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Air purifier use, shift work, and long-term exposure to fine particulate matter (PM2.5) are linked to platelet abnormality. However, the role of air purifier use and shift work in the individual or joint associations of PM2.5 and its components with platelet indices are largely unknown. A total of 8772 participants were recruited from a population of subway workers in China. PM2.5 and its component data were obtained from the Tracking Air Pollution in China dataset. The role of air purifier use and shift work in the association between PM2.5 and its components and platelet indices were analyzed. Among shift workers without air purifier use, positive associations of PM2.5 and each component in PM2.5 with the mean platelet volume (MPV) or platelet counts (PLT) were observed, whereas negative associations of PM2.5 and each component in PM2.5 with the platelet distribution width (PDW) were observed. Furthermore, estimated changes (95%CIs) in PLT, MPV, and PDW in response to each 10th percentile increment in the mixture of PM2.5 and its components were 0.8657 (0.2496, 1.4819), 0.0192 (0.0054, 0.0329), and −0.0648 (−0.0945, −0.0351), respectively, and sulfate in PM2.5 was the major contributor to those associations. Long-term exposure to PM2.5 and its components was related to increased platelet disorders among shift workers without air purifier use, and those associations were mainly attributed to sulfate in PM2.5.

Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%.

Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things (IoT) ecosystem due to their ability to collect and transmit data from remote and hard-to-reach areas. Ensuring collision-free navigation for these UAVs is crucial in achieving this goal. However, existing UAV collision-avoidance methods face two challenges: conventional path-planning methods are energy-intensive and computationally demanding, while deep reinforcement learning (DRL)-based motion-planning methods are prone to make UAVs trapped in complex environments—especially for long trajectories with unpredictable obstacles—due to UAVs’ limited sensing ability. To address these challenges, we propose a hybrid collision-avoidance method for the real-time navigation of UAVs in complex environments with unpredictable obstacles. We firstly develop a Human-in-the-Loop DRL (HL-DRL) training module for mapless obstacle avoidance and secondly establish a global-planning module that generates a few points as waypoint guidance. Moreover, a novel goal-updating algorithm is proposed to integrate the HL-DRL training module with the global-planning module by adaptively determining the to-be-reached waypoint. The proposed method is evaluated in different simulated environments. Results demonstrate that our approach can rapidly adapt to changes in environments with short replanning time and prevent the UAV from getting stuck in maze-like environments.

Nowadays, the contradiction between strict construction land supply restriction and excessive construction land demand is extremely prominent. Construction land reduction (CLR) is a policy innovation for economically developed regions designed to solve the tight constraints of the construction land quota as urban development continues in China, however, it leads to a lack of spatial justice. In this study, we address a gap in land use regulation literature regarding regional economic development in fast-developing nations by presenting a quantitative investigation of spatial justice in Shanghai, China. We theoretically analyze the connotation of spatial justice in CLR and its influence on residents’ policy acceptance of CLR. Based on theoretical analysis and using household questionnaires from JJ Town in W District, Shanghai, China, we investigate how spatial justice affects residents’ policy acceptance of CLR through an ordered probit model. The results show that (1) spatial justice strengthens residents’ policy acceptance of CLR; (2) both policy familiarity and participation are important influencing factors that contribute to residents’ policy acceptance of CLR; (3) age, education, household income, the contracting land scale and household population structure also affect residents’ policy acceptance of CLR. (4) Robustness tests support the above findings. Thus, in the process of CLR, it is essential to fully consider the realization of spatial justice to ensure the development of remote suburbs, especially the regions experiencing a net reduction in their construction land.

