Wireless sensor networks (WSN) are widely used in various fields such as military, industrial and transportation for real-time monitoring, sensing and data collection of different environments or objects. However, the development of WSN is hindered by several limitations, including energy, storage space, computing power and data transmission rate. Among these, the availability of power energy plays a crucial role as it directly determines the lifespan of WSN. To extend the life cycle of WSN, two key approaches are power supply improvement and energy conservation. Therefor, we proposed an energy harvesting system and a low energy consumption mechanism for WSN. Firstly, we delved into the energy harvesting technology of WSN, explored the utilization of solar energy and mechanical vibration energy to ensure a continuous and dependable power supply to the sensor nodes, and analyzed the voltage output characteristics of bistable piezoelectric cantilever. Secondly, we proposed a neighbor discovery mechanism that utilizes a separation beacon, is based on reply to ACK, and can facilitate the identification of neighboring nodes. This mechanism operates at a certain duty cycle ratio, significantly reduces idle listening time and results in substantial energy savings. In comparison to the Disco and U-connect protocols, our proposed mechanism achieves a remarkable reduction of 66.67% and 75% in the worst discovery delay, respectively. Furthermore, we introduced a data fusion mechanism based on integer wavelet transform. This mechanism effectively eliminates data redundancy caused by spatio-temporal correlation, results in a data compression rate of 5.42. Additionally, it significantly reduces energy consumption associated with data transmission by the nodes.

Most of the current methods for stabilizing electric arc furnace (EAF) slag are time-consuming and cannot be completely stabilized. In view of this, this study aimed to apply microbial‑induced calcium carbonate precipitation (MICP) technology in the stabilization of EAF reducing slag, and this was to be achieved by using the reaction between carbonate ions and free calcium oxide (f-CaO) in reducing slag to form a more stable calcium carbonate to achieve the purpose of stabilization. The test results showed that, when the EAF reducing slag aggregates (ERSAs) were immersed in Bacillus pasteurii bacteria solution or water, the f-CaO contained in it would react such that stabilization was achieved. The titration test results showed that the f-CaO content of the ERSAs immersed in the bacterial solution and water decreased. The expansion test results of the ERSAs that were subjected to hydration showed that the seven-day expansion of ERSAs after biomineralization could meet the Taiwan regulation requirement of a less than 0.5% expansion rate. The thermogravimetric analysis showed that both the experimental group and the control group might contain calcium carbonate compounds. The results of the X-ray diffraction analysis showed that the CaCO3 content in the ERSAs that were immersed in the bacterial solution was significantly higher than those that were immersed in water. Moreover, the compressive strength test results of concrete prepared with ERSAs showed that the compressive strength of the control group concrete began to decline after 28 days. In contrast, the experimental group concrete had a good stabilization effect, and there was no decline in compressive strength until the age of 180 days. At the age of 240 days, the surface cracks of the experimental group were particularly small, while the surface of the control group showed obvious cracks. These results confirmed that a mineralization reaction with B. pasteurii bacteria could be used as a stabilization technology for ERSAs.

Concrete cracks and local damage can affect the bond performance between concrete and steel bars, thereby reducing the durability of reinforced concrete structures. Compared with general concrete crack repair methods, biomineralization repair not only has effective bonding capabilities but is also particularly environmentally friendly. In view of this, this study aimed to apply biomineralization technology to repair damaged fiber-reinforced lightweight aggregate concrete (LWAC). Two groups of LWAC specimens were prepared. The control group used lightweight aggregates (LWAs) without bacterial spores and nutrient sources, and the experimental group used LWAs containing bacterial spores and nutrient sources. These specimens were first subjected to compression tests and pull-out tests, respectively, and thus were damaged. After the damaged specimen healed itself in different ways for 28 days, secondary compression and pull-out tests were conducted. The self-healing method of the control group involved placing the specimens in an incubator. The experimental group was further divided into experimental group I and experimental group II. The self-healing method of experimental group I was the same as that of the control group. The self-healing method of experimental group II involved soaking the specimen in a mixed solution of urea and calcium acetate for two days, and then taking it out and placing it in an incubator for two days, with a cycle of four days. The test results showed that, compared with the control group specimens, the relative bond strength ratio of the experimental group II specimens increased by 17.9%. Moreover, the EDS and XRD analysis results confirmed that the precipitate formed at the crack was calcium carbonate, which improved the compressive strength and bond strength after self-healing. This indicates that the biomineralization maintenance method used in experimental group II is more effective.

Future healthcare professionals can play a significant role in managing those with thoughts of suicide. This scoping review aims to determine healthcare students’ knowledge and attitude towards suicide. A systematic search was conducted across five databases: PubMed, CINAHL, EMBASE, PsycINFO, Cochrane, and DARE. Only primary studies written in English, from database inception until 31st December 2023 were included. A total of 44 studies were included. Overall, healthcare students demonstrated low to moderate confidence in managing patients with thoughts of suicide and low levels of relevant education despite acknowledging their importance in preventing suicide. Increased personal exposure to suicide was associated with increased suicide literacy, confidence in managing patients with thoughts of suicide, and reduced stigma. Demographics and culture were shown to affect knowledge and attitude as well. Further research is needed to better understand various contributing factors to healthcare students’ knowledge and attitude regarding suicide. Evaluating healthcare curricula should be considered to implement effective suicide training program.

In order to analyze the spatial distribution characteristics of the cultural landscape of the Yangtze River (Nanjing section) and show the role of the Yangtze River culture in promoting regional economic development and cultural radiation, Based on the Knowledge graph technology and 3S technology, this paper conducts geographical positioning and spatial analysis of the Yangtze River cultural landscape in Nanjing, and analyzes the surrounding environment by combining remote sensing images and topographic data. Researches show that: The Yangtze River cultural landscape in Nanjing includes 74 landscape points of seven landscape types, and the distribution of landscape points shows a clustering trend, but the spatial distribution and aggregation degree of different types of cultural landscapes are different. Due to the influence of climate environment and water resources distribution, the surrounding cultural landscape has a low altitude and high vegetation coverage, showing obvious distribution characteristics along the river. This study is helpful to improve the coordination degree between cultural landscape protection and planning management, and can provide useful reference for the protection of cultural landscape of the Yangtze River.

As the most important city in China, Beijing has experienced an economic soar, large-scale population growth and eco-environment changes in recent 20 years.Evaluating climate and human-induced vegetation changes could reveal the relationship of vegetation-climate-human activities and provide important insights for the coordination of economic growth and environmental protection. Based on a long-term MODIS vegetation index dataset, meteorological data (temperature, precipitation) and impervious surface data, the Theil-Sen regression and the Mann-Kendall method are used to estimate vegetation change trend in this study and the residual analysis is utilized to distinguish the impacts of climate factors and human activities on vegetation restoration and degradation from 2000 to 2019 in Beijing. Our results show that the increasing vegetation areas account for 80.2\% of Beijing. The restoration of vegetation is concentrated in the urban core area and mountainous area, while the degradation of vegetation is mainly concentrated in the suburbs. In recent years, the vegetation in most mountainous areas has changed from restoration to significant restoration, indicating that the growth of mountain vegetation has continued to restore. We also found that in the process of urban expansion, vegetation browning occurred in 53.1\% of the urban built-up area, while vegetation greening occurred in the rest part. We concluded that precipitation is the main climatic factor affecting the growth of vegetation in Beijing mountain areas through correlation analysis. Human activities have significantly promoted the vegetation growing in the northern mountainous area thanks to the establishment of environmental protection areas. The negative correlation between vegetation and the impervious surface tends to gradually expand outwards, which is consistent with the trend of urban expansion. The positive correlation region remains stable, but the positive correlation is gradually enhanced. The response of vegetation to urbanization demonstrated a high degree of spatial heterogeneity. These findings indicated that human activities played an increasingly important role in influencing vegetation changes in Beijing.

One-step oxidative esterification of 2, 5-furandiformaldehyde (DFF) derived from biomass to prepare Dimethyl Furan-2, 5-dicarboxylate (FDMC) not only simplifies the catalytic process and increases purity of product, but also avoids the polymerization of 5-hydroxymethylfurfural (HMF) at high temperature condition. Gold supported on a series of acidic oxide, alkaline oxide and hydrotalcite were prepared by colloidal deposition to explore the effect of support on the catalytic activities. The Au/Mg3Al-HT catalyst exhibited the best catalytic activity in all catalysts, 97.8% selectivity of FDMC at 99.9% conversion of DFF. And this catalyst is suitable to oxidative esterification of benzaldehyde and furfural as well. X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and CO2 temperature programmed desorption (CO2-TPD) were performed to characterize the catalysts. The results indicated that the medium and strong basic sites in the heterogeneous catalysts benefited for the absorption of intermediate spices, further facilitated the oxidative esterification of aldehyde groups. While, neutral or acidic support tended to aldol condensation reaction. It was worth noting that basicity on the support surface reduce electronic state of the Au nanoparticle (Auδ-), thus enhance the catalytic selectivity of oxidative esterification. This finding demonstrated the support plays crucial role in oxidative esterification.

In this paper, applicability of the modified water-based yttria stabilized zirconia (YSZ) suspensions, including different dispersants, to produce plasma sprayed coatings with “columnar microstructure” is investigated. The effects of processing parameters like suspension liquid phase ethanol content, change of substrate roughness, and spray distance on the resulting coatings morphologies are investigated. The results showed that increasing ethanol concentration, substrate surface roughness, and the spray distance promote formation of coatings with columnar structure. Moreover, application of modified water-based suspensions including α-Terpineol dispersant has led to deposition of columnar coatings with comparable morphologies of coatings deposited from ethanol-based suspensions.

Microscale surgery on single cells and small organisms have enabled major advances in fundamental biology and in engineering biological systems. Examples of applications range from wound healing and regeneration studies to the generation of hybridoma to produce monoclonal antibodies. Even today, these surgical operations are often performed manually, but they are labor-intensive and lack reproducibility. Microfluidics has emerged as a powerful technology to control and manipulate cells and multicellular systems at the micro- and nanoscale with high precision. Here, we review the physical and chemical mechanisms of microscale surgery, and the corresponding design principles, applications, and implementations in microfluidic systems. We consider four types of surgical operations: 1) Sectioning, which splits a biological entity into multiple parts, 2) ablation, which destroys part of an entity, 3) biopsy, which extracts materials from within a living cell, and 4) fusion, which joins multiple entities into one. For each type of surgery, we summarize the motivating applications and the microfluidic devices developed. Throughout the review, we highlight existing challenges and opportunities. We hope that this review will inspire scientists and engineers to continue to explore and improve microfluidic surgical methods.

As a result of the influence of geographical environment and historical heritage, food preference has significant regional differentiation characteristics. However, the spatial structure of food culture represented by the cuisine culture at the regional level has not yet been explored from the perspective of geography. This study aims to explore such patterns by focusing on the restaurants of the eight most famous cuisines in Mainland China. Initially, the density based geospatial hotspot detector method is proposed to analyze and mapping the spatial quantitative characteristics of the eight major cuisines. A heuristic method for geographical regionalization based on machine learning was used to analyze spatial distribution patterns in accordance with the proportion of these cuisines in each prefecture-level city. Results show that some types of single-category cuisines have a stronger spatial concentration effect in the present, whereas others have a strong diffusion trend. In the comprehensive analysis of multicategory cuisines, the eight major cuisines formed a new structure of geographical regionalization of Chinese cuisine culture. This study is helpful to understand regional structure characteristics of food preference, and the density based hotspot detector proposed in this paper can also be used in the analysis of other type of POI data.

Many classic visual monocular SLAM systems have been developed over the past decades, however, most of them will fail when dynamic scenarios dominate. DM-SLAM is proposed for handling dynamic objects in environments based on ORB-SLAM. The article mainly concentrates on two aspects. Firstly, DLRSAC is proposed to extract static features from the dynamic scene based on awareness of nature difference between motion and static, which is integrated into initialization of DM-SLAM. Secondly, we design candidate map points selection mechanism based on neighborhood mutual exclusion to balance the accuracy of tracking camera pose and system robustness in motion scenes. Finally, we conduct experiments in the public dataset and compare DM-SLAM with ORB-SLAM. The experiments verify the superiority of the DM-SLAM.

Detection of early stage corrosion on slender steel members is crucial for preventing buckling failures of steel structures. An active photoacoustic fiber optic sensors (FOS) system has been reported for early stage steel corrosion detection of steel plates and rebars using surface ultrasonic waves. The objective of this paper is to investigate the surface corrosion/rust detection problem on steel rods using numerically simulated surface ultrasonic waves. The finite element method (FEM) is applied in simulating the propagation of ultrasonic waves on steel rod models. Transmission mode of damage detection is adopted, in which one source (transmitter) and one sensor (receiver) are considered. In this research, radial displacements at the receiver were simulated and analyzed by short-time Fourier transform (STFT) for detecting, locating, and quantifying a surface rust located between the transmitter and the receiver. From our time domain and frequency domain analyses, it is found that the presence, location, and dimensions (length, width, and depth) of surface rust can be estimated by ultrasonic waves propagating through the surface rust.

