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Metamorphosis control is pivotal in preventing the outbreak of jellyfish, and it is often studied using common model organisms. The widespread use of the ultraviolet blocking agent homosalate in cosmetics poses a threat to marine ecosystems. Although the impact of homosalate on marine organisms has been extensively examined, there is a notable absence of research on its effects on jellyfish metamorphosis and the underlying mechanisms, warranting further investigation. In this study, we first established a study model by using 5-methoxy-2-methylindole to induce Aurelia coerulea metamorphosis, and selected homosalate as a PI3K agonist and an ERK agonist, while we used YS-49 as a specific PI3K agonist, as well as ERK knockdown, to observe their effect on the metamorphosis of Aurelia coerulea. The results showed that an Aurelia coerulea metamorphosis model was established successfully, and the PI3K agonist homosalate, YS-49, and the knockdown of ERK molecules could significantly delay the metamorphosis development of Aurelia coerulea. We propose that activating PI3K/Akt and inhibiting the ERK pathway are involved in the delayed development of Aurelia coerulea, which provides a new strategy for the prevention and control of jellyfish blooms.

Heart failure is a major global health concern, especially in Ethiopia. Numerous studies have analyzed heart failure data to inform decision-making, but these often struggle with limitations to accurately capture death dynamics and account for within-cluster dependence and heterogeneity. Addressing these limitations, this study aims to incorporate dependence and analyze heart failure data to estimate survival time and identify risk factors affecting patient survival. The data, obtained from 497 patients at Jimma University Medical Center in Ethiopia were collected between July 2015 and January 2019. Residence was considered as the clustering factor in the analysis. We employed the Bayesian accelerated failure time (AFT), and Bayesian AFT shared gamma frailty models, comparing their performance using the Deviance Information Criterion (DIC) and Watanabe–Akaike Information Criterion (WAIC). The Bayesian log-normal AFT shared gamma frailty model had the lowest DIC and WAIC, with well-capturing cluster dependency that was attributed to unobserved heterogeneity between patient residences. Unlike other methods that use Markov-Chain Monte-Carlo (MCMC), we applied the Integrated Nested Laplace Approximation (INLA) to reduce computational load. The study found that 39.44% of patients died, while 60.56% were censored, with a median survival time of 34 months. Another interesting finding of this study is that adding frailty into the Bayesian AFT models boosted the performance in fitting the heart failure dataset. Significant factors reducing survival time included age, chronic kidney disease, heart failure history, diabetes, heart failure etiology, hypertension, anemia, smoking, and heart failure stage.

As the need for effective antiviral treatment intensifies, such as with the coronavirus disease 19 (COVID-19) infection, it is crucial to understand that while the mechanisms of action of these drugs or compounds seem apparent, they might also interact with unexplored targets, such as cell membrane ion channels in diverse cell types. In this review paper, we demonstrate that many different drugs or compounds, in addition to their known interference with viral infections, may also directly influence various types of ionic currents on the surface membrane of the host cell. These agents include artemisinin, cannabidiol, memantine, mitoxantrone, molnupiravir, remdesivir, SM-102, and sorafenib. If achievable at low concentrations, these regulatory effects on ion channels are highly likely to synergize with the identified initial mechanisms of viral replication interference. Additionally, the immediate regulatory impact of these agents on the ion-channel function may potentially result in unintended adverse effects, including changes in cardiac electrical activity and the prolongation of the QTc interval. Therefore, it is essential for patients receiving these related agents to exercise additional caution to prevent unnecessary complications.

The reliability of the multicomponent stress–strength system was investigated under the two-parameter Burr X distribution model. Based on the structure of the system, the type II censored sample of strength and random sample of stress were obtained for the study. The maximum likelihood estimators were established by utilizing the type II censored Burr X distributed strength and complete random stress data sets collected from the multicomponent system. Two related approximate confidence intervals were achieved by utilizing the delta method under the asymptotic normal distribution theory and parametric bootstrap procedure. Meanwhile, point and confidence interval estimators based on alternative generalized pivotal quantities were derived. Furthermore, a likelihood ratio test to infer the equality of both scalar parameters is provided. Finally, a practical example is provided for illustration.

Radar echo extrapolation is a critical technique for short-term weather forecasting. Timely warnings of severe convective weather events can be provided according to the extrapolated images. However, traditional echo extrapolation methods fail to fully utilize historical radar echo data, resulting in limited accuracy for future radar echo prediction. Existing deep learning echo extrapolation methods often face issues such as high-threshold echo attenuation and blurring distortion. In this paper, we propose a UNet-based multi-branch feature extraction model named MBFE-UNet for radar echo extrapolation to mitigate these issues. We design a Multi-Branch Feature Extraction Block, which extracts spatiotemporal features of radar echo data from various perspectives. Additionally, we introduce a Temporal Cross Attention Fusion Unit to model the temporal correlation between features from different network layers, which helps the model to better capture the temporal evolution patterns of radar echoes. Experimental results indicate that, compared to the Transformer-based Rainformer, the MBFE-UNet achieves an average increase of 4.8% in the critical success index (CSI), 5.5% in the probability of detection (POD), and 3.8% in the Heidke skill score (HSS).

