Search Results (1,382)

Accurate semantic segmentation of indoor architectural elements, such as staircases and elevators, is critical for safe and efficient robotic navigation, particularly in complex multi-floor environments. Traditional fusion methods struggle with occlusions, reflections, and low-contrast regions. In this paper, we propose a novel feature fusion module, Skip-Connection Fusion (SCF), that dynamically integrates RGB (Red, Green, Blue) and depth features through an adaptive weighting mechanism and skip-connection integration. This approach enables the model to selectively emphasize informative regions while suppressing noise, effectively addressing challenging conditions such as partially blocked staircases, glossy elevator doors, and dimly lit stair edges, which improves obstacle detection and supports reliable human–robot interaction in complex environments. Extensive experiments on a newly collected dataset demonstrate that SCF consistently outperforms state-of-the-art methods, including PSPNet and DeepLabv3, in both overall mIoU (mean Intersection over Union) and challenging-case performance. Specifically, our SCF module improves segmentation accuracy by 5.23% in the top 10% of challenging samples, highlighting its robustness in real-world conditions. Furthermore, we conduct a sensitivity analysis on the learnable weights, demonstrating their impact on segmentation quality across varying scene complexities. Our work provides a strong foundation for real-world applications in autonomous navigation, assistive robotics, and smart surveillance. Full article

Bilberry (Vaccinium myrtillus L.) fruits represent the recognized wellspring of bioactive compounds with various documented bioactivities. Although bilberry leaves are often treated as industrial by-products, they also represent a valuable source of phytochemicals with potential dermocosmetic applications. In this study, extracts of bilberry fruits and leaves were prepared using both conventional solvents (water and 50% ethanol) and natural deep eutectic solvents (NaDES) as green, biodegradable alternatives. The aim of this study was to examine the UV protective activity and inhibitory potential of those extracts against some enzymes (tyrosinase, hyaluronidase, collagenase) that are important in terms of skin conditioning and skin aging. The results of in vitro tests have shown the superiority of NaDES extracts compared to conventional extracts regarding all tested bioactivities. In addition, bilberry leaves extracts were more potent compared to fruit extracts in all cases. The most potent extract was bilberry leaf extract made with malic acid–glycerol, which exhibited strong anti-tyrosinase (IC₅₀ = 3.52 ± 0.26 mg/mL), anti-hyaluronidase (IC₅₀ = 3.23 ± 0.30 mg/mL), and anti-collagenase (IC₅₀ = 1.84 ± 0.50 mg/mL) activities. The correlation analysis revealed correlation between UV protective and anti-tyrosinase, UV protective and anti-collagenase as well as between anti-hyaluronidase and anti-collagenase activity. UV protection and anti-tyrosinase activity correlated significantly with chlorogenic acid and hyperoside contents in extracts. The extracts with the best activities also demonstrated a good safety profile in a 24 h in vivo study on human volunteers. Full article

The ongoing degradation of global forests has severely weakened ecosystem service functions, and traditional judicial remedies have struggled to quantify intangible ecological losses. China has become an important testing ground for restorative justice through the establishment of specialized environmental courts and the practice of environmental public interest litigation. Since 2015, China has actively explored and institutionalized the application of the concept of restorative justice in its environmental justice reform. This concept emphasizes compensating environmental damages through actual ecological restoration acts rather than relying solely on financial compensation. This shift reflects a deep understanding of the limitations of traditional environmental justice and an institutional response to China’s ecological civilization construction, providing critical support for forest ecosystem restoration and enabling ecological restoration activities, such as replanting and re-greening, habitat reconstruction, etc., to be enforced through judicial decisions. This study conducts a qualitative analysis of judicial rulings in forest restoration cases to systematically evaluate the effectiveness of restorative justice in compensating for losses in forest ecosystem service functions. The findings reveal the following: (1) restoration measures in judicial practice are disconnected from the types of ecosystem services available; (2) non-market values and long-term cumulative damages are systematically underestimated, with monitoring mechanisms exhibiting fragmented implementation and insufficient effectiveness; (3) management cycles are set in violation of ecological restoration principles, and acceptance standards lack function-oriented indicators; (4) participation of key stakeholders is severely lacking, and local knowledge and professional expertise have not been integrated. In response, this study proposes a restorative judicial framework oriented toward forest ecosystem services, utilizing four mechanisms: independent recognition of legal interests, function-matched restoration, application of scientific assessment tools, and multi-stakeholder collaboration. This framework aims to drive a paradigm shift from formal restoration to substantive functional recovery, providing theoretical support and practical pathways for environmental judicial reform and global forest governance. Full article