Poa crymophila is a perennial, cold-tolerant, native grass species, widely distributed to the Qing-hai-Tibet Plateau. However, the molecular mechanism behind the cold stress tolerance and role of key regulatory genes and pathways of P. crymophila are poorly understood yet. Therefore, the present study investigated the physiological and transcriptome responses of P. crymophila’s roots, stems, and leaves under cold stress to explore the molecular mechanism of cold tolerance. Results of the present study suggested that cold stress significantly changed the physiologic characteristics of roots, stems, and leaves of P. crymophila. In addition, the transcriptome results showed that 4434, 8793, and 14942 differentially expressed genes (DEGs) were identified in roots, stems, and leaves, respectively; however, 464 DEGs were commonly identified in these three tissues. The Gene On-tology (GO) results showed that a large numbers of DEGs were significantly enriched in the pho-tosynthetic related categories in leaves. In addition, the “response to stimulus” category was sig-nificantly enriched in roots and stems. The Kyoto Encyclopedia of Genes and Genomes (KEGG) results showed that DEGs involved in “phenylpropanoid biosynthesis” were significantly enriched in roots, “photosynthesis” and “circadian rhythm-plant” pathways significantly enriched in stems and leaves, starch and sucrose metabolism, and galactose metabolism were pathways significantly enriched in three tissues. Weighted gene coexpression network analysis (WGCNA) identified Hub genes involved in P. crymophila cold response. This study provides new insights into the molecular mechanisms underlying the cold tolerance of P. crymophila belowground and aboveground tissues. In addition, specific genes involved in Ca2+ signaling, ROS scavenging system, hormones, circadian clock, photosynthesis, and transcription factors (TFs) were identified in P. crymophila. The identi-fication of key genes may provide valuable resources for further functional genomic and breeding studies.

How people recognize linguistic and emotional prosody in different listening conditions is essential for understanding the complex interplay between social context, cognition, and communication. The perception of both lexical tones and emotional prosody depends on prosodic features including pitch, intensity, duration, and voice quality. However, it is unclear which aspect of prosody is perceptually more salient and resistant to noise. This study aimed to investigate the relative perceptual robustness of emotional prosody and lexical tone recognition in quiet and in the presence of multi-talker babble noise. Forty young adults with normal hearing listened to monosyllables either with or without background babble noise and completed two identification tasks, one for emotion recognition and the other for lexical tone recognition. Compared with emotional prosody, lexical tones were more perceptually salient in multi-talker babble noise. Native Mandarin Chinese participants identified lexical tones more accurately and quickly than vocal emotions at the same signal-to-noise ratio. Lexical tone perception is also more robust against babble speech noise degradation than emotional prosody perception for native Mandarin Chinese listeners. Acoustic and cognitive dissimilarities between linguistic prosody and emotional prosody may have led to the phenomenon, which calls for further explorations into the underlying psychobiological and neurophysiological mechanisms.

This prospective study aimed to evaluate the ability of point-of-care Doppler ultrasound measurements of carotid-corrected flow time and changes in corrected carotid flow time (FTc) induced by volume expansion to predict fluid responsiveness in patients undergoing robot-assisted gynecological surgery in a modified head-down lithotomy position. FTc was measured using Doppler images of the common carotid artery before and after volume expansion. The stroke volume index at each time point was recorded using the MostCare instrument. Fluid responsiveness was defined as a stroke volume index ≥10% increase after volume expansion. Half (50%) of the 52 patients responded positively. The areas under the receiver operating characteristic curves measured to predict fluid responsiveness for the corrected carotid flow time and changes in the FTc were 0.82 [95% confidence interval:0.705–0.937; P < 0.0001] and 0.67 (95% confidence interval:0.520–0.815; P < 0.05), respectively. The optimal cut-off value for the FTc and the change in FTc were 356.5 ms and 19.5 ms, respectively. FTc is a more reliable predictor of fluid responsiveness than FTc induced by volume expansion in gynecological patients undergoing robot-assisted laparoscopic surgery in the modified head-down lithotomy position.

An atomic-scale approach is employed to simulate the formation of precipitates with different lattice misfits in the early stages of aging of supersaturated aluminum alloys. Simulation results reveal that the increase of lattice misfit could significantly promote the nucleation rate of precipitates, which results in a larger number and smaller size of the precipitates. The morphologies of the precipitates also vary with the degree of lattice misfit. Moreover, the higher the lattice misfit, the earlier the nucleation of the second phase occurs, which can substantially inhibit the movement of dislocations. The research on the lattice misfit of precipitation can provide theoretical guidance for the design of high-strength aluminum alloy.

Crop breeding is an important global strategy to meet sustainable food demand. CRISPR-Cas is a most promising gene-editing technology for rapid and precise generation of novel germplasm, and leads to revolutionary crop breeding innovation. In this review, we summarize recent advance of CRISPR/Cas technology in gene function analyses and generation of new germplasms with increased yield, improved product quality, and enhanced resistance to biotic and abiotic stress. We highlight their applications and breakthroughs in agriculture including crop de novo domestication, decoupling the gene pleiotropy tradeoff, crop hybrid seed conventional production, hybrid rice asexual reproduction, and double haploid breeding. Moreover, the challenges and development of CRISPR/Cas technology in crops are also discussed.

For the traditional indoor visible light communication heterogeneous network switching algorithm is only concerned with the research of vertical switching algorithm, however, the research of horizontal vertical collaborative switching combined with multi-carrier modulation technology is seldom mentioned, based on the above, this paper proposes a horizontal-vertical collaborative switching strategy based on the residency time of the communication blind area. Firstly, we use asymmetrically clipped optical OFDM (ACO-OFDM) for signal processing to reduce the bit error rate (BER) of the system by reducing the multipath interference generated during signal transmission.Secondly, the BER is used to determine the communication blind area and to calculate its dwell time. Finally, the threshold time is set as the channel interruption time in the communication blind zone, and the horizontal-vertical cooperative switching is performed by comparing with the dwell time. The simulation results show that the average network throughput is 195.28Mbps, and the average number of handovers is 1.35. Compared with instant vertical handover (IVHO) scheme and dwell vertical handover (DVHO) scheme, the average network throughput is improved by 37.21% and 28.45%, respectively, and the average switching times are reduced by 28.05% and 5.27%, respectively.

The COVID-19 pandemic has profoundly changed the world and human society in various ways. Infection-related figures are key indicators of the direct impact of the pandemic. In addition to the virus, many vulnerable populations (e.g. patients with complex health conditions) have suffered direct or indirect health impacts, such as delayed treatment or diagnosis of cancer and severe mental illness due to stay-at-home restrictions, or financial issues. Studying these kinds of impacts can provide the community with insights and understanding of how the pandemic has affected patients with health conditions and help inform decision-makers design policies that support various vulnerable populations. This paper surveys analytical work conducted on how COVID-19 has impacted patients with complex health conditions. We categorise vulnerable populations into three specific cohorts: patients with chronic disease, patients with cancer and patients with end-of-life care. We then investigate how each of these cohorts has been impacted by the pandemic. We also identify each cohort from an analytics perspective by leveraging various data collection strategies and analytical methods. The goals of this paper are to provide evidence on how to improve the quality of life for vulnerable populations and to inform a national response strategy for future pandemics, which seems inevitable in a highly globalised economy.

Micro-blade design is an important factor in the cutting of single cells and other biological structures. This paper describes the fabrication process of three dimensional (3D) micro-blades for the cutting of single cells in a microfluidic “guillotine” intended for fundamental wound repair and regeneration studies. Our microfluidic guillotine consists of a fixed 3D micro-blade centered in a microchannel to bisect cells flowing through. We show that the Nanoscribe two-photon polymerization direct laser writing system is capable of fabricating complex 3D micro-blade geometries. However, structures made of the Nanoscribe IP-S resin have low adhesion to silicon, and they tend to peel off from the substrate after at most two times of replica molding in poly(dimethylsiloxane) (PDMS). Our work demonstrates that the use of a secondary mold replicates Nanoscribe printed features faithfully for at least 10 iterations. Finally, we show that complex micro-blade features can generate different degrees of cell wounding and cell survival rates compared with simple blades possessing a vertical cutting edge fabricated with conventional 2.5D photolithography. Our work lays the foundation for future applications in single cell analyses, wound repair and regeneration studies, as well as investigations of the physics of cutting and the interaction between the micro-blade and biological structures.

Microglial cells, the resident macrophages of the CNS, exist in a process-bearing, ramified/surveying phenotype under resting conditions. Upon activation by cell damaging factors they get transformed to an amoeboid phenotype releasing various cell products including pro-inflammatory cytokines, chemokines, proteases, reactive oxygen/nitrogen species and the excytotoxic ATP and glutamate. In addition, they engulf pathogenic bacteria or cell debris and phagocytose them. However, already resting/surveying microglia has a number of important physiological functions in the CNS; they e.g. shield small disruptions of the blood-brain barrier by their processes, dynamically interact with synaptic structures and clear surplus synapses during development. In neurodegenerative illnesses they aggravate the original disease by a microglia-based compulsory neuroinflammatory reaction. Therefore, the blockade of this reaction improves the outcome of Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, etc. The function of microglia is regulated by a whole array of purinergic receptors classified as P2Y12, P2Y6, P2Y4, P2X4, P2X7, A2A, A3, and being targets for endogenous ATP, ADP, or adenosine. ATP is sequentially degraded by the ecto-nucleotidases and 5’-nucleotidase enzymes to the weak adenosine agonist inosine as an end-product. The appropriate selective agonists/antagonists for purinergic receptors as well as the respective enzyme inhibitors may profoundly interfere with microglial functions and reconstitute the homeostasis of the CNS disturbed by neuroinflammation.

Chronic heat stress (HS), aggravated by global warming, reduces the production efficiency of the buffalo dairy industry. Here, we conducted a proteomic analysis to investigate the adaptation strategies used by buffalo in response to heat stress. Seventeen differentially abundant proteins with known functions were detected using label-free quantification (LFQ), and five of these differentially expressed proteins were validated with parallel reaction monitoring (PRM). These five proteins were associated with various aspects of heat stress, including decreased heat production, increased blood oxygen delivery, and enhanced natural disease resistance. Lipase (LPL), glutathione peroxidase 3 (GPX3), cathelicidin-2 (CATHL2, LL-37), ceruloplasmin (CP), and hemoglobin subunit alpha 1 (HBA1) were shown to play cooperative roles in the tolerance of chronic HS in dairy buffalo. We found that high levels of HBA1 increased blood oxygen transport capacity. Our results increase our understanding of the adaptation of dairy buffalo to chronic heat stress.

Abstract：The brain often switches freely between focused attention and divergent thinking, and the default mode network (DMN) is activated during brain rest. The discovery, function and characteristics of default mode network all indicate that learning does not stop when the brain "rests". Mental disease can lead to dysfunctional default mode networks, which affects learning adversely. Therefore, default mode network plays an important role in learning. Neural activities such as β-wave rhythm regulation, "subconscious" divergence mode initiation, hippocampal function and neural replay occur during default mode, which also explain that "rest" promotes learning. This paper reveals the role and neural mechanism of default mode network in learning from the neural level, discusses the essential role default mode network plays in learning, and default mode network enables rest to promote learning.

Two-dimensional (2D) materials show advantages as surface enhanced Raman scattering (SERS) substrate over traditional noble metals, such as chemically inert flat surface and uniform SERS signals. However, due to the low density of free electrons of 2D materials, the enhancement mechanism is mainly restricted to chemical enhancement instead of electromagnetic enhancement, resulting a relatively low enhancement factor. In this work, we reported a sensitive 2D SERS substrate based on chemical vapor deposited MoN nanosheets with electromagnetic enhanced mechanism. The maximum enhancement factor can be 3×105 with a limit of detected concentration of 4×10-8 M, which is comparable to those noble metal SERS substrates without hot spots. The MoN nanosheets show strong surface plasmon resonance in the visible range as evidenced by the UV-vis absorption spectroscopy and first principle calculation. The MoN nanosheets SERS substrates exhibit excellent Raman signal uniformity within the nanosheets. Meanwhile, Distinct thickness dependent sensitivity was observed in the MoN nanosheets, higher sensitivity can be achieved by decreasing the thickness of the nanosheets. Furthermore, the MoN nanosheets SERS substrate also show high thermal stability, which can be annealed in air for more than 300℃ and maintain its SERS activity. The high stability of MoN nanosheets allows it to be reused for 20 cycles without obvious signal decay. The high sensitivity, stability, reusability and Raman signal uniformity, makes MoN nanosheets an ideal 2D SERS substrate for practical applications.

Supplemental light is widely applied in greenhouses for promotions of the product and flavors of strawberries in global markets. Present selections of colored lights are, however, quite empirical or qualitative, from the aspect of photometry or colorimetry. The accurate control of chromatic parameters of supplemental light and their chromatic influences on fruit quality has been under-studied. In this study, color parameters including correlated color temperatures (CCTs) and illuminance of supplement lights are precisely controlled using a digitally color-coding method. We systematically investigate the chromatic effect of supplemental light on five parameters of strawberries: plant height, single weight, fruit hardness, soluble solids, and titratable acids. Results show that the supplement light generally lowers down the single weight and the fruit hardness, increases the plant height, the contents of soluble solids and titratable acids. The chromatic dependences for the five parameters are different and might be strengthened, weakened, or shifted by light illuminance. We demonstrate the beneficial roles of supplemental light in accelerating the maturation and enhancing the flavor of strawberries in greenhouses cultivation. The results pro-vide valuable guidance for the effective cultivation of strawberries. Moreover, the controlling method for accurate colors is ready for the implementation of supplemental lights in other fruits or plants.

In order to reduce the risk of grid transient voltage instability brought about by the new energy and system dynamic loads and the increasing proportion of new energy access, this paper proposes a differentiated dynamic reactive power compensation configuration method for suppressing transient voltage dip instability. The method first establishes a dynamic reactive power device configuration index system based on a multi-binary table, and then establishes a differentiated dynamic reactive power compensation device configuration model based on the node instability risk characteristics by combining with the regional division of system transient voltage instability risk. The model takes the investment cost and suppression effect as the optimization objectives, and realizes differentiated dynamic reactive power compensation by flexibly adjusting the optimization weights of economic benefits and reactive power compensation effects in the configuration process; Compared with the traditional compensation methods, differentiated compensation can significantly improve the economic benefits of reactive power compensation configuration. Finally, taking the IEEE39 node test system as an example, the improved TLBO algorithm is used to solve the model, which verifies the accuracy and effectiveness of the method proposed in this paper.

Pandemics caused by viruses have threatened lives of thousands of people. Viral infection is a complex and diverse process, and substantial studies have been complemented in understanding the biochemical and molecular interactions between viruses and hosts. However, the physical microenvironment where infections implement is often less carefully considered, and the role of mechanobiology in viral infection remains elusive. Mechanobiology focuses on sensation, transduction and response to intracellular and extracellular physical factors by tissues, cells and extracellular matrix. The intracellular cytoskeleton and mechanosensors have been proved to be extensively involved in virus life cycle. Furthermore, innovative methods in vivo and in vitro are being utilized to elucidate how extracellular factors including stiffness, forces and topography in regulating viral infection. Our current review covers how physical factors from different sources coordinate virus infection. We further discuss how this knowledge can be harnessed in future research on cross-fields of mechanobiology and virology.

We analyzed the SARS-CoV-2 spike (S) protein amino acid sequence extracted from 11,542 viral genomic sequences submitted to the Global Initiative on Sharing All Influenza Data (GISAID) database through April 27, 2020. Consistent with prior reports, we found a major S protein mutation, D614 to G614, that was represented in 56% of all the analyzed sequences. All other mutations combined were less than 10%. After parsing the data geographically, we found most of the Chinese patient samples showed D614 (97%). By contrast, most patient samples in many European countries showed G614 (51 to 88%). In the United States, the genotypic distribution in California and Washington was similar to Asian countries, while the distribution in other US states was comparable to Europe. We observed a dramatic increase in the frequency of G614 over time in multiple regions, surpassing D614 when both were present, suggesting G614 S protein virus outcompetes D614 S protein virus. To gain insight into the consequences of the D614G mutation, homology modeling using a multi-template threading mechanism with ab initio structural refinement was performed for a region of the S protein (S591 to N710) spanning the D614G mutation and the S1 furin cleavage site. Molecular models of this region containing D614 or G614 revealed a major difference in secondary structure at the furin domain (RRARS, R682 to S686). The D614 model predicted a random coil structure in the furin domain whereas the G614 model predicted an alpha helix. Critical residues in the cleavage domain of G614 model were found to better align with the PDB structure of a furin inhibitor. Thus, homology modeling studies suggest a potential mechanism whereby the D614G mutation may confer a competitive advantage at the furin binding domain that may contribute to the rise of the D614G SARS-CoV-2 mutant.

Alterations in root morphology and physiology are important strategies in plants to adapt to low-phosphorus (P) environments. Maize genotypes differed in nitrogen (N) efficiency may also respond differently to low P stress. This study aimed to investigate the responses of root morphological and physiological traits of these two maize cultivars to P deficit and how these traits were linked with the acquisition of soil P. Two maize cultivars, XY335 (N efficient) and ZD958 (N inefficient), were cultivated for 40 days in a calcareous loamy soil amended with (high P) or without (low P) P. Functional root traits were used to evaluate the morphological and physiological responses to low P supply. Two separate short-term experiments determined the correlation between P uptake rate and P supply intensity (hydroponic) or root hair length under two P treatments (rhizobox). Low P status significantly simulated biomass allocation to roots, specific root length and exudations of carboxylates, while decreased root diameter and rhizosphere pH in both maize cultivars. Two cultivars had different total root length and root surface area under low P stress: increased in ZD958 and decreased in XY335. Both genotypes developed longer root hair under P deficit. ZD958 (greater biomass and shoot P content) has a greater capability at accessing soil P than XY335. Rhizosphere exudation of citric acid was significantly higher in ZD958 than in XY335, while there was not significant genotypic difference in rhizosphere pH and exudation of malic acid and acid phosphatase activity. ZD958 had higher P uptake rate than XY335 when solution P was between 12.5 and 250 µM. This study identified ZD958 as a P-efficient genotype, which better adapted to low P stress by altering root physiological traits (exudation of citric acid and P uptake rate), rather than root morphological traits (total root length, root surface area, root hair length). Our results highlight the importance of analyzing root morphological and physiological traits to enhance our understanding of the physiological mechanisms of P acquisition.

Primary urethral malignancies are rare in clinical practice and elderly female are prone to suffer. The cause of PUC is unknown and may be related to long-term chronic inflammation, infection, sexual intercourse, pregnancy and irritation of the urethra. Incipient symptom of PUC is not distinctness and can easily lead to misdiagnosis. The final diagnosis relied on transvaginal ultrasound-guided mass biopsy and urethral biopsy. Early tumor treatment is mainly based on surgical resection. For advanced tumors, it is recommended to adopt a comprehensive treatment plan combining surgery, radiotherapy and chemotherapy We report this case with the aim of bringing attention to that primary urethral cancer progresses rapidly and has a poor prognosis, it is urged to call for a need of diagnostic and treatment specification.

The interaction between different regions normally is reflected by the form of the stream. For example, the interaction of the flow of people and flow of information between different regions can reflect the structure of cities’ network, and also can reflect how the cities function and connect to each other. Since big data has become increasingly popular, it is much easier to acquire flow data for various types of individuals. Currently, it is a hot research topic to apply the regional interaction model, which is based on the summary level of individual flow data mining. So far, previous research on spatial interaction methods focused on point-to-point and area-to-area interaction patterns. However, there are a few scholars who study the hotspot interaction pattern between two regional groups with some predefined neighborhood relationship by starting with two regions. In this paper, a method for identifying a similar hotspot interaction pattern between two regional groups has been proposed, and the Geo-Information-Tupu methods are applied to visualize the interaction patterns. For an example of an empirical analysis, we discuss China’s air traffic flow data, so this method can be used to find and analyze any hotspot interaction patterns between regional groups with adjoining relationships across China. Our research results indicate that this method is efficient in identifying hotspot interaction flow patterns between regional groups. Moreover, it can be applied to any analysis of flow space that is used to excavate regional group hotspot interaction patterns.

The COVID-19 pandemic has led to a significant and enduring influence on global health, in-cluding maternal and fetal well-being. Evidence suggests that placental dysfunction is a potential consequence of SARS-CoV-2 infection during pregnancy, which may result in adverse outcomes such as preeclampsia and preterm birth. However, the molecular mechanisms underlying this association remain unclear, and it is uncertain whether a mature placenta can protect the fetus from SARS-CoV-2 infection. To address the above hiatus, we conducted a transcriptome-based study of the placenta in both maternal and fetal compartments. We collected placental samples from 16 women, immediately after term delivery, of which seven had confirmed SARS-CoV-2 in-fection by PCR before parturition. Notably, we did not detect any viral load in either the maternal or fetal compartments of the placenta, regardless of symptomatic status. We extracted total RNA from placental tissues, separately from maternal and fetal compartments, constructed cDNA li-braries, and sequenced them to assess mRNA and small RNA expression. Our analysis revealed 727 differentially expressed genes (DEG) in the maternal placental tissue, with 608 upregulated and 109 downregulated in SARS-CoV-2-positive women compared with healthy, negative wom-en. In contrast, the fetal compartment did not exhibit any significant changes in gene expression with SARS-CoV-2 infection. Specifically, we observed significant downregulation of seven genes belonging to the pregnancy-specific glycoprotein (PSG), related to the immunoglobulin superfam-ily in the maternal compartment with active SARS-CoV-2 infection (fold change range from -13.70 to -5.28, FDR ≤ 0.05). Additionally, comparing symptomatic women with healthy, we identified 14,223 DEGs, with high expression of the inflammatory cytokine IL6 in the maternal placenta of the symptomatic women. Furthermore, KEGG analysis revealed that pathways relat-ed to viral infection, vascular smooth muscle contraction, and oxytocin signaling were altered significantly in symptomatic women. Overall, our study sheds light on the molecular mechanisms underlying the reported clinical risk of preeclampsia and preterm delivery in women with SARS-CoV-2 infection. Nonetheless, studies with larger sample sizes are warranted to further deepen our understanding of the molecular mechanisms of the placenta’s anti-viral effects in ma-ternal SARS-CoV-2 infection.

Background: Esophageal cancer was hardly diagnosed in early stage, and more potential biomarkers should be found. Methods: 252 patients and normal controls which recruited in Renmin Hospital of Wuhan University, were divided into esophageal carcinoma group (105 cases), disease control group (75 cases) and the control group of healthy people (72 cases). Moreover, TISIDB and GEPIA databases were used to investigate the different expression of EC and normal tissues, and explore the roles of C1q in tumor-immune system interactions in EC. Results: The concentration of serum C1q in EC group is 196.8(180~219.4) mg/L, which is higher than the level of DC [178.10(153.70~200.85) mg/L]and HC [183.00(167.75~201.00) mg/L] (P<0.05). A higher expression level of C1q was observed in Ⅲ and Ⅳ grades [214(192~237.3) mg/ml] than grades Ⅰ and Ⅱ [180.95(172.03~193.85) mg/L] (P<0.05). C1q was positively correlated with eosinophils, active CD8 T cells, myeloid derived suppressor cells, natural killer cells, monocytes and macrophages (r = 0.373; r = 0.659; r = 0.846; r = 0.760; r = 0.499; r = 0.757; P<0.05). Conclusion: The concentrations of C1q increased in EC and related to the severity of EC, which had potential value of diagnosis of EC. There were correlations in C1q and tumor-infiltrating lymphocytes.

Worldwide initiatives currently aim to reduce free sugar intakes, but success will depend on consumer attitudes towards sugar and the alternatives. This work aimed to explore attitudes towards sugar, sweeteners and sweet-tasting foods in the general public of the UK, including attitudes towards personal consumption and related policies. Focus groups and interviews were conducted with 34 adults (7 males, ages: 18-65 years). Thematic analysis identified six themes: ‘Value’ (e.g. pleasure, emotions), ‘Angle’ (e.g. disinterest), ‘Personal Relevance’ (to be concerned and/or change one’s own behaviour), ‘Personal Responsibility’ (one has an active relationship with these food items), ‘Understanding’ (the acquisition, comprehension and application of information surrounding these food items) and ‘It’s Not Up to Me’ (a passive approach towards these food items, because intake is subjected to other factors). Both positive and negative attitudes towards sugar, sweeteners and sweet-tasting foods were expressed in all themes, largely dependent on the individual. Potential strategies for reducing free sugar intakes were also reported, but differences in likely value were suggested by different individuals. Future work should assess associations between attitudes and intakes. For greatest population benefit, evidence of the dominant attitudes in those in greatest need of reduced free sugar intakes would be of value.

In order to obtain higher thermoelectric and mechanical properties in non-polar thermoplastic vulcanizates (TPVs), the Butyl rubber/Polypropylene (TPVs)/hydroxylated graphene (HGE) composites with nanosheet network were prepared through masterbatch technique and based on thermodynamic calculations, using polypropylene-graft-maleic anhydride (PP-MA) as a compatibilizer. The FTIR and Raman spectra revealed the introduced maleic anhydride group on PP-MA can form strong interfacial interaction with hydroxyl-containing functional groups on HGE. Morphology study indicated the rubber particles in the composites occupied the most volume of the PP phase, as expected to hinder the aggregation of HGE and form the effective nanosheet network. The nanosheet network can be combined with the IIR cross-linked particles during the dynamic vulcanization process to improve the interface bonding between PP and IIR, thus increasing the tensile strength of TPVs. When the content of HGE reached the percolation threshold (2 wt.%), the nanosheet network of HGE was formed, and the AC conductivity, dielectric permittivity and thermal conductivity increased sharply. The prepared TPVs/HGE nanocomposites have significantly improved in mechanical properties, thermal properties and dielectric properties, which provides a guarantee for their potential application as multifunctional TPVs polymers.

The acoustic emission (AE) technique was applied to monitor the pitting corrosion of carbon steel in NaHCO₃ + NaCl solutions. The open circuit potential (OCP) measurement and the corrosion morphology in-situ capturing using optical microscope were conducted during AE monitoring. The corrosion micromorphology was characterized with scanning electron microscope (SEM). The propagation behavior and AE features of natural pitting on carbon steel were investigated. After the performing of signal processing including pre-treatment, shape preserving interpolation and denoising for raw AE waveforms, three types of AE signals can be classified in the correlation diagrams of new waveform parameters. Finally, a 2D pattern recognition method was established to calculate the similarity of different continuous AE graphics, which is quite effective to distinguish the localized corrosion from uniform corrosion.

The associations between dietary sodium intake (DSI), dietary potassium intake (DPI), and kidney stone disease (KSD) are not clear. We examined The National Health and Nutrition Examination Survey 2011-2018 to determine the independent associations between daily DSI, DPI, DSI/DPI and prevalent KSD. 19,405 participants were included for analysis, of which 1,895 had KSD. Higher DSI was not associated with increased odds of KSD in regression analysis when DSI was modeled as a continuous variable (OR=0.99, 95% CI: 0.99-1.00, p=0.2), or when comparing highest quartile of DSI to lowest quartile (OR=0.84, 95% CI: 0.68–1.04, p=0.1). Unlike DSI, higher DPI associated strongly with reduced odds of KSD in regression analysis when DPI was modeled as a continuous variable (OR=0.99, 95% CI: 0.99-0.99, p=0.02), or when comparing highest quartile of DPI to lowest quartile (OR =0.75, 95% CI: 0.60–0.94, p=0.01). Lastly, higher DSI/DPI also associated strongly with increased odds of KSD in regression analysis when DSI/DPI was modeled as a continuous variable (OR=1.1, 95% CI: 1.01-1.20, p=0.03), or when comparing highest quartile of DPI to lowest quartile (OR =1.30, 95% CI: 1.10–1.70, p=0.008). All the observed relationships were independent of total calorie intake. In conclusion, both lower DPI and higher DSI/DPI associate with an increased risk of KSD. Future prospective studies are needed to clarify these causal relationships.

This study presents a research plan that utilizes data obtained from wearable devices to identify human activities and gain insights into human behavior. We developed a model capable of classifying activities similar to human behavior and evaluated the effectiveness and generalization capabilities of this model. The data underwent initial preprocessing, including standardization and normalization. Additionally, recognizing the inherent similarities between human activity behaviors, we introduced a multi-layer classifier model. The first layer is a random forest model based on stepwise regression, which may encounter reduced accuracy for similar activities. The second layer employs a Support Vector Machine (SVM) model based on Kernel Fisher Discriminant Analysis (KFDA). KFDA is used to reduce the dimensionality of data points with potential confusion, followed by SVM for classification. The model was experimentally evaluated and applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that our approach achieved recognition accuracies of 99.71%, 98.71%, 99.12%, and 97.6% on these datasets, indicating excellent recognition performance. Furthermore, to assess the model's generalization ability, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.

Feruloylacetone (FER) is a natural degradant of curcumin after heating, which structurally reserves some functional groups of curcumin. It is not as widely discussed as its original counterpart in previous studies and, therefore, its anti-cancer efficacy is investigated herein. This study focuses on the suppressive effect of FER on colon cancer, as the efficacious effect of curcumin on this typical cancer type has been well evidenced. In addition, demethoxy-feruloylacetone (DFER) was applied to compare the effect that might be brought on by the structural differences of the methoxy group. It was revealed that both FER and DFER inhibited the proliferation of HCT116 cells, possibly via suppression of the phosphorylated mTOR/STAT3 pathway. Notably, FER could significantly repress both the STAT3 phosphorylation and protein levels. Furthermore, both samples showed the capability of arresting HCT116 cells at the G2/M phase via the activation of p53/p21 and upregulation of cyclin-B. In addition, ROS elevation and changes in mitochondrial membrane potential were revealed, as indicated by p-atm elevation. The apoptotic rate rised to 36.9% and 32.2% after being treated by FER and DFER, respectively. In summary, both compounds exhibited an anti-cancer effect, and FER showed a greater pro-apoptotic effect, possibly due to the presence of the methoxy group on the aromatic ring.

Cloud robot is becoming popular and security of cloud robot is important. However, the researches of cloud robot safety are a few. This work develops a security policy to defense DDoS attack of cloud robot. In this policy, complex, but accurate calculation models are deployed on the cloud, simple but efficient calculation models are deployed on the robot. In the cloud, there are master server and standby server. The master server transfers parameters of complex but accurate models to the standby server periodically and the master executes the start-stop backup policy. Specifically, this work proposes and proves an algorithm to dynamically adjust the interval of parameter transfer. According to a PDRA feature of Netflow, when DDoS attacks, the master server sends warning signals to the robot and standby server. The robot runs local models to avoid stopping work until it is connected to the standby server. Then, standby server provides service to the robot until the master server recover. Finally, this work implemented a gesture recognition cloud robot based on convolutional neural network, hidden Markov model and PDRA feature of Netflow to verify the policy. Experiment shows that the security policy to defense DDoS attack for cloud robot is effective.

With the rapid development of MEMS (Micro-electro-mechanical Systems) fabrication technologies, manifolds microelectrodes with various structures and functions have been designed and fabricated for applications in biomedical research, diagnosis and treatment through electrical stimulation and electrophysiological signal recording. The flexible MEMS microelectrodes exhibit multi-aspect excellent characteristics beyond stiff microelectrodes based on silicon or SU-8, which comprising: lighter weight, smaller volume, better conforming to neural tissue and lower fabrication cost. In this paper, we mainly reviewed key technologies on flexible MEMS microelectrodes for neural interface in recent years, including: design and fabrication technology, flexible MEMS microelectrodes with fluidic channels and electrode-tissue interface modification technology for performance improvement. Furthermore, the future directions of flexible MEMS microelectrodes for neural interface were described including transparent and stretchable microelectrodes integrated with multi-aspect functions and next-generation electrode-tissue interface modifications facilitated electrode efficacy and safety during implantation. Finally, the combinations among micro fabrication techniques with biomedical engineering and nanotechnology represented by flexible MEMS microelectrodes for neural interface will open a new gate to human lives and understanding of the world.

This paper proposes a novel symmetrical active-switched coupled-inductor high-step-up DC/DC converter. A new active-switched coupled inductor is used to allow much higher voltage gain of the proposed converter. The symmetrical circuit structure of the proposed converter evenly divides large input currents and reduce input current ripple by operating in interleaved mode so power density is increased. The switches also have low voltage stress. The operating principle and design considerations for the converter topology are described. The proposed converter with an input voltage of 40 V, an output voltage of 400 V and an output of 1000 W at a switching frequency of 50 kHz is implemented. The simulations and experimental results are thus depicted to verify the theoretical analysis. The highest efficiency is 95.5% at 200 W measured by using a HIOKI 3390. Consequently, the proposed converter is suitable for renewable energy generation systems that require high power, high voltage gain and high efficiency.

To further explore the bearing capacity and sustainable construction of vacuum drainage pipe (VDP) piles, field tests were carried out to compare and analyze the ultimate single pile bearing capacity, pile side friction and pile end resistance of ordinary piles and vacuum drainage pipe piles. The effect of the VDP pile on energy conservation and emission reduction in practical projects is discussed. The results shows that the VDP pile basically eliminated the soil squeeze and did not cause excessive relative displacement of pile and soil after vacuum consolidation. The VDP pile enhances pile side friction resistance, resulting in a 17.6% increase in ultimate bearing capacity compared to traditional piles. The VDP pile method can reduce carbon emissions by 31.4% compared to traditional methods. This study provides guidance for the production and design of VDP piles in the future and demonstrates their potential for energy conservation and emission reduction benefits compared to traditional methods.

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman Filter to enhance the perception performance. In addition, a back-end UAV path planning for surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.

Plants produce above- and below-ground biomass. However, our understanding of both production and decomposition of below-ground biomass is poor, largely because of the difficulties of accessing study materials. Below-ground organic matter decomposition studies are scanty and especially rare in the tropics. Here, we used a litter bag experiment to quantify the mass loss and nutrients dynamics of decomposing twigs and fine roots from an arbuscular mycorrhizal fungal associated tree, Parashorea chinensis, in a tropical rain forest in Southwest China. Overall, twig litter decomposed 1.9 times faster than fine roots (decay rate (k) twig=0.255, root=0.134). The difference in decomposition rates can be explained by a difference in phosphorus (P) concentration, availability and use by decomposers or C quality. Both materials showed an increase in N concentration, with final measurements still higher than initial levels. This suggests N may not be available due to microbial immobilization. Both carbon and nitrogen dynamics were significantly predicted by mass loss and showed a negative and positive relationship, respectively. Our study results imply that fine roots carbon and nitrogen contribute more to soils organic matter and enlarge the resident time. Therefore, better understanding of carbon cycle requires better understanding of mechanisms governing below ground biomass decomposition.

Purpose: Understanding how older adults perceive and interpret emotional cues in speech prosody contributes to our knowledge of cognitive aging. This study provides a systematic review with meta-analysis to investigate the extent of the decline in affective prosody recognition (APR) among older adults in terms of overall and emotion-specific performance and explore potential moderators that may cause between-study heterogeneity. Method: Incorporated into the literature search were six electronic databases, with a specific emphasis on studies comparing the APR performance of older adults with that of younger adults. This comparison was focused on basic emotions. Meta-regression analyses were executed to pinpoint potential moderators related to demographic and methodological characteristics. Results: A total of 19 studies were included in the meta-analysis, involving 560 older adults with a mean age of 69.15 years. The findings indicated a substantial negative effect size (g = －1.21). Furthermore, the magnitude of aggregated effect sizes showed a distinct valence-related recognition pattern with positive prosody exhibiting smaller effect sizes. Years of education were found to moderate the overall performance effect estimate while age and gender significantly influenced the effect estimate of happiness. Conclusions: The results confirmed a significant decline in APR ability among older adults, but this decline was unbalanced across basic emotions. Educational level emerged as a significant factor influencing APR ability. Moreover, participants with a higher mean age exhibited notably poorer performance in recognizing happy prosody. These findings underscore the need to further investigate the neurobiological mechanisms for APR decline associated with aging.

The treatment, disposal, and resource utilization of waste mud are challenges for engineering construction. This study investigates the road performance of waste mud–solidified soil and reveals the mechanism of how solidifying materials influence the strength and deformation characteristics of waste mud. Waste mud, collected from a highway reconstruction project, was mixed with ordinary Portland cement as a solidifying material. Unconfined compressive strength tests, consolidated undrained triaxial shear tests, resonant column tests, and consolidation compression tests were conducted to evaluate the solidification effect. The test results show that with an increase in cement content from 5% to 9%, the unconfined compressive strength of the waste mud–solidified soil increased by over 100%, the curing time was extended from 3 days to 28 days, and the unconfined compressive strength increased by approximately 70%. However, an increase in initial water content from 40% to 60% reduced the unconfined compressive strength by 50%. With the increase of cement content from 5% to 9%, the cohesion and friction angles increased by approximately 78% and 24%, respectively. The initial shear modulus under dynamic shear increased by approximately 38% and the shear strain corresponding to a damping ratio decay to 70% of the initial shear modulus decreased by nearly 11%. The compression coefficient decreased by approximately 55%. Scanning electron microscopy and X-ray diffraction tests showed that a higher cement content led to the formation of more hydration reaction products, especially an increase in the content of AlPO4. The bonding material between soil particles significantly increased, exerting a bonding and filling effect on soil particles and firmly adhering them together. Consequently, the strength of the waste mud–solidified soil increased significantly while its compressibility decreased.

Microbial-mediated biogeochemical cycling of iron is widespread in anaerobic environments such as sediments and deep Earth aquifers, and is one of the major pathways for metabolising iron in nature. Organic matter containing quinone groups can act as electron shuttles to transfer electrons between microorganisms and Fe(III) oxides, facilitating the biotransformation of iron; this process also influences the transport transformation of pollutants. In this paper, the reduction of Fe(III) oxides by aniline in the heterogeneous iron-reducing bacterium Shewanella oneidensis MR-1 mediated by the electron shuttle AQS was investigated. The results showed that aniline significantly promoted the reduction of Fe(III) by MR-1 in the AQS-mediated reaction, and the best Fe(III) reduction was achieved in the reaction group of "GT+MR-1+0.5mM AQS+3μM aniline", in which the Fe(II) production was significantly higher than that in the reaction groups of only microorganisms and no aniline reaction. The amount of Fe(II) produced was significantly higher than that of the microorganism-only and aniline-free reaction groups, and the addition of aniline also significantly increased the consumption of sodium lactate by MR-1 in the reaction system. The solid mineral Fe(II)-O content increased to 41.32% after the reaction. However, the structure of needle ferrite is relatively stable, and the surface morphology did not change significantly by SEM, and the generation of other secondary minerals was not observed in the XRD results. The results of this experimental study can help to understand the biogeochemical cycle of iron in multicomponent reactions and are of great significance for environmental management.

Water electrolysis is a compelling technology for the production of environmentally-friendly hydrogen, minimizing carbon emissions. The electrolysis process of water heavily relies on the effective and steady oxygen evolution reaction (OER) taking place at the anode. Herein, we in-troduce a highly promising catalyst for OER called CoSe2@NiFeOOH arrays, which are supported on a nickel foam. This catalyst, referred to as CoSe2@NiFeOOH/NF, is fabricated through a two-step process involving the selenidation of a Co-based porous metal organic framework and subsequent electrochemical deposition on a nickel foam. The CoSe2@NiFeOOH/NF catalyst demonstrates outstanding activity for the OER in an alkaline electrolyte. It exhibits a low over-potential (η) of 254 mV at 100 mA cm−2, a small Tafel slope of 73 mV dec−1, and excellent high stability. The high performance of CoSe2@NiFeOOH/NF can be attributed to the combination of the high conductivity of the inner layer and the synergistic effect between CoSe2 and NiFeOOH. This study offers an effective method for the fabrication of highly efficient catalysts for the OER.

Ultrasonic-guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects experienced in metallic pipelines. Signal processing of the guided waves is often challenged due to the complexity of operational conditions and environments in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for signal process and data classification of complex systems, thus great potential for damage detection of critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model is utilized for decoding ultrasonic guided waves for damage detection of metallic pipelines, and twenty-nine features are extracted as input to classify different types of defects from metallic pipes. The prediction capacity of the CNN-LSTM model is assessed by comparing it to the CNN and LSTM. The results demonstrate that the CNN-LSTM hybrid model exhibits much higher accuracy, with 94.8%, as compared to those of CNN and LSTM. Interestingly, the results also reveal that predetermined features, including time-, frequency-, and time-frequency domains, could significantly improve the robustness of the deep learning approaches, even though the deep learning approaches are often believed they include automated feature extraction, without the hand-crafted steps as the shallow learning do. Furthermore, the CNN-LSTM model displays higher performance when the noise level is relatively low (e.g., SNR=9 or higher), as compared to the other two models, but its prediction drops gradually with the increase of the noise.

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robot, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon or heuristic search algorithm based CCPP methods, when coping with the field with irregular boundaries, obstacles and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, which introduces the idea of chromosome pairs and multi-points mutation to improve its global optimization ability in complex environments, reduce the repeated work areas and the number of turns and U-turns, and thereby improve the operation efficiency. The simulation and experimental results on simple regular fields showed that the proposed improved genetic algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, although the proposed CCPP method increased the number of turns by 13.76%, the area of repeated operation and the number of U-turns were decreased by 38.54% and 35.00% respectively, and saves the battery voltage by 7.82% on average compared to the boustrophedon-based CCPP method. This proved that the proposed CCPP method has strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.

Accurate measurement of low-level thoron gas and high-accuracy calibration of thoron measurement devices are essential for assessing and preventing thoron radiological risks. This study aimed to develop a thoron gas measurement technique using an airflow-through scintillation cell for both low-level measurement and high-accuracy calibration. To achieve this, a compartment model was developed to estimate the influence of progeny deposition and accumulation on the wall of the scintillation cell to prevent overestimation of thoron. A self-developed scintillation cell was utilized to implement and validate this technique. The lower detection limit and measurement uncertainty were then evaluated to assess the feasibility of the technique for low-level measurement and high-accuracy calibration. The results showed that the compartment model effectively addressed the influence of the progeny deposition. The measurement technique achieved a lower detection limit below 100 Bq m-3 even with the coexistence of 100 Bq m-3 of radon and attained a measurement uncertainty (k = 2) below 10% when the concentration of thoron exceeded 1,000 Bq m-3. In summary, this study developed a reliable and practical thoron gas measurement technique using an airflow-through scintillation cell with consideration for progeny deposition, and is expected to contribute to the assessment and prevention of thoron radiological risk.

Although several protocols for genetic transformation of citrus have been published, it is highly desirable to further improve its efficiency. Here we report treatments of Agrobacterium cells and citrus explants prior to and during co-cultivation process to enhance transformation efficiency using a commercially used rootstock 'Carrizo' Citrange as a model plant. We pre-cultured Agrobacterium cells in a 1/10 MS, 0.5 g/L 2-(N-morpholino) ethanesulfonic acid (MES) and 100 µM acetosyringone liquid medium for 6 hours at 25 ℃ before used to infect citrus explants. We incubated epicotyl segments in an MS liquid medium containing 13.2 µM 6-BA, 4.5 µM 2,4-D, 0.5 µM NAA for 3 hours at 25℃ prior to Agrobacterium infection. In the co-cultivation medium, we added 30 µM paclobutrazol and 10 µM lipoic acid. Each of these three treatments significantly increased the efficiencies of transformation. When the three treatments were combined, we observed that the transformation efficiency was enhanced from 11.5% to 52.3%. The improvement of genetic transformation efficiency mediated by these three simple treatments may facilitate more efficient applications of transgenic and gene editing technologies for functional characterization of citrus genes and for genetic improvement of cultivated citrus varieties.

In recent years, land supply in Hong Kong has fallen sharply and fails to keep pace with the growth of population and number of households. Land shortage becomes urgent and dire. With the inadequate provision of residential land, housing supply is also adversely affected.Insufficient housing supply leads to surging property and rental price in the private market. According to the Rating and Valuation Department (RVD), the overall private domestic price index peaked in June 2017 at 336.1. Keen housing demand is also observed in the private rental market. RVD statistics showed that the domestic rental index also reached the peak at 183.6 in June 2017 and the vacancy rate of about 3.8% as at the end-2016, which is much lower than the average vacancy rate of 5% from 1996 to 2015 (Development Bureau, 2017).In addition, there is soaring housing demand in public housing. As at the end-December 2021, the average waiting time for general applicants was 5.7 years, which is nearly double of the Housing Authority’s target for providing the first flat offer to general applicants at around three years on average (Housing Authority, 2021). Identifying and securing sufficient land to meet both current and anticipated pressing housing demand becomes a key challenge of the government. In the light of this, the government has established a dedicated task force (Task Force on Land Supply) in September 2017 and appointed 30 professionals from different disciplines, such as planning, engineering, academia and think tank, to take a Marco-review on the land supply sources, look for feasible options as well as consider the pros and cons of various options. 18 land supply options were worked out for the public to make choices in the public engagement exercise. Among the 18 choices, the development area on the Periphery of Country Parks was regarded as one of the land supplies sources. This option does not only include the Hong Kong Housing Society’s studies on developing Two Pilot Areas on the Periphery of Tai Lam and Man On Shan Country Parks, but also include developing more areas on the periphery of Country Parks. The proposed option received overwhelming attention from the public and aroused debates in various perspectives. Its ecological value, recreational value, financial viability and even legitimacy were discussed. However, spatial analysis on the suitability of having residential development in Country Parks has not been conducted. Therefore, in this study, multi criteria GIS analysis is conducted to evaluate the geographic suitability of Hong Kong Country Parks for residential purpose with the consideration of various criteria.

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that causes the new Coronavirus disease (COVID-19). The symptoms range from mild to severe with a higher incidence of severe cases seen in patients with risk factors such as older age and comorbidities. COVID-19 is mainly spread through the inhalation of respiratory droplets from coughing or sneezing or via contact with droplet-contaminated surfaces. Paramedics should be aware that some aerosol generating procedures (AGPs) may put them at a higher risk of contracting the virus via possible airborne transmission. The use of remote triage clinical assessment is likely to increase as a result of the pandemic. There is no curative drug treatment for the virus and some medications may exacerbate its effects or make patients more susceptible to it. Paramedics should accept that feeling stressed by the pandemic is a natural response. Official guidelines and advice are evolving continually as the evidence on SARS-CoV-2 and COVID-19 grows. Paramedics should keep up to date with the latest clinical guidance from their employers.

Chinese investors are easily affected by hot topics on the media and Internet and likely take irrational behavior. They tend to consume rationally and invest emotionally. This research aims to analyze the Baidu index’s effect on share price of solar power industry in China. Three main models are established to identify the correlation between Baidu index and stock price of solar industry. The media index and searching index of Baidu are chosen as an independent variable to measure the influence from media and Internet, and stock price as the dependent variable. To improve and complete the analysis, Hexun index is taken into consideration as mediator variable. This study applied Multiple Linear Regression Model and VAR (Vector auto-regression) Model. The research mainly uses the tools of SPSS, Eviews and Excel to conduct data analysis. The test is based on the theories of Granger Causality, unit root test, F-test, and t-test. The research proves the relationship between the close price of stock and Baidu index, and it also created a model for further forecasting.

Bile acids (BAs) play a crucial role in the human body's defense against infections caused by bacteria, fungi, and viruses. BAs counteract infections not only through interactions with intestinal bacteria exhibiting bile salt hydrolases (BSHs) activity but also directly combat infections. Building upon our research group's previous discoveries highlighting the role of BAs in combating infections, we have initiated an in-depth investigation into the interactions between BAs and microbiota. Leveraging existing literature, we offer a comprehensive analysis of the relationships between BAs and 16 key microbiota. This investigation encompasses bacteria (e.g., Clostridioides difficile, Staphylococcus aureus, Escherichia coli, Enterococcus, Pseudomonas aeruginosa, Mycobacterium tuberculosis, Bacteroides, Clostridium scindens, Streptococcus thermophilus, Saccharomyces boulardii, Clostridium butyricum, Lactic Acid Bacteria), fungi (e.g., Candida albicans), and viruses (e.g., Coronavirus SARS-CoV-2, influenza virus, norovirus). Our research underscores the critical role of the interplay between BAs and intestinal microbiota, including Bacteroides, Clostridium scindens, Streptococcus thermophilus, Saccharomyces boulardii, Clostridium butyricum, and Lactic Acid Bacteria, in maintaining intestinal homeostasis and combating infections. It is imperative to note that Primary bile acid (PBA) and Secondary bile acid (SBA) often exhibit distinct roles in the anti-infection process. In the antimicrobial action of BAs, SBA demonstrate antagonistic properties against a wide range of microbiota, with the exception of Norovirus. Given the intricate interplay between BAs and intestinal microbiota, and their regulatory effects on infections, we assert that BAs hold significant potential as a novel approach for preventing and treating intestinal microbial infections.

Flow experience, characterized by deep immersion and complete engagement in a task, is highly recognized for its positive psychological impacts. However, previous studies have been restricted to use a single type of task, and the exploration on its neural correlates has been limited. This study aimed to explore the neural correlates of flow experience with the employment of multifac-eted flow-induction tasks. Six tasks spanning mindfulness, artistic tasks, free recall, and varying levels of Tetris complexity (easy, flow, and hard conditions) were employed to have a relatively complete coverage of the known flow-induction tasks for a better induction of individualized flow experience. Twenty-eight participants were recruited to perform these six tasks, with a sin-gle-channel prefrontal electroencephalography (EEG) recording. Significant positive correlations were observed between the subjective flow scores of individual’s best-flow-experience task and the EEG activities at delta, gamma, and theta bands, peaking at latencies around 2 minutes after task onset. The outcomes of our multiple regression analysis yield a maximum coefficient of de-termination (R²) of 0.279. Our findings report the EEG correlates of flow experience in naturalistic settings and highlight the potential of portable and unobtrusive EEG technology for an objective measurement of flow experience.

The increasing popularity of unconventional wells has sparked a heightened interest in evaluating and predicting their production performance. With the use of extended reach well structures, these wells are able to generate and access larger reservoir volumes. Hence, it is crucial to understand how the well's lateral trajectory affects its transient production performance. The assessment of lateral trajectory undulating amplitude effect on the flow behavior is performed in the current study based on the experimental results obtained at the University of North Dakota, Undulated Two-Phase (UTP) Flow Loop, injecting air and water mixture through a variable undulating amplitude section followed by a vertical section. The experiments showed that the undulating amplitude increase induced lower translational velocity, frequency, and length, with a consistent slug acceleration through the system profile from the inlet to the vertical section and a decreasing frequency when the slugs travel through the vertical section. Measured data shows that an increase of the horizontal pressure losses and variability is expected with a higher undulation amplitude, translating the fact that larger instabilities are observed for higher amplitudes. The numerical simulation predicted lower translational velocity and frequency, higher slug’s lengths, and similar vertical pressure losses when compared to the experimental results.

Background and Objectives: Acute lung injury (ALI) is an inflammatory response in the lung caused by bacteria, viruses, trauma and other factors that results in lack of alveolar surfactant and subsequently respiratory distress. Endothelial-to-mesenchymal transition (EndoMT) is closely related to ALI-induced pulmonary fibrosis development and progression. This study examined the effect of milk fat globulin factor 8 (MFGE8) knockdown and overexpression on lipopolysaccharide (LPS)-induced EndoMT. Methods: Total MFGE8 activity in patients with acute respiratory distress syndrome (ARDS) and healthy volunteers was assessed using a colorimetric kit. In vitro, cell morphology was observed by microscopy, and invasion and migration were tested by Transwell and scratch assays. Specific siRNAs were transfected into HLMECs to downregulate MFGE8, and CD31 and α-SMA protein expression was detected by fluorescence microscopy and FCM. MFGE8, BMP, Smad 1, Smad4, Smad5, Snail, CD31 and α-SMA protein and gene expression was assessed by western blotting (WB) and qRT‒PCR. In vivo, the changes in cell morphology and alveolar septum in the lung of mice (C57BL/6, aged 7-8 weeks, male) were observed by H&E staining. In addition, ECM deposition in lung tissue was detected by Masson staining. CD31 and α-SMA protein expression in lung tissue was measured by WB. Results: The serum level of MFGE8 was lower in the ARDS group than in the control group. MFGE8 was identified as a protective factor associated with hospital survival. In vitro, the treatment of human lung microvascular endothelial cells (HLMECs) with LPS for 96 h clearly changed the cells from the typical cobblestone shape of ECs to spindle-like fibroblasts. The scratch and Transwell assay results indicated that endothelial cell migration and invasion were enhanced in the LPS group compared with the control group. Fluorescence microscopy, FCM, WB and PCR showed that LPS greatly inhibited CD31 (endothelial marker) expression and increased α-SMA (mesenchymal marker) expression. These data showed that LPS could induce EndoMT in HLMECs. Notably, siRNA-treated HLMECs yielded the same results. The administration of rhMFGE8 to HLMECs in the LPS or LPS+siRNA group ameliorated the changes in cell morphology and decreased cell migration and invasion. rhMFGE8 attenuated the effect of LPS or LPS+siRNA on EndoMT induction by increasing CD31 and decreasing α-SMA protein and gene expression. Moreover, activation of BMP/Smad1/5-Smad4 signalling in response to LPS and Snail (related to EndoMT transcription factors) expression were increased by MFGE8 knockdown but inhibited by rhMFGE8. In vivo, H&E staining revealed a thickened alveolar septum in the LPS group, and the thickness increased over time; in contrast, rhMFGE8 reversed this effect. ECM deposition occurred early in ALI induced by LPS and increased over time, and the administration of rhMFGE8 reversed this effect. WB showed that LPS inhibited CD31 protein expression and increased α-SMA protein expression in lung tissue. Conclusions: rhMFGE8 exerts a protective effect early in LPS-induced EndoMT through BMP/Smad1/5/Smad4 signalling and could be a therapeutic target in ALI.

The fall armyworm (FAW), Spodoptera frugiperda, has become one of the most important pests on corn in China since it invaded in 2019. Although FAW has not been reported to cause widespread damage to rice plants in China, it has been sporadically found feeding in the field. If FAW infests rice in China, the fitness of other insect pests on rice may be influenced. However, how FAW and other insect pests on rice interact remains unknown. In this study, we found that the infestation of FAW larvae on rice plants prolonged the developmental duration of the brown planthopper (BPH, Nilaparvata lugens (Stål)) eggs, and plants damaged by gravid BPH females did not induce defenses that influenced the growth of FAW larvae. Moreover, co-infestation by FAW larvae on rice plants did not influence the attractiveness of volatiles emitted from BPH-infested plants to Anagrus nilaparvatae, an egg parasitoid of rice planthoppers. FAW larvae were able to prey on BPH eggs laid on rice plants and grew faster compared to those larvae that lacked available eggs. Studies revealed that the delay in development of BPH eggs on FAW-infested plants was probably related to the increase in levels of jasmonoyl-isoleucine, abscisic acid and the defensive compounds in the rice leaf sheaths on which BPH eggs were laid. These findings indicate that if FAW invades rice plants in China, the population density of BPH may be decreased by intraguild predation and induced plant defenses, whereas the population density of FAW may be increased.

Airborne virus, such as COVID-19, caused pandemics all over the world. Virus-containing particles produced by infected individuals are suspended in the air for extended periods of time, actually results in viral aerosols and the spread of infectious diseases. Aerosol collection and detection devices are essential for limiting the spread of airborne virus diseases. This review provides an overview of the primary mechanisms and enhancement techniques for collecting and detecting airborne viruses. Indoor virus detection strategies for scenarios with varying ventilations are also summarized based on the excellent performance of existing advanced comprehensive devices. This review provides guidance for the development of future aerosol detection devices and aids in the control of airborne transmission diseases, such as COVID-19, monkeypox, and other airborne transmission viruses.

In this paper, we propose an unsupervised blind restoration model for images in hybrid degradation scenes. The proposed model encodes the content information and degradation information of images and then uses the attention module to disentangle the two kinds of information. It can improve the ability of disentangled presentation learning for a generative adversarial network (GAN) to restore the images in hybrid degradation scenes, enhance the detailed features of restored image and remove the artifact combining the adversarial loss, cycle-consistency loss, and perception loss. The experimental results on the DIV2K dataset and medical images show that the proposed method outperforms existing unsupervised image restoration algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual evaluation.

With the continuous development of urbanization and industrialization in the world, concrete is widely used in various engineering constructions as an engineering material. However, the consequent problem of durability of concrete structures is also becoming increasingly prominent. As an important additional measures, protective coating can effectively improve the durability of concrete performance. Moreover, the uniformity of the concrete surface coating will directly affect its protective effect. Therefore, we propose a nondestructive inspection and evaluation method of coating uniformity based on infrared imaging and cluster analysis for concrete surface coating uniformity detection and evaluation. Based on the obtained infrared images, a series of processing and analysis of the images were carried out using MATLAB software to obtain the characteristics of the infrared images of concrete surface. Finally, by extracting the temperature distribution data of the pixel points on the concrete surface, an evaluation method of concrete surface coating uniformity based on a combination of cluster analysis and hierarchical analysis was established. The evaluation results show that the determination results obtained by this method are consistent with the actual situation. This study has a positive contribution to the testing of concrete surface coating uniformity and its evaluation.

In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN’s use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyper-parameters—learning rate and batch size of the online learning phase—highlights their roles in optimizing the learning process and enhancing the framework’s overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.

The quality of cellular products used in biological research can directly impact the ability to ob-tain accurate results. Epstein-Barr virus (EBV) is a latent virus that spreads extensively world-wide, and cell lines used in experiments may carry EBV and pose an infection risk. The presence of EBV in a single cell line can contaminate other cell lines used in the same laboratory, affecting experimental results. We developed three EBV detection systems: (1) a polymerase chain reac-tion (PCR)-based detection system, (2) a recombinase polymerase amplification (RPA)-based de-tection system, and (3) a combined RPA-lateral flow assay (LFA) detection system. The mini-mum EBV detection limits were 1×103 copy numbers for the RPA-based and RPA-LFA systems and 1×104 copy numbers for the PCR-based system. Both the PCR and RPA detection systems were applied to 192 cell lines, and the results were consistent with those obtained by the EBV assay methods specified in the pharma-ceutical industry standards of the People's Republic of China. 10 EBV-positive cell lines were identified. The combined RPA-LFA system is simple to operate, allowing for rapid results visualization. This system can be implemented in laborato-ries and cell banks as part of a daily quality control strategy to ensure cell quality and experi-mental safety and may represent a potential new technique for the rapid detection of EBV in clinical samples.

Tight gas, a category of Deep Green Energy Resources, rely on advanced intelligent monitoring methods for their extraction. Conventional logging for reservoir evaluation relies on logging data and the manual setting of evaluation criteria to classify reservoirs. However, the complexity and heterogeneity of tight reservoirs pose challenges in accurately identifying target layers by using traditional well-logging techniques. Machine learning may hold the key to solving this problem, as it enables computers to learn without being explicitly programmed and manually added rules. Therefore, it is possible to make reservoir evaluation via using machine learning methods. In this paper, the reservoir quality index (RQI) and porous geometric parameters obtained from the optimized inversion of the spherical-tubular model are adopted to evaluate the reservoir. Then, three different machine learning approaches, the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm, are utilized for reservoir classification. The selected dataset covers more than 7,000 samples from five wells. The data from four wells are arranged as the training dataset, and the data of the remaining one well is designed as the testing dataset to calculate the prediction accuracies of different machine learning algorithms. Among them, accuracies of RF, SVM, and XGB are all higher than 90%, and XGB owns the highest result by reaching 97%. Machine learning based approaches can greatly assist reservoir prediction by implementing the well-logging data. The research highlights the application of reservoir classification with a higher prediction accuracy by combining machine learning algorithms with NMR logging based pore structure characterization, which can provide a guideline for sweet spot identification within the tight formation. This not only optimizes resource extraction but also aligns with the global shift towards clean and renewable energy sources, promoting sustainability and reducing the carbon footprint associated with conventional energy production. In summary, the fusion of machine learning and NMR logging based reservoir evaluation plays a crucial role in advancing both energy efficiency and the transition to cleaner energy sources.

Ferroptosis is an iron-dependent and lipid peroxidation-driven cell death cascade, occurring when there is an imbalance of redox homeostasis in the cell. Nuclear factor E2 related factor 2 (NFE2L2, also known as NRF2) is key for cellular antioxidant responses, which promotes downstream gene transcription by binding to their antioxidant response elements (AREs). Numerous studies suggest that NRF2 assumes an extremely important role in the regulation of ferroptosis, for its various functions on iron, lipid and amino acid metabolism and so on. Many pathological states are relevant to ferroptosis. In cancer cells, ferroptosis is frequently found abnormal suppression. While during tissue damages, ferroptosis is recurrently promoted, resulting in a large number of cell deaths and ultimately loss of the functions of the corresponding organs. Therefore, targeting NRF2-related signaling pathways, to induce or inhibit ferroptosis, has become a great potential therapy for combating cancers, as well as preventing neurodegenerative and ischemic diseases. In this review, a brief overview of the research process of ferroptosis over the past decade will be presented. In particular, the mechanisms of ferroptosis and a focus on the regulation of ferroptosis by NRF2 will be discussed. Finally, the review will briefly list some clinical applications of targeting the NRF2 signaling pathway in the treatment of diseases.

Silicon has a theoretical specific capacity of 4200 mAh g−1, which is considered to have significant potential for anode materials in lithium-ion batteries (LIBs). However, the volume change that occurs in silicon during charging and discharging affects the development of commercial applications. In this work, a yolk-shell structured Si@void@C anode material has been developed to address this problem. The silicon nanoparticle yolk material is obtained by recycling kerf-loss (KL) Si waste from the photovoltaic industry, the carbon shell is prepared via a hydrothermal process with glucose, and the sacrificial interlayer is Al2O3. The synthesized material is used to fabricate anodes that show a high reversible capacity of 836 mAh g−1 at the current density of 0.1 A g−1 after 100 cycles, with a Coulomb efficiency of 71.4%. This work demonstrates a relatively low-cost approach to turn KL Si waste into value-added materials for LIBs.

3D point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we design an augmented sampling and grouping (ASG) module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to mine point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first try to use a linearly-normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module is adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that our method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which was 0.5% higher than PCT. Our network also archived 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.

The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ) of 0.01-10s-1, the deformation temperature (T) of 1273-1453K and the maximum deformation is 0.916. The data of the stress-strain ( - )were obtained. Based on the - data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and , it has a fairly high prediction accuracy. And it can predict flow stress ( ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a FEM of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

Generator automatic voltage control device (AVC) is an automatic voltage and reactive power optimization control terminal on the power plant side. Aiming at improving the performance of generator automatic voltage control device, this paper introduces a detection method of AVC substation performance based on virtual dispatching terminal environment. During the testing, the upper computer of redundant power plant is connected to the virtual dispatching terminal environment to simulate the sending instructions of AVC master station, the behavior of AVC sub stations and the excitation system of generators. The performance of AVC substation is evaluated according to the feedback adjustment results, which can effectively detect the abnormal behavior of AVC substation in power plant.

