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In the present study, the effect of the Nb element on the lattice parameters, phase stability and martensitic transformation behaviors of Ti-Zr-based shape memory alloys was extensively investigated using the first-principles calculation. The lattice parameters of both the β parent phase and α′ martensite phase gradually decreased with Nb content increasing. For the α″ martensite phase, the lattice constant (a) gradually increased with the increase in Nb content, whereas the lattice constants (b and c) continuously decreased due to the addition of Nb. Based on the formation energy and density of state, β→α′ martensitic transformation occurred, as the Nb content was not more than 12.5 at.%. However, the Ti-Zr-Nb shape memory alloys with a Nb content higher than 12.5 at.% possessed the β→α″ martensitic transformation. However, both the largest transformation strain and sensitivity of critical stress to temperature (dσ/dT) can be optimized by controlling 12.5 at.% Nb in the Ti-Zr-Nb shape memory alloy, which was favorable to obtaining the largest elastocaloric effect. [ABSTRACT FROM AUTHOR]

The growth and development of apricot flower organs are severely impacted by spring frosts. To better understand this process, apricot flowers were exposed to temperatures ranging from 0 °C to −8 °C, including a control at 18 °C, in artificial incubators to mimic diverse low-temperature environments. We aimed to examine their physiological reactions to cold stress, with an emphasis on changes in phenotype, membrane stability, osmotic substance levels, and antioxidant enzyme performance. Results reveal that cold stress induces significant browning and cellular damage, with a sharp increase in browning rate and membrane permeability below −5 °C. Soluble sugars and proteins initially rise as osmoprotectants, but their content decreases at lower temperatures. Proline content consistently increases, suggesting a protective role. Antioxidant enzyme activities, including catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), and ascorbate peroxidase (APX), exhibit a complex pattern, with initial increases followed by declines at more severe cold conditions. Correlation and principal component analyses highlight the interplay between these responses, indicating a multifaceted adaptation strategy. The findings contribute to the understanding of apricot cold tolerance and inform breeding efforts for improved crop resilience. [ABSTRACT FROM AUTHOR]

This study explores the application of the fully convolutional network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG), thunderstorms, and short-term heavy rain (STHR) in Gansu. The training data come from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations. The performance of the proposed FCN model, based on 2017 to 2021 training datasets, demonstrated a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when tested against the 2022 dataset. Operational deployment in 2023 yielded an average critical success index (CSI) of 24.3%, a probability of detection (POD) of 62.6%, and a false alarm ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model's optimal performances in terms of hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model's ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future. [ABSTRACT FROM AUTHOR]

The advancement of autonomous driving technology has urgently necessitated enhanced safety testing measures. Traditional road testing methods face significant challenges due to their high costs and prolonged durations. In response to the inefficiencies of manual scenario construction and the difficulties in selecting effective scenarios using common scenario generation methods in autonomous driving safety testing, this study proposes an innovative automatic SG system based on PreScan2021.1.0. The SG process is significantly simplified by this system's capability to swiftly and accurately generate a vast array of specific scenarios through the input of scene parameters. The results indicate that this system achieves SG at a rate 2.5-fold faster than manual methods, alongside substantial improvements in accuracy. This system introduces a novel approach to virtual simulation, which is vital for the progress of autonomous driving safety. It offers a new paradigm for quickly and precisely generating test scenarios for autonomous driving. [ABSTRACT FROM AUTHOR]

Alpinia plants are widely cherished for their vibrant and captivating flowers. The unique feature of this genus lies in their labellum, a specialized floral structure resulting from the fusion of two non-fertile staminodes. However, the intricate process of pigment formation, leading to distinct color patterns in the various labellum segments of Alpinia, remains a subject of limited understanding. In this study, labellum tissues of two Alpinia species, A. zerumbet (yellow–orange flowers) and A. oxyphylla (white–purple flowers), were sampled and analyzed through morphological structure observation, metabolite analysis, and transcriptome analyses. We found that hemispherical/spherical epidermal cells and undulate cell population morphology usually display darker flower colors, while flat epidermal cells and cell populations usually exhibit lighter flower colors. Metabolomic analysis identified a high concentration of anthocyanins, particularly peonidin derivatives, in segments with orange and purple pigments. Additionally, segments with yellow pigments showed significant accumulations of flavones, flavanols, flavanones, and xanthophylls. Furthermore, our investigation into gene expression levels through qRT-PCR revealed notable differences in several genes that participated in anthocyanin and carotenoid biosynthesis among the four pigmented segments. Collectively, these findings offer a comprehensive understanding of pigmentation in Alpinia flowers and serve as a valuable resource for guiding future breeding efforts aimed at developing Alpinia varieties with novel flower colors. [ABSTRACT FROM AUTHOR]

