Search Results (4,915)

Amyloid cardiomyopathy (ACM), driven by transthyretin (TTR) and immunoglobulin light chain (LC) amyloid fibrils, remains a major clinical challenge due to limited mechanistic understanding and insufficient preclinical models. Human-induced pluripotent stem cells (iPSCs) have emerged as a transformative platform to model ACM, offering patient-specific and genetically controlled systems. In this review, we summarize recent advances in the use of iPSC-derived cardiomyocytes (iPSC-CMs) in both two-dimensional (2D) monolayer cultures and three-dimensional (3D) constructs—including spheroids, organoids, cardiac microtissues, and engineered heart tissues (EHTs)—for disease modeling, mechanistic research, and drug discovery. While 2D culture of iPSC-CMs reproduces hallmark proteotoxic phenotypes such as sarcomeric disorganization, oxidative stress, and apoptosis in ACM, 3D models provide enhanced physiological relevance through incorporating multicellularity, extracellular matrix interactions, and mechanical load-related features. Genome editing with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 further broadens the scope of iPSC-based models, enabling isogenic comparisons and the dissection of mutation-specific effects, particularly in transthyretin-related amyloidosis (ATTR). Despite limitations such as cellular immaturity and challenges in recapitulating aging-associated phenotypes, ongoing refinements in differentiation, maturation, and dynamic training of iPSC-cardiac models hold great promise for overcoming these barriers. Together, these advances position iPSC-based systems as powerful human-relevant platforms for modeling and elucidating disease mechanisms and accelerating therapeutic development to prevent ACM. Full article

Photocatalytic CO₂ reduction holds great potential for sustainable solar fuel production, yet its practical application is often limited by inefficient charge separation and poor product selectivity. The photothermal effect presents a viable strategy to address these challenges by reducing activation energies and accelerating reaction kinetics. In this work, we report a rationally designed CN-B/Ti₃C₂ heterojunction that effectively leverages photothermal promotion for enhanced CO₂ reduction. The black carbon nitride (CN-B) framework, synthesized via a one-step calcination of urea and Phloxine B, exhibits outstanding photothermal conversion, reaching 131.4 °C under 300 mW cm⁻² illumination, which facilitates CO₂ adsorption and charge separation. Coupled with Ti₃C₂ MXene, the optimized composite (3:1) achieves remarkable CO and CH₄ production rates of 80.21 and 35.13 μmol g⁻¹ h⁻¹, respectively, without any cocatalyst—representing a 2.9-fold and 8.8-fold enhancement over CN-B and g-C₃N₄ in CO yield. Mechanistic studies reveal that the improved performance stems from synergistic effects: a built-in electric field prolongs charge carrier lifetime (3.15 ns) and reduces interfacial resistance, while localized heating under full-spectrum light further promotes CO₂ activation. In situ Fourier transform infrared (FTIR) spectroscopy confirms the accelerated formation of key intermediates (*COOH and *CO). The catalyst also maintains excellent stability over 24 h. This study demonstrates the promise of combining photothermal effects with heterojunction engineering for efficient and durable CO₂ photoreduction. Full article

The photocatalytic reduction of carbon dioxide (CO₂) into energy-dense fuels using visible light provides a sustainable approach for solar-to-chemical energy transformation. Among the diverse metal molecular systems developed, ruthenium (II) (Ru(II)) complexes have emerged as promising catalysts due to their superior redox properties, strong visible light absorption, and customizable ligand structures. This review explores recent advances in Ru(II)-catalyzed CO₂ photoreduction, with particular attention given to catalyst design strategies, mechanistic pathways, and system integration methodologies. Key configurations, including photosensitizer/catalyst (PS/Cat) mixed systems, covalently bonded dyads, and hybrid/supramolecular frameworks, are evaluated in terms of efficiency, turnover numbers (TON), and selectivity. A critical analysis of challenges such as competing H₂ generation, inefficient charge transfer, and limited long-term stability is presented. Emerging trends toward the use of pincer ligands, transition metal integration, and self-photosensitizing frameworks are discussed as potential approaches for improving efficiency. Overall, this review offers insights into the structural and mechanistic features driving CO₂ photoreduction and provides perspectives for the rational design of next-generation Ru-based photocatalytic systems for efficient solar CO₂ conversion and the photocatalytic reduction of carbon dioxide (CO₂) into energy-dense fuels using visible light. Full article

Autonomous driving is a cornerstone of intelligent transportation systems, where visual elements such as traffic signs, lights, and pedestrians are critical for safety and decision-making. Yet, existing datasets often lack diversity, underrepresent rare scenarios, and suffer from class imbalance, which limits the robustness of object detection models. While earlier reviews have examined general image enhancement, a systematic analysis of dataset augmentation for traffic visual elements remains lacking. This paper presents a comprehensive investigation of enhancement techniques tailored for transportation datasets. It pursues three objectives: establishing a classification framework for autonomous driving scenarios, assessing performance gains from augmentation methods on tasks such as detection and classification, and providing practical insights to guide dataset improvement in both research and industry. Four principal approaches are analyzed, including image transformation, GAN-based generation, diffusion models, and composite methods, with discussion of their strengths, limitations, and emerging strategies. Nearly 40 traffic-related datasets and 10 evaluation metrics are reviewed to support benchmarking. Results show that augmentation improves robustness under challenging conditions, with hybrid methods often yielding the best outcomes. Nonetheless, key challenges remain, including computational costs, unstable GAN training, and limited rare scene data. Future work should prioritize lightweight models, richer semantic context, specialized datasets, and scalable, efficient strategies. Full article

The application of orchard inspection robots has become increasingly widespread. How-ever, achieving autonomous navigation in unstructured environments continues to pre-sent significant challenges. This study investigates the Simultaneous Localization and Mapping (SLAM) navigation system of an orchard inspection robot and evaluates its performance using Light Detection and Ranging (LiDAR) technology. A mobile robot that integrates tightly coupled multi-sensors is developed and implemented. The integration of LiDAR and Inertial Measurement Units (IMUs) enables the perception of environmental information. Moreover, the robot’s kinematic model is established, and coordinate transformations are performed based on the Unified Robotics Description Format (URDF). The URDF facilitates the visualization of robot features within the Robot Operating System (ROS). ROS navigation nodes are configured for path planning, where an improved A* algorithm, combined with the Dynamic Window Approach (DWA), is introduced to achieve efficient global and local path planning. The comparison of the simulation results with classical algorithms demonstrated the implemented algorithm exhibits superior search efficiency and smoothness. The robot’s navigation performance is rigorously tested, focusing on navigation accuracy and obstacle avoidance capability. Results demonstrated that, during temporary stops at waypoints, the robot exhibits an average lateral deviation of 0.163 m and a longitudinal deviation of 0.282 m from the target point. The average braking time and startup time of the robot at the four waypoints are 0.46 s and 0.64 s, respectively. In obstacle avoidance tests, optimal performance is observed with an expansion radius of 0.4 m across various obstacle sizes. The proposed combined method achieves efficient and stable global and local path planning, serving as a reference for future applications of mobile inspection robots in autonomous navigation. Full article

Weeds continue to pose a serious challenge to agriculture, reducing both the productivity and quality of crops. In this paper, we explore how modern deep learning, specifically Vision Transformers (ViTs), can help address this issue through fast and accurate weed classification. We developed a transformer-based model trained on the DeepWeeds dataset, which contains images of nine different weed species collected under various environmental conditions, such as changes in lighting and weather. By leveraging the ViT architecture, the model is able to capture complex patterns and spatial details in high-resolution images, leading to improved prediction accuracy. We also examined the effects of model optimization techniques, including fine-tuning and the use of pre-trained weights, along with different strategies for handling class imbalance. While traditional oversampling actually hurt performance, dropping accuracy to 94%, using class weights alongside strong data augmentation boosted accuracy to 96.9%. Overall, our ViT model outperformed standard Convolutional Neural Networks, achieving 96.9% accuracy on the held-out test set. Attention-based saliency maps were inspected to confirm that predictions were driven by weed regions, and model consistency under location shift and capture perturbations was assessed using the diverse acquisition sites in DeepWeeds. These findings show that with the right combination of model architecture and training strategies, Vision Transformers can offer a powerful solution for smarter weed detection and more efficient farming practices. Full article

