Search Results (20,418)

Silver nanoparticles (AgNPs) synthesized via fungal-mediated biosynthesis have gained attention as eco-friendly alternatives to chemically produced nanomaterials, with broad biomedical potential. Fungi represent particularly attractive systems because their secretomes contain diverse biomolecules, including enzymes, proteins, polysaccharides, and secondary metabolites, capable of reducing silver ions and stabilizing the resulting nanoparticles. Despite extensive investigation, the molecular mechanisms underlying fungal-mediated AgNP formation remain poorly defined. This review critically examines the key biomolecular drivers involved in this process, with emphasis on nitrate reductases, oxidoreductases, extracellular proteins, polysaccharides, and secondary metabolites as potential reducing and capping agents. Proposed mechanisms, including nitrate reductase-dependent, superoxide-mediated, and metabolite-driven pathways, are evaluated. The influence of process parameters such as silver nitrate concentration, incubation time, culture medium composition, pH, temperature, and fungal species on nanoparticle yield, size, and stability is also assessed. Analysis of the current literature highlights significant knowledge gaps, including limited application of proteomic and metabolomic approaches, a lack of causal mechanistic studies, and insufficient standardization of experimental protocols. Overall, evidence indicates that fungal AgNP biosynthesis is governed by complex interactions among multiple biomolecular classes rather than a single universal mechanism, underscoring priorities for improving reproducibility, scalability, and mechanistic understanding. Full article

The ability to detect individual photons with exquisite temporal precision has transformed single-photon avalanche diodes (SPADs) into indispensable tools across photonics, with quantum communication emerging as one of their most demanding frontiers. In recent years, device engineering breakthroughs, including refined junction geometries, advanced avalanche quenching schemes and scalable array integration, have redefined the limits of SPAD performance. Parallel advances in fabrication precision and CMOS-compatible architectures have not only expanded spectral sensitivity from the visible to the near-infrared but also enabled systematic suppression of dark count rates, afterpulsing, and timing jitter. These developments have directly impacted the feasibility and robustness of quantum key distribution and time-correlated single-photon counting, where detection efficiency and noise suppression determine system fidelity. This review unifies recent progress in SPAD technology, weaving together innovations in device design, fabrication strategies, and parameter optimization to reveal their collective influence on next-generation quantum-enabled photonic systems. Looking forward, the convergence of hybrid material platforms, on-chip photonic–electronic co-integration, and intelligent quenching control is poised to elevate SPAD performance to meet, and potentially exceed, the stringent requirements of future quantum communication infrastructures. Full article

Rice cultivation is a major source of methane (CH₄) emission in the agricultural sector, with a significantly higher global warming potential than carbon dioxide. Accurate and scalable quantification of CH₄ from rice paddies is essential for carbon accounting. This study presents an automated framework for estimating rice CH₄ emissions from irrigated paddies in the central plain of Thailand, integrating multi-sensor Synthetic Aperture Radar (SAR) observations with the IPCC methodology. The framework combines Sentinel-1 C-band SAR time series for phenological detection, ALOS-2 PALSAR-2 L-band full-polarimetric SAR for water regime classification, and IPCC water-scaling factors corresponding to Continuous Flooding, Single Drainage, or Multiple Drainage regimes. Evaluated across five stratified holdout sets, the phenology detection algorithm achieved planting and harvesting date Mean Absolute Errors of 6.1 ± 1.4 and 8.3 ± 1.7 days, with a 97.0% ± 2.7% operational detection rate. Water regime classification employed rice growth stage-specific Support Vector Machine classifiers with Radial Basis Function kernels (SVM-RBF), achieving per-stage test Balanced Accuracy ranging from 0.59 to 0.89. End-to-end integration using a four-track counterfactual decomposition yielded a full-pipeline mean absolute error of 18.5 ± 4.5 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$ (21.4% of the mean ground-based CH₄ calculation) and a mean bias of 3.5 ± 5.8 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$. Water level classification was confirmed as the dominant algorithmic uncertainty source, while the IPCC Tier 1 emission factor structural range (−32% to +48% of the default) exceeded all algorithmic errors combined. The proposed framework provides a spatially explicit approach for integrating multi-frequency SAR data into IPCC-compliant methane estimation, supporting Monitoring, Reporting, and Verification applications. Full article

