Potts and percolation models on bowtie lattices

We give the exact critical frontier of the Potts model on bowtie lattices. For the case of $q=1$, the critical frontier yields the thresholds of bond percolation on these lattices, which are exactly consistent with the results given by Ziff et al [J. Phys. A 39, 15083 (2006)]. For the $q=2$ Potts model on the bowtie-A lattice, the critical point is in agreement with that of the Ising model on this lattice, which has been exactly solved. Furthermore, we do extensive Monte Carlo simulations of Potts model on the bowtie-A lattice with noninteger $q$. Our numerical results, which are accurate up to 7 significant digits, are consistent with the theoretical predictions. We also simulate the site percolation on the bowtie-A lattice, and the threshold is $s_c=0.5479148(7)$. In the simulations of bond percolation and site percolation, we find that the shape-dependent properties of the percolation model on the bowtie-A lattice are somewhat different from those of an isotropic lattice, which may be caused by the anisotropy of the lattice.Comment: 18 pages, 9 figures and 3 table

Bioaccumulation of eight heavy metals in cave animals from Dashui and Malang caves, Guizhou Province, China

Eight heavy metals content in the dominant animal groups, soil and water in Malang and Dashui caves were examined. The results showed that zinc contents in Porcellio scaber from Dashui and Malang caves were 448.80 and 598.00 mg/kg, respectively, which is the highest among all these 8 metals, while Pb was not detected in Diestrammena marmorata and Rhinolophidae pearsoni, suggesting that both animals were incapable of or poor in Pb accumulation. The highest average value of enrichment coefficient for soil-born Cd in animals from Dashui cave was 4.15, while that for water-born Zn was 91723.84. By contrast, the highest average value of enrichment coefficient for soil-born Cd in animals from Malang cave was 8.48, and that for water-born Zn was 708102.64.Key words: Bioaccumulation, heavy metal, cave animals, China

MS-NHHO: A Swarm Intelligence Optimization Algorithm Incorporating Cognitive Science for Malicious Traffic Detection

The diversification of attacks jeopardizes cyberspace's normal operation. This paper proposes a new Harris Hawks Optimization Based on Multiple Strategies (MS-NHHO), inspired by humans' limited cognitive load, collective decision-making, and dynamic learning mechanisms for processing complex information. This paper utilizes the elite chaos reverse learning strategy to improve the algorithm's convergence speed and population diversity. Then, the dynamic adaptive weights are introduced into the escape energy decline mechanism to improve the algorithm's global exploration and local exploitation ability. Finally, the Gaussian random walk strategy enhances the algorithm's anti-stagnation ability. The experimental results confirm the usefulness of the three optimization strategies. Meanwhile, MS-NHHO exhibits satisfactory performance in terms of computational cost, detection performance, and efficiency in several scenarios

Sublattice extraordinary-log phase and new special point of the antiferromagnetic Potts model

We study the surface criticality of a three-dimensional classical antiferromagnetic Potts model, whose bulk critical behaviors belongs to the XY model because of emergent O(2) symmetry. We find that the surface antiferromagnetic next-nearest neighboring interactions can drive the extraordinary-log phase to the ordinary phase, the transition between the two phases belongs to the universality class of the well-known special transition of the XY model. Further strengthening the surface next-nearest neighboring interactions, the extraordinary-log phase reappears, but the main critical behaviors are dominated on the sublattices of the model; the special point between the ordinary phase and the sublattice extraordinary-log phase belongs to a new universality class.Comment: 6 pages, 7 figure

Vibration de-icing method with piezoelectric actuators

This work presents the analytic and experimental research of a vibration de-icing method for aircrafts with piezoelectric transducer as the actuator. The maximum displacement of the structure will be achieved when excited at the natural frequencies. The vibration induces shear stress at the interface of the ice and structure, which leads to the shedding off of ice. A shear model of linear Bernoulli-Euler type is derived with ice attached to a flat plate, which is capable of predicting the shear stress along the interface and gives guidance to the choice of vibration modes for de-icing. The finite element method (FEM) is used to get the relationship between the length of piezoelectric actuator and the vibration intensity of the modes to be excited. The optimal length is determined in order to maximize the shear stress. The results indicate that peak values of the shear stress at the interface appear at the edges of the ice, and the amplitudes depend upon the strains on the surface of plate underlying the edges of ice. For a specific mode the maximum excitation happens when the length of piezoelectric actuator is an odd integer multiple of the half wavelength of that mode. Actually the optimal length would be a slightly longer due to the influence of the actuator. Finally, experimental investigations on a clamped aluminum plate are carried out to verify the results of the analysis. The power consumption for vibration de-icing is about 36.5 w/m2, which is only 1.57 % of the power consumption when using the latest electro-thermal de-icing method

