Search Results (4,954)

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

Soda lakes are ecologically significant habitats characterized by high salinity, alkaline pH, and intense evaporation. These milieus are hostile to most life, though these lakes could be a rich source for discovering novel halotolerant and halophilic cyanobacterial taxa. The Indian subcontinent is endowed with shallow saline–alkaline lakes whose cyanobacterial diversity has been little explored. The present study was undertaken to explore the cyanobacterial diversity in an inland saline–alkaline lake (Sambhar Lake) in India using a polyphasic approach. Two thin, filamentous strains encapsulated within thick sheaths and capable of nodule formation under normal light conditions were recovered. Both isolates exhibited growth at up to 4% salinity, indicating their halotolerant nature. The studied strains exhibited <95% 16S rRNA gene similarity with closely related taxa from the genera Thainema and Insularia and formed a distinct evolutionary lineage in phylogenetic tree supported by a high bootstrap value. Additionally, the secondary structures of the 16S-23S Internal Transcribed Spacer (ITS) regions (D1-D1′ and BoxB) of the studied strains showed remarkable differences from phylogenetically closely related taxa, indicating these strains represent a new genus in the Nodosilineales: Krienitziella sambharensis gen. et sp. nov., in accordance with the International Code of Nomenclature for Algae, Fungi, and Plants (ICN). Full article

Geometric morphometrics is an important component of quantitative research on insect morphology, widely applied in taxonomy, intraspecific variation, and phylogenetic studies. However, systematic research in this field remains limited, with few comprehensive summaries of research trends, hotspots, and core theories. This study, based on scientometric methods, analyzed 1321 publications indexed in the Web of Science database up to 31 December 2025, and presents a meta-scientific review from a macro perspective, revealing the research trends, hotspots, and future directions in the field. The results show that: (1) annual publications exhibit overall growth, while research methods evolved from single landmark analysis to multimodal and interdisciplinary approaches; (2) scientists from Brazil, the USA, and France are major contributors, with studies spanning morphology, taxonomy, and ecology; (3) taxonomic studies centered on wing shape analysis constitutes a major research hotspot, closely related to phylogeny, allometry, and sexual dimorphism; (4) highly co-cited studies provide the main theoretical and methodological foundations for the field. Future research, building on existing hotspots, will further integrate geometric morphometrics with genomics, ecological functional data, three-dimensional geometric morphometrics, and artificial intelligence-assisted approaches to advance integrative taxonomy within interdisciplinary and data-driven frameworks. Full article

Using transaction cost economics (TCE) and agency theory, this paper examines how blockchain, smart contracts, and decentralized autonomous organizations (DAOs) reconfigure financial services across payments, wealth management, real estate, and corporate governance. Three research questions are addressed: (1) What are the quantifiable efficiency gains from blockchain-based real-time settlement compared with legacy systems? (2) How do blockchain technologies reduce intermediation and agency costs in wealth management and real estate? (3) Finally, to what extent do DAOs resolve or transform traditional corporate governance problems? By combining a present-value model calibrated to U.S. Automated Clearing House (ACH) data ($86.2 trillion in annual volume), comparative institutional analysis, and synthesis of empirical evidence from pilot implementations and on-chain governance metrics, this paper makes three principal contributions. First, real-time settlement yields approximately $12 billion in annual opportunity cost savings at the baseline 7.5% discount rate, with sensitivity analysis producing a range of $8–15 billion. The majority of gains accrue from moving to same-day or within-hour settlement. Second, tokenization and smart contract escrow substantially reduce real estate intermediation costs, blockchain-based digital identity streamlines wealth management onboarding, and a stablecoin taxonomy classifies fiat-collateralized, crypto-collateralized, and algorithmic designs by risk profile. Third, on-chain data reveal persistent governance token concentration (Gini > 0.98) and low voter participation (typically below 10%), exposing a gap between DAO theory and practice. Blockchain-specific risks are mapped to National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0, and mechanism design solutions, such as quadratic voting and AI-assisted proposal evaluation, are proposed to address whale dominance. Effective adoption requires hybrid architecture combining on-chain automation with off-chain structures for accountability and regulatory compliance. Full article

This paper presents a taxonomy-based survey of AI-driven network optimization mechanisms relevant to the transition from fifth generation (5G) to sixth generation (6G) mobile communication systems. In contrast to earlier generational shifts that are often described as technology replacement cycles, the 5G-to-6G evolution is increasingly characterized in the literature as a prolonged period of coexistence, hybrid operation, and progressive integration of new capabilities across radio, edge, core, and service layers. To structure this transition, the paper organizes prior work into a transition-oriented taxonomy covering migration strategies, AI-enabled closed-loop control, RAN disaggregation and edge intelligence, core virtualization and slice orchestration, spectrum-aware coexistence, service-driven requirements, and security-aware governance. Rather than introducing a new optimization algorithm or an experimentally validated architecture, the contribution of this survey is analytical and integrative. Specifically, it consolidates fragmented research directions into a reference view of how AI-driven control mechanisms are distributed across spectrum, RAN, edge, and core domains during hybrid 5G–6G operation. In addition, the paper includes a structured evidence synthesis of performance trends, deployment maturity signals, and recurring methodological limitations reported across the literature. The review indicates that meeting anticipated 6G objectives, including ultra-low latency, high reliability, scalability, and improved energy efficiency, depends less on isolated enhancements at individual protocol layers and more on coordinated cross-layer optimization supported by AI-native control loops. At the same time, the surveyed literature reveals persistent gaps in service-to-control mapping, security-aware orchestration, interoperability across heterogeneous domains, and reproducible evaluation methodologies for hybrid 5G–6G environments. The survey is intended to provide researchers, network operators, and standardization stakeholders with a structured analytical basis for assessing how AI-driven optimization can support the staged evolution from 5G systems toward 6G-ready infrastructures. Full article

