Search Results (616)

Dynamic beam shaping opens new possibilities for improving the quality and productivity of industrial laser material processing applications such as welding and cutting. However, dynamic beam shaping involves time constants and frequencies that must be selected correctly to successfully modify a given laser process. This paper proposes a standardized nomenclature for the possible types of dynamic beam shaping and the resulting dynamic process modifications, and relates these to characteristic time constants and frequencies at which the process modifications have a particularly strong influence on the process. These characteristic frequencies define three process regimes that have distinctly different effects on the process. An overview of typical time constants and frequencies in laser processes aids in understanding the occurrence of characteristic frequencies. Knowledge of the process regimes allows for a systematic selection of frequencies in dynamic beam shaping to achieve targeted dynamic process modifications, e.g., for pore reduction. Using a laser system capable of dynamic beam shaping at frequencies of up to 80 MHz, the influence of the three process zones on the porosity of the weld was demonstrated using deep welds in cast aluminum as an example. Full article

Increasing frequency and intensity of extreme precipitation events, together with altered soil saturation dynamics, have significantly increased the occurrence of shallow landslides. These processes are closely linked to climate change and increasingly affect mountainous and hilly regions worldwide, where rainfall-induced pore pressure variations and transient infiltration govern slope instability. Despite growing recognition of climate-driven slope failures, most conventional geomechanical analyses still rely on static assumptions and simplified boundary conditions, which are insufficient to capture the pronounced temporal variability of hydro-climatic forcing. To address this gap, this study introduces a conceptual and methodological framework for a proactive landslide cadaster, developed within the Climate Adaptive Resilience Evaluation (CARE) framework. Rather than serving as a static inventory of past events, the proposed cadaster functions as a structured, updatable repository of climate–geomechanical parameters that directly support advanced landslide analyses. The core innovation lies in the formalization of the climate–geomechanical interface, which enables the transformation of climatic and hydrological variables into parameters directly applicable in geomechanical modeling. These parameters encompass climatic, hydrological, geomechanical, and thermo-hydraulic processes and are assigned to spatially referenced locations, complemented by documented landslide occurrences. Their spatial distribution forms a network of reference points that allows interpolation, continuous updating, and reuse across multiple analyses. In this way, the cadaster becomes a proactive, process-based data infrastructure, serving as the foundational input for scenario-based landslide susceptibility, hazard, and risk assessments within the CARE analytical workflow. The conceptual framework is illustrated through an example from Slovenia, focusing on the Visole area near Maribor, where selected data types and workflow steps are presented for demonstration purposes. Full article

The current energy transition has shifted the power system paradigm, including distributed resources (mostly renewables) and energy storage systems, the proper incorporation of which is beneficial for the power system but can also cause issues such as network instability, grid congestion or issues with power quality. Moreover, the exponential electrification of loads, especially ones with dynamic behavior, due to most sectors switching to electric mode, with prominent examples including mobility, heating, hydrogen production and marine applications, can pose challenges for the system operators. The purpose of this paper is to highlight the effects of this transition from the perspective of the distribution and transmission systems in Europe generally, but also in Greece specifically, by presenting key performance indicators (technical, economic, environmental, and social) related to expected EU targets, as well as selected real-life applications, future trends and challenges. Full article

Historical water management infrastructures, often comprising underground environments, represent a significant example of the interplay between built heritage and the natural substrate. This study proposes an interdisciplinary, integrated and multi-scalar investigative methodology for such structures. Through the analysis of the case study of Palombaro Purgatoro Vecchio, a large historical public water cistern located in Matera in Italy, this paper presents a rigorous methodology replicable in different contexts. Bibliographic and archival research establish the knowledge base regarding the structure’s historical evolution; territorial and hydromorphic analyses, supported by GIS, highlight the dynamics of the surrounding watersheds. Meanwhile, a digital survey integrating SLAM and photogrammetry provides geometric-dimensional data, serving as the foundation for analysing construction techniques and materials. The selection of accessible and manageable technologies promotes a practical, replicable investigative methodology aimed at the protection, comprehension, enhancement and dissemination of water UBH. Full article