Currently, various pruning strategies including different methods and distribution types are commonly used to reduce the number of FLOPs and parameters in deep learning models. However, their impact on actual energy savings remains insufficiently studied, particularly in resource-constrained settings. To address this, we introduce PruneEnergyAnalyzer, an open-source Python tool designed to evaluate the energy efficiency of pruned models. Starting from the unpruned model, the tool calculates the energy savings achieved by pruned versions provided by the user, and generates comparative visualizations based on previously applied pruning hyperparameters such as method, distribution type (PD), compression ratio (CR), and batch size. These visual outputs enable the identification of the most favorable pruning configurations in terms of FLOPs, parameter count, and energy consumption. As a demonstration, we evaluated the tool with 180 models generated from three architectures, five pruning distributions, three pruning methods, and four batch sizes, using another previous library (e.g. FlexiPrune). This experiment revealed the significant impact of the network architecture on Energy Reduction, the non-linearity between FLOPs savings and energy savings, as well as between parameter reduction and energy efficiency. It also showed that the batch size strongly influences the energy consumption of the pruned model. Therefore, this tool can support researchers in making pruning policy decisions that also take into account the energy efficiency of the pruned model. Full article

Electrochromic devices (ECDs) capable of modulating both visible color and infrared (IR) emissivity are promising for applications in smart thermal camouflage and multifunctional displays. However, conventional transmissive ECDs suffer from limited IR modulation due to the low IR transmittance of transparent electrodes. Here, we report a reflection-type electrochromic zinc-ion battery (HWEC-ZIB) using a self-doped polyaniline nanorod film (SP(ANI-MA)) as the active layer. By positioning the active material at the device surface, this structure avoids interference from transparent electrodes and enables broadband and efficient IR emissivity tuning. To prevent electrolyte-induced IR absorption, a thermal lamination encapsulation method is employed. The optimized device achieves emissivity modulation ranges of 0.28 (3–5 μm) and 0.19 (8–14 μm), delivering excellent thermal camouflage performance. It also exhibits a visible color change from earthy yellow to deep green, suitable for various natural environments. In addition, the HWEC-ZIB shows a high areal capacity of 72.15 mAh cm⁻² at 0.1 mA cm⁻² and maintains 80% capacity after 5000 cycles, demonstrating outstanding electrochemical stability. This work offers a versatile device platform integrating IR stealth, visual camouflage, and energy storage, providing a promising solution for next-generation adaptive camouflage and defense-oriented electronics. Full article

Tocopherols, major lipid-soluble components of vitamin E, are essential natural products with significant nutritional and pharmacological value. Their structural diversity and uneven distribution across vegetable oils require accurate analytical strategies for compositional profiling, quality control, and authenticity verification, amid concerns over food fraud and regulatory demands. Analytical challenges, such as matrix effects in complex oils and the cost trade-offs of green extraction methods, complicate these processes. This review examines recent advances in tocopherol analysis, focusing on extraction and detection techniques. Green methods like supercritical fluid extraction and deep eutectic solvents offer selectivity and sustainability, though they are costlier than traditional approaches. On the analytical side, hyphenated techniques such as supercritical fluid chromatography-mass spectrometry (SFC-MS) achieve detection limits as low as 0.05 ng/mL, improving sensitivity in complex matrices. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) provides robust analysis, while spectroscopic and electrochemical sensors offer rapid, cost-effective alternatives for high-throughput screening. The integration of chemometric tools and miniaturized systems supports scalable workflows. Looking ahead, the incorporation of Artificial Intelligence (AI) in oil authentication has the potential to enhance the accuracy and efficiency of future analyses. These innovations could improve our understanding of tocopherol compositions in vegetable oils, supporting more reliable assessments of nutritional value and product authenticity. Full article

Urban greening initiatives, including the incorporation of street trees, have been widely recognized for a variety of environmental benefits. However, their economic impact on retail, in particular, the impact of street trees on the visibility of signs, has been underexplored. Street trees can obscure retail signs, potentially reducing customer engagement and discouraging retailers from paying higher rents for such locations. This paper investigates how the blocking of retail signage by street trees affects monthly rent in developed commercial districts in Seoul. It identifies, through Google Street View and state-of-the-art deep-learning-based semantic segmentation methods, environmental elements such as street trees, sidewalks, and buildings; quantifies their proportions; and analyzes their impact on rent using OLS regression, controlling for socio-economic variables. The results reveal that rents significantly diminish when street trees blocking views of retail signs increase. Our findings require more nuanced consideration by planners and policymakers in balancing both environmental and economic demands toward sustainable street design and planning. Full article

