potential method to represent them through high-resolution voxels. However, it brings a substantial computational burden. To this end, this paper proposes a fast inverse design method, whose core is an advanced deep generative AI algorithm, to generate voxel-based mechanical metamaterials. Specifically, we use the self-conditioned diffusion model, capable of generating a microstructure with a resolution of $128^3$ to approach the specified homogenized tensor matrix in just 3 seconds. Accordingly, this rapid reverse design tool facilitates the exploration of extreme metamaterials, the sequence interpolation in metamaterials, and the generation of diverse microstructures for multi-scale design. This flexible and adaptive generative tool is of great value in structural engineering or other mechanical systems and can stimulate more subsequent research., Comment: 13 pages, 6 figures

Mengying Yao,1– 3,* Dawei Lian,1– 3,* Meizhu Wu,1– 3 Yuting Zhou,1– 3 Yi Fang,1– 4 Siyu Zhang,1– 3 Wenqiang Zhang,1– 3 Yanyan Yang,1– 4 Renfeng Li,1– 3 Hong Chen,1– 3 Youqin Chen,5 Aling Shen,1– 4 Jun Peng1– 3 1Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People’s Republic of China; 2Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People’s Republic of China; 3Fujian Collaborative Innovation Center for Integrative Medicine in Prevention and Treatment of Major Chronic Cardiovascular Diseases, Fuzhou, Fujian, People’s Republic of China; 4Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People’s Republic of China; 5Department of Pediatrics, Case Western Reserve University School of Medicine, Rainbow Babies and Children’s Hospital, Cleveland, OH, USA*These authors contributed equally to this workCorrespondence: Aling Shen; Jun Peng, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, People’s Republic of China, Tel +86 18050286255 ; +86 15806070011, Email saling86@hotmail.com; pjunlab@hotmail.comPurpose: This study aimed to investigate the molecular mechanisms of isoliensinine, a kind of bibenzyl isoquinoline alkaloid which isolated from a TCM named Lotus Plumule (Nelumbo nucifera Gaertn), in treating renal interstitial fibrosis (RIF) by using RNA sequencing, KEGG analysis and in vivo experimental approaches.Methods: Spontaneous hypertension rats (SHRs) were randomly assigned into five groups, consisting of SHR, SHR+Isoliensinine-L (2.5 mg/kg/day), SHR+Isoliensinine-M (5 mg/kg/day), SHR+Isoliensinine-H (10 mg/kg/day), and SHR+Valsartan (10 mg/kg/day) groups (n = 6 for each group). A control group of Wistar Kyoto rats (n = 6) was also included. Rats were treated intragastrically with isoliensinine, valsartan, o

Understanding crowd behavior is crucial for energy demand-side management.Inthispaper,weemploythefluiddynamicsconcept potential flow to model the energy demand shift patterns of the crowd in both temporal and spatial dimensions. To facilitate the use of the proposed method, we implement a visual analysis platform that allows users to interactively explore and interpret the shift patterns. The effectiveness of the proposed method will be evaluated through a hands-on experience with a real case study during the conference demonstration.

Yanyan Yang,1 Yao Qin,2 Ziyi Wang,3 Angyu Sun4 1School of Business Administration, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, People’s Republic of China; 2School of Business, Macau University of Science and Technology, Macau, People’s Republic of China; 3School of Business Administration, Zhongnan University of Economics and Law, Wuhan, Hubei, People’s Republic of China; 4Faculty of Arts and Social Science, Dalhousie University, Halifax, NS, CanadaCorrespondence: Ziyi Wang, Email 15999955121@163.comPurpose: The purpose of this study is to investigate whether the emotional labor of service employees affects customer service misbehavior and repurchase intention and to explore the mechanism and boundary conditions.Methods: We collected a total of 252 pairs of employee–customer valid matching data and used SPSS 24.0 and Mplus7.0 statistical analysis tools to perform statistical analysis and hypothesis testing.Results: The results showed that employees’ surface acting has a significant positive impact on customer misbehavior and negative impact on repurchase intention via perceived face threat, while deep acting has a significant negative impact on customer misbehavior and positive impact on repurchase intention via perceived face threat. And customer face threat sensitivity not only moderates the relationship between service employee emotional labor and customer perceived face threat but also moderates the indirect effect of surface acting on customer misbehavior and repurchase intention via customer perceived face threat.Conclusion: Based on face theory, this study explained how and when emotional labor of service employees may affect customer service misbehavior and repurchase intention. These results contribute to the emotional labor and customer service misbehavior literature by introducing perceived face threat as an underlying mechanism and face threat sensitivity as a boundary condition. In addition, this study suggests that service

