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Carbon emission reduction is crucial for mitigating global climate change, and green fiscal policies, through providing economic incentives and reallocating resources, are key means to achieve carbon reduction targets. This paper uses data covering 248 cities from 2003 to 2019 and applies a multi-period difference-in-differences model (DID) to thoroughly assess the impact of energy conservation and emission reduction (ECER) fiscal policies on enhancing carbon emission (CE1) reduction and carbon efficiency (CE2). It further analyzes the mediating role of Green Innovation (GI), exploring how it strengthens the impact of ECER policies. We find that: (1) ECER policies significantly promote the improvement of carbon reduction and CE2, a conclusion that remains robust after excluding the impacts of concurrent policy influences, sample selection biases, outliers, and other random factors. (2) ECER policies enhance CE1 reduction and CE2 in pilot cities by promoting green innovation, and this conclusion is confirmed by Sobel Z tests. (3) The effects of ECER policies on CE1 reduction and the improvement of CE2 are more pronounced in higher-level cities, the eastern regions and non-resource cities. This research provides policy makers with suggestions, highlighting that incentivizing green innovation through green fiscal policies is an effective path to achieving carbon reduction goals. [ABSTRACT FROM AUTHOR]

The increasing personalized product demands bring reformation to the manufacturing paradigm. Traditional manufacturing systems seldom analyze and give feedback on the data collected during production. The bottleneck between the physical and digital worlds of manufacturing systems is the lack of interoperability. In this paper, a digital twin-based self-organizing manufacturing system (DT-SOMS) is presented under the individualization paradigm. On the basis of the interconnection between smart workpieces and smart resources via decentralized digital twin models, a decentralized self-organizing network is established to achieve intelligent collaboration between tasks and resources. The mechanism of job-machine optimal assignment and adaptive optimization control is constructed to improve the capabilities of reconfiguration and responsiveness of the DT-SOMS. An implement case is designed to illustrate that the proposed DT-SOMS can realize synchronized online intelligence in the configuration of resources and response to disturbances. [ABSTRACT FROM AUTHOR]

The influence of Co content on the microstructure and corrosion behavior of Al0.3CoxCrFeNi (x = 0.25, 0.5 and 1.0) high entropy alloys were investigated. The results indicated that the increased content of Co improve the corrosion resistance of alloy, which can be attributed to the distinction of phase composition in alloys caused by different Co content. The Al0.3CoxCrFeNi alloy shows a double-phase (FCC + BCC) structure for x = 0.25 and 0.5, and BCC phase consists of (Al, Ni)-rich B2 phase and (Cr, Fe, Co)-rich A2 phase. The increase of Co content induces the transformations of crystal-structures from BCC to FCC, resulting in the decrease of BCC phase proportion. The Al0.3CoCrFeNi alloy exhibits the single FCC structure, and all elements tend to be homogeneously distributed. The difference in the potential of FCC and BCC phases indicates the BCC phase acts as anode resulting to localized corrosion, while B2 phase preferentially dissolves compared to the A2 phase due to the lower potential. Therefore, the existence of selective corrosion is found in the alloys with double-phase, by contrast, the alloy with single FCC phase shows random pitting corrosion. [ABSTRACT FROM AUTHOR]

Emotion-cause pair extraction (ECPE) is a challenging natural language understanding task that aims to extract all potential emotion-cause clause pairs in a document. Existing works have adopted an end-to-end sequence tagging framework to solve this task. However, these approaches struggle to solve the overlapping pairs problem, which means more than one emotion-cause pairs share the same emotion or cause clause. And they suffer from poor generalization ability when the document contains multiple emotion-cause pairs. To address the mentioned issues, we propose a Bidirectional Multi-Label Sequence Tagging (BMST) framework in this paper. Specifically, the proposed method can leverage two sequence tagging modules to achieve bidirectional emotion-cause pair extraction, each of which integrates relevant distance information into the label scheme and employs a multi-label classification strategy. To improve the generalization ability of the model, we propose a joint reasoning mechanism that can exploit the two sequence tagging probabilities to interactively decode emotion-cause pairs. Besides, we design a distance-aware graph attention network to model the relationship between clauses of different distances. Experimental results show that BMST achieves state-of-the-art performance, demonstrating the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Seawater splitting is a prospective approach to yield renewable and sustainable hydrogen energy. Complex preparation processes and poor repeatability are currently considered to be an insuperable impediment to the promotion of the large-scale production and application of electrocatalysts. Avoiding the use of intricate instruments, corrosion engineering is an intriguing strategy to reduce the cost and presents considerable potential for electrodes with catalytic performance. An anode comprising quinary AlCoCrFeNi layered double hydroxides uniformly decorated on an AlCoCrFeNi high-entropy alloy is proposed in this paper via a one-step corrosion engineering method, which directly serves as a remarkably active catalyst for boosting the oxygen evolution reaction (OER) in alkaline seawater. Notably, the best-performing catalyst exhibited oxygen evolution reaction activity with overpotential values of 272.3 and 332 mV to achieve the current densities of 10 and 100 mA·cm−2, respectively. The failure mechanism of the obtained catalyst was identified for advancing the development of multicomponent catalysts. [ABSTRACT FROM AUTHOR]

