Search Results (4,963)

Russian-language resources for medical natural language processing (NLP) are expanding rapidly; however, their fragmentation, uneven curation, and limited clinical reliability hinder the development of safe machine learning systems for prognosis, prevention, and precision medicine. We provide the first systematic survey of Russian medical NLP datasets and analyze their suitability for clinically meaningful tasks as defined by the MedHELM taxonomy. We additionally perform expert clinical validation of three representative public corpora—RuMedPrimeData (real outpatient notes), MedSyn (synthetic clinical notes), and RuMedNLI (translated natural language inference)—assessing clinical plausibility, diagnosis accuracy, and logical consistency. Experts identified substantial reliability issues: across randomly sampled subsets of each corpus, only approximately 20% of RuMedPrimeData records, fewer than 15% of MedSyn records, and approximately 55% of RuMedNLI pairs met essential quality criteria, which can hinder downstream ML systems built on these data. To support robust applications—ranging from medical chatbots and triage assistants to predictive and preventive models—we outline practical requirements for high-quality datasets: coordinated, expert-validated, machine-readable corpora aligned with clinical guidelines and insurance logic, standardized de-identification, and transparent provenance. Strengthening these data foundations will enable the development of reliable, reproducible, and clinically relevant AI systems suitable for real-world healthcare applications. Full article

Indonesia’s rapid digital growth has been accompanied by escalating cyber threats, with public discourse on social media emerging as a critical but underutilized source of threat intelligence. This discourse is characterized by informal language and local nuances that render existing international cybercrime taxonomies ineffective, creating a gap in scalable, locally relevant threat analytics. This study introduces the Indonesian Cybercrime Threat Taxonomy (ICTT), a novel five-dimensional framework tailored to Indonesian online environments. An end-to-end OSINT pipeline was developed to collect 2344 samples from X (formerly Twitter) and YouTube, employing weak supervision with 12 high-precision regex patterns to generate training labels. A state-of-the-art IndoBERT model was fine-tuned on this data, and its performance was compared against rule-based and hybrid classification models. On a manually annotated gold-standard dataset of 600 samples, both the IndoBERT and hybrid models achieved 96.8% accuracy, significantly outperforming the rule-based baseline (66.7%). The models demonstrated strong generalization across both social media platforms, and the hybrid approach provided an effective balance of high performance and interpretability. This research demonstrates that informal public discourse can be systematically transformed into structured threat intelligence. The ICTT and the accompanying hybrid classification system provide a scalable, interpretable, and locally relevant foundation for cyber threat analytics in Indonesia, establishing a methodological blueprint for other low-resource language contexts. Full article

The increasing complexity and uncertain system of modern discrete event system (DES) challenge traditional model-based control approaches, while artificial intelligence (AI) techniques offer powerful data-driven decision-making capabilities but lack formal guarantees. This review surveys recent research on the integration of AI with DES and supervisory control theory. Following a systematic literature mapping methodology, the literature is organized using a taxonomy based on three orthogonal perspectives: control and decision paradigm, system capability and property, and application and operational objectives. The review highlights how learning-based methods enhance adaptability and performance in DES, while also exposing persistent challenges related to safety, nonblocking behavior, data efficiency, and interpretability. By structuring existing approaches and identifying open issues, this review provides a coherent overview of the current research landscape and outlines key directions for future work on AI-enabled DES. Full article

Meditation-related difficulties have been systematically documented in contemporary contemplative science, yet the prescriptive resources preserved in the ancient Buddhist commentarial literature remain underutilized in comparative research. This study analyzes the case of five hundred monks in the Paramatthajotikā I’s commentary on the Karaṇīya-metta-sutta. During intensive practice, these monks experienced complex psychosomatic symptoms—perceptual disturbances, fear, somatic distress, and cognitive impairment—and received from the Buddha an integrated prescription of five protective practices (pañca rakkhā). Through Pāli textual and comparative analysis with Lindahl et al.’s taxonomy of meditation-related difficulties, this study demonstrates that the monks’ symptoms correspond systematically to the perceptual, affective, somatic, and cognitive domains of the modern taxonomy, with the critical difference residing in interpretive frameworks rather than in the phenomena themselves. The five practices—loving-kindness meditation, protective chant recitation, contemplation of impurity, mindfulness of death, and the arousal of religious urgency—constitute a sequentially structured system progressing from the emotional reframing of fear to the deconstruction of bodily and existential attachment, culminating in the restoration of soteriological motivation. This study argues that Paramatthajotikā I’s prescriptive system provides a historically grounded, soteriologically oriented complement to contemporary contemplative science, particularly in bridging the gap between phenomenological classification and meaning-centered intervention. Full article