Oil pollution remains a persistent global environmental challenge due to the recalcitrance and toxicity of lipid-rich contaminants in terrestrial and aquatic ecosystems. Bacterial lipases (EC 3.1.1.3) play a pivotal role in the initial stages of bioremediation by catalysing the hydrolysis of complex lipids into more bioavailable intermediates, thereby facilitating downstream microbial degradation and mineralisation. This review critically examines the mechanistic basis of lipase-mediated hydrocarbon degradation, with emphasis on enzyme structure–function relationships, catalytic pathways, and regulation under environmentally relevant conditions. In addition to conventional applications in soil and wastewater bioremediation, emerging strategies involving immobilised enzymes, microbial consortia, and waste-derived substrates are evaluated for their effectiveness and scalability. Attention is given to advances in molecular and omics approaches, including metagenomics, transcriptomics, and proteomics, which have expanded the discovery of novel lipases but remain limited in their ability to predict in situ functionality. The review highlights the growing role of protein engineering and artificial intelligence in tailoring lipase properties; however, it also critically assesses current limitations, including insufficient experimental validation and challenges in translating computational predictions to complex environmental systems. Furthermore, integrating multi-omics data into quantitative and predictive frameworks is identified as a key future direction for improving bioremediation efficiency. Despite significant progress, major gaps persist in linking enzyme activity to real-world degradation performance and in developing standardized, scalable approaches. This review therefore provides a comprehensive and critical synthesis of current knowledge while identifying strategic research priorities required to advance bacterial lipases as robust tools for sustainable bioremediation of lipid-based pollutants. Full article

This work reports the development of innovative enzyme–titanium nanobiohybrids synthesized via a protein-assisted approach to obtain efficient and sustainable photocatalysts for environmental remediation. By addressing the limitations of conventional TiO₂ nanoparticle synthesis, this strategy enables controlled material properties under milder, potentially scalable conditions for enhanced ROS-driven degradation of persistent dye pollutants. This work employs a bio-assisted synthesis approach using β-glucosidase as a protein scaffold, TiCl₄ as the titanium precursor, and H₂O₂ in bicarbonate buffer at room temperature, eliminating the need for harsh conditions and high temperatures. The biological moiety guides the nanoparticle formation, controlling size and morphology while preventing aggregation, all performed under mild conditions. X-ray diffraction determined that the Ti hybrid was composed of TiO₂ brookite species. TEM analyses demonstrated the formation of well-dispersed nanostructures of around 700 nm. The resulting nanobiohybrids showed excellent photocatalytic activity, achieving >99% Rhodamine B degradation under UV light in only 1 h compared to visible light. The catalyst was capable of degrading Rhodamine B at a concentration approximately 36 times above the recommended threshold for water. Furthermore, a preactivation of the catalyst by direct exposition of it to UV-395 nm light greatly enhanced the efficiency in the photocatalytic process, being inactive in visible light. The Ti–enzyme hybrid showed excellent recyclability over five consecutive cycles and retained good activity after storage, demonstrating its stability. This study introduces a sustainable and efficient route for synthesizing Ti-based nanobiohybrids, providing a promising strategy for advanced photocatalytic applications in water treatment and environmental remediation. Full article

Despite the growing adoption of Augmented Reality (AR) in industrial maintenance, no structured methodology exists to systematically identify which operations are best suited for effective AR deployment. This study addresses this gap by proposing a five-step, Multi-Criteria Decision-Making (MCDM)-based selection framework for determining AR-compatible maintenance operations in high-speed railway systems. The framework—applied under the AFNOR FD X 60-000 standard—integrates maintenance-level compatibility analysis, multi-criteria filtering across five dimensions (operational frequency, execution complexity, safety impact, traceability, and scalability), and expert validation involving 100 railway maintenance professionals. Applied to 12 candidate operations at a high-speed railway maintenance facility in Morocco, the framework identified OP10 (insulating oil level verification of the Main Transformer) as the optimal pilot use case, confirming expert consensus (Kruskal–Wallis: H = 18.479, p < 0.001). The selected operation was subsequently integrated into a hybrid AR–Deep Reinforcement Learning architecture employing a Deep Q-Learning (DQL) agent for adaptive decision support, deployed on a Magic Leap 2 head-mounted device via a Unity-based rendering pipeline with hybrid marker-based and markerless computer vision tracking through Vuforia Engine. Experimental validation conducted over three months under simulated and semi-operational conditions yielded a 34–47% reduction in intervention time, a 55–70% decrease in human error rates, and a 28–42% decline in failure-related costs. While results are currently limited to a single-site context, the proposed methodology is directly transferable to any asset-intensive, regulated maintenance environment beyond the railway sector. Full article