Document-level event extraction aims to extract event information from a passage, which is more challenging than sentence-level event extraction. Despite some success, the existing document-level event extraction methods still face the following two shortcomings: (a) Insufficient Dependencies: the dependencies between event type and role type are not fully exploited in a document. (b) Redundancy: The redundant role features in document-level event extraction hinder the performance improvement. In this paper, we propose dual-channel Conditional Interaction with Tree Pruning (CITP) to solve the above two challenges simultaneously. For the insufficient dependencies issue, CITP constructs a dual-channel condition interaction module to simultaneously extract both event types and role types in a document, which improves the feature interaction between event types and role types. Also, it can effectively enhance candidate argument features and sentence features with event-aware and role-aware. For the redundancy issue, CITP expands the ordered tree and adopts the classified role types as guidance to prune unnecessary branches, which reduces the impact of redundant role features and improves event extraction performance. Experimental results on the widely used ChFinAnn dataset have proved that our model achieves state-of-the-art compared to other models, with higher effectiveness. [ABSTRACT FROM AUTHOR]

Understanding the influence of purities on the electrochemical performance of pure aluminum (Al) in alkaline media for Al—air batteries is significant. Herein, we comprehensively investigate secondary phase precipitate (SPP)-induced localized corrosion of pure Al in NaOH solution mainly based on quasi-in-situ and cross-section observations under scanning electron microscopy coupled with finite element simulation. The experimental results indicate that Al—Fe SPPs appear as clusters and are coherent with the Al substrate. In alkaline media, Al—Fe SPPs exhibit more positive potentials than the substrate, thus aggravating localized galvanic corrosion as cathodic phases. Moreover, finite element simulation indicates that the irregular geometry coupled with potential difference produces the non-uniform current density distribution inside the SPP cluster, and the current density on the Al substrate gradually decreases with distance. [ABSTRACT FROM AUTHOR]

As few-shot relation classification requires less data to train the neural network models, high costs related to data collection and labeling are eliminated. Compared with the traditional few-shot task, the interdisciplinary few-shot relation classification faces many semantic gap issues (e.g., common sense, subject terminology and insufficient migratable information), bringing the linguistic bias between the testing phase and the training phase. Previous methods mainly focus on the training phase itself and neglect the inherent syntactic and semantic heterogeneity in terms of gap issues. In this paper, we propose a Domain-Aware Prototypical Network (DPNet) to cast the interdisciplinary few-shot relation classification as a covariate shift task. First, the relational keyword masking mechanism, which masks the keywords with a certain probability, is adopted to learn the contextual structure information. Second, to reduce the influence of subject-specific terminology, the adaptive word mover's distance (WMD) is proposed to calculate the similarity among test samples by weighting each word in the sentence. Third, the knowledge distillation strategy integrates each category distribution characteristic into a prototypical network, which supplements additional information for metric learning in task-adaptive feature sub-space. We conduct various experiments on two widely used benchmarks (FewRel 2.0 and DDI-13 dataset). Experimental results show that our model outperforms related methods by a significant margin. [ABSTRACT FROM AUTHOR]

It is vital to reveal the effect of microstructure features on the corrosion behavior of pure aluminum anode in alkaline electrolyte for Al-air batteries. In this paper, we extensively studied the influence of mechanical deformation on the corrosion microstructure and then corrosion features of pure aluminum anode. The results demonstrate that the cold-rolling deformation could facilitate the grain size refinement of pure aluminum. Surprisingly, the aluminum anodes with smaller grain sizes and larger grain boundary areas could effectively improve the corrosion resistance. The further characterization of the microstructure indicates that the deformation is capable of causing the fracture of the cathodic secondary phase precipitate clusters thus weakening the galvanic corrosion effects. The results of this paper could gain insights into the design and processing of pure aluminum for Al-air battery. [ABSTRACT FROM AUTHOR]

