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Previous studies have rarely investigated stochastic robustness of flexible cable dome structures under impact loads. In this study, the dynamic responses and failure modes of a Geiger cable dome structure were analysed under impact loads. The number of components damaged by impact and the loss of internal force in components were used to establish a structural damage index and an equation of failure mode control. Following this, by establishing the impact resistance failure limit state equation and combining the probabilistic and statistical characteristics of each design parameter of the structure, the probability of disproportionate failure of the structure undergoing disproportional damage under the impact load was calculated. Then, the stochastic robustness index I rob based on the probability of structural failure was proposed, and the key factors affecting the structural robustness were analysed through parameter analysis. The research results indicated the following. First, cable dome structures exhibited three types of failure modes under impact loads. Second, the stochastic robustness index fully reflected the probability of a cable dome structure sustaining disproportional damage under impact loads. Increasing the mass or speed of the impactor both increased the probability of the structure sustaining disproportional damage. An impact occurred closer to the centre of the structure, both the probability of overall structural failure and structural robustness performance increase, but the probability of the structure sustaining disproportional damage decreases. Third, increasing the prestress and cross-sectional area of the components might increase their impact resistance and structural robustness performance. [ABSTRACT FROM AUTHOR]

In recent years, semi-supervised learning (SSL) methods based on pseudo-labeling algorithms have been widely applied and achieved significant success. However, most existing deep semi-supervised learning methods suffer from the problem of distribution shift between the source and target domains, as well as the issue of "cognitive bias" in pseudo labeling algorithms, where the model's errors are difficult to rectify as they accumulate through the pseudo-labeling process. This paper introduces the concept of causal invariance and proposes an incremental repeated labeling strategy with a high confidence threshold to enhance the utilization of unlabeled samples. It effectively solves the issue of distribution discrepancy between the source and target domains in the field of semi-supervised learning, as well as the problem of pseudo label "cognitive bias", thus improving the accuracy of the model. Extensive experiments on CIFAR-10, CIFAR-100, SVHN, STL-10, PACS and VLCS demonstrate that semi-supervised incremental models based on causal invariance have a significant improvement in domain generalization ability compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hot-carrier transistors are a class of devices that leverage the excess kinetic energy of carriers. Unlike regular transistors, which rely on steady-state carrier transport, hot-carrier transistors modulate carriers to high-energy states, resulting in enhanced device speed and functionality. These characteristics are essential for applications that demand rapid switching and high-frequency operations, such as advanced telecommunications and cutting-edge computing technologies1–5. However, the traditional mechanisms of hot-carrier generation are either carrier injection6–11 or acceleration12,13, which limit device performance in terms of power consumption and negative differential resistance14–17. Mixed-dimensional devices, which combine bulk and low-dimensional materials, can offer different mechanisms for hot-carrier generation by leveraging the diverse potential barriers formed by energy-band combinations18–21. Here we report a hot-emitter transistor based on double mixed-dimensional graphene/germanium Schottky junctions that uses stimulated emission of heated carriers to achieve a subthreshold swing lower than 1 millivolt per decade beyond the Boltzmann limit and a negative differential resistance with a peak-to-valley current ratio greater than 100 at room temperature. Multi-valued logic with a high inverter gain and reconfigurable logic states are further demonstrated. This work reports a multifunctional hot-emitter transistor with significant potential for low-power and negative-differential-resistance applications, marking a promising advancement for the post-Moore era.A mixed-dimensional hot-emitter transistor based on mixed-dimensional graphene/germanium Schottky junctions uses stimulated emission of heated carriers, achieving an ultralow subthreshold swing and a high negative differential resistance. [ABSTRACT FROM AUTHOR]

In this paper, we adopt q-deformed binary operations, such as q-addition, q-subtraction, q-multiplication, and q-division, to construct the q-deformed Schrödinger equation in one dimension. We explore the mathematics involving q-deformed binary operations. We q-deform the ordinary commutator and the definition of Fock space in q-deformed quantum mechanics. As examples, we discuss the particle in a box and the harmonic oscillator. [ABSTRACT FROM AUTHOR]

