Search Results (692)

With the rapid development of high-end equipment manufacturing, the number and size of complex surfaces continue to increase. Five-axis machining has become the dominant machining method. Effective prediction of cutting force and stability is of great significance for improving machining efficiency and quality. However, due to the complex and time-varying cutting geometry in five-axis machining of complex surfaces, low prediction efficiency has become a key issue restricting the research and engineering application of cutting force and stability. To address this issue, this study introduces the concept of dimensional compression and establishes an efficient prediction model for cutting force and stability. Each tool position along the tool path is discretized into inclined plane milling based on finite difference, thereby simplifying the research object. The tool twist angle and feed deflection angle are defined to describe the spatial relationship in five-axis machining. Using these two angles as new basis variables, a compressed space is constructed, and a mapping relationship between tool position and spatial point sets is established, further reducing the dimensionality of the research object. The cutting edge contact interval is determined using the spatial constraint method. Based on the full discretization method, the cutting force and stability of inclined plane milling are predicted, and the results are uniformly stored in the compressed space to form a sample point library. Consequently, the prediction process of complex surface five-axis machining is transformed into a process of sample point retrieval, significantly improving computational efficiency. Cutting force and vibration experiments in five-axis machining of complex surfaces are conducted. The results show that the predicted results are in good agreement with the experimental measurements, validating the accuracy of the proposed model and demonstrating its capability to guide practical machining. Full article

In the global transition toward low-carbon power systems with high renewable energy penetration, pumped storage has emerged as a strategic cornerstone for modern power grids. However, the collaborative planning of pumped storage and backbone-grids faces critical challenges, including the lack of explicit quantification of the resilience value of pumped storage and the coarse treatment of N-1 connectivity constraints. This paper proposes a bi-objective resilient backbone-grid planning approach that integrates the pumped-storage hub effect, aiming to minimize total life-cycle costs and the system resilience mismatch index. The proposed framework incorporates network connectivity, N-1 connectivity (edge connectivity ≥ 2), and dual-scenario power flow security as rigid constraints. Furthermore, a three-stage constrained evolutionary algorithm TER-NSGA-II is developed. During the N-1 connectivity reinforcement phase, the max-flow min-cut theorem is employed to achieve precise validation and guidance for edge-connectivity enhancement. Case studies on the IEEE 118-bus system, together with extended validation on the IEEE 300-bus system, show that the proposed method can explicitly quantify the resilience value of pumped storage, obtain Pareto solutions that balance economy and resilience under strict edge-connectivity constraints, and demonstrate competitive overall performance in terms of solution-set quality, feasible-domain search stability, and scalability compared with NSGA-II and the more recent NSGA-III/NG benchmark. Full article

This study presents a simulation-assisted engineering framework intended to support comparative machining parameter selection for quartz sintered materials. The approach integrates CAD/CAM-based analysis, an illustrative Design of Experiments (DOE) framework, and preliminary experimental validation to improve process planning and machining quality. The analysis focuses on key technological parameters, including cutting speed (vc), feed rate (f), and depth of cut (ap), evaluated across cutting, milling, and finishing stages. The results indicate that feed rate is the dominant parameter influencing process stability, surface quality, and edge integrity. A practical transition region of approximately 1200 mm/min was identified, above which increased vibration, defect formation, and surface degradation occur. The complementary DOE analysis confirms the relative importance of process parameters and reveals interaction effects, particularly between feed rate and depth of cut, which significantly influence defect formation under high-load conditions. Preliminary industrial observations provide trend-oriented support for the simulation-predicted process behavior. Based on the integrated analysis, a preliminary technological operating region was identified (vc = 1080–1320 m/min, f = 800–1200 mm/min, ap = 0.5–1.0 mm), suggesting a practical compromise between machining efficiency and surface integrity. The proposed methodology provides preliminary engineering support for comparative process planning and defect-reduction-oriented parameter selection in the machining of brittle materials. The novelty of this work lies in the integration of CAD/CAM simulation, DOE-based interaction analysis, and experimental validation for supporting the identification of a practical technological operating region for machining brittle materials. The presented results should therefore be interpreted as engineering-oriented comparative process-planning guidelines rather than statistically generalized machining laws. The presented study should be interpreted as an exploratory simulation-assisted engineering investigation intended to support comparative process planning rather than as a fully experimentally validated machining model. Full article

