Search Results (4,196)

Amid escalating global climate change and the urgent international demand for low-carbon development, enhancing the greenization level of manufacturing industrial chains has emerged as a critical policy priority. This study investigates the impact of environmental regulation on the greenization of China’s manufacturing industrial chains using provincial panel data from 30 regions (2010–2022), employing two-way fixed effects and spatial Durbin models. The results demonstrate that environmental regulation significantly promotes industrial chain greenization through three pathways: industrial structure rationalization, green technology innovation, and industrialization advancement. Heterogeneity analysis reveals stronger regulatory effects in regions characterized by coal-dependent energy structures, low shares of energy-intensive industries, and underdeveloped digital economies, while negligible impacts are observed in areas with cleaner energy mixes, high energy-intensive industrial concentrations, or advanced digitalization. Spatial econometric results confirm positive spatial spillovers, indicating that environmental regulation in one region enhances neighboring areas’ greenization through policy coordination and technology diffusion. Based on these findings, this study proposes tailored policy recommendations, including strengthening regulation in coal-reliant regions, optimizing industrial structures in energy-intensive hubs, and fostering cross-regional governance synergy to mitigate pollution haven effects. The research provides novel insights into achieving sustainable manufacturing transitions under the “dual carbon” framework. Full article

This study investigates effective antioxidants to stabilize chlorophyll, a valuable and most abundant but unstable natural green pigment, adsorbed on a silica surface. Although fixing chlorophyll on silica offers some protection, significant photo-induced oxidative degradation still occurs. To enhance photostability, the prepared chlorophyll–silica composites were combined with various well-known antioxidants. The stability of these samples was evaluated by the deterioration ratio of the chlorophyll under visible light irradiation. The results showed that gallic acid provided the most significant stabilization effect. This was attributed to its moderate hydrophilicity, allowing it to be positioned near the chromophore part of the chlorophyll molecule adsorbed on the silica surface. Further tests with the derivatives of gallic acid revealed that smaller molecular size and less steric hindrance were also crucial for effectiveness as an antioxidative stabilizer. Pyrogallol and gallic acid, being the smallest molecules, performed best. It was concluded that the ability of an antioxidant to approach a chlorophyll molecule is essential for stabilization. This requires an appropriate balance of hydrophilicity and a small molecular size. Considering the nontoxicity together, gallic acid is recommended as a superior stabilizer for chlorophyll on silica surfaces. Full article

This paper presents a fixed-time fault-tolerant control strategy based on an improved long short-term memory network for dynamic positioning of unmanned marine vehicles subject to signal quantization, disturbances, and input saturation. Firstly, an improved long short-term memory network optimized by an adaptive mixed-gradient algorithm is developed to accurately estimate external disturbances. Secondly, a fixed-time extended state observer is designed to rapidly predict thruster faults. Subsequently, within a fixed-time control framework, a novel terminal sliding-mode surface incorporating signal quantization parameters is constructed. In addition, a dynamic uniform quantization strategy with tunable sensitivity is introduced to effectively alleviate the performance degradation induced by quantization errors. Based on this, a fixed-time fault-tolerant controller is constructed. Finally, simulation results and comparative experiments are provided to demonstrate the effectiveness of the proposed control scheme. Full article

