Interpolation and extrapolation methods for WLAN-based positioning

WLAN-based positioning is obtaining more and more attention in the research field no-wadays. In order to create better Location Based Services (LBSs), the demand to achieve higher user location accuracy is increasing. This thesis aims at studying the ef-fect of different interpolation and extrapolation methods in the WLAN-based indoor positioning, based on collected WLAN data. Depending on the embraced positioning method, there are various errors in WLAN-based positioning, such as calibration error, measurement errors, shadowing, etc. The motivation of this work came from trying to decrease the positioning error in the ab-sence of complete information about the indoor environment. This can be done by using interpolation and extrapolation methods, which are widely used in image processing nowadays. However, they are also an available and efficient way to deal with WLAN-based positioning studies. Among interpolation methods, Delaunay triangulation can partly avoid introducing dis-tortions in the measurement databases. Therefore, it makes sense to investigate triangula-tion based methods and to study their usefulness in the WLAN context. Practically, it is very hard to extrapolate appropriately and the implementation of the extrapolation is much more complex than the one of the interpolation. Thus in this thesis, simple extrapo-lation methods have been performed. The results here are based on measurement data. The performance of each method is analyzed in terms of the error between the received signal strengths (RSS) coming from the measurements and the RSS obtained through interpolation and extrapolation. WLAN data was collected from several buildings of Tampere University of Technology. Results show that extrapolation methods may increase the RSS estimation error some-times because it is very hard to predict the outside range. However, with more accurate extrapolation, the error would decrease. The performances of natural neighbor, linear and cubic interpolation are similar. The highest impact on RSS estimation comes from the extrapolation

Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis

Game theory, as an analytical tool, is frequently utilized to analyze human behavior in social science research. With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs as substitutes for humans in game experiments, enabling social science research. However, despite numerous empirical researches on the combination of LLMs and game theory, the capability boundaries of LLMs in game theory remain unclear. In this research, we endeavor to systematically analyze LLMs in the context of game theory. Specifically, rationality, as the fundamental principle of game theory, serves as the metric for evaluating players' behavior -- building a clear desire, refining belief about uncertainty, and taking optimal actions. Accordingly, we select three classical games (dictator game, Rock-Paper-Scissors, and ring-network game) to analyze to what extent LLMs can achieve rationality in these three aspects. The experimental results indicate that even the current state-of-the-art LLM (GPT-4) exhibits substantial disparities compared to humans in game theory. For instance, LLMs struggle to build desires based on uncommon preferences, fail to refine belief from many simple patterns, and may overlook or modify refined belief when taking actions. Therefore, we consider that introducing LLMs into game experiments in the field of social science should be approached with greater caution.Comment: AAAI 202

Self-Hint Prompting Improves Zero-shot Reasoning in Large Language Models via Reflective Cycle

Chain-of-Thought (CoT) has brought a fresh perspective to improve the reasoning ability of large language models (LLMs). To relieve the burden of manual design in CoT, Zero-shot CoT has pioneered a direct interaction with LLMs. Based on it, researchers attempt to optimize reasoning paths through various prompting approaches like reflection, selection, and planning. However, few studies have focused on the possibility of combining all these strategies through a cognitive theory. Inspired by experiential learning, this paper proposes a new zero-shot prompting method based on Kolb's reflective cycle, named Self-Hint prompting. Specifically, Self-Hint prompting introduces an automated iterative interaction approach to simulate the conscious reflection process, which uses intermediate observations as hints to guide LLMs. We have conducted comprehensive experiments on various math reasoning benchmarks. The empirical results on GPT models demonstrate the effectiveness of our method. Proposed Self-Hint prompting consistently outperforms other zero-shot baselines

Computational understanding of the structural and electronic properties of the GeS-graphene contact

Two-dimensional (2D) metal-semiconductor junctions have shown significant potential for nanoelectronic and optoelectronic applications. Herein, the structural and electronic properties of a germanium monosulfide/graphene (GeS/G) van der Waals (vdW) heterostructure were explored using first-principles calculations. It was discovered that the structural rigidity and mechanical anisotropy of GeS could be significantly improved by loading graphene. In addition, the intrinsic characteristics of the atomic layer GeS and graphene were well preserved, and the formation of a p-type Schottky contact in the equilibrium state was demonstrated; moreover, the Schottky barrier height of the interface was sensitive to the external condition and could be reduced to zero via applying normal strain or a perpendicular electric field. These insightful results pave the way for experimental research and the design of other 2D nanomaterial-based electronic and optoelectronic devices

Time-Enabled and Verifiable Secure Search for Blockchain-Empowered Electronic Health Record Sharing in IoT

The collection and sharing of electronic health records (EHRs) via the Internet of Things (IoT) can enhance the accuracy of disease diagnosis. However, it is challenging to guarantee the secure search of EHR during the sharing process. The advent of blockchain is a promising solution to address the issues, owing to its remarkable features such as immutability and anonymity. In this paper, we propose a novel blockchain-based secure sharing system over searchable encryption and hidden data structure via IoT devices. EHR ciphertexts of data owners are stored in the interplanetary file system (IPFS). A user with proper access permissions can search for the desired data with the data owner’s time-bound authorization and verify the authenticity of the search result. After that, the data user can access the relevant EHR ciphertext from IPFS using a symmetric key. The scheme jointly uses searchable encryption and smart contract to realize secure search, time control, verifiable keyword search, fast search, and forward privacy in IoT scenarios. Performance analysis and proof demonstrate that the proposed protocol can satisfy the design goals. In addition, performance evaluation shows the high scalability and feasibility of the proposed scheme

A Multivariate Load Trading Optimization Method for Energy Internet Based on LSTM and Gaming Theory

