Search Results (1,533)

Mangrove ecosystems are extremely sensitive to compounded stress, as evidenced by the widespread degradation and mortality of the pioneer mangrove species Avicennia marina along the Guangxi coast in recent years. However, research on how mangrove ecosystems respond to compound biotic stressors remains limited. Therefore, the present study aimed to systematically examine the ecological response mechanisms of A. marina under dual threats from the burrowing isopod Sphaeroma terebrans and the defoliating moth Hyblaea puera. Two contrasting sites were selected: Guchengling (subject to chronic stem-boring and sudden defoliator outbreaks) and Tieshangang (free from compounded stress). Photosynthetic capacity, metabolic function, and root structural integrity were all compromised considerably by chronic boring stress. During insect outbreaks, 15.33 ha of mangroves were destroyed due to impairments that breached the ecological threshold. In contrast, the healthier Tieshangang community exhibited strong ecological resilience, with rapid green canopy regeneration following defoliation and notable recovery in the normalized difference vegetation index. To enable early identification and precise intervention in mangrove decline, a comprehensive health index model was developed that includes root–canopy coordination, root length, and boring density. Field validation results, showing 100% agreement with expert evaluations across 19 validation sites (Cohen’s κ = 1.0), confirmed the high accuracy of the model. This study highlights the importance of identifying sensitive zones and undertaking timely ecological restoration, thereby providing a scientific basis and a practical tool that could facilitate early warning and timely management of mangrove degradation events. Full article

The ultimate objective of research on hypernetwork robustness is to enhance its capability to withstand external attacks and natural disasters. For hypernetworks such as telecommunication networks, public safety networks, and military networks—where security requirements are extremely high—achieving higher communication robustness is essential. This study integrates the structural characteristics of hypernetworks with an optimization method for communication robustness by combining four key indicators: hyper-betweenness centrality, hyper-centrality of feature subgraph, hyper-centrality of Fiedler, and hyperdistance entropy. Using the best improvement performance ($BIP\_T$) as the evaluation metric, simulation experiments were conducted to comparatively analyze the effectiveness of these four indicators in enhancing the communication robustness of Barabási–Albert (BA), Erdos–Renyi (ER), and Newman–Watts (NW) hypernetworks, and theoretically derives the hyperedge addition threshold $\theta$. The results show that all four indicators effectively improve the communication robustness of hypernetworks, although with varying degrees of optimization. Among them, hyper-betweenness centrality demonstrates the most significant optimization effect, followed by hyper-centrality of feature subgraph and hyper-centrality of Fiedler, while hyperdistance entropy exhibits a relatively weaker effect. Furthermore, these four indicators and the proposed communication robustness optimization method exhibit strong generalizability and have been effectively applied to the WIKI-VOTE social hypernetwork. Full article

Biomarker-based prevention is rapidly expanding, driven by advances in molecular diagnostics, genetic profiling, and commercial direct-to-consumer (DTC) testing. General practitioners (GPs) increasingly encounter biomarker results of uncertain relevance, often introduced outside the guideline frameworks. This creates new challenges in interpretation, communication, and equitable resource use in primary care. This narrative review synthesizes evidence from population-based studies, guideline frameworks, consensus statements, and communication research to evaluate the predictive value, limitations, and real-world implications of biomarkers in asymptomatic adults. Attention is given to polygenic risk scores, DTC genetic tests, neurodegenerative and cardiovascular biomarkers, and emerging multi-omics and aging markers. Several biomarkers, including high-sensitivity cardiac troponins, N-terminal pro–B-type natriuretic peptide, lipoprotein(a), coronary artery calcium scoring, and plasma p-tau species, showed robust predictive validity. However, many widely marketed biomarkers lack evidence of clinical utility, offer limited actionable benefits, or perform poorly in primary care populations. Unintended consequences, such as overdiagnosis, false positives, psychological distress, diagnostic cascades, and widening inequities, are well documented. Patients often misinterpret unvalidated biomarker results, whereas DTC testing amplifies demand without providing adequate counseling or follow-up. Only a minority of biomarkers currently meet the thresholds of analytical validity, clinical validity, and clinical utility required for preventive use in general practices. GPs play a critical role in contextualizing biomarker results, guiding shared decision-making, and mitigating potential harm. The responsible integration of biomarkers into preventive medicine requires clear communication, strong ethical safeguards, robust evidence, and system-level support for equitable, patient-centered care. Full article

