Search Results (899)

The Bell–Bloom magnetometer is promising for mobile applications, but its accuracy is limited by heading errors. A recently identified source of such error is a phase shift in the magnetic resonance, which arises from the superposition of two signals, i.e., the primary resonance from synchronous pumping and a secondary resonance, 90${}^{\circ }$ out-of-phase, driven by the AC light shift of the pump laser. Through Bloch equation modeling and experiment, we uncover a counter-intuitive mechanism: although initiated by the AC light shift, the phase shift’s magnitude is determined solely by the pump light’s average power (DC component) and is independent of its AC modulation. This occurs because the amplitude ratio of the two resonances depends exclusively on the DC-power-induced atomic polarization. Based on this insight, we propose a novel DC compensation scheme by adding a continuous counter-polarized beam to cancel the net DC pumping. Theoretically, this simultaneously suppresses both the AC-light-shift-induced phase shift and the DC-light-shift-induced frequency shift. The scheme’s advantage is its simplified approach to polarization control, avoiding the need for high-speed polarization modulation or major hardware changes as the beams share the same optical path. This makes it highly suitable for demanding mobile applications like aerial magnetic surveying and wearable bio-magnetic sensing in unshielded environments. Full article

This study investigates the biodeterioration of mosaic surfaces in a semi-confined archaeological environment along the Phlegraean coast (Baiae, Italy), focusing on the interaction between salt efflorescence and phototrophic biofilms. A multi-analytical approach was employed, integrating in situ observations with ex situ analyses, including SEM/EDS, FTIR spectroscopy, and metabarcoding (16S and 18S rRNA), to characterize both abiotic and biotic alteration patterns. Results highlight subtle traces of spatial differentiation: samples from the more exposed sector showed a more consistent colonization by halotolerant and halophilic taxa, particularly among Halobacteria and Rubrobacter, along with abundant sodium, chloride, and sulfate signals suggestive of active salt crystallization. Protected areas exhibit a comparable presence of salts with less diverse halophilic communities that vary along a vertical gradient of light exposure. The integration of chemical and biological data supports a model in which salt stress and biofilm development are co-dependent and synergistic in driving surface degradation. These findings emphasize the need for context-specific conservation strategies that account for the combined action of environmental salinity and microbial communities on historical materials. Full article

Nitric oxide (NO), a reactive nitrogen species (RNS), plays a role in multiple biological functions and signal transduction. However, the mechanisms by which NO counteracts stress tolerance in microbes have been poorly explored. In addition, the decomposition of salty food waste poses a significant challenge for food-degrading microbes. Therefore, we investigated how S-nitrosocysteine (CysNO) affects the cellular salt stress response of Burkholderia vietnamiensis TVV75, a strain isolated from a commercial food waste composite. Under the additional 2% NaCl treatment, increased reactive oxygen species (ROS) inhibited bacterial cell growth and viability. In contrast, CysNO treatment alleviated the cellular ROS levels and growth inhibition by augmenting the superoxide dismutase (SOD) and catalase (CAT) activities. CysNO supplementation also promotes the nitrate reduction pathway in B. vietnamiensis TVV75 under salt stress, suggesting NO-mediated nitrogen metabolism for microbial adaptation to salt stress. Furthermore, CysNO restored the intracellular lipid-degrading lipase enzyme activities, which were compromised by salt stress alone. This restoration was accompanied by a concentration-dependent increase in the relative expression of the lipA (lipase A) and ELFPP (esterase lipase family protein) genes. These results suggest that external NO supplementation can regulate redox balance, nitrate reduction, and lipase activity to maintain microbial cell growth in high-salt environments, pinpointing a NO-dependent salt stress adaptation strategy for salt-sensitive microbes involved in food waste decomposition. Full article

