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The microstructure and mechanical properties have a significant impact on the service performance of materials. This paper compared the microstructure and mechanical properties of 2024 aluminum alloy subjected to turning and ultrasonic surface rolling process (USRP). It was found that both turning and USRP can cause grain refinement and increased microhardness. The grain refinement induced by USRP was attributed to dislocation proliferation and T-phase fragmentation; At the same time, a quantitative relationship model between microhardness and microstructure during turning and USRP was constructed. The results showed that precipitation had the greatest impact on the microhardness induced by turning, while dislocations and grain refinement had the greatest impact on the microhardness induced by USRP; In addition, the mechanical performance response mechanism of the materials after turning and USRP was elucidated, and it was found that USRP can significantly enhance the yield strength of the materials.

The fatigue load that a material experiences and its microstructure are important factors influencing fatigue crack propagation behavior. This study employed laser scanning microscopy and electron backscatter diffraction (EBSD) technology, along with fatigue crack propagation experiments, to investigate the fatigue crack propagation behavior of 2024 aluminum alloy under varying stress ratios (R). The results showed that the stress amplitude (σp) was the main factor controlling the fatigue crack propagation life (N). Additionally, detailed characterization of the fatigue crack propagation path was conducted using crystal models and EBSD. A fatigue crack propagation model for 2024 aluminum alloy under different R-values was established based on the grain twist angle and Schmid factor. Finally, the impact of R on the crack tip shielding (Ks) was systematically analyzed, elucidating the intrinsic mapping relationship between R, microstructure, and crack propagation characteristics.

Abstract Federated learning is a widely used distributed learning approach in recent years, however, despite model training from collecting data become to gathering parameters, privacy violations may occur when publishing and sharing models. A dynamic approach is proposed to add Gaussian noise more effectively and apply differential privacy to federal deep learning. Concretely, it is abandoning the traditional way of equally distributing the privacy budget ϵ and adjusting the privacy budget to accommodate gradient descent federation learning dynamically, where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created manually. It also incorporates adaptive threshold cropping to control the sensitivity, and finally, moments accountant is used to counting the ϵ consumed on the privacy‐preserving, and learning is stopped only if the ϵtotal by clients setting is reached, this allows the privacy budget to be adequately explored for model training. The experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy, which is significantly better than the differential privacy method used by TensorFlow.

High-performance manufacturing (HPM) is essential for high-quality equipment and parts manufacturing, which requires not only geometric accuracy but also control of surface integrity to satisfy functional performance. Surface topography and residual stress are important elements of surface integrity. However, they are always investigated individually, and their collaborative formation pattern has been ignored for a long time. In this study, surface topography will be measured by a 3D laser scanning microscope and analyzed from the perspective of spectral wavelength (λ) along with its corresponding amplitude. Whereas the residual stress (RS) is going to be analyzed by X-Ray diffraction and finite element (FE) model. Thereafter we introduce the formation pattern of both surface topography and RS. Hereby, simulated points clouds of machined surface topography are generated by superimposing the normally distributed random numbers that take into account the springback of the workpiece material response on tool kinematics and geometry. Furthermore, the RS is divided into mechanical loads induced (RSM) and thermal loads induce (RST) according to the mechanism of formation, which were obtained in cutting speed (Y-direction) and feed direction (Z-direction) by the Mohr circle. Our experimental and numerical results showed that an increase in the feed rate (from 0.05 mm/r to 0.15 mm/r) significantly increases the surface roughness and cutting temperature. The increase in temperature increased the RST by up to 286.1%, resulting in a reduction of compressive RS in Z-direction and an increase of tensile RS in Y-direction. Moreover, the increase in cutting speed (from 40 m/min to 80 m/min) results in a more regular surface and a reduction in surface roughness because of the smaller springback. At the same time, it also leads to a decrease of RSM in Z-direction and an increase of RSM in Y-direction. In addition, the increment in cutting speed leads to a minor increase in temperature and RST (maximum increase of 47.0%). Combining the effects of RSM and RST, RS in both directions increases slightly.