Shaoxing rice wine is a notable exemplar of Chinese rice wine. Its superior quality is strongly correlated with the indigenous natural environment. The results indicated that Firmicutes (75%), Actinobacteria (15%), Proteobacteria (5%), and Bacteroidetes (3%) comprised the prevailing bacterial groups. Among the main bacterial genera, Lactobacillus was the most abundant, accounting for 49.4%, followed by Lactococcus (11.9%), Saccharopolyspora (13.1%), Leuconostoc (4.1%), and Thermoactinomyces (1.1%). The dominant fungal phyla were Ascomycota and Zygomycota. Among the dominant genera, Saccharomyces (59.3%) prevailed as the most abundant, followed by Saccharomycopsis (10.7%), Aspergillus (7.1%), Thermomyces (6.2%), Rhizopus (4.9%), Rhizomucor (2.2%), and Mucor (1.3%). The findings demonstrate that the structure of the bacterial and fungal communities remains stable in the environment, with their diversity strongly influenced by climatic conditions. The continuous fluctuations in environmental factors, such as temperature, air pressure, humidity, rainfall, and light, significantly impact the composition and diversity of microbial populations, particularly the dominant bacterial community. [ABSTRACT FROM AUTHOR]

Real-time quantitative PCR (RT-qPCR) has become a widely used method for exploring plant gene expression level. The method requires using some stably expressed genes as a reference to accurately normalize the RT-qPCR data. However, under various stresses and hormone treatments, the levels of most reference genes vary. Environmental variations also influence their expression levels. The lack of validated, stably expressed reference genes can mislead the study of gene function in pears. "Huangguan" pears have recently become the focus of research on stress resistance mechanisms, such as high resistance. Therefore, the aim of this study was to select the optimal reference genes in Huangguan pears, and we analyzed the expression of the genes EF1α, ACT, SKD1, YLS8, UBQ, GAPDH, TUB, and WDP in a series of pear leaf sets under various stresses and hormone treatments. Using different statistical algorithms, we found that under various treatments, the WDP gene had more stable expression, ACT was the most stable under MeJA treatment, YLS8 was the most valuable reference gene under ABA hormone and heat stress conditions, and GAPDH showed worst results compared to other housekeeping genes, except under heat stress. These results will supply valuable and updated information for the selection of housekeeping genes in pears under biotic and abiotic stresses in the future. [ABSTRACT FROM AUTHOR]

The Doi–Onsager molecular theory is capable of providing a rather accurate description of the local behavior of molecules; however, its computation is extremely time-consuming, since some higher-dimensional variables are typically involved. Therefore, establishing a computable reduced model that can capture essential physical properties is an important issue. In this work, we derived a reduced Q-tensor hydrodynamic theory that described smectic phases with density variations from the Doi–Onsager molecular theory using the Bingham closure approximation. The coefficients in the tensor model were derived from those in the molecular model. The energy dissipation law was inherited from the tensor model. Some special cases for the model were also discussed. [ABSTRACT FROM AUTHOR]

We used the recently introduced stress tensor trajectory Uσ space construction within the framework of next-generation quantum theory of atoms in molecules (NG-QTAIM) for a chirality investigation of alanine when subjected to a non-structurally distorting electric field. The resultant sliding of the axial-bond critical point (BCP) responded significantly, up to twice as much, in the presence of the applied electric field in comparison to its absence. The bond flexing, a measure of bond strain, was always lower by up to a factor of four in the presence of the electric field, depending on its direction and magnitude. An achiral character of up to 7% was found for alanine in the presence of the applied electric field. The achiral character was entirely absent in the presence of the lowest value of the applied electric field. Future applications, including molecular devices using left and right circularly polarized laser pulses, are briefly discussed. [ABSTRACT FROM AUTHOR]

We used next-generation QTAIM (NG-QTAIM) to explain the cis-effect for two families of molecules: C2X2 (X = H, F, Cl) and N2X2 (X = H, F, Cl). We explained why the cis-effect is the exception rather than the rule. This was undertaken by tracking the motion of the bond critical point (BCP) of the stress tensor trajectories Tσ(s) used to sample the Uσ-space cis- and trans-characteristics. The Tσ(s) were constructed by subjecting the C1-C2 BCP and N1-N2 BCP to torsions ± θ and summing all possible Tσ(s) from the bonding environment. During this process, care was taken to fully account for multi-reference effects. We associated bond-bending and bond-twisting components of the Tσ(s) with cis- and trans-characteristics, respectively, based on the relative ease of motion of the electronic charge density ρ(rb). Qualitative agreement is found with existing experimental data and predictions are made where experimental data is not available. [ABSTRACT FROM AUTHOR]

Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios. [ABSTRACT FROM AUTHOR]

