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Considering its Venturi effect, long corridor terrain is considered as one of the outstanding wind-energy resources, but has not been developed due to unclear inflow turbulence effects on wind turbines. To advance the wind-power technology in long corridors and reveal the physics behind it, we conducted a series of data-fusion analyses combining field measurements in the Hexi Corridor, Gansu, China, and numerical simulations and summarize the effort here. In this study, the classical spectrum for the atmospheric boundary layer for wind-energy research, the IEC Kaimal spectrum (also known as IECKwind, International Elector technical Commission 61400-1) cannot describe the wind profile in corridors. Here, we proposed a new spectrum to describe the wind in the Hexi Corridor by modifying the IEC Kaimal spectrum, and we call it the IECK-HX spectrum. The results demonstrate that it preferentially resides at lower frequencies (larger spatial scales) compared with IECKwind. Large-scale motions (LSMs) and very large-scale motions (VLSMs) coexist in the Hexi Corridor atmospheric boundary layer, which can be well described by the IECK-HX spectrum, and these two dominate the large fluctuations in the power, thrust, and blade root flapwise moment of wind turbines, while the high-frequency small-scale coherent structures mainly cause high-frequency fluctuations in wind-turbine load. Furthermore, IEC Kaimal spectrum can not describe the VLSMs, and the generated flow presents significant fluctuations and thereby reduces the power output in high wind-speed regions. This study provides fundamental support for wind-energy scientists and engineers who are interested in wind energy in corridor terrains.

Abstract Optimizing the wind farm layout requires accurately quantifying the wind-turbine wake distribution to minimize interference between wakes. Thus, the accuracy of wind turbine wake superposition models is critical. The sum of squares (SS) model is currently touted as the most accurate, but its application in engineering is hampered by its overestimation of the velocity deficit of the mixed wake. Therefore, previous work relied on approximate power calculations for performing optimization. The physical meaning of the SS model is unclear, which makes optimization difficult. In this study, a univariate linear correction idea is proposed based on the linear increase phenomenon of the SS method error. The unknown coefficients are obtained by fitting experimental data. The results demonstrate that the proposed method can accurately quantify the full-wake two-dimensional distribution of the mixed wake.

Recent findings indicate that air pollution contributes to the onset and advancement of chronic obstructive pulmonary disease (COPD). Nevertheless, there is insufficient research indicating that air pollution is linked to COPD in the region of inland northwest China. Daily hospital admission records for COPD, air pollutant levels, and meteorological factor information were collected in Jiuquan for this study between 1 January 2018 and 31 December 2019. We employed a distributed lag non-linear model (DLNM) integrated with the generalized additive model (GAM) to assess the association between air pollution and hospital admissions for COPD with single lag days from lag0 to lag7 and multiday moving average lag days from lag01 to lag07. For example, the pollutant concentration on the current day was lag0, and on the prior 7th day was lag7. The present and previous 7-day moving average pollutant concentration was lag07. Gender, age, and season-specific stratified analyses were also carried out. It is noteworthy that the delayed days exhibited a different pattern, and the magnitude of associations varied. For NO2 and CO, obvious associations with hospitalizations for COPD were found at lag1, lag01–lag07, and lag03–lag07, with the biggest associations at lag05 and lag06 [RR = 1.015 (95%CI: 1.008, 1.023) for NO2, RR = 2.049 (95%CI: 1.416, 2.966) for CO], while only SO2 at lag02 was appreciably linked to hospitalizations for COPD [1.167 (95%CI: 1.009, 1.348)]. In contrast, short-term encounters with PM2.5, PM10, and O3 were found to have no significant effects on COPD morbidity. The lag effects of NO2 and CO were stronger than those of PM2.5 and PM10. Males and those aged 65 years or older were more vulnerable to air pollution. When it came to the seasons, the impacts appeared to be more pronounced in the cold season. In conclusion, short-term encounters with NO2 and CO were significantly correlated with COPD hospitalization in males and the elderly (≥65).

IntroductionLyophyllum decastes (Fr.) Singer polysaccharides (LDSPs) have been verified to possess strong biological properties. However, the effects of LDSPs on intestinal microbes and their metabolites have rarely been addressed.MethodsThe in vitro-simulated saliva-gastrointestinal digestion and human fecal fermentation were used to evaluate the effects of LDSPs on non-digestibility and intestinal microflora regulation in the present study.ResultsThe results showed a slight increase in the content of the reducing end of the polysaccharide chain and no obvious change in the molecular weight during in vitro digestion. After 24 h in vitro fermentation, LDSPs were degraded and utilized by human gut microbiota, and LDSPs could be transformed into short-chain fatty acids leading to significant (p

Metal–organic frameworks (MOFs) derivatives are developing family of functional materials for electromagnetic wave (EMW) absorption. Their tailored structures, controllable compositions, high porosity, and versatile functions offer immense advantages for the construction of excellent EMW absorption materials. Nevertheless, it is crucial and challenging to understand the unique role of rationally designing art and tailoring the microstructures of MOF‐derived materials for EMW absorption. In this review, advances in rational architectural design strategy and the elaborate control of microstructures are outlined to promote the EMW absorption performance of MOF‐derived materials. In addition, the derived key information regarding the superiority and composition–structure–performance relationships of the engineered MOF‐derived materials with advanced components and nanostructures is comprehensively summarized. Finally, the insight into the challenges of future development in MOF‐derived EMW absorption materials is presented.

The frequent appearance of sandy and dusty weather in Northwest China impacts the wind turbine. Meanwhile, the non-constant phenomena, dynamic stall speed during the wind turbine operation, will lead to large load fluctuations and unsafe operation. However, few studies have been conducted at home and abroad on the effect of particle parameters on the dynamic stall of airfoils. This paper investigates the impact of particle parameters on the airfoil dynamic stall through numerical simulation of the coupling between the continuous phase and discrete phase by using the SST k-ω turbulence model for a two-dimensional NACA 0012 airfoil. The effect of particle parameters on the airfoil dynamic stall aerodynamic performance, the impact of the flow field around the airfoil, and the particles motion were studied, respectively. The investigation shows a reduction in the aerodynamic performance of the airfoil, due to the addition of particles. The effect is more prominent under a large angle of attack and less under a small angle of attack. When the angle of attack increases, the loss rate of lift coefficient in the windy and sandy environment gradually decreases, while irregular fluctuations emerge when the angle of attack decreases, and the overall rate of change increases more significantly, compared to the stage of the increasing angle of attack. For the particle diameter under 50 μm, the larger the particle diameter, the more significant the change of lift coefficient becomes, as well as the larger the vortex volume near the airfoil’s leading edge, and a large number of particles gather at the suction surface of the airfoil. For the particle diameter of 50 μm, the lift coefficient decreases at any angle of attack of the airfoil movement to the oscillation cycle, the vortex volume decreases, and a large number of particles gather at the pressure surface of the airfoil. However, for particle diameters above 50 μm, the lift coefficient gets reduced, followed by a decrease in the vortex volume near the airfoil leading edge with the increase of particle diameter, so that plenty of particles gather on the pressure surface of that airfoil. At the stage of increasing the airfoil angle of attack, with the increase of particle concentration, there is a gradual decrease of the peak lift coefficient and stall angle of attack of the airfoil, as well as a corresponding decrease of the drag coefficient divergence angle of attack and peak value. In contrast, when the airfoil angle of attack is decreased, the airflow reattachment process obviously lags behind that of the clean air. As the particle concentration increases, the airfoil separation point occurs earlier, and the higher the concentration, the earlier the separation point. The erosion maximum airfoil erosion rate increases with the particle concentration.

With the advancements in wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which has also been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, high energy consumption, and unreliable transmission, influencing the good quality of service (QoS). In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability and energy efficiency. The on-site architecture adopted by SROA is different from most architectures in that the data center is deployed underwater, which makes the sink nodes closer to the data source. A clustering method is introduced in SROA, which makes the network adapt to the changes in the network scale and avoid single-point failure. Moreover, the Q-learning algorithm is applied to seek optimal routing policies, in which the characteristics of underwater acoustic communication such as residual energy, end-to-end delay, and link quality are considered jointly when constructing the reward function. Furthermore, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. The scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively decreasing energy consumption and end-to-end delay.