Due to the different composition of cutting fluid will affect the failure stress of the material in the shear deformation area, so the rational use of cutting fluid can optimize the cutting force and the surface integrity of parts. Four kinds of hat-shaped samples with shear band widths were designed. The effects of different cutting fluids on mechanical properties of ultrahigh strength steel at different shear strain rates were studied in this paper by SHPB, and verified by cutting experiments. The stress–strain curve, shear failure stress, fracture morphology and cutting force were studied systematically. Experimental results show that the stress remains stable for a period of time with the increases of strain after strain hardening at γs= 10.03 × 104 1/s. All shear fracture is ductile fracture and at γs

Mode-division multiplexing (MDM) is a promising multiplexing technique to further improve the transmission capacity of optical communication and on-chip optical interconnection systems. Furthermore, the multimode optical switch is of great importance in the MDM system, since it makes the MDM system more flexible by directly switching multiple spatial signals simultaneously. In this paper, we proposed a mode-independent optical switch based on the graphene–polymer hybrid waveguide platform that could process the TE11, TE12, TE21 and TE22 modes in a few-mode waveguide. The presented switch is independent of the four guided modes, optimizing the buried position of graphene capacitors in the polymer waveguide to regulate the coplanar interaction between the graphene capacitors and spatial modes. The TE11, TE12, TE21 and TE22 modes can be regulated simultaneously by changing the chemical potential of graphene capacitors in a straight waveguide. Our presented switch can enable the independent management of the spatial modes to be more flexible and efficient and has wide application in the MDM transmission systems.

ObjectiveRandom skin flaps have many applications in plastic and reconstructive surgeries. However, distal flap necrosis restricts wider clinical utility. Mitophagy, a vital form of autophagy for damaged mitochondria, is excessively activated in flap ischemia/reperfusion (I/R) injury, thus inducing cell death. Aldehyde dehydrogenase-2 (ALDH2), an allosteric tetrameric enzyme, plays an important role in regulating mitophagy. We explored whether ALDH2 activated by N-(1,3-benzodioxol-5-ylmethyl)-2,6-dichlorobenzamide (Alda-1) could reduce the risk of ischemic random skin flap necrosis, and the possible mechanism of action.MethodsModified McFarlane flap models were established in 36 male Sprague-Dawley rats assigned randomly to three groups: a low-dose Alda-1 group (10 mg/kg/day), a high-dose Alda-1 group (20 mg/kg/day) and a control group. The percentage surviving skin flap area, neutrophil density and microvessel density (MVD) were evaluated on day 7. Oxidative stress was quantitated by measuring the superoxide dismutase (SOD) and malondialdehyde (MDA) levels. Blood perfusion and skin flap angiogenesis were assessed via laser Doppler flow imaging and lead oxide-gelatin angiography, respectively. The expression levels of inflammatory cytokines (IL-1β, IL-6, and TNF-α), vascular endothelial growth factor (VEGF), ALDH2, PTEN-induced kinase 1 (PINK1), and E3 ubiquitin ligase (Parkin) were immunohistochemically detected. Indicators of mitophagy such as Beclin-1, p62, and microtubule-associated protein light chain 3 (LC3) were evaluated by immunofluorescence.ResultsAlda-1 significantly enhanced the survival area of random skin flaps. The SOD activity increased and the MDA level decreased, suggesting that Alda-1 reduced oxidative stress. ALDH2 was upregulated, and mitophagy-related proteins (PINK1, Parkin, Beclin-1, p62, and LC3) were downregulated, indicating that ALDH2 inhibited mitophagy through the PINK1/Parkin signaling pathway. Treatment with Alda-1 reduced neutrophil infiltration and expressions of inflammatory cytokines. Alda-1 significantly upregulated VEGF expression, increased the MVD, promoted angiogenesis, and enhanced blood perfusion.ConclusionALDH2 activation can effectively enhance random skin flap viability via inhibiting PINK1/Parkin-dependent mitophagy. Moreover, enhancement of ALDH2 activity also exerts anti-inflammatory and angiogenic properties.