Patchoulene, the characteristic sesquiterpene of patchouli essential oil, is highly valued in the perfume industry for its distinctive woody note and fixative properties. Beyond its olfactory applications, patchoulene has demonstrated promising biological activities, including anti-inflammatory, antimicrobial, and neuroprotective effects. Current production relies mainly on extraction from Pogostemon cablin plants, which requires long growth cycles (≥8 months), exhibits low yields, and imposes significant environmental constraints. To overcome these limitations, this study aimed to enhance the Whole-cell yield of patchoulene synthase (PcPTS) through structure-informed protein engineering. A semi-rational design approach was employed, combining homology modeling, molecular docking, evolutionary analysis, and molecular dynamics simulations to identify functional residues within the enzyme active site. Ala-scanning mutagenesis highlighted Thr532 as essential for catalytic activity, and coevolutionary analysis indicated synergistic effects between Phe456 and Thr532. Site-directed mutagenesis was conducted to generate single (F456M, T532Y) and double (F456M/T532Y, designated M2) mutants. The double mutant M2 showed a 3.62-fold increase in patchoulene production compared to the wild-type enzyme. In silico analyses suggested that the enhanced performance of M2 originates from improved substrate positioning, reduced structural flexibility, and strengthened molecular interactions, collectively contributing to a lower energy barrier for catalysis. This study provides an effective strategy for the rapid optimization of terpenoid synthases and facilitates the development of microbial cell factories for sustainable and high-yield production of plant-derived terpenoids. Full article

The environmental protection expenditure of enterprises is an important investment in the process of developing green innovation. Although a large number of researchers have evaluated the factors promoting green innovation, few have studied the impact of environmental protection expenditure of enterprises on green innovation, and signal transmission is an important mechanism. This paper selects the data of China’s Shanghai and Shenzhen A-share listed companies from 2012 to 2023, obtains the data of enterprise environmental protection investment through a manual collection and keyword screening method, and analyzes the micro impact of enterprise environmental protection investment on green innovation output by using a two-way fixed corresponding method. The empirical results show that the environmental protection expenditure of enterprises will significantly promote green innovation. At the same time, the green innovation effect of enterprise environmental protection expenditure is also different due to enterprise scale, financing constraints, and life cycle. At the same time, this paper verifies the signal transmission mechanism of enterprises’ environmental protection expenditure, that is, enterprises’ investment in environmental protection which will release positive signals to the outside so as to strive for more social resources. Further empirical analysis verifies the promotion effect of enterprise environmental protection expenditure on performance. This paper asserts that enterprises should play a positive role in environmental protection investment, and government departments should increase support for enterprises. Full article

In order to further explore the role of infill walls in the progressive collapse resistance of reinforced concrete (RC) spatial frames, based on ANSYS/LS-DYNA finite element analysis software, the refined numerical models of pure RC spatial frames and infilled RC spatial frames were constructed, respectively. By comparing it with the experimental results, the validity and accuracy of the model are verified. Subsequently, the effects of column removal devices and infill wall openings on the progressive collapse resistance of RC spatial frames were studied. The results show that the residual displacement of the model with a complete column removal device is 238.1% higher than that of the model with an incomplete column removal device, and the stiffness is reduced by 68.8%. The results obtained by an incomplete column removal device are often unsafe. The open-hole infill wall will form a diagonal strut in the corresponding area. The strength of the strut near the fixed end has the most significant effect on the structural stiffness after the column is removed and plays a controlling role. The reduction in the effective area of the strut reduces the strength of the strut and weakens the structural stiffness. When the opening is arranged in the mid-span position, the structural stiffness decreases more significantly. Full article

Spatial Allocation Problems (SAP) in multistory buildings present significant challenges, as they require the simultaneous satisfaction of complex geometric constraints and conflicting functional requirements. To address this problem, this study proposes an integrated pipeline utilizing Generative Design (GD) methodologies within a Building Information Modeling (BIM) environment to automate and optimize a 3.3 m modular multi-story spatial allocation. The core of the proposed methodology lies in the clear distinction and application of design requirements formalized as ‘Hard Constraints’ (mandatory conditions for feasibility) and ’Soft Objectives’ (metrics for performance evaluation). Hard constraints include the implementation of a boundary constraint, ensuring that all spaces remain within defined limits, and a vertical alignment constraint for fixed elements (e.g., cores), thereby ensuring geometric and structural validity. To quantify functional efficiency, three soft objectives were defined: positional preference, circulation efficiency, and functional cohesion. The methodology was validated using a four-story case study. The implemented system successfully generated numerous valid design alternatives that satisfied all hard constraints while simultaneously optimizing the three soft objectives. Aimed at architects, building designers, and computational specialists, this study offers significant practical value by providing a tool that automates the complex initial phases of space planning. This allows designers to rapidly explore and quantitatively evaluate a wide array of optimized and valid layouts. This study contributes to a systematic framework for balancing geometric validity and functional efficiency during the early design stages by presenting a concrete procedure for integrating GD and BIM to solve complex SAPs. Full article

