Search Results (38,116)

The prediction of Remaining Useful Life (RUL) constitutes a vital aspect of Prognostics and Health Management (PHM), providing capabilities for the assessment of mechanical component health status and prediction of failure instances. Recent studies on feature extraction, time-series modeling, and multi-task learning have shown remarkable advancements. However, most deep learning (DL) techniques predominantly focus on unimodal data or static feature extraction techniques, resulting in a lack of RUL prediction methods that can effectively capture the individual differences among heterogeneous sensors and failure modes under complex operational conditions. To overcome these limitations, an adaptive RUL prediction framework named ADAPT-RULNet is proposed for mechanical components, integrating the feature extraction capabilities of attention-enhanced deep learning (DL) and the decision-making abilities of deep reinforcement learning (DRL) to achieve end-to-end optimization from raw data to accurate RUL prediction. Initially, Functional Alignment Resampling (FAR) is employed to generate high-quality functional signals; then, attention-enhanced Dynamic Time Warping (DTW) is leveraged to obtain individual degradation stages. Subsequently, an attention-enhanced of hybrid multi-scale RUL prediction network is constructed to extract both local and global features from multi-format data. Furthermore, the network achieves optimal feature representation by adaptively fusing multi-source features through Bayesian methods. Finally, we innovatively introduce a Deep Deterministic Policy Gradient (DDPG) strategy from DRL to adaptively optimize key parameters in the construction of individual degradation stages and achieve a global balance between model complexity and prediction accuracy. The proposed model was evaluated on aircraft engines and railway freight car wheels. The results indicate that it achieves a lower average Root Mean Square Error (RMSE) and higher accuracy in comparison with current approaches. Moreover, the method shows strong potential for improving prediction accuracy and robustness in varied industrial applications. Full article

Concrete diagnosis is an important task in making informed decisions about reconstructing or repairing buildings. Among the different approaches for evaluating its characteristics, methods based on electromagnetic waves have been proposed in the literature over the years. In this context, the characterization of concrete complex dielectric permittivity ${ϵ}_r\left(f\right)$ (where f is the frequency) has received considerable attention, taking into account that its values and its frequency behavior are both sensitive to a series of physical parameters, which in turn can significantly influence the mechanical performance of concrete. Recently, data-driven techniques have emerged as alternatives for modeling material properties due to their regression and generalization potential. Following this research line in this work, we investigated the potential of Gaussian Process Regression to model ${ϵ}_r\left(f\right)$ by comparing its performance with that of the model most employed to characterize the concrete dielectric permittivity: the universal Jonscher model. The inherent ability to provide predictions accompanied by confidence intervals, which allows the assessment of the reliability of the permittivity estimate across frequency, and the related error metrics demonstrate that GPR can effectively characterize ${ϵ}_r\left(f\right)$ in an effective manner, outperforming the Jonscher model in terms of accuracy in all the cases considered in our study. Full article

Accidental opioid overdose and Sudden Unexpected Death in Epilepsy (SUDEP) represent major forms of preventable mortality, often involving sudden-onset catastrophic events that could be survivable with rapid detection and intervention. The current physiological monitoring technologies are potentially applicable, but face challenges, including complex setups, poor patient compliance, high costs, and uncertainty about community-based use. Paradoxically, simple clinical observation in supervised injection facilities has proven highly effective, suggesting observable changes in central body motion may be sufficient to detect life-threatening events. We describe a novel wearable biosensor for continuous central body motion monitoring, offering a potential early warning system for life-threatening events. The biosensor incorporates a low-power, triaxial MEMS accelerometer within a discreet, chest-worn device, enabling long-term monitoring with minimal user burden. Two system architectures are described: stored data for retrospective analysis/research, and an in-development system for real-time overdose detection and response. Early user research highlights the importance of accuracy, discretion, and trust for adoption among people who use opioids. The initial clinical data collection, including the OD-SEEN study, demonstrates feasibility for capturing motion data during real-world opioid use. This technology represents a promising advancement in non-invasive monitoring, with potential to improve the outcomes for at-risk populations with multiple health conditions. Full article