Hydrogen energy is a pivotal carrier for achieving carbon neutrality, requiring green and efficient production via water electrolysis. However, the anodic oxygen evolution reaction (OER) involves a sluggish four-electron transfer process, resulting in high overpotentials, while the prohibitive cost and complex preparation of precious metal catalysts impede large-scale commercialization. In this study, we develop a FeCo-based bimetallic deep eutectic solvent (FeCo-DES) as a multifunctional reaction medium for engineering a three-dimensional (3D) coral-like FeOOH-Co₂(OH)₃Cl/NF composite via a mild one-step impregnation approach (70 °C, ambient pressure). The FeCo-DES simultaneously serves as the solvent, metal source, and redox agent, driving the controlled in situ assembly of FeOOH-Co₂(OH)₃Cl hybrids on Ni(OH)₂/NiOOH-coated nickel foam (NF). This hierarchical architecture induces synergistic enhancement through geometric structural effects combined with multi-component electronic interactions. Consequently, the FeOOH-Co₂(OH)₃Cl/NF catalyst achieves a remarkably low overpotential of 197 mV at 100 mA cm⁻² and a Tafel slope of 65.9 mV dec⁻¹, along with 98% current retention over 24 h chronopotentiometry. This study pioneers a DES-mediated strategy for designing robust composite catalysts, establishing a scalable blueprint for high-performance and low-cost OER systems. Full article

The equivalent source method (ESM) is a core algorithm in integrated radiation-propagation acoustic field modeling. However, in challenging marine environments, including deep-sea and polar regions, where sound speed profiles exhibit strong vertical gradients, the ESM must increase waveguide stratification to maintain accuracy. This causes computational costs to scale exponentially with the number of layers, compromising efficiency and limiting applicability. To address this, this paper proposes a modal equivalent source (MES) model employing normal modes as basis functions instead of free-field Green’s functions. This model constructs a set of normal mode bases using full-depth hydroacoustic parameters, incorporating water column characteristics into the basis functions to eliminate waveguide stratification. This significantly reduces the computational matrix size of the ESM and computes acoustic fields in range-dependent waveguides using a single set of normal modes, resolving the dual limitations of inadequate precision and low efficiency in such environments. Concurrently, for the construction of basis functions, this paper also proposes a fast computation method for eigenvalues and eigenmodes in waveguide contexts based on phase functions and difference equations. Furthermore, coupling the MES method with the Finite Element Method (FEM) enables integrated computation of underwater target radiation and propagation fields. Multiple simulations demonstrate close agreement between the proposed model and reference results (errors < 4 dB). Under equivalent accuracy requirements, the proposed model reduces computation time to less than 1/25 of traditional ESM, achieving significant efficiency gains. Additionally, sea trial verification confirms model effectiveness, with mean correlation coefficients exceeding 0.9 and mean errors below 5 dB against experimental data. Full article

Moringa oleifera (MO), a nutritionally and pharmacologically potent species, is emerging as a sustainable candidate for applications across bioenergy, agriculture, textiles, pharmaceuticals, and biomedicine. This review explores recent advances in MO-based biotechnologies, highlighting novel extraction methods, green nanotechnology, and clinical trial findings. Although MO’s resilience offers promise for climate-smart agriculture and public health, challenges remain in standardizing cultivation and verifying therapeutic claims. This work underscores MO’s translational potential and the need for integrative, interdisciplinary research. MO is used in advanced materials, like electrospun fibers and biopolymers, showing filtration, antibacterial, anti-inflammatory, and antioxidant properties—important for the biomedical industry and environmental remediation. In textiles, it serves as an eco-friendly alternative for wastewater treatment and yarn sizing. Biotechnological advancements, such as genome sequencing and in vitro culture, enhance traits and metabolite production. MO supports green biotechnology through sustainable agriculture, nanomaterials, and biocomposites. MO shows potential for disease management, immune support, metabolic health, and dental care, but requires further clinical trials for validation. Its resilience is suitable for land restoration and food security in arid areas. AI and deep learning enhance Moringa breeding, allowing for faster, cost-effective development of improved varieties. MO’s diverse applications establish it as a key element for sustainable development in arid regions. Full article