The mechanisms of chronic pain are complex, and genetic factors play an essential role in the development of chronic pain. Neuropathic pain (NP) and inflammatory pain (IP) are two primary components of chronic pain. Previous studies have uncovered some common biological processes in NP and IP. However, the shared genetic mechanisms remained poorly studied. We utilized multi-omics systematic analyses to investigate the shared genetic mechanisms of NP and IP. First, by integrating several genome-wide association studies (GWASs) with multi-omics data, we revealed the significant overlap of the gene co-expression modules in NP and IP. Further, we uncovered the shared biological pathways, including the previously reported mitochondrial electron transport and ATP metabolism, and stressed the role of genetic factors in chronic pain with neurodegenerative diseases. Second, we identified 24 conservative key drivers (KDs) contributing to NP and IP, containing two well-established pain genes, IL1B and OPRM1, and some novel potential pain genes, such as C5AR1 and SERPINE1. The subnetwork of those KDs highlighted the processes involving the immune system. Finally, gene expression analysis of the KDs in mouse models underlined two of the KDs, SLC6A15 and KCNQ5, with unidirectional regulatory functions in NP and IP. Our study provides strong evidence to support the current understanding of the shared genetic regulatory networks underlying NP and IP and potentially benefit the future common therapeutic avenues for chronic pain.

Heat pipes transfer heat via phase transformation of the working fluid, where the working fluid will keep the temperature constant and absorb or release a large amount of latent heat during phase transformation. With the development of heat pipe technology, the isothermal performance of heat pipes has been gradually emphasized in many application fields. Most studies focused on the average temperature characteristics of one heat pipe or several heat pipes with the same type, and lacked a comprehensive analysis on the isothermal performance of different heat pipes. In this paper, previous studies on the application fields of the isothermal performance of heat pipes, the isothermal level of heat pipes used in different fields, and the methods to improve the isothermal performance of heat pipes are summarized. The parameters of the wick have little effect on the temperature uniformity of the heat pipe, while the arrangement of the wick has more influence on the uniformity of the heat pipe. The most suitable charge rate is 15% to 30% of the total inner volume, and the best start-up performance and isothermal performance is at approximately 45°.

For high-precision thermocouple calibration, the uniformity of the temperature field provided by the metal temperature equalizing block is low, and the structure of the gas-controlled heat pipe is complex. In order to improve the thermocouple calibration equipment, heat pipe technology can be used to provide the stability and uniformity of the temperature field of the equipment. The concentric annular heat pipe (CAHP) is completely placed in the heating furnace to provide a uniform temperature field, and limited studies consider this heating mode for alkali metal CAHPs. Specifically, no information is available on the effect of heating temperatures on the temperature distribution of the internal pipe of potassium CAHP. In this study, a temperature comparison based on potassium CAHP for high-precision thermocouple calibration was manufactured. The temperature stability and temperature uniformity of CAHP were measured, and the effects of heating temperature and heating mode on the isothermal performance in the metering wells of CAHP were studied. The CAHP can provide a very stable and uniform temperature field. Under uniform heating at 400 °C, the maximum temperature difference within 16 cm was 0.174 °C. After adjusting the heating mode, the maximum temperature difference was within 16 cm 0.095 °C. The CAHP can effectively reduce the influence of heating temperature fluctuation on the temperature in the metering well; the maximum temperature change rate of the metering wells affected by the heating furnace temperature was 0.0942 °C/°C.

In the geyser boiling mode, the working fluid state is divided into a boiling process and a quiet process, and the sodium-potassium (Na-K) alloy heat pipe can discontinuously transfer heat at each boiling. The overheating of the liquid working fluid at the bottom causes short-term boiling and forms slug bubble, the strong condensing ability quickly conducts heat from the evaporator section. And geyser boiling can occur before the working fluid forms continuous flow, so it transfers more heat at lower temperatures than natural convection cooling. In this study, the heat transfer process of a Na-K alloy heat pipe with forced convection cooling under different heating power was experimental studied. The geyser boiling mode can make the Na-K alloy heat pipe work below 650 °C and reduce the start-up time. In the process of geyser boiling, the heat transfer quantity was increased by the boiling frequency and the amount of vapor produced in a single boiling. The boiling temperature had no obvious change with the increased of heating power, and the condenser section temperature increased with the heating power.