The building sector represents a major frontier in the global response to climate change, accounting for approximately one-third of global energy consumption and a comparable share of energy-related carbon dioxide emissions. This review conducts a PRISMA-ScR–based scoping synthesis of technological, behavioural, and policy pathways to achieve energy efficiency and deep decarbonization in buildings. It systematically examines passive design principles, high-performance envelopes, efficient HVAC and lighting systems, renewable energy integration, building energy modelling, and retrofit strategies. The study also addresses the role of regulatory instruments, energy codes, and certification schemes in accelerating sectoral transformation. The synthesis identifies three cross-cutting drivers of decarbonization: integrated design across building systems, digitalization enabling predictive and adaptive operation, and robust policy frameworks ensuring large-scale implementation. The review concludes that while most technologies required to reach zero-emission buildings are already available, their potential remains underutilized due to fragmented policies, limited retrofit rates, and behavioural barriers. Coordinated implementation across technology, governance, and user engagement is essential to realise a net-zero building sector. Full article

Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing on growth performance and environmental control. Systems incorporating Internet of Things technology achieved a growth rate of 0.94 cm/day versus 0.16 cm/day for conventional setups, due to precise water temperature control, optimized lighting, data acquisition, targeted nutrients, and reduced pest incidence. The integration of Industry 4.0 principles further enhances sustainable production and resource management. Statistical validation under diverse conditions is recommended. Future work will add environmental sensors, refine mechanical design, and explore machine learning for adaptive control, highlighting the potential of Internet of Things–based hydroponics to transform agriculture through intelligent, efficient, and eco-friendly cultivation. Full article

This study presents the valorisation of cocoa waste (CW) by transforming it into edible packaging films using apple pectin (AP) as a gelling agent. Several properties, including microstructure, optical characteristics, sorption, wetting, barrier functionality, mechanical strength, structure, and antioxidant activity, were investigated. The analyses concluded that increasing the concentration of CW from 0 to 50% in pectin films enhanced UV light protection and caused a reorganisation in the film’s microstructure, resulting in both higher surface roughness and improved mechanical resistance. Specifically, the tensile strength increased from 7.28 to 19.14 MPa. The addition of CW reduced the lightness (parameter L*) from 82.58 to 28.58, making the films darker. Measurements of the water contact angle, which was in the range of 38.25 to 73.23; gas permeability, in the range from 5.53 to 19.52 × 10⁻¹⁶ g/m·Pa·s for oxygen and from 9.62 to 40.82 × 10⁻¹⁶ g/m·Pa·s for carbon dioxide; and adsorption indicated a reduction in water vapour sorption rates, suggesting that the films have average barrier properties against moisture. Fourier-transform infrared spectroscopy analysis confirmed no interactions between CW and the polymer matrix, showing the typical functional groups of pectin, such as carbonyl (C=O) and hydroxyl (-OH) groups. The incorporation of CW significantly increased the antioxidant properties of the developed films, attributed to the bioactive compounds present in CW. These films have potential for use as active food packaging thanks to the CW addition. They could be particularly beneficial for extending the shelf life of products sensitive to oxidation, such as oily products. Excellent sealability indicated suitability for use as pouches for fried products, such as instant coffee or powders. This study underscores the possibility of using apple pectin films with cocoa waste as sustainable components in eco-friendly packaging materials. This idea aligns with circular economic and waste reduction principles. This approach contributes to the development of innovative solutions for sustainable food packaging. Full article

Embankment piping and leakage are primary causes of flood control infrastructure failure, accounting for more than 90% of embankment failures worldwide and posing significant threats to public safety and economic stability. Current manual inspection methods are labor-intensive, hazardous, and inadequate for emergency flood season monitoring, while existing automated approaches using thermal infrared imaging face limitations in cost, weather dependency, and deployment flexibility. This study addresses the critical scientific challenge of developing reliable, cost-effective automated detection systems for embankment safety monitoring using Unmanned Aerial Vehicle (UAV)-based visible light imagery. The fundamental problem lies in extracting subtle textural signatures of piping and leakage from complex embankment surface patterns under varying environmental conditions. To solve this challenge, we propose the Embankment-Frequency Network (EmbFreq-Net), a frequency-enhanced deep learning framework that leverages frequency-domain analysis to amplify hazard-related features while suppressing environmental noise. The architecture integrates dynamic frequency-domain feature extraction, multi-scale attention mechanisms, and lightweight design principles to achieve real-time detection capabilities suitable for emergency deployment and edge computing applications. This approach transforms traditional post-processing workflows into an efficient real-time edge computing solution, significantly improving computational efficiency and enabling immediate on-site hazard assessment. Comprehensive evaluations on a specialized embankment hazard dataset demonstrate that EmbFreq-Net achieves 77.68% mAP@0.5, representing a 4.19 percentage point improvement over state-of-the-art methods, while reducing computational requirements by 27.0% (4.6 vs. 6.3 Giga Floating-Point Operations (GFLOPs)) and model parameters by 21.7% (2.02M vs. 2.58M). These results demonstrate the method’s potential for transforming embankment safety monitoring from reactive manual inspection to proactive automated surveillance, thereby contributing to enhanced flood risk management and infrastructure resilience. Full article

