Search Results (371)

The C₄F₇N eco-friendly switchgear shows significant application potential, and quantifying its carbon footprint can accelerate the low-carbon transition in the power industry. A life cycle assessment (LCA) model for a 12 kV C₄F₇N eco-friendly switchgear is established in this study, and the carbon footprint across four stages—raw material acquisition, transportation, operation, and recycling—is accurately quantified. Sensitivity analysis of key raw material parameters and Monte Carlo simulation are used to further quantify the impact of uncertainty in these key sensitive parameters. Results indicate that the operational stage contributes the most to the switchgear’s carbon footprint, amounting to 24,794.77 kgCO₂e, mainly due to electricity consumption. Within this stage, C₄F₇N gas leakage contributes minimally at 2.21 kgCO₂e. The raw material acquisition stage follows with 3005.57 kgCO₂e, where C₄F₇N gas, aluminum, and stainless steel are identified as the primary contributing materials. Sensitivity analysis shows that electricity, C₄F₇N, aluminum, and stainless steel are the resources that have the greatest impact on the switchgear’s carbon footprint. Compared with traditional SF₆ switchgear, the C₄F₇N switchgear has a 23.8% lower total carbon footprint, with its total carbon footprint reaching 26,771.58 kgCO₂e compared to 35,136.48 kgCO₂e for SF₆ switchgear. This advantage stems largely from C₄F₇N’s much lower global warming potential—2090 versus 25,200 for SF₆—which reduces gas-related emissions by 96.6%. These findings substantiate the practical viability of C₄F₇N-based eco-friendly switchgear and provide strategies for the power sector to achieve a low-carbon transition. Full article

Optical phase modulation is essential for a wide range of silicon photonic integrated circuits used in communication applications. In this study, an optical phase shifter utilizing photo-elastic effects is proposed, where mechanical stress is induced by electrostatic micro-electro-mechanical systems (MEMS) with actuators arranged in a comb drive configuration. The design incorporates suspended serpentine silicon nitride (SiN) optical waveguides. Through extensive numerical simulations, it is shown that the change in the effective refractive index (n_eff) of the optical waveguide is a function of the voltage applied to the electrostatic actuators and that such n_eff tuning can be achieved for a broad range of wavelengths. Implemented within one arm of an unbalanced Mach–Zehnder interferometer (MZI), the phase shifter achieves a phase change of π when the stressed optical path measures 4.7 mm, and the actuators are supplied with 80 V DC and consume almost no power. This results in a half-wave voltage-length product (V_πL) of 37.6 V·cm. Comparative analysis with contemporary optical phase shifters highlights the proposed design’s superior power efficiency, compact footprint, and simplified fabrication process, making it a highly efficient component for reconfigurable MEMS-based silicon nitride photonic integrated circuits. Full article

This paper presents a lightweight, genre-conditioned photo curation framework that restructures user-selected image sequences based on cinematic shot scale patterns. Unlike prior frame-level approaches, our method explicitly models sequential rhythm and genre style. The proposed pipeline integrates (1) a MobileNetV3-based shot scale classifier optimized for on-device efficiency, (2) a conditional variational autoencoder (cVAE) for embedding temporal shot rhythms conditioned on genre, and (3) a similarity-driven adaptation module that adjusts sequences through swap and crop operations guided by latent distance reduction. Deployed as an iOS application, the system processes an 8-image sequence in ~2.02 s with a footprint under 3 MB. Quantitative evaluations show that the classifier achieved 69.9% Top-1 accuracy (F1 = 0.646), and that adaptation reduced latent distance by 22.7% compared to shuffled baselines. On-device tests confirmed practical feasibility. A user study (n = 24) using Likert ratings revealed that the method improved rhythm perception among film/media experts, though effects on genre recognition and preference were less consistent for general users. Overall, this work contributes a novel, style-aware, and mobile-ready sequencing framework that advances beyond prior frame-level methods and supports applications in memory curation, interactive storytelling, and mobile authoring. Full article

