Decision-making Capabilities Research Articles

What legal basis for an EU tax?

Article 311 TFEU is a correct legal basis for the introduction and design of an EU tax, due to the historical and teleological meaning of “own resources”, the capability of decisions to have normative content and the scope of its wording.However, when an EU tax has the dual goal of raising revenues for the EU budget and harmonizing the domestic tax systems of the Member States, there is a need for a supporting legal basis.

Robust and energy-efficient RPL optimization algorithm with scalable deep reinforcement learning for IIoT

The increasing complexity and quantity of the Industrial Internet of Things (IIoT) pose new challenges to the traditional routing protocol for low-power and lossy networks (RPL) in terms of dynamic management, data transmission reliability, and energy efficiency optimization. This paper proposes a scalable deep reinforcement learning (DRL) algorithm with a multi-attention actor double critic model for routing optimization (MADC) to meet the requirements of IIoT for efficient and intelligent routing decisions while improving data transmission reliability and energy efficiency. Specifically, MADC employs the centralized training and decentralized execution (CTDE) learning paradigm to decouple the model’s training and inference tasks, which reduces the difficulty and computational cost of model learning and improves the training efficiency. In addition, a lightweight actor network based on multi-scale convolutional attention mechanism is designed in MADC, which can provide intelligent and real-time decision-making capabilities for resource-constrained nodes with low computational and storage complexities. Moreover, a scalable critic network utilizing multiple attention mechanisms is proposed. It is not only suitable for dynamic and changing network environments but also can more comprehensively and accurately evaluate local observation states, providing more accurate and efficient guidance for model optimization. Furthermore, MADC incorporates a double critic network architecture to mitigate potential overestimation issues during training, thereby ensuring the model’s robustness and reliability. Simulation results demonstrate that MADC outperforms existing RPL optimization algorithms in terms of energy efficiency, data transmission reliability, and adaptability.

Towards sustainable cognitive digital twins: A portfolio management tool for waste mitigation

The rapid integration of Cognitive Digital Twins (CDTs) across various industries has revolutionized operational practices, leading to enhanced efficiency and improved decision-making capabilities. However, this technological advancement is accompanied by significant environmental implications, particularly regarding the management of electronic waste (e-waste) and digital waste. While e-waste primarily concerns the disposal of obsolete hardware, digital waste encompasses inefficiencies, redundancies, and unnecessary consumption of computational resources in the digital environment. This paper emphasizes the importance of understanding the lifecycle impacts of CDTs and advocates for the implementation of sustainable practices in their management. To address these challenges, we introduce Digital Twin Portfolio Management (DTPM) as a systematic framework for optimizing the CDT ecosystem. We also present the Digital Twin Triple Bottom Line as a framework for assessing the technical, economic, and environmental impacts of CDT implementations, ensuring that organizations can identify inefficiencies and align their operations with broader sustainability objectives. The findings elucidate the various types of waste generated by CDTs, establishing a critical link between digital asset management and environmental sustainability. Additionally, the study advocates for improved APM practices tailored to the unique challenges posed by CDTs, contributing valuable insights to the evolving discourse on IT portfolio management. Future research directions are also discussed, including the need for expanded case studies and longitudinal investigations to enhance the generalizability and understanding of CDT lifecycle management.

Data-Driven Agropreneurship (DDA): Empowering Farmers through Predictive Analytics

Data-Driven Agropreneurship (DDA) represents a transformative approach in modern agriculture, utilizing predictive analytics to empower farmers with data-driven decision-making capabilities. This paper explores the significant benefits of adopting DDA, including improved crop yields, optimized resource management, and enhanced market opportunities. Despite these advantages, several challenges impede its widespread adoption, such as data access, technological literacy, and inadequate infrastructure. This paper also discusses potential solutions to overcome these barriers, emphasizing the importance of government support, public-private partnerships, and capacity-building initiatives. By addressing these challenges, stakeholders can foster an environment conducive to the successful implementation of DDA, ultimately leading to a more sustainable, productive, and profitable agricultural sector.