Federated learning effectively addresses the issues of data privacy and communication overhead in traditional deep learning through distributed local training. However, its open architecture is seriously threatened by backdoor attacks, where malicious clients can implant triggers to control the global model. To address these issues, this paper proposes a novel three-stage defense mechanism based on local clients. First, through text readability analysis, each client’s local data is independently evaluated to construct a global scoring distribution model, and a dynamic threshold is used to precisely locate and remove suspicious samples with low readability. Second, frequency analysis and perturbation are performed on the remaining data to identify and disrupt triggers based on specific words while preserving the basic semantics of the text. Third, n-gram distribution analysis is employed to detect and remove samples containing abnormally high-frequency word sequences, which may correspond to complex backdoor attack patterns. Experimental results show that this method can effectively defend against various backdoor attacks with minimal impact on model accuracy, providing a new solution for the security of federated learning. Full article

Sustainability in the context of machine learning (ML) plays an important role for accessible models by both researchers as well as clinicians. This article describes a reproducibility study on PiDeeL, a metabolic-pathway-informed deep learning model. It serves to test the hypothesis that the requirement of a simple provision of all digital artifacts is not sufficient to reproduce the computational experiment(s). The reproduction and modification of the computational model foundational to the previous findings shall promote documentation and evaluation of existing scientific models and confirm their applicability. The modification of the original model is based on measuring emissions of training machine learning models using CodeCarbon. Two different systems with different CPU as well as GPU specifications and Windows Subsystem Linux could be tested after guide and code adaptions due to initial incomplete replication attempts given the threshold of computation completion without error message(s). Emissions equivalent to 0.3–0.6 kg of CO₂ per run were shown. Encountered issues along the replication attempts call for refined guidelines on documentation and processing of computational approaches in scientific studies by publishers as well as the scientific community. Thorough peer review including algorithmic reproduction would be necessary to ensure model reusability. Full article

Aim: This cross-sectional study aimed to evaluate and compare parents’ and children’s preferences for full-coverage restorative treatment options of primary molars, including stainless steel crowns (SSCs), zirconia crowns (ZCs), and BioFlx crowns. Additionally, the study evaluates the influence of providing a brief overview of the advantages and disadvantages of each full-coverage restorative treatment option on parental preference. Methods: The study was conducted at the pediatric dental clinics at King Abdulaziz University Faculty of Dentistry (KAUFD) in Jeddah, Saudi Arabia, from January to May 2024. Healthy Arabic-speaking children aged 6–12 years attending KAUFD for routine dental treatment, along with at least one parent who agreed to participate, were included. Three typodont models with a SSC, a ZC, and a BioFlx crown were prepared and cemented by an expert pediatric dentist. The participating children and their parents were simultaneously and independently shown the prepared typodont models and asked to indicate which treatment option they preferred most. Subsequently, a trained pediatric dentist presented a brief overview of the advantages and disadvantages of each treatment option to the parents. Then, parents were asked to re-evaluate their preferences. The threshold for significance was set at p < 0.05. Results: A total of 172 children and their parents were included. The most preferred full-coverage restorative treatment among children was SSC (39.0%), while among parents, ZC (60.5%) was the most preferred. After providing a brief overview, the most preferred option among parents was SSC (39.5%), with ZC and BioFlx crowns being equally preferred (30.2%). Significantly more children with no history of dental pain or discomfort (49.1%) (p = 0.023) or with a history of previous dental treatment involving SSC (40.2%) (p = 0.045) preferred SSC. The ZC was significantly more preferred by parents of female children (70.65%) (p = 0.027) and by parents of children with a history of dental treatment (60.6%) (p = 0.018). Conclusions: The study revealed that parental demands and expectations often differ from those of their children, leading to notable differences between children’s and parents’ preferences. After a brief overview, parental preference shifted from ZC to SSC, highlighting the importance of effective communication and education when making treatment decisions for pediatric patients. Full article

