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Flute grinding is a crucial process in solid end-mill manufacturing, because the generated flute parameters, i.e. helix angle, rake angle, core radius, and flute width, have significant effects on the cutting performance of end-mills. However, the flute grinding of non-cylindrical solid end-mills is still challenging due to the variation of the flute parameters along the tool axis. This paper proposes an accurate and efficient method of wheel path planning for flute grinding of non-cylindrical solid end-mills based on circular arc projection. The wheel path determined by the new method can accurately generate the designed helix angle, rake angle, core radius, and flute width, even if all of these parameters vary along the tool axis. The helix angle and rake angle are ensured by the conjugate condition between the wheel surface and the cutting-edge curve. Based on circular arc projection, equivalent errors of the core radius and flute width are introduced to quantify the machining errors of the two parameters, without the need to predict the flute profile. Simulations and experiments are conducted to validate the proposed method. Three types of non-cylindrical end-mills i.e. tapered end-mill, tapered barrel end-mill, and barrel end-mill, are considered in the validation. The measured results indicate that the generated flute parameters coincide well with the designed ones at different axial positions of the end-mills.

Solar evaporation is an ecofriendly and practical method for seawater desalination. The photothermal layer, which absorbs solar energy and converts it to thermal energy, plays a crucial role in enhancing the efficiency of the evaporator. However, structural design methods for photothermal layers are often complex and energy-intensive. This work reports a simple and efficient strategy for fabricating a necklace-like beaded nanofiber self-organized honeycomb-structured photothermal material. The honeycomb-like cavities form numerous microscale thermal traps, achieving thermal localization while maintaining high energy utilization efficiency, which not only increases light absorption but also facilitates the diffusion and escape of steam. Besides, the hydrophobic honeycomb layer separates the photothermal layer and the interface water, which reduces considerable heat conduction loss and achieves an effective antisalting performance. These functional features endow the evaporator with an evaporation efficiency of 92.9%, and the evaporation rate reaches 2.11 kg m–2h–1at 1 sun irradiance, demonstrating its great potential for practical solar-driven seawater desalination under natural sunlight.

The microfluidic impedance flow cytometer (m-IFC) using constricted microchannels is an appealing choice for the high-throughput measurement of single-cell mechanical properties. However, channels smaller than the cells are susceptible to irreversible blockage, extremely affecting the stability of the system and the throughput. Meanwhile, the common practice of extracting a single quantitative index, i.e., total cell passage time, through the constricted part is inadequate to decipher the complex mechanical properties of individual cells. Herein, this study presents a long-term stable and multifeature m-IFC based on a constricted channel for single-cell mechanical phenotyping. The blockage problem is effectively overcome by adding tiny xanthan gum (XG) polymers. The cells can pass through the constricted channel at a flow rate of 500 μL/h without clogging, exhibiting high throughput (∼240 samples per second) and long-term stability (∼2 h). Moreover, six detection regions were implemented to capture the multiple features related to the whole process of a single cell passing through the long-constricted channel, e.g., creep, friction, and relaxation stages. To verify the performance of the multifeature m-IFC, cells treated with perturbations of microtubules and microfilaments within the cytoskeleton were detected, respectively. It suggests that the extracted features provide more comprehensive clues for single-cell analysis in structural and mechanical transformation. Overall, our proposed multifeature m-IFC exhibits the advantages of nonclogging and high throughput, which can be extended to other cell types for nondestructive and real-time mechanical phenotyping in cost-effective applications.

To realize the coordinated planning of distribution system (DS) with multiple integrated energy microgrids (IEMs), this paper proposes a mixed game-based and carbon-oriented two-stage planning model for photovoltaic (PV) capacity and energy storage system (ESS). Firstly, the mixed game-based planning model is constructed by comprising the Stackelberg game with the nested cooperative game in the first stage. Specifically, the distribution system operator (DSO) and the IEMs alliance play a Stackelberg game as the leader and follower respectively, making strategic decisions on invested PV and ESS through interactive price and power. Within the alliance, multiple IEMs reach an agreement on trading power and price to fairly share cooperation benefits through cooperative game. Secondly, the demand response model that considers carbon emission flow (CEF) and tiered carbon price is presented to reduce carbon emissions in the second stage. Furthermore, the model reformulation and distributed algorithm are developed to protect the privacy of multi-agents. The bisection-based method and the prediction-correction-based alternating direction method of multipliers (PCB-ADMM) are leveraged to solve the mixed game model for improving convergence. Finally, several cases in the modified IEEE 33-bus DS with multiple IEMs verify the effectiveness of the proposed model and methods.

Federated learning (FL) is a distributed framework for machine learning that enables collaborative training of a shared model across data silos while preserving data privacy. However, the FL aggregation server faces a challenge in waiting for a large volume of model parameters from selected nodes before generating a global model, which leads to inefficient communication and aggregation. Although transmitting only the signs of stochastic gradient descent (SignSGD) reduces the transmission load, it decreases model accuracy, and the time waiting for local model collection remains substantial. Moreover, the security of FL is severely compromised by prevalent poisoning, backdoor, and DDoS attacks, causing ineffective and inaccurate model training. To overcome these challenges, this paper proposes a Blockchain-based Asynchronous SignSGD (BASS) architecture for efficient and secure federated learning. By integrating a blockchain-based semi-asynchronous aggregation scheme with sign-based gradient compression, BASS considerably improves communication and aggregation efficiency, while providing resistance against attacks. Besides, a novel node-summarized sign aggregation algorithm is developed for the blockchain leaders to ensure the convergence and accuracy of the global model. An open-source prototype is developed, on top of which extensive experiments are conducted. The results validate the superiority of BASS in terms of efficiency, model accuracy, and security.