Quantitative ¹H NMR (qNMR) is a versatile analytical tool that provides simultaneous structural and quantitative information without the need for analyte-specific standards. This review summarizes its key methodological fundamentals and broad applications in both pharmaceutical and biomedical analysis. In drug analysis, qNMR enables content determination and purity assessment of small molecules, polysaccharides and glycoconjugates, synthetic polymers, and complex herbal medicines. In biomedical analysis, it serves as a powerful platform for metabolomics profiling, real-time monitoring of cellular processes, and absolute quantification of metabolites in biofluids and tissues. Recent and emerging technological advancements, including hyperpolarization, quantum mechanical spectral analysis, artificial intelligence, and deep learning, hold great promise for further enhancing sensitivity, resolving power, and automation. With ongoing integration into pharmacopoeial standards and regulatory frameworks, qNMR is poised to expand its role in both routine quality control and cutting-edge biomedical research. Full article

Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., “Parsha (Scab),” “Brown spotting,” “White-Gray spotting,” and “Rust,” which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management. Full article

Ultra-precision machining of ferrous alloys remains challenging because conventional diamond tools suffer severe thermochemical wear, whereas ultrasonic vibration-assisted cutting requires complex and costly equipment. This study investigates single-crystal sapphire as an alternative cutting-tool material for ultra-precision machining of both non-ferrous and ferrous metals. A sapphire tool was fabricated from a polished wafer, laser-shaped into an equilateral triangular insert, vacuum-brazed onto a tungsten carbide carrier, and finished by ultra-fine grinding to yield a well-defined cutting edge. Ultra-precision turning experiments were conducted on copper and 420 stainless steel using a Moore Nanotech 350FG lathe, and the performance of the sapphire tool was benchmarked against conventional diamond (copper) and cubic boron nitride (CBN) tools (stainless steel) under comparable cutting conditions. Surface roughness (Ra) and topography were characterized using an optical surface profiler, while scanning electron microscopy and atomic force microscopy were employed to assess tool wear and cutting-edge geometry. The sapphire tool produced mirror-like surfaces with average surface roughness (Ra) values of 6.4 nm on copper and 39.1 nm on 420 stainless steel, compared with 1.3 nm for diamond on copper and 92.9 nm for CBN on stainless steel. Across both materials, sapphire generated regular, stable tool marks and exhibited minimal wear, with no catastrophic edge degradation or clear evidence of severe chemical interaction with the steel workpiece. These results demonstrate that sapphire is a viable tool material for extending diamond turning-level surface quality to stainless steel without ultrasonic assistance. Full article

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety. Full article

This study investigates the influence of process parameters on dimensional accuracy and microhardness in fiber laser cutting of low-carbon steel. A full factorial design of experiments (DOE) with three factors—cutting speed, focal position, and assist gas pressure—was applied to evaluate their effects on dimensional deviations and microhardness in the heat-affected zone (HAZ). The results showed that focal position is the most significant factor affecting all evaluated dimensional responses, while cutting speed has a strong influence on circular and linear dimensions. The effect of assist gas pressure was found to be response-dependent, being insignificant for inner diameter deviation but significant for selected linear features and through interaction effects with focal position. Statistical analysis confirmed the presence of significant interaction effects between process parameters. Microhardness measurements revealed a substantial increase in hardness from the base material toward the cut edge, indicating microstructural transformations caused by rapid thermal cycles during laser cutting. While this increase in hardness may enhance wear resistance, it may also lead to increased brittleness and reduced toughness. The findings provide a detailed insight into the relationship between process parameters and dimensional accuracy, highlighting the importance of parameter optimization and interaction effects in contributing to improved quality of laser-cut components. Full article