The genus Erysimum (Brassicaceae) comprises more than 150 species distributed mainly across Europe, Central Asia, East Asia, the Middle East, North Africa and North America, many of which are traditionally used for treating cardiovascular, respiratory, and inflammatory disorders. Plants of this genus are rich in various groups of secondary metabolites, including cardenolides, glucosinolates and isothiocyanates released from them, sterols, phenolic compounds such as flavonoids and tannins, and other secondary metabolites. This review synthesizes its unique phytochemical profile, characterized by the coexistence of ancestral glucosinolates and independently evolved cardenolides. Over 100 cardenolide structures based on 15 aglycones have been reported from Erysimum, although the structural characterization of several compounds remains inconsistent or incomplete, with some glycosides still absent in major chemical databases. A variety of pharmacological activities have been documented for extracts and isolated constituents, including cardiotonic, anti-inflammatory, antioxidant, antimicrobial, and cytotoxic effects, supporting the therapeutic potential of the genus. Ecologically, the genus employs a two-tiered defense strategy where strophanthidin-based compounds deter butterfly oviposition and digitoxigenin-based compounds repel larval feeding. This review summarizes current knowledge on the taxonomy, distribution, phytochemical composition, and biological activities of Erysimum species, with a focus on cardenolide diversity, structural ambiguities, and research gaps that require further investigation. Full article

Diffusion models have emerged as a powerful class of generative models, demonstrating impressive results across visual domains such as image and video synthesis. This survey provides a comprehensive taxonomy of generative models, with a particular focus on diffusion models and their applications in enhancing visual fidelity for text-to-image and text-to-video generation. We discuss the theoretical foundations of diffusion models, including their formulation through stochastic differential equations, and analyze the forward noising and reverse denoising processes that enable stable training and high-quality generation. The survey further categorizes diffusion architectures, including pixel-space and latent-space models, and examines their design choices, training strategies, and trade-offs across different resolution regimes. In addition, we review noise characteristics in real-world imaging domains and discuss their implications for diffusion-based models. Denoising strategies are analyzed by distinguishing between in-model denoising mechanisms and external denoising techniques used in preprocessing and post-processing pipelines. The survey also summarizes commonly used datasets and evaluation metrics for generative modeling, providing a practical perspective on benchmarking and model comparison. Finally, we discuss current challenges, including computational efficiency, scalability, and robustness to diverse noise distributions, and outline potential directions for future research. This survey aims to provide a structured reference for understanding diffusion models and their applications in visual generation tasks. Full article

Integrating Lean Construction (LC), the Circular Economy (CE), and Construction 5.0 (C5.0) remains challenging in emerging delivery contexts. This difficulty increases when procurement routines determine which practices become enforceable across tendering, contracting, and site execution. This study prioritized barriers to LCCE5.0 implementation in Morocco and translated expert judgments into actionable recommendations. A structured literature review informed the barrier inventory and conceptual framing. The study proposed a three-layer, life-cycle LCCE5.0 framework that links governance, operational routines, and digital enablers. It operationalized 40 critical barrier factors across six dimensions and five life-cycle macro-phases. A two-round Delphi study was conducted with 22 Moroccan experts using a 7-point Likert scale. Barriers were ranked using Round 2 (T2) medians with ties resolved using the interquartile range. Top-box agreement (ratings of 6–7) and consensus tiers were reported. The ranking showed strong stability across rounds, with 92.5% of barrier factors remaining stable. Kendall’s W at T2 equaled 0.817 (p < 0.001), indicating high panel consensus. Results indicated that constraints clustered in upstream governance. Three procurement-centered regulatory and contractual barriers topped the ranking (Mdn_T2 = 7). These barriers reflected missing CE procurement guidelines, limited weighting of environmental criteria, and the absence of circularity and digital requirements in tenders. Six additional barriers reinforced this procurement bottleneck. They included limited owner commitment, weak enforcement authority, limited top-management commitment, and regulatory instability. They also included low interorganizational trust, limited risk-sharing contracts, and tool-centered deployment of LCCE5.0 practices. These findings support procurement-focused recommendations to institutionalize auditable circular requirements and data-enabled verification in tendering and contracting routines. The proposed LCCE5.0 mechanism and the resulting recommendations require empirical validation beyond this Delphi-based prioritization. Full article

