Search Results (559)

Pincer nail is a severe variant of nail deformity characterized by excessive transverse curvature of the nail, impinging on both lateral nail folds. The distal end is the most curved and grows inward, squeezing both lateral nail folds, resulting in varying degrees of pain and discomfort, making it difficult for patients to wear shoes, hindering walking, and severely impacting the quality of life. Although most previous nail surgical procedures have demonstrated good outcomes, these complicated operations result in a long recovery period and painful nail extraction. Here we present a tissue-preserving combination therapy of nail grinding followed by limited nail fold resection without nail plate extraction. By preserving the integrity of the nail bed and matrix, this approach avoids the extensive dissection and avulsion trauma inherent in conventional procedures, offering reduced postoperative pain, faster recovery, and minimal cosmetic alteration. Full article

Cellulose nanofibrils (CNFs) are promising renewable materials, but high fibrillation energy remains a major barrier to industrial adoption. This narrative, mechanism-focused review examines CNF production from an engineering perspective, integrating the coupled roles of feedstock structure, interfacial chemistry, hydrodynamic stress transfer, and process boundary definition. This review examines energy consumption in CNF production by linking interfacial cohesion within cellulose fiber walls, hydrodynamic stress generation in fibrillation devices, and nonproductive energy-dissipation pathways. The principal mechanical routes—high-pressure homogenization, microfluidization, grinding and refining, and high-consistency extrusion—are compared with chemical, enzymatic, and interfacial pretreatments that reduce cohesive resistance or suppress re-agglomeration. Attention is given to feedstock composition, hornification history, solids content, and process boundary definitions, because these factors strongly influence both specific energy consumption and total process energy. Across the literature, meaningful energy reduction is achieved not by equipment choice alone but by co-optimizing feedstock design, pretreatment chemistry, and stress-transfer efficiency while limiting viscous losses, elastic recovery, and fibril reassociation. The review also highlights persistent comparability problems caused by inconsistent reporting of solids content, pass number, product quality, and system boundaries. A unified framework is proposed in which energy-efficient CNF production depends on three coupled objectives: lowering interfacial cohesion, improving productive stress localization, and reducing dissipation across the full process chain while evaluating energy demand against clearly defined process boundaries and product quality endpoints. Full article

To promote the high-value utilization of coal gasification slag (CGS) resources and mitigate the environmental issues caused by its accumulation, CGS was separated into five fractions by particle size (2.36–4.75 mm, 1.18–2.36 mm, 0.60–1.18 mm, 0.30–0.60 mm, and 0.15–0.30 mm) and subsequently ground into CGS powders (CGSPs). The physicochemical properties of both CGS and the obtained CGSP were systematically characterized, and the effects of CGSP on the hydration behavior and engineering performance of ordinary Portland cement (OPC) were investigated. The results revealed significant differences in physical properties and composition among the various particle-size fractions of CGS and their corresponding CGSP. When 40 wt.% CGSP was used to replace Portland cement, the C2.36 fraction exhibited the highest early-age compressive strength (16.91 MPa and 25.3 MPa at 3 d and 7 d, respectively), which is attributed to its favorable chemical composition and abundant glassy components. In contrast, the C0.6 fraction achieved the highest 28 d compressive strength (50.0 MPa). The C0.15 fraction showed the lowest strength at all ages, may be mainly due to its high residual carbon content and low reactivity. Overall, the compositional differences among CGS fractions of different particle sizes govern the formation and evolution of hydration products, and the proposed strategy of “classified grinding and quality-oriented utilization” provides an effective approach for the high-value application of CGS in cement-based materials. Full article

This study investigated the effects of roasting temperature (130 °C, 150 °C, 180 °C) and grinding fineness (10, 50, and 100 mesh) on the nutritional quality and physicochemical properties of high-resistant starch rice flour (Youtangdao 3). The results revealed that the process of whole-grain moist roasting, followed by milling and sieving, markedly enhanced the resistant starch (RS) content while concurrently reducing the fraction of rapidly digestible starch (RDS). Specifically, roasting at 130 °C with coarse grinding (R130-10) yielded the highest absolute RS content (27.45 g/100 g). However, for fine-particle fractions essential for food texture, roasting at 150 °C (R150-100) proved optimal by achieving the highest RS proportion within the starch matrix (89.36% by Englyst method). Structural analysis via X-ray diffraction (XRD) and scanning electron microscopy (SEM) revealed that roasting facilitated the reorganization of starch chains. This molecular rearrangement led to a pronounced increase in crystallinity and induced distinct morphological alterations within the granules. Furthermore, Rapid Visco Analyzer (RVA) profiling indicated a substantial decline in peak viscosity for all roasted samples, indicating a significant loss of pasting ability and an increase in structural rigidity. In conclusion, the combination of moderate roasting temperature and precise particle size control effectively optimizes the nutritional value of high-RS rice flour. These insights provide a theoretical basis for developing high-RS rice flour ingredients. Full article

