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In this work, a bionic structure was redesigned for a specific type of crossbeam and the corresponding modification strategies were formulated. Three modification schemes were put forward and the corresponding parameterized analysis models established. Sensitivity analysis results guided the variable parameter selection and the total design of an orthogonal test. Data obtained were utilized to establish a neural network model that reflects the dynamic nonlinear mapping relation. Then, a collaborative optimization design method was proposed that was based on the neural network and the genetic algorithm for secondary design of the rib thickness. Through optimizing the neural network model by NSGA-II, Pareto optimal results were obtained. The results showed that the mass of the newly designed crossbeam had been reduced by 3.74%, while the first three orders of natural frequency had been increased by 6.67%, 3.47% and 14.20%, respectively. The bionic modification structure and the collaborative optimization design method were proved to be valuable for redesign in the remanufacturing field. [ABSTRACT FROM AUTHOR]

[Display omitted] Solid-state Li batteries employing Li-metal anodes and solid Li/Li 1.3 Al 0.3 Ti 1.7 (PO 4) 3 (LATP) electrolytes have emerged as promising next-generation energy storage devices due to their high energy density and safety. However, their performance is seriously limited by the irreversible reactivity of LATP with the Li-metal anode and the poor solid–solid interfacial contact between them, which result in relatively low ionic conductivity at the interface. The present work addresses these issues by presenting a method for modifying the Li/LATP interface in situ by applying 2-(trimethylsilyl) phenyl trifluoromethanesulfonate (2-(TMS)PTM) as a new type of electrolyte additive between the Li anode and the LATP electrolyte when assembling the battery, and then forming a uniform and thin interfacial layer via redox reactions occurring during the application of multiple charge–discharge cycles to the resulting battery. As a result of the significantly improved chemical compatibility between the Li anode and the LATP electrolyte, an as-assembled battery delivers a high reversible capacity of 165.7 mAh g−1 and an outstanding capacity retention of 86.2% after 300 charge–discharge cycles conducted at a rate of 0.2C and a temperature of 30 °C. Accordingly, this work provides a new strategy for developing advanced solid-state Li metal batteries by tailoring the interface between the Li anode and the solid electrolyte. [ABSTRACT FROM AUTHOR]

Sensor techniques and emerging CNN models have greatly facilitated the development of collaborative fault diagnosis. Existing CNN models apply different fusion schemes to achieve reliable fault identification based on multisensor data. Few CNN models, however, take into account both the intrinsic correlations and the distribution gap between different signals, which may result in a limited exploration of multisource data. To address this issue, a novel convolutional fusion framework called a collaborative fusion convolutional neural network (CFCNN) is developed in this paper. More specifically, a multiscale shrinkage denoising module (MSDM) is developed first to extract multilevel modality-specific features from different mechanical signals. Then, drawing inspiration from the intermediate fusion scheme, a central fusion module (CFM) is introduced to explore the intrinsic correlations and integrate cross-modal features. Moreover, an online label smoothing training (OLST) strategy is applied to reduce overfitting and promote better classification performance of CFCNN. The developed CFCNN is expected to shed new light on collaborative fault diagnosis using the intermediate fusion scheme. The efficacy of the developed CFCNN is verified through the cylindrical rolling bearing dataset and the planetary gearbox dataset. • A novel convolutional fusion framework named CFCNN is developed for collaborative fault recognition. • A central fusion module (CFM) is put forward for effective feature fusion. • An online label smoothing (OLS) algorithm is introduced to reduce overfitting and facilitate model convergence. • Two case studies are conducted to verify the effectiveness of the proposed diagnostic framework. [ABSTRACT FROM AUTHOR]

Radiation damage effects were important issues for the development of CdZnTe detectors in space-exploration field applications. In this study, we explored the radiation damage mechanisms on CdZnTe crystals based on the Kinchin-Pease model. The type and evolution of irradiation induced defects under different gamma ray doses were investigated by the photoluminescent technique. Laser beam induced transient current measurement was applied to investigate the influence of defects on the charge transport property. Our results demonstrated that the radiation induced defects accumulated negative space charges, which significantly distorted the electric field distribution and reduced electron mobility by enhancing the ionized impurities scattering effect. By correlating the results with 241Am gamma ray radiation response spectra measurement, the effects of radiation damage on the detector performance were discussed. [ABSTRACT FROM AUTHOR]

Recent progress on intelligent fault diagnosis is mainly attributed to the explosive development of convolutional neural networks (CNNs). Many existing CNN-based fault diagnosis models can extract abundant features from the measured vibration signals but cannot explore enough discriminative features under strong noise conditions. This poses a challenge for industrial applications. To address this problem, we develop a new deep CNN model, called a multireceptive field denoising residual convolutional network (MF-DRCN). The major contributions are: a multireceptive field denoising (MFD) block is designed to enhance the deep features extracted by the CNN model and filter out the interference feature information; an adaptive feature integration (AFI) module is embedded in the CNN model to adaptively integrate features, so as to make better use of the extracted information; and an end-to-end CNN model called MF-DRCN is developed based on MFD and AFI. The experimental results demonstrate that the MF-DRCN has better feature extraction and antiinterference capabilities than the other seven competitive methods. Specifically, under strong noise conditions with SNR = −6 dB, the MF-DRCN achieves 84.51% and 86.45% diagnostic accuracy, respectively, on the planetary gearbox dataset and the industrial pump dataset, which suggests the MF-DRCN is a promising intelligent fault diagnosis approach. [ABSTRACT FROM AUTHOR]

The on-machine monitoring of tool wear is of great significance to improve the machining efficiency and reliability of CNC machine tools. Although numerous approaches have been developed for condition monitoring of milling tool, two major problems still exist: (a) The actual manufacturing data is complicated due to the different combinations of the cutting speed, cutting feed, and depth of cut. (b) Abnormal data collection is extremely expensive and difficult to obtain. To fully explore the transferable wear-related information from multi-source data, an attention-based cross-domain generative adversarial network is developed in this study. First, an adaptive weighted feature selection network based on attention mechanism is established to extract shared features from source and target domain. Second, an auxiliary classifier generative adversarial network is introduced to utilize the shared features for transfer data augmentation. Finally, a new objective function of generative adversarial network's discriminator is built with correlation alignment regularization term to further utilize the wear-related information for improving the accuracy of tool wear prediction. Experiments are conducted on a machine tool to verify the effectiveness of the proposed cross-domain adaptive generation adversarial network based on the attention mechanism (CDAGAN) method. The results show that the proposed method can identify different tool wear states and capture the specific tool wear frequency feature on multi-machining parameters. • A new adaptive weighted generative adversarial network named CDAGAN is proposed. • The method can extract wear-related transferable features from the frequency spectrum for multi-working conditions tool wear prediction. • A spectrum similarity evaluation method is proposed to assess the data augmentation quality. [ABSTRACT FROM AUTHOR]