As industrial robots become increasingly essential to modern manufacturing and automation systems, ensuring their durability and operational integrity has emerged as a key concern. Traditional defect detection methods typically depend on labeled datasets and supervised learning techniques, which can be difficult and impractical to implement in real-world industries. In contrast, unsupervised learning presents a compelling alternative by facilitating anomaly detection and fault diagnosis without the need for labeled data. This article offers a thorough analysis of unsupervised learning techniques used in the health monitoring of industrial robots. We explore significant trends and key algorithms, such as clustering, autoencoders, and generative models, assessing their effectiveness in identifying faults and performance degradation. The research addresses the unique challenges associated with high-dimensional sensor data, variable operating conditions, and the lack of ground truth labels. Additionally, we highlight unresolved research questions and potential future directions, emphasizing the need for scalable, interpretable, and real-time solutions. This survey serves as a foundational reference for researchers and practitioners aiming to develop resilient and autonomous health monitoring systems for industrial robots. Full article

This review examines hydrogen-assisted fatigue crack growth rate (HA-FCGR) in pipeline steels with a focus on implications for integrity assessment of hydrogen transport systems. Existing natural gas pipelines offer a cost-effective pathway for hydrogen transmission; however, hydrogen embrittlement (HE) significantly alters fatigue behavior. This paper integrates scientometric analysis with a systematic review to evaluate the influence of material microstructure, welds, loading conditions, hydrogen pressure, and environmental variables on fatigue crack growth rates (FCGR). The synthesis confirms that HA-FCGR is most pronounced in the Paris region and is strongly governed by hydrogen pressure and loading frequency, while the role of material strength is less definitive than traditionally assumed. Recent advances in machine learning demonstrate strong predictive capability for FCGR; however, their integration into risk-based inspection and pipeline integrity frameworks remains limited. To bridge the gap between laboratory-scale understanding and field implementation, the concept of a near-real-world hydrogen pipeline testbed is introduced, enabling synchronized measurement of pressure cycling, material degradation, and system-level response. The review identifies critical research needs, including weld-focused fatigue datasets, realistic pressure-cycle validation, uncertainty-aware modeling, and integration of physics-based and data-driven approaches for decision-making. These findings provide a pathway toward reliable and scalable integrity assessment for hydrogen transport in existing pipeline infrastructure. Full article

Background: Chronic low back pain (CLBP) is the leading cause of years lived with disability globally, affecting over 600 million individuals. Intervertebral disc degeneration (IVDD) is a principal structural contributor, yet conventional treatments, including pharmacotherapy, physical therapy, and surgical intervention, do not reverse the underlying degenerative pathology. Regenerative medicine has introduced a spectrum of biological therapies for IVDD, including cell-based mesenchymal stromal cell (MSC) therapy, platelet-derived products such as platelet-rich plasma (PRP) and platelet lysate, extracellular vesicle-based approaches using MSC-derived extracellular vesicles (EVs), and secretome-based therapies using MSC-derived secretomes. However, these approaches have largely been studied in isolation, without a unified framework to compare their respective advantages and limitations in CLBP secondary to IVDD. Accordingly, this narrative review aims to provide an integrated and comparative evaluation of these regenerative strategies within a single translational and clinical context. Methods: For this narrative review, PubMed, Scopus, and Web of Science were searched from January 2000 to January 2026 using terms combining regenerative modalities with intervertebral disc degeneration, and chronic low back pain. Randomized controlled trials (RCTs), prospective cohort studies, systematic reviews, and preclinical studies with translational relevance were included. Results: Intradiscal MSC therapy has demonstrated safety across multiple phase I–III trials, but two recent landmark RCTs (RESPINE and the Mesoblast phase III trial) failed to meet primary efficacy endpoints, highlighting the gap between preclinical promise and clinical outcomes. PRP has the largest clinical evidence base, with level II evidence supporting short- to medium-term pain relief for discogenic pain, although standardization remains a critical barrier. Platelet lysate, MSC-derived EVs, and MSC-derived secretomes show compelling preclinical data, including extracellular matrix restoration, anti-inflammatory modulation, and attenuation of nucleus pulposus cell apoptosis, but remain at early translational stages for spinal applications, with no completed RCTs. The hostile disc microenvironment (avascular, hypoxic, acidic, and nutrient-poor) poses unique challenges for all regenerative modalities, differing fundamentally from other musculoskeletal applications. Conclusions: The studies included in this narrative review suggest that no single regenerative modality has yet shown consistent and unequivocal efficacy for CLBP secondary to IVDD across clinical trials. Cell-free approaches offer manufacturing, scalability, and safety advantages over cell-based therapies, but lack clinical validation. Future progress requires standardized preparation protocols, disc-specific delivery systems, patient phenotyping strategies, and rigorously designed comparative clinical trials. This narrative review provides a framework for researchers and clinicians to evaluate these therapies in context rather than isolation. Full article

