Search Results (87)

Background/Objectives: Advanced drug delivery systems (DDSs) are essential for targeted delivery, controlled release, and reduced systemic toxicity, but their clinical adoption is limited by biological barriers, manufacturing complexities, and cost. The aim of this systematic review is to critically evaluate the quantitative relationships between platform design, overcoming biological barriers, and clinical translation outcomes for DDS developed between 2016 and 2025. Methods: A comprehensive literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science (January 2016–April 2025) in accordance with the PRISMA 2020 guidelines. Included studies focused on experimental or clinical data for nanocarrier platforms (liposomes, lipid nanoparticles, polymer systems, biomimetic carriers, extracellular vesicles). Data on platform characteristics, interactions with barriers, pharmacokinetics, manufacturing, and clinical outcomes were extracted and synthesized in narrative form due to the significant methodological heterogeneity. Results: An analysis of 77 included studies confirms that successful clinical translation depends on matching the physicochemical properties of the carrier (size, surface chemistry, material) to specific biological barriers. Liposomes and lipid nanoparticles (LNPs) remain the most clinically validated platforms, exploiting the EPR effect and liver tropism, respectively. Key engineering solutions include stealth coatings, ligand-mediated targeting, and stimulus-responsive materials to overcome barriers such as mononuclear phagocyte system clearance, the blood–brain barrier, and mucosal barriers. Microfluidic and continuous manufacturing processes enable reproducibility, but scalability, cost, and immunogenicity (e.g., anti-PEG responses) remain key translational challenges. Engineered extracellular vesicles, biomimetic carriers, and 3D/4D-printed systems combined with AI-driven design demonstrate the potential for personalized, adaptive delivery. Conclusions: Cutting-edge DDSs have validated their clinical value, but realizing their full potential requires a holistic, patient-centered design approach integrating barrier-specific engineering, scalable manufacturing, and rigorous safety assessment from the earliest stages of development. Further progress will depend on standardizing methods for new platforms (e.g., extracellular vesicles), implementing digital and AI tools, and ensuring translational feasibility as a fundamental principle. Full article

Post-translational modifications (PTMs) are crucial chemical alterations occurring on proteins post-synthesis, impacting various cellular processes. During viral infections, PTMs are shown to play a multitude of roles in viral replication, host interaction, and immune evasion. Thus, these modifications can influence infectivity, with direct impact on the anti-viral host immune responses and potentially viral adaptation across species. This field is still scarcely explored, whilst understanding PTMs is not only important to advance the knowledge of virus pathology but also potentially to provide insights for vaccine development. In this review, we attempt to summarize the latest findings mainly published over the last 10 years, focusing on the roles of PTMs involved in virus infection and anti-viral immune responses, in the context of relevant human respiratory infections: influenza A virus (IAV), respiratory syncytial virus (RSV), and SARS-CoV-2. We decided to concentrate on these three viruses because they currently represent a global health problem due to recurrent outbreaks and pandemic potential. A deeper characterization of the PTMs may help in understanding virus–host interaction with possible implications on curative strategies. Further, we will report on cutting-edge technologies to study in vitro virus infection in different cellular-based systems. In particular, we describe and discuss the application of 2D and 3D lung organoid cell-culture systems as in vitro models to mimic respiratory environments and to study the PTMs in a controlled setting. Finally, we will discuss the importance of PTMs in the context of next-generation vaccine design, especially for their potential role to offer effective protection against respiratory viruses. Full article

The mining industry places high priority on occupational safety, process quality and operational efficiency. Roadheaders are widely deployed in coal mines to support fully automated excavation, reducing workers’ physical strain and improving overall safety. This article examines an automatic control system for a roadheader cutting head designed to increase mining efficiency, reduce energy consumption and maintain stable performance under varying coal and rock conditions. The system integrates advanced control algorithms with geological strength index (GSI) analysis and asynchronous motor control strategies. GSI-based adaptive speed control conserves energy and increases cutting efficiency compared to manual control. By reducing dynamic load fluctuations, transitions between different cutting zones become smoother, which decreases equipment wear. The proposed control system incorporates speed feedback loops that use a proportional–integral (PI) controller with field-oriented control (FOC), as well as super-twisted sliding mode control (STSMC) with FOC. FOC with STSMC improves roadheader productivity by applying advanced control strategies, adaptive speed regulation and precise geological strength analysis. It is also better able to handle disturbances and sudden loads thanks to STSMC’s nonlinear control robustness. The result is safer, more efficient, and more cost-effective mining that can be implemented across a wide range of underground mining scenarios. Full article

