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In this paper, the stress distribution and evolution of LPwC and LSP in thin-walled model are compared by simulation. The mechanical properties and microstructure changes of LPwCed TC4 titanium alloy thin-walled components were further analyzed by experiments, and the mechanism of LPwC induced grain refinement was analyzed in detail. The results show that compared with LSP, the stress field introduced by LPwC is more uniform, the depth of the influence layer is shallower, and the shock wave energy decays faster. At the same time, LPwC can control the deformation of the specimen more accurately. At 30 mJ energy, the high cycle fatigue limit of the LPwCed TC4 titanium alloy thin-walled sample is 467.1 MPa, which is 14.3 % higher than that of the untreated sample. The maximum hardness can reach 367.2 HV0.1, which is 12.0 % higher than that of the untreated sample, and the depth of the influence layer is 0.3 mm. The surface residual stress is −61.43 MPa, and the depth of the influence layer can reach 250 μm. At the same time, the formation of dynamic recrystallized grains under the action of LPwC promotes the texture orientation of the sample to change from [ 10−10 ] to [-12-10 ].

Water table configuration gives rise to hierarchically nested groundwater flow systems. However, there remains a lack of comprehensive understanding regarding the controlling factors of water table and its impact on flow systems. Moreover, it remains challenging to identify characteristics of water table space variation through limited groundwater observations at the regional scale. Based on two ideal two-dimensional cross-section analytical models, this study presents a simplified approach to preliminarily assess the nonlinear interactions between water table variation and three driving factors: Topography, geology and climate. Two criteria, C1 and C2, are utilized to address issues at different scales ranging from basin to local: (i) the influence of various factors on water table configuration; and (ii) the influence of water table on groundwater flow pattern. Then, the Ordos Plateau is taken as an example to explore the role of the water table in nested groundwater systems using the provided approach and criterion. The application of this approach in the Ordos Plateau demonstrates its appropriateness as a practical method for preliminarily determining the characteristics of water table configuration and its impact on flow systems. The study explores the mechanism influencing spatial variation in the water table and improves understanding of the interaction between topography, geology, and climate on groundwater flow patterns.

Abstract Dynamic metabolic reprogramming occurs at different stages of myogenesis and contributes to the fate determination of skeletal muscle satellite cells (MuSCs). Accumulating evidence suggests that mutations in myostatin (MSTN) have a vital role in regulating muscle energy metabolism. Here, we explored the metabolic reprogramming in MuSCs and myotube cells in MSTN and FGF5 dual-gene edited sheep models prepared previously, and also focused on the metabolic alterations during myogenic differentiation of MuSCs. Our study revealed that the pathways of nucleotide metabolism, pantothenate and CoA biosynthesis were weakened, while the unsaturated fatty acids biosynthesis were strengthened during myogenic differentiation of sheep MuSCs. The MSTN and FGF5 dual-gene editing mainly inhibited nucleotide metabolism and biosynthesis of unsaturated fatty acids in sheep MuSCs, reduced the number of lipid droplets in per satellite cell, and promoted the pentose phosphate pathway, and the interconversion of pentose and glucuronate. The MSTN and FGF5 dual-gene editing also resulted in the inhibition of nucleotide metabolism and TCA cycle pathway in differentiated myotube cells. The differential metabolites we identified can be characterized as biomarkers of different cellular states, and providing a new reference for MSTN and FGF5 dual-gene editing in regulation of muscle development. It may also provide a reference for the development of muscle regeneration drugs targeting biomarkers.

Abstract Design thinking (DT) is becoming an innovative and popular teaching method. Recently, DT has been used as an unconventional method to develop skills of problem-solving, creativity, and innovation. However, its effects on student learning are unclear. This research aimed to examine the DT’s effects on student learning. The meta-analytic result based on 25 articles showed that DT positively affected student learning (r = 0.436, p =3 months); (4) for secondary school and university students; (5) on student learning engagement, motivation, problem-solving skills, and academic achievement; (6) with the model of Observe, Synthesize, Ideate, and Prototype, and Empathize, Define, Ideate, Prototype, Test; (7) when team size is

To overcome challenges in the traditional melt casting process of zine alloys, such as oxidative burnout, compositional segregation, and grain coarsening, this study carried out hot compression deformation tests on Zn-0.5Mg-0.05Fe alloys prepared by powder metallurgy. The results show that increasing deformation temperature and decreasing strain rate reduce rheological stress. The hot compression deformation process exhibits three phases, as rheological stress increases, work hardening, dynamic softening, and steady-state fluidization are observed, respectively. An intrinsic equation for the hot compression process is derived as = 1.334 × 1010 × [sinh(0.0053σ)]7.62947exp(-109.931691/RT). Processing conditions for Zn-0.5Mg-0.05Fe alloy are determined from a processing map, suggesting that a safe zone of 25°C–130 °C deformation temperatures with 0.13s−1-0.36s−1 strain rates, and 120°C–200 °C deformation temperatures with 0.01s−1-0.6s−1 strain rates. Electron Back-scattering Patterns (EBSD) and Transmission Electron Microscope (TEM) results show both discontinuous dynamic recrystallization (DDRX) and continuous dynamic recrystallization (CDRX) during the hot deformation compression process. DRX reduces deformation resistance, refines the grains, and improves the mechanical properties of the alloys. This study provides theoretical insights and process guidance for preparing degradable zinc alloys with excellent mechanical properties.

Abstract Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T.

A new near-α titanium alloy Ti-0.3Mo-0.8Ni-2Al-1.5Zr was designed based on Ti-0.3Mo-0.8Ni (TA10). The hot compression tests were carried out at temperatures of 800–970 °C and strain rates of 0.01–5 s−1. The results show that the flow softening phenomenon in the α + β phase region is more distinct than that in the single β phase region. The Arrhenius constitutive models based on strain compensation in the α+β phase region and β phase region were established respectively, and the correlation coefficient between the predicted flow peak stress values and the experimental values reached 0.99365. The hot processing map of the alloy was established, the processing parameters of the peak efficiency: 800 ≤ T ≤ 818 °C, 0.01 ≤ ε˙ ≤ 0.0235 s−1, and 960 ≤ T ≤ 970 °C, 0.01 ≤ ε˙ ≤ 0.0213 s−1. The microstructure evolution and deformation mechanism of the compressed specimens were researched by electron backscatter diffraction (EBSD) technique. There were two nucleation mechanisms for dynamic recrystallization (DRX): sub-grains growth nucleation and high angle grain boundaries (HAGBs) bulging nucleation, the former was continuous dynamic recrystallization (CDRX), and the latter was discontinuous dynamic recrystallization (DDRX). The flow stress in the α+β phase region was softened by CDRX and dynamic recovery (DRV), while the principal deformation mechanisms in the β phase region are DRV and DDRX. The dislocation strain energy and recrystallization ability at different temperatures were quantitatively analyzed by the kernel average misorientation (KAM) and the geometrically necessary dislocation (GND), and the results showed that more complete recrystallization occurred at lower temperature in the α + β phase region. The HCP slip was principally completed by prismatic ⟨a⟩ and pyramidal ⟨c+a⟩ slip systems during the hot compression, while BCC was a mixed mode of {110}, {112} and {113} multiple slips.

Both titanium (Ti) and magnesium (Mg) exhibit exceptional qualities for biomedical applications. The rigorous selection of a suitable material for advancing artificial mandible implantation is of paramount importance. The porous titanium scaffold was fabricated by 3D printing, and subsequently Ti–Mg composites with gradient magnesium content were prepared by pressureless infiltration. Ti–Mg composites with three distinct mass ratios are prepared based on the different melting points of Ti and Mg. A protective coating has been applied to the surface of the composites using plasma electrolytic oxidation (PEO) to further improve their properties. A comprehensive investigation of the obtained samples was conducted, including the macroscopic and microstructures, element compositions, parameters such as thickness, roughness, and water contact angle of the coating, as well as the corrosion behavior and biocompatibility evaluations. Cytocompatibility evaluation and morphology was done in 6-time diluted extracts on tissue culture plastic surface. The PEO-Ti-Mg-900 (with 20% magnesium content) has shown the best behavior in corrosion resistance, cell proliferation and cytotoxicity, indicating its further application in the artificial mandible implantation.

Abstract Detrital composition of sandstone is the most important data for siliciclastic studies including sandstone classification, provenance analysis, oil and gas exploration. A large amount of detrital composition data has accumulated over the past decades, however, they are scattered in publications without unified standards. Here we constructed a global dataset of detrital components of sandstones from 646 peer‐reviewed publications using Gazzi‐Dickinson method. A total of 19,861 samples from Precambrian to Quaternary are involved in this dataset. For each sample, we present details on reference information, geographic information, geological background, depositional age and the original data. It is a high‐quality dataset for the information on each sandstone sample from different studies which was standardized. The dataset can be used widely, such as for stratigraphic comparison, provenance analysis, exploring the general laws of the source‐to‐sink process and geological engineering.

ObjectiveCopy number changes at Chromosomal 16p13.11 have been implicated in a variety of human diseases including congenital cardiac abnormalities. The clinical correlation of copy number variants (CNVs) in this region with developmental abnormalities remains controversial as most of the patients inherit the duplication from an unaffected parent.MethodsWe performed CNV analysis on 164 patients with defective left-right (LR) patterning based on whole genome-exome sequencing (WG-ES) followed by multiplex ligation-dependent probe amplification (MLPA) validation. Most cases were accompanied with complex congenital heart disease (CHD).ResultsCNVs at 16p13.11 were identified in a total of 21 cases, accounting for 12.80% (21/164) evaluated cases. We observed a marked overrepresentation of chromosome 16p13.11 duplications in cases when compared with healthy controls according to literature reports (15/164, 9.14% versus 0.09% in controls). Notably, in two independent family trios, de novo 16p13.11 micro-duplications were identified in two patients with laterality defects and CHD. Moreover, 16p13.11 micro-duplication was segregated with the disease in a family trio containing 2 affected individuals. Notably, five coding genes, NOMO1, PKD1P3, NPIPA1, PDXDC1, and NTAN1, were potentially affected by micro-CNV at 16p13.11 in these patients.ConclusionOur study provides new family-trio based evidences to support 16p13.11 micro-duplications predispose individuals to defective cardiac left-right patterning and laterality disorder.

To reduce the distortion effects of traveling wavefronts for fault location in distribution networks, a novel fault traveling wave detection method employing Robust Local Mean Decomposition (RLMD), Teager Energy Operator (TEO) and Incremental Difference Ratio (IDR) is proposed. According to the dispersion characteristic of traveling waves with multi-branch lines, the sampled faulty traveling wave signal is filtered using the RLMD method, then the TEO is used to determine the first half-wave time interval of the fault traveling wave. Finally, the IDR is proposed to detect the initial moment of the faulty traveling wave. And according to the fault traveling wave transmission path of multi-branch lines, the complex multi-branch line fault location problem is transformed into a three-terminal fault location problem. The fault traveling wave location method is proposed based on the three-terminal fault matrix, which achieves the fault location through the gradual reduction of the fault area range. Simulation and experiment results indicate that the method proposed in this paper can effectively calculate the arrival time of faulty traveling waves, and is more suitable for fault detection in multi-branch distribution lines compared to existing methods. The new proposed faulty location method can achieve reliable location to reduce location errors.

Due to the enclosed environment of greenhouse grape production, the supply of CO2 required for photosynthesis is often insufficient, leading to photosynthetic downregulation and reduced yield. Currently, the optimal CO2 concentration for grape production in greenhouses is unknown, and the precise control of actual CO2 levels remains a challenge. This study aims to investigate the effects of different CO2 concentrations on the photosynthetic characteristics and yield of grapes, to validate the feasibility of a CO2 gas irrigation system, and to identify the optimal CO2 concentration for greenhouse grape production. In this study, a CO2 gas irrigation system combining CO2 enrichment and gas irrigation techniques was used with a 5-year-old Eurasian grape variety (Vitis vinifera L.) ‘Flame Seedless.’ Four CO2 concentration treatments were applied: 500 ppm (500 ± 30 µmol·mol−1), 700 ppm (700 ± 30 µmol·mol−1), 850 ppm (850 ± 30 µmol·mol−1), and 1,000 ppm (1,000 ± 30 µmol·mol−1). As CO2 concentration increased, chlorophyll a, chlorophyll b, and carotenoids in grape leaves all reached maximum values at 700 ppm and 850 ppm during the same irrigation cycle, while the chlorophyll a/b ratio was lower than at other concentrations. The net photosynthetic rate (Pn) and water use efficiency (WUE) of grape leaves were the highest at 700 ppm. The transpiration rate and stomatal conductance at 700 ppm and 850 ppm were significantly lower than those at other concentrations. The light saturation point and apparent quantum efficiency reached their maximum at 850 ppm, followed by 700 ppm. Additionally, the maximum net photosynthetic rate, carboxylation efficiency, electron transport rate, and activities of SOD, CAT, POD, PPO, and RuBisCO at 700 ppm were significantly higher than at other concentrations, with the highest yield recorded at 14.54 t·hm−2. However, when the CO2 concentration reached 1,000 ppm, both photosynthesis and yield declined to varying degrees. Under the experimental conditions, the optimal CO2 concentration for greenhouse grape production was 700 ppm, with excessive CO2 levels gradually inhibiting photosynthesis and yield. The results provide a theoretical basis for the future application of CO2 fertilization and gas irrigation techniques in controlled greenhouse grape production.

The repair and regeneration of bone defects are highly challenging orthopedic problems. Recently, Mg-based implants have gained popularity due to their unique biodegradation and elastic modulus similar to that of human bone. The aim of our study is to develop a magnesium alloy with a controllable degradation that can closely match bone tissue to help injuries heal in vivo and avoid cytotoxicity caused by a sudden increase in ion concentration. In this study, we prepared and modified Mg-3 Zn, Mg-3Zn-1Y, and Mg-2Zn-1Mn by hot extrusion, and used Mg-2.5Y-2.5Nd was as a control. We then investigated the effect of additions of Y and Mn on alloys’ properties. Our results show that Mn and Y can improve not only compression strength but also corrosion resistance. The alloy Mg-2Zn-1Mn demonstrated good cytocompatibility in vitro, and for this reason we selected it for implantation in vivo. The degraded Mg-2Zn-1Mn implanted a bone defect area did not cause obvious rejection and inflammatory reaction, and the degradation products left no signs of damage to the heart, liver, kidney, or brain. Furthermore, we find that Mg-2Zn-1Mn can promote an osteoinductive response in vivo and the formation of bone regeneration.

The introduction of alleles into commercial crop breeding pipelines is both time consuming and costly. Two technologies that are disrupting traditional breeding processes are doubled haploid (DH) breeding and genome editing (GE). Recently, these techniques were combined into a GE trait delivery system called HI-Edit (Haploid Inducer-Edit) [1]. In HI-Edit, the pollen of a haploid inducer line is reprogrammed to deliver GE traits to any variety, obviating recurrent selection. For HI-Edit to operate at scale, an efficient transformable HI line is needed, but most maize varieties are recalcitrant to transformation, and haploid inducers are especially difficult to transform given their aberrant reproductive behaviors. Leveraging marker assisted selection and a three-tiered testing scheme, we report the development of new Iodent and Stiff Stalk maize germplasm that are transformable, have high haploid induction rates, and exhibit a robust, genetically-dominant anthocyanin native trait that may be used for rapid haploid identification. We show that transformation of these elite “HI-Edit” lines is enhanced using the BABYBOOM and WUSCHEL morphogenetic factors. Finally, we evaluate the HI-Edit performance of one of the lines against both Stiff Stalk and non-Stiff Stalk testers. The strategy and results of this study should facilitate the development of commercially scalable HI-Edit systems in diverse crops.

Phonon polaritons (PhPs) in two-dimensional van der Waals crystals, such as hexagonal boron nitride (hBN) and α-phase molybdenum trioxide (α-MoO3), exhibit remarkable spatial confinement and extremely low optical losses, offering possibilities for the development of novel micro and nanophotonic devices at mid-infrared wavelengths. Here, we demonstrate the potential of the proposed Tamm structure to achieve dual-band high quality-factor perfect absorption within the mid-infrared range. This structure utilizes upper and lower spacer layers to isolate the anisotropic materials hBN and α-MoO3 from one-dimensional photonic crystals, where the Tamm phonon polaritons (TPhPs) are respectively excited at the interfaces, facilitating strong coupling with incident light and leading to significant dual-band perfect absorption phenomena. The results demonstrate that due to polariton resonances, the absorption peaks exhibit extremely narrow absorption bandwidths, with the maximum quality-factor reaching up to 297. In addition, tunable resonant wavelengths and polarization-dependent absorption properties are also demonstrated in the proposed Tamm structure. This study provides an alternative and straightforward approach for constructing dynamic and multifunctional optical devices such as optical modulators, tunable multi-band filters and biosensors.

Infection with human papillomavirus (HPV) typically leads to cervical cancer, skin related cancers and many other tumors. HPV is mainly responsible for evading immune tumor monitoring in HPV related cancers. Toll like receptors (TLRs) are particular pattern recognition molecules. When the body is facing immune danger, it can lead to innate and direct adaptive immunity. TLR plays an important role in initiating antiviral immune responses. HPV can affect the expression level of TLR and interfere with TLR related signaling pathways, resulting in sustained viral infection and even carcinogenesis. This paper introduces the HPV virus and HPV related cancers. We discussed the present comprehension of TLR, its expression and signaling, as well as its role in HPV infection. We also provided a detailed introduction to immunotherapy methods for HPV related diseases based on TLR agonists. This will provide insights into methods that support the therapeutic method of HPV related conditions with TLR agonists

The integration of distributed generation (DG) units into distribution networks (DNs) has brought about several operational challenges, including voltage issues and increased power loss. Energy storage equipped soft open points (E-SOPs) can accurately and flexibly control active and reactive power flows to address these problems. Additionally, the photovoltaic (PV) inverter and the network reconfiguration (NR) play a significant role in voltage control by adjusting the reactive power and the topology of the DN, respectively. However, due to differences in response times, there is a lack of systematic coordination between NR and the inverters of the E-SOP and PV. This paper proposes a multi-time-scale voltage control model that includes day-ahead NR scheduling, inter-day droop control optimization of the PV and E-SOP, and real-time local droop control. Considering the uncertainties of renewable DG outputs and loads, a robust optimization method is used in the day-ahead stage to obtain a reliable network structure. Then, with more accurate intra-day predictions, a stochastic optimization method is used to obtain the optimal state-of-charge interval, aiming to provide a flexible regulation range for battery energy storage to cope with the power fluctuations during the real-time stage. In addition, to address the intra-day voltage control model with bilinear constraints of the droop control function, a particle swarm optimization method is used. The results are verified on a 33-bus DN system through comparative analyses, showing effective performance.

Amidst the swift progression of thermal radiation, the application of high-temperature spectral radiation characteristics in metallic materials has become pervasive across domains such as temperature measurement and material identification. X-Point is the intersection of the metal emissivity isotherms. The scope of X-point emissivity has been inadequately explored in prior investigations. The present study employs a concerted approach that amalgamates the simulation of the Lorentz-Drude model theory and the emissivity measurement based on the energy contrast method, to comprehensively investigate the X-point emissivity of metal materials. The results demonstrate that: (1) the simulation of X-point emissivity is achieved for the first time by the incorporation of the Lorentz-Drude model of electron-phonon scattering. (2) Validation of the model's feasibility is demonstrated by the measurement of the X-point emissivity of tungsten, and the inaugural measurement of vanadium's X-point emissivity. (3) Pioneeringly, X-point emissivity finds application in metal temperature measurement, exhibiting an accuracy 0.3 % superior to that of pyrometers. This research establishes an effective methodology for the model simulation of metal X-point emissivity and its application in radiation temperature measurement scenarios.

In recent years, a surge in malicious network incidents and instances of network information theft has taken place, with malware identified as the primary culprit. The primary objective of malware is to disrupt the normal functioning of computers and networks, all the while surreptitiously gathering users’ private and sensitive information. The formidable concealment and latency capabilities of malware pose significant challenges to its detection. In light of the operational characteristics of malware, this paper conducts an initial analysis of prevailing malware detection schemes. Subsequently, it extracts fuzzy features based on the distinct characteristics of malware traffic. The approach then integrates traffic detection techniques with Type II fuzzy recognition theory to effectively monitor malware-related traffic. Finally, the paper classifies the identified malware instances according to fuzzy association rules. Experimental results showcase that the proposed method achieves a detection accuracy exceeding 90%, with a remarkably low false alarm rate of approximately 5%. This method adeptly addresses the challenges associated with malware detection, thereby making a meaningful contribution to enhancing our country’s cybersecurity.

With the urbanization construction and the advancement of the carbon peaking and carbon neutrality goals, urban energy systems are characterized by coupling multi-energy networks and a high proportion of renewable energy. Urban energy systems need to improve the quality of energy use, as well as to achieve energy conservation and emission reduction. Inter-seasonal heat technology has satisfactory engineering application prospects in promoting renewable energy consumption and the energy supply of urban multi-energy systems. Considering inter-seasonal heat storage and electric hydrogen production, a joint optimization method of planning and operation is proposed for the urban multi-energy flow system. First, the operation framework of inter-seasonal heat storage and electric hydrogen production system is established, which clarifies the energy flow of the urban multi-energy system. Secondly, aiming at the goals of minimizing the equipment’s annual investment cost and the multi-energy system annual operation cost, combined with the time series period division method, a planning operation model has been established considering multi-objectives. Through case study, it is shown that the proposed model can promote the renewable energy consumption and reduce the operation cost of the whole system.

Aluminum/Polytetrafluoroethylene (Al/PTFE) is a representative energetic material, which can be used in many fields including defense weapon fragments, insensitive penetrator or oil-well perforation. However, the insufficient mechanical strength of traditional ingredient has seriously restricted its penetration ability, and traditional additives improve its compressive strength at the expense of reactivity. Hence, innovative component design and advanced manufacturing technology should be applied to Al/PTFE composite. In this investigation, TiH2 as a new reinforcement is introduced into Al/PTFE to improve both strength and reactivity. Quasi-static and dynamic compressive tests were conducted, and the impact reaction process was recorded by high-speed camera. The results shown Al/PTFE/TiH2 composites are all elasto-plastic materials, significant strain hardening and strain rate hardening phenomenon were observed. The compressive strength increased first and then decreased with increase in TiH2 content, reached the maximum of 185.1 MPa at the strain rate of 5000 s−1 when TiH2 content was 30 %, which increased by 30.4 % compared to traditional Al/PTFE. A newly-developed drop-weight device was used to quantitatively characterize reactivity, and the reaction efficiency reached 8.2 % when TiH2 content was 30 %, increased by 43.2 % compared to Al/PTFE. The formation of oriented PTFE nano-fibers was considered the important strengthening mechanism. The established Johnson–Cook model was agreed well with experimental results. Combining the results of thermogravimetry-differential scanning calorimetry (TG-DSC) tests with X-ray diffraction (XRD) analysis of reaction residues, the reaction mechanism was clarified. The impact initiation reaction was in a mechano-chemical manner and the reactivity was speculated to be the results of multiple actions between Al/PTFE and TiH2.

The arid inland river basins in northwestern China have been experienced various stages of soil and water resources over-exploitation over the past 50 years, as well as ecological protection and restoration in recent years, which has significantly influenced the regional groundwater and ecological environment. However, the long-term evolution of groundwater and its ecological effects remain unclear. In this study, the Kongque River Basin is chosen as a study area, and the spatio-temporal evolution of groundwater flow field is analyzed based on the comparison of recent groundwater monitoring data and historical data. The ecological effects of long-term groundwater evolution are discussed by analyzing the influence of regional groundwater to Populus euphratica in the riparian zone. The results show that from 1971 to 2021, the groundwater flow in the Kongque River Basin has undergone drastic variation due to excessive groundwater exploitation, and the groundwater levels have shown a general downward trend. The total area where the groundwater levels have decreased by more than 40 m is 204.69 km2, as well as 1735.81 and 1018.56 km2 for 20−40 and 5−20 m, and eventually led to the formation of a regional drawdown funnel, which have seriously affected the water consumption of Populus euphratica in the middle and lower reaches. From 1971 to 2016, the regional groundwater level depth in the riparian zone of the Kongque River Basin decreased by more than 10 m, with a degradation and shrinkage of Populus euphratica. After the implementation of the ecological water transport project since 2016, the groundwater level depth has gradually increased by 2−4 m, and reached the critical groundwater level of Populus euphratica. From 2014 to 2018, the regional NDVI increased by 17%, which indicates that the coverage rate of natural vegetation has improved. This study can improve the understanding of evolution patterns in groundwater and ecological environment in arid inland river basins of the northwestern China, and provide scientific references for sustainable development and utilization of groundwater and ecological protection in similar regions.

Advances in artificial intelligence (AI), including intelligent machines, are opening new possibilities to support teaching and learning in higher education. This research has found a ‘proof of concept’ in the application of machine learning in the assessment of educators’ use of four key microskills, drawn from an internationally established framework. The analysis of teaching videos where these microskills were demonstrated multiple times in front of a green screen or in a space formed the data set. Multiple videos of this nature were recorded to allow for increased analysis and deconstruction of the video components to enable the application of machine learning. The results showed how AI can be used to support the collaborative and reflective practice of educators in a time when online teaching has become the norm. Having achieved a ‘proof of concept’, this research has laid the groundwork to allow for the whole framework of ten microskills to be applied in this way thus adding a new dimension to its use. Providing such critical information that is not currently available in such a systematic and personalised way to educators in the higher education sector can also support the validity of formative assessment practices.

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved adaptive K-means clustering algorithm is designed for load pattern recognition and avoiding local sub-optimal clustering centroids. We also design bi-directional long-short-term memory neural networks with an attention mechanism to filter important load information and perform load forecasting for each recognized load pattern. The numerical results on the public load dataset show that our proposed method effectively forecasts the residential load with a high accuracy.

Seawater intrusion significantly affects the microbial communities within coastal aquifers. Investigating the spatial distribution of groundwater microbial communities in coastal regions is crucial for understanding seawater intrusion. The primary objective of this study is to develop a novel microbial index-based method for detecting seawater intrusion. Groundwater microbial samples were collected and sent to the laboratory in the west coastal area of Longkou City, Shandong Province. By characterizing the microbial community within the whole interval of seawater intrusion into fresh groundwater and discussing the effects of salt-freshwater displacement intensities on groundwater microbial communities, including diversity, structure, and function, using indoor domestication experiments, we reveal the response of microorganisms to the seawater intrusion process under in situ environmental conditions. The results show that the microbial community diversity is highest in environments with a seawater mixing proportion (P(sm)) of 2.5% and lowest in those with a P(sm) of 75%. When considering species abundance and evolutionary processes, the microbial community structure is similar at higher P(sm) levels, while it is similar at lower P(sm) levels based on the presence or absence of species. Tenericutes, Flavobacteriia, Rhodobacterales, Flavobacteriales, Rhodobacteraceae, Flavobacteriaceae, Cohaesibacteraceae, and Cohaesibacter are significantly positively correlated with the P(sm). Strong salt-freshwater displacement enhanced the richness and evenness of the microbial community, whereas weak displacement showed the opposite trend. Strong displacement affects the functional profiles of the microbial community. This study effectively addressed the challenge of obtaining samples in coastal areas and also incorporated salt-freshwater displacement intensities, which can more comprehensively describe the microbial community characteristics within the groundwater of coastal aquifers.

Introduction: Hair loss has always bothered many people, with numerous individuals potentially facing the issue of sparse hair.Methods: Due to a scarcity of accurate research on detecting sparse hair, this paper proposes a sparse hair cluster detection model based on improved object detection neural network and medical images of sparse hair under dermatoscope to optimize the evaluation of treatment outcomes for hair loss patients. A new Multi-Level Feature Fusion Module is designed to extract and fuse features at different levels. Additionally, a new Channel-Space Dual Attention Module is proposed to consider both channel and spatial dimensions simultaneously, thereby further enhancing the model’s representational capacity and the precision of sparse hair cluster detection.Results: After testing on self-annotated data, the proposed method is proven capable of accurately identifying and counting sparse hair clusters, surpassing existing methods in terms of accuracy and efficiency.Discussion: Therefore, it can work as an effective tool for early detection and treatment of sparse hair, and offer greater convenience for medical professionals in diagnosis and treatment.

Intracerebral haemorrhage (ICH) is a lethal cerebrovascular disorder with a high mortality and morbidity. Although it is a major public health problem, there is no effective treatment for ICH. After ICH, the primary and secondary mechanisms are mentioned when discussing brain injury. The transcription factor, nuclear factor-kappa B (NF-kB), is an important regulator of inflammatory responses. The role of platelet factor 4 (PF4) in ICH is unclear. To study the effect of PF4 on inflammatory response of rats in ICH, a rat model of striatum ICH was established by injecting autologous blood from the autogenous femoral artery into the right striatum of rats. Forty-eight hours after ICH, the expression of PF4, NF-kB (P-P65) and inflammatory changes in rats were determined with WB and ELISA. Heme was used to induce PC12 cell damage, simulate the ICH model in vitro, and detect PF4, P-P65 and striatal inflammatory changes. Short hairpin RNA (shRNA-PF4) was used to knock-down the expression of PF4 in PC12 cells to detect changes in inflammatory factors. The results showed that 48 hours after surgery, the behavioural score of cerebral haemorrhage was the lowest. The expression of PF4 and P-P65 in the striatum of the ICH group was significantly higher compared with the sham surgery group. The expression of interleukin (IL)-6 and IL-1b in the ICH group was also greatly improved. After inhibiting NF-kB expression, PF4 expression was decreased. In short, ICH enhances the expression of PF4, which induces an inflammatory response in rats with cerebral haemorrhage through the NF-kB signalling pathway. Reducing the expression of PF4 can attenuate the inflammatory response.

Abstract Neural Process (NP) fully combines the advantages of neural network and Gaussian Process (GP) to provide an efficient method for solving regression problems. Nonetheless, limited by the dimensionality of the latent variable, NP has difficulty fitting the observed data completely and predicting the targets perfectly. To remedy these drawbacks, the authors propose a concise and effective improvement of the latent path of NP, which the authors term Multi‐Latent Variables Neural Process (MLNP). MLNP samples multiple latent variables and integrates the representations corresponding to the latent variables in the decoder with adaptive weights. MLNP inherits the desirable property of linear computation scales of NP and learns the approximate distribution over objective functions from contexts more flexibly and accurately. By applying MLNP to 1‐D regression, real‐world image completion, which can be seen as a 2‐D regression task, the authors demonstrate its significant improvement in the accuracy of prediction and contexts fitting capability compared with NP. Through ablation experiments, the authors also verify that the number of latent variables has a great impact on the prediction accuracy and fitting capability of MLNP. Moreover, the authors also analyze the roles played by different latent variables in reconstructing images.

Fibroblast growth factor 5 (FGF5) plays key roles in promoting the transition from the anagen to catagen during the hair follicle cycle. The sheep serves as an excellent model for studying hair growth and is frequently utilized in various research processes related to human skin diseases. We used the CRISPR/Cas9 system to generate four FGF5-edited Dorper sheep and only low levels of FGF5 were detected in the edited sheep. The density of fine wool in GE sheep was markedly increased, and the proportion of fine wool with a diameter of 14.4–20.0 μm was significantly higher. The proliferation signal in the skin of gene-edited (GE) sheep was stronger than in wild-type (WT) sheep. FGF5 editing decreased cortisol concentration in the skin, further activated the activity of antioxidant enzymes such as Glutathione peroxidase (GSH-Px), and regulated the expression of Wnt signaling pathways containing Wnt agonists (Rspondins, Rspos) and antagonists (Notum) in hair regeneration. We suggest that FGF5 not only mediates the activation of antioxidant pathways by cortisol, which constitutes a highly coordinated microenvironment in hair follicle cells, but also influences key signals of the Wnt pathway to regulate secondary hair follicle (SHF) development. Overall, our findings here demonstrate that FGF5 plays a significant role in regulating SHF growth in sheep and potentially serves as a molecular marker of fine wool growth in sheep breeding.

Due to the overlap of initial traveling wave signals, the traveling wave propagation process in hybrid distribution lines is complicated to analyze. The most significant challenge posed by the traditional passive traveling wave-locating method for hybrid distribution lines lies in identifying the fault section and distinguishing the reflected wave from the fault point or the hybrid connection points. Based on this approach, with the application of the aerial-mode component of the pulse signal generated at the fault point, a fault-section-identification and fault-locating scheme for hybrid distribution feeders with active pulse injection is proposed. When power in a line is cut after a single-to-line ground (SLG) fault occurs, the same pulse is injected into the three phases from the neutral point of the coupling capacitor bank to construct the zero-mode component, which propagates to the SLG fault three-phase asymmetrical point, producing an aerial-mode component that is reflected back to the first end of the line. With the application of the arrival time of an aerial-mode wavefront, it is simple to locate the SLG fault for arbitrary forms of hybrid lines. The simulation results confirm the feasibility of the fault-locating scheme under different feeders, different fault locations, and fault resistances. The results of the experiments confirm the high practical value of the proposed method.

Abstract The effect of laser shock peening (LSP) on the microhardness and tensile properties of laser cladding (LC) 30CrMnSiNi2A high-strength steel was studied. After LSP, the microhardness of the cladding zone reached approximately 800 HV0.2, which was 25% higher than that of the substrate, while the cladding zone without LSP had an approximately 18% increase in its microhardness. Two strengthening processes were designed: groove LSP + LC + surface LSP versus LC + surface LSP. The former's tensile strength and yield strength were less than 10% weaker than those of forged materials, which is the best mechanical property recovery found in LC samples. The microstructural characteristics of the LC samples were analysed by scanning electron microscopy (SEM) and electron backscatter diffraction. Under the action of the laser-induced shock wave, the grain size of the LC sample surface was refined, the low-angle grain boundaries on the surface layer increased significantly, and the austenite grain length was reduced from 30–40 μm in the deep layer to 4–8 μm in the surface layer. In addition, LSP modulated the residual stress field, hence preventing the weakening effect of the LC process's thermal stress on the components' mechanical properties.

The genus Cyclocosmia Ausserer, 1871 previously included ten species from North America and Asia, six of which have been recorded from China.A new species, Cyclocosmia ruyi Yu & Zhang sp. n., is described and diagnosed, based on both sexes from Guangxi Province, China. Morphological characters for the early stages of juveniles of the new species are also provided.

Abstract Background In the field of genomics and personalized medicine, it is a key issue to find biomarkers directly related to the diagnosis of specific diseases from high-throughput gene microarray data. Feature selection technology can discover biomarkers with disease classification information. Results We use support vector machines as classifiers and use the five-fold cross-validation average classification accuracy, recall, precision and F1 score as evaluation metrics to evaluate the identified biomarkers. Experimental results show classification accuracy above 0.93, recall above 0.92, precision above 0.91, and F1 score above 0.94 on eight microarray datasets. Method This paper proposes a two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization (EF-BDBA), which can effectively reduce the dimension of microarray data and obtain optimal biomarkers. In the first stage, we propose an ensemble filter feature selection method. The method combines an improved fast correlation-based filter algorithm with Fisher score. obviously redundant and irrelevant features can be filtered out to initially reduce the dimensionality of the microarray data. In the second stage, the optimal feature subset is selected using an improved binary differential evolution incorporating an improved binary African vultures optimization algorithm. The African vultures optimization algorithm has excellent global optimization ability. It has not been systematically applied to feature selection problems, especially for gene microarray data. We combine it with a differential evolution algorithm to improve population diversity. Conclusion Compared with traditional feature selection methods and advanced hybrid methods, the proposed method achieves higher classification accuracy and identifies excellent biomarkers while retaining fewer features. The experimental results demonstrate the effectiveness and advancement of our proposed algorithmic model.

IntroductionWhether university graduates successfully make the transition from the university to work is critical for their career development. However, a comprehensive measurement of university-to-work success (UWS) that applies across different contexts remains lacking.MethodsTo address this gap, we adapted and validated the first comprehensive UWS measurement, the university-to-work success scale (UWSS), among samples of Chinese university graduates with three studies. We also provided new construct validity evidence for the scale and examined its measurement invariance across gender.ResultsThe findings of the current study showed that the 24-item UWSS-Chinese version clearly showed four factors (career satisfaction, income and financial independence, confidence in career future, and adaptation to work) that were consistent with the original scale. Moreover, construct validity analysis revealed that UWSS was positively associated with proposed antecedents (i.e., career adaptability) and outcome (i.e., adult identity). It also showed incremental validity over general indicators of career success (i.e., career adaptability) in predicting adult identity establishment. Additionally, the measurement also showed measurement invariance across gender.DiscussionOverall, these findings implied that the UWSS-Chinese version had good psychometric properties to be used in future studies and practice in China.

Abstract With the rapid development of social economy, power supply reliability of the distribution network has become particularly important. The distribution network has long been faced with problems such as overvoltage, single‐phase grounding failure and grounding protection. Passive, active, and hybrid arc suppression devices (ASDs) are proposed to solve these problems. Passive ASDs have the advantages of high reliability, low control complexity and good economic benefits, and have been widely used in distribution networks. With the wide application of power electronic components in distribution network, the active part and harmonic part of ground current, which cannot be effectively eliminated by passive ASDs, increase sharply. Therefore, an active arc suppression method is proposed. However, due to the high cost and low reliability of the active equipment, the hybrid ASD has better arc suppression effect. In this paper, the existing ASDs are reviewed, and specific simulation models are established to verify the principle and performance of three different ASDs. The advantages and disadvantages of the three ASDs are compared in detail, and the possible problems and research directions in the future research of arc suppression technology are prospected.

Abstract Energy conservation and carbon reduction in building energy is an important way to achieve the global goal of ‘carbon neutrality’. Common low‐carbon operation strategies of buildings rely on price incentives to guide users’ behaviour, which is difficult to make users aware of the impact of their energy consumption behaviour on carbon emissions. In this paper, the power system's dynamic carbon emission factors (CEF) were used to release information on energy consumption and carbon emission to building users. At the same time, the differential effects of building envelope and external temperature in the Building Information Modelling were considered. An optimisation method of building low‐carbon energy consumption strategy considering both the building and power carbon emission was established to improve the comprehensive carbon reduction ability of the building and power system. The simulation results show that the proposed method effectively coordinates the building virtual energy storage and demand response. By incorporating the dynamic energy carbon transaction cost into the objective function, the target signal of carbon reduction is transmitted to users so that the volatility of the renewable Energy and other random energy behaviours can be considered in the dynamic CEF.

Abstract Background Crop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time. Results We proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline. Conclusions The results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops.

This article proposes a skin disease segmentation network RSUnet based on network feature aggregation module and edge enhanced attention mechanism. The network subject adopts an encoding decoding structure, dividing the left encoding layer into four layers according to the order of feature map resolution from large to small. Multiple convolutional layers are used to capture the contextual information of the lesion image and extract image features. Using ResNext as the backbone feature extraction network and SwinTransformer as the auxiliary feature extraction network, fully absorbing the advantages of CNN and Transformer. In order to better combine the two and improve the segmentation of skin disease images, which may include hair, blood vessels, bubbles, etc., causing errors in the segmentation results. Propose a network feature aggregation module and input it into the next layer of feature extraction convolutional block bottleneck in ResNext. Secondly, considering that the edge shape of skin diseases is variable and fuzzy, and the segmentation task is difficult, an edge enhancement attention mechanism is proposed to increase the weight allocation of edges and enhance the edge details of the lesion area. The RSUnet designed based on the above ideas achieved better Jaccard, accuracy, recall, and dice values on the ISIC2018 dataset for skin disease segmentation compared to neural networks such as Unet, Unet++, DeepLabV3+, PSPNet, and FPN.

In recent years, 3D animation technology has developed at a rapid pace and has been used in a variety of fields. In the field of architecture, the use of bespoke 3D animation techniques to simulate the exterior and interior details of a building can not only help designers to communicate their design ideas, but can also be used in marketing to give purchasers a more visual understanding of the building. However, the production process of 3D animation is limited by storage capacity and bandwidth. In order to reduce the load of 3D data on the bandwidth, it is necessary to choose an appropriate compression method to compress the 3D data. This study introduces the Motion Picture Experts Group format algorithm to optimise the traditional algorithm for compressing 3D animation in the hope of achieving better compression results. The experimental results showed that for the quantization table filling method, linear function compression worked best. For the choice of sub-block size, the compression effect of sub-block size of A is the best and the reconstruction error is lower. The compression effect is significantly improved by introducing a local linear embedding algorithm for dimensionality reduction, and the combination of a digital zero processing algorithm and a discrete cosine transform results in better compression. In comparative experiments of 3D animation compression based on the Motion picture expert group format algorithm, the Motion Picture Experts Group format algorithm outperforms other algorithms. The experiments confirm the superior compression performance of the Motion picture expert group format algorithm and show that the Motion picture expert group formatalgorithm has some application value in architectural animation adaptation.

Target detection for hyperspectral images (HSIs) is one of the significant techniques in remote sensing data processing. Targets generally comprise various object categories with complex features and of varying sizes. Target detection is often used in complex application scenarios in which accurately and efficiently acquiring detection results can be challenging. The development of advanced target detection approaches is becoming increasingly necessary in both military and civilian fields. This article proposes an alternating direction method of multiplier (ADMM)-based parallel approach for hyperspectral target detection. Different from existing methods performing target detection solely on HSIs, our approach performs the fusion of hyperspectral and multispectral data to leverage both spectral and spatial information prior. For each task or data partition, the parallel processing of the computation load on multiple computing nodes can substantially reduce the computation time. In addition, we introduce a novel weighted ADMM, which takes the influence of different variables on convergence into account, to further enhance the computational efficiency of the target detection model. Experiments on real-world HSI datasets demonstrate that our proposed parallel method not only produces more accurate detection results than direct detection methods, but also achieves significant acceleration ratio compared with the serial processing flow.

Currently, little is known about the characteristics of polyphenol oxidase from wheat bran, which is closely linked to the browning of wheat product. The wheat PPO was purified by ammonium sulfate precipitation, DEAE-Sepharose ion-exchange column, and Superdex G-75 chromatography column. Purified wheat PPO activity was 11.05-fold higher, its specific activity was 1365.12 U/mg, and its yield was 8.46%. SDS-PAGE showed that the molecular weight of wheat PPO was approximately 21 kDa. Its optimal pH and temperature were 6.5 and 35 °C for catechol as substrate, respectively. Twelve phenolic substrates from wheat and green tea were used for analyzing the substrate specificity. Wheat PPO showed the highest affinity to catechol due to its maximum Vmax (517.55 U·mL−1·min−1) and low Km (6.36 mM) values. Docking analysis revealed strong affinities between catechol, gallic acid, EGCG, and EC with binding energies of −5.28 kcal/mol, −4.65 kcal/mol, −4.21 kcal/mol, and −5.62 kcal/mol, respectively, for PPO. Sodium sulfite, ascorbic acid, and sodium bisulfite dramatically inhibited wheat PPO activity. Cu2+ and Ca2+ at 10 mM were considered potent activators and inhibitors for wheat PPO, respectively. This report provides a theoretical basis for controlling the enzymatic browning of wheat products fortified with green tea.

Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency.

Locoweed is a collective name for a variety of plants, such as Oxytropis and Astragalus L. When these plants are infected by some fungi or endophytes, they will produce an alkaloid (swainsonine) that is harmful to livestock. Chronic toxicity characterized by neurological disorders occurs in livestock overfed on locoweed, and swainsonine (SW) is considered a major toxic component. The mechanism of the SW synthesis of endophytic fungi from locoweed remains unknown. In order to further discover the possible synthetic pathway of SW, in this study, a mycotoxin (SW) producer, Alternaria oxytropis isolate, UA003, isolated from Locoweed plants, and its mutant were subjected to transcriptomic analyses to ascertain the genes involved in the synthesis of this toxin. Mutant strain A. oxytropis E02 was obtained by ethyl methanesulfonate (EMS) mutagenesis treatment, and the strains were sequenced with different culture times for transcriptomic analysis and screening of differentially expressed genes. The results show a highly significant (p < 0.01) increase in SW yield in the A. oxytropis E02 strain obtained by EMS mutagenesis treatment compared to A. oxytropis UA003. A total of 637 differentially expressed genes were screened by transcriptome sequencing analysis, including 11 genes potentially associated with SW biosynthesis. These genes were screened using GO and KEGG data annotation and analysis. Among the differential genes, evm.TU.Contig4.409, evm.TU.Contig19.10, and evm.TU.Contig50.48 were associated with L-lysine biosynthesis, the L-pipecolic acid pathway, and the α-aminoadipic acid synthesis pathway. This study provides new insights to elucidate the mechanism of SW synthesis of endophytic fungi in locoweed and provides data support for further exploration of A. oxytropis genomics studies.

