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A new system model reflecting the clustered structure of distributed storage is suggested to investigate interplay between storage overhead and repair bandwidth as storage node failures occur. Large data centers with multiple racks/disks or local networks of storage devices (e.g. sensor network) are good applications of the suggested clustered model. In realistic scenarios involving clustered storage structures, repairing storage nodes using intact nodes residing in other clusters is more bandwidth-consuming than restoring nodes based on information from intra-cluster nodes. Therefore, it is important to differentiate between intra-cluster repair bandwidth and cross-cluster repair bandwidth in modeling distributed storage. Capacity of the suggested model is obtained as a function of fundamental resources of distributed storage systems, namely, node storage capacity, intra-cluster repair bandwidth and cross-cluster repair bandwidth. The capacity is shown to be asymptotically equivalent to a monotonic decreasing function of number of clusters, as the number of storage nodes increases without bound. Based on the capacity expression, feasible sets of required resources which enable reliable storage are obtained in a closed-form solution. Specifically, it is shown that the cross-cluster traffic can be minimized to zero (i.e., intra-cluster local repair becomes possible) by allowing extra resources on storage capacity and intra-cluster repair bandwidth, according to the law specified in the closed-form. The network coding schemes with zero crosscluster traffic are defined as intra-cluster repairable codes, which are shown to be a class of the previously developed locally repairable codes.

This paper presents a numerical modeling study of coupled thermodynamic, multiphase fluid flow and heat transport associated with underground compressed air energy storage (CAES) in lined rock caverns. Specifically, we explored the concept of using concrete lined caverns at a relatively shallow depth for which constructing and operational costs may be reduced if air tightness and stability can be assured. Our analysis showed that the key parameter to assure long-term air tightness in such a system was the permeability of both the concrete lining and the surrounding rock. The analysis also indicated that a concrete lining with a permeability of less than 1×10 -18 m 2 would result in an acceptable air leakage rate of less than 1%, with the operational pressure range between 5 and 8 MPa at a depth of 100 m. It was further noted that capillary retention properties and the initial liquid saturation of the lining were very important. Indeed, air leakage could be effectively prevented when the air-entry pressure of the concrete lining is higher than the operational air pressure and when the lining is kept moist at a relatively high liquid saturation. Our subsequent energy-balance analysis demonstrated that the energy loss for a daily compression and decompression cycle is governed by the air-pressure loss, as well as heat loss by conduction to the concrete liner and surrounding rock. For a sufficiently tight system, i.e., for a concrete permeability off less than 1×10 -18 m 2 , heat loss by heat conduction tends to become proportionally more important. However, the energy loss by heat conduction can be minimized by keeping the air-injection temperature of compressed air closer to the ambient temperature of the underground storage cavern. In such a case, almost all the heat loss during compression is gained back during subsequent decompression. Finally, our numerical simulation study showed that CAES in shallow rock caverns is feasible from a leakage and energy efficiency viewpoint. Our numerical approach and energy analysis will next be applied in designing and evaluating the performance of a planned full-scale pilot test of the proposed underground CAES concept.

Federated learning has been spotlighted as a way to train neural networks using data distributed over multiple nodes without the need for the nodes to share data. Unfortunately, it has also been shown that data privacy could not be fully guaranteed as adversaries may be able to extract certain information on local data from the model parameters transmitted during federated learning. A recent solution based on the secure aggregation primitive enabled privacypreserving federated learning, but at the expense of significant extra communication/computational resources. In this paper, we propose communicationcomputation efficient secure aggregation which substantially reduces the amount of communication/computational resources relative to the existing secure solution without sacrificing data privacy. The key idea behind the suggested scheme is to design the topology of the secret-sharing nodes as sparse random graphs instead of the complete graph corresponding to the existing solution. We first obtain the necessary and sufficient condition on the graph to guarantee reliable and private federated learning in the information-theoretic sense. We then suggest using the Erdős-Rényi graph in particular and provide theoretical guarantees on the reliability/privacy of the proposed scheme. Through extensive real-world experiments, we demonstrate that our scheme, using only 20 ∼ 30% of the resources required in the conventional scheme, maintains virtually the same levels of reliability and data privacy in practical federated learning systems.