Severe trauma could induce sepsis due to the loss of control of the infection, which may eventually lead to death. Accurate and timely diagnosis of sepsis with severe trauma remains challenging both for clinician and laboratory. Combinations of markers, as opposed to single ones, may improve diagnosis. We compared the diagnostic characteristics of routinely used biomarkers of sepsis alone and in combination, trying to define a biomarker panel to predict sepsis in severe patients. This prospective observational study included patients with severe trauma (Injury severity score, ISS = 16 or more) in the emergency intensive care unit (EICU) at a university hospital. Blood samples were collected and plasma levels of procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6) and serum amyloid A (SAA) were measured using commercial enzyme linked immunosorbent assay (ELISA) kits. A total of 100 patients were eligible for analysis. Of these, 52 were diagnosed with sepsis. CRP yielded the highest discriminative value followed by PCT. In multiple logistic regression, SAA, CRP, and PCT were found to be independent predictors of sepsis. Bioscore which was composed of SAA, CRP, and PCT was shown to be far superior to that of each individual biomarker taken individually. Therefore, compared with single markers, the biomarker panel of PCT, CRP, and SAA was more predictive of sepsis in severe polytrauma patients. [ABSTRACT FROM AUTHOR]

In our research, we explore the impact of a geometric non-minimal coupling between fermions and curvature scalars on the positioning of fermions within a brane structure. Our investigation encompasses massless fermionic modes and massive modes, revealing that specifically fermionic modes with left chirality are situated within the brane. This insight allows us to gain a more direct understanding of how the system's geometry influences the placement of fermion fields on the brane. To delve deeper into the precise localization of massless fermionic modes, we employ entropic information measurements. By varying the parameters that govern the curvature scalars, we observe the ability to adjust the localization of these fermionic modes on the brane. Importantly, the BBM uncertainty relation remains satisfied in all scenarios, particularly for f 1 , 2 , 3 . We leverage the Shannon entropy as a highly effective metric to yield accurate results regarding the positioning of massless fermionic modes within our model. In the case of massive fermionic modes, we utilize relative probability analysis to pinpoint the resonant modes, and we identify the existence of these resonant modes in both f 1 , 2 , 3 cases. Ultimately, we ascertain that these parameters influence the placement of resonant modes by increasing the amplitude of the massive modes in the core of the brane. [ABSTRACT FROM AUTHOR]

The exact solutions of 1D Schrödinger equation subject to a complex periodic potential V (x) = - [ i a sin (b x) + c ] 2 ( a , b , c ∈ R ) are found as a confluent Heun function (CHF) H C (α , β , γ , δ , η ; z) . The energy spectra which are solved exactly by calculating the Wronskian determinant are found as real except for complex values. It is found that the eigenvalues obtained by two constraints on the CHF are not reliable or complete any more since they are only one small part of those evaluated by the Wronskian determinant. The wave functions are illustrated when eigenvalues are substituted into the eigenfunctions. We also notice that the energy spectra remain invariant when one substitutes a → - a or b → - b or c → - c due to the P T symmetry with the property V (x) = V (- x) ∗ . [ABSTRACT FROM AUTHOR]

With the industrialization of society, air pollution has become a critical environmental issue, leading to excessive morbidity and mortality from cardiovascular and respiratory diseases in humans. Accurate air pollution prediction has strongly promoted air quality control, which is important for human health. However, previous studies have failed to model spatiotemporal dependencies simultaneously with non-Euclidean distributions considering meteorological factors. In this study, a novel multigraph convolutional neural network for air pollution prediction is proposed. First, a spatial graph, an air pollution pattern graph and a meteorological pattern graph are constructed to model different relationships among non-Euclidean areas. Second, the graph convolutional network is applied to learn and incorporate the information of neighbour nodes of the corresponding graph, and then the graphs after convolution are fused. Finally, the fused matrix of GCNs is input into the gate recurrent units to capture temporal dependencies. Experimental results on the real dataset collected at air quality monitoring stations in Beijing validate the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