As a critical technology for next-generation communication networks, integrated sensing and communication (ISAC) aims to achieve the harmonious coexistence of communication and sensing. The degrees-of-freedom (DoF) of ISAC is limited due to multiple performance metrics used for communication and sensing. Reconfigurable Intelligent Surfaces (RIS) composed of metamaterials can enhance the DoF in the spatial domain of ISAC systems. However, the availability of perfect Channel State Information (CSI) is a prerequisite for the gain brought by RIS, which is not realistic in practical environments. Therefore, under the imperfect CSI condition, we propose a decomposition-based large deviation inequality approach to eliminate the impact of CSI error on communication rate and sensing Cramér-Rao bound (CRB). Then, an alternating optimization (AO) algorithm based on semi-definite relaxation (SDR) and gradient extrapolated majorization-maximization (GEMM) is proposed to solve the transmit beamforming and discrete RIS beamforming problems. We also analyze the complexity and convergence of the proposed algorithm. Simulation results show that the proposed algorithms can effectively eliminate the influence of CSI error and have good convergence performance. Notably, when CSI error exists, the gain brought by RIS will decrease with the increase of the number of RIS elements.

Robust imaging under challenging conditions, such as starlit nights, has broadened the adoption of thermal infrared (TIR) cameras for nighttime driving scenes. Given that TIR images are monochromatic, which makes them difficult to interpret by humans and limits the applicability of RGB-based algorithms, it is reasonable to perform colorization of nighttime TIR (NTIR) images by converting them into corresponding daytime color images (NTIR2DC). Despite the impressive results achieved by previous NTIR2DC methods, how to improve the colorization performance of small-sample categories without semantic annotation is under-explored. To address this issue, we propose a novel learning framework called Memory-guided cOllaboRative atteNtion Generative Adversarial Network (MornGAN), which is inspired by the analogical reasoning mechanisms of humans. Specifically, we first propose an online semantic distillation module to mine and refine the semantic cues of NTIR images. Then, a memory-guided sample selection strategy and adaptive collaborative attention loss are devised to enhance the semantic preservation of small-sample categories. Further, a new conditional gradient repair loss is introduced for reducing edge distortion during translation. Extensive experiments on the NTIR2DC task show that the proposed MornGAN significantly outperforms other image-to-image translation methods in terms of semantic preservation and edge consistency, which helps improve the object detection accuracy remarkably.

This study explores the influence of cryogenic supersonic fine particle bombardment (CSFPB) on a Ni–W–Co–Ta medium heavy alloy (MHA), and focus on the effect of gas pressure and impact time on the surface integrity, microstructural evolution, and mechanical properties of the MHA. CSFPB treatment creates a gradient structure, including surface layer with nanograins down to 9.0 nm due to severe plastic deformation, subsurface layer with high density dislocation structures and deformation twins due to weakened impact energy, and undeformed matrix. A key advantage of CSFPB is seen to be the cryogenic environment, which promotes superior surface integrity by reduced roughness. As the impact duration and gas pressure increase, the depth of deformation layer, surface hardness and strength of the MHA progressively rise. The optimal combination of strength and ductility is achieved at a gas pressure of 1.0 MPa for a duration of 90 s. This setting results in a maximum ultimate tensile strength of 1351 MPa and yield strength 796 MPa, with uncompromising elongation. However, excessively long treatment durations or high gas pressures can lead to the formation of surface microcracks, ultimately reducing the MHA's strength. Therefore, CSFPB offers the benefit of improved surface integrity through the cryogenic environment while maintaining a desirable balance between enhanced strength and ductility.

ABSTRACTTo solve the problem of inaccurate prediction caused by the lack of representativeness of samples due to the small sample size of the collected clinical data when using machine learning methods to predict drug concentration in plasma and describe the hysteresis phenomenon of drug effect lagging behind plasma drug concentration, this paper proposes a pharmacokinetic-pharmacodynamic (PK-PD) model based on the SSA-1DCNN-Attention network and the semicompartment method. First, a one-dimensional convolutional neural network (1DCNN) is established, and the attention mechanism is introduced to determine the importance of each physiological and biochemical parameter. The sparrow search algorithm (SSA) is used to optimize the parameters of the network to improve the prediction accuracy after data enhancement through the synthetic minority oversampling technique (SMOTE) method. After constructing the time-concentration relationship of the drug through the SSA-1DCNN-Attention network, the concentration-effect relationship of the drug is established by using the semicompartment method to synchronize the drug effect with the concentration. At last, the phenobarbital (PHB) combined with Cynanchum otophyllum saponins to treat epilepsy was taken as an example to validate the proposed method.

Recommendation serving with deep learning models is one of the most valuable services of modern E-commerce companies. In production, to accommodate billions of recommendation queries with stringent service level agreements, high-performant recommendation serving systems play an essential role in meeting such daunting demand. Unfortunately, existing model serving frameworks fail to achieve efficient serving due to unique challenges such as 1) the input format mismatch between service needs and the model's ability and 2) heavy software contentions to concurrently execute the constrained operations. To address the above challenges, we propose RecServe, a high-performant serving system for recommendation with the optimized design of structured features and SessionGroups for recommendation serving. With structured features, RecServe packs single-user-multiple-candidates inputs by semi-automatically transforming computation graphs with annotated input tensors, which can significantly reduce redundant network transmission, data movements, and useless computations. With session group, RecServe further adopts resource isolations for multiple compute streams and cost-aware operator scheduler with critical-path-based schedule policy to enable concurrent kernel execution, further improving serving throughput. The experiment results demonstrate that RecServe can achieve maximum performance speedups of 12.3$\boldsymbol{\times}$× and $22.0\boldsymbol{\times}$22.0× compared to the state-of-the-art serving system on CPU and GPU platforms, respectively.

We consider the persistence of affine periodic solutions for perturbed affine periodic systems. Such (Q, T)-affine periodic solutions have the form x(t+ T) = Qx(t) for all t∈ R, where T> 0 is fixed and Qis a nonsingular matrix. These are a kind of spatiotemporal symmetric solutions, e.g. spiral waves. We give the averaging method for the existence of affine periodic solutions in two situations: one in which the initial values of the affine periodic solutions of the unperturbed system form a manifold, and another that does not rely on the structure of the initial values of the unperturbed system’s affine periodic solutions. The transversal condition is determined using the Brouwer degree. We also present a higher order averaging method for general degenerate systems by means of the Brouwer degree and a Lyapunov-Schmidt reduction.