Abstract The dynamics of synaptic vesicles (SVs) within presynaptic domains are tightly controlled by synapsin1 phosphorylation; however, the mechanism underlying the anchoring of synapsin1 with F-actin or SVs is not yet fully understood. Here, we found that Syn1 is modified with protein palmitoylation, and examining the roles of Syn1 palmitoylation in neurons led us to uncover that Syn1 palmitoylation is negatively regulated by its phosphorylation; together, they manipulate the clustering and redistribution of SVs. Using the combined approaches of electron microscopy and genetics, we revealed that Syn1 palmitoylation is vital for its binding with F-actin but not SVs. Inhibition of Syn1 palmitoylation causes defects in SVs clustering and a reduced number of total SVs in vivo. We propose a model in which SVs redistribution is triggered by upregulated Syn1 phosphorylation and downregulated Syn1 palmitoylation, and they reversibly promote SVs clustering. The crosstalk of Syn1 palmitoylation and phosphorylation thereby bidirectionally manipulates SVs dynamics in neurons.

The maintenance service network is always designed as a multi-level service network to provide timely maintenance service for failed machinery, and is rarely studied in agriculture. Thus, this paper focuses on a three-level maintenance service network location–allocation problem in agriculture, which contains several spare part centres, service stations, and service units. This research aims to obtain the optimal location of spare part centres and service stations while determining service vehicle allocation results for service stations, and the problem can be called a multi-level facility location and allocation problem (MLFLAP). Considering contiguity constraints and hierarchical relationships, the proposed MLFLAP is formulated as a mixed-integer linear programming (MILP) model integrating with P-region and set covering location problems to minimize total service costs, including spare part centre construction costs, service vehicle usage costs, and service mileage costs of service stations. The Benders decomposition-based solution method with several improvements is then applied to decompose the original MLFLAP into master problem and subproblems to find the optimal solutions effectively. Finally, a real-world case in China is proposed to evaluate the performance of the model and algorithm in agriculture, and sensitivity analysis is also conducted to demonstrate the impact of several parameters.

Cutting fluids are essential for the machining of difficult-to-cut materials in production. In addition to four basic functions including cooling, lubrication, cleaning, and antirust, cutting fluids also have an effect on the reduction of hardness and ductility of materials. Focused on the effect of cutting fluid composition in the material deformation processes, the influence of two universal cutting fluids (denoted as Blasocut and E709) on high strain rate dynamic mechanical properties of Ni-based superalloy was experimentally investigated and verified by cutting process. The shear stress-shear strain curve, maximum shear stress and shear failure stress, milling force as well as chip formation were systematically investigated. It is indicated that the results from Split Hopkinson Pressure Bar tests are consistent with those from cutting experiments. The Rehbinder effect for the samples under E709 conditions is more significant and the stress fluctuation is the largest, which is up to 14%. Brittle fracture exists under a narrow shear bandwidth 100 μm with high strain rate 10 × 104 s−1, while ductile fracture occurs under a wide shear bandwidth 400 μm with low strain rate 3 × 104 s−1. The cutting force amplitude under E709 is larger than that of Blasocut. The chip burrs under E709 and water are more severe than those under Blasocut. This research reveal significant connection between the cutting fluids and the dynamic mechanical properties or the cutting performance of the metal, which is also helpful to the control of mechanical properties and fatigue resistance of the machined surface.