The development of biodegradable films with antioxidant properties offers a promising approach to food preservation. This study focused on creating and characterising mango starch-based films enriched with mango peel extract (MPE) at concentrations of 0, 1, and 2%, using peels from mangoes (Mangifera indica var. Corazon) at organoleptic maturity, obtained as residual byproducts (peel and seed) for active food packaging applications. An MPE extraction yield of 35.57 ± 2.74% was achieved using ultrasound-assisted extraction (UAE), confirming its rich phenolic content and antioxidant activity as a natural alternative to synthetic preservatives. Rheological analysis revealed that the films exhibited pseudoplastic behavior, with complex viscosity reducing as angular frequency increased. Incorporating MPE at concentrations up to 1% enhanced the films’ viscoelastic properties, while a 2% addition significantly altered their frequency and temperature dependence. The rheological modeling showed that the fractional Maxwell model with two springpots described the films more accurately than the generalized Maxwell model. This approach offered a clearer understanding of their viscoelastic response, especially under changes in frequency and temperature. Mechanical characterization indicated that adding MPE improved film strength while reducing solubility. Although film thickness remained unchanged, increasing MPE concentration led to greater opacity and darker coloration. These changes offer advantages in food packaging by enhancing UV protection and reducing oxidative degradation. Crucially, the incorporation of MPE significantly increased the phenolic content and antioxidant capacity of the films, as confirmed by ABTS assays. These findings strongly support the potential of MPE-based films for active packaging, providing a sustainable and functional alternative for preserving light-sensitive food products. Among the tested formulations, films with 1% MPE demonstrated the most effective balance of rheological stability, mechanical strength, and antioxidant capacity. Full article

Accurate knowledge of a patient’s anatomy during every treatment fraction in proton therapy is an important prerequisite to ensure a correct dose deposition in the target volume. Adaptive proton therapy aims to detect those changes and adjust the treatment plan accordingly. One way to trigger a daily re-planning of the treatment is to take a proton radiograph from the beam’s-eye view before the treatment to check for possible changes in the water equivalent thickness (WET) along the path due to daily changes in the patient’s anatomy. In this paper, the Two-Plane Imaging System (TPIS) is presented, comprising two ATLAS IBL silicon pixel-detector modules developed for the tracking detector of the ATLAS experiment at CERN. The prototype of the TPIS is described in detail, and proof-of-principle WET images are presented, of two-step phantoms and more complex phantoms with bone-like inlays (WET 10 to 40$\mathrm{m}$$\mathrm{m}$). This study shows the capability of the TPIS to measure WET images with high precision. In addition, the potential of the TPIS to accurately determine WET changes over time down to 1$\mathrm{m}$$\mathrm{m}$ between subsequently taken WET images of a changing phantom is shown. This demonstrates the possible application of the TPIS and ATLAS IBL pixel-detector module in adaptive proton therapy. Full article

Mycobacterium avium complex (MAC) infections present significant therapeutic challenges due to their inherent antibiotic resistance, demanding innovative treatment approaches. This study investigated the antimicrobial and antioxidant potential of a novel compound, Fullerene Gallium Phosphonate (FGP), and compared its effects against a previously tested similar compound, Fullerene Disodium Phosphonate (FDSP). Results of experiments using MAC cultures and infected THP-1 macrophages treated with varying FGP and FDSP concentrations (1, 10, 100 µg/mL) revealed that FGP demonstrated greater efficacy than FDSP in reducing M. avium colony-forming units (CFU), achieving a nearly 3-fold reduction by day 8, compared to a 2-fold decrease with FDSP. In infected macrophages, FGP significantly decreased bacterial load at 1 and 10 µg/mL (p < 0.01). FGP also lowered oxidative stress, reflected by a significant reduction in malondialdehyde (MDA) levels on day 4 (p < 0.05) and decreased IL-6 (2-fold) and TNF-α levels (3-fold) by day 8, indicating both antimicrobial and anti-inflammatory effects. However, FGP paradoxically increased MAC burden at its highest concentration and showed no significant difference in efficacy of different concentrations. These findings suggest that FGP may serve as a promising candidate for antimycobacterial therapy with dual antibacterial and antioxidant effects. Further research is crucial to fully elucidate its mechanism of action and find the optimal therapeutic window. Full article

