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We prove if B is a cluster-tilted algebra, then B is τB-tilting finite if and only if B is representation-finite.

Magnetic rotation and antimagnetic rotation are exotic rotational phenomena observed in weakly deformed or near-spherical nuclei, which are respectively interpreted in terms of the shears mechanism and two shearslike mechanism. Since their observations, magnetic rotation and antimagnetic rotation phenomena have been mainly investigated in the framework of tilted axis cranking based on the pairing plus quadrupole model. For the last decades, the covariant density functional theory and its extension have been proved to be successful in describing series of nuclear ground-states and excited states properties, including the binding energies, radii, single-particle spectra, resonance states, halo phenomena, magnetic moments, magnetic rotation, low-lying excitations, shape phase transitions, collective rotation and vibrations, etc. This review will mainly focus on the tilted axis cranking covariant density functional theory and its application for the magnetic rotation and antimagnetic rotation phenomena.

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

Optical measurement of mechanical parameters is gaining significant commercial interest in different industry sectors. Torsion, twist and rotation are among the very frequently measured mechanical parameters. Recently, twist/torsion/rotation sensors have become a topic of intense fiber-optic sensor research. Various sensing concepts have been reported. Many of those have different properties and performances, and many of them still need to be proven in out-of-the laboratory use. This paper provides an overview of basic approaches and a review of current state-of-the-art in fiber optic sensors for measurements of torsion, twist and/or rotation.Invited Paper

Negative refraction usually demands complex structure engineering while it is very natural for massless Dirac fermions (MDFs) across the p-n junction (PNJ), this leads to Dirac electron optics. The emergent Dirac materials may exhibit hitherto unidentified phenomenon due to their nontrivial band structures in contrast to the isotropic MDFs in graphene. Here, as a specific example, we explore the negative refraction induced caustics and Veselago focusing of tilted MDFs across 8-Pmmn borophene PNJs. To this aim, we develop a technique to effectively construct the electronic Green's function (GF) in PNJs with arbitrary junction directions. Based on analytical discussions and numerical calculations, we demonstrate the strong dependence of interference pattern on the junction direction. As the junction direction perpendicular to the tilt direction, Veselago focusing or normal caustics (similar to that in graphene) appears resting on the doping configuration of the PNJs, otherwise anomalous caustics (different from that in graphene) occurs which is manipulated by the junction direction and the doping configuration. Finally, the developed GF technique is generally promising to uncover the unique transport of emergent MDFs, and the discovered anomalous caustics makes tilted MDFs potential applications in Dirac electron optics.

In this study, we propose a tilted fiber Bragg grating (TFBG) humidity sensor fabricated using the phase mask method to produce a TFBG that was then etched with five different diameters of 20, 35, 50, 55 and 60 μm, after which piezoelectric inkjet technology was used to coat the grating with graphene oxide. According to the experimental results, the diameter of 20 μm yielded the best sensitivity. In addition, the experimental results showed that the wavelength sensitivity was −0.01 nm/%RH and the linearity was 0.996. Furthermore, the measurement results showed that when the relative humidity was increased, the refractive index of the sensor was decreased, meaning that the TFBG cladding mode spectrum wavelength was shifted. Therefore, the proposed graphene oxide film TFBG humidity sensor has good potential to be an effective relative humidity monitor.

A cosmological model with tilted plane symmetry in the presence of a bulk viscous fluid and heat flow has been the subject of numerous types of enquiry. It was discovered that the cosmological constant∧ decreases with time. For the new number of n=3/2, we see that the expansion scalarθ increases, and the presence of the tilt angle, λ, sinhλ varies from imaginary to real. We also go through the physical and geometrical characteristics of the cosmological model as a potential new approach.

Transverse thermoelectric conversion holds significant potential in addressing complex challenges faced by classical Seebeck/Peltier modules. A promising transverse thermoelectric phenomenon is the anomalous Nernst effect originating from nontrivial band structures in magnetic topological materials. However, the currently reported performance of the anomalous Nernst effect in topological materials, e.g., Co 2 MnGa, remains insufficient for practical thermoelectric applications. Here, we unveil an unconventional availability of the anomalous Nernst effect by integrating magnetic topological materials into artificially tilted multilayers, known to exhibit the structure-induced transverse thermoelectric conversion due to the off-diagonal Seebeck effect. Our experiments reveal that the transverse thermoelectric performance in Co 2 MnGa-based artificially tilted multilayers is improved through the hybrid action of the anomalous Nernst and off-diagonal Seebeck effects, with the magnetization-dependent performance modulation being one order of magnitude greater than the performance achievable with the anomalous Nernst effect alone. This synergy underscores the importance of hybrid transverse thermoelectric conversion and paves a way for advancing thermoelectric applications using magnetic materials. The anomalous Nernst effect is a key for transverse thermoelectric applications. Here, the authors show an intense performance improvement of the anomalous Nernst effect via hybrid actions with the off-diagonal Seebeck effect in artificial materials.

Liquid unidirectional transport exhibits critical applications from water harvesting to microfluidics. Despite extensive progress, implementation of liquid unidirectional transport that is not subjected to the liquid surface tension and injecting velocity also remains a great challenge. Here, a tilted‐sector arrayed tube for excellent liquid unidirectional transport is proposed that applies to a vast width domain of liquid surface tension and injecting velocity. In addition, the transport direction is abnormally against the tilted direction of structure, in stark contrast to the traditional understanding that is along tilted direction. This excellent and unique liquid unidirectional transport is caused by synergistic effects of tilted sectors and tube structures, which induce a unique 3D liquid propagation mode as well as a large Laplace pressure asymmetry between the front and rear sides of the liquid. Moreover, the antigravity climbing, circuit isolating, and chemical reaction controlling can be achieved based on the excellent liquid unidirectional transport. It is envisioned that the design can be extensively applied in microfluidics, lab‐on‐a‐chip devices, and biochemistry microreactors. A tilted‐sector arrayed tube for excellent liquid unidirectional transport is proposed that can operate liquid over a vast width domain of liquid surface tension and injecting velocity. These outstanding findings extend the practical applications of liquid unidirectional transport in microfluidic and biotechnological systems.

In this article, we develop a Bayesian semiparametric analysis of moment condition models by casting the problem within the exponentially tilted empirical likelihood (ETEL) framework. We use this framework to develop a fully Bayesian analysis of correctly and misspecified moment condition models. We show that even under misspecification, the Bayesian ETEL posterior distribution satisfies the Bernstein-von Mises (BvM) theorem. We also develop a unified approach based on marginal likelihoods and Bayes factors for comparing different moment-restricted models and for discarding any misspecified moment restrictions. Computation of the marginal likelihoods is by the method of Chib ( 1995 ) as extended to Metropolis-Hastings samplers in Chib and Jeliazkov in 2001 . We establish the model selection consistency of the marginal likelihood and show that the marginal likelihood favors the model with the minimum number of parameters and the maximum number of valid moment restrictions. When the models are misspecified, the marginal likelihood model selection procedure selects the model that is closer to the (unknown) true data-generating process in terms of the Kullback-Leibler divergence. The ideas and results in this article broaden the theoretical underpinning and value of the Bayesian ETEL framework with many practical applications. The discussion is illuminated through several examples. Supplementary materials for this article are available online.