Surface quality has a significant impact on the service life of machine parts. Grinding is often the last process to ensure surface quality and accuracy of material formation. In this study, a high-quality surface was developed by determining the coefficient of friction in grinding a quartz fiber-reinforced silica ceramic composite. By processing the physical signals in the grinding process, a multi-objective function was established by considering grinding parameters, i.e., surface roughness, coefficient of friction, active energy consumption, and effective grinding time. The weight vector coefficients of the sub-objective functions were optimized through a multi-objective evolutionary algorithm based on the decomposition (MOEA/D) algorithm. The genetic algorithm was used to optimize the process parameters of the multi-objective function, and the optimal range for the coefficient of friction was determined to be 0.197~0.216. The experimental results indicated that when the coefficient of friction tends to 0.197, the distribution distance of the microscopic data points on the surface profile is small and the distribution uniformity is good. When the coefficient of friction tends to 0.216, the surface profile shows a good periodic characteristic. The quality of a grinding surface depends on the uniformity and periodicity of the surface’s topography. The coefficient of friction explained the typical physical characteristics of high-quality grinding surfaces. The multi-objective optimization function was even more important for the subsequent high-quality machining of mechanical parts to provide guidance and reference significance.

Imbalanced learning problems often occur in application scenarios and are additionally an important research direction in the field of machine learning. Traditional classifiers are substantially less effective for datasets with an imbalanced distribution, especially for high-dimensional longitudinal data structures. In the medical field, the imbalance of data problem is more common, and correctly identifying samples of the minority class can obtain important information. Moreover, class imbalance in imbalanced AD (Alzheimer’s disease) data presents a significant challenge for machine learning algorithms that assume the data are evenly distributed within the classes. In this paper, we propose a random forest-based feature selection algorithm for imbalanced neuroimaging data classification. The algorithm employs random forest to evaluate the value of each feature and combines the correlation matrix to choose the optimal feature subset, which is applied to imbalanced MRI (magnetic resonance imaging) AD data to identify AD, MCI (mild cognitive impairment), and NC (normal individuals). In addition, we extract multiple features from AD images that can represent 2D and 3D brain information. The effectiveness of the proposed method is verified by the experimental evaluation using the public ADNI (Alzheimer’s neuroimaging initiative) dataset, and results demonstrate that the proposed method has a higher prediction accuracy and AUC (area under the receiver operating characteristic curve) value in NC-AD, MCI-AD, and NC-MCI group data, with the highest accuracy and AUC value for the NC-AD group data.

ABSTRACT: Developing mechanically robust, biocompatible and stretchable electronic skins (e-skins) are highly desirable for detecting physiological and physical signals accurately. Silk fibroin (SF) hydrogels as an emerging class of cross-linked polymer networks have shined a spotlight on wearable bioelectronics. However, the structural and functional characteristic of SF hydrogels produced through conventional methods unable satisfied application in e-skins due to the low tensile property and environmental stability. Here, an environment-stable, stretchable and highly sensitive e-skin is fabricated by photo-cross-linked chemically modified SF hydrogel (MSFH) by glycidyl methacrylate (GMA). This device exhibits excellent pressure-sensing and strain-sensing performances and high adhesion to diverse material surfaces. Moreover, this e-skin platform is capable of detecting stretching motions for gesture recognition, establishing a promising candidate for intelligent soft robotics, human-machine interface and continuous health-monitoring.

BackgroundThe efficacy of allergen immunotherapy (AIT) in treating pediatric allergy has been clearly demonstrated, however, many patients hesitate to initiate AIT due to weekly hospital visits during the 3-4 months up-dosing phase. Meanwhile, rush immunotherapy (RIT) shortens the duration of the up-dosing phase to 7 days. However, considering that patients receiving RIT are exposed to the allergens during a much shorter period of time and thus may be at a greater risk of systemic reactions, RIT is currently underused, especially in children. This study investigated the utility of combination treatment with RIT plus 1 dose of pretreatment anti-IgE in children with respiratory allergies.MethodsIn this retrospective study, we reviewed records of children with allergic rhinitis (AR) and/or allergic asthma (AA) sensitized to dust mite allergens receiving RIT+1 dose of pretreatment anti-IgE (the RIT group) or conventional immunotherapy (the CIT group) at our hospital from January 2020 to March 2021. Data such as visual analogue scale (VAS) scores, comprehensive symptom and medication score (CSMS), allergy blood test results, adverse reactions, compliance and cost were collected and analyzed.Results40 patients in the RIT group and 81 patients in the CIT group were included in this study. Both treatments were well tolerated and patients in the 2 treatment groups had comparable local and systemic reactions. Compared to CIT, RIT + anti-IgE combination led to significantly faster symptomatic improvement as demonstrated by significantly decreased VAS and CSMS starting as early as 1 month after AIT initiation (P 0.05).ConclusionsRIT + 1 dose of pretreatment anti-IgE combination has practical advantages over CIT, including comparable safety, better compliance, and probably a faster onset of clinical efficacy at no additional cost, so it can be an useful regimen for the treatment of Chinese children with respiratory allergies.