The land demands of ground-mounted PV systems raise concerns about competition with agriculture, particularly in regions with limited productive farmland. Agrivoltaics, which integrates solar energy generation with agricultural use, offers a potential solution. While agrivoltaics has been extensively studied, less is known about its feasibility and impacts in complex temperate maritime climates such as Aotearoa New Zealand, in particular, the effects of PV-induced shading on ground-level light availability and vegetation. This study modelled the spatial and seasonal distribution of ground-level irradiation and Photosynthetic Photon Flux Density (PPFD) beneath fixed-tilt PV arrays at the Tauhei solar farm in the Waikato region. It quantifies and maps PPFD to evaluate light conditions and its implications for vegetation growth. The results reveal significant spatial and temporal variation over a year. The under-panel ground irradiance is lower than open-field GHI by 18% (summer), 22% (spring), 16% (autumn), and 3% (winter), and this seasonal reduction translates into PPFD gradients. This variation supports a precision agrivoltaic strategy that zones land based on irradiance levels. By aligning crop types and planting schedules with seasonal light profiles, land productivity and ecological value can be improved. These findings are highly applicable in Aotearoa New Zealand’s pasture-based systems and show that effective light management is critical for agrivoltaic success in temperate maritime climates. This is, to our knowledge, the first spatial PPFD zoning analysis for fixed-tilt agrivoltaics, linking year-round ground-light maps to crop/pasture suitability. Full article

Environmental sensitivity illnesses—including fibromyalgia syndrome (FMS), myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and multiple chemical sensitivity (MCS)—are chronic, disabling disorders characterized by hypersensitivity to environmental stimuli, persistent fatigue, widespread pain, and neurocognitive and autonomic dysfunction. Although their diagnostic criteria differ, increasing evidence suggests overlapping clinical features and shared biological mechanisms. A unifying hypothesis highlights the gut–brain–immune axis, where alterations in the intestinal microbiome, epithelial barrier dysfunction, and aberrant immune signaling interact with central sensitization and systemic metabolic dysregulation. Recent studies demonstrate reduced microbial diversity, depletion of anti-inflammatory taxa (e.g., Faecalibacterium prausnitzii, Bifidobacterium), and enrichment of pro-inflammatory Clostridium species across these conditions. These shifts likely alter production of short-chain fatty acids, amino acid metabolites, and complex lipids, with downstream effects on mitochondrial function, neuroinflammation, and host energy metabolism. Moreover, emerging clinical interventions—including probiotics, prebiotics, synbiotics, and fecal microbiota transplantation—suggest a potential role for microbiome-targeted therapies, though controlled evidence remains limited. This review synthesizes current knowledge on microbiome alterations in FMS, ME/CFS, and MCS, emphasizing their convergence on metabolic and immune pathways. By integrating microbial, immunological, and neurophysiological perspectives, we propose a microbiome-centered framework for understanding environmental sensitivity illnesses and highlight avenues for translational research and therapeutic innovation. Full article

Coalbed methane (CBM) reservoirs in southwestern China are characterized by thick, multi-layered coal sequences partitioned into several independent pressure systems by impermeable strata. Commingled production from multiple coal seams in such multi-superposed CBM systems often suffers from severe inter-layer interference, leading to suboptimal gas recovery. To address this challenge, we developed a systematic four-step optimization workflow integrating geological data screening, pressure compartmentalization analysis, and numerical reservoir simulation. The workflow identifies the key “main” coal seams and evaluates various co-production layer combinations to maximize gas recovery while minimizing negative interference. We applied this method to a CBM well (LC-C2) in the Western Guizhou–Eastern Yunnan region, which penetrates three discrete CBM pressure systems. In the case study, single-layer simulations first revealed that one seam (No. 7 + 8) contributed over 30% of the total gas potential, with a few other seams (e.g., No. 18, 13, 4, 16) providing moderate contributions and many seams yielding negligible gas. Guided by these results, we simulated five commingling scenarios of increasing complexity. The optimal scenario was to co-produce the seams from the two higher-pressure systems (a total of six seams) while excluding the low-pressure shallow seams. This optimal six-seam configuration achieved a 10-year cumulative gas production of approximately 2.53 × 10⁶ m³ (about 700 m³/day average)—roughly 75% higher than producing the main seam alone, and even about 15% greater than a scenario involving all available seams. In contrast, including all three pressure systems (ten seams) led to interference effects where the high-pressure seams dominated flow and the low-pressure seams contributed little, resulting in lower overall recovery. The findings demonstrate that more is not always better in multi-seam CBM production. By intelligently selecting a moderate number of compatible seams for co-production, the reservoir’s gas can be extracted more efficiently. The proposed quantitative optimization approach provides a practical tool for designing multi-seam CBM wells and can be broadly applied to similar geologically compartmentalized reservoirs. Full article