As an important part in the mining crushing and grinding circuit, the high-pressure grinding roll (HPGR) is essential for energy efficiency, cost reduction, quality improvement and efficiency gains; therefore, it plays a key role in sustaining stable production and intelligent control of mining operations. Its instantaneous throughput and processing capacity directly influence the efficiency of the comminution system, the compatibility of production scheduling, and overall energy consumption. Therefore, these metrics serve as important indicators for intelligent optimization and stable operation. However, it is a difficult task to predict the throughput of HPGRs by traditional purely data-driven models. The process is characterized by strong nonlinearity, significant time-lag effects, and limited physical consistency. To address these challenges, this work develops a tailored throughput prediction framework for HPGRs by combining Gated Recurrent Units with Physics-Informed Neural Networks (GRU-PINN), which integrates an HPGR-specific volumetric throughput mechanism as a dedicated physical constraint. This approach first exploits the GRU network’s “reset” and “update” gates to selectively filter historical operating information while adaptively updating the current process features. This allows the model to better capture complex long-term temporal dependencies in the production data. Additionally, based on volumetric analysis and the principles of bed comminution, the HPGR throughput formula is incorporated into the loss function as a physical constraint. Together, these components establish a joint optimization framework that combines data-driven learning with physical constraints to correct prediction biases generated by the neural network during abrupt changes in operating conditions. The proposed GRU-PINN model was validated using real operational data collected from an industrial mining site. The results show that the proposed framework significantly improves the physical consistency of HPGR throughput predictions and, at the same time, effectively corrects prediction biases, which are commonly observed in conventional purely data-driven models under complex operating conditions. It also exhibits improved robustness and lower inference latency. Compared with other benchmark models, the proposed method displays superior overall performance across key evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), prediction accuracy, and inference speed. These findings confirm that the proposed method greatly enhances the physical interpretability of the prediction model without compromising accuracy. Consequently, it lays a solid foundation for process parameter optimization and intelligent control of HPGR system. Full article

Reliable online condition monitoring is essential for maintaining process stability and guaranteeing machining quality in precision grinding. Against this background, this study proposes an electrostatic induction-based measurement strategy and further performs systematic comparisons with conventional force-based monitoring methods. First, an electrostatic sensor model is established to quantitatively characterize charge transfer behaviors during grinding interactions. A three-axis experimental grinding platform is deployed to correlate electrostatic responses and mechanical force signals with critical grinding variables, including grinding speed, depth of cut, feed rate, and wheel wear severity. To achieve quantitative and objective evaluation, a unit-free Dynamic Sensitivity Change Degree (DSCD) index is introduced. Comparative results based on the DSCD index reveal that electrostatic signals exhibit better performance than traditional force-based signals in tracking grinding wheel speed variations and progressive wear evolution under the tested conditions. Meanwhile, the DSCD fluctuation in electrostatic signals remains within 1% under varying cutting depths, indicating favorable linear stability within the scope of the present experiments. Furthermore, a Generalized Conditional Variational Auto-Encoder (G-CVAE) model is developed to augment insufficient wheel wear datasets. The generated synthetic signals exhibit high fidelity and enable accurate and robust classification of grinding wheel wear states. This study verifies the existence of explicit quantitative correlations between electrostatic induction signals and key grinding parameters as well as wheel wear conditions. The proposed monitoring method can provide high-quality, reliable data support for subsequent grinding condition assessment and intelligent process decision-making. Full article

Highland barley (HB) is nutritionally rich but its low gluten content limits its use in wheat-based staple products. This study systematically compared the effects of substituting wheat flour with superfine grinding modified highland barley flour (SG-HBF) or ultrasonically modified highland barley flour (US-HBF) at 10–30% ratios on the properties and quality of wheat flour, dough, and noodles. Results showed that SG-HBF reduced the peak viscosity, breakdown, and setback value of the blended flour, enhanced its thermal stability and anti-aging properties; whereas, US-HBF significantly increased the viscosity. Noodles maintained good sensory and cooking quality when SG-HBF ≤ 15% or US-HBF ≤ 20%. Beyond these thresholds, the cooking loss increased sharply and overall acceptability declined. At the same substitution ratio, SG-HBF outperformed US-HBF in terms of water distribution, cooking loss, and sensory scores, offering better processing efficiency, while US-HBF provides higher springiness and lower broken rate, suitable for products requiring noodle integrity. This study provides a reference for the application of modified HBF in wheat-based products. Full article