In the aspect of water purification, a photoactive hybrid material based on reduced graphite oxide (RGO) with covalently, coordinatively, and through van der Waals interactions bonded zirconium(IV) phthalocyanine (PcZr) is proposed. In the material, the phthalocyanine complex plays the role of photosensitizer, while RGO is considered a carrier, ensuring high surface area and supporting PcZr activation. The central metal atom of PcZr directly interacts with lateral active oxygen-containing surface groups of graphite oxide, mainly –OH and –COOH. Thus, the proposed method of synthesis under solvothermal conditions allowed obtaining a relatively high concentration of the dye (0.2 wt.%) in the system based on a partially reduced and exfoliated graphite oxide. Optical studies confirmed the presence of PcZr through absorption and luminescence spectra. Additionally, effective generation of reactive oxygen species was demonstrated by testing the transformation of a dye indicator (diphenylisobenzofuran). Photocatalytic activity of the system was confirmed by photooxidizing selected organic dyes (methylene blue, Rhodamine B, Brilliant Green, and Eriochrome Black T) in a water medium, tested in slightly acidic conditions under red light. The greatest overall decrease in absorption during the photodegradation test was observed for Brilliant Green, reaching 88% after 3 h of irradiation. Full article

A major bottleneck in evaluating the environmental health implications of micro-nanoplastics (MNPs) is the inadequacy of analytical techniques for their precise quantification within complex environmental and biological matrices. Additionally, there is a conspicuous paucity of studies addressing environmentally relevant, photo-aged MNPs. In this study, the effects of UV aging on toxicity and bioavailability were investigated utilizing inductively coupled plasma mass spectrometry (ICP-MS)-traceable 25 nm gold-core polystyrene shell nanoplastics (AuPS25 NPs) and a triculture small intestinal epithelium (SIE) model coupled with simulated digestions to mimic physiological bio-transformations post-ingestion. Employing dynamic light scattering (DLS), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS), the physicochemical and morphological alterations of AuPS25 NPs as a function of UV exposure time were investigated, revealing significant photo-oxidation within 14 days. Toxicological evaluations demonstrated that, contrasting with un-aged AuPS25 NPs, the digesta from UV-aged AuPS25 NPs at oral concentrations of 4 and 40 µg/mL weakened barrier integrity by ~15% and ~18% and heightened cytotoxicity by ~4.3% and ~5.4%, respectively. Although the NP translocation rates were similar for both aged and un-aged PS NPs, the uptake by SIE of aged AuPS25 NPs was significantly higher, reaching 72.2% at 4 µg/mL and 59.2% at 40 µg/mL. In contrast, less than 0.5% of the un-aged PS NPs at both 4 µg/mL and 40 µg/mL were taken up by SIE. These findings highlight the imperative to integrate environmentally aged MNPs into toxicological assessments, as they facilitate “real-world” MNPs. Finally, the use of ICP-MS-traceable core–shell MNPs enables the identification and quantification of PS MNPs in cell lysates and biological media via ICP-MS, showcasing the use of such a tracer MNP approach in cellular uptake and in vivo biokinetic studies. Full article