Subfraction P3 was further performed on silica gel (200-300 mesh) column chromatography (CC) eluted with PE-EtOAc (stepwise, from 30:1-10:1) and cyclohexane-acetone (gradient, from 35:1-15:1), and then crystallized from PE-EtOAc to afford compounds B 1 b (42 mg) and B 2 b (24 mg). Subfraction E2 was isolated by silica gel (200-300 mesh) CC with CH SB 2 sb Cl SB 2 sb -MeOH (stepwise, from 80:1-20:1), and then purified by Sephadex LH-20 CC with CH SB 2 sb Cl SB 2 sb -MeOH (1:1) to give compounds B 10 b (16 mg) and B 11 b (34 mg). Subfraction P5 was subjected to silica gel (200-300 mesh) CC, eluting with PE-EtOAc (gradient, from 30:1-10:1) and PE-acetone (stepwise, from 40:1-15:1), and then crystallized from PE-EtOAc to give compounds B 3 b (12 mg) and B 4 b (26 mg). Subfraction E1 was performed on silica gel (200-300 mesh) CC with PE-acetone (40:1-12:1), and then on Sephadex LH-20 CC with CH SB 2 sb Cl SB 2 sb -MeOH (1:1) to yield compounds B 8 b (20 mg) and B 9 b (31 mg). [Extracted from the article]

Flexible control of Goos–Hänchen (G–H) shift is revisited in an optomechanical cavity. The optomechanical cavity consists of a Laguerre–Gaussian rovibrational cavity. One of the mirrors of a rovibrational cavity can rotate and vibrate simultaneously which can generate rotational and vibrational modes. We examine the behavior of the G–H shift under various factors and discuss its characteristics. The rovibrational cavity leads to double-transparency windows where we obtain the G–H shifts at these transparency windows. Surprisingly, we find magnitude-enhanced G–H shifts by considering gain and loss in the mirrors of the rovibrational cavity. When the gain of one mirror is simultaneously equal to the loss of another mirror, then the probe light propagates more in the cavity and one can get an enhanced G–H shift. [ABSTRACT FROM AUTHOR]

A rapid identification method for flue-cured tobacco quality was proposed based on Raman spectroscopy. Considering the critical quality factors of flue-cured tobacco-like oil content, softness, and glossiness, four statistical methods, random forest, K-nearest neighbor, logistic regression, and partial least squares, can effectively improve the accuracy of quality identification. We randomly collected 149 flue-cured tobacco samples from multiple producing areas in China. After Raman spectroscopy analysis, Savitzky–Golay convolution smoothing and multi-scatter correction were done. The functional groups were analyzed to select characteristic peaks as features for discriminant analysis. The results show that the Raman spectroscopic information can distinguish the quality of flue-cured tobacco with an accuracy greater than 95%, whereas the partial least-squares approach delivers an accuracy of 100%. We conclude that Raman spectroscopy can be considered a vital avenue for identifying the quality of flue-cured tobacco. [ABSTRACT FROM AUTHOR]

With the rapid development of network technology, the Internet has accelerated the generation of network traffic, which has made network security a top priority. In recent years, due to the limitations of deep packet inspection technology and port number-based network traffic identification technology, machine learning-based network traffic identification technology has gradually become the most concerned method in the field of traffic identification with its advantages. As the learning ability of deep learning in machine learning becomes more substantial and more able to adapt to highly complex tasks, deep learning has become more widely used in natural language processing, image identification, and computer vision. Therefore, more and more researchers are applying deep learning to network traffic identification and classification. To address the imbalance of current network traffic, we propose a traffic identification model based on generating adversarial deep convolutional networks (GADCN), which effectively fits and expands traffic images, maintains a balance between classes of the dataset, and enhances the dataset stability. We use the USTC-TFC2016 dataset as training and test samples, and experimental results show that the method based on GADCN has better performance than general deep learning models. [ABSTRACT FROM AUTHOR]

In social networks, the community detection algorithm is very important for understanding the structures and the functions of these networks. A lot of researches have been done on the overlapping community detection algorithms as the overlapping is a significant feature of such networks. However, though many algorithms have been introduced to detect overlapping communities, the detection of the overlapping community is still a challenging task. In fact, the traditional static methods which partitioned the network structure could not efficiently obtain the latest community structure. The problems of high computational complexity and low identification accuracy need to be solved. To address these issues, in this paper, we propose a New Overlapping Community Detection algorithm based on improved KNN (called NOCD), which can timely adjust the community structure based on different network changes, and ultimately obtains the results of the community partitions with a high degree of Q module. To deal with the weighted social networks, NOCD adopts similarity instead of distance to evaluate the network. The experimental results show that the proposed NOCD algorithm compared with the COPRA, the CPM, the DeCom, the PLPA, and the AI-LPA algorithms can effectively improve the detection accuracy, the efficiency of parallel computing, and reduce the time complexity. [ABSTRACT FROM AUTHOR]

