Search Results (5,026)

Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security. Full article

Residential buildings must now be designed and retrofitted as adaptive climate–health–work systems rather than as static housing units. This structured literature review synthesises peer-reviewed journal and conference evidence on residential taxonomy, ventilation, indoor environmental quality, overheating, airborne infection resilience, post-pandemic occupancy changes and future performance benchmarks. The review shows that single-family and multifamily buildings remain the most practical first-order categories because they differ in envelope exposure, ventilation pathways, system ownership, governance, retrofit feasibility and occupant control. Single-family dwellings generally provide greater household autonomy, roof-based renewable potential and room-level intervention flexibility, but can also carry higher envelope losses, lower density and stronger dependence on occupant operation. Multifamily buildings benefit from compactness and shared infrastructure, yet face additional risks from common services, vertical shafts, stack effects, corridor pressurisation, inter-zonal airflow and collective maintenance. Ventilation evidence indicates that natural, exhaust-only, supply, balanced heat-recovery, hybrid, demand-controlled and filtration-based strategies cannot be ranked universally; their effectiveness depends on climate, airtightness, pollutant source, occupancy, maintenance and governance. This review further shows that overheating, cooling-demand growth, airborne infection preparedness and remote work are shifting residential performance from winter-centric energy efficiency toward year-round thermal resilience, clean-air delivery and prolonged-occupancy functionality. A future taxonomy is therefore proposed around adaptive performance attributes, including thermal resilience, clean-air capacity, ventilation controllability, energy flexibility, remote-work readiness, vulnerability and retrofit potential. The core contribution is a hypothesis-generating, decision-support and benchmark-development framework for aligning residential design, retrofit and policy with health, indoor environmental quality, energy efficiency and carbon performance. Full article

Arthrinium-like fungi in the family Apiosporaceae are taxonomically complex and still require a thorough characterization despite recent phylogenetic reassessments. This study aimed to investigate the diversity and taxonomic position of endophytic Arthrinium-like fungi associated with Itea japonica and I. riparia in Thailand. Two fungal strains discovered from healthy stems of these hosts were characterized by integrative approaches including morphology, multi-locus phylogenetic analyses based on ITS, LSU, TEF1-α, TUB2 sequence data, and nucleotide base–pair comparisons. One isolate from I. japonica is introduced as Nigrospora iteae sp. nov. supported by distinct morphological traits, a well-resolved phylogenetic placement, and significant nucleotide difference from its closest relatives. The second isolate was identified as Apiospora vietnamensis and is reported herein as a new host record for I. riparia based on morphological congruence, a close phylogenetic relationship, and TUB2 nucleotide similarity with the type strain. In addition, a new species, Apiospora fici, originally described from dead leaves of Ficus septica in Taiwan, is reclassified based on updated phylogenetic analyses to clarify its taxonomic placement within Apiosporaceae. Furthermore, Nigrospora wurfbainiae nom. nov. is proposed as a replacement name for the later homonym N. guangdongensis. A summary of important morphological characteristics, host relationships, current distribution, and biological activities of Nigrospora species is provided. This study emphasizes the previously unrecognized fungal diversity within Itea hosts and offers new insights into species diversity and phylogenetic relationships within the Apiosporaceae. Full article

This study presents an adaptive learning framework that integrates fuzzy logic and learning analytics to support personalized education and multi-factor student assessment. The proposed system combines cognitive and behavioral indicators to provide an interpretable representation of the learner’s state within a dynamic digital learning environment. The architecture is based on adaptive learner modeling and classroom-level monitoring mechanisms, enabling personalized guidance, adaptive content sequencing, and continuous performance monitoring at both individual and classroom levels. A core contribution of the approach is a fuzzy logic-based evaluation mechanism that aggregates multiple signals, including quiz performance, time spent on theory, help-seeking behavior, and system interaction patterns. These inputs are transformed into fuzzy sets and combined through inference rules to produce interpretable learning level estimates aligned with Bloom’s taxonomy. The approach is grounded in Vygotsky’s Zone of Proximal Development, supporting adaptive scaffolding and targeted instructional interventions. The evaluation results demonstrate a strong correlation between the model outputs and conventional exam performance (r ≈ 0.91), while exhibiting reduced variability (SD ≈ 0.15 compared to SD ≈ 0.20), indicating a more stable representation of learner performance. Furthermore, statistical analysis confirms that the differences between traditional and model-based scores are significant (p < 0.01), suggesting that the proposed approach captures additional dimensions of learner behavior beyond conventional grading metrics. Overall, the findings indicate that integrating fuzzy reasoning with behavioral analytics enables a more interpretable, stable, and pedagogically grounded approach to learner assessment, supporting adaptive and interpretable personalized learning. Full article

