Measuring the reactivity of intelligent agent programs

The booming of intelligent agent technology over past few decades brings a surging number of agent applications in various areas. There also have a large number of designs as well as programming languages been introduced in the literature in the agent oriented area. However, very little work has been dedicated to define quality measures for the development of an agent-based system. Previous efforts mostly focus on adopting classical measures such as using coupling (degree of program dependency) and cohesion (degree of function relationship in single module) to measure the quality of agent programs. I maintain that its time to work on a set of software quality measures that are specific to the distinct characteristics of agent-based systems. In this thesis, two methods are purposed to measure the reactivity of agent systems, which provide indications on how agent systems respond to changes in their environment in a timely fashion. A prototype tool is developed integrated with Jason, a well-known agent-oriented programming platform, to calculate reactivity of each agent in agent system based on their plan libraries, and conducted several experiments to demonstrate the reliability of reactivity measure. In addition, an agent behavioural profile is introduced which is an overview of relationships of actions in agent plan library. Based on agent behavioural profile, definitions of agent behavioral profile identity, entailment as well as preservation were proposed, which ensure original agent\u27s behaviours could be preserved while performing reactivity enhancement

Adsorption behavior of silica nanofluid on coal and its injection enhancement mechanism

Coal seam water injection has proven effective in mitigating coal and gas outburst disasters in mines. However, its drawbacks, including the poor wettability of coal seams and the susceptibility to filtration loss of injected water, have led to low construction efficiency and uneven control outcomes. In this study, we propose a novel approach to address these issues by utilizing a water-based silica nanofluid to alter surface wettability. The four-stage deposition process of nanoparticles on the coal surface is identified, and the time-varying behaviour of modified coal wettability is revealed under the influence of key parameters, such as particle concentration. These findings provide a foundation for the application of nanofluids in modifying the wettability of reservoirs

ftp ejde.math.txstate.edu (login: ftp) PYRAMIDAL CENTRAL CONFIGURATIONS AND PERVERSE SOLUTIONS

Abstract. For n-body problems, a central configuration (CC) plays an important role. In this paper, we establish the relation between the spatial pyramidal central configuration (PCC) and the planar central configuration. We prove that the base of PCC is also a CC and we also prove that for some given conditions a planar CC can be extended to a PCC. In particular, if the pyramidal central configuration has a regular polygon base, then the masses of base are equal and the distance between the top vertex and the base is fixed and the mass of the top vertex is selective. Furthermore, the pyramidal central configuration gives rise to an example of a perverse solution in R3

RS5M: A Large Scale Vision-Language Dataset for Remote Sensing Vision-Language Foundation Model

Pre-trained Vision-Language Foundation Models utilizing extensive image-text paired data have demonstrated unprecedented image-text association capabilities, achieving remarkable results across various downstream tasks. A critical challenge is how to make use of existing large-scale pre-trained VLMs, which are trained on common objects, to perform the domain-specific transfer for accomplishing domain-related downstream tasks. In this paper, we propose a new framework that includes the Domain Foundation Model (DFM), bridging the gap between the General Foundation Model (GFM) and domain-specific downstream tasks. Moreover, we present an image-text paired dataset in the field of remote sensing (RS), RS5M, which has 5 million RS images with English descriptions. The dataset is obtained from filtering publicly available image-text paired datasets and captioning label-only RS datasets with pre-trained VLM. These constitute the first large-scale RS image-text paired dataset. Additionally, we tried several Parameter-Efficient Fine-Tuning methods on RS5M to implement the DFM. Experimental results show that our proposed dataset are highly effective for various tasks, improving upon the baseline by $8 \% \sim 16 \%$ in zero-shot classification tasks, and obtaining good results in both Vision-Language Retrieval and Semantic Localization tasks. \url{https://github.com/om-ai-lab/RS5M}Comment: RS5M dataset v

Manipulating dc currents with bilayer bulk natural materials

The principle of transformation optics has been applied to various wave phenomena (e.g., optics, electromagnetics, acoustics and thermodynamics). Recently, metamaterial devices manipulating dc currents have received increasing attention which usually adopted the analogue of transformation optics using complicated resistor networks to mimic the inhomogeneous and anisotropic conductivities. We propose a distinct and general principle of manipulating dc currents by directly solving electric conduction equations, which only needs to utilize two layers of bulk natural materials. We experimentally demonstrate dc bilayer cloak and fan-shaped concentrator, derived from the generalized account for cloaking sensor. The proposed schemes have been validated as exact devices and this opens a facile way towards complete spatial control of dc currents. The proposed schemes may have vast potentials in various applications not only in dc, but also in other fields of manipulating magnetic field, thermal heat, elastic mechanics, and matter waves