Search Results (17)

Dynamic scheduling systems often rely on structured domain knowledge for real-time optimization and decision making. However, much of this knowledge originates from unstructured text, such as operational manuals, maintenance logs, and expert records. Extracting machine readable decision categories from such raw corpora without manual intervention remains a significant challenge. To address this issue, we propose a framework using large language models (LLMs) that automatically discovers latent semantic dimensions from unstructured text without any predefined schema. The LLM performs unsupervised semantic clustering via hierarchical prompts, autonomously inducing concept categories. We validate our method on Traditional Chinese Medicine (TCM), a domain characterized by highly nested, multi-dimensional knowledge structures. This makes it a challenging and representative benchmark for testing the generalizability of knowledge discovery methods. From 12,163 TCM herb records (64,107 independent short sentences), the DeepSeek-V3.2 model with batch-wise clustering generates 458 raw labels. These labels are further merged into 22 broad semantic categories. The resulting groups can be interpreted as potential decision dimensions or state abstractions for knowledge driven optimization systems. For evaluation, we adopt a dual approach: (1) semantic cosine similarity computed by Qwen3-Embedding-8B shows that 19 concepts achieve an average internal coherence above 60%; (2) domain experts rate the reasonableness of sampled concepts on a 1 to 5 scale, yielding an average score of 4.64. Experimental results demonstrate that our method autonomously induces high quality, interpretable concept taxonomies from large-scale unstructured text. We further validate the transferability of the method on an aircraft assembly manual, where it induces 12 semantically coherent concepts (average example coherence 59.6%) that align well with a domain ontology. This work offers a transferable paradigm for integrating unstructured domain knowledge into intelligent scheduling and optimization frameworks, with potential implications for dynamic scheduling systems. Full article

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots. Full article

In flood disaster emergency decision-making, the dynamic changes in expert trust relationships directly affects the efficiency of reaching a decision consensus. This paper constructs a dynamic evolution model of expert trust relationships in flood disaster emergency decision-making from the perspective of preference distance: the initial trust matrix and weights of experts based on four dimensions including cooperation intensity, social relations, background similarity, and subjective initial trust; the cognitive trust is quantified by using the intuitionistic fuzzy Hamming distance, and the trust relationship is dynamically update through the exponential fusion method; the Louvain community discovery algorithm is introduce to achieve dynamic clustering of experts and weight updates of experts in combination with the dynamic changes in trust relationships; and a consensus feedback adjustment mechanism is designed to optimize the preferences of experts with lower consensus. At the same time, the dynamic trust model is verified by combining a flood disaster case. Case validation shows that the model completes consensus iteration in just four rounds, with the maximum increase in cognitive trust due to opinion convergence reaching 0.18 during the evolution process. The model effectively captures changes in trust among experts during decision-making, improving consensus convergence speed while ensuring that the final solution aligns with the comprehensive considerations required in emergency scenarios. This study provides a quantitative tool for large-group decision-making in flood emergencies under high-pressure, information-poor environments; one that balances dynamic trust evolution with efficient consensus building. Full article

Artificial intelligence (AI) applications have attracted widespread attention and have proven to be highly successful in understanding messages across various dimensions. These applications have the potential to assist astronomers in exploring the massive amounts of astronomical data. In fact, the integration of AI techniques with astronomy began some time ago, significantly advancing our understanding of the universe by aiding in exoplanet discovery, galaxy morphology classification, gravitational wave event analysis, and more. In particular, AI is now recognized as a crucial component in time-domain astronomy, particularly given the rapid evolution of targeting transients and the increasing number of candidates detected by powerful surveys. A notable success is SN 2023tyk, the first transient discovered and spectroscopically classified without human inspection, an achievement made even more remarkable given that it was identified by the Zwicky Transient Facility, which detects millions of alert sources every night. There is no doubt that AI will play a crucial role in future astronomical observations across various messenger channels, aiding in transient discovery and classification, and helping, or even replacing, observers in making real-time decisions. This review paper examines several cases where AI is transforming contemporary astronomy, especially time-domain astronomy. We discuss the AI algorithms and methodologies employed to date, highlight significant discoveries enabled by AI, and outline future research directions in this rapidly evolving field. Full article

