mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Optimization of the grain boundary diffusion process by doping gallium and zirconium in Nd-Fe-B sintered magnets

As the channel for grain boundary diffusion (GBD) in Nd-Fe-B magnets, grain boundary (GB) phases have a very important effect on GBD. As doping elements that are commonly used to regulate the GB phases in Nd-Fe-B sintered magnets, the influences of Ga and Zr on GBD were investigated in this work. The results show that the Zr-doped magnet has the highest coercivity increment (7.97 kOe) by GBD, which is almost twice that of the Ga-doped magnet (4.32 kOe) and the magnet without Ga and Zr (3.24 kOe). Microstructure analysis shows that ZrB_2 formed in the Zr-doped magnet plays a key role in increasing the diffusion depth. A continuous diffusion channel in the magnet can form because of the presence of ZrB_2. ZrB_2 can also increase the defect concentration in GB phases, which can facilitate GBD. Although Ga can also improve the diffusion depth, its effect is not very obvious. The micromagnetic simulation based on the experimental results also proves that the distribution of Tb in the Zr-doped magnet after GBD is beneficial to coercivity. This study reveals that the doping elements Ga and Zr in Nd-Fe-B play an important role in GBD, and could provide a new perspective for researchers to improve the effects of GBD.
Influence of viscous force on the dynamic process of micro-sphere in optical tweezers

With the advantages of noncontact, high accuracy, and high flexibility, optical tweezers hold huge potential for micromanipulation and force measurement. However, the majority of previous research focused on the state of the motion of particles in the optical trap, but paid little attention to the early dynamic process between the initial state of the particles and the optical trap. Note that the viscous forces can greatly affect the motion of micro-spheres. In this paper, based on the equations of Newtonian mechanics, we investigate the dynamics of laser-trapped micro-spheres in the surrounding environment with different viscosity coefficients. Through the calculations, over time the particle trajectory clearly reveals the subtle details of the optical capture process, including acceleration, deceleration, turning, and reciprocating oscillation. The time to equilibrium mainly depends on the corresponding damping coefficient of the surrounding environment and the oscillation frequency of the optical tweezers. These studies are essential for understanding various mechanisms to engineer the mechanical motion behavior of molecules or microparticles in liquid or air.
Raman imaging analysis of intracellular biothiols independent of the aggregation of sensing substrates

The content of biothiols in cells is highly associated with the occurrence and development of several diseases. However, due to their active chemical properties, thiol-contained molecules are normally volatile during the detection process, rendering precise analysis of intracellular biothiols challenging. In this study, 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB) is covalently modified on the surface of gold nanorods (AuNRs), constructing sensing substrates for in situ Raman imaging analysis of biothiols in cells. AuNRs are able to serve as ideal surface-enhanced Raman scattering substrates, and thus Raman signals of DTNB are greatly amplified by AuNRs. Meanwhile, the disulfide bond of DTNB can be broken by thiols, thereby releasing part of DTNB from the surface of AuNRs. As a result, three kinds of main biothiols are sensitively quantified with DTNB-modified AuNRs according to the variation of Raman signals, and DTNB-modified AuNRs exhibit far better analytical performance than a commercial probe. In addition, the sensing substrates can be readily delivered to cytoplasm with the transmembrane of AuNRs, and are capable of responding to biothiols in cells. Notably, the Raman approach is established by the breaking of chemical bonds rather than the aggregation of substrates, which is more inclined to analyze intracellular biothiols with a desirable spatial resolution. Therefore, fluctuation of biothiols in glioma cells is evidently observed via Raman imaging. Overall, this work provides an alternative strategy for designing Raman sensors to visualize active molecules in cells.
Emissive metallacages for biomedical applications

Owing to their appealing three-dimensional structures and tunable photophysical properties, emissive metallacages have been widely applied in recognition and sensing, adsorption and separation, catalysis, etc. Recently, the application of emissive metallacages in biomedical fields has emerged as a hot research topic, because multiple biological functionalities can be facilely integrated into metallacage-based platforms to deliver different functions. In this review, the applications of emissive metallacages in bio-imaging, delivery and cancer theranostics are systematically summarized. The dilemmas and challenges of metallacage-based biomedical materials are also raised at the end of this review. We hope this review would provide some guidance for the construction of novel emissive metallacages with biological functions, and further advance the development of emissive metallacages as biomedical materials.
A rapid modeling method for urban microscale meteorology and its applications

