Search Results (820)

Continuous-Variable Quantum Key Distribution (CV-QKD) offers practical advantages for secure communication, but laser linewidth-induced phase noise remains a critical performance limitation. This work presents a comprehensive simulation-based analysis quantifying the impact of laser linewidth on secret key rate (SKR) in Gaussian-modulated coherent-state CV-QKD systems. We develop a detailed noise model incorporating detector electronics, Raman scattering, phase recovery, ADC quantization, and laser relative intensity noise. Through systematic parameter sweeps spanning linewidths from 10 Hz to 250 kHz, modulation variances from 1 to 20 SNU, and fiber distances up to 100 km, we identify three distinct operational regimes and optimization strategies for both transmitted local oscillator (TLO) and local–local oscillator (LLO) configurations under homodyne and heterodyne detection. Results show that metropolitan-scale links (50 km) require linewidths below 5 kHz to maintain secure operation, with performance decreasing beyond 25 kHz. We demonstrate that modulation variance must be jointly optimized with laser quality, with optimal values decreasing from 3–4 SNU at narrow linewidths to 2–2.5 SNU at moderate linewidths. The analysis reveals asymmetric sensitivity in LLO systems where local oscillator linewidth degrades performance more strongly than signal laser linewidth. These quantitative findings provide practical design guidelines for achieving secure CV-QKD operation over metropolitan distances with realistic hardware constraints, supporting deployment of defense-grade quantum communication networks. Full article

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts. Full article

In response to the demand for reference material under the EU Maximum Levels for Nickel (Ni) limit in soybeans (15 mg/kg) in 2024, this study explored the technical difficulty of ensuring the homogeneity of Ni reference material in the soybean matrix. Multi-scale characterization (LA-ICP-MS, ICP-MS, FT-IR, etc.) verified that Ni was specifically enriched in embryo and the finer powder (mainly embryo). Based on this finding, we innovatively proposed the span [(D₉₀ − D₁₀)/D₅₀] as a rapid predictor to evaluate homogeneity, offering a potential screening tool to optimize grinding conditions and reduce reliance on time-consuming traditional homogeneity assessments (Ni-RSD by ICP-MS). A positive correlation between span and homogeneity was observed, which was attributed to the inhomogeneous distribution of low-Ni tissue (seed coat). By optimizing the crushing process (hammer cyclone milling, room temperature: 20 °C, 15,000 r/min, ≤ 0.45 mm sieve), a homogeneity uncertainty of 1.00% was obtained. This finding helps in ensuring the homogeneity of reference materials from other high-fat oilseed matrixes. Full article

This study used Computational Fluid Dynamics (CFD) with the Reynolds-Averaged Navier–Stokes (RANS) k-ω Shear Stress Transport (SST) model to evaluate how crop presence and evapotranspiration affect airflow and thermal stratification in a naturally ventilated tropical tomato greenhouse. Three configurations were simulated: SP-SC-R (No Plants—No crop thermal load—Radiation), CP-SC-R (Crop Present—No crop thermal load—Radiation), and CP-CC-R (Crop Present—Crop thermal load (233.68 W·m⁻²)—Radiation). Mesh independence analysis yielded numerical uncertainties of 1.58% (velocity) and 1 × 10⁻⁶ (temperature). Vegetation reduced canopy air velocity by 55% (from 4 m·s⁻¹ to values below 2 m·s⁻¹). Evapotranspiration enhanced buoyancy-driven mixing, decreasing temperature gradients by up to 1.5 °C, but thermal stratification persisted above 4.5 m in all cases (vertical gradients 0.31–0.42 °C·m⁻¹; maximum roof temperature 37.95 °C). Extreme wind speeds (greater than 20 m·s⁻¹) occurred in the leeward span but above the main foliage. Natural ventilation alone is insufficient for tomato cultivation under tropical conditions. Practical recommendations include increasing roof vent area, installing windbreak baffles, and adopting hybrid ventilation. Future work should use unsteady, RANS/large-eddy simulation (LES), porous media models based on leaf area density (LAI), and field validation. This study demonstrates that coupling crop geometry and evapotranspiration is essential for realistic greenhouse CFD modelling in warm climates. Full article

