Search Results (308)

Weak-lensing observations of the Perseus Cluster now indicate a massive sub-halo associated with NGC 1264 and a connecting mass bridge in a system long treated as a benchmark relaxed cool-core cluster. Perseus is also known from X-ray observations to host large-scale gas sloshing and an ancient cold front extending to several hundred kiloparsecs. This paper uses Perseus as a motivation for a narrower population question: do nominally relaxed clusters retain merger history information in residual mass–gas offsets after the obvious signatures of an active merger have faded? A candidate remnant stress–energy interpretation is introduced as one possible covariant language for such a long-lived structure, but the empirical test does not require acceptance of that interpretation. The work then carries out a literature-based pilot test using the cold front outer radius as an independent merger history proxy, published mass–gas or gas tracer offsets for relaxed/cool-core systems, and a separate control cohort of actively dissociative mergers. The resulting three-regime comparison separates young active mergers, relaxed low-offset systems, and relaxed systems with sourced offsets above 5 kpc. For all seven Regime 3 (relaxed, offset $>5$ kpc) systems with vetted cold front/history proxies and sourced mass–gas offset measurements, the directional rank-order association has the predicted sign, ${\rho }_s=0.68$, with $p_{\mathrm{one}-\mathrm{sided}}\approx 0.047$ ($p_{\mathrm{two}-\mathrm{sided}}\approx 0.094$, $N=7$). The one-sided statistic crosses the conventional $5\%$ threshold. The sample mixes lensing–X-ray centroid offsets, BCG/X-ray peak offsets, and weak-lensing sub-halo separations, and the result is not a decisive population detection: it is a suggestive directional signal in a small heterogeneous archival pilot. Its significance is that a framework-derived directional diagnostic, specified before the sample was assembled, is non-zero in the predicted sense and can now be tested with a homogeneous weak-lensing/X-ray/SZ survey. Full article

Low-carbon scheduling of regional integrated energy systems (RIES) based only on energy-balance models may overlook the physical operating limits of distribution, gas, and heating networks, resulting in a gap between scheduling outcomes and actual operating boundaries. To address this issue, this paper proposes a framework integrating bi-objective scheduling, three-network posterior verification, and closed-loop repair. A mixed-integer linear programming model is first formulated with operating cost and carbon emissions as the two objectives, and an adaptive ε-constraint strategy is used to improve the characterization of the compromise region on the Pareto front. Posterior verification models are then established for the distribution, gas, and heating networks to assess the physical feasibility of representative solutions. When infeasibility is detected, a boundary-shrinking repair mechanism is triggered to iteratively update the scheduling boundaries. Case results show that the adaptive refined strategy improves the resolution of the compromise region by 3.2 times with only a 20.4% increase in computational time. Compared with the cost-optimal solution, the carbon-optimal solution reduces carbon emissions but increases peak purchased electricity from 7.333 MW to 11.1 MW, further tightening the lower-voltage margin of the distribution network. The results show that posterior physical verification and closed-loop repair provide additional support for evaluating and improving the engineering feasibility of RIES scheduling solutions. Full article

To address the cross-sensitivity and non-linear coupling issues caused by the coexistence of hydrogen, carbon monoxide, ammonia, and nitrogen dioxide in industrial environments, a flow-through quantitative detection system based on a MEMS gas sensor array was designed and constructed. The steady-state peak sampling method was employed for feature extraction from high-dimensional time-series data, and regression prediction models were developed using a traditional BP neural network and BP neural networks optimized by four swarm intelligence algorithms (ALA, AOO, SFOA, and SDO). The experimental results indicate that the intelligent optimization algorithms excel in decoupling the “cross-response” phenomenon, with all optimized models outperforming the traditional BP network. Among them, the SDOBP (Sledge Dog Optimizer-BP) model demonstrated the best overall performance, achieving the highest accuracy in carbon monoxide and hydrogen detection, with the Root Mean Square Error for hydrogen reduced to 2.17, an 84.2% improvement over the traditional model. The system achieves high-precision quantitative inversion of multi-component gases in complex environments, providing an effective means for industrial environmental safety monitoring. Full article