This study presents a systematic comparative benchmark between two distinct paradigms for solving nonlinear partial differential equations (PDEs): the data-driven Physics-Informed Neural Networks (PINNs) and the analytical Homotopy Analysis Method (HAM). We apply both methods to a unified family of canonical PDEs, the Burgers, Huxley, Fisher, Burgers–Huxley, and Burgers–Fisher equations, under identical problem setups, domain discretization, and validation metrics. PINNs incorporate physical laws directly into neural network training by minimizing a loss function that enforces PDE residuals, yielding physically consistent solutions even for strongly nonlinear problems. HAM provides approximate analytical solutions using a unified framework, and the same initial guess, auxiliary linear operator, and auxiliary function across all equations despite their distinct nonlinearities. The controlled, consistent application of both methods enables a fair, reproducible comparison across this equation family. The results provide a quantitative performance map under identical conditions, delineating when PINNs (high accuracy, long-term stability, and generalization capability) are preferable, versus when HAM (computational speed, short-term analytic approximation, and lower memory footprint) offers advantages. While the finite radius of convergence of the truncated HAM series is theoretically expected, our controlled comparison quantifies for the first time how this degradation varies across equation types, revealing that the choice between methods depends on specific problem requirements including error tolerance, available computational resources, and temporal horizon. The novelty lies not in solving each equation individually, but in deriving a performance taxonomy that systematically connects equation features (shocks, stiffness, and reaction–diffusion coupling) to optimal solver choice—providing previously unavailable, evidence-based guidance for the scientific computing community. This study establishes the first rigorous, controlled comparative benchmark between analytic and data-driven PDE solvers across a spectrum of nonlinearities, providing a reproducible baseline for future hybrid scientific machine learning solvers. Full article

Nematodes in the genus Oscheius (Rhabditidae) have traditionally been regarded as free-living bacteriophagous or necromenic associates of insects. Over the past two decades, however, multiple Oscheius species and isolates have been shown to express facultative pathogenicity toward insects and, in some cases, parasitism of mollusks. This has stimulated interest in Oscheius as a complementary group of biological control agents that may function under conditions limiting classical entomopathogenic nematodes (EPNs) of the genera Steinernema and Heterorhabditis. Here, we synthesize current knowledge on Oscheius taxonomy and diversity, life-history strategies, bacterial associations and virulence mechanisms, evidence for control of insect and mollusk pests, and recent advances in chemo-ecology relevant to host finding. We emphasize that Oscheius represents a continuum of ecological strategies, and we adopt conservative terminology in which “entomopathogenic” is reserved for Oscheius species/isolates that meet operational criteria of insect pathogenicity. Finally, we highlight key barriers to wider implementation—strain variability, bacterial partner instability, non-target and community effects, and production/quality control needs—and propose research priorities for the development of robust, field-reliable Oscheius-based biocontrol. Full article

In the contexts of higher education and Education for Sustainable Development, universities face the challenge of preparing professionals capable of addressing complex urban issues related to Sustainable Development Goal 11 (SDG 11). Learning landscapes, grounded in the theory of Multiple Intelligences and Bloom’s Taxonomy, have been proposed as a pedagogical framework to support the development of sustainability competencies and higher-order thinking; however, evidence regarding their applicability and viability in university teaching remains limited. This study examines an exploratory learning landscape–based training experience oriented toward SDG 11, focusing on university faculty perceptions. A design-based research approach with mixed-methods design was employed, emphasizing the co-construction, pilot implementation, and formative assessment of learning landscapes within a technical-scientific faculty development program. The results indicate generally positive faculty perceptions, particularly in terms of satisfaction, perceived learning, and professional development. Participants also reported pedagogical usefulness and perceived potential to enhance student motivation and engagement. However, stable curricular integration emerged as the main challenge, mainly due to design workload and the need for institutional support. Overall, the findings provide initial empirical evidence on the perceived value and limitations of learning landscapes in sustainability-oriented higher education and point to the need for further research and institutional conditions to support their implementation. Full article

Marine byproducts generated from seafood processing represent valuable reservoirs of structurally and functionally distinct biomolecules, whose composition reflects species, habitat, and processing history. This systematic review identified which marine byproducts have been most extensively studied between 2020 and 2025, with emphasis on their composition, valorisation, and suitability for tracing their geographic origin. Following the PRISMA protocol, 6443 publications were initially retrieved, of which 96 peer-reviewed studies were included for data extraction and analysis. The five most frequently investigated byproducts—skin, bones, scales, shells, and roe—were identified as rich sources of proteins (collagen and gelatin), minerals (hydroxyapatite and calcium carbonate), polysaccharides (chitin), lipids (notably polyunsaturated fatty acids (PUFAs), docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA)), and vitamin B12. Collagen properties, particularly imino acid content, hydroxylation degree, crosslinking density, and thermal stability, correlate more strongly with environmental temperature than taxonomy, supporting their potential as markers for tracing geographic origin. The mineral fractions, dominated by hydroxyapatite in bones and scales, or calcium carbonate in shells, provided complementary inorganic fingerprints based on calcium-to-phosphorus ratios, carbonate substitution, trace element composition, and thermal analyses. While the lipid profile alone could not completely discriminate fish roe, proteomic techniques, such as MALDI-TOF MS, make it possible to reliably identify species. Collectively, these byproducts offer complementary organic and inorganic markers that support integrated strategies that allow tracing their origin and fostering their sustainable valorisation, overcoming a key technical bottleneck for their use. However, their large-scale conversion into market-ready products remains limited by technical complexity, process variability, and cost-related constraints. Full article

