Social Commerce and Consumer Intention to Purchase Evidence From Popular Social Networks in Iran

The aim of this study is to investigate the relationship of consumer behavior in s-commerce by proposing a research model that incorporates various factors. To conclude, perceived usefulness and perceived enjoyment have a positive relationship with attitude. The proposed model that examines the mediating effects of attitude on variables. It is reported that perceived usefulness could be mediated by the effect of attitude, however, perceived enjoy fullness doesn\u27t not. As with any study of this nature, a number of limitations have to be pointed out. The first limitation is the issue of generalization. Small sample size collected in this study lead to the poor-fit structural model. Thus, the findings must be interpreted with caution and cannot be conclusively used to generalize to all the consumer behavior towards s-commerce. The second limitation relates to the cross-sectional nature of the current study. Future research to investigate consumer behavior towards s-commerce by utilizing longitudinal methodology can be adopted. Also, variables such as trust could be added to capture more efficient results

Semantically Intelligent Distributed Leader Election (SIDLE) Algorithm for WSAN Part of IoT Systems

This paper introduces the deployment of a group of Wireless Sensor and Actuator Network (WSAN) part of Internet of Thing (IoT) systems in rural regions deployed by a drone dropping sensors and actuators at a certain position as a mesh of a hexagonal form. Nodes are heterogeneous in hardware and functionality thus not all nodes are able to transfer data directly to the base station. Primitive ones are only capable of collecting local data. However, ones that are more sophisticated are equipped with long-range radio telemetry and more computational power. Power optimization is one of the crucial factors in designing WSANs. Total power consumption must be minimized, as sensors are self-managed. It is not feasible to collect sensors on time bases and recharge the batteries. Therefore, energy consumption optimization and harvesting green energy are other factors that are considered. In this regard, protocols are designed in a way to support such requirements. The preprocessed data are first collected and combined by the leaders at each hexagonal cell. Then, the information packets are sent to the head clusters. Consequently, head clusters reprocess the received information and depict a better global view of the zone, using a variety of the received information. Finally, the processed information is sent to the nearest base station or a mobile drone.Comment: The First International Conference of Smart City, 2019, Apadana University, Shiraz, Iran https://www.civilica.com/Paper-SMARTCITYC01-SMARTCITYC01_100.htm

Optimal Robot-Environment Interaction Using Inverse Differential Riccati Equation

An optimal robot-environment interaction is designed by transforming an environment model into an optimal control problem. In the optimal control, the inverse differential Riccati equation is introduced as a fixed-end-point closed-loop optimal control over a specific time interval. Then, the environment model, including interaction force is formulated in a state equation, and the optimal trajectory is determined by minimizing a cost function. Position control is proposed, and the stability of the closed-loop system is investigated using the Lyapunov direct method. Finally, theoretical developments are verified through numerical simulation

Fault-tolerant neuro adaptive constrained control of wind turbines for power regulation with uncertain wind speed variation

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation, and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation, or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust, adaptive, and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme

ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives

This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in topic clusters. Our experiments show that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles

ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics

This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framework for discovering evolving topics. ANTM maintains the temporal continuity of evolving topics by extracting time-aware features from documents using advanced pre-trained Large Language Models (LLMs) and employing an overlapping sliding window algorithm for sequential document clustering. This overlapping sliding window algorithm identifies a different number of topics within each time frame and aligns semantically similar document clusters across time periods. This process captures emerging and fading trends across different periods and allows for a more interpretable representation of evolving topics. Experiments on four distinct datasets show that ANTM outperforms probabilistic dynamic topic models in terms of topic coherence and diversity metrics. Moreover, it improves the scalability and flexibility of dynamic topic models by being accessible and adaptable to different types of algorithms. Additionally, a Python package is developed for researchers and scientists who wish to study the trends and evolving patterns of topics in large-scale textual data