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A bstract We consider the triality fusion category discovered in the c = 1 Kosterlitz-Thouless theory [ 1 ]. We analyze this fusion category using the tools from the group theoretical fusion category and compute the simple lines, fusion rules and F -symbols. We then studied the physical implication of this fusion category including deriving the spin selection rule, computing the asymptotic density of states of irreducible representations of the fusion category symmetries, and analyzing its anomaly and constraints under the renormalization group flow. There is another set of F -symbols for the fusion categories with the same fusion rule known in the literature [ 2 ]. We find these two solutions are different as they lead to different spin selection rules. This gives a complete list of the fusion categories with the same fusion rule by the classification result in [ 3 ].

A wet-bulb temperature of 35 ∘ C is widely used as the threshold for human survivability, but the wet-bulb temperature is not a particularly accurate metric for human heat stress. For a person in the shade, a more accurate metric is the heat index, which is based on a model of human thermoregulation that accounts for metabolic heat, radiation, respiratory ventilation, and finite wind speeds. The heat index has two critical values: the highest heat index for which a healthy core temperature can be maintained and the highest heat index that is survivable. It is shown here that a wet-bulb temperature of 35 ∘ C corresponds to conditions between these two critical values. For example, in a world warmer than pre-industrial by 10 ∘ C, about 30% of the world’s population would be exposed once or more per year to a wet-bulb temperature above 35 ∘ C, but the heat index reveals that less than 2% would be exposed to fatal conditions while over 60% would be exposed to conditions that would cause hyperthermia.

A bstract The Tambara-Yamagami (TY) fusion category symmetry TY A χ ϵ describes the enhanced non-invertible self-duality symmetry of a 2-dim QFT under gauging a finite Abelian group A . We generalize the enhanced non-invertible symmetries by considering twisted gauging which allows stacking A -SPTs before and after the gauging. Such noninvertible symmetries can be obtained from invertible anyon permutation symmetries of the 3-dim SymTFT. Consider a finite group G formed by (un)twisted gaugings of A , a 2-dim QFT invariant under topological manipulations in G admits non-invertible G-ality defects . We study the classification and the physical implication of the G -ality defects using the SymTFT and the group-theoretical fusion categories, with three concrete examples. 1) Triality with A = ℤ N × ℤ N where N is coprime with 3. The classification was previously determined by Jordan and Larson where the data is similar to the TY fusion categories, and we determine the anomaly of these fusion categories. 2) p -ality with A = ℤ p × ℤ p where p is an odd prime. We consider two such categories P ± , m which are distinguished by different choices of the symmetry fractionalization, a new data that does not appear in the TY classification, and show that they have distinct anomaly structures and spin selection rules. 3) S 3 -ality with A = ℤ N × ℤ N . We study their classification explicitly for N < 20 via SymTFT, and provide a group-theoretical construction for certain N . We find N = 5 is the minimal N to admit an S 3 -ality and N = 11 is the minimal N to admit a group-theoretical S 3 -ality.

Astilbin, a dihydroflavonol derivative found in many food and medicine plants, exhibited multiple pharmacological functions. In the present study, the ethanol extraction of astilbin from the rhizome of smilax glabra Roxb was optimized by response surface methodology (RSM) using Box-Behnken design. Results indicated that the obtained experimental data was well fitted to a second-order polynomial equation by using multiple regression analysis, and the optimal extraction conditions were identified as an extraction time of 40 min, ethanol concentration of 60%, temperature of 73.63 °C, and liquid-solid ratio of 29.89 mL/g for the highest predicted yield of astilbin (15.05 mg/g), which was confirmed through validation experiments. In addition, the anti-inflammatory efficiency of astilbin was evaluated in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Results showed that astilbin, at non-cytotoxicity concentrations, significantly suppressed the production of nitric oxide (NO) and tumor necrosis factor-α (TNF-α), as well as the mRNA expression of inducible nitric oxide synthase (iNOS) and TNF-α in LPS-induced RAW 264.7 cells, but did not affect interleukin-6 (IL-6) release or its mRNA expression. These effects may be related to its up-regulation of the phosphorylation of p65, extracellular signal-regulated kinases 1/2 (ERK1/2) and c-Jun N-terminal kinase (JNK).

In this paper, we introduce and investigate composite inertial gradient-based algorithms with a line-search process for solving a variational inequality problem (VIP) with a pseudomonotone and Lipschitz continuous mapping and a common fixed-point problem (CFPP) of finitely many nonexpansive mappings and a strictly pseudocontractive mapping in the framework infinite-dimensional Hilbert spaces. The proposed algorithms are based on an inertial subgradient–extragradient method with the line-search process, hybrid steepest-descent methods, viscosity approximation methods and Mann iteration methods. Under weak conditions, we prove strong convergence of the proposed algorithms to the element in the common solution set of the VIP and CFPP, which solves a certain hierarchical VIP defined on this common solution set.

Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate and fails to correspond to changes in respiratory airflow. Current artificial intelligence image technologies can effectively address this issue. To enhance the accuracy of epiglottis obstruction assessment and replace obstruction degrees with obstruction ratios, this study developed a computer vision system with a deep learning-based method for calculating epiglottis obstruction ratios. The system employs a convolutional neural network, the YOLOv4 model, for epiglottis cartilage localization, a color quantization method to transform pixels into regions, and a region puzzle algorithm to calculate the range of a patient’s epiglottis airway. This information is then utilized to compute the obstruction ratio of the patient’s epiglottis site. Additionally, this system integrates web-based and PC-based programming technologies to realize its functionalities. Through experimental validation, this system was found to autonomously calculate obstruction ratios with a precision of 0.1% (ranging from 0% to 100%). It presents epiglottis obstruction levels as continuous data, providing crucial diagnostic insight for surgeons to assess the severity of epiglottis obstruction in patients.

The heat index, or apparent temperature, was never defined for extreme heat and humidity, leading to the widespread adoption of a polynomial extrapolation designed by the United States National Weather Service. Recently, however, the heat index has been extended to all combinations of temperature and humidity, presenting an opportunity to reassess past heat waves. Here, three-hourly temperature and humidity are used to evaluate the extended heat index over the contiguous United States during the years 1984–2020. It is found that the 99.9th percentile of the daily maximum heat index is highest over the Midwest. Identifying and ranking heat waves by the spatially integrated exceedance of that percentile, the Midwest once again stands out as home to the most extreme heat waves, including the top-ranked July 2011 and July 1995 heat waves. The extended heat index can also be used to evaluate the physiological stress induced by heat and humidity. It is found that the most extreme Midwest heat waves tax the cardiovascular system with a skin blood flow that is elevated severalfold, approaching the physiological limit. These effects are not captured by the National Weather Service’s polynomial extrapolation, which also underestimates the heat index by as much as 10  ∘ C (20  ∘ F) during severe heat waves.

Chinese consumers comprise a unique subculture that exerts a considerable influence on the market and are treated as a collective group by researchers. However, few studies have examined the effects of collectivism and consumer attitudinal attributes on consumer ethics. Although the practice of religion was prohibited in China before economic reforms in the late 1970s, religion remains a major factor that affects the ethical judgment of consumers. The present study, based on the Hunt-Vitell model, examines the influence of culture (collectivism and religion) and personal characteristics (attitude toward business) on consumer ethics. A total of 284 Chinese consumers were surveyed. Structural equation modeling was used to test hypothesized relationships in the research model. The results indicate that collectivism had a significant explanatory power for four dimensions of consumer ethical beliefs: (a) actively benefiting from illegal activities; (b) passively benefiting from questionable activities; (c) actively benefiting from deceptive legal activities; and (d) engaging in no harm and no foul activities. However, consumer attitude toward business significantly explained only the passive dimension of consumer ethics, and religious beliefs significantly explained only the active dimension of consumer ethical beliefs.

In a real Hilbert space, let GSVI and CFPP represent a general system of variational inequalities and a common fixed point problem of a countable family of nonexpansive mappings and an asymptotically nonexpansive mapping, respectively. In this paper, via a new subgradient extragradient implicit rule, we introduce and analyze two iterative algorithms for solving the monotone bilevel equilibrium problem (MBEP) with the GSVI and CFPP constraints, i.e., a strongly monotone equilibrium problem over the common solution set of another monotone equilibrium problem, the GSVI and the CFPP. Some strong convergence results for the proposed algorithms are established under the mild assumptions, and they are also applied for finding a common solution of the GSVI, VIP, and FPP, where the VIP and FPP stand for a variational inequality problem and a fixed point problem, respectively.

To determine the living regions of individuals based on amino acids in fingermark residues and to establish a rapid and accurate regional classification method using machine learning. Methods: A total of 71 fingermark donors from six different provinces in various regions of China were selected. The content of 18 amino acids in their fingermarks was detected using UHPLC-QQQ-MS/MS. Classification models were established using various machine learning algorithms, and the cross-validation accuracy of 72 combinations, including feature engineering, classification algorithms, and optimization algorithms, was compared. Results: UHPLC-QQQ-MS/MS successfully quantified 16 amino acids. Significant differences in the relative content of amino acids were found between the fingermarks from the eastern and western regions of China, as well as among neighboring provinces. The combination of SFS+SVM+BO was identified as the optimal classification model, achieving an accuracy of 90.14 %. Conclusion: The study found regional differences in the relative content of amino acids in fingermarks and established a regional classification method combining UHPLC-QQQ-MS/MS and machine learning. The method developed in this study can be applied to incomplete or distorted fingermarks, and the experimental results can be directly used in police investigations. This research uncovers the multidimensional information carried by fingerprint substances, demonstrating innovation and application value. It not only saves and shortens investigation time and provides investigative leads, but also enables previously unusable physical evidence to play a role again, enhancing the profiling of suspects. •Established a fingermark amino acid testing method using UHPLC-QQQ-MS/MS.•Relative amino acid contents in fingermarks show significant differences across populations from six provinces.•Compared 72 algorithm combinations, with SFS+SVM+BO achieving a classification accuracy of 90.14 %.