Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. The sum square of wavelet packet energy change rate, a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. BPNN, GA-BPNN, PSO-BPNN and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. The sum square of wavelet packet energy change rate, a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. BPNN, GA-BPNN, PSO-BPNN and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges

Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis

In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect and propose an Interactive Graph Convolutional Networks (InterGCN) model for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph. Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words. Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect. Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT

Rechargeable Li/Cl2_2 battery down to -80 {\deg}C

Low temperature rechargeable batteries are important to life in cold climates, polar/deep-sea expeditions and space explorations. Here, we report ~ 3.5 - 4 V rechargeable lithium/chlorine (Li/Cl2) batteries operating down to -80 {\deg}C, employing Li metal negative electrode, a novel CO2 activated porous carbon (KJCO2) as the positive electrode, and a high ionic conductivity (~ 5 to 20 mS cm-1 from -80 {\deg}C to 25 {\deg}C) electrolyte comprised of 1 M aluminum chloride (AlCl3), 0.95 M lithium chloride (LiCl), and 0.05 M lithium bis(fluorosulfonyl)imide (LiFSI) in low melting point (-104.5 {\deg}C) thionyl chloride (SOCl2). Between room-temperature and -80 {\deg}C, the Li/Cl2 battery delivered up to ~ 30,000 - 4,500 mAh g-1 first discharge capacity and a 1,200 - 5,000 mAh g-1 reversible capacity (discharge voltages in ~ 3.5 to 3.1 V) over up to 130 charge-discharge cycles. Mass spectrometry and X-ray photoelectron spectroscopy (XPS) probed Cl2 trapped in the porous carbon upon LiCl electro-oxidation during charging. At lower temperature down to -80 {\deg}C, SCl2/S2Cl2 and Cl2 generated by electro-oxidation in the charging step were trapped in porous KJCO2 carbon, allowing for reversible reduction to afford a high discharge voltage plateau near ~ 4 V with up to ~ 1000 mAh g-1 capacity for SCl2/S2Cl2 reduction and up to ~ 4000 mAh g-1 capacity at ~ 3.1 V plateau for Cl2 reduction. Towards practical use, we made CR2032 Li/Cl2 battery cells to drive digital watches at -40 {\deg}C and light emitting diode at -80 {\deg}C, opening Li/Cl2 secondary batteries for ultra-cold conditions

Aspect-invariant sentiment feature learning : adversarial multi-task learning for aspect-based sentiment analysis

In most previous studies, the aspect-related text is considered an important clue for the Aspect-based Sentiment Analysis (ABSA) task, and thus various attention mechanisms have been proposed to leverage the interactions between aspects and context. However, it is observed that some sentiment expressions carry the same polarity regardless of the aspects they are associated with. In such cases, it is not necessary to incorporate aspect information for ABSA. More observations on the experimental results show that blindly leveraging interactions between aspects and context as features may introduce noises when analyzing those aspect-invariant sentiment expressions, especially when the aspect-related annotated data is insufficient. Hence, in this paper, we propose an Adversarial Multi-task Learning framework to identify the aspect-invariant/dependent sentiment expressions without extra annotations. In addition, we adopt a gating mechanism to control the contribution of representations derived from aspect-invariant and aspect-dependent hidden states when generating the final contextual sentiment representations for the given aspect. This essentially allows the exploitation of aspect-invariant sentiment features for better ABSA results. Experimental results on two benchmark datasets show that extending existing neural models using our proposed framework achieves superior performance. In addition, the aspect-invariant data extracted by the proposed framework can be considered as pivot features for better transfer learning of the ABSA models on unseen aspects

Tunable spin and valley excitations of correlated insulators in Γ\Gamma-valley moir\'e bands

Moir\'e superlattices formed from transition metal dichalcogenides (TMDs) have been shown to support a variety of quantum electronic phases that are highly tunable using applied electromagnetic fields. While the valley character of the low-energy states dramatically affects optoelectronic properties in the constituent TMDs, this degree of freedom has yet to be fully explored in moir\'e systems. Here, we establish twisted double bilayer WSe$_2$ as an experimental platform to study electronic correlations within $\Gamma$-valley moir\'e bands. Through a combination of local and global electronic compressibility measurements, we identify charge-ordered phases at multiple integer and fractional moir\'e band fillings $\nu$. By measuring the magnetic field dependence of their energy gaps and the chemical potential upon doping, we reveal spin-polarized ground states with novel spin polaron quasiparticle excitations. In addition, an applied displacement field allows us to realize a new mechanism of metal-insulator transition at $\nu = -1$ driven by tuning between $\Gamma$- and $K$-valley moir\'e bands. Together, our results demonstrate control over both the spin and valley character of the correlated ground and excited states in this system