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m,named URBAN (Urban Rapid & Building-Aware Neighborhood),which is capable of rapid assessment of meteorological fields over key urban areas,including wind speed,air temperature,humidity and thermal comfort index,with the execution time less than 10 minutes consuming 1 CPU core.URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly.To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings,we propose a new parameterization method based on stream functions,which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field.The model can provide various results for the meteorological service of large outdoor activities,including conventional meteorological elements (wind,temperature,humidity,radiation,etc.) and the Universal Thermal Comfort Index,which is derived from the relationship between physiological processes and environmental meteorological conditions.In this paper,URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event.By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region,the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed,2-meter-height air temperature and humidity are 0.98 m s~(-1),1.37 °C and 7.37%,respectively.
Effectively tuning the stability and optoelectronic properties of halide perovskites by B-site alloying

Halide perovskites (ABX3),as promising candidates for solar cell photovoltaic devices,have recently attracted increasing research interest,but many issues remain unsolved for their practical applications.One such issue is their poor stability,which largely reduces device lifetime and efficiency.In recent decades,using inorganic ions or organic molecules for replacement or alloying the A-site atoms in halide perovskites has improved thermodynamic stability to some extent.However,because the energy band edge structures of halide perovskites are contributed mainly by Bsite atoms (X is usually a halogen),the effect of B-site substitution on the optoelectronic properties of this functional material must be investigated.
A Note on a Question of I.Laine and C.C.Yang

The aim of this paper is to study the order of meromorphic solutions of linear difference equation A_n(z)f(z + n) +···+ A_1(z)f(z + 1) + A_0(z)f(z)=0,where the coefficients A_j (z)(j=0,...,n) are entire functions.We obtain some results by giving some restrictions on coefficients of above equation with no dominating coefficient and partially answer a question of I.Laine and C.C.Yang.
Dissecting the genetic basis of grain color and pre-harvest sprouting resistance in common wheat by association analysis

Pre-harvest sprouting (PHS) adversely affects wheat quality and yield,and grain color (GC) is associated with PHS resistance.However,the genetic relationship between GC and PHS resistance remains unclear.In this study,168 wheat varieties (lines) with significant differences in GC and PHS resistance were genotyped using an Illumina 90K iSelect SNP array.Genome-wide association study (GWAS) based on a mixed linear model showed that 67 marker-trait associations (MTAs) assigned to 29 loci,including 17 potentially novel loci,were significantly associated with GC,which explained 1.1-17.0% of the phenotypic variation.In addition,100 MTAs belonging to 54 loci,including 31 novel loci,were significantly associated with PHS resistance,which accounted for 1.1-14.7% of the phenotypic variation.Subsequently,two cleaved amplified polymorphic sequences (CAPS) markers,2B-448 on chromosome 2B and 5B-301 on chromosome 5B,were developed from the representative SNPs of the major common loci Qgc.ahau-2B.3/Qphs.ahau-2B.4 controlling GC/PHS resistance and PHS resistance locus Qphs.ahau-5B.4,respectively.Further validation in 171 Chinese mini-core collections confirmed significant correlations of the two CAPS markers with GC and PHS resistance phenotypes under different environments (P<0.05).Furthermore,the wheat public expression database,transcriptomic sequencing,and gene allelic variation analysis identified TraesCS5B02G545100,which encodes glutaredoxin,as a potential candidate gene linked to Qphs.ahau-5B.4.The new CAPS marker CAPS-356 was then developed based on the SNP (T/C) in the coding sequences (CDS) region of TraesCS5B02G545100.The high-density linkage map of the Jing 411/Hongmangchun 21 recombinant inbred lines (RILs) constructed based on specific locus amplified fragment sequencing markers showed that CAPS-356 collocated with a novel QTL for PHS resistance,supporting the role of TraesCS5B02G545100 as the potential candidate gene linked to Qphs.ahau-5B.4.These results provide valuable information for the map-based cloning of Qphs.ahau-5B.4 and breeding of PHS resistant white-grained varieties.
Development and pathogenicity analysis of full-length infectious cDNA clones of citrus yellow mottle-associated virus in citrus plants