Operational decisions significantly influence organizational performance and individual well-being. Decision mining offers a method to discover and analyze decision logic from decision logs, enhancing decision-making processes. However, evaluating the quality of decision discovery algorithms remains a challenge. While precision, fitness, generalization, and simplicity are well-established quality dimensions in process mining, their adaptation to the decision mining domain is underexplored. This study adapts these four dimensions to the necessary characteristics of decision models, providing a framework for evaluating decision discovery algorithms. Using a design science research approach, we develop tailored metrics and functions and demonstrate their application through a practical example of environmental permit management modeled in Decision Model and Notation (DMN). Precision measures how the discovered decision model reproduces the observed fact types and values from the decision log, detecting over-specification in the decision model. Fitness evaluates how completely the decision model covers the behavior in the decision log, identifying missing or under-specified elements in the decision model. Generalization assesses the model’s robustness to unseen decision cases by quantifying how well the discovered decision model performs beyond the training data. Simplicity captures the complexity in the discovered decision model in relation to a human actor-specified threshold. These insights guide decision model improvements, contributing to higher transparency, accountability, and fairness in operational decision-making processes. This research bridges a gap in the body of knowledge by providing a concrete methodology for evaluating decision discovery algorithms. The results support organizations in aligning decision models with regulatory requirements and public values, while also laying a foundation for future research. Full article

Locus of control refers to the way in which people perceive whether they have control over situations in their lives or whether these situations are the result of external circumstances. Locus of control subsequently influences individuals’ motivation, decision-making, and ability to accept responsibility. How locus of control manifests itself in the behavior of a particular individual can be influenced by several factors. In this article, we focused on how elements of different dimensions of human resource diversity can influence locus of control. For the research, we chose a quantitative approach using a questionnaire measuring the locus of control, along with additional questions. The main aim of the presented research was to identify the relationship between sociodemographic variables and the locus of control orientation of individual groups of respondents. The research sample consisted of N = 384 participants who completed the reduced standardized Rotter locus of control scale. The results focused on differences in individuals’ locus of control in terms of age, gender, type of work experience, and marital status and to what extent these sociodemographic variables can be a predictor of individuals’ locus of control. Hypotheses testing was performed using IBM SPSS 23 software. Th theoretical application of the research findings lies in the discovery that the locus of control (LoC) is not influenced by simple characteristics but must be understood in a more complex way. The practical application lies in the fact that professional experience can influence how employees perceive their level of control over their ability to affect their work and outcomes. Full article

This paper introduces Semantic Knowledge Advanced Tool for Extraction Browsing Organisation Annotation Retrieval and Discovery (SKATEBOARD), a tool designed to facilitate knowledge exploration through the application of semantic technologies. The demand for advanced solutions that streamline Knowledge Extraction, management, and visualisation, characterised by abundant information, has grown substantially in the current era. Graph-based representations have emerged as a robust approach for uncovering intricate data relationships, complementing the capabilities offered by AI models. Acknowledging the transparency and user control challenges faced by AI-driven solutions, SKATEBOARD offers a comprehensive framework encompassing Knowledge Extraction, ontology development, management, and interactive exploration. By adhering to Linked Data principles and adopting graph-based exploration, SKATEBOARD provides users with a clear view of data relationships and dependencies. Furthermore, it integrates recommendation systems and reasoning capabilities to augment the knowledge discovery process, thus introducing a serendipity effect generated by the SKATEBOARD interface exploration. This paper elucidates SKATEBOARD’s functionalities while emphasising its user-centric design. After reviewing related research, we provide an overview of the SKATEBOARD pipeline, demonstrating its capacity to bridge RDF and LPG representations. Subsequent sections delve into Knowledge Extraction and exploration, culminating in the evaluation of the tool. SKATEBOARD empowers users to make informed decisions and uncover valuable insights within their data domains, with the added dimension of serendipitous discoveries facilitated by its interface exploration capabilities. Full article