Many studies acknowledge the significance of assessment frameworks for urban heritage sites in preserving their identities. Due to the pandemic and its impact on heritage sites and visitors, the spatial features and identities of many heritage sites have undergone inevitable shifts, challenging the current assessment frameworks. As numerous urban heritage sites are being revitalised post COVID-19, this study aims to explore how heritage-assessment frameworks can be adapted during the pandemic to sustainably capture the identity of urban heritage sites, particularly from a spatial perspective. Methodologically, the study first examines existing urban-heritage-assessment frameworks, including typologies, embedded spatial attributes, and analysis methods, through a literature review. The research adopts the methodology framework for collecting and assessing evidence to demonstrate the cultural significance outlined in the ‘Guidance on identifying place and object of state-level social value in Victoria’ under Criterion G by the Heritage Council of Victoria. Chinatown, Melbourne, serves as the case study to address the research questions, utilising qualitative data from archival review and field observation. The results highlight the shortcomings of current heritage assessments, particularly in urban contexts, emphasising the overlooked importance of spatial attributes for understanding urban identity. This is exemplified by the exacerbated identity crisis in Chinatown, Melbourne, during the COVID-19 pandemic. Therefore, the study recommends future heritage assessments incorporate spatial attributes with a thematic approach tailored to diverse cultural-heritage backgrounds in the post-pandemic era. The study acknowledges the sample size and encourages future studies to test the framework with case studies of varied backgrounds.

Buffalo meat is gaining popularity for its nutritional properties, such as its low fat and cholesterol content. However, it is often unsatisfactory to consumers due to its dark color and low tenderness. There is currently limited research on the regulatory mechanisms of buffalo meat quality. Xinglong buffalo are raised in the tropical Hainan region and are undergoing genetic improvement from draught to meat production. For the first time, we evaluated the meat quality traits of Xinglong buffalo using the longissimus dorsi muscle and compared them to Hainan cattle. Furthermore, we utilized a multi-omics approach combining transcriptomics and metabolomics to explore the underlying molecular mechanism regulating meat quality traits. We found that the Xinglong buffalo had significantly higher meat color redness but lower amino acid content and higher shear force compared to Hainan cattle. Differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were identified, with them being significantly enriched in nicotinic acid and nicotinamide metabolic and glycine, serine, and threonine metabolic pathways. The correlation analysis revealed that those genes and metabolites (such as: GAMT, GCSH, PNP, L-aspartic acid, NADP+, and glutathione) are significantly associated with meat color, tenderness, and amino acid content, indicating their potential as candidate genes and biological indicators associated with meat quality. This study contributes to the breed genetic improvement and enhancement of buffalo meat quality.

Lung ischemia-reperfusion injury (LIRI) is a prevalent occurrence in various pulmonary diseases and surgical procedures, including lung resections and transplantation. LIRI can result in systemic hypoxemia and multi-organ failure. Hydroxycitric acid (HCA), the primary acid present in the peel of Garcinia cambogia, exhibits anti-inflammatory, antioxidant, and anticancer properties. However, the effects of HCA on LIRI remain unknown. To investigate the impact of HCA on LIRI in mice, the mice were randomly divided into four groups: the control group, the I/R model group, and the I/R + low- or high-dose HCA groups. Human umbilical vein endothelial cells (HUVECs) were subjected to hypoxia for 12 h followed by reoxygenation for 6 h to simulate in vitro LIRI. The results demonstrated that administration of HCA effectively attenuated lung injury, inflammation, and edema induced by ischemia reperfusion. Moreover, HCA treatment significantly reduced malondialdehyde (MDA) and reactive oxygen species (ROS) levels while decreasing iron content and increasing superoxide dismutase (SOD) levels after ischemia-reperfusion insult. Mechanistically, HCA administration significantly inhibited Hif-1α and HO-1 upregulation both in vivo and in vitro. We found that HCA could also alleviate endothelial barrier damage in H/R-induced HUVECs in a concentration-dependent manner. In addition, overexpression of Hif-1α counteracted HCA-mediated inhibition of H/R-induced endothelial cell ferroptosis. In summary, these results indicate that HCA alleviated LIRI by inhibiting oxidative stress and ferroptosis through the Hif-1α pathway.