Investigation of Turn Number of the Coil on Tube Forming Performance in Electromagnetic Pulse Forming

Electromagnetic pulse forming (EMPF) is an environmentally friendly and high-speed forming method without pollution generating. As the key component for energy conversion, the coil has a significant influence on the forming performance. Consequently, this paper aims to study the effect of the turn number of the coil on the performance of the tube forming through the theoretical analysis model and experiments. Based on the connection among the electrical parameters of the coil, the current parameters of the discharge pulse, and electromagnetic force, a theoretical model is built to analyze the qualitative connection between the number of turns of the coil and the amount of forming. Then, the EMPF experiments with the same discharge energy of 3.4kJ are carried out to verify the established model through the established EMPF equipment. The experimental results are consistent with the theoretical analysis. The results show that the peak value of the current amplitude and rise time of the pulse decrease respectively with the increase of the number of turns of the coil because the increments of the number of turns lead to the rising coil inductance. But the forming amount of the tube increases first and then decreases. So there is an optimal value of the turn number for the coil to generate the maximum Lorentz force to deform the workpiece. Therefore, it is worthwhile to choose the proper number of coil turn to improve the effect of EMPF

Domain Adaptation with Incomplete Target Domains

Domain adaptation, as a task of reducing the annotation cost in a target domain by exploiting the existing labeled data in an auxiliary source domain, has received a lot of attention in the research community. However, the standard domain adaptation has assumed perfectly observed data in both domains, while in real world applications the existence of missing data can be prevalent. In this paper, we tackle a more challenging domain adaptation scenario where one has an incomplete target domain with partially observed data. We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption. We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains. The experimental results demonstrate the effectiveness of the proposed method

Unsupervised identification of synonymous query intent templates for attribute intents

ABSTRACT Among all web search queries there is an important subset of queries containing entity mentions. In these queries, it is observed that users are most interested in requesting some attribute of an entity, such as "Obama age" for the intent of age, which we refer to as the attribute intent. In this work we address the problem of identifying synonymous query intent templates for the attribute intent. For example, "how old is [Person]" and "[Person]'s age" are both synonymous templates for the age intent. Successful identification of the synonymous query intent templates not only can improve the performance of all existing query annotation approaches, but also could benefit applications such as instant answers and intent-based query suggestion. In this work we propose a clustering framework with multiple kernel functions to identify synonymous query intent templates for a set of canonical templates jointly. Furthermore, signals from multiple sources of information are integrated into a kernel function between templates, where the weights of these signals are tuned in an unsupervised manner. We have conducted extensive experiments across multiple domains in FreeBase, and results demonstrate the effectiveness of our clustering framework for finding synonymous query intent templates for attribute intents

Platelet/high-density lipoprotein cholesterol ratio as a biomarker of depression in individuals with chronic opioid use

BackgroundThe comorbidity of depression and opioid use is increasingly recognized as a significant public health concern. Chronic opioid use can alter biological systems, including lipid metabolism and inflammatory responses, potentially contributing to depressive symptoms. The platelet/high-density lipoprotein cholesterol ratio (PHR) has emerged as a biomarker associated with both cardiovascular and mental health outcomes. This study investigates the relationship between PHR and depression in individuals with chronic opioid use.MethodsA cross-sectional analysis was conducted using data from the National Health and Nutrition Examination Survey (NHANES) (2007–2018). A total of 843 participants with prescription opioid use were included. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9), with a score ≥10 indicating clinically significant depression. PHR was calculated from platelet counts and HDL cholesterol levels and categorized into quartiles. Weighted logistic regression and restricted cubic spline regression were employed to evaluate associations and potential nonlinear relationships, adjusting for demographic, socioeconomic, lifestyle, and clinical covariates.ResultsHigher PHR quartiles were significantly associated with increased odds of depression, even after full adjustment for confounders (OR for Q3: 3.40; 95% CI: 1.95–5.94; OR for Q4: 4.12; 95% CI: 2.21–7.12). A nonlinear relationship was observed, with depression risk increasing sharply beyond a specific PHR threshold. Subgroup analyses revealed stronger associations in younger participants and those with obesity, with significant interaction effects for age and BMI.ConclusionElevated PHR is independently associated with depression in individuals with chronic opioid use, suggesting its potential as a biomarker for identifying at-risk populations. The findings underscore the need to address systemic inflammation and lipid dysregulation as part of integrated mental health care for opioid users