Plant community assembly is the central issue in community ecology. As plant traits differ in different life history stages, the form, intensity and mechanism of interspecific interactions may change with the ontogenetic process of plants. However, our understanding of interspecific interaction mechanisms during germination is still limited. Here, we conducted a laboratory germination experiment using 5 dominant species in Chongming Dongtan (Spartina alterniflora, Scirpus mariqueter, Phragmites australis, Suaeda glauca and Tripolium vulgare) to assess their germination performance in control (monoculture), allelopathy and mixture treatments. The results indicated that seeds could affect germination performance of neighbours through both allelopathy and microenvironment modification. Salinity of the solution in petri dishes after germination decreased in all treatments, and was negatively correlated with the number of total germinated seeds. Seed leachate of invasive Spartina alterniflora inhibited the germination of native Scirpus mariqueter and Phragmites australis, but not the two halophytes Suaeda glauca and Tripolium vulgare. The salt absorption by Spartina alterniflora seeds had the opposite effects compared with that of its seed leachate. On the other hand, seed leachate of native species promoted the germination of invasive Spartina alterniflora. The effect of microenvironment modification of Scirpus mariqueter and Phragmites australis was positive, whereas that of Suaeda glauca and Tripolium vulgare was neutral or negative. Considering seed-to-seed interactions is an important perspective to understand the underlying mechanisms of community dynamics, species diversity maintenance and invasion of alien species, and can improve the effectiveness in the management of invaded coastal wetlands.

Lung cancer predominates in cancer-related deaths worldwide, with lung adenocarcinoma (LUAD) being a common histological subtype of lung cancer. The aim at this study was to search for biomarkers associated with the progression and prognosis of LUAD. We have integrated the expression profiles of 1174 lung cancer patients from five GEO datasets (GSE18842, GSE19804, GSE30219, GSE40791 and GSE68465) and identified a set of differentially expressed genes. Functional enrichment analysis showed that these genes are closely related to the progression of LUAD, such as cell cycle, mitosis and adhesion. Cytoscape software was used to establish a protein-protein interaction (PPI) network to analyze important modules using Molecular Complex Detection (MCODE), and finally CCNB1, BUB1B and TTK were selected for further study. The study found that compared with non-tumor lung tissue, CCNB1, BUB1B and TTK are highly expressed in LUAD. Kaplan-Meier analysis showed that CCNB1, BUB1B and TTK were negatively correlated with the overall survival and disease-free survival of patients. Gene set enrichment analysis (GSEA) demonstrated that for the samples of any hub gene highly expressed, most of the functional gene sets enriched in cell cycle. In summary, CCNB1, BUB1B and TTK can be used as biomarkers of poor prognosis of LUAD. The high expression of CCNB1, BUB1B and TTK can accelerate the progression of LUAD and lead to shorter survival, suggesting that they may be potential targets for treatment in LUAD.

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

This paper proposes a radial alternating materials phononic crystal (RAM-PnC). By simulating the band gap structure of the phononic crystal, it is verified that there is a complete acoustic band gap at the resonant frequency of 175.14 MHz, which can prevent the propagation of elastic waves in a specific direction. The proposed alternately arranged radial phononic crystal structure is applied to the thin-film piezoelectric-on-silicon (TPOPS) MEMS resonator. The finite element simulation method increases the anchor quality factor (Qanchor) from 60596 to 659536011 at the operating frequency of 175.14 MHz, which is about 10,000 times higher. The motion resistance of the RAM-PnC resonator is reduced from 156.25 Ω to 48.31 Ω compared with the traditional resonator. At the same time, the insertion loss of the RAM-PnC resonator is reduced by 1.1 dB compared with the traditional resonator.

Track fasteners play a pivotal role in infrastructure inspection for high-speed rail. Yet, images taken by drones often capture shadows cast by electrical towers flanking the high-speed rail tracks. These shadows can hinder the visibility of the track fasteners, thereby impacting detection efficiency and accuracy considerably. The present paper introduces an end-to-end shadow removal algorithm, rooted in generative adversarial network training. The comprehensive network framework is segmented into three sub-networks: pseudo-mask generation, shadow removal, and result refinement. We have integrated a Fourier convolutional residual module to bolster the feature extraction capability of the generator network. This integration ensures the network retains a global receptive field, even in its more superficial layers. By employing an overall weighted loss function, we enhance the quality of the images produced without shadows. Further, a perceptual loss function has been incorporated to retain the structural information of objects, setting the stage for subsequent defect detection. Our results highlight that Pse-ShadowNet adeptly eradicates fastener shadows while maintaining vital visual features, including object position, structure, texture, edges, and other key visual elements. Consequently, the reconstructed images are detailed and showcase superior image quality.

Xaa-Pro dipeptidase (XPD, EC 3.4.13.9; also known as prolidase) catalyzes the hydrolysis of iminopeptide bond in the trans Xaa-Pro dipeptides (Xaa represents any amino acid except proline), which makes it find wide applications in food, medical and environmental protection fields. In the present study, a novel Xaa-Pro dipeptidase from Aspergillus phoenicis ATCC 14332 (ApXPD) was molecularly and biochemically characterized. Reclassification based on phylogenetic analysis and version 12.5 MEROPS database showed that this enzyme was the only fungal XPD in the unassigned subfamily which shared the highest sequence similarity with Xanthomonas campestris prolidase but not with that from the more related fungal species A. niudulans. As compared with other prolidases, ApXPD also contained an N-terminal tail (residues 81-89) and an additional region (PAPARLREKL), and used a different arginine residue for dipeptide selectivity. After heterologous expression and partial purification, recombinant ApXPD was highly active and stable over the alkaline range from 8.5 to 10.0, with a maximum activity at pH 9.0 and more than 80% activity retained after 1-h incubation at pHs of 8.5-10.0 (55oC). It also had an apparent optimum temperature of 55oC and remained stable at 20-30oC. Moreover, this enzyme was a cobalt dependent prolidase that only cleaved dipeptides Lys-Pro, Gly-Pro and Ala-Pro rather than other dipeptides, tripeptides and tetrapeptides. All these distinct features make A. phoenicis ATCC 14332 XPD unique among currently known prolidases, thus defining a novel Xaa-Pro dipeptidase subfamily.

Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide. This study aimed to test the efficacy of a newly-developed symptom self-management program on health outcomes among inpatients post-AMI. A pilot randomised, controlled trial was utilised. A convenience sample of 90 was recruited from a tertiary hospital in Singapore. Adult inpatients with post-AMI were randomly assigned to the three groups: IManage-HSet, IManage-FF or control group. Sample size was computed using power analysis. Data were collected through self-reported questionnaires and physiological instruments. Data analyses included univariate statistics, analysis of variance and repeated measure analysis of variance. Ninety participants were recruited with 30 in each group. Participants in the IManage-HSet and IManage-FF group had significantly greater relaxation (ηp2 = 0.11), lower objective stress (ηp2 =0.14), and greater knowledge (ηp2 = 0.62) than those of the control group. The two intervention groups had lower subjective stress (ηp2 = 0.04), depression (ηp2 = 0.01), and anxiety (ηp2 = 0.03) than the control but such differences did not achieve statistical significance. Furthermore, most participants reported that they were satisfied with the programs and they provided positive comments on the contents, educational materials and relaxation practice. This pilot study supports the potential benefits and feasibility of the IManage-VR and IManage-FF interventions on stress, perceived relaxation, knowledge and patient satisfaction. Contents of the symptom management interventions could be added into current cardiac rehabilitation programs. Future RCTs with multi-centered sites and larger sample size are required to further test the intervention. This study was prospectively registered with the International Standard Randomised Controlled Trial on the 5th August 2014 with registration number ISRCTN61027816.

Sarcopenia becomes more common with age, being most prevalent among elderly individuals. According to the EWGSOP, muscle mass is one of the criteria for the evaluation of sarcopenia. The main methods for evaluating muscle mass are CT, MRI, and DXA, but these methods are difficult to apply in the field due to equipment costs, radioactivity, and lack of portability. BIVA and PhA are alternative approaches for assessing somatic cell mass and volume and do not require predictive equations. These variables are clinically relevant parameters that indicate cell health, especially cell membrane integrity and cell function. This study in sarcopenic and nonsarcopenic elderly volunteers aimed to determine the BIVA distribution pattern among individual sarcopenia patients; to evaluate the relationship between PhA and muscle strength, muscle quality, and physical function; and to find any correlates of PhA. The sample comprised 134 free-living elderly individuals of both sexes aged 69–91 years. Anthropometric parameters, grip strength, DXA findings, BIA results, and physical performance (the 6-meter walk test) were measured. Impedance vector distributions were evaluated in sarcopenia patients and healthy elderly individuals using BIVA. According to the AWGS criteria, sarcopenia was diagnosed according to DXA findings, grip strength and physical performance test results. Group differences were evaluated using the t test, Mann‒Whitney U test, and Hotelling's T2 test. Correlation analysis was performed to identify variables significantly associated with PhA. Linear regression analysis was performed to determine whether PhA was associated with muscle strength, muscle quality and physical function. BIVA detected a significant difference between the sarcopenia and non-sarcopenia groups (both sexes) due to higher R/H values and lower phase angles in a few individuals, whereas Xc/Ht values did not differ between the two groups. The sarcopenia group had a significantly lower PhA than the non-sarcopenia group among both males (p&lt;0.01) and females (p&lt;0.05). PhA was significantly correlated with age, ASM, HGS, and muscle quality in both sexes and significantly correlated with ASM/Ht2 and physical performance in males. PhA was a significant indicator of muscle strength in both males (β = 2.6; p&lt;0.01) and females (β = 3.4; p&lt;0.01), a significant indicator of muscle quality in both males (β = 0.07; p&lt;0.05) and females (β = 0.17; p&lt;0.01), and a significant indicator of physical performance in males (β = 0.3; p&lt;0.01). BIVA can detect changes in muscle mass in individuals with sarcopenia and is a practical method for the assessment of sarcopenia in the field. PhA is a good indicator of muscle strength, muscle quality and physical performance (in males). These methods can help diagnose sarcopenia in elderly individuals with reduced mobility.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

With the improvement of software copyright protection awareness, code obfuscation technology plays a crucial role in protecting key code segments. As the obfuscation technology becomes more and more complex and diverse, it has spawned a large number of malware variants, which make it easy to evade the detection of anti-virus software. Malicious code detection mainly depends on binary code similarity analysis. However, the existing software analysis technologies are difficult to deal with the growing complex obfuscation technologies. To solve this problem, this paper proposes a new obfuscation-resilient program analysis method, which is based on the data flow transformation relationship of the intermediate representation and the graph network model. In our approach, we first construct the data transformation graph based on LLVM IR. Then, we design a novel intermediate language representation model based on graph networks, named DFSGraph, to learn the data flow semantics from DTG. DFSGraph can detect the similarity of obfuscated code by extracting the semantic information of program data flow without deobfuscation. Extensive experiments prove that our approach is more accurate than existing deobfuscation tools when searching for similar functions from obfuscated code.

Multi-modal Motion-assisted Memory Desensitization and Reprocessing Therapy (3MDR), an interactive, virtual-reality assisted, exposure-based intervention for PTSD, has shown promising results for treatment-resistant Posttraumatic Stress Disorder (TR-PTSD) among military members (MMs) and Veterans in Randomized Controlled Trials. Previous research has suggested that emotional regulation (ER) and emotional dysregulation (ED) may be factors which are correlated with symptom severity and maintenance of TR-PTSD. This embedded mixed-methods pilot study (n=9) sought to explore the impact of 3MDR on ER and ED of MMs and Veterans. Difficulties in Emotional Regulation Scale (DERS-18) data was collected at baseline, prior to each session, and at 1 week, 1 month and 3 months post-intervention and analyzed using a Wilcoxon signed-ranks test. Qualitative data collected from sessions, debriefs, and follow-up interviews were transcribed and descriptively analyzed. Results demonstrated statistically significant decreases in DERS-18 scores from pre-intervention to post-intervention at each timepoint. Qualitatively, participants perceived improvements in ER within specified DERS-18 domains. We describe how 3MDR’s unique and novel approach may address ED through cognitive-motor stimulation, narration, divergent thinking, reappraisal of aversive stimuli, dual-task processing, and reconsolidation of traumatic memories. Further investigation is underway to better understand the underlying neurobiological mechanisms by which 3MDR addresses ER and PTSD.

The reinforcement learning problem of complex action control in the Multi-player wargame is a hot research topic in recent years. In this paper , a game system based on turn-based confrontation is designed and implemented with the state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based the DQN(Deep Q Network) to model the complex game behaviors. Then, a priori- knowledge based algorithm PK-DQN(Prior Knowledge- Deep Q Network) is introduced to improve the DQN algorithm, which accelerates the convergence speed and stability of the algorithm. The experiments demonstrate, the correctness of the PK-DQN algorithm is validated and its performance surpass the conventional DQN algorithm. Furthermore, the PK-DQN algorithm shows effectiveness in defeating the high level of rule-based opponents, which provides promising results for the exploration of the field of smart chess and intelligent game deduction.

Recent preclinical studies have shown the potential benefits of short-term calorie reduction (SCR) on cancer treatment. In this integrative review, we aimed to identify and synthesize current evidence regarding the feasibility, process, and effects of SCR in cancer patients receiving chemotherapy. PubMed, Cumulative Index to Nursing and Allied Health Literature, Ovid Medline, PsychINFO, and Embase were searched for original research articles using various combinations of Medical Subject Heading terms. Among the 311 articles identified, seven studies met the inclusion criteria. The majority of the reviewed studies was small randomized controlled trials or cohort study with fair quality. The results suggest that SCR is safe and feasible. SCR is typically arranged around the chemotherapy with the duration ranging from 24 to 96 hours. Most studies examined the protective effects of SCR on normal cells during chemotherapy. The evidence supports that SCR had the potential to enhance both physical and psychological wellbeing of patients during chemotherapy. SCR is a cost-effective intervention with great potential. Future well-controlled studies with sufficient sample sizes are needed to examine the full and long-term effects of SCR and its mechanism of action.

