Search Results (27,424)

Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and invasive. While vision-based systems offer a more accessible alternative, they have not been extensively explored for eye-writing recognition. Additionally, the natural instability of eye movements and variations in writing styles result in inconsistent signal lengths, which reduces recognition accuracy and limits the practical use of eye-writing systems. To address these challenges, we propose a novel vision-based eye-writing recognition approach that utilizes a webcam-captured dataset. A key contribution of our approach is the introduction of a Discrete Fourier Transform (DFT)-based length normalization method that standardizes the length of each eye-writing sample while preserving essential spectral characteristics. This ensures uniformity in input lengths and improves both efficiency and robustness. Moreover, we integrate a hybrid deep learning model that combines 1D Convolutional Neural Networks (CNN) and Temporal Convolutional Networks (TCN) to jointly capture spatial and temporal features of eye-writing. To further improve model robustness, we incorporate data augmentation and initial-point normalization techniques. The proposed system was evaluated using our new webcam-captured Arabic numbers dataset and two existing benchmark datasets, with leave-one-subject-out (LOSO) cross-validation. The model achieved accuracies of 97.68% on the new dataset, 94.48% on the Japanese Katakana dataset, and 98.70% on the EOG-captured Arabic numbers dataset—outperforming existing systems. This work provides an efficient eye-writing recognition system, featuring robust preprocessing techniques, a hybrid deep learning model, and a new webcam-captured dataset. Full article

Background: Cancer’s multifactorial complexity demands innovative polypharmacological strategies that can simultaneously target multiple oncogenic pathways. Natural products, with their inherent chemical diversity, offer promising multi-target therapeutic potential. This study comprehensively investigates the anticancer mechanisms of Tunisian Myrtus communis essential oils (McEOs) using an integrated computational-experimental framework to elucidate their polypharmacological basis and therapeutic potential. Methods: McEO composition was characterized via GC-MS analysis. Antiproliferative activity was evaluated against HeLa (cervical), MCF-7 (breast), and Raji (lymphoma) cancer cell lines using MTT assays. A multi-scale computational pipeline integrated network pharmacology, molecular docking against eight key oncoproteins, and 100 ns all-atom molecular dynamics simulations to elucidate molecular mechanisms and target interactions. Results: GC-MS revealed a 1,8-cineole-rich chemotype (38.94%) containing significant sesquiterpenes. McEO demonstrated potent differential cytotoxicity: HeLa (IC₅₀ = 8.12 μg/mL) > MCF-7 (IC₅₀ = 19.59 μg/mL) > Raji cells (IC₅₀ = 27.32 μg/mL). Network pharmacology quantitatively explained this differential sensitivity through target overlap analysis, showing higher associations with breast (23%) and cervical (18.3%) versus lymphoma (5.5%) cancer pathways. Molecular docking identified spathulenol as a high-affinity Androgen Receptor (AR) antagonist (XP GScore: −9.650 kcal/mol). Molecular dynamics simulations confirmed exceptional spathulenol-AR complex stability, maintaining critical hydrogen bonding with Asn705 for 96% of simulation time. Conclusions: McEO exerts sophisticated multi-target anticancer effects through synergistic constituent interactions, notably spathulenol’s potent AR antagonism. This integrated computational-experimental approach validates McEO’s polypharmacological basis and supports its therapeutic potential, particularly for hormone-dependent malignancies, while establishing a robust framework for natural product bioactivity deconvolution. Full article

In plain river networks, a sluggish flow due to the flat terrain and hydraulic structures significantly reduces water’s capacity for self-purification, leading to persistent water pollution that threatens aquatic ecosystems and human health. Despite being critical, effective water quality prediction proves challenging in such regions, with current models lacking either physical interpretability or temporal accuracy. To address this gap, both a process-based model (MIKE 21) and a deep learning model (CNN-LSTM-Attention) were developed in this study to predict key water quality indicators—dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP)—in a typical river network area in Jiaxing, China. This site was selected for its representative complexity and acute pollution challenges. The MIKE 21 model demonstrated strong performance, with R² values above 0.88 for all indicators, offering high spatial resolution and mechanistic insight. The CNN-LSTM-Attention model excelled in capturing temporal dynamics, achieving an R² of 0.9934 for DO. The results indicate the complementary nature of these two approaches: while MIKE 21 supports scenario-based planning, the deep learning model enables highly accurate real-time forecasting. The findings are transferable to similar river network systems, providing a robust reference for selecting modeling frameworks in the design of water pollution control strategies. Full article

