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The fusion of global contextual information with local cropped block details is crucial for segmenting ultra-high resolution images. In this study, A novel fusion mechanism termed global–local deep fusion (GL-Deep Fusion) is introduced, based on an enhanced transformer architecture that efficiently integrates global contextual information and local details. Specifically, we propose the global–local synthesis networks (GLSNet), a dual-branch network where one branch processes the entire original image, while the other branch handles cropped local patches as input. The feature fusion of different branches in GLSNet is achieved through GL-Deep Fusion, significantly enhancing the accuracy of ultra-high resolution image segmentation. Identifying tiny overlapping items is a task where the model excels, demonstrating its particular effectiveness. To optimize GPU memory utilization, a dual-branch architecture was meticulously designed. This architecture proficiently leverages the features it extracts and seamlessly integrates them into the enhanced transformer framework of GL-Deep Fusion. Benchmarks on the DeepGlobe and Vaihingen datasets demonstrate the efficiency and accuracy of the proposed model. It significantly reduces GPU memory usage by 24.1% on the DeepGlobe dataset, enhancing segmentation accuracy by 0.8% over the baseline model. On the Vaihingen dataset, our model delivers a Mean F1 score of 90.2% and achieves a mIoU of 90.9%, highlighting its exceptional memory efficiency and segmentation precision.

The intelligent diagnosis key technology of orchard nutrients provides a decision-making basis for precision fertilization, which has important research significance. This article reviewed the recent research literature, compared and analyzed existing technologies, and summarized solved and unresolved problems. It aimed to find breakthroughs to further improve the level of intelligent diagnosis key technology for orchard nutrients, and promote the implementation and application of the technology. Research had found that the current rapid nutrient detection technologies were mostly based on spectral data, with a focus on preprocessing algorithms and regression models. Hyperspectral technology shows good performance in predicting tree and soil nutrients due to its large number of characteristic variables. Meanwhile, preprocessing algorithms such as filtering, transformation, and feature band selection had also solved the problem of data redundancy. However, there were few studies for small and trace elements, and field applications. Laser breakdown-induced spectroscopy has good prospects for soil nutrient detection, as it can simultaneously detect multiple nutrients. There had been some studies on the technology for generating suitable nutrient standards for orchards in terms of soil and tree nutrients, but it requires a long and extensive experiment, which is time-consuming and laborious. A universal and rapid method needs to be studied to meet the construction needs of suitable nutrient standards for different varieties of fruit trees.

Metal–insulator transition has long been one of the key subjects in condensed matter systems. Herein, the emergence of a large energy gap (Eg, 0.8–1.0 eV) in Bi(110) two‐atomic‐layer nanoribbons grown on a SnSe(001) substrate is reported, which normally has an intrinsic semimetal‐like characteristic. The existence of this abnormally large Eg in Bi(110) is, however, determined by Bi coverage. When coverage is above ≈64 ± 2%, Eg vanishes, and instead, a Bi(110) semimetal‐like phase appears through a singular insulator–metal transition. Measurements using qPlus atomic force microscopy demonstrate that either insulating or semimetal‐like Bi(110) possesses a distorted black phosphorous structure with noticeable atomic buckling. Density function theory fully reproduces the semimetal‐like Bi(110) on SnSe(001). However, none of the insulating phases with this large Eg could be traced. Although the underlying mechanism of the large Eg and the insulator‐metal transition requires further exploration, experiments demonstrate that similar results can be achieved for Bi grown on SnS, the structural analog of SnSe, exhibiting an even larger Eg of ≈2.3 eV. The experimental strategy may be generalized to utilization of group‐IV monochalcogenides to create Bi(110) nanostructures with properties unachievable on other surfaces, providing an intriguing platform for exploring the interesting quantum electronic phases.