In this work, we report the fabrication and characterization of single-film and double-film composite epitaxial garnet structures based on single-crystalline films (SCFs) and bulk single-crystal (SC) scintillators for enhanced α–γ discrimination in mixed radiation fields. These composite scintillators consist of TbAG:Ce and YAG:Ce SCFs grown by liquid-phase epitaxy (LPE) on Czochralski-grown Gd₃Ga_2.5Al_2.5O₁₂ (GAGG:Ce) bulk SC substrates. Single- and double-film architectures were designed to optimize the energy absorption and pulse-shape discrimination (PSD) performance for low-penetrating α-particles and high-energy γ-rays. Energy calibration was performed using different γ-ray sources (⁵⁷Co, ⁵¹Cr, and ¹³⁷Cs), enabling the conversion of detector signals to a calibrated electron-equivalent energy scale (keVee). Integration gates were systematically optimized, yielding maximum figures of merit (FOM) of 1.4 for the GAGG:Ce SC substrate, 1.9 for the single-film composite, and 5.0 for the double-film composite, demonstrating a progressive improvement in α–γ discrimination with increasing structural complexity. Two-dimensional PSD density maps reveal well-separated α and γ events, with the highest separation observed for the double-film composite. These results indicate that the engineering of LPE-grown composites provides tunable scintillation decay profiles, enhanced temporal separation, and increased light yields, making them promising candidates for applications such as mixed radiation field detection, dosimetry, and radiation monitoring. Full article

In early-warning scenarios for thermal runaway in energy storage batteries, carbon monoxide (CO) may interfere with hydrogen detection and reduce the reliability of signal interpretation. To mitigate CO cross-interference under representative mixed-gas conditions and improve sensing stability, a fiber-optic microelectromechanical systems (MEMS) hydrogen sensor based on a Pd–Cu alloy-sensitive layer was developed. The sensor employs a single-cantilever structure and a reflective Fabry–Pérot (F–P) interferometer for optical readout. Comparative experiments were carried out using sensors coated with pure Pd and Pd–Cu-sensitive layers under pure H₂, CO background interference, and temperature-fluctuation conditions. The Pd–Cu sensor exhibited a good linear response over 0–500 ppm H₂, with a sensitivity of 0.0845 nm/ppm. Under a mixed atmosphere of 200 ppm H₂ and 500 ppm CO, the Pd–Cu sensor measured 198 ppm, whereas the pure Pd sensor measured 176 ppm, corresponding to relative errors of approximately 1% and 12%, respectively. In addition, the Pd–Cu sensor showed faster response/recovery behavior and better output stability after temperature compensation. These results indicate that, under the investigated conditions, the selected Pd–Cu-sensitive layer can effectively reduce CO-induced interference and improve the accuracy and stability of fiber-optic MEMS hydrogen sensing, supporting its feasibility for representative early-warning-related monitoring scenarios in energy storage batteries. Full article

Illegal, unreported, and unregulated (IUU) fishing threatens marine ecosystems in the Western Pacific. Conventional patrol strategies under-utilize the available multi-source surveillance data. This study proposes a maritime patrol-routing framework that integrates AIS fishing effort, Sentinel-1 SAR dark-vessel detections, and GFW vessel encounter records into a Surveillance Priority Index (SPI) over the study domain (0–20°N, 140–160°E). An Adaptive Priority-Boosted Ant Colony Optimization (APB-ACO) algorithm with two-phase deadline-aware route construction and best-of-N adaptive strategy selection produces patrol routes that cover high-priority cells within a 72 h window while minimizing total distance. Across 30 random seeds and a benchmark suite (PB-ACO, GA, PSO, DQN, NSGA-II), APB-ACO yields the shortest mean route ($21,658\pm 9$ km, $7\%$ shorter than PB-ACO, $p<0.001$), the lowest variance ($46\times$ lower standard deviation than PB-ACO), and 100% high-priority coverage at default settings; a scalability analysis across 2–20% high-priority task ratios shows that the coverage gap over PB-ACO widens with the HP ratio. The problem is also formalized as a Mixed-Integer Linear Program (Priority-Constrained VRPTW), positioning APB-ACO as a constructive metaheuristic for an NP-hard operational problem. The framework’s principal limitation is that, in the tested three-vessel scenario, the 500 km inter-vessel communication constraint is violated more than 1,100 times per 72 h mission and is repaired post hoc; integrating this constraint into the optimizer is identified as a near-term extension. The results provide a methodological foundation for surveillance-driven patrol planning rather than a validated tool for operational IUU interdiction. Full article