Deep hard-rock and geothermal drilling expose polycrystalline diamond compact (PDC) cutter chamfers to coupled thermal shock, abrasive wear, and intermittent impact, which accelerates edge spalling and degrades the quality of on-bit monitoring signals. This bench-scale proof-of-concept study evaluates a surface-gradient architecture that combines shallow cobalt leaching in the chamfer region with a thin silicon carbide (SiC) interlayer and a nanocrystalline diamond topcoat. Commercial 13 mm PDC cutters were treated within a surface-gradient design window of $t_{\mathrm{SiC}}$ = 0–1.0 μm and $L_{\mathrm{deCo}}$ = 0–200 μm, and were examined by cross-sectional microscopy, XPS/ToF-SIMS, Raman stress mapping, scratch adhesion, apparent fracture toughness, laser-flash thermal transport, thermal-shock cycling, 400 °C pin-on-disc wear, instrumented impact loading, bench granite-drilling signal acquisition, and finite-element correlation. The optimized configuration ($t_{\mathrm{SiC}}\approx 0.7\mathsf{\mu }\mathrm{m}$, $t_{\mathrm{D}}\approx 5\mathsf{\mu }\mathrm{m}$, and $L_{\mathrm{deCo}}\approx 100\mathsf{\mu }\mathrm{m}$) reduced the 95th-percentile tensile residual stress at the chamfer from about 0.48 to 0.26 GPa, reached a scratch critical load of about 28 N, compared with about 16 N for the topcoat-only condition and about 25 N for the SiC-plus-topcoat condition, cut high-temperature wear volume by about 40%, and shifted the characteristic spalling energy from about 0.8 to 1.3 J. In bench-scale granite drilling, the same design stabilized frictional response and improved simple pre-spall discrimination metrics, raising ROC-AUC from about 0.65 to 0.87. These bench-scale results provide proof-of-concept evidence that surface-gradient design can improve PDC chamfer durability and signal discriminability, while the proposed signal metrics have yet to be validated under field-scale downhole conditions. Full article

Regarding the current issues in Xinjiang, China, during the harvesting of cotton stalks, the lack of specialized, efficient, and durable cutting blades for cotton stalks causes uneven cutting, high power consumption, and short blade life. In this study, a biomimetic serrated blade was designed based on the Trictenotomidae mandible for efficient, low-power-consumption cutting. The biomimetic design, FEM-SPH coupled simulation, bench test, combined with response surface methodology, and field test were used. The simulation results showed that under the same working conditions, the maximum shear stress was 34.81% lower than that for the ordinary blade and 22.05% lower than that for the ordinary serrated blade. And the bench test results showed that cutting power consumption was reduced by about 20.12% and 15.69% compared to the ordinary cutting blade and serrated cutting blade, respectively. When cutting velocity was 1.3 m/s, cutting inclination angle was 11°, and ratio of cutting velocity and feeding velocity was 1.1, the biomimetic serrated cutting blade could achieve effective cutting of cotton stalks and obtain better quality of cutting—the cutting power per unit area and the cutting-edge angle after cutting cotton stalks were 52.08 kJ/m² and 6°, respectively. The research results can provide a theoretical basis and support for the utilization of cotton stalks out of the field, as well as the cutting of other similar crop stalks. Full article

Winter pruning is a key management practice in viticulture that directly affects vine architecture, yield balance, and grape quality. At the same time, it is a highly labor-intensive operation, and the selective identification of appropriate cutting locations remains one of the main challenges limiting the automation of pruning in vineyards. Advances in machine vision provide new opportunities to support the development of robotic pruning systems. The objective of this study was to develop and evaluate a vision-based method for estimating grapevine pruning points and cutting lines using instance segmentation outputs generated by YOLO models. A dataset of 1500 RGB images of dormant grapevines was collected under field conditions in the Nobilis vineyard located in southeastern Poland. Two annotation strategies were implemented to define pruning regions. YOLO-based instance segmentation models were trained and evaluated for detecting cutting-related structures. Based on the predicted segmentation masks, a geometry-based method termed PCAcutSeg-V was developed to estimate class-dependent cutting points and cutting lines using principal component analysis applied to object contours. The results indicate that YOLOv8 and YOLO11 architectures achieved the highest segmentation performance among the evaluated models. The simplified annotation strategy provided more stable geometric inputs for the PCAcutSeg-V method, enabling more reliable estimation of cutting points and cutting lines compared with the extended annotation approach. When combined with the PCAcutSeg-V method, the proposed perception–geometry pipeline achieved high effectiveness in pruning decision estimation. The method was further implemented in a real-time processing pipeline using an RGB camera and an edge computing platform, where it maintained performance consistent with the results obtained from offline image analysis. These findings demonstrate that combining deep learning-based instance segmentation with deterministic geometric reasoning enables accurate and interpretable estimation of grapevine pruning locations and provides a promising foundation for future autonomous pruning systems. Full article