The conventional time-dependent perturbation theory in quantum mechanics is established within the framework of Hermitian Hamiltonians, applicable for describing quantum transitions and associated energy level responses in such systems. However, this theory has fundamental limitations when applied to non-Hermitian systems. Consequently, researchers have systematically extended time-dependent perturbation theory to non-Hermitian systems, establishing a corresponding mature framework. Building on this foundation, this study extends the theory to investigate the transition dynamics induced by non-Hermitian interactions in non-Hermitian Hamiltonian systems. We employ a biorthogonal basis representation for a three-level non-Hermitian system. This work investigates a system comprising an unperturbed static non-Hermitian Hamiltonian and a periodically driven time-dependent perturbation Hamiltonian. Taking the three-level system as a concrete example, we combine analytical methods with numerical simulations to solve and analyze its dynamical evolution equations. These complementary approaches reveal that when system parameters complete a full cycle around an exceptional point, the transitional behavior exhibits specific evolutionary patterns. In this system, quantum transition probabilities exhibit significant asymmetry and non-conservation that depend on the initial and final states, revealing inherent directional characteristics in the dynamical process. Furthermore, for a three-level, periodically driven non-Hermitian system with time-dependent perturbations, this asymmetry is even more pronounced, manifesting as a distinct disparity between forward and reverse transition probabilities. The periodic driving actively amplifies the asymmetry in the transition process. By designing the perturbation spectrum, selective manipulation of specific quantum states can be achieved. Moreover, transition probabilities can be significantly enhanced under resonance conditions, while non-Hermiticity further breaks the system’s inherent symmetry, leading to substantial amplification of transitions in a single direction. By precisely tuning the drive frequency, interactions between specific coupling channels can be selectively enhanced or suppressed. The amplification of channel asymmetry by non-Hermitian properties provides a novel mechanism for directional control of quantum states and opens new pathways for realizing related quantum technologies. Full article

In this paper, a parameterized intuitionistic fuzzy distance measure-based combined compromise solution (PIFDM-CoCoSo) framework is proposed to solve the multi-criteria decision-making (MCDM) problem with completely unknown criterion weight information. Firstly, a new parameterized intuitionistic fuzzy distance measure $d_{\lambda }$ is proposed. This measure dynamically adjusts the evaluation of individual and group consensus information by introducing the parameter $\lambda$. Numerical examples show the discriminative ability of the proposed distance measure for intuitionistic fuzzy information. On the basis of $d_{\lambda }$, a new intuitionistic fuzzy entropy measure is derived to determine the objective criterion weight. Secondly, the two proposed measures are integrated into the comprehensive compromise solution framework to form the PIFDM-CoCoSo model. Finally, the proposed PIFDM-CoCoSo model is applied to the stock selection case. Sensitivity analysis and comparative analysis verify the robustness and effectiveness of the optimal solution. The proposed parameterized measure and ensemble model provide a flexible and effective tool for dealing with complex MCDM problems under intuitionistic fuzzy uncertainty. Full article

The digitization of healthcare has been accompanied by a rapid expansion of electronic health records (EHRs); however, a significant proportion of critical patient data, specifically medication regimens, remains entrapped within unstructured clinical narratives. The inability to seamlessly compute this data hinders advancements in pharmacovigilance, clinical decision support, and population health management. This study presents a comprehensive, rigorous evaluation of the feasibility of deploying Large Language Models (LLMs) to automate the extraction of structured dosage information (Dose, Daily Frequency, Duration) from outpatient antimicrobial clinical notes sourced from the Collaboration to Harmonize Antimicrobial Registry Measures (CHARM) registry. We scrutinized the performance of five distinct open-weight architectures, namely GPT-OSS:20B, Gemma 2:9B, Mistral 7B, Qwen3:14B and Llama 3.2, across both Zero-Shot and Retrieval Augmented Generation (RAG)-based Few-Shot prompting paradigms. Our analysis reveals a fundamental architectural trade-off: the reasoning-optimized GPT-OSS:20B dominates the zero-shot landscape (F1 > 0.90) by leveraging abstract schema understanding, whereas the instruction-tuned Gemma 2:9B excels in the few-shot setting (F1 ~ 0.99), effectively utilizing examples as guardrails to surpass larger models. Conversely, smaller models (Mistral, Llama) exhibit a prohibitive “hallucination barrier,” rendering them unsafe for unsupervised clinical application. Furthermore, we identify “Inconsistent Unit Handling” and “Complex Temporal Logic” as persistent failure modes that resist simple scaling laws. This report provides a definitive framework for selecting model architectures based on the availability of few-shot examples and highlights the necessity of dynamic RAG strategies to achieve production-grade reliability in medical informatics. Full article