Commencing in December 2019 with the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), three years of the coronavirus disease 2019 (COVID-19) pandemic have transpired. The virus has consistently demonstrated a tendency for evolutionary adaptation, resulting in mutations that impact both immune evasion and transmissibility. This ongoing process has led to successive waves of infections. This study offers a comprehensive assessment spanning genetic, phylogenetic, phylodynamic, and phylogeographic dimensions, focused on the trajectory of the SARS-CoV-2 epidemic in Cyprus. Based on a dataset comprising 4700 viral genomic sequences obtained from affected individuals between October 2021 and October 2022, our analysis is presented. Over this timeframe, a total of 167 distinct lineages and sublineages emerged, including variants such as Delta and Omicron (1, 2, and 5). Notably, during the fifth wave of infections, Omicron subvariants 1 and 2 gained prominence, followed by the ascendancy of Omicron 5 in the subsequent sixth wave. Additionally, during the fifth wave (December 2021–January 2022), a unique set of Delta sequences with genetic mutations associated with Omicron variant 1, dubbed “Deltacron”, was identified. The emergence of this phenomenon initially evoked skepticism, characterized by concerns primarily centered around contamination or coinfection as plausible etiological contributors. These hypotheses were predominantly disseminated through unsubstantiated assertions within the realms of social and mass media, lacking concurrent scientific evidence to validate their claims. Nevertheless, the exhaustive molecular analyses presented in this study have demonstrated that such occurrences would likely lead to a frameshift mutation—a genetic aberration conspicuously absent in our provided sequences. This substantiates the accuracy of our initial assertion while refuting contamination or coinfection as potential etiologies. Comparable observations on a global scale dispelled doubt, eventually leading to the recognition of Delta-Omicron variants by the scientific community and their subsequent monitoring by the World Health Organization (WHO). As our investigation delved deeper into the intricate dynamics of the SARS-CoV-2 epidemic in Cyprus, a discernible pattern emerged, highlighting the major role of international connections in shaping the virus’s local trajectory. Notably, the United States and the United Kingdom were the central conduits governing the entry and exit of the virus to and from Cyprus. Moreover, notable migratory routes included nations such as Greece, South Korea, France, Germany, Brazil, Spain, Australia, Denmark, Sweden, and Italy. These empirical findings underscore that the spread of SARS-CoV-2 within Cyprus was markedly influenced by the influx of new, highly transmissible variants, triggering successive waves of infection. This investigation elucidates the emergence of new waves of infection subsequent to the advent of highly contagious and transmissible viral variants, notably characterized by an abundance of mutations localized within the spike protein. Notably, this discovery decisively contradicts the hitherto hypothesis of seasonal fluctuations in the virus’s epidemiological dynamics. This study emphasizes the importance of meticulously examining molecular genetics alongside virus migration patterns within a specific region. Past experiences also emphasize the substantial evolutionary potential of viruses such as SARS-CoV-2, underscoring the need for sustained vigilance. However, as the pandemic’s dynamics continue to evolve, a balanced approach between caution and resilience becomes paramount. This ethos encourages an approach founded on informed prudence and self-preservation, guided by public health authorities, rather than enduring apprehension. Such an approach empowers societies to adapt and progress, fostering a poised confidence rooted in well-founded adaptation. Full article

In recent years, the interdisciplinary field of quantum computing has rapidly developed and garnered substantial interest from both academia and industry due to its ability to process information in fundamentally different ways, leading to hitherto unattainable computational capabilities. However, despite its potential, the full extent of quantum computing’s impact on healthcare remains largely unexplored. This survey paper presents the first systematic analysis of the various capabilities of quantum computing in enhancing healthcare systems, with a focus on its potential to revolutionize compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization. Through a comprehensive analysis of existing literature, we have developed taxonomies across different dimensions, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, providing a panoramic view of the quantum computing paradigm for healthcare. Our survey aims to aid both new and experienced researchers in quantum computing and healthcare by helping them understand the current research landscape, identifying potential opportunities and challenges, and making informed decisions when designing new architectures and applications for quantum computing in healthcare. Full article

In broader terms, a Smart City improves the quality of life of its citizens through the effective use of innovative (digital) solutions. While innovative Smart City solutions keep growing, attention has been paid to resilience-making within Smart Cities, recognising that disasters are unavoidable. In light of the characteristics of a Smart City (smartness requirements) being inchoate and vague, different Smart Cities develop their own smartness criteria. Regardless of the Smart City type, smartness criteria need to adequately embed resilience. Integrating the resilience concept provides a strategic direction for Smart Cities and there is a significant positive relationship between the two concepts, Smart Cities, and urban resilience. Although Smart Cities are increasingly growing in popularity all around the world, there is a lack of research to guide a Smart City to define its smartness reflecting on disaster resilience. This paper intends to address this research gap by setting out a set of smartness criteria (with particular reference to urban (city) resilience) which should compulsorily feature in any type of Smart City that desires to be resilient. The study undertakes a systematic literature review to provide a new dimension, depth, and value to existing research discoveries. The findings are presented by structuring ten urban (city) resilience dimensions built upon six Smart City dimensions: smart economy, smart governance, smart people, smart mobility, smart living, and smart environment. Our findings make a niche contribution to knowledge by guiding Smart Cities that intend to build, enhance, and/or sustain resilience, to develop smartness criteria/smart characteristics reflecting on urban resilience. The research outcomes will be of large importance to Smart City policymakers, administrators, project managers, etc. to efficiently manage extreme events timely with optimal resource allocation and will be of specific interest to all the stakeholders (for instance, the innovators) in a Smart City ecosystem who may use the research outcomes as a decision-making tool. Full article

