Search Results (8,444)

Artificial metalloenzymes incorporating cobalt protoporphyrin IX (Co-PPIX) are promising for sustainable hydrogen production; however, slow protein preparation and a lack of suitable detection methods limit the systematic optimization of their catalytic performance. Here, we report a streamlined workflow that combines the direct in vivo incorporation of Co-PPIX into cytochrome b₅₆₂ (cyt b₅₆₂) variants with a colorimetric assay for hydrogen evolution, scalable to hundreds of mutants. We screened 103 members of a mutant library and selected the variant Co-Mut25, which displayed activity double than wild type on the screen, and produced over 70% more hydrogen than WT as assessed by gas chromatography. This approach enables the rapid and scalable identification of high-performing cobalt–protein catalysts and expands the toolkit for artificial hydrogenase development. Full article

The aviation industry, responsible for approximately 2.5–3.5% of global greenhouse gas emissions, faces increasing pressure to adopt sustainable energy solutions. Hydrogen, with its high gravimetric energy density and zero carbon emissions during use, has emerged as a promising alternative fuel to support aviation decarbonization. However, its large-scale implementation remains hindered by cryogenic storage requirements, safety risks, infrastructure adaptation, and economic constraints. This study aims to identify and evaluate the primary technical and operational risks associated with hydrogen utilization in aviation through a comprehensive Monte Carlo Simulation-based risk assessment. The analysis specifically focuses on four key domains—hydrogen leakage, cryogenic storage, explosion hazards, and infrastructure challenges—while excluding economic and lifecycle aspects to maintain a technical scope only. A 10,000-iteration simulation was conducted to quantify the probability and impact of each risk factor. Results indicate that hydrogen leakage and explosion hazards represent the most critical risks, with mean risk scores exceeding 20 on a 25-point scale, whereas investment costs and technical expertise were ranked as comparatively low-level risks. Based on these findings, strategic mitigation measures—including real-time leak detection systems, composite cryotank technologies, and standardized safety protocols—are proposed to enhance system reliability and support the safe integration of hydrogen-powered aviation. This study contributes to a data-driven understanding of hydrogen-related risks and provides a technological roadmap for advancing carbon-neutral air transport. Full article

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

Aim: To report on the clinical and genetic spectrum of retinopathy associated with CDHR1 variants in a Hungarian cohort. Methods: A retrospective cohort study was conducted at a single tertiary care referral center. The study enrolled nine patients harboring biallelic variants in the CDHR1 gene. Detailed clinical history, multimodal imaging, electroretinography, and molecular genetics are presented. Results: We identified four CDHR1 variants predicted to cause loss-of-function and five phenotypes (cone dystrophy, central areolar choroidal dystrophy, cone-rod dystrophy, rod-cone dystrophy, and late-onset macular dystrophy). The most frequent variant was the synonymous CDHR1 c.783G>A (p.Pro261=) variant (10/18 alleles, 55.6%). A novel splice acceptor site variant, CDHR1 c.349-1G>A, and a novel intronic variant, CDHR1 c.1168-10A>G, were also detected. Fundus examination revealed macular atrophy with or without peripheral retinal changes. Full-field electroretinography, available in seven patients, demonstrated decreased light-adapted and extinguished dark-adapted responses in both the rod-cone dystrophy group and patients with macular involvement. OCT imaging indicated ellipsoid zone disruption with foveal sparing in two out of nine patients and severe retinal damage in rod-cone dystrophy cases. Conclusions: The predominant clinical manifestations of cone dystrophy, cone-rod dystrophy, and macular dystrophy in the Hungarian patient cohort showed heterogeneity, with a rod-cone dystrophy phenotype observed in five of nine cases (55.6%). The natural history of CDHR1-associated retinopathy typically follows a slow progression, providing a therapeutic window, which makes the disease a candidate for gene therapy. Full article

