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Selecting a set of deterministic (e.g., P10, P50 and P90) models is an important and difficult step in any uncertainty quantification workflow. In this paper, we propose to use multi-objective optimization to find a reasonable balance between often conflicting features that must be captured by these models. We embed this approach into a streamlined uncertainty quantification workflow that seamlessly integrates multi-realization history-matching (MHM), production forecasting with uncertainty ranges and representative, deterministic model selection. Some uncertain parameters strongly impact simulated responses representing historic (production) data and are selected as active parameters for history-matching, whereas others are important only for forecasting. An ensemble of conditional realizations of active history match parameters is generated in the MHM stage using a distributed optimizer, integrated with either randomized-maximum-likelihood (RML) or Gaussian-mixture-model (GMM). This ensemble is extended with unconditional realizations of forecast parameters generated by sampling from their prior distribution. Based on production forecasting results from simulations of this ensemble representing the posterior uncertainty distribution, representative (P10/P50/P90) models are selected using multi-objective optimization. In addition to matching target values of the primary and a few secondary key performance indicators (e.g., cumulative oil/gas/water production, recovery factor, etc.), selected representative models often must satisfy other requirements or constraints, e.g., the value of some key parameters must be within a user specified tight range. It can be quite difficult to find a set of representative models that satisfy all requirements. Even more challenging, some requirements may be conflicting with others such that no single model can satisfy all requirements. To overcome these technical difficulties, this paper proposes formulating different requirements and constraints as objectives and applying a multi-objective optimization strategy to find a set of Pareto optimal solutions based on the concept of dominance. One or more representative models can then be selected from the set of optimal solutions according to case dependent preferences or requirements. The proposed method is tested and validated on a realistic example. Our results confirm that the proposed method is robust and efficient and finds acceptable solutions with no violation or minimal violations of constraints (when conflicting constraints are present). These results suggest that our advanced multi-objective optimization technique can select high-quality representative models by striking a balance between conflicting constraints. Thus, a better decision can be made while running much fewer simulations than would be required with traditional methods.

AIMS Previous research has demonstrated that autistic individuals often experience difficulties accessing dental care, both as a result of autism specific difficulties and practitioners' attitudes towards autism. However, very little research exists that explores dental professionals' experiences of providing care to their autistic patients. The aim of this study was to investigate the strategies UK-based dental professionals' use when working with autistic patients METHODS AND RESULTS: In this study, dental professionals (n = 16) from a variety of specialty roles (special care, paediatrics, orthodontics) were interviewed. We asked participants to talk through, in depth, specific cases they had encountered in their practice, what sorts of accommodations they had provided, and what concerns had arisen during appointments. Thematic analysis was used to analyses the data and revealed four main themes: the unique dental needs associated with being autistic, effective adaptations to practice, the crucial role of the caregiver, and the importance of specialist knowledge CONCLUSION: Recommendations for how dentists can improve the dental experiences of autistic patients can be drawn from the specialist dentists' responses in this study. These include involving autistic patients in decisions about their treatment and being flexible and willing to work with autistic patients and their caregivers.

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

Background Sarcopenia is prevalent worldwide and has a detrimental effect on the quality of life in cirrhosis patients. This study aimed to explore how the 5-year survival rate differs between young and older cirrhotic patients, as well as between patients with and without sarcopenia. Methods We retrospectively evaluated 320 cirrhotic patients. All patients were followed-up every 6 months for up to 5 years. Patients were divided into two groups (148 with sarcopenia and 172 without sarcopenia) according to the third lumbar skeletal muscle mass index [L3-SMI]. Furthermore, we compared difference in survival rates between young (

Objective To evaluate the efficacy of acupotomy in the treatment of rheumatoid arthritis by meta-analysis. Methods Randomized controlled trials of acupotomy in the treatment of rheumatoid arthritis were searched by computer in PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang, VIP and China Biomedical Literature Service System. The retrieval time was up to April 2, 2022. Literature screening and data extraction were performed independently by two researchers. We evaluate the risk of bias in the included studies and stata16 software was used for Meta-analysis. Results A total of 13 randomized controlled trials involving 1,103 subjects were included. The clinical efficacy of acupotomy combined therapy for rheumatoid arthritis was improved compared with the control group. Lysholm’s knee function score was improved. The visual analog scale (VAS) was improved. Disease activity score (DAS28) was decreased in 28 joints. Decreased morning stiffness time; Decreased C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and rheumatoid factor (RF). Conclusion The stability of the results of this study is satisfactory, which proves that acupotomy has good efficacy in the treatment of rheumatoid arthritis.