Although numerous studies have focused on the potential application of heterotrophic nitrification–aerobic denitrification (HNAD) bacteria in wastewater treatment, research exploring their potential in aquaculture biofloc systems remains limited. In this study, a promising HNAD strain, identified as Stutzerimonas stutzeri MJ20, was isolated from mature biofloc. This strain efficiently utilized low-cost carbon sources (e.g., glucose) and small-molecule carbon sources (e.g., sodium acetate and sodium succinate). Under conditions with glucose as the carbon source, a carbon-to-nitrogen (C/N) ratio of 15, pH 6–9, temperature 25–35 °C, salinity 0–35‰, and shaker speed of 0–150 rpm, it achieved removal rates of 95–100% for NH₄⁺-N, NO₂⁻-N, and NO₃⁻-N at initial concentrations of 100 mg/L each. Even at higher concentrations (up to 200 mg/L NH₄⁺-N and 500 mg/L for both NO₂⁻-N and NO₃⁻-N), removal rates exceeded 99%. Under mixed nitrogen sources, strain MJ20 demonstrated efficient nitrogen removal, preferentially utilizing NH₄⁺-N, with only minimal and transient accumulation of nitrite and nitrate. Genomic analysis revealed that MJ20 carries key denitrification genes, including napA, nirS, norB and nosZ, and possesses complete pathways for nitrate reduction to nitrogen gas and ammonia assimilation, although typical autotrophic nitrification genes were not detected. Combined genomic data and autotrophic culture experiments indicated that, in addition to utilizing various organic carbon sources, the strain also exhibited certain autotrophic growth capabilities. Furthermore, MJ20 showed strong flocculation ability (flocculation rate > 96% within 16 h), sensitivity to multiple common antibiotics, and no toxicity to zebrafish, demonstrating favorable biosafety. In simulated seawater aquaculture wastewater with a C/N ratio of 5, it achieved a total nitrogen removal rate exceeding 94% within 72 h. These results indicate that strain MJ20 possesses comprehensive advantages, including efficient nitrogen removal, broad carbon source adaptability, strong environmental resilience, minimal accumulation of intermediate nitrogen products, excellent flocculation ability, and high biosafety. These traits highlight its potential for application in biofloc systems and in treating aquaculture tail water with a low C/N ratio. This study provides theoretical insights and practical guidance for screening HNAD bacteria suitable for biofloc systems. Full article

Instance segmentation in heterogeneous waste scenes remains challenging due to object variability, deformable shapes, partial occlusion, and large appearance differences across packaging types. This study presents a YOLOv11-based deep learning framework for mixed plastic waste instance segmentation, developed to connect visual perception with reliable material quantification. The framework integrates curated instance-level annotations, strict split isolation, multi-stage optimization, training strategy ablation, and seed-robustness analysis to support reproducible model selection. Experimental results on a held-out test set show that the optimized model achieves a mask mAP@50:95 of 0.9337, indicating strong segmentation performance under heterogeneous waste-scene conditions. To extend the analysis beyond standard vision metrics, the framework incorporates a physics-informed mask-to-mass module that converts predicted masks into class-specific mass estimates using geometric calibration and material priors. Applied to a representative stream of 1253 detected objects, the system estimated a total plastic mass of 15.48 ± 1.08 kg, corresponding to a theoretical H₂ potential of 0.41 ± 0.04 kg and a greenhouse-gas avoidance of 34.57 ± 4.15 kg CO₂e. Overall, the proposed framework extends waste-scene understanding beyond vision-level assessment toward physically grounded, data-driven decision support for smart material recovery systems. Full article

Background/Objectives: Intramuscular fat deposition is a key determinant of beef quality in Japanese Black cattle, and the fat area ratio of the rib eye (FAR) is highly correlated with Beef Marbling Standard scores. Methods: To identify genetic variants underlying variation in the FAR, we conducted a genome-wide association study (GWAS) followed by whole-genome sequence–based fine mapping in a Hyogo Japanese Black population (n = 432). Animals were genotyped using the Illumina BovineSNP50v3 BeadChip, and association analysis was performed using residuals derived from a linear mixed model accounting for fixed and random effects. Results: A significant association signal was detected on BTA17 (λ = 1.09), with the top single nucleotide polymorphism (SNP) located at 17:72,329,662 (p = 3.60 × 10⁻⁶). To refine the candidate region, we analyzed whole-genome resequencing data from 42 Hyogo Japanese Black cattle and identified a distinct linkage disequilibrium (LD) block spanning 71–74 Mbp on BTA17. Among 4292 variants within genes showing LD (r² ≥ 0.1) with the top SNP, 96 variants with strong LD and predicted functional effects were selected for validation. Genotyping in the Hyogo population revealed that a missense variant in gamma-glutamyltransferase 1 (GGT1) (c.589G>A, p.Asp197Asn) showed the strongest association with FAR (p = 3.89 × 10⁻⁶). A 5′UTR variant in GGT1 (c. −256G>T) and a missense variant in solute carrier family 5 member 1 (SLC5A1) (c.32C>T, p.Thr11Met) also exhibited significant associations and strong LD with the top SNP (r² > 0.7). GGT1 is involved in glutathione metabolism, whereas SLC5A1 encodes a sodium–glucose cotransporter implicated in nutrient sensing and metabolic regulation. Conclusions: Although functional validation is required, these variants represent strong positional and biological candidates underlying the BTA17 quantitative trait loci (QTL). The identified polymorphisms may provide useful molecular markers for optimizing genetic improvement of marbling-related traits within the Hyogo Japanese Black population. Full article