The effects of sintering temperatures on the microstructures and mechanical properties of titanium carbide particles reinforced iron matrix composites (TiC/Fe MCs) fabricated by the spark plasma sintering (SPS) process with pure element powders have been systematically investigated. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron back scattering diffractometer (EBSD), and energy dispersive spectroscopy (EDS) have been conducted for microstructural analysis. The results show that with increasing sintering temperatures, the porosity of the composites initially decreases and then increases. Simultaneously, the grain size gradually diminishes while element diffusion becomes more uniform. Upon reaching a critical sintering temperature (1120 °C), the original grain size disappears and carbides undergo decomposition and reprecipitation to reach an equilibrium state, with which optimal comprehensive properties can be achieved (porosity decreases to a minimum of 3.85%, grain size of 2.69 μm, Vickers hardness reaches 595 HV0.5, bending strength is at 662 MPa, coefficient of friction is at 0.74, and wear loss to 0.21 mg). These property enhancements have been attributed to reduced porosity in the composites, decreased grain size, and improved anchoring effect of carbides within the matrix. Additionally, the primary fracture mechanisms and wear mechanisms of TiC/Fe MCs with different process parameters have been analyzed. When the temperature is below 1080 °C, intergranular fracture predominates, whereas transgranular and ductile fractures become predominant above this threshold. When the temperature is below 1120 °C, fatigue wear, oxidation wear, and abrasive wear are predominantly observed. Conversely, when the temperature exceeds 1120 °C, oxidation wear and abrasive wear become the primary mechanisms.

Domino-coils wireless power transfer (WPT) technology has emerged as an effective means of ensuring reliable power supply for online monitoring equipment in smart grid. Nonetheless, it encounters persistent challenges when integrating domino-coils into composite insulators, notably the issues of large coil size and low power transmission efficiency (PTE) over medium and long distances. In response, this article constructs a T equivalent model of domino-coils WPT system to deduce the optimal efficiency condition for any number of coils. Specifically, it is determined that PTE is maximized when the current ratio of nonadjacent coils equals the mutual inductance ratio of adjacent coils. Building upon this insight and considering practical constraints such as coil volume and transmission distance, an optimization model is formulated for single-layer domino-coils with uniform spacing under the optimal efficiency condition and given output power specification, which is subsequently determined using a particle swarm algorithm. Experimental validation is conducted utilizing a seven-coil system experiment platform, achieving an output power of 12.23 W and a PTE of 85.94% at a transmission distance of 50 cm. These findings demonstrate significant enhancements in PTE, coil volume reduction, and ease of integration into thin and uniformly distributed insulator.

In this study, particle-reinforced aluminium matrix composites (PRAMCs) of an Al–Cu–Li alloy were prepared using nano-sized TiB₂+TiC particles. The relationship between TiB₂+TiC nanoparticles and T1precipitates during the ageing process, as well as the influence of TiC + TiB₂ particles on the growth process of T1precipitates during the ageing process, were investigated. The grain sizes of the A0 and A1 samples were found to be 249.89 μm and 86.42 μm, respectively. The dislocation density is greater in the deformed A1 sample. The coefficient of thermal expansion (CTE) effect generated by TiC and TiB₂ particles stimulates the precipitation process of T1precipitates. The A1 alloy reached the peak ageing state in a shorter time and yielded a greater number of T1precipitates. The precipitation process of T1precipitate is: SSSS→T1P→T1. The transition from T1P(face-centered cubic, FCC) to T1(hexagonal close packing, HCP) entails a modification in the order of packing. The recently formed T1Pprecipitate is attached to the established T1precipitate and extends along the c-axis through the shared copper-rich layer that has formed on both sides of the mature T1precipitate. The mechanical properties of sample A1 are optimal at T = 22h, with a yield strength, tensile strength, and elongation of 520 MPa, 553 MPa, and 8.2%, respectively. In comparison to the A0 sample, the yield strength and tensile strength exhibited an increase of 5.05% and 5.13%, respectively.

Chronic kidney disease (CKD) is a major global health concern and the third leading cause of premature death. Renal fibrosis is the primary process driving the progression of CKD, but the mechanisms behind it are not fully understood, making treatment options limited. Here, we find that the E3 ligase TRIM65 is a positive regulator of renal fibrosis. Deletion of TRIM65 results in a reduction of pathological lesions and renal fibrosis in mouse models of kidney fibrosis induced by unilateral ureteral obstruction (UUO)- and folic acid. Through screening with a yeast-hybrid system, we identify a new interactor of TRIM65, the mammalian cleavage factor I subunit CFIm25 (NUDT21), which plays a crucial role in fibrosis through alternative polyadenylation (APA). TRIM65 interacts with NUDT21 to induce K48-linked polyubiquitination of lysine 56 and proteasomal degradation, leading to the inhibition of TGF-β1-mediated SMAD and ERK1/2 signaling pathways. The degradation of NUDT21 subsequently altered the length and sequence content of the 3′UTR (3′UTR-APA) of several pro-fibrotic genes including Col1a1, Fn-1, Tgfbr1, Wnt5a, and Fzd2. Furthermore, reducing NUDT21 expression via hydrodynamic renal pelvis injection of adeno-associated virus 9 (AAV9) exacerbated UUO-induced renal fibrosis in the normal mouse kidneys and blocked the protective effect of TRIM65 deletion. These findings suggest that TRIM65 promotes renal fibrosis by regulating NUDT21-mediated APA and highlight TRIM65 as a potential target for reducing renal fibrosis in CKD patients.

In this paper, we develop a non-coherent differential chaos shift keying based index modulation by using initial value index (IVI-DCSK) to convey additional information for wireless communications. In the proposed scheme, $m_{c}$ mapped bits are carried by $2^{m_{c}}$ chaotic sequences by exploiting the quasi-orthogonality of different chaotic signals, while the modulated bit is carried by DCSK. To diminish the multiuser interference, the references allocated to different users are sent in individual time slots, while the information-bearing sequences for the mapped bits of users are sent simultaneously. We then derive the bit error rate (BER) expression of multi-user IVI-DCSK over multipath Rayleigh fading channels. The theoretical and consistent simulation results show that the proposed IVI-DCSK achieves significant gains over the conventional chaotic-based index modulations, i.e., permutation index DCSK (PI-DCSK) and code index modulation DCSK (CIM-DCSK). This gain can be more than 4 dB in fading channels with high multipath delay. In addition, it achieves higher energy and spectral efficiencies over the latter ones. The superiority of the proposed scheme is further verified in practical ultra-wideband (UWB) communications. Thus, this proposed scheme is efficient and promising for chaotic-based low-complexity communications, such as in wireless local area network (WLAN) and indoor applications.

In this paper, we construct non-binary codes of length N which correct errors under a non-binary segment-N maxburst-D deletion and maxburst-S insertion (NB-SBDI( $N,D,S$ )) channel without knowing the codeword boundaries. In this NB-SBDI( $N,D,S$ ) channel, at most a single non-binary burst (a block of consecutive bits/symbols) of deletions or insertions of length up to D or S, respectively, exists in a block of N consecutive non-binary symbols. One code named as BM-DB-MDS consists of a maximum distance separable (MDS) code, a block of periodic de Bruijn (DB) symbols, and a block of proposed periodic binary marker (BM) patterns with a period of $S+D+2$ . The other code called BM-MDS code consists of a BM code and an MDS code. We show that the rates of BM-DB-MDS and BM-MDS codes achieve $\frac {\lambda }{\lambda +1}\left ({{1-\frac {1}{2t}}}\right)$ , $\lambda \in \mathbb {N}^{+}$ and $1-\frac {1}{t}$ , respectively, when $N\rightarrow +\infty $ , where $\lambda $ represent the MDS code shortening factor, and $\frac {1}{t}$ is the rough proportion of the maximum length of burst deletions or insertions allowed in a code.

Moisture content significantly impacts the electrical strength of oil-paper insulation. Acquiring moisture content accurately and promptly at different points in oil-paper insulation is crucial to assess the risks associated with oil-filled equipment. Moisture primarily resides in the insulating paper within oil-paper insulation, and the change in moisture content in pressboard is a dynamic process, especially after localized dampening. Besides the exchanges between oil and paper, the predominant movement of moisture occurs within different regions of the insulating paper. Previous studies have primarily focused on moisture exchange at the interface between insulating paper and insulating oil. Due to the lack of nondestructive testing methods, exploration of the migration and diffusion of moisture within the internal structure of insulating pressboard is limited. This aspect is pivotal for accurately assessing moisture distribution within the insulation, thereby directly influencing the evaluation of insulation risks. In this study, the rapid and nondestructive sensing capabilities of terahertz (THz) spectroscopy are used to quantify moisture content in solid insulation. The integral value of the absorption coefficient spectrum within the frequency range of 0.3–1 THz serves as a characteristic parameter for quantifying moisture content in insulating paper. A 2-D moisture-diffusion model for localized dampening in pressboard is established based on the Fick model. Using this approach, the diffusion behavior of localized dampening in new and aged oil-impregnated pressboard is demonstrated, and the diffusion coefficient within the pressboard is calculated. A relationship between this coefficient and moisture concentration is determined. Finally, the obtained diffusion coefficient is used to numerically deduce 3-D localized moisture-diffusion behavior within the pressboard.

The frequency and voltage stability of grid-forming virtual synchronous generator (GFM-VSG) grid-tied system becomes significant in inertia and damping support when GFM-VSG is attached to the power network. In this article, we propose a generic open-loop system model for the frequency–voltage induced-stability analysis, where the dynamics of rate of change of frequency (RoCoF) is a dynamic process that acts on the rate of change of voltage and then, in turn, reacts on RoCoF. In addition, frequency self-stability is evaluated by magnitude–phase feedback analytical model, where the frequency dynamics are identified by the interaction between RoCoF and frequency bias (FB). Also, it is found that inertia is the origin of occurrence of low-frequency oscillation, which induces a natural phase bias between RoCoF and FB. It is found that GFM-VSG can operate stably in weak grid but cannot operate well in ultrastrong grid condition. Finally, theoretical analysis is validated by simulations and experiments.

As deep learning (DL) technologies become ubiquitous, GPU clusters are deployed for inference tasks with consistent service level objectives (SLOs). Efficiently utilizing multiple GPUs is crucial for throughput and cost-effectiveness. This article addresses the challenges posed by dynamic input and NVIDIA MIG in scheduling DL workloads. We present ElasticBatch, a scheduling system that simplifies configuration through bucketization and employs a machine learning-based pipeline to optimize settings. Our experiments demonstrate that ElasticBatch achieves a 50% reduction in GPU instances compared to MIG disablement, increases GPU utilization by 1.4% to 6.5% over an ideal scheduler and significantly reduces profiling time. This research contributes to the discourse on efficient utilization of GPU clusters. ElasticBatch's effectiveness in mitigating challenges posed by dynamic inputs and NVIDIA MIG underscores its potential to optimize GPU cluster performance, providing tangible benefits in terms of reduced instances, increased utilization, and significant time savings in real-world deployment scenarios.

The dual-active-bridge (DAB) dc–dc converter with the input-series output-series (ISOS) structure is applicable for flexible interconnection of a power grid, where both the input and output sides are high voltage. However, the bidirectional series structure and bidirectional power flow pose significant challenges to voltage balance between modules. To address the issue, this letter proposes a voltage balance scheme, in which the LC resonant branch is integrated into the ISOS DAB dc–dc converter to balance the voltage of the input side, while the control strategy eliminates the unbalanced voltage of the output side by phase-shift ratio regulation. Hardware-in-the-loop test results indicate that the proposed scheme simultaneously realizes voltage balance on both the input and output sides of the ISOS DAB dc–dc converter.