This work addresses the design of a predefined-time adaptive fuzzy control scheme for high-order nonlinear systems with nonstrict-feedback structures, subject to unmodeled dynamics and input time delay. To mitigate the influence of unmodeled dynamics, a predefined-time auxiliary dynamic signal is incorporated into the controller design. Meanwhile, the adverse effects caused by input delay are handled by integrating a Padé approximation with the introduction of an intermediate state variable. Fuzzy logic systems are utilized to approximate the unknown nonlinear terms present in the system dynamics. Based on a recursive backstepping framework and a power-type Lyapunov function formulation, an adaptive fuzzy tracking controller with predefined-time convergence characteristics is constructed. A detailed stability analysis demonstrates that the closed-loop system achieves practical predefined-time convergence, while appropriate selection of design parameters guarantees that the tracking errors remain confined within a small bounded region around the origin. Finally, the effectiveness and advantages of the proposed control strategy are validated through a numerical example and a practical example. Full article

The spontaneous emergence of macroscopic dissipative structures in systems driven by generalized chemical potentials is well established in non-equilibrium thermodynamics. Examples include atmospheric/oceanic currents, hurricanes and tornadoes, Rayleigh–Bénard convection cells and reaction–diffusion patterns. Less well recognized, however, are microscopic dissipative structures that form when the driving potential excites internal molecular degrees of freedom (electronic states and nuclear coordinates), typically via high-energy photons or coupling with ATP. Examples include dynamic nanoscale lipid rafts, kinesin or dynein motors along microtubules, and spatiotemporal Ca²⁺ signaling waves propagating through the cytoplasm. The thermodynamic dissipation theory of the origin of life asserts that the core biomolecules of all three domains of life originated as self-organized molecular dissipative structures—chromophores or pigments—that proliferated on the Archean ocean surface to absorb and dissipate the intense “soft” UV-C (205–280 nm) and UV-B (280–315 nm) solar flux into heat. Thermodynamic coupling to ancillary antenna and surface-anchoring molecules subsequently increased photon dissipation and enabled more complex dissipative processes, including photosynthesis, to dissipate lower-energy but higher-intensity UV-A and visible light. Further thermodynamic coupling to abiotic geophysical cycles (e.g., the water cycle, winds, and ocean currents) ultimately led to today’s biosphere, efficiently dissipating the incident solar spectrum well into the infrared. This paper reviews historical considerations of UV light in life’s origin and our proposal of UV-C molecular dissipative structuring of three classes of fundamental biomolecules: nucleobases, fatty acids, and pigments. Increases in structural complexity and assembly into larger complexes are shown to be driven by the thermodynamic imperative of enhancing solar photon dissipation. We conclude that thermodynamic selection of dissipative structures, rather than Darwinian natural selection, is the fundamental creative force in biology at all levels of hierarchy. Full article

This critical and selective review synthesizes the accumulating body of biological evidence supporting a process we term epigenetic–genetic coupling as a mechanistic basis for Lamarckian inheritance of somatically acquired adaptations. We propose that evolutionary processes in mammals and higher vertebrates can involve deaminase-driven, reverse transcriptase-mediated, RNA-templated targeted homologous recombination. We contrast well-established examples of “Soft”, reversible epigenetic inheritance with historical and contemporary evidence suggestive of stable, DNA-integrated “Hard” Lamarckian transgenerational inheritance. Our analysis indicates that the establishment of “Hard” Lamarckian inheritance may require specific population dynamics, including inbreeding or interbreeding among phenotypically affected offspring, together with sustained and defined environmental stimuli over one or more generations to consolidate the acquired traits at the genomic level. We also present molecular and cellular evidence supporting RNA-to-DNA genetic feedback mechanisms involving targeted genomic integration, primarily mediated by the DNA repair–associated reverse transcriptase activity of DNA polymerase η. Finally, we review diversification mechanisms in molecular and cellular immunology that now routinely employ single-molecule, real-time, long-read genomic sequencing (6–8 kb). We recommend the broader application of these technologies in future breeding and experimental programs across other somatic systems. Their deployment offers a robust strategy for securing definitive “Hard” molecular evidence of Lamarckian acquired inheritance in diverse biological contexts; including somatically acquired immunity, as well as adaptive behavioral and central nervous system phenotypes. This is compatible with our over-arching goal—to provide an experimental road map of conceptual options to drive future experimentation in acquired inheritance breeding programs. Full article

Silk production is a classic example of a domestication trait, yet the cell-type-specific driver of its enhancement in the silkworm Bombyx mori remains unresolved. To address this, we integrated extensive bulk RNA-seq data with a single-nucleus RNA-seq atlas of silk glands (SGs) from domestic B. mori and wild B. mandarina for deconvolution analysis. This identified phenotype-associated cell subpopulations (Scissor+ and Scissor− cells) that enrich in B. mori and B. mandarina, respectively. Transcriptomic characterization revealed that B. mori SG cells exhibit a pervasive “pro-synthesis” transcriptional state, with concerted upregulation of silk protein genes and metabolic pathways. Conversely, B. mandarina cells maintained a “protective–adaptive” state, enriched for stress response and xenobiotic metabolism genes. Pseudotime analysis further delineated the cell state transitions, pinpointing key dynamic gene expression linked to high silk yield. Our findings demonstrate that domestication reshaped the silk gland cellular landscape, promoting a systemic shift toward a synthesis-optimized cell state. This study offers a new framework at the cellular level to elucidate the evolution of complex traits under selection. Full article

If our observable Universe is only a tiny region of a vastly larger and conformally older spacetime, then the usual formulations of the classical flatness and horizon problems of the Hot Big Bang can be reinterpreted as artifacts manifesting an observational selection effect; we occupy a small causal domain of a much larger causally-connected and possibly non-flat spacetime. A sufficiently large positive cosmological constant, $\Lambda$, sets the future asymptotic horizon scale of the observable Universe, ∼${\Lambda }^{-1/2}$, thereby implying that the observable Universe may simply be a minute patch of a far larger pre-existing one, hereafter a Small Patch Hypothesis. Importantly, this observational bound is purely geometric; regardless of when the Universe is observed, the maximum accessible scale is finite and fixed by $\Lambda$, independent of inflationary dynamics, anthropic arguments, or assumptions about the global hosting spacetime. The externally possibly frozen past-eternal state implied by a pre-existing, causally connected spacetime motivates, but does not strictly require, viewing the perturbation field as being in (or arbitrarily close to) a coarse-grained maximum-entropy—equilibrium—configuration. Conditionalizing only on fixed mean and variance, a Gaussian distribution uniquely emerges, while the absence of entropy gradients corresponds to adiabaticity. In this work these features are therefore treated as plausible maximum-ignorance priors for super-horizon perturbations, rather than as rigorously derived consequences of a fully developed microscopic notion of gravitational entropy. In this sense, inflation becomes one viable realization of the proposed Small Patch Hypothesis. Here, one particular non-inflationary alternative is considered for illustrative purposes in which a primordial spectrum $P_{\zeta }\left(k\right)$ of the gauge-invariant perturbation $\zeta$ that pre-dates the Big Bang grows logarithmically toward large scales, $k\to 0$, and in fact diverges at some finite $k_c$. If $k_c\ll {\Lambda }^{-1/2}$, then our local cosmic patch probes only the regime where $\zeta \ll 1$ and appears exceptionally smooth. Over the comparatively narrow observable window, this $P_{\zeta }\left(k\right)$ mimics a slightly red-tilted, inflation-like spectrum. Rather than introducing high-energy new fields, this perspective frames large-scale homogeneity, isotropy, Gaussianity, adiabaticity, and the observed thermodynamic Arrow of Time as possible consequences of restricted observational access to a much larger Universe in equilibrium, rather than signatures of a unique early-Universe mechanism. Current observations cannot distinguish this logarithmically running spectrum from the standard power-law one, but future probes—for example high-resolution 21-cm measurements of the Dark Ages—may be able to falsify it. Full article

Interference patterns are increasingly explored in contemporary architectural façades as visual configurations generated through the superposition of repetitive and layered geometric structures. This study examines the role of interference patterns in contemporary architecture, with particular attention to the perceptual effects and illusion-related phenomena that may emerge during their observation. The research is based on a comparative, case-based analysis of selected architectural examples in which interference patterns are introduced through façade articulation, layered glazing systems, spatial textures, or form-related strategies. The analysed material is classified into four groups: semi-spatial façades, façade graphics applied to multi-layer glass systems, spatial textures, and interference embedded in the overall building form. The analysis focuses on identifying recurring perceptual effects associated with interference patterns, such as illusion-related phenomena, including visual aliasing, motion parallax, apparent depth, figure–ground ambiguity, flicker effects, and dynamic perspective. The comparative analysis indicates that interference patterns can significantly influence the perception of architectural space within its urban context. This influence extends beyond visual appearance and aesthetic composition, contributing to architectural communication, meaning-making processes, and the cognitive engagement of the viewer with spatial and visual structures. The study provides a structured analytical framework that may support further research on perceptual strategies in contemporary architectural design. Full article

In disaster emergency response, spatial location information embedded within social media texts holds substantial value for the rapid localization of affected areas and the implementation of precise rescue operations. Existing research predominantly employs natural language processing and deep learning technologies for geographic information extraction; however, two critical limitations persist: first, insufficient integration of textual semantic features for disaster relevance determination, resulting in inadequate correlation between extracted results and actual disaster locations; second, absence of mechanisms for identifying affected sites in multi-location contexts, thereby compromising decision support efficacy. Addressing these challenges, this study proposes a hierarchical disaster location information extraction framework that integrates semantic understanding. The framework operates through a three-tier hierarchy: data-level adversarial augmentation, semantic-level dynamic parsing, and parameter-level scale optimization. It achieves three core functionalities: (1) precise determination of disaster relevance for geographic location information; (2) identification of affected areas in multi-location contexts; (3) establishment of a logarithmic scaling relationship between LLM parameter scale and optimal prompt sample size. Full article

Integrating machine learning (ML) with Statistical Process Control (SPC) is important for Industry 4.0 environments. Contemporary manufacturing data exhibit high-dimensionality, autocorrelation, non-stationarity, and class imbalance, which challenge classical SPC assumptions. This systematic review, conducted following the PRISMA 2020 guidelines, provides a problem-driven synthesis that links these data challenges to corresponding methodological families in ML-based SPC. Specifically, we review approaches for (1) high-dimensional and redundant data (dimensionality reduction and feature selection), (2) autocorrelated and dynamic processes (time-series and state-space models), and (3) data scarcity and imbalance (cost-sensitive learning, generative modeling, and transfer learning). Nonlinearity is treated as a cross-cutting property within each category. For each, we outline the mathematical rationale of representative algorithms and illustrate their use with industrial examples. We also summarize open issues in interpretability, thresholding, and real-time deployment. This review offers structured guidance for selecting ML techniques suited to complex manufacturing data and for designing reliable online monitoring pipelines. Full article