This study presents the development of a low-cost H₂ gas sensor made from a titanium dioxide–zinc oxide composite by means of a simple, cost-effective screen-printing method. The sensing material was created by mixing titanium dioxide and zinc oxide nanoparticles with an organic binder, which was screen-printed onto a glass substrate containing silver electrodes. These samples were then characterized using X-ray diffraction (XRD) and field-emission scanning electron microscopy (FESEM). The XRD results confirmed that the films boasted well-defined crystallinity, with predominant anatase and hexagonal ZnO phases, as well as uniformity of grains. Sensor performance was evaluated in a custom-built chamber at hydrogen concentrations of 100 to 1000 ppm and at operating temperatures of 100 °C, 200 °C, and 300 °C. The results indicate improved sensor performance as the operating temperature increased to 300 °C, with the best sensitivity values of 0.99, 1.17, and 1.31 at hydrogen concentrations of 100, 500, and 1000 ppm, respectively. The sensor showed stable and reproducible response characteristics, and its responses were retimed after a few hundred seconds. Low-cost fabrication, ease of processing, and reliable sensor performance make titanium oxide–zinc oxide composites promising candidates for hydrogen detection. Full article

Herein, RATSGO (Real-time Automated Training and Sensing for Gas Odor), a fully automated live-animal olfactory training platform, for the detection of GBL as a sexual assault-facilitating drug is reported. The system integrates four distinct operant conditioning-based training paradigms, all executed without human intervention, to enhance learning speed, consistency, and scalability. Using this fully automated framework, four rats were trained to identify γ-butyrolactone (GBL). Three of the four animals successfully reached the predefined learning completion criterion, whereas one failed to meet the criterion. Across 320 automated trials, the GBL rats achieved a mean detection accuracy of 90%, with sensitivity and specificity values of 97% and 82%, respectively. The corresponding positive and negative predictive values (PPV and NPV) were 85% and 96%. When challenged with GBL diluted in drinking water (180 trials), performance remained high, yielding 88% accuracy, 89% sensitivity, 87% specificity, 85% PPV, and 90% NPV. Similarly, in experiments involving GBL mixed with whisky (200 trials), the rats demonstrated robust recognition capability, achieving 90% overall accuracy, perfect sensitivity (100%), 84% specificity, 79% PPV, and 100% NPV. Importantly, odor discrimination performance was preserved when reassessed four months after the completion of training, indicating strong long-term retention of the learned odor representations. Collectively, these findings confirm that the RATSGO system supports rapid, stable, and precise odor learning, underscoring its promise as a practical and extensible biological sensing platform for chemical detection applications. Full article

This study investigates the spatiotemporal variability of ambient methane (CH₄) using a drone-deployable Aeris Technologies MIRA Strato LDS midwave-infrared analyzer. Laboratory calibration with NOAA-certified gas standards and Standard Reference Material (SRM) for CH₄ demonstrated high measurement precision across a range of concentrations (R² = 0.9986, slope = 0.9678). Field validation conducted during a two-week intercomparison with a Picarro G2301 in September 2023 confirmed the MIRA Strato’s reliability under ambient conditions (R² = 0.9845; slope = 0.9438), indicating strong agreement with the reference analyzer. Diurnal patterns revealed peak CH₄ concentrations (~2.2 ppm) between 04:00–08:00 LT and minima (~2.1 ppm) between 13:00–17:00 LT, consistent with nocturnal boundary-layer stability and daytime convective mixing. Across 14 midday UAV flights from October 2023 to September 2024, CH₄–altitude slopes ranged from −3.05 × 10⁻⁴ to +1.41 × 10⁻⁴ ppm/m, reflecting variable stratification and uplift regimes. The highest flight concentration (2.23 ppm) was observed on 19 October under stable conditions, while the lowest (2.03 ppm) was observed on 14 August under elevated vertical mixing. These extremes reflect seasonal background accumulation and convective transport effects. Temperature was the most consistent predictor, with regression coefficients ranging from −0.021 to +0.008 ppm/°C, while ethane (C₂H₆) coefficients were significant but confounded due to measurements below detection limits. The analyzer maintained strong signal stability throughout (mean CV ≈ 0.0066; max = 0.0114), and remote sensing validation with TROPOMI supported observed seasonal accumulation trends. These results demonstrate the MIRA Strato’s capability to resolve near-surface CH₄ dynamics and characterize convective transport in complex atmospheric environments. Full article

