Comparing remifentanil and sufentanil in stress reduction during neurosurgery: a randomised controlled trial.

Background In most scenarios, anaesthesiologists titrate opioids to control nociceptive surgical stress based on intraoperative haemodynamic changes. Remifentanil was reported to cause more profound cardiovascular depression than sufentanil. A concern is that this direct cardiovascular depression might counteract the hypertension and tachycardia caused by surgical manipulation and mask inadequate analgesia. Objective To compare remifentanil and sufentanil, titrated to maintain a comparable haemodynamic range (within 20% of baseline) and combined with the same propofol regimen, in stress reduction measured as plasma levels of putative mediators of surgical stress. Setting Huashan Hospital of Fudan University, Shanghai, China. Method Forty-five patients undergoing supratentorial glioma resection were randomised to the remifentanil group or the sufentanil group. Main outcome measures Plasma concentrations of cortisol, epinephrine, norepinephrine, interleukin-6, interleukin-10 and lymphocyte counts were analysed before anaesthesia, 1 h after incision, at the end of surgery and 24 h after incision using enzyme-linked immunosorbent assay and an automatic haematology analyser. Recovery profiles during emergence from anaesthesia were also compared. Results Except for a lower epinephrine concentration in the remifentanil group 24 h after incision (median [interquartile range], 4.2 [3.4-6.1] vs. 8.4 [4.8-12.5] ng/ml; P = 0.003), stress biomarkers were not significantly different between the two groups. Patients in the sufentanil group had lower grades in coughing, restlessness (P = 0.001 and < 0.001, respectively) and a lower incidence of postoperative shivering (P = 0.007). Conclusion Compared to that of sufentanil, the direct cardiovascular depression of remifentanil does not mask the clinical manifestation of inadequate analgesia when both drugs are titrated according to haemodynamic variables in neurosurgery.

Comparing two airway management strategies for moderately sedated patients undergoing awake craniotomy: A single-blinded randomized controlled trial.

BACKGROUND: In the monitored anesthesia care (MAC) setting for awake craniotomy (AC), maintaining airway patency in sedated patients remains challenging. This randomized controlled trial aimed to compare the validity of the below-epiglottis transnasal tube insertion (the tip of the tube placed between the epiglottis and vocal cords) and the nasopharyngeal airway (simulated by the above-epiglottis transnasal tube with the tip of the tube placed between the epiglottis and the free edge of the soft palate) with respect to maintaining upper airway patency for moderately sedated patients undergoing AC. METHODS: Sixty patients scheduled for elective AC were randomized to receive below-epiglottis (n = 30) or above-epiglottis (n = 30) transnasal tube insertion before surgery. Moderate sedation was maintained in the pre- and post-awake phases. The primary outcome was the upper airway obstruction (UAO) remission rate (relieved obstructions after tube insertion/the total number of obstructions before tube insertion). RESULTS: The UAO remission rate was higher in the below-epiglottis group [100% (12/12) vs 45% (5/11); P = .005]. The tidal volume values monitored through the tube were greater in the below-epiglottis group during the pre-awake phase (P < .001). End-tidal carbon dioxide (EtCO2 ) monitored through the tube was higher in the below-epiglottis group at bone flap removal (P < .001). During the awake phase, patients' ability to speak was not impeded. No patient had serious complications related to the tube. CONCLUSION: The below-epiglottis tube insertion is a more effective method to maintain upper airway patency than the nasopharyngeal airway for moderately sedated patients undergoing AC.

CeO2-x quantum dots with massive oxygen vacancies as efficient catalysts for the synthesis of dimethyl carbonate.

CeO2-x quantum dots with massive oxygen vacancies are obtained by a one-step single molecular synthesis strategy. The yield of dimethyl carbonate from CO2 and methanol is more than 5 times that for commercial CeO2 nanoparticles.

Spatial Vulnerability of Network Systems under Spatially Local Hazards.