This study presents a Systematic Literature Review (SLR) of gamified platforms for cybersecurity education and training, with a particular focus on environments that incorporate attack–defence simulation. The review examines how the literature addresses gamification, User eXperience (UX), didactics, platform design, and sustainability-related deployment conditions across higher education as the primary reference context, while also considering adjacent applied training contexts when they provide transferable evidence for platform design, deployment, or evaluation, including online learning. We analysed 172 studies published between 1 January 2015 and 8 March 2026. The findings show that the field combines authentic hands-on practice with challenge-based learning, feedback-rich progression, and diverse technical formats, including cyber range environments, serious games, CTF-oriented platforms, cloud-based infrastructures, and modular training systems. The review also indicates that the educational value of these approaches depends on the alignment between pedagogical structure, interaction design, and technical architecture, as well as on safe experimentation, adaptability, governance, and long-term maintainability. The review contributes an evidence-based taxonomy and configurational synthesis of recurrent design patterns across UX, didactics, gamification, architecture, and sustainability, and it identifies implications for future empirical research and for the design of sustainable, learner-centred cybersecurity teaching platforms. Full article

The application of Internet of Things (IoT) technology to Digital Twin (DT) in smart manufacturing has opened significant opportunities for real-time monitoring, predictive maintenance, and closed-loop control; however, the inherent latency that exists in these architectures (the temporal gap between a physical event and its reflection in a digital model) remains one of the most significant and least systematically understood barriers to fulfill its full potential. This paper aims to propose a formal four-layer taxonomy of latency sources in IoT-based Digital Twin systems for smart manufacturing and to review the current approaches and tools that are available for their measurement. The PRISMA protocol has been used to perform a systematic literature review, where 58 primary survey studies published between 2020 and 2026 were extracted from IEEE Xplore, Elsevier Scopus, Google Scholar and arXiv, with all the studies being coded along six dimensions (architectural layer, application domain, latency metrics reported, evaluation methodology, quantitative impact, and enabling technologies). The proposed taxonomy presents 28 different types of latencies under four layers: (L1) network, (L2) compute, (L3) data, and (L4) end-to-end (E2E), whose magnitudes vary from 0.1 ms for local network propagation to tail latencies above 500 ms in production (P99). Three categories and three cross-layer interaction patterns are formalized here and are absent from prior partial taxonomies. Among the most promising results is the finding that several high-impact interventions require no infrastructure investment: a protocol migration from Modbus to WebSocket reduces telemetry latency by 32%, while Age of Information-aware synchronization and clock drift correction deliver substantial data layer gains through software updates alone, yet remain underutilized. The review identifies a systematic under-reporting of tail-latency percentiles across the corpus, the lack of a cross-protocol jitter benchmark, and a predominance of simulation-based evaluation over real-hardware measurement. The systematic review contributions of this paper (the formal four-layer taxonomy, the proportional metric audit across the 58 papers, and the formalization of three cross-layer interaction patterns) are derived from cross-corpus analysis. The investigation also identifies three open research directions (a standardized manufacturing IoT-DT benchmark, cross-layer joint optimization frameworks, and wireless TSN validation on real manufacturing testing grounds) that together form a well-organized and practical basis to advance both the science and the application of ultra-low-latency Digital Twin technology in the industrial field. Full article

