Search Results (198)

Fibromyalgia (FM) is a complex chronic syndrome marked by widespread musculoskeletal pain, neurocognitive dysfunction (“fibro-fog”), and autonomic disturbances. Clinical management remains challenging due to subjective symptom reporting and the lack of definitive diagnostics. Emerging evidence points to a multifactorial origin involving central sensitization, neuroendocrine imbalance, and systemic immune-inflammatory alterations. A wide array of candidate biomarkers has been reported in FM, encompassing neurotransmitters (serotonin, norepinephrine), excitatory and inhibitory amino acids, metabolic and glycolytic enzymes, stress-related proteins, autoantibodies, oxidative stress markers and pro-inflammatory cytokines. This molecular heterogeneity reflects the systemic and multidimensional nature of FM. However, most of these biomarkers have been primarily investigated in serum or plasma, where analytical validation and reference ranges are more established. In contrast, the exploration of salivary biomarkers—although highly attractive due to its non-invasive, stress-free, and repeatable collection—remains comparatively limited. Saliva contains a reduced concentration range of many systemic markers and is strongly influenced by circadian rhythms, stress, flow rate, and oral health conditions. While promising candidates such as α-amylase, cortisol, calgranulins, and selected metabolic enzymes have shown potential in saliva, many proposed FM-related biomarkers lack full analytical validation, standardized protocols, and clinically defined reference intervals in this matrix. In this context, non-invasive electrochemical biosensors represent a transformative technological approach. Advanced electrode architectures incorporating nucleic acid probes, redox reporters, and nanostructured materials offer high sensitivity in low-volume and low-concentration biofluids such as saliva. The integration of multiplexed biomarker panels into portable platforms could enable real-time, longitudinal monitoring of FM pathophysiology, supporting phenotype stratification, personalized therapeutic adjustment, and objective disease activity tracking. Full article

A sustainable way to recycle used tires and improve the functionality of asphalt pavements is through the use of crumb rubber modified asphalt (CRMA). However, its application during high-temperature construction raises environmental and occupational health concerns due to the release of significant quantities of odorous and potentially harmful gases. Therefore, this study selected α-Amyl cinnamic aldehyde (ACA) as a deodorant and added it to CRMA at proportions of 0.5%, 1.0%, 1.5%, and 2.0% to prepare DCRMA. A number of common tests, such as softening point, ductility, penetration, Brookfield rotational viscosity, and segregation analysis, were used to evaluate the basic characteristics of the modified asphalt. A self-developed asphalt fume monitoring device was used to quantitatively analyze the changes in VOCs, H₂S gas concentration, and solid particle content in the asphalt fumes to assess the deodorization effect of ACA on CRMA. Furthermore, the deodorization mechanism of ACA on CRMA was explored in depth using microscopic methods, such as fluorescence microscopy (FM) and Fourier transform infrared spectroscopy (FTIR). The findings demonstrated that ACA can increase the softening point and viscosity of CRMA while decreasing its penetration and ductility. The storage stability was optimal at a 1.0% ACA addition. Additionally, as the ACA content increased, the concentrations of VOCs, H₂S gas, and solid particles in the asphalt fumes continued to decrease. FM results indicated that when the ACA content did not exceed 1.0%, it promoted the swelling degree of CR in the asphalt. FTIR results showed that ACA can reduce the characteristic peak intensity of CRMA. This study offers important technical references and practical support for the environmentally friendly use of CRMA. Full article

To enhance the comprehensive performance and interfacial adhesion of conventional emulsified asphalt, an epoxy-functionalized waterborne polyurethane modified emulsified asphalt (EFPU-MEA) was developed using an epoxy-functionalized waterborne polyurethane (EFPU) emulsion and an isocyanate curing agent. Experimental evaluations show that the EFPU-MEA achieves a tensile strength of 1.11 ± 0.05 MPa and an elongation at break of 782.5 ± 45%, demonstrating a well-balanced flexibility and deformation resistance. The interfacial bond between EFPU-MEA and aggregates exhibited robust durability under various stressors, including thermal fluctuations, low-temperature cracking, chemical corrosion, and moisture damage. Quantitative “sandwich” pull-out and shear tests determined the optimal modifier content and spraying quantity to be 15–20% and 1.0 kg/m², respectively. Under these conditions, the system maintained high bond strength following severe freeze–thaw cycles and chemical erosion. Mechanistically, fluorescence microscopy (FM) confirmed a uniform dispersion of EFPU within the asphalt matrix, providing effective physical reinforcement. Furthermore, surface free energy (SFE) analysis and Fourier Transform Infrared (FTIR) spectroscopy revealed that internal chemical crosslinking restructures the binder’s surface thermodynamics, significantly increasing the surface polarity and adhesion work. Finally, road performance tests—including marshall stability, wet track abrasion, and rutting resistance—verified the engineering durability of the EFPU-MEA mixture. These findings provide a theoretical and practical basis for the use of EFPU-MEA in extending the service life of high-grade highway pavements. Full article

