Search Results (29,242)

Iron oxide nanoparticles (IONPs) have emerged as key materials in magnetic hyperthermia (MH), a minimally invasive cancer therapy capable of selectively inducing apoptosis, ferroptosis, and other cell death pathways while sparing surrounding healthy tissue. This review synthesizes advances in the design, functionalization, and biomedical application of magnetic nanoparticles (MNPs) for MH, highlighting strategies to optimize heating efficiency, biocompatibility, and tumor targeting. Key developments include tailoring particle size, shape, and composition; doping with metallic ions; engineering multicore nanostructures; and employing diverse surface coatings to improve colloidal stability, immune evasion, and multifunctionality. We discuss preclinical and clinical evidence for MH, its integration with chemotherapy, radiotherapy, and immunotherapy, and emerging theranostic applications enabling simultaneous imaging and therapy. Special attention is given to the role of MNPs in immunogenic cell death induction and metastasis prevention, as well as novel concepts for circulating tumor cell capture. Despite promising results in vitro and in vivo, clinical translation remains limited by insufficient tumor accumulation after systemic delivery, safety concerns, and a lack of standardized treatment protocols. Future progress will require interdisciplinary innovations in nanomaterial engineering, active targeting technologies, and real-time treatment monitoring to fully integrate MH into multimodal cancer therapy and improve patient outcomes. Full article

The rapid rise in atmospheric CO₂ concentrations has intensified the need for scalable, sustainable, and economically viable carbon sequestration technologies. This study introduces a cost-effective CaO/Ca(OH)₂-based mineralization process that not only enables efficient CO₂ removal but also allows the simultaneous recovery of high-purity calcite nanoparticles as value-added products. The process involves hydrating CaO, followed by controlled carbonation under optimized CO₂ flow rates, temperature conditions, and and additive use, yielding nanocrystalline calcite with an average particle size of approximately 100 nm. Comprehensive characterization using X-ray diffraction, transmission electron microscopy, and energy-dispersive X-ray spectroscopy confirmed a polycrystalline structure with exceptional chemical purity (99.9%) and rhombohedral morphology. Techno-economic analysis further demonstrated that coupling CO₂ sequestration with nanoparticle production can markedly improve profitability, particularly when utilizing CaO/Ca(OH)₂-rich industrial residues such as steel slags or lime sludge as feedstock. This hybrid, multi-revenue strategy—integrating carbon credits, nanoparticle sales, and waste valorization—offers a scalable pathway aligned with circular economy principles, enhancing both environmental and economic performance. Moreover, the proposed system can be applied to CO₂-emitting plants and facilities, enabling not only effective carbon dioxide removal and the generation of carbon credits, but also the production of calcite nanoparticles for diverse applications in agriculture, manufacturing, and environmental remediation. These findings highlight the potential of CaO/Ca(OH)₂-based mineralization to evolve from a carbon management technology into a platform for advanced materials manufacturing, thereby contributing to global decarbonization efforts. Full article

Polyploidization is a rapid breeding strategy for producing new varieties with superior agronomic traits. Kenaf (Hibiscus cannabinus L.), an important fiber crop, exhibits high adaptability to diverse stress conditions. However, comprehensive studies on polyploid induction, screening, and genetic identification in kenaf remain unreported. This study first established an optimal tetraploid induction system for diploid kenaf seeds using colchicine. The results showed that a 4-h treatment with 0.3% colchicine yielded the highest tetraploid induction rate of 37.59%. Compared with diploids, tetraploid plants displayed distinct phenotypic and physiological characteristics: dwarfism with shortened internodal distance, increased stem thickness, larger and thicker leaves with deeper green color and serration, as well as enlarged flowers, capsules, and seeds. Physiologically, tetraploid leaves featured increased chloroplast numbers in guard cells, reduced stomatal density, and larger pollen grains, elevated chlorophyll content. Further analyses revealed that tetraploid kenaf had elevated contents of various trace elements, enhanced photosynthetic efficiency, prolonged growth duration, and superior agronomic traits with higher biomass (54.54% higher fresh weight, 79.17% higher dry weight). These findings confirm the effectiveness of colchicine-induced polyploidization in kenaf, and the obtained tetraploid germplasm provides valuable resources for accelerating the breeding of elite kenaf varieties with improved yield and quality. Full article

Lane detection is a fundamental component of perception systems in autonomous driving. Despite significant progress in this area, existing methods still face challenges in complex scenarios such as abnormal weather, occlusions, and curved roads. These situations typically demand the integration of both the global semantic context and local visual features to predict the lane position and shape. This paper presents ORANet, an enhanced lane detection framework built upon the baseline CLRNet. ORANet incorporates two novel modules: Enhanced Coordinate Attention (EnCA) and Channel–Spatial Shuffle Attention (CSSA). EnCA models long-range lane structures while effectively capturing global semantic information, whereas CSSA strengthens the precise extraction of local features and provides optimized inputs for EnCA. These components operate in hierarchical synergy, collectively establishing a complete enhancement pathway from refined local feature extraction to efficient global feature fusion. The experimental results demonstrate that ORANet achieves greater performance stability than CLRNet in complex roadway scenarios. Notably, under shadow conditions, ORANet achieves an F1 score improvement of nearly 3% over CLRNet. These results highlight the potential of ORANet for reliable lane detection in real-world autonomous driving environments. Full article