Understory-cultivated Panax ginseng possesses high pharmacological and economic value; however, its visual quality grading predominantly relies on subjective manual assessment, constraining industrial scalability. To address challenges including fine-grained morphological variations, boundary ambiguity, and complex natural backgrounds, this study proposes Ginseng-YOLO, a lightweight and deployment-friendly object detection model for automated ginseng grade classification. The model is built on the YOLOv11n (You Only Look Once11n) framework and integrates three complementary components: (1) C2-LWA, a cross-stage local window attention module that enhances discrimination of key visual features, such as primary root contours and fibrous textures; (2) ADown, a non-parametric downsampling mechanism that substitutes convolution operations with parallel pooling, markedly reducing computational complexity; and (3) Slide Loss, a piecewise IoU-weighted loss function designed to emphasize learning from samples with ambiguous or irregular boundaries. Experimental results on a curated multi-grade ginseng dataset indicate that Ginseng-YOLO achieves a Precision of 84.9%, a Recall of 83.9%, and an mAP@50 of 88.7%, outperforming YOLOv11n and other state-of-the-art variants. The model maintains a compact footprint, with 2.0 M parameters, 5.3 GFLOPs, and 4.6 MB model size, supporting real-time deployment on edge devices. Ablation studies further confirm the synergistic contributions of the proposed modules in enhancing feature representation, architectural efficiency, and training robustness. Successful deployment on the NVIDIA Jetson Nano demonstrates practical real-time inference capability under limited computational resources. This work provides a scalable approach for intelligent grading of forest-grown ginseng and offers methodological insights for the design of lightweight models in medicinal plants and agricultural applications. Full article

Developing a more productive, resource-efficient, and climate-smart rubber agroforestry model is essential for the sustainable growth of natural rubber cultivation. In this study, we evaluated whether a double-row rubber plantation intercropped with the medicinal crop Ficus hirta Vahl. (DR-F) could achieve this goal, using a single-row rubber plantation (SR) as the control. We assessed the feasibility of the DR-F system based on productivity, solar utilization efficiency (SUE), partial factor productivity of applied nitrogen (PFPN), carbon efficiency (CE), net ecosystem carbon balance (NECB), and carbon footprint (CF). No significant difference was observed in rubber tree biomass between the DR-F (10.49 t·ha⁻¹) and SR (8.49 t·ha⁻¹) systems. However, the DR-F system exhibited significantly higher total biomass productivity (23.34 t·ha⁻¹) than the SR systems due to the substantial contribution from intercropped Ficus hirta Vahl., which yielded 12.84 t·ha⁻¹(p < 0.05). The root fresh weight yield of Ficus hirta Vahl. reached 17.55 t·ha⁻¹, generating an additional profit of 20,417 CNY ha⁻¹. The DR-F system also exhibited higher solar radiation interception and greater availability of soil nutrients. Notably, the roots of rubber trees and Ficus hirta Vahl. did not overlap at a 4 m distance from the rubber trees. The DR-F system achieved higher SUE (0.64%), PFPN (51.40 kg·kg⁻¹ N), and CE (6.93 kg·kg⁻¹ C) than the SR system, with the SUE and PFPN differences being statistically significant (p < 0.05). Although the NECB remained unaffected, the DR-F system demonstrated significantly higher productivity and a substantially lower CF (0.33 kg CO₂·kg⁻¹, a 56% reduction; p < 0.05). In conclusion, the DR-F system represents a more sustainable and beneficial agroforestry approach, offering improved productivity, greater resource use efficiency, and reduced environmental impact. Full article

Onboard the MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms, which are processed to provide high-resolution atmospheric emission spectra. These spectra enable the derivation of temperature and humidity profiles, among other parameters, with exceptional spectral resolution. In this study, we evaluate a novel, rapid retrieval approach in the interferogram domain, aiming for near-real-time (NRT) analysis of large spectral datasets anticipated from next-generation tropospheric sounders, such as MTG-IRS. The Partially Sampled Interferogram (PSI) method, applied to trace gas retrievals from IASI, has been sparsely explored. However, previous studies suggest its potential for high-accuracy retrievals of specific gases, including CO, CO₂, CH₄, and N₂O at the resolution of a single IASI footprint. This article presents the results of a study based on retrieval in the interferogram domain. Furthermore, the optical pathway differences sensitive to the parameters of interest are studied. Interferograms are generated using a fast Fourier transform on synthetic IASI spectra. Finally, the relationship to the total column of carbon monoxide is explored using three different algorithms—from the most intuitive to a complex neural network approach. These algorithms serve as a proof of concept for interferogram classification and rapid predictions of surface temperature, as well as the abundances of H₂O and CO. IASI spectra simulations were performed using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on least squares estimation. The climatological library TIGR was employed to generate IASI interferograms from LARA spectra. TIGR includes 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration profiles across a pressure grid from the surface to the top of the atmosphere. Our study focuses on CO, a critical trace gas for understanding air quality and climate forcing, which displays a characteristic absorption pattern in the 2050–2350 ${\mathrm{cm}}^{-1}$ wavenumber range. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals. Starting with intuitive retrieval algorithms, we progressively increased complexity, culminating in a neural network-based algorithm. The results of the NN study demonstrate the feasibility of fast interferogram-domain retrievals, paving the way for operational applications. Full article