The cell wall polysaccharides were extracted from Sparassis latifolia fruit bodies by acid–alkali and superfine-grinding assisted methods, and the chemical characterization and in vitro immunity activities of these polysaccharide fractions were studied and compared. Results showed that superfine-grinding assisted extraction exhibited the highest yield of polysaccharides (SP, 20.80%) and low β-glucan content (19.35%) compared with alkaline extracts. The results revealed that the 20% ethanol precipitated fraction (20E) from SP was mainly composed of β-(1→3)-glucan and α-(1→4)-glucan. With the increase of ethanol precipitation, the fractions (30E, 40E, 50E) were identified as α-(1→4)-glucan with different molecular weights and conformations. Cell wall polysaccharides extracted through NaOH (NSP) and KOH (KSP) extraction had similar yields with 8.90% and 8.83%, respectively. Structural analysis indicated that the purified fraction from KSP (KSP-30E) was a β-(1→3)-glucan backbone branched with β-(1→6)-Glcp, while the purified fraction from NSP (NSP-30E) mainly contained β-(1→3)-glucan with a small number of α-linked-Glcp. The two fractions both exhibited rigid chain conformation in aqueous solutions. All polysaccharide fractions exerted the activity of activating Dectin-1 receptor in vitro, and the KSP-30E mainly identified as β-(1→3)-glucan with the terminal group via 1→6-linkage attached at every third residue exhibited a stronger enhancing effect than other fractions. Results suggested that KOH extraction could be efficient for the preparation of bioactive β-(1→3, 1→6)-glucan as a food ingredient.

The computer numerical control machine is important industrial equipment, and its reliability has been one of the most important symbols to measure the modernization of advanced manufacturing and it is critical in the aspects of reliability design improvement, fault monitoring, and fault repair for the computer numerical control machine. The computer numerical control machine’s assembly process is a significant part in its manufacturing process, and assembly operation is a major factor in determining the whole machine’s quality, and assembly process quality analysis is necessary for computer numerical control machines, in which reliability allocation is an essential part of its reliability design. In order to quickly locate the fault of computer numerical control machine tool and accurately judge the fault grade, a method of fault classification decision of computer numerical control machine tool based on motion micro-unit is proposed, which includes the following steps: (1) from the point of the decomposition of system function, the computer numerical control machine tool is decomposed layer by layer into the layer of micro-actions, and the conceptual model of motion unit is given; (2) from the level of action, the types of fault modes of motion units are comprehensively analyzed and summarized; and (3) combining grey clustering theory and rough set theory, a fast and accurate fault classification decision-making method is formed. Finally, the validity of this method is verified by an example analysis of motion micro-units of a computer numerical control machine rack. The contributions of this work can be summarized as (1) the proposed grey fixed weight clustering analysis, (2) the graded fault classification using the decision table approach, (3) the knowledge reduction of decision rules using the rough set theory, and (4) the quicker and accurater decision and effectiveness validated by the given study case.

Hazard assessment is critical for a biomass gasification station because it includes various hazardous factors. Set pair analysis (SPA) is a convenient and effective method for hazard assessment, but it has limitations, including (1) the inability to reflect the difference when the data belong to the same hazard grade and (2) the assessment results lack precision and accuracy. This study proposes an improved method designated as general set pair analysis (GSPA). Connection measure degree (CMD), which is based on the cosine function, as well as weighting deviation degree (WDD), relative membership degree (RMD), and comprehensive index (CI) were proposed in GSPA, and the algorithms were generated. The calculated results of these different methods can be utilized to overcome the shortcomings of SPA during hazard assessment. A case study of two biomass gasification stations in Shenyang City, Liaoning Province, Northeast China, was performed. The hazard assessment results of the GSPA were compared with those of SPA. The results showed that GSPA is a more effective, precise, and accurate method for hazard assessment of a biomass gasification station.

Biomass gasification stations are facing many hazard factors, therefore, it is necessary to make hazard assessment for them. In this study, a novel hazard assessment method called extended set pair analysis (ESPA) is proposed based on set pair analysis (SPA). However, the calculation of the connection degree (CD) requires the classification of hazard grades and their corresponding thresholds using SPA for the hazard assessment. In regard to the hazard assessment using ESPA, a novel calculation algorithm of the CD is worked out when hazard grades and their corresponding thresholds are unknown. Then the CD can be converted into Euclidean distance (ED) by a simple and concise calculation, and the hazard of each sample will be ranked based on the value of ED. In this paper, six biomass gasification stations are introduced to make hazard assessment using ESPA and general set pair analysis (GSPA), respectively. By the comparison of hazard assessment results obtained from ESPA and GSPA, the availability and validity of ESPA can be proved in the hazard assessment for biomass gasification stations. Meanwhile, the reasonability of ESPA is also justified by the sensitivity analysis of hazard assessment results obtained by ESPA and GSPA.