Candidate ranking is the process of selecting the candidate with the best matching probability to the question after generating candidates in the knowledge base question answering (KBQA) task. It is a representative problem in mining matching relationships between candidates and questions. Previous research works always model questions and candidate representations separately, ignoring their impact on each other. The text information is too short to capture rich features in the KBQA task. Therefore, our work presents an attention neural network (ANN) fused with structural features (SF-ANN) to rank candidate facts jointly. First, two types of attention mechanisms are used to capture the correlation between the question and the candidate fact: a mutual-attention mechanism that captures the correspondence between the sentence components of a question and each part of a candidate and an intra-attention mechanism that captures the self-dependency of the concatenation between a question and a candidate fact. Second, an ANN is designed for fusing these two types of attention mechanisms to deeply couple interactive information of the input. Finally, knowledge base structural features are introduced to supplement the semantic information to increase the richness of the information. Three mutual attention mechanisms are applied for fusing them into the ANN, resulting in higher information gain. The experimental results on the SimpleQuestions (SimpleQ) benchmark demonstrate that the proposed model achieves a higher ranking accuracy (82.9%) than the state-of-the-art models. Moreover, the ablation study on SimpleQ and WebQuestionsSP (WebQSP) shows that leveraged features and the propoesd ANN both contribute to performance improvement. [ABSTRACT FROM AUTHOR]

Mg and Ti alloy are hard to be bonded due to their huge discrepancy in physical and metallurgical characteristics. As an intermediate element, Ni is feasible for joining of Mg and Ti. The interfacial microstructure and mechanical properties of laser-welded–brazed AZ31 Mg/Ti-6Al-4V joints in butt configuration were investigated with different thicknesses of electrodeposited Ni interlayer. The interfacial reaction products evolved from an ultra-thin Ti3Al layer to Ti2Ni layer mingled with Ti3Al, and more Mg3AlNi2 compounds were produced in the fusion zone (FZ) as Ni coating thickness increased. Slag inclusions and cracks were observed in interfacial layer when coating thickness exceeded 2.5 μm. The quantity of Mg3AlNi2 and the thickness of interfacial layer decreased from the top to bottom of groove of the same joint owing to high thermal gradient during laser welding. The chemical potential of Al and Ni indicated that Al-Ti reaction product was produced more easily than Ni-Ti in 0.001Mg-(Ni-Al-Ti) system. The thermodynamic calculation results of Gibbs free energy suggested the preferential formation Ti-Al phase and Ti-Ni phase along the interface while the Mg-Al-Ni ternary phase in the FZ. Fracture loads of joints increased first followed by a decline with the increase in coating thickness. The joint fractured at the Mg base metal, reaching a maximum value of 3900 N with the Ni coating thickness of 1.5 μm. In the case of using thinner or thicker Ni coating, crack propagated along the interface due to the insufficient metallurgical bonding or defects existed in the interfacial layer. The characteristic of fracture surface changed from the tear ridges and terraces to river pattern when interfacial failure occurred. [ABSTRACT FROM AUTHOR]

Metallurgical bonding between immiscible system AZ31B magnesium (Mg) and Ti-6Al-4V titanium (Ti) was achieved by adding Cu interlayer using laser welding-brazing process. Effect of the laser power on microstructure evolution and mechanical properties of Mg/Cu-coated Ti joints was studied. Visually acceptable joints were obtained at the range of 1300 to 1500 W. The brazed interface was divided into three parts due to temperature gradient: direct irradiation zone, intermediate zone and seam head zone. Ti3Al phase was produced along the interface at the direct irradiation zone. Ti-Al reaction layer grew slightly with the increase in laser power. A small amount of Ti2(Cu,Al) interfacial compounds formed at the intermediate zone and the (α-Mg + Mg2Cu) eutectic structure dispersed in the fusion zone instead of gathering when increasing the laser power at this zone. At the seam head zone, Mg-Cu eutectic structure was produced in large quantities under all cases. Joint strength first increased and then decreased with the variation of the laser power. The maximum fracture load of Mg/Cu-coated Ti joint reached 2314 N at the laser power of 1300 W, representing 85.7% joint efficiency when compared with Mg base metal. All specimens fractured at the interface. The feature of fracture surface at the laser power of 1100 W was characterized by overall smooth surface. Obvious tear ridge and Ti3Al particles were observed at the fracture surface with increase in laser power. It suggested atomic diffusion was accelerated with more heat input giving rise to the enhanced interfacial reaction and metallurgical bonding in direct irradiation zone, which determined the mechanical properties of the joint. [ABSTRACT FROM AUTHOR]

The vision of smart shop floor is based on the notion of Industry 4.0 that denotes technologies and concepts related to Cyber-Physical Production Systems (CPPS). In smart shop floors, CPPS monitors physical processes, creates a virtual copy of the physical world, and makes decentralized decisions. CPPS allows virtual world to store data, process data, communicate, and cooperate with each other in real time. This paper presents architecture of smart shop floor based on physical, logical, and communication layers that embed intelligent approaches within manufacturing processes. Every physical entity in the smart shop floor is regarded as an autonomous intelligent logical unit that performs operations guided by distributed control functions. Moreover, computing power and optimization approaches are embedded into each logical unit to make decisions to agilely respond to frequent occurrence of unexpected disturbances at the shop floor. A test platform has been set up to demonstrate how physical entities can be cooperative and autonomous logical units that can automatize the shop floor operations. The results verify the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]