Analysis of the Implementation for the State-of-art AI Techniques in Self-driving

Analysis of the Implementation for the State-of-art AI Techniques in Self-driving

Application of PID Control Technology in Unmanned Aerial Vehicles

Abstract. The rapid advancement of unmanned aerial vehicle (UAV) technology underscores the essential role of PID (proportional-integral-derivative) control in ensuring flight stability, particularly through precise motor speed adjustments. Thus, this paper systematically analyzes the fundamental principles and limitations of traditional PID control, further highlighting its insufficient adaptability to dynamic external conditions, which includes load fluctuations and wind disturbances. In response to these challenges, it explores adaptive control strategies such as self-tuning PID and fuzzy logic PID, points out how these advanced methods can effectively improve flight stability by dynamically adjusting control parameters, and demonstrates their superiority over traditional PID through comparative analysis. In addition, the paper anticipates future developments in UAV control systems, thereby emphasizing the integration of artificial intelligence and machine learning technologies into PID control. And this integration seeks to optimize control strategies and markedly enhance the autonomous decision-making capabilities and adaptability of UAVs. Meanwhile, the paper also predicts the development trend of hybrid control systems, that is, combining PID with other advanced control methods (such as linear quadratic control) to enhance the overall control capabilities of UAVs. In short, it underscores the necessity for continuous innovation and in-depth research in response to the dynamic flight environment and the diverse mission requirements.

Transforming Alzheimer's Digital Caregiving through Large Language Models.

Alzheimer's Disease and Related Dementias (AD/ADRD) present significant caregiving challenges, with increasing burdens on informal caregivers. This study examines the potential of AI-driven Large Language Models (LLMs) in developing digital caregiving strategies for AD/ADRD. The objectives include analyzing existing caregiving education materials (CEMs) and mobile application descriptions (MADs) and aligning key caregiving tasks with digital functions across different stages of disease progression. We analyzed 38 CEMs from the National Library of Medicine's MedlinePlus, along with associated hyperlinked web resources, and 57 MADs focused on AD digital caregiving. Using ChatGPT 3.5, essential caregiving tasks were extracted and matched with digital functionalities suitable for each stage of AD progression, while also highlighting digital literacy requirements for caregivers. The analysis categorizes AD caregiving into 4 stages-Pre-Clinical, Mild, Moderate, and Severe-identifying key tasks, such as behavior monitoring, daily assistance, direct supervision, and ensuring a safe environment. These tasks were supported by digital aids, including memory- enhancing apps, Global Positioning System (GPS) tracking, voice-controlled devices, and advanced GPS tracking for comprehensive care. Additionally, 6 essential digital literacy skills for AD/ADRD caregiving were identified: basic digital skills, communication, information management, safety and privacy, healthcare knowledge, and caregiver coordination, highlighting the need for tailored training. The findings advocate for an LLM-driven strategy in designing digital caregiving interventions, particularly emphasizing a novel paradigm in AD/ADRD support, offering adaptive assistance that evolves with caregivers' needs, thereby enhancing their shared decision-making and patient care capabilities.

Real-Time Milk Quality Control Using Multi-Spectral Sensing and Edge Computing: Advancing On-Site Detection of Milk Components with XGBoost

This study explores the use of edge computing technologies to enhance the quality control processes in the dairy industry. Traditional milk quality control methods can be time-consuming and sometimes inadequate, whereas this new approach offers real-time data processing and rapid decision-making capabilities. The objective of the study is to assess the effectiveness of evaluating various spectral characteristics of milk in predicting critical parameters such as protein and fat content. In this research, a multi-channel sensor capable of collecting spectral data at various wavelengths was utilized. The collected data were processed using advanced machine learning models, where XGBoost and other regression models were assessed for their accuracy in predicting protein and fat content. The findings demonstrate the suitability of this technology for quality control in the dairy industry. The results reveal that edge computing-based systems can determine milk quality more quickly and accurately. This technology holds significant potential for overcoming the challenges faced in milk quality control, particularly in developing countries. This study provides valuable insights into how the use of edge computing can enhance operational efficiency and ensure product quality in the dairy industry. This research represents an important step towards developing more effective quality control mechanisms in the dairy industry and aims to establish a robust foundation for future studies. Recommendations focus on the adaptation of this technology to other food safety applications and its diversification for widespread industrial use.