The analytical solutions of an asymmetric double well potential V (x) = a x 2 - b x 3 + c x 4 are found to be a triconfluent Heun function H T (α , β , γ ; z) . It should be emphasized that these potential parameters are taken arbitrarily without any restriction on them. The wave functions which depend on the potential parameters are shrunk toward to the origin for given b and c when the parameter a increases, while they are moved far from the origin and toward to the left when the parameter b increases for given a and c. Also, when the parameter c increases for given a and b they have the similar property to the case when the parameter a increases. [ABSTRACT FROM AUTHOR]

Bacterial natural product biosynthetic genes, canonically clustered, have been increasingly found to rely on hidden enzymes encoded elsewhere in the genome for completion of biosynthesis. The study and application of lanthipeptides are frequently hindered by unclustered protease genes required for final maturation. Here, we establish a global correlation network bridging the gap between lanthipeptide precursors and hidden proteases. Applying our analysis to 161,954 bacterial genomes, we establish 5209 correlations between precursors and hidden proteases, with 91 prioritized. We use network predictions and co-expression analysis to reveal a previously missing protease for the maturation of class I lanthipeptide paenilan. We further discover widely distributed bacterial M16B metallopeptidases of previously unclear biological function as a new family of lanthipeptide proteases. We show the involvement of a pair of bifunctional M16B proteases in the production of previously unreported class III lanthipeptides with high substrate specificity. Together, these results demonstrate the strength of our correlational networking approach to the discovery of hidden lanthipeptide proteases and potentially other missing enzymes for natural products biosynthesis. Many natural product biosynthetic gene clusters are found not to harbor a full set of necessary genes and must utilize enzymes encoded elsewhere (hidden) in the genome for biosynthesis. Here, the authors establish a global correlation network bridging the gap between lanthipeptide precursors and hidden proteases. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We calculate the Shannon entropies numerically for a rectangular asymmetric multiple quantum well system with a constant total length. This quantum system is designed as an asymmetric multiple well with equal barriers but unequal arithmetic sequence (AS) wells. We show how the number of wells and confining potential depth affect the Shannon entropy density and the Shannon entropy. When increasing the confined potential depth V conf , the magnitude of the position entropy density decreases while that of the momentum entropy density increases, but there is a very slight difference when the confined potential depth reaches a large value. Also, the oscillation frequency of the position entropy density inside the quantum barriers decreases while that of the position entropy density inside the quantum wells increases. When the potential well depth reaches a large value, the moving particle is mainly confined in a relatively wide potential well, and the position entropy density disappears in other barriers and potential wells. As the number of wells increases, the oscillation frequency of the position entropy density decreases inside the barriers but increases inside the quantum wells. It is very interesting to see that the S x and S p do not always decrease or increase monotonically with the confined potential depth V conf , but their sum always satisfies the BBM inequality. [ABSTRACT FROM AUTHOR]

According to a long-span pedestrian suspension bridge, the sectional models of the human-footbridge system were proposed, and then the force measurement tests were performed to study the effects of the pedestrian density (ρ), pedestrian permutation, and wind attack angle (α) on the drag force coefficient (CH), lift force coefficient (CV), and pitching moment coefficient (CM), respectively. So, it is found that: (1) with the increase of ρ, both CH and CV with α < −2° increase, while both CV with α ≥ −2° and CM with ρ ≠ 0.0 P/m2 decrease. (2) when ρ > 0.2 P/m2, there is a significant effect of the pedestrian permutations on CH and CM, while the effect of the pedestrian permutations on CV is small. Then, the static wind forces were calculated and applied to the pedestrian suspension bridge, and the effects of ρ and pedestrian permutations on the aerostatic response were studied. So, it is found that: 1) with the increase of ρ, the aerostatic response increases; 2) the effects of the pedestrian permutations with ρ ≤ 0.5 P/m2 on the aerostatic response can be ignored, while there are disadvantageous effects of the pedestrian permutations with ρ > 0.5 P/m2 and larger wind-blocking area on the aerostatic response. [ABSTRACT FROM AUTHOR]