A variety of myricetin derivatives bearing ferulic acid amide scaffolds were designed and synthesized. The structures of all title compounds were determined by 1 H NMR, 13 C NMR, 19 F NMR and HRMS. Preliminary bioassays suggested that some of the target compounds exhibited remarkable antiviral activities. In particular, compound 4l possessed significant protection activity against tobacco mosaic virus (TMV), with an half maximal effective concentration (EC50) value of 196.11 μg/mL, which was better than commercial agent ningnamycin (447.92 μg/mL). Meanwhile, microscale thermophoresis (MST) indicated that compound 4l have strong binding capability to tobacco mosaic virus coat protein (TMV-CP) with dissociation constant (Kd) values of 0.34 μmol/L, which was better than ningnamycin (0.52 μmol/L). These results suggest that novel myricetin derivatives bearing ferulic acid amide scaffolds may be considered as an activator for antiviral agents.

Fresh water is valuable nonrenewable resource and plays an important role of maintaining economic and social development. Condisering its large population and consumption potential, water resources deficit will certainly impede basic industries sustainable development of China in the near future. Application of sewage irrigation, to some extent, was regarded as an alternative way to solve the problem of agricultural irrigation water shortage in some areas (such as North China). However, accompanied with extensive implementation of sewage irrigation, some problems on sewage irrigation in agriculture are gradually obvious, especially serious pollution and destruction for farmland. In this paper, the effects of sewage irrigation on soil physical (soil bulk density, soil resistance to penetration and field capacity), chemical (pH, soil organic matter, nitrogen, phosphrous, patassium, heavy metal and organic pollutants) and biological characteristics (soil microorganism and enzyme activities) of farmland in China were systematically reviewed on the base of the current utilization status of China’s farmland sewage irrigation and some feasible suggestions were put forward to the development prospect for the future. This review will be beneficial for promoting healthy development of sewage irrigation and providing theoretical support for reclamation and high efficiency of effluents in China.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

The signal transduction pathways of estrogen receptors (ER) mainly includes gene pathway and non-gene pathway. Studies have shown that the gene pathway of ER is related with the expression of nuclear proteins, and this is the key issue for our current research. With the GEO database analysis, Human centromere protein F (CENPF) is highly expressed in adenocarcinoma of lung (LUAD), and the co-expression of CENPF and ERβ was found in the nucleus of LUAD cells. Meanwhile, CENPF and ERβ2/5 were related with T stage and poor prognosis (P<0.05). Knockdown of CENPF gene significantly inhibited the biological effects of LUAD cells, the tumor growth of mice and the expression of ERβ2/5 (P<0.05). Further, group experiments showed that knockdown CENPF inhibits biological effects of LUAD cells mediated by ERβ pathway. All the results indicated that both CENPF and ERβ2/5 play important roles in the progression of LUAD, and knockdown of CENPF can inhibit the progression of LUAD by inhibiting the expression of ER2/5. Thus, the development of inhibitors against ERβ2/5 subtype and CENPF remained more effective in improving the therapeutic effect of LUAD.

Large scale cropland erosion rates mapping and dynamic monitoring are critical for agricultural planning but extremely challenging. In this study, by using field investigation data collected from 20,155 land parcels in 2,781 sample units in the National Soil Erosion Survey, land use change data for two decades from the National Land Use/Cover Database of China (NLUD-C), we proposed a new point to surface approach to quantitatively assess long-term cropland erosion based on the CSLE model and non-homogeneous voting. The results show that cropland in Yunnan suffers from serious problem with unsustainable mean soil erosion rate of 40.47t/(ha·a) and erosion ratio of 70.11%, which are significantly higher than those of other land types. Engineering control measures (ECMS) have a profound impact on reducing soil erosion, soil erosion rate of cropland with and without ESMs differs by more than five times. Over the past two decades, the cropland area in Yunnan continues to decrease, with a net reduction of 7461.83 km2 and a ratio of −10.55%, which causes corresponding 0.32×108 t (12.12% ) decrease in cropland soil loss. We also quantified the impact of different LUCC scenarios on cropland erosion, and extraordinarily high variability was found in soil loss in different basins and periods. Conversion from cropland to forest contributes the most to cropland erosion reduction, while conversion from grassland to cropland contributes 56.18% of the increase in soil erosion. Considering the current speed of cropland regulation, it is the sharp reduction in land area that leads to cropland erosion reduction rather than treatments. The dilemma between the Grain for Green Policy and Cropland Protecting Strategy in mountainous areas should be treated carefully with shared understanding and collaborations from different roles.

In order to master the structure state of subway shield tunnel, reduce the diseases and avoid the potential safety hazards, the lateral convergence deformation of subway shield tunnels should be predicted. For the non-stationarity small sample size of subway shield tunnel lateral convergence deformation value, the existing prediction models have poor performance in terms of accuracy and stability. In this paper, a lateral convergence model of subway shield tunnel based on Kalman algorithm is constructed based on Kalman filtering theory. The model is efficient, adaptive and robust, and can accurately predict the lateral convergence deformation of subway shield tunnel. Taking the horizontal diameter of 200 rings shield segment in the interval section of a subway tunnel as an example, it is proved that the residuals of the Kalman prediction model are small, the residual distribution conforms to the normal distribution, and the prediction effect is great. The model is suitable for the prediction of more than 5 periods of data, and the prediction accuracy of the model is improved with the increase of the number of data periods. In addition, the Kalman model is compared with GM(1,1) model and GM-Markov model, and RMSE and MAPE are introduced as evaluation indexes. The results show that the Kalman model has higher prediction accuracy and is more suitable for predicting the lateral convergence deformation of subway shield tunnel.

East Lake in Wuhan, China, harbors a high number of freshwater fish species of great conservation value, concurrently serving as vital resources for local livelihoods. However, the ecosystem is threatened by an array of anthropogenic activities, thus requiring consistent monitoring of the local fish community to enable more efficacious conservation management. In place of conventional surveying methods, we undertook the first analysis of the fish distribution within East Lake via metabarcoding of environmental DNA (eDNA). The accuracy and efficacy of eDNA metabarcoding relies heavily upon selecting an appropriate primer set for PCR amplification. Given the varying environmental conditions and taxonomic diversity across distinct study systems, it remains a challenge to propose an optimal genetic marker for universal use. Thus, it becomes necessary to select PCR primers suitable for the composition of fish in the East Lake. Here, we evaluated the performance of two primer sets, Mifish-U and Metafish, designed to amplify 12S rRNA barcoding genes in fishes. Our results detected a total of 116 taxonomic units and 51 fish species, with beta diversity analysis indicating significant differences in community structure diversity between the 6 sampling locations encompassing East Lake. While it was difficult to accurately compare the species-level discriminatory power and amplification bias of the two primers, Mifish outperformed Metafish in terms of taxonomic specificity for fish taxa and reproducibility. These findings will assist with primer selection for eDNA-based fish monitoring and biodiversity conservation in the East Lake and other freshwater ecosystems.

Due to the ever increasing global warming, the scientific community is concerned with finding immediate solutions to reduce or utilize carbon dioxide (CO2) and convert it in useful compounds. In this context, the reductive process of CO2 methanation is well investigated and attractive due to its simplicity. However, it requires the development of highly active catalysts. In this mini-review, the focus is on biochar-immobilized nanocatalysts for CO2 methanation. We summarize the recent literature on the topic, reporting strategies for the design of biochar with immobilized nanocatalysts and their performances in CO2 methanation. We review the thermochemical transformation of biomass into biochar and its decoration with CO2 methanation catalysts. We also tackle direct methods of obtaining biochar-nanocatalysts, in one pot, from nanocatalyst precursor-impregnated biomass. We review the effect of the initial biomass nature, as well as the conditions that permit to tune the perfomances of the composite catalysts. Finally, we discuss the CO2 methanation performances and how they could be improved keeping in mind low operation cost and particularly sustainability.

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, result in generated results failing to achieve a high identity similarity with the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllablely transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images and controllable face swapping.

Jacobian-free Newton Krylov (JFNK) is an attractive method to solve nonlinear equations in nuclear engineering systems, and has been successfully applied to steady-state neutron diffusion (k-eigenvalue) problems and multi-physics coupling problems. Preconditioning technique plays an important role in JFNK algorithm, significantly affecting computational efficiency. The key point is how to automatically construct a high-quality preconditioning matrix that can improve the convergence rate, and perform the preconditioning matrix factorization efficiently. An efficient reordering-based ILU(k) preconditioner is proposed to achieve the above objectives. In detail, the finite difference technique combined with the coloring algorithm is utilized to construct an efficient preconditioning matrix with low computational cost automatically. Furthermore, the reordering algorithm is employed for the ILU(k) to reduce the additional non-zero elements and pursue robust computational performance. A two-dimensional LRA neutron steady-state benchmark problem is used to evaluate the performance of the proposed preconditioning technique, and steady-state neutron diffusion problem with thermal-hydraulic feedback is also utilized as a supplement. The results show that coloring algorithms can efficiently and automatically construct the preconditioning matrix. The reordering-based ILU(k) preconditioner shows excellent robustness, avoiding the effect of fill-in levels k choice in incomplete LU factorization.

Validation of the TROPOspheric Monitoring Instrument (TROPOMI) SIF product is a necessity to evaluate its feasibility in various applications. A few validation works have been conducted through direct comparison with in-situ SIF retrievals or cross-comparison with similar satel-lite-based SIF or vegetation index (VI) products. Nevertheless, the influence of the geolocation mismatch between the validation pixel and reference data on validation results was never con-sidered. This study, for the first time, quantifies the geolocation shift of the TROPOMI validation pixel based on a geometric location matching method and then the uncertainty caused by the ge-olocation mismatch. The results indicate that the geolocation shift of the TROPOMI pixel shows large temporal variations, with a standard deviation of 2.45 km and 1.96 km in the across- and along-track directions. The mean shifts are 0.07 km and 0.25 km in the across- and along-track directions respectively, indicating eastward shifts in the across-track direction and northward shifts in the along-track direction in general. More than half (42/54) of the time periods showed relative uncertainty larger than 5%, with the maximum even reaching 55.7%. Hence, the uncertainty caused by geolocation mismatch should be fully considered in the validation of satellite SIF products, especially over heterogeneous surfaces.

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications, such as the military, where the dynamic grasp of enemy targets can help improve the early warning capability of naval defence, and the civilian detection of illegal fishing vessels can help improve the level of maritime management. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposes an attention mechanism for multiscale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), is proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our model is more lightweight, only 18.43M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy AP50 of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 4.9% and 5% higher than that of YOLOv7, respectively.

In this study, seventeen faceted gem-quality chrysoberyls exhibiting an attractive canary yellow color were investigated by a series of gemological, spectral, and chemical methods. Microscopic observation reveals distinct growth lines and inclusions, including CO2 fluids, carbon, and mineral crystals such as calcite, quartz, sillimanite, and mica, identified by the Raman spectrum. The FTIR spectrum exhibits the 2405 and 2160 cm-1 feature peaks and a 3223 cm-1 peak in all samples, which can be accompanied by the 3112 cm-1 shoulder, 3301, and 3412 and 3432 cm-1 peaks. The UV-Vis spectrum shows a Fe-related peak at 440 nm, along with the 650–660 nm band and the absorption band in the blue zone of the visible light. The chemical results of EDXRF reveal a V-poor, Cr-poor, and Fe-rich feature. The spectral and chemical results could contribute to explaining the origin of the canary yellow color, which originates from the abundant amount of Fe with very little influence from Cr or V.

Plants of the golden Camellia group are not only high-quality camellia germplasm , but also a plant group with rich medicinal and nutritional value, which are used as food, cosmetics and traditional medicine in China. There are approximately 50 species in this group around the world, and more than 30 species of golden camelliaplants have been published in China. The leaves and flowers of these species have similar shapes, so they are often confused as species. Our study used simplified genome sequencing technology to construct a phylogenetic tree of plants in the Chinese golden Camellia group and describe the evolutionary relationships . At the same time, the secondary metabolic indexes of total phenols, total flavonoids, total anthocyanins and ellagic acid in the leaves were determined., and principal component clustering analysis was performed . The results showed that the chemical classification and genetic phylogenetic trees showed some of the same trends, indicating that Camellia chinensis can be used as Biomarkers for chemical type. The research results provide phylogenetic information for the genotype and performance diversity of golden camelliaregionally distributed in China. At the same time, molecular biology phylogenetic methods are used to understand or explain the characteristics of secondary metabolites of golden Camellia , and provide theoretical basis for the search and development of potential bioactive substances.