Food spoilage and contamination remain pressing global challenges, undermining food security and safety while driving economic losses. Conventional preservation strategies, including thermal treatments, refrigeration, and synthetic additives, often compromise nutritional quality and raise sustainability concerns, thereby necessitating natural, effective alternatives. Curcumin, a polyphenolic compound derived from Curcuma longa, has demonstrated broad-spectrum antimicrobial, antioxidant, and anti-inflammatory activities, making it a promising candidate for food preservation. However, its poor solubility, instability, and low bioavailability limit direct applications in food systems. Advances in nanotechnology have enabled the development of nanoformulated curcumin, enhancing solubility, stability, controlled release, and functional efficacy. This review examines the antimicrobial mechanisms of curcumin and its nanoformulations, including membrane disruption, oxidative stress via reactive oxygen species, quorum sensing inhibition, and biofilm suppression. Applications in active and smart packaging are highlighted, where curcumin nanoformulation not only extends shelf life but also enables freshness monitoring through pH-responsive color changes. Evidence across meats, seafood, fruits, dairy, and beverages shows improved microbial safety, oxidative stability, and sensory quality. Multifunctional systems, such as hybrid composites and stimuli-responsive carriers, represent next-generation tools for sustainable packaging. However, challenges remain with scale-up, migration safety, cytotoxicity, and potential promotion of antimicrobial resistance gene (ARG) transfer. Future research should focus on safety validation, advanced nanocarriers, ARG-aware strategies, and regulatory frameworks. Overall, nanoformulated curcumin offers a natural, versatile, and eco-friendly approach to food preservation that aligns with clean-label consumer demand. Full article

The construction sector drives nearly half of global material extraction, energy use, emissions, and waste, yet environmental impact assessment (EIA) remains a static document, fragmented and disconnected from dynamic ecological systems. Here, we propose an upgrade to a five-dimensional (5D) EIA framework that integrates space-time analysis (3D + time = 4D) with real-time monitoring and impact quantification (5D) to account for environmental footprint and prevent irreversible impacts. The methodology included an analysis of over 100 EIA permits and reports, supplemented by interviews, reviews of technologies and process and systems analysis. Central to this approach is the inclusion of 4D building information models (BIM) and nature’s self-cleansing capacity, which is often overlooked in conventional assessments. The proposed Integrated Environmental Decision Support Information System (I-EDSIS) would enable continuous impact tracking, cumulative effect evaluation, and insights into patterns for adaptive mitigation. Drawing on a national-scale case study, we show that building permits correlate with NO_x and PM₁₀ (r = 0.96), while pollutant levels vary by up to 1.5–3 times across months and within a day, revealing potential for time-sensitive adaptive construction and less ecological disruption. This perspective argues for reframing EIA as a proactive tool for sustainability, transparency, active durability, cross-sectoral data integration, and resilience-based development. Full article

Lighting measurements and calculation is an old and widespread process, evolving with the variety of technologies that use light or operate efficiently depending on the natural or artificial light conditions in the ambient environment. The complexity of human activities gives rise to different techniques and approaches to lighting effect analysis, and this paper aims to clarify which type of units, photometric or radiometric, are appropriate, and which light measurement and calculation techniques are optimal for evaluating the environmental microclimate intended for an activity. Quantitative lighting analysis is common and accessible through the measuring devices, calculation formulas, and simulation software available. In contrast, qualitative analysis remains less prevalent, partly due to its complexity and the need to consider human perception as a central component in assessing lighting impact, as emphasized by the human-centric lighting paradigm. Current evaluation frameworks distinguish between the quantitative and qualitative approaches, with actinic calculations addressing biologically relevant aspects of lighting in specific environmental contexts. Full article

Gasification of residual biomass has emerged as an efficient thermochemical conversion process, applicable to a wide range of uses, such as electricity generation; chemical manufacturing; and the production of liquid biofuels, BioSNG (biomass-based synthetic natural gas), and hydrogen. Thus, gasification of biomass residues not only constitutes an important contribution toward decarbonizing the economy but also promotes the efficient utilization of renewable resources. Although a variety of gasification technologies are available, there are no clear guidelines for selecting the type of gasifier appropriate depending on the feedstock and the desired downstream products. Herein, we propose a gasifier classification model based on an extensive literature review, combined with a multi-criteria decision-making approach. A comprehensive and up-to-date literature review was conducted to gain a thorough understanding of the current state of knowledge in biomass gasification. The different features of the different types of gasifiers, in the context of biomass gasification, are presented and compared. The gasifiers were reviewed and evaluated considering criteria such as processing capacity, syngas quality, process performance, feedstock flexibility, operational and capital costs, environmental impact, and specific equipment features. A multi-criteria classification methodology was evaluated for assessing biomass gasifiers. A case study of such methodology was a applied to determine the best gasifiers for BioSNG inclusion in the natural gas distribution system in a small-scale scenario. Validation was conducted by comparing the matrix findings with commercially implemented gasification projects worldwide. Full article

Accurate spatial information of cropland is crucial for precision agricultural management and ensuring national food security. High-resolution remote sensing imagery combined with deep learning algorithms provides a promising approach for extracting detailed cropland information. However, due to the diverse morphological characteristics of croplands across different agricultural landscapes, existing deep learning methods encounter challenges in precise boundary localization. The advancement of large-scale vision models has led to the emergence of the Segment Anything Model (SAM), which has demonstrated remarkable performance on natural images and attracted considerable attention in the field of remote sensing image segmentation. However, when applied to high-resolution cropland extraction, SAM faces limitations in semantic expressiveness and cross-domain adaptability. To address these issues, this study proposes a dual-branch framework integrating SAM and a semantically aware network (SAM-SANet) for high-resolution cropland extraction. Specifically, a semantically aware branch based on a semantic segmentation network is applied to identify cropland areas, complemented by a boundary-constrained SAM branch that directs the model’s attention to boundary information and enhances cropland extraction performance. Additionally, a boundary-aware feature fusion module and a prompt generation and selection module are incorporated into the SAM branch for precise cropland boundary localization. The former aggregates multi-scale edge information to enhance boundary representation, while the latter generates prompts with high relevance to the boundary. To evaluate the effectiveness of the proposed approach, we construct three cropland datasets named GID-CD, JY-CD and QX-CD. Experimental results on these datasets demonstrated that SAM-SANet achieved mIoU scores of 87.58%, 91.17% and 71.39%, along with mF1 scores of 93.54%, 95.35% and 82.21%, respectively. Comparative experiments with mainstream semantic segmentation models further confirmed the superior performance of SAM-SANet in high-resolution cropland extraction. Full article

Utilization of solid waste as building material (USB) is a promising strategy that effectively addresses the challenges of safety and environmental pollution posed by solid waste and alleviates the scarcity of natural resources to facilitate the sustainable production of building materials. However, USB implementation and promotion have not yet matured in China because of various barriers. Therefore, this study employed the GT-DEMATEL-ISM-MACMIC model to identify the critical factors in USB implementation and examine the interactions and relationships among barriers to propose targeted recommendations. The results identified 33 barriers and revealed a distinct causal hierarchy. It was found that the macro-level barriers at the apex of the hierarchy, ‘incomplete policies and legislation’, ‘poor supervision and regulation of solid waste’, and ‘insufficient financial subsidies and incentives’, are critical barriers to USB implementation. A key outcome of this study is the identification of the most critical and obstinate barrier path evolution in USB implementation, where incomplete policies and regulations (P1, P2) lead to underdeveloped markets and capital (M6, E2), as well as low stakeholder motivation (S4), which in turn, exacerbates policy inertia and traps USB development in a state of deadlock. Conversely, detail-level barriers at the technical and managerial levels, such as ‘limited innovation in management models’ and ‘single type and limited application of renewable building material’, tend to be less influential than other barriers. Therefore, USB promotion can be achieved by strengthening policies and legislation, improving policy systems, and increasing financial subsidies. The results of this study will assist China and other developing countries in identifying critical barriers to USB implementation, offer practical approaches for promoting USB implementation, and provide methodological guidance for similar studies. Full article

