Search Results (9,724)

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

Background/Objective: The identification of novel non-invasive diagnostic and prognostic biomarkers is urgently needed in pleural mesothelioma (PM). While soluble mesothelin-related peptides (SMRP) are the most established circulating biomarker, their prognostic value is limited. A wide range of microRNAs (miRs) play diverse roles in regulating gene expression in PM. MiR-21 has been shown to be upregulated in mesothelioma tissue; nevertheless, the diagnostic and prognostic utility of miR-21 in the circulation and its association with survival in PM have not been extensively investigated to date. The objective of the current study was to evaluate miR-21 as a potential blood-based diagnostic and prognostic biomarker in PM. Methods: Plasma samples from PM patients (n = 94) were collected at the time of diagnosis, prior to treatment. Sex- and age-matched healthy individuals (n = 30) served as controls. MiR-21 levels were measured using quantitative RT-PCR and normalized to miR-16, and potential correlations with clinicopathological data were analyzed. Serum SMRP levels were measured in matched patients (n = 84), and a direct comparative analysis of miR-21 and SMRP was conducted. In situ hybridization (ISH) was used to confirm the presence of miR-21 in tumor cells. Results: Plasma miR-21 levels were significantly elevated in PM patients compared to healthy controls (p < 0.001), demonstrating good diagnostic performance (AUC 0.81). The localization of miR-21 in PM cells was confirmed by ISH. High miR-21 levels were associated with significantly shorter median overall survival (12.4 vs. 24.3 months, p < 0.001). Elevated SMRP levels were also associated with reduced survival (12.4 vs. 19.5 months, p = 0.032); however, SMRP did not retain independent prognostic significance in multivariable analysis. In contrast, high-circulating miR-21 was confirmed as an independent predictor for poor survival (HR 3.12, p < 0.001). Conclusions: Our findings highlight that circulating miR-21 is a potential non-invasive biomarker with both diagnostic and independent prognostic value in pleural mesothelioma and outperforms SMRP in multivariable survival analysis. Further research is warranted to validate its role in the biology of this disease and to assess its correlation with outcome and treatment responses. Full article

Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains unclear whether these tools, in the absence of reference standards, can reliably annotate real-world experimental LC-HRMS data and whether they are sufficient for this task. Methods: This study assesses the performance and limitations of four widely used in silico structure prediction tools (MassFrontier, MetFrag, MS-FINDER, and SIRIUS/CSI:FingerID) when applied to an experimentally acquired feature set previously used to differentiate patients with depressive disorders from healthy controls. To ensure uniform evaluation across tools under realistic but optimized conditions, the quality of MS/MS data was improved using a parallel reaction monitoring method, allowing acquisition of interpretable fragmentation spectra for 26 of the 28 detected features. Results: For most features, all tools were able to suggest structure candidates. However, none of the tools proved sufficient as a standalone solution for reliable metabolite annotation. Due to their different algorithms, each tool had strengths and weaknesses in fragmentation interpretation, candidate generation, and ranking, resulting in incomplete or inconsistent annotations. While the combined application of all four tools provided a substantial improvement in putative annotation over conventional spectral library matching, the in silico structure prediction tools often prioritized chemically implausible, biologically irrelevant, or artifactual candidates. Consequently, manual expert evaluation was required to assess the chemical plausibility and biological relevance of the proposed structures. This ultimately reduced the number of biologically plausible metabolites putatively associated with disease to ten. Conclusions: Overall, these results demonstrate that existing in silico annotation tools can substantially support the annotation of experimental metabolomics data, but are insufficient on their own. Reliable identification of metabolites in complex biological matrices still depends on high-quality MS/MS data acquisition, the combined use of complementary tools, and mandatory post-annotation expert curation. Full article