Optineurin (OPTN) is a multifunctional adaptor protein that regulates diverse cellular processes, including inflammatory signaling, autophagy, vesicular trafficking, and immune responses. This multifaceted role of OPTN is made possible by the presence of a complex structure comprising multiple domains that interact with different proteins to exert various functions important for modulating key signaling processes. Mutations in OPTN are linked with several human pathologies including glaucoma, Paget’s disease of bone, Crohn’s disease, and neurodegenerative diseases such as amyotrophic lateral sclerosis, and dementia. Emerging evidence suggests that OPTN has a complex and context-dependent role in cancer biology as well. It is upregulated in pancreatic ductal adenocarcinoma and hepatocellular carcinoma but downregulated in lung and colorectal cancers, indicating its dual role as a potential oncogene or tumor suppressor depending on the cellular environment. Additionally, OPTN plays a critical role in preventing immune evasion in colorectal cancer by maintaining interferon-gamma receptor 1 (IFNGR1) expression and supporting dendritic cell-mediated T-cell priming, thereby enhancing antitumor immune responses. Despite its significance in oncogenic pathways and immune regulation, the therapeutic potential of targeting OPTN in cancer remains largely unexplored. This review aims to provide a comprehensive understanding of OPTN’s pleiotropic functions, highlighting its role in autophagy, inflammation, immune surveillance, and cancer progression. By elucidating its diverse regulatory mechanisms, we seek to encourage further research into the therapeutic implications of OPTN in cancer treatment and immunotherapy. Full article

Lamiophlomis rotata (Benth.) Kudo is a typical alpine medicinal plant. However, the mechanism underlying its adaptation to high altitudes remains incompletely understood. In this study, we integrated transcriptome and metabolome analyses. Specifically, we used third-generation sequencing for building a reference transcriptome and second-generation sequencing for differential gene expression analysis. Our findings revealed that the activation of the hydrogen sulfide signaling pathway and the reprogramming of amino acid metabolism are probable adaptation mechanisms. Different from previous reports, the hydrogen sulfide signaling may regulate the activity of cellulose synthase in addition to enhancement of antioxidant capacity and accumulation of osmolytes. By altering the agronomic traits of plants in a cell wall remodeling-dependent manner, it enables L. rotata to adapt to alpine stress. The accumulated amino acids not only store energy-efficient organic nitrogen as precursors for the synthesis of secondary metabolites but also act as signaling molecules to activate defense responses. Additionally, we propose a potential link between the hydrogen sulfide signaling pathway and amino acid metabolism. Overall, this study systematically explores the adaptation mechanism of L. rotata to high-altitude environments, offering a novel perspective for understanding the growth, development, stress responses, and secondary metabolic processes of alpine plants. Full article

Over the service life of several decades, structural damage detection is crucial for ensuring the safety and durability of engineering structures. However, existing methods often overlook the spatiotemporal coupling in multi-sensor data, hindering the full exploitation of structural dynamic evolution and spatial correlations. This paper proposes an autoencoder model integrating Temporal Convolutional Networks (TCN) and Graph Attention Networks (GAT), termed TCNGAT-AE, to establish an unsupervised damage detection method. The model utilizes the TCN module to extract temporal dependencies and dynamic features from vibration signals, while leveraging the GAT module to explicitly capture the spatial topological relationships within the sensor network, thereby achieving deep fusion of spatiotemporal features. The proposed method adopts an “offline training-online detection” framework, requiring only data from the healthy state of the structure for training, and employs reconstruction error as the damage indicator. To validate the proposed method, two sets of experimentally measured data are utilized: one from the Z-24 concrete box-girder bridge under ambient excitation, and the other from the Old Ada Bridge under vehicle load excitation. Additionally, ablation studies are conducted to analyze the effectiveness of the spatiotemporal fusion mechanism. Results demonstrate that the proposed method achieves effective damage detection in both different structural types and excitation scenarios. Furthermore, the explicit modeling of spatiotemporal features significantly enhances detection performance, with the anomaly detection rate showing substantial improvement compared to baseline models utilizing only temporal or spatial modeling. Moreover, this end-to-end framework processes raw vibration signals directly, avoiding complex preprocessing. This makes it highly suitable for practical and near-real-time monitoring. The findings of this study demonstrate that the damage detection method based on TCNGAT-AE can be effectively applied to structural safety monitoring in complex engineering environments, and can be further integrated with real-time monitoring systems of critical structures for online analysis. Full article