Highlights: A confined thermal expansion strategy to fabricate liquid metal (LM)-based monoliths with continuous LM network at ultra-low content. The results show a strong integration advantage of LM-based monoliths in density, mechanical strength, electromagnetic interference shielding effectiveness, and near field shielding effectiveness, as well as multi-functions such as magnetic actuation. Liquid metal (LM) has become an emerging material paradigm in the electromagnetic interference shielding field owing to its excellent electrical conductivity. However, the processing of lightweight bulk LM composites with finite package without leakage is still a great challenge, due to high surface tension and pump-out issues of LM. Here, a novel confined thermal expansion strategy based on expandable microsphere (EM) is proposed to develop a new class of LM-based monoliths with 3D continuous conductive network. The EM/LM monolith (EM/LMm) presents outstanding performance of lightweight like metallic aerogel (0.104 g cm−1), high strength (3.43 MPa), super elasticity (90% strain), as well as excellent tailor ability and recyclability, rely on its unique gas-filled closed-cellular structure and refined LM network. Moreover, the assembled highly conducting EM/LMm exhibits a recorded shielding effectiveness (98.7 dB) over a broad frequency range of 8.2–40 GHz among reported LM-based composites at an ultra-low content of LM, and demonstrates excellent electromagnetic sealing capacity in practical electronics. The ternary EM/LM/Ni monoliths fabricated by the same approach could be promising universal design principles for multifunctional LM composites, and applicable in magnetic responsive actuator. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC) is a common malignancy in the genitourinary system and the current theranostic approaches are unsatisfactory. Sensitivity and specificity of current diagnosis methods are not ideal and high recurrence and progression rates after initial treatment indicate the urgent need for management improvements in clinic. Nanotechnology has been proposed as an effective method to improve theranosis efficiency for both non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). For example, gold nanoparticles (AuNPs) have been developed for simple, fast and sensitive urinary sample test for bladder cancer diagnosis. Nanoparticles targeting bladder cancers can facilitate to distinguish the normal and abnormal bladder tissues during cystoscopy and thus help with the complete removal of malignant lesions. Both intravenous and intravesical agents can be modified by nanotechnology for targeted delivery, high anti-tumor efficiency and excellent tolerability, exhibiting encouraging potential in bladder cancer treatment. Photosensitizers and biological agents can also be delivered by nanotechnology, intermediating phototherapy and targeted therapy. The management of bladder cancer remained almost unchanged for decades with unsatisfactory effect. However, it is likely to change with the fast-developed nanotechnology. Herein we summarized the current utility of nanotechnology in bladder cancer diagnosis and treatment, providing insights for the future designing and discovering novel nanoparticles for bladder cancer management. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of convolutional neural networks (CNNs) has significantly advanced the progress of intelligent fault diagnosis. Most currently-available CNN-based diagnostic models are built on the premise that the monitored machine operates under stable conditions. However, in real-world scenarios, rotary machinery usually operates at varying speeds, making the fault-related pulse features susceptible to noise oversaturation. To extract discriminative features from mechanical signals under non-stationary conditions, a global contextual feature aggregation network (GCFAN) is developed in this paper. To begin with, a global contextual module (GCM) is embedded in the CNN architecture to explore multimodal features. Then, a multiscale attention module (MSAM) is introduced to guide the model to focus on global and local discriminative information. Further, a multiscale feature enhancement module (MFEM) is established to enlarge the receptive field and eliminate useless features. Finally, the GCFAN architecture is constructed based on these improvements. To achieve favourable diagnostic results under fluctuating variable speed conditions, we apply the label smoothing algorithm and the AMSGrad algorithm to assist the training of the model. Two case studies using the benchmark variable speed bearing dataset and the HF-MS variable speed gearbox dataset were carried out to test the practicality of the developed approach. Experimental results demonstrated that the developed GCFAN performs better than seven state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Collaborative fault diagnosis has become a hot research topic in fault detection and identification, greatly benefiting from emerging multisensory fusion techniques and newly developed convolutional neural network (CNN) models. Existing CNN models take advantage of various fusion techniques to identify machine health status by utilizing multiple sensory signals. Nevertheless, a few of them are able to simultaneously explore modality-specific features and intrinsic shared features among multi-source signals, limiting the capability of the exploration of multisource data. To address this issue, this paper proposes a novel convolutional network called a graph-guided collaborative convolutional neural network (GGCN) for highly-effective fault diagnosis of electromechanical systems. The main contributions of this study include: (1) developing a novel graph-guided CNN algorithm for collaborative fault detection; (2) establishing a graph reasoning fusion module (GRFM) to explore the inherent correlations between multisource signals; and (3) advancing the current approaches by taking into account both the distribution gap and the intrinsic correlation between different signals simultaneously. The developed GGCN is expected to shed new light on collaborative fault diagnosis using the graph-convolution-based intermediate fusion scheme. Two experimental datasets namely, the cylindrical rolling bearing dataset and the planetary gearbox dataset, are applied in this paper to verify the efficacy of the GGCN. Experimental results demonstrate that GGCN outperforms seven other state-of-the-art approaches, particularly under noisy conditions. [ABSTRACT FROM AUTHOR]

In this paper, a gradient stable node-based smoothed discrete shear gap method (GS-DSG) using 3-node triangular elements is presented for Reissner–Mindlin plates in elastic-static, free vibration, and buckling analyses fields. By applying the smoothed Galerkin weak form, the discretized system equations are obtained. In order to carry out the smoothing operation and numerical integration, the smoothing domain associated with each node is defined. The modified smoothed strain with gradient information is derived from the Hu–Washizu three-field variational principle, resulting in the stabilization terms in the system equations. The stabilized discrete shear gap method is also applied to avoid transverse shear-locking problem. Several numerical examples are provided to illustrate the accuracy and effectiveness. The results demonstrate that the presented method is free of shear locking and can overcome the temporal instability issues, simultaneously obtaining excellent solutions. [ABSTRACT FROM AUTHOR]

The continent of China developed through the coalescence of three major cratons (North China, Tarim and Yangtze) and continental micro‐blocks through the processes of oceanic crust disappearance and acceretionary‐collision of continental crusts. The strata of the Chinese continental landmass are subdivided into 12 tectonic‐strata regions. Based on the composition of geological features among the three main cratons, continental micro‐blocks and other major global cratons, their affinities can be preliminarily deduced during the Tonian period, using evidence from sedimentary successions, paleobiogeography, tectonic and magmatic events. The Yangtze and Tarim cratons show that they have close affinities during the assembly‐dispersal milestone of the Rodinia Supercontinent. The sedimentary record and magmatic age populations in the blocks suggest that there was a widespread, intensive magmatic event that resulted from a subduction process during ∼1000–820 Ma, related to continental rifting around the Yangtze and Tarim cratons. However, they differ greatly from the North China Craton. The continental micro‐blocks in the Panthalassic Ocean could have some missing connection with the North China Craton that persisted until the Middle‐Late Devonian. In contrast, the Alxa Block showed a strong affinity with the Tarim Craton. The revised Tonian paleogeography of the Rodinia Supercontinent is a good demonstration of how to show the relationship between the main cratons and the continental micro‐blocks. [ABSTRACT FROM AUTHOR]

Herein, we report the design and preparation of a carbon foam composite with the addition of SiO 2 aerogel, aimed to obtain a novel thermal insulation material. SiO 2 aerogel was formed via a sol-gel process and then mixed with the mesophase pitch, followed by further foaming and carbonization process to synthesize the SiO 2 aerogel/carbon foam composite. The effects of SiO 2 aerogel content on the morphology and performance of composite product were studied to understand the relationship between the microstructure and the thermal insulation performance and mechanical strength of the composite. The results showed that the addition of SiO 2 aerogel had a significantly effect on the microstructure of the pore cells. The composite with SiO 2 aerogel addition showed better thermal insulation performance as compared with the pristine carbon foam; and the composite with 11 wt % SiO 2 aerogel content possessed a minimum thermal conductivity of 0.254 W/m·K. Meanwhile, the mechanical strength of the resultant composite was found co-enhanced with the addition of SiO 2 aerogel. Our study paves a new avenue to realize the simultaneous enhancement of thermal insulation performance and mechanical strength of carbon foam. [ABSTRACT FROM AUTHOR]

Core IdeasVegetation restoration altered the diversity and structure of the N‐fixing microbial community.Dominant phyla changed from Actinobacteria in farmland to Proteobacteria in revegetation types.The N to P ratio was the key environmental factor regarding the composition of N‐fixing microorganisms.Study data can be a reference for developing N‐fixing microbial fertilizers suitable for this region. In this study, we determined the variations in the diversity and structure of nitrogen‐fixing microorganisms during afforestation and how this variability correlates with the soil physicochemical factors. Soils were sampled from replicated plots representing three revegetation types (Robinia pseudoacacia L. [Rp], Caragana korshinskii Kom [Ck], and abandoned land) with adjacent farmland as the control at a research site in Ansai District, Yan'an City, Shaanxi Province, China. Using high‐throughput sequencing of the nifH rRNA gene, we found that revegetation significantly increased the Shannon diversity index, and for the richness index Chao1 was highest in Ck and have no significant difference in other three lands. From farmland to vegetation types abandoned land, Ck, and Rp, the dominant phylum of nifH microorganisms changed from Actinobacteria to Proteobacteria. Furthormore, variation in the Chao1 richness index was significantly influenced by pH (p = 0.002), soil organic carbon (SOC; p = 0.025) and total nitrogen (TN; p = 0.015). The Shannon diversity index was significantly influenced by SOC, nitrate nitrogen (NN), C to P ratio, TN, AN, N to P ratio, TP, and sand. The changes of nifH microorganism composition at the phylum level were significantly correlated with the soil N to P ratio, which explained 53.1% of the variation among environmental factors (F = 11.8, p = 0.002). The ratio of N to P (interpretation rate 65.2%, F = 18.8, p = 0.002) was also a key factor that influenced the composition at the genus level. Based on the diversity and composition, Ck was a more advantageous recovery method to increase the capacity for N fixation. This study provides a basis for the study of N‐fixation characteristics in the Loess Hilly Region. [ABSTRACT FROM AUTHOR]

Abstract The effects of vegetation restoration on plant characteristics and soil physicochemical properties have been widely documented; however, knowledge of the variation in soil ecoenzymatic activity and stoichiometry remains limited, particularly with respect to their relationship with plant and soil variables in restored ecosystems. Here, the vegetation and soil from one farmland and two restored land-use types (Robinia pseudoacacia plantations: RP and abandoned lands: AL) of four age classes (10, 18, 28, and 43-years) were investigated and sampled on the Loess Plateau, China. The activity of C, N, and P-acquiring enzymes (β-1,4-glucosidase, BG; β-1,4-N-acetylglucosaminidase, NAG; leucine aminopeptidase, LAP; and alkaline phosphatase, AP) and other major influencing factors (soil physical properties, soil nutrient contents, and microbial biomass) were determined. It was found that both restoration time and vegetation types significantly affect plant characteristics, soil physical properties, soil nutrient content, and microbial biomass. Soil BG, NAG + LAP, and AP enzyme activities were higher in RP and AL than in farmland, and increased with restoration time in AL. For RP, the activity of these enzymes increased from year 10 to year 28, then decreased from year 28 to year 43. Soil ecoenzymatic C:P and N:P activity ratios were ordered: farmland > AL > RP, and decreased with restoration time in AL and RP; thus, P limitation was stronger in RP than in AL and increased with restoration time in both AL and RP. Soil ecoenzymatic C:N:P acquisition ratios of farmland, AL, and RP deviated from the 1:1:1 ratio and depended on the availability of environmental nutrients and demand for microbial nutrients. The vegetation characteristics and soil physical properties were closely related to the nutrient acquisitions of microbes and, ultimately, contributed towards shaping soil ecoenzymatic activity and stoichiometry (particularly vegetation coverage, belowground biomass, soil water content, soil bulk density, and pH). Furthermore, variation in soil ecoenzymatic activity and stoichiometry was better accounted by dissolved nutrients in the soil (particularly C and N) and microbial biomass (particularly N and P) than by plant characteristics and soil properties. Overall, this study demonstrates that the C:N:P stoichiometry of soil microbes and ecoenzymatic activity is nutrient dependent, rather homeostatic, with the potential to influence nutrient cycling on the Loess Plateau. Graphical abstract Image 1 Highlights • Soil ecoenzymatic C:N:P acquisition ratios deviated from the 1:1:1. • Soil ecoenzymatic activity ratio was not in homeostasis but resource dependent. • P limitation of microbial metabolisms increased with restoration time. • Soil ecoenzymatic stoichiometry were influenced by both plant and soil properties. • Soil dissolved nutrients and microbial biomass have stronger effect on soil ecoenzymatic stoichiometry than plant and soil properties. [ABSTRACT FROM AUTHOR]

We report doping at B-Site in TmFeO 3 with In3+ ion of varying concentration (x = 0.02, 0.05, 0.07, 0.1) to explore the effect of In3+ ion substitution on structural, magnetic and vibrational properties. XRD study revealed that all the synthesized samples exhibit distorted perovskite orthorhombic (Pbnm) crystal symmetry. The immediate observable effect was an increase in lattice parameters with increasing doping up from x = 0.0 to 0.1 as evidenced by diffraction peaks shifted towards lower angle. Spin reorientation transition (Γ 4 →Γ 2) in all prepared samples occurs at temperature (∼85–92 K) to be a process involving the continuous rotation of Fe3+ spins. M-H curves display the coexistence of weak ferromagnetism and antiferromagnetic ordering at all the measured temperatures with reduction in coercive field and remanence values. Varied magnetic behaviors were observed in this series of compounds, which are believed to be associated with the different interactions of Fe and rare earth ions. The occurrence of two-magnon and two-phonon modes in Raman spectra at temperature around 100 K attributed to spin-reorientation transition exhibited by TmFe 1-x In x O 3. This might be unambiguous evidence for spin-phonon coupling in the prepared compounds. The coupling and possible presence of two phonon and magnon pair scattering and other energy carriers have been used to explain several recently discovered thermally driven spin transport phenomena. • XRD data reveals that samples TmFe 1-x In x O 3 possess the orthorhombic structure (Pbnm). • Spin reorientation transition (Γ 4 → Γ 2) arises at temperature between 85 and 92 K. • M-H curves display the coexistence of weak ferromagnetism and antiferromagnetic ordering • Evidences for spin-phonon coupling are also comes from Raman spectra of In-doped TmFeO 3. • Orthoferrites are very attractive option to fulfill the demand of miniaturization data storage and spintronics devices. [ABSTRACT FROM AUTHOR]