Citrus yellow mottle-associated virus (CiYMaV) belonging to the subgenus Mandarivirus within the genus Potexvirus,was first identified in 2018 from Pakistan (CiYMaV-PK),where it is endemic in several regions.Here,three full-length cDNA clones (pCiYMaV-FL-1,pCiYMaV-FL-18,and pCiYMaV-FL-22) corresponding to the genomic RNA of CiYMaV were constructed and then agroinfiltrated on Chandler pummelo (Citrus grandis) seedlings using the vacuum infiltration method.All the inoculated plants developed severe vein yellowing,leaf mottling,and dwarfing symptoms by 40 days post-infiltration (dpi).The results of a direct tissue blot immunoassay and reverse transcription polymerase chain reaction detection showed 94.7-100% infection rates of pCiYMaV-FL at 60 dpi.Despite there being no observed difference among the three clones in the severity of symptom,pCiYMaV-FL-22 showed the highest accumulation levels of viral RNA and coat proteins.Moreover,pCiYMaV-FL-22 successfully infected seven other citrus varieties and induced symptoms in five of them.Transmission electron microscopy identified the presence of filamentous virus particles in extracts from systemic leaves of the plants infected with pCiYMaV-FL-22 at 6-months post-infiltration.Taken together,the results indicate that Koch's postulates were fulfilled for CiYMaV in citrus plants.This is the first report of full-length infectious cDNA clones of CiYMaV,and thus,the data provide a basis for further study of the molecular mechanisms of virus infection and the development of a viral vector to express foreign genes in citrus plants.
Exploring the nano-fungicidal efficacy of green synthesized magnesium oxide nanoparticles (MgO NPs) on the development,physiology,and infection of carrot (Daucus carota L.) with Alternaria leaf blight (ALB):Molecular docking

In this research,green synthesized magnesium oxide nanoparticles (MgO NPs) from lemon fruit extracts and their fungicidal potential was evaluated against Alternaria dauci infection on carrot (Daucus carota L.) under greenhouse conditions.The scanning and transmission electron microscopy (SEM and TEM) and ultra-violet (UV) visible spectroscopy were used to validate and characterize MgO NPs.The crystalline nature of MgONPs was determined using selected area electron diffraction (SAED).MgO NPs triggered substantial antifungal activity against A.dauci when exposed to 50 and 100 mg L~(-1) concentrations but the higher antifungal potential was noticed in 100 mg L~(-1) under invitro conditions.In fungal inoculated plants,a marked decrease in growth,photosynthetic pigments,and an increase in phenol,proline contents,and defense-related enzymes of carrot were seen over control (distilled water).However,foliar application of MgO NPs at 50 and 100 mg L~(-1) resulted in significant improvement of plant growth,photosynthetic pigments,phenol and proline contents,and defense enzymes activity of carrots with and without A.dauci infection.Spraying of MgO NPs at 100 mg L~(-1) had more plant length (17.11%),shoot dry weight (34.38%),plant fresh weight (20.46%),and root dry weight (49.09%) in carrots when challenged with A.dauci over inoculated control.The leaf blight indices and percent disease severity were also reduced in A.dauci inoculated plants when sprayed with MgO NPs.The non-bonding interactions of Alternaria genus protein with nanoparticles were studied using molecular docking.
A network perspective of human-nature interactions in dynamic and fast-changing landscapes

Increasing and intensifying the use of land represents a prominent sustainability challenge of particular importance in regions undergoing rapid change while at the same time exhibiting large natural and anthropocentrically induced variability. To reconcile the needs for both human prosperity and healthy ecosystems, a more integrated understanding of key biophysical and adaptation processes is paramount in such dynamic and deeply entangled social and environmental contexts. Interdisciplinary research utilizing a network perspective provides a novel methodological and theoretical approach to that end. We review and synthesize recent network-centric studies, and use this network perspective to show how rangeland managers in a dynamic pastoral region in the Qinghai Province of China form social relationships based on geographic proximity, social status and shared grazing areas. The results indicate that adaption to biophysical and socioeconomic changes is partly a social process in that rangeland managers develop their adaptive capacity jointly and in concert with others they trust and with whom they share grazing areas. Avenues for further development of this network perspective, in terms of how it might contribute important new insights about how to sustainably use land in dynamic landscapes undergoing rapid change, are suggested.
Proteomics of adjacent-to-tumor samples uncovers clinically relevant biological events in hepatocellular carcinoma

