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This study aimed to elucidate the molecular mechanisms of hypoxia/reoxygenation (H/R) injury in human cardiac microvascular endothelial cells (HCMECs) by regulating ferroptosis. H/R model was established with HCMECs and before the reperfusion, ferroptosis inhibitor ferrostatin-1 or ferroptosis inducer erastin was all administered. Wound-healing assay was performed to detect the migration ability of cells in each group, and the angiogenesis ability was determined by tube formation assay. The level of reactive oxygen species (ROS) was detected by flow cytometry. Transmission electron microscopy (TEM) was used to observe the state of mitochondria. The expressions of related proteins in HCMECs were assessed by Western blot. From the results, H/R injury could inhibit the migration and angiogenesis, induce the ROS production, and cause the mitochondrial damage of HCMECs. Ferroptosis activator erastin could aggravate H/R injury in HCMECs, while the ferroptosis inhibitor ferrostatin-1 could reverse the effects of H/R on HCMECs. Western blot results showed that H/R or/and erastin treatment could significantly induce ACSL4, HGF, VEGF, p-ERK, and uPA protein expression and inhibit GPX4 expression. The addition of ferrostatin-1 resulted in the opposite trend of the proteins expression above to erastin treatment. What is more, overexpression of ENPP2 markedly suppressed the damaging effect of H/R on HCMECs and reversed the effects of H/R or erastin treatment on the expression of related proteins. These results demonstrated a great therapeutic efficacy of ENPP2 overexpression in preventing the development of H/R injury through inhibiting oxidative stress and ferroptosis.

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, especially deep learning method, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives. Specifically, we first summarize the existing traffic prediction methods, and give a taxonomy. Second, we list the state-of-the-art approaches in different traffic prediction applications. Third, we comprehensively collect and organize widely used public datasets in the existing literature to facilitate other researchers. Furthermore, we give an evaluation and analysis by conducting extensive experiments to compare the performance of different methods on a real-world public dataset. Finally, we discuss open challenges in this field., to be published in IEEE Transactions on Intelligent Transportation Systems

To store and process big data, large-scale data centers are built to interconnect a huge number of servers, which is a challenging task due to the network scalability and performance. In this article, we study the underlying common principles behind the existing data center network architectures on dual-port servers. We find that the existing architectures hardly achieve both large scalability and bisection width simultaneously. As a result, we propose a novel fabric of switches (FoS), which is recursively constructed on the flexible division of switch ports by means of a complete graph. The novel FoS benefits data center networks by flexible design space and double-exponential scalability, as well as small diameter and large bisection width. DPCell is constructed by inserting a dual-port server between any two adjacent switches in the novel FoS, which inherits good properties of the embedded FoS. Comparison with other architectures shows DPCell can achieve both large scalability and bisection width with a small diameter. Evaluation results show that DPCell demonstrates good performance in average routing delay and average load on servers under different degrees of network traffic.

Smoothed particle hydrodynamics (SPH) is adopted to simulate the peak impact load, the water entry process and the water entry characteristics of a re-entry capsule with the complex capsule motions modelled by a developed six degrees of freedom fluid-solid coupling model. Diffused particle distribution is developed and used in the simulations to decrease memory and computational cost. The results show that the six degrees of freedom model and diffused particle distribution are valid. The results obtained by the improved SPH method match well with experimental results, and the maximal impact load of the capsule may reach more than 10 G, even tens of G. In addition, the peak impact loads are significantly affected by the capsule vertical velocity, the capsule mass, and the pitch angle, while are slightly affected by the horizontal velocity. More concretely, the peak impact load increases with the increase of vertical velocity, and decreases with the increase of mass. However, there is no definite law between the peak load and the pitch angle. These results can provide a foundation for guiding capsule design.

Fluorescent probes for nitric oxide (NO), or more frequently for its oxidized surrogate dinitrogen trioxide (N2O3), have enabled scientists to study the contributions of this signaling molecule to many physiological processes. Seeking to improve upon limitations of other probes, we have developed a family of fluorescent probes based on a 2-amino-3′-dialkylaminobiphenyl core. This core condenses with N2O3 to form benzo[c]cinnoline structures, incorporating the analyte into the newly formed fluorophore, which results in product fluorescence with virtually no background contribution from the initial probe. We varied the substituents in the core in order to optimize both the reactivity of the probes with N2O3 and their cinnoline products' fluorescence wavelengths and brightness. The top candidates were then applied to cultured cells to verify that they could respond to NO within cellular milieus, and the top performer, NO530, was compared with a “gold standard” commercial probe, DAF-FM, in a macrophage-derived cell line, RAW 264.7, stimulated to produce NO. NO530 demonstrated similar or better sensitivity and higher selectivity for NO than DAF, making it an attractive potential alternative for NO tracking in various applications., A thorough structure-property relationship study has produced much-improved fluorescence probes for nitric oxide surrogate, e.g. N2O3, compared to the original NO550.

For learning graph representations, not all detailed structures within a graph are relevant to the given graph tasks. Task-relevant structures can be $localized$ or $sparse$ which are only involved in subgraphs or characterized by the interactions of subgraphs (a hierarchical perspective). A graph neural network should be able to efficiently extract task-relevant structures and be invariant to irrelevant parts, which is challenging for general message passing GNNs. In this work, we propose to learn graph representations from a sequence of subgraphs of the original graph to better capture task-relevant substructures or hierarchical structures and skip $noisy$ parts. To this end, we design soft-mask GNN layer to extract desired subgraphs through the mask mechanism. The soft-mask is defined in a continuous space to maintain the differentiability and characterize the weights of different parts. Compared with existing subgraph or hierarchical representation learning methods and graph pooling operations, the soft-mask GNN layer is not limited by the fixed sample or drop ratio, and therefore is more flexible to extract subgraphs with arbitrary sizes. Extensive experiments on public graph benchmarks show that soft-mask mechanism brings performance improvements. And it also provides interpretability where visualizing the values of masks in each layer allows us to have an insight into the structures learned by the model., Comment: The Web Conference (WWW), 2021

Gait detection plays an important role in areas where spatial-temporal gait parameters are needed. Inertial sensors are now sufficiently small in size and light in weight for collection of human gait data with body sensor networks (BSNs). However, gait detection methods usually rely on careful sensor alignment and a set of rule-based thresholds, which are brittle or difficult to implement. This paper presents an adaptive method for gait detection, which models human gait with a hidden Markov model (HMM), and employs a neural network (NN) to deal with the raw measurements and feed the HMM with classifications. Six gait events are involved for a detailed analysis, i.e., heel strike, foot flat, mid-stance, heel off, toe off, and mid-swing. In order to obtain enough gait data for training a gait model, the gait events are labeled by a rule-based detection method, in which the predefined rules are verified with an optical motion capture system. Experiments were conducted by nine subjects, based on a dual-sensor configuration with one sensor on each foot. Detection performance is quantified using metrics of accuracy, sensitivity and specificity, and the averaged performance values are 98.11%, 94.32% and 98.86% respectively with a timing error less than 2.5 ms.

People have their own tastes on visual appearances of products from various categories. For many of them, the tastes are affected by the current fashion trend. Studying visual appearances and fashion trend makes us understand the composition of users’ preferences and their purchase choices. However, since the fashion is changing over time, it is complex to model time-aware and non-time-aware variables simultaneously. In this paper, we present VIsually-aware Temporal rAting modeL with topics using review text to help mine visual dynamics and non-visual features for rating prediction task. Understanding the reviews will help the Recommender Systems (RSs) know whether a user is attracted by the appearance of an item, and which aspect of an item’s appearance contributes most to its ratings. To achieve this, we incorporate the visual information into the rating predicting function and introduce a topic model that can automatically classify words in an item’s reviews into non-visual words that explain the coherent feature, and visual words that are associated with its visual appearances in each time period, respectively. We run experiments on eleven real-world public datasets and the results show that our model performs better on predicting ratings than many of the state-of-the-art RSs, such as PMF, timeSVD++, HFT, JMARS, ETDR, and TVBPR+.

Mg-Al layered double hydroxides (LDHs) adsorbent was synthesized in situ on γ-Al2O3 for the removal of Cr(VI) from aqueous solution. The material was characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy, Raman spectroscopy, scanning electronic microscopy and thermogravimetry and differential thermal analysis. Compared to the LDHs powder, the calcined LDHs sorbent prepared in situ on γ-Al2O3 had higher specific surface area and was easy to recover and reuse. The adsorptive capacity for removing Cr(VI) from aqueous solution was resulting from the memory effect of LDHs based on the XRD results. Both the pseudo-second-order kinetic model and the Langmuir model fit the experimental data well. Furthermore, the adsorbent exhibits excellent sorption–regeneration performances.

As the way of easing urban traffic congestion, taxi ridesharing can effectively protect the environment and solve the difficulty of passengers taking taxis when taxi demand is high. In this paper, we formally define the dynamic ride-sharing problem and propose a taxi candidates-reduction ride-sharing scheduling algorithm based on dynamic grid. Regarding the congestion situation of the multiple road condition, the concept of speed decay zone was purposed to simulate this area. To solve the problem of low satisfaction in the congested situation, we devise a dynamic grid division strategy that reduces the grid size of the hotspot area to satisfy the specific needs of passengers in rush hour, and efficiently screen candidate taxis by dynamic grid index. We perform the experiments using the request dataset generated by the taxi request simulator of Beijing Chaoyang district. The performance shows that our approach reduce 35.5% computation without losing average satisfaction compared with existing ridesharing algorithm.

Underlying network provides infrastructures for cloud computing in data centers. The server-centric architectures integrate network and compute, which place routing intelligence on servers. However, the existing multi-port server based architectures suffer from determined scale and large path length. In this paper, we propose FleCube, a flexibly-connected architecture on multi-port servers without using any switches. FleCube is recursively constructed on division of multiple ports in a server by means of complete graph. FleCube benefits data center networks by flexible scale and low diameter, as well as large bisection width and small bottleneck degree. Furthermore, we develop multi-path routing (MPR) to take advantage of parallel paths between any two servers. MPR adopts random forwarding to distribute traffic load and relieve network congestion. Analysis and comparisons with existing architectures show the advantages of FleCube. Evaluations under different degrees of network traffic demonstrate the merits of FleCube and the proposed routings.

Given a massive set of records, similarity join is to find pairs of records with similarity score greater than a threshold. In this paper, we address the problem of scaling up similarity join for general metric distance functions using MapReduce. First, we propose a novel index structure, Similarity Join Tree (SJT), which partitions data based on the underlying data distribution, and distributes similar records to the same group. Different from existing approaches, SJT can prune a large number of comparisons within reduce tasks by utilizing the by-product results generated in partitioning data. Then, to avoid the straggler reduce tasks, we design a graph partition algorithm by extending the well known Fiduccia---Mattheyses algorithm which can ensure load balancing while minimizing communication cost and redundancy in all reduce tasks. Experimental results using real data sets show that our approach is more effective and scalable compared to state-of-the-art algorithms.

Human CD4+ T cells play an important role in the immune response to Mycobacterium tuberculosis (MTB). However, little is known about the spectratyping characteristics of the CD4+ T-cell receptor (TCR) α- and β-chains CDR3 region in tuberculosis (TB) patients. We sorted MTB peptide E7-bound CD4+ T cells by using E7/HLA-DR tetramers constructed with different HLA-DRB1 alleles and extracted the CDR3 amino-acid sequences of TCR α- and β-chains. The results showed that the CDR3 sequences of E7-bound CD4+ T cells were completely or partially identical in a single patient. The sequences of MTB peptide C5-bound CD4+ T cells shared another, and non-peptide bound CD4+ T cells, as well as unbound CD4+ T cells with tetramers were different from each other. Specifically, diverse CDR3 sequences of E7-bound CD4+ T cells displayed similar protein tertiary structure in one TB patient. In summary, the TCR α- and β-chains of CDR3 lineage of CD4+ T cells in TB patients apparently drifted, and the predominant CDR3 sequences of TCR α- and β-chains that recognized the MTB antigen exhibited peptide specificity, and certain HLA-DR restriction was also established. This study elucidates the possible causes and mechanisms of peptide-specific CD4+ T-cell-related presentation against MTB.