When a robot performs a grinding operation, the steady-state force/position tracking accuracy of its end-effector is critical to achieving high grinding quality. To solve this problem, a fuzzy adaptive impedance method is incorporated into the robot’s compliant control framework. Firstly, the robot dynamics model is established based on the Newton–Euler method. To describe the robot dynamics more comprehensively, a linear friction compensation model is also introduced. Secondly, a dynamic feedforward trajectory-tracking controller is proposed based on the dynamic model, and its stability is verified using a Lyapunov function. The impedance parameters are adjusted in real time according to the feedback contact force and its rate of change, thereby enabling dynamic equilibrium between the end contact force and end position. This allows the robot end-effector to exhibit compliance during external environmental interactions. Finally, a control platform of a force/position compliance controller was constructed, and two grinding conditions of plane and arc were designed to validate the effectiveness of force/position compliance control based on impedance control. Compared with the fixed impedance approach, the proposed method reduces overshoot by 11.6% (plane) and 12.45% (arc), improves surface roughness from Ra 0.042 μm to Ra 0.021 μm, and achieves faster force tracking with fewer oscillations. Full article

Peanut butter combines nutritional density with two hazard profiles that are often discussed separately: persistence of enteric pathogens in a low-moisture, lipid-rich matrix and pre-processing contamination with aflatoxins. This structured narrative review synthesizes evidence identified in PubMed, Scopus, Web of Science, and Google Scholar between January and June 2026 and asks whether food-matrix characteristics change not only nutrient release but also the reliability and timing of safety controls. The evidence is strongest for prolonged survival and matrix-enhanced heat resistance of Salmonella, the heterogeneous distribution of aflatoxins among kernels, and the limited corrective value of roasting for contaminated lots. Direct studies that measure nutritional and safety outcomes within the same peanut butter formulation are scarce; the proposed framework is therefore an integrative interpretation rather than a quantitatively validated model. In our assessment, the practical value of the matrix concept lies in three decisions: controlling aflatoxins before grinding, validating microbial lethality in the actual product, and protecting post-lethality areas from recontamination. These conclusions support hazard-specific risk management that preserves nutritional and sensory quality without treating shelf stability as evidence of microbiological safety. Full article

Efficient blasting design is increasingly regarded not only as a geomechanical problem but also as a managerial challenge related to energy use, fragmentation quality, downstream comminution costs, and environmental performance. This study develops a shock-adiabat-based analytical model for predicting the radius of the crushing zone around borehole explosive charges and demonstrates its applicability as a decision support tool for energy- and cost-oriented blasting management. The model integrates shock wave propagation parameters, particle velocity behind the shock front, and the physical and mechanical properties of limestone, sandstone, and granite. The calculated crushing zone radiation was compared with a previously developed analytical model based on borehole pressure and validated using finite element simulations in SolidWorks Simulation. The discrepancy between the proposed shock adiabat model and the reference analytical solution did not exceed 6%, while the difference between analytical estimates and numerical simulations remained below 5%. The results show that borehole diameter, compressive strength, and explosive–rock interface pressure significantly affect the crushing zone radius and, consequently, the volume of rock effectively fragmented during blasting. A scenario-based assessment further indicates that improved prediction and management of the crushing zone may reduce downstream crushing and grinding energy demand by approximately 10–20%, generating potential cost savings and indirect CO₂ emission reductions. The proposed method therefore supports the management of blasting energy efficiency, fragmentation quality, operational costs, and sustainability performance in mineral extraction systems. Full article

Taking pickling sludge generated from the steel rolling process at a Chinese steel mill as the subject of study, this research investigates a combined mineral processing and metallurgical process involving gasification-reduction roasting, magnetic separation, and waste acid recovery. The main elements in this pickling sludge are Fe and Cl, with contents of 46.50% and 12.70%, respectively. The primary component is chlorine-containing iron oxide, and a significant amount of amorphous material is also present. The study investigated the effects of various calcination temperatures, calcination times, reducing agent dosages, and material thicknesses on gasification-reduction performance indicators. The results indicate that using a reduction calcination–grinding–magnetic separation process, with coal as the reducing agent, a calcination temperature of 1100 °C, a reducing agent dosage of 15%, and a calcination time of 2 h, the chlorine volatilization rate exceeds 97%. Furthermore, when the roasted ore is ground to a particle size where 85% passes through a −0.076 mm screen and is recovered via magnetic separation, an iron concentrate with a grade of over 69.50% can be obtained. This iron concentrate meets the quality requirements for high-grade iron concentrates used in direct reduced iron (DRI) production. The small amount of tailings from the iron concentration process can be utilized in the production of bricks, cement, and other products, thereby achieving the efficient comprehensive utilization of acid washing sludge. Full article