In this research, oat resistant starch (ORS) was prepared by autoclaving-retrogradation cycle (ORS-A), enzymatic hydrolysis (ORS-B), and ultrasound combined enzymatic hydrolysis (ORS-C). Differences in their structural features, physicochemical properties and digestive properties were studied. Results of particle size distribution, XRD, DSC, FTIR, SEM and in vitro digestion showed that ORS-C was a B + C-crystal, and ORS-C had a larger particle size, the smallest span value, the highest relative crystallinity, the most ordered and stable double helix structure, the roughest surface shape and strongest digestion resistance compared to ORS-A and ORS-B. Correlation analysis revealed that the digestion resistance of ORS-C was strongly positively correlated with RS content, amylose content, relative crystallinity and absorption peak intensity ratio of 1047/1022 cm−1 (R1047/1022), and weakly positively correlated with average particle size. These results provided theoretical support for the application of ORS-C with strong digestion resistance prepared by ultrasound combined enzymatic hydrolysis in the low GI food application.

The reliability of urban transportation systems is crucial for ensuring smooth traffic flow and minimizing disruptions caused by external factors. This study focuses on improving the stability and efficiency of transportation systems through the calibration of a refined link performance function while building upon the U.S. Bureau of Public Roads (BPR) model. To achieve this, we propose three customized algorithms—Newton’s method, Bayesian optimization, and the differential evolutionary algorithm—to calibrate the key parameters. Additionally, we conducted a sensitivity analysis to assess the influences of the model parameters on link performance. Numerical experiments conducted in Yuyao City demonstrate the applicability and efficacy of the proposed model and solution algorithms. Our results reveal that the Newton approach is notably more efficient than the Bayesian optimization algorithm and the differential evolutionary algorithm.

The pivotal role of oxygen electrocatalysis in the realm of energy conversion and storage is unmistakably significant. In an endeavor to diminish the reliance on precious metals, the development of innovative catalysts exhibiting exceptional bifunctional durability and heightened activity for both the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) has garnered considerable scholarly interest. Employing a straightforward two-step methodology, we have successfully synthesized uniformly distributed MnCo2O4 and CoMn2O4 nanoparticles of diminutive size, meticulously anchored onto carbon nanotubes (CNTs). Owing to the synergistic covalent interplay between the spinel oxide nanoparticles and CNTs, these nanocomposites demonstrate ORR activity on par with, and notably superior OER activity compared to, commercially available Pt/C catalysts. The onset potential of MnCo2O4-CNTs stands at 1.03 V vs. RHE, maintaining 95.76% of its initial current density following a 10,000-s chronoamperometry test. Furthermore, MnCo2O4-CNTs outperform CoMn2O4-CNTs in OER catalysis. The outstanding performance of MnCo2O4-CNTs is attributed to the higher content of Co3+ ions, which are active for the oxygen electrocatalysis.

Aiming at the problem of cable breakage of an asymmetric single-tower cable-stayed bridge by taking a single-tower cable-stayed bridge that has been in operation for more than 20 years as an engineering example, a spatial finite element model that reflected the actual stress state of the structure was established and a method for solving the time point of cable breakage was proposed. The results show that the strength of the main girder, pylon and cable could meet the stress requirements of the vibration caused by successive cable breaks, and the maximum dynamic displacement of the main girder did not exceed the stiffness limit. Although the breakage of the stay cable caused the stress in the adjacent position and the corresponding position on the opposite side of the tower to increase sharply, the stress fluctuation in other positions was small. The fluctuation of the dynamic stress of the cable tower was relatively small, but the displacement of the tower top changed greatly. The dynamic response of the cables breaking in succession was less than that of simultaneous breaking, and the structural vibration caused by cable breaking was not as severe as that caused by simultaneous breaking.

The high theoretical capacity of lithium (Li) has sparkled its intensive research as the anode for Li batteries. However, the dendritic growth due to the uneven Li deposition causes premature cell failure and dramatically restricts the application of Li anode. Herein, inspired by the pore sizes effects of anodized aluminum oxide (AAO) membranes on Li+ transport performance, 20 nm aperture membrane (AAO-20) with enhanced Li+ transport can achieve higher Li+ concentration near the deposition substrate, possibly preventing rapid Li+ depletion compared to other aperture sizes. Subsequently, smaller Li nucleation overpotential and larger exchange current density in AAO-20 reveal the enhanced kinetics at deposition interface, which is derived from the enhanced Li+ transport in nanochannels, leading to a superior Li deposition. With AAO-20 which can stabilize Li anode, Li-Cu, Li-Li, and Li-LiFePO4 cells demonstrate high Coulombic efficiency, superior cycling stability, and excellent capacity retention, respectively. Such findings can be helpful for the development of high-energy Li metal batteries.