Classic fluorescent dyes, such as coumarin, naphthalimide, fluorescein, BODIPY, rhodamine, and cyanines, are cornerstones of various spectroscopic and microscopic methods, which hold a prominent position in biological studies. We recently found that 9-amino-benzo[c]cinnoliniums make up a novel group of fluorophores that can be used in biological studies. They are featured with a succinct conjugative push-pull backbone, a broad absorption band, and a large Stokes shift. They are potentially useful as a small-molecule alternative to R-phycoerythrin to pair with fluorescein in multiplexing applications.

A new tomato juice concentration technology was presented by CO2 hydrate formation. The CO2 hydrate equilibrium conditions were measured by isochoric pressure search method and tomato juice concentration experiments were carried out in a high-pressure stirred reactor. Moreover, dehydration ratio was defined and CO2 hydrate formation rate constants were calculated with different feed pressure. The effects of feed pressure, temperature, and juice volume on dehydration ratio were investigated. The results show that the tomato juice used in this work nearly has no effect on CO2 hydrate phase equilibrium conditions, but can accelerate CO2 hydrate formation. And the dehydration ratio increased with increasing the feed pressure from 1.81 to 3.95 MPa, the maximum dehydration ratio can reach 63.2% with feed pressure of 3.95 MPa, and the optimum tomato juice volume is 80 mL. These results demonstrated that removal of water with the help of CO2 hydrate is an efficient technology for tomato juice concentration.

A nanostructured Ag loaded Bi3NbO7 photocatalyst was synthesized by the co-precipitation combined with photodeposition method, and characterized by XRD, XPS, SEM, BET nitrogen adsorption–desorption, and UV–vis DRS. Photocatalytic activities of the Ag/Bi3NbO7 samples were evaluated by the degradation of methyl orange under visible light irradiation. It was shown that the best photocatalytic activity was obtained on sample with Ag loading level of 2 wt%. It was suggested that the increase in photocatalytic activity of Ag/Bi3NbO7 photocatalyst was explained on the basis of the electron trapping ability, the electron exciting ability and the surface plasmon resonance effect of metallic Ag particles.

Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45% of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time.

Mg-Al layered double hydroxides (LDHs) adsorbent was synthesized in situ on γ-Al

Owing to its attractive features such as fast identification and relatively long interrogating range over the classical barcode systems, radio-frequency identification (RFID) technology possesses a promising prospect in many practical applications such as inventory control and supply chain management. However, unknown tags appear in RFID systems when the tagged objects are misplaced or unregistered tagged objects are moved in, which often causes huge economic losses. This paper addresses an important and challenging problem of unknown tag identification in large-scale RFID systems. The existing protocols leverage the Aloha-like schemes to distinguish the unknown tags from known tags at the slot level, which are of low time-efficiency, and thus can hardly satisfy the delay-sensitive applications. To fill in this gap, two filtering-based protocols (at the bit level) are proposed in this paper to address the problem of unknown tag identification efficiently. Theoretical analysis of the protocol parameters is performed to minimize the execution time of the proposed protocols. Extensive simulation experiments are conducted to evaluate the performance of the protocols. The results demonstrate that the proposed protocols significantly outperform the currently most promising protocols.

The tartrate-intercalated MgAl layered double hydroxide (MgAl-TA LDH) was prepared by ion-exchange method at the acid solution using MgAl-CO3 LDH as the precursor. The adsorption of Cu2+ on MgAl-TA LDH was studied and some influence factors such as contact time, initial Cu2+ concentration, adsorbent dosage, solution pH, and adsorption temperature were investigated. Three kinds of adsorption isotherms (Langmuir model, Freundlich model, and Redlich–Peterson model) were investigated; the results indicated that equilibrium was well described by Langmuir isotherm, predicting the adsorption of Cu2+ on MgAl-TA adsorbents was a monolayer adsorption. The equilibrium kinetic adsorption data were fitted to the pseudo-second order kinetic equation. Furthermore, the adsorption of Cu2+ was controlled mainly by chemical process combined with intra-particle diffusion. Parameters of adsorption thermodynamic suggested that the interaction of Cu2+ adsorbed by MgAl-TA LDH adsorbents was spontaneous and endothermic.

