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Promoting osteogenic differentiation and inhibiting osteoclast formation remain significant challenges in the treatment of osteoporosis. With the growing understanding of osteoporosis, increasing literature has highlighted the regulatory role of m6A methylation in this condition. However, there is currently no reliable method to stably regulate cellular m6A methylation levels. Here, we report a novel approach utilizing alendronate (aln)-modified mesoporous silica nanoparticles (MSNs) to deliver sodium bicarbonate and piR7472, modulating cellular behavior. Our experimental results demonstrate that Aln modification enables the nanoparticles to stably target hydroxyapatite, thereby accumulating in osteoporotic regions. Sodium bicarbonate suppresses osteoclastogenesis, while piR7472 enhances m6A methylation, promoting osteogenic differentiation of bone marrow stromal cells (BMSCs). Computed tomography (CT) and hematoxylin and eosin (HE) staining showed that after 2 weeks of treatment with MSNs-Na@piR7472, cortical bone thickened, trabecular bone density increased, collagen fiber thickness improved, and both the number and staining area of osteoclasts were significantly reduced. These findings indicate a marked improvement in osteoporosis.

Rainfall infiltration affects permafrost-related slope stability by changing the pore water pressure in soil. In this study, the infiltration responses under rainfall conditions were elucidated. The instantaneous profile method and filter paper method were used to obtain the soil–water characteristic curve (SWCC) and hydraulic conductivity function (HCF). During the rainfall infiltration test, the vertical patters of volumetric moisture contents, total hydraulic head or suction and wetting front were recorded. Advancing displacement and rate of the wetting front, the cumulative infiltration, the instantaneous infiltration rate, and the average infiltration rate were determined to comprehensively assess the rainfall infiltration process, along with SWCC and HCF. Additionally, the effects of dry density and runoff on the one-dimensional vertical infiltration process of soil columns were evaluated. The results showed that the variation curve of wetting front displacement versus time obeys a power function relationship. In addition, the infiltration rate–time relationship curve and the unsaturated permeability curve could be roughly divided into three stages, and the SWCC and HCF calculated by volumetric moisture content are more sensitive to changes in dry density than to changes in runoff or hydraulic head height.

As a technological advantage of ultrasonic non-destructive testing, fully focused imaging can accurately feedback the defective characteristics of the inspected object, greatly improving the detection efficiency. This article aims to address the challenges of outdated and low detection rates in the detection technology of branch pipe fillet welds. The full matrix acquisition (FMC) and total focus method (TFM) ultrasonic detection technology are used for detection and defect image feature analysis. Firstly, a multi-mode, fully focused real-time imaging software system was developed to address the specificity of the detection object; secondly, a phased array detection system based on 64 elements was constructed; finally, a region wavelet denoising method based on TFM images was proposed to solve the problem of artifacts caused by poor coupling; and based on the feature extraction method for a minimum rectangle, we analyzed the size, position, angle, and other information regarding defects. Through experiments, it has been found that this technology can effectively improve the detection efficiency of branch pipe weld defects, with a detection rate of 100%. Based on the partition fusion denoising method, the defect imaging quality can be further improved; at the same time, based on the feature extraction method, the error is 0.1 mm, the length range of various defects is 2.3 mm–6.3 mm, the width range is 0.6 mm–0.8 mm, and the angle range is 52°–75°, which can provide an application basis for the localization, classification, and risk assessment of corner weld defects in branch pipes.

Nowadays, the demand for more personalized healthy food recipes is increasing over time, but traditional personalized recipe recommendation systems often tend to overlook the differences that exist in different users’ health conditions. This paper aims to address the issue by proposing an innovative personalized health recipe recommendation framework. This framework is based on different health needs, aiming to address users with different health conditions. More explicitly, the hybrid recommendation algorithm based on facial health recognition is designed to recommend the most suitable recipes for the user by taking into account the user’s health status and preferences. In addition, the fitness factor will adjust the recommendation results to best meet the user’s taste preferences and health goals. Experimental results and user research results show that the recommendation results of this framework are more accurate compared to existing systems, and therefore, users are more satisfied.

Dendrite formation and water-triggered side reactions on the surface of Zn metal anodes severely restrict the commercial viability of aqueous zinc-ion batteries (AZIBs). In this work, we introduce erythritol (Et) as an electrolyte additive to enhance the reversibility of zinc anodes, given its cost-effectiveness, mature technology, and extensive utilization in various domains such as food, medicine, and other industries. By combining multiscale theoretical simulation and experimental characterization, it was demonstrated that Et molecules can partially replace the coordination H2O molecules to reshape the Zn2+ solvation sheath and destroy the hydrogen bond network of the aqueous electrolyte. More importantly, Et molecules tend to adsorb on the zinc anode surface, simultaneously inhibit water-triggered side reactions by isolating water and promote uniform and dense deposition by accelerating the Zn2+ diffusion and regulating the nucleation size of the Zn grain. Thanks to this synergistic mechanism, the Zn anode can achieve a cycle life of more than 3900 h at 1 mA cm−2 and an average Coulombic efficiency of 99.77%. Coupling with δ-MnO2 cathodes, the full battery delivers a high specific capacity of 228.1 mAh g−1 with a capacity retention of 76% over 1000 cycles at 1 A g−1.

