Research on Fatigue Strain and Fatigue Modulus of Concrete

Concrete fatigue strain and fatigue modulus evolution play a vital role in the evaluation of the material properties. In this paper, by analyzing the advantages and disadvantages of existing concrete strain analysis methods, the level-S nonlinear fatigue strain model was proposed. The parameters’ physical meaning, the ranges, and the impact on the shape of the curve were all discussed. Then, the evolution model of fatigue modulus was established based on the fatigue strain evolution model and the hypothesis of fatigue modulus inversely related fatigue strain amplitude. The results indicate that the level-S model covered all types of fatigue strain evolution. It is very suitable for the description of strain evolution of concrete for its strong adaptability and high accuracy. It was found that the fitting curves coincided with the experimental curves very well, and the correlation coefficients were all above 0.98. The evolution curves of fatigue strain modulus both have three stages, namely, variation phase, linear change stage, and convergence stage. The difference is that the fatigue strain evolution curve is from the lower left corner to the upper right corner, but the fatigue modulus evolution curve is from the upper left corner to the right lower corner

Strong anisotropic enhancement of photoluminescence in WS₂ integrated with plasmonic nanowire array

Layered transition metal dichalcogenides (TMDCs) have shown great potential for a wide range of applications in photonics and optoelectronics. Nevertheless, valley decoherence severely randomizes its polarization which is important to a light emitter. Plasmonic metasurface with a unique way to manipulate the light-matter interaction may provide an effective and practical solution. Here by integrating TMDCs with plasmonic nanowire arrays, we demonstrate strong anisotropic enhancement of the excitonic emission at different spectral positions. For the indirect bandgap transition in bilayer WS2, multifold enhancement can be achieved with the photoluminescence (PL) polarization either perpendicular or parallel to the long axis of nanowires, which arises from the coupling of WS2 with localized or guided plasmon modes, respectively. Moreover, PL of high linearity is obtained in the direct bandgap transition benefiting from, in addition to the plasmonic enhancement, the directional diffraction scattering of nanowire arrays. Our method with enhanced PL intensity contrasts to the conventional form-birefringence based on the aspect ratio of nanowire arrays where the intensity loss is remarkable. Our results provide a prototypical plasmon-exciton hybrid system for anisotropic enhancement of the PL at the nanoscale, enabling simultaneous control of the intensity, polarization and wavelength toward practical ultrathin photonic devices based on TMDCs

Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technolog

Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tolerant Deterministic Ordering

Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes Prophet, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for pre-executed results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. Prophet orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that Prophet improves the throughput by $3.11\times$ and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding

Group Time-based One-time Passwords and its Application to Efficient Privacy-Preserving Proof of Location

Time-based One-Time Password (TOTP) provides a strong second factor for user authentication. In TOTP, a prover authenticates to a verifier by using the current time and a secret key to generate an authentication token (or password) which is valid for a short time period. Our goal is to extend TOTP to the group setting, and to provide both authentication and privacy. To this end, we introduce a new authentication scheme, called Group TOTP (GTOTP), that allows the prover to prove that it is a member of an authenticated group without revealing its identity. We propose a novel construction that transforms any asymmetric TOTP scheme into a GTOTP scheme. Our approach combines Merkle tree and Bloom filter to reduce the verifier\u27s states to constant sizes. As a promising application of GTOTP, we show that GTOTP can be used to construct an efficient privacy-preserving Proof of Location (PoL) scheme. We utilize a commitment protocol, a privacy-preserving location proximity scheme, and our GTOTP scheme to build the PoL scheme, in which GTOTP is used not only for user authentication but also as a tool to glue up other building blocks. In the PoL scheme, with the help of some witnesses, a user can prove its location to a verifier, while ensuring the identity and location privacy of both the prover and witnesses. Our PoL scheme outperforms the alternatives based on group digital signatures. We evaluate our schemes on Raspberry Pi hardware, and demonstrate that they achieve practical performance. In particular, the password generation and verification time are in the order of microseconds and milliseconds, respectively, while the computation time of proof generation is less than $1$ second

Group Time-based One-Time Passwords and its application to efficient privacy-preserving Proof of Location

Time-based One-Time Password (TOTP) provides a strong second factor for user authentication. In TOTP, a prover authenticates to a verifier by using the current time and a secret key to generate an authentication token (or password) which is valid for a short time period. Our goal is to extend TOTP to the group setting, and to provide both authentication and privacy. To this end, we introduce a new authentication scheme, called Group TOTP (GTOTP), that allows the prover to prove that it is a member of an authenticated group without revealing its identity. We propose a novel construction that transforms any asymmetric TOTP scheme into a GTOTP scheme. Our approach combines Merkle tree and Bloom filter to reduce the verifier’s states to constant sizes. As a promising application of GTOTP, we show that GTOTP can be used to construct an efficient privacy-preserving Proof of Location (PoL) scheme. We utilize a commitment protocol, a privacy-preserving location proximity scheme, and our GTOTP scheme to build the PoL scheme, in which GTOTP is used not only for user authentication but also as a tool to glue up other building blocks. In the PoL scheme, with the help of some witnesses, a user can prove its location to a verifier, while ensuring the identity and location privacy of both the prover and witnesses. Our PoL scheme outperforms the alternatives based on group digital signatures. We evaluate our schemes on Raspberry Pi hardware, and demonstrate that they achieve practical performance. In particular, the password generation and verification time are in the order of microseconds and milliseconds, respectively, while the computation time of proof generation is less than 1 second

