Search Results (349)

This study re-evaluates the hydrogeological framework of the Bohemian Cretaceous Basin (Czech Republic), where preliminary surveys unexpectedly identified old groundwater in several springs and abstraction wells. Traditional distinction into a Cenomanian (A) and a single Turonian (C) aquifer failed to explain the observed hydraulic head discrepancies and the occurrence of old groundwater. By integrating the spatial correlations of hundreds of well logs with hydraulic head data, environmental tracers (chemistry, ²H, ³H, ¹³C, ¹⁴C, ¹⁸O, ³⁹Ar, ⁸⁵Kr, CFCs, SF₆, and noble gases), and field measurements, we objectively delineated the hydrostratigraphic architecture of the basin. The results demonstrate three distinct aquifers (A, Ca, and Cb), challenging long-standing interpretations. Several flow systems were identified, with mean residence times of the old water exceeding 300 years. The hydrogeochemical and isotopic evidence confirmed mixing of Holocene groundwater between Ca and Cb aquifers while excluding Last Glacial Period fossil groundwater that is typical of the A aquifer. These findings highlight the necessity of a multi-proxy approach to validate conceptual models in seemingly “well-understood” regions. The newly characterized subdivision of Turonian aquifers is critical for protecting old groundwater resources, optimizing the design of geothermal and water supply wells to prevent hydraulic short-circuiting, and identifying previously unrecognized groundwater resources currently discharging to the Jizera River. Full article

In large-scale dam construction, the efficiency of concrete transport operations is fundamentally governed by the coordination between horizontal hauling and vertical hoisting capacities. Traditional experience-based scheduling approaches often fail to capture the stochastic, cyclic, and resource-coupled nature of these transport systems. This study developed a closed queuing network-based stochastic simulation framework to model dam concrete transportation as a finite-population cyclic service system. The process was abstracted into sequential service stages with stochastic service times, and a structured state-space representation combined with time-step simulation was constructed to describe dynamic resource occupation and task transitions under varying truck and cable crane configurations. Application to a real large-scale dam project revealed a characteristic multi-stage performance evolution pattern governed by capacity matching mechanisms. As the truck fleet size increased, system performance transitioned from a transport-limited regime to a capacity-coordination regime and ultimately to a hoisting-saturated regime in which further fleet expansion yielded diminishing returns. Sensitivity analysis demonstrated that hoisting capacity imposed an upper bound on system throughput, while adaptive fleet reconfiguration could restore operational equilibrium under constrained equipment availability. The results indicated that dam concrete transport should be treated as a dynamic capacity regulation problem rather than a static allocation task. The proposed framework provides an interpretable and quantitative decision-support tool for equipment configuration, bottleneck identification, and adaptive scheduling in large-scale hydraulic infrastructure projects. Full article

Quantum computing exploits the principles of quantum mechanics to perform computation. Information is stored in qubits and processed with a sequence of quantum gates arranged as circuits. Verifying the correctness of quantum circuits is becoming essential as hardware scales in qubit count and architectural complexity. Traditional testing and naive simulation do not scale and quickly become computationally infeasible because the state space grows exponentially. This creates a strong need for more powerful and scalable verification techniques. Formal methods offer a viable solution by providing mathematically rigorous and scalable verification techniques that address these scalability challenges through abstraction, symbolic reasoning, and probabilistic guarantees. This study examines how formal methods are applied to quantum-circuit verification. Specifically, four families of formal techniques: barrier certificates, abstract interpretation, model checking, and theorem proving are examined, along with the theoretical foundations and practical applications of these techniques. Finally, the study highlights open challenges and identifies promising directions for future research. An extensive set of references is included to support further study and exploration. Full article