Efficient separation of cocoa shell and kernel is a critical operation in semi-finished cocoa processing, where conventional mechanical methods such as grinding, cutting, and rubbing often generate excessive heat, leading to cocoa butter melting and degradation of kernel quality due to its high fat content. To overcome these limitations, this study pro-poses a dynamic impact-based framework for a small-scale centrifugal cracking system, in which fracture is induced by controlled kinetic impact rather than compressive loading. A combined theoretical and experimental investigation was conducted on roasted cocoa beans at a small industrial scale. Mechanical characterization showed that the mean and maximum shell fracture forces were 23.515 N and 54.382 N, respectively, while kernel fracture forces were significantly higher at 91.896 N and 195.327 N. A dynamic analysis of the centrifugal cracker identified a critical rotational speed range of 812–975 rpm, corresponding to impact velocities of 17.14–20.57 m/s and kinetic energies of 0.17–0.25 J per bean. Experimental validation indicated an optimal operating range of 860–900 rpm, achieving less than 1.1% uncracked beans and less than 2% fine nibs (<3 mm). Below 800 rpm, incomplete cracking was observed, whereas speeds above 950 rpm increased kernel fragmentation. These results demonstrate that precise control of impact energy is the key factor governing efficient centrifugal cracking performance in cocoa processing. Full article

Sensory evaluation is the reference method for assessing specialty coffee quality; however, the descriptive narratives generated by certified Q Arabica Graders remain an underutilized source of information. This study developed an integrated analytical framework combining conventional sensory evaluation with natural language processing (NLP) to characterize the evolution of specialty coffee quality during accelerated storage under different packaging systems. Green and roasted coffee stored in eight packaging configurations were subjected to accelerated storage at 40, 50, and 60 °C, and sensory evaluations were performed according to the Specialty Coffee Association protocol. Textual sensory descriptions were analyzed using descriptor frequency analysis, term frequency–inverse document frequency (TF–IDF) weighting, co-occurrence networks, topic modeling, and topic prevalence analysis. The results demonstrated that the evaluated packaging–product configurations (PPCs), together with storage temperature, influenced the sensory stability of specialty coffee under accelerated storage conditions. Vacuum packaging and multilayer laminated bags more effectively preserved desirable sensory attributes and higher cup scores, whereas elevated temperatures and coffee grinding accelerated quality deterioration, leading to the progressive replacement of freshness-related descriptors by undesirable storage-related sensory characteristics. The combined application of multiple text-mining approaches consistently revealed systematic semantic changes in sensory perception that complemented conventional cup scores and provided a more comprehensive characterization of quality evolution during storage. These findings demonstrate that integrating conventional sensory evaluation with natural language processing transforms expert sensory narratives into reproducible quantitative information, providing a reproducible analytical framework for the objective characterization and comparison of sensory changes during accelerated storage of specialty coffee. Full article

In conventional CNC machining, the workpiece clamping pose is registered with a preset CAD model under multiple geometric constraints to establish the machining reference frame. The tool path, generated from this model, is subsequently used to produce components of identical geometry. However, this paradigm proves inadequate when a final shape must accommodate morphological variations specific to each individual blank. Manual grinding, as an alternative, is not only inefficient and hazardous but also relies heavily on subjective quality assessment. To address these challenges, we propose an adaptive local-region milling strategy tailored for blanks with similar yet non-identical surface morphologies, enabling the finished geometry to adjust dynamically to each workpiece. Under conditions of under-constrained clamping, visual positioning is first employed to automatically locate the target regions. Line laser scanning is then conducted over the planned area to acquire high-density point clouds. Through segmentation, points lying outside the region to be machined are extracted, from which a theoretical post-machining surface is reconstructed. Milling toolpaths are subsequently planned based on this reconstructed model to compensate for surface variations across different blanks. Experimental validation on a three-axis CNC milling machine demonstrates that the proposed adaptive strategy effectively replaces manual grinding by removing the bulk of the machining allowance from locally variant surfaces. With the reconstructed model serving as the reference, 77.1 percent of the machining errors fall below 0.055 mm. These results confirm that the method yields a smooth and level surface finish, thereby meeting the fundamental requirements for such adaptive machining tasks. Full article

Accurate and moderate rice processing is a key strategy for ensuring national food security and improving population nutritional status. This article synthesizes the technical scope and development trajectory of moderate processing, examines the evolution from multistage light grinding to AI-driven targeted grinding, and analyzes the balance between processing accuracy and eating quality, nutritional retention, storage stability, and safety risks. Evidence suggests that the core of moderate processing lies in the dynamic alignment of rice variety with processing targets, supported by intelligent online detection technologies that enable precise control. China’s rice processing industry is currently transitioning from experience-driven to data-driven operations; however, it continues to face challenges, including underdeveloped evaluation standards, high technology adoption costs, and limited consumer awareness. Future efforts should prioritize loss reduction, nutritional preservation, and intelligent processing to support coordinated advancement across the precision and moderate rice processing value chain. Moderate processing of rice preserves more native nutrients and plays an important role in improving residents’ dietary structure and safeguarding public health, achieving the best balance between saving food, preserving nutrients, and improving efficiency. Full article