A flight of shallow convective clouds during the SCMS95 (Small Cumulus Microphysics Study 1995) observation project is simulated by the large eddy simulation (LES) version of the Weather Research and Forecasting Model (WRF-LES) with spectral bin microphysics (SBM). This study focuses on relative dispersion of cloud droplet size distributions, since its influencing factors are still unclear. After validation of the simulation by aircraft observations, the factors affecting relative dispersion are analyzed. It is found that the relationships between relative dispersion and vertical velocity, and between relative dispersion and adiabatic fraction are both negative. Furthermore, the negative relationships are relatively weak near the cloud base, strengthen with the increasing height first and then weaken again, which is related to the interplays among activation, condensation and evaporation for different vertical velocity and entrainment conditions. The results will be helpful to improve parameterizations related to relative dispersion (e.g., autoconversion and effective radius) in large-scale models.

We report on the growth of SiC nanowires on a single crystal Si substrate by pyrolysis of polycarbosilane and using two catalyst (Al2O3 and Ni) films with different thickness (2, 4, and 6 nm). The catalyst films were deposited on the Si substrate, and the SiC nanowires were grown according to two mechanisms, i.e., the oxide-assisted growth mechanism and vapor- liquid-solid mechanism. As a result, pearl-chain-like SiC nanowires and straight SiC nanowires were obtained. The prepared nanowires exhibited excellent photoluminescence properties, emission spectra displaying two emission peaks at 395 and 465 nm, and have good thermal stability below 1000 °C. The experimental results revealed the importance of the catalyst in controlling the morphology and properties of SiC nanowires.

In this study, gelatin and pullulan were successfully prepared as a novel type of protein−polysaccharide composite nanofibrous membrane by electrospinning at room temperature with deionized water as the solvent. The effects of gelatin content on the properties of the solution, as well as the morphology of the resultant nanofibers, were investigated. Scanning electron microscopy (SEM) was utilized to observe the surface morphology. Fourier transform infrared spectroscopy (FTIR) was used to study the interaction between gelatin and pullulan. Incorporation of pullulan with gelatin will improve the spinnability of the mixed aqueous solution due to lower surface tension. Moreover, the conductivity of the solution had a greater effect on the fiber diameters, and the as-spun fibers became thinner as the viscosity and the surface tension increased due to the addition of the polyelectrolyte gelatin. Gelatin and pullulan formed hydrogen bonds, and the intermolecular hydrogen bonds increased while the intramolecular hydrogen bond decreased, which resulted in better mechanical properties. The electrospun gelatin/pullulan nanofibers could mimic both the structure and the composition of the extracellular matrix, and thus could be applied in tissue engineering.

A broadband microwave absorbing composite with a multi-scale layered structure is proposed, in which a reduced graphene oxide (RGO) film sandwiched between two layers of epoxy glass fiber laminates serves as the frequency selective surface (FSS). RGO films with the desired electrical properties were synthesized directly by hydrothermal reaction, vacuum filtration, and heat treatment without subsequent processing. With the novel layer-by-layer structure ranging from micro to macro scale, the optimized composite exhibits excellent microwave absorption performance with a total thickness of 3.2 mm. Its reflection coefficient (RC) is less than −10 dB in the entire X and Ku band, reaching a minimum value of −32 dB at 10.2 GHz and an average RC of −22.8 dB from 8 to 18 GHz. Enhanced microwave absorption of the composites is achieved through the optimization of layer thickness in the sandwich structure to promote destructive interference. Improved impedance matching by the introduction of FSS along with the polarization and conduction loss of layered graphene films also contribute to the increased absorption.

The mechanical and dielectric properties of two types of amorphous silicon nitride (Si3N4) fibers prior to and following annealing at 800 °C were studied. The tensile strengths of the Si3N4 fiber bundles were measured using unidirectional tensile experimentation at room temperature, whereas the permittivity values were measured at 8.2–12.4 GHz using the waveguide method. The results demonstrated that the tensile strength and dielectric properties of Si3N4 fibers were correlated to the corresponding composition, microstructure, and intrinsic performance of electrical resistance. The Si3N4 fibers with a lower content of amorphous SiNxOy presented an improved thermal stability, a higher tensile strength, a higher conductivity, and a significantly stable wave-transparent property. These were mainly attributed to the highly pure composition and decomposition of less amorphous SiNxOy.