Direct laser deposition (DLD), as a popular metal additive manufacturing process, shows advantages of technical flexibility and high efficiency to gain a high-performance alloy steel component. However, during the processing of DLD, the deposited steel layer is affected by the subsequent layer depositing. The DLD block shows different microstructure and mechanical properties at the bottom, middle and top of the deposited parts. To date, there are few research works about the effects of inter-layer interval time and laser power on the microstructure evolution and mechanical properties of the deposited layers. In this study, the idle time and laser power layer by layer during DLD of 12CrNi2 steel were controlled to cause the deposited layers to maintain a high cooling rate, while the bottom deposited layer was subjected to a weak tempering effect. Results show that a high proportion of martensite is produced, which improves the strength of the deposited layer. Under the laser scanning strategy of laser power 2,500 W, scanning velocity 5 mm·s−1, powder feeding rate 11 g·min−1, overlap rate 50%, and a laser power difference of 50 W and a 2 min interval, the tensile strength of the deposited layer of 12CrNi2 steel is in the range of 873–1,022 MPa, and the elongation is in the range of 16.2%–18.9%. This study provides a method to reduce the tempering effect of the subsequent deposition layers on the bottom layers, which can increase the proportion of martensite in the low-alloy high-strength steel, so as to improve the yield strength of the alloy steel. [ABSTRACT FROM AUTHOR]

Weber's law reveals the relationship between human perception and perceptual stimuli. Inspired by the theory, this paper designs a multi-level convolution correlation feature statistic method for image retrieval. Firstly, the difference between a central pixel and its neighbors is described by Weber's law through computing the differential excitation of image. Then, a multi-level saliency map is obtained by binary transformation and convolution operation. Thirdly, to exploit spatial correlation information of the image, a pixels pair-wise correlation and hierarchy statistic model is constructed. Finally, all intermediate features are concatenated into one histogram, which includes salient color and texture features. Extensive experiments demonstrate the proposed method of this paper has excellent performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We studied a class of exponential potential model V (x) = - a e - b x ( a > 0 , b > 0 ) and found that its solutions are given by the Bessel functions, but the energy spectra E = - b 2 (n + 1 / 2) 2 / 8 which are derived from the quantization condition do not correspond to any discrete bound states. The energy levels which are calculated by the boundary condition J ν (2 2 a / b) = 0 at the origin are in good agreement with the numerical results. We illustrate the wave functions through varying the potential parameters a, b and notice that they are pull back to the origin when the potential parameter a or b increases. [ABSTRACT FROM AUTHOR]

Owing to the joint effects of ecosystem fragility, anthropogenic disturbance and climate change, alpine grasslands (alpine meadow, alpine steppe and alpine desert) have experienced serious degradation during the past several decades. Grasslands degradation has severely affected the delivery of ecosystem multifunctionality (EMF) and services, and then threatens the livelihood of local herdsmen and ecological security of China. However, we still lack comprehensive insights about the effects of degradation and climatic factors on EMF of alpine grasslands, especially for alpine desert ecosystem. Therefore, we applied a large-scale field investigation to answer this question. Our results suggested grassland degradation significantly decreased the belowground ecosystem multifunctionality (BEMF) and EMF of alpine grasslands and aboveground ecosystem multifunctionality (AEMF) of alpine meadow, while did not reduce the AEMF of alpine steppe and desert. Except for the insignificant difference between degraded steppe and degraded desert in AEMF, the alpine meadow showed the highest AEMF, BEMF and EMF, alpine steppe ranked the second and alpine desert was the lowest. AEMF, BEMF and EMF of health alpine grasslands were strongly affected by mean annual precipitation (MAP) (19%–51%) and mean annual temperature (MAT) (9%–36%), while those of degraded meadow and degraded desert were not impacted by precipitation and temperature. AEMF and BEMF showed a synergistic relationship in healthy alpine grasslands (12%–28%), but not in degraded grasslands. Our findings emphasized the urgency of implementing the feasible ecological restoration project to mitigate the negative influences of grassland degradation on EMF of alpine ecosystems. [ABSTRACT FROM AUTHOR]

Collapsibility determination in loess area is expensive, and it also requires a large amount of experimentation. This paper aims to find the association rules between physical parameters and collapsibility of the loess in Xining through the method of data mining, so to help researchers predict the collapsibility of loess. Related physical parameters of loess collapsibility, collected from 1039 samples, involve 13 potential influence factors. According to Grey Relational Analysis, the key influence factors that lead to collapsing are identified from these potential influence factors. Subsequently, take the key influence factors, δs (coefficient of collapsibility) and δzs (coefficient of collapsibility under overburden pressure) as input items, and use the Apriori algorithm to find multiple association rules between them. Then, through analysing the results of association rules between these key influence factors and collapsibility, the evaluation criteria for collapsibility in this area is proposed, which can be used to simplify the workload of determining collapsibility. Finally, based on these research results, recommendations for projects construction were made to ensure the safety of construction in the area. [ABSTRACT FROM AUTHOR]