In order to obtain Al–Al2O3–ZrO2composite refractories with excellent oxidation resistance, erosion resistance and thermal shock stability, metallic aluminium and fused Al2O3–ZrO2were used as the raw materials, while phenolic resin was used as the binding agent. The microstructure was characterised by means of X-ray diffraction and scanning electron microscopy tests. Based on the reaction between zirconia and metallic aluminium under N2flowing at 1300 °C, the reaction mechanism of zirconia and metallic aluminium in-situ reaction at 1300 °C to generate (Al2OC)x(AlN)1−xsolid solution, zirconium nitride, and aluminium–zirconium alloy was revealed, and a model for the evolution of the phases in the aluminium-added Al–Al2O3–ZrO2specimen was constructed to realise the Al–Al2O3–ZrO2composite refractories phase controlling. Based on the good corrosion resistance of ZrO2to CaO and the excellent results of the in-situ reaction of metallic aluminium to generate a large number of non-oxide reinforcing phases capable of replacing elemental C, this work is of great significance for the study of refractory in erosion-heavy environments, such as calcium-treated steel.

The pronounced brittleness of hard Laves phase intermetallics is detrimental to their tribological properties at room temperature. In this study, we utilized a heterogeneous structure to engineer an ultrastrong dual-phase (Laves + B2) AlCoFeNiNb high-entropy alloy that exhibits a low wear rate (3.82×10−6mm3/(N·m)) at room temperature. This wear resistance in the ball-on-disc sliding friction test with the counterpart of Al2O3balls stems from the activated deformation ability in the ultrafine Laves lamellae under heterogeneous interface constraints. Furthermore, as tribological stress intensifies, the surface deformation mechanism transitions from dislocation slip on the basal and pyramidal planes to a unique combination of local shear and grain rotation within the Laves phase. Our study illuminates fresh perspectives for mitigating the embrittling effect of Laves phase intermetallics under tribological loading and for the development of wear-resistant materials.

Flexible output regulation and high efficiency are key requirements for wireless power transfer systems. However, it is a challenge to maintain high efficiency in wide output voltage range applications due to the limited regulation range of converters. To address this issue, in this article, two power transmission channels based on the LCC-LCC compensation network are used to improve system efficiency within a wide operation range. Multiple operation modes of two power channels are utilized to obtain multiple operation ranges with different optimal output voltages. Then, a multimode control strategy for two power channels is proposed to achieve optimal efficiency with different output voltage ranges. By properly switching the operation mode of these power channels, the system can always operate in an optimal range, thus the overall efficiency can achieve a high level even in not only heavy load, but also light load conditions. An experimental prototype with 100 to 420 V output voltage is constructed. Experimental results show that the proposed method can achieve an overall efficiency of 93.8% to 96.5%.

Photocatalysis properties enhanced by metal and organic polymer has been received more interest because of their ability to directly or indirectly degrade pollutants. The effect of PDA (polydopamine) and Ag nanoparticles on the different phases of ZnO to degrade organic dyes under visible and UV-vis light was investigated. ZnO@PDA, ZnO-Ag, and ZnO-Ag@PDA nanoparticles were synthesized. It’s shown that Ag particles with sizes of less than 20 nm were deposited evenly on the ZnO. There were a few changes in the structure of ZnO@PDA or ZnO-Ag @PDA. When the ZnO was coated by PDA, the photocatalytic activity could be enhanced. The photocatalytic activity under UV-vis and visible light of the ZnO@PDA were effectively improved. The degradation rate of ZnO-Ag@PDA was 97.9% under UV-vis light for 20 mins, which was 16.1% higher than that of ZnO. The photocatalytic activity of ZnO-Ag@PDA reached the maximum after polymerizing for 2 hours. The photocatalytic efficiency of ZnO-Ag@PDA-2h under UV-vis for 30 minutes can reach higher than 99.01%. The photocatalytic performance decreased rapidly with the increasing cycles. When the number of cycles was 5, the degradation rate was 65.84%. Afterward, the degradation rate changed small and became stable.

In recent years, various transition metal compounds have been extensively studied to deal with the problems of slow reaction kinetics and the shuttle effect of lithium–sulfur (Li–S) batteries. Nevertheless, their catalytic performance still needs to be further improved by enhancing intrinsic catalytic activity and enriching active sites. Doping is an effective means to boost the catalytic performance through adjusting the electron structure of the catalysts. Herein, the electron structure of CoSe2is adjusted by doping P, S with different p electron numbers and electronegativity. After S doping (S–CoSe2), the content of Co2+increases, and charge is redistributed. Furthermore, more electrons are transferred between Li2S4/Li2S and S–CoSe2, and optimal Co–S bonds are formed between them with optimized d–p orbital hybridization, making the bonds of Li2S4/Li2S the longest and easy to break and decompose. Consequently, the Li–S batteries with the S–CoSe2-modified separator achieve improved rate performance and cycling performance, benefiting from the better bidirectional catalytic activity. This work will provide reference for the selection of the anion doping element to enhance the catalytic effect of transition metal compounds.

A redox-neutral coupling of allyl alcohols with trifluoromethyl ketones has been developed via Ni–Ti bimetallic catalysis. This innovative method allows for the efficient synthesis of various β-tertiary trifluoromethyl alcohol-substituted ketones with yields of up to 98%. The reaction is scalable and compatible with a wide range of substrates, including complex bioactive molecules. Mechanistic studies suggest that the rate-determining step involving β-H elimination and the presence of the Ti-based Lewis acid, as well as a hydroxyl group on the substrates, is crucial for driving the reactivity of this transformation.

Low-density parity-check (LDPC) codes with protograph-based raptor-like (PBRL) structure have been chosen as the data channel coding scheme for the fifth-generation (5G) enhanced mobile broad band (eMBB) services. However, these 5G LDPC codes are not optimized for the scenarios of Ultra Reliable and Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC) in which small transport block sizes are usually used. In this paper, a new method is proposed to construct PBRL LDPC codes for URLLC and mMTC. This method incorporates degree-distribution optimization, base matrix derived from parity-check matrix of quadratic residue codes, masking technique and modified progressive-edge-growth (PEG) algorithm. Simulation results show that the proposed PBRL LDPC codes outperform 5G LDPC short codes in terms of error-correcting performance.

While neural BP-based (NBP) decoders exhibit superior error correction performance compared to belief-propagation (BP) decoders, the NBP decoder's high computational and memory requirements impede its practical deployment in communication systems. To overcome this challenge, we propose a Coded Neural BP (CNBP) scheme to accelerate the NBP decoder in distributed environments, while considering storage constraints and providing resilience to stragglers. The key idea is to reformulate the primary operations of the NBP decoder as matrix-vector multiplications by introducing weight matrices and transformations. Based on this, the acceleration of the NBP decoder is achieved by speeding up matrix-vector multiplications using coded distributed computing. Extensive experiments conducted on Amazon EC2 cluster demonstrate that CNBP achieves notable acceleration and scalability performance without any loss in error correction performance.

Mobile crowdsensing (MCS) and wireless power transfer (WPT) are two promising paradigms to enhance sensing coverage and prolong the operational time of users. However, current research on the MCS and the unmanned aerial vehicle (UAV)-enabled WPT faces two challenges, namely, neglecting the impact of the computing capabilities of users on resource allocation and employing an omnidirectional energy transmission model. To address the above challenges, we propose a UAV-assisted data collection framework that considers both the adaptive compression of users influenced by environmental factors and the directional energy transmission model. We formulate a problem of maximizing the amount of collected data of the UAV under the proposed framework by jointly optimizing the compression rate, the WPT time, the uploading time, the transmit power of users, and the UAV trajectory. To tackle the formulated non-convex optimization problem, we propose an iterative algorithm using the multi-variable decoupling method and the successive convex approximation (SCA) method. Simulation results verify that the proposed approach is more efficient than the existing scheme without data compression.

In this study, three types of three-layer functionally graded materials (FGMs) were designed by mainting the average content of the reinforcement constant and changing the span of the composition. The B4C particle-reinforced Al-matrix FGMs and a homogeneous composite (HC) with the same average composition were prepared using the laser additive manufacturing technique. The FGMs had a higher average secondary-phase content and grain size than the HC. Large (black) and reticulated (white) secondary phases corresponding to B4C phase, T phase, and MgZn2were observed in the as-deposited FGM samples. The content of the secondary phase in the middle layer was generally low. The bottom layer, which was in direct contact with the substrate, exhibited the smallest grain size. The hardness and wear resistance significantly improved owing to the high B4C content in the top layer. The flexural behaviors of each layer of the FGM in different directions were systematically studied. The plasticity of the single-layer material decreased with increase in the B4C content. When the bottom layer had a high B4C content, the flexural ability significantly reduced. Among all the samples, G2 with 8% B4C particle content in the top layer exhibited the best comprehensive mechanical properties. The hardness value, wear rate of the top layer, maximum bending stress, and maximum bending strain of the FGM were 177.5 HV, 0.367 × 10−3mm3/(N·m), 585.6 MPa, and 7.36%, respectively. This study can provide a reference value for the future design and development of wear-resistant gradient materials for aerospace applications.

To enhance the thermo-hydraulic performance of cooling channels, this investigation examines the influence of distinct cross-sectional shapes (i.e., triangular, rectangular, and hexagonal) of twisted pin fins and their arrangements in straight and cross rows. An ambient air cooling test platform was established to numerically and experimentally investigate the flow and heat transfer characteristics of 360° twisted pin fins at Re=15 200–22 800. The findings reveal that straight rows exhibit higher Nuvalues than cross rows for triangular and rectangular twisted pin fins, and Nuincreases with Re. In contrast, for hexagonal twisted pin fins, only straight rows at Re=19 000 exhibit superior overall thermal performance compared to cross rows. Notably, the heat transfer performance of the cooling channel with hexagonal twisted fins surpasses both triangular and rectangular configurations, especially at high Reynolds numbers (Re=22 800). Although the heat transfer coefficient of the cooling channel with hexagonal twisted fins is significantly enhanced by 132.71% compared to the flat channel, it also exhibits the highest thermal resistance and relative friction among the three types of twisted fins, the maximum of which are 2.14 and 16.55. Furthermore, the hydrothermal performance factor (HTPF) of the cooling channels with different types of twisted pin fins depends on the Reynolds number and arrangement modes. At Re=15 200, the highest HTPF achieved for the cross-row hexagonal twisted pin fins is 0.99.

To improve the power density of magnetic field energy harvesters (MFEHs) and supply stable voltage output, a maximum-power-point tracking (MPPT) method with constant voltage (CV) output for the MFEH in the saturated region is proposed. The proposed MFEH system consists of a bidirectional buck–boost converter connected with a supercapacitor and an active rectifier. The bidirectional buck–boost converter is employed for CV output control, and MPPT under CV output conditions is realized by controlling the conduction time of the active rectifier. Besides, the calculation formula for the optimal conduction time in the saturated region is derived, which is constant and does not vary with the primary current. Subsequently, the optimal coil turn design method of the MFEH under optimal conduction time control is proposed, which can be used to guide the design of the coil turns. An experimental prototype is constructed to verify the effectiveness of the proposal. Experimental results demonstrate that the output voltage can be maintained at 3.3 V, and CV output can be realized. Besides, compared to conventional methods, the output power of the MFEH with the proposed method at 50 Arms increases from 0.47 to 0.60 W, achieving a 27% improvement.

This paper proposes a three-dimensional non-orthogonal multiple access (3D-NOMA) high-security transmission in a seven-core fiber based on chaotic selection mapping of constellations. The transmission scheme uses a four-dimensional hyperchaotic model for the first selection mapping mask and the second selection mapping mask for 3D constellation points. The first selection mapping mask randomly permutes the information of each mapped constellation point to complete the first layer of encryption. The second selection mapping mask maps the constellation points onto the hollow sphere domain in 3D space through encrypted data preprocessing mapping rules to complete the second layer of encryption. The joint mapping of two-layer encryption provides greater security and flexibility for constellation encryption. An ample key space∼10150 with an effective information transmission of 35.51 Gb/s was demonstrated experimentally in a 2 km 7-core fiber. The 3D orthogonal frequency division multiplexing (3D-OFDM) is transmitted simultaneously under the same conditions, and compared with 3D-OFDM, the proposed 3D-NOMA scheme could potentially expand the number of users without obvious performance degradation. The experimental results show that the scheme can effectively protect the system from illegal optical network unit attacks and is ideal for physically securing 3D-NOMA in the future.