Upon careful examination, numerous wind turbine collapses can be attributed to the failure of the tower bolts. Nowadays, the Schmidt–Neuper algorithm is extensively accepted in wind turbine tower bolt design. It is not advisable to utilize the finite-element method, notwithstanding the effect of the flange gap. To quantitatively investigate the influence of flange gaps on bolt fatigue, a nonlinear finite-element model of a flange segment incorporating bolt pretension and contact elements is herein proposed. Three distinct types of flange gaps are defined intentionally. It is possible to determine the nonlinear relationship between the wall load and bolt internal force. The fatigue damage of bolts was thus computed using the obtained nonlinear curve. Comparing with the results with those of Schmidt–Neuper method revealed the bolt fatigue damage is susceptible to a specified flange gap.

Eye diseases can cause great inconvenience to people’s daily life; therefore, it is necessary to study the causes of ocular diseases and related physiological processes. Raman spectroscopic imaging (RSI) is a non-destructive, non-contact detection technique with the advantages of label-free, non-invasive and highly specific. Compared with other mature imaging technologies, RSI can provide real-time molecular information and high-resolution imaging at relatively low cost, making it very suitable for quantitative detection of biological molecules. RSI can reflect the overall situation of the sample, revealing the content distribution of the same substance in different areas of the sample. This review focuses on the recent advances in ophthalmology, with particular emphasis on the powerful use of RSI techniques, as well as its combination with other imaging techniques. Finally, we prospect the wider application and future potential of RSI approaches in ophthalmology.

Rainfall is the main cause of erosion damage in loose slope deposits. During rainfall infiltration, fine particles in the soil mass will move with water infiltration, thus changing the localized particle distribution of the soil mass, which, in turn, causes changes in the pore water pressure and volumetric water content within the slope and ultimately affects slope stability. In order to develop advanced soil and water conservation programs to prevent slope damage, it is crucial to understand and accurately reproduce the particle migration and aggregation characteristics of soils under different rainfall conditions. Therefore, this paper systematically investigates the soil particle migration characteristics of the soil body under rainfall conditions by simulating the internal erosion of the lateritic soil slope body under rainfall conditions via slope internal erosion simulation experiments and experimentally analyzing the migration and aggregation of fine particles in the slope body, as well as the changed rules regarding pore water pressure and volumetric water content at different locations of the slope body with rainfall. The results of this study show that (1) with the infiltration of rainfall, the fine particles in the slope body mainly infiltrate in the vertical direction in an early stage of rainfall; in a later stage, there is vertical downward and down-slope seepage. Therefore, fine particles always gather at the toe of the slope, which leads to relatively high water content and pore water pressure at the toe of the slope, and thus, the slope is always damaged from the toe of the slope. (2) Inside the slope, the fine particles always gather at the smallest pore diameter. With the enhancement of hydrodynamic force, they will be lost again, which leads to a sudden decrease in the local volumetric water content of the slope, and the pore space increases. Then, it is filled with seepage water, which makes the pore water pressure fluctuate or increase. (3) Based on the particle distribution parameter, the present study produced a distribution map of the fine particle content of the slope body under different rainfall intensities and established a model of the dynamic change of fine particles, which improves the understanding of the effect of the change in the fine particle composition of the slope body on the water content and the pore water pressure and may be helpful for the assessment of the initiation of the mudslides.

As a new rock breaking method, CO2 transient cracking has been widely used in rock excavation projects in recent years. However, in the actual construction process, there are often situations where the fracturing effect varies due to different rock mass structures. Through theoretical analysis and on-site cracking tests, this article studies the effect of CO2 transient cracking under the control of different rock mass structures. The results show that: (1) the dynamic compressive strength of rock directly determines the number and range of dynamic impact fractures; the original fractures of rock mass and those caused by dynamic impact in the first stage jointly determine the effect of high-pressure gas expansion in the second stage. (2) The arrangement of holes along the strata is conducive to the action of high-pressure expanding gas along the soft structural plane in the rock mass, which is conducive to the fracturing of the rock mass; the amount of crack formation is small, but the influence range is large. (3) The cracking effect of carbon dioxide transient cracking applied to massive rock mass is better than that of monolithic rock mass, while the cracking effect of layered rock mass with soil interlayer is poor. The research results are of great significance for improving the effectiveness of carbon dioxide transient-induced cracking excavation and guiding actual construction.

Pitaya, or dragon fruit, is a typical tropical fruit with an appealing taste and diverse health benefits to humans. The plantation of pitaya in Guizhou province in China has greatly boosted the income of local farmers and alleviated poverty. However, the frequent occurrence of postharvest diseases has brought large economic loss. To find a solution, we set out to identify the postharvest disease-causing agents of Guizhou pitaya. Several fungi were isolated from diseased pitaya and identified as species based on the ITS1 sequence similarity. Of them, Penicillium spinulosum, Phoma herbarum, Nemania bipapillata, and Aspergillus oryzae were, for the first time, found to cause dragon fruit disease. In consideration of their prevalence in postharvest fruit diseases, Alternaria alternata H8 and Fusarium proliferatum H4 were chosen as representative pathogens for the drug susceptibility test. Among the tested drugs and plant extracts, 430 g/L tebuconazole and 45% prochloraz were found to be the most potent fungicides against H8 and H4, respectively. The research provides insights into the mechanism and control of postharvest diseases of dragon fruits in Guizhou, China, and thus could be of economic and social significance to local farmers and the government.