Graphical abstract We fabricate the ultrathin and flexible four corners needle zinc oxide whiskers/silver/waterborne polyurethane (T-ZnO/Ag/WPU) conductive films to satisfy different EMI shielding demand by tuning the shielding network structure. Highlights • The specially designed 3D T-ZnO/Ag nanofiller is synthesized successfully. • The ultrathin and flexible T-ZnO/Ag/WPU EMI shielding film is fabricated. • The film with uniform structure shows an EMI SE of 42 dB at thickness of 0.02 mm. • The film with deposited layer structure exhibits an ultra-high EMI SE of 87 dB. Abstract With recent emergence of portable and wearable electronics, it is imperative to design a novel electromagnetic interference (EMI) shielding material which possesses flexibility, ultra-thinness and excellent EMI shielding effectiveness (SE) to prevent the adverse effect of electromagnetic radiation. Herein, we fabricate the ultrathin and flexible four corners needle zinc oxide whiskers/silver/waterborne polyurethane (T-ZnO/Ag/WPU) conductive films to realize different EMI shielding demand by tuning the nanofiller dispersion morphology. The T-ZnO/Ag/WPU composite film with uniform structure (prepared by blade coating method) shows excellent flexibility and EMI SE of over 42 dB at a thickness of only 0.02 mm. Highly reliable shielding ability is achieved with 92% EMI SE retention after 1000 folding cycle tests. The T-ZnO/Ag/WPU composite film with deposited layer structure (prepared by casting method) exhibits an ultrahigh EMI SE of over 87 dB at a thickness of 0.25 mm. At the same time, the T-ZnO/Ag/WPU composite shows great potential in printable circuit. The ultrathin and flexible T-ZnO/Ag/WPU composite films with excellent EMI SE are highly promising for applications in next-generation flexible electronics, especially the portable and wearable electronic devices. [ABSTRACT FROM AUTHOR]

Abstract Afforestation greatly alters soil nitrogen (N) dynamics and soil microbial community, but the response of soil N dynamics to microbial activity, particularly to that of N-fixing microbes, remains unclear. In the present study, soil samples were collected from areas with a chronosequence of 17, 27 and 42 years of Robinia pseudoacacia L (RP17a, RP27a, and RP42a) and a sloped farmland (FL) as a control. The nifH gene was sequenced with the Illumina MiSeq platform to analyze soil microbial diversity and composition, and soil N fractions (TN, AN, NN, MBN, DON) were estimated. The soil N stocks, N fractions, and diversity of N-fixing microorganisms increased with the chronosequence of R. pseudoacacia. Beta diversity, analyzed using nonmetric multidimensional scaling, revealed that afforestation effectively improved soil microbial communities. The profiles of the N-fixing microorganism communities at sites 17a, 27a and 42a were clearly separated from the farmland, and soil N-fixing microbial community compositions generally changed in the farmland from Actinobacteria-dominated to Proteobacteria- dominated with stand ages. Redundancy analysis revealed that soil N fractions were positively correlated with the main species, particularly for AN, which had a 34.7% explanatory space in axis one and was significantly correlated with the Proteobacteria at the phylum level. Results implied the evolutionary tendency of the dominant microbial groups, and demonstrated the variation of the N fractions and some other physical-chemistry properties along the chronosequence of R. pseudoacacia, and that AN was the most sensitive N fraction to the N-fixing microorganism composition. Thus, our results provide evidence that the variation of soil compositions of N-fixing microbial communities is linked to the level of N fractions, especially the content of AN. Highlights • Soil diazotrophs abundance, diversity, and compositions changed along with the chronosequence. • With the increase in ages, the difference in species composition decreased. • Soil N fractions were significantly correlated with the major microbial species. • AN was the most sensitive N fraction to N-fixing microorganisms. [ABSTRACT FROM AUTHOR]

Highlights • A facile and cost-effective methodology was employed to pattern Au nanoparticles. • The density and inter-particle spacing of Au nanoparticles could be controlled. • The substrate for modulating the dispersion and density of plasmonic nanoparticles. Abstract Surface-enhanced Raman spectroscopy (SERS) is a promising technique for future high-sensitivity chemical sensors. The main deficiencies that restrict the large-scale application of SERS lie in its high cost, low reproducibility, and poor signal uniformity. In this letter, we report the primary exploration of a facile dip coating – thermal shrinking method to produce gold (Au) nanoparticle decorated flexible polymer substrate for SERS. Briefly, Au nanoparticles dispersed in DI-water were anchored on to a polystyrene film via controlled dip coating, which was followed by a thermal treatment process to shrink the polymer film. We observe a polymer volume shrinking rate of 20% after heating, which results in a particle density increase of more than 10 times. Significant enhancement of Raman signals was observed for the R6G molecules anchored on the shrunk substrate, demonstrating our methodology is feasible, reliable, and promising for future large-scale application of SERS-based chemical sensors. [ABSTRACT FROM AUTHOR]

Zinc telluride (ZnTe) has immense potential for a variety of optical and electro-optical devices. However, the structural inhomogeneity originating from non-stoichiometric melt growth restricts the extensive application of ZnTe-based devices. In this work, we report the structural homogenization of Te-rich grown ZnTe bulk crystals by annealing under saturated Zn vapor. Te inclusions smaller than 10 μm are eliminated successfully by regulating the annealing temperature (873–953 K) and annealing time (50–250 h). Simultaneously, the concentration of zinc vacancies has decreased significantly after annealing, which was confirmed from the 10 K photoluminescence (PL) spectra. Based on the annealing results, a mechanism of eliminating Te inclusions in the ZnTe crystal was proposed, according to the processes of Zn atom diffusion and the reaction of Zn atoms with Te inclusions. The diffusion path is determined by Zn vacancies in the ZnTe crystal. Finally, the Zn atom diffusion coefficient in the temperature range from 873 K to 953 K is estimated to be 9.73 × 10−8 to 2.33 × 10−6 cm2 s−1 in view of the proposed model, with a diffusion activation energy of ∼2.21 eV. [ABSTRACT FROM AUTHOR]

Abstract In this study, the development of the north-eastern Tibetan Plateau is investigated via an integrated study of the sedimentology, magnetostratigraphy, palaeogeography, and cross-section comparisons of the Neogene sedimentary basins of the Tibetan Plateau. From 23 to 17 Ma, this region was characterized by widespread angular unconformities and a sedimentary hiatus, indicating the comprehensive uplift of the plateau. The tectonic event that created the Tibetan Plateau triggered the evolution of sedimentary basins. During the Middle Miocene climatic optimum event, the growing Qilian Shan formed a barrier, leading to cooler and drier conditions on the western side of the mountain. However, humid and warm conditions persisted on the eastern side of the range. After this event, this region was influenced by a global cooling trend and enhanced tectonic activity in the north-eastern Tibetan Plateau. At approximately 10–8 Ma, sedimentary data, which were consistent with low-temperature thermochronological records, indicated the inversion of the surrounding mountains and rapid exhumation of the Laji Shan. Since 5.0–3.6 Ma, these intermontane basins gradually shrank and disappeared due to the overall uplift of the Tibetan Plateau and the accelerated global cooling trend and were associated with unconformities and growth strata that formed during stepwise-north-eastward thrusting tectonism. Highlights • During 23–17 Ma, the northeastern Tibetan Plateau was covered by widespread angular unconformities. • The tectonic event creating the Tibetan Plateau triggered the evolution of the sedimentary basins. • Growing Qilian blocking the eastern Asian moisture during the Middle Miocene climatic optimum, lead to westward aridification. • Among 10–8 Ma, the granularity and sedimentary rates of sedimentary basins increased abruptly. • Scince 5.0–3.6 Ma, these basins gradually withered away controlled by the overall uplift of the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