Background: The peak power density of the hard X-ray nano-probe beamline is the highest at SSRF (Shanghai Synchrotron Radiation Facility). The peak power density of the front-end is 77.43 kW/mrad2, which is about 1.7 times higher than the others, for the fixed mask 1 (FM1) which is 11,440 mm away from the light source. However, adopting the traditional design may cause the major power absorbed by the downstream fixed mask 2 (FM2). Purpose: In order to evenly absorb the heat and ensure smooth beam transmission. Methods: The synchrotron beam tracing was performed, and the separate mask absorption approach was adopted. Additionally, finite element analysis (FEA) was conducted to analyze the thermodynamic behavior of FMs. Result: Currently, the front-end has been successfully designed and is running smoothly.

Three types of 7185+TiB2/TiC aluminum matrix composites with different Zn/Mg ratios were prepared in this work through a water-cooled copper casting process. The effects of Zn/Mg ratio on the microstructure and mechanical properties of 20-mm-thick composites were studied by optical microscopy, electron back scattering diffraction, transmission electron microscopy, and tensile mechanical testing. The results showed that the addition of appropriate TiB2/TiC particles refined the as-cast microstructure of the alloy, and with a decrease in Zn/Mg ratio, the grain size distribution became more uniform, the crystalline phase content gradually increased, and the uniformity of the alloy increased. After aging, as the Zn/Mg ratio decreased, the size of the precipitated phase decreased, and the strengthening effect of the alloy improved. The tensile strengths of the surface layer, 1/4 layer, and center position of the A3 alloy were 558, 568, and 572 MPa, respectively. The strength difference among the different thickness layers was the smallest, and the elongation rate was greater than 12%, resulting in the best uniformity.

Recent single-arm studies involving neoadjuvant camrelizumab, a PD-1 inhibitor, plus chemotherapy for resectable locally advanced esophageal squamous cell carcinoma (LA-ESCC) have shown promising results. This multicenter, randomized, open-label phase 3 trial aimed to further assess the efficacy and safety of neoadjuvant camrelizumab plus chemotherapy followed by adjuvant camrelizumab, compared to neoadjuvant chemotherapy alone. A total of 391 patients with resectable thoracic LA-ESCC (T1b-3N1-3M0 or T3N0M0) were stratified by clinical stage (I/II, III or IVA) and randomized in a 1:1:1 ratio to undergo two cycles of neoadjuvant therapy. Treatments included camrelizumab, albumin-bound paclitaxel and cisplatin (Cam+nab-TP group; n= 132); camrelizumab, paclitaxel and cisplatin (Cam+TP group; n= 130); and paclitaxel with cisplatin (TP group; n= 129), followed by surgical resection. Both the Cam+nab-TP and Cam+TP groups also received adjuvant camrelizumab. The dual primary endpoints were the rate of pathological complete response (pCR), as evaluated by a blind independent review committee, and event-free survival (EFS), as assessed by investigators. This study reports the final analysis of pCR rates. In the intention-to-treat population, the Cam+nab-TP and Cam+TP groups exhibited significantly higher pCR rates of 28.0% and 15.4%, respectively, compared to 4.7% in the TP group (Cam+nab-TP versus TP: difference 23.5%, 95% confidence interval (CI) 15.1–32.0, P< 0.0001; Cam+TP versus TP: difference 10.9%, 95% CI 3.7–18.1, P= 0.0034). The study met its primary endpoint of pCR; however, EFS is not yet mature. The incidence of grade ≥3 treatment-related adverse events during neoadjuvant treatment was 34.1% for the Cam+nab-TP group, 29.2% for the Cam+TP group and 28.8% for the TP group; the postoperative complication rates were 34.2%, 38.8% and 32.0%, respectively. Neoadjuvant camrelizumab plus chemotherapy demonstrated superior pCR rates compared to chemotherapy alone for LA-ESCC, with a tolerable safety profile. Chinese Clinical Trial Registry identifier: ChiCTR2000040034.

The CNC machining of non-zero genus surfaces in B-rep models has become a prevalent challenge in modern manufacturing. The computational complexity inherent in generating tool paths for such geometries remains a significant hurdle. In this study, an efficient path planning algorithm tailored for porous structures of this nature is presented. Initially, we create a parametric grid using the adaptive iso-scallop height method and subsequently utilize a marching cells algorithm to construct a cell grid capable of preserving arbitrary boundaries. Based on the graph structure naturally induced by marching cells, we employ a designated weighting method to establish a minimum spanning tree, upon which a path with a singular start and end point is generated. We conduct various experiments on examples from industrial scenarios as well as synthetic examples. The results show the superior performance and effectiveness of our method concerning scallop height limits, sharp turns, and structural stability.

In-depth understanding of the multi-energy consumption behavior pattern is the essential to improve the management of multi-energy system (MES). This paper proposes a data-driven three-stage adaptive pattern mining approach for multi-energy loads, which addresses the issues of complex multi-dimensional time-series mining, uncommon daily loads discovery, typical load classification and parameter setting requiring user intervention. In the first stage, the relative state changes over time between different energy loads are excavated based on Autoplait, which realizes time pattern discovery, segmentation and match for multi-dimensional loads. In the second stage, adaptive affinity propagation (AAP) considering trend similarity distance (TSD) is proposed to classify loads into common and uncommon clusters, where uncommon loads are eliminated and daily pattern is obtained by taking average of common loads. In the third stage, AAP with windows dynamic time warping (WDTW) identifies various profiles to obtain typical pattern of daily loads. Specifically, pattern mining provides the key information of multi-energy loads, which is significant to the applications for the demand side, such as load scene compression, load forecasting and demand response analysis. A case study uses MES data from Arizona State University to verify the effectiveness and practicality of the proposed approach.