Background: Chronic kidney disease (CKD) is a major public health problem, primarily affecting patients with diabetes mellitus and/or hypertension. Early detection is critical to delay progression to renal replacement therapy. Therefore, this study aims to evaluate the implementation of a digital module for CKD detection in at-risk patients within the Peruvian Social Health Insurance system (EsSalud) during 2024. Materials and Methods: Data were collected through the Renal Health Module (MOSARE), a digital tool integrated into EsSalud’s electronic health record for monitoring patients at risk of CKD. Key indicators were assessed using the RE-AIM framework: reach, effectiveness, adoption, implementation, and maintenance. Quantitative analyses evaluated patient identification, screening, and diagnosis, while qualitative analyses of technical documents identified barriers, facilitators, and stakeholder perceptions. Results: In 2024, MOSARE was implemented in a population of 1,667,856 insured individuals with CKD-related risk conditions, predominantly women (55.7%) and adults aged > 55 years (88.3%). Nationwide, 93,266 patients were screened (5.59% reach), with 34.3% diagnosed with CKD. Screening coverage showed substantial geographic variability (0.35–29.59%). A total of 32,004 CKD cases were identified, with 84.2% in early stages (1–3A). Adoption reached 66.8% of level I and II healthcare facilities, with variability across networks. Regarding maintenance, 65% of trained facilities sustained active use. Implementation gaps were associated with interoperability issues, resource limitations, and training variability. Conclusions: MOSARE proved to be a feasible and operationally sensitive tool for CKD screening and risk identification, improving integration of renal care. However, addressing technical, training, and resource barriers is essential to ensure sustainability and scalability. Full article

This paper presents an adaptive neural-network-based algorithm for chattering mitigation in sliding mode control (SMC) of parallel multicellular DC/AC power converters. Although conventional SMC provides strong robustness against parameter uncertainties, external disturbances, and load variations, its discontinuous control action often generates chattering, resulting in excessive switching activity and reduced converter performance. To address this limitation, a computationally efficient adaptive neural network is integrated into the SMC framework to approximate the discontinuous switching term and generate a smooth control signal. The proposed algorithm updates neural network parameters online through an adaptive learning mechanism, enabling real-time compensation of modeling uncertainties while preserving the inherent robustness of SMC. The resulting adaptive neural network sliding mode control (ANN-SMC) algorithm is formulated to ensure accurate output voltage tracking, balanced operation of converter cells, and reduced switching oscillations. Extensive simulation studies are conducted under different operating scenarios, including load variations and system disturbances. The performance of the proposed method is evaluated against classical SMC using quantitative indicators related to tracking accuracy, dynamic response, robustness, and chattering suppression. The results demonstrate that the ANN-SMC algorithm significantly reduces high-frequency oscillations while improving transient behavior and maintaining robust operation. These findings indicate that the proposed adaptive learning-based control algorithm constitutes an effective and scalable solution for advanced power conversion systems operating under uncertain conditions. Full article

In this paper, we introduce SPADE, a framework engineered for building Digital Twins through Multi-Agent Systems. The architecture is inherently scalable and distributed, aligning perfectly with the demands of modern Digital Twin environments. We implement the Agents and Artifacts meta-model via the SPADE Artifacts extension, which serves as a structured interface connecting autonomous agents with their physical system counterparts. To demonstrate the framework’s efficacy, we detail a case study involving urban traffic management in Valencia, Spain. In this implementation, we model 386 street segments as individual agents responsible for managing traffic flows and coordinating redistribution efforts. The research delineates a MAS-based communication strategy spanning the entire network and introduces a consensus algorithm specifically designed to manage traffic rerouting when a street is closed. Finally, we present results from a series of experimental trials and evaluate the system’s broader potential. By synthesizing diverse data sources and providing an interactive dashboard for visualizing network conditions, this work demonstrates how SPADE can serve as a robust foundation for Digital Twin development, illustrating its potential for real-world urban applications through a conceptual implementation grounded in open sensor data. Full article