As the scaling of silicon-based CMOS technology approaches its physical limits, two-dimensional (2D) materials have emerged as promising alternatives for future electronic devices. Among them, MoS₂ is a leading candidate due to its fascinating semiconducting nature and compatibility with CMOS processes. However, high contact resistance at the metal–MoS₂ interface remains a major bottleneck, limiting device performance. In this study, we report the fabrication and characterization of graphene–MoS₂ (Gr–MoS₂) lateral heterostructure FETs, where monolayer graphene, synthesized by inductively coupled plasma chemical vapor deposition (ICP-CVD), is directly used as the source and drain. Bilayer MoS₂ is selectively grown along graphene edges via edge-guided CVD, forming a chemically bonded in-plane junction without transfer steps. Electrical measurements reveal that the Gr–MoS₂ FETs exhibit a threefold increase in average field-effect mobility (3.9 vs. 1.1 cm² V⁻¹ s⁻¹) compared to conventional MoS₂ FETs. Y-function analysis shows that the contact resistance is significantly reduced from 85.8 kΩ to 20.5 kΩ at V_G = 40 V. These improvements are attributed to the replacement of the conventional metal–MoS₂ contact with a graphene–metal contact. Our results demonstrate that lateral heterostructure engineering with graphene provides an effective and scalable strategy for high-performance 2D electronics. Full article

Extreme natural disasters can force microgrids into islanded operation, where low system inertia and asynchronous, time-varying communication delays present severe challenges to frequency stability. These challenges threaten not only short-term reliability but also the sustainable operation of renewable-dominated energy systems. Existing frequency control methods are often unable to robustly handle heterogeneous delays, thereby limiting the resilience of power systems with high shares of renewables. To address this issue, we propose a parametric Riccati equation-based frequency control method that adaptively adjusts control parameters to balance system robustness and optimality under asynchronous delays. Controller stability is guaranteed by Barbalat’s lemma. The main contributions include: (i) developing a microgrid frequency control model that incorporates asynchronous delays, (ii) designing a delay-aware controller using the parametric Riccati equation, and (iii) validating its effectiveness on a modified New England 39-bus system. Simulation results confirm that the proposed method enhances frequency stability under disaster-induced islanding scenarios. By ensuring robust and reliable operation of renewable-rich power systems, the proposed approach contributes to the sustainable integration of renewable energy, reduces blackout risks, and supports long-term environmental and socio-economic sustainability goals. Full article

This study explores the influence of the urban soundscape on pedestrian perception and walking experience in the historic and lower parts of Béjaïa, Algeria. More precisely, the analysis investigates how variations in soundscape configuration relate to perceived comfort, safety, and walking pleasantness across five morphologically distinct urban zones. A mixed-method approach combining quantitative tools (LAeq acoustic measurements) and qualitative methods (soundwalks, sound diaries, and mental maps) was applied in accordance with ISO 12913. The study involved 50 participants for the sound diaries and 58 for the soundwalks. Results show that natural and social sounds enhance perceived comfort and safety, while mechanical noise is associated with discomfort and avoidance behaviors. In the morning, moderate to strong correlations were observed between sound comfort and visual perception (ρ = 0.58, p = 0.001, 95% CI [0.27; 0.80]), as well as between sound comfort and walking pleasantness (ρ = 0.40, p = 0.033, 95% CI [0.05; 0.67]). The study highlights the need to integrate soundscape considerations into urban planning and heritage conservation strategies. Full article