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots. Full article

Phlebotomine sand flies are dipterans that transmit leishmaniasis, bartonellosis, and arboviruses of public health importance. Colombia is a tropical country with high annual incidences of arboviruses, such as dengue and, more recently, yellow fever, all of which have similar symptoms. This study characterized the viruses circulating in phlebotomine sand flies in two departments in the Colombian Caribbean. Between August 2023 and December 2024, a descriptive study was conducted in the Departments of Córdoba and Cesar in Colombia. Four municipalities were selected per department, and four insect captures were performed using CDC light traps. Specimens were taxonomically identified and organized into groups according to species and study area, and total RNA was extracted for NGS analysis. Short sequences were quality-assessed, assembled using MEGAHIT to obtain contigs, and classified using DIAMOND-MEGAN6 to select viral genomic sequences for phylogenetic analysis. Thirteen viral families were identified, including a virus from the family Rhabdoviridae in Pi. evansi in the department of Cesar and another from the family Dicistroviridae in Lutzomyia gomezi in both departments. Two genome segments of the family Phenuiviridae were found in Lutzomyia gomezi in the department of Córdoba, Colombia. Sand flies harbor a diverse range of viral families, some of which are previously undescribed, and can be studied to determine their taxonomy and assess their potential to infect vertebrate cells or their interactions with medically important pathogens such as Leishmania spp. Full article

We study convergence in multi-agent reinforcement learning (MARL) through the lens of sufficient conditions, using a single-point-of-failure analysis applied to multi-agent policy iteration integrated with linear programming (MAPI-LP), where results are proven for pure coordination games, and extension to broader settings is conjectured. We identify two sufficient conditions for convergence to Markov Perfect Equilibrium (MPE). The first is stability in best-response space that emerges from value monotonicity. The second, monotonic best-response space shrinking (MBRSS), is a novel condition requiring that each agent’s best-response space contracts monotonically across iterations until it collapses to a stable space. Furthermore, we show that MBRSS does not necessarily imply monotonic improvement in this setting. However, value monotonicity and stability in best-response space imply each other when a complementary condition is applied. Building on this hierarchical relationship, we propose conjectures on sufficient condition relationships in both serial and parallel MARL. In addition, we propose conjectures on generalized MBRSS to arbitrary finite repeated games and validation of stability in best-response space. We further discuss connections between MBRSS and existing related frameworks, and outline directions toward a taxonomy of sufficient conditions for MARL convergence. Full article

This exploratory study investigates the application of the EU Taxonomy within the Romanian capital market, focusing on companies included in the BET Index and analyzing discrepancies between declarative sustainability commitments and actual technical compliance. Employing documentary content analysis and descriptive statistical design, the research introduces a convergence matrix that compares declarative intensity with taxonomic potential, mainly reflected through eligible turnover and complemented by CapEx-related indicators. The findings suggest a systemic execution gap. Despite significant eligibility in certain cases, technical alignment tends to remain very limited, mainly due to the bureaucratic and practical constraints involved in demonstrating compliance with the Do No Significant Harm criteria. Consequently, the analysis identifies a possible cross-sectional decoupling pattern: entities with low eligibility tend to compensate through extensive narrative disclosures, whereas those with higher eligibility maintain more pragmatic and technical communication. Furthermore, Capital Expenditure appears to be used as a forward-looking mechanism to project future transition efforts. The study concludes that, in emerging markets, the EU Taxonomy currently operates as a regulatory-technical proxy, emphasizing the need to distinguish substantive compliance from formal, narrative-driven disclosure. Full article

Angelica dahurica (A. dahurica) is an important medicinal plant in China; however, its production is affected by viral infections, leading to reduced yields and quality. In this study, we identified a novel carlavirus, tentatively named Angelica carlavirus virus (AnCV), in the leaves of A. dahurica exhibiting mosaic and leaf crinkling symptoms. Notably, the complete genome of AnCV was 8562 nt long and contained six open reading frames, with a genomic organization typical of the genus Carlavirus. AnCV exhibited 44.5–57.8% nucleotide identity at the whole-genome level with known members of the genus Carlavirus. In the polymerase gene and coat protein regions, the highest nucleotide and amino acid identities were 59.4–60.0% and 46.5–55.8%, respectively, which were below the species demarcation criteria established by the International Committee on Taxonomy of Viruses for the genus Carlavirus. Importantly, 10 AnCV isolates clustered within subgroup I of the genus Carlavirus, forming a relatively distinct branch. Moreover, 119 of the 280 A. dahurica samples were positive for AnCV (detection rate of 42.58%). Our study revealed that AnCV is a novel member of the genus Carlavirus that infects A. dahurica, providing a theoretical basis for the monitoring and control of viral diseases in A. dahurica. Full article