Heterocapsa bohaiensis is an emerging harmful dinoflagellate increasingly reported from coastal regions of the Pacific. However, an available molecular assay offering rapid and sensitive detection is still lacking. This study developed a SYBR Green real-time quantitative PCR (qPCR) assay for the identification and quantification of H. bohaiensis. Species-specific primers (F: 5′-CCATCGAACCAGAACTCCGT-3′; R: 5′-AGTGTAGTGCACCGCATGTC-3′) were designed and the assay was optimized and evaluated using laboratory cultures for specificity, sensitivity, and quantitative performance. Primer screening and melt-curve analysis confirmed that the selected primer pair produced a single, specific amplification peak for H. bohaiensis, with no cross-reactivity observed in non-target species (Chlorella pyrenoidosa, Phaeocystis globosa, Skeletonema costatum, Alexandrium tamarense) or mixed algal communities. The standard curve displayed strong linearity (R² = 0.9868) and a high amplification efficiency (102.5%). The limit of detection (LOD) was approximately 2–3 cells per reaction, as determined from 24 replicates of 5-cell equivalents and verified at ~2.7-cell equivalents. This sensitivity was comparable to or exceeded that reported for assays targeting other HABs forming dinoflagellates. Quantitative results derived from the qPCR assay closely matched microscopic cell counts, with a relative error of 10.79%, falling within the acceptable threshold for phytoplankton surveys. In summary, this study established and validates a species-specific qPCR assay for H. bohaiensis under controlled laboratory conditions. The method shows strong potential for incorporation into HAB monitoring programs, early-warning systems, and future ecological investigations of this emerging species. Full article

One of the major challenges for effective intrusion detection systems (IDSs) is continuously and efficiently incorporating changes on cyber-attack tactics, techniques, and procedures in the Internet of Things (IoT). Semi-automated cross-organizational sharing of IDS data is a potential solution. However, a major barrier to IDS data sharing is privacy. In this article, we describe the design, implementation, and evaluation of FedPrIDS: a privacy-preserving federated learning system for collaborative network intrusion detection in IoT. We performed experimental evaluation of FedPrIDS using three public network-based intrusion datasets: CIC-IDS-2017, UNSW-NB15, and Bot-IoT. Based on the labels in these datasets for attack type, we created five fictitious organizations, Financial, Technology, Healthcare, Government, and University and evaluated IDS accuracy before and after intelligence sharing. In our evaluation, FedPrIDS showed (1) a detection accuracy net gain of 8.5% to 14.4% from a comparative non-federated approach, with ranges depending on the organization type, where the organization type determines its estimated most likely attack types, privacy thresholds, and data quality measures; (2) a federated detection accuracy across attack types of 90.3% on CIC-IDS-2017, 89.7% on UNSW-NB15, and 92.1% on Bot-IoT; (3) maintained privacy of shared NIDS data via federated machine learning; and (4) reduced inter-organizational communication overhead by an average 50% and showed convergence within 20 training rounds. Full article

Salinity shock severely impairs the partial denitrification/anammox (PD/A) process, leading to prolonged functional deterioration and slow reactivation of anaerobic ammonium-oxidizing bacteria (anammox). To develop an effective strategy for mitigating salinity-induced inhibition, this study systematically examined the role of exogenous hydroxylamine (NH₂OH) in accelerating PD/A recovery using short-term batch assays and long-term reactor operation. Hydroxylamine exhibited a clear concentration-dependent effect on system reactivation. In batch tests, low-dose hydroxylamine (10 mg/L) markedly enhanced anammox activity, increasing the ammonium oxidation rate to 5.5 mg N/(g VSS·h), representing a 42.5% increase, indicating its potential to stimulate key nitrogen-transforming pathways following salinity stress. During continuous operation, hydroxylamine at 5 mg/L proved optimal for restoring reactor performance, achieving stable nitrogen removal with 87% NH₄⁺-N removal efficiency. The nitrite transformation ratio (NTR) reached approximately 80% within 13 cycles, 46 cycles ahead of the control, while simultaneously promoting the enrichment of key functional microbial taxa, including Thauera and Candidatus Brocadia. Hydroxylamine addition also triggered the production of tyrosine- and tryptophan-like proteins within extracellular polymeric substances, which enhanced protective and metabolic functionality during recovery. In contrast, a higher hydroxylamine dosage (10 mg/L) resulted in persistent NO₂⁻-N accumulation, substantial suppression of Candidatus Brocadia (declining from 0.67% to 0.09%), and impaired system stability, highlighting a dose-sensitive threshold between stimulation and inhibition. Overall, this study demonstrates that controlled low-level hydroxylamine supplementation can effectively reactivate salinity-inhibited PD/A systems by enhancing nitrogen conversion, reshaping functional microbial communities, and reinforcing stress-response mechanisms. These findings provide mechanistic insight and practical guidance for improving the resilience and engineering application of PD/A processes treating saline wastewater. Full article