Featured Application: The specific findings of this research will deep our understanding for the mechanisms of bio-decontamination from material surfaces by plasma technology. The main objective of this work was to fully understand the bio-decontamination process in a reduced-pressure oxygen plasma. Gram-negative Escherichia coli species was chosen as the target microorganism in this test. The comparison of decontamination efficacy between plasma total and UV radiation individually under various treatment parameters and tests of DNA agarose electrophoresis were made to evaluate the inactivation effect of UV radiation. The quantity of protein leakage and the concentration of malondialdehyde (MDA), which are markers of the end products of lipid peroxidation, in bacterial suspension after treatment were determined to estimate the contribution of both charged particles and free radicals for bacterial death. In addition, a scanning electronic microscope was used to visualize the plasma effect on microorganisms. The results showed that the essential action of the oxygen plasma on Escherichia coli is believed to be attributed to the fast and intense etching on cell membrane by electrons and ions. Attacks on polyunsaturation fatty acid (PUFA) in the cell membrane by oxygen free radicals and the destruction of the DNA in the cell by UV radiation are accessorial during an effective decontamination process. [ABSTRACT FROM AUTHOR]

The growth of secondary organic aerosols (SOA) is a vital cause of the outbreaks of winter haze in North China. Intermediate volatile organic compounds (IVOCs) are important precursors of SOA. Therefore, the chemical characteristics, source, and SOA production of IVOCs during haze episodes have attracted much attention. Hourly time resolution IVOC samples during two haze episodes collected in Hebei Province in North China were analyzed in this study. Results showed that: (1) the concentration of IVOCs measured was within the range of 11.3~85.1 μg·cm−3 during haze episodes, with normal alkanes (n-alkanes), polycyclic aromatic hydrocarbons (PAHs), branched alkanes (b-alkanes), and the residue unresolved complex mixture (R-UCM) accounting for 8.6 ± 2.3%, 6.8 ± 2.2%, 24.1 ± 3.8%, and 60.5 ± 6.5% of IVOCs, respectively. NC12-nC15 in n-alkanes, naphthalene and its alkyl substitutes in PAHs, b-alkanes in B12–B16 bins, and R-UCM in B12–B16 bins are the main components, accounting for 87.0 ± 0.2%, 87.6 ± 2.9%, 85.9 ± 5.4%, 74.0 ± 8.3%, respectively. (2) Based on the component characteristics of IVOCs and the ratios of n-alkanes/b-alkanes in emission sources and the hourly variation of IVOCs during haze episodes, coal combustion (CC), biomass burning (BB), gasoline vehicles (GV), and diesel vehicles (DV)were identified as important emission sources of IVOCs in Hebei Province. (3) During haze episodes, temporal variation of the estimated SOA production based on different methods (such as IVOCs concentration, OC/ECmin tracer, and the PMF model) were similar; however, the absolute values were different. This difference may be due to the transformation of IVOCs to SOA affected by various factors such as SOA production from different IVOC components, meteorological conditions, atmospheric oxidation, etc. [ABSTRACT FROM AUTHOR]

Plasma is a surface decontamination tool that is widely used in the food fields for pesticide degradation. In this study the effect of plasma on pesticide elimination from the surface of corn and the corn quality changes were tested as functions of power, air flow rate, treatment time, and frequency. Results indicated that plasma treatment for 60 s at 1000 mL·min−1 air flow rate, power of 20 W, and frequency of 1200 Hz, achieved the largest degradation efficiency up to 86.2% for chlorpyrifos and 66.6% for carbaryl, both of which were below the maximum residues limit of grains. Most importantly, after plasma treatment, there was a remarkable decrease (p < 0.05) in moisture content and starch content for treated corn compared with control. The acid value for treated corn showed a prominent increase (p < 0.05), but within the acceptable range of the standard. The vitamin B2 content of treated corn did not show a significant difference (p > 0.05). All results of this study demonstrated that plasma treatment is a promising technology with the ability to remove pesticide residues on corn while maintaining its quality within acceptable limits. [ABSTRACT FROM AUTHOR]

Limited data are available on the effects of 5-aminolevulinic acid (ALA) on plant photosynthesis in relation to the nitrogen (N) level. In this study, we investigate photosynthetic responses to ALA in canola plants (Brassica napus L.). We used wild-type plants without ALA addition (controls), wild-type plants with exogenous ALA application, and transgenic plants that endogenously overproduced ALA. The plants were grown hydroponically in nutrient solutions with low, middle, and high concentrations of N. Our results indicate that plants in both treatment groups had higher chlorophyll contents and net photosynthetic rates and lower intracellular CO2 concentrations in the leaves, as compared to controls. Furthermore, simultaneous measurement of prompt chlorophyll fluorescence and modulated 820-nm reflections showed that the active photosystem II (PS II) reaction centers, electron transfer capacity, and photosystem I (PS I) activity were all higher in treated plants than controls at all N levels; however, the responses of some photochemical processes to ALA were significantly affected by the N level. For example, under low N conditions only, a negative ΔK peak appeared in the prompt chlorophyll fluorescence curve, indicating a protective effect of ALA on electron donation via activation of the oxygen-evolving complex. Taken together, our findings suggest that ALA contributes to the promotion of photosynthesis by regulating photosynthetic electron transport under various N levels. These findings may provide a new strategy for improving photosynthesis in crops grown in N-poor conditions or reduced N-fertilization requirements. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs .