Search Results (4,946)

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

In today’s digital era, cyberattacks pose a critical threat to networks of all scales, from local systems to global infrastructures. Intrusion detection systems (IDSs) are essential for identifying and mitigating such threats. However, existing machine learning-based IDS often suffer from low detection accuracy, heavy reliance on manual feature extraction, and limited coverage of attack categories. To address these limitations, we propose a modular, deployment-ready intrusion detection framework that integrates multiple heterogeneous datasets through a hybrid transformer–multilayer perceptron (Transformer–MLP) architecture. The system employs three parallel Transformer–MLP models, each specialized for a distinct dataset, whose probabilistic outputs are fused using a weighted decision-level strategy. Unlike traditional feature-level fusion, this strategy ensures module independence, eliminates the need for global retraining when adding new components, and provides seamless modular scalability. The framework accurately identifies twenty-one traffic categories, including one benign and twenty attack classes, derived from a unified mapping across multiple heterogeneous sources to ensure a consistent cross-dataset taxonomy. By combining advanced contextual representation learning with ensemble-based probabilistic fusion, the framework demonstrates high detection accuracy and practical applicability in real-world network environments. The Transformer module captures complex contextual dependencies, while the MLP performs final classification. Class imbalance is mitigated via adaptive synthetic sampling (ADASYN), synthetic minority over-sampling technique (SMOTE), edited nearest neighbor (ENN), and class weight adjustments. Empirical evaluation demonstrates the framework’s high effectiveness: for binary classification, it achieves 99.98% on CICIDS2017, 99.19% on NSL-KDD, and 99.98% on NF-BoT-IoT-v2; for two-stage multi-class classification, 99.56%, 99.55%, and 97.75%; and for one-phase multi-class classification, 99.73%, 99.07%, and 98.23%, respectively. Moreover, the framework enables real-time deployment with 4.8–6.9 ms latency, 9800–14,200 fps throughput, and 412–458 MB memory. These results outperform existing multi-dataset IDS approaches, highlighting the architectural effectiveness, robustness, and practical applicability of the proposed framework. Full article

The genus Xylaria comprises numerous species, particularly prevalent in tropical ecosystems such as those of Ecuador. Despite its ecological importance, the taxonomy of the genus remains challenging, and much of its diversity in the Neotropics remains under-documented. This study provides a preliminary characterization of the Xylaria diversity at the Estación Científica San Francisco, an Andean biodiversity hotspot in Southern Ecuador. Through an integrated approach including detailed macro- and micro-morphological descriptions and nuclear ribosomal DNA (nrDNA ITS and LSU) phylogenetic analyses, 20 Xylaria specimens were examined. As a result, ten species were recognized: Xylaria adscendens, X. cf. anisopleura, X. apiculata, X. curta, X. enterogena, X. fissilis, X. globosa, X. aff. telfairii, X. tuberoides, and X. aenea, the latter representing a new record for Ecuador. The phylogenetic analysis presented here serves as a preliminary systematic positioning of these specimens within the genus rather than a comprehensive global reconstruction. While these ribosomal markers provided preliminary insights into species relationships, partial incongruence with morphospecies highlights the evolutionary complexity of certain lineages and underscores the need for future multilocus studies. Furthermore, four additional phylotypes found in their anamorphic state are documented, suggesting that local diversity exceeds current records. By providing detailed morphological documentation supported by preliminary barcode data from a poorly sampled region, this study contributes vital information to the global understanding of Xylaria and underscores the importance of Southern Ecuador as a reservoir of fungal diversity. Full article