We (Public Interest Group on Cancer Research) started a podcast and guest blog series on cancer in 2024. Our objective in this Commentary is to describe our experience with this series, insights gained, adjustments made to our approach, and our recommendations for future series. Our group identified and invited guests to contribute a blog or podcast episode on cancer, lived experience of cancer, cancer care and research, or advocacy. The podcast episodes were recorded using the WebEx platform (version 45.9.0.33069) and edited using the Kdenlive software (version 23.08.4). The blogs and podcasts were edited, finalized, and posted online for public access. In this manuscript, we utilized descriptive statistics to define and summarize information about the podcast episodes, guest blogs, and categorical responses to guest feedback survey questions, while we presented the responses to open-ended survey questions as quotes and summaries. As a result, during the period of January 2024–July 2025, we aired 28 podcast episodes and 13 guest blogs involving 36 guests. Guests included people from various backgrounds (such as people with lived experience, advocates, scientists, and healthcare providers) and members of equity-deserving communities (such as women, Indigenous and 2SLGBTQIA+ communities). We contemplated and learned as we proceeded with this project and implemented changes to address the issues that arose. In most cases the guests had positive experiences; however, in rare cases, university practices or federal policies prevented guest compensation, creating an unusual barrier. In conclusion, podcasting and blogging are practical public engagement instruments that provide space for sharing messages and knowledge to communicate with members of the public. Systematic barriers, such as policies that hamper guest compensation, need to be fixed for equitable participation, compensation, and engagement. As there is an increased interest in public engagement and knowledge mobilization activities, our learnings shared in this commentary may help other groups initiate or improve their public engagement practices. Full article

We developed a self-consistent double-null description of an evaporating white-hole spacetime by embedding the outgoing Vaidya solution in a coordinate system that remains regular across the future horizon. Starting from the radiation-coordinate form, we specialize in retarded time so that a monotonically decreasing mass function $M\left(u\right)$ encodes outgoing positive-energy flux. Expressing the metric in null coordinates $(u,v)$, Einstein’s equations for a single-directional null-dust stress–energy tensor, $T_{uu}=\rho \left(u\right)$, then reduce to one first-order PDE for the areal radius: ${\mathsf{\partial }}_{v}r=B\left(u\right)\left(1-2M\left(u\right)/r\right)$. Its integral, $r+2M\left(u\right)ln|r-2M(u\left)\right|=v-C\left(u\right)$, defines an implicit foliation of outgoing null cones. The metric coefficient follows algebraically as $f(u,v)=1-2M\left(u\right)/r$. Residual gauge freedom in $B\left(u\right)$ and $C\left(u\right)$ is fixed so that u matches the Bondi retarded time at null infinity, while v remains analytic at the apparent horizon, generalizing the Kruskal prescription to dynamical mass loss. In the limit $M\left(u\right)\to M$, the construction reduces to the familiar Eddington–Finkelstein and Kruskal forms. Our solution, therefore, provides a compact analytic framework for studying white-hole evaporation, Hawking-like energy fluxes, and back-reaction in spherically symmetric settings without encountering coordinate singularities. Full article

