Search Results (747)

Wave-edge milling tools can suppress chatter by introducing periodic harmonic variations along the cutting edge, which change the tooth-passing time delays between adjacent teeth. However, their stability is affected by coupled waveform parameters, such as amplitude, wavelength, and phase, making it difficult to screen suitable parameter combinations efficiently. This numerical/modeling-based study uses a previously validated multi-delay dynamic model to investigate the probabilistic relationship between pitch angle variance (PAV) and stability region area (SRA). A large number of feasible waveform-parameter combinations are generated under geometric constraints, and a PAV-based stratified sampling strategy is used to retain 54 representative parameter sets from six PAV layers for stability lobe diagram construction and SRA calculation. The results show that PAV has weak pointwise predictive capability for individual SRA values, with Pearson = 0.4234, Spearman = 0.4720, and R² = 0.179. However, the stratified statistical results reveal a clear layer-wise probabilistic tendency: the mean SRA increases from 17.962 to 20.996 in units of rpm·m, and the probability of obtaining an above-median SRA increases from 11.1% to 88.9%. The high-value tail case with PAV > 0.06 further indicates that a higher PAV does not necessarily guarantee a larger SRA for an individual parameter set. Therefore, PAV should not be used as a deterministic predictor or stand-alone tool-selection criterion, but can serve as a low-cost auxiliary probabilistic pre-screening descriptor before high-fidelity SLD/SRA evaluation. Full article

Proper nitrogen (N) management is essential for increasing the productivity of sugarcane (Saccharum spp.) and reducing the economic and environmental impacts associated with excessive fertilizer use. This study compared the performance of two portable spectroradiometers, FieldSpec 3 and HandHeld 2, in estimating foliar nitrogen content based on hyperspectral data in the visible and near-infrared regions, obtained throughout the crop cycle. The experiment was conducted in Piracicaba, São Paulo, Brazil, under four N rates: 0, 60, 120, and 180 kg ha⁻¹. Spectral measurements were taken at the foliar and canopy levels at eight evaluation times, accompanied by laboratory determination of N content. Partial Least Squares Regression (PLSR) and Random Forest (RF) models were fitted using the spectral data and days after cutting (DAC), included as a categorical factor and evaluated using 10-fold internal cross-validation, based on the metrics R², RMSE, MAE, and Willmott’s refined agreement index (dr). The foliar data performed better with PLSR (R² = 0.727; RMSE = 1.381 g kg⁻¹; MAE = 1.109; dr = 0.917) than canopy data (R² = 0.591; RMSE = 1.489 g kg⁻¹; MAE = 1.157; dr = 0.866). PLSR also outperformed RF at both acquisition levels. The green (~550 nm) and red edge (~740 nm) regions were the most relevant for N estimation. Under the evaluated conditions, model performance was associated with the spectral acquisition level and conditions, the instrumental configuration, and the modeling strategy employed. Full article

Maceration is one of the most important tools available to winemakers for shaping the chemical composition, sensory profile, and overall style of red wines. However, understanding the consequences of different maceration choices remains challenging due to the diversity of available techniques, the complexity of extraction phenomena, and the fragmented nature of the available literature, which spans a wide range of technological strategies, cultivars, vintages, winemaking conditions, and analytical approaches. This review thus provides a comprehensive overview of traditional, advanced and emerging maceration techniques used in red winemaking. Conventional approaches, including pre-fermentative cold maceration, fermentative maceration, extended post-fermentative maceration, whole-bunch fermentation, and carbonic maceration, are discussed alongside technological modifications such as cryoextraction, thermovinification, flesh-release and Accentuated Cut Edges, as well as emerging and non-thermal technologies including pulsed electric fields, ultrasound, high-pressure processing, microwaves and ohmic heating. Particular attention is given to the mechanisms governing extraction, with emphasis on how processing conditions and tissue-disruption phenomena influence grape cell wall integrity, membrane permeability, and the release of targeted constituents from different grape tissues. Finally, the advantages, limitations, and practical implications of each technique are highlighted for researchers and winemakers, indicating the need for an integrated consideration of chemical, microbiological, sensory, technological, and economic factors when optimizing red wine production. Full article

