DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Since semantic trajectories can discover more semantic meanings of a user\u27s interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method

Molecular Cloning and Characterization of Juvenile Hormone Acid Methyltransferase in the Honey Bee, Apis mellifera, and Its Differential Expression during Caste Differentiation

Juvenile hormone acid methyltransferase (JHAMT) is an enzyme involved in one of the final steps of juvenile hormone biosynthesis in insects. It transfers a methyl group from S-adenosyl-L-methionine (SAM) to the carboxyl group of either farnesoic acid (FA) or JH acid (JHA). Several genes coding for JHAMT have been cloned and characterized from insects from different orders, and they have been shown to play critical roles in metamorphosis and reproduction. However, the significance of JHAMT in Hymenopteran insects is unknown. We used RACE amplification method to clone JHAMT cDNA from the honey bee, Apis mellifera (AmJHAMT). The full length cDNA of AmJHAMT that we cloned is 1253bp long and encodes a 278-aa protein that shares 32-36% identity with known JHAMTs. A SAM-binding motif, conserved in the SAM-dependent methyltransferase (SAM-MT) superfamily, is present in AmJHAMT. Its secondary structure also contains a typical SAM-MT fold. Most of the active sites bound with SAM and substrates (JHA or FA) are conserved in AmJHAMT as in other JHAMT orthologs. Phylogenetic analysis clustered AmJHAMT with the other orthologs from Hymenoptera to form a major clade in the phylogenetic tree. Purified recombinant AmJHAMT protein expressed in E. coli was used to produce polyclonal antibodies and to verify the identity of AmJHAMT by immunoblotting and mass spectrometry. Quantitative RT-PCR and immunoblotting analyses revealed that queen larvae contained significantly higher levels of AmJHAMT mRNA and protein than worker larvae during the periods of caste development. The temporal profiles of both AmJHAMT mRNA and protein in queens and workers showed a similar pattern as the JH biosynthesis. These results suggest that the gene that we cloned codes for a functional JHAMT that catalyzes the final reactions of JH biosynthesis in honey bees. In addition, AmJHAMT may play an important role in honey bee caste differentiation.The research was supported by the earmarked funds for Modern Agro-industry Technology Research System (No.CARS-45-KXJ3) and National Natural Science Foundation of China (No.30571409) to SKS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A scaling law for distinct electrocaloric cooling performance in low-dimensional organic, relaxor and anti-ferroelectrics

Electrocaloric (EC) materials show promise in eco-friendly solid-state refrigeration and integrable on-chip thermal management. While direct measurement of EC thin-films still remains challenging, a generic theoretical framework for quantifying the cooling properties of rich EC materials including normal-, relaxor-, organic- and anti-ferroelectrics is imperative for exploiting new flexible and room-temperature cooling alternatives. Here, we present a versatile theory that combines Master equation with Maxwell relations and analytically relates the macroscopic cooling responses in EC materials with the intrinsic diffuseness of phase transitions and correlation characteristics. Under increased electric fields, both EC entropy and adiabatic temperature changes increase quadratically initially, followed by further linear growth and eventual gradual saturation. The upper bound of entropy change (∆Smax) is limited by distinct correlation volumes (V cr ) and transition diffuseness. The linearity between V cr and the transition diffuseness is emphasized, while ∆Smax = 300 kJ/(K.m3) is obtained for Pb0.8Ba0.2ZrO3. The ∆Smax in antiferroelectric Pb0.95Zr0.05TiO3, Pb0.8Ba0.2ZrO3 and polymeric ferroelectrics scales proportionally with V cr −2.2, owing to the one-dimensional structural constraint on lattice-scale depolarization dynamics; whereas ∆Smax in relaxor and normal ferroelectrics scales as ∆Smax ~ V cr −0.37, which tallies with a dipolar interaction exponent of 2/3 in EC materials and the well-proven fractional dimensionality of 2.5 for ferroelectric domain walls