Dielectrics with improved energy density have long-standing demand for miniature and lightweight energy storage capacitors for electrical and electronic systems. Recently, polyvinylidene fluoride (PVDF)-based ferroelectric polymers have shown attractive energy storage performance, such as high dielectric permittivity and high breakdown strength, and are regarded as one of the most promising candidates. However, the non-negligible energy loss and inferior temperature stability of PVDF-based polymers deteriorated the energy storage performance or even the thermal runaway, which could be ascribed to vulnerable amorphous regions at elevated temperatures. Herein, a new strategy was proposed to achieve high energy density and high temperature stability simultaneously of PVDF/PMMA blends by in situ polymerization. The rigidity of the amorphous region was ideally strengthened by in situ polymerization of methyl methacrylate (MMA) monomers in a PVDF matrix to obtain PVDF/PMMA blends. The atomic force microscopic study of the microstructure of etched films showed the ultra-homogenous distribution of PMMA with high glass transition temperature in the PVDF matrix. Consequently, the temperature variation was remarkably decreased, while the high polarization response was maintained. Accordingly, the high energy density of ∼8 J/cm3 with ∼80% efficiency was achieved between 30 and 90 °C in PVDF/PMMA films with 39–62% PMMA content, outperforming most of the dielectric polymers. Our work could provide a general solution to substantially optimize the temperature stability of dielectric polymers for energy storage applications and other associated functions.

Mobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce the time delays and computational complexity in cloud platforms. To improve task completion and coverage rates, how to design a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes has raised huge challenges for Mobile CrowdSensing. In this study, we propose a Reputation-based Collaborative User Recruitment algorithm (RCUR) under a certain budget in an edge-aided Mobile CrowdSensing system. We first introduce edge computing into MCS and build an edge-aided MCS system in urban scenarios. Moreover, we analyze the influence of user reputation on user recruitment. Then we establish a user reputation module to deduce the user reputation equation by combining the user’s past reputation score with an instantaneous reputation score. Finally, we utilize the sensing ability of edge nodes and design a collaborative sensing method. We use the greedy method to help choose the appropriate users for the tasks. Simulation results compared with the other three algorithms prove that our RCUR approach can significantly achieve better performance in task completion rate and task coverage rate.

It is highly desirable to enhance the photogenerated charge separation of g-C3N4 by constructing efficient heterojunctions, especially with an additional organic constitution for solar–hydrogen conversion. Herein, g-C3N4 nanosheets have been modified controllably with nano-sized poly(3-thiophenecarboxylic acid) (PTA) through in situ photopolymerization and then coordinated with Fe(III) via the -COOH groups of modified PTA, forming an interface of tightly contacted nanoheterojunctions between the Fe(III)-coordinated PTA and g-C3N4. The resulting ratio-optimized nanoheterojunction displays a ~4.6-fold enhancement of the visible-light photocatalytic H2 evolution activity compared to bare g-C3N4. Based on the surface photovoltage spectra, measurements of the amount of •OH produced, photoluminescence (PL) spectra, photoelectrochemical curves, and single-wavelength photocurrent action spectra, it was confirmed that the improved photoactivity of g-C3N4 is attributed to the significantly promoted charge separation by the transfer of high-energy electrons from the lowest unoccupied molecular orbital (LUMO) of g-C3N4 to the modified PTA via the formed tight interface, dependent on the hydrogen bond interaction between the -COOH of PTA and the -NH2 of g-C3N4, and the continuous transfer to the coordinated Fe(III) with -OH favorable for connection with Pt as the cocatalyst. This study demonstrates a feasible strategy for solar-light-driven energy production over the large family of g-C3N4 heterojunction photocatalysts with exceptional visible-light activities.

An electrochemical immunosensor has been developed for the rapid detection and identification of potentially harmful bacteria in food and environmental samples. This study aimed to fabricate a microwire-based electrochemical immunosensor (MEI sensor) for selective detection of Escherichia coli and Staphylococcus aureus in microbial cocktail samples using dielectrophoresis (DEP)-based cell concentration. A gold-coated tungsten microwire was functionalized by coating polyethylenimine, single-walled carbon nanotube (SWCNT) suspension, streptavidin, biotinylated antibodies, and then bovine serum albumin (BSA) solutions. Double-layered SWCNTs and 5% BSA solution were found to be optimized for enhanced signal enhancement and nonspecific binding barrier. The selective capture of E. coli K12 or S. aureus cells was achieved when the electric field in the bacterial sample solution was generated at a frequency of 3 MHz and 20 Vpp. A linear trend of the change in the electron transfer resistance was observed as E. coli concentrations increased from 5.32 × 102 to 1.30 × 108 CFU/mL (R2 = 0.976). The S. aureus MEI sensor fabricated with the anti-S. aureus antibodies also showed an increase in resistance with concentrations of S. aureus (8.90 × 102–3.45 × 107 CFU/mL) with a correlation of R2 = 0.983. Salmonella typhimurium and Listeria monocytogenes were used to evaluate the specificity of the MEI sensors. The functionalization process developed for the MEI sensor is expected to contribute to the sensitive and selective detection of other harmful microorganisms in food and environmental industries.

