Graph Attention-based Deep Reinforcement Learning for solving the Chinese Postman Problem with Load-dependent costs

Recently, Deep reinforcement learning (DRL) models have shown promising results in solving routing problems. However, most DRL solvers are commonly proposed to solve node routing problems, such as the Traveling Salesman Problem (TSP). Meanwhile, there has been limited research on applying neural methods to arc routing problems, such as the Chinese Postman Problem (CPP), since they often feature irregular and complex solution spaces compared to TSP. To fill these gaps, this paper proposes a novel DRL framework to address the CPP with load-dependent costs (CPP-LC) (Corberan et al., 2018), which is a complex arc routing problem with load constraints. The novelty of our method is two-fold. First, we formulate the CPP-LC as a Markov Decision Process (MDP) sequential model. Subsequently, we introduce an autoregressive model based on DRL, namely Arc-DRL, consisting of an encoder and decoder to address the CPP-LC challenge effectively. Such a framework allows the DRL model to work efficiently and scalably to arc routing problems. Furthermore, we propose a new bio-inspired meta-heuristic solution based on Evolutionary Algorithm (EA) for CPP-LC. Extensive experiments show that Arc-DRL outperforms existing meta-heuristic methods such as Iterative Local Search (ILS) and Variable Neighborhood Search (VNS) proposed by (Corberan et al., 2018) on large benchmark datasets for CPP-LC regarding both solution quality and running time; while the EA gives the best solution quality with much more running time. We release our C++ implementations for metaheuristics such as EA, ILS and VNS along with the code for data generation and our generated data at https://github.com/HySonLab/Chinese_Postman_Proble

Symmetry-preserving graph attention network to solve routing problems at multiple resolutions

Travelling Salesperson Problems (TSPs) and Vehicle Routing Problems (VRPs) have achieved reasonable improvement in accuracy and computation time with the adaptation of Machine Learning (ML) methods. However, none of the previous works completely respects the symmetries arising from TSPs and VRPs including rotation, translation, permutation, and scaling. In this work, we introduce the first-ever completely equivariant model and training to solve combinatorial problems. Furthermore, it is essential to capture the multiscale structure (i.e. from local to global information) of the input graph, especially for the cases of large and long-range graphs, while previous methods are limited to extracting only local information that can lead to a local or sub-optimal solution. To tackle the above limitation, we propose a Multiresolution scheme in combination with Equivariant Graph Attention network (mEGAT) architecture, which can learn the optimal route based on low-level and high-level graph resolutions in an efficient way. In particular, our approach constructs a hierarchy of coarse-graining graphs from the input graph, in which we try to solve the routing problems on simple low-level graphs first, then utilize that knowledge for the more complex high-level graphs. Experimentally, we have shown that our model outperforms existing baselines and proved that symmetry preservation and multiresolution are important recipes for solving combinatorial problems in a data-driven manner. Our source code is publicly available at https://github.com/HySonLab/Multires-NP-har

Moral Foundations and Public Perceptions of Carbon Capture and Storage with Induced Seismicity

Moral foundations held by the public significantly influence attitudes towards energy transition policies like carbon capture and storage (CCS). This study examined the relationships between moral foundations and public perception of CCS with induced seismicity risks in a nationally representative survey of Americans, while controlling for political party and orientation. The binding moral foundation of Loyalty and the individualizing foundation of Care were associated with support for CCS, despite the risk of small earthquakes. In contrast, the individualizing foundation of Fairness and the binding foundations of Authority and Purity were correlated with opposition to CCS when considering the possibility of induced seismicity. An interaction effect was observed between the moral foundation of Loyalty and political orientation. Liberals and moderates tended to increase their support for CCS with the risk of earthquakes as their in-group loyalty increased, while conservatives' support remained unchanged with increasing in-group loyalty. These findings suggest effective energy transition strategies should consider moral foundation dynamics in policy design and public messaging, particularly for climate mitigation aspects involving seismicity risks. Tailoring approaches to align with distinct moral concerns of different population segments could enhance public acceptance of carbon-mitigating energy solutions. Policymakers and communicators should address underlying moral foundations shaping public attitudes to develop more targeted strategies for building support, especially for methods with inherent risks.N

Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework called MGL4MEP that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators

PURIFICATION AND PROPERTIES OF AN AZO-REDUCTASE FROM BACILLUS SP.