Neural scaling laws describe how model performance improves as a power law with size, but existing work has focused almost entirely on models above 100 M parameters. The regime below 20 million parameters, where TinyML and edge AI systems operate, remains largely unexamined. We train 90 models spanning 22 K to 19.8 M parameters across two architecture families (a plain ConvNet and MobileNetV2) on CIFAR-100, varying width while holding depth and training protocol fixed. Both architectures follow approximate power laws, with exponents of $\alpha =0.156$ (ScaleCNN) and $\alpha =0.106$ (MobileNetV2). However, the power law does not hold uniformly: local exponents decay with scale, and MobileNetV2 saturates at 19.8 M parameters (${\alpha }_{\mathrm{local}}=0.006$), hitting a data wall. The structure of errors also changes with scale. The Jaccard overlap between error sets of the smallest and largest ScaleCNN models is only 0.35; compression changes which inputs are misclassified, not merely how many. Small models develop a triage strategy, concentrating capacity on easy classes (Gini of per-class accuracy: 0.26 at 22 K params vs. 0.09 at 4.7 M) while effectively abandoning the hardest ones (bottom-5 class accuracy: 10% vs. 53%). The smallest models achieve the lowest ECE values (0.013 vs. peak 0.110 at mid-size), reversing the typical overconfidence–capacity relationship, though this partly reflects a global-mean matching artifact rather than well-calibrated per-bin confidence. On CIFAR-100, aggregate accuracy alone is therefore a misleading basis for edge deployment decisions; validation must happen at the target model size. All findings in this study are based on CIFAR-100 (32 × 32, 100 classes); their generalizability to other datasets, resolutions, and architectures remains to be verified. Full article

In this work, we present a detailed comparison of the SuSAv2 (SuperScaling Approach version 2) and RDWIA (Relativistic Distorted-Wave Impulse Approximation) models with measurements of charged-current neutrino-induced single-pion production from different experiments (T2K, MINERvA and MiniBooNE), studying the differences between the two theoretical descriptions. The neutrino energy range in these experiments spans from hundreds of MeV to roughly 20 GeV, and the nuclear targets are mainly composed of ${}^{12}$C. The SuSAv2 model uses the single-nucleon inelastic structure functions from the ANL-Osaka DCC model, which allows for a separation of pion production channels, distinguishing between the ${\pi }^{+}$, ${\pi }^{-}$ and ${\pi }^0$ final states. In the RDWIA approach, the Hybrid model developed by the Ghent group is used for the description of the boson–pion–nucleon vertex. Full article

Growth in the elderly population will inevitably increase the demand for care and assistance, which must be matched by a sufficient number of individuals capable of providing the care and assistance required. This study aims to estimate the present and future caregiver support ratio (CSR) at the national level across Europe and at the Italian subnational level. Italy was selected due to its higher proportion of elderly citizens compared with other EU countries. The CSR is defined as the number of potential caregivers aged 45–64 years (the age range most commonly involved in caregiving) per citizen aged 80 and over (the most likely to require long-term caregiving). Data were obtained from Eurostat for the EU-level analysis, whereas those pertaining to Italy were sourced from the Italian National Institute of Statistics. CSR projections were made for the decades spanning 2030 to 2080. The findings show that the ratio of potential caregivers aged 45–64 to individuals aged 80 or over will steadily decline over the coming decades, implicating challenges for gerontological social policies. The results reveal variation in the CSR for the 27 European countries—with a decline from 5:1 in 2025 to 2:1 by 2050—and across the 20 Italian regions, as well as differences in the projected trends in CSR variations over the medium (until 2050) and long term (until 2080). Technology may offer possible solutions to address some of the challenges associated with the aging demographic. Full article

Background: Familial hemiplegic migraine (FHM) is a rare and severe form of migraine disorder featuring aura symptoms that include hemiplegia during attacks. While pathogenic missense variants in CACNA1A, ATP1A2, and SCN1A can cause FHM or its sporadic form, they explain less than 20% of suspected hemiplegic migraine cases, suggesting the involvement of other genes or genetic variations, potentially including copy number variations (CNVs). PPRT2 gene variants including CNVs have also been implicated in hemiplegic migraine. Methods: Multiplex ligation-dependent probe amplification (MLPA) assays were used to investigate the presence of CNVs in the CACNA1A, SCN1A, ATP1A2, and PRRT2 genes in a cohort of 170 unrelated probands suspected to have FHM who had tested negative for pathogenic missense or small indel variants within these genes. Potential CNVs were subsequently confirmed using quantitative PCR. Results: In 15 patients referred for FHM genetic testing, various CNVs in the target genes were detected by MLPA and subsequently validated by quantitative PCR. CACNA1A exon duplications were identified in six patients and deletions found in two. Two patients had ATP1A2 exon deletions, while one had a duplication. For SCN1A, exon deletions were found in three patients and a duplication in one. PRRT2 exon deletions were detected in five patients, with a single nucleotide polymorphism (SNP) array confirming a deletion spanning PRRT2 and neighbouring loci including 26 genes in one of those. Three patients had CNVs in more than one FHM gene. Conclusions: Our study demonstrates the presence of CNVs in FHM genes in a subset of hemiplegic migraine cases (~9%), suggesting a likely role in the disorder and highlighting the need to explore structural variation in addition to the commonly interrogated genetic mutation points. These findings contribute to further understanding of genetic mechanisms that underlie hemiplegic migraine and may inform improved diagnostic and therapeutic strategies. Full article