Microlens arrays have been widely employed to control the reflection, refraction, and diffraction characteristics of light due to its distinctive surface properties. Precision glass molding (PGM) is the primary method for the mass production of microlens arrays, of which pressureless sintered silicon carbide (SSiC) is a typical mold material due to its excellent wear resistance, high thermal conductivity, high-temperature resistance, and low thermal expansion. However, the high hardness of SSiC makes it hard to be machined, especially for optical mold material that requires good surface quality. The lapping efficiency of SSiC molds is quite low. and the underlying mechanism remains insufficiently explored. In this study, an experimental study has been performed on SSiC. A spherical lapping tool and diamond abrasive slurry have been utilized and various parameters have been carried out to achieve fast material removal. The material removal characteristics and damage mechanism have been illustrated in detail. The findings reveal that the material removal mechanism involves a combination of ploughing, shearing, micro-cutting, and micro-fracturing, which aligns well with the results obtained from finite element method (FEM) simulations. This study serves as preliminary reference for the optimization of the precision machining of SSiC PGM molds with high efficiency and good surface quality.

Undesirable chatter is one of the key problems that restrict the improvement of robot milling quality and efficiency. The prediction of chatter stability, which is used to guide the selection of process parameters, is an effective method to avoid chatter in robot milling. Due to the weak stiffness of the robot, deformation caused by milling forces becomes an unavoidable problem, which will change the tool–workpiece contact area and affect the stability prediction. However, it is often simplified and neglected. In this paper, a multipoint contact dynamic model of robot milling is established, which considers the influence of force-induced deformation on the regenerative effect and process damping. The tool–workpiece contact area is discretized into a finite number of nodes along the axial direction so that the force and deformation at each node can be calculated separately. The different contact forms of the tool–workpiece under different process parameters are discussed in different cases, and the interaction process between cutting force and force-induced deformation is analyzed in detail. An iterative strategy is used to calculate the deformation of each node and the result of the tool–workpiece contact boundary. Finally, chatter stability of robot milling is predicted by a fully discrete method. Robot milling experiments were carried out to verify the predicted results. The results show that force-induced deformation is an important factor improving the stability prediction accuracy of robot milling, and a more accurate prediction result can be obtained by simultaneously considering force-induced deformation and process damping.

The surface integrity of ultra-high-strength steel has a significant influence on service performance, and cutting fluid plays an important role in maintaining surface integrity in production. In this paper, the surface integrity of ultra-high-strength steel 45CrNiMoVA was investigated under three cutting fluids: HY-103 (micro-emulsion), TRIM E709 (emulsion), and Vasco 7000 (micro-emulsion) from the aspects of cutting force, surface morphology, residual stress, micro hardness, microstructure, etc. The results showed that the changing trend of the cutting forces in three directions is HY-103 > Vasco 7000 > TRIM E709. The TRIM E709 contains the maximum lubricants, which reduce cutting force and Sa roughness, while the Vasco 7000 contains the minimum corrosive elements, which results in the least pitting. Both tangential and axial stresses under cutting fluid are tensile stresses. TRIM E709 and Vasco 7000 are reduced axially by 4.45% and 7.60% relative to HY-103, respectively. The grain refinement layer depths of HY-103, TRIM E709, and Vasco 7000 are 9 μm, 4 μm, and 8 μm, respectively, and TRIM E709 can induce recrystallized grains to grow along {001} of the sample cross section, which results from the lowest cooling rate. This work may provide an innovative control strategy for cutting fluid to improve surface integrity and service performance.