The threshold voltage distribution technique is an effective way to reduce the static power consumption of integrated circuits. Several gate-level-based distribution algorithms have been proposed, but the optimization effect and run time still need further optimization when applied to very large-scale integration (VLSI) designs. This paper presents a triple-threshold path-based static power optimization methodology (TPSPOM) for low-power system-on-chip. This method obtains the path weights and cell weights from paths’ timing constraints and cells’ delay-to-power ratios, then uses them as indexes to distribute each cell to low-threshold voltage (LVT), standard-threshold voltage (SVT), or high-threshold voltage (HVT). The experimental results based on a 28 nm circuit containing 385,781 cells show that the TPSPOM method reduces static power consumption by 15.16% more than the critical-path aware power consumption optimization methodology (CAPCOM). At the same time, run time is reduced by 96.85%.

Low-frequency electromagnetic waves have strong penetration and low attenuation performance, making them widely used in long-wave communication areas such as underwater communication and earthquake prediction. Mechanical antennas are seen as a promising alternative to traditional low-frequency resonant antennas, as they offer the potential for miniaturization and low power consumption. However, existing communication systems using mechanical antennas are usually rudimentary, incompatible, and inconvenient to use. To address these problems, a novel speech communication system based on a piezoelectric electret mechanical antenna was proposed. This communication system includes a hardware system, a communication protocol for mechanical antennas, and a software system with a modular structure. This system includes hardware, a communication protocol, and a modular software system. The results of the experiments showed that this communication system provides real-time speech communication with a user-friendly design, high compatibility with different communication devices, and easy customization to meet specific requirements. This design holds great potential for applications in mineral detection, seismic research, submarine communication, industrial RF control, and other related mechanical antenna applications.

Self-supporting electrodes usually show excellent electrocatalytic performance which does not require coating steps, additional polymer binders, and conductive additives. Rapid in situ growth of highly active ingredient on self-supporting electric conductors is identified as a straight forward path to prepare binder-free and integrated electrodes. Here, Pd-doped Co3O4 loaded on carbon nanofiber materials through electrospinning and heat treatment was efficiently synthesized, and used as a free-standing electrode. Benefiting from its abundant active sites, high surface area and effective ionic conduction capability from three-dimensional (3D) nanofiber framework, Pd-Co3O4@CNF works as bifunctional oxygen electrode and exhibits superior activity and stability superior to commercial catalysts.

As the need for medical services has grown in recent years, medical image critical point detection has emerged as a new subject of research for academics. In this paper, a search decision network method is proposed for medical image landmark detection. Unlike the conventional coarse-to-fine methods which generate bias prediction due to poor initialization, our method is to use the neural network structure search strategy to find a suitable network structure and then make reasonable decisions for robust prediction. To achieve this, we formulate medical landmark detection as a Markov decision process and design a shooting reward function to interact with the task. The task aims to maximize the discount of the received value and search for the optimal network architecture over the entire search space. Furthermore, we embed the central difference convolution, which typically extracts the data invariant feature representation, into the architectural search space. In experiments using standard accessible datasets, our approach achieves a detection accuracy of 98.59% in the 4 mm detection range. Our results demonstrate that, on standard datasets, our proposed approach consistently outperforms the majority of methods.

Protein oxidation leads to covalent modification of structure and deterioration of functional properties of quinoa protein. The objective of this study was to investigate the effects of ultrasonic treatment on the functional and physicochemical properties of quinoa protein oxidation aggregates. In this concern, 2,2′-azobis (2-amidinopropane) dihydrochloride (AAPH) was selected as oxidative modification of quinoa protein. The microstructure of quinoa protein displayed by scanning electron microscope (SEM) indicated that oxidation induced extensive aggregation, leading to carbonylation and degradation of sulfhydryl groups. Aggregation induced by oxidation had a negative effect on the solubility, turbidity, emulsifying stability. However, according to the analysis of physicochemical properties, ultrasonic significantly improved the water solubility of quinoa protein. The quinoa protein treated by ultrasonic for 30 min exhibited the best dispersion stability in water, which corresponded to the highest ζ-potential, smallest particle size and most uniform distribution. Based on the FT-IR, SDS-PAGE and surface hydrophobicity analysis, the increase of α-helix, β-turn and surface hydrophobicity caused by cavitation effect appeared to be the main mechanism of quinoa protein solubilization. In addition, the hydrophobic region of the protein was re-buried by excessive ultrasonic treatment, and the protein molecules were reaggregated by disulfide bonds. Microstructural observations further confirmed that ultrasonic treatment effectively inhibited protein aggregation and improved the functional properties of quinoa protein.