Background/Objective: The growing volume and complexity of cases presented to emergency departments underline the urgent need for effective clinical-risk-management strategies. Increasing demands for quality and safety in healthcare highlight the importance of predictive tools in supporting timely and informed clinical decision-making. This study aims to evaluate the performance and usefulness of predictive models for managing the clinical risk of people who visit the emergency department. Methods: A systematic review was conducted, including primary observational studies involving people aged 18 and over, who were not pregnant, and who had visited the emergency department; the intervention was clinical-risk management in emergency departments; the comparison was of early warning scores; and the outcomes were predictive models. Searches were performed on 10 November 2024 across eight electronic databases without date restrictions, and studies published in English, Portuguese, and Spanish were included in this study. Risk of bias was assessed using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies as well as the Prediction Model Risk-of-Bias Assessment Tool. The results were synthesized narratively and are summarized in a table. Results: Four studies were included, each including between 4388 and 448,972 participants. The predictive models identified included the Older Persons' Emergency Risk Assessment score; a new situation awareness model; machine learning and deep learning models; and the Vital-Sign Scoring system. The main outcomes evaluated were in-hospital mortality and clinical deterioration. Conclusions: Despite the limited number of studies, our results indicate that predictive models have potential for managing the clinical risk of emergency department patients, with the risk-of-bias study indicating low concern. We conclude that integrating predictive models with artificial intelligence can improve clinical decision-making and patient safety. Full article

Late pregnancy represents a critical period for the onset of eating disorder symptoms, particularly in the presence of psychological and relational vulnerabilities. Among these, the quality of the romantic relationship has received limited empirical attention, despite its potential role in shaping women’s psychological adjustment, influencing both mood and body image. The present study examined the association between romantic relationship quality and eating disorder symptoms during the third trimester of pregnancy, considering the mediating roles of depressive symptoms and body dissatisfaction. A sample of 231 Italian pregnant women (Mage = 32.3 years) completed four self-report measures: the Dyadic Adjustment Scale-7, the Edinburgh Postnatal Depression Scale, the Body Image in Pregnancy Scale, and the Eating Disorder Examination Questionnaire-Short. A serial mediation model was tested, including pre-pregnancy body mass index as a covariate. Results indicated that lower romantic relationship quality was associated with greater eating disorder symptoms through higher depressive symptoms and body dissatisfaction, which acted both independently and sequentially. These findings highlight the complex interplay between relational and psychological factors in the development of disordered eating during pregnancy, emphasizing the need for early screening and integrated interventions addressing both interpersonal and intrapersonal domains. Full article

Background: Serous papillary adenofibroma cyst (SPAC) of the ovary is a rare benign epithelial tumor that can mimic borderline or malignant ovarian neoplasms. Reports in young women are particularly scarce. Purpose: The aim of this study is to present a rare clinical case of ovarian SPAC in a young woman and to review the existing literature, highlighting diagnostic challenges and implications for fertility-preserving management. Methods: We present a clinical case of ovarian SPAC in a 41-year-old woman and conducted a narrative literature review. The search was performed in PubMed, Scopus, and Web of Science to identify reports published between 2000 and 2025. Additional relevant articles were also identified through manual screening of reference lists from selected papers. Results: MRI revealed a well-encapsulated septated cystic lesion with solid nodular components and post-contrast enhancement. Tumor markers, including CA 19-9, were elevated. Laparoscopic surgery with intraoperative frozen section confirmed the diagnosis of SPAC, allowing fertility-preserving management. Histopathology established the final diagnosis. Conclusions: This case emphasizes the importance of considering SPAC in the differential diagnosis of complex adnexal masses. Early recognition and intraoperative frozen section can guide conservative surgical strategies, avoiding overtreatment and preserving reproductive potential in young patients. Full article