With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area. Full article

A continual increase in the influence of exogenic conditions on a company’s ability to sustain and develop and increasing complexity of the management processes correlated with the discovery of new dimensions of an enterprise, which determine its potential—these all lead to a growing interest in the search for the factors which might facilitate the elevation of the level of integrity of a business model of an enterprise/organisational and managerial system of an enterprise. Currently, it is absolutely necessary to perceive an enterprise not only in the terms of a rational economic system that, in its activity, concentrates on the generation of a positive financial result, and the creation of its value for the stockholders, but also as the system, at the same time, is meant to act responsibly in relation to its stakeholders, society and the market. The observation of successful enterprises shows clearly that the company values make up an element indispensable for the creation of effective strategies of development, and, at the same time, an element guaranteeing them an appropriate level of strategic integrity in external and internal dimensions, paired with the preservation of flexibility for necessary reconfiguration. Some special attention should be paid to the fact that, in order to build permanent competitive advantage, in the context of the strategic dimension of company management, values should play a key role also in the enterprises within heavy industry, including the energy sector. This article aims to present the significance of selected aspects of strategic dimension management in the context of company strategy in one of the largest energy sector companies in Poland. It is based on an analysis of the results of research carried out in the second part of 2021 and at the beginning of 2022. Qualitative studies based on a questionnaire survey and in-depth interviews were conducted among this institution’s managers who are responsible for formulating the company strategy. The study results have shown that social and employees’ personal values, organisational culture, and management by values, have a substantial impact on the character and quality of the strategic dimension management of the analysed company. The article aims to show the role of social values in determining efficiency in the area of managing the strategic dimension of a company. In the context of this aim, the following important research questions should be asked: What is the influence of social values and values represented by the TMT (top management team) on a given company’s efficiency in the context of strategic decisions; and how do these factors influence the stability of the company development, and do they ensure the organisation’s resilience to short-term external changes? Full article

The regeneration and reuse of abandoned healthcare facilities represent one of the most complex issues in the broader field of disused public architectural heritage and its valorization. The leading causes of an elevated quantity of abandoned hospitals are the lack of resilience of these structures, as well as the evolution of the regulatory framework used to increase the quality standards of the National Health System and the constant changes caused by medical discoveries. In addition, the transfer to a new building typically does not involve consideration of the future of the dismissed facility with a lack of a strategic view for its regeneration, thus causing its progressive degradation. Although their large dimensions and unbuilt areas make recovery plans complex, the re-functionalization of these facilities represents an excellent opportunity for social and economic development, as several case studies demonstrate. This paper selects some useful examples of the reconversion and reuse of disused social and healthcare buildings through an accurate comparison that highlights the importance of the topic and the possible actions to be taken into consideration. Although this research focuses on a limited number of case studies, the paper gives rise to some strategies that can be applied to several current cases of disused buildings that could be used to support Decision Makers (DMs) from different countries. Full article

The aim of this study is to develop a reliable 5D (x, y, z, time, flow dimension) model for medical decision making. Sophisticated techniques for the assessment of serious stenosis were developed using time-dependent instantaneous pressure gradients through the aorta (flow rate, Reynolds number, velocity, etc.). A 74 cardiac MRI scan and 3057 scans were performed on a 10-year-old patient with congenital valve and valvular aortic stenosis on sensitive MRI and coarctation (operated and then dilated) in the sense of shone syndrome. The occlusion rate was estimated to be 80.5%. The stenosis area was approximately 15 mm long and 10 mm high. The fluid solver (NS) exhibited a significant shear stress of −3.735 × 10⁻⁵ Pa within the first 10 iterations. There was a significant drop in the flux mass of −0.0050 (kg/s), as well as high blood turbulence in vortex field lines and low geometry Reynolds cells. The fifth dimension was used for negative velocity prediction (−81.4 cm/s). The discoveries of the 5D aortic simulation are convincing based on the evaluation of its physical and biomedical features. Full article

We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm-specific labeling decisions. We first improved two of these techniques, a KNN variant of the density peaks clustering method dpc, and a weighted-mode variant of knnclust, so the four methods use the same input KNN graph and only differ by their labeling rules. We propose two regularization schemes for hyperspectral image analysis: (i) a graph regularization based on mutual nearest neighbors (MNN) prior to clustering to improve cluster discovery in high dimensions; (ii) a spatial regularization to account for correlation between neighboring pixels. We demonstrate the relevance of the proposed methods on synthetic data and hyperspectral images, and show they achieve superior overall performances in most cases, outperforming the state-of-the-art methods by up to 20% in kappa index on real hyperspectral images. Full article