Antarctic krill, an important marine resource, contains significant arsenic levels, predominantly as the low-toxicity arsenobetaine (AsB). However, the stability of AsB during post-harvest storage and its transformations during human digestion are poorly understood, which is critical for a comprehensive safety assessment. This research investigated the effects of temperature, light exposure, and in vitro simulated digestion on the stability and transformation dynamics of major arsenic species in Antarctic krill. The results revealed that AsB predominated among the arsenic species. AsB remained stable during long-term frozen storage (−18 °C for 5 months) and short-term refrigeration (4 °C for 2 days). However, AsB content decreased significantly during storage at ambient temperature (25 °C) and after prolonged light exposure (>8 h), indicating that improper storage conditions can lead to its degradation. During simulated gastrointestinal digestion, a significant transformation of arsenic species was observed. The content of toxic inorganic As(III) decreased significantly during the gastric phase, while the less-toxic AsB content markedly increased. Furthermore, dimethylarsinic acid (DMA) and As(V) were newly detected during the intestinal phase. These findings demonstrate that arsenic stability in krill is highly dependent on storage conditions. Moreover, the transformations during digestion—notably the decrease in As(III) and increase in AsB—suggest a potential reduction in overall arsenic toxicity upon consumption. This provides a critical theoretical basis for developing storage guidelines and improving human health risk assessments for Antarctic krill products. Full article

In the context of rapid urbanization, human activities have profoundly transformed urban thermal environments. However, most existing studies have focused on single cities or relatively uniform climatic contexts, and the long-term dynamics between land surface temperature (LST) and nighttime light (NTL) across urban–rural gradients in diverse climates remain insufficiently explored. This gap limits a systematic understanding of how human activities and thermal environments co-evolve under varying regional conditions. To address this gap, we selected ten representative cities spanning multiple climate zones in China. Using MODIS LST and NTL datasets from 2000 to 2020, we developed an urban–rural gradient analysis framework to systematically assess the spatiotemporal response patterns and coupling mechanisms between LST and NTL. Our findings reveal the following: (1) From 2000 to 2020, NTL exhibited a pronounced upward trend across all climate zones, most notably in the marginal tropical humid region, while LST changes were relatively moderate. (2) LST and NTL displayed power-law distributions along urban–rural transects, marked by steep declines in monocentric cities and gradual transitions in polycentric cities, with sharper thermal gradients in northern and inland areas and more gradual transitions in southern and coastal regions. (3) The long-term increase in NTL was most evident in suburban areas (0.94 nW/cm²/sr/a), surpassing that in urban cores (0.68 nW/cm²/sr/a) and rural zones (0.60 nW/cm²/sr/a), with inland cities (0.84 nW/cm²/sr/a) outpacing their coastal counterparts. Although LST changes were modest, suburban warming (0.16 ± 0.08 °C/a) was over twice that of urban and rural areas. Notably, the synergistic escalation of light and heat was most pronounced in tropical and subtropical cities. (4) Eastern coastal cities exhibited strongly synchronized rises in NTL and LST, whereas cities in the plateau, temperate semi-arid, and mid-temperate arid regions showed clear decoupling. Along urban–rural gradients, NTL–LST correlations generally weakened from urban centers to peripheries, yet coupling coordination peaked in fringe areas (mean = 0.63), underscoring pronounced spatial heterogeneity. This study advances our understanding of the spatiotemporal coupling of urban light and heat under varying climatic and urbanization contexts, offering critical insights into managing urban thermal environments. Full article

Images used in real-world applications such as image or video retrieval, outdoor surveillance, and autonomous driving suffer from poor weather conditions. When designing robust computer vision systems, removing adverse weather such as haze, rain, and snow is a significant problem. Recently, deep-learning methods offered a solution for a single type of degradation. Current state-of-the-art universal methods struggle with combinations of degradations, such as haze and rain streaks. Few algorithms have been developed that perform well when presented with images containing multiple adverse weather conditions. This work focuses on developing an efficient solution for multiple adverse weather removal, using a unified quaternion neural architecture called CMAWRNet. It is based on a novel texture–structure decomposition block, a novel lightweight encoder–decoder quaternion transformer architecture, and an attentive fusion block with low-light correction. We also introduce a quaternion similarity loss function to better preserve color information. The quantitative and qualitative evaluation of the current state-of-the-art benchmarking datasets and real-world images shows the performance advantages of the proposed CMAWRNet, compared to other state-of-the-art weather removal approaches dealing with multiple weather artifacts. Extensive computer simulations validate that CMAWRNet improves the performance of downstream applications, such as object detection. This is the first time the decomposition approach has been applied to the universal weather removal task. Full article