Despite increasing interest in fungal remediation systems for the treatment of persistent contaminants, the mechanisms governing fungal transformation of halogenated organic compounds remain poorly resolved. The aim of this study was to determine whether the transformation of halogenated nitrophenols is driven by isolated extracellular enzymes and cofactor-dependent oxidative activity or instead reflects coordinated system-level fungal metabolism. To address this question, we investigated the transformation of 2-chloro-4-nitrophenol (2C4NP) and 5-fluoro-2-nitrophenol (5F2NP) by ascomycete fungi Caldariomyces fumago (C. fumago) and Curvularia sp. under varying nutrient and cofactor conditions. Whole-culture transformation, crude supernatant activity, purified enzyme assays, intracellular detoxification responses, and genome-resolved functional annotation were integrated to evaluate the relative contributions of extracellular and intracellular processes. Transformation was strongly dependent on fungal species, substrate identity, nutrient availability, and cofactor composition. C. fumago achieved complete transformation of 2C4NP and up to 85.3% transformation of 5F2NP, whereas Curvularia sp. exhibited strict Na₃VO₄-dependent transformation of 5F2NP. Crude supernatants retained partial transformation capacity, achieving ~40–45% substrate depletion under conditions supporting whole-culture activity. Purified chloroperoxidase and laccase showed negligible independent activity and did not reproduce whole-culture transformation behavior. Lignin peroxidase activity was consistently induced during contaminant exposure and peaked during periods of maximum transformation. Cytochrome P450 inhibition did not prevent transformation. Baseline glutathione S-transferase activity was detected in both fungi, and comparative genome analysis identified conserved intracellular detoxification-associated enzyme alongside divergent extracellular oxidative enzyme repertoires. Together, these findings demonstrate that transformation of halogenated nitrophenols by fungi cannot be explained by isolated extracellular enzymes alone but is consistent with coordinated extracellular and intracellular system-level metabolism. These findings highlight an underexplored role for integrated fungal metabolic systems in bioremediation and provide a mechanistic basis for developing a scalable fungal platform for treatment of persistent halogenated contaminants. Full article

Visible solid waste pollution in water bodies has intensified, threatening ecosystems and public health. This study proposes a low-complexity approach based on Convolutional Neural Networks (CNNs) for the automatic detection of visible litter in rivers using images. The methodology involves a curated dataset from multiple sources and the application of Transfer Learning with the MobileNetV2 architecture, chosen for its computational efficiency. The model achieved stable performance across 20 independent runs, with an average test accuracy of 89.7%, an F1-score of 90.9%, and an AUC of 0.996. Notably, the representative model selected for qualitative illustration produced zero false negatives on the test set; this result reflects the behavior of a specific model instance and should be interpreted as an illustrative outcome rather than the guaranteed operational performance of the method. The results indicate that the proposed approach is a viable, scalable, and accessible technological alternative for automated river monitoring. Future integration with web applications and geospatial platforms could enhance its utility for environmental agencies and public managers. Full article

Sodium (Na) metal anodes suffer from poor processability, severe volume fluctuation, unstable interfacial chemistry, and uncontrolled dendrite growth during cycling, which significantly hinder their practical application. Herein, a Na/Na₂In composite foil is fabricated through an in situ spontaneous alloying reaction enabled by a simple rolling–folding process using Na and indium (In) foils as precursors. Structural characterizations confirm the complete conversion of metallic In into the Na₂In alloy phase, forming a continuous architecture with uniformly distributed Na₂In networks embedded within the Na matrix. Owing to the sodiophilic and mechanically robust Na₂In framework, the Na/Na₂In composite anode effectively regulates Na plating/stripping behavior and suppresses dendritic growth, thereby maintaining a dense and stable electrode morphology during repeated charge/discharge processes. As a result, the Na/Na₂In symmetric cell exhibits stable cycling for over 900 h at 0.5 mA cm⁻² and 1 mAh cm⁻² with low polarization hysteresis, whereas the pure Na counterpart fails after only 143 h. Moreover, full cells paired with NaFe_1/3Ni_1/3Mn_1/3O₂ cathodes deliver enhanced cycling stability, retaining 87% of the initial capacity after 100 cycles at 0.5 C, together with improved rate capability. This work demonstrates a scalable mechanical fabrication strategy for high-stability Na metal composite anodes and provides new insights into the practical development of high-energy-density Na metal batteries. Full article