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The study presents an investigation into the preparation and characterization of modified hemp fiber reinforced recycled construction waste bricks for ground and road applications. By incorporating 1 % hemp fiber and a 70/30 ratio of recycled concrete aggregate to recycled brick aggregate, the optimal combination was identified, resulting in a maximum compressive strength of 36.2 MPa, a 38.7 % improvement over the control bricks. The addition of 2 % hemp fiber increased the flexural strength by 85.7 %, while the thermal conductivity decreased by 41.5 % with the inclusion of 2 % hemp fiber and 100 % recycled brick aggregate. Scanning electron microscopy and X-ray diffraction analysis confirmed the compatibility of hemp fibers with the cement matrix and their role in enhancing the microstructure of the bricks and hydration process. These findings highlight the potential of hemp fiber-reinforced recycled waste bricks as environmentally friendly high-performance materials for sustainable ground and road construction.

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.

Optical mode switch is a key device for reconfigurable mode-division-multiplexing (MDM) technique. Here, an ultra-broadband 1×2 mode switch based on cascaded Mach-Zehnder interferometer and asymmetric Y-branch is proposed. The input E00 or E10 mode will be converted to E00 mode and switched between two output ports by changing the working state of electrode. When E00 mode is launched, the measured insertion loss of fabricated switch is 3.3 dB (off-state)/3.4 dB (on-state), and corresponding crosstalk is −18.8 dB (off-state)/−16.1 dB (on-state) at 1550 nm, respectively. Due to the adoption of polymer with high thermo-optic coefficient as the waveguide material, the measured switching power is 10.3 mW. The captured far-field output mode patterns prove the wavelength independence of proposed mode switch within the range from 1500 to 1630 nm. This broadband operation mode switch has potentials in wavelength-division-multiplexing & MDM complex systems, as well as cost-effective low-end switching applications.

Multi-release is critical for modern open source software product in order to satisfy more customer requirements. Masked data, a kind of missing data, is the system failure data when the exact cause of the failures might be unknown. That is, the cause of the system failures may be any one of the objects. However, due to the influence of the test strategy in real project, the cause of the system failures may be a subset of the system objects, not any one of the objects. In this paper, the mathematical description of general masked data is presented based on the traditional masked data. Furthermore, a novel multi-release open source software (OSS) reliability model based on general masked data is proposed. Different from traditional multi-release OSS reliability model, the proposed approach is based on additive model with general masked data other than change point model. And then, the maximum likelihood estimation (MLE) process of the model parameters is derived in detail, and expectation maximization (EM) algorithm is used to solve the extremely complicated problem of the log-likelihood function. Finally, two data sets from real open source software project are applied to the proposed approach, and the results show that the proposed reliability model is useful and powerful.

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.

By exploiting the electro-absorption characteristic of graphene, we proposed and theoretically demonstrated an electro-absorption optical modulator (EOM) based on the graphene-buried polymer waveguides. By burying the graphene layers inside the polymer waveguide, acquiring a strong graphene-light interaction when the graphene layers buried in the center of core, which bring about an important change of the optical mode and effective mode index in the waveguide. The width of the graphene capacitor was also optimized to enhance the bandwidth of the device. Calculations show that the proposed EOM can realize 29 dB extinction ratio, 7 dB modulation depth and 42 GHz bandwidth with an 800 μm-long active region. The required switching voltage form its minimum to maximum absorption state is 1.6 V and 1.55 pJ/bit energy consumption can be achieved. The proposed modulator can remedy the lack of high-speed optical modulator on the passive polymer waveguide and have a potential application for polymer-based photonic integrated circuits.

In this paper, an on-chip electro-optic (EO) modulator with loss compensation based on polymeric active-integrated waveguides was demonstrated. An erbium-doped waveguide amplifier was investigated and integrated with the EO modulator to compensate for signal loss. Polymeric active-integrated waveguides were based on the Mach-Zehnder interferometer structure, which consists of an amplified waveguide formed by two symmetric Y-junction branches and an EO waveguide formed by two decoupled waveguide arms. The dimensions of the polymeric active-integrated waveguides and the modulator were carefully designed and simulated. Moreover, a six-level spectroscopic model pumped at 980 nm was presented. The rate equations and propagation equations were solved, and the gain characteristics were simulated. The internal gain of 4.65 dB was achieved when the signal power was 0.1 mW at 1550 nm, the pump power was 100 mW at 980 nm, the Er3+ concentration was $9.3\times 10^{25}/\text{m}^{3}$ , and the Yb3+ concentration was $8.6\times 10^{26}/\text{m}^{3}$ in one Y-junction branch with a length of 1.5 cm. With the integrated waveguide amplifier, the loss of the EO modulator can be compensated at 9.3 dB in the two symmetric Y-junction branches. The light output intensity was also statistically presented. The proposed device with active-integrated waveguides could be used in polymer-based photonics integrated circuits.

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.

Compact high-performance electro-optic (EO) modulators are key elements for optical communications. An electro-absorption modulator is theoretically designed. Due to the introduction of graphene film over the silicon slot waveguide, the modulation efficiency is strongly improved. The effect of waveguide dimensions, dielectric insulator thickness, and carrier mobility on performances of the proposed EO modulator have been comprehensively investigated. For the 120-μm-long graphene-on-silicon waveguide modulator, the extinction ratio 28 dB and insertion loss 1.28 dB can be obtained at 1550 nm when drive voltage is 1.91 and 0.59 V, respectively. The 3 dB bandwidth of 117 GHz can be obtained at a small power consumption of 212 fJ/bit. The proposed modulator possesses a high figure-of-merit of 20.5. Its benefits of small size, power saving, and process compatible promises possible applications in on-chip signal processing.

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 study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.

Background: As global healthcare system is overwhelmed by novel coronavirus disease (COVID-19), early identification of risks of adverse outcomes becomes the key to optimize management and improve survival. This study aimed to provide a CT-based pattern categorization to predict outcome of COVID-19 pneumonia.Methods: One hundred and sixty-five patients with COVID-19 (91 men, 4–89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pattern 0 (negative), Pattern 1 (bronchopneumonia pattern), Pattern 2 (organizing pneumonia pattern), Pattern 3 (progressive organizing pneumonia pattern), and Pattern 4 (diffuse alveolar damage pattern). Clinical findings were compared across different categories. Time-dependent progression of CT patterns and correlations with clinical outcomes, i.e.„ discharge or adverse outcome (admission to ICU, requiring mechanical ventilation, or death), with pulmonary sequelae (complete absorption or residuals) on CT after discharge were analyzed.Results: Of 94 patients with outcome, 81 (86.2%) were discharged, 3 (3.2%) were admitted to ICU, 4 (4.3%) required mechanical ventilation, 6 (6.4%) died. 31 (38.3%) had complete absorption at median day 37 after symptom onset. Significant differences between pattern-categories were found in age, disease severity, comorbidity and laboratory results (all P < 0.05). Remarkable evolution was observed in Pattern 0–2 and Pattern 3–4 within 3 and 2 weeks after symptom-onset, respectively; most of patterns remained thereafter. After controlling for age, CT pattern significantly correlated with adverse outcomes [Pattern 4 vs. Pattern 0–3 [reference]; hazard-ratio [95% CI], 18.90 [1.91–186.60], P = 0.012]. CT pattern [Pattern 3–4 vs. Pattern 0–2 [reference]; 0.26 [0.08–0.88], P = 0.030] and C-reactive protein [>10 vs. ≤ 10 mg/L [reference]; 0.31 [0.13–0.72], P = 0.006] were risk factors associated with pulmonary residuals.Conclusion: CT pattern categorization allied with clinical characteristics within 2 weeks after symptom onset would facilitate early prognostic stratification in COVID-19 pneumonia.

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.

The submicron-level structured surface induces viewing-angle-dependent iridescence, and has wide applications in multi-color printing, micro-display projection, invisibility cloak technology. In this study, several types of two-level structures, which consist of the first-order micro geometric features corresponding to the pattern shape and the second-order submicron grooves corresponding to the diffraction grating, are designed to directly induce a variety of iridescent patterns based on their shape regulations. To fabricate these two-level iridescent structures with high accuracy in a single step, vibration-assisted fly cutting (VAFC) is proposed. VAFC involves the low-frequency vibration of the workpiece in the vertical direction during the feed in the horizontal direction and the high-speed rotation of the diamond cutting tool. A 3D theoretical model is established for the numerical simulation of the generating process of the iridescent structures and the regulation of the first-order geometric feature by vibration parameters. As the key parameter to the iridescent color, the spacing of the second-order submicron grooves is flexibly controlled by the feed rate. A VAFC platform is developed to fabricate the two-level structures and an optical detection system is setup to test the surface iridescence. Various vivid colors are regulated by the two-level structures with high saturation and uniformity.

Surface-layer defects of M-plane sapphire caused by ultrasonic vibration-assisted cutting were investigated in this paper through comparison cutting experiments. Topography analysis results indicate that when the cutting direction is not along a-axis or c-axis, the spalling defects of conventional cutting groove are asymmetric because the probabilities of activated cleavage/twinning systems vary with machining direction; however, the vibrational cutting groove presents better symmetry and less spalling owning to the propagation of stress wave. The cross-section of cutting groove was obtained by focus ion beam cutting. The cross-section analysis shows that the subsurface defects of conventional cutting groove involve median cracks and lateral cracks, and the main propagating path of cracks is towards downward, which is the cause of the generation of serious spalling; however, lateral cracks are the main defect in the subsurface of the vibrational cutting groove, and they mainly propagate towards the free surface and give rise to material removal. The subsurface quality of cutting groove with ultrasonic assistance is better than that of the conventional cutting groove. It is evident that ultrasonic vibration can prevent the median crack from extending towards bulk material and contribute to improve the uniformity of groove quality.

Bioresorbable polymeric stents have attracted great interest for coronary artery disease because they can provide mechanical support first and then disappear within a desired time period. The conventional manufacturing process is laser cutting, and generally they are fabricated from tubular prototypes produced by injection molding or melt extrusion. The aim of this study is to fabricate and characterize a novel bioresorbable polymeric stent for treatment of coronary artery disease. Polycaprolactone (PCL) is investigated as suitable material for biomedical stents. A rotary 3D printing method is developed to fabricate the polymeric stents. Surface modification of polymeric stent is performed by immobilization of 2-N, 6-O-sulfated chitosan (26SCS). Physical and chemical characterization results showed that the surface microstructure of 3D-pinted PCL stents can be influenced by 26SCS modification, but no significant difference was observed for their mechanical behavior. Biocompatibility assessment results indicated that PCL and S-PCL stents possess good compatibility with blood and cells, and 26SCS modification can enhance cell proliferation. These results suggest that 3D printed PCL stent can be a potential candidate for coronary artery disease by modification of sulfated chitosan (CS).

In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.

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.

A new method had been developed for the analysis of hydrogen isotopic composition of trace hydrocarbons in natural gas samples by using solid phase microextraction (SPME) combined with gas chromatography-isotope ratio mass spectrometry (GC/IRMS). In this study, the SPME technique had been initially introduced to achieve the enrichment of trace content of hydrocarbons with low abundance and coupled to GC/IRMS for hydrogen isotopic analysis. The main parameters, including the equilibration time, extraction temperature, and the fiber type, were systematically optimized. The results not only demonstrated that high extraction yield was true but also shows that the hydrogen isotopic fractionation was not observed during the extraction process, when the SPME device fitted with polydimethylsiloxane/divinylbenzene/carbon molecular sieve (PDMS/DVB/CAR) fiber. The applications of SPME-GC/IRMS method were evaluated by using natural gas samples collected from different sedimentary basins; the standard deviation (SD) was better than 4‰ for reproducible measurements; and also, the hydrogen isotope values from C1 to C9 can be obtained with satisfying repeatability. The SPME-GC/IRMS method fitted with PDMS/DVB/CAR fiber is well suited for the preconcentration of trace hydrocarbons, and provides a reliable hydrogen isotopic analysis for trace hydrocarbons in natural gas samples.

A broadband polarization-insensitive graphene modulator has been proposed. The dual built-in orthogonal slots waveguide allows polarization independence for the transverse electric (TE) mode and the transverse magnetic (TM) mode. Due to the introduction of metal slots in both the vertical and horizontal directions, the optical field as well as the electro-absorption of graphene are enhanced by the plasmonic effect. The proposed electro-optic modulator shows a modulation depth of 0.474 and 0.462 dB/μm for two supported modes, respectively. An ultra-low effective index difference of 0.001 can be achieved within the wavelength range from 1100 to 1900 nm. The 3 dB-bandwidth is estimated to be 101 GHz. The power consumption is 271 fJ/bit at a modulation length of 20 μm. The proposed modulator provides high speed broadband solutions in microwave photonic systems.

It has been a long-standing challenge to produce air-stable few- or monolayer phosphorene. Here, the authors demonstrate a new highly controllable method for fabricating high quality, air-stable mono- and few-layer phosphorene films, enabling precise defect engineering in monolayer phosphorene.

Iron−nickel based ultra-high strength steel (wt. 18~20% Ni) is characterized by its high strength and low thermal conductivity, and is normally used to make key components by forming and machining processes. The optimization of these processes is based on a deep understanding of the mechanical and dynamic behaviors under high strain, high strain rate, and high temperature. In this paper, the relationship of stress to strain, strain rate, and temperature is systematically investigated by the dynamic compression tests combined with quasi-static compression tests, and the hardening and softening is associated with the transformation in microstructures. According to the analysis, dynamic recrystallization around 600 °C is assumed to be one important influencing factor, hence hot deformation equations are established and the critical strain for dynamic recrystallization and the volume fraction of the dynamic recrystallized grains are defined.