An effective and reliable fusion method for conflicting information is proposed in this paper. Compared with a single-sensor system, a multi-sensor fusion system can comprehensively combine the redundancy and complementarity of multi-sensor information to obtain better system performance. Hence, the multi-sensor fusion system has become one of the research hotspots. However, due to lack knowledge about the measurement environment and limited sensor accuracy, the multi-sensor system inevitably appears to have imperfect, uncertain and inconsistent information. To solve the problem, we introduce one powerful uncertainty reasoning method: Dempster–Shafer theory (DS theory). With convincing measurement and a forceful combination of uncertain information, DS theory is widely applied in various fields, like decision-making, expert systems, target tracking, monitoring systems, etc. Nevertheless, DS theory will produce counter-intuitive fusion results when the pieces of evidence are highly conflicting. To address this issue, we raise an improved DS combination method for conflicting information fusion in this paper. First of all, the modified Minkowski distance function and the betting-commitment distance function are separately employed to revise potentially conflicting pieces of evidence. The procedure availably solves the conflicting situations caused by unreliable and imprecise evidence sources, which enhances the consistency among pieces of evidence. Then, based on two revised pieces of evidence, a conflicting redistribution strategy based on locally conflicting analyses is put forward. The approach dexterously combines two revised pieces of evidence to avoid conflicting situations caused by compulsive normalization, which further improves the accuracy and convergence speed of the multi-sensor fusion system. Finally, two experimental analyses with consistent information and conflicting information reveal the remarkable effectiveness and priority of the proposed algorithm for the multi-sensor fusion system. Consequently, this paper has certain value for the multi-sensor fusion system.

SiBCN ceramics were introduced into porous Si3N4 ceramics via a low-pressure chemical vapor deposition and infiltration (LPCVD/CVI) technique, and then the composite ceramics were heat-treated from 1400 °C to 1700 °C in a N2 atmosphere. The effects of annealing temperatures on microstructure, phase evolution, dielectric properties of SiBCN ceramics were investigated. The results revealed that α-Si3N4 and free carbon were separated below 1700 °C, and then SiC grains formed in the SiBCN ceramic matrix after annealing at 1700 °C through a phase-reaction between free carbon and α-Si3N4. The average dielectric loss of composites increased from 0 to 0.03 due to the formation of dispersive SiC grains and the increase of grain boundaries.

To improve the thermal and mechanical stability of SiCf/SiC or C/SiC composites with SiBN interphase, SiBN coating was deposited by low pressure chemical vapor deposition (LPCVD) using SiCl4-BCl3-NH3-H2-Ar gas system. The effect of the SiCl4 flow rate on deposition kinetics was investigated. Results show that deposition rate increases at first and then decreases with the increase of the SiCl4 flow rate. The surface of the coating is a uniform cauliflower-like structure at the SiCl4 flow rate of 10 mL/min and 20 mL/min. The surface is covered with small spherical particles when the flow rate is 30 mL/min. The coatings deposited at various SiCl4 flow rates are all X-ray amorphous and contain Si, B, N, and O elements. The main bonding states are B-N, Si-N, and N-O. B element and B-N bonding decrease with the increase of SiCl4 flow rate, while Si element and Si-N bonding increase. The main deposition mechanism refers to two parallel reactions of BCl3+NH3 and SiCl4+NH3. The deposition process is mainly controlled by the reaction of BCl3+NH3.

A new DS (Dempster-Shafer) combination method is presented in this paper. As data detected by a single sensor are characterized by not only fuzziness, but also partial reliability, the development of multi-sensor information fusion becomes extremely indispensable. The DS evidence theory is an effective means of information fusion, which can not only deal with the uncertainty and inconsistency of multi-sensor data, but also handle the inevitably ambiguity and instability under noise or possible interference. However, the application of DS evidence theory has some limitations when multi-sensor data are conflicting. To address this issue, the DS evidence theory is modified in this paper. Adopting the idea of cluster analysis, we firstly introduce the Lance distance function and spectral angle cosine function to revise original evidence separately before the combination of evidence. Then, based on the modifications of original evidence, an improved conflict redistribution strategy is ulteriorly raised to fuse multi-sensor information. Finally, the numerical simulation analyses demonstrate that the improvement of the DS evidence theory available in this paper overcomes the limitations of conventional DS evidence theory, and realizes more reliable fusion with multi-sensor conflicting information compared to the existing methods.