A novel orange juice concentration method by C2H4 hydrate formation was presented. The experiments were carried out in a high-pressure stirred reactor. The changes of temperature and pressure during C2H4 hydrate formation were discussed under different pressure (2.10 and 4.43 MPa). Dehydration ratio was defined in order to evaluate the separation efficiency. It was found that secondary nucleation happened under higher initial pressure. The dehydration ratios were 20.2 and 99.3% with initial pressure at 2.10 and 4.43 MPa, respectively. The results demonstrated that removal of water by formation of C2H4 hydrate is a potential technology for orange juice concentration.

Owing to its superior properties, such as fast identification and relatively long interrogating range over barcode systems, Radio Frequency Identification (RFID) technology has promising application prospects in inventory management. This paper studies the problem of complete identification of missing RFID tag, which is important in practice. Time efficiency is the key performance metric of missing tag identification. However, the existing protocols are ineffective in terms of execution time and can hardly satisfy the requirements of real-time applications. In this paper, a Multi-hashing based Missing Tag Identification (MMTI) protocol is proposed, which achieves better time efficiency by improving the utilization of the time frame used for identification. Specifically, the reader recursively sends bitmaps that reflect the current slot occupation state to guide the slot selection of the next hashing process, thereby changing more empty or collision slots to the expected singleton slots. We investigate the optimal parameter settings to maximize the performance of the MMTI protocol. Furthermore, we discuss the case of channel error and propose the countermeasures to make the MMTI workable in the scenarios with imperfect communication channels. Extensive simulation experiments are conducted to evaluate the performance of MMTI, and the results demonstrate that this new protocol significantly outperforms other related protocols reported in the current literature.

New organic-inorganic composite of 2,4-dihydroxybenz ophenone (BP-1) encapsulation into dodecylbenzene-sulfonate (DBS) modified layered double hydroxide (LDH) was successfully prepared. The surface, structural,thermal and absorption properties of the BP-1/DBS-LDH nanohybrid was characterized by BET analysis,scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TG) and diffuse reflectance UV-Vis absorbance spectra (DRUV-vis). Theinterlayer configuration of composite and the adsorption mechanism of BP-1 on MgAl-DBS-LDH werediscussed. It was suspected that DBS anions located in the form of monolayer arrangement with a 75° antiparallel angle between dodecylbenzen esulfonate chain axis. The diffuse reflectance UV-Vis absorbance resultsrevealed that the UV absorbing wavelength of BP-1/DBS-LDH evidently extends to about 400 nm, whichshows that the BP-1/DBS-LDH has the potential application as a UV absorber.

Dynamic spectrum access and adaptive spectrum allocation can significantly improve the resource utilization in dynamic spectrum market. To achieve this, it is important to stimulate the primary license holders to open up their under-utilized spectrum for sharing and adaptive allocation so as to maximize the secondary user's transmission reliability with the minimum cost. In this paper, we propose a highly available spectrum allocation service model that adopts a parallel mechanism to improve the transmission reliability. A greedy algorithm is presented for minimizing the secondary user's cost with satisfied reliability requirement. Furthermore, a dynamic programming algorithm is developed for maximizing the secondary user's reliability with satisfied cost requirement. Simulation results show that, by using the proposed service model, spectrum utilization can be improved significantly and purchase price from secondary users is relatively stable.

With the rapid development of wireless networking technology, real time video multicast applications have become feasible. These applications are bandwidth hungry and have stringent delay constraints. Furthermore, in wireless networks, different users generally have different receiving capabilities. Therefore, it is challenging to provide scalable support for such applications. To address this challenge, in this paper, we propose to use layered video and a P2P mode approach. In our solution, a video is encoded into multiple layers and broadcast to the multicast group users. Users form a P2P network to recover the loss of multicast video packets. This can significantly improve the overall received video quality. More importantly, our solution can provide incentives for users to help each other. With the proposed scheme, when a user helps another one, it can also improve its own received video quality.