This study presents the design and validation of an automated skip loading system for vertical shaft hoisting operations, aimed at addressing inefficiencies in current manual systems that contribute to consistent underperformance in meeting daily production targets. Initial assessments revealed a task completion rate of 91.6%, largely due to delays and inaccuracies in manual ore loading and accounting. To resolve these challenges, an automated system was developed using a bin and conveyor mechanism integrated with a suite of industrial automation components, including a programmable logic controller (PLC), stepper motors, hydraulic cylinders, ultrasonic sensors, and limit switches. The system is designed to transport ore from the draw point, halt when one ton is detected, and activate the hoisting process automatically. Digital simulations demonstrated that the automated system reduced loading time by 12% and increased utilization by 16.6%, particularly by taking advantage of the 2 h post-blast idle period. Financial evaluation of the system revealed a positive Net Present Value (NPV) of $1,019,701, a return on investment (ROI) of 69.7% over four years, and a payback period of 2 years and 11 months. The study concludes that the proposed solution significantly improves operational efficiency and recommends further enhancements to the hoisting infrastructure to fully optimize performance. Full article

The incentives in fee-for-service healthcare payment systems to increase clinical volume often work in opposition to efforts to coordinate care or improve care delivery in partnership with community-based services. There has been increasing interest in and adoption of value-based care as an alternative healthcare delivery model in which clinician reimbursement is based on measures of healthcare quality and patient outcomes, meant to shift the focus from generating volume toward providing more efficient, coordinated care. In this commentary, we discuss potential benefits, challenges, and unintended consequences of this fundamental shift in payment systems and the specific implications for autism services, highlighting critical areas of focus for future research and policy development. Full article

In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of $1\times {10}^{-3}$ by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

Intelligent sensing systems are increasingly used in wearable devices, enabling advanced tasks across various application domains including robotics and human–machine interaction. Ensuring these systems are energy autonomous is highly demanded, despite strict constraints on power, memory and processing resources. To meet these requirements, embedded neural networks must be optimized to achieve a balance between accuracy and efficiency. This paper presents an integrated approach that combines Hardware-Aware Neural Architecture Search (HW-NAS) with optimization techniques—weight reshaping, quantization, and their combination—to develop efficient classifiers for a multisensory glove. HW-NAS automatically derives 1D-CNN models tailored to the NUCLEO-F401RE board, while the additional optimization further reduces model size, memory usage, and latency. Across three datasets, the optimized models not only improve classification accuracy but also deliver an average reduction of 75% in inference time, 69% in flash memory, and more than 45% in RAM compared to NAS-only baselines. These results highlight the effectiveness of integrating NAS with optimization techniques, paving the way towards energy-autonomous wearable systems. Full article

CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated protein) nucleases enable precise genome editing, but off-target cleavage remains a critical challenge. Here, we report the development of MAD7_HF, a high-fidelity variant of the MAD7 nuclease engineered through a bacterial screening system leveraging the DNA gyrase-targeting toxic gene ccdB. This system couples survival to efficient on-target cleavage and minimal off-target activity, mimicking the transient action required for high-precision editing. Through iterative selection and sequencing validation, we identified MAD7_HF, harboring three substitutions (R187C, S350T, K1019N) that enhanced discrimination between on- and off-target sites. In Escherichia coli assays, MAD7_HF exhibited a >20-fold reduction in off-target cleavage across multiple mismatch contexts while maintaining on-target efficiency comparable to wild-type MAD7. Structural modeling revealed that these mutations stabilize the guide RNA-DNA hybrid at on-target sites and weaken interactions with mismatched sequences. This work establishes a high-throughput bacterial screening strategy that allows the identification of Cas12a variants with improved specificity at a given target site, providing a useful framework for future efforts to develop precision genome-editing tools. Full article

Current medical informatics systems face critical challenges, including limited scalability across distributed institutions, insufficient real-time AI-driven decision support, and lack of standardized interoperability for heterogeneous medical data exchange. To address these challenges, this paper proposes a novel distributed web system architecture for medical informatics, integrating artificial intelligence techniques and cloud-based services. The system ensures interoperability via HL7 FHIR standards and preserves data privacy and fault tolerance across interconnected medical institutions. A hybrid AI pipeline combining principal component analysis (PCA), K-Means clustering, and convolutional neural networks (CNNs) is applied to diffusion tensor imaging (DTI) data for early detection of neurological anomalies. The architecture leverages containerized microservices orchestrated with Docker Swarm, enabling adaptive resource management and high availability. Experimental validation confirms reduced latency, improved system reliability, and enhanced compliance with medical data exchange protocols. Results demonstrate superior performance with an average latency of 94 ms, a diagnostic accuracy of 91.3%, and enhanced clinical workflow efficiency compared to traditional monolithic architectures. The proposed solution successfully addresses scalability limitations while maintaining data security and regulatory compliance across multi-institutional deployments. This work contributes to the advancement of intelligent, interoperable, and scalable e-health infrastructures aligned with the evolution of digital healthcare ecosystems. Full article