The electric spindle serves as a critical component in enabling a highly dynamic response, stable torque output, and precise motion control for the main cutting operations of CNC machine tools. The design precision and control performance of its drive motor directly influence the geometric accuracy, surface quality, and overall machining efficiency of the workpiece, thereby determining the comprehensive performance of advanced CNC systems. This paper begins with a systematic review of the global industrial layout of CNC machine tool and electric spindle manufacturers, highlighting regional clustering patterns and technological development trends across key manufacturing regions. Subsequently, it classifies and elaborates on the differentiated technical requirements for the electric spindle motor in terms of wide-speed-range servo capability, high-efficiency operation, adaptability to high-speed and high-power cutting loads, and precision maintenance under high-speed conditions, based on the process characteristics of different types of CNC machine tools. A comprehensive overview of the current state of research is provided with respect to electric spindle motor design and control technologies. Finally, forward-looking perspectives are presented on future development directions, particularly in the areas of multi-physics coupling co-design and the integration of intelligent control algorithms, aiming to offer a solid theoretical foundation and strategic guidance for the advancement and engineering application of high-performance electric spindles. Full article

Due to the shortage of rural labor and an increasingly aging population, promoting the mechanized plucking of bulk tea and improving plucking efficiency have become urgent problems for tea plantations. Previous bulk tea plucking machines have not fully adapted to tea plantations in hilly areas, necessitating enhancements in the performance of cutter profiling. In this paper, we present an automatic cutter profiling method based on an RGB-D camera, which utilizes the depth information of bulk tea shoots to tackle the issues mentioned above. Specifically, we use improved super-green features and the Otsu method to detect and segment the shoots from the RGB images of the tea canopy taken from different lighting conditions. Furthermore, the cutting pose based on the depth value of the tea shoots can be generated as a basis for cutter profiling. Lastly, the profiling task is completed by the upper computer controlling motors to adjust the cutter pose. Field tests were conducted in the tea plantation to verify the proposed profiling method’s effectiveness. The average bud and leaf integrity rate, leakage rate, loss rate, tea making rate, and qualified rate were 81.2%, 0.91%, 0.66%, and 90.4%, respectively. The results show that the developed algorithm can improve cutting pose calculation accuracy and that the harvested bulk tea shoots meet the requirements of machine plucking quality standards and the subsequent processing process. Full article

Drilling is fundamental to the assembly of aerospace structures, where millions of fastening holes must meet stringent structural and geometric requirements. Despite significant technological advances, hole quality remains sensitive to nonlinear and stochastic interactions between mechanics, thermal effects, tribology, and structural configuration. This review consolidates recent advances in intelligent drilling, focusing on how sensors and artificial intelligence (AI) are integrated to enable process understanding, prediction, and control. In-process monitoring modalities (e.g., cutting forces/torque, vibration, acoustic emission, motor current/active power, infrared thermography, and vision) are examined with respect to signal characteristics, feature design, and modelling choices for real-time anomaly detection, tool condition monitoring, and phase/interface recognition. Predictive quality modelling of burr, delamination, roughness, and roundness is discussed across statistical learning, kernel methods, and neural and hybrid models. Offline parameter optimisation via surrogate-assisted and evolutionary algorithms is considered alongside adaptive control strategies. Practical aspects of robotic drilling and multifunctional end-effectors are highlighted as enablers of embedded sensing and feedback. Finally, cross-cutting challenges (e.g., limited, heterogeneous datasets and model generalisability across materials, tools, and geometries) are outlined, together with research directions including curated multi-sensor benchmarks, multi-source transfer learning, physics-informed machine learning, and explainable AI to support trustworthy deployment in aerospace manufacturing. Full article

While 3D seismic reflection is well established in hydrocarbon exploration at the kilometer scale in relatively simple offshore settings, its application to shallow faulting in continental basins is rare, owing to difficulties in adapting acquisition and processing to rugged terrains and complex near-surface conditions. We present the first high-resolution 3D seismic study of a seismogenic fault in a structurally complex intramontane basin at depths < 200 m. The survey focuses on the Pantano–Ripa Rossa Fault, ruptured during the 1980 Mw 6.9 Irpinia earthquake, the largest Italian event of the past century. This fault cuts across the Pantano di San Gregorio Magno, a small basin filled with Quaternary sediments and showing modest cumulative displacement. Our results demonstrate that in such environments, where morphotectonic analysis and 2D geophysics provide limited constraints, high-resolution 3D seismic imaging is crucial to resolve fault geometry and to assess surface-faulting hazard. The 3D volume reveals a ~35–40 m wide intra-basin deformation zone beneath the 1980 rupture, composed of synthetic and antithetic splays, and highlights lateral variations in fault geometry and stratigraphy. Deformation is distributed and complex, with fault-controlled depocenters, variable sedimentary architectures, and rapid basement-depth changes—features unresolved by 2D data. We infer that the Pantano–Ripa Rossa Fault is relatively young, active since the late Middle Pleistocene, and developed in the hanging wall of the NE-dipping southern basin-bounding fault, challenging previous models that located the master fault along the northern basin margin. Full article

Apple represents one of the most economically significant fruit crops worldwide, and the performance of its scion is largely determined by the physiological and genetic characteristics of the rootstock. Despite their superior ecological adaptability and growth-controlling attributes, many dwarfing apple rootstocks exhibit inherently poor rooting competence, which poses a critical limitation to their large-scale clonal propagation and commercial utilization. Adventitious root (AR) formation is a pivotal yet highly intricate developmental process that governs the success of asexual propagation. It is orchestrated by a complex network of hormonal signaling, transcriptional regulation, metabolic reprogramming, and environmental cues. Over the past decade, remarkable advances have elucidated the physiological, biochemical, and molecular frameworks underpinning AR formation in apple rootstocks. This review provides an integrative synthesis of current progress in vegetative propagation techniques—including cutting, layering, and tissue culture—and systematically dissects the endogenous and exogenous factors influencing AR development. Particular emphasis is placed on the regulatory interplay among phytohormones, carbohydrate and nitrogen metabolism, phenolic compounds, transcription factors (such as WUSCHEL-RELATED HOMEOBOX (WOX), LATERAL ORGAN BOUNDARIES DOMAIN (LBD), and RESPONSE FACTOR (ARF families), and epigenetic modulators that collectively coordinate root induction and emergence. Furthermore, emerging insights into multi-omics integration and genotype-specific molecular regulation are discussed as strategic pathways toward enhancing propagation efficiency. Collectively, this review establishes a comprehensive theoretical framework for optimizing the asexual propagation of apple rootstocks and provides critical molecular guidance for breeding novel, easy-to-root genotypes that can drive the sustainable intensification of global apple production. Full article

Functional hydrogels are cutting-edge materials that are important in various fields, such as biomedical engineering, agriculture, pollution control, artificial organs, electronics, and domestic products. They are essential to contemporary scientific and industrial advancements because of their adaptability and versatility. The new synthesis techniques and multidimensional applications of different kinds of hydrogels are the goals of this study. The special qualities of hydrogels are one of the main reasons for their widespread use. Because of their stimulus-responsivity, these materials may alter their properties in response to external environmental signals, including light exposure, pH, and temperature. Their biodegradability and biocompatibility make them appropriate for ecological and medicinal applications, while their intrinsic flexibility guarantees adaptation across many applications. Furthermore, the ability of hydrogels to self-heal and be reused enhances their sustainability and efficiency. The preparation of hydrogels with these unique qualities necessitates exacting preparation methods and cautious raw material selection based on the application. To improve their operation and make sure they satisfy the required performance standards in various sectors, a variety of chemical and physical modifications are used. The functional processes of hydrogels in each sector are thoroughly examined in this review, which offers in-depth information on their interactions, efficacy, and the science underlying their uses. By providing a comprehensive overview, this analysis hopes to provide readers with a solid knowledge of potential hydrogels, empowering them to investigate new avenues for research and optimize their uses across a range of sectors. Full article

In automated sorting and grasping of livestock meat cuts, the ideal assumption of symmetric mass distribution is often violated due to irregular morphology and soft tissue deformation. Under the combined effects of gripping forces and gravity, the originally balanced configuration evolves into an asymmetric state, resulting in dynamic shifts of the center of gravity (CoG) that undermine the stability and accuracy of robotic grasping. To address this challenge, this study proposes a CoG trajectory prediction method tailored for meat-cut grasping tasks. First, a dynamic model is established to characterize CoG displacement during grasping, quantitatively linking gripping force to CoG shift. Then, the prediction task is reformulated as a nonlinear state estimation problem, and a Small-Target Bayesian–Probability Hypothesis Density (STB-PHD) algorithm is developed. By incorporating historical error feedback and adaptive covariance adjustment, the proposed method compensates for asymmetric perturbations in real time. Extensive experiments validated the effectiveness of the proposed method: the Optimal Sub-Pattern Allocation (OSPA) metric reached 4.82%, reducing the error by 4.35 percentage points compared to the best baseline MGSTM (9.17%). The task completion time (TC Time) was 6.15 s, demonstrating superior performance in grasping duration. Furthermore, the Average Track Center Distance (ATCD) reached 8.33%, outperforming the TPMBM algorithm (8.86%). These results demonstrate that the proposed method can accurately capture CoG trajectories under deformation, providing reliable control references for robotic grasping systems. The findings confirm that this approach enhances both stability and precision in automated grasping of deformable objects, offering valuable technological support for advancing intelligence in meat processing industries. Full article

The growing demand for automated production systems is driving continuous innovation in smart and data-driven manufacturing technologies. In the field of production metrology, the trend is shifting from using measurement laboratories to integrating measurement systems directly into production processes. This has led the Institute of Manufacturing Technology at TU Vienna together with its partners to develop a roughness measurement device that can be directly integrated into machine tools. Building on this foundation, this study tries to find applications beyond mere surface roughness assessment and demonstrates how the device could be applied in broader contexts of manufacturing process monitoring. By linking surface measurements with tool wear monitoring, the study establishes a correlation between surface roughness and wear progression of indexable inserts in milling. It demonstrates how in situ data can support predictive maintenance and the real-time adjustment of cutting parameters. This represents a first step toward integrating in situ metrology into closed-loop control in machining. The experimental setup followed ISO 8688-1 guidelines for tool life testing. Indexable inserts were operated throughout their entire service life while surface roughness was continuously recorded. In parallel, cutting edge conditions were documented at defined intervals using focus variation microscopy. The results show a consistent three-phase pattern: initially stable roughness, followed by a steady increase due to flank wear, and an abrupt decrease in roughness linked to edge chipping. These findings confirm the potential of integrated roughness measurement for condition-based monitoring and the development of adaptive machining strategies. Full article

Achieving optimal lubrication during machining processes, particularly turning of stainless steel, remains a significant challenge due to dynamic variations in cutting conditions that affect tool life, surface quality, and environmental impact. Conventional Minimum Quantity Lubrication (MQL) systems provide fixed flow rates and often fail to adapt to changing process parameters, limiting their effectiveness under fluctuating thermal and mechanical loads. To address these limitations, this study proposes an ambient-aware adaptive Auto-Tuned MQL (ATM) system that intelligently controls both nanofluid concentration and lubricant flow rate in real time. The system employs embedded sensors to monitor cutting zone temperature, surface roughness, and ambient conditions, linked through a feedback-driven control algorithm designed to optimize lubrication delivery dynamically. A Taguchi L9 design was used for experimental validation on AISI 304 stainless steel turning, investigating feed rate, cutting speed, and nanofluid concentration. Results demonstrate that the ATM system substantially improves machining outcomes, reducing surface roughness by more than 50% and cutting force by approximately 20% compared to conventional MQL. Regression models achieved high predictive accuracy, with R-squared values exceeding 99%, and surface analyses confirmed reduced adhesion and wear under adaptive lubrication. The proposed system offers a robust approach to enhancing machining performance and sustainability through intelligent, real-time lubrication control. Full article