The machining process of aluminum alloy usually produces built-up edge and tool sticking problems due to their low hardness and large plastic deformation, which may further affect the machined surface quality and tool life. This paper aims to investigate the influence of different cutting fluids on the machined surface quality and tool life during the milling process of 7050 aluminum alloy. A novel cutting fluid (QC-2803) was considered in the study, which is synthesized by addition of alkyl alcohol amide and chlorinated polyolefin, and the traditional cutting fluid (CCF-10) was used as the control group. The physical and chemical properties of two cutting fluids were characterized. The milling process of 7050 aluminum alloy was carried out under two different cutting fluid conditions. The machined surface morphology, cutting force and tool wear morphology were observed during the process. Results show that the surface tension of the novel cutting fluid is significantly lower than that of the traditional cutting fluid, which makes it easier to produce a lubricating film between the aluminum alloy and tool, and further benefits the machined surface quality and tool life. As a result, the surface roughness and cutting force are reduced by ~20.0% and ~42.9%, respectively, and the tool life is increased by 25.6% in the case of the novel cutting fluid (QC-2803). The results in this paper revealed the important laws of cutting fluid with metal surface quality, cutting performance and tool wear, which helps to control the machined surface quality and tool life by the selection of cutting fluid during metal milling.

In order to improve the tool life and micro-hole machining quality, the H-shaped chisel edge micro-drill (HCE-MD) was developed in this paper. The HCE-MD was characterized by the inner edge formed through the chisel edge thinning. In the micro-drilling process, the inner edge can perform positive rake cutting, so the machining area of the workpiece extruded by cutting edge with a negative rake angle is reduced. Based on this, the distribution of rake angle near the chisel edge corner is improved. Then, the HCE-MD was fabricated on the six-axis CNC grinding machine. The grinding process parameters of the micro-drill were optimized based on the orthogonal grinding test and grey relational grade theory. The size and shape accuracy of the micro-drill were controlled by the multi-axis linkage grinding method and the movement-axis micro compensation method. Finally, the 0.25 mm HCE-MD was fabricated with the cutting edge radius of 1.94 μm and the flank surface roughness of 0.25 μm. The drilling performance of HCE-MD was evaluated through comparative drilling experiments. The experimental results show that, compared with common micro drill, the HCE-MD produced lower thrust force and better micro-hole roundness accuracy, and reduced the micro-drill wear on the chisel edge and the flank.

Superalloy parts place high demands on machined surface integrity and serviceability. In the machining process of superalloys, the cutting fluid is usually used to improve the machining performance. Cutting fluids with cooling and lubrication functions have a relatively large effect on the surface microstructure and residual stress as well. The corrosion damage caused by cutting fluid to the machined surface, during machining and residual, are also worth considering. In this paper, the machining performance of typical binary Ni Cr solid solution, age-hardened, nickel-based superalloy NiCr20TiAl T6, under two commonly used cutting fluids, Blasocut and E709, was analyzed, including cutting performance, surface quality, machining surface corrosion characteristics, and so on. The results showed that the surface residual stress could be improved by adding both cutting fluids compared with the deionized water. Blasocut had better lubrication properties, which could reduce friction and heat production. Pitting holes were found on the polished surface after 45 days with E709 cutting fluid, which was more corrosive than Blasocut. According to this research, a reasonable cutting fluid can be selected to reduce the surface corrosion and improve the service life and performance of parts.