In order to solve the problems of excessive elastic deformation and excessive inertia force existed in the drive mechanism of traditional die-cutting machine, a lightweight drive rod with full symmetrical structure is proposed as the main force bearing component of the drive mechanism based on the kinematics analysis. The elastic deformation and inertia force of the lightweight drive rod are verified by static simulation analysis, and show that the weight of the drive rod is significantly reduced under the same deformation conditions, the traditional one. Further compared with, taking the minimum elastic deformation and lightweight as the optimization objectives, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to optimize the structural parameters of the drive rod. The results show that under the working conditions of 350 T die-cutting force and 125 r/min rotating speed, the elastic deformation of lightweight drive rod after structural optimization is smaller (the maximum deformation is 0.00988 mm) and the weight is lighter (27% less). The research data presented in this paper can be used as the theoretical basis for future research on die-cutting mechanism. The lightweight drive rod proposed in this study can be used in die-cutting devices with high die-cutting speed and high die-cutting accuracy.

Air and water pollution poses a serious threat to public health and the ecological environment worldwide. Particulate matter (PM) is the major air pollutant, and its primary sources are processes that require high temperatures, such as fossil fuel combustion and vehicle exhaust. PM0.3 can penetrate and seriously harm the bronchi of the lungs, but it is difficult to remove PM0.3 due to its small size. Therefore, PM0.3 air filters that are highly efficient and resistant to high temperatures must be developed. Polyimide (PI) is an excellent polymer with a high temperature resistance and a good mechanical property. Air filters made from PI nanofibers have a high PM removal efficiency and a low air flow resistance. Herein, zeolitic imidazolate framework-8 (ZIF-8) was used to modify PI nanofibers to fabricate air filters with a high specific surface area and filtration efficiency. Compared with traditional PI membranes, the ZIF-8/PI multifunction nanofiber membranes achieved super-high filtration efficiency for ultrafine particles (PM0.3, 100%), and the pressure drop was only 63 Pa. The filtration mechanism of performance improvement caused by the introduction of ZIF-8/PI nanofiber membrane is explored. Moreover, the ZIF-8/PI nanofiber membranes exhibited excellent thermal stability (300 C) and efficient water–oil separation ability (99.85%).

The Yinchuan–Chongqing high-speed railway (HSR) is one of the “ten vertical and ten horizontal” comprehensive transportation channels in the National 13th Five-Year Plan for Mid- and Long-Term Railway Network. However, the choice of node stations on this line is controversial. In this paper, the problem of high-speed railway station selection is transformed into a classification problem under the edge graph structure in complex networks, and a Scatter-GNN model is proposed to predict stations. The article first uses the Node2vec algorithm to perform a biased random walk on the railway network to generate the vector representation of each station. Secondly, an adaptive method is proposed, which derives the critical value of edge stations through the pinching rule, and then effectively identifies the edge stations in the high-speed railway network. Next, the calculation method of Hadamard product is used to represent the potential neighbors of edge sites, and then the attention mechanism is used to predict the link between all potential neighbors and their corresponding edge sites. After the link prediction, the final high-speed railway network is obtained, and it is input into the GNN classifier together with the line label to complete the station prediction. Experiments show that: Baoji and Hanzhong are more likely to become node stations in this north–south railway trunk line. The Scatter-GNN classifier optimizes the site selection strategy by calculating the connection probabilities between two or more candidate routes and comparing their results. This may reduce manual selection costs and ease geographic evaluation burdens. The model proposed in this paper can be used as an auxiliary strategy for the traditional route planning scheme, which may become a new way of thinking to study such problems in the future.

Intelligent vehicle-following control presents a great challenge in autonomous driving. In vehicle-intensive roads of city environments, frequent starting and stopping of vehicles is one of the important cause of front-end collision accidents. Therefore, this paper proposes a subsection proximal policy optimization method (Subsection-PPO), which divides the vehicle-following process into the start–stop and steady stages and provides control at different stages with two different actor networks. It improves security in the vehicle-following control using the proximal policy optimization algorithm. To improve the training efficiency and reduce the variance of advantage function, the weighted importance sampling method is employed instead of the importance sampling method to estimate the data distribution. Finally, based on the TORCS simulation engine, the advantages and robustness of the method in vehicle-following control is verified. The results show that compared with other deep learning learning, the Subsection-PPO algorithm has better algorithm efficiency and higher safety than PPO and DDPG in vehicle-following control.

Phosphorus-doped activated carbon (P@AC), prepared by wet impregnation method, can serve as efficient metal-free catalyst for the dehydroaromatization of n-hexane to benzene under mild conditions. The effect of phosphorus amount and phosphorus sources including phenylphosphonic acid (PPOA), phosphorus acid (H3PO4), triphenylphosphine (TPP), and tributylphosphine (TBP) on catalytic performance of P@AC were investigated. Combining with characterization analysis, functional groups containing -P(O)(OH) on the carbon surface are believed to be the active sites for dehydroaromatization catalysis.

Although tremendous progress has been made in preparing sulfur nanoparticles to achieve high-rate lithium sulfur (Li-S) batteries, the synthetic methods involved are usually toxic, complicated and costly. To solve the above problem, a simple and green method through high pressure steam is developed to prepare sulfur nanoparticles (

Quasiparticles, especially the ones near the Josephson junctions in the superconducting qubits, are known as an important source of decoherence. By injecting quasiparticles into a quantum chip, we characterized the diffusion feature by measuring the energy relaxation time and the residual excited-state population of a transmon qubit. From the extracted transition rates, we phenomenologically modeled the quasiparticle diffusion in a superconducting circuit that contained “hot” nonequilibrium quasiparticles in addition to low-energy ones.

In order to study the dynamic response characteristics of a pile-soil foundation when an adjacent tunnel exists, both model tests and numerical simulations were performed in this study. Taking the peak vibration acceleration, peak vibration velocity, and peak vibration displacement as the evaluation indexes of the dynamic response, the dynamic response of the pile-soil foundation with an adjacent tunnel under high-speed train loads is studied. A method of dividing the affected zones of the dynamic response based on the safety threshold is provided in this paper, which can evaluate the safety of the adjacent tunnel under the action of train load. Additionally, the effect of train speed on dynamic response is further analyzed. The results show that the dynamic response index inside the foundation decays significantly with increasing distance from the surface when the train load is applied, regardless of the presence or absence of the adjacent tunnel. Compared with an ordinary pile-soil foundation, the dynamic response indexes inside the foundation increase significantly when there is an adjacent tunnel. With the increase in the horizontal distance from the pile foundation, the distribution characteristics of the horizontal transverse direction of each dynamic response index inside the foundation change from a “wavy” distribution to a monotonically decreasing distribution. According to the safety threshold of vibration velocity, the foundation is divided into the hazardous affected zone, strongly affected zone, and weakly affected zone, and then the corresponding vibration reduction measures are adopted. With the increase in train speed, the effect of tunnel structure on the attenuation of dynamic response in a pile-soil foundation becomes more obvious.

Currently, precious metal group materials are known as the efficient and widely used oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) catalysts. The exorbitant prices and scarcity of the precious metals have stimulated scale exploration of alternative non-precious metal catalysts with low-cost and high performance. Layered double hydroxides (LDHs) are a promising precursor to prepare cost-effective and high-performance catalysts because they possess abundant micropores and nitrogen self-doping after pyrolysis, which can accelerate the electron transfer and serve as active sites for efficient OER. Herein, we developed a new highly active NiFeMn-layered double hydroxide (NFM LDH) based electrocatalyst for OER. Through building NFM hydroxide/oxyhydroxide heterojunction and incorporation of conductive graphene, the prepared NFM LDH-based electrocatalyst delivers a low overpotential of 338 mV at current density of 10 mA cm−2 with a small Tafel slope of 67 mV dec−1, which are superior to those of commercial RuO2 catalyst for OER. The LDH/OOH heterojunction involves strong interfacial coupling, which modulates the local electronic environment and boosts the kinetics of charge transfer. In addition, the high valence Fe3+ and Mn3+ species formed after NaOH treatment provide more active sites and promote the Ni2+ to higher oxidation states during the O2 evolution. Moreover, graphene contributes a lot to the reduction of charge transfer resistance. The combining effects have greatly enhanced the catalytic ability for OER, demonstrating that the synthesized NFM LDH/OOH heterojunction with graphene linkage can be practically applied as a high-performance electrocatalyst for oxygen production via water splitting.

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.

Generalized Hilbert transform (GHT) has been widely and successfully used in signal processing and interpretation for many years. However, the order parameter in GHT can only be chosen in a certain range, and the GHT result is not stable with respect to the chosen order parameter, which usually leads to poor-stability and low-resolution outputs. To improve the stability of GHT and broaden its order parameter chosen range, and finally achieve high-efficiency and high-resolution results, we extend GHT by introducing the sign function, the two frequency integration parameters, and propose the Lp-order time–frequency spectra (LTFS). In LTFS, the sign function aims to broaden the chosen range of the order parameter into any positive numbers, and then leads to high-stability and high-resolution LTFS results; the two frequency integration parameters aim to optimize the computation efficiency and output resolution of LTFS. Synthetic one-dimensional (1D) channel data testing, synthetic two-dimensional (2D) seismic data, and actual three-dimensional (3D) seismic data examples demonstrate favorable capability of LTFS for edge detection.

Monolayer 2H-MoS2 has been widely noticed as a typical transition metal dichalcogenides (TMDC) for surface-enhanced Raman scattering (SERS). However, monolayer MoS2 is limited to a narrow range of applications due to poor detection sensitivity caused by the combination of a lower density of states (DOS) near the Fermi energy level as well as a rich fluorescence background. Here, surfaced S and Mo atomic defects are fabricated on a monolayer MoS2 with a perfect lattice. Defects exhibit metallic properties. The presence of defects enhances the interaction between MoS2 and the detection molecule, and it increases the probability of photoinduced charge transfer (PICT), resulting in a significant improvement of Raman enhancement. Defect-containing monolayer MoS2 enables the fluorescence signal of many dyes to be effectively burst, making the SERS spectrum clearer and making the limits of detection (LODs) below 10−8 M. In conclusion, metallic defect-containing monolayer MoS2 becomes a promising and versatile substrate capable of detecting a wide range of dye molecules due to its abundant DOS and effective PICT resonance. In addition, the synergistic effect of surface defects and of the MoS2 main body presents a new perspective for plasma-free SERS based on the chemical mechanism (CM), which provides promising theoretical support for other TMDC studies.