This study presents a novel methodology for the development of a chemically defined medium (CDM) using genome-scale metabolic network and flux balance analysis. The genome-based in silico analysis identified two amino acids and four vitamins as non-substitutable essential compounds to be supplemented to a minimal medium for the sustainable growth of Mannheimia succiniciproducens, while no substitutable essential compounds were identified. The in silico predictions were verified by cultivating the cells on a CDM containing the six non-substitutable essential compounds, and it was further demonstrated by observing no cell growth on the CDM lacking any one of the non-substitutable essentials. An optimal CDM for the enhancement of cell growth and succinic acid production, as a target product, was formulated with a single-addition technique. The fermentation on the optimal CDM increased the succinic acid productivity by 36%, the final succinic acid concentration by 17%, and the succinic acid yield on glucose by 15% compared to the cultivation using a complex medium. The optimal CDM also lowered the sum of the amounts of by-products (acetic, formic, and lactic acids) by 30%. The strategy reported in this paper should be generally applicable to the development of CDMs for other organisms, whose genome sequences are available.

Clustered distributed storage models real data centers where intra-and cross-cluster repair bandwidths are different. In this paper, exact-repair minimum-storage-regenerating (MSR) codes achieving capacity of clustered distributed storage are designed. Focus is given on two cases: = 0 and = 1/(n−k), where is the ratio of the available cross-and intra-cluster repair bandwidths, n is the total number of distributed nodes and k is the number of contact nodes in data retrieval. The former represents the scenario where cross-cluster communication is not allowed, while the latter corresponds to the case of minimum cross-cluster bandwidth that is possible under the minimum storage overhead constraint. For the = 0 case, two types of locally repairable codes are proven to achieve the MSR point. As for = 1/(n − k), an explicit MSR coding scheme is suggested for the two-cluster situation under the specific of condition of n = 2k.

BackgroundSufentanil is a potent opioid analgesic frequently used in clinical anesthesia. Double-lumen endobronchial intubation induces profound cardiovascular responses in comparison with ordinary endotracheal intubation because of the larger tube diameter and direct irritation of the carina.ObjectivesThe purpose of this study was to determine the effective bolus dose of sufentanil to attenuate hemodynamic changes in response to laryngoscopic double-lumen endobronchial intubation.Patients and MethodsWe randomly assigned 72 patients aged 18 - 65 years and with an American Society of Anesthesiologists physical status of 1 or 2 to one of four sufentanil dose groups: NS, S0.1, S0.2, or S0.3. The respective doses for the groups were as follows: normal saline, 0.1 mcg/kg of sufentanil, 0.2 mcg/kg of sufentanil, and 0.3 mcg/kg of sufentanil. Blood pressure and heart rate were recorded during the pre-anesthesia period at baseline, pre-intubation, immediate post-intubation, and every minute during 5 minutes after intubation.ResultsBaseline mean arterial pressures in the NS, S0.1, S0.2, and S0.3 groups were 89.8 ± 12.1, 89.2 ± 10.9, 88.8 ± 13.6, and 90.7 ± 11.1, respectively. At immediate post-intubation, the mean arterial pressures in the NS, S0.1, S0.2, and S0.3 groups were 129.7 ± 14.7, 120.7 ± 14.2, 120.8 ± 17.2, and 96.7 ± 10.4, respectively. At immediate post-intubation, the mean arterial pressure in the NS, S0.1, and S0.2 groups significantly increased from baseline (P < 0.001), but the S0.3 group showed no difference. In the time point comparison at immediate post- intubation, the S0.3 group had a significantly lower mean arterial pressure than did the NS, S0.1, and S0.2 groups (P < 0.001).ConclusionsWe found that 0.3 mcg/kg of sufentanil attenuates cardiovascular responses to double-lumen endobronchial intubation without adverse effects.

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