In the digital context, how to achieve symmetrical integration between subjective evaluation and structural stability becomes the key to improving the design effect of iterative product optimization. In this paper, we propose an iterative design method for CIHFS-DEMATEL products that incorporates structural symmetry analysis. The method is based on online review mining and constructs a credibility-based interval hesitant fuzzy set (CIHFS) to symmetrically express the ambiguity and credibility differences in the decision-maker’s subjective evaluation. In turn, a novel exact score function called credibility interval hesitant fuzzy score function (CHFSF), incorporating information symmetric weights, is proposed to realize the bidirectional symmetric mapping between subjective fuzzy inputs and objective exact outputs. Subsequently, the CIHFS-DEMATEL model is introduced to identify the causal paths and a symmetric interaction structure between potential users’ demands. Finally, the demand module mapping matrix is constructed to realize the symmetric decision-making closure loop from demand to solution. Taking the “Intelligent Classified Trash Can” as a case study, we verify the superiority of the method in terms of recognition accuracy, rationality of weight allocation, and structural stability. This study emphasizes the structural symmetry between “input–evaluation–output”, which provides a theoretical foundation and practical framework for the optimal design of products with complex multi-source information. Full article

Epithelial–mesenchymal transition (EMT) and inflammasome signalling are intercon-nected processes which underpin tumour progression, metastasis, and therapeutic re-sistance. Inflammasomes such as NLRP3 encourage pro-inflammatory states (IL-1β, IL-18, NF-κB) and the activation of signalling pathways like TGF-β that promote mes-enchymal traits crucial for EMT. EMT transcriptional programmes can then in turn modulate the inflammasome via NF-κB/TGF-β signalling, creating self-perpetuating mechanisms of cellular plasticity and dysregulated therapeutic response. We have re-viewed the mechanistic evidence for EMT–inflammasome crosstalk in cancer and discussed the potential therapeutic implications. The function of the EMT-inflammasome axis is clearly context-dependent, with the cancer type, stage, and the complexity of the tumour microenvironment heavily contributing. The crosstalk between EMT and the inflammasome is an overlooked mechanism of tumour evolution, and targeting inflammasomes like NLRP3, or their downstream signalling pathways, offers a promising therapeutic avenue, with the objective of inhibiting metastasis and overcoming drug resistance. Full article

Reactive oxygen species (ROS) are formed by the incomplete reduction of oxygen and play a crucial role in both physiological function and pathological process, being controlled by enzymatic and non-enzymatic antioxidant systems. However, excessive ROS production can exceed the body’s antioxidant capacity, resulting in oxidative stress and causing cell death and oxidation of important biomolecules. In this context, the inhibition and/or modulation of ROS has been shown to be effective in reducing pain, oxidative stress, and inflammation. Among ROS, superoxide anion (O₂^•−) is the first free radical to be formed through the mitochondrial electron transport chain (ETC) or by specific enzymes systems, such as the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) complex. O₂^•− plays a significant role in the development and maintenance of pain associated with inflammatory conditions through direct or indirect activation of primary nociceptive neurons and, consequently, peripheral and central sensitization. Experimentally, potassium superoxide (KO₂, a O₂^●− donor) is used to initiate O₂^●− mediated inflammatory and nociceptive responses, making it important for studying the mechanisms associated with ROS-induced pain and evaluating potential therapeutic molecules. This review addresses the production and regulation of O₂^•−, highlighting its biosynthesis, redox control, and its physiological and pathological roles in the development of inflammatory pain, as well as the pharmacological therapies under development aimed at its generation and/or action. Full article