The sugar transporter (STP) gene family is a key regulator of plant development, which is crucial for the efficient transport and utilization of sugars during plant growth and development. In this study, we identified the MeSTP7 gene, which is highly expressed in cassava fibrous roots, early storage roots, and under hormonal treatment, including IAA, MeJA, ABA, and GA3, and abiotic stressors, such as mannitol and NaCl. A strong response was observed with exoqenous IAA. Transfecting MeSTP7 into Arabidopsis promoted early seedling growth, particularly in lateral root development. The content of endogenous hormones (IAA and MeJA) as well as soluble sugars (sucrose, fructose, and glucose) was elevated in transgenic Arabidopsis. Hormone treatments with IAA, MeJA, GA3, and ABA on transgenic Arabidopsis revealed that transgenic Arabidopsis responded positively to added 20 μM IAA. They also exhibited co-induced regulation of lateral root formation by GA3, MeJA, and ABA. qRT-PCR analysis showed that overexpression of MeSTP7 upregulated the expression of IAA14, ARF7, and ARF19 in Arabidopsis. Under IAA treatment, the expression of these genes was similarly upregulated but downregulated under MeJA treatment. These results suggest that MeSTP7 may promote Arabidopsis seedling development by increasing the content of sucrose, glucose, and fructose in roots, which in turn influences IAA-based hormonal signaling.

Water quality and aquatic ecosystems along lakeshores are vital for ecological balance and human well-being. However, research has primarily focused on plankton, with benthic niches being largely overlooked. To enhance understanding of benthic microbial communities, we utilized 16S and 18S rRNA sequencing alongside multivariate statistical methods to analyze samples from the shoreline of Lake Taihu in Huzhou City, Zhejiang Province. Our results reveal a marked difference in species composition between benthic and planktonic microorganisms, with benthic cyanobacteria predominantly comprising filamentous genera like Tychonema, while 95% of planktonic cyanobacteria were Cyanobium. The β-diversity of benthic microorganisms was notably higher than that of planktonic counterparts. The neutral community model indicated that stochastic processes dominated planktonic microbial assembly, while deterministic processes prevailed in benthic communities. Null models showed that homogeneous selection influenced benthic community assembly, whereas planktonic communities were affected by undominated processes and dispersal limitation. Network analysis indicated that planktonic networks were more stable than benthic networks. Importantly, dominant benthic cyanobacterial genera posed potential toxin risks, highlighting the need for enhanced monitoring and ecological risk assessment. Overall, these findings enhance our understanding of benthic and planktonic microbial communities in lakeshores and offer valuable insights for aquatic assessment and management in eutrophicated environments.

Bedforms are complex and varied features on riverbeds created by the interaction between flow and sediment particles. Bedform evolution is crucial for understanding sediment transport, bed resistance, and the ecological environment of rivers. In this study, a series of movable-bed dune experiments were conducted utilizing advanced 3D topographic laser scanning technology to precisely measure the 2D and 3D dune bedforms under various conditions. This enabled the detailed analysis of the bedform parameters, including the dune length and height, the crest height, and the trough depth. Furthermore, this study examined the relationship between bed resistance and dune morphology and investigated how the intensity of bedload transport affects bed resistance. The findings indicate that bedload mass transport on the riverbed significantly contributes to increased bed resistance. Given the limitations of the traditional bedload transport rate formulas for dune bedforms, this research introduces a new formula derived from the experimental data. The accuracy of this formula was confirmed using the nonlinear least squares method. Our study enhances the accuracy of sediment transport predictions and provides scientific support for river management and engineering practices.

The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information of similar historical problems in the HEA field. To address this issue, a transferable meta-learning model (MTL-AMWO) based on an adaptive migration walrus optimizer is proposed to predict the phases of HEAs. Firstly, a transferable meta-learning algorithm frame is proposed, which consists of meta-learning based on adaptive migration walrus optimizer, balanced-relative density peaks clustering, and transfer strategy. Secondly, an adaptive migration walrus optimizer model is proposed, which adaptively migrates walruses according to the changes in the average fitness value of the population over multiple iterations. Thirdly, balanced-relative density peaks clustering is proposed to cluster the samples in the source and target domains into several clusters with similar distributions, respectively. Finally, the transfer strategy adopts the maximum mean discrepancy to find the most matching historical problem and transfer its optimal solution information to the target domain. The effectiveness of MTL-AMWO is validated on 986 samples from six datasets, including 323 quinary HEAs, 366 senary HEAs, and 297 septenary HEAs. The experimental results show that the MTL-AMWO achieves better performance than other algorithms.

Hypoallergenic processing is an area worthy of continued exploration. In the treatment of the peanut protein (PP), pH shift was applied by acidic (pH 1.0–4.0) and alkaline (pH 9.0–12.0) treatment, after which the pH was adjusted to 7.0. Following pH-shift treatment, PP showed a larger particle size than in neutral solutions. SDS-PAGE, CD analysis, intrinsic fluorescence, UV spectra, and surface hydrophobicity indicated the protein conformation was unfolded with the exposure of more buried hydrophobic residues. Additionally, the IgE-binding capacity of PP decreased after pH-shift treatment on both sides. Label-free LC–MS/MS results demonstrated that the pH-shift treatment induced the structural changes on allergens, which altered the abundance of peptides after tryptic digestion. Less linear IgE-binding epitopes were detected in PP with pH-shift treatment. Our results suggested the pH-shift treatment is a promising alternative approach in the peanut hypoallergenic processing. This study also provides a theoretical basis for the development of hypoallergenic food processing.

Tofu has captivated researchers’ attention due to its distinctive texture and enrichment in nutritional elements, predominantly soybean protein. Coagulants play a critical role in promoting coagulation during tofu production, directly influencing its texture, quality, and physicochemical characteristics. Currently, the impact of coagulant characteristics on coagulation, as well as the underlying mechanisms, remain unclear. This review provides a summary of research progress on salt coagulants, acid coagulants, enzyme coagulants, novel coagulants, polysaccharide additives, and various coagulant formulations. The coagulation mechanisms of various coagulants are also discussed. Accordingly, this paper seeks to offer reliable theoretical guidance for the development of novel coagulants and the realization of fully automated tofu production.

Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing method developed on the principle of stirring friction. Benefits from its solid-phase properties, compared with traditional additive manufacturing based on melting–solidification cycles, AFSD solves the problems of porosity, cracks, and residual stress caused by the melting–solidification process, and has a significant improvement in efficiency. In AFSD, the interaction between feedstocks and high-speed rotating print heads suffers severe plastic deformation at high temperatures below the melting point, ending up in fine, equiaxed recrystallized grains. The above characteristics make components by AFSD show similar mechanical behaviors to the forged ones. This article reviews the development of AFSD technology, elaborates on the basic principles, compares the macroscopic formability and material flow behavior of AFSD processes using different types of feedstocks, summarizes the microstructure and mechanical properties obtained from the AFSD of alloys with different compositions, and finally provides an outlook on the development trends, opportunities, and challenges to the researchers and industrial fields concerning AFSD.

The evolution of microstructures and mechanical properties with tempering temperature of a novel 2.5 GPa grade ultra-high strength steel with synergistic precipitation strengthening was investigated. With increasing tempering temperature, the experimental steel initially progressed from ε-carbides to M3C and then to M2C, followed by further coarsening of the M2C carbides and β-NiAl. Concurrently, the martensite matrix gradually decomposed and austenitized. The ultimate tensile strength and yield strength initially increased and subsequently decreased with rising tempering temperature, reaching peak value at 460 and 470 °C, respectively. Conversely, the ductility and toughness initially decreased and then increased with rising tempering temperature, reaching a minimum at 440 °C. The increase in strength was attributed to the secondary hardening effects resulting from carbide evolution and the precipitation of β-NiAl. The subsequent decrease in strength was due to the recovery of martensite and coarsening of precipitates. The decrease in ductility and toughness was linked to the precipitation of M3C, while their subsequent increase was primarily attributed to the dissolution of M3C and an increase in the volume fraction of reverted austenite. The high dislocation density of martensite, the film of reverted austenite, nanoscale M2C carbides, and ultrafine β-NiAl obtained during tempering at 480 °C resulted in the optimal mechanical properties of the experimental steel. The strength contributions from M2C carbides and β-NiAl were 1081 and 597 MPa, respectively.

Depression is a complex and common mental illness affecting physical and psychological health. Panax ginseng C. A. Mey is a traditional Chinese medicine with abundant pharmacological activity and applications in regulating mood disorders. 20 (S)-Protopanaxadiol is the major intestinal metabolite of ginsenoside and one of the active components in ginseng. In this study, we aimed to investigate the therapeutic effects of 20 (S)-Protopanaxadiol on neuronal damage and depression, which may involve mitochondrial dynamics. However, the mechanism underlying the antidepressant effects of 20 (S)-Protopanaxadiol is unelucidated. In the present study, we investigated the potential mechanisms underlying the antidepressant activity of 20 (S)-Protopanaxadiol by employing a corticosterone-induced HT22 cellular model and a chronic unpredicted mild stress (CUMS)-induced animal model in combination with a network pharmacology approach. In vitro, the results showed that 20 (S)-Protopanaxadiol ameliorated the corticosterone (CORT)-induced decrease in HT22 cell viability, decrease in 5-hydroxytryptamine (5-HT) levels, and increase in nitric oxide (NO) and malondialdehyde (MDA) levels. Furthermore, 20 (S)-Protopanaxadiol exerted improvement effects on the CORT-induced increase in HT22 cell mitochondrial reactive oxygen species, loss of mitochondrial membrane potential, and apoptosis. In vivo, the results showed that 20 (S)-Protopanaxadiol ameliorated depressive symptoms and hippocampal neuronal damage in CUMS mice, and sirtuin1 (SIRT1) and peroxisome proliferator-activated receptor-1-Alpha (PGC-1α) activity were activated in the hippocampus of mice, thereby alleviating mitochondrial dysfunction and promoting the clearance of damaged mitochondria. In both in vivo and in vitro models, after inhibiting SIRT1 expression, the protective effect of 20 (S)-Protopanaxadiol on mitochondria was significantly weakened, and dynamin-related protein 1 (DRP1)-mediated mitochondrial division was significantly reduced. These findings suggest that 20 (S)-Protopanaxadiol may exert neuroprotective and antidepressant effects by attenuating DRP1-mediated mitochondrial dysfunction and apoptosis by modulating the SIRT1/PGC-1α signaling pathway.