Network modelling reveals the mechanism underlying colitis-associated colon cancer and identifies novel combinatorial anti-cancer targets

The connection between inflammation and tumourigenesis has been well established. However, the detailed molecular mechanism underlying inflammation-associated tumourigenesis remains unknown because this process involves a complex interplay between immune microenvironments and epithelial cells. To obtain a more systematic understanding of inflammation-associated tumourigenesis as well as to identify novel therapeutic approaches, we constructed a knowledge-based network describing the development of colitis-associated colon cancer (CAC) by integrating the extracellular microenvironment and intracellular signalling pathways. Dynamic simulations of the CAC network revealed a core network module, including P53, MDM2, and AKT, that may govern the malignant transformation of colon epithelial cells in a pro-tumor inflammatory microenvironment. Furthermore, in silico mutation studies and experimental validations led to a novel finding that concurrently targeting ceramide and PI3K/AKT pathway by chemical probes or marketed drugs achieves synergistic anti-cancer effects. Overall, our network model can guide further mechanistic studies on CAC and provide new insights into the design of combinatorial cancer therapies in a rational manner

Gongsun Longzi’s “form”: Minimal word meaning

Inspired by Gongsun Longzi’s “form-naming” idea about word meaning, this paper argues that 1) the internal lexicon contains only the list of word-meaning pairs, with no additional information either as part of word meaning or as a structural level above it; 2) the meaning of word is a minimal C-Form, the identifying conceptual meaning that individuates a concept; 3) C-Form is the interface between word meaning and concept meaning; and 4) a sentence has a minimal semantic content, consisting of the minimal meanings of the words composing it, which is propositional and truth-evaluable, and contextual elements contribute nothing to the meaning of language expressions. This paper adheres to semantic minimalism, believing meanwhile that meaning holism helps in semantics inquiry, since reflection on language meaning differs from language meaning itself. 

Efficient Frozen Gaussian Sampling Algorithms for Nonadiabatic Quantum Dynamics at Metal Surfaces

In this article, we propose a Frozen Gaussian Sampling (FGS) algorithm for simulating nonadiabatic quantum dynamics at metal surfaces with a continuous spectrum. This method consists of a Monte-Carlo algorithm for sampling the initial wave packets on the phase space and a surface-hopping type stochastic time propagation scheme for the wave packets. We prove that to reach a certain accuracy threshold, the sample size required is independent of both the semiclassical parameter $\varepsilon$ and the number of metal orbitals $N$, which makes it one of the most promising methods to study the nonadiabatic dynamics. The algorithm and its convergence properties are also validated numerically. Furthermore, we carry out numerical experiments including exploring the nuclei dynamics, electron transfer and finite-temperature effects, and demonstrate that our method captures the physics which can not be captured by classical surface hopping trajectories.Comment: 41 pages, 10 figure

Multiscale lubrication simulation based on fourier feature networks with trainable frequency

Rough surface lubrication simulation is crucial for designing and optimizing tribological performance. Despite the growing application of Physical Information Neural Networks (PINNs) in hydrodynamic lubrication analysis, their use has been primarily limited to smooth surfaces. This is due to traditional PINN methods suffer from spectral bias, favoring to learn low-frequency features and thus failing to analyze rough surfaces with high-frequency signals. To date, no PINN methods have been reported for rough surface lubrication. To overcome these limitations, this work introduces a novel multi-scale lubrication neural network architecture that utilizes a trainable Fourier feature network. By incorporating learnable feature embedding frequencies, this architecture automatically adapts to various frequency components, thereby enhancing the analysis of rough surface characteristics. This method has been tested across multiple surface morphologies, and the results have been compared with those obtained using the finite element method (FEM). The comparative analysis demonstrates that this approach achieves a high consistency with FEM results. Furthermore, this novel architecture surpasses traditional Fourier feature networks with fixed feature embedding frequencies in both accuracy and computational efficiency. Consequently, the multi-scale lubrication neural network model offers a more efficient tool for rough surface lubrication analysis