Cognitive radio (CR) has become a tempting technology that achieves significant improvement in spectrum utilization. To resolve the hidden terminal problem, collaborative spectrum sensing (CSS), which profits from spatial diversity, has been studied intensively in recent years. As CSS is vulnerable to the attacks launched by malicious secondary users (SUs), certain CSS security schemes based on the Dempster–Shafer theory of evidence have been proposed. Nevertheless, the available works only focus on the real-time difference of SUs, like the difference in similarity degree or SNR, to evaluate the credibility of each SU. Since the real-time difference is unilateral and sometimes inexact, the statistical information comprised in SUs’ historical behaviors should not be ignored. In this paper, we propose a robust CSS method based on evidence theory and credibility calculation. It is executed in four consecutive procedures, which are basic probability assignment (BPA), holistic credibility calculation, option and amelioration of BPA and evidence combination via the Dempster–Shafer rule, respectively. Our scheme evaluates the holistic credibility of SUs from both the real-time difference and statistical sensing behavior of SUs. Moreover, considering that the transmitted data increase with the number of SUs increasing, we introduce the projection approximation approach to adjust the evidence theory to the binary hypothesis test in CSS; on this account, both the data volume to be transmitted and the workload at the data fusion center have been reduced. Malicious SUs can be distinguished from genuine ones based on their historical sensing behaviors, and SUs’ real-time difference can be reserved to acquire a superior current performance. Abounding simulation results have proven that the proposed method outperforms the existing ones under the effect of different attack modes and different numbers of malicious SUs.

Device-to-device (D2D) communications bring significant improvements of spectral efficiency by underlaying cellular networks. However, they also lead to a more deteriorative interference environment for cellular users, especially the users in severely deep fading or shadowing. In this paper, we investigate a relay-based communication scheme in cellular systems, where the D2D communications are exploited to aid the cellular downlink transmissions by acting as relay nodes with underlaying cellular networks. We modeled two-antenna infrastructure relays employed for D2D relay. The D2D transmitter is able to transmit and receive signals simultaneously over the same frequency band. Then we proposed an efficient power allocation algorithm for the base station (BS) and D2D relay to reduce the loopback interference which is inherent due to the two-antenna infrastructure in full-duplex (FD) mode. We derived the optimal power allocation problem in closed form under the independent power constraint. Simulation results show that the algorithm reduces the power consumption of D2D relay to the greatest extent and also guarantees cellular users’ minimum transmit rate. Moreover, it also outperforms the existing half-duplex (HD) relay mode in terms of achievable rate of D2D.

To improve the ability of autonomous navigation for Unmanned Surface Vehicles (USVs), multi-sensor integrated navigation based on Inertial Navigation System (INS), Celestial Navigation System (CNS) and Doppler Velocity Log (DVL) is proposed. The CNS position and the DVL velocity are introduced as the reference information to correct the INS divergence error. The autonomy of the integrated system based on INS/CNS/DVL is much better compared with the integration based on INS/GNSS alone. However, the accuracy of DVL velocity and CNS position are decreased by the measurement noise of DVL and bad weather, respectively. Hence, the INS divergence error cannot be estimated and corrected by the reference information. To resolve the problem, the Adaptive Information Sharing Factor Federated Filter (AISFF) is introduced to fuse data. The information sharing factor of the Federated Filter is adaptively adjusted to maintaining multiple component solutions usable as back-ups, which can improve the reliability of overall system. The effectiveness of this approach is demonstrated by simulation and experiment, the results show that for the INS/CNS/DVL integrated system, when the DVL velocity accuracy is decreased and the CNS cannot work under bad weather conditions, the INS/CNS/DVL integrated system can operate stably based on the AISFF method.