The interplay between the hydrophobic interactions generated by the nonpolar region and the proximal functional groups within nanometers of the nonpolar region offers a promising strategy to manipulate the intermolecular hydrophobic attractions in an artificial molecule system, but the outcomes of such modulations in the building of a native protein architecture remain unclear. Here we focus on the intermediate filament (IF) coiled-coil superfamily to assess the conservation of positively charged residue identity via a biostatistical approach. By screening the disease-correlated mutations throughout the IF superfamily, 10 distinct hotspots where a cation-to-cation substitution is associated with a pathogenic syndrome have been identified. The analysis of the local chemical context surrounding the hotspots revealed that the cationic diversity depends on their separation distance to the hydrophobic domain. The nearby cationic residues flanking the hydrophobic domain of a helix (separation 1 nm) tolerate higher levels of variation and replaceability. We attribute this bias in the conservation degree of the cationic residue identity to reflect the interplay between the proximal cations and the hydrophobic interactions.

A flexible and stretchable electrode based on polydimethylsiloxane (PDMS)-Ag nanosheet composite with low resistance and stable properties has been investigated. Under the synergistic effect of the excellent flexibility and stretchability of PDMS and the excellent electrical conductivity of Ag nanosheets, the electrode possesses a resistivity as low as 4.28 Ωm, a low resistance variation in the 0–50% strain range, a stable electrical conductivity over 1000 cycles, and a rapid recovery ability after failure caused by destructive large stretching. Moreover, the conductive mechanism of the flexible electrode during stretching is explained by combining experimental tests, theoretical models of contact point-tunneling effect, and finite element simulation. This research provides a simple and effective solution for the structure design and material selection of flexible electrodes, and an analytical method for the conductive mechanism of stretchable electrodes, which has potential for applications in flexible electronic devices, smart sensing, wearable devices, and other fields.

The Siamese-based object tracking algorithm regards tracking as a similarity matching problem. It determines the object location according to the response value of the object template to the search template. When there is similar object interference in complex scenes, it is easy to cause tracking drift. We propose a real-time Siamese network object tracking algorithm combined with a compensating attention mechanism to solve this problem. Firstly, the attention mechanism is introduced in the feature extraction module of the template branch and search branch of the Siamese network to improve the feature representation of the network to the object. The attention mechanism of the search branch enhances the feature representation of both the target and the similar backgrounds simultaneously. Therefore, based on the above two-branch attention, we propose a compensated attention model, which introduces the attention selected by the template branch into the search branch, and improves the discriminative ability of the search branch to the object by using the feature attention weighting of the template branch to the object. Experimental results on three popular benchmarks, including OTB2015, VOT2018, and LaSOT, show that the accuracy and robustness of the algorithm in this paper are adequate. It improved occlusion cases, similar object interference, and high-speed motion. The processing speed on GPU reaches 47 fps, which can achieve real-time object tracking.

Proper control of a 3d transition metal-based catalyst with advanced structures toward oxygen evolution reaction (OER) with a more feasible synthesis strategy is of great significance for sustainable energy-related devices. Herein, carbon nanotube interconnected NiCo layered double hydroxide rhombic dodecahedral nanocages (NiCo-LDH RDC@CNTs) were developed here with the assistance of a feasible zeolitic imidazolate framework (ZIF) self-sacrificing template strategy as a highly efficient OER electrocatalyst. Profited by the well-fined rhombic dodecahedral nanocage architecture, CNTs’ interconnected characteristic and structural feature of the vertically aligned nanosheets, the as-synthesized NiCo-LDH RDC@CNTs integrated large exposed active surface areas, enhanced electron transfer capacity and multidimensional mass diffusion channels, and thereby collaboratively afforded the remarkable electrocatalytic performance of the OER. Specifically, the designed NiCo-LDH RDC@CNTs exhibited a distinguished OER activity, which only required a low overpotential of 255 mV to reach a current density of 10 mA cm−2 for the OER. For the stability, no obvious current attenuation was detected, even after continuous operation for more than 27 h. We certainly believe that the current extraordinary OER activity combined with the robust stability of NiCo-LDH RDC@CNTs enables it to be a great candidate electrocatalyst for economical and sustainable energy-related devices.