We revisit the one-dimensional quantum system of a sextic potential added with a centrifugal term, V (x) = a x - 2 + b x 2 + c x 4 + d x 6 , where the parameters a, b, c and d are arbitrary. We find that its solutions can be expressed as a Biconfluent Heun function H B (α , β , γ , δ ; z) , while the associated energy spectrum is determined by the parameter δ . The semi-exact solutions of wave functions fully consist with the properties of the potential. [ABSTRACT FROM AUTHOR]

Community detection plays an important role in the analysis of complex networks. However, overlapping community detection in real networks is still a challenge. To address the problems of pre-input parameters and label redundancy, an improved label propagation algorithm (ILPA) that adopts a method based on the influence factor is proposed in this paper. Theoretical analysis and experimental results on both synthetic and real datasets show that the ILPA detects that the overlapping community has higher accuracy compared to other existing methods. [ABSTRACT FROM AUTHOR]

Currently, the robustness analysis of structure is primarily related to rigid structures, and little has been done to study the robustness of spatial flexible cable-strut tensile structures with larger span, more complex forms, lower redundancy, more sensitivity to unexpected interference such as construction deviations, etc. Based on the H ∞ theory, this paper starts with the fundamental theory and develops analysis method for the robustness of flexible cable-strut tensile structures, and then the relationships between the structural robustness and some design parameters such as initial pre-stress level, load distribution form, structural span, cross-section area, rise-span ratio, etc. are analysed through a case study of a concrete cable-strut tensile structure in China. On this basis, the effects of construction errors such as element-length error and supporting node position error are further analysed. Finally, the importance of each kind of element is analysed by means of conceptual removal of element. The results show that the higher the initial pre-stress level, the larger the element cross-section, the smaller the structural span, the larger the rise-span ratio and the more uniform the load distribution, the stronger the structural robustness. In the limited construction error range, the element-length error and the supporting node position error have little effects on the robustness of the structure. The hoop cables are the more important elements and the elements in inner region of the structure are of comparatively lower importance in the cable dome structure in this study. [ABSTRACT FROM AUTHOR]

We propose theoretical schemes to realize the cyon and anyon by atoms which possess non-vanishing electric dipole moments. To realize a cyon, besides the atom, we need a magnetic field produced by a long magnetic-charged filament. To realize an anyon, however, apart from these we need a harmonic potential and an additional magnetic field produced by a uniformly distributed magnetic charges. We find that the atom will be an anyon when cooled down to the negligibly small kinetic energy limit. The relationship between our results and the previous ones is investigated from the electromagnetic duality. [ABSTRACT FROM AUTHOR]

This work carried out liquid–solid two-phase jet experiments and simulations to study the erosion behavior of 304 stainless steel at 30° impingement. The single-phase impinging jet was simulated using dense grid by one-way coupling of solid phase due to its dilute distribution. The simulation results agreed well with experiments. It was found that after impinging particle attrition occurred and particles became round with decreasing length-ratio and particle breakage occurred along the "long" direction. Both experiment and simulations found that the erosion generated on the sample could be divided into three regions that were nominated as stagnant region, cutting transition region and wall jet region. Most particle–wall impacts were found to occur in the cutting transition region and the wall jet region. In the cutting transition region, holes and lip-shaped hogbacks were generated in the same direction as the flow imping. In the wall jet region, furrows and grooves were generated. The averaged grooves depth tended to become constant with the progress of impinging and reach the steady state of erosion in the end. In addition, it was found that impinging effect increased erosion and anti-wear rate. [ABSTRACT FROM AUTHOR]