In order to improve system performance such as throughput, heterogeneous network (HetNet) has become an effective solution in Long Term Evolution-Advanced (LET-A). However, co-channel interference leads to degradation of the HetNet throughput, because femtocells are always arranged to share the spectrum with the macro base station. In this paper, in view of the serious cross-layer interference in double layer HetNet, the Stackelberg game model is adopted to analyze the resource allocation methods of the network. Unlike the traditional system models only focusing on macro base station performance improvement, we take into account the overall system performance and build a revenue function with convexity. System utility functions are defined as the average throughput, which does not adopt frequency spectrum trading method, so as to avoid excessive signaling overhead. Due to the value scope of continuous Nash equilibrium of the built game model, the gradient iterative algorithm is introduced to reduce the computational complexity. As for the solution of Nash equilibrium, one kind of gradient iterative algorithm is proposed, which is able to intelligently choose adjustment factors. The Nash equilibrium can be quickly solved; meanwhile, the step of presetting adjustment factors is avoided according to network parameters in traditional linear iterative model. Simulation results show that the proposed algorithm enhances the overall performance of the system.

Collaborative spectrum sensing (CSS) was envisioned to improve the reliability of spectrum sensing in centralized cognitive radio networks (CRNs). However, secondary users (SUs)’ changeable environment and ease of compromise make CSS vulnerable to security threats, which further mislead the global decision making and degrade the overall performance. A popular attack in CSS is the called spectrum sensing data falsification (SSDF) attack. In the SSDF attack, malicious cognitive users (MUs) send false sensing results to the fusion center, which significantly degrades detection accuracy. In this paper, a comprehensive reputation-based security mechanism against dynamic SSDF attack for CRNs is proposed. In the mechanism, the reliability of SUs in collaborative sensing is measured with comprehensive reputation values in accordance with the SUs’ current and historical sensing behaviors. Meanwhile a punishment strategy is presented to revise the reputation, in which a reward factor and a penalty factor are introduced to encourage SUs to engage in positive and honest sensing activities. The whole mechanism focuses on ensuring the correctness of the global decision continuously. Specifically, the proposed security scheme can effectively alleviate the effect of users’ malicious behaviors on network decision making, which contributes greatly to enhancing the fairness and robustness of CRNs. Considering that the attack strategy adopted by MUs has been gradually transforming from simplicity, fixedness and singleness into complexity, dynamic and crypticity, we introduce two dynamic behavior patterns (true to false and then to true (TFT) and false to true and then to false (FTF)) to further validate the effectiveness of our proposed defense mechanism. Abundant simulation results verify the rationality and validity of our proposed mechanism.

Knowledge concerning the complicated changes of mass and heat transfer is desired to improve the performance and durability of unitized regenerative fuel cells (URFCs). In this study, a transient, non-isothermal, single-phase, and multi-physics mathematical model for a URFC based on the proton exchange membrane is generated to investigate transient responses in the process of operation mode switching from fuel cell (FC) to electrolysis cell (EC). Various heat generation mechanisms, including Joule heat, reaction heat, and the heat attributed to activation polarizations, have been considered in the transient model coupled with electrochemical reaction and mass transfer in porous electrodes. The polarization curves of the steady-state models are validated by experimental data in the literatures. Numerical results reveal that current density, gas mass fractions, and temperature suddenly change with the sudden change of operating voltage in the mode switching process. The response time of temperature is longer than that of current density and gas mass fractions. In both FC and EC modes, the cell temperature and gradient of gas mass fraction in the oxygen side are larger than that in the hydrogen side. The temperature difference of the entire cell is less than 1.5 K. The highest temperature appears at oxygen-side catalyst layer under the FC mode and at membrane under a more stable EC mode. The cell is exothermic all the time. These dynamic responses and phenomena have important implications for heat analysis and provide proven guidelines for the improvement of URFCs mode switching.

Heterogeneous networks (HetNets) give users the opportunity to access different access points(APs), which will simultaneously affect user performance and system performance, so user association in HetNets plays a critical role in enhancing the load balancing and the system sum-throughput of networks. Meanwhile, the incremental sum-throughput currently fails to meet the escalating data demands. Besides, ensuring fairness amongst users constitutes another urgent issue in the radio resource management (RRM) of HetNets. What is more, few works consider the maximum service user number constraint in femtocell access points (FAPs). To solve the aforementioned problem, this paper associates users to APs by considering system sum-throughput and fairness at the same time in HetNets under a maximum service user number constraint of FAPs; accordingly, the user association problem is formulated. By releasing constraint, the optimal user association algorithm is obtained by Lagrangian function, and based on this optimal solution, a low complexity suboptimal user association algorithm is proposed. At last, this paper investigates the relationship between system sum-throughput and maximum service user number of FAPs. Numerical simulation results show that the proposed algorithm can improve sum-throughput and fairness at the same time at a specific maximum service user number of FAPs.