A flexible and highly conductive nylon porous membrane (NPM)/nickel (Ni) composite film with outstanding electromagnetic interference (EMI) shielding performance is fabricated via a facile electroless deposition method. The NPM, with a large specific surface area, is amenable to the electroless deposition of Ni and the enhancement of interaction between Ni and NPM. Highly conductive Ni layers are uniformly deposited on the upper and lower surfaces of NPM to construct a sandwich structure that provides numerous interfaces to reflect, scatter, and absorb electromagnetic waves. The resultant NPM/Ni film exhibits an ultra-high EMI shielding effectiveness (EMI SE) of 77 dB with a film thickness of only 100 μm. Moreover, the NPM/Ni film shows cyclic EMI shielding stability under repeated mechanical deformation, with 85% retention after 300 bending cycles. Our work provides an effective approach to fabricate reliable metal-polymer composite films that show potential for applications in EMI shielding. [ABSTRACT FROM AUTHOR]

As a member of the bismuth iodide family compounds, Cs3Bi2I9 perovskite shows potential applications in photovoltaic devices and radiation detectors, due to the absence of toxic lead and its high air and moisture stability. In this work, we report Cs3Bi2I9 bulk crystals grown by a low temperature solvent volatilized crystallization method. The morphology of the single crystals exhibits a well symmetric hexagonal shape, with a layered structure along the [00l] direction. Three defect related transitions (iodine substituting bismuth (IBi) to the valence band maximum (VBM), the conduction band minimum (CBM) to caesium substituting bismuth (CsBi1−/2−), and the CBM to caesium substituting iodine (CsI1+/2+)) are identified in 10 K photoluminescence spectra, with dissociation energies of 47.87 meV, 45.28 meV, and 52.01 meV, respectively. The anisotropic resistivities along the [00l] and [l00] directions are compared, with values of 11.08 GΩ cm and 0.77 GΩ cm, respectively, which is attributed to the difference of carrier mobility. This is further certified by the first principles calculations, which demonstrate that the effective masses along the [00l] and [l00] directions are 3.22me (electrons)/2.14me (holes) and 0.327me (electrons)/0.946me (holes), respectively. The Cs3Bi2I9 device exhibits fast photoresponse to optical light, with an on–off ratio of ∼24 along [l00], which is fifteen times higher than that along [00l]. This photoelectric anisotropy dictates the potential applications in photovoltaic devices and radiation detectors. [ABSTRACT FROM AUTHOR]

Air pollution epidemiology studies of ambient fine particulate matter (PM 2.5 ) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM 2.5 exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM 2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM 2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (F inf_home , Tier 2), indoor concentrations (C in , Tier 3), personal exposure factors (F pex , Tier 4), and personal exposures (E, Tier 5) for ambient PM 2.5 . We applied EMI to predict daily PM 2.5 exposure metrics (Tiers 1–5) for 174 participant-days across the 13 months of DEPS. Individual model predictions were compared to a subset of daily measurements of F pex and E (Tiers 4–5) from the DEPS participants. Model-predicted F pex and E corresponded well to daily measurements with a median difference of 14% and 23%; respectively. Daily model predictions for all 174 days showed considerable temporal and house-to-house variability of AER, F inf_home , and C in (Tiers 1–3), and person-to-person variability of F pex and E (Tiers 4–5). Our study demonstrates the capability of predicting individual-level ambient PM 2.5 exposure metrics for an epidemiological study, in support of improving risk estimation. [ABSTRACT FROM AUTHOR]

The potential of the layered gadolinium hydroxide (LGdH) [Gd2(OH)5]Cl·yH2O (LGdH-Cl) for simultaneous drug delivery and magnetic resonance imaging was explored in this work. Three non-steroidal anti-inflammatory drugs (diclofenac [dic], ibuprofen [ibu], and naproxen [nap]) were intercalated into LGdH-Cl for the first time, using three different routes (ion exchange intercalation, coprecipitation, and exfoliation-self-assembly). X-ray diffraction, elemental microanalysis and IR spectroscopy confirmed successful incorporation of the drug into the interlayer spaces of the LGdH in all cases. From a comparison of the guest anion sizes and interlayer spacings, the active ingredients are believed to adopt intertwined bilayer configurations between the LGdH layers. The materials prepared by coprecipitation in general have noticeably higher drug loadings than those produced by ion exchange or self-assembly, as a result of the incorporation of some neutral drug into the composites. The LGdH-drug intercalates are stable at neutral pH, but rapidly degrade in acidic conditions to free Gd3+ into solution. While LGdH-nap releases its drug loading into solution very rapidly (within ca. 1.5 h) at pH 7.4, LGdH-dic shows sustained release over 4 h, and LGdH-ibu extends this to 24 h. The latter composites therefore can be incorporated into enteric-coated tablets to provide sustained release in the small intestine. The drug intercalates are highly biocompatible and retain the proton relaxivity properties of the parent LGdH-Cl, with the materials most promising for use as negative contrast agents in MRI. Overall, the LGdH-drug intercalation compounds appear to have great potential for use in theranostic applications. [ABSTRACT FROM AUTHOR]

A reverse microemulsion approach has been explored for the synthesis of layered gadolinium hydroxide (LGdH) nanoparticles in this work. This method uses oleylamine as a multifunctional agent, acting as surfactant, oil phase and base. 1-butanol is additionally used as a co-surfactant. A systematic study of the key reaction parameters was undertaken, including the volume ratio of surfactant (oleylamine) to water, the reaction time, synthesis temperature, and the amount of co-surfactant (1-butanol) added. It proved possible to obtain pristine LGdH materials at temperatures of 120 °C or below with an oleylamine: water ratio of 1:4. Using larger amounts of surfactant or higher temperatures caused the formation of Gd(OH) 3 , either as the sole product or as a major impurity phase. The LGdH particles produced have sizes of ca. 200 nm, with this size being largely independent of temperature or reaction time. Adjusting the amount of 1-butanol co-surfactant added permits the size to be varied between 200 and 300 nm. [ABSTRACT FROM AUTHOR]

Radiation detectors with different thickness are needed to detect gamma rays with various energies. In this paper, a post-growth annealing method was used to improve the properties of CdMnTe:In (CMT:In) crystals with different thickness for gamma-ray detectors. The results indicated that Te inclusions in CMT:In crystals with different thickness were reduced remarkably after annealing. Both the resistivity and IR transmittance of annealed CMT:In crystals with different thickness increased obviously, which suggested that the crystal quality was improved. For the detectors fabricated by annealed CMT:In slices with 1 mm, 2 mm and 5 mm thickness, the energy resolutions were enhanced about 252%, 193% and 141%, respectively. And ( μτ ) e values were enhanced about 80%, 80% and 76%, respectively. The performance of the detectors was greatly improved after annealing. [ABSTRACT FROM AUTHOR]

Conductive ultrahigh-molecular-weight polyethylene (UHMWPE) composite with segregated nickel (Ni) structure which exhibited highly efficient electromagnetic interference (EMI) shielding performance was fabricated in this work. By synthesizing nickel coated UHMWPE particles via electroless deposition and then hot compressing, the prearranged thin Ni layer could only selectively locate at the boundary between UHMWPE regions due to the specific high melting viscosity of UHMWPE, and thus constructed a well connected segregate Ni conductive network in UHMWPE matrix. Owing to the advantage of segregated network structure, the UHMWPE/Ni composite shows a low percolation threshold and high conductivity, and exhibits an EMI shielding effectiveness (SE) of average 55 dB with Ni content of only 2.58 vol% in X-band. This result indicates the segregated UHMWPE/Ni composite can serve as an ultra-efficient material for EMI shielding applications. [ABSTRACT FROM AUTHOR]