Marine-derived bioactive peptides (MBPs) are emerging as promising natural agents for regulating inflammatory responses. MBPs, typically obtained through enzymatic hydrolysis of proteins from various marine organisms such as fish, mollusks, and algae, exhibit diverse biological activities, including antioxidant, immunomodulatory, and anti-inflammatory effects. The ability of MBPs to modulate key inflammatory mediators such as TNF-α, IL-6, and COX-2, primarily through pathways like NF-κB and MAPK, highlights the therapeutic potential of MBPs in managing chronic inflammatory diseases. However, most existing studies are confined to in vitro assays or animal models, with limited translation to human clinical applications. This review explores the stability, bioavailability, and metabolic rate of MBPs under physiological conditions, which remain poorly understood. In addition, a lack of standardized protocols for peptide extraction, purification, and efficacy evaluation hinders comparative analysis across studies and also different proteomics approaches for separation, purification, identification, and quantification of marine-derived peptides with therapeutic properties. The structure–function relationship of MBPs is also underexplored, limiting rational design and targeted applications in functional foods or therapeutic products. These limitations are largely due to a lack of consolidated information and integrated research efforts. To address these challenges, this review summarizes recent progress in identifying MBPs with anti-inflammatory potentials, outlines key mechanisms, and highlights current limitations. Additionally, this review also emphasizes the need to enhance mechanistic understanding, optimize delivery strategies, and advance clinical validation to fully realize the therapeutic potential of MBPs. Full article

Climate risks are one of the major challenges facing sustainable development. This study examines how physical and transition climate risks influence the volatility and correlation of fossil energy futures and clean energy stock indices, using a mixed-frequency modeling framework. Taking the Paris Agreement as the starting point for the global energy transition, we aim to compare the impacts of climate risks on various fossil energy assets and clean energy assets and investigate how the dynamic linkages between clean energy and fossil energy assets have evolved under the influence of climate risks. The results show that climate risks have increased the volatility of fossil energy and clean energy assets to varying degrees. Correlation patterns vary by energy type: crude oil futures and clean energy indices exhibit a decoupling trend under climate risks, while natural gas futures show a more consistent, positive linkage. These findings not only provide useful guidance for investors in formulating more effective strategies under increasing climate risks but also offer policymakers valuable insights into designing optimal approaches to balance decarbonization objectives with energy security. Full article

Allergenic proteins in natural rubber latex (NRL) pose significant health risks, particularly in rubber gloves. This study evaluated RNA interference (RNAi) technology for silencing HbREF (rubber elongation factor) and HbSRPP (small rubber particle protein) genes in Hevea brasiliensis to reduce latex allergen content. Double-stranded RNA (dsRNA) targeting these genes demonstrated high stability at 25–37 °C for 6 h and under UV/outdoor conditions for 72 h, but degraded rapidly above 50 °C. Among the three delivery methods tested, direct injection achieved the highest efficiency (>90% gene silencing within 12 h), followed by root drenching (54–84%) and foliar spray (46–70%). HbREF silencing achieved 98–99% expression reduction within 3 h, while HbSRPP showed dose-dependent responses (70–90% silencing) without off-target effects. Gene silencing affected downstream rubber synthesis genes HbCPT (cis-prenyltransferase) and HbRME (rubber membrane elongation protein) (37–58% reduction) while upstream genes remained unaffected. HbREF silencing reduced Hev b1 allergen by 64.04% and Hev b3 by 12.51%, whereas HbSRPP silencing decreased Hev b3 by 71.54% and Hev b1 by 13.48%. Both treatments caused only a 11–13% reduction in dry rubber content. This RNAi approach effectively reduces major latex allergens while maintaining rubber production, demonstrating commercial potential for developing hypoallergenic rubber products through precision agriculture biotechnology. Full article