Vegetable tannins (VTs) are natural polyphenolic compounds widely used in leather tanning as sustainable alternatives to chrome-based processes. Traditionally, only a limited number of commercially available tannins, such as mimosa, quebracho, and chestnut, are employed globally, often requiring long-distance transportation with associated environmental and economic costs. This review systematically explores recent advances (2015–2025) in the identification and evaluation of alternative VT sources derived from underutilized plant species in Africa and Asia. Chemical composition, extraction efficiency, and tanning performance, including hydrothermal stability, tensile strength (TS), elongation at break (EB%), and tear strength (Ts), are critically analyzed and compared with conventional agents. Particular focus is given to the tannin/non-tannin ratio (T/N), a key indicator of tanning potential. Promising results were found for extracts from Acacia xanthophloea, Cassia singueana, Solanum incanum, Pontederia crassipes, and Xylocarpus granatum. Preliminary environmental assessments (COD, BOD, TDS) also suggest comparable impacts to standard tannins. However, performance variability due to species, plant part, seasonality, and extraction conditions remains a challenge. This review underscores the potential of regionally sourced VTs to support proximity-based economies and reduce the environmental footprint of the leather industry, while highlighting the need for further studies to optimize extraction protocols and scale industrial application. Full article

Ratoon rice is a planting system that efficiently utilizes temperature and light resources. However, multiple fertilization applications are typically required to maintain stable rice yields. Improper fertilization not only poses challenges to scarce labor resources but also increases carbon footprints (CFs). Research on the effects of different fertilization strategies on greenhouse gas (GHG) emissions, yield, CF, and ecosystem net economic benefits (NEEBs) in ratoon rice systems remains limited. A two-year field experiment was conducted to evaluate the effects of one conventional fertilization strategy and four optimized fertilization strategies on GHG emissions, yield, CF, and NEEBs in the ratoon rice system. The conventional fertilization strategy applied urea in five splits (FFP, 280 kg N·ha⁻¹). The optimized strategies included (1) one-time side deep application controlled-release fertilizer (CRF, 280 kg N·ha⁻¹); (2) CRF with 20% N replaced by organic fertilizer (OF + CRF1); (3) the same as (2) with a 10% N reduction (OF + CRF2, 252 kg N·ha⁻¹); and (4) the same as (2) with a 20% N reduction (OF + CRF3, 224 kg N·ha⁻¹). The results showed that compared with FFP, optimized fertilization treatments reduced CH₄ and N₂O emissions by 28.69% to 55.27% and 25.08% to 40.32%, respectively. They also increased the annual rice yields by 2.22% to 19.52% (except OF + CRF3). Optimizing fertilization treatments reduced annual CF, CF_Y, and CF_EC by 26.66% to 49.59%, 34.11% to 51.12%, and 25.35% to 41.47%, respectively. These treatments also increased NEEBs by 8.27% to 34.23%. Among them, OF + CRF1 and OF + CRF2 treatments achieved the highest NEEB. In summary, CRF treatments can balance ratoon rice yield and environmental benefits. Replacing part of the N with organic fertilizer further enhances annual yield and NEEBs. Full article

Background and Objectives: Although cyanoacrylate–polylactic acid (CA + PLA) patches shorten the time to hemostasis after partial hepatectomy, their long-term biocompatibility remains uncertain. We compared the 5-month histopathological footprint of a novel CA + PLA patch (Study group) with a licensed fibrinogen/thrombin matrix (TachoSil^® group) and electrocautery (Control group). Methods: Thirty-three male Wistar rats underwent a 3 × 1.5 cm hepatic segment resection and were randomized to the Control (n = 5), Study (n = 14), or TachoSil^® (n = 14) group. The animals were sacrificed on postoperative day (POD) 50, 100, or 150. Blinded semiquantitative scoring (0–3) was used to capture inflammation intensity, and the number of neutrophils (PMNs), lymphocytes (Ly’s), isolated histiocytes, and foreign-body giant cells (FBGCs). Results: The proportions of animals in each group across the different sacrifice time points were homogeneous (χ² = 4.34, p = 0.36). The median inflammation remained mild (2 [IQR 1–2]) in the Control and Study groups but lower in the TachoSil^® group (1 [1–2], p = 0.47). The FBGC scores differed markedly (score ≥ 2: 64% in Study, 0% in Control, 14% in TachoSil^®; p < 0.001). Fibrosis occurred almost exclusively in the Study group (79% vs. 0%; χ² = 22.4, p < 0.001). Mature vessels were most frequently observed in the TachoSil^® group (50%, aOR = 5.1 vs. Study, p = 0.04). Abscesses only developed in the Study group (29%, p = 0.046). Within the TachoSil^® group, inflammation (ρ = −0.62, p = 0.019) and Ly infiltration (ρ = −0.76, p = 0.002) declined with time; no significant temporal trends emerged in the Study group. Conclusions: At the five-month follow-up, there was an exuberant foreign-body reaction, dense collagen deposition, and a higher abscess rate around the CA + PLA patch compared with both TachoSil^® and cautery. Conversely, TachoSil^® evolved toward a mature, well-vascularized scar with waning inflammation. These findings underscore the importance of chronic-phase evaluation before clinical adoption of new hemostatic biomaterials. Full article