Chao Tian,1,* Yanyan Yang,2,* Bing Li,3 Meixin Liu,4 Xiangqin He,4 Liang Zhao,4 Xiaoxia Song,4 Tao Yu,4,5 Xian-Ming Chu1,6 1Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 2Department of Immunology, Basic Medicine School, Qingdao University, Qingdao, People’s Republic of China; 3Department of Genetics, Basic Medicine School, Qingdao University, Qingdao, People’s Republic of China; 4Department of Cardiac Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 5Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 6Department of Cardiology, The Affiliated Cardiovascular Hospital of Qingdao University, Qingdao, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tao Yu, Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China, Tel/Fax +86-532-82991791, Email yutao0112@qdu.edu.cn Xian-Ming Chu, Department of Cardiology, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266100, People’s Republic of China, Tel +86-532-82913257, Email chuxianming@qdu.edu.cnPurpose: To explore the therapeutic mechanism of bone marrow mesenchymal stem cells derived exosomes (BMSC-Exos) for doxorubicin (DOX)-induced cardiotoxicity (DIC) and identify the long noncoding RNAs’ (lncRNAs’) anti-inflammation function derived by BMSC-Exos.Materials and Methods: High-throughput sequencing and transcriptome bioinformatics analysis of lncRNA were performed between DOX group and BEC (bone marrow mesenchymal stem cells derived exosomes coculture) group. Elevated lncRNA (ElncRNA) in the cardiomyocytes of BEC group compared with DOX group were confirmed. Based on the location and co-expression relationship between ElncRNA and its target genes, we predicted two target genes

Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC. In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task. We propose SCOPE which builds on top of a shared encoder two parallel decoders, one for the primary CSC task and the other for a fine-grained auxiliary CPP task, with a novel adaptive weighting scheme to balance the two tasks. In addition, we design a delicate iterative correction strategy for further improvements during inference. Empirical evaluation shows that SCOPE achieves new state-of-the-art on three CSC benchmarks, demonstrating the effectiveness and superiority of the auxiliary CPP task. Comprehensive ablation studies further verify the positive effects of adaptivity and granularity of the task. Code and data used in this paper are publicly available at https://github.com/jiahaozhenbang/SCOPE., Comment: To appear at the main conference of EMNLP 2022

Understanding the relative importance of dispersal and environmental selection in shaping aquatic mycoplankton communities can help to predict fungus-driven ecological processes such as mycoflux or mycoloop and to maintain biodiversity in river ecosystems. In the presented study, a transect of the Elbe River (7th stream order number) that extended from the shallow freshwater zone through the estuary up to the river plume, and to the adjacent ocean waters, was sampled. Mycoplankton community structure and underlying assemblage processes were investigated using tag sequencing and Quantitative Process Estimates (QPE) analysis. Mycoplankton communities formed three significantly different biomes, by which the most structuring factor of salinity was superimposed by secondary effects of the Elbe deepening works. The underlying processes differed drastically: migrations of taxa played only a minor role. Instead, the species present in upstream sections responded to the strong physico-chemical environmental changes and a change from an autotrophic to a heterotrophic system was observed. Downstream, neither selection nor dispersal dominated but microbial features such as the cell tolerance to salinity variability, host dependency, and life strategy could help to explain the observed patterns. The results showed that the assemblage processes can change over relatively short distances. It indicates that the processes are not static and that the relative importance of a process can vary under different conditions and between members of a community.++