Bud eye identification is a critical step in the intelligent seed cutting process for potatoes. This study focuses on the challenges of low testing accuracy and excessive weighted memory in testing models for potato bud eye detection. It proposes an improved potato bud eye detection method based on YOLOv5s, referred to as the YOLO-SCA model, which synergistically optimizing three main modules. The improved model introduces the ShuffleNetV2 module to reconstruct the backbone network. The channel shuffling mechanism reduces the model’s weighted memory and computational load, while enhancing bud eye features. Additionally, the CBAM attention mechanism is embedded at specific layers, using dual-path feature weighting (channel and spatial) to enhance sensitivity to key bud eye features in complex contexts. Then, the Alpha-IoU function is used to replace the CloU function as the bounding box regression loss function. Its single-parameter control mechanism and adaptive gradient amplification characteristics significantly improve the accuracy of bud eye positioning and strengthen the model’s anti-interference ability. Finally, we conduct pruning based on the channel evaluation after sparse training, accurately removing redundant channels, significantly reducing the amount of computation and weighted memory, and achieving real-time performance of the model. This study aims to address how potato bud eye detection models can achieve high-precision real-time detection under the conditions of limited computational resources and storage space. The improved YOLO-SCA model has a size of 3.6 MB, which is 35.3% of the original model; the number of parameters is 1.7 M, which is 25% of the original model; and the average accuracy rate is 95.3%, which is a 12.5% improvement over the original model. This study provides theoretical support for the development of potato bud eye recognition technology and intelligent cutting equipment. Full article

Cellular plasticity enables cells to dynamically adapt their phenotype in response to environmental cues, a process central to development, tissue repair, and disease. Among the most studied plasticity programs is epithelial–mesenchymal transition (EMT), a transcriptionally controlled process by which epithelial cells acquire mesenchymal traits. Originally described in embryogenesis, EMT is now recognized as a key driver in both tumor progression and fibrotic remodeling. In cancer, EMT and hybrid epithelial/mesenchymal (E/M) states promote invasion, metastasis, stemness, therapy resistance, and immune evasion. In fibrotic diseases, partial EMT (pEMT) contributes to fibroblast activation and excessive extracellular matrix deposition, sustaining organ dysfunction mainly in the kidney, liver, lung, and heart. This review integrates recent findings on the molecular regulation of EMT, including signaling pathways (TGF-β, WNT, NOTCH, HIPPO), transcription factors (SNAIL, ZEB, TWIST), and regulatory layers involving microRNAs and epigenetic modifications. Moreover, we discuss the emergence of pEMT states as drivers of phenotypic plasticity, functional heterogeneity, and poor prognosis. By comparing EMT in cancer and fibrosis, we reveal shared mechanisms and disease-specific features, emphasizing the translational relevance of targeting EMT plasticity. Finally, we explore how cutting-edge technologies, such as single-cell transcriptomics and lineage tracing, are reshaping our understanding of EMT across pathological contexts. Full article

Municipal solid waste incineration (MSWI) plays a key role in advancing environmental sustainability. However, the current main furnace temperature control methods are difficult to solve the problems of strong coupling, equipment wear, and frequent disturbances. To solve the above problems, in this article, we propose a static and dynamic hybrid event-triggering mechanism-based interval type-II fuzzy broad adaptive compensation model predictive control (SDHETM-IT2FB-ACMPC). Firstly, a furnace temperature prediction model based on the interval type-2 fuzzy broad learning system (IT2FBLS) is constructed, and the IT2FB-MPC method is obtained, which solve the problem of variable coupling. Secondly, DETM based on historical error information is designed using sliding window method and combined with SETM to form SDHETM to drive the update of control variable to reduce the problem of equipment wear. Finally, the adaptive compensation control law of the adaptive compensation optimization control (ACOC) algorithm can compensate for the influence of the disturbance and the event-triggered mechanism on the control effect, and overcome the problem of frequent disturbances. Experimental results show that the proposed method reduces ISE to 0.2821, IAE to 0.1930, and DEV^max to 6.6269—reductions of 79%, 59%, and 8% compared to traditional NMPC—while cutting control actions by 71%. The results prove that IT2FB-MPC has excellent control performance for furnace temperature, and that SDHETM and ACOC can effectively reduce the triggering times and effectively compensate for the influence caused by disturbances and the lack of control variable updates. The proposed method successfully solves the control difficulties of furnace temperature in the MSWI process. Full article