Skin aging is fundamentally a breakdown in cellular quality control rather than a strictly aesthetic phenomenon. To address this, the present conceptual review provides an interdisciplinary analysis of the biological foundations of skin aging, spotlighting ubiquitination as the master regulator of proteostasis. Complex, interdependent mechanisms are responsible for protecting skin cells, mediating crucial interplay between the ubiquitin-proteasome system (UPS), autophagy, and molecular chaperones. By examining how the UPS governs proliferation, oxidative stress responses, and inflammation, the consequences of its age-related dysregulation become evident. Bridging advanced cell biology with regenerative dermatology, the presented analysis pivots away from direct pharmacological modulation. Instead, it emphasizes biologically grounded strategies to protect and stabilize the cellular environment. In conclusion, the review establishes a “well-aging” paradigm that integrates cutting-edge proteostasis research with practical applications for cosmetic formulation design and proactive skincare. Full article

Conventional polyolefin battery separators are limited by inherent deficiencies in thermal stability, electrolyte wettability, and environmental sustainability, which collectively hinder the advancement of high-energy-density energy storage systems. In this context, biomass macromolecular materials, including cellulose, chitin/chitosan, and lignin, have emerged as promising candidates for next-generation separators owing to their environmental benefits, exceptional hydrophilicity, and superior thermal resistance. This review systematically evaluates the molecular characteristics of these three biomass systems, alongside core gel-state processing and network-forming processes such as electrospinning, solution casting, nonwoven technology, and hydrogel-assisted film formation. It further highlights their cutting-edge applications in lithium-ion, lithium–sulfur, zinc-ion, and solid-state batteries, emphasizing their behavior as polymer gel electrolytes and gel-derived structural matrices. To overcome key challenges associated with mechanical robustness, interfacial compatibility, and network uniformity, advanced modification strategies are critically discussed, including surface chemical functionalization, multicomponent hybrid composite formulation, and rational three-dimensional structural engineering. Overall, current research evidence demonstrates that rationally designed biomass-based gel networks and membranes can effectively suppress metal dendrite growth, immobilize soluble polysulfide intermediates via supramolecular interactions, and reduce interfacial impedance in solid-state systems, thereby offering a viable pathway toward safer, more sustainable, and commercially competitive high-energy-density batteries. Full article

Proanthocyanidin A₂ (PA₂) is a class of bioactive small-molecule natural polyphenols, while selenocysteine (Sec) represents an essential functional selenium-containing amino acid in humans and has emerged as a cutting-edge research hotspot in the field of natural bioactive factors. This review systematically summarizes the natural resource distribution and biological signaling pathways of PA₂, as well as the in vivo metabolic profiles of Sec. It further elaborates on the core physiological and pathological processes co-regulated by PA₂ and Sec, including oxidative stress homeostasis, inflammatory immune responses, and metabolic reprogramming. Malignant tumors, severe diseases triggered by disrupted organismal homeostasis, serve as the primary research model for exploring the synergistic regulatory effects of PA₂ combined with Sec. Accumulating evidence has validated the independent anti-tumor efficacy of PA₂ and Sec in respective research domains; nevertheless, studies investigating their synergistic bioactivities remain scarce. Starting from the natural resource distribution of PA₂ and the respective core biological functions of the two agents, this work systematically dissects their individual roles in modulating oxidative stress, inflammatory responses, tumor malignant proliferation and apoptosis, and proposes putative synergistic regulatory mechanisms with hypothetical mechanistic validation. The findings are expected to provide theoretical references and innovative research strategies for developing combinatorial interventions of natural polyphenols and selenocysteine against tumors and related disorders, as well as for constructing antioxidant damage defense systems. Full article

Molecularly imprinted membranes (MIMs) have demonstrated tremendous potential in the field of high-efficiency separation due to their specific molecular recognition capabilities. This review aims to elucidate the underlying mechanisms governing MIMs’ performance and, moving beyond traditional classification frameworks, systematically reconstructs the classification system for MIMs from the perspectives of the spatial distribution of imprinted sites, the chemical topology of the matrix, and mass transfer kinetics. The article focuses on the decisive influence of key physical parameters such as pore size, specific surface area, hydrophilicity/hydrophobicity, and swellability on separation efficiency. It provides an in-depth analysis of the spatial matching between pore size and target molecules, the nonlinear relationship between specific surface area and adsorption capacity, and the mechanisms by which mechanical strength and swelling behavior constrain the long-term stability of the membranes. Addressing the common bottlenecks faced by MIMs “high mass transfer resistance and poor accessibility of recognition sites” this paper critically summarizes cutting-edge morphological optimization strategies, such as multi-level pore construction, nanocomposite reinforcement, and surface topological engineering, aiming to elucidate how microstructural regulation can achieve a synergistic enhancement of both high throughput and high selectivity. Finally, by reviewing breakthroughs in MIMs applications for biomedical extraction and environmental pollutant remediation, this review not only clarifies the principles governing material suitability across different scenarios but also provides a systematic technical reference for the development of next-generation, high-performance, industrial-scale MIMs. Full article