Photocatalytic degradation is one of the most promising emerging technologies for environmental pollution control. However, the preparation of efficient, low-cost photocatalysts still faces many challenges. TiO2 is a widely available and inexpensive photocatalyst material, but improving its catalytic degradation performance has posed a significant challenge due to its shortcomings, such as the easy recombination of its photogenerated electron–hole pairs and its difficulty in absorbing visible light. The construction of homogeneous heterojunctions is an effective means to enhance the photocatalytic performances of photocatalysts. In this study, a TiO2(B)/TiO2(A) homogeneous heterojunction composite photocatalyst (with B and A denoting bronze and anatase phases, respectively) was successfully constructed in situ. Although the construction of homogeneous heterojunctions did not improve the light absorption performance of the material, its photocatalytic degradation performance was substantially enhanced. This was due to the suppression of the recombination of photogenerated electron–hole pairs and the enhancement of the carrier mobility. The photocatalytic ability of the TiO2(B)/TiO2(A) homogeneous heterojunction composite photocatalyst was up to three times higher than that of raw TiO2 (pure anatase TiO2).

Surface charge transfer doping has attracted much attention in modulating the optical and electrical behavior of 2D transition metal dichalcogenides (TMDCs), where finding controllable and efficient dopants is crucial. Here, 1,1,2,2-tetraphenylethylene (TPE) derivative molecules with aggregation-induced emission (AIE) effect were selected as adjustable dopants. By designing nitro and methoxyl functional groups and surface coating, controlled p/n-type doping can be achieved on a chemical vapor deposition (CVD) grown monolayer, MoS2. We investigated the electron transfer behavior between these two dopants and MoS2 with fluorescence, Raman, X-ray photoelectron spectra and transient absorption spectra. 1,1,2,2-Tetrakis(4-nitrophenyl)ethane (TPE-4NO2) with a negative charge aggregation can be a donor to transfer electrons to MoS2, while 1,1,2,2-Tetrakis(4-methoxyphenyl)ethane (TPE-4OCH3) is the opposite and electron-accepting. Density functional theory calculations further explain and confirm these experimental results. This work shows a new way to select suitable dopants for TMDCs, which is beneficial for a wide range of applications in optoelectronic devices.

Synchrosqueezing transform (SST) can effectively improve time-frequency precision and resolution by squeezing time-frequency spectra via instantaneous frequencies, and it has been applied in many diverse disciplines; however, the precision of estimated instantaneous frequencies during SST is usually affected by the time-sample interval of the inputted signal; this usually leads to low-precision or inaccurate SST results and limits its further application. To obtain high-precision and high-resolution SST results with high efficiency, we propose a high-precision and high-resolution SST via time-frequency instantaneous phases (HSST); in HSST, time-frequency instantaneous phases with period-jumps removal are used for high-precision instantaneous frequencies estimation and SST. Two synthetic signal examples show that HSST can minimize the impact of the time-sample interval to achieve high-precision and high-resolution SST results with high efficiency. A real 3D seismic data application demonstrates that HSST has fantastic performance in time-frequency precision and resolution enhancement, and it can be widely used in digital signals processing and interpretation fields.

Rapid and effective detection of pesticide residues from complex surfaces of fruits and vegetables has important significance. Herein, we report a novel three-dimensional (3D) hierarchical porous functional surface-enhanced Raman scattering (SERS) substrate, which is fabricated by successive two-step hydrothermal synthesis strategy of silver nanoparticles (Ag NPs) and cobalt oxide nanowires (Co3O4 NWs) on the 3D copper foam framework as Cu@Co3O4@Ag-H. The strategy offers a new avenue for localized plasmonic materials distribution and construction, which exhibits better morphology regulation ability and SERS activity (or hotspots engineering) than physical spurring obtained Cu@Co3O4@Ag-S. The developed Cu@Co3O4@Ag-H possesses large surface area and rich hotspots, which contributes to the excellent SERS performance, including homogeneity (RSD of 7.8%), sensitivity (enhancement factor, EF of 2.24 × 108) and stability. The Cu@Co3O4@Ag-H not only provides plenty of Electromagnetic enhancement (EM) hotspots but also the trace detection capability for droplet rapid sensing within 2 s. Cu@Co3O4@Ag-H substrate is further developed as an effective SERS sensing platform for pesticide residues detection on the surfaces of fruits and vegetables with excellent LOD of 0.1 ppm, which is lower than the most similar reported works. This work offers new potential for bioassay, disease POCT diagnosis, national security, wearable flexible devices, energy storage and other related fields.

Recently, alkaline direct methanol fuel cells have made great progress with the development of alkaline electrocatalysis, and a wide variety of catalysts have been explored for methanol oxidation reaction (MOR)and oxygen reduction reaction (ORR). However, the slow kinetics of the MOR and ORR remain a great challenge. In this paper, self-supported defect-rich AuCu was obtained by a convenient one-pot strategy. Self-supported AuCu presented a branched, porous nanostructure. The nanobranch consisted of several 13 nm skeletons, which connected in the kink of the structure. Different growth directions co-existed at the kink, and the twin boundaries and dislocations as defects were observed. When the Au-based nanostructure functioned as an electrocatalyst, it showed robust MOR and ORR performance. For the MOR, the forward peak current was 2.68 times greater than that of Au/C; for the ORR, the activity was close to that of Pt/C and significantly better than that of Au/C. In addition, it possessed superior electrochemical stability for MOR and ORR. Finally, an in-depth exploration of the impact of surface defects and electrochemical Cu removal on MOR and ORR activity was carried out to explain the MOR and ORR’s catalytic performance.