The rosette-like dislocation clusters around Te inclusions in as-grown CdZnTe/CdTe crystals and those introduced by the micro-indentation on CdZnTe/CdTe surface are studied experimentally. The extended dislocation patterns are formed around Te inclusions in both CdZnTe and CdTe crystals, owing to the build-in stress. Two mutually orthogonal tetrahedrons are observed in CdZnTe crystal. However, the “double-arms” dislocation rosette pattern extended along 〈1 1 0〉 direction is observed in CdTe crystal. The Peierls kink pair mechanism and the Hirsch effects are used to explain the discrepancy of these two different rosette patterns. Similar dislocation rosette patterns are observed on indentation surface of CdZnTe crystal. The dislocation rosette patterns are found to be independent of the indenter orientation, but completely determined by the crystallographic properties of zinc-blende structure of the crystal. Furthermore, the Te(g) and Cd(g) dislocation arms are found to be mixed and bended with each other in CdTe crystal under high indentation stress, making it different from that generated around Te inclusions. A model concerning the comprehensive impact of stress field and electronic polarities dislocations is proposed to clarify the dislocation bending phenomenon. [ABSTRACT FROM AUTHOR]

Road traffic simulation is a useful tool for studying road traffic and evaluating solutions to traffic problems. Large-scale agent-based road traffic simulation is computationally intensive, which triggers the need for conducting parallel simulation. This paper deals with the synchronization problem in parallel agent-based road traffic simulation to reduce the overall simulation execution time. We aim to reduce synchronization operations by introducing some redundant computation to the simulation. There is a trade-off between the benefit of reduced synchronization operations and the overhead of redundant computation. The challenge is to minimize the total overhead of redundant computation and synchronization. First, to determine the amount of redundant computation, we proposed a way to define extended layers of partitions in the road network. The sizes of extended layers are determined by the behavior of agents and the topology of road networks. Second, due to the dynamic nature of road traffic, a heuristic was proposed to adjust the amount of redundant computation according to traffic conditions during simulation run-time to minimize the overall simulation execution time. The efficiency of the proposed method was investigated in a parallel agent-based road traffic simulator using real-world network and trip data. Results have shown that the method can reduce synchronization overhead and improve the overall performance of the parallel simulation significantly. [ABSTRACT FROM PUBLISHER]

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km 2 versus a coarser resolution of 36 × 36 km 2 . Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error. [ABSTRACT FROM AUTHOR]

As a type of AII1−xMnxBVI alloy, Zn1−xMnxTe ingots with a diameter of 30 mm are grown as large single crystals by a temperature gradient solvent method under Te-rich conditions. The orange-red Zn1−xMnxTe crystals are cut and processed into size-appropriate wafers for fundamental studies, as well as for THz spectroscopy and magnetization response analyses. Mn segregation associated with the growth conditions is identified in the as-grown crystals, and an enriched higher concentration of Mn is observed inside the Te inclusion. To evaluate the effect of Mn on the Zn1−xMnxTe crystal, the site occupation is calculated via an ab initio study, and is further confirmed by electrical and optical property measurements. Mn tends to substitute Zn to form MnZn in Zn1−xMnxTe, which results in a low density of free charge carriers, through which the THz detection sensitivity is enhanced by 15–25% compared to the intrinsic ZnTe. Moreover, evident paramagnetic magnetization behavior is observed at variable temperatures due to the random distribution of an isolated Mn (Mnioct,Te6) spin with S = 5/2. We note that further optimization of the THz performance can be achieved by optimizing the growth process and tailoring the Mn content. [ABSTRACT FROM AUTHOR]

CNN-based intelligent fault diagnosis methodologies have demonstrated excellent performance in machine health condition monitoring and safety assessment. However, the majority of existing CNN models are developed on the basis of undisturbed and balanced distribution of samples, which is inconsistent with real industrial scenarios. To tackle this issue, a global contextual multiscale fusion network (GCMFN) is developed in this study. The main contributions of this study are highlighted and summarized as follows: (1) a multi-dilated fusion layer and a non-local activation module are developed as the building units of GCMFN to guide the model for exploring multiscale features; (2) a global contextual denoising module is applied to amplify important features and eliminate interference features, and (3) an online label smoothing algorithm is utilized to promote the better diagnostic performance of GCMFN under imbalanced scenarios. Three experiments using the benchmark motor dataset, the planetary gearbox dataset, and the industrial pump dataset are implemented to test the applicability of the proposed GCMFN in machine health state identification. The experimental results show that GCMFN is competent and a promising diagnostic tool for various machine reliability monitoring tasks. • A multi-dilated fusion layer (MFL) and a non-local activation module (NAM) are developed to explore multilevel features. • A global contextual denoising module (GCDM) is introduced to explore robust features and filter out irrelevant information. • A label smoothing (LS) algorithm is utilized to promote the training of GCMFN. • The effectiveness of GCMFN is verified by extensive experimental results. [ABSTRACT FROM AUTHOR]

As a type of AII1−xMnxBVI alloy, Zn1−xMnxTe ingots with a diameter of 30 mm are grown as large single crystals by a temperature gradient solvent method under Te-rich conditions. The orange-red Zn1−xMnxTe crystals are cut and processed into size-appropriate wafers for fundamental studies, as well as for THz spectroscopy and magnetization response analyses. Mn segregation associated with the growth conditions is identified in the as-grown crystals, and an enriched higher concentration of Mn is observed inside the Te inclusion. To evaluate the effect of Mn on the Zn1−xMnxTe crystal, the site occupation is calculated via an ab initio study, and is further confirmed by electrical and optical property measurements. Mn tends to substitute Zn to form MnZn in Zn1−xMnxTe, which results in a low density of free charge carriers, through which the THz detection sensitivity is enhanced by 15–25% compared to the intrinsic ZnTe. Moreover, evident paramagnetic magnetization behavior is observed at variable temperatures due to the random distribution of an isolated Mn (Mnioct,Te6) spin with S = 5/2. We note that further optimization of the THz performance can be achieved by optimizing the growth process and tailoring the Mn content. [ABSTRACT FROM AUTHOR]

Local electronic imaging using a scanning microwave impedance microscope (MIM) is performed on as-grown CdZnTe bulk crystals at room temperature. Well-defined Te-rich secondary phase (SP) particles embedded in CdZnTe crystals are clearly observed in the MIM images. The high contrast observed via MIM is attributed to the discrepance between the dielectric response and conductivity of Te-SP particles and the CdZnTe matrix. A homogeneous phase structure is found within Te-SP particles according to the uniform MIM signal, which is further confirmed as the rhombohedral structure. The size effect of Te-SP on the response microwave intensity is discussed. It is demonstrated that the MIM signal is essentially independent of the domain sizes for Te-SP smaller than 10 µm. In contrast, for Te-SP larger than 10 µm, a roughly linear size dependence of the signal amplitude appears. In addition, the local area surrounding Te-SP is examined. A thin transition layer is observed surrounding the inclusion and is ascribed to Zn micro-segregation. For some Te-SP particles, a wide dark region surrounds the particle and is attributed to a high concentration of extended defects. For this Te-SP induced higher defect region, the microwave response is lower owing to the high electron relaxation polarization. Schematic diagram depicting the scanning microwave impedance microscope (MIM) configuration. [ABSTRACT FROM AUTHOR]

