Microstructures and constituents of super-high strength aluminum alloy ingots made through LFEC process

Ingots of a new super-high strength Al-Zn-Mg-Cu-Zr alloy were produced respectively by low frequency electromagnetic casting (LFEC) and by conventional direct chill (DC) casting process. Microstructure and constituents of the ingots were studied. The results indicated that the LFEC process significantly refines microstructure and constituents of the alloy, and to some extent, decreases the area (or volume) fraction of constituents and eutectic structure precipitated at grain boundaries. But, no difference in the type of constituents was observed between LFEC and DC ingots. The results also showed LFEC process can improve the as-cast mechanical properties

How Australian construction contractors responded to the economic downturn

The Global Financial Crisis (GFC) in 2008 had a significant impact on the world economy and the construction industry was no exception. This study investigates the major impacts of the 2008 GFC on the Australian construction industry and, in particular how the Australian construction contractors responded to the economic downturn. A total of 35 senior managers from the Top 100 Australian construction companies were interviewed. The findings indicate that construction companies, particularly the large ones were not affected in any significant way but are expecting some difficult financial times over the next few years and are taking actions to minimize the upcoming adverse impacts. The most common strategy adopted by Australian construction contractors is to concentrate on core business while avoiding aimless bidding. Similarly, great focus is placed on retaining human resources in order to maintain the skill set so that the company can respond quickly when market conditions improves. The research findings will provide construction contractors with insights on how to establish and sustain competitive advantages during economic slowdown and become more resilient in the future

Position-Aware Contrastive Alignment for Referring Image Segmentation

Referring image segmentation aims to segment the target object described by a given natural language expression. Typically, referring expressions contain complex relationships between the target and its surrounding objects. The main challenge of this task is to understand the visual and linguistic content simultaneously and to find the referred object accurately among all instances in the image. Currently, the most effective way to solve the above problem is to obtain aligned multi-modal features by computing the correlation between visual and linguistic feature modalities under the supervision of the ground-truth mask. However, existing paradigms have difficulty in thoroughly understanding visual and linguistic content due to the inability to perceive information directly about surrounding objects that refer to the target. This prevents them from learning aligned multi-modal features, which leads to inaccurate segmentation. To address this issue, we present a position-aware contrastive alignment network (PCAN) to enhance the alignment of multi-modal features by guiding the interaction between vision and language through prior position information. Our PCAN consists of two modules: 1) Position Aware Module (PAM), which provides position information of all objects related to natural language descriptions, and 2) Contrastive Language Understanding Module (CLUM), which enhances multi-modal alignment by comparing the features of the referred object with those of related objects. Extensive experiments on three benchmarks demonstrate our PCAN performs favorably against the state-of-the-art methods. Our code will be made publicly available.Comment: 12 pages, 6 figure

4-Des­oxy-4β-(4-methoxy­carbonyl-1,2,3-triazol-1-yl)podophyllotoxin dichloro­methane solvate

In the title compound {systematic name: methyl 1-[12-oxo-10-(3,4,5-trimethoxy­phen­yl)-4,6,13-trioxa­tetra­cyclo­[7.7.0.03,7.011,15]hexa­deca-1,3(7),8-trien-16-yl]-1H-1,2,3-triazole-4-carboxyl­ate dichloro­methane solvate}, C26H25N3O9·CH2Cl2, the tetra­hydro­furan ring and the six-membered ring fused to it both display envelope conformations

Tetrakis[μ-3-(3-pyridyl)acrylato-κ2 O:O′]bis{(1,10-phenanthroline-κ2 N,N′)[3-(3-pyridyl)acrylato-κ2 O,O′]europium(III)} pentahydrate

The europiumIII ion in the title compound, [Eu2(C8H6NO2)6(C12H8N2)2]·5H2O, is coordinated by seven carboxyl­ate O atoms and two N atoms from one phenanthroline mol­ecule. The carboxyl­ate groups of 3-(3-pyrid­yl)acrylate link pairs of europium(III) ions, forming centrosymmetric dinuclear units, which further assemble into a sheet parallel to the (001) plane through hydrogen-bonding inter­actions involving the uncoordinated water mol­ecules. One water molecule is disordered

Counterfactual Fairness with Partially Known Causal Graph

Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrimination and bias through causal effects. Though causality-based fair learning is attracting increasing attention, current methods assume the true causal graph is fully known. This paper proposes a general method to achieve the notion of counterfactual fairness when the true causal graph is unknown. To be able to select features that lead to counterfactual fairness, we derive the conditions and algorithms to identify ancestral relations between variables on a \textit{Partially Directed Acyclic Graph (PDAG)}, specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. Interestingly, we find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided: the sensitive attributes do not have ancestors in the causal graph. Results on both simulated and real-world datasets demonstrate the effectiveness of our method

Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition

Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%

Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central Universities and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin