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This paper investigates the three-dimensional crack propagation and damage evolution process of metallic column shells under internal explosive loading. The calibration of four typical failure parameters for 40CrMnSiB steel was conducted through experiments and subsequently applied to simulations. The numerical simulation results employing the four failure criteria were compared with the differences and similarities observed in freeze-recovery tests and ultra-high-speed tests. This analysis addressed the critical issue of determining failure criteria for the fracture of a metal shell under internal explosive loads. Building upon this foundation, the damage parameter Dc, linked to the cumulative crack density, was defined based on the evolution characteristics of a substantial number of cracks. The relationship between the damage parameter and crack velocity over time was established, and the influence of the internal central pressure on the damage parameter and crack velocity was investigated. Variations in the fracture modes were found under different failure criteria, with the principal strain failure criterion proving to be the most effective for simulating 3D crack propagation in a pure shear fracture mode. Through statistical analysis of the shell penetration fracture radius data, it was determined that the fracture radius remained essentially constant during the crack evolution process and could be considered a constant. The propagation velocity of axial cracks ranged between 5300 m/s and 12600 m/s, surpassing the Rayleigh wave velocity of the shell material and decreasing linearly with time. The increase in shell damage exhibited an initial rapid phase, followed by deceleration, demonstrating accelerated damage during the propagation stage of the blast wave and decelerated damage after the arrival of the rarefaction wave. This study provides an effective approach for investigating crack propagation and damage evolution. The derived crack propagation and damage evolution law serves as a valuable reference for the development of crack velocity theory and the construction of shell damage evolution modes.

Abstract Toxoplasma gondii causes lifelong infection in most definitive and intermediate hosts. Clinical cases of toxoplasmosis in captive cheetahs have been reported. However, there are few reports of viable T. gondii strains isolated from cheetahs. Here, T. gondii infection was investigated using molecular and serological assays in cheetahs from China. Modified agglutination test (MAT) (cut-off: 1:25) indicated that all six examined cheetahs (n = 6) showed T. gondii antibodies. Toxoplasma gondii DNA was detected in three out of five cheetahs. Two viable T. gondii strains were isolated from the striated muscles of two cheetahs using mice bioassay. They were designated as TgCheetahCHn1 and TgCheetahCHn2. Genetic characterization of DNA derived from tachyzoites was performed using RFLP-PCR of 10 markers. Toxoplasma gondii TgCheetahCHn1 is ToxoDB PCR-RFLP genotype #319, and the alleles of ROP18/ROP5 types were 3/7. TgCheetahCHn2 is ToxoDB genotype #9, and the alleles of ROP18/ROP5 were 3/6. The average survival time of TgCheetahCHn1-infected Swiss mice was 22 ± 1 days (n = 23), and the mice did not have detectable T. gondii-specific antibodies until 117 ± 30 days post-inoculation (n = 8), therefore, TgCheetahCHn1 had intermediate virulence. TgCheetahCHn2 was avirulent for Swiss mice. Few brain tissue cysts (0–50) were observed in the mice inoculated with TgCheetahCHn1 or TgCheetahCHn2. The results provide direct evidence of cheetah as intermediate host of T. gondii.

This study proposed three types of Al–Hf reactive materials with particle size ratios (α), which were almost completely dense (porosity of

In modern electronic warfare, the jamming environment of radar is more complex than ever. The airborne jammer adapts its jamming method based on diverse raid missions and stages. Recently, the reinforcement learning–based radar anti-jamming method has made some progress in the confrontation scenario of single jamming; however, the gap with respect to actual complex multi-jamming scenarios is large. To address this issue, this paper proposes a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in the complex domain to optimize the anti-jamming strategy of frequency agile radar. First, according to the stage characteristics of the raid mission, noise spot jamming, range deception jamming , and dense false target forwarding jamming models are established. The three jamming sequence strategies were designed to simulate actual jamming scenarios. Second, a reinforcement learning reward function that integrates the signal-to-noise ratio and target trajectory integrity is constructed for the multi-jamming scenario model. Thus, a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in a complex domain is proposed, which is based on the complex domain characteristics of the jamming signal. Finally, radar anti-jamming simulation experiments are performed based on the three jamming sequence strategies. The results show that the proposed method can effectively deal with the main-lobe jamming problem of complex multi-jamming scenarios under time-sequence conditions. Moreover, the average decision-making accuracy was improved, and the average decision-making time was reduced to 405.3 ms compared with the two classical reinforcement learning algorithms.