Arthropod pest species represent a serious threat to agriculture and forestry. Canada is no exception, with over 1200 recorded pest species. Although a consolidated dataset would benefit research, management, and policy, information on these species has never been compiled into a unitary database. This publication merges available information to create a dataset for the agroforest pest insects of Canada and updates the taxonomy for these records based on the Catalog of Life and GBIF. Each species record includes (1) their global distribution, (2) feeding guild for adults and larval stages (when available), (3) host plant data (when available) and (4) identifiers to obtain DNA barcodes from the Barcode of Life Data Systems (when available). Full article

Background/Objectives: Healthcare workers (HCWs) have a crucial role in addressing vaccine hesitancy in ethnic minority populations as they are a trusted source of information. The aim of this systematic review is to synthesise and evaluate behaviour change techniques (BCTs) and strategies in interventions aimed at HCWs to promote vaccine uptake among ethnic minority populations. Methods: The literature was systematically searched in peer-reviewed databases and the grey literature. Studies were included if they reported interventions for respiratory and routinely recommended vaccine-preventable diseases which were delivered by HCWs to increase vaccine uptake in ethnic minority groups. Interventions were coded using the Behaviour Change Wheel (BCW) and BCT Taxonomy. Results: From 7250 records identified, 14 studies were included in the review. Vaccines targeted by interventions included influenza, pneumococcal disease, pertussis, tetanus, diphtheria, meningitis and hepatitis B. Seven BCW intervention types, six policy options and 22 BCTs were identified. Main intervention types used were persuasion, enablement and education. Effective interventions had multi-components and were tailored to specific populations. Staff training to improve vaccine recommendation and dialogue with patients, and prompts/cues were associated with positive effects, but there was no strong evidence to recommend one specific intervention strategy over another as effectiveness was linked to a multitude of BCTs and intervention types. Conclusions: Several strategies aimed at HCWs can be used and tailored to increase vaccine uptake among ethnic minority communities; however, this does not address all issues related to low vaccine uptake. While HCWs are necessary, without system-level enablement, they cannot fully address barriers to vaccine uptake. Full article

Bovine viral diarrhea (BVD) is a major cause of economic losses in the global cattle industry, particularly in countries characterized by intensive livestock production systems. Pestivirus tauri, formerly known as Bovine viral diarrhea virus type 2 (BVDV-2), is the current taxonomic designation according to the International Committee on Taxonomy of Viruses (ICTV). Between 2005 and 2018, Pestivirus tauri was detected in cattle herds in mainland Italy, particularly in the Lombardy region. Four viral strains were successfully isolated in cell cultures and subjected to whole-genome sequencing. Phylogenetic reconstruction placed all Italian isolates within the Pestivirus tauri subgenotype c, a lineage encompassing strains reported in Asia, Europe and the United States. Consistently, comparative sequence identity analyses indicated the highest similarity with the Parker strain (USA, 1991) and the Potsdam 1600 strain (Germany, 2000). These results contribute to a more detailed understanding of Pestivirus tauri genomic architecture and evolutionary dynamics, providing a valuable resource for comparative genomic studies. Such data are crucial for exploring viral diversity and evolution, optimizing the design of diagnostic primers and probes, and advancing insights into the molecular epidemiology of Pestivirus. Full article

We examine the population structure, habitat associations, spatial ecology, morphometrics, meristics and reproductive attributes of two species in the genus Malacoraja of Canada. M. senta, the only shelf-dwelling species of the genus, is also atypical of Rajidae, and marine fish in general, in forming disjunct populations. This unusual spatial structure appears to be the result of a fragmented thermal habitat. At the northern, coldest extent of their range, M. senta occur only within the troughs where temperatures are >3 °C, comparable to the thermal habitat further south. M. spinacidermis, consistent with its other congeners, is slope-dwelling, reaching the highest density at >900 m, concentrating in 3.1–4.0 °C. The two species are of a similar size and body proportions but less spiny for M. spinacidermis. Body and tail size and spine counts underwent allometric changes with growth. L₅₀ could not be determined for all populations, but Laurentian population L₅₀ was 45 cm for females, 51 cm for males; Funk males, 45 cm. Size at first maturity was similar between species. This pattern of maturity is reflected in the secondary sexual characteristics. There was partial separation of maturity stages by depth for M. senta, with immature fish distributing in greater depths. Full article

Elastic cloud systems are increasingly employing machine learning (ML) to automate resource scaling in response to variable workloads and stringent service-level objectives. However, current ML-based autoscalers are fragmented across different platforms, objectives, and evaluation frameworks. This survey examines 60 primary studies conducted between 2015 and 2025, categorising them according to a five-dimensional taxonomy that includes goal, decision logic, scaling mode, control scope, and deployment. This study classifies supervised, unsupervised, and reinforcement learning approaches and analyzes their integration into practical frameworks, including Kubernetes-based controllers and cloud provider services. This paper summarizes the application of machine learning to workload prediction, proactive and hybrid horizontal–vertical scaling, and adaptive policy optimization. Additionally, it synthesises common evaluation practices, encompassing workloads, metrics, and benchmarks. The analysis identifies ongoing challenges: actuation delays and telemetry lag, the intricacies of hybrid scaling, coordination across multi-service and edge-cloud deployments, and the constrained joint consideration of cost, SLO, and energy objectives. The identified gaps necessitate additional research on unified machine learning-driven orchestration, multi-agent and federated control, standardised benchmarks, and sustainability-aware autoscaling. Full article