An analysis of the use of 3D game engine technology in visualising sustainability data

Purpose Responding to calls for accountants to engage with modern technologies and explore data visualisation within a three-dimensional (3D) environment, this study aims to explore whether social and environmental accounting (SEA) data visualisation is a promising use case for 3D game engine technology. Design/methodology/approach Drawing on visual perception and embodiment theories, this study uses photo-elicitation, a qualitative research method, to explore the usefulness of two-dimensional (2D) and 3D visualisations of sustainability information in a 3D virtual environment. This study provides three stimuli: numerical data, 2D visualisations and 3D visualisations, and asks open-ended questions regarding future applications. Twelve semi-structured interviews were conducted with academics, preparers and users of sustainability reports to obtain responses to these stimuli. Findings The key finding is that visualisation of SEA information may indeed be a strong use case for 3D game technology, but only for certain data and for certain audiences. Presenting information within a 3D virtual environment offered enhanced engagement and contextual understanding but reduced navigation speed and data clarity. Participants were enthusiastic about the potential of a museum-like experience, incorporating interactivity and community, but felt that the appropriate audience was more likely to be novices than experts. Practical implications This study suggests that deploying 3D game engine technology can be a powerful tool for presenting sustainability information but requires significant resources. The optimum audience is likely to be novices, and a key design principle is to ensure the virtual environment supports, rather than overwhelms, the information presented within that environment. Originality/value This study introduces a novel application of 3D visualisation technology within the SEA context, offering original insights into its potential to enhance user understanding and decision-making capabilities. This study highlights the technology’s value not as a replacement for traditional reporting but as a supplementary educational tool. The study also provides a novel setting for the photo-elicitation method, demonstrating this approach’s utility in a 3D environment.

Automatic Reading and Reporting Weather Information from Surface Fax Charts for Ships Sailing in Actual Northern Pacific and Atlantic Oceans

This study is aimed to improve the intelligence level, efficiency, and accuracy of ship safety and security systems by contributing to the development of marine weather forecasting. The accurate and prompt recognition of weather fax charts is very important for navigation safety. This study employed many artificial intelligent (AI) methods including a vectorization approach and target recognition algorithm to automatically detect the severe weather information from Japanese and US weather charts. This enabled the expansion of an existing auto-response marine forecasting system’s applications toward north Pacific and Atlantic Oceans, thus enhancing decision-making capabilities and response measures for sailing ships at actual sea. The OpenCV image processing method and YOLOv5s/YOLO8vn algorithm were utilized to make template matches and locate warning symbols and weather reports from surface weather charts. After these improvements, the average accuracy of the model significantly increased from 0.920 to 0.928, and the detection rate of a single image reached a maximum of 1.2 ms. Additionally, OCR technology was applied to retract texts from weather reports and highlighted the marine areas where dense fog and great wind conditions are likely to occur. Finally, the field tests confirmed that this auto and intelligent system could assist the navigator within 2–3 min and thus greatly enhance the navigation safety in specific areas in the sailing routes with minor text-based communication costs.

Capabilities and Application of Blockchain-Based Artificial Intelligence

Technologies have a profound and far-reaching influence on numerous aspects of human life, shaping industries, societal interactions, and economic structures. As the pace of technological advancements accelerates, the convergence of two of the most transformative technologies - blockchain and artificial intelligence (AI) - holds great potential to drive innovation and create unprecedented opportunities for various sectors. This paper delves into the synergy between blockchain and AI, examining the unique advantages that their integration can offer. Blockchain provides a decentralized and secure framework, while AI adds advanced data processing and decision-making capabilities. Together, these technologies can enhance security, transparency, and efficiency in various applications. This research explores real-world examples of how blockchain-based AI systems are being applied across industries and how they illustrate their potential to revolutionize business processes and societal interactions.

Transforming Insights into Action: The Influence of Business Analytics on Corporate Strategy Development

The revolutionary impact of business analytics on corporate strategy in modern firms is investigated in this research paper. Integrating analytics into strategic planning has become essential for gaining a competitive edge in a time of rapid technological innovation and data abundance. The significance of this study is in its capacity to clarify how data-driven decision-making can improve organizational performance and flexibility in a changing marketplace. The main goal of this study is to investigate the effects of employing business analytics in the development of corporate strategies, with a particular emphasis on how these processes affect decision-making and the overall efficacy of strategic initiatives. By analyzing these dynamics, the study hopes to offer firms looking to use analytics for strategic success practical insights. The scope of the study includes a thorough investigation of how business analytics impacts many aspects of corporate strategy, such as risk management, customer relationship management, and operational efficiency. The main issue discussed is the discrepancy between conventional strategic planning techniques and the requirement for a more data-centric strategy that can adapt to changing market conditions. The idea that better strategic results can result from the efficient application of business analytics forms the basis of the theoretical framework that directs this study. Using secondary data sources including case studies, industry reports, and scholarly literature, a qualitative research methodology is used to accomplish these goals. This methodology makes it easier to conduct a comprehensive analysis of current business analytics methods and knowledge in strategic situations. Organizations that successfully incorporate analytics into their strategic planning procedures report better performance metrics and increased decision-making capabilities, according to the main findings of the analysis. Potential biases in secondary data and the difficulty of extrapolating results across several businesses are drawbacks, though. In terms of theory and practice, this study has consequences. Theorists can better grasp how business analytics may transform strategic frameworks, and practitioners can learn about the most effective ways to put data-driven plans into reality. The findings of this study ultimately highlight how important it is for businesses to develop a strong data culture that values analytics as a fundamental part of their strategic decision-making procedures.