Joint Research on Environmental Science and Technology for the Eart

Molecular cloning gene and nucleotide sequence of the gene encoding an endo-1,4-β-glucanase from Bacillus sp VLSH08 strain applying to biomass hydrolysis: Research article

Bacillus sp VLSH08 screened from sea wetland in Nam Dinh province produces an extracellular endo-1,4-beta-glucanase. According to the results of the classified Kit API 50/CHB as well as sequence of 1500 bp fragment coding for 16S rRNA gene of the Bacillus sp VLSH 08 strain showed that the taxonomical characteristics between the strain VLSH 08 and Bacillus amyloliquefaciene JN999857 are similar of 98%. Culture supernatant of this strain showed optimal cellulase activity at pH 5.8 and 60 Celsius degree and that was enhanced 2.03 times in the presence of 5 mM Co2+. Moreover, the gene encoding endo-1,4-beta-glucanase from this strain was cloned in Escherichia coli using pCR2.1 vector. The entire gene for the enzyme contained a 1500-bp single open reading frame encoding 500 amino acids, including a 29-amino acid signal peptide. The amino acid sequence of this enzyme is very close to that of an EG of Bacillus subtilis (EU022560.1) and an EG of Bacillus amyloliquefaciene (EU022559.1) which all belong to the cellulase family E2. A cocktail of enzyme containing this endo-1,4-beta-glucanase used for biomass hydrolysis indicated that the cellulose conversion attained to 72.76% cellulose after 48 hours.Chủng vi khuẩn Bacillus sp VLSH08 được tuyển chọn từ tập hợp chủng vi khuẩn phân lập ở vùng ngập mặn tỉnh Nam Định có khả năng sinh tổng hợp enzyme endo-1,4-beta-glucanase ngoại bào. Kết quả phân loại chủng vi khuẩn Bacillus sp VLSH08 bằng Kit hóa sinh API 50/CHB cũng như trình tự gen mã hóa 16S rRNA cho thấy độ tương đồng của chủng Bacillus sp VLSH08 và chủng Bacillus amyloliquefaciene JN999857 đạt 98%. Dịch lên men của chủng được sử dụng làm nguồn enzyme thô để nghiên cứu hoạt độ tối ưu của enzyme ở pH 5,8 và nhiệt đô 60oC. Hoạt tính enzyme tăng 2,03 lần khi có mặt 5 mM ion Co2+. Đồng thời, gen mã hóa cho enzyme endo-1,4-betaglucanase cũng được tách dòng trong tế bào Escherichia coli sử dụng vector pCR 2.1. Gen mã hóa cho enzyme này có chiều dài 1500 bp, mã hóa cho 500 axit amin, bao gồm 29 axit amin của chuỗi peptid tín hiệu. So sánh cho thấy trình tự gen endo-1,4-beta-glucanase của chủng Bacillus sp VLSH08 có độ tương đồng cao với enzyme này của chủng Bacillus subtilis (EU022560.1) và của chủng Bacillus amyloliquefaciene (EU022559.1). Tất cả các enzyme nhóm này đều thuộc họ cellulase E2. Enzyme của chủng này cũng đã được phối trộn với các enzyme khác tạo thành cocktail để thủy phân sinh khối cho kết quả cellulose bị thủy phân 72,76% sau 48 giờ

Finite-element analysis of the deformation of thin Mylar films due to measurement forces.

Significant deformation of thin films occurs when measuring thickness by mechanical means. This source of measurement error can lead to underestimating film thickness if proper corrections are not made. Analytical solutions exist for Hertzian contact deformation, but these solutions assume relatively large geometries. If the film being measured is thin, the analytical Hertzian assumptions are not appropriate. ANSYS is used to model the contact deformation of a 48 gauge Mylar film under bearing load, supported by a stiffer material. Simulation results are presented and compared to other correction estimates. Ideal, semi-infinite, and constrained properties of the film and the measurement tools are considered

Quantification of uncertainty in machining operations for on-machine acceptance.

Manufactured parts are designed with acceptance tolerances, i.e. deviations from ideal design conditions, due to unavoidable errors in the manufacturing process. It is necessary to measure and evaluate the manufactured part, compared to the nominal design, to determine whether the part meets design specifications. The scope of this research project is dimensional acceptance of machined parts; specifically, parts machined using numerically controlled (NC, or also CNC for Computer Numerically Controlled) machines. In the design/build/accept cycle, the designer will specify both a nominal value, and an acceptable tolerance. As part of the typical design/build/accept business practice, it is required to verify that the part did meet acceptable values prior to acceptance. Manufacturing cost must include not only raw materials and added labor, but also the cost of ensuring conformance to specifications. Ensuring conformance is a substantial portion of the cost of manufacturing. In this project, the costs of measurements were approximately 50% of the cost of the machined part. In production, cost of measurement would be smaller, but still a substantial proportion of manufacturing cost. The results of this research project will point to a science-based approach to reducing the cost of ensuring conformance to specifications. The approach that we take is to determine, a priori, how well a CNC machine can manufacture a particular geometry from stock. Based on the knowledge of the manufacturing process, we are then able to decide features which need further measurements from features which can be accepted 'as is' from the CNC. By calibration of the machine tool, and establishing a machining accuracy ratio, we can validate the ability of CNC to fabricate to a particular level of tolerance. This will eliminate the costs of checking for conformance for relatively large tolerances

Genetic study of congenital bile-duct dilatation identifies de novo and inherited variants in functionally related genes

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