Rivers are transport systems and supply adjacent ecosystems with nutrients. They also serve human well-being, for example as a source of food. Microorganismic biodiversity is an important parameter for the ecological balance of river ecosystems. Despite the knowledge that fungi are key players in freshwater nutrient cycling and food webs, data on planktonic fungi of streams with higher stream order is scarce. This study aims to fill this knowledge gap by a fungi-specific 18S rRNA gene tag sequencing approach, investigating mycoplankton diversity in the Elbe River along a transect from shallow freshwater, to the estuary and river plume down to the adjacent marine waters (sections of 7th stream order number). Using multivariate analyses and the Quantitative Process Estimates (QPE) method, the questions of how mycoplankton communities as part of the river continuum change along the transect, what factors, spatial and environmental, play a role, and what assembly processes, such as selection or dispersion, operate along the transect were addressed. The partitioning of mycoplankton communities into three significant distant biomes was mainly driven by local environmental conditions that were partly under spatial control. The assembly processes underlying the biomes also differed significantly. Thus, variable selection dominated the upstream sections, while undominated processes like ecological drift dominated the sections close to the river mouth and beyond. Dispersal played a minor role. The results suggest that the ecological versatility of the mycoplankton communities changes along the transect as response for example to a drastic change from an autotrophic to a heterotrophic system caused by an abrupt increase in the river depth. Furthermore, a significant salinity-dependent occurrence of diverse basal fungal groups was observed, with no clade found exclusively in marine waters. These results provide an important framework to help understand patterns of riverine myco

Rivers are transport systems and supply adjacent ecosystems with nutrients. They also serve human well-being, for example as a source of food. Microorganismic biodiversity is an important parameter for the ecological balance of river ecosystems. Despite the knowledge that fungi are key players in freshwater nutrient cycling and food webs, data on planktonic fungi of streams with higher stream order is scarce. This study aims to fill this knowledge gap by a fungi-specific 18S rRNA gene tag sequencing approach, investigating mycoplankton diversity in the Elbe River along a transect from shallow freshwater, to the estuary and river plume down to the adjacent marine waters (sections of 7th stream order number). Using multivariate analyses and the Quantitative Process Estimates (QPE) method, the questions of how mycoplankton communities as part of the river continuum change along the transect, what factors, spatial and environmental, play a role, and what assembly processes, such as selection or dispersion, operate along the transect were addressed. The partitioning of mycoplankton communities into three significant distant biomes was mainly driven by local environmental conditions that were partly under spatial control. The assembly processes underlying the biomes also differed significantly. Thus, variable selection dominated the upstream sections, while undominated processes like ecological drift dominated the sections close to the river mouth and beyond. Dispersal played a minor role. The results suggest that the ecological versatility of the mycoplankton communities changes along the transect as response for example to a drastic change from an autotrophic to a heterotrophic system caused by an abrupt increase in the river depth. Furthermore, a significant salinity-dependent occurrence of diverse basal fungal groups was observed, with no clade found exclusively in marine waters. These results provide an important framework to help understand patterns of riverine myco

Providing accurate measurement of the magnetic field intensity and its vector components is one of the primary objectives of the China Seismo-Electromagnetic Satellite (CSES). The high precision magnetometer (HPM) payload assembled on CSES is designed to achieve this goal. In this study, the data format, naming convention, and content of the CSES HPM Level 2 scientific data products are introduced, as a reference for users who are interested in this data set. In particular, flags for potential magnetic field disturbances from the platform and payloads are discussed. Possible scientific applications are also outlined. A preliminary validation of the data is conducted through comparison with magnetic data from the ESA’s Swarm constellation, and the result demonstrates that the HPM data of CSES are of good quality. Taking the intense geomagnetic storm that occurred on August 25–26, 2018 as an example, the magnetic field variations and the expansion of the field-aligned currents (FACs) during this storm are discussed. We finally show that the CSES HPM data can be used to derive a satellite-derived index equivalent to the Dst index, which agrees well to the index during this event. Our analysis thus suggests a high scientific potential of the HPM data.

Using magnetic field data from the China Seismo-Electromagnetic Satellite (CSES) mission, we derive a global geomagnetic field model, which we call the CSES Global Geomagnetic Field Model (CGGM). This model describes the Earth’s magnetic main field and its linear temporal evolution over the time period between March 2018 and September 2019. As the CSES mission was not originally designed for main field modelling, we carefully assess the ability of the CSES orbits and data to provide relevant data for such a purpose. A number of issues are identified, and an appropriate modelling approach is found to mitigate these. The resulting CGGM model appears to be of high enough quality, and it is next used as a parent model to produce a main field model extrapolated to epoch 2020.0, which was eventually submitted on October 1, 2019 as one of the IGRF-13 2020 candidate models. This CGGM candidate model, the first ever produced by a Chinese-led team, is also the only one relying on a data set completely independent from that used by all other candidate models. A successful validation of this candidate model is performed by comparison with the final (now published) IGRF-13 2020 model and all other candidate models. Comparisons of the secular variation predicted by the CGGM parent model with the final IGRF-13 2020–2025 predictive secular variation also reveal a remarkable agreement. This shows that, despite their current limitations, CSES magnetic data can already be used to produce useful IGRF 2020 and 2020–2025 secular variation candidate models to contribute to the official IGRF-13 2020 and predictive secular variation models for the coming 2020–2025 time period. These very encouraging results show that additional efforts to improve the CSES magnetic data quality could make these data very useful for long-term monitoring of the main field and possibly other magnetic field sources, in complement to the data provided by missions such as the ESA Swarm mission.[Figure not avail

In recent years, the increasing prevalence of hate speech in social media has been considered as a serious problem worldwide. Many governments and organizations have made significant investment in hate speech detection techniques, which have also attracted the attention of the scientific community. Although plenty of literature focusing on this issue is available, it remains difficult to assess the performances of each proposed method, as each has its own advantages and disadvantages. A general way to improve the overall results of classification by fusing the various classifiers results is a meaningful attempt. We first focus on several famous machine learning methods for text classification such as Embeddings from Language Models (ELMo), Bidirectional Encoder Representation from Transformers (BERT) and Convolutional Neural Network (CNN), and apply these methods to the data sets of the SemEval 2019 Task 5. We then adopt some fusion strategies to combine the classifiers to improve the overall classification performance. The results show that the accuracy and F1-score of the classification are significantly improved.

In the context of urbanization and the rapid growth of energy demand, understanding the spatial and temporal dynamics of urban energy use is crucial for identifying energy-saving potentials. In this demo, we present a visual analysis tool, VAP, that allows users to explore the dynamics of urban energy use at different spatial and temporal scales. In contrast to traditional statistical and machine learning methods, the visual analysis based tool focuses on analytical thinking, user interactions and answering business questions by examining different visual analysis views. In the demonstration, conference attendees will interact with VAP and learn its capabilities in discovering typical consumption patterns and spatio-temporal shift patterns from a real-world case study of electricity.

User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or more family members, and formalizes the simulation processes under different conditions. A data generator is implemented to create fine-grained activity sequences that require only a small sample of time-use survey data as a seed. This paper evaluates the data generator by comparing the generated synthetic data with real data, and comparing other related work. The results show the effectiveness of the proposed modelling approach and the efficiency of generating realistic residential user activities.

Human action recognition remains an important yet challenging task. This work proposes a novel action recognition system. It uses a novel Multiple View Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM) formulation combined with appearance information. Multiple stream 3D Convolutional Neural Networks (CNNs) are trained on the different views and time resolutions of the region adaptive Depth Motion Maps. Multiple views are synthesised to enhance the view invariance. The region adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. Dedicated 3D CNN streams for multi-time resolution appearance information (RGB) are also included. These help to identify and differentiate between small object interactions. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multiple class Support Vector Machines (SVM)s. Average score fusion is used on the output. The developed approach is capable of recognising both human action and human-object interaction. Three public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view actions and MSR 3D daily activity are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms., Comment: 14 pages, 6 figures, 13 tables. Submitted

Lu Li,1,* Zongfu Pan,2,* Si Yang,1 Wenya Shan,1 Yanyan Yang1 1Department of Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China; 2Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, People’s Republic of China *These authors contributed equally to this work Purpose: The number of people with type 2 diabetes (T2D) is growing rapidly worldwide. Islet β-cell dysfunction and failure are the main causes of T2D pathological processes. The aim of this study was to elucidate the underlying pathways and coexpression networks in T2D islets. Materials and methods: We analyzed the differentially expressed genes (DEGs) in the data set GSE41762, which contained 57 nondiabetic and 20 diabetic samples, and developed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein–protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed as well. Moreover, a weighted correlation network analysis (WGCNA) was applied to screen critical gene modules and coexpression networks and explore the biological significance. Results: We filtered 957 DEGs in T2D islets. Then GO and KEGG analyses identified that key pathways like inflammatory response, type B pancreatic cell differentiation, and calcium ion-dependent exocytosis were involved in human T2D. Three significant modules were filtered from the PPI network. Ribosome biogenesis, extrinsic apoptotic signaling pathway, and membrane depolarization during action potential were associated with the modules, respectively. Furthermore, coexpression network analysis by WGCNA identified 13 distinct gene modules of T2D islets and revealed four modules, which were strongly correlated with T2D and T2D biomarker hemoglobin A1c (HbA1c). Functional annotation showed that these modules mainly enriched KEGG pathway