Plant tissue culture is a crucial part of biotechnology that supports crop improvement, plant conservation, and other related fields. Although tissue culture has been successfully used in many plants, low regeneration and transformation rates still exist in some species. Agrobacterium-mediated transformation is commonly used in genetic engineering, but its effectiveness depends heavily on establishing a reliable in vitro regeneration system through organogenesis or somatic embryogenesis. Over the past decade, substantial progress has been made in understanding the molecular basis of regeneration; however, most reviews have focused on individual aspects such as hormone regulation or transcription factor networks in isolation. In contrast, this review provides a comprehensive and integrated framework that systematically links four critical layers—wound signaling, hormonal regulation, developmental regulators, and epigenetic modifications—into a unified regulatory network governing plant regeneration. Furthermore, we highlight recent cutting-edge advances, including artificial intelligence-assisted prediction, single-cell and spatial transcriptomics, epigenome editing, and CRISPR-based activation systems, and we discuss their transformative potential in overcoming genotype-dependent recalcitrance. By synthesizing classical regulatory mechanisms with emerging technologies, this review offers a forward-looking perspective that distinguishes it from earlier publications and provides both theoretical foundations and practical strategies for improving plant regeneration and genetic transformation. Full article

The performance of Network-on-Chip (NoC) architectures is severely limited by the high dynamic power consumption and interconnect crosstalk with the scaling of deep sub-micron (DSM) technology. The coupling capacitance between wires dominates the self-capacitance in dense interconnects, and the coupling transitions contribute the most to the link power dissipation. In this paper, we propose a new low-power error correction coding scheme called Parity-based Multi-bit Transient Error Correction (PMTEC), which can simultaneously deal with self-transitions and coupling transitions in NoC links. The proposed method reduces the effective switching activity by employing a balanced encoding strategy, unlike existing coding techniques, which typically degrade the signal integrity or impose significant power overheads for particular data patterns. Experimental analysis demonstrates that PMTEC achieves strong link power reductions on various data profiles, including text, image, and video traffic, without the negative overheads of cutting-edge techniques like Duplicated Two-Dimensional Parities (DTDPs) and Crosstalk-Aware Transient Error Correction (CATEC). Furthermore, the codec’s parametric synthesis reveals a very small footprint, taking up only 2472.018 $\mathsf{\mu }{\mathrm{m}}^2$ of silicon area and using a negligible 0.219 $\mathsf{\mu }$W of power, which are 87.57% and 99.20% reductions over CATEC, respectively. The suggested work offers a scalable and energy-efficient solution for dependable on-chip communication in power-constrained systems, despite requiring a 15.371 ns latency trade-off to correct 8-bit bursts. Full article

Background: Osteoarthritis (OA) is the most common degenerative joint disease globally, with its pathogenesis yet to be fully elucidated. Accumulating evidence has redefined OA as a systemic low-grade inflammatory disorder. While the gut microbiota–joint axis is widely recognized as a key regulatory pathway in OA progression, a definitive causal and mechanistic framework linking microbiota-derived metabolites to OA pathology has not yet been established. Aim of Review: This review aims to comprehensively evaluate the roles of microbiota-derived metabolites in OA pathogenesis by integrating cutting-edge multi-omics data, causal evidence from Mendelian randomization studies, and advanced translational strategies. We propose a conceptual framework linking microbial dysbiosis to joint degeneration and discuss potential therapeutic targets for OA. Key Scientific Concepts of Review: Microbiota-derived signals, including lipopolysaccharides, peptidoglycans, short-chain fatty acids, bile acids, tryptophan metabolites, and hydrogen sulfide, are associated with mucosal barrier impairment, aberrant immune activation, and metabolic–endocrine dysfunction. These systemic host responses, in turn, may collectively contribute to the three core pathological hallmarks of OA: cartilage degeneration, synovial inflammation, and subchondral bone remodeling. We highlight novel regulatory pathways, including bile acid–glucagon-like peptide-1(GLP-1) signaling, aryl hydrocarbon receptor (AhR) modulation, and ferroptosis regulation, as potential critical mediators of OA. Causal evidence from multi-omics and Mendelian randomization analyses is synthesized to move beyond simple descriptive associations. Furthermore, we discuss translational strategies, such as metabolite-targeted interventions (GUDCA, IPA, HDCA) and engineered bacterial extracellular vesicle delivery systems, providing a potential framework for the precision theranostics of OA. Full article