As the population increases, the demand for power continues to rise. As fossil fuel resources reduce, wind energy emerges as a sustainable alternative and helps address adverse effects of global warming and environmental pollution caused by fossil fuels. Thus, this study focuses on increasing the efficiency of wind turbines by improving their energy conversion. In this study, the NREL Phase VI wind turbine blade was modified by adding a Gurney flap at trailing edge along the entire span. Computational fluid dynamics simulations using ANSYS CFX 19.2 were performed on the modified blades to evaluate their aerodynamic performance. Three different flap lengths were investigated with six wind speeds varying from 5 m/s to 20 m/s. The results obtained were compared with those from NREL Phase VI original shape and a blade equipped with a winglet. Computational domain was divided into a rotating cylindrical region and a stationary rectangular part. The aerodynamic parameters calculated include torque, thrust, and normal and tangential forces coefficients. At low velocities, the addition of a Gurney flap had an insignificant impact on torque and thrust, whereas at medium to high wind speeds, significant increases were observed on torque, indicating more power production. Full article

This study investigates the influence of key parameters—vehicle speed, weight, loading lane, and pavement roughness—on the Dynamic Amplification Factor (DAF) and ride comfort of a beam–arch composite continuous rigid-frame bridge under vehicle–bridge coupling. A six-span bridge is analyzed using a spatial beam-element model in ANSYS and a typical three-axle vehicle model is adopted to conduct the coupled dynamic response analysis. Based on the modal and structural characteristics of this bridge, key response indices are selected, including vertical displacement and bending moment at midspan, longitudinal displacement and bending moment at pier top, arch crown displacement, and tensile force in the long hanger. Control sections are identified in Span 4 (midspan, arch crown, long hanger) and at the top of Pier 16. The results demonstrate that pavement roughness significantly influences ride comfort, with the root mean square (RMS) value varying up to 107%, whereas the loading lane shows a negligible effect. Vehicle speed effects are divided into two distinct regimes: at 60 km/h and within 70–90 km/h, with dynamic responses in the higher speed range approximately 22% greater. Increasing vehicle weight raises the peak dynamic response by up to 77.68%, but does not lead to a proportional increase in DAF. Transverse loading eccentricity has a more pronounced impact on vertical bridge responses (>20% change) than on longitudinal responses (<10% change). Deterioration in pavement roughness elevates both dynamic response and DAF, with maximum increases reaching 27.97% and 28%, respectively. Full article

This study aims to reveal the control mechanism of key geometric parameters (flange thickness and flange edge thickness) of H-shaped cross-section on the bending performance of UHPC piles. Through conducting bending tests, combined with digital image correlation (DIC) technology and finite element simulation, the mechanical behavior was studied, and based on the principal strain field obtained from DIC, a strain field concentration index was proposed. The results show that: as the load ratio increases, the strain field concentration and the peak value of the mid-span principal strain continuously increase, and the crack evolution changes from dispersed development to localized control; near the limit state, the strain field concentration can reach approximately 0.28, and the peak value of the principal strain increases in an increasing trend, approximately 20% or more. Under the specific conditions of this test, in terms of ductility and energy absorption, when the flange thickness is constant, increasing the flange thickness of the web increases the energy absorption of the component by approximately 6% to 10%, while the ductility coefficient decreases by approximately 9% to 15%; when the web thickness is constant, increasing the flange thickness reduces the ductility coefficient by approximately 21% to 25%, and the energy absorption decreases by approximately 27% to 29%. The strain field concentration can effectively reflect the evolution process of the localization of bending cracks in H-shaped UHPC piles and can be used for quantitative analysis of their ductility degradation and energy absorption characteristics. It should be clarified that this study does not claim to isolate the effect of a single parameter. Full article

The proliferation of sensor networks in critical infrastructure, healthcare monitoring, and smart city applications demands robust privacy-preserving mechanisms for data verification. Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) offer a promising cryptographic primitive that enables data integrity verification without revealing sensitive sensor readings. However, the practical feasibility of deploying zk-SNARKs in resource-constrained sensor network environments remains insufficiently characterized. This paper presents a systematic benchmarking study of the Groth16 zk-SNARK protocol across eight representative circuit types spanning six orders of magnitude in computational complexity, from basic arithmetic operations (1 constraint) to ECDSA signature verification (1,510,185 constraints). Using an automated open-source benchmarking framework built on the Circom-snarkjs toolchain, we conducted 160 statistically controlled measurements (20 iterations per circuit) with cold/warm separation, collecting proof generation time, verification time, proof size, memory consumption, and witness generation overhead. Our results demonstrate that Groth16 proofs maintain a constant size of $804.7\pm 1.7$ bytes and near-constant verification time of $0.662\pm 0.032$ s regardless of circuit complexity, with coefficients of variation below 5% across all circuit types. Proof generation time exhibits sub-linear scaling ($\alpha =0.256$, $R^2=0.608$), with statistically significant differences between circuit categories confirmed by one-way ANOVA ($F=355.0$, $p<{10}^{-79}$, ${\eta }^2=0.94$). We identify three operational deployment tiers for sensor network architectures and estimate energy budgets for battery-powered devices. These findings provide actionable guidance for the design of privacy-preserving data verification systems in next-generation sensor networks. Full article

Rivers are a key pathway for the transport of plastics into the ocean. Studies of plastic pollution in Arctic rivers remain limited due to the inaccessibility of sampling sites and work in extreme weather conditions. This work presents the results of a three-year (2019–2021) survey of floating large microplastics (0.5–5 mm) and meso/macroplastics (>5 mm) in the Northern Dvina River, an actively navigated river that drains a densely populated region into the White Sea. Sampling was conducted during the ice-free periods (May–October) along a ∼3.5 km transect using a Neuston net, providing a multi-year dataset spanning three ice-free seasons. A critical methodological advancement was the calculation of plastic river–sea flux using the discharge of the sampled surface layer (upper 20 cm), which constitutes only ∼3% of the river’s total discharge, rather than the total discharge itself. Observed microplastic concentrations (average 0.003 items ${\mathrm{m}}^{-3}$) were low compared to many European rivers, and lower than those reported in the adjacent Barents and Kara Seas. Microplastic abundance was significantly lower during the high-water season than during the low-water season, which resulted in practically no seasonal variability in microplastic fluxes from the river to the White Sea (average 0.3 items ${\mathrm{s}}^{-1}$). A notable finding was that in some cases, meso/macroplastics outnumbered microplastics by item count, underscoring the river’s role as a significant source of larger plastic debris. A geospatial assessment of Arctic rivers’ pollution potential was performed, using socio-economic indicators such as near-delta population density and port activity. This study identified the Northern Dvina River as a major contributor of microplastics among the Arctic rivers. Full article

Hydrogen is recognized as a promising energy vector for the decarbonization of energy production. Besides the undoubted benefits, its utilization poses some technological challenges in the generation, transportation, storage and utilization phases, which must be carefully assessed. The aim of this work is to assess the effect of methane substitution with hydrogen in a dual-fuel diesel/methane engine on fuel conversion efficiency and pollutant emission levels. Therefore, an extensive experimental campaign has been designed in which a hydrogen/methane mixture with variable composition is ignited with a pilot injection of diesel fuel. The engine was operated in naturally aspirated or supercharged conditions, and conventional or alternative combustion strategies were implemented, spanning a pilot injection timing over a broad range of values. The results show that the effect of a variation in H₂ percentage of up to 20% strongly depends on air intake pressure and pilot injection timing. In particular, engine efficiency and HC and CO emissions are penalized as H₂ percentage increases; however, this penalty can be mitigated in naturally aspirated conditions if a late pilot SOI strategy is adopted. In terms of NO_x, a reduction is observed as H₂ percentage increases. Late SOIs determine the lowest levels of NO_x emissions in both naturally aspirated and supercharged conditions. Full article

This study aims to present a parametric analysis in the optimization problem for steel-concrete composite cellular beams with steel deck slabs. A comparative analysis was carried out considering two scenarios, namely, (i) in the first scenario, the slab span and applied loads were varied, adopting slab configurations from a manufacturer’s catalog for spans of 10 m to 20 m with a step of 2.5 m; (ii) in the second scenario, the same span and loading conditions were considered; however, slab optimization was performed by introducing reinforcement in order to evaluate the resulting impacts on the structural design. In both analyzed scenarios, the objective function was defined as the composite system’s CO₂ emissions. The design constraints were defined based on literature recommendations, and to solve the optimization problem, the Particle Swarm Optimization (PSO) algorithm was also adopted. The results demonstrate that the PSO algorithm was effective in identifying optimal solutions and that the introduction of slab reinforcement, combined with optimal design, led to CO₂ emission reductions of up to 25% at the highest load levels analyzed. Full article