Co-doped ZnAl layered double hydroxides (LDH) were papered by coprecipitation. The prepared samples were characterized by multiple techniques including X-ray Diffraction (XRD), Brunauer−Emmett−Teller (BET) surface area, Scanning Electronic Microscopy (SEM), Transmission Electron Microscopy (TEM), X-ray Photoelectron Spectroscopy (XPS) and UV−Vis Diffuse-Reflectance Spectroscopy (UV−Vis DRS). The incorporation of Co 2+ into the ZnAl LDH sheets as CrO 6 octahedron forms a new energy level which contributes for the excitation of electrons under visible light. The doped Co 2+ at a reasonable content also serves as photo-generated charges separator and improves the visible light photocatalytic activity of ZnAl LDH. A degradation mechanism based on the hydroxyl radical as the active species was proposed. Copyright © 2018 BCREC Group. All rights reserved Received: 3rd February 2018; Revised: 8th July 2018; Accepted: 13rd July 2018 How to Cite : Li, D., Fan, L., Qi, M., Shen, Y., Liu, D., Li, S. (2018). Enhanced Visible-Light-Driven Photocatalytic Activity of ZnAl Layered Double Hydroxide by Incorporation of Co 2+ . Bulletin of Chemical Reaction Engineering & Catalysis , 13 (3): 502-511 (doi:10.9767/bcrec.13.3.2168.502-511) Permalink/DOI: https://doi.org/10.9767/bcrec.13.3.2168.502-511

With the development of wireless communication technology, various wireless networks have been deployed. Heterogeneous networks will be dominant in the next generation wireless networks. In such networks, providing a seamless handoff and quality of service (QoS) guarantees is one of the key issues for real-time services. In this paper, we propose a horizontal handoff scheme that reduces the horizontal handoff latency based on the location and movement pattern of a mobile node (MN). We also present a vertical handoff scheme for providing seamless services between Wi-Fi and WiMAX networks. Finally, by performance evaluation using computer simulations, we show that the proposed scheme can reduce the handoff latency significantly.

Although there are several successful commercial deployments of live P2P streaming systems, the current designs; lack incentives for users to contribute bandwidth resources; lack adaptation to aggregate bandwidth availability; and exhibit poor video quality when bandwidth availability falls below bandwidth supply. In this paper, we propose, prototype, deploy, and validate LayerP2P, a P2P live streaming system that addresses all three of these problems. LayerP2P combines layered video, mesh P2P distribution, and a tit-for-tat-like algorithm, in a manner such that a peer contributing more upload bandwidth receives more layers and consequently better video quality. We implement LayerP2P (including seeds, clients, trackers, and layered codecs), deploy the prototype in PlanetLab, and perform extensive experiments. We also examine a wide range of scenarios using trace-driven simulations. The results show that LayerP2P has high efficiency, provides differentiated service, adapts to bandwidth deficient scenarios, and provides protection against free-riders.

The load-balanced (LB) switch proposed by C.S. Chang et al. consists of two stages. First, a load-balancing stage converts arriving packets into uniform traffic. Then, a forwarding stage transfers packets from the line-cards to their final output destination. Load-balanced switches do not need a centralized scheduler and can achieve 100% throughput for a broad class of traffic distributions. However, load-balanced switches may cause packets at the output port to be out of sequence. Several schemes have been proposed to tackle the out of- sequence problem of the load-balanced switch. They are either too complex to implement, or introduce a large additional delay. In this paper, we present a practical load-balanced switch, called the Byte-Focal switch, which uses packet-by-packet scheduling to significantly improve the delay performance over switches of comparable complexity. We prove that the queues at the input need only finite buffering, and that the overall switch is stable under any traffic matrix. Our analysis shows that the average queuing delay is roughly linear with the switch size N, and although the worst case resequencing delay is N2, the average resequencing delay is much smaller. This means that we can reduce the required resequencing buffer size significantly.