A hazard is often spatially local in a network system, but its impact can spread out through network topology and become global. To qualitatively and quantitatively assess the impact of spatially local hazards on network systems, this article develops a new spatial vulnerability model by taking into account hazard location, area covered by hazard, and impact of hazard (including direct impact and indirect impact), and proposes an absolute spatial vulnerability index (ASVI) and a relative spatial vulnerability index (RSVI). The relationship between the new model and some relevant traditional network properties is also analyzed. A case study on the spatial vulnerability of the Chinese civil aviation network system is conducted to demonstrate the effectiveness of the model, and another case study on the Beijing subway network system to verify its relationship with traditional network properties.

Deterministic Agent-Based Path Optimization by Mimicking the Spreading of Ripples.

Inspirations from nature have contributed fundamentally to the development of evolutionary computation. Learning from the natural ripple-spreading phenomenon, this article proposes a novel ripple-spreading algorithm (RSA) for the path optimization problem (POP). In nature, a ripple spreads at a constant speed in all directions, and the node closest to the source is the first to be reached. This very simple principle forms the foundation of the proposed RSA. In contrast to most deterministic top-down centralized path optimization methods, such as Dijkstra's algorithm, the RSA is a bottom-up decentralized agent-based simulation model. Moreover, it is distinguished from other agent-based algorithms, such as genetic algorithms and ant colony optimization, by being a deterministic method that can always guarantee the global optimal solution with very good scalability. Here, the RSA is specifically applied to four different POPs. The comparative simulation results illustrate the advantages of the RSA in terms of effectiveness and efficiency. Thanks to the agent-based and deterministic features, the RSA opens new opportunities to attack some problems, such as calculating the exact complete Pareto front in multiobjective optimization and determining the kth shortest project time in project management, which are very difficult, if not impossible, for existing methods to resolve. The ripple-spreading optimization principle and the new distinguishing features and capacities of the RSA enrich the theoretical foundations of evolutionary computation.

[Effects of Phenol on Activity and Microfauna Community Structure of Activated Sludge].

In order to investigate the effects of phenol on activity and microfauna community structure of activated sludge, phenol with different concentrations were added into the wastewater for activated sludge culturing in a SBR reactor, then the activity parameters of TTC-ETS and INT-ETS of activated sludge were measured, and the dynamics of microfauna community was analyzed. The results indicated that TTC-ETS activity was more applicable to evaluate the effects of phenol on sludge activity than INT-ETS activity. The higher the concentration of phenol was, the more seriously the sludge activity was inhibited. When 50 mg·L-1 of phenol was added into the influent, the inhibition rate of sludge activity was (20.75±10.43)%, while it increased to (39.73±26.92)% with a great fluctuation at the phenol concentration of 100 mg·L-1. The inhibition rate of sludge activity fluctuated at about 40% in the later period of sludge culturing with 300 mg·L-1 of phenol. The inhibitory effects on microfauna community in activated sludge increased with increasing phenol concentration. Phenol had different effects on various groups of microfauna community, i.e., only one group (testate amoebae) was inhibited significantly at a low feeding concentration of phenol, many groups (sessile ciliates, testate amoebae, crawling ciliates, carnivorous ciliates) were inhibited at the phenol concentrations of 100 mg·L-1 and 300 mg·L-1, whereas a few groups (flagellates, nematodes, etc.) were promoted at the phenol concentration of 300 mg·L-1. There was a certain degree of relationship between sludge activity and microfauna under the influence of phenol, which means that Centropyxis aculeata, Plagiocampa mutabilis etc., may be bio-indicators for low sludge activity, and Epistylis lacustris, Bodo lens and Pleuromonas jaculans can be used as bio-indicators for high sludge activity in the activated sludge used to treat phenolic wastewater.

Evolutionary computation with spatial receding horizon control to minimize network coding resources.

The minimization of network coding resources, such as coding nodes and links, is a challenging task, not only because it is a NP-hard problem, but also because the problem scale is huge; for example, networks in real world may have thousands or even millions of nodes and links. Genetic algorithms (GAs) have a good potential of resolving NP-hard problems like the network coding problem (NCP), but as a population-based algorithm, serious scalability and applicability problems are often confronted when GAs are applied to large- or huge-scale systems. Inspired by the temporal receding horizon control in control engineering, this paper proposes a novel spatial receding horizon control (SRHC) strategy as a network partitioning technology, and then designs an efficient GA to tackle the NCP. Traditional network partitioning methods can be viewed as a special case of the proposed SRHC, that is, one-step-wide SRHC, whilst the method in this paper is a generalized N-step-wide SRHC, which can make a better use of global information of network topologies. Besides the SRHC strategy, some useful designs are also reported in this paper. The advantages of the proposed SRHC and GA for the NCP are illustrated by extensive experiments, and they have a good potential of being extended to other large-scale complex problems.

Calculating complete and exact Pareto front for multiobjective optimization: a new deterministic approach for discrete problems.

Searching the Pareto front for multiobjective optimization problems usually involves the use of a population-based search algorithm or of a deterministic method with a set of different single aggregate objective functions. The results are, in fact, only approximations of the real Pareto front. In this paper, we propose a new deterministic approach capable of fully determining the real Pareto front for those discrete problems for which it is possible to construct optimization algorithms to find the k best solutions to each of the single-objective problems. To this end, two theoretical conditions are given to guarantee the finding of the actual Pareto front rather than its approximation. Then, a general methodology for designing a deterministic search procedure is proposed. A case study is conducted, where by following the general methodology, a ripple-spreading algorithm is designed to calculate the complete exact Pareto front for multiobjective route optimization. When compared with traditional Pareto front search methods, the obvious advantage of the proposed approach is its unique capability of finding the complete Pareto front. This is illustrated by the simulation results in terms of both solution quality and computational efficiency.

Characteristics of biofilm attaching to carriers in moving bed biofilm reactor used to treat vitamin C wastewater.

In order to investigate characteristics of biofilm attaching firmly to carriers in the moving bed biofilm reactor (MBBR) used for vitamin C wastewater treatment, experiments were undertaken with instrumental analysis methods. Scanning electron microscopy (SEM) micrographs of MBBR biofilms revealed that there were rod-shaped microbes and cocci in the biofilm, and microbes were embedded within medium substances and the biofilm matrix adhered firmly to carriers, leading to the formation of a smooth compacted surface at the base of the biofilm. Transmission electron microscopy (TEM) analysis revealed that extracellular polymeric substances (EPS) layer surrounded cell, sequestered inorganics to form a mixed structure, which ensured firm attachment of the biofilm to the carrier. X-ray diffraction (XRD) experiments and thermogravimetry analysis revealed that (i) the biofilm contained many inorganic substances, about 70.5%, and the inorganic substances contained multiple classes of inorganic with a high boiling point; (ii) inorganic elements such as calcium and phosphorous were selectively absorbed and accumulated in the biofilm as insoluble compounds with amorphous phases, rendering the biofilm highly resistant to detachment. Fourier-transform infrared (FTIR) spectroscopy showed carbohydrates were the main EPS.

Deterministic ripple-spreading model for complex networks.

This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

A ripple-spreading genetic algorithm for the aircraft sequencing problem.

When genetic algorithms (GAs) are applied to combinatorial problems, permutation representations are usually adopted. As a result, such GAs are often confronted with feasibility and memory-efficiency problems. With the aircraft sequencing problem (ASP) as a study case, this paper reports on a novel binary-representation-based GA scheme for combinatorial problems. Unlike existing GAs for the ASP, which typically use permutation representations based on aircraft landing order, the new GA introduces a novel ripple-spreading model which transforms the original landing-order-based ASP solutions into value-based ones. In the new scheme, arriving aircraft are projected as points into an artificial space. A deterministic method inspired by the natural phenomenon of ripple-spreading on liquid surfaces is developed, which uses a few parameters as input to connect points on this space to form a landing sequence. A traditional GA, free of feasibility and memory-efficiency problems, can then be used to evolve the ripple-spreading related parameters in order to find an optimal sequence. Since the ripple-spreading model is the centerpiece of the new algorithm, it is called the ripple-spreading GA (RSGA). The advantages of the proposed RSGA are illustrated by extensive comparative studies for the case of the ASP.