Concept Of Agent Research Articles

Mitigation of climate change. Risk and uncertainty research gaps in the specification of mitigation actions

In this perspective paper, we are concerned with the general problem of how to increase the probability of achieving the expected outcomes of climate change mitigation actions. Climate change mitigation actions prevent, limit, reduce, delay or slow the rate of environmental changes produced by greenhouse gas emissions. A mitigation action can fail to achieve its intended outcome or create an adverse outcome elsewhere, which means uncertainty about mitigation outcomes or risk. Thus, mitigation risk can be defined by the deviation from a given mitigation goal and the associated uncertainties. We observe a lack of take-up of crucial concepts associated with risk in the climate change mitigation literature. Next, the concepts of risk, risk perception, risk acceptance and agents’ concerns are sometimes used interchangeably. As discussed in this paper, this has resulted in a lack of research about, for example, critical causes of mitigation failure. This situation means that some crucial knowledge gaps remain unaddressed or little researched. In this paper, we strive to identify those research gaps that need to be addressed in managing mitigation actions. Based on literature on risk, clarifications and distinctions regarding the potential meaning, scope, roles and implications among key concepts, such as risk knowledge, mitigation risk, uncertainty, agents’ concerns, risk perception and risk acceptance are given. A key distinction is that the central concept of concerns is associated with agentsʼ objectives, interests, visions, needs, preferences, norms, criteria or values and is different from risk perception. Following this, the gaps this perspective paper discusses are identified and justified by the analysis of how specialised literature in mitigation of climate change reflects aspects linked to the risk concepts. The discussed gaps entail the identification and operationalisation of agents’ concerns, the lack of knowledge about the influence of risk perception and risk acceptance on the weighting of agents’ concerns, and the impacts of the disparity in power relations among agents involved in mitigation.

In silico evaluation of corneal patch eluting anti-VEGF agents concept

This study introduces a novel approach utilizing a temporary drug-eluting hydrogel corneal patch to prevent neovascularization, alongside a numerical predictive tool for assessing the release and transport kinetics of bevacizumab (BVZ) after the keratoplasty. A key focus was investigating the impact of tear film clearance on the release kinetics and drug transport from the designed corneal patch. The proposed tear drug clearance model incorporates the physiological mechanism of lacrimal flow (tear turnover), distinguishing itself from previous models. Validation against experimental data confirms the model’s robustness, despite limitations such as a 2D axisymmetrical framework and omission of blink frequency and saccadic eye movements potential effects. Analysis highlights the significant influence of lacrimal flow on ocular drug transport, with the corneal patch extending BVZ residence time compared to topical administration. This research sets the stage for exploring multi-layer drug-eluting corneal patches as a promising therapeutic strategy in ocular health.

Multi-agent platform to support trading decisions in the FOREX market

Trading decisions often encounter risk and uncertainty complexities, significantly influencing their overall performance. Recognizing the intricacies of this challenge, computational models within information systems have become essential to support and augment trading decisions. The paper introduces the concepts of trading software agents, investment strategies, and evaluation functions that automate the selection of the most suitable strategy in near real-time, offering the potential to enhance trading effectiveness. This approach holds the promise of significantly increasing the effectiveness of investments. The research also seeks to discern how changing market conditions influence the performance of these strategies, emphasizing that no single agent or strategy universally outperforms the rest. In summary, the overarching objective of this research is to contribute to the realm of financial decision-making by introducing a pragmatic platform and strategies tailored for traders, investors, and market participants in the FOREX market. Ultimately, this endeavor aims to empower people with more informed and productive trading decisions. The contributions of this work extend beyond the theoretical realm, demonstrating a commitment to address the practical challenges faced by traders and investors in real-time decision-making within the financial markets. This multidimensional approach to financial decision support promises to enhance investment effectiveness and contribute to the broader field of algorithmic trading.

Agent in Kurdish language (Bahdini subdialect)

The agent in the Kurdish language, especially in modern grammar, is known as a semantic role, limiting its function to the sentence level. To contradict this view, an attempt has been made in this study to explain the agent at both morphological and syntactic levels. At the morphological level, some derivational bound morphemes play a major role in forming some nouns that carry within themselves the concept of the agent known as agent nouns. Also at the sentence level, in addition to the semantic features of the noun phrase that identify and distinguish the agent role from other roles, it is expected that the verb phrase can contribute greatly in the identification and recognition of the agent. For this purpose, Larson's theory (1988) has been employed to deconstruct the VP-shell into the main verb phrase VP and the light verb phrase (carrier of the concept of agent in the VP). The sentences are analyzed according to Chomsky's minimalist program principles. Research data is collected from the everyday speech of Bahdini dialect speakers. Results show that Larson's theory of deconstructing the VP could be considered the best and only way to analyze the agent in the Bahdini sentence; and the morpheme (ان), in addition to its agentive and causative functions, can also function as a light verb.

Fire, Vulcanus, Archeus, and Alchemy: A Hybrid Close-Distant Reading of Paracelsus’s Thought on Active Agents

The Swiss physician and philosopher Theophrastus von Hohenheim, called Paracelsus (1493-1541) is known for his strong advocacy of medical alchemy. That his natural philosophy was tied to medical alchemy is perhaps uncontroversial, but just how it was so is less straightforward. This article provides an insight into the connection between the two by means of Paracelsus’s concept of active agents, with an emphasis on fire, its master Vulcanus, and the associated term Archeus. I will show how these evolved in the thought of Paracelsus by taking an evolutionary view of his works. The complexity of his views comes to light as a result of employing the method of hybrid reading, which integrates the traditional close reading technique with the much more novel one of distant reading. The latter is part of the emerging field of digital and computational humanities, and involves the analysis of corpora by means of digital tools, including computer programming (Python). In the article, I will attempt to combine distant and close reading and showcase how such hybrid reading approaches may provide new insights into historical corpora.

MODEL OF PROCESS FOR ENSURING FAULT TOLERANCE IN INTERNET OF THINGS NETWORKS

The paper presents a model for providing fault tolerance in the Internet of Things network, describes key concepts, entities and connections, and also defines the main stages and processes that are included in providing fault tolerance. This model is the basis of the functioning of the fault tolerance system. The concept of a fault tolerance system that integrates into existing Internet of Things networks is proposed. The concept of fault tolerance agents is introduced, which make up the basis of the fault tolerance system, and which communicate with each other to ensure the exchange of information about the occurrence of a fault. Two local fault tolerance mechanisms are proposed, which determine the functionality of the agents.

Proof of Concept of Natural and Synthetic Antifouling Agents in Coatings.

Marine biofouling, caused by the deposition and accumulation of marine organisms on submerged surfaces, represents a huge concern for the maritime industries and also contributes to environmental pollution and health concerns. The most effective way to prevent this phenomenon is the use of biocide-based coatings which have proven to cause serious damage to marine ecosystems. Several research groups have focused on the search for new environmentally friendly antifoulants, including marine and terrestrial natural products and synthetic analogues. Some of these compounds have been incorporated into marine coatings and display interesting antifouling activities caused by the interference with the biofilm-forming species as well as by the inhibition of the settlement of macroorganisms. This review highlights the proof-of-concept studies of emerging natural or synthetic antifouling compounds in coatings, from lab-made to commercial ones, performed between 2019 and 2023 and their results in the field or in in vivo laboratorial tests.

Network Information Security Monitoring Under Artificial Intelligence Environment

At present, network attack means emerge in endlessly. The detection technology of network attack must be constantly updated and developed. Based on this, the two stages of network attack detection (feature selection and traffic classification) are discussed. The improved bat algorithm (O-BA) and the improved random forest algorithm (O-RF) are proposed for optimization. Moreover, the NIS system is designed based on the Agent concept. Finally, the simulation experiment is carried out on the real data platform. The results showed that the detection precision, accuracy, recall, and F1 score of O-BA are significantly higher than those of references [17], [18], [19], and [20], while the false positive rate is the opposite (P < 0.05). The detection precision, accuracy, recall, and F1 score of O-RF algorithm are significantly higher than those of Apriori, ID3, SVM, NSA, and O-RF algorithm, while the false positive rate is significantly lower than that of Apriori, ID3, SVM, NSA, and O-RF algorithm (P < 0.05).

Borich 요구도와 The Locus for Focus model을 활용한 대학생의 챗GPT 현 수준 및 중요도 분석

Objectives The purpose of this study is to analyze the perception level of university students regarding ChatGPT using the Borich Needs and The Locus for Focus model. Methods For this purpose, a total of 199 undergraduate students from University A in the metropolitan area were selected as participants, and the research was conducted using SPSS 25.0 and Excel software programs. Statistical significance was analyzed using t-tests to examine the differences between the current level and im-portance in IPA analysis.Borich'sNeeds and The Locus for Focus Model were examined in a 2x2 matrix, divided into four quadrants. Results The key findings of this study are as follows: First, in the IPA analysis, the reinforcement and maintenance area(HH) revealed 2 items related to understanding ChatGPT, 5items related to ChatGPT principles and applica-tions, and 1 item related to data and machine learning. The concentration management area(HL) included 1 item related to ChatGPT principles and applications, and 3 items related to data and machine learning. The low priority area(LL) consisted of 1 item related to understanding ChatGPT, 2 items related to ChatGPT principles and applica-tions, and 4 items related to social impact. The excessive area(LH) was characterized bybiaseddata in the social impact category. Second, it was found that the differences between importance and current level were higher in terms of importance for all items, except for those related to data bias. This difference was statistically significant. The priority order according to Borich's needs includes 'structuredand unstructured data', 'sensorsand perception', 'computer vision', 'concepts of AI agents' and 'conceptsand application learning of machines'. Third, The Locus for Focus model analysis revealed that the HH area consisted of 9 items, including 'speech recognition and language understanding', 'problem solving and exploration', 'representation and deduction of information', 'concepts and ap-plication learning of machines', 'concepts and application of artificial neural networks', 'data attributes', 'structured and unstructured data', 'classification models', and 'machine learning model implementation'. Fourth, a total of 6 items, including 'speech recognition and language understanding', 'representation and deduction of information', 'concepts and application learning of machines', 'concepts and application of artificial neural networks', 'structured and unstructured data', and 'classification models' were consistently rated as high priority and demanded the high-est attention. Conclusions The findings of this study indicate the need to explore ways to apply ChatGPT in education. Furthermore, it proposes a long-term education plan to enhance awareness of the social impact of ChatGPT and provide practical strategies for learning and applying it.

Strengthening AI via ToM and MC dimensions

Theory of Mind (ToM) highlights the social-cognitive ability of the individual to communicate and interact effectively with the members of each social group. Essentially, it is the cornerstone of social knowledge that allows the recognition and understanding of the thoughts, intentions, and feelings of all involved, promoting social interaction and engagement. Metacognition (MC) is a higher mental ability of the biological mind and is characterized by the observation, control, evaluation, differentiation, and readjustment of the cognitive mechanism, aiming at its optimal performance and maintaining the homeostasis of mental, social, and emotional becoming of an organism. The rapid development of technology in recent decades has promoted the development of Artificial Intelligence (AI) intertwined with the need to integrate ToM and MC capabilities, enriching human communication. This paper investigates how the above-described human cognitive functions are involved in the conception and development of an artificial agent and their influence on human society. The conclusions suggest the importance of being able to read beliefs, emotions, and other factors, but also introspection by an intelligent system for social benefit, including the necessary ethical constraints.

The Concept of Akrasia in D. Husak’s Theory of Criminal Responsibility and the Advantages of Its Alternative

This article explores two concepts of akrasia and their relation to the ascription of responsibility in Douglas Husak’s theory of criminal responsibility. Ever since Plato’s Protagoras, the problem of akrasia has been tightly related to the issue of moral knowledge. By using influential texts of ancient and Christian ethics, as well as contemporary research on akratic behavior, this article outlines a different conception of akrasia, one that is based on intrapersonal conflict. In his theory, Husak employs the concept of akrasia related to moral knowledge-ignorance, albeit sourcing its presuppositions from contemporary skeptics rather than from ancient philosophy. Yet, Husak’s theory is not skeptical. The article highlights the main drawbacks of this theory. These drawbacks are grounded in Husak’s conception of the akratic agent and the ascription of responsibility to such an agent. Their analysis reveals the advantages of the concept of akrasia based on intrapersonal conflict for constructing a non-skeptical theory of criminal responsibility.

The Concept of Akrasia in D. Husak’s Theory of Criminal Responsibility and the Advantages of Its Alternative

This article explores two concepts of akrasia and their relation to the ascription of responsibility in Douglas Husak’s theory of criminal responsibility. Ever since Plato’s Protagoras, the problem of akrasia has been tightly related to the issue of moral knowledge. By using influential texts of ancient and Christian ethics, as well as contemporary research on akratic behavior, this article outlines a different conception of akrasia, one that is based on intrapersonal conflict. In his theory, Husak employs the concept of akrasia related to moral knowledge-ignorance, albeit sourcing its presuppositions from contemporary skeptics rather than from ancient philosophy. Yet, Husak’s theory is not skeptical. The article highlights the main drawbacks of this theory. These drawbacks are grounded in Husak’s conception of the akratic agent and the ascription of responsibility to such an agent. Their analysis reveals the advantages of the concept of akrasia based on intrapersonal conflict for constructing a non-skeptical theory of criminal responsibility.

A Novel Strategy to Substantially Reduce the Incidence of Periprosthetic Joint Infection Following Total Hip Arthroplasty with Antimicrobial Agents.

Periprosthetic joint infections (PJI) are devastating complications following total hip arthroplasty (THA) and are the most common reason for revision following primary arthroplasty. Although several devices, techniques, and procedures have been developed to combat this serious complication, there is little consensus as to how to prevent the development of PJI at the time of index arthroplasty. This article reviews the concept and implementation of a novel antimicrobial agent to substantially reduce the incidence of PJI. The regular implementation of this infection prophylaxis should be successful in drastically reducing the rate of PJI following primary THA.

A Systematic Approach to Autonomous Agents

Agents and agent-based systems are becoming essential in the development of various fields, such as artificial intelligence, ubiquitous computing, ambient intelligence, autonomous computing, and intelligent robotics. The concept of autonomous agents, inspired by the observed agency in living systems, is also central to current theories on the origin, development, and evolution of life. Therefore, it is crucial to develop an accurate understanding of agents and the concept of agency. This paper begins by discussing the role of agency in natural systems as an inspiration and motivation for agential technologies and then introduces the idea of artificial agents. A systematic approach is presented for the classification of artificial agents. This classification aids in understanding the existing state of the artificial agents and projects their potential future roles in addressing specific types of problems with dedicated agent types.

The schizophrenic board secretary: Embedded agent between multiple stakeholders and financial misconduct

While agency theory has emphasized the importance of corporate governance in preventing financial misconduct, monitoring bodies do not always function well, especially in transition economies. By integrating the stakeholder-agency perspective with prospect theory, this study provides a new explanation of such dysfunction by introducing an embedded agent concept that manifests in a “schizophrenic” status of agents who need to satisfy multiple stakeholders and their conflicting interests. Drawing on a sample of small-to-medium-sized enterprises (SMEs) listed on the Chinese National Equities and Exchange and Quotations system from 2014 to 2017, it is shown that having a board secretary did not help reduce financial misconduct as the government expected, due to an embedded agent problem. However, their dysfunctional status would largely disappear, and they would perform their monitoring responsibilities if given higher equity shares and stricter subnational regulations, two key contingencies that better align embedded agents’ interests with one group of stakeholders. Overall, this study contributes to the corporate governance literature by introducing the embedded agent concept and its theoretical boundaries, along with a focus on transition economies.

A careful approach to artificial intelligence: the struggles with epistemic responsibility of healthcare professionals

ABSTRACT Machine learning approaches are being developed to contribute to the treatment of patients and the organisation of care. These new approaches are created in complex environments that include data and computational models as well as new practices, roles and competencies. In such settings, individualised conceptions of agents bearing responsibility need rethinking. In response, we elaborate on the concept of epistemic responsibility based on De la Bellacasa’s work on care (Bellacasa, M.P. de la. (2017). Matters of care: Speculative ethics in more than human worlds. University of Minnesota Press). To better understand these complex environments and the dynamics of responsibility, we use an ethnographic approach and followed Dutch healthcare professionals who learned the basics of (supervised) machine learning, while they pursued a project in their organisations during a four-month-long course. The professionals struggled with different interdependencies and this brought responsibility-in-the-making into relief. Rather than seeing (growing) relations and impure entanglements as standing in the way of responsibility, we show how connections are worthy of inquiry. We argue that connections are essential to knowledge and that producing epistemic responsibility means considering these embedded relations. In contrast to calls for control and clarification of machine learning techniques, and warnings that they create irresponsible black boxes, our care approach shows how responsibility-in-the-making reveals opportunities for ethical reflection and action. Our approach attends to how humans and non-humans are engaged in caring, reveals patterns around kinds of responsibility, and points to opportunities for avoiding neglect and irresponsibility.

A methodological approach by capillary electrophoresis coupled to mass spectrometry via electrospray interface for the characterization of short synthetic peptides towards the conception of self-assembled nanotheranostic agents

Nanostructures formed by the self-assembling peptide building blocks are attractive materials for the design of theranostic objects due to their intrinsic biocompatibility, accessible surface chemistry as well as cavitary morphology. Short peptide synthesis and modification are straightforward and give access to a great diversity of sequences, making them very versatile building blocks allowing for the design of thoroughly controlled self-assembled nanostructures. In this work, we developed a new CE-DAD-ESI-MS method to characterize short synthetic amphiphilic peptides in terms of exact sequence and purity level in the low 0.1 mg.mL−1 range, without sample treatment. This study was conducted using a model sequence, described to have pH sensitive self-assembling property. Peptide samples obtained from different synthesis processes (batch or flow, purified or not) were thus separated by capillary zone electrophoresis (CZE). The associated dual UV and MS detection mode allowed to evidence the exact sequence together with the presence of impurities, identified as truncated or non-deprotected sequences, and to quantify their relative proportion in the peptide mixture. Our results demonstrate that the developed CE-DAD-ESI-MS method could be directly applied to the characterization of crude synthetic peptide products, in parallel with the optimization of peptide synthetic pathway to obtain controlled sequences with high synthetic yield and purity, which is crucial for further design of robust peptide based self-assembled nanoarchitectures.

The Study of Fundamental Concepts of Agent and Multi-agent Systems

The concept of n intelligent agent is a concept that is born from the area of artificial intelligence; in fact, a commonly-accepted definition relates the discipline of artificial intelligence with the analysis and design of autonomous entities capable of exhibitin intelligent behavior. From that perspective, it is assumed that an intelligent agent must be able to perceive its environment, reason about how to achieve its objectives, act towards achieving them through the application of some principle of rationality, and interact with other intelligent agents, being artificial or human [1]. Multi-agent systems are a particular case of a distributed system, and its particularity lies in the fact that the components of the system are autonomous and selfish, seeking to satisfy their own objectives. In addition, these systems also stand out for being open systems without a centralized design [2]. One main reason for the great interest and attention that multi-agent systems have received is that they are seen as an enabling technology for complex applications that require distributed and parallel processing of data and operate autonomously in complex and dynamic domains.

Reinforcement Learning as a Path to Autonomous Intelligent Cyber-Defense Agents in Vehicle Platforms

Technological advancement of vehicle platforms exposes opportunities for new attack paths and vulnerabilities. Static cyber defenses can help mitigate certain attacks, but those attacks must generally be known ahead of time, and the cyber defenses must be hand-crafted by experts. This research explores reinforcement learning (RL) as a path to achieve autonomous, intelligent cyber defense of vehicle control networks—namely, the controller area network (CAN) bus. We train an RL agent for the CAN bus using Toyota’s Portable Automotive Security Testbed with Adaptability (PASTA). We then apply the U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory’s methodology for quantitative measurement of cyber resilience to assess the agent’s effect on the vehicle testbed in a contested cyberspace environment. Despite all defenses having similar traditional performance measures, our RL agent averaged a 90% cyber resilience measurement during drive cycles executed on hardware versus 41% for a naïve static timing defense and 98% for the bespoke timing-based defense. Our results also show that an RL-based agent can detect and block injection attacks on a vehicle CAN bus in a laboratory environment with greater cyber resilience than prior learning approaches (1% for convolutional networks and 0% for recurrent networks). With further research, we believe there is potential for using RL in the autonomous intelligent cyber defense agent concept.

Improvement and analysis of toner based on triboelectric

Toner in the laser printer has played a significant role recently, however, the pollution of toner is not optimistic, toner produces ozone in the process of printing, and because of the characteristics of toner insoluble in water, when it is exposed to air, toner which are small particles will easily to enter the human body and keep in the human kidney, which has a great impact on the health. In the article, the experimental model of quantified triboelectricity is proposed according to the physical principle of toner, which uses mercury as a liquid metal to quantify triboelectricity, aiming to obtain the gain and loss ability of different materials, so as to improve the effect of toner. At the same time, the article includes the concept of charge control agents (CCAs), which is a chemical substance that keeps the charge on the surface of the toner to ensure charged on the toner surface, obtains the basic selection conditions that need to be paid attention to in the selection of CCAs materials, aiming to provide the direction of toner improvement. The article provides the direction and idea of toner improvement by proposing the concept of quantifying triboelectricity and CCAs.