In February 2024, the European Union published its Industrial Carbon Management Strategy, setting ambitious goals for carbon capture and storage (CCS), carbon capture and utilisation (CCU), and related technologies. Industrial decarbonisation will require a mix of solutions, CCUS, electrification, hydrogen and hydrogen-derived fuels, and energy efficiency, which are all dependent on affordable clean energy. Although carbon management technologies could contribute substantially to climate targets, their deployment has been slowed by technical barriers and public concerns. Sotacarbo has created a research centre dedicated to developing and testing carbon capture, utilisation, and storage technologies. Within this framework, the new Sotacarbo Fault Laboratory (SFL) was designed to investigate gas migration in faults and to test monitoring systems capable of detecting potential short- and long-term CO₂ leakages. This paper presents a preliminary study, including seismic full-waveform simulations for time-lapse surveys before and after CO₂ injection, and a suite of geophysical methods used to characterise the Matzaccara Fault within the Eocene Sulcis Basin. The results of the application of integrated geophysical methods support the selection of a safe and suitable injection-well location and demonstrate the value of these methods for detailed fault characterisation in CCUS applications. Full article

Rabies virus (RABV) causes fatal encephalitis once it invades the central nervous system (CNS), and treatment options are extremely limited at this stage. We investigated the recombinant RABV variants SPBN, SPBNGA (glycoprotein substitution R333E), SPBNGAK (R333E plus N194K), SPBNGAS (R333E plus N194S), and TriGAS (three copies of the R333E/N194S glycoprotein). We evaluated their cellular tropism and immune activation in an intracerebral mouse infection model using immunohistochemistry and confocal immunofluorescence. SPBNGAK (R333E/N194K) resulted in mixed neuronal and astrocytic infection and lethal disease. In contrast, the R333E/N194S mutations in the GAS variants were associated with reduced neuronal infection and apparent astrocyte-restricted infection patterns. This tropism shift coincided with microglial activation (allograft inflammatory factor 1, amoeboid transformation) and astrocytic activation (nestin), along with T-cell infiltration and endothelial activation that persisted beyond viral clearance. SPBNGAK-infected astrocytes expressed nestin, while GAS variant-infected astrocytes remained nestin-negative and were rapidly cleared. Intracerebral co-inoculation of astrocytotropic TriGAS with the lethal neurotropic DOG4 strain was associated with survival and a marked reduction in detectable DOG4 neuronal infection. These findings suggest that glycoprotein-mediated astrocyte tropism may be associated with altered immune responses after rabies CNS invasion. While mechanistic causality cannot be inferred, these observations may inform the design of future studies exploring astrocyte-restricted RABV infection in therapeutic-related contexts. Full article

The Internet of Things (IoT) is increasingly deployed at the edge under resource and environmental constraints, which limits the practicality of traditional intrusion detection systems (IDSs) on IoT hardware. This paper presents two IDS configurations. First, we develop a baseline IDS with fixed hyperparameters, achieving 99.20% accuracy and ~0.002 ms/sample inference latency on a desktop machine; this configuration is suitable for high-performance platforms but is not intended for constrained IoT deployment. Second, we propose a lightweight, edge-oriented IDS that applies ANOVA-based filter feature selection and uses a genetic algorithm (GA) for the bounded hyperparameter tuning of the classifier under stratified cross-validation, enabling efficient execution on Raspberry Pi-class devices. The lightweight IDS achieves 98.95% accuracy with ~4.3 ms/sample end-to-end inference latency on Raspberry Pi while detecting both low-volume and high-volume (DoS/DDoS) attacks. Experiments are conducted in a Raspberry Pi-based real lab using an up-to-date mixed-modal dataset combining system/network telemetry and heterogeneous physical sensors. Overall, the proposed framework demonstrates a practical, hardware-aware, and reproducible way to balance detection performance and edge-level latency using established techniques for real-world IoT IDS deployment. Full article