Zero-valent iron (Fe(0)) nanoparticles have a wide range of applications, including catalysis, energy storage, and even reported roles in human neurochemistry. This study demonstrated that [Fe₃(CO)₁₂] dissolves in N,N-Dimethylformamide (DMF) within a minute to resolve the dissolution problem of this complex. Dodecylamine (DDA) was used to produce DDA-coated Fe(0) at 383 K in 30 s with a microwave reactor. The powder X-ray diffraction (PXRD) of the Fe(0) profile indicated a pure-phase face-centred cubic (FCC) structure with Fm$\overline{3}$m space group. Varying the synthesis time from 30 s to 5 min did not significantly affect the unit cell parameters (3.5276 (±0.0001) and 3.5391 (±0.0001) Å). Microwave use yielded well-dispersed, pure Fe(0) nanoparticles, and the particle size, shape, elemental analysis, and surface oxidation of the Fe(0) nanoparticles were studied using scanning electron microscopy and dispersive X-ray spectroscopy (SEM/EDX). Annular Dark-Field Scanning Transmission Electron Microscopy (ADF-STEM) and Fourier-transform infrared (FT-IR) spectroscopy confirmed the surface coating of Fe(0) nanoparticles with DDA. Thermogravimetric analysis (TGA) was used to demonstrate the surface adsorption of DDA on Fe(0) nanoparticles. In addition, STEM showed that the average nanoparticle size under the stated synthesis conditions was 25.7 nm. This comparatively straightforward procedure offers advantages over existing practical approaches to the synthesis of Fe(0) nanoparticles, including safety, speed and reaction control. Full article

Generative recommendation systems based on Large Language Models leverage their reasoning capabilities to capture users’ latent interests. However, aligning continuous user behavioral embeddings with the discrete semantic space of LLMs remains a challenge. Direct alignment often leads to semantic mismatch and hallucination issues. Furthermore, existing methods typically rely on multi-stage training strategies to adapt to variations in feature distributions, thereby limiting training efficiency. To address the aforementioned issues, we propose SBT-Rec, a structured behavioral tokenization framework. Specifically, we first design a hierarchical discrete structure discovery module, utilizing a recursive residual quantization mechanism to decompose continuous behavioral vectors into discrete behavioral atoms to resolve modality discrepancies. Second, the multi-scale behavioral semantic reconstruction module reconstructs behavioral representations via residual superposition, thereby reducing data noise. Third, a residual-aware modality distribution aligner is introduced to transform behavioral features into input tokens compatible with the LLM via non-linear mapping. Finally, based on structured discrete representations, we propose a single-stage behavioral-semantic adaptive optimization strategy, achieving end-to-end parameter-efficient fine-tuning. Experiments on the MovieLens, LastFM, and Steam datasets demonstrate that SBT-Rec outperforms existing baseline models in terms of recommendation accuracy, training efficiency, and noise robustness. Full article

Although extensive research has been conducted on the regenerants for unmodified and SBS-modified asphalt, in-depth studies on the activation of regenerants to restore the SBS cross-linked network while preserving their diffusion performance have not yet been reported. This study quantitatively evaluated the activation effect of self-healing regenerants on SBS cross-linked networks by testing the activation degree of 6%, 8%, and 10% cross-linked networks with self-healing regenerants; the phase structure of SBS-modified asphalt before and after regeneration was examined using fluorescence microscopy (FM); the underlying mechanism of the reactive regenerant was elucidated by Fourier Transform Infrared Spectroscopy (FTIR) and Gel Permeation Chromatography (GPC); furthermore, the rheological response characteristics of the reactive regenerant and conventional regenerant were comparatively analyzed. The findings indicated that the SBS cross-linked network self-healing regenerant exhibited a more pronounced activation effect on aged asphalt. Specifically, when the dosage of the regenerant reaches 8%, its repairing effect on the cross-linked network becomes particularly significant. Reconstructing the cross-linked network structure of SBS-modified asphalt enabled the recovery of the viscoelastic properties of the recycled asphalt. Nevertheless, an excessive dosage of the regenerant failed to further enhance the cross-linked structure in a meaningful way and might even exert an adverse impact on the high-temperature performance of the recycled asphalt. Full article

Improving both the high- and low-temperature performance of asphalt is still difficult in modern pavement applications. This performance imbalance has motivated the development of new modification strategies that can enhance temperature stability while maintaining construction workability. In this research, a low-molecular-weight elastic polyolefin (POL) with inherent compatibility was introduced as a novel asphalt modifier. POL was incorporated at five dosages (0%, 2%, 4%, 6%, and 8% by weight of asphalt) to investigate its effects on the fundamental physical, rheological, and low-temperature properties of the asphalt. The rheological behavior was characterized by dynamic shear rheometer (DSR) and bending beam rheometer (BBR), while the modification mechanism and dispersion morphology were analyzed through Fourier-transform infrared spectroscopy (FT-IR) and fluorescence microscopy (FM). The results reveal that POL markedly improves the high-temperature performance and workability of asphalt, with the rutting factor increasing by two- to eightfold. POL modification improved the thermal stability of asphalt, shifting the maximum decomposition temperature from 455.2 °C for the base binder to 461–463 °C, while the total mass loss remained nearly constant at 80–83%. Microscopic observations confirm that POL forms a physically blended network within the asphalt matrix, exhibiting a green fluorescent structure that becomes progressively continuous with increasing dosage. The most homogeneous dispersion and optimal compatibility occur at a POL dosage of 6%, beyond which phase segregation emerges and low-temperature properties deteriorate. Accordingly, a 6% POL dosage is recommended for achieving balanced performance. These findings provide theoretical and practical guidance for the development of balanced performance and thermally stable POL-modified asphalt materials. Full article

Foundation models (FMs) have become a paradigm shift in the field of artificial intelligence, allowing one large-scale pretrained model to be customized for a broad set of downstream tasks using very little task-specific data. These models, which include GPT, CLIP, BERT, and vision transformers, have altered the scope of transfer learning and multimodal understanding and are built on top of enormous datasets and self-supervised learning. The paper provides a broad view of the modern state of foundation models, with an emphasis on their technological foundation, training, and cross-domain use in fields like natural language processing, computer vision, healthcare, robotics and scientific discovery. We also explore the main opportunities that FMs offer, as well as state-of-the-art methods and techniques for the development of foundation models. we discuss their applications in natural language processing, computer vision, healthcare, etc. Furthermore, their limitations and challenges are also investigated. Lastly, future prospects are discussed so that professionals and scientists obtain a better understanding of the importance of foundation models for addressing their research goals. Full article

In view of the depletion of high-grade magnesite resources in China, this study presents a comparative analysis of two industrial fused magnesia products produced via a flotation–fusion route. A low-grade magnesite (DSQLM-3, MgO 41.48 wt.%) was upgraded by reverse flotation to a concentrate (FDSQLM-3, MgO 47.55 wt.%) with >97% SiO₂ removal. Two fused magnesia samples (FM-1 from natural high-grade ore DSQLM-1; FFM-3 from concentrate FDSQLM-3) were produced under identical arc-furnace melting (2800 °C, 4 h), followed by natural cooling. Although FFM-3 showed higher MgO (97.61 vs. 97.25 wt.%), its bulk density was comparable to FM-1 (3.45 vs. 3.46 g/cm³). XRD/Rietveld refinement and SEM-EDS indicated that CMS dominated the Ca–silicate assemblage in FM-1, whereas β/γ-C₂S was observed in FFM-3, coinciding with a higher CaO/SiO₂ (C/S) ratio (2.85 vs. 0.68). Image analysis further showed higher grain boundary microcrack metrics in FFM-3. These observations are consistent with reports in the literature stating that the β → γ transformation of C₂S during cooling involves ~12% volume expansion that can contribute to cracking; however, cooling history and composition were not independently controlled in this industrial comparison, so the relationships are interpreted as data-supported associations rather than isolated causality. The results suggest that beneficiation strategies may benefit from managing residual oxide balance (especially C/S ratio) in addition to reducing total impurities. Mechanical and thermomechanical properties were not measured and should be evaluated in future work. Full article

Emerging technologies are transforming Facilities Management (FM), enabling more efficient and accurate building inspections and maintenance. Mixed Reality (MR), which integrates virtual content into real-world environments, has shown potential for improving operational performance and technician training. This study presents the development and evaluation of an MR-assisted system designed to support facility operations in academic buildings. The system was tested across three case scenarios, namely plumbing, lighting, and fire sprinkler systems, using Microsoft HoloLens^®. A mixed-methods approach combined a post-use questionnaire and semi-structured interviews with twelve FM professionals, including technicians, inspectors, and managers. Results indicated that 66.67% of participants found the MR interface highly effective in visualizing systems and guiding maintenance steps. 83.33% agreed that checklist integration enhanced accuracy and learning. Technical challenges, including model drift, latency, and occasional software crashes, were also observed. Overall, the study confirms the feasibility of MR for FM training and inspection, offering a foundation for broader implementation and future research. The findings provide valuable insights into how MR-based visualization and interaction tools can enhance efficiency, learning, and communication in facility operations. Full article