Wireless technologies grow rapidly and benefit almost every aspect of our daily lives. To fulfill various application requirements, researchers have proposed and developed many different communication protocols. The assignments of wireless channels, which are known as the most precious resources in wireless networks, cause intensive research interests from both academic and industrial fields. Every different kind of wireless network has its own feature, which puts extra constraints on the channel assignment. As the emerging technology, wireless sensor networks (WSNs) are well known for its feature of easy deployment, low cost and etc. Since most of the commercial WSNs-oriented products using ISM 2.4GHz band for communications, the communication costs in terms of energy and time become a very challenging issue for real applications. To tackle this problem, we first comprehensively study the channel feature in WSNs. Then proposed Double-Plate, a fair channel assignment algorithm, which aims to ensure high throughput and fairness simultaneously. With the observation of inefficient channel usage among the full spectrum band, the new concept of cognitive radio networks (CRNs) is initiated by researchers, where spectrum users are divided into primary users with license and secondary users without license but can opportunistically use the spectrum. While CRNs calls for spectrums sensing from secondary users before they can use any available pre-assigned channels to primary users, the spectrum sensing technology becomes one key issue for a successful deployment in CRNs. To utilize the WSNs for spectrum sensing, we initiate a cooperative boundary detection algorithm which utilizes WSNs to detect the signal boundary of each primary user. We then also study the more complicated case where multiple primary channels exist. This scenario is more realistic in practice. While more sensors can definitely provide more accurate sensing result per primary channel, we must find a best t

Traditional RF-based indoor positioning approaches use only Radio Signal Strength Indicator (RSSI) to locate the target object. But RSSI suffers significantly from the multi-path phenomenon and other environmental factors. Hence, the localization accuracy drops dramatically in a large tracking field. To solve this problem, this paper introduces one more resource, the dynamic of RSSI, which is the variance of signal strength caused by the target object and is more robust to environment changes. By combining these two resources, we are able to greatly improve the accuracy and scalability of current RF-based approaches. We call such hybrid approach COCKTAIL. It employs both the technologies of active RFID and Wireless Sensor Networks (WSNs). Sensors use the dynamic of RSSI to figure out a cluster of reference tags as candidates. The final target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Experiments show that COCKTAIL can reach a remarkable high degree of localization accuracy to 0.45m, which outperforms significantly to most of the pure RF-based localization approaches.

Spectrum sensing is one of the key enabling technologies in Cognitive Radio Networks (CRNs). In CRNs, secondary users (SUs) are allowed to exploit the spectrum opportunities by sensing and accessing the spectrum, which exhibit many critical limitations in practical environments. In this paper, we propose a new sensing service model that uses dedicated wireless spectrum sensor networks (WSSN) for spectrum sensing. The major challenge in WSSN is the design of data fusion, for which the traditional fusion scheme will produce a large amount of errors. We formulate the problem as a boundary detection problem with notable unknown erroneous inputs. To solve the problem, we propose a novel cooperative boundary detection scheme that intelligently incorporates the cooperative spectrum sensing concept and the recent advances in support vector machine (SVM). Cooperative boundary detection consists of two major components, a declaration calibration algorithm and a boundary derivation algorithm. We prove that cooperative spectrum sensing can asymptotically approach the optimal solution. A prototype system as well as simulation experiments show that compared with the traditional approaches, cooperative boundary detection can reduce the errors by up to 95% with an average reduction about 85%.