The escalating problem of bacterial drug resistance poses a severe threat to global public health, with Acinetobacter baumannii (A. baumannii) exhibiting particularly high resistance rates that are closely linked to its Quorum Sensing (QS) system. This narrative review synthesizes current knowledge on the A. baumannii QS system, focusing on its essential role in mediating antimicrobial resistance, as well as the latest progress in developing QS inhibitors. Key findings indicate that the A. baumannii QS system enhances bacterial survival by promoting biofilm formation, and regulating the expression of efflux pump and resistance genes. However, significant translational challenges remain, including the risk of inducing resistance, the poor bioavailability and suboptimal pharmacokinetic properties, and their reduced efficacy in complex biological systems. In conclusion, while QS inhibitors represent a promising therapeutic target, overcoming current developmental bottlenecks requires combining them with traditional antibiotics, exploring novel drug delivery strategies, and integrating cutting-edge tools to accelerate drug discovery and clinical application. Full article

Within the domain of Earth observation, tracking terrestrial transitions via high-resolution optical data plays a fundamental role. Nevertheless, current methods face critical challenges, including the difficulty in collaborative modeling of local details and global features and the singularity of bi-temporal difference representation, along with insufficient cross-scale feature communication, thereby constraining both the precision and resilience of models when applied to complicated environments. To solve these problems, we propose LADENet (Locally Adaptive Mamba and Multi-scale Feature Enhancement Network), an innovative framework that synergizes CNN, Transformer, and Mamba paradigms. By leveraging customized local contextual refinement alongside sophisticated hierarchical fusion, this integration delivers highly precise and resilient detection performance. LADENet adopts a weight-sharing multi-level Transformer encoder combined with a sequence reduction mechanism to generate multi-scale global features, achieving precise alignment of bi-temporal features and global context modeling while reducing computational complexity. To realize accurate localization and local enhancement of changed regions, we design a dual spatiotemporal adaptive local feature marking module based on State-Space Scanning (SSS). This module screens high-saliency changed regions through an adaptive scanning strategy, realizes pixel-aligned spatiotemporal feature fusion via cross-temporal state-space scanning, and introduces a sliding window boundary calibration mechanism to alleviate boundary information loss caused by window segmentation. To strengthen the feature representation of changed regions, a dual-branch difference enhancement module is constructed, which collaboratively captures global change trends and fine-grained local features through an attention-enhanced difference branch and a multi-scale convolution concatenation branch, effectively suppressing background interference. To address the semantic gap between cross-scale features, a global cross-scale spatial feature fusion decoder is proposed, which balances local detail preservation and global context perception through the synergy of spatial attention and two-dimensional selective scanning, completing refined multi-scale feature fusion and spatial resolution recovery. To rigorously validate the proposed LADENet, comprehensive experiments were conducted across four widely adopted bi-temporal benchmarks: LEVIR-CD, WHU-CD, CLCD-CD, and GVLM-CD. The presented architecture establishes substantial superiority over existing cutting-edge methodologies across primary evaluation criteria. Specifically, it yields an F1-measure of 91.06% alongside an IoU of 85.28% in the LEVIR-CD tests, while registering 90.51% (F1) and 82.45% (IoU) for WHU-CD. Similarly, robust outcomes are delivered on CLCD-CD (82.15% F1, 72.83% IoU) as well as GVLM-CD (89.12% F1, 77.78% IoU). These results demonstrate that LADENet possesses excellent detection accuracy, boundary delineation capability and generalization performance in diverse and intricate bi-temporal observation environments. Full article