Cytokine concentration in peripheral blood of patients with colorectal cancer

IntroductionThe role of tumour secretory cytokines and peripheral circulatory cytokines in tumour progression has received increasing attention; however, the role of tumour-related inflammatory cytokines in colorectal cancer (CRC) remains unclear. In this study, the concentrations of various cytokines in the peripheral blood of healthy controls and patients with CRC at different stages were compared.MethodsPeripheral blood samples from 4 healthy participants and 22 colorectal cancer patients were examined. Luminex beads were used to evaluate concentration levels of 40 inflammatory cytokines in peripheral blood samples.ResultsIn peripheral blood, compared with healthy controls and early stage (I + II) CRC patients, advanced CRC (III + IV) patients had increased concentrations of mononuclear/macrophage chemotactic-related proteins (CCL7, CCL8, CCL15, CCL2, and MIF), M2 polarization-related factors (IL-1β, IL-4), neutrophil chemotactic and N2 polarization-related cytokines (CXCL2, CXCL5, CXCL6, IL-8), dendritic cells (DCs) chemotactic-related proteins (CCL19, CCL20, and CCL21), Natural killer (NK) cell related cytokines (CXCL9, CXCL10), Th2 cell-related cytokines (CCL1, CCL11, CCL26), CXCL12, IL-2, CCL25, and CCL27, and decreased IFN-γ and CX3CL1 concentrations. The differential upregulation of cytokines in peripheral blood was mainly concentrated in CRC patients with distant metastasis and was related to the size of the primary tumour; however, there was no significant correlation between cytokine levels in peripheral blood and the propensity and mechanism of lymph node metastasis.DiscussionDifferent types of immune cells may share the same chemokine receptors and can co-localise in response to the same chemokines and exert synergistic pro-tumour or anti-tumour functions in the tumour microenvironment. Chemokines and cytokines affect tumour metastasis and prognosis and may be potential targets for treatment

Fatigue Strain and Damage Analysis of Concrete in Reinforced Concrete Beams under Constant Amplitude Fatigue Loading

Concrete fatigue strain evolution plays a very important role in the evaluation of the material properties of concrete. To study fatigue strain and fatigue damage of concrete in reinforced concrete beams under constant amplitude bending fatigue loading, constant amplitude bending fatigue experiments with reinforced concrete beams with rectangular sections were first carried out in the laboratory. Then, by analyzing the shortcomings and limitations of existing fatigue strain evolution equations, the level-S nonlinear evolution model of fatigue strain was constructed, and the physical meaning of the parameters was discussed. Finally, the evolution of fatigue strain and fatigue damage of concrete in the compression zone of the experimental beam was analyzed based on the level-S nonlinear evolution model. The results show that, initially, fatigue strain grows rapidly. In the middle stages, fatigue strain is nearly a linear change. Because the experimental data for the third stage are relatively scarce, the evolution of the strain therefore degenerated into two phases. The model has strong adaptability and high accuracy and can reflect the evolution of fatigue strain. The fatigue damage evolution expression based on fatigue strain shows that fatigue strain and fatigue damage have similar variations, and, with the same load cycles, the greater the load level, the larger the damage, in line with the general rules of damage

Machine learning-based model for recognizing the failure modes of FRP-strengthened RC beams in flexure

Plate end (PE) debonding and intermediate crack (IC) debonding are the main failure modes of fiber reinforced polymer (FRP) strengthened reinforced concrete (RC) beams in flexure. Different failure modes exhibit different failure characteristics. Therefore, accurately identifying the failure mode is of great significance in selecting methods to prevent the debonding of the strengthened beams. This study first established a primary indicator system through literature research. Then one hundred and eighty-eight FRP-strengthened RC beams containing PE and IC debonding were collected from the published literature of forty-eight researchers. After that, correlation analysis and grey correlation analysis were used to study the data of two failure modes. Finally, the indicator system for the prediction of failure modes was established. After the indicator system was established, six machine learning algorithms, including K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF), back propagation neural network (BPNN), logistic regression (LR), and support vector machine (SVM), were used to build the prediction model of failure modes. The evaluation of the models shows that the coefficient of variation of the accuracy of the decision tree is merely 5.4%, which has the best robustness; the average accuracy of the random forest for the two failure modes reaches 93% and 98%, which has the highest precision