This study presents the development of highly efficient Surface-Enhanced Raman Scattering (SERS) substrates through femtosecond (fs) laser processing of crystalline silicon (Si), resulting in mountain-like microstructures. These microstructures, when decorated with gold nanoparticles (Au NPs), exhibit remarkable SERS performance due to the creation of concentrated hotspots. The enhanced Raman signals originate from the excitation of localized surface plasmon resonance (LSPR) of the Au NPs and the multi-scale rough morphology of the Si substrates. Finite-element method simulations confirm the electromagnetic field enhancement in narrow gaps, supporting the experimental observations. The fabricated substrates show high uniformity, oxidation resistance, long-term stability, and exceptional reproducibility, making them ideal for molecular detection, especially in food safety applications. A remarkable enhancement factor (EF) of 1010 is attained in the detection of Malachite Green (MG), boasting a limit of detection (LoD) as low as 10−14 M. This underscores the immense potential of this technique for achieving highly sensitive and dependable SERS-based sensing capabilities.

The continuous global escalation of soil salinization areas presents severe challenges to the stability and growth of agricultural development across the world. In-depth research on sugar beet (Beta vulgaris L.), an important economic and sugar crop with salt tolerance characteristics, is crucial for to determine its salt-tolerance mechanisms, which has important practical implications for production. This review summarizes the multifaceted effects of salt stress on sugar beet, ranging from individual plant responses to cellular and molecular adaptations. Sugar beet exhibits robust salt-tolerance mechanisms, including osmotic regulation, ion balance management, and the compartmentalization of toxic ions. Omics technologies, including genomics, transcriptomics, proteomics, post-translational modification omics and metabolomics, have played crucial roles in elucidating these mechanisms. Key genes and pathways involved in salt tolerance in sugar beet have been identified, paving the way for targeted breeding strategies and biotechnological advancements. Understanding these mechanisms not only enhances our knowledge of sugar beet’s adaptation strategies but also provides insights for improving salt tolerance in other crops. Future studies should focus on analyzing gene expression changes in sugar beet under salt stress to gain insight into the molecular aspects of its salt-tolerance mechanisms. Meanwhile, the effects of different environmental conditions on sugar beet adaptation strategies should also be investigated to improve their growth potential in salinized soils.

In power grid surveying, it is often necessary to fuse panchromatic and multispectral imagery for the design of power lines. Despite the abundance of deep learning networks for fusing these images, the results often suffer from spectral information loss or structural blurring. This study introduces a fusion model specifically tailored for power grid surveying that significantly enhances the representation of spatial–spectral features in remote sensing images. The model comprises three main modules: a TransforRS-Mamba module that integrates the sequence processing capabilities of the Mamba model with the attention mechanism of the Transformer to effectively merge spatial and spectral features; an improved spatial proximity-aware attention mechanism (SPPAM) that utilizes a spatial constraint matrix to greatly enhance the recognition of complex object relationships; and an optimized spatial proximity-constrained gated fusion module (SPCGF) that integrates spatial proximity constraints with residual connections to boost the recognition accuracy of key object features. To validate the effectiveness of the proposed method, extensive comparative and ablation experiments were conducted on GF-2 satellite images and the QuickBird (QB) dataset. Both qualitative and quantitative analyses indicate that our method outperforms 11 existing methods in terms of fusion effectiveness, particularly in reducing spectral distortion and spatial detail loss. However, the model’s generalization performance across different data sources and environmental conditions has yet to be evaluated. Future research will explore the integration of various satellite datasets and assess the model’s performance in diverse environmental contexts.

The process of urbanization leads to the rapid expansion of construction land and brings a series of ecological and environmental problems. The ecological network, as a linear landscape element, is of great significance to improve the quality of the regional ecological environment. In this study, the Morphological Spatial Pattern Analysis (MSPA) and the model of Minimum Cumulative Resistance (MCR) were used to construct the ecological corridors in the central city of Fuzhou, and the land use pattern under the constraints of the ecological network was simulated and quantified by the patch-level land use simulation (PLUS) tool with the results of the identification of ecological corridors. Meanwhile, with the help of InVEST habitat quality model, the regional habitat quality under different development scenarios was compared. The results show that (1) 19 ecological sources and 35 ecological corridors were identified; (2) under the constraints of ecological corridors, the area of forested land in the study area in 2027 was increased by 1.57% and the area of built-up land was reduced by 0.55% compared with that in 2022; (3) and under the constraints of ecological corridors, the mean value of habitat quality in Fuzhou City improved by 0.0055 and 0.0254 compared with 2022 and 2027 natural evolution scenarios, respectively. The study provides decision-making assistance for the construction of ecological corridors from the perspective of land use planning.

Middle borosilicate glass tubes are critical materials for medical packaging, with the drawing machine playing a pivotal role in their production process. However, the effects of the drawing wheel’s motion parameters on the glass tube remain underexplored. Therefore, based on the finite element method simulation and validation experiment, the effects of experimental factors (angle of upper and lower drawing wheel, AUD; friction factor of glass and rubber, FFGR; and distance of upper and lower drawing wheel, DUD) on experimental indexes (maximum effective stress of glass tube, MES; rotational speed of glass tube, RSG; maximum and average forward driving force, MFDF and AFDF; maximum and average rotational driving force, MRDF and ARDF; total energy consumption and power, TEC and TPD; maximum and average forward driving power, MFDP and AFDP; and maximum and average rotational driving power, MRDP and ARDP) were analyzed. The results indicated that compared to FFGR and AUD, the influence of DUD on experimental indexes was the highest. The positive influence of AUD on RSG, ARDP, and MRDP, the positive influence of FFGR and the negative influence of DUD on MRDF, ARDF, TEC, TPD, ARDP, and MRDP were found, respectively. These findings will provide a theoretical reference for the optimization of drawing machines.

Exopolysaccharides (EPSs) produced by microorganisms play an important role in biotolerance and reducing heavy metal (HM) contamination by limiting the migration of HMs into plants. However, research on the application of EPS-producing marine bacteria for soil heavy metal remediation remains limited, particularly regarding their mechanisms of HM immobilization in soil and impact on plant growth. In this study, the EPS-producing marine bacterium Micrococcus antarcticus HZ was investigated for its ability to immobilize Pb and produce EPSs in soil filtrate. The effects on the growth quality and biomass of pakchoi (Brassica chinensis L.), as well as bacterial communities in inter-root soil contaminated with Pb, were also investigated. The results indicated that HZ could reduce the Pb concentration in the soil filtrate, achieving a removal rate of 43.25–63.5%. The EPS content and pH levels increased in the presence of Pb. Pot experiments showed that adding HZ significantly increased the biomass of pakchoi (9.45–14.69%), vitamin C (Vc) (9.69–12.92%), and soluble protein content (22.58–49.7%). HZ reduced the Pb content in the roots (17.52–47.48%) and leaves (edible tissues) (43.82–52.83%) of pakchoi. HZ increased soil enzyme activities (alkaline phosphatase, dehydrogenase, and urease), and the contents of ammonium nitrogen and nitrate nitrogen. Additionally, HZ also increased the relative abundance of beneficial bacteria (e.g., Proteobacteria, Cyanobacteria, and Chlorobacteria) in the inter-root soil, which have prophylactic and heavy-metal fixation functions. In summary, HZ reduces effective Pb content in edible tissues, roots, and inter-root soil by regulating inter-root soil microbial community structure, increasing soil pH, nitrogen content, and soil enzyme activity, and altering dominant phylum abundance.

Male Idesia polycarpa, which display distinct morphological and physiological traits, exhibit greater adaptability to stressful environments than females. However, the connection between this adaptability and rhizosphere processes remains unclear. Here, we investigate the differences in root bacterial community structures between male and female plants at different developmental stages, identifying bacterial strains associated with plant sex through functional predictions. This study aims to inform the optimal allocation of male and female plants during cultivation and provide a theoretical basis for sex identification and breeding. Samples from seven-year-old male and female plants were collected during the flowering (May) and fruit ripening (October) stages. Rhizosphere nutrient content and bacterial diversity were analyzed using Illumina high-throughput sequencing technology. The results demonstrate that total nitrogen (TN), total carbon (TC), and available potassium (AK) varied between sexes at different times. No significant differences between male and female plants were observed in the Shannon, Simpson, and Chao1 indexes during the flowering period. However, the Chao1 and Shannon indexes were significantly higher at fruit maturity in male rather than female plants. The predominant phyla of rhizosphere bacteria were Pseudomonadota, Acidobacteriota, and Actinomycetes. Interestingly, from flowering to fruit ripening, the dominant phyla in both male and female plants shifted from Actinomycetes to Pseudomonadota. A significant correlation was observed between pH and AK and rhizosphere bacteria (p < 0.05), with metabolism being the main functional difference. This study provides preliminary insights into the functional predictions and analyses of bacteria associated with Idesia polycarpa. The above findings lay the groundwork for further investigation into the sex-specific differences in microbial flora across different developmental stages, elucidating the mechanisms underlying flora changes and offering theoretical support for the high-quality management of Idesia polycarpa.

The integrated stress response, especially stress granules (SGs), contributes to host immunity. Typical G3BP1+ stress granules (tSGs) are usually formed after virus infection to restrain viral replication and stimulate innate immunity. Recently, several SG-like foci or atypical SGs (aSGs) with proviral function have been found during viral infection. We have shown that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein induces atypical N+/G3BP1+ foci (N+foci), leading to the inhibition of host immunity and facilitation of viral infection. However, the precise mechanism has not been well clarified yet. In this study, we showed that the SARS-CoV-2 N (SARS2-N) protein inhibits dsRNA-induced growth arrest and DNA damage-inducible 34 (GADD34) expression. Mechanistically, the SARS2-N protein promotes the interaction between GADD34 mRNA and G3BP1, sequestering GADD34 mRNA into the N+foci. Importantly, we found that GADD34 participates in IRF3 nuclear translocation through its KVRF motif and promotes the transcription of downstream interferon genes. The suppression of GADD34 expression by the SARS2-N protein impairs the nuclear localization of IRF3 and compromises the host’s innate immune response, which facilitates viral replication. Taking these findings together, our study revealed a novel mechanism by which the SARS2-N protein antagonized the GADD34-mediated innate immune pathway via induction of N+foci. We think this is a critical strategy for viral pathogenesis and has potential therapeutic implications.

Quantitative classification and ordination are instrumental in improving our understanding of plant community patterns and facilitating effective conservation efforts in national mountain ecosystems worldwide. However, there has been a lack of relevant research focused on arid and semi-arid mountain ecosystems. This study aims to address this gap by investigating the Ningxia Helan Mountain National Nature Reserve (located in Northwest China). We conducted a comprehensive study on the patterns of plant communities and their association with environmental factors across a broad elevation range from 1200 m a.s.l. to 2600 m a.s.l. Our findings revealed the presence of 121 angiosperm species across 41 families, with vegetation classified into six distinct groups through two-way indicator species analysis (TWINSPAN) along the elevational gradient. Notably, the communities of Ulmus, Prunus, and Stipa in the middle elevation range exhibited the highest Shannon–Wiener (SW) and Simpson (SN) diversity indices, and these indices followed a single-peak pattern with increasing elevation. Canonical correspondence analysis (CCA) further revealed six distinct yet interrelated plant communities, revealing elevation (ELE) and the biological aridity index (BK) as the most influential environmental factors influencing plant communities’ distribution. This understanding is critically important for biodiversity conservation and the management of ecosystems in arid and semi-arid mountain ecosystems.

Dendrocalamus brandisii is a high-quality bamboo species that can be used for both bamboo shoots and wood. The nutritional components and flavors of D. brandisii vary from different geographical provenances. However, the unique biological characteristics of bamboo make morphological classification methods unsuitable for distinguishing them. Although the new cultivar ‘Manxie No.1’ has significant differences in the branch characteristics and the color of shoot sheaths compared to the D. brandisii, it still lacks precise genetic information at the molecular level. This study identified 231,789 microsatellite markers based on the whole genome of D. brandisii and analyzed their type composition and distribution on chromosomes in detail. Then, using TP-M13-SSR fluorescence-labeling technology, 34 pairs of polymorphic primers were screened to identify the new cultivar ‘Manxie No.1’ and 11 different geographical provenances of D. brandisii. We also constructed DNA fingerprinting profiles for them. At the same time, we mapped six polymorphic SSRs to the gene of D. brandisii, among which SSR673 was mapped to DhB10G011540, which is related to plant immunity. The specific markers selected in this study can rapidly identify the provenances and the new cultivar of D. brandisii and help explore candidate genes related to some important traits.

To address the challenges of sparse point clouds in current MIMO millimeter-wave radar environmental mapping, this paper proposes a dense 3D millimeter-wave radar point cloud environmental mapping algorithm. In the preprocessing phase, a radar SLAM-based approach is introduced to construct local submaps, which replaces the direct use of radar point cloud frames. This not only reduces data dimensionality but also enables the proposed method to handle scenarios involving vehicle motion with varying speeds. Building on this, a 3D-RadarHR cross-modal learning network is proposed, which uses LiDAR as the target output to train the radar submaps, thereby generating a dense millimeter-wave radar point cloud map. Experimental results across multiple scenarios, including outdoor environments and underground tunnels, demonstrate that the proposed method can increase the point cloud density of millimeter-wave radar environmental maps by over 50 times, with a point cloud accuracy better than 0.1 m. Compared to existing algorithms, the proposed method achieves superior environmental map reconstruction performance while maintaining a real-time processing rate of 15 Hz.