Urban expansion will squeeze the green space system and cause ecological fragmentation. The question of how to expand cities more scientifically and build eco-cities has become an important topic of sustainable urban construction. This paper takes Shanghai as a research case. A deep neural network combined with an attention mechanism model measures the comprehensive level of the built environment and green space pattern of urbanization and quantitatively analyzes the coordinated relationship between the two using the coupled degree of coordination model. Subsequently, the K-Means clustering model was used for spatial clustering to determine the governance and construction directions for different spatial areas and was, finally, combined with the LightGBM model plus SHAP to analyze the importance and threshold effect of the indicators on the degree of coupled coordination. The results of the study show that (1) the core area of the city shows a high state of coordination, indicating that Shanghai has a better green space construction in the central city, but the periphery shows different imbalances; (2) three different kinds of areas are identified, and different governance measures as well as the direction of urbanization are proposed according to the characteristics of the different areas; and (3) this study finds that the structural indicators of the built environment, such as Average Compactness, Weighted Average Height, and Land Use Diversity, have a significant influence on the coupling coordination degree and have different response thresholds. The results of the study provide theoretical support for regional governance and suggestions for the direction of urban expansion for sustainable urbanization. Full article

This study provides a thorough examination of the utilization of hydrophobic deep eutectic solvents (HDESs) for the extraction of 1,2-dichloroethane (1,2-DCA) from effluent, with an emphasis on a sustainable and environmentally friendly approach. The extraction efficacy of six HDES systems was initially evaluated, and the combinations of thymol/camphor (Thy/Cam) and menthol/thymol (Men/Thy) exhibited superior performance. Subsequently, these two HDESs were chosen for a comprehensive parametric analysis. The impact of contact time demonstrated that extraction equilibrium was reached at 15 min for both systems, thereby achieving a balance between high efficiency and time efficiency. Next, the impact of the HDES-to-water mass ratio was investigated. A 1:1 ratio was determined to be the most effective, as it minimized solvent consumption and provided high efficiency. An additional examination of the molar ratios of the HDES components revealed that the 1:1 ratio exhibited the most effective extraction performance. This was due to the fact that imbalances in the solvent mixture resulted in diminished efficiency as a result of disrupted molecular interactions. The extraction efficiency was significantly influenced by the initial concentration of 1,2-DCA, with higher concentrations resulting in superior results as a result of the increased mass transfer driving forces. In general, the Men/Thy and Thy/Cam systems have shown noteworthy stability and efficiency under different conditions, which makes them highly suitable for large-scale applications. Full article

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article

In this study, the ultrasonic-assisted extraction of deep eutectic solvents (UADES) for tea polysaccharides was optimized, and their physicochemical properties and antioxidant activities were analyzed. The optimal DES comprised choline chloride (CC) and ethylene glycol (EG) in a molar ratio of 1:3, with a water content of 40%. The optimized condition was an extraction temperature of 61 °C, an ultrasonic power of 480 W, and an extraction time of 60 min. The UADES extraction rate of polysaccharides (ERP) was 15.89 ± 0.13%, significantly exceeding that of hot water (HW) extraction. The polysaccharide content in the UADES-extracted tea polysaccharides (UADESTPs) was comparable to that of hot-water-extracted tea polysaccharides (HWTPs) (75.47 ± 1.35% vs. 74.08 ± 2.51%); the UADESTPs contained more uronic acid (8.35 ± 0.26%) and less protein (12.91%) than HWTP. Most of the UADESTPs (88.87%) had molecular weights (Mw) below 1.80 × 10³ Da. The UADESTPs contained trehalose, glucuronic acid, galactose, xylose, and glucose, with molar ratios of 8:16:1:10. The free radical scavenging rate and total reducing power of the UADESTPs were markedly superior to those of the HWTPs. Moreover, the UADESTPs had a better alleviating effect on H₂O₂-induced oxidative injury in HepG2 cells. This study develops an eco-friendly and efficient extraction method for tea polysaccharides, offering new insights for the development of tea polysaccharides. Full article

Given the importance of turbidity as a key indicator of water quality, this study investigates the use of a convolutional neural network (CNN) to classify water samples into five turbidity-based categories. These classes were defined using ranges inspired by Mexican environmental regulations and generated from 33 laboratory-prepared mixtures with varying concentrations of suspended clay particles. Red, green, and blue (RGB) images of each sample were captured under controlled optical conditions, and turbidity was measured using a calibrated turbidimeter. A transfer learning (TL) approach was applied using EfficientNet-B0, a deep yet computationally efficient CNN architecture. The model achieved an average accuracy of 99% across ten independent training runs, with minimal misclassifications. The use of a lightweight deep learning model, combined with a standardized image acquisition protocol, represents a novel and scalable alternative for rapid, low-cost water quality assessment in future environmental monitoring systems. Full article