The airfoil plays an important role in improving the performance of wind turbines. However, there is less research dedicated to the airfoils for Vertical Axis Wind Turbines (VAWTs) compared to the research on Horizontal Axis Wind Turbines (HAWTs). With the objective of maximizing the aerodynamic performance of the airfoil by optimizing its geometrical parameters and by considering the law of motion of VAWTs, a new airfoil, designated the LUT airfoil (Lanzhou University of Technology), was designed for lift-driven VAWTs by employing the sequential quadratic programming optimization method. Afterwards, the pressure on the surface of the airfoil and the flow velocity were measured in steady conditions by employing wind tunnel experiments and particle image velocimetry technology. Then, the distribution of the pressure coefficient and aerodynamic loads were analyzed for the LUT airfoil under free transition. The results show that the LUT airfoil has a moderate thickness (20.77%) and moderate camber (1.11%). Moreover, compared to the airfoils commonly used for VAWTs, the LUT airfoil, with a wide drag bucket and gentle stall performance, achieves a higher maximum lift coefficient and lift⁻drag ratios at the Reynolds numbers 3 × 105 and 5 × 105.

Software-defined networking (SDN) is widely used in multiple types of data center networks, and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers. However, the network topology of each control domain of SDN will affect the performance of the multidomain network, so performance evaluation is required before the deployment of the multi-domain SDN. Besides, there is a high cost to build real multi-domain SDN networks with different topologies, so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network. As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically, this paper proposes an automated multi-domain SDN topology performance evaluation framework, which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network. The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies. We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster. Then, we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments. Test results show that Space Shuffle has the most stable performance in a single-domain environment, and Fat-tree has the best performance in a multi-domain environment. Also, this tool has the characteristics of simplicity and stability, which can meet the needs of multi-domain SDN topology performance evaluation. [ABSTRACT FROM AUTHOR]

Delay and stability are two key factors that affect the performance of multicast data transmission in a network. However, current algorithms of tree generation hardly meet the requirements of low delay and high stability simultaneously. Given a general network, the generation algorithm of a multicast tree with minimum delay and maximum stability is an NPhard problem, without a precise and efficient algorithm. To address these challenges, this paper studies the generation of low-delay and high-stability multicast trees under the model of spanning tree based on stability probability, degree-constrained, edge-weighted for multicast (T-SDE). A class of algorithms was proposed which creates the multicast tree greedy on the ratio of fan-out to delay (RFD) and probability of stability of terminal to obtain a high performance in multicast. The proposed algorithms greedily select terminals with a large RFD and a high probability of stability as forwarding nodes in the generation of the multicast tree, where the larger RFD and higher stability of upstream nodes are beneficial to achieve a low transmission delay and high stability in multicast. The proposed RFD can be compatible with the original model, which can take advantage of network connectivity during the generation of a multicast tree. This paper carries out simulation experiments on Matlab R2016b to measure the performance of the proposed algorithm. Experimental results show that the proposed algorithm can provide a smaller height, higher stability, and a lower transmission delay of the resulting multicast tree than other solutions. The spanning tree of the proposed algorithms can support low transmission delay and high stability in multicast transmission. [ABSTRACT FROM AUTHOR]

Metal mineral resources are critical materials for the world economic development. In a situation of weak supply of terrestrial rare metals, many countries are expanding the direction of resource extraction from a land-based approach to a land-sea approach, with an increased emphasis on marine resource extraction. Aiming at the problem of efficient exploration of deep-sea resources, it is crucial to investigate the equipment and technology used in the deep-sea mining (DSM). First, the history of scientific technologies and equipment for DSM is systematically discussed, and the technical characteristics of submarine-drag bucket mining system, continuous line bucket mining system, shuttle vessel mining system, and pipe lift mining system are analyzed, the development and current status of the three types of deep-sea vehicles, transport equipment, and mining vessel are summarized. Subsequently, on the basis of current research and development trends, the research developments on the technologies for DSM equipment are synthesized in three areas of mining, transportation, and support platforms. The progress and achievements of four major mining technologies have been reviewed, i.e., harvesting, traction-drive, tracking-planning, and plume evolution assessment. Research developments in four transport technologies are summarized, including pneumatic lifting technology, hydraulic lifting technology, transition parts assurance technology, and mechanical behavior technology. Research direction and progress of two support platform technologies are discussed, involving the launch and recovery technology and heave compensation technology. Finally, the key scientific and technical issues are analyzed and the studies are carried out to meet the environment and safety challenges in the future. It is proposed that four key technologies need to have a breakthrough, which consist of green travel and collection technology, ultra-deep safe mine lifting technology, global synergistic safeguard technology, and digital environmental assessment technology, given the current state of development in the three areas of economy, environment, equipment and technology, as well as the scientific goals of deep-sea mining technology, which is to shift from equipment controllability to safe and green mining. These initiatives provide a theoretical support for the research and construction of the DSM equipment manufacturing system.