Graphene-based micro-ring modulators are promising candidates for next-generation optical interconnects, offering compact footprints, broadband operation, and CMOS compatibility. However, most demonstrations to date have relied on conventional straight bus coupling geometries, which limit design flexibility and require extremely small coupling gaps to reach critical coupling. This work presents a comprehensive comparative analysis of straight, bent, and racetrack bus geometries in graphene-on-silicon nitride (Si₃N₄) micro-ring modulators operating near 1.31 µm. Based on finite-difference time-domain simulation results, a proposed racetrack-based modulator structure demonstrates that extending the coupling region enables critical coupling at larger gaps—up to 300 nm—while preserving high modulation efficiency. With only 6–12% graphene coverage, this geometry achieves extinction ratios of up to 28 dB and supports electrical bandwidths approaching 90 GHz. Findings from this work highlight a new co-design framework for coupling geometry and graphene coverage, offering a pathway to high-speed and high-modulation-depth graphene photonic modulators suitable for scalable integration in next-generation photonic interconnects devices. Full article

The rice-wheat rotation is the main agricultural cropping system in Jiangsu Province, playing a vital role in ensuring food security and promoting economic development. However, current research on rice-wheat systems mainly focuses on in-situ controlled experiments at the point scale, with limited studies addressing carbon footprint (CF) and energy balance (EB) at the regional scale and long time series. Therefore, we analyzed the evolution patterns of the CF and EB of the rice-wheat system in Jiangsu Province from 1980 to 2022, as well as their influencing factors. The results showed that the sown area and total yield of rice and wheat exhibited an increasing–decreasing–increasing trend during 1980–2022, while the yield per unit area increased continuously. The CF of rice and wheat increased by 4172.27 kg CO₂ eq ha⁻¹ and 2729.18 kg CO₂ eq ha⁻¹, respectively, with the greenhouse gas emissions intensity (GHGI) showing a fluctuating upward trend. Furthermore, CH₄ emission, nitrogen (N) fertilizer, and irrigation were the main factors affecting the CF of rice, with proportions of 36%, 20.26%, and 17.34%, respectively. For wheat, N fertilizer, agricultural diesel, compound fertilizer, and total N₂O emission were the primary contributors, accounting for 42.39%, 22.54%, 13.65%, and 13.14%, respectively. Among energy balances, the net energy (NE) of rice exhibited an increasing and then fluctuating trend, while that of wheat remained relatively stable. The energy utilization efficiency (EUE), energy productivity (EPD), and energy profitability (EPF) of rice showed an increasing and then decreasing trend, while wheat decreased by 46.31%, 46.31%, and 60.62% during 43 years, respectively. Additionally, N fertilizer, agricultural diesel, and compound fertilizer accounted for 43.91–45.37%, 21.63–25.81%, and 12.46–20.37% of energy input for rice and wheat, respectively. Moreover, emission factors and energy coefficients may vary over time, which is an important consideration in the analysis of long-term time series. This study analyzes the ecological and environmental effects of the rice-wheat system in Jiangsu Province, which helps to promote the development of agriculture in a green, low-carbon, and high-efficiency direction. It also offers a theoretical basis for constructing a low-carbon sustainable agricultural production system. Full article

The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies—BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10—and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein using multiscale modeling, which combined molecular simulations with the ensemble-based mutational scanning of the binding interfaces and binding free energy computations. A central theme emerging from this work is that the unique binding strength and resilience to immune escape of the BD55-1205 antibody are determined by leveraging a broad epitope footprint and distributed hotspot architecture, additionally supported by backbone-mediated specific interactions, which are less sensitive to amino acid substitutions and together enable exceptional tolerance to mutational escape. In contrast, BD-604 and OMI-42 exhibit localized binding modes with strong dependence on side-chain interactions, rendering them particularly vulnerable to escape mutations at K417N, L455M, F456L and A475V. Similarly, P5S-1H1 and P5S-2B10 display intermediate behavior—effective in some contexts but increasingly susceptible to antigenic drift due to narrower epitope coverage and concentrated hotspots. Our computational predictions show strong agreement with experimental deep mutational scanning data, validating the accuracy of the models and reinforcing the value of binding hotspot mapping in predicting antibody vulnerability. This work highlights that neutralization breadth and durability are not solely dictated by epitope location, but also by how binding energy is distributed across the interface. The results provide atomistic insight into mechanisms driving resilience to immune escape for broadly neutralizing antibodies targeting the ACE2 binding interface—which stems from cumulative effects of structural diversity in binding contacts, redundancy in interaction patterns and reduced vulnerability to mutation-prone positions. Full article

Addressing climate change requires not only knowledge but also psychological resilience. This study examined whether integrating Mental Mode Management (MMM) self-regulation training with climate education improves pro-environmental outcomes and emotional responses to climate change. In a randomised 2 × 2 design, 44 participants were assigned to either a control group (CG; n = 21), which received a six-week climate education programme, or an experimental group (MMM; n = 23), which received the same education plus MMM training. Pro-environmental attitudes, behaviours, carbon emissions, climate change anxiety, mindfulness, and executive functions were assessed at baseline and post-intervention. A follow-up was also conducted six months later. Both groups showed increased pro-environmental attitudes post-intervention (η² = 0.3) and reduced food-related emissions (η² = 0.107). No changes were observed in pro-environmental behaviour scores or global carbon footprint. While neither intervention affected overall climate anxiety or cognitive impairment, functional impairment increased in the CG and decreased in the MMM group (η² = 0.177), with mindfulness facet acting with awareness moderating this effect. These findings contribute to sustainability research by showing that integrating climate education with psychological training enhances environmental awareness and fosters emotionally resilient engagement with climate challenges, supporting individual-level contributions to broader sustainability goals. Full article

This study investigates the feasibility of producing low-carbon FeCr via metallothermic smelting of Cr concentrate and chromite spinel powder using a complex FeAlSiCa alloy as the reductant in an induction furnace. The proposed approach offers an alternative to conventional carbothermic and oxygen-blown technologies, reducing both the carbon footprint and airborne emissions. Three charge compositions were tested with varying FeAlSiCa additions (12, 14, and 16 kg per 100 kg of Cr source) and partial replacement of Cr concentrate with up to 20% CSP. Thermodynamic and microstructural analyses were conducted, and the effects of the slag basicity, temperature profiles, and holding time were assessed. In optimal conditions, Cr recovery reached up to 80% with minimal Cr₂O₃ losses in slag, and the resulting alloys met ISO 5448-81 requirements for nitrogen-containing low-carbon FeCr. Microstructural examination revealed the formation of Fe-Cr solid solutions and CrN phases, with V incorporation from the FeAlSiCa alloy. The process proved stable and energy-efficient, producing compact, non-disintegrating slag. This study highlights the potential of induction furnace smelting and chromite spinel powder valorization as a sustainable path for FeCr production. Full article

Variable-rate nitrogen (VR-N) application allows farmers to optimize nitrogen (N) input site-specifically within field boundaries, enhancing both economic efficiency and environmental sustainability. In this study, VR-N technology was applied to durum wheat in two small-scale commercial fields (3–4 ha each) located in distinct agro-climatic zones of Thessaly, central Greece. A real-time VR-N application algorithm was used to calculate N rates based on easily obtainable near-real-time data from unmanned aerial vehicle (UAV) imagery, tailored to the crop’s actual needs. VR-N implementation was carried out using conventional fertilizer spreaders equipped to read prescription maps. Results showed that VR-N reduced N input by up to 49.6% compared to the conventional uniform-rate N (UR-N) application, with no significant impact on wheat yield or grain quality. In one of the fields, the improved gain of VR-N when compared to UR-N was 7.2%, corresponding to an economic gain of EUR 163.8 ha⁻¹, while in the second field—where growing conditions were less favorable—no considerable VR-N economic gain was observed. Environmental benefits were also notable. The carbon footprint (CF) of the wheat crop was reduced by 6.4% to 22.0%, and residual soil nitrate (NO₃⁻) levels at harvest were 13.6% to 36.1% lower in VR-N zones compared to UR-N zones. These findings suggest a decreased risk of NO₃⁻ leaching and ground water contamination. Overall, the study supports the viability of VR-N as a practical and scalable approach to improve N use efficiency (NUE) and reduce the environmental impact of wheat cultivation which could be readily adopted by farmers. Full article