Gender-based and sexual violence are major public health concerns, and norms about consent are central to their prevention. This study examines how attitudes toward sexual consent relate to digital sexual consent and to the occasional feeling of distrust in public consent campaigns and institutions. We conducted a cross-sectional online survey embedded in the evaluation of a municipal consent campaign in Zaragoza (Spain). Adults (N = 404; 56.7% women) completed a 14-item short version of the Sexual Consent Scale–Revised, two items on digital sexual consent, and three items on institutional reluctance (perceived “sermonizing” tone, distrust in effectiveness, and lack of personal identification with the message). Correlation and multiple regression models with robust standard errors were estimated, controlling for gender, age, education, income, relationship status, and social media use. Attitudes toward sexual consent were strongly and positively associated with digital sexual consent. Gender was the most consistent sociodemographic correlate: men showed less egalitarian attitudes than women across all consent measurements. Institutional reluctance was systematically related to less supportive consent attitudes: perceiving institutional messages as exaggerated or personally irrelevant predicted lower support for sexual and digital consent norms, whereas trust in the campaign’s effectiveness was associated with more egalitarian attitudes. The findings support the continuity between sexual and digital consent and highlight gender and institutional trust as key determinants for the prevention of gender-based and sexual violence. Public health and social policies should integrate digital consent into consent education and co-design campaigns that minimize defensive reactions and rebuild trust in institutions. Full article

The skin serves as the first line barrier of innate immunity, protecting the body from external influences and maintaining its homeostasis. Exogenous and endogenous stress factors alter the structure and functional properties of the skin. The search for compounds capable of counteracting these processes has allowed the identification of peptides as promising ingredients of products for medicinal and cosmetic applications. This review comprehensively examines the mechanisms of action and dermatological applications of two distinct classes of natural products—endogenous human peptides and those derived from marine organisms. Human peptides exhibit numerous biological functions, including antimicrobial and immunomodulatory ones, as well as promoting antioxidant protection and wound healing. Microbiome-associated peptides are an underestimated but powerful regulator of skin aging through immunomodulation, inflammation control, barrier function maintenance, and selection of the proper microbial community. Peptides from marine organisms exhibit significant structural diversity and a broad spectrum of biological activity, including regenerative effects and effects on antibiotic-resistant microorganisms. This review summarizes current data obtained from in vitro, ex vivo, and clinical studies demonstrating a broad potential of peptides for maintaining skin health. Both peptide classes represent powerful, targeted strategies for innovative dermatological interventions aimed at promoting skin rejuvenation, protection, and overall homeostasis. Full article

The middle and lower reaches of the Yangtze River Basin occupy a strategically pivotal position in regional development; yet extreme drought-runoff events pose severe threats to water supply and ecological security. Despite this, systematic research gaps persist, including the lack of a unified definition, standardized identification criteria, and clear understanding of formation mechanisms for extreme drought-runoff. To address these limitations, this study focused on extreme drought-runoff in the basin, utilizing 1956–2024 discharge data from four mainstream hydrological stations and meteorological data from 171 stations. Quantitative discrimination criteria were established via Pearson-III frequency analysis; meteorological characteristics were analyzed using the Meteorological Drought Comprehensive Index; and formation mechanisms were explored through partial correlation analysis and multiple linear regression. This study innovatively proposed a basin-wide three-level quantitative discrimination criterion for drought-runoff based on the June–November flow frequency of key mainstream stations, which is distinguished from single-indicator drought identification methods (SPI/SPEI/SSI) by integrating basin-scale hydrological coherence and seasonal drought characteristics. The results revealed basin-wide extreme drought-runoff in 2006 and 2022, severe drought-runoff in 1972 and 2011, and relatively severe drought-runoff in 1959, 1992, and 2024. Typical extreme drought-runoff events were characterized by sustained low precipitation and high temperatures. Meteorological factors emerged as the primary driver during June–September, while reservoir operation and riverine water intake played secondary roles. Notably, the large-scale reservoir group in the Yangtze River Basin (53 key control reservoirs) helped alleviate drought-runoff impacts from December to May (non-flood season) via water supplementation. These findings provide a robust scientific basis for precise drought-runoff prediction and the development of targeted adaptation strategies in the Yangtze River Basin. Full article

This study explored the continuous monitoring of hydrogen-induced cracking (HIC) in high-strength steel tension wires used in metal-based flexible pipes, exposed to a H₂S-saturated aqueous environment, using acoustic emission (AE) techniques. Armor wire samples were subjected to sour conditions under controlled environments for 24 and 96 h. To reinforce and validate the AE findings, a comprehensive characterization was performed, including X-ray microtomography, optical microscopy, and scanning electron microscopy. The experimental results demonstrated that AE techniques effectively monitored the evolution of HIC damage in the armor wire samples, enabling the identification of distinct damage stages and cracking phenomena. These findings confirm that AE can serve as a valuable complementary tool during HIC testing, optimizing test duration and providing insights into the kinetics of the cracking process. Full article

Background/Objectives: In recent years, research on psychosis has increasingly focused on prevention, aiming to implement early interventions that mitigate or reduce its impact. Within this framework, the analysis of linguistic markers in individuals with at-risk mental states (ARMS) has proven valuable for identifying those at risk and predicting psychosis onset. Artificial intelligence tools, particularly natural language processing (NLP), have emerged as effective resources for detecting these language-based indicators. This study aims to synthesize the existing scientific evidence on linguistic markers analyzed through NLP techniques in individuals with ARMS. Methods: A systematic review following the PRISMA 2020 protocol was conducted. Three databases (PubMed, PsycInfo, and Scopus) were searched for published articles from their inception to October 2025. Rayyan software was used to manage references and article downloads. Out of ninety initial search results, fifteen studies involving 1313 participants from diverse groups were included in the review. Results: The findings indicated that alterations in semantic coherence, syntactic complexity, referential cohesion, and speech/content poverty differentiated ARMS individuals from healthy controls. Several of these markers, analyzed with NLP methods, predicted the onset of psychosis with accuracy levels ranging from 79% to 100%, although these findings should be interpreted with caution due to the significant methodological heterogeneity and variability in sample sizes across the included studies. Conclusions: NLP techniques offer a powerful approach for detecting language alterations that distinguish ARMS individuals and provide meaningful predictions of psychosis onset, highlighting their potential as a complement to traditional clinical assessments for early identification and prevention. Full article

Network Intrusion Detection Systems (NIDS) face increasing challenges from sophisticated cyber threats, particularly zero-day attacks that evade signature-based methods. While supervised learning is effective for known attack classification, it struggles with novel threats, whereas anomaly-based approaches suffer from high false positive rates and unstable thresholds. To address these limitations, this paper proposes a decision-level adaptive intrusion-detection framework combining hierarchical CNN-based closed-set classification with autoencoder-based zero-day detection in a cascade architecture. The framework enables deployment-time adaptation by dynamically adjusting class-specific confidence thresholds and fusion parameters without model retraining. Experiments on the CSE-CIC-IDS2018 dataset demonstrate strong closed-set performance, achieving 98.98% accuracy and a macro-F1-score of 0.9342, with improved recall for minority attack classes under adaptive thresholding. Under a zero-day evaluation protocol in which Web_Attacks and Infiltration are excluded from training and validation, the proposed approach achieves an F1-score of 0.9319 while maintaining a low false positive rate of 0.0019. The framework is further evaluated on the Simulated University Network Environment (SUNE) dataset representing campus network traffic, achieving 96.18% closed-set accuracy and 97.54% accuracy in the integrated cascade setting. These results demonstrate that the proposed framework effectively balances minority attack detection, zero-day identification, and false-alarm control in dynamic and resource-constrained network environments. Full article

The use of biostimulants in agriculture is increasing; however, their effects on raspberry remain insufficiently understood. The aim of this study was to evaluate the impact of foliar-applied biostimulants on yield and growth in three primocane raspberry cultivars grown under field conditions using multispectral imaging based on unmanned aerial vehicles. An experiment included a control and four foliar biostimulant treatments based on animal-derived amino acids, plant-derived amino acids, seaweed extract, and seaweed extract combined with animal-derived amino acids. Biostimulant effects on primocane raspberry were found to vary substantially depending on cultivar, environmental conditions, and formulation type, with measurable impacts on both yield formation and vegetative growth. These responses were further supported and characterized using multispectral UAV-based mutlispectral imaging, which enabled effective detection of treatment-related physiological changes. This approach was based on the analysis of relative percentage changes between consecutive measurements of selected vegetation indices, allowing the identification of dynamic physiological responses over time. These findings highlight the need for a more targeted approach to biostimulant use, taking into account cultivar-specific responses and environmental variability. Future research should extend this framework to a broader range of genotypes, cultivation systems, and biostimulant formulations, while integrating remote sensing with other analytical methods to better understand plant physiological responses. Such developments may support the transition toward data-driven and precision-guided biostimulant application strategies in sustainable crop production. Full article

The precise identification of terminal area arrival procedures is crucial for airspace planning, traffic management, and safety analysis. Traditional methods are limited in automatically detecting specific procedural maneuvers from large amounts of trajectory data. This paper proposes a methodology with knowledge-based filtering to automatically identify three common air traffic control arrival procedures, namely Point Merge System, Vector for Space, and Trombone, from historical trajectory data. After clustering the landing trajectories in the terminal area, we identify the predominant flight patterns. Then, a knowledge-based filtering algorithm, designed based on knowledge of the procedure and geometry criteria, is employed to precisely extract trajectories with different procedure patterns. Experimental results demonstrate that this method effectively identifies the distinct procedural trajectories. An in-depth analysis of the extracted trajectories reveals significant characteristics and differences in their spatial distribution, trajectory structure, and operational efficiency. This work provides data-driven decision support for evaluating terminal area operational performance and arrival procedures. Full article

This article presents the potential for maliciously influencing a control system by interfering with the program code of an industrial controller, using a temperature control system for a heating process based on resistive electric heating as an example. The presented attack scenarios are crucial for the energy efficiency of electric heating systems, which is related to the issue of cybersecurity in the area of energy security. The aim of this research was to demonstrate that a cyberattack involving the malicious manipulation of the setpoint can be carried out in a manner invisible to the heating process operator and be difficult to detect using classical time-domain control quality indicators (time-response specifications). The first involves incorporating proportional elements with mutually inverted gains into the input and output of a closed-loop system. The second method is based on adding an additional transfer function G_m(s) in parallel to the control system. The difference between the correct and manipulated setpoints is introduced into the input, and the output signal is added to the actual (hidden) value of the controlled variable. In the first method, at the moment of starting the control system, there is a difference between the apparent (falsified) value and the ambient temperature. In the second method, the inclusion of an additional G_m(s) ensures that the apparent (falsified) value of the controlled variable matches the temperature at the moment of starting the system. PID control enables achieving satisfactory control quality in heating processes, which are characterized by high inertia and time delays. Compared to classical PID regulation, advanced control methods can, under certain conditions, provide better performance in terms of quality indicators. However, due to their high computational complexity and sensitivity to model uncertainty—particularly in methods relying on accurate system identification—PID controllers continue to be widely used in industrial practice. For this reason, the present study focuses on a control system based on a PID controller as a practical solution. Based on the results, it was found that the most effective manipulation occurred within the range from 0.9 to 1.1 of the actual setpoint value for both the first and second method, using a model with $T_m$ between 5 s and 30 s. In these cases, the quality indicators referenced to the nominal values, determined for the falsified control system responses to a step change in the setpoint, were as follows: overshoot—0.97 and 1.30 (method 1), and 0.90 and 1.10 (method 2 for 5 s), 0.75 and 1.30 (method 2 for 30 s); settling time—1.06 (method 1), and 0.98 and 1.17 (method 2 for 5 s), 0.85 and 1.14 (method 2 for 30 s). The settling times determined for the system’s response to a disturbance were: 1.00 and 1.15 (method 1), and 1.13 and 1.16 (method 2 for 5 s), 1.12 and 1.02 (method 2 for 30 s). Based on the conducted analysis, it was demonstrated that the relatively simple setpoint manipulation methods presented can effectively mask the impact of malicious interference on the temperature value in the control system of a heating process. Full article

Background: Konjac (Amorphophallus spp.) is an economically important crop valued for the glucomannan content in its corms. Currently, the konjac industry faces germplasm degeneration due to long-term asexual propagation. Developing tissue culture and genetic transformation techniques is essential for its genetic improvement. The WUSCHEL-related homeobox (WOX) transcription factors are critical regulators of somatic embryogenesis and stem cell maintenance in plants. Methods: In this study, we performed genome-wide identification and characterization of WOX genes in the A. konjac reference genome. Furthermore, comparative transcriptomic analyses and functional verification were conducted in A. bulbifer. Results: A total of 12 AkWOX genes were identified in A. konjac, and their structural features were documented. Comparative transcriptomic analysis of A. bulbifer revealed that AbWOX genes were differentially expressed between embryogenic calli (EC) and non-embryogenic calli (nEC). Notably, AbWOX2 was significantly upregulated in EC. Overexpression of AbWOX2 significantly promoted callus proliferation and shoot regeneration in A. bulbifer. Furthermore, AbWOX2-overexpressing lines exhibited a 5.3-fold increase in genetic transformation efficiency (from 5.12% to 27.31%) compared to the control. Conclusions: We characterized the diverse expression patterns of the WOX gene family in Amorphophallus. Crucially, we identified specific individual members—most notably the markedly upregulated AbWOX2—that function as pivotal drivers of somatic embryogenesis and serve as promising candidates for enhancing regeneration and genetic engineering efficiency in Amorphophallus species. Full article

Background: Early vascular aging (EVA) is a common complication of type 2 diabetes mellitus. Early identification is crucial in middle-aged individuals with T2DM, as vascular stiffness can occur gradually for years before cardiovascular disease. However, EVA is rarely considered in routine care. Adropin is a vasoprotective peptide that may counter-regulate endothelin-1 (ET-1). Therefore, this study aims to examine the association between circulating adropin, ET-1, oxLDL, MMP-2, VEGFA, and EVA. Methods: This observational study included 300 adults aged 25–55 years (150 T2DM; 150 age/sex-matched controls). ePWV was calculated from age and mean blood pressure. EVA was classified using a residual-based, age-specific ePWV threshold derived from controls. Associations were tested using correlation and logistic regression. ROC and decision curve analyses were performed to evaluate diagnostic performance and clinical utility. Results: EVA prevalence was 38.6% overall, occurring in 7.3% of controls and increasing across T2DM with good and poor glycemic control (56.1% and 80.95%, respectively, p < 0.001). Compared with normal vascular aging, EVA showed lower adropin and higher ET-1, oxLDL and MMP-2, with lower VEGFA (all p < 0.05). In fully adjusted models, adropin (OR 0.991 per pg/mL; p < 0.001) and ET-1 (OR 1.017 per pg/mL, p = 0.005) remained independently associated with EVA. A combined adropin + ET-1 predictor improved discrimination (AUC 0.901, 95% CI 0.868–0.934), at a predicted-probability cutoff of 0.607, 78.7% sensitivity and 87.0% specificity. Conclusions: In middle-aged T2DM, EVA was associated with lower adropin and higher ET-1 in T2DM. These findings support an association between these biomarkers and the EVA phenotype. Full article

(1) Background: Potentially arthropod-pathogenic and plant-associated Metarhizium fungi are of high interest for basic research, biological pest control and plant growth promotion. Unambiguous species delineation enabling the taxonomic assignment of new isolates and the identification of new Metarhizium species is of crucial importance for both research and application. Recently, the new species Metarhizium hybridum and Metarhizium parapingshaense were introduced on the basis of phylogenomic studies. (2) Methods: Neighbor- joining and Bayesian inference-based phylogenetic reconstruction of ribosomal intergenic spacer (rIGS) sequences were used to critically evaluate new species introductions. A species-discriminating diagnostic PCR tool for Metarhizium was adapted to M. hybridum and M. parapingshaense. GenBank database mining was performed to identify cryptic descriptions of the new species. (3) Results: The introduction of M. hybridum and M. parapingshaense was corroborated by rIGS sequence comparison. Data mining revealed cryptic first descriptions of M. hybridum from Canada, China, Colombia, Costa Rica, Cuba, Honduras, Mexico, New Zealand, the USA and the Philippines, and of M. parapingshaense from China, India, Japan, the Philippines and South Korea. (4) Conclusions: Results support the reliability of rIGS as a single taxonomic marker for species-level identification of Metarhizium fungi. Species-discriminating diagnostic PCR was successfully adapted to enable the sequencing-independent identification of the confirmed new species M. hybridum and M. parapingshaense. Full article