Micro-motors are widely used in industrial applications, which require effective fault diagnosis to maintain safe equipment operation. However, fault signals from micro-motors often exhibit weak signal strength and ambiguous features. To address these challenges, this study proposes a novel fault diagnosis method. Initially, the Jump plus AM-FM Mode Decomposition (JMD) technique was utilized to decompose the measured signals into amplitude-modulated–frequency-modulated (AM-FM) oscillation components and discontinuous (jump) components. The proposed process extracts valuable fault features and integrates them into a new time-domain signal, while also suppressing modal aliasing. Subsequently, a novel Global Relationship Matrix (GRM) is employed to transform one-dimensional signals into two-dimensional images, thereby enhancing the representation of fault features. These images are then input into an Optimized Convolutional Neural Network (OCNN) with an AdamW optimizer, which effectively reduces overfitting during training. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy rate of 99.0476% for multiple fault types, outperforming four comparative methods. This approach offers a reliable solution for quality inspection of micro-motors in a manufacturing environment. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

We present LazyNet, a compact one-step neural-ODE model for single-cell CRISPR activation/interference (A/I) that operates directly on two-snapshot (“pre → post”) measurements and yields parameters with clear mechanistic meaning. The core log–linear–exp residual block exactly represents multiplicative effects, so synergistic multi-locus responses appear as explicit components rather than opaque composites. On a 53k-cell × 18k-gene neuronal Perturb-seq matrix, a three-replica LazyNet ensemble trained under a matched 1 h budget achieved strong threshold-free ranking and competitive error (genome-wide r ≈ 0.67) while running on CPUs. For comparison, we instantiated transformer (scGPT-style) and state-space (RetNet/CellFM-style) architectures from random initialization and trained them from scratch on the same dataset and within the same 1 h cap on a GPU platform, without any large-scale pretraining or external data. Under these strictly controlled, low-data conditions, LazyNet matched or exceeded their predictive performance while using far fewer parameters and resources. A T-cell screen included only for generalization showed the same ranking advantage under the identical evaluation pipeline. Beyond prediction, LazyNet exposes directed, local elasticities; averaging Jacobians across replicas produces a consensus interaction matrix from which compact subgraphs are extracted and evaluated at the module level. The resulting networks show coherent enrichment against authoritative resources (large-scale co-expression and curated functional associations) and concordance with orthogonal GPX4-knockout proteomes, recovering known ferroptosis regulators and nominating testable links in a lysosomal–mitochondrial–immune module. These results position LazyNet as a practical option for from-scratch, low-data CRISPR A/I studies where large-scale pretraining of foundation models is not feasible. Full article

The increasing adoption of highly variable renewable energy has introduced unprecedented volatility into the National Electricity Market (NEM), rendering traditional linear price forecasting models insufficient. The Australian Energy Market Operator (AEMO) spot price forecasts often struggle during periods of volatile demand, renewable variability, and strategic rebidding. This study evaluates whether transformer architectures can improve intraday NEM price forecasting. Using 34 months of market data and weather conditions, several transformer variants, including encoder–decoder, decoder-only, and encoder-only, were compared against the AEMO’s operational forecast, a two-layer LSTM baseline, the Temporal Fusion Transformer, PatchTST, and TimesFM. The decoder-only transformer achieved the best accuracy across the 2–16 h horizons in NSW, with nMAPE values of 33.6–39.2%, outperforming both AEMO and all baseline models. Retraining in Victoria and Queensland produced similarly strong results, demonstrating robust regional generalisation. A feature importance analysis showed that future-facing predispatch and forecast covariates dominate model importance, explaining why a decoder-only transformer variant performed so competitively. While magnitude estimation for extreme price spikes remains challenging, the transformer models demonstrated superior capability in delivering statistically significant improvements in forecast accuracy. An API providing real-time forecasts using the small encoder–decoder transformer model is available. Full article

Industrial deliverables in the AEC/FM sector are increasingly specified, validated, and governed by open standards. However, the machine-readable delivery specifications rarely propagate intact into the real-time collaborative 3D scene descriptions required by digital twins, XR, large-scale simulation, and visualization. This paper proposes a pipeline that transforms industrial deliverables into semantically faithful, queryable, and render-ready open scene descriptions. Unlike existing workflows that focus on geometric translation via connectors or intermediate formats, the proposed pipeline aligns defined delivery specifications with schema-aware USD composition so that contractual semantics remain executable in the scene. The pipeline comprises delivery specification, which records required objects, attributes, and provenance as versioned rule sets; semantically bound scene realization, which builds an open scene graph that preserves spatial hierarchy and identifiers, while linking rich properties through lightweight references; and interactive sustainment, which lets multiple engines render, analyze, and update the scene while allowing rules to be re-applied at any time. It presents a prototype and roadmap that make open scene description a streaming-ready execution layer for building deliverables, enabling consistent semantics, and reuse across diverse 3D engines. Full article