Digital literacy (DL) among farmers serves as a vital link between digital technology and green sustainable development, significantly enhancing agricultural green total factor productivity (AGTFP). This study employs panel data from the China Family Panel Studies (CFPS) covering 2014–2020, applying a two-way fixed effects model and machine learning techniques to examine the influence of farmers’ digital literacy on AGTFP. The results indicate that DL positively contributes to AGTFP. Further heterogeneity analysis shows stronger effects among male farmers, households with low trust, and those within the working-age population. Mechanism analysis indicates that social capital accumulation mediates the relationship, whereas agricultural socialization services strengthen the positive impact of DL on AGTFP. Additional analysis using machine learning models reveals that the impact of farmers’ digital literacy on AGTFP changes over time. Specifically, entertainment and learning-oriented network use enhances AGTFP, whereas work-related, social, and lifestyle-related use suppresses it. This study offers a more nuanced understanding by shifting from traditional macro-level frameworks to a micro-level perspective focused on farmers’ digital literacy. Moreover, the innovative application of explainable machine learning provides empirical evidence for the underlying drivers of AGTFP. Full article

Underwater Optical Camera Communication (UOCC) has emerged as a promising paradigm for short-range, high-bandwidth, and secure data exchange in autonomous underwater vehicles (AUVs). UOCC performance strongly depends on exposure time and International Standards Organization (ISO) sensitivity—two parameters that govern photon capture, contrast, and bit detection fidelity. However, optical propagation in aquatic environments is highly susceptible to turbidity, scattering, and illumination variability, which severely degrade image clarity and signal-to-noise ratio (SNR). Conventional systems with fixed imaging settings cannot adapt to time-varying conditions, limiting communication reliability. While validating the feasibility of deep learning for exposure prediction, this baseline lacked environmental awareness and generalization to dynamic scenarios. To overcome these limitations, we introduce a Real-to-Sim-to-Deployment framework that couples a physically calibrated emulation platform with a Hybrid CNN-MLP Model (HCMM). By fusing optical images, environmental states, and camera configurations, the HCMM achieves substantially improved parameter prediction accuracy, reducing RMSE to 0.23–0.33. When deployed on embedded hardware, it enables real-time adaptive reconfiguration and delivers up to 8.5 dB SNR gain, surpassing both static-parameter systems and the prior CNN baseline. These results demonstrate that environment-aware multimodal learning, supported by reproducible optical channel emulation, provides a scalable and robust solution for practical UOCC deployment in positioning, inspection, and laser-based underwater communication. Full article

As offshore wind power moves into deeper waters, large-scale electrical platforms are key to efficient power transmission. However, their heavy topside modules create major installation challenges. As traditional lifting methods are inadequate, the float-over method has become a viable solution for installing topside modules, but it is essential to study the structural responses to collisions during the process to ensure construction and equipment safety. This study establishes a physical model of the offshore converter station at a 1:65 scale based on the elastic force-gravity similarity principle. Assuming the barge carrying the topside module descends at a constant speed, the study investigates the dynamic response of the platform during the float-over mating process. Float-over collision tests are conducted to obtain the platform’s acceleration, strain, and displacement responses and to analyze the effects of collision speed, offset position, and Leg Mating Unit (LMU) stiffness on the dynamic structural response characteristics. The results show that as collision speed increases from 10 mm/s to 50 mm/s, the topside acceleration response increases up to 5.7 times. Beam strain remains mostly unchanged, and displacement increases first, then decreases. Under fixed descent velocity, x-offset increases jacket strain and converter valve acceleration, while y-offset raises platform acceleration and reduces valve acceleration by approximately 20 percent. At 50 mm/s, higher LMU stiffness causes the acceleration response to first drop, then rise. These findings support safe float-over installation. Full article

To address the problem of the degraded positioning accuracy of the long-baseline undifferenced network RTK (URTK) under extreme space weather conditions, herein, we propose a user-side atmospheric delay estimation strategy based on the undifferenced network RTK concept to enhance positioning performance in geomagnetic storm environments. First, an ambiguity-resolution model that jointly estimates atmospheric error parameters is used to fix the carrier-phase integer ambiguities for long-baseline reference stations. The accurately fixed inter-station ambiguities are then linearly transformed to recover station-specific undifferenced integer ambiguities; undifferenced observation errors at each reference station are computed to generate corresponding undifferenced correction terms. Lastly, recognizing that ionospheric delays vary sharply during geomagnetic storms and can severely compromise the availability of regional undifferenced correction models, we estimate the residual atmospheric parameters on the user side after applying regional corrections. Experimental results show that the server side is not significantly impacted during geomagnetic storms and can continue operating normally. On the user side, augmenting the solution with atmospheric parameter estimation effectively improves positioning availability. Under strong geomagnetic storms, the proposed mode improves user-station positioning accuracy by 63.7%, 60.7%, and 64.4% in the east (E), north (N), and up (U) components, respectively, relative to the conventional user-side solution; under moderate storm conditions, the corresponding improvements are 16.7%, 10.0%, and 11.1%. Full article

Each year, hundreds of female koalas are presented to koala hospitals suffering from a range of morbidities, many of which require euthanasia for animal welfare reasons. These koalas represent a possible resource for genetic recovery by means of oocyte retrieval for genome banking or use in assisted reproductive technology. To examine the feasibility of koala oocyte recovery, this study conducted a preliminary survey of follicular activity and disease presence in fixed ovarian tissues from koala cadavers in South East Queensland. Ovarian activity and pathology were assessed by gross examination and histology. Bursal pathology was categorized into koalas with no, small (<10 mm diameter), moderate (10–20 mm diameter), or large (>20 mm diameter) sized bursae, whereas uterine pathology was diagnosed by an experienced reproductive pathologist. Antral follicles were observed in 94.4% of ovaries recovered from koalas with no bursal or uterine pathology (n = 18/44), 95.2% of the ovaries of koalas with bursal but no uterine pathology (n = 11/44), 100% of the ovaries of koalas showing only uterine pathology (n = 4/4) and 89.5% of ovaries from koalas with both bursal and uterine pathology (n = 11/44). Of the fixed ovarian tissue suitable for PCR Chlamydia detection (35/44), none were positive. As proof of concept, oocytes were also collected and evaluated from six koala cadavers within 2 h post-mortem. Although further studies are required to determine the quality and viability of the retrieved koala oocytes, our preliminary survey provides strong evidence that ovarian activity mostly continues unabated, irrespective of reproductive pathology, and that oocytes can be recovered successfully. Full article

Transformer-based models have accelerated EEG motor imagery (MI) decoding by using self-attention to capture long-range temporal structures while complementing spatial inductive biases. This systematic survey of Scopus-indexed works from 2020 to 2025 indicates that reported advances are concentrated in offline, protocol-heterogeneous settings; inconsistent preprocessing, non-standard data splits, and sparse efficiency frequently reporting cloud claims of generalization and real-time suitability. Under session- and subject-aware evaluation on the BCIC IV 2a/2b dataset, typical performance clusters are in the high-80% range for binary MI and the mid-70% range for multi-class tasks with gains of roughly 5–10 percentage points achieved by strong hybrids (CNN/TCN–Transformer; hierarchical attention) rather than by extreme figures often driven by leakage-prone protocols. In parallel, transformer-driven denoising—particularly diffusion–transformer hybrids—yields strong signal-level metrics but remains weakly linked to task benefit; denoise → decode validation is rarely standardized despite being the most relevant proxy when artifact-free ground truth is unavailable. Three priorities emerge for translation: protocol discipline (fixed train/test partitions, transparent preprocessing, mandatory reporting of parameters, FLOPs, per-trial latency, and acquisition-to-feedback delay); task relevance (shared denoise → decode benchmarks for MI and related paradigms); and adaptivity at scale (self-supervised pretraining on heterogeneous EEG corpora and resource-aware co-optimization of preprocessing and hybrid transformer topologies). Evidence from subject-adjusting evolutionary pipelines that jointly tune preprocessing, attention depth, and CNN–Transformer fusion demonstrates reproducible inter-subject gains over established baselines under controlled protocols. Implementing these practices positions transformer-driven BCIs to move beyond inflated offline estimates toward reliable, real-time neurointerfaces with concrete clinical and assistive relevance. Full article