Modified Substrate Specificity of a Methyltransferase Domain by Protein Insertion Into an Adenylation Domain of the Bassianolide Synthetase

Background: Creating designer molecules using a combination of select domains from polyketide synthases and/or nonribosomal peptide synthetases (NRPS) continues to be a synthetic goal. However, an incomplete understanding of how protein-protein interactions and dynamics affect each of the domain functions stands as a major obstacle in the field. Of particular interest is understanding the basis for a class of methyltransferase domains (MT) that are found embedded within the adenylation domain (A) of fungal NRPS systems instead of in an end-to-end architecture. Results: The MT domain from bassianolide synthetase (BSLS) was removed and the truncated enzyme BSLS-ΔMT was recombinantly expressed. The biosynthesis of bassianolide was abolished and N-desmethylbassianolide was produced in low yields. Co-expression of BSLS-ΔMT with standalone MT did not recover bassianolide biosynthesis. In order to address the functional implications of the protein insertion, we characterized the N-methyltransferase activity of the MT domain as both the isolated domain (MTBSLS) and as part of the full NRPS megaenzyme. Surprisingly, the MTBSLS construct demonstrated a relaxed substrate specificity and preferentially methylated an amino acid (L-Phe-SNAC) that is rarely incorporated into the final product. By testing the preference of a series of MT constructs (BSLS, MTBSLS, cMT, XLcMT, and aMT) to L-Phe-SNAC and L-Leu-SNAC, we further showed that restricting and/or fixing the termini of the MTBSLS by crosslinking or embedding the MT within an A domain narrowed the substrate specificity of the methyltransferase toward L-Leu-SNAC, the preferred substrate for the BSLS megaenzyme. Conclusions: The embedding of MT into the A2 domain of BSLS is not required for the product assembly, but is critical for the overall yields of the final products. The substrate specificity of MT is significantly affected by the protein context within which it is present. While A domains are known to be responsible for selecting and activating the biosynthetic precursors for NRPS systems, our results suggest that embedding the MT acts as a secondary gatekeeper for the assembly line. This work thus provides new insights into the embedded MT domain in NRPSs, which will facilitate further engineering of this type of biosynthetic machinery to create structural diversity in natural products

Hofstadter states and reentrant charge order in a semiconductor moir\'e lattice

The emergence of moir\'e materials with flat bands provides a platform to systematically investigate and precisely control correlated electronic phases. Here, we report local electronic compressibility measurements of a twisted WSe$_2$/MoSe$_2$ heterobilayer which reveal a rich phase diagram of interpenetrating Hofstadter states and electron solids. We show that this reflects the presence of both flat and dispersive moir\'e bands whose relative energies, and therefore occupations, are tuned by density and magnetic field. At low densities, competition between moir\'e bands leads to a transition from commensurate arrangements of singlets at doubly occupied sites to triplet configurations at high fields. Hofstadter states (i.e., Chern insulators) are generally favored at high densities as dispersive bands are populated, but are suppressed by an intervening region of reentrant charge-ordered states in which holes originating from multiple bands cooperatively crystallize. Our results reveal the key microscopic ingredients that favor distinct correlated ground states in semiconductor moir\'e systems, and they demonstrate an emergent lattice model system in which both interactions and band dispersion can be experimentally controlled

Embedding refinement framework for targeted aspect-based sentiment analysis

The state-of-the-art approaches to Targeted Aspect-Based Sentiment Analysis (TABSA) are mostly deep learning models based on attention mechanisms. One problem in most previous studies is that embeddings of targets and aspects are either pre-trained from large external corpora or randomly initialized. We argue that affective commonsense knowledge and words indicative of sentiment could be used to learn better target and aspect embeddings. We therefore propose an embedding refinement framework called RAEC (Refining Affective Embedding from Context), in which sentiment concepts extracted from affective commonsense knowledge and word relative location information are incorporated to derive context-affective embeddings. Furthermore, a sparse coefficient vector is exploited in refining the embeddings of targets and aspects separately. In this way, embeddings of targets and aspects can capture the highly relevant affective words. Experimental results on two benchmark datasets show that our framework can be easily integrated with existing embedding-based TABSA models and achieves state-of-the-art results compared to models relying on pre-trained word embeddings or built on other embedding refinement methods