Electrolysis of water to produce hydrogen is crucial for developing sustainable clean energy and protecting the environment. However, because of the multi-electron transfer in the oxygen evolution reaction (OER) process, the kinetics of the reaction is seriously hindered. To address this issue, we designed and synthesized hollow CoP/FeP4 heterostructural nanorods interwoven by carbon nanotubes (CoP/FeP4@CNT) via a hydrothermal reaction and a phosphorization process. The CoP/FeP4@CNT hybrid catalyst delivers prominent OER electrochemical performances: it displays a substantially smaller Tafel slope of 48.0 mV dec−1 and a lower overpotential of 301 mV at 10 mA cm−2, compared with an RuO2 commercial catalyst; it also shows good stability over 20 h. The outstanding OER property is mainly attributed to the synergistic coupling between its unique CNT-interwoven hollow nanorod structure and the CoP/FeP4 heterojunction, which can not only guarantee high conductivity and rich active sites, but also greatly facilitate the electron transfer, ion diffusion, and O2 gas release and significantly enhance its electrocatalytic activity. This work offers a facile method to develop transition metal-based phosphide heterostructure electrocatalysts with a unique hierarchical nanostructure for high performance water oxidation.

α″-Fe16N2 nanomaterials with a shape anisotropy for high coercivity performance are of interest in potential applications such as rare-earth-free permanent magnets, which are difficult to synthesize in situ anisotropic growth. Here, we develop a new and facile one-pot microemulsion method with Fe(CO)5 as the iron source and tetraethylenepentamine (TEPA) as the N/C source at low synthesis temperatures to fabricate carbon-coated tetragonal α″-Fe16N2 nanocones. Magnetocrystalline anisotropy energy is suggested as the driving force for the anisotropic growth of α″-Fe16N2@C nanocones because the easy magnetization direction of tetragonal α″-Fe16N2 nanocrystals is along the c axis. The α″-Fe16N2@C nanocones agglomerate to form a fan-like microstructure, in which the thin ends of nanocones direct to its center, due to the magnetostatic energy. The lengths of α″-Fe16N2@C nanocones are ~200 nm and the diameters vary from ~10 nm on one end to ~40 nm on the other end. Carbon shells with a thickness of 2–3 nm protect α″-Fe16N2 nanocones from oxidation in air atmosphere. The α″-Fe16N2@C nanocones synthesized at 433 K show a room-temperature saturation magnetization of 82.6 emu/g and a coercive force of 320 Oe.

Olivine-structure LiFePO4 is considered as promising cathode materials for lithium-ion batteries. However, the material always sustains poor electron conductivity, severely hindering its further commercial application. In this work, zinc oxide and carbon co-modified LiFePO4 nanomaterials (LFP/C-ZnO) were prepared by an inorganic-based hydrothermal route, which vastly boosts its performance. The sample of LFP/C-xZnO (x = 3 wt%) exhibited well-dispersed spherical particles and remarkable cycling stability (initial discharge capacities of 138.7 mAh/g at 0.1 C, maintained 94.8% of the initial capacity after 50 cycles at 0.1 C). In addition, the cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) disclose the reduced charge transfer resistance from 296 to 102 Ω. These suggest that zinc oxide and carbon modification could effectively minimize charge transfer resistance, improve contact area, and buffer the diffusion barrier, including electron conductivity and the electrochemical property. Our study provides a simple and efficient strategy to design and optimize promising olivine-structural cathodes for lithium-ion batteries.

Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the convolution neural network (CNN) to improve the image quality of PA bone imaging in a numerical model. To be more adaptive for imaging bone samples with complex structure, an attention block U-net (AB-U-Net) network was designed from the standard U-net by integrating the attention blocks in the feature extraction part. The k-wave toolbox was used for the simulation of photoacoustic wave fields, and then the direct reconstruction algorithm—time reversal was adopted for generating a dataset of deep learning. The performance of the proposed AB-U-Net network on the reconstruction of photoacoustic bone imaging was analyzed. The results show that the AB-U-Net based deep learning method can obtain the image presented as a clear bone micro-structure. Compared with the traditional photoacoustic reconstruction method, the AB-U-Net-based reconstruction algorithm can achieve better performance, which greatly improves image quality on test set with peak signal to noise ratio (PSNR) and structural similarity increased (SSIM) by 3.83 dB and 0.17, respectively. The deep learning method holds great potential in enhancing PA imaging technology for bone disease detection.

Mechanical damage in form of dents, cracks, gouges, and scratches are common in pipelines. Sometimes, these damages form in proximity of each other and act as one defect in the pipe wall. The combined defects have been found to be more injurious than individual defects. One of the combined defects in pipeline comprises of a crack in a dent, also known as dent-crack defect. This paper discusses the development of finite element models using extended finite element criterion (XFEM) in Abaqus to predict burst pressure of specimens of API X70 pipeline with restrained and unrestrained concentric dent-crack defects. The models are calibrated and validated using results of full-scale burst tests. The effects of crack length, crack depth, dent depth, and denting pressure on burst pressure are investigated. The results show that restrained dent-crack defects with shallow cracks (depth less than 50% wall thickness) inside dents do not affect pipeline operations at maximum allowable operating pressure if crack lengths are less than 200 mm. Releasing restrained dent-cracks when the pressure is at maximum allowable operating pressure can cause propagation of deep cracks (depth of 50% wall thickness or more) longer than 60 mm. However, only very long cracks (200 mm and higher) propagate to burst the pipe. Cracks of depth less than 20% of wall thickness inside dents formed at zero pressure are not propagated by the maximum allowable operating pressure. Dent-crack defects having dents of depth less than 2% outside diameter of pipe behave as plain cracks if the dents are formed at zero denting pressure but are more injurious than plain cracks if the dents are formed in pressurized pipes.

