Soil’s Hidden Power : The Stable Soil Organic Carbon Pool Controls the Burden of Persistent Organic Pollutants in Background Soils

Persistent organic pollutants (POPs) tend to accumulate in cold regions by cold condensation and global distillation. Soil organic matter is the main storage compartment for POPs in terrestrial ecosystems due to deposition and repeated air–surface exchange processes. Here, physicochemical properties and environmental factors were investigated for their role in influencing POPs accumulation in soils of the Tibetan Plateau and Antarctic and Arctic regions. The results showed that the soil burden of most POPs was closely coupled to stable mineral-associated organic carbon (MAOC). Combining the proportion of MAOC and physicochemical properties can explain much of the soil distribution characteristics of the POPs. The background levels of POPs were estimated in conjunction with the global soil database. It led to the proposition that the stable soil carbon pools are key controlling factors affecting the ultimate global distribution of POPs, so that the dynamic cycling of soil carbon acts to counteract the cold-trapping effects. In the future, soil carbon pool composition should be fully considered in a multimedia environmental model of POPs, and the risk of secondary release of POPs in soils under conditions such as climate change can be further assessed with soil organic carbon models

Demographic, clinical and antibody characteristics of patients with digital ulcers in systemic sclerosis: data from the DUO Registry

OBJECTIVES: The Digital Ulcers Outcome (DUO) Registry was designed to describe the clinical and antibody characteristics, disease course and outcomes of patients with digital ulcers associated with systemic sclerosis (SSc). METHODS: The DUO Registry is a European, prospective, multicentre, observational, registry of SSc patients with ongoing digital ulcer disease, irrespective of treatment regimen. Data collected included demographics, SSc duration, SSc subset, internal organ manifestations, autoantibodies, previous and ongoing interventions and complications related to digital ulcers. RESULTS: Up to 19 November 2010 a total of 2439 patients had enrolled into the registry. Most were classified as either limited cutaneous SSc (lcSSc; 52.2%) or diffuse cutaneous SSc (dcSSc; 36.9%). Digital ulcers developed earlier in patients with dcSSc compared with lcSSc. Almost all patients (95.7%) tested positive for antinuclear antibodies, 45.2% for anti-scleroderma-70 and 43.6% for anticentromere antibodies (ACA). The first digital ulcer in the anti-scleroderma-70-positive patient cohort occurred approximately 5 years earlier than the ACA-positive patient group. CONCLUSIONS: This study provides data from a large cohort of SSc patients with a history of digital ulcers. The early occurrence and high frequency of digital ulcer complications are especially seen in patients with dcSSc and/or anti-scleroderma-70 antibodies

Reduction of the Variety of Phenolic Compounds in Bio-oil via the Catalytic Pyrolysis of Pine Sawdust

The objective of this study was to evaluate phenolic compounds produced from the catalytic pyrolysis of pine sawdust by commercial catalysts. Eight types of commercial catalysts consisting of SiO2, montmorillonite, α-Fe2O3, HZSM-5 (Si:Al = 25:1), ZnO, γ-Fe2O3, HZSM-5 (Si:Al = 50:1), and nano-HZSM-5 (Si:Al = 50:1) were screened in a fixed bed reactor at a reaction temperature of 500 °C and a vapor residence time of 3 s. All the tested commercial catalysts exhibited different catalytic performances for the adjustment of the composition of the bio-oil. HZSM-5 (Si:Al = 25:1) significantly increased hydrocarbon production in the bio-oil, which is helpful for improving its heating value. The different types of phenols were reduced significantly from 17 to 7 with nano-HZSM-5 (Si:Al = 50:1); however, the phenols content also decreased from 32.6% to 23.28% compared with non-catalytic pyrolysis. Meanwhile, the addition of nano-HZSM-5 (Si:Al = 50:1) to the raw material provided the highest amount of furans (up to 38.8%) among the tested commercial catalysts. The inexpensive ZnO and γ-Fe2O3 also were surprisingly effective for the reduction of the variety of phenolic compounds detected by GC/MS, reducing that number from 17 to 10

Energy Cooperation in Ultradense Network Powered by Renewable Energy Based on Cluster and Learning Strategy

A new method about renewable energy cooperation among small base stations (SBSs) is proposed, which is for maximizing the energy efficiency in ultradense network (UDN). In UDN each SBS is equipped with energy harvesting (EH) unit, and the energy arrival times are modeled as a Poisson counting process. Firstly, SBSs of large traffic demands are selected as the clustering centers, and then all SBSs are clustered using dynamic k-means algorithm. Secondly, SBSs coordinate their renewable energy within each formed cluster. The process of energy cooperation among SBSs is considered as Markov decision process. Q-learning algorithm is utilized to optimize energy cooperation. In the algorithm there are four different actions and their corresponding reward functions. Q-learning explores the action as much as possible and predicts better action by calculating reward. In addition, ε greedy policy is used to ensure the algorithm convergence. Finally, simulation results show that the new method reduces data dimension and improves calculation speed, which furthermore improves the utilization of renewable energy and promotes the performance of UDN. Through online optimization, the proposed method can significantly improve the energy utilization rate and data transmission rate

Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions

In recent years, the formation control of multi-mobile robots has been widely investigated by researchers. With increasing numbers of robots in the formation, distributed formation control has become the development trend of multi-mobile robot formation control, and the consensus problem is the most basic problem in the distributed multi-mobile robot control algorithm. Therefore, it is very important to analyze the consensus of multi-mobile robot systems. There are already mature and sophisticated strategies solving the consensus problem in ideal environments. However, in practical applications, uncertain factors like communication noise, communication delay and measurement errors will still lead to many problems in multi-robot formation control. In this paper, the consensus problem of second-order multi-robot systems with multiple time delays and noises is analyzed. The characteristic equation of the system is transformed into a quadratic polynomial of pure imaginary eigenvalues using the frequency domain analysis method, and then the critical stability state of the maximum time delay under noisy conditions is obtained. When all robot delays are less than the maximum time delay, the system can be stabilized and achieve consensus. Compared with the traditional Lyapunov method, this algorithm has lower conservativeness, and it is easier to extend the results to higher-order multi-robot systems. Finally, the results are verified by numerical simulation using MATLAB/Simulink. At the same time, a multi-mobile robot platform is built, and the proposed algorithm is applied to an actual multi-robot system. The experimental results show that the proposed algorithm is finally able to achieve the consensus of the second-order multi-robot system under delay and noise interference

A Bayesian approach to pilot-pivotal trials for bioequivalence assessment

Abstract Background To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. Methods We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. Results Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. Conclusions The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size

Phase-field simulation of magnetic double-hole nanoring and its application in random storage

As an ideal high-density storage unit, magnetic nanorings have become a research hotspot in recent years. We can both study the evolution of microscopic state of magnetization and acquire macroscopic magnetic properties by micromagnetic simulation, which has thus been widely used. However, traditional micromagnetism cannot simulate complex stress state. Due to the introduction of microelasticity theory, the phase field method for magnetic materials can be used to calculate the coupling effect of stress and magnetic field. However, the computing model usually needs to satisfy periodic boundary condition. In this paper, the phase field simulation combined with the finite element method is employed. By using user defined element, the evolution of magnetic domain structures of the double-hole nanorings has been studied. In different diameter of the holes and external magnetic field direction, we have found seven kinds of magnetic domain evolution mechanism. Among them, the twin-vortex evolution mechanism with high stability and low demagnetization interference characteristics of advantages, has good application prospect in magnetic random-access memory (MRAM) unit

Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System

Aiming at the problem of the wide range and great difficulty in the future of battlefield airspace control, based on the unique advantages of an airspace grid system in an airspace grid representation and time–space binary computing, this paper designs a pre-clustering method for mission airspace based on airspace location correlation under the condition of future large-scale air combat missions in order to realize the block control of battlefield airspace. This method reduces the whole 3D battlefield space projection to a 2D plane and regards the task airspace projection as “obstacles” in the task area; Multi-A * algorithm is used to generate the airspace clustering line surrounding the task airspace, and the airspace association clustering problem is transformed into a multiple “start point-end point” path planning problem with autonomous optimization. Through the experiment, it was found that clustering the airspace can effectively improve the management and control efficiency of large-scale battlefield airspace