It is very important to promote plant growth and decrease the nitrogen leaching in soil, to improve nitrogen (N) utilization efficiency. In this experiment, we designed a new fertilization strategy, fruit tree hole storage brick (FTHSB) application under subsurface drip irrigation, to characterise the effects of FTHSB addition on N absorption and utilization in grapes. Three treatments were set in this study, including subsurface drip irrigation (CK) control, fruit tree hole storage brick A (T1) treatment, and fruit tree hole storage brick B (T2) treatment. Results showed that the pore number and size of FTHSB A were significantly higher than FTHSB B. Compared with CK, T1 and T2 treatments significantly increased the biomass of different organs of grape, N utilization and 15N content in the roots, stems and leaves, along with more prominent promotion at T1 treatment. When the soil depth was 15–30 cm, the FTHSB application significantly increased the soil 15N content. But when the soil depth was 30–45 cm, it reduced the soil 15N content greatly. T1 and T2 treatments obviously increased the activities of nitrite reductase (NR) and glutamine synthetase (GS) in grape leaves, also the urease activity(UR) in 30 cm of soil. Our findings suggest that FTHSB promoted plant N utilization by reducing N loss in soil and increasing the enzyme activity related to nitrogen metabolism. In addition, this study showed that FTHSB A application was more effective than FTHSB B in improving nitrogen utilization in grapes.

The ability to selectively emit in specific spectral bands, known as spectral selective emission, is highly valuable in thermal photovoltaic systems, infrared stealth, radiation heat dissipation, and other fields. This paper presents a planarized molybdenum/zinc sulfide (Mo/ZnS) multilayer film design to achieve infrared stealth under high temperatures. By utilizing the distinctive radiation characteristics of an ultra-thin Mo layer and impedance matching, selective emission in the infrared band can be achieved. The fabricated multilayer films have low emissivity in the atmospheric window (ε(3–5μm) = 0.19; ε(8–14μm) = 0.29), but high emissivity in the non-atmospheric window (ε(5–8μm) = 0.7). In addition to ensuring infrared stealth performance, the films can also withstand high temperatures up to 723K with good high-temperature resistance. Moreover, the proposed design has several advantages, including structural simplicity, ductility, and ease of mass production. This work could bring added convenience to many applications, including infrared stealth, in the future.

As a prescribed antioxidant, N-Acetylcysteine is used in treating disease and supportive care. However, N-acetylcysteine’s role in cancer therapy is controversial and needs to be investigated thoroughly. Presently, renal cell carcinoma is one of the most common forms of malignancy and needs new anti-cancer drugs with high efficacy and low side effect. Herein, we studied the effect of N-acetylcysteine on cell proliferation, oxidant stress, and apoptosis on human renal cell carcinoma cell line A498 and tried to understand the mechanisms driving these effects. The results show that N-acetylcysteine could enhance antioxidative activities against oxidative damage and suppress the expression of the directly apoptotic protein to inhibit apoptosis when used at low concentration (10 mM), thereby killing A498 cells. Therefore, N-acetylcysteine has two contradictory activities towards renal cell carcinoma cells.

Abstract Solid-state cooling technology based on electrocaloric effect (ECE) has been advanced as an alternative to replace the vapour-compression approach to overcome the releasing of the global warming gases. However, the development in high ECE materials is still a challenge. In this work, polarization merging strategy was proposed to achieve a large ECE in xBa(Sn0.07Ti0.93)O3-(1−x)Ba(Hf0.1Ti0.9)O3 ferroelectric ceramics, where x = 0, 0.2, 0.4, 0.6, 0.8, and 1. Ba(Sn0.07Ti0.93)O3 with an orthorhombic phase and Ba(Hf0.1Ti0.9)O3 with a rhombohedral phase at room temperature were prepared beforehand as precursors, and phase-coexisted xBSnT-(1−x)BHfT ceramics were formed via a solid-state reaction approach. Phase coexisting structures were confirmed using the X-ray diffraction. The merged polarization was confirmed by the dielectric and ferroelectric properties. Optimal ECEs were obtained for 0.2BSnT-0.8BHfT ceramics, i.e., adiabatic temperature change ΔT = 2.16±0.08 K at 80 °C and 5 MV/m, and ΔT = 3.35±0.09 K at 80 °C and 7 MV/m.

With the explosive growth of distributed renewable energy connected to the power system, the application of AC–DC hybrid distribution network with power electronic transformer (PET) becomes more and more extensive. An equivalent loss model is formulated from the perspective of the internal topology of PET. Then, a novel PET power flow model is established to obtain the relationship between the three ports. On this basis, reactive power of PV nodes connecting PET and distributed renewable generation is modified by establishing an equivalent impedance matrix, thus the forward–backward sweep load flow calculation method is improved for power flow calculation and volt/var control of distribution network. Different from traditional reactive optimization methods, a multi-objective reactive power optimization model considering the PET and two types of photovoltaic converters is established in this paper, with the targets of minimizing voltage deviation and power loss simultaneously. Finally, the numerical simulation shows the accuracy, rationality, and feasibility of the proposed method.

Complex yet lethal wounds with uncontrollable bleeding hinder conventional hemostats from clotting blood at the source or deep sites of injury vasculature, thereby causing massive blood loss and significantly increased mortality. Inspired by the attack action of torpedoes, we synthesized microcluster (MC) colloidosomes equipped with magnetic-mediated navigation and “blast” systems to deliver hemostats into the cavity of vase-type wounds. CaCO3/Fe2O3 (CF) microparticles functionalized with Arg-Gly-Asp (RGD) modified polyelectrolyte multilayers were co-assembled with oppositely charged zwitterionic carbon dots (CDs) to form MC colloidosomes, which were loaded with thrombin and protonated tranexamic acid (TXA-NH3+). The composite microparticles moved against blood flow under magnetic mediation and simultaneously disassembled for the burst release of thrombin stimulated by TXA-NH3+. The CO2 bubbles generated during disassembly produced a “blast” that propelled thrombin into the wound cavity. Severe bleeding in a vase-type hemorrhage model in the rabbit liver was rapidly controlled within ∼60 s. Furthermore, in vivo subcutaneous muscle and liver implantation models demonstrated excellent biodegradability of MC colloidosomes. This study is the first to propose a novel strategy based on the principle of torpedoes for transporting hemostats into vase-type wounds to achieve rapid hemostasis, creating a new paradigm for combating trauma treatment.