Accident investigation reports are text documents that systematically review and analyze the cause and process of accidents after accidents have occurred and have been widely used in the fields such as transportation, construction and aerospace. With the aid of accident investigation reports, the cause of the accident can be clearly identified, which provides an important basis for accident prevention and reliability assessment. However, since accident record reports are mostly composed of unstructured data such as text, the analysis of accident causes inevitably relies on a lot of expert experience and statistical analyses also require a lot of manual classification. Although, in recent years, with the development of natural language processing technology, there have been many efforts to automatically analyze and classify text. However, the existing methods either rely on large corpus and data preprocessing methods, which are cumbersome, or extract text information based on bidirectional encoder representation from transformers (BERT), but the computational cost is extremely high. These shortcomings make it still a great challenge to automatically analyze accident investigation reports and extract the information therein. To address the aforementioned problems, this study proposes a text-mining-based accident causal classification method based on a relational graph convolutional network (R-GCN) and pre-trained BERT. On the one hand, the proposed method avoids preprocessing such as stop word removal and word segmentation, which not only preserves the information of accident investigation reports to the greatest extent, but also avoids tedious operations. On the other hand, with the help of R-GCN to process the semantic features obtained by BERT representation, the dependence of BERT retraining on computing resources can be avoided.

The rational design of direct Z-scheme heterostructural photocatalysts using solar energy is promising for energy conversion and environmental remediation, which depends on the precise regulation of redox active sites, rapid spatial separation and transport of photoexcited charge and a broad visible light response. The Bi2WO6 materials have been paid more and more attention because of their unique photochemical properties. In this study, S2− doped Bi2WO6-x coupled with twin crystal ZnIn2S4 nanosheets (Sov−BWO/T−ZIS) were prepared as an efficient photocatalyst by a simple hydrothermal method for the removal of tetracycline hydrochloride (TCH). Multiple methods (XRD, TEM, XPS, EPR, UV vis DRS, PL etc.) were employed to systematically investigate the morphology, structure, composition and photochemical properties of the as-prepared samples. The XRD spectrum indicated that the S2− ions were successfully doped into the Sov−BWO component. XPS spectra and photoelectrochemical analysis proved that S2− served as electronic bridge and promoted captured electrons of surface oxygen vacancies transfer to the valence band of T−ZIS. Through both experimental and in situ electron paramagnetic resonance (in situ EPR) characterizations, a defined direct Z-scheme heterojunction in S-BWO/T−ZIS was confirmed. The improved photocatalytic capability of S-BWO/T−ZIS results ascribed that broadened wavelength range of light absorption, rapid separation and interfacial transport of photoexcited charge, precisely regulated redox centers by optimizing the interfacial transport mode. Particularly, the Sov−50BWO/T−ZIS Z-scheme heterojunction exhibited the highest photodegradation rate was 95% under visible light irradiation. Moreover, this heterojunction exhibited a robust adsorption and degradation capacity, providing a promising photocatalyst for an organic pollutant synergistic removal strategy.

WRKY transcription factors (TFs) have been reported to respond to biotic and abiotic stresses and regulate plant growth and development. However, the molecular mechanisms of WRKY TFs involved in drought stress and regulating plant height in rice remain largely unknown. In this study, we found that transgenic rice lines overexpressing OsWRKY55 (OsWRKY55-OE) exhibited reduced drought resistance. The OsWRKY55-OE lines showed faster water loss and greater accumulation of hydrogen peroxide (H2O2) and superoxide radical (O2−·) compared to wild-type (WT) plants under drought conditions. OsWRKY55 was expressed in various tissues and was induced by drought and abscisic acid (ABA) treatments. Through yeast two-hybrid assays, we found that OsWRKY55 interacted with four mitogen-activated protein kinases (MAPKs) that could be induced by drought, including OsMPK7, OsMPK9, OsMPK20-1, and OsMPK20-4. The activation effects of the four OsMPKs on OsWRKY55 transcriptional activity were demonstrated by a GAL4-dependent chimeric transactivation assay in rice protoplasts. Furthermore, OsWRKY55 was able to reduce plant height under normal conditions by decreasing the cell size. In addition, based on a dual luciferase reporter assay, OsWRKY55 was shown to bind to the promoter of OsAP2-39 through a yeast one-hybrid assay and positively regulate OsAP2-39 expression. These results suggest that OsWRKY55 plays a critical role in responses to drought stress and the regulation of plant height in rice, further providing valuable information for crop improvement.