Spoofing attacks remain among the most critical security threats in Connected and Autonomous Vehicles (CAVs). This work introduces a lightweight, two-level spoofing detection framework based on Kalman filtering, designed for real-time deployment in vehicular platoons that communicate over Dedicated Short-Range Communications (DSRC). At the first level, a heuristic residual-based detector identifies abnormal measurement deviations using adaptive statistical thresholds. At the second level, a Mahalanobis distance test assesses model consistency using covariance-aware anomaly scoring at a 95% confidence level. The combination of these complementary mechanisms enables both rapid alerting and robust statistical verification without the need for machine-learning training or centralized processing. Simulation results from 20 independent nodes demonstrate that the proposed approach achieves an average F1-score of 0.92 and Area Under the ROC Curve (AUC) of 0.72, outperforming standalone detectors while maintaining low computational cost. Compared with deep learning and adaptive Extended Kalman Filter (EKF) approaches, the proposed framework achieves similar detection performance while substantially reducing computational complexity and enabling full real-time operation, making it suitable for embedded in-vehicle security modules. Full article

Several commercial seismometers now support CSTP, the real-time communication protocol used in the China Earthquake Early Warning System, but there is still no simple, flexible, and low-cost solution to archive CSTP streams or integrate them into existing data processing systems. In this study, we design and implement SharpCEEWPServer, a lightweight, out-of-the-box graphical server that integrates client management, real-time data reception, visualization, and archiving, and can, via RingServer, convert CSTP real-time streams into widely supported SeedLink streams. Hardware compatibility is evaluated using four commercial CSTP-capable instruments, a forwarding chain is built to assess forwarding functionality and reliability, and concurrency performance is tested using simulated networks with different station counts. The stability under network impairment scenarios and the performance of the forwarding system were also analyzed. The results show that the server can reliably receive and forward real-time data streams, and that laptop-class hardware is sufficient to withstand the load imposed by an M7.0 earthquake scenario when receiving real-time streams from 1000 three-component seismometers. However, the current version’s latency performance can only meet the needs of non-early warning networks. Overall, the proposed server significantly lowers the deployment and usage threshold for new CSTP-capable instruments and provides an efficient, low-cost integration solution for temporary networks in earthquake emergency response and seismic arrays. Full article

Climate warming is one of the most pressing global changes, with profound consequences for biodiversity, ecosystem functioning, and the provision of ecosystem services. Although warming is expected to alter soil nutrient cycling and plant community structure, the mechanisms through which it reshapes ecosystem multifunctionality (EMF) remain insufficiently understood. Here, we conducted a 3-year field warming experiment in an alpine grassland to assess how warming influences plant diversity, soil nutrients, and their joint effects on EMF. We found that plant α-diversity declined in both control and warming groups in 2021 and partially recovered by 2023, though recovery was weaker under warming. In contrast, β-diversity (turnover) showed a continuous increasing trend under warming across years, although differences from the control were not statistically significant. EMF, evaluated with single- and multi-threshold approaches, exhibited a consistent decline, with warming accelerating this reduction and producing more complex bimodal fluctuations within intermediate threshold ranges (55–75% and 80–90%). Warming also restructured the functional drivers of EMF: soil organic carbon (SOC) and available nitrogen (AN) emerged as dominant regulators, whereas the contributions of total nitrogen and turnover weakened. Collectively, these findings demonstrate that warming not only alters biodiversity patterns and ecosystem functions but also reshapes the soil–plant–function feedbacks that sustain EMF. By identifying SOC and AN as critical mediators, this study highlights a mechanistic pathway through which climate warming may undermine ecosystem resilience and long-term sustainability, providing insights essential for predicting terrestrial ecosystem responses under future climate scenarios. Full article

This paper addresses the tracking control problem for stochastic nonlinear systems (SNSs) subject to distributed time-varying delays and output dead zones. A novel dynamic event-triggered control scheme is proposed by integrating the backstepping technique with a fuzzy logic system (FLS). The FLS is employed to approximate unknown nonlinear functions, while a Nussbaum-type function is incorporated to mitigate the effects of the output dead zone. The challenges posed by distributed time-varying delays are effectively overcome by constructing novel double-integral Lyapunov–Krasovskii functionals. Furthermore, the introduced dynamic event-triggering mechanism, which features a relative threshold and an adaptive parameter, significantly reduces the network communication burden while maintaining desired system performance. Based on Lyapunov stability theory, it is rigorously proven that all signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to validate the feasibility and effectiveness of the proposed control approach. Full article

Second language acquisition (SLA) is critical for refugee integration and a determinant of health and health care access. Although numerous studies have examined language barriers and health communication, the reciprocal relationship between health and second language acquisition remains underexplored in public health research. This qualitative study draws on interviews with twenty Syrian refugees (nine men and eleven women, aged 22–65) resettled in Norway. Data were collected through semi-structured interviews and analysed using reflexive thematic analysis. Two overarching themes were identified: (1) Learning under strain: health problems and post-migratory stressors constrained SLA; and (2) Relational support: reciprocal interactions with neighbours, colleagues, and volunteers enabled both language learning and functional health. These social arenas acted as low-threshold, health-promoting settings that mitigated isolation and strengthened belonging. The study highlights that language operates as a social determinant of health: inclusive, relational spaces facilitate both SLA and health by enhancing communicative participation and access to care. Refugee integration policy should therefore support accessible community spaces outside formal education to strengthen social inclusion, health literacy and refugees’ ability to navigate health and welfare services. Full article

This work presents an experimental evaluation of a high-speed analog-to-digital conversion method based on passive reference waveform crossings combined with time-to-digital converter (TDC) time-tagging. Unlike conventional level-crossing event-driven analog-to-digital converters (ADCs) that require dynamically updated digital-to-analog converters (DACs), the proposed architecture compares the input waveform against a broadband periodic sampling function without active threshold control. Crossing instants are detected by a high-speed comparator and converted into rising and falling edge timestamps using a multi-channel TDC. A commercial ScioSense GPX2-based time-tagger with 30 ps single-shot precision was used for validation. A range of test signals—including 5 MHz sine, sawtooth, damped sine, and frequency-modulated chirp waveforms—were acquired using triangular, sinusoidal, and sawtooth sampling functions. Stroboscopic sampling was demonstrated using reference frequencies lower than the signal of interest, enabling effective undersampling of periodic radio frequency (RF) waveforms. The method achieved effective bandwidths approaching 100 MHz, with amplitude reconstruction errors of 0.05–0.30 RMS for sinusoidal signals and 0.15–0.40 RMS for sawtooth signals. Timing jitter showed strong dependence on the relative slope between the acquired waveform and sampling function: steep regions produced jitter near 5 ns, while shallow regions exhibited jitter up to 20 ns. The study has several limitations, including the bandwidth and dead-time constraints of the commercial TDC, the finite slew rate and noise of the comparator front-end, and the limited frequency range of the generated sampling functions. These factors influence the achievable timing precision and reconstruction accuracy, especially in low-gradient signal regions. Overall, the passive waveform-crossing method demonstrates strong potential for wideband, sparse, and rapidly varying signals, with natural scalability to multi-channel systems. Potential application domains include RF acquisition, ultra-wideband (UWB) radar, integrated sensing and communication (ISAC) systems, high-speed instrumentation, and wideband timed antenna arrays. Full article