Real Option, Debt Agency Conflicts and Corporate Investment Decisions

This paper studies enterprise investment decisions under debt agency conflicts by the method of real option. It is different from the existed literatures that this paper considers not only the influence of investment goals alienation on investment decisions without bankruptcy agency, but also analyzes the investment decisions distortion under bankruptcy agency conflicts. The study result shows that enterprises with debt usually exist overinvestment; on the one hand, the overinvestment comes from excessive pursuing of the tax benefits under the investment goals alienation, on the other hand, it comes from the protection for shareholders’ investment risk under strategic default bankruptcy. What’s more, under the lower debt level, the former is the dominant mechanism for investment decisions distortion; while under the higher debt level, the influence of the later is prominent

Drilling for fissures and exploiting common ground in the discourse of oil production: An enhanced eco-discourse analysis, Part 2

This is the second part of a two-part article which proposes an enhanced approach to eco-discourses after weighing the (dis)advantages of mainstream Critical Discourse Analysis (CDA) and Positive Discourse Analysis (PDA). Part I explored the theoretical grounding for an enhanced PDA, introduced the research method and then, based on the adapted analytic framework of Stibbe (2016), undertook a critical analysis of the discourses of Shell Oil Company (SOC). Part II uses the same analytic framework to analyse Greenpeace USA’s (GPU) discourse and compare it to the SOC discourse. The emphasis in Part II is on the exploration of potential fissures in the discourses across difference, and the possible common grounds upon which to design alternative discourses that are empathetic, comprehensible and legitimate to a coalition of social forces. Practically, Part II finds that the two groups use similar discourse strategies, such as salience and framing, but with different orientations. Methodologically, Part II argues that corpus-aided comparative discourse analysis, with a focus on discourse semantics, will facilitate the identification of ‘greenwashing’ strategies that strengthen and stabilize current hegemonic social order; this part also points to avenues of alternative discourses which exploit the inherent contradictions or fissures within that hegemonic order. Theoretically, the paper suggests that within an enhanced Positive Discourse Analysis approach, it is also important to seek out points of convergence between progressive positions and to articulate these within a hybrid, counter-hegemonic discourse that maximizes its potential for uptake, while it destabilizes the prevailing discourses at precisely the fissure points identified

Remote sensing traffic scene retrieval based on learning control algorithm for robot multimodal sensing information fusion and human-machine interaction and collaboration

In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning

A Simple Graphene NH₃ Gas Sensor via Laser Direct Writing.

Ammonia gas sensors are very essential in many industries and everyday life. However, their complicated fabrication process, severe environmental fabrication requirements and desorption of residual ammonia molecules result in high cost and hinder their market acceptance. Here, laser direct writing is used to fabricate three parallel porous 3D graphene lines on a polyimide (PI) tape to simply construct an ammonia gas sensor. The middle one works as an ammonia sensing element and the other two on both sides work as heaters to improve the desorption performance of the sensing element to ammonia gas molecules. The graphene lines were characterized by scanning electron microscopy and Raman spectroscopy. The response and recovery time of the sensor without heating are 214 s and 222 s with a sensitivity of 0.087% ppm-1 for sensing 75 ppm ammonia gas, respectively. The experimental results prove that under the optimized heating temperature of about 70 °C the heaters successfully help implement complete desorption of residual NH₃ showing a good sensitivity and cyclic stability

SurrealDriver: Designing Generative Driver Agent Simulation Framework in Urban Contexts based on Large Language Model

Simulation plays a critical role in the research and development of autonomous driving and intelligent transportation systems. However, the current simulation platforms exhibit limitations in the realism and diversity of agent behaviors, which impede the transfer of simulation outcomes to the real world. In this paper, we propose a generative driver agent simulation framework based on large language models (LLMs), capable of perceiving complex traffic scenarios and providing realistic driving maneuvers. Notably, we conducted interviews with 24 drivers and used their detailed descriptions of driving behavior as chain-of-thought prompts to develop a `coach agent' module, which can evaluate and assist driver agents in accumulating driving experience and developing human-like driving styles. Through practical simulation experiments and user experiments, we validate the feasibility of this framework in generating reliable driver agents and analyze the roles of each module. The results show that the framework with full architect decreased the collision rate by 81.04% and increased the human-likeness by 50%. Our research proposes the first urban context driver agent simulation framework based on LLMs and provides valuable insights into the future of agent simulation for complex tasks.Comment: 12 pages, 8 figure

Resource Management for MEC Assisted Multi-layer Federated Learning Framework

In this paper, a mobile edge computing (MEC) assisted multi-layer architecture is proposed to support the implementation of federated learning in Internet of Things (IoT) networks. In this architecture, when performing a federated learning based task, data samples can be partially offloaded to MEC servers and cloud server rather than only processing the task at the IoT devices. After collecting local model parameters from devices and MEC servers, cloud server makes an aggregation and broadcasts it back to all devices. An optimization problem is presented to minimize the total federated training latency by jointly optimizing decisions on data offloading ratio, computation resource allocation and bandwidth allocation. To solve the formulated NP hard problem, the optimization problem is converted into quadratically constrained quadratic program (QCQP) and an efficient algorithm is proposed based on semidefinite relaxation (SDR) method. Furthermore, the scenario with the constraint of indivisible tasks in devices is considered and an applicable algorithm is proposed to get effective offloading decisions. Simulation results show that the proposed solutions can get effective resource allocation strategy and the proposed multi-layer federated learning architecture outperforms the conventional federated learning scheme in terms of the learning latency performance

Removal of Formaldehyde Using Highly Active Pt/TiO 2

Formaldehyde (HCHO) is one of the major indoor air pollutants. TiO2 supported Pt catalysts were prepared by sol-gel method and used to eliminate HCHO at room temperature without irradiation. The reduced Pt/TiO2 catalyst (denoted as Pt/TiO2-H2) showed much higher activity than that calcined in air (denoted as Pt/TiO2-air). More than 96% of the conversion of HCHO was obtained over 0.5 wt% Pt/TiO2-H2, on which highly dispersed metallic Pt nanoparticles with very small size (~2 nm) were identified. Metallic Pt rather than cationic Pt nanoparticles provide the active sites for HCHO oxidation. Negatively charged metallic Pt nanoparticles facilitate the transfer of charge and oxygen species and the activation of oxygen

The molecular biological characteristics of Vibrio vulnificus isolated in Guangzhou

ObjectiveTo investigate the molecular and biological characteristics of Vibrio vulnificus （V. vulnificus）isolated from Guangzhou.MethodsThirty-eight strains of V. vulnificus were collected from Guangzhou， and whole-genome sequences were obtained. The population structure of V. vulnificus was inferred by utilizing 50 publicly available genome sequences obtained from NCBI. FineSTRUCTURE software was employed for this analysis. Antibiotic resistance and virulence factors were identified using CARD， ResFinder， and VFDB databases.ResultsFour well-supported phylogenetic groups or lineages （L1-L4） were identified， and all genomes of the strains in Guangzhou were classified into L1 （47%） and L2 （53%）. The predominant ST were ST357， ST157， ST136， ST139， ST345， ST303， and so on， which showed regional aggregation. Multiple genome identification of V. vulnificus revealed 11 drug resistance genes： acrF， CRP， catB9， rpoC， ugd， and others. MCR genes related to polymyxin resistance were identified in V. vulnificus for the first time. The species also carries 23 virulence genes in five classes encoding flagellin， type Ⅱ secretory system protein， capsular polysaccharide， RtxA toxin， iron overload， and other virulence-related genes.ConclusionVibrio vulnificus in Guangzhou undergoes highly homologous recombination and carries a variety of antimicrobial resistance genes and virulence-related genes. Therefore， monitoring and management of Vibrio vulnificus should be strengthened

Scalable synthesis of multicomponent multifunctional inorganic core@mesoporous silica shell nanocomposites

Integrating multiple materials with different functionalities in a single nanostructure enables advances in many scientific and technological applications. However, such highly sophisticated nanomaterials usually require complex synthesis processes that complicate their preparation in a sustainable and industrially feasible manner. Herein, we designed a simple general method to grow a mesoporous silica shell onto any combination of hydrophilic nanoparticle cores. The synthetic strategy, based on the adjustment of the key parameters of the sol-gel process for the silica shell formation, allows for the embedment of single, double, and triple inorganic nanoparticles within the same shell, as well as the size-control of the obtained nanocomposites. No additional interfacial adhesive layer is required on the nanoparticle surfaces for the embedding process. Adopting this approach, electrostatically stabilized, small-sized (from 4 to 15 nm) CeO2, Fe3O4, Gd2O3, NaYF4, Au, and Ag cores were used to test the methodology. The mean diameter of the resulting nanocomposites could be as low as 55 nm, with high monodispersity. These are very feasible sizes for biological intervention, and we further observed increased nanoparticle stability in physiological environments. As a demonstration of their increased activity as a result of this, the antioxidant activity of CeO2 cores was enhanced when in core-shell form. Remarkably, the method is conducted entirely at room temperature, atmospheric conditions, and in aqueous solvent with the use of ethanol as co-solvent. These facile and even "green" synthesis conditions favor scalability and easy preparation of multicomponent nanocomposite libraries with standard laboratory glassware and simple benchtop chemistry, through this sustainable and cost-effective fabrication process.This work was financially supported by the National Natural Science Foundation of China (31950410536 to E.C. and 22005221 to M.Z.), the Wuyi University (2018TP010 to E.C., 2018TP011 and 2020FKZX05 to M.Z., and 2019TD02 to J.P.), Guangdong Science and Technology Department (2019A050512006 to E.C.), the Academy of Finland (309374 to J.M.R.), and the Instituto de Salud Carlos III of Spain (PI19/00774 to G.F-V and G.C.), co-financed by FEDER, European Union, “A way of making Europe”