In this paper, we study the trade-off between network throughput and fairness in a multi-channel enabled wireless sensor network (WSN). Traditional approaches attempt to solve the two problems in an isolated manner without a joint design. Although there are schemes aiming to improve either throughput or fairness, respectively, our empirical studies show that solutions to these two problems cannot be simply combined to achieve better throughput and fairness simultaneously. Away from the traditional belief, the number of channels in WSNs with Telosb sensor nodes operating at 2.4 GHz band can be up to 83 and the orthogonal channels can be up to 27. The switching overhead in terms of time and energy cost is relatively small. The averaged switching time for a sender is about 0.34 ms and the additional energy consumption caused by channel switching is no more than 2\% when 10(4) packets with 43 bytes of each are sending out continuously. Furthermore, we observe a buffer wall phenomenon which is one of the main reasons causing network throughput degradation and unfairness. To strike a better trade-off between the network throughput and fairness, we design a novel multi-channel assignment algorithm, targeting at maximizing the minimal data sending rate. The key idea of the proposed algorithm is to level down the buffer wall so that the buffer usage of nodes can be evenly distributed. As such, the bandwidth of bottleneck nodes can be fully utilized and the unfairness due to the node locality can be removed. We prove that the achieved data sending rate is no less than 4/9 of the optimal rate in theory. Our experimental results based on both chain and uniformly random sensor deployment topologies show that the minimal data sending rate can be improved by up to 100\% comparing with the existing work TMCP. (C) 2010 Elsevier B.V. All rights reserved.

A GLObal Smart Space (GLOSS) provides support for interaction amongst people, artefacts and places while taking account of both context and movement on a global scale. Crucial to the definition of a GLOSS is the provision of a set of location-aware services that detect, convey, store and exploit location information. We use one of these services, hearsay, to illustrate the implementation dimensions of a GLOSS. The focus of the paper is on both local and global software architecture to support the implementation of such services. The local architecture is based on XML pipe-lines and is used to construct location-aware components. The global architecture is based on a hybrid peer-to-peer routing scheme and provides the local architectures with the means to communicate in the global context., Comment: 4th International Conference on Mobile Data Management (MDM 2003)

This document describes the Gloss software currently implemented. The description of the Gloss demonstrator for multi-surface interaction can be found in D17. The ongoing integration activity for the work described in D17 and D8 constitutes our development of infrastructure for a first smart space. In this report, the focus is on infrastructure to support the implementation of location aware services. A local architecture provides a framework for constructing Gloss applications, termed assemblies, that run on individual physical nodes. A global architecture defines an overlay network for linking individual assemblies. Both local and global architectures are under active development.

In this paper, we study the trade-off between network throughput and fairness in a multi-channel enabled wireless sensor network (WSN). Traditional approaches attempt to solve the two problems in an isolated manner without a joint design. Our empirical studies show that solutions to these two problems cannot be simply combined. Away from the traditional belief, the number of channels in WSNs with Telosb sensor nodes operating at 2.4GHz band can be up to 83 and the orthogonal channels can be up to 27. The switching overhead in terms of time and energy cost is relatively small. Furthermore, we observe a buffer wall phenomenon which is one of the main reasons causing network throughput degradation and unfairness. To strike a better trade-off between the network throughput and fairness, we design a novel multi-channel assignment algorithm, targeting at maximizing the minimal data sending rate. The key idea of the proposed algorithm is to level down the buffer wall so that the buffer usage of nodes can be evenly distributed. As such, the bandwidth of bottleneck nodes can be fully utilized and the unfairness due to the node locality can be removed. We prove that the achieved data sending rate is no less than 4/9 of the optimal rate in theory. Our experimental results show that the minimal data sending rate can be improved by up to 100% comparing with the existing work TMCP. ©2009 IEEE.

Remote-sensing data about the Earth's environment is being created at an ever-increasing rate and distributed among heterogeneous remote sites. Traditional models of distributed computing are inadequate to support such complex applications, which generally involve a large quantity of data. In this paper, we explore an approach based on mobile agent techniques for autonomous data process and information discovery on the Synthetic Aperture Radar Atlas (SARA) digital library, which consists of distributed multi-agency archives of multi-spectral remote-sensing imagery of the Earth. Our goal is to enable automatic and dynamic configuration of distributed parallel computing and to efficiently support on-demand processing of such a remote-sensing archive. The architecture design and implementation status of the prototype system , which applied the approach, is reported here.