Design of a Gomoku AI Based on the Alpha-Beta Pruning Search Algorithm

Abstract. This paper presents the implementation of a Gomoku AI based on the alpha-beta pruning search algorithm and the Negamax algorithm. Gomoku, a traditional board game known for its strategic depth, poses significant challenges in AI development due to the exponential increase in possible moves. The AI leverages the computational efficiency of Alpha-Beta Pruning, which enhances the Negamax algorithm by reducing the number of nodes that need to be evaluated in the game tree. This combination allows for faster decision-making without compromising accuracy. Additionally, a value evaluation function is used to assess board states and guide the AI in selecting optimal moves. In the results the performance of the AI was tested through simulations, demonstrating great performance in move selection and computational efficiency compared to traditional methods. The paper also explores potential improvements, including the integration of reinforcement learning (RL) techniques to further enhance the AI's adaptability and strategic decision-making capabilities.

Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages

Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represented in many African languages. While state-of-the-art Afrocentric pre-trained language models (PLMs) have been developed for various natural language processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain largely unexplored. In this study, we propose a novel approach that combines Afrocentric PLMs with XAI techniques for sentiment analysis. We demonstrate the effectiveness of incorporating attention mechanisms and visualization techniques in improving the transparency, trustworthiness, and decision-making capabilities of transformer-based models when making sentiment predictions. To validate our approach, we employ the SAfriSenti corpus, a multilingual sentiment dataset for South African under-resourced languages, and perform a series of sentiment analysis experiments. These experiments enable comprehensive evaluations, comparing the performance of Afrocentric models against mainstream PLMs. Our results show that the Afro-XLMR model outperforms all other models, achieving an average F1-score of 71.04% across five tested languages, and the lowest error rate among the evaluated models. Additionally, we enhance the interpretability and explainability of the Afro-XLMR model using Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These XAI techniques ensure that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in AI-driven NLP technologies, particularly in the context of African languages.

Tactile-GAT: tactile graph attention networks for robot tactile perception classification

As one of the most important senses in human beings, touch can also help robots better perceive and adapt to complex environmental information, improving their autonomous decision-making and execution capabilities. Compared to other perception methods, tactile perception needs to handle multi-channel tactile signals simultaneously, such as pressure, bending, temperature, and humidity. However, directly transferring deep learning algorithms that work well on temporal signals to tactile signal tasks does not effectively utilize the physical spatial connectivity information of tactile sensors. In this paper, we propose a tactile perception framework based on graph attention networks, which incorporates explicit and latent relation graphs. This framework can effectively utilize the structural information between different tactile signal channels. We constructed a tactile glove and collected a dataset of pressure and bending tactile signals during grasping and holding objects, and our method achieved 89.58% accuracy in object tactile signal classification. Compared to existing time-series signal classification algorithms, our graph-based tactile perception algorithm can better utilize and learn sensor spatial information, making it more suitable for processing multi-channel tactile data. Our method can serve as a general strategy to improve a robot’s tactile perception capabilities.

The Role of Machine Learning in Improving Robotic Perception and Decision Making

Machine learning, specifically through Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL), significantly enhances robotic perception and decision-making capabilities. This research explores the integration of CNNs to improve object recognition accuracy and employs sensor fusion for interpreting complex environments by synthesizing multiple sensory inputs. Furthermore, RL is utilized to refine robots real-time decision-making processes, which reduces task completion times and increases decision accuracy. Despite the potential, these advanced methods require extensive datasets and considerable computational resources for effective real-time applications. The study aims to optimize these machine learning models for better efficiency and address the ethical considerations involved in autonomous systems. Results indicate that machine learning can substantially advance robotic functionality across various sectors, including autonomous vehicles and industrial automation, supporting sustainable industrial growth. This aligns with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 8 (Decent Work and Economic Growth), by promoting technological innovation and enhancing industrial safety. The conclusion suggests that future research should focus on improving the scalability and ethical application of these technologies in robotics, ensuring broad, sustainable impact.