Type 1 Diabetes (T1D) is thought to result from the interaction of genetic and environmental factors, with different studies highlighting a potential role for the gut microbiota and its metabolites in modulating immune responses and disease development. We hypothesized that patients with T1D exhibited altered levels of circulating bacterial metabolites compared with healthy controls (HC), and that these metabolite profiles were associated with key demographic, clinical, and analytical features of the disease. A total of 91 T1D patients and 58 HC were recruited. Plasma samples were collected and analyzed with gas chromatography coupled to mass spectrometry for the detection of the metabolites: short-chain fatty acids (SCFAs: acetate [AA], propionate [PA], isobutyrate [IBA], butyrate [BA], isovalerate [IVA], valerate [VA], and methyl valerate [MVA]), medium-chain fatty acids (MCFAs: hexanoate [HxA] and heptanoate [HpA]) and para-cresol (p-cresol). We also calculated the ratios between the different SCFAs with AA. T1D patients showed significantly higher circulating AA levels than HC, along with reduced PA/AA and IBA/AA ratios, indicating an altered SCFA profile. SCFA diversity was lower in T1D patients, with reduced detection of BA, and total SCFA levels were increased mainly due to elevated AA. AA levels were higher and SCFA ratios lower in women with T1D compared with healthy women, while p-cresol levels were higher in men with T1D than in healthy men. In T1D patients, AA levels positively correlated with HbA1c, whereas PA/AA, IBA/AA, and BA/AA ratios showed negative correlations, particularly in women. MV/AA and non-AA/AA ratios were inversely associated with glucose levels, again, mainly in women. p-cresol levels correlated positively with age and ferritin and were higher in T1D patients with liver dysfunction or hypertension. Therefore, we can conclude that T1D is associated with a marked alteration in circulating gut-derived metabolites, characterized by increased AA levels, particularly in women, and an imbalance in SCFA ratios that correlates with glycemic control. These findings, together with the associations observed for p-cresol and metabolic comorbidities, support a role for the gut microbiota–host metabolic axis in T1D. Full article

Lung cancer (LC) remains the leading cause of cancer-related death worldwide, largely due to late-stage diagnosis and the limited performance of current screening strategies. In this preliminary study, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) was used to comprehensively characterize the urinary volatilome of LC patients and healthy controls (HCs), with the dual aim of defining an LC-associated volatilomic signature and identifying volatile organic metabolites (VOMs) with discriminatory potential. A total of 56 VOMs spanning multiple chemical classes were identified, revealing a distinct metabolic footprint between groups. LC patients exhibited markedly increased levels of terpenoids and aldehydes, consistent with heightened oxidative stress, including lipid peroxidation, and perturbed metabolic pathways, whereas HCs showed a predominance of sulphur-containing compounds and volatile phenols, likely reflecting active sulphur amino acid metabolism and/or microbial-derived processes. Multivariate modelling using partial least squares-discriminant analysis (PLS-DA, R² = 0.961; Q² = 0.941; p < 0.001), supported by hierarchical clustering, demonstrated robust and clearly separated group stratification. Among the detected VOMs, octanal, dehydro-p-cymene, 2,6-dimethyl-7-octen-2-ol and 3,7-dimethyl-3-octanol displayed the highest discriminative power, emerging as promising candidate urinary biomarkers of LC. These findings provide proof-of-concept that HS-SPME/GC-MS-based urinary volatilomic profiling can capture disease-specific molecular signatures and may serve as a non-invasive approach to support the early detection of LC, warranting validation in independent cohorts and integration within future multi-omics diagnostic frameworks. Full article

Ethanolamine (EA) is a widely used yet toxic volatile organic compound (VOC). However, existing gas sensors for EA detection face persistent challenges in achieving exceptional sensitivity and low detection limits at room temperature (RT). In this study, a novel and high-performance EA sensor based on the MoO₃/BiOI composite was prefabricated using hydrothermal and cyclic impregnation methods. The response value toward 100 ppm EA reached 861.3, which was 3.5-times higher compared to that of pure MoO₃. In addition, the MoO₃/BiOI composite exhibited a low detection limit (0.13 ppm), excellent selectivity, short response/recovery times, exceptional repeatability and long-term stability. The outstanding gas sensing performance of the MoO₃/BiOI is attributed to the formation of a p-n heterojunction, synergistic effects between the two materials, abundant adsorbed oxygen species and superior charge transfer efficiency. The sensor developed in this work effectively addresses the long-standing challenges, demonstrating unprecedented practical application potential for EA gas detection. Simultaneously, this study provides a novel strategy, a new approach and a promising material for the subsequent development of advanced amine sensors. Full article

We present a new method for mapping global gas flaring using a multiyear spatio-temporal database of VIIRS Nightfire (VNF) nighttime infrared detections from the Suomi NPP, NOAA-20, and NOAA-21 satellites. The method is designed to resolve closely spaced industrial combustion sources and to produce a stable, physically meaningful flare catalog suitable for long-term monitoring and emissions analysis. The method combines adaptive spatial aggregation of high-temperature detections with a hierarchical clustering that super-resolves individual flare stacks within oil and gas fields. Post-processing yields physically consistent flare footprints and attraction regions, allowing separation of closely spaced sources. Flare clusters are assigned to operational categories (e.g., upstream, midstream, LNG) using prior catalogs combined with AI-assisted expert interpretation. In this step, a multimodal large language model (LLM) provides contextual classification suggestions based on geospatial information, high-resolution daytime imagery, and detection time-series summaries, while final attribution is performed and validated by domain experts. Compared with annual flare catalogs commonly used for national flaring estimates, the new catalog demonstrates substantially improved performance. It is more selective in the presence of intense atmospheric glow from large flares, identifies approximately twice as many active flares, and localizes individual stacks with ~50 m precision, resolving emitters separated by ~400–700 m. For the well-defined class of downstream flares at LNG export facilities, the catalog achieves complete detectability. These improvements support more accurate flare inventories, facility-level attribution, and policy-relevant assessments of gas flaring activity. Full article