Neural network training often suffers from spectral asymmetry, where gradient energy is disproportionately allocated to high-frequency components, leading to suboptimal convergence and reduced efficiency. This paper introduces Gradient Spectral Normalization (GSN), a novel optimization technique designed to restore spectral symmetry by dynamically reshaping gradient distributions in the frequency domain. GSN transforms gradients using FFT, applies layer-specific energy redistribution to enforce a symmetric balance between low- and high-frequency components, and reconstructs the gradients for parameter updates. By tailoring normalization schedules for attention and MLP layers, GSN enhances inference performance and improves model accuracy with minimal overhead. Our approach leverages the principle of symmetry to create more stable and efficient neural systems, offering a practical solution for resource-constrained environments. This frequency-domain paradigm, grounded in symmetry restoration, opens new directions for neural network optimization with broad implications for large-scale AI systems. Full article

Over the past decade, digitalization in the logistics sector has heightened the significance of autonomous systems and AI-based applications, while the integration of advanced deep learning technologies with air cargo carriers has ushered in a new era in the logistics industry. This study systematically addresses the current applications of these technological advances in logistics planning, the challenges faced, and perspectives for the future. These developments are transforming the role of UAVs and autonomous systems in logistics operations by improving last-mile efficiency and reducing costs. Key functions of autonomous vehicles, including environmental perception, decision-making, and route optimization, have shown notable progress through deep learning algorithms. However, major obstacles remain to their widespread adoption, particularly in terms of energy efficiency, data security, and the absence of a mature regulatory framework. Accordingly, this paper discusses these issues in detail and highlights areas for further research. This systematic literature review reveals the disruptive potential of AACV for the logistics industry and presents findings that can guide both academic inquiry and industrial practice. The results underscore that establishing a sustainable and efficient logistics ecosystem is essential in the context of these emerging technologies. Full article

Broiler chickens are commonly reared in closed housing systems with limited exposure to sunlight, thereby relying entirely on dietary sources of vitamin D. The hydroxylated metabolite 25-hydroxycholecalciferol [25(OH)D₃] has been proposed as a more potent form than native vitamin D₃ (cholecalciferol). This study evaluated the effects of dietary supplementation with vitamin D₃ alone or in combination with 25(OH)D₃ on growth performance, bone characteristics, and cecal microbiota in Ross 308 broilers. A total of 952 one-day-old male chicks were allocated to four treatments: a negative control (no vitamin D₃), a positive control (vitamin D₃ according to Ross 308 specifications), and a positive control supplemented with 25(OH)D₃ at 1394 or 2788 IU/kg, in a randomized design with 17 replicates per treatment and 14 birds per replicate. Over a 40-day feeding trial, diets containing vitamin D₃ (positive control) or supplemented with 25(OH)D₃ significantly improved final body weight, weight gain, average daily gain, and feed conversion ratio compared with the negative control (p < 0.01), with no significant differences among the positive control and 25(OH)D₃-supplemented groups, with a clear linear dose-dependent response. Although tibia ash and bone-breaking strength were not significantly affected, linear responses indicated a slight numerical trend toward improved skeletal mineralization with increasing 25(OH)D₃. Microbiota analysis indicated that 25(OH)D₃ affected cecal microbial ecology: low-dose inclusion showed reduced species richness and evenness, whereas high-dose inclusion restored richness to levels comparable to the positive control and enriched taxa associated with fiber fermentation and bile acid metabolism while reducing Lactobacillus dominance. In conclusion, supplementation with 25(OH)D₃ in addition to vitamin D₃ enhanced growth performance and selectively shaped the cecal microbiota of broilers, with suggestive benefits for bone mineralization. These findings highlight 25(OH)D₃ as a more potent source of vitamin D than cholecalciferol alone and support its practical use in modern broiler nutrition to improve efficiency, skeletal health, and microbial balance. Full article

This study reports the development of electrospun poly(methyl methacrylate) (PMMA) fiber mats incorporating ibuprofen (IBU)-intercalated layered double hydroxides (LDH) for enhanced transdermal drug delivery systems (TDDS). IBU, in its anionic form, was successfully intercalated into LDH, which possesses anion exchange capabilities, and subsequently embedded into PMMA fibers via electrospinning. In vitro drug release experiments demonstrated that UPMMA–LDH–IBU fibers exhibited significantly higher IBU release than PMMA–IBU controls. This enhancement was attributed to the improved hydrophilicity and water absorption imparted by the LDH, as confirmed by contact angle and water uptake measurements. Furthermore, artificial skin permeation tests revealed that the UPMMA–LDH–IBU fibers maintained comparable release rates to those observed during buffer immersion, indicating that the rate-limiting step was the diffusion of IBU within the fiber matrix rather than the interface with the skin or buffer. These findings highlight the critical role of LDH in modulating drug release behavior and suggest that UPMMA–LDH–IBU electrospun fiber mats offer a promising and efficient platform for advanced TDDS applications. Full article