Reconstructing a spline surface from a given arbitrary topological triangle mesh is a fundamental and challenging problem in computer-aided design and engineering. This article introduces a novel surface fitting method utilizing G-NURBS capable of handling control meshes with arbitrary topologies. This method employs adaptive control point adjustment, guided by the geometric attributes of the input model, ensuring precise representation of sharp features such as edges and corners. Two primary strategies are employed: A parameter correspondence approach designed for sharp features and a control mesh iterative refinement technique that incorporates geometrical feature information. The proposed method has been tested and evaluated on various CAD models to demonstrate its effectiveness. This method can achieve higher fitting accuracy while faithfully preserving the geometrical features with fewer control points.

Predictive position control (PPC) method with long predictive horizon is an effective method to eliminate the cascade structure and improve the dynamic response for the permanent magnet synchronous motor (PMSM). However, traditional PPC method needs a time-consuming prediction process and does not always guarantee satisfactory position performance. In this article, a simplified constrained PPC method is proposed, the Laguerre functions are designed to construct the control trajectory of position system, which offers two control degrees to improve the position dynamic response and simplify programming of prediction process. Besides that, the optimization model with current constraints, speed constraints, and voltage constraints are constructed with the frame of Laguerre functions. The optimization model with constraints is solved with an optimal method and suboptimal method. The proposed PPC method is experimentally verified and compared with field-oriented control (FOC) method and a traditional PPC method, the results have proved that proposed method has excellent position performance and low calculation burden.

•Protonation can significantly enhance the superconducting properties of FeSe.•H+insertion modifies the flux dynamics and increases the pinning energy.•The transition between strong and weak pinning is related to the second peak effect.•The behavior of flux in Hx-FeSe is dominated by different types of defects.•Vortex phase diagrams of FeSe and Hx-FeSe were established.

Multiphase rectifiers are widely employed to enhance the power level of inductive power transfer systems. However, due to the parameter deviations of circuits, unbalanced currents are inevitable, leading to a comparatively lower system power level. Thus, a novel current balancing method for multiple-phase rectifiers is proposed to address the issue. The idea of the proposed method is to split the multiple-strand Litz wire used in the receiving coil into multiple windings, connect each winding to a single corresponding phase of the multiple-phase rectifier, and then compensate each winding independently. As a result, the loop impedance deviations are reduced, and the unbalanced currents are alleviated. The advantage of the proposed method is that no extra voltage-ampere capacity is required to achieve the current balancing. Finally, a prototype is built to validate the performance of the proposed approach. The experiment results show that the proposed method is effective. With the proposed method, the maximum deviation of the currents significantly decreases by 27.6% at a maximum from 28.4% to 0.8%.

The point cloud keypoint detection algorithm like USIP that uses downsampling first and then fine-tuning the sampling points cannot effectively detect the defect part of the single view defect point cloud, resulting in the inability to output the keypoints of the defect part. Therefore, this paper proposes the twin structure key point detection algorithm named TSKPD based on the idea of contrastive learning, which uses two single-view defect point clouds to synthesize relatively more complete key points for learning, so as to promote the network model to learn the features of the complete point cloud. The robustness of key point detection of point cloud is effectively improved, and the detection of single view defect point cloud is realized. The test results on ModelNet40 and ShapeNet datasets show that the coverage rate of TSKPD on the missing part of the single view defect point cloud is 12.62 higher than the existing optimal algorithm.

The study of thermoelasticity problems holds significant importance in the field of engineering. When analyzing non-Fourier thermoelastic problems, it was found that as the thermal relaxation time increases, the finite element solution will face convergence difficulties. Therefore, it is necessary to use alternative methods to solve. This paper proposes a physics-informed neural network (PINN) based on the DeepXDE deep learning library to analyze thermoelastic problems, including classical thermoelastic problems, thermoelastic coupling problems, and generalized thermoelastic problems. The loss function is constructed based on equations, initial conditions, and boundary conditions. Unlike traditional data-driven methods, this approach does not rely on known solutions. By comparing with analytical and finite element solutions, the applicability and accuracy of the deep learning method have been validated, providing new insights for the study of thermoelastic problems.

• It was found that the stress corrosion crack growth behaviour is controlled by the mechano-electrochemical effect at the crack tip. • The hydrogen embrittlement occurs at crack tip even under anodic potential, and hydrogen embrittlement is the main factor of crack growth. • A model of mechano-electrochemical effect at crack tip is developed to study the phenomenon. • The reason for the promotion or inhibition of anodic potential on crack propagation is revealed. • The critical value of applied potential affecting crack growth is the crack tip potential of −0.0591 pH. Here, the stress corrosion crack growth behaviour of a high-strength low-alloy steel under anodic potential was investigated in artificial seawater. As applied potential shifts positively, the crack growth rate first increases and then decreases. The crack growth behaviour is controlled by the mechano-electrochemical effect at the crack tip. Even under anodic potential, hydrogen embrittlement (HE) occurs at the crack tip, and HE is the main factor of crack growth. A model of mechano-electrochemical effect at the crack tip is developed to study the phenomenon. It was found that applied potentials change the crack growth behaviour via involvement in HE and anodic dissolution at the crack tip. The critical value of applied potential affecting crack growth is the crack tip potential of –0.0591 pH. [Display omitted] [ABSTRACT FROM AUTHOR]

The healthcare burden imposed by bacterial infections demands robust and accessible diagnostic methods that can be performed outside hospitals and centralized laboratories. Here, we report Pathogen Assay with Ratiometric Luminescence (PEARL), a sensitive and easy-to-operate platform for detecting pathogenic bacteria. The PEARL leveraged a color-changeable CRISPR–Cas12a sensor and recombinase polymerase amplification to elicit ratiometric bioluminescence responses to target inputs. This platform enabled robust and visualized identification of attomolar bacteria genome deoxyribonucleic acid according to the color changes of the reactions. In addition, the components of the color-changeable Cas12a sensor could be lyophilized for 3 month storage at ambient temperature and then be fully activated with the amplicons derived from crude bacterial lysates, reducing the requirements for cold-chain storage and tedious handling steps. We demonstrated that the PEARL assay is applicable for identifying the infections caused by Pseudomonas aeruginosain different clinical specimens, including sputa, urines, and swabs derived from wounds. These results revealed the potential of PEARL to be used by untrained personnel, which will facilitate decentralized pathogen diagnosis in community- and resource-limited regions.