Thrombophilia is considered one of the key mechanisms underlying reproductive disorders. Clinical heterogeneity of reproductive disorders and a lack of stratification by phenotype often limit interpretation. Therefore, evaluating thrombophilia-associated genetic markers separately in fetal loss syndrome, postpartum hemorrhage (PPH), and hypertensive disorders of pregnancy is essential. Background/Objectives: To assess the frequency of thrombophilia-related genetic polymorphisms in women with various reproductive disorders and evaluate their association with clinical–anamnestic characteristics and obstetric antiphospholipid syndrome. Methods: A total of 132 women with reproductive disorders (fetal loss syndrome, postpartum hemorrhage, preeclampsia). Results: Statistically significant differences were found when comparing between the groups. Thus, heterozygous F13 genetic polymorphisms were statistically more common in the group with a history of preeclampsia compared to the group with PPH (the G/A genotype was detected in 22.2% versus 10.7%, p = 0.045), and heterozygous ITGA2 gene genetic polymorphisms were also more common (the C/T genotype was detected in 66.7% versus 42.9%, p = 0.023). In women with a history of PPH, homozygous ITGA2 genetic polymorphisms were statistically more common (the T/T genotype was detected 2.6 times more often—21.4% versus 8.8% compared to the group with fetal loss syndrome, p = 0.022; and 3.8 times more often—21.4% versus 5.6% compared to the group with PE, p = 0.022). Conclusions: A study of thrombophilia gene polymorphisms in women with reproductive disorders showed that the G/A genotype of F13, the C/T genotype of ITGA2, and the A/G genotype of MTR:2756 were significantly more common in women with preeclampsia than in the group with postpartum hemorrhage; the T/T genotype of the ITGA2 gene was detected in postpartum hemorrhage. The MTHFR 1286A > C (A/C) polymorphism was associated with a reduced risk of postpartum hemorrhage. In contrast, the MTR 2756A > G (A/G) genotype was associated with an increased risk of preeclampsia. Full article

Port wine production involves the addition of grape spirit to halt fermentation and retain natural sweetness. This spirit, produced by distilling wine and its by-products, must comply with legal standards, including a mandatory sensory assessment. Because grape spirit influences Port wine’s volatile composition, this study investigated the odour-active compounds present in several grape spirits intended for fortification. Volatile compounds were extracted by liquid–liquid extraction, concentrated, and analysed using gas chromatography–olfactometry (GC-O) and gas chromatography–mass spectrometry (GC-MS). In GC-O, based on frequency detection, a panel of assessors sniffed the extracts to determine the presence of aroma compounds. The results revealed a wide range of odour-active compounds in grape spirits, belonging to several chemical families such as esters, alcohols, terpenic compounds and acids. These compounds exhibited both pleasant aromas, such as fruity, floral and caramel notes as well as undesirable ones like cheese and foot odour. Most of these compounds originate from the fermentation process and are also found in other unaged distilled beverages, including young Cognac, Calvados and fruit spirits. This research highlights the aromatic complexity of grape spirits and, for the first time, determined the aroma thresholds for 25 of 36 the compounds studied at an ethanol content of 20%. Full article

Gas-insulated transmission lines (GILs) are increasingly deployed in dense urban power networks, where complex construction activities may introduce external mechanical impacts and pose risks to pipeline structural integrity. However, existing GIL monitoring approaches mainly emphasize electrical and gas-state parameters, while lightweight solutions capable of rapidly detecting and localizing impact-induced structural anomalies remain limited. To address this gap, this paper proposes an intelligent end-edge-cloud monitoring system for impact anomaly detection in GIL pipelines. Numerical simulations are first conducted to analyze the dynamic response characteristics of the pipeline under impacts of varying magnitudes, orientations, and locations, revealing the relationship between impact scenarios and vibration mode evolution. An end-tier multi-parameter intelligent sensor is then developed, integrating triaxial acceleration and angular velocity measurement with embedded lightweight computing. Laboratory impact experiments are performed to acquire sensor data, which are used to train and validate a multi-class extreme gradient boosting (XGBoost) model deployed at the edge tier for accurate impact-location identification. Results show that, even with a single sensor positioned at the pipeline midpoint, fusing acceleration and angular velocity features enables reliable discrimination of impact regions. Finally, a lightweight cloud platform is implemented for visualizing structural responses and environmental parameters with downsampled edge-side data. The proposed system achieves rapid sensor-level anomaly detection, precise edge-level localization, and unified cloud-level monitoring, offering a low-cost and easily deployable solution for GIL structural health assessment. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