Idiopathic pulmonary fibrosis (IPF) is a fatal and age-related pulmonary disease. Nintedanib is a receptor tyrosine kinase inhibitor, and one of the only two listed drugs against IPF. Regorafenib is a novel, orally active, multi-kinase inhibitor that has similar targets to nintedanib and is applied to treat colorectal cancer and gastrointestinal stromal tumors in patients. In this study, we first identified that regorafenib could alleviate bleomycin-induced pulmonary fibrosis in mice. The in vivo experiments indicated that regorafenib suppresses collagen accumulation and myofibroblast activation. Further in vitro mechanism studies showed that regorafenib inhibits the activation and migration of myofibroblasts and extracellular matrix production, mainly through suppressing the transforming growth factor (TGF)-β1/Smad and non-Smad signaling pathways. In vitro studies have also indicated that regorafenib could augment autophagy in myofibroblasts by suppressing TGF-β1/mTOR (mechanistic target of rapamycin) signaling, and could promote apoptosis in myofibroblasts. In conclusion, regorafenib attenuates bleomycin-induced pulmonary fibrosis by suppressing the TGF-β1 signaling pathway.

A fast calculation method for a full parallax high-resolution hologram is proposed based on elemental light field image (EI) rendering. A 3D object located near the holographic plane is firstly rendered as multiple EIs with a pinhole array. Each EI is interpolated and multiplied by a divergent sphere wave and interfered with a reference wave to form a hogel. Parallel acceleration is used to calculate the high-resolution hologram because the calculation of each hogel is independent. A high-resolution hologram with the resolution of 200,000 × 200,000 pixels is calculated within only eight minutes. Full parallax high-resolution 3D displays are realized by optical reconstructions.

Magnetic graphene composites (MGCs), which are composed of magnetic nanoparticles with graphene or its derivatives, played an important role in sensors development. Due to the enhanced electronic properties and the synergistic effect of magnetic nanomaterials and graphene, MGCs could be used to realize more efficient sensors such as chemical, biological, and electronic sensors, compared to their single component alone. In this review, we first reviewed the various routes for MGCs preparation. Then, sensors based on MGCs were discussed in different groups, including optical sensors, electrochemical sensors, and others. At the end of the paper, the challenges and opportunities for MGCs in sensors implementation are also discussed.

Upconversion Nanoparticles (UCNPs) enable direct measurement of the local temperature with high temporal and thermal resolution and sensitivity. Current studies focusing on small animals and cellular systems, based on continuous wave (CW) infrared excitation sources, typically lead to localized thermal heating. However, the effects of upconversion bioimaging at the molecular scale, where higher infrared intensities under a tightly focused excitation beam, coupled with pulsed excitation to provide higher peak powers, is not well understood. We report on the feasibility of 800 and 980 nm excited UCNPs in thermal sensing under pulsed excitation. The UCNPs report temperature ratiometrically with sensitivities in the 1 × 10−4 K−1 range under both excitation wavelengths. Our optical measurements show a ln(I525/I545) vs. 1/T dependence for both 800 nm and 980 nm excitations. Despite widespread evidence promoting the benefits of 800 nm over 980 nm CW excitation in avoiding thermal heating in biological imaging, in contrary, we find that given the pulsed laser intensities appropriate for single particle imaging, at both 800 and 980 nm, that there is no significant local heating in air and in water. Finally, in order to confirm the applicability of infrared imaging at excitation intensities compatible with single nanoparticle tracking, DNA tightropes were exposed to pulsed infrared excitations at 800 and 980 nm. Our results show no appreciable change in the viability of DNA over time when exposed to either wavelengths. Our studies provide evidence for the feasibility of exploring protein-DNA interactions at the single molecule scale, using UCNPs as a reporter.