Leveraging artificial intelligence (AI)―a cutting-edge technological tool―to drive the new quality transformation of Chinese manufacturing is a crucial foundation for China’s steady advancement of the new real economy, as well as an inevitable requirement for China to align with contemporary economic and technological trends. This study constructs a multi-sectoral equilibrium model to theoretically analyze the focal points of the new quality transformation in Chinese manufacturing and the impact AI has on it, followed by corresponding empirical tests. The results indicate that (1) AI has a positive impact on the qualitative transformation of China’s manufacturing sector; a one-unit increase in a firm’s AI level leads to a 0.171-unit increase in the sector’s qualitative transformation level. (2) This impact exhibits heterogeneity at the firm, industry, and regional levels. At the firm level, the impact varies depending on firm size, digitalization level, operational performance, internal control strength, and governance quality. At the industry level, the impact varies depending on technology intensity, industrial structure, strategic importance, and green development level. At the regional level, heterogeneity is reflected in geographical location, natural resource endowments, and the degree of urban agglomeration. (3) Artificial intelligence promotes the new quality transformation of Chinese manufacturing through the following mechanisms: reducing time lag costs and transaction costs in market penetration mechanisms; enhancing the quality of cutting-edge factor combinations and key core technologies in advanced innovation mechanisms; and improving resource utilization and operational management efficiency in lean production mechanisms. Full article

The assessment of histopathological features has evolved considerably, transitioning from traditional manual measurements to more sophisticated, technology-assisted approaches. Classical histological evaluation, while foundational and highly reliable, is inherently labor-intensive and subject to inter-observer variability. With the advent of digital pathology, these practices have been progressively enhanced by image processing software, which offers capabilities such as segmentation, feature extraction, and data visualization. However, despite their promise, the integration of machine learning into this branch of pathology faces notable challenges, such as the need for large, high-quality annotated datasets and the integration into existing workflows, which remain significant hurdles. Looking forward, the role of specialists in histological evaluation remains crucial in this evolving landscape. While automation streamlines routine tasks, the expertise of pathologists is indispensable in validating results and interpreting findings in scientific contexts. This comprehensive review explores the trajectory of histological evaluation methods, from manual and classical strategies to cutting-edge digital tools, highlighting the benefits, limitations, and implications of each approach in contemporary practice. Full article

Lipid nanoparticles (LNPs) have emerged as a groundbreaking delivery platform, revolutionizing the development of nucleic acid-based medicines for gene delivery and gene therapy. This review provides an insightful industrial perspective on the production process of LNPs, focusing on cutting-edge manufacturing equipment, downstream processing and the crucial transition from laboratory to large scale. While LNP production in the discovery phase relies on a small scale (µL to mL) for screening various LNP formulation candidates, transferring to preclinical (up to hundreds of mL) and clinical/commercial scales (up to liters) requires a robust and reproducible manufacturing process. Thus, mixing technologies throughout these scales must be carefully selected and require precision, scalability and high reproducibility to meet the target quality of the LNP drug product. Key mixing technologies in mRNA-LNP production primarily include microfluidic systems and impinging jet mixers (IJMs). In this review, we discuss key critical process parameters (CPPs) in LNP preparation, including flow rate ratio (FRR) or total flow rate (TFR), in relation to associated critical quality attributes (CQAs) across multiple manufacturing scales. We further assess the impact of downstream processing, specifically tangential flow filtration (TFF), on the formulation’s CQAs. In particular, the review highlights the importance of maintaining CQAs along each step of the process and emphasizes the role of robust analytical methods in ensuring product quality and safety. Additionally, we touch on current challenges associated with these advanced delivery vehicles, such as their long-term stability, and introduce the readership to innovative stabilization strategies aimed to extent LNP shelf-life. Full article

Non-intrusive load monitoring (NILM), as a key technology for decomposing power loads by analyzing aggregate electrical signals, holds significant importance for advancing refined energy management and achieving carbon peaking and carbon neutrality goals. This paper systematically reviews the technical processes of event-based and state-based NILM methods. It focuses on analyzing key technical challenges in typical application scenarios, such as real-time feedback, energy efficiency optimization, and demand response. These challenges include balancing high real-time performance with accuracy, leveraging edge computing while ensuring privacy protection, and addressing issues like unknown load identification and user behavior modeling. Furthermore, this paper discusses cross-cutting challenges related to data quality, algorithm transferability, system integration, and cost. This review aims to provide a systematic, scenario-based analytical framework to facilitate the transition of NILM from theoretical research to practical application, offering insights for subsequent technological development and engineering implementation. Full article