Focus on node cloud computing environment due to the randomness of the user task arrival caused a large number of idle energy consumption. To solve this problem, a dynamic energy-efficient scheme in virtual machines based on user task characteristic is proposed. First, according to the amount of user tasks arrival and the expectations of task completion time, user tasks characteristic information table is established. Thus it can predict the request arrival probability and user expect to reach the finish time. Then, according to user expectations of completion time, psychological expectations QOS value of the user is obtained, and the threshold based on the integration of energy-saving value is dynamically adjusted. Finally, it can achieve energy-saving by dynamic integrating of the user task arrival rate and closing idle node. Experimental results show that the method can reach a better balance of performance and energy saving to meet user requirement. [ABSTRACT FROM AUTHOR]

The k-nearest neighbors outlier detection is a simple yet effective widely renowned method in data mining. The actual application of this model in the big data domain is not feasible due to time and memory restrictions. Several distributed alternatives based on MapReduce have been proposed to enable this method to handle large-scale data. However, their performance can be further improved with new designs that fit with newly arising technologies. Furthermore, it gives to each attribute the same importance to outlier. There are several approaches to enhance its precision, with the entropy-based outlier detection being among the most successful ones. Entropy-based outlier detection computes attribute entropy of the data set to weighted distance formula for the outlier detection. Apart from the existing the k-nearest neighbors outlier detection to handle big datasets, there is not an entropy-based outlier detection to manage that volume of data. In this paper, we propose an entropy-based outlier detection based on Spark. It presents three separately stages. The first stage computes attribute entropy. The second stage finds the k nearest neighbors and calculates the degrees of outliers using the attribute entropy computed previously. The third stage ranks each point on the degrees of outliers and declares the top n points in this ranking to be outliers. Extensive experimental results show the advantages of the proposed method. This algorithm can improve the outlier detection precision, reduce the runtime and realize the effective large scale dataset outlier detection. [ABSTRACT FROM AUTHOR]

Nowadays, a large number of positioning studies based on RSSI technology mainly focused on the coordinate position by measuring the distance using the certain algorithm. The difference between calculated distance and real distance became rather lager due to the poor solution to improve the impact of environment to RSSI, and the bad repeatability. Resulting in the inaccuracy of positioning. According to the RSSI distance measurement technology and Zigbee communication technology, this paper investigated the distance between the unknown node and anchor node based on the "RSSI-Distance". As the distance was less than 10 m or not, the position can be calculated by the "Mini-Max Positioning Method" or "Maximum Likelihood Estimation Method", respectively. The results showed that this accuracy of this positioning system can be limited within 3 m. [ABSTRACT FROM AUTHOR]

Inspired on the classical Bayesian method also called mutual information, by generalizing the classical case to quantum approach to study the multipartite correlation in quantum image by using the flexible representation of quantum images (FRQI) method. Some important entanglement measures are calculated including von Neumann entropy, conditional entropy, joint entropy, discord and mutual information. We take two typical images of size 2 × 2 pixels and 8 × 8 pixels and study their entanglement measures from different classical and quantum point of view. The quantum image of size 2 × 2 is studied under the change of color for one pixel, but the quantum image of 8 × 8 is treated under translation transform. We find that the classical joint entropy remains invariant under the change of color for one pixel but the quantum entropy is sensitive to such change. It is shown that the total correlation IT could arrive to the double amount of the classical joint entropy. The discord in the classical case is zero, but it varies much in the quantum case. [ABSTRACT FROM AUTHOR]

With the rapid development of broadband wireless technology and the wide demand of multimedia mobile services, various broadband mobile multimedia services are coming into being. However, the limited radio resources do not adequately guarantee the quality of service requirements for these multimedia mobile services. In this paper, the improved chaotic neural network technology is applied to three typical broadband wireless communication systems. Through the theoretical analysis and the simulation, the proposed algorithm can make full use of the chaotic neural power to search the optimal solution, which can achieve the purpose of further optimizing the wireless resources. At the same time, it also make the positive attempt to promote cross-disciplinary integration. [ABSTRACT FROM AUTHOR]

We study quantum system with a symmetric sine hyperbolic type potential V (x) = V 0 [ sinh 4 (x) - k sinh 2 (x) ] , which becomes single or double well depending on whether the potential parameter k is taken as negative or positive. We find that its exact solutions can be written as the confluent Heun functions H c (α , β , γ , δ , η ; z) , in which the energy level E is involved inside the parameter η . The properties of the wave functions, which is strongly relevant for the potential parameter k, are illustrated for a given potential parameter V 0 . It is shown that the wave functions are shrunk to the origin when the negative potential parameter |k| increases, while for a positive k which corresponding to a double well, the wave functions with a certain parity are changed sensitively. [ABSTRACT FROM AUTHOR]