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) based on deep reinforcement learning (DRL) has made great progress. However, most studies assume relatively simple task scenarios and do not consider the impact of complex task scenarios on UAV flight performance. This paper proposes a DRL-based autonomous navigation algorithm for UAVs, which enables autonomous path planning for UAVs in high-density and highly dynamic environments. This algorithm proposes a state space representation method that contains position information and angle information by analyzing the impact of UAV position changes and angle changes on navigation performance in complex environments. In addition, a dynamic reward function is constructed based on a non-sparse reward function to balance the agent’s conservative behavior and exploratory behavior during the model training process. The results of multiple comparative experiments show that the proposed algorithm not only has the best autonomous navigation performance but also has the optimal flight efficiency in complex environments.

With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.

Toxoplasmosis is one of the most common zoonotic diseases in the world. Felines excrete Toxoplasma gondii oocysts, which play a key role in the transmission of this protozoon. Pathological diagnoses were performed on four carcasses of captive tigers collected from 2019 to 2021 in China, and T. gondii was surveyed using serology, molecular analysis, and aetiology. Striated muscle samples of the tigers (n = 4) were bioassayed in mice. DNA derived from T. gondii tachyzoites was isolated and characterized using PCR–RFLP. The pathological diagnoses revealed that ageing, declined immune function, liver, and kidney failures caused the deaths in the tigers examined. A modified agglutination test (cut–off: 1:25) revealed that IgG antibodies to T. gondii were 100% (4/4) in the captive tigers. Two viable T. gondii strains (TgTigerCHn3 and TgTigerCHn4) were isolated from tiger striated muscles and seeded on the Vero cell culture for further propagation. The genotypes of TgTigerCHn3 and TgTigerCHn4 were ToxoDB#20 and ToxoDB#2, respectively. The two strains were avirulent for Swiss mice, which matched the ROP18 and ROP5 gene alleles of TgtigerCHn3 (3/4) and TgtigerCHn4 (3/3). Few brain tissue cysts (0–213) were observed in the mice after inoculation with TgTigerCHn3 and TgTigerCHn4. This is the first documented isolation of T. gondii ToxoDB#20 and ToxoDB#2 from tigers. The results provide additional direct evidence of tiger as intermediate hosts for T. gondii. Tigers in the zoos may potentially transmit T. gondii to other animals and humans.

As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model’s lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks.

Wallabies and other marsupials are highly susceptible to Toxoplasma gondii. In this study, 26 macropod samples were collected (8 red kangaroos, 4 Parma wallabies, 8 red-necked wallabies, 5 albino red-necked wallabies and 1 Eastern grey kangaroo), including tissue (n = 9) and serum (n = 17) samples. According to the modified agglutination test (MAT) results (cutoff 1:25), 50% (95% Cl: 32.06–67.94%) of the macropods had T. gondii antibodies. Among them, species, survival state, and sampling date were risk factors for T. gondii susceptibility (P