The regulatory Community Multiscale Air Quality (CMAQ) model is a means to understanding the sources, concentrations and regulatory attainment of air pollutants within a model's domain. Substantial resources are allocated to the evaluation of model performance. The Regionalized Air quality Model Performance (RAMP) method introduced here explores novel ways of visualizing and evaluating CMAQ model performance and errors for daily Particulate Matter ≤ 2.5 μm (PM2.5) concentrations across the continental United States. The RAMP method performs a non-homogenous, non-linear, non-homoscedastic model performance evaluation at each CMAQ grid. This work demonstrates that CMAQ model performance, for a well-documented 2001 regulatory episode, is non-homogeneous across space/time. The RAMP correction of systematic errors outperforms other model evaluation methods as demonstrated by a 22.1% reduction in Mean Square Error compared to a constant domain wide correction. The RAMP method is able to accurately reproduce simulated performance with a correlation of r = 76.1%. Most of the error coming from CMAQ is random error with only a minority of error being systematic. Areas of high systematic error are collocated with areas of high random error, implying both error types originate from similar sources. Therefore, addressing underlying causes of systematic error will have the added benefit of also addressing underlying causes of random error. [ABSTRACT FROM AUTHOR]

The migration of dislocations in substrate-grade CdZnTe (CZT) single crystals during temperature gradient annealing under Cd/Zn vapor has been investigated. The etch pit density (EPD) and configuration of dislocations have been evaluated before and after annealing in CZT crystals with and without Cd-rich second phase (Cd-SP) particles, respectively. After Cd/Zn overpressure annealing, dislocation reduction in CZT crystals was observed. However, dislocation walls with 120° intervals along <211> crystalline direction were observed in the both types of CZT crystals. The formation of these dislocation walls can be attributed to the reaction of <110> dislocations. Moreover, it is considered that the release of the restored stress during annealing act as the domain driving force for dislocation migration, by comparing the variation of dislocation configuration in CZT crystals with and without Cd-SP particles. [ABSTRACT FROM AUTHOR]

In this paper, an effective annealing method in which CdMnTe:In (CMT:In) single crystals were coated with CMT powders of the same composition was used to improve the crystal quality of CMT:In crystals. The results indicated that the density of Te inclusions decreased as the annealing time increased. The resistivity and IR transmittance of annealed CMT:In crystals were enhanced obviously. The resistivity of 120 h annealed crystal increased even two orders of magnitude. The reduction of full-width at-half-maximum (FWHM) and the increase of the intensity of X-ray rocking curve indicated an improvement of the crystal quality. PL measurements also showed the crystal quality improved after annealing. No characteristic peak of 241 Am γ-ray could be observed in the detector fabricated with as-grown crystal. Remarkably, for the detector fabricated with annealed crystals, the peak of 241 Am γ-ray appeared. And the energy resolution and μτ value were improved as the annealing time increased. Specially, 120 h annealed CMT:In crystal with 10.11% energy resolution and 1.20×10 −3 cm 2 /V μτ value has the best detector performance. [ABSTRACT FROM AUTHOR]

Herein, we study the crystallographic configuration of rosette-like dislocation clusters introduced by indentation in CdZnTe (CZT) crystal and the effects of the dislocations on the carrier transport process. The Vickers hardness test was used to generate dislocations artificially on the (1̅1̅1̅) Te face of the CZT crystal at room temperature. Etch pit density (EPD) revealed by Everson etchant showed that the dislocation density increased from 4.9 × 104 cm−2 to a concentration too high to be evaluated after the indentation. A model was proposed to explain the rosette-like dislocation clusters. It was verified that the tetrahedron with small size and high EPD belonged to Te(g) dislocations on the (1̅1̅1̅) Te face. The hexagon-like distribution of pile-ups formed during the indentation was determined by the zinc-blende structure of CZT, rather than the direction of the indenter. The dislocation pile-up during propagation was observed and analyzed based on the rule proposed by Lomer and Cottrell. The ion beam induced charge (IBIC) measurements showed low carrier collection efficiency (CCE) in the dislocation generation areas, which confirms that the dislocations and related defects can significantly degrade the CCE of a CZT detector. Measurements of low temperature photo-luminescence (PL) showed that the emission peak located at 1.485 eV highly increased in the indentation area and was responsible for the performance degradation of the detectors. [ABSTRACT FROM AUTHOR]

CNN-based fault diagnosis approaches have achieved promising results in improving the safety and reliability of rotating machinery. Most of the existing CNN models are developed on the assumption that the collected data is high-quality. However, since rotating machinery usually operates under fluctuating conditions, the critical pulse information of the measured vibration signals is easily submerged in noise. To promote the adaptability of CNN in noisy industrial scenes, an attention-based multiscale denoising residual convolutional neural network (AM-DRCN) is put forward in this study. First of all, a multiscale denoising module (MDM) is introduced as the basic building unit to help the network explore multiscale features and filter out irrelevant information. Then, a feature enhancement module (FEM) is leveraged to expand the receptive field and make full use of the side-out features. Further, a joint attention module (JAM) is explored to integrate the extracted features effectively. Finally, a lightweight CNN model named AM-DRCN is developed based on the above improvements. The practicality and effectiveness of AM-DRCN for monitoring machine health and stability states are verified through three case studies. • A multiscale denoising module (MDM) is developed to explore multilevel features and filter out irrelevant information. • A feature enhancement module (FEM) is used to make the CNN model perceive more contextual information. • A joint attention module (JAM) is introduced to make the CNN model pay more attention to discriminative features. • An end-to-end CNN model called AM-DRCN is developed, and its effectiveness has been verified by extensive experimental results. [ABSTRACT FROM AUTHOR]

Convolutional neural networks, with a powerful ability for feature representation, have made vast inroads into motor fault diagnosis. However, most of the existing CNN models cannot favorably handle the data generated in variable-speed scenarios. First, the continuous irregular fluctuation of the motor makes the time domain interval between two adjacent fault pulses change continuously. Secondly, due to the complex transmission path of the signal under unstable conditions, the noise distribution is complex. To address this problem, a global contextual residual convolutional neural network is proposed. The major novelties fall into three aspects. First, to make full use of the features from all intermediate layers and explore multiscale information, a new hierarchical structure is adopted in the CNN model. Second, since different features are of different importance for fault detection tasks, the global context module is explored to guide the model to pay more attention to global discriminant features. Third, the features learned by the network can either promote each other or contradict each other, so a multi-feature fusion layer is introduced to integrate these features adaptively. Case studies using the benchmark motor dataset and the industrial motor bearing dataset are performed to validate the superiority of the GC-ResCNN. • A new hierarchical structure is designed to fully exploit the feature information. • A global context block is introduced to guide the model to learn more discriminative features. • A multi-feature fusion layer is introduced to integrate the extracted features adaptively. • Two case studies are implemented to validate the efficacy and the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Recent advances in convolutional neural networks (CNN) have boosted the research on reliability monitoring of rotating machinery. In actual industry production, the mechanical equipment often operates under variable speed and strong noise conditions, so the discriminative fault-related features of the collected vibration signals are easily buried by interference information. Thus, it poses a huge challenge for the existing CNN models to achieve favorable diagnostic results. To address this issue, we put forward a dually attentive multiscale network (DAMN) for mechanical fault diagnosis. To begin with, a new hierarchical structure is built to make full use of the features from intermediate convolutional layers. Then, to explore abundant discriminative information from mechanical signals, a dually attentive multiscale module (DAMM) is introduced to guide the CNN model to extract multiscale and multilevel features. Further, a feature reinforcement module (FRM) is designed to expand receptive field and filter out unrelated interference information. Finally, embarking on the above improvements, an end-to-end CNN model named DAMN is built for intelligent fault diagnosis. Experimental results show that DAMN outperforms seven state-of-the-art methods for health recognition of rotating machinery. • A new hierarchical structure is introduced to make full use of the feature information extracted by CNN models. • The DAMM is designed to excavate the internal multiscale information of mechanical signals. • The FRM is embedded in the CNN model to enhance important features and filter out interference information. • Extensive experimental results demonstrate that DAMN outperforms seven state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To interpret the interplay mechanism of dislocations associated with secondary phase (SP) particles, ＜111＞ CdZnTe crystals are annealed at isothermal and thermal gradient conditions under Cd/Zn and Te overpressure, respectively. The etching pit arrangement surrounding Te-SP is evaluated to gain an insight into the generation and multiplication of induced dislocations. It is demonstrated that Te-SP dissociation dominates the volume variation during isothermal annealing. This dissociation is enhanced under Cd/Zn overpressure due to an exothermic reaction, which results in the activation of dislocation glide and climb systems and, simultaneously, the diffusion of Tei. In turn, ‘radial-shaped’ dislocation rosettes and ‘star-shaped’ dislocation clusters appear on the {111}B surface after preferential etching. However, for thermal gradient annealing, Te-SP thermomigration predominates. The induced dislocation density is related to the Te droplet drift velocity. Meanwhile, Cd/Zn and Te overpressure could promote or slow down the migration. Interestingly, no remarkable dislocation rosettes or clusters are observed after temperature gradient annealing under Te overpressure. [ABSTRACT FROM AUTHOR]