This paper introduces the Bateson Game, a signaling game in which ambiguity over the governing rules of interaction (interpretive frames), rather than asymmetry of information about player types, drives strategic outcomes. We formalize the communication paradox of the “double bind” by defining a class of games where a Receiver acts under uncertainty about the operative frame, while the Sender possesses private information about the true frame, benefits from manipulation, and penalizes attempts at meta-communication (clarification). We prove that the game’s core axioms preclude the existence of a separating Perfect Bayesian Equilibrium. More significantly, we show that under boundedly rational learning dynamics, the Receiver’s beliefs can become locked into one of two pathological states, depending on the structure of the Sender’s incentives. If the Sender’s incentives are cyclical, the system enters a persistent oscillatory state (an “ambiguity trap”). If the Sender’s incentives align with reinforcing a specific belief or if the Sender has a dominant strategy, the system settles into a stable equilibrium (a “certainty trap”), characterized by stable beliefs dictated by the Sender. We present a computational analysis contrasting these outcomes, demonstrating empirically how different parametrizations lead to either trap. The Bateson Game provides a novel game-theoretic foundation for analyzing phenomena such as deceptive AI alignment and institutional gaslighting, demonstrating how ambiguity can be weaponized to create durable, exploitative strategic environments. Full article

Reliable fault diagnosis of milling machines is essential for maintaining operational stability and cost-effective maintenance; however, it remains challenging due to limited labeled data and the highly non-stationary nature of acoustic emission (AE) signals. This study introduces an optimized Few-Shot Learning framework (FSL) that integrates time–frequency analysis with attention-guided representation learning and distribution-aware classification for data-efficient fault detection. The framework converts AE signals into Continuous Wavelet Transform (CWT) scalograms, which are processed using a self-attention-enhanced ResNet-50 backbone to capture both local texture features and long-range dependencies in the signal. Adaptive prototype computation with learnable importance weighting refines class representations, while Mahalanobis distance-based matching ensures robust alignment between query and prototype embeddings under limited sample conditions. To further strengthen discriminability, contrastive loss with hard negative mining enforces compact intra-class clustering and clear inter-class separation. Comprehensive experiments under 7-way 5-shot settings and 5-fold stratified cross-validation demonstrate consistent and reliable performance, achieving a mean accuracy of 98.86% ± 0.97% (95% CI: [98.01%, 99.71%]). Additional evaluations across multiple spindle speeds (660 rpm and 1440 rpm) confirm that the model generalizes effectively under varying operating conditions. Grad-CAM++ activation maps further illustrate that the network focuses on physically meaningful fault-related regions, enhancing interpretability. The results verify that the proposed framework achieves robust, scalable, and interpretable fault diagnosis using minimal labeled data, offering a practical solution for predictive maintenance in modern intelligent manufacturing environments. Full article

The present work investigated the effect of different storage temperatures (10 °C, 30 °C, and 50 °C) on the mechanical, structural, chemical, and microstructural properties of a set of sustainable mortars with gray waste. Three types of mortar were investigated: (1) Type A, prepared from a proportion of 1 part cement: 1 part hydrated lime: 6 parts sand; (2) Type B, prepared from a proportion of 1 part cement: 1 part hydrated lime: 6 parts sand: 0.1 part waste; and (3) Type C, prepared from a proportion of 0.9 part cement: 1 part hydrated lime: 6 parts sand: 0.1 part waste. The waste incorporation reduced compressive strength by 8%, while partial cement replacement reduced by 33%. The cement storage at 10 °C preserved the compressive strength, whereas storage at 50 °C increased it by 8.8%. In type B mortar, the waste incorporation improved compressive strength by 19% at 50 °C. The most substantial enhancement occurred in type C mortar, where cement replacement with residue and storage at 50 °C led to a 27% increase. These results highlight the potential of higher storage temperatures to mitigate cement degradation in humid environments. Furthermore, XRD analysis revealed that cement storage temperature did not affect the formation of primary cement phases, as degradation products were chemically similar to hydration products. However, sustainable mortars exhibited changes in the C-S-H phase signal when the cement is stored for 90 days at 30 °C. Finally, SEM and EDS analyses identified variations in Ca, Si, and O proportions depending on storage conditions. Full article

Recent advancements in deep learning have significantly improved fault diagnosis methods. However, challenges such as insufficient feature extraction, limited long-range dependency modeling, and environmental noise continue to hinder their effectiveness. This paper presents a novel mixture of multi-view convolutional (MOM-Conv) layers integrating the Mixture of Experts (MOE) mechanism. This design effectively captures and fuses both local and contextual information, thereby enhancing feature extraction and representation. This proposed approach aims to improve prediction accuracy under varying noise conditions, particularly in rolling ball bearing systems characterized by noisy signals. Additionally, we propose the Mamba-enhanced adaptive self-attention long short-term memory (MASA-LSTM) model, which effectively captures both global and local dependencies in ultra-long time series data. This model addresses the limitations of traditional models in extracting long-range dependencies from such signals. The architecture also integrates a multi-step temporal state fusion mechanism to optimize information flow and incorporates adaptive parameter tuning, thereby improving dynamic adaptability within the LSTM framework. To further mitigate the impact of noise, we transform vibration signals into denoised multi-channel representations, enhancing model stability in noisy environments. Experimental results show that our proposed model outperforms existing state-of-the-art approaches on both the Paderborn and Case Western Reserve University bearing datasets, demonstrating remarkable robustness and effectiveness across various noise levels. Full article

Background/Objectives: Lightweight and portable functional near-infrared spectroscopy (fNIRS) systems enable neuromonitoring in clinical environments such as operating rooms. Patient posture is known to influence physiology, behavior, and brain activity, and may affect fNIRS measurements. However, the effects of some postures commonly used in clinical care—such as Fowler’s and semi-Fowler’s—remain largely unexamined in fNIRS research. Methods: We conducted a singular study in a mock operating room exploring the effects of five postures—standing, upright sitting, Fowler’s, semi-Fowler’s, and supine—on fNIRS data during resting-state conditions and under various auditory stimuli. We collected hemodynamic data and extracted the characteristic hemodynamic response function (HRF) at each posture in response to the presented auditory stimulus and the amplitude of the resting-state signal. Results: For the auditory task condition, we found that posture had no statistically significant impact on the amplitude of the global HRF for Fowler’s and semi-Fowler’s postures. We also found no significant relationships across different postures when analyzing the amplitude of the global resting-state signal; however, binning of frequency-dependent postural effects revealed statistically significant differences between Fowler’s and semi-Fowler’s postures at low frequencies (f < 0.09 Hz). Conclusions: Our results suggest posture effects need not require complex data processing pipelines or data segmentation efforts on an auditory task-induced condition or on the general analysis of the global resting signal; however, not all reclined postures are equivalent, and we recommend that researchers report the angle of reclination measurements for seated data collection sessions for improved reliability and data context. Full article