Morphological characters are widely used for species delimitation in Scolytinae, yet their stability under intraspecific variation remains poorly understood. Here, we analyzed phenotypic variation in specimens currently assigned to Phloeosinus baumanni across its distribution in Mexico to evaluate the effects of sexual dimorphism and locality on morphological traits. A total of 25 characters were examined using univariate and multivariate analyses. Pronounced sexual dimorphism was detected, with significant differences in more than half of the evaluated traits and a clear separation between sexes in multivariate space, particularly in body size, cephalic proportions, and elytral declivity features. Locality-associated variation was also detected, particularly within the better-sampled localities of the Transmexican Volcanic Belt. However, localities showed broad overlap in morphospace, with no consistent clustering across biogeographic provinces. Several traits commonly used for species delimitation exhibited substantial variation associated with both sex and geography. These results indicate sex- and locality-associated phenotypic variation among specimens currently assigned to P. baumanni and highlight the risk of taxonomic overestimation when variable morphological traits are overemphasized. Incorporating intraspecific variability into taxonomic frameworks is essential to improve species delimitation in Scolytinae. Full article

In this contribution, we describe a new species from the Estación de Biología Chamela (IBUNAM), located in Jalisco, Mexico. Mastigoproctus claritae sp. nov. has a stridulatory organ like M. mexicanus and M. xetame. However, it is distinguished from these species by the presence of a proventral marginal smooth keel in the chelicerae, a retrolateral surface pedipalp femur with congregated and blunt cristulae, a prodorsal margin spine S3 that is longer than the other spines in males, and symmetric, peanut-shaped spermatheca seminal receptacles in females. Mastigoproctus claritae sp. nov. represents the twenty-third species of the genus and the twelfth species documented in Mexico. Full article

Wood-inhabiting fungi play important ecological roles in forest ecosystems by participating in wood decomposition and nutrient cycling and represent a highly diverse group within Basidiomycota. In this study, four new wood-inhabiting species of Sistotrema from China are described and illustrated based on morphological examination and phylogenetic analyses of ITS, nLSU, nuc-SSU, and RPB2 sequence data. Sistotrema armandii is characterized by a hypochnoid hymenial surface, ventricose to suburniform basidia, and long sterigmata. Sistotrema caeruleogriseum is separated by its slightly tuberculate hymenial surface, subclavate to suburniform basidia with 4-sterigmata, and subglobose to globose basidiospores. Sistotrema luteum is typified by angular to irregular pores, thin and slightly fimbriate dissepiments, and globose basidiospores. Sistotrema tenuissimum is distinguished by a farinaceous hymenial surface, a monomitic hyphal system with thin- to slightly thick-walled generative hyphae, and barrel-shaped to suburniform basidia with 4 or 6 long sterigmata. The four new species and their phylogenetically related and morphologically similar species are discussed. This study expands the current knowledge of species diversity and phylogenetic relationships within the genus Sistotrema in China. Full article

Web Application Programming Interfaces (Web APIs) have become fundamental components of modern software ecosystems. At the same time, they have emerged as major attack surfaces in web applications and distributed services. Although many web API vulnerabilities are well documented, a critical gap remains in understanding how insecure development practices, usability limitations, and developer-related issues contribute to recurring API security problems. To address this gap, this study presents a systematic review of web API security research using a PRISMA-guided methodology and a taxonomy-driven analytical approach. The review synthesizes findings from 50 selected studies covering web API architectural styles, usability concerns, authentication and access-control weaknesses, and common vulnerabilities. These vulnerabilities include SQL Injection (SQLi), Cross-Site Scripting (XSS), Broken Authentication, and Denial-of-Service (DoS) attacks within the context of the OWASP API Security Top 10 framework. The findings indicate that recurring web API vulnerabilities are associated not only with technical weaknesses but also with API usability issues, insecure development practices, inconsistent security guidance, and increasing implementation complexity. The review also identifies persistent research gaps involving usability-security integration, API evolution, secure-by-design development practices, and empirical validation of security tools and frameworks. By synthesizing these dimensions into a unified conceptual perspective, this study provides researchers and practitioners with a clearer understanding of the factors contributing to web API insecurity. The study also highlights directions for developing more resilient and developer-aware API security practices. Full article