Traditional detectors for spectrum sensing in cognitive radio networks always become disabled when noise uncertainty is severe. Shannon entropy-based detection methods have aroused widespread attention in recent years due to the characteristics of effective anti-noise uncertainty. However, in existing entropy-based sensing schemes, the uniform quantization method cannot guarantee the maximum entropy distribution when primary users do not exist, and cannot effectively distinguish between two hypotheses, which severely limits the promotion of detection performance. Moreover, the Shannon entropy-based sensing schemes are prone to misconvergence occurring when estimating entropy values, thus causing failure detection, which leads to system detection inefficiency and resource waste. These are the two major serious defects in Shannon entropy-based detectors, which restrict the performance improvement. In this paper, a novel non-uniform quantized exponential entropy-based (NQEE) detector is proposed for local sensing to deal with the problems of maximum entropy distribution and detection failure. To further improve the reliability of the detection, a collaborative spectrum sensing algorithm based on an NQEE detector with multiple fusion rules is presented. Simulation results verify that the detection performance of the improved local entropy-based detector is superior to the existing Shannon entropy-based detectors and is proved to be robust to noise power uncertainty. In addition, the novel collaborative detection algorithm outperforms the traditional collaborative spectrum detection method to a great degree.

This study aimed to investigate the incorporation of rutin into electrospun pullulan and poly(vinyl alcohol) (PVA) nanofibers to obtain ultraviolet (UV)-resistant properties. The effect of weight ratios between pullulan and PVA, and the addition of rutin on the nanofibers’ morphology and diameters were studied and characterized by scanning electron microscopy (SEM). Fourier transform infrared (FTIR) analysis was utilized to investigate the interaction between pullulan and PVA, as well as with rutin. The results showed that the inclusion of PVA results in the increase in the fiber’s diameter. The addition of rutin had no obvious effect on the fibers’ average diameters when the content of rutin was less than 7.41%. FTIR results indicated that a hydrogen bond formed between pullulan and PVA, also between these polymers and rutin. Moreover, the addition of rutin could enhance the mechanical properties due to its stiff structure and could decrease the transmittance of UVA and UVB to be fewer than 5%; meanwhile, the value of ultraviolet protection factor (UPF) reached more than 40 and 50 when the content of rutin was 4.46% and 5.67%, respectively. Therefore, the electrospun pullulan/PVA/rutin nanofibrous mats showed excellent UV resistance and have potential applications in anti-ultraviolet packaging and dressing materials.

Inter-cell interference (ICI) is the main factor affecting system capacity and spectral efficiency. Effective spectrum resource management is an important and challenging issue for the design of wireless communication systems. The soft frequency reuse (SFR) is regarded as an interesting approach to significantly eliminate ICI. However, the allocation of resource is fixed prior to system deployment in static SFR. To overcome this drawback, this paper adopts a distributed method and proposes an improved dynamic joint resource allocation algorithm (DJRA). The improved scheme adaptively adjusts resource allocation based on the real-time user distribution. DJRA first detects the edge-user distribution vector to determine the optimal scheme, which guarantees that all the users have available resources and the number of iterations is reduced. Then, the DJRA maximizes the throughput for each cell via optimizing resource and power allocation. Due to further eliminate interference, the sector partition method is used in the center region and in view of fairness among users, the novel approach adds the proportional fair algorithm at the end of DJRA. Simulation results show that the proposed algorithm outperforms previous approaches for improving the system capacity and cell edge user performance.