Rapid, standardised discrimination of soil taxonomic units remains challenging when relying solely on conventional field descriptions and laboratory analyses, particularly at high sampling densities. This study evaluated whether proximal spectroscopy and hyperspectral imaging can support the classification of Brazilian Soil Classification System (SiBCS) suborders and pedogenetic horizons when surface and subsurface spectra are treated separately. Six intact soil monoliths (0.12 × 1.60 m) were collected in Paraná State, southern Brazil, representing one Organossolo (Ooy), three Latossolos (LVd, LVd1, and LVd2) and two Argissolos (PVAd and PVd). For each monolith, 800 spectra were acquired per sensor with a non-imaging VIS–NIR–SWIR spectroradiometer (350–2500 nm), and 800 spectra per sensor per monolith were extracted from the SWIR hyperspectral images (1200–2450 nm). Principal component analysis (PCA) was used to summarise spectral variability, and supervised classification was performed via k-nearest neighbours, random forest, decision tree and gradient boosting for suborders (10-fold cross-validation), and a neural network was used for within-profile horizon classification. PCA indicated that most of the spectral variance was captured by a dominant axis, with clearer separation among suborders in the SWIR space than in the full VIS–NIR–SWIR range. With respect to suborder classification, subsurface spectra outperformed surface spectra, and SWIR outperformed VIS–NIR–SWIR: the best accuracies were 0.96 for subsurface SWIR (gradient boosting; AUC = 0.99; MCC = 0.95) and 0.89 for surface SWIR (k-nearest neighbours; AUC = 0.98; MCC = 0.87). Within-profile horizon classification via VIS–NIR–SWIR achieved accuracies of 0.84–0.97 with the Neural Network, with most misclassifications occurring between adjacent horizons. Overall, subsurface SWIR information provided the most reliable basis for taxonomic discrimination, whereas horizon classification was feasible but reflected gradual spectral transitions along the profile. Full article

Ants are vital bioindicators that contribute to soil health and food webs, making accurate identification essential for biodiversity monitoring and conservation. However, traditional taxonomic methods are time-consuming and require specialized expertise, limiting large-scale data collection and public participation. This paper presents AntID_APP, a web-based application designed to support citizen scientists in Taiwan by enabling real-time, image-based detection and the identification of native ant genera. Fine-tuned YOLO models first detect ants in user-uploaded images and then classify them at the genus level. The models were trained on a curated dataset of 60,429 open-access images from iNaturalist, covering 54 native ant species. To ensure robustness in real-world conditions, we applied targeted data augmentation and evaluated multiple YOLO versions (v9–v12). The best-performing model achieved a mean Average Precision (mAP50: 0.935–0.948, mAP50-95: 0.777–0.807) for the detection task, followed by accurate genus-level identification. The application features an intuitive interface and a lightweight asynchronous server architecture, allowing users to upload images and receive both visual detection results (bounding boxes) and genus predictions efficiently. By combining high accuracy with accessibility, AntID_APP offers a scalable solution for biodiversity monitoring and public engagement in ecological research. Full article

Power electronic converters are being pushed toward higher power density and switching frequency, turning both design and operation into multi-objective, multi-physics optimization problems. While analytical rules and gradient-based methods remain essential, they often struggle with non-convex, mixed-integer trade-offs that include thermal behavior, Electromagnetic Interference/Electromagnetic Compatibility (EMI/EMC), and reliability constraints. This review surveys intelligent optimization approaches for power electronics across design-time, commissioning-time, and run-time horizons. We propose a deployment-oriented taxonomy of intelligent optimization approaches covering metaheuristics, surrogate-assisted and learning-guided design, constrained optimization via model predictive control, reinforcement learning-based supervisory policies, and hybrid physics-informed methods. For each family, we summarize typical tasks, computational and data requirements, robustness, interpretability, and validation maturity, highlighting where intelligent methods provide clear benefits and where classical approaches remain preferable. Reliability- and diagnostics-oriented optimization is discussed with emphasis on residual-based monitoring, stress-aware operation, and lifetime proxies. Practical adoption barriers—model–reality mismatch, data scarcity, real-time determinism, and certification—are synthesized into recurring design patterns that improve deployability. Finally, a conceptual cognitive design framework is proposed that couples virtual engineering, physics-informed surrogates, human-in-the-loop validation, and knowledge reuse in a closed-loop workflow, offering a structured perspective on how intelligent optimization may be integrated more reliably into industrial design practice. Full article