Smart energy networks, including electricity distribution and district heating, are increasingly operated as sensor-enabled infrastructures where maintenance decisions must be made under heterogeneous and time-varying operating conditions. In these settings, time-to-event data are rarely complete; preventive actions and limited observation horizons routinely introduce censoring and truncation, so models and validation procedures must account for partially observed lifetimes to avoid biased inference and misleading performance estimates. This review surveys survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation, spanning non-parametric, semi-parametric, parametric, and learning-based approaches, with emphasis on censoring-aware formulations and the use of static and time-varying covariates derived from sensor, inspection, and contextual information. A structured taxonomy and a systematic mapping of model families to data types, core assumptions (proportional hazards versus parametric distributional structure), and decision-oriented outputs such as risk ranking, horizon failure probabilities, and RUL distributions are presented. Evaluation practice is also synthesized by covering discrimination metrics, censoring-aware RUL accuracy measures, and probabilistic assessment via proper scoring rules, including the time-dependent Brier score and Integrated Brier Score (IBS). The review provides researchers and practitioners with a practical guide to selecting, fitting, and evaluating survival models for risk-informed maintenance planning in smart energy networks. Full article

The isolation of locally adapted rhizobial strains with high symbiotic activity represents an effective strategy for increasing soybean yield under extreme environmental conditions. In this study, seven novel strains were isolated from nodules of soybeans grown in a greenhouse using field soil from the Lower Volga region. Five genomes were assembled into complete circular chromosomes, whereas two strains yielded near-complete chromosomes containing single repeat-mediated junctions. All strains had putative plasmids that were independently validated as circular by long-read mapping and confirmed by the presence of characteristic replication and conjugation-associated genes. Genome sequences of strains were about 11 Mb, and GC contents were 63.1–63.3%. Comparative genome analyses demonstrated that all strains had average nucleotide identity values of 95.4% with Bradyrhizobium japonicum USDA 6^T and 96.3% with Bradyrhizobium barranii 144S4^T, forming a distinct cluster in phylogenetic trees. No significant differences were detected between B. japonicum and B. barranii that would explain the species boundary. Therefore, it is proposed to unite all novel strains into the subspecies Bradyrhizobium japonicum subsp. saratovii subsp. nov., and all other strains of B. japonicum and B. barranii we suggest dividing into four subspecies: Bradyrhizobium japonicum subsp. japonicum subsp. nov., Bradyrhizobium japonicum subsp. barranii comb. nov., Bradyrhizobium japonicum subsp. apii comb. nov., and Bradyrhizobium japonicum subsp. saratovii subsp. nov. The proposed taxonomic framework expands current knowledge of the biodiversity of soybean symbiotic bacteria and contributes to a better understanding of the distribution and the evolution of bacteria Bradyrhizobium spp. in previously unexplored regions. Full article

This article introduces Memetic/Metaphorical Digital Twins (MDTs) as a novel extension of Digital Twin typologies by twinning conceptual schemes, complementing Industrial, Human, and Cognitive Digital Twins. MDTs embed cultural, organizational, and semiotic knowledge into digital frameworks, enabling the recombination and evolution of knowledge structures across disciplines. Drawing on Schlaile’s economic perspectives and Mavromatidis’s architectural lens of entropy and constructal thermodynamics, this study demonstrates how MDTs can address systemic challenges in communication, knowledge transfer, and design. A Digital Community Platform, under development for supporting decentralized Personal Knowledge Management Systems (PKMS), provides the operational foundation, integrating iterative KM cycles to support knowledge co-creation. Its logic and logistics substitute the traditional document paradigm with a memetic approach by utilizing memes as replicable, adaptive knowledge units, thereby mimicking biological evolution and ecosystem resilience in digital platform environments. It aims to offer distributed, decentralized, bottom-up, affordable, knowledge-worker-centric applications prioritizing personalization, mobility, generativity, and entropy reduction; its mission is to serve a knowledge-co-creating community characterized by highly diverse individual Abilities, Contexts, Means, and Ends (ACME) facing increasingly volatile, uncertain, complex, and ambiguous futures (VUCA). A Boundary Object Taxonomy to Omnify Memetic Storytelling (BOTTOMS) is proposed to further structure atomic units of meaning—such as memes, mythemes, narratemes, and reputemes—into a unified framework for authorship and dissemination. The article situates MDTs within a design science research paradigm, outlines current implementation progress, and identifies future developments, including AI-supported curation, personalized metrics, and expanded boundary objects. Together, these contributions position MDTs as a universal framework for adaptive, transdisciplinary knowledge co-creation. Full article