The fourth industrial revolution has been discussed generously in literature, as it centers around offering high value and customized products or services to the consumer by harnessing the potential of cutting-edge technologies. It comes as no surprise that it has brought about a paradigm shift in the manufacturing and services sector; however, it is imperative to analyze the variables which influence its adoption. Although there has been an increasing number of studies helping us to understand the adoption of Industry 4.0, there is no structured and process-oriented implementation roadmap that brings together contextual factors for the adoption, nor a step-by-step methodology regarding improvements. Therefore, the authors have conducted a review in which the barriers to Industry 4.0 adoption have been analyzed in a manufacturing context and their corresponding drivers have been discussed. The study reveals that top management commitment, clear strategy, and a skilled workforce play a significant role in the adoption of Industry 4.0 technologies. Afterwards, the authors have developed a conceptual framework for Industry 4.0 adoption by combining DMAIC with a Technology–Organization–Environment (TOE) framework. The recommended framework is designed to facilitate sustainable digital transformation, helping organizations navigate through a structured ability-building process, upskill their workforce, and embrace technologies that align with sustainability objectives. From an academic perspective, the research makes key contributions to technology management literature by utilizing the TOE approach in a proper manner through DMAIC principles. For practitioners, the research work provides an easy four-step process that can assist them in adopting Industry 4.0 technologies in a proper manner. Full article

Reliable visual detection of small floating objects on the water surface is a prerequisite for environmental monitoring and clean-up tasks performed by unmanned surface vehicles (USVs) on inland waterways. Such scenes are routinely degraded by low illumination at dawn and dusk, strong specular reflections, ripple-induced clutter, and large object-scale variations, which together cause missed detections, false alarms, and unstable localization. Aiming at these practical challenges, this study conducts a scenario-oriented optimization and experimental validation based on the lightweight YOLOv11n detector. We integrate multiple mature attention mechanisms, regression loss functions and data augmentation strategies to develop an improved scheme, YOLOv11-LREP, for floating object detection. The detailed optimizations are as follows: (i) a Coordinate Attention (CoordAtt) module is inserted at the top of the backbone to enhance positional encoding and highlight obstacle-related semantic regions; (ii) three Efficient Channel Attention (ECA) modules are embedded at the multi-scale fusion nodes of the Neck so that reflection- and ripple-induced spurious channel responses can be suppressed at almost no extra cost; (iii) the Powerful-IoU (PIoU) loss replaces the original regression loss to enforce four-side boundary alignment and stabilize convergence on small, blurred-edge targets; and (iv) a joint low-light and reflection augmentation strategy, together with CutMix region-level mixing, broadens the training distribution along the illumination and occlusion axes. Experiments on the public FloW-Img dataset, split into 1200 training and 800 validation images (2024 instances) and run under a fixed random seed (seed = 0, deterministic = true), show that YOLOv11-LREP attains AP₅₀ = 80.1%, AP_50:95 = 38.5%, and AP_S = 24.3% with only 2.84 M parameters and 9.3 GFLOPs. On an NVIDIA RTX 4060 Laptop GPU, the model runs at 3.3 ms total per 640 × 640 image (≈303 FPS), satisfying real-time perception requirements while retaining lightweight deployability. The ablation results indicate that different components contribute differently to localization accuracy, small-object sensitivity, and robustness, and that the final configuration provides a balanced trade-off rather than the best value for every individual metric. A systematic threshold sensitivity analysis (F₁ fluctuation < 0.2%) demonstrates the stability of the final model. Full article

Pompe disease (PD) is a rare, autosomal recessive neuromuscular disorder caused by mutations in the gene encoding acid alpha-glucosidase (GAA). The resulting deficiency in GAA, a lysosomal enzyme, leads to the pathological accumulation of glycogen, primarily in cardiac and skeletal muscles. PD presents as a clinical continuum spanning two major phenotypes: infantile-onset Pompe disease (IOPD), the most severe form, typically characterized by onset before 12 months of age, rapid hypertrophic cardiomyopathy, and severe hypotonia; and late-onset Pompe disease (LOPD), which manifests between 12 months of age and adulthood, and is characterized by progressive axial and proximal muscle weakness and respiratory insufficiency. Enzyme replacement therapy (ERT), available since 2006, has improved survival, particularly in IOPD, but is limited by variable efficacy and limited penetration of the blood–brain barrier, necessitating new approaches. In this comprehensive review, we focus on advances in the understanding and management of PD. First, we explore recent diagnostic advances and the characterization of multisystem involvement in PD. Next, we critically discuss the advantages and limitations of current ERT approaches, and advances achieved with next-generation ERT (avalglucosidase alfa, cipaglucosidase alfa + miglustat). Finally, we summarize cutting-edge, potentially curative strategies, including substrate reduction therapy and novel experimental therapies (e.g., gene therapy) that seek to circumvent the limitations of ERT, provide durable effects, and potentially penetrate the central nervous system. Full article