Normal adjacent tissues (NATs) of hepatocellular carcinoma (HCC) differ from healthy liver tissues and their heterogeneity may contain biological information associated with disease occurrence and clinical outcome that has yet to be fully evaluated at the proteomic level. This study provides a detailed description of the heterogeneity of NATs and the differences between NATs and healthy livers and revealed that molecular features of tumor subgroups in HCC were partially reflected in their respective NATs. Proteomic data classified HCC NATs into two subtypes (Subtypes 1 and 2), and Subtype 2 was associated with poor prognosis and high-risk recurrence. The pathway and immune features of these two subtypes were characterized. Proteomic differences between the two NAT subtypes and healthy liver tissues were further investigated using data-independent acquisition mass spectrometry, revealing the early molecular alterations associated with the progression from healthy livers to NATs. This study provides a high-quality resource for HCC researchers and clinicians and may significantly expand the knowledge of tumor NATs to eventually benefit clinical practice.
From aspiration to implementation of the 30% solution

In closing the COP15 of the Convention on Biological Diversity in late 2022,Huang Runqiu,China's Minister of Ecology and Environment and COP15 president,celebrated the agreement to protect ≥30% of the world by 2030.Recognizing Indigenous and traditional territories,where applicable,he said the agreement marked a ‘historic moment' in global efforts to save nature,calling the deal ‘a package we can all be proud of'.The challenges in implementing this solution are formidable,as a recent international effort led by Xiaoli Shen,Mingzhang Liu and Keping Ma explains.This timely article addresses the critical issue of how to disaggregate the global 30% target at the country level and identifies the different responsibilities of each country and the possible paths for implementation.
Non-planar dielectrics derived thermal and electrostatic field inhomogeneity for boosted weather-adaptive energy harvesting

Weather-adaptive energy harvesting of omnipresent waste heat and rain droplets,though promising in the field of environmental energy sustainability,is still far from practice due to its low electrical output owing to dielectric structure irrationality and unscalability.Here we present atypical upcycling of ambient heat and raindrop energy via an all-in-one non-planar energy harvester,simultaneously increasing solar pyroelectricity and droplet-based triboelectricity by two-fold,in contrast to conventional counterparts.The delivered non-planar dielectric with high transmittance confines the solar irradiance onto a focal hotspot,offering transverse thermal field propagation towards boosted inhomogeneous polarization with a generated power density of 6.1 mWm~(-2) at 0.2 sun.Moreover,the enlarged lateral surface area of curved architecture promotes droplet spreading/separation,thus travelling the electrostatic field towards increased triboelectricity.These enhanced pyroelectric and triboelectric outputs,upgraded with advanced manufacturing,demonstrate applicability in adaptive sustainable energy harvesting on sunny,cloudy,night,and rainy days.Our findings highlight a facile yet efficient strategy,not only for weather-adaptive environmental energy recovery but also in providing key insights for spatial thermal/electrostatic field manipulation in thermoelectrics and ferroelectrics.
Photovoltaphores: pharmacophore models for identifying metal-free dyes for dye-sensitized solar cells

Dye-sensitized solar cells (DSSCs) are cost-effective, sustainable, and versatile electricity producers, allowing them to be incorporated into a variety of devices. In this work, we explore the usage of pharmacophore modeling to identify metal-free dyes for DSSCs by means of virtual screening. Pharmacophore models were built based on experimentally tested sensitizers. Virtual screening was performed against a large dataset of commercially available compounds taken from the ZINC15 library and identified multiple virtual hits. A subset of these hits was subjected to DFT and time-dependent-DFT calculations leading to the identification of two compounds, TSC6 and ASC5, with appropriate molecular orbitals energies, favorable localization, and reasonable absorption UV-vis spectra. These results suggest that pharmacophore models, traditionally used in drug discovery and lead optimization, successfully predicted electronic properties, which are in agreement with the theoretical requirements for sensitizers. Such models may therefore find additional usages as modeling tools in materials sciences.
Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters (e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors (such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method with a generative adversarial interpolation network (GAIN) network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.
Automated deep learning system for power line inspection image analysis and processing:architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing.Owing to its excellent performance in computer vision,deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection.Despite their excellent performance,electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope,the need for constant retraining and optimization,and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning.In this study,an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems.This system design is based on the three critical design principles of generalizability,extensibility,and automation.Pre-trained models,fine-tuning (downstream task adaptation),and automated machine learning,which are closely related to these design principles,are reviewed.In addition,an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented.A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition.The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition,respectively.This demonstrates that the system design concept is reasonable and the system architecture feasible.