In order to locate source signal more accurately in authorized frequency bands, a novel primary user localization algorithm based on compressive sensing (PU-CSL) in cognitive radio networks (CRNs) is proposed in this paper. In comparison to existing centroid locating algorithms, PU-CSL shows higher locating accuracy for integrally exploring correlation between source signal and secondary users (SUs). Energy detection is first adopted for collecting the energy fingerprint of source signal at each SU, then degree of correlation between source signal and SUs is reconstructed based on compressive sensing (CS), which determines weights of centroid coordinates. A weighted centroid scheme is finally utilized to estimate source position. Simulation results show that PU-CSL has smaller maximum error of positioning and root-mean-square error. Moreover, the proposed PU-CSL algorithm possess excellent location accuracy and strong anti-noise performance.

The Kv1.3 channel-acting scorpion toxins usually adopt the conserved anti-parallel β-sheet domain as the binding interface, but it remains challenging to discover some highly selective Kv1.3 channel-acting toxins. In this work, we investigated the pharmacological profile of the Kv1.3 channel-acting BmKTX-D33H, a structural analogue of the BmKTX scorpion toxin. Interestingly, BmKTX-D33H, with its conserved anti-parallel β-sheet domain as a Kv1.3 channel-interacting interface, exhibited more than 1000-fold selectivity towards the Kv1.3 channel as compared to other K+ channels (including Kv1.1, Kv1.2, Kv1.7, Kv11.1, KCa2.2, KCa2.3, and KCa3.1). As expected, BmKTX-D33H was found to inhibit the cytokine production and proliferation of both Jurkat cells and human T cells in vitro. It also significantly improved the delayed-type hypersensitivity (DTH) responses, an autoreactive T cell-mediated inflammation in rats. Amino acid sequence alignment and structural analysis strongly suggest that the “evolutionary” Gly11 residue of BmKTX-D33H interacts with the turret domain of Kv1 channels; it appears to be a pivotal amino acid residue with regard to the selectivity of BmKTX-D33H towards the Kv1.3 channel (in comparison with the highly homologous scorpion toxins). Together, our data indicate that BmKTX-D33H is a Kv1.3 channel–specific blocker. Finally, the remarkable selectivity of BmKTX-D33H highlights the great potential of evolutionary-guided peptide drug design in future studies.

A two-dimensional, single-phase, isothermal, multicomponent, transient model is built to investigate the transport phenomena in unitized regenerative fuel cells (URFCs) under the condition of switching from the fuel cell (FC) mode to the water electrolysis (WE) mode. The model is coupled with an electrochemical reaction. The proton exchange membrane (PEM) is selected as the solid electrolyte of the URFC. The work is motivated by the need to elucidate the complex mass transfer and electrochemical process under operation mode switching in order to improve the performance of PEM URFC. A set of governing equations, including conservation of mass, momentum, species, and charge, are considered. These equations are solved by the finite element method. The simulation results indicate the distributions of hydrogen, oxygen, water mass fraction, and electrolyte potential response to the transient phenomena via saltation under operation mode switching. The hydrogen mass fraction gradients are smaller than the oxygen mass fraction gradients. The average mass fractions of the reactants (oxygen and hydrogen) and product (water) exhibit evident differences between each layer in the steady state of the FC mode. By contrast, the average mass fractions of the reactant (water) and products (oxygen and hydrogen) exhibit only slight differences between each layer in the steady state of the WE mode. Under either the FC mode or the WE mode, the duration of the transient state is only approximately 0.2 s.

Natural hazard threats have grown as a result of climate change, fast demographic development, and major urbanization. Devastating floods have occurred in several areas of the world recently, including the Kingdom of Saudi Arabia, which is located in a region with a dry environment. In arid or semi-arid regions, rapidly forming flash floods associated with debris flowing down over dry water courses leading to a potential threat to both lives and property. Being located at the coastal plain of western Saudi Arabia, Jeddah City has witnessed an unexpected amount of rainfall events in recent years. Such extreme rainfall events, integrated with other factors, namely topography, land use, surface runoff, etc., have led to flood generation, which is alarming indeed. Herein, this paper addresses the varying climatic classifications of the Kingdom, its risk and vulnerability, followed by reasoning about the impact of flash flood events and the associated casualties and property losses. Further, it reports about the existing strategies of the government and proposes a systematic way forward on how to alleviate such events in future. Thus, risk variables have been discovered and integrated in the context of climate change and rising anthropogenic strain on coastal communities to give planners and decision makers tools to assure effective and appropriate flood risk management.