With in situ imaging systems becoming more common, precise, and economically viable, use of these systems has grown dramatically, including both automated classification and biomass estimations. However, a rather large and overlooked portion of these efforts is reliable detection and classification of these organisms as they pass through the imaging device. This paper focuses on the development of an end-to-end classification CNN-based algorithm for marine zooplankton using the in situ Ichthyoplankton Imaging System (ISIIS-DPI) from Bellamare (La Jolla, CA, USA). Our novel approach considers many issues with automated segmentation and classification, including over-segmentation, noise segmentation, and organism size input. This allows for classifications in diverse water types, demonstrated by the comparison of three datasets created in conjunction with this project, each with very different water properties and zooplankton communities (Florida Gulf coast; Trondheimsfjord, Norway; Sargasso Sea). Our segmented image dataset contains 70,624 regions of interest (ROIs) across four organism classes—Chaetognath, Crustacean, Gelatinous, and Larvacean—with two classes dedicated to detritus. Four common network architectures—Resnet, Xception, GoogleNet, and Darknet—are trained on this dataset, with final test accuracies in the range of 95.94–96.09%. Following this initial training, a secondary level of classification is introduced. The base Gelatinous class is further divided into six groups. The same four CNN architectures are used once again, with final accuracies in the range of 86.12–90.40%, showing the ability to taxonomically classify down to the order level. The present work introduces a versatile, adaptable, scalable and autonomous segmentation and classification algorithm using niched networks mirroring taxonomy, and is fully contained in a publicly available MATLAB R2025a custom graphical user interface. Full article

As Unmanned Aerial Vehicles (UAVs) become integral to critical infrastructure, ranging from precision agriculture to emergency disaster recovery, their security becomes a matter of systemic resilience. This paper provides a comprehensive thematic survey of the security landscape for unmanned devices, bridging the gap between low-level hardware vulnerabilities and high-level mission failures. We propose a multidimensional taxonomy that categorizes challenges into hardware roots of trust, swarm intelligence threats, and domain-specific applications. A primary focus is placed on the Resource–Security Paradox, where the energy cost of heavy cryptographic or AI defenses directly reduces flight endurance, creating a trade-off that adversaries exploit through battery-exhaustion attacks. Beyond standard threats, we analyze emerging risks in additive manufacturing supply chains, the “Sim-to-Real” gap in AI-driven perception, and the legal necessity of Digital Forensic Readiness (DFR) for post-incident attribution. Through a systematic review of defensive frameworks, including lightweight encryption, Mamba-KAN anomaly detection, and blockchain-anchored logging, we evaluate the effectiveness of current solutions against complex adversarial models. Finally, we identify critical research gaps, providing a roadmap for security-by-design in the next generation of critical infrastructure swarms. Full article

The naviculoid genus Luticola exhibits a high degree of morphological convergence, complicating species delimitation when based solely on traditional morphometrics. Here, we describe Luticola edaphica sp. nov., a new species isolated from the forest soils of Mount Sestra (Primorsky Territory, Russian Far East) using an integrative taxonomic approach (phylogenetic, morphological, ultrastructural, and life cycle data). Molecular phylogenetic analysis, based on the chloroplast rbcL gene, placed the new strain within the Luticola clade, showing the closest affinity to L. tenera. However, L. edaphica is distinguished from similar Luticola species by a unique combination of morphological traits (structure of the valvocopula, maximal valve length and width, position and number of striae in 10 µm, central area, and distal raphe ends). A comprehensive study of its life cycle revealed that L. edaphica is homothallic and capable of both cis- and trans-anisogamy, with the latter being reported for the genus for the first time. Full article