The increasing reliance of Internet of Things (IoT) applications on low-power wide-area network technologies, particularly Long Range Wide Area Network (LoRaWAN), has amplified the need for security monitoring approaches that go beyond attack-specific signatures and generic traffic anomalies. Existing solutions are often tailored to individual threat scenarios or rely on statistical indicators, which limits their ability to systematically capture protocol-level misuse in an interpretable manner. This paper addresses this gap by proposing a protocol-aware validation methodology based on a Digital Twin abstraction of LoRaWAN communication behavior. The Over-The-Air Activation (OTAA) procedure is modeled as a finite-state machine that encodes expected message sequences, timing constraints, and specification-driven state transitions. Observed network events are continuously evaluated against this formal state model, enabling the identification of protocol-level deviations indicative of anomalous or non-conformant behavior. Illustrative examples include replay behavior, timing inconsistencies, and integrity-related anomalies, although the framework is not limited to predefined attack categories. The results demonstrate that state machine-based Digital Twin provides a structured and extensible foundation for protocol-aware security validation and Security Operation Center (SOC)-oriented telemetry enrichment. In this sense, the presented approach represents a concrete step toward protocol-aware intrusion detection for LoRaWAN networks by establishing a state-synchronized semantic validation layer upon which higher-level detection mechanisms can be built. Full article

In evolutionary computation, distinct clusters that address different subproblems evolve independently of each other, which makes it difficult to exchange genetic information between them. However, a vaguely defined task within one system may be expressed more clearly within another. Effective interaction methods enable subsystems to collaborate more effectively in solving global tasks. By analysing how ambiguous intentions regarding electricity consumption influence actual behaviour in real-world scenarios, we discovered that transaction and negotiation patterns within electricity markets can effectively support this process. By introducing time and third parties, the study presents a semiautomatic, interpretable reasoning community logic system that enables machines to express transaction negotiation patterns. Through formalised operations, it facilitates the conversion of intentions, uncovering hidden relationships within global structures through this liberated form of expression. This paper examines its impact on computational and search paradigms through case studies, enabling collaborative approaches and granularity control via dynamic anchor points, and explores automated peer-to-peer transactions and electricity monetisation within highly abstracted power trading processes. Full article

Despite the relative hydrological abundance of northwestern Greece, the Arta Plain exhibits persistent spatial and seasonal mismatches between irrigation demand and the effective capacity of the public network. To clarify the factors mediating between available water resources and actual irrigation coverage, this study applies an integrated framework combining quantitative irrigation modelling (FAO CROPWAT 8.0) with qualitative insights from semi-structured interviews with farmers and institutional stakeholders. Annual irrigation demand was estimated at approximately 49.1 hm³. Although this volume could theoretically be met through available surface water, in practice, it is constrained by conveyance losses and infrastructure degradation. Under these conditions, meeting irrigation needs shifts toward private abstractions. The interviews indicate systematic groundwater use for the four dominant crops; as a share of modelled demand, groundwater use corresponds to approximately 41% of irrigation requirements, with higher reliance in perennial and water-intensive crops such as kiwifruit and citrus, where supply stability is critical. These findings indicate that irrigation dysfunctions in the Arta Plain do not stem from hydrological insufficiency but from structural misalignments between infrastructure, institutional organization, and prevailing practices. Addressing these inefficiencies requires coordinated interventions, including targeted infrastructure rehabilitation, adoption of precision irrigation technologies, transparent volumetric monitoring, and participatory management processes. Overall, the study provides a transparent logic for interpreting irrigation performance when monitoring data are incomplete by linking modelled demand with operational delivery constraints and evidence from primary water users. Full article

Mapping thematic structure in large scientific corpora enables the systematic analysis of research trends and conceptual organization. This work presents an unsupervised framework that leverages large language models (LLMs) as fixed semantic inference operators guided by structured soft prompts. The framework transforms raw abstracts into normalized semantic representations that reduce stylistic variability while retaining core conceptual content. These representations are embedded into a continuous vector space, where density-based clustering identifies latent research themes without predefining the number of topics. Cluster-level interpretation is performed using LLM-based semantic decoding to generate concise, human-readable descriptions of the discovered themes. Experiments on ICML and ACL 2025 abstracts demonstrate that the method produces coherent clusters reflecting problem formulations, methodological contributions, and empirical contexts. The findings indicate that prompt-driven semantic normalization combined with geometric analysis provides a scalable and model-agnostic approach for unsupervised thematic discovery across large scholarly corpora. Full article