Social recommendation can effectively alleviate the problems of data sparseness and the cold start of recommendation systems, attracting widespread attention from researchers and industry. Current social recommendation models use social relations to alleviate the problem of data sparsity and improve recommendation performance. Although self-supervised learning based on user–item interaction can enhance the performance of such models, multi-auxiliary information is neglected in the learning process. Therefore, we propose a model based on self-supervision and multi-auxiliary information using multi-auxiliary information, such as user social relationships and item association relationships, to make recommendations. Specifically, the user social relationship and item association relationship are combined to form a multi-auxiliary information graph. The user–item interaction relationship is also integrated into the same heterogeneous graph so that multiple pieces of information can be spread in the same graph. In addition, we utilize the graph convolution method to learn user and item embeddings, whereby the user embeddings reflect both user–item interaction and user social relationships, and the item embeddings reflect user–item interaction and item association relationships. We also design multi-view self-supervising auxiliary tasks based on the constructed multi-auxiliary views. Signals generated by self-supervised auxiliary tasks can alleviate the problem of data sparsity, further improving user/item embedding quality and recommendation performance. Extensive experiments on two public datasets verify the superiority of the proposed model.

Magnesium alloys have been widely used as lightweight engineering structural materials, but their service performances are severely restricted by corrosion failure. In this paper, the influence of corrosive medium and surface defect energy on the corrosion behavior of rolled ZK61M alloy was investigated. The corrosion tests were conducted in different concentrations of sodium chloride solution for different durations, and the polarization curves and electrochemical impedance spectroscopy were reported. The surface morphology of rolled ZK61M alloy before and after corrosion tests were analyzed. The results showed that the corrosion tendency became stronger with the increase of the concentration of corrosive medium and the number of surface defects of ZK61M alloy. Moreover, the initial corrosion pattern was the pitting caused by micro galvanic corrosion at the surface defect, which gradually developed into uniform corrosion. Furthermore, the main damage occurred at the grain boundary, resulting in the destruction of grain bonding force and the removal of material along the rheological layer. The oxidation corrosion mechanism was mainly the anodic dissolution mechanism.

For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-task learning (MTL) model for simultaneous estimation of surface roughness and tool wear. To address the problem, a new MTL model with shared layers and two task-specific layers was proposed. A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features from cutting force signals. To enhance the performance of the MTL model, the scaled exponential linear unit (SELU) was introduced as the activation function of SDAE. Moreover, a dynamic weight averaging (DWA) strategy was implemented to dynamically adjust the learning rate of different tasks. Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients. Frequency-domain features were extracted from the power spectrum obtained by the Fourier transform. After that, all features were combined as the input vectors of the proposed MTL model. Finally, surface roughness and tool wear were simultaneously predicted by the trained MTL model. To verify the superiority and effectiveness of the proposed MTL model, nickel-based superalloy Haynes 230 was machined under different cutting parameter combinations and tool wear levels. Some other intelligent algorithms were also implemented to predict surface roughness and tool wear. The results showed that compared with the support vector regression (SVR), kernel extreme learning machine (KELM), MTL with SDAE (MTL_SDAE), MTL with SCAE (MTL_SCAE), and single-task learning with PSAE (STL_PSAE), the estimation accuracy of surface roughness was improved by 30.82%, 16.67%, 14.06%, 26.17%, and 16.67%, respectively. Meanwhile, the prediction accuracy of tool wear was improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For practical engineering application, the dimensional deviation and surface quality of the machined parts can be controlled through the established MTL model.

Impact loading is an important cause of fracture failure of ultrahigh-strength steel parts during service. Revealing the fracture mechanism of ultrahigh-strength steel under impact loading has important reference significance for the material preparation, part design, and manufacturing of such steel. Based on the split Hopkinson pressure bar (SHPB) test, the mechanical response characteristics of 45CrNiMoVA steel under impact loading were analyzed, and the true stress–true strain curves under a high strain rate (103 s−1) were obtained. It was found that under the simultaneous action of forward and tangential loading forces, a severe plastic deformation layer with a thickness of 20–30 μm was generated in the near impact-loading end face, which is the main cause for crack initiation and propagation. Under the condition of a high strain rate, the plastic flow stress of 45CrNiMoVA steel was characterized by the equilibrium of strain hardening and strain softening, and its impact fracture toughness decreased by 43.6%, resulting in increased quasi-cleavage fracture. Hence, severe surface plastic deformation during 45CrNiMoVA steel machining should be avoided, as it may lead to early failure.

