Prediction of wave force on netting under strong nonlinear wave action

Under the strong nonlinear wave environment, accurate simulation of wave force for aquaculture netting is an effective guarantee for cage design and safety. In this paper, the horizontal wave forces of a nylon square-mesh netting panel were obtained through a series of strong nonlinear regular wave tests, and their nonlinearity was analyzed by amplitude spectrum. Moreover, the Morison equation based on fifth-order Stokes wave theory was used to reasonably predict the wave force on the netting. The results showed that both wave and wave force have strong nonlinearity, especially the latter. The frequency domain characteristics of the test wave and wave force are similar, while the higher frequency components of the test force are more apparent. The predicted wave forces are in good agreement with the test values in time and frequency domain, and zero or higher frequency components of predicted force are more prominent with the increase of wave steepness. When the range of the Keulegan-Carpenter number is 35-120, the average drag and inertia coefficient of the predicted force are 2.4 and 2.1, respectively. The results can provide a more accurate assessment of the nonlinear wave force on aquaculture facilities

VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference

Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged as a prominent concern in causal inference research. While existing models tackle observed confounders, the presence of unobserved confounders remains a significant challenge, distorting causal inference and impacting counterfactual outcome accuracy. To address this, we propose a novel variational learning model of unobserved confounders for counterfactual inference (VLUCI), which generates the posterior distribution of unobserved confounders. VLUCI relaxes the unconfoundedness assumption often overlooked by most causal inference methods. By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes. Extensive experiments on synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance in inferring unobserved confounders. It is compatible with state-of-the-art counterfactual inference models, significantly improving inference accuracy at both group and individual levels. Additionally, VLUCI provides confidence intervals for counterfactual outcomes, aiding decision-making in risk-sensitive domains. We further clarify the considerations when applying VLUCI to cases where unobserved confounders don't strictly conform to our model assumptions using the public IHDP dataset as an example, highlighting the practical advantages of VLUCI.Comment: 15 pages, 8 figure

Requirements-driven self-repairing against environmental failures

Self-repairing approaches have been proposed to alleviate the runtime requirements satisfaction problem by switching to appropriate alternative solutions according to the feedback monitored. However, little has been done formally on analyzing the relations between specific environmental failures and corresponding repairing decisions, making it a challenge to derive a set of alternative solutions to withstand possible environmental failures at runtime. To address these challenges, we propose a requirements-driven self-repairing approach against environmental failures, which combines both development-time and runtime techniques. At the development phase, in a stepwise manner, we formally analyze the issue of self-repairing against environmental failures with the support of the model checking technique, and then design a sufficient and necessary set of alternative solutions to withstand possible environmental failures. The runtime part is a runtime self-repairing mechanism that monitors the operating environment for unsatisfiable situations, and makes self-repairing decisions among alternative solutions in response to the detected environmental failures

Dichlorido[N-(2-pyridylmethyl­idene)benzene-1,4-diamine]zinc(II)

In the title compound, [ZnCl2(C12H11N3)], the ZnII atom is four-coordinated by two N atoms from an N-(2-pyridylmethyl­ene)benzene-1,4-diamine ligand and two Cl atoms in a distorted tetra­hedral geometry. In the crystal, the complex mol­ecules are connected by N—H⋯Cl and C—H⋯Cl hydrogen bonds into a two-dimensional layer structure parallel to (110)

Does the COVID-19 pandemic affect the tourism industry in China? Evidence from extreme quantiles approach

The tourism industry carries great significance in the economic development of any country. It has been observed that the COVID-19 crisis has affected global travel and tourism more than any other sector globally as well as in China. The travel restrictions, home isolation, and quarantine orders have given massive damage to China’s once thriving tourism industry. Despite this phenomenal impact, the existing literature has a dearth of empirical studies related to the impact of the COVID-19 pandemic on the tourism industry. This study attempts to reflect a thorough picture of the current scenario and the crisis effects under different intensities reflected through quantiles of Covid-19 related deaths. The study has utilized the QARDL model and the Wald test on the daily time series data of COVID-19 intensity, the real effective exchange rate, oil prices, and the tourism development index from January 1, 2020, to March 15, 2021. The outcomes indicate that COVID-19 related deaths have a negative, but significant impact on China’s tourism in the long run and short run. The oil prices also show a negative influence on tourism in the long run, but there is no significant impact of the oil prices on tourism in the short run. At the same time, the increase in the real effective exchange rates tends to support tourism in the long run, but does not influence tourism development in the short run

Detecting differences across multiple instances of code clones

Clone detectors find similar code fragments (i.e., instances of code clones) and report large numbers of them for industrial systems. To maintain or manage code clones, developers often have to in-vestigate differences of multiple cloned code fragments. However, existing program differencing techniques compare only two code fragments at a time. Developers then have to manually combine several pairwise differencing results. In this paper, we present an approach to automatically detecting differences across multiple clone instances. We have implemented our approach as an Eclipse plugin and evaluated its accuracy with three Java software systems. Our evaluation shows that our algorithm has precision over 97.66% and recall over 95.63 % in three open source Java projects. We also conducted a user study of 18 developers to evaluate the use-fulness of our approach for eight clone-related refactoring tasks. Our study shows that our approach can significantly improve de-velopers ’ performance in refactoring decisions, refactoring details, and task completion time on clone-related refactoring tasks. Au-tomatically detecting differences across multiple clone instances also opens opportunities for building practical applications of code clones in software maintenance, such as auto-generation of appli-cation skeleton, intelligent simultaneous code editing