Yersinia enterocolitica is a foodborne pathogen capable of biofilm formation and virulence modulation in response to environmental signals. Among these, glucose—present at physiologically relevant concentrations in the human body—may serve as a regulatory cue affecting infection-associated pathways, including those governed by the pYV virulence plasmid. Although the role of glucose has been investigated under host-mimicking conditions, its impact in non-host environments remains poorly understood. This study was designed to evaluate the glucose-dependent physiological responses of two isogenic Y. enterocolitica strains, KT0001 (pYV-negative) and KT0003 (pYV-positive), under non-host conditions (26 °C). Both strains were cultured in TYE medium containing 0–3% glucose. Comparative analyses were conducted under identical in vitro conditions to elucidate plasmid-associated phenotypic differences. Glucose elicited markedly divergent responses. In KT0001, growth remained unaffected; however, biofilm formation declined by 77.7%, accompanied by a 90% reduction in surface hydrophobicity, a 40% decrease in motility, and a 59% drop in intracellular cyclic AMP—suggesting classical carbon catabolite repression. Conversely, KT0003 exhibited 86% growth inhibition but maintained biofilm levels. This was associated with substantial extracellular polymeric substance induction (~20-fold increase in polysaccharides and ~4.7-fold in extracellular DNA) and nearly fivefold elevation in cyclic AMP levels, despite concurrent decreases in motility (64%) and hydrophobicity (40%). These findings indicate that glucose functions as a strain-specific modulator in Y. enterocolitica. In particular, KT0003’s response suggests that the pYV plasmid enables the bacterium to interpret glucose as a host-associated cue, even under non-host conditions, potentially initiating virulence-related adaptations prior to host contact. Full article

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

Conductivity sensors play a vital role in monitoring production data in oil wells to ensure efficient oilfield operations, and their service performance depends on the durability of Invar alloy electrodes. The alloy electrodes are susceptible to damage from abrasive solid particles, corrosive media, and oil fluids in downhole environments. The degradation of the alloy electrodes directly compromises the signal stability of conductivity sensors, resulting in inaccurate monitoring data. Inspired by the intrinsic oleophobic properties of fish scales, we developed a fluorinated boron-doped diamond (FBDD) film with biomimetic micro–nano structures to enhance the wear resistance, corrosion resistance, and amphiphobicity of Invar alloy electrodes. The fish scale architecture was fabricated through argon-rich hot-filament chemical vapor deposition (90% Ar, 8 h) followed by fluorination. FBDD-coated electrodes surpass industrial benchmarks, exhibiting a friction coefficient of 0.08, wear rate of 5.1 × 10⁻⁷ mm³/(N·mm), corrosion rate of 3.581 × 10⁻³ mm/a, and oil/water contact angles of 95.32°/106.47°. The following underlying improvement mechanisms of FBDD films are proposed: (i) the wear-resistant matrix preserves the oleophobic nanostructures during abrasive contact; (ii) the corrosion barrier maintains electrical conductivity by preventing surface oxidation; (iii) the oil-repellent surface minimizes fouling that could mask corrosion or wear damage. Full article

Gas Detection Systems (GDSs) are critical safety technologies deployed in semiconductor wafer fabrication facilities to monitor the presence of hazardous gases. A GDS receives input from gas detectors equipped with consumable gas sensors, such as electrochemical (EC) and metal oxide semiconductor (MOS) types, which are used to detect toxic, flammable, or reactive gases. However, over time, sensors degradations, accuracy drift, and cross-sensitivity to interference gases compromise their intended performance. To maintain sensor accuracy and reliability, routine manual calibration is required—an approach that is resource-intensive, time-consuming, and prone to human error, especially in facilities with extensive networks of gas detectors. This systematic review (PROSPERO on 11th October 2025 Registration number: 1166004) explored minimizing or eliminating the dependency on manual calibration. Findings indicate that using properly calibrated gas sensor data can support advanced data analytics and machine learning algorithms to correct accuracy drift and improve gas selectivity. Techniques such as Principal Component Analysis (PCA), Support Vector Machines (SVMs), multivariate regression, and calibration transfer have been effectively applied to differentiate target gases from interferences and compensate for sensor aging and environmental variability. The paper also explores the emerging potential for integrating calibration-free or self-correcting gas sensor systems into existing GDS infrastructures. Despite significant progress, key research challenges persist. These include understanding the dynamics of sensor response drift due to prolonged gas exposure, synchronizing multi-sensor data collection to minimize time-related drift, and aligning ambient sensor signals with gas analytical references. Future research should prioritize the development of application-specific datasets, adaptive environmental compensation models, and hybrid validation frameworks. These advancements will contribute to the realization of intelligent, autonomous, and data-driven gas detection solutions that are robust, scalable, and well-suited to the operational complexities of modern industrial environments. Full article