A discernible gap has materialized between the expectations for object detection tasks in optical remote sensing images and the increasingly sophisticated design methods. The flexibility of deep learning object detection algorithms allows the selection and combination of multiple basic structures and model sizes, but this selection process relies heavily on human experience and lacks reliability when faced with special scenarios or extreme data distribution. To address these inherent challenges, this study proposes an approach that leverages deep reinforcement learning within the framework of vision tasks. This study introduces a Task-Risk Consistent Intelligent Detection Framework (TRC-ODF) for object detection in optical remote sensing images. The proposed framework designs a model optimization strategy based on deep reinforcement learning that systematically integrates the available information from images and vision processes. The core of the reinforcement learning agent is the proposed task-risk consistency reward mechanism, which is the driving force behind the optimal prediction allocation in the decision-making process. To verify the effectiveness of the proposed framework, multiple sets of empirical evaluations are conducted on representative optical remote sensing image datasets: RSOD, NWPU VHR-10, and DIOR. When applying the proposed framework to representative advanced detection models, the mean average precision (mAP@0.5 and mAP@0.5:0.95) is improved by 0.8–5.4 and 0.4–2.7, respectively. The obtained results showcase the considerable promise and potential of the TRC-ODF framework to address the challenges associated with object detection in optical remote sensing images.

Highly pathogenic influenza A(H5N8) viruses had caused several outbreaks among wild bird and poultry populations across the globe, and strikingly, caused human infection, posing serious public health concerns. In this study, we conducted influenza surveillance in China during 2021 to monitor the evolution of influenza viruses in poultry. A total of 35 influenza viruses were obtained in chickens, ducks, and geese, of which 30 H5N8 viruses, 3 H5N1 viruses, and 2 H5N6 viruses. Phylogenetic analysis suggested all of H5N1, H5N6, and H5N8 isolates were derived from clade 2.3.4.4b H5N8 viruses during 2020/21 season, and notably, the internal genes of H5N1 and H5N6 viruses shared different genetic heterogeneity with H5N8 viruses and had been reassorted with wild bird-origin H5N1 viruses from Europe. By contrast, almost all H5N8 viruses exhibited only one phylogenic cluster with wild bird-origin H5N8 viruses in China and Korea, indicating that H5N8 viruses in China were more stable. Besides, we found that Korea is the main output geographic location in the spread of these H5N8 viruses to northern and eastern China, and especially, the co-circulation of H5N8 viruses occurred within China, with central China acted as a seeding population during the H5N8 epidemic. The statistical support was strong for viral migration from wild birds to chickens and ducks, indicating that 2.3.4.4b poultry-origin H5N8 viruses during 2020–2021 were originated from wild birds. Our findings provide novel insights into evolution and transmission dynamics of H5 subtype influenza viruses among poultry after novel H5N8 viruses invaded China for nearly one year.

Toxoplasma gondii infects most warm-blooded animals, including humans. Felids can serve as both intermediate and definitive hosts for T. gondii. However, there is no direct evidence to prove the caracal (Caracal caracal) is an intermediate host for T. gondii. Here, we report T. gondii infection in two caracals in a zoo from China. Antibodies against T. gondii were found in both caracals by modified agglutination test (MAT) (cut-off titer: 1:25). Tissue cysts were observed in the leg and tongue muscles of caracal case# 1. These cysts were confirmed as T. gondii by immunohistochemical staining and T. gondii was detected by polymerase chain reaction (PCR). Viable T. gondii strain was isolated from the striated muscles of caracal case# 2 and designated as TgCaracalCHn1. DNA from tachyzoites obtained from cell cultures was characterized by RFLP-PCR using ten markers (SAG1, SAG3, SAG2, BTUB, c22-8, GRA6, c29-2, PK1, L358, and Apico) and the virulence genes (ROP5 and ROP18). The results indicate that this isolate belongs to ToxoDB genotye #2 (Type III). The virulence of this isolate was evaluated in BALB/c mice. A dose of 104 TgCaracalCHn1 tachyzoites was non-lethal to mice. Tissue cysts were found in brain tissues of infected mice. This result confirmed that the TgCaracalCHn1 is non-virulent to mice. Current study documents first isolation of viable T. gondii strain from caracal and also indicates that caracal can act as new intermediate host for T. gondii.