We report an ultra-broadband and compact TM-pass polarizer based on graphene-buried polymer waveguides. The characteristic parameters of the polarizer were carefully designed and optimized. The standard microfabrication processes were employed to fabricate the device. The presented polarizers exhibit high polarization-dependent transmission imposing a TE mode cutoff while leaving the TM mode almost unaffected. We experimentally demonstrated the polarizer that has an ultra-high extinction ratio of more than 22.9 dB and 41.9 dB for the monolayer graphene film placed on the surface of core layer and buried in the center of core layer, respectively, and as low insertion loss as ~4.0 dB for the TM mode with the bandwidth over 110 nm. The presented polarizer has the advantages of high extinction ratio, ultra-broadband, low cost, and easy integration with other polymer-based planar lightwave devices.

In this paper, the pain point that cold chain transportation urgently needs for an efficient disinfection method is pointed out. Thus, this work aims at solving the problems and improving the disinfection efficiency in cold chain transportation. While Ultraviolet-C (UVC) irradiation is an effective method by which to kill viruses, it is difficult to apply the commonly used UVC-LED disinfection light source to ice-covered cold chain transportation due to its uneven light field distribution. Thus, the light field regulation of UVC-LED disinfection for cold chain transportation is studied. A UVC-LED chip with a wavelength of 275 nm was used as a light source, and parallel light was obtained by collimating lenses. Then, microlens array homogenization technology was used to shape the UVC light into a uniform light spot, with an energy space uniformity rate of 96.4%. Moreover, a simulation was conducted to compare the effects of the ice layer on the absorption of UVC light. Finally, an experiment was carried out to verify that the disinfection efficiency can be increased nearly by 30% with the proposed system by disinfecting E. coli (Escherichia coli), and the results indicate that the proposed system is an effective disinfection solution during cold chain transportation.

Scintillation single crystal fibers (SCFs) have great potential applications in the new generation of high-energy ray and particle detectors due to their morphological advantages. In this work; Ce:LuAG SCFs with a diameter of 1 mm were grown along the direction of [111] by laser-heated pedestal growth (LHPG) method using a transparent ceramic as the source rod; and a doping concentration was 0.1 at%, 0.3 at%, 1 at%, respectively. The effects of growth rate and annealing in air on the scintillation and optical properties of SCF are discussed in detail. The results of analyzing the absorption spectra; radioluminescence (RL) spectra; pulse-height spectra and fluorescence lifetime of SCFs show that the SCF maintains excellent scintillation performance while having a fiber structure. Therefore; Ce:LuAG SCF is a potential candidate material for detector.

Coronary artery disease (CAD) is the leading killer of humans worldwide. Bioresorbable polymeric stents have attracted a great deal of interest because they can treat CAD without producing long-term complications. Bioresorbable polymeric stents (BMSs) have undergone a sustainable revolution in terms of material processing, mechanical performance, biodegradability and manufacture techniques. Biodegradable polymers and copolymers have been widely studied as potential material candidates for bioresorbable stents. It is a great challenge to find a reasonable balance between the mechanical properties and degradation behavior of bioresorbable polymeric stents. Surface modification and drug-coating methods are generally used to improve biocompatibility and drug loading performance, which are decisive factors for the safety and efficacy of bioresorbable stents. Traditional stent manufacture techniques include etching, micro-electro discharge machining, electroforming, die-casting and laser cutting. The rapid development of 3D printing has brought continuous innovation and the wide application of biodegradable materials, which provides a novel technique for the additive manufacture of bioresorbable stents. This review aims to describe the problems regarding and the achievements of biodegradable stents from their birth to the present and discuss potential difficulties and challenges in the future.