Previous implementation of distributed Gauss-Newton (DGN) optimization algorithm runs multiple optimization threads in parallel, employing a synchronous running mode (S-DGN). As a result, it waits for all simulations submitted in each iteration to complete, which may significantly degrade performance because a few simulations may run much longer than others, especially for time-consuming real-field cases. To overcome this limitation and thus improve the DGN optimizer's execution, we propose two asynchronous DGN (A-DGN) optimization algorithms in this paper. The A-DGN optimizer is a well-parallelized and efficient derivative-free (DFO) method. The A-DGN optimizer generates multiple initial guesses by sampling from the prior probability distribution of uncertain parameters in the first iteration. It then runs multiple simulations on high-performance-computing (HPC) clusters in parallel. A checking time interval is introduced to control the optimization process. The A-DGN optimizer checks the status of all running simulations after every checking time frame. A new simulation case is proposed immediately once the simulation of an optimization thread is completed, without waiting for the completion of other simulations. Thus, each A-DGN optimization thread becomes independent. The two A-DGN optimization algorithms are 1) the local-search algorithm to locate multiple maximum-a-posteriori (MAP) estimates and 2) the integrated global-search algorithm with the randomized-maximum-likelihood (RML) method to generate hundreds of RML samples in parallel for uncertainty quantification. We modified the training-data data set updating algorithm using the iteration index for each thread to implement the asynchronous running mode. The sensitivity matrix at the best solution of each optimization thread is estimated by linear interpolation of a subset of the training data closest to the best solution, using the modified QR decomposition method. A new simulation case (or search point) is generated by solving the Gauss-Newton trust-region subproblem (GNTRS), together with the estimated sensitivity matrix, using the more efficient and robust GNTRS solver that we developed recently. The proposed A-DGN optimization method is tested and validated on a synthetic problem and then applied to a real-field deep-water reservoir model. Numerical tests confirm that the proposed A-DGN optimization method can converge to solutions with matching quality comparable to those obtained by the S-DGN optimizer, saving on the time required for the optimizer to converge by a factor ranging from 1.3 to 2 when compared to the S-DGN optimizer depending on the problem. The new A-DGN optimization algorithm presented in this paper helps improve efficiency and robustness in solving history-matching or inversion problems, especially for uncertainty quantification of subsurface model parameters and production forecasts of real-field reservoirs by conditioning to production data.

The investigation of potential stylistic differences between translated and non-translated texts has been proven to be a promising line in corpus-based translation studies, yet similar research on the product of interpreting seems both smaller in scale and slower in development. This study compares the use of hedges in interpreted and spontaneous English speeches collected in similar settings. Specifically, we gathered the two types of speech data from Chinese and American political press briefings respectively and analyzed their differences in the employment of various hedging devices. The results show that interpreted speeches not only contain significantly fewer hedges but they are also dependent on a narrower range of such devices than spontaneous speeches, which is consistent with the tendency towards simplification in translations. Also, our findings suggest that interpreted and spontaneous speeches tend to follow distinct hedging patterns in terms of preferred linguistic choices. Potential factors triggering such differences are discussed.

High-radix routers that switch and route packets across large-scale racks become increasingly important and can crucially determine the latency and bandwidth of the interconnection network for super-computers and data centers. With the omnipresence of big data analysis there is a pressing need for scalable and self-diagnosing routers to construct more high-performance and reliable interconnect networks. This paper proposes an optimized aggregated-tile router micro-architecture with the reconfigurable multi-level routing and intelligent adaptive routing schemes to reduce the hardware requirement and optimize the router performance. The experiment results show that our approach achieves 98% throughput and maintains the same delay performance even better than YARC router while providing up to 40–50% reduction in memory consumption as well as global wire complexity. Moreover, by aggregating a built-in CPU and the intellectual network management structure, our router runs heuristic search algorithms to automatically reconstruct route tables for failure links or routers without any intervention of control plane, thereby implementing the automatical fault-tolerance and fault-recovery effects when maximizing the network performance.

This article investigates the role of translations from English in language change in Chinese. It employs a new corpus, the Chinese Diachronic Composite Corpus (CDCC), which incorporates a parallel corpus and comparable corpus in three sampling periods in the twentieth century, and a refe­rence corpus as a starting point in the timeframe. We examine whether explicitness in English–Chinese translations has exerted an impact on the target language, focusing on adversative conjunctions as a measure of explicitness. The results of the study demonstrate that: (1) translated Chinese texts have changed in step with original Chinese texts in the frequency of adversative conjunctions; (2) translated Chinese texts and original Chinese texts are interrelated throughout the three periods, but the correlation between them has changed perceptibly over the three sample points; and (3) source language interference found in translated Chinese texts increases over the three periods.