Abstract Background High-solids enzymatic hydrolysis has attracted increasing attentions for the production of bioethanol from lignocellulosic biomass with its advantages of high product concentration, water saving, and low energy and capital costs. However, the increase of solids content would worsen the rheological properties, resulting in heat/mass transfer limitation and higher mixing energy. To address these issues, ball milling was applied to corn stover prior to enzymatic hydrolysis, and the rheological behaviors and digestibility of ball-milled corn stover under high-solids loading were investigated. Results Ball milling significantly modified the physicochemical properties of corn stover. The apparent viscosity of slurries at 30% solid loading decreased by a factor of 500 after milling for 60 min, and the yield stress was less than 10 Pa. The dramatic decrease of viscosity and yield stress enabled the hydrolysis process to be conducted in shake flask, and remained good mixing. Meanwhile, the estimated energy consumption for mixing during saccharification decreased by 400-fold compared to the untreated one. The resultant hydrolysate using 10 FPU g−1 solids was determined to contain 130.5 g L−1 fermentable sugar, and no fermentation inhibitors were detected. Conclusions The proposed ball milling pretreatment improved rheological behavior and sugar yield of high-solids corn stover slurry. Ball milling enables high-solids slurry to maintain low viscosity and yield stress while obtaining a non-toxic high-concentration fermentable syrup, which is undoubtedly of great significance for inter-unit processing, mixing and downstream process. In addition, the energy input for ball milling could be balanced by the reduced mixing energy. Our study indicates ball milling a promising pretreatment process for industrial bioethanol production.

The shock and spalling behavior of a compositionally complex high strength low-alloy steel (HSLA) was studied using plate impact testing. The free surface velocity of the specimen in the range of 194~938 m/s was measured by a displacement interferometer system for any reflector (DISAR). The Hugoniot elastic limit (HEL), spallation fracture and microstructural evolution of the HSLA under an impact stress of 3.04~18.66 GPa were analyzed. Shock Hugoniots were obtained from the measured particle velocities and calculated shock velocities. The velocity curves show clear signs of HEL and velocity fallback, indicating a transition from elastic to plastic and spalling behavior. When the impact velocity exceeds 757 m/s, the particle velocity rises to the peak and then increases again, indicating that an α→ε phase transition occurred, with a threshold of 13.51 GPa. It was found that the impact velocity is linearly related to the particle velocity of the HSLA. As the impact stress increased, the HEL remained within the range of 1.32~1.50 GPa, while the spalling strength presented an upward trend with the increasing impact stresses. Metallographic analysis shows that the impact failure is dominated by brittle fracture at lower velocities, while more ductile fracture occurs at higher velocities.

SLAM in dynamic environments is still a severe challenge for most feature-based SLAM systems. Moving objects will lead to terrible errors in the calculation of frame tracking and local mapping. We propose a novel method for keypoints selection to lower the negative effect brought by moving objects during map construction. To address this challenge, we concentrate on the combination of coarse semantic information and a feature-based SLAM system. In this article, a modified CenterNet object detector is proposed as the moving object detection thread for providing coarse semantic information and 2D location. The modified CenterNet can provide a faster and more accurate prediction. For each frame in a sequence, objects in a scenario will be classified into two motion states, non-static and static, according to the category prediction from the moving object detection thread. Then important processing called semantic data association is presented for motion removal in frame tracking thread and local mapping thread. In this way, the keypoints on the non-static objects will not be chosen for calculation. Finally, on this basis, a modified real-time SLAM system is presented. Experimental results in TUM indoor dataset show that the proposed SLAM system outperforms the state-of-the-art ORB-SLAM2 in dynamic environments. Besides that, our method has a better speed-accuracy trade-off than other deep learning-based SLAM systems in dynamic environments.

Most terahertz (THz) radar systems can only work in the near-field region, because the THz source power is limited and the size of the target scattered near field is up to tens of kilometers. Such conditions will result in the conventional radar range equation being unsuitable. Therefore, the near-field radar cross section (RCS) formula is given according to the numerical simulation on different targets. By modifying the parameters in the near field, including the gain of radar antennas and the RCS of targets, the generalized radar range equation is proposed. The THz radar working efficiency in the whole range and the simulation of the near-field RCS simulation model were employed to validate its effectiveness. Through comparison with the radar range equation, it can be concluded that the calculation results of the proposed equation are smaller in the near field, and the outcomes in the far field are identical. The proposed generalized radar range equation can be applied to the whole radiation area including the near field and the far field. Furthermore, more complicated real targets are calculated according to the generalized radar range equation and it can be extended from the submillimeter wave band to a much wider band range. Finally, the near-field radar theory is established, which shows its potential application to the radar cross section estimation in the extremely high frequency and fine design of THz radar systems.

Due to its drug resistant nature, β-lactamase represents a serious challenge for public health. Extended-spectrum β-lactamase (ESBL) producing Klebsiella pneumoniae clones are increasingly reported worldwide. Little is known about the prevalence and biological characteristics of drug-resistant strains in zoos. During routine surveillance at the Zhengzhou Zoo of China, we found Klebsiella pneumoniae isolate in healthy Red Kangaroos (Macropus Rufus) with severe MDR. The Klebsiella pneumoniae were especially resistant to Cefuroxime Sodium (MIC, > 64 μg/mL), Ceftriaxone (MIC, >8 μg/mL) and Cefepime (MIC, >64 μg/mL), and belonged to ST290. Subsequently, whole genome sequencing (WGS) showed that the Chrome Chr-M297-1 harbored blaDHA–3, blaSHV–1, blaCTX–M–14, fosA5, dfrA3, sul3, etc., and pM297-1.1 [222,864 bp, IncFIB(K)], which carried nine antimicrobial genes including blaCTX–M–14, blaTEM–191, aph(3″)-Ib, aph(6)-Id and qnrS1, etc., and pM297-1.2 [225,763 bp, IncFII(K)] carried 22 antimicrobial genes including blaTEM–1, blaCTX–M–3, aph(3′)-Ia, aac(3)-IIa, aac(6′)-Ib-cr, aadA16, qnrB2, qnrS1, qacEΔ1, mphA, sul1, and dfrA27, etc. A traceability analysis then revealed that these two plasmids were highly similar to those recovered from human clinical samples in some southern cities in Sichuan Province, China (>99%), suggesting that these plasmids are spreading in China. Furthermore, two plasmids harboring conjugal transfer genes facilitated the transmission of antimicrobial genes by conjugation with E. coli J53. Our research shows that the transmission and adaptation of Klebsiella pneumoniae producing ESBLs is occurring in zoo environments, suggesting that zoos may be becoming important potential reservoirs for clinically important drug-resistant genes. It is therefore necessary to monitor the emergence and spread of drug-resistant gene strains in captive wild animals held in zoo environments.

Magnetic resonance (MR) images can detect small pathological tissue with the size of 3–5 image pixels at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. High-resolution MR images can provide clearer structural details and help doctors to analyze and diagnose the disease correctly. In this paper, MR super-resolution based on the multiple optimizations-based Enhanced Super Resolution Feed Back Network (ESRFBN) is proposed. The method realizes network optimization from the three perspectives of network structure, data characteristics and heterogeneous network integration. Firstly, a super-resolution network structure based on multi-slice input optimization is proposed to make full use of the structural similarity between samples. Secondly, aiming at the problem that the L1 or L2 loss function is based on a per-pixel comparison of differences, without considering human visual perception, the optimization method of multiple loss function cascade is proposed, which combines the L1 loss function to retain the color and brightness characteristics and the MS-SSIM loss function to retain the contrast characteristics of the high-frequency region better, so that the depth model has better characterization performance; thirdly, in view of the problem that large deep learning networks are difficult to balance model complexity and training difficulty, a heterogeneous network fusion method is proposed. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, which broadens the width of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features. The experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super-resolution method effectively improves the super-resolution performance.

QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

The optimizing design for enhancement of the micro performance of manipulator based on analytical models is investigated in this paper. By utilizing the established uncanonical linear homogeneous equations, the quasi-static analytical model of the micro-manipulator is built, and the theoretical calculation results are tested by FEA simulations. To provide a theoretical basis for a micro-manipulator being used in high-precision engineering applications, this paper investigates the modal property based on the analytical model. Based on the finite element method, with multipoint constraint equations, the model is built and the results have a good match with the simulation. The following parametric influences studied show that the influences of other objectives on one objective are complicated. Consequently, the multi-objective optimization by the derived analytical models is carried out to find out the optimal solutions of the manipulator. Besides the inner relationships among these design objectives during the optimization process are discussed.

As technology scales, negative bias temperature instability (NBTI) becomes one of the primary failure mechanisms for Very Large Scale Integration (VLSI) circuits. Meanwhile, the leakage power increases dramatically as the supply/threshold voltage continues to scale down. These two issues pose severe reliability problems for complementary metal oxide semiconductor (CMOS) devices. Because both the NBTI and leakage are dependent on the input vector of the circuit, we present an input vector control (IVC) method based on a linear programming algorithm, which can co-optimize circuit aging and power dissipation simultaneously. In addition, our proposed IVC method is combined with the supply voltage assignment technique to further reduce delay degradation and leakage power. Experimental results on various circuits show the effectiveness of the proposed combination method.

The reliability of Very Large Scale Integration (VLSI) circuits has become increasingly susceptible to transient faults induced by environmental noise with the scaling of technology. Some commonly used fault tolerance strategies require statistical methods to accurately estimate the fault rate in different parts of the logic circuit, and Monte Carlo (MC) simulation is often applied to complete this task. However, the MC method suffers from impractical computation costs due to the size of the circuits. Furthermore, circuit aging effects, such as negative bias temperature instability (NBTI), will change the characteristics of the circuit during its lifetime, leading to a change in the circuit’s noise margin. This change will increase the complexity of transient fault rate estimation tasks. In this paper, an NBTI-aware statistical analysis method based on probability voltage transfer characteristics is proposed for combinational logic circuit. This method can acquire accurate fault rates using a discrete probability density function approximation process, thus resolving the computation cost problem of the MC method. The proposed method can also consider aging effects and analyze statistical changes in the fault rates. Experimental results demonstrate that, compared to the MC simulation, our method can achieve computation times that are two orders of magnitude shorter while maintaining an error rate less than 9%.

This study proposed three types of AleHf reactive materials with particle size ratios (a), which were almost completely dense (porosity of <5.40%) owing to their preparation using hot-pressing technology. Microstructure characteristics and phase composition were analyzed, and the influence of particle size ratios on dynamic mechanical behavior and damage mechanism were investigated. The prepared sample with a - 0.1 exhibited continuous wrapping of the Hf phase by the Al phase. HfeHf contact (continuous Hf phase) within the sample gradually increased with increasing a, and a small amount of fine Hf appeared for the sample with a - 1. The reactive materials exhibited clear strain-rate sensitivity, with flow stress s0.05 and failure strain ef increasing approximately linearly with increasing strain rate _e. It is found that the plastic deformation of the material increased with increasing strain rate. As a increased from 0.1 to 1, the flow stress gradually increased. Impact failure of the material was dominated by ductile fracture with a large Al phase plastic deformation band for lower a, while brittle fracture with crushed Hf particles occurred at higher a. Finally, a constitutive model based on BP neural network was proposed to describe the stress-strain relationships of the materials, with an average relative error of 2.22%. [ABSTRACT FROM AUTHOR]