Energy Internet is a complex nonlinear system. There are many stakeholders in the load trading market, which is usually regarded as a multi-player gaming. Although gaming theory has been introduced to solve Multivariate Load trading problems, different conditions should be considered to accurately optimize the multivariate load trading problem. For example, the selling side needs to reduce the reserve capacity and improve profits, but the consumer side needs to reduce costs and minimize the impact on its own electricity consumption. These contradictory conditions require multiple Nash equilibrium to achieve obviously. To address this issue, a unified architecture of the power system cloud trading is constructed in this paper, which is combined with the multiple load classification of the power system. In addition, according to the power market operation mechanism, a price-guided multivariate load trading game strategy is designed. More importantly, a multivariate load trading optimization method based on LSTM (Long Short-Term Memory) and gaming theory is proposed in this work. LSTM is introduced for real time prediction, which can be combined with the game theory for strategy searching. The global stability and optimal solution theory prove the feasibility of the proposed neural network, and finally the effectiveness of the proposed method is verified by using numerical simulation

Multi‐round load control method considering the safe electricity consumption for industrial users based on situational awareness

Abstract This paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here

Tear trough deformity: different types of anatomy and treatment options

Aim : To explore the efficacy of tear trough deformity treatment with the use of hyaluronic acid gel or autologous fat for soft tissue augmentation and fat repositioning via arcus marginalis release. Material and methods : Seventy-eight patients with the tear trough were divided into three groups. Class I has tear trough without bulging orbital fat or excess of the lower eyelid skin. Class II is associated with mild to moderate orbital fat bulging, without excess of the lower eyelid skin. Class III is associated with severe orbital fat bulging and excess of the lower eyelid skin. Class I or II was treated using hyaluronic acid gel or autologous fat injections. Class III was treated with fat repositioning via arcus marginalis release. The patients with a deep nasojugal groove of class III were treated with injecting autologous fat into the tear trough during fat repositioning lower blepharoplasty as a way of supplementing the volume added by the repositioned fat. Results : Seventy-eight patients with tear trough deformity were confirmed from photographs taken before and after surgery. There were some complications, but all had complete resolution. Conclusions : Patients with mild to moderate peri-orbital volume loss without severe orbital fat bulging may be good candidates for hyaluronic acid filler or fat grafting alone. However, patients with more pronounced deformities, severe orbital fat bulging and excess of the lower eyelid skin are often better served by fat repositioning via arcus marginalis release and fat grafting

Enhanced stability of lead-free double perovskite Cs2Na1-xBi1-xMn2xCl6 microcrystals and their optoelectronic devices under high humidity environment by SiO2 encapsulation

The current studies on lead-free double perovskites (DPs) have mainly focused on the optimization of their optical properties, while less attention has been paid to their optical stability under high humidity environment. However, optoelectronic devices will inevitably be exposed to high humidity environment in practical applications so that investigating the optical stability of lead-free DPs under high humidity environment will be especially important for its subsequent development. We prepared lead-free DP Cs2Na1−xBi1−xMn2xCl6 microcrystals (MCs) with different Mn2+ content by the solution method at room temperature, where the photoluminescence quantum yield (PLQY) of Cs2Na0.8Bi0.8Mn0.4Cl6 MCs achieved a maximum of 22.3% with 20% doping of Mn2+, subsequently tested for stability under high humidity environment. The results indicated that the structure and fluorescence properties of Cs2Na0.8Bi0.8Mn0.4Cl6 MCs were destroyed after being contacted with water molecules and that Cs2Na0.8Bi0.8Mn0.4Cl6 MCs was subsequently restored again by using hydrochloric acid solution, but its fluorescence was not able to reach initial intensity, which severely affected its application under high humidity environment. Therefore, the encapsulation of Cs2Na0.8Bi0.8Mn0.4Cl6 MCs using water stable and highly transparent SiO2 was proposed to obtain Cs2Na0.8Bi0.8Mn0.4Cl6@SiO2 core-shell MCs, its optical properties were unaffected after stored in aqueous solution for 10 days. The assembled light emitting diode devices kept constant illumination output in harsh environment of 85% relative humidity, which laid a solid foundation for the subsequent development of lead-free DP optoelectronic devices

Enhanced stability of lead-free double perovskite Cs2AgInxBi1-xCl6 crystals under a high humidity environment by surface capping treatment

Lead-free double perovskite (DP) crystals have received considerable attention for solving the toxicity and instability issues of traditional lead-based perovskites. However, recent studies have focused mainly on the improved optical performance of lead-free DP crystals, with less attention paid to their optical stability under high humidity environments. In this study, ligand-free lead-free DP Cs2AgIn0.93Bi0.07Cl6 nanocrystals (NCs) with excellent optical properties were prepared as research objects, and the larger surface area of the NCs caused them to be rapidly transformed into other phases after contact with water molecules, accompanied by a complete loss of optical properties, which significantly limited their application under high humidity environments. Therefore, surface capping treatment for Cs2AgIn0.93Bi0.07Cl6 NCs with an oleic acid (OA) ligand and polyvinylidene difluoride (PVDF) was proposed to achieve Cs2AgIn0.93Bi0.07Cl6 NCs-OA and Cs2AgIn0.93Bi0.07Cl6/PVDF composite films (CFs) with high water stability. In particular, the optically outstanding Cs2AgIn0.93Bi0.07Cl6/PVDF CFs exhibited stable fluorescence emission even after being stored in water solution for 10 days, and the assembled LED device operated well for a long time under 85% relative humidity (RH), which will bring a new dawn for the commercialization of next-generation lead-free DP optoelectronic devices