Bi2Se3 possesses a two-dimensional layered rhombohedral crystal structure, where the quintuple layers (QLs) are covalently bonded within the layers but weakly held together by van der Waals forces between the adjacent QLs. It is also pointed out that Bi2Se3 is a topological insulator, making it a promising candidate for a wide range of electronic and optoelectronic applications. In this study, we investigate the growth of high-quality Bi2Se3 thin films on mica by the molecular beam epitaxy technique. The films exhibited a layered structure and highly c-axis-preferred growth orientation with an XRD rocking curve full-width at half-maximum (FWHM) of 0.088°, clearly demonstrating excellent crystallinity for the Bi2Se3 deposited on the mica substrate. The growth mechanism was studied by using an interface model associated with the coincidence site lattice unit (CSLU) developed for van der Waals epitaxies. This high (001) texture favors electron transport in the material. Hall measurements revealed a mobility of 726 cm2/(Vs) at room temperature and up to 1469 cm2/(Vs) at 12 K. The results illustrate excellent electron mobility arising from the superior crystallinity of the films with significant implications for applications in conducting electrodes in optoelectronic devices on flexible substrates.

With the continuous progress of machine vision technology, crack detection in pipelines has been greatly improved. For crack detection in deep holes, inner tubes, and other environments, it is not only necessary to detect the existence of cracks, but also to collect important information regarding the crack detection direction for further analysis. Because shooting with a frontal field of view causes the real side wall images to produce certain distortions, the detection and calibration of cracks requires a certain amount of professional technology and time. It usually takes a long time to collect the image to eliminate the distortion, and then to identify the crack and mark the direction according to the data line. Therefore, a simple and efficient end-to-end neural network model for crack recognition and three-dimensional visualization are proposed by using a cascade network and simple recognition technology in conjunction with inertial navigation equipment. In addition, we screen the crack data via pixel calibration and eliminate the ambiguous data to make the visualization more accurate. Experiments in pipelines and burrows show that the accuracy, performance, and efficiency of the proposed method reached a high level.

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.

A novel high-speed 3D shape measurement technology called temporal Fourier transform profilometry (TFTP for short) is proposed by combining the merits of Fourier transform profilometry (FTP) and phase-measuring profilometry (PMP). Instead of using the digital light projector, a mechanical projector is employed to generate multi-period phase-shifting fringe patterns sequentially. During the reconstruction process, the phase value of each pixel is calculated independently along the temporal axis and no spectrum filtering operation is performed in a spatial domain. Therefore, high-frequency components containing the detailed information of the measured object effectively remain. The proposed method is suitable for measuring isolated dynamic objects. Only one frame of deformed fringe pattern is required to retrieve one 3D shape of the measured object, so it has the obvious advantage if measuring the dynamic scene at a high speed. A low-cost self-made mechanical projector with fast projection speed is developed to execute the principle-proof experiments, whose results demonstrate the feasibility of measuring isolated dynamic objects.

Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.

Micro-nano composite material was prepared to adsorb Hg(II) ions via the co-precipitation method. Oyster shell (OS), Fe3O4 nanoparticles, and humic acid (HA) were used as the raw materials. The adhesion of nanoparticles to OS displayed by scanning electron microscopy (SEM), the appearance of the (311) plane of standard Fe3O4 derived from X-ray diffraction (XRD), and the transformation of pore sizes to 50 nm and 20 μm by mercury intrusion porosimetry (MIP) jointly revealed the successful grafting of HA-functionalized Fe3O4 onto the oyster shell surface. The vibrating sample magnetometer (VSM) results showed superparamagnetic properties of the novel adsorbent. The adsorption mechanism was investigated based on X-ray photoelectron spectroscopy (XPS) techniques, which showed the process of physicochemical adsorption while mercury was adsorbed as Hg(II). The effects of pH (3−7), initial solution concentration (2.5−30 mg·L−1), and contact time (0−5 h) on the adsorption of Hg(II) ions were studied in detail. The experimental data were well fitted to the Langmuir isotherm equation (R2 = 0.991) and were shown to follow a pseudo-second-order reaction model (R2 = 0.998). The maximum adsorption capacity of Hg(II) was shown to be 141.57 mg·g−1. In addition, this new adsorbent exhibited excellent selectivity.

The capability for recognizing pedestrian semantic attributes, such as gender, clothes color and other semantic attributes is of practical significance in bank smart surveillance, intelligent transportation and so on. In order to recognize the key multi attributes of pedestrians in indoor and outdoor scenes, this paper proposes a deep network with dynamic weights and joint loss function for pedestrian key attribute recognition. First, a new multi-label and multi-attribute pedestrian dataset, which is named NEU-dataset, is built. Second, we propose a new deep model based on DeepMAR model. The new network develops a loss function, which joins the sigmoid function and the softmax loss to solve the multi-label and multi-attribute problem. Furthermore, the dynamic weight in the loss function is adopted to solve the unbalanced samples problem. The experiment results show that the new attribute recognition method has good generalization performance.