Leveraging artificial intelligence in economics and finance: Enhancing decision-making and market efficiency

Abstract. Artificial Intelligence (AI) has revolutionized the fields of economics and finance by providing advanced tools for decision-making, predictive analysis, and market efficiency. This paper examines the integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment. By enhancing these models with AI, we demonstrate significant improvements in prediction accuracy and decision-making capabilities. Case studies illustrate a 15% improvement in GDP growth prediction accuracy, a 20% reduction in option pricing errors, and a 20% decrease in credit default rates. The paper also discusses the future prospects of AI, including advancements in quantum computing and ethical considerations like data privacy and algorithmic bias. Implementation challenges such as high costs and data integration issues are addressed, providing a comprehensive roadmap for organizations to effectively leverage AI. This study underscores the transformative potential of AI in shaping the future of economic and financial landscapes, driving innovation and operational efficiency.

A trustworthy reinforcement learning framework for autonomous control of a large-scale complex heating system: Simulation and field implementation

Traditional control approaches heavily rely on hard-coded expert knowledge, complicating the development of optimal control solutions as system complexity increases. Deep Reinforcement Learning (DRL) offers a self-learning control solution, proving advantageous in scenarios where crafting expert-based solutions becomes intricate. This study investigates the potential of DRL for supervisory control in a unique and complex heating system within a large-scale university building. The DRL framework aims to minimize energy costs while ensuring occupant comfort. However, the trial-and-error learning approach of DRL raises concerns about the trustworthiness of executed actions, hindering practical implementation. To address this, the study incorporates action masking, enabling the integration of hard constraints into DRL to enhance user trust. Maskable Proximal Policy Optimization (MPPO) is evaluated alongside standard Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC). Simulation results reveal that MPPO achieves comparable energy savings (8% relative to the baseline control) with fewer comfort violations than other methods. Therefore, it is selected among the candidate algorithms and experimentally implemented in the university building over one week. Experimental findings demonstrate that MPPO reduces energy costs while maintaining occupant comfort, resulting in a 36% saving compared to a historical day with similar weather conditions. These results underscore the proactive decision-making capability of DRL, establishing its viability for autonomous control in complex energy systems.

Engaging in cyber hygiene: the role of thoughtful decision-making and informational interventions.

The effectiveness of human-centric cybersecurity largely depends on end-users' adherence to security and privacy behaviors. Understanding and predicting variations in the adoption of these safeguards is crucial for both theoretical advancement and practical application. While existing frameworks are often adapted from health science literature, there is potential to enhance these models by incorporating criminological constructs relevant to online victimization. This study introduce rational choice theory of thoughtfully reflective decision-making (TRDM) into the information security domain. TRDM suggests that variations in cognitive decision-making capabilities influence behavioral outcomes, particularly in the context of security and privacy practices. The study employed a field experiment to test the applicability of TRDM in predicting end-users' engagement in security and privacy behaviors. Participants were exposed to security-related warnings, with the hypothesis that thoughtfully reflective decision-makers would be more likely to adopt robust protective behaviors. Data was collected on participants' responses to these security warnings, as well as their overall adherence to privacy and security practices. The findings support the theoretical framework: individuals exhibiting thoughtfully reflective decision-making tendencies demonstrated a higher likelihood of engaging in privacy and security behaviors. Specifically, participants with higher TRDM scores were more likely to adopt protective behaviors when warned of the consequences of non-compliance. These results indicate that cognitive decision-making capabilities significantly influence the likelihood of engaging in cybersecurity practices. The study challenges the prevailing one-size-fits-all approach to cybersecurity by highlighting the importance of individual differences in cognitive decision-making. Thoughtfully reflective decision-makers are better equipped to adopt preventive security measures, suggesting the need for more tailored interventions in cybersecurity education and risk assessment. This research contributes to the development of sophisticated risk assessment tools aimed at mitigating vulnerabilities and reducing users' susceptibility to digital threats. Incorporating TRDM into information security models provides a more nuanced understanding of user behavior, offering insights into how cognitive processes influence cybersecurity adherence.

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework’s practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.