Traffic identification is currently an important challenge for network management and security. In this paper, we propose a novel application identification method named as MPNN to improve the efficiency and flexibility of application identification. MPNN is based on a structure of multiple neural networks, and it uses an individual neural network module to handle a single application; therefore, it can effectively utilize the characteristics of every application; meanwhile, the minimum Bayes method is used in every neural network module. The MPNN method has the following advantages: it can handle more complex network behavior and extend the identified object from complete TCP flows to all TCP+UDP flows. It can improve the identification accuracy of every application, especially for those applications which containing much less traffic than others. The process of changing identified applications become much easier. Due to adopting parallel processing, it has much lower time and space complexity. The theoretical analysis and experimental results show that MPNN could achieve 95% identification accuracy. [ABSTRACT FROM AUTHOR]

Construction errors are inevitable in real cable-strut tensile structures. Nevertheless, the relevant work, especially the monitoring scheme design work, to control the construction errors is lacking. At present, the monitoring schemes always lay out the monitored members in the places with great internal force or great deformation, do not consider the method to control the construction errors and do not explain the method to lay out the monitored members in fundamental theory. To address this situation, the element length error, which is an important construction error affecting the bearing performance, is considered as the factor variable and the fundamental equation of pre-stress deviation and element length error is derived firstly. Next the methods to express the pre-stress deviation, which are only derived from the active cable length errors or from both the active cable and passive cable length errors, are discussed. After that, based on the condition that the errors can be solved and compensated, the least number of monitored members is determined. Moreover, those members sensitive to cable length deviation of active cables are selected as monitored members. In order to evaluate the effect level caused by passive cable length deviation, two evaluation parameters Δ and ρ are further discussed. Finally, one cable-strut tensile structure example is employed to verify the proposed method and the results of the example studies indicate that the least necessary number of monitored members can be achieved for the accurate solution and compensation of active cable length deviations. Different members have different sensitivities to the change of the length in active cables and those members with great sensitivities can be chosen in prior as monitored members. The evaluation parameters Δ and ρ can be used to analyze the source of errors and to evaluate the error effect level caused by the passive cable length deviation. [ABSTRACT FROM AUTHOR]

Active control experiments on a newly proposed herringbone ribbed cable dome are described in this study. The cables of the dome are designed to have the ability to change length in order to adjust the geometrical configuration and the force distribution of the structure. Thereby, the dome is adaptable to different load cases. To begin with, for achieving the control amount for the active control test, an active control algorithm based on a nonlinear force method is presented. Then, an assembly and pre-stressing procedure is implemented. Active adjustment tests on three possible types of adjustable cables are performed to provide a practical method for the following active control test. The active control test demonstrates the applicability of the active control algorithm to achieve both force control and shape control. The method can be used to prevent failure of the cable domes due to slackening of the ridge cables and excessive displacements of the central section of the cable dome. The experiments verify the proposed control algorithm and the feasibility of the cable dome to adapt to excessive full span load and maintain the integrity of the structure. [ABSTRACT FROM AUTHOR]

Although Act1 (adaptor for IL-17 receptors) is necessary for IL-17-mediated inflammatory responses, Act1- (but not ll17ra-, ll17rc-,or ll17rb-) deficient mice develop spontaneous SLE- and Sjögren's-like diseases. Here, we show that Act1 functions as a negative regulator in T and B cells via direct inhibition of STAT3. Mass spectrometry analysis detected an Act1--STAT3 complex, deficiency of Act1 (but not ll17ra-, ll17rc-,or ll17rb) results in hyper IL-23- and IL-21-induced STAT3 activation in T and B cells, respectively. IL-23R deletion or blockade of IL-21 ameliorates SLE- and Sjögren's-like diseases in Act1-/- mice. Act1 deficiency results in hyperactivated follicular Th17 cells with elevated IL-21 expression, which promotes T--B cell interaction for B cell expansion and antibody production. Moreover, anti-IL-21 ameliorates the SLE- and Sjögren's-like diseases in Act1-deficient mice. Thus, IL-21 blocking antibody might be an effective therapy for treating SLE- and Sjögren's-like syndrome in patients containing Act1 mutation. [ABSTRACT FROM AUTHOR]