In today's data centers, memory-based key-value systems, such as Memcached and Redis, play an indispensable role in providing high-speed data services. The rapidly growing capacity and quickly falling price of DRAM memory in the past years have enabled us to create a large memory-based key-value store, which is able to serve hundreds of Gigabytes to even Terabytes of key-value data all in memory. Unfortunately, CPU cache in modern processors has not seen a similar growth in capacity, still remaining at the level of a few dozens of Megabytes. Such an extremely low cache-to-memory ratio (less than 0.1%) poses a significant new challenge---the limited CPU cache is becoming a severe performance bottleneck that hinders us from fully exploiting the great potential of high-speed memory-based key-value stores. To address this critical challenge, we propose a highly cache-efficient scheme, called Cavast , to optimize the cache utilization of large-capacity in-memory key-value stores. Our goal is to maximize cache efficiency and system performance without any hardware changes. We first present two light-weight, software-only mechanisms to enable user to indirectly control the cache content at application level. Then we propose a set of optimization policies to address several critical design issues that impair cache's efficacy in the current key-value store systems. By carefully reorganizing the data layout in memory, redesigning the hash indexing structure, and offloading garbage collection, we can effectively improve the utilization of the limited cache space. We have developed a module in Linux as a kernel-level support, and implemented two prototypes based on Memcached and Redis with the proposed Cavast scheme. Our experimental studies show promising results. On a 6-core Intel Xeon processor with only 15-MB cache, we can raise the cache hit ratio up to 82.7% with a very small cache-to-memory ratio (0.023%), and significantly increase the key-value system throughput by a factor of up to 4.2.

Multiple myeloma (MM) is still an incurable malignancy of plasma cells. Proteasome inhibitors (PIs) work as the backbone agent and have greatly improved the outcome in majority of newly diagnosed patients with myeloma. However, drug resistance remains the major obstacle causing treatment failure in clinical practice. Here, we investigated the effects of Indirubin-3'-monoxime (I3MO), one of the derivatives of Indirubin, in the treatment of MM.MM patient primary samples and human cell lines were examined. I3MO effects on myeloma treatment and the underling molecular mechanisms were investigated via in vivo and in vitro study.Our results demonstrated the anti-MM activity of I3MO in both drug- sensitive and -resistance MM cells. I3MO sensitizes MM cells to bortezomib-induced apoptosis. Mechanistically, I3MO acts as a multifaceted regulator of cell death, which induced DNA damage, cell cycle arrest, and abrogates NF-κB activation. I3MO efficiently down-regulated USP7 expression, promoted NEK2 degradation, and suppressed NF-κB signaling in MM. Our study reported that I3MO directly bound with and caused the down-regulation of PA28γ (PSME3), and PA200 (PSME4), the proteasome activators. Knockdown of PSME3 or PSME4 caused the inhibition of proteasome capacity and the overload of paraprotein, which sensitizes MM cells to bortezomib-mediated growth arrest. Clinical data demonstrated that PSME3 and PSME4 are over-expressed in relapsed/refractory MM (RRMM) and associated with inferior outcome.Altogether, our study indicates that I3MO is agent triggering proteasome inhibition and represents a promising therapeutic strategy to improve patient outcome in MM.A full list of funding can be found in the acknowledgements.

Introduction and Objectives Hypoxia-inducible factor-1α is critically involved in the pathogenesis of liver diseases. Its inhibitor genistein attenuated D-galactosamine (D-GalN)-induced liver damage. However, the role of genistein in acute-on-chronic liver failure (ACLF) is unclear. The influence of genistein on reactive oxygen species (ROS) and hepatocyte functions were evaluated in a rat model of ACLF. Material and methods Genistein [20 mg/ (kg. day)]/coenzyme Q10 [10 mg/ (kg. day)]/lipoic acid [20 mg/ (kg. day)] was administered via the intra-gastric route daily for 6 weeks as co-treatment to the rats in the experimental groups. Then, 100 μg/kg LPS combined with 0.5 g/kg D-GalN was injected intraperitoneally to attack the rats. Results Genistein significantly attenuated LPS/D-GalN-induced ACLF, characterized by ameliorated gross appearance and microscopic histopathology of liver, reduced AST level in serum, whereas increased levels of ATP, ADP/O, and respiratory control ratio (RCR) in mitochondria. Genistein suppressed necrosis and ROS production. Conclusion These results suggested that genistein could protect against ACLF through inhibiting cellular ROS production and necrosis, improving RCR, and decreasing permeability transition pores in mitochondrial, which was similar as mitochondrial protective agent coenzyme Q10.