Highlights: A flexible freestanding TaS2 film (thickness = 3.1 μm) exhibits an ultralow void ratio of 6.01%, an ultra-high electrical conductivity of 2,666 S cm−1, an electromagnetic interference shielding effectiveness (EMI SE) of 41.8 dB, an absolute EMI SE (SSE/t) of 27,859 dB cm2 g−1, and excellent flexibility withstand 1,000 bends without rupture. The TaS2 composite films exhibit excellent EMI shielding properties and higher tensile strength with better mechanical flexibility, making them suitable for EMI shielding practical applications. Low-dimensional transition metal dichalcogenides (TMDs) have unique electronic structure, vibration modes, and physicochemical properties, making them suitable for fundamental studies and cutting-edge applications such as silicon electronics, optoelectronics, and bioelectronics. However, the brittleness, low toughness, and poor mechanical and electrical stabilities of TMD-based films limit their application. Herein, a TaS2 freestanding film with ultralow void ratio of 6.01% is restacked under the effect of bond-free van der Waals (vdW) interactions within the staggered 2H-TaS2 nanosheets. The restacked films demonstrated an exceptionally high electrical conductivity of 2,666 S cm−1, electromagnetic interference shielding effectiveness (EMI SE) of 41.8 dB, and absolute EMI SE (SSE/t) of 27,859 dB cm2 g−1, which is the highest value reported for TMD-based materials. The bond-free vdW interactions between the adjacent 2H-TaS2 nanosheets provide a natural interfacial strain relaxation, achieving excellent flexibility without rupture after 1,000 bends. In addition, the TaS2 nanosheets are further combined with the polymer fibers of bacterial cellulose and aramid nanofibers via electrostatic interactions to significantly enhance the tensile strength and flexibility of the films while maintaining their high electrical conductivity and EMI SE.This work provides promising alternatives for conventional materials used in EMI shielding and nanodevices. [ABSTRACT FROM AUTHOR]

• A novel variational decomposition model is proposed for low-illumination image enhancement. The model can estimate the detail-preserving reflectance map and the piece-wise smoothed illumination map while suppressing the noise interference. • The alternating direction method of multipliers (ADMM) is introduced to solve the refined function effectively. • Extensive experimental results have shown that the proposed approach yields comparative and even better performance in comparison with some state-of-the-art techniques in both subjective and objective evaluation. The images captured under low-light conditions often show low sharpness and low contrast, which inevitably influences the quality of the images and degrades the performance of many computer vision systems designed for pattern recognition. Although many low-light image enhancement methods have been put forward to deal with the problem, the existing methods may bring about contrast distortion and insufficient or excessive enhancement of the source images. To tackle this challenge, a novel variational enhancement model is presented. This method focuses on enhancing the visibility of low-illumination images while preserving the details and the texture information. To begin with, we introduce a new variational model, which includes novel regularization terms for the illumination, the reflectance and the noise component. Specifically, we use the l 1 norm to implement the piece-wise continuousness operation on the reflectance map, present a novel structure-preserving regularizer to assure the scale-aware structure smoothness of the illumination map, and take noise map estimation into account to suppress the noise of the given image. Then, an alternating direction method of multipliers (ADMM) is utilized to solve the specified problem accurately. The extensive experimental results have shown that the proposed approach yields comparative and even better performance in comparison with some state-of-the-art techniques in both qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

Transverse magnetic modes propagate freely for any frequency in a parallel plated waveguide. Here, it is shown that when two thin plasmonic materials are attached to the metal walls of the waveguide, a bandgap emerges due to the excited surface plasmon polaritons. As the plasmonic materials become thin enough, a critical thickness is observed. For any width larger than such a value, the bandgap is fixed, otherwise a tunable bandgap can be achieved (different widths induce different bandgaps). For application, such a tunable bandgap system can be utilized to design a band-stop filter for terahertz frequencies with a high quality factor. [ABSTRACT FROM AUTHOR]

The Tarim Basin, as the largest inland basin on the planet, provides a valuable opportunity to understand the mountain building of the northern Tibetan Plateau and its effects on basin development. Here we present a synthesis of sedimentology, zircon U-Pb geochronology, and bulk-rock geochemistry of Neogene sediments in the Qimugan section, southwest Tarim Basin. Spatial variation in zircon U-Pb age distributions from early Miocene clasts at Qimugan and Oytag suggest significant dextral strike-slip on the Kashgar-Yecheng transfer system likely commenced during the Oligocene-early Miocene. Over time, age peaks of ∼20 and ∼107 Ma in a middle Miocene sample at Qimugan suggest significant headwater erosion of the ancient Yarkand River reached the southeast Pamir-Karakoram hinterland as it does today. This is coincident with a relatively steady decrease in chemical weathering of source terranes during the middle-late Miocene, suggesting a climate transition from warm and/or humid to cool and/or dry in the Pamir- Karakoram. Under global cooling, middle Miocene changes in both provenance and geochemistry at Qimugan require topographic growth of the Pamir-Karakoram interior at that time, coeval with initial formation of the fold-thrust system and doming of the Muztaghata massif in the eastern Pamir, in addition to a prominent depocenter shift and sediment load in the southwest Tarim Basin. Subsequently, stable sediment provenance and depocenters suggest the current tectonic-sedimentary configuration in the eastern Pamir-southwest Tarim Basin has been established since the middle-late Miocene. These observations can be explained by a model of crustal contraction below the southeast Pamir-Karakoram and strain propagation to the Tarim Basin, possibly related to resumed Indian crust subduction. Our results thus support compressional deformation extended to all margins of the northern Tibetan Plateau by the middle-late Miocene. [ABSTRACT FROM AUTHOR]

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods. [ABSTRACT FROM AUTHOR]