In this study, a nested ensemble filtering (NEF) approach is advanced for uncertainty parameter estimation and uncertainty quantification of a traffic noise model. As an extension of the ensemble Kalman filter (EnKF) and particle filter methods, the proposed NEF method improves upon the ensemble Kalman filter (EnKF) method by incorporating the sample importance resampling (SIR) procedures into the EnKF update process. The NEF method can avoid the overshooting problem (abnormal value (e.g., outside the predefined ranges, complex values) in parameter or state samples) existing in the EnKF update process. The proposed NEF method is applied to the traffic noise prediction on the Trans-Canada Highway in the City of Regina to demonstrate its applicability. The results indicate that: (a) when determining parameters in the traffic noise prediction model, the NEF method provides accurate estimation; (b) the model parameters can be recursively corrected with the NEF method whenever a new measurement becomes available; (c) the uncertainty in the traffic noise model (should be the noise itself) can be well reduced and quantified through the proposed NEF approach.

Scutellaria baicalensis is a well-known medicinal plant that produces biologically active flavonoids, such as baicalin, baicalein, and wogonin. Pharmacological studies have shown that these compounds have anti-inflammatory, anti-bacterial, and anti-cancer activities. Therefore, it is of great significance to investigate the genetic information of S. baicalensis, particularly the genes related to the biosynthetic pathways of these compounds. Here, we constructed the full-length transcriptome of S. baicalensis using a hybrid sequencing strategy and acquired 338,136 full-length sequences, accounting for 93.3% of the total reads. After the removal of redundancy and correction with Illumina short reads, 75,785 nonredundant transcripts were generated, among which approximately 98% were annotated with significant hits in the protein databases, and 11,135 sequences were classified as lncRNAs. Differentially expressed gene (DEG) analysis showed that most of the genes related to flavonoid biosynthesis were highly expressed in the roots, consistent with previous reports that the flavonoids were mainly synthesized and accumulated in the roots of S. baicalensis. By constructing unique transcription models, a total of 44,071 alternative splicing (AS) events were identified, with intron retention (IR) accounting for the highest proportion (44.5%). A total of 94 AS events were present in five key genes related to flavonoid biosynthesis, suggesting that AS may play important roles in the regulation of flavonoid biosynthesis in S. baicalensis. This study provided a large number of highly accurate full-length transcripts, which represents a valuable genetic resource for further research of the molecular biology of S. baicalensis, such as the development, breeding, and biosynthesis of active ingredients.

In this paper, vibration of axially moving nanobeams is studied using Eringen’s two-phase nonlocal integral model. Geometric nonlinearity is taken into account for the integral model for the first time. Equations of motion for the beam with simply supported and fixed⁻fixed boundary conditions are obtained by Hamilton’s Principle, which turns out to be nonlinear integro-differential equations. For the free vibration of the nanobeam, the critical velocity and the natural frequencies are obtained numerically. Furthermore, the effects of parameters on critical velocity and natural frequency are analyzed. We have found that, for the two-phase nonlocal integral model, regardless of the boundary conditions considered, both the critical velocity and the natural frequency increase with the nonlocal parameter and the geometric parameter.

The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.

Needle segmentation is a fundamental step for needle reconstruction and image-guided surgery. Although there has been success stories in needle segmentation for non-microsurgeries, the methods cannot be directly extended to ophthalmic surgery due to the challenges bounded to required spatial resolution. As the ophthalmic surgery is performed by finer and smaller surgical instruments in micro-structural anatomies, specifically in retinal domains, difficulties are raised for delicate operation and sensitive perception. To address these challenges, in this paper we investigate needle segmentation in ophthalmic operation on 60 Optical Coherence Tomography (OCT) cubes captured during needle injection surgeries on ex-vivo pig eyes. Furthermore, we developed two different approaches, a conventional method based on morphological features (MF) and a specifically designed full convolution neural networks (FCN) method, moreover, we evaluate them on the benchmark for needle segmentation in the volumetric OCT images. The experimental results show that FCN method has a better segmentation performance based on four evaluation metrics while MF method has a short inference time, which provides valuable reference for future works.