Bolt loosening detection is crucial for ensuring the safe operation of equipment. This paper presents a vision-based real-time detection method that identifies bolt loosening by recognizing anti-loosening line markers at bolt connections. The method employs the YOLOv10-S deep learning model for high-precision, real-time bolt detection, followed by a two-step Fast-SCNN image segmentation technique. This approach effectively isolates the bolt and nut regions, enabling accurate extraction of the anti-loosening line markers. Key intersection points are calculated using ellipse and line fitting techniques, and the loosening angle is determined through spatial projection transformation. The experimental results demonstrate that, for high-resolution images of 2048 × 1024 pixels, the proposed method achieves an average angle detection error of 1.145° with a detection speed of 32 FPS. Compared to traditional methods and other vision-based approaches, this method offers non-contact measurement, real-time detection capabilities, reduced detection error, and general adaptability to various bolt types and configurations, indicating significant application potential.

The charging load of electric vehicles (EV) is one of the most rapidly increasing loads in current power distribution systems. It may cause distribution transformer/feeder overload without proper coordination or control, especially in residential area where household load and EV charging load are sharing transformer capacity. Existing smart meter-based orderly charging control (OCC) approaches commonly require costly but unreliable communication schemes to control EV charging behavior. In this work, a smart meter-based distributed controller is designed to establish a meter-to-EV communication interface with low cost and enhanced reliability, based on the state-of-the-art charging standard. An event-driven OCC algorithm is developed, and then, deployed in the data hub (concentrator) of the AMI with an easy-to-implement optimization formulation. The effectiveness of the proposed approach is validated using a numerical case study and a practical field test in Hangzhou, China. Both results indicate promising advantages of the proposed OCC approach in reducing the peak load of emerging EV charging demand by more than 30%.

The high-speed development of the liquor industry brings sorghum demand, which is increasingly strong at the moment. Still, its harvesting mechanization level is low, and with the design of a longitudinal flow sparse and dense curved-teeth sorghum threshing technology, the harvester’s work quality is improved by the reduction of seed impurities. This article describes the working principle of the harvester, the overall distribution of threshing elements, and force analysis of the threshing aspects to determine the structure of the threshing elements. The orthogonal test was carried out, with a sparse–dense interphase threshing drum as the research object, selecting operating speed, threshing element bending angle, and threshing element mounting angle as the test factors, with the entrainment loss rate and the net threshing rate as the assessment indexes for the three-factor, three-level test, and the use of Design-Expert to establish a mathematical regression model between the factors and the two indicators, resulting in the following optimized parameters: when the operating speed is 1.0 m·s−1, the bending angle of the threshing element is 80°, and the mounting angle of the threshing element is 45°, the loss rate of entrainment is 1.89%, and the net threshing rate is 95.53%. The machine’s design indexes are in line with relevant national standards and can meet the demand for mechanized harvesting of sorghum.

To improve the expandability, seeding accuracy, and operating speed range of the electric drive system (EDS) of precision seeders, this study constructed an EDS based on a controller area network (CAN) bus and designed a motor controller based on a field-orientated control (FOC) algorithm. Full-factorial bench and field tests based on seed spacing (0.1, 0.2, and 0.3 m) and operating speed (3, 6, 9, 12, and 15 km/h) were carried out to evaluate the performance of the EDS. The results of bench tests showed that seeding quality varied inversely with operating speed and positively with seed spacing. The average quality of feed index (QFI) at 0.1, 0.2, and 0.3 m seed spacing in bench tests was 88.38%, 96.67%, and 97.36%, with the average coefficient of variation (CV) being 20.13%, 16.27%, and 13.20%. Analysis of variance confirmed that both operating speed and seed spacing had a significant effect on QFI and CV (p < 0.001). The analysis of motor rotational speed accuracy showed that the relative error of motor rotational speed above 410 rpm did not exceed 2.24%, and the relative error had less influence on the seeding quality. The average QFI was 85.93%, 95.91%, and 96.24%, with the average CV being 21.12%, 15.50%, and 16.49% at 0.1, 0.2, and 0.3 m seed spacing in field tests. The methods and results of this study can provide a reference for the design and optimization of the EDS in a maize precision seeder and provide an effective solution for the improvement of maize yields.

With the growing world population, food demand has also increased, resulting in increased agricultural waste and livestock manure production. Wheat straw and cow dung are rich nutrient sources and, if not utilized properly, may lead to environmental pollution. Keeping in view the cultivation of Agaricus bisporus on straw/manure-based substrate, the current study aimed to optimize the conventional manure preparation technique to reduce nutrient losses and keep the quality of manure at its best. The treatments were considered as traditional and optimized schemes for mushroom substrate preparation. The results achieved herein indicated that the nutrient losses were low in the optimum scheme. For carbon (C), the loss was 43.55% at the substrate stage in the traditional scheme and reduced to 37.75% in the optimum scheme. In the case of nitrogen (N), the loss was 22.01% in the traditional scheme and was lower (18.49%) in the optimum scheme. The nutrient concentration in Agaricus bisporus was higher with the optimum scheme compared with the traditional scheme. It was 1.74% for C, 7.17% for N, 3.58% for phosphorus (P), and 4.92% for potassium (K). The optimum scheme also improved the Agaricus bisporus yield per unit area (84.55%) and the total