Flood change detection from remote sensing data can be used to identify post-disaster flooded areas, providing decision support for emergency rescue and post-disaster reconstruction. Although the combination of SAR and optical images effectively addresses obscuration by clouds and rain, the inherent difference in their imaging mechanisms poses a challenge to improving the accuracy of flood area change detection. Furthermore, existing flood inundation change detection methods based on heterogeneous remote sensing imagery struggle to distinguish small ground objects within the background from the actual inundated regions. Therefore, a pseudo-Siamese hybrid dilation spiral feature network (PSiam-HDSFNet) is proposed in this paper. Firstly, the feature extraction pipeline progressively processes optical and SAR images through five-layer Enhanced Deep Residual Blocks and five-layer Residual Dense Blocks, respectively. A Hybrid Dilated Pyramid (HDP) module based on a sawtooth wave-like dilated coefficient is designed to enhance multi-scale semantics of deep features in order to selectively reinforce semantic features in flood areas and weaken the noise semantics from small ground objects. Then, a Spiral Feature Pyramid (SFP) module is designed to make the deep features of SAR and optical images more consistent in spatial structure and numerical distribution patterns, so that the features of flood areas become more prominent while the noise semantics from small ground objects are further suppressed. After that, the Galerkin-type attention with linear complexity is introduced to the decoder, rapidly reconstructing the abstract semantic information of floods into interpretable flood features. Finally, the Align OPT-SAR (AlignOS) method is designed to align SAR and optical image features, enabling subsequent flood area detection. Seven metrics are adopted in the comparison between PSiam-HDSFNet and the other 14 methods. The results indicate that PSiam-HDSFNet improves change detection accuracy by extracting and processing depth features of these two images without image domain translation, and its F1 scores are improved by 7.704%, 7.664%, 4.353%, and 1.111% in the four flood coverage categories detection tasks compared to the suboptimum. Full article

Botnet attacks in Internet of Things (IoT) environments often occur as multi-stage campaigns, making early and reliable detection difficult across distributed and privacy-sensitive networks. Centralized detection approaches are often limited by heterogeneous traffic characteristics, severe data imbalance, and the need to aggregate large volumes of raw network data, raising scalability and privacy concerns. To address these challenges, this paper proposes FDA, a federated learning-based and data mining-driven framework for stage-aware botnet attack detection in IoT networks. FDA operates at network gateways, where anomalous traffic is first detected and then abstracted into compact and interpretable patterns using Frequent Itemset Mining (FIM). This pattern-based representation reduces noise and local traffic bias, enabling more robust learning across different IoT networks. Lightweight neural network models are trained locally at gateways, and a global model is learned through federated aggregation of model parameters, avoiding direct sharing of raw network data while enabling gateways to collaboratively learn evolving attack patterns across different IoT networks. Experimental results show that FDA achieves anomaly detection F1-scores above 99% across all gateways and multi-stage botnet attack classification F1-scores in the range of 48–49%, which are comparable to centralized machine-learning baselines while operating under decentralized and privacy-preserving constraints. Overall, FDA provides a practical, privacy-preserving, and effective solution for distributed botnet attack stage detection in real-world IoT deployments. Full article

Emerging digital technologies, including immersive environments (VR/AR/XR) and Vision–Language Models (VLMs), have the potential to reshape digital pathology and medical imaging. While immersive tools can enhance spatial visualization and procedural training, VLM-based copilots offer cognitive and workflow support. Their combined impact on cytopathology remains largely conceptual and preclinical. This Conceptual Exploratory Narrative Review (CENR) examines how immersive technologies and VLM-based copilots may jointly influence cytopathologists’ professional workflow, training, and diagnostic processes, introducing the notion of the “augmented cytopathologist.” A structured exploratory approach integrated peer-reviewed literature, position papers, preprints, gray literature (technical reports, white papers, conference abstracts, blogs), and cross-disciplinary perspectives. Database searches (PubMed, Web of Science, Scopus) confirmed a limited number of studies addressing immersive or AI-assisted cytopathology imaging. Thematic analysis focused on four conceptual dimensions: (1) technological capabilities and maturity; (2) workflow and educational applications; (3) professional implications and cytopathologist role; and (4) responsible use of LLMs and VLMs as supportive tools. This approach emphasizes interpretation of emerging trends over aggregation of empirical data, enabling conceptual synthesis of early-stage implementations and perspectives in the field. Immersive technologies facilitate three-dimensional visualization, procedural skill development, and collaborative engagement, whereas VLMs support report generation, literature retrieval, and decision guidance. Together, they offer a synergistic model for perceptual and cognitive augmentation. Key challenges include technical maturity, interoperability, workflow integration, regulatory compliance, and ethical oversight. Figures illustrate representative examples of (1) remote collaborative immersive evaluation and (2) integration of immersive visualization with VLM-based copilots, highlighting potential applications in training and workflow support. The CENR underscores the potential of combining immersive tools and AI copilots to support cytopathology, particularly for education, workflow efficiency, and cognitive augmentation. Adoption should be incremental and carefully governed, emphasizing augmentative rather than transformative use. Future research should focus on clinical validation, scalable integration, and regulatory and ethical frameworks to realize the concept of the augmented cytopathologist in practice. Full article

Static analysis is a critical methodology for ensuring the quality, security, and safety of embedded, cyber-physical, and electronic software systems, particularly as such systems become increasingly complex and tightly coupled with hardware and real-time constraints. Through a systematic study of the literature, this paper summarizes the State-of-the-Art in static program analysis. We develop a comprehensive taxonomy of fundamental techniques, including model checking, abstract interpretation, data-flow analysis, and symbolic execution, and examine their application in modern analysis tools used in electronic and safety-critical systems. The survey thoroughly reviews applications across key domains, including vulnerability detection, automotive and embedded software verification, smart contract auditing, and AI-enabled electronic systems. We also critically analyze persistent challenges, including tool integration, scalability limitations, and the trade-off between analysis precision and soundness. Finally, by discussing emerging trends and future research directions—such as machine-learning-enhanced analysis and hybrid static–dynamic techniques—this work provides a structured framework to guide future research and industrial practice in the development of reliable electronic systems. Full article

Background/Objectives: Integrating technology with caring is essential in modern healthcare, yet the clinical applicability of nursing theories remains underexplored. Locsin’s Technological Competency as Caring in Nursing (TCCN) theory emphasizes the competent use of technology to address patients holistically, rather than focusing solely on health concerns. Here, we explored the interpretive clarity of the TCCN Instrument–Revised for Practice (TCCNI-RePract) items and identified the conceptual barriers encountered by psychiatric nurses when engaging with its theoretical constructs. Methods: This concurrent mixed-methods study surveyed 291 psychiatric nurses across five large hospitals in the Kansai region of Japan. Quantitative data on the TCCNI-RePract perception dimension were examined using descriptive statistics and normality testing. Qualitative open-ended responses were analyzed using reflexive thematic analysis. To ensure rigor and integration, a joint display was utilized to bridge both data strands. Results: Quantitative findings indicated that nurses strongly endorsed core values of caring (high agreement) but perceived theoretical constructs (wholeness and technological knowing) as significantly more difficult to interpret than concrete, behavior-oriented items. Qualitative analysis revealed four major themes: (1) fragmented understanding of “technology and caring,” (2) struggles with abstract and philosophical language, (3) moral and emotional tensions in caring relationships, and (4) contextual barriers to integrating caring and technology. We found a “semantic gap,” where the professional endorsement of caring values was not automatically translated into the mastery of theoretical lexicon. Conclusions: While psychiatric nurses identify with the moral core of TCCN, a substantial gap exists between abstract theory and clinical practice. For effectiveness, middle-range theories require “clinical translation” that resonates with the moral, emotional, and organizational realities of psychiatric settings. Full article

This study investigates how digital representation practices shape spatial meaning-making, aesthetic coherence, and narrative construction in interior design education. Responding to a notable gap in the literature regarding the limited examination of student-produced visuals as visual–cultural artifacts, the research analyzes how emerging designers employ digital tools to construct spatial identity and atmosphere. The dataset consists of 816 images produced by 34 fifth semester interior design students within a design studio project focused on the adaptive reuse of a standardized school building. The study adopts a hybrid methodological framework that combines Gillian Rose’s multi-sited visual analysis with Braun and Clarke’s reflexive thematic analysis, alongside a semiotic interpretation informed by Kress and van Leeuwen, Barthes, and Manovich. The analysis reveals three recurring themes across the projects: a fluid spatial identity articulated through guided circulation and rhythmic compositional strategies; digital nature abstractions developed through software-mediated organic metaphors; and institutional comfort atmospheres characterized by symmetry, tonal neutrality, and controlled relationships between material and light. Overall, the findings demonstrate that digital visualization tools function not only as technical means of representation but also as mediating environments that interact with students’ design intentions, visual culture exposure, and pedagogical frameworks, shaping spatial thinking and aesthetic coherence. In this respect, the study provides critical and timely insights into the evolving pedagogical structure of digital interior design education. Full article

Quantitative evaluation of human motion from image data requires both high geometric precision and mathematical interpretability. To address the limitations of pixel-level posture analysis and empirical performance scoring, this study proposes a geometry-driven quantitative modeling framework for image-based motion evaluation. Sub-pixel edge detection based on quadratic polynomial interpolation is first employed to construct a precise continuous representation of limb contours from image sequences. By abstracting the human arm as a spatial rigid-body system, posture evaluation is reformulated as an optimization problem governed by geometric constraints and physical principles. An optimal swing trajectory is obtained by minimizing the total kinetic energy of the system, which is solved numerically using Newton’s iterative method, avoiding the explicit solution of highly coupled inverse kinematics. To further analyze the contribution of multiple performance-related variables within a unified quantitative framework, a hybrid feature attribution strategy integrating Random Forest, XGBoost, and LightGBM is introduced. The proposed mixed feature mining approach reduces model dependency and enhances the robustness of factor importance ranking. The effectiveness of the proposed framework is validated using image data collected from a cloud-based table tennis classroom. The experimental results demonstrate that the geometry-driven modeling approach provides stable, interpretable, and discriminative evaluation outcomes, indicating its potential applicability to broader image-based human motion analysis tasks. Full article

Background: Intervertebral motion is a fundamental aspect of spinal biomechanics, crucial for understanding lumbar spine function, pain mechanisms, and surgical outcomes. Various methods exist for measuring and interpreting it, each with its own advantages, limitations, and specific applications. However, a comprehensive and standard taxonomy of study types for the measurement and interpretation of in vivo intervertebral motion in the lumbar spine is lacking. Objectives: This review aimed to systematically identify, characterise, and categorise the diverse study types deposited in the literature. Eligibility criteria: Only studies in English and of lumbar spine intervertebral motion in living subjects were considered, and only those that employed objective measurement of motion sequences were included. Sources of evidence: A comprehensive literature search was performed in PubMed, CINAHL, and SCOPUS for articles published between January 2000 and October 2025. Charting methods: After removal of duplicates, all studies were subjected to Title and abstract screening, followed by full-text screening of potentially eligible studies. Data selected were charted into tables under the headings: author, year, country, purpose, technology, participants, measurement, interpretation, radiation dosage, and significance of findings. Results: Forty-nine studies were abstracted and are described under 11 study types. These formed a taxonomy constituting the following six categories: normal biomechanical mechanisms, pathological and injury mechanisms, direct kinematic measurement, spinal stabilisation, dynamic radiography, and clinical markers. The resulting taxonomy will serve as a resource for researchers, clinicians, and policymakers by facilitating a more coherent understanding of the field and promoting standardisation in research design and reporting. Full article