This review explores the variety of diamond-based sensing applications, emphasizing their material properties, such as high Young’s modulus, thermal conductivity, wide bandgap, chemical stability, and radiation hardness. These diamond properties give excellent performance in mechanical, pressure, thermal, magnetic, optoelectronic, radiation, biosensing, quantum, and other applications. In vibration sensing, nano/poly/single-crystal diamond resonators operate from MHz to GHz frequencies, with high quality factor via CVD growth, diamond-on-insulator techniques, and ICP etching. Pressure sensing uses boron-doped piezoresistive, as well as capacitive and Fabry–Pérot readouts. Thermal sensing merges NV nanothermometry, single-crystal resonant thermometers, and resistive/diode sensors. Magnetic detection offers FeGa/Ti/diamond heterostructures, complementing NV. Optoelectronic applications utilize DUV photodiodes and color centers. Radiation detectors benefit from diamond’s neutron conversion capability. Biosensing leverages boron-doped diamond and hydrogen-terminated SGFETs, as well as gas targets such as NO₂/NH₃/H₂ via surface transfer doping and Pd Schottky/MIS. Imaging uses AFM/NV probes and boron-doped diamond tips. Persistent challenges, such as grain boundary losses in nanocrystalline diamond, limited diamond-on-insulator bonding yield, high temperature interface degradation, humidity-dependent gas transduction, stabilization of hydrogen termination, near-surface nitrogen-vacancy noise, and the cost of high-quality single-crystal diamond, are being addressed through interface and surface chemistry control, catalytic/dielectric stack engineering, photonic integration, and scalable chemical vapor deposition routes. These advances are enabling integrated, high-reliability diamond sensors for extreme and quantum-enhanced applications. Full article

High-precision short-term photovoltaic (PV) power prediction has become a critical technology in ensuring grid accommodation capacity, optimizing dispatching decisions, and enhancing plant economic benefits. This paper proposes a long short-term memory (LSTM)-based short-term PV power prediction method with the genetic algorithm (GA)-optimized adaptive fusion of space-based cloud imagery and ground-based meteorological data. The effective integration of satellite cloud imagery is conducted in the PV power prediction system, and the proposed method addresses the issues of low accuracy, poor robustness, and inadequate adaptation to complex weather associated with using a single type of meteorological data for PV power prediction. The multi-source heterogeneous data are preprocessed through outlier detection and missing value imputation. Spearman correlation analysis is employed to identify meteorological attributes highly correlated with PV power output. A dedicated dataset compatible with LSTM algorithm-based prediction models is constructed. An LSTM prediction model with a GA algorithm-based adaptive multi-source heterogeneous data fusion method is proposed, and the ability to construct a precise short-term PV power prediction model is demonstrated. Experimental results demonstrate that the proposed method outperforms single-source LSTM, single-source CNN-LSTM, and dual-source CNN-Transformer models in prediction accuracy, achieving an RMSE of 0.807 kWh and an MAPE of 6.74% on a critical test day. The proposed method enables real-time precision forecasting for grid dispatch centers and lightweight edge deployment at PV plants, enhancing renewable energy integration while effectively mitigating grid instability from power fluctuations. Full article

The study of high-redshift active galactic nuclei (AGN) and their small-scale environment is necessary to investigate the different processes that control and influence the evolution of massive galaxies. In this paper we present a case study of cid_1253 ($z=2.15$) and its companion galaxy using archive CO(3–2) and 340 GHz continuum observations with the Atacama Large Millimeter/submillimeter Array, supplemented by multi-wavelength photometry. Previous studies treated the system as a whole, without separating its components in order to match large-beam infrared observations. Our goal is to study cid_1253 and its companion separately by re-analysing the available archive data of the system. Based on our analysis, the companion galaxy is not only more gas-rich ($M_{{\mathrm{H}}_2}\sim {10}^{11}{\mathrm{M}}_{\odot }$) but also has a higher dust mass, indicative of obscured star formation. Moreover, as cid_1253 is not detected at 340 GHz, it is possible that a large fraction of the unresolved, Herschel-detected infrared emission is associated with the companion, rather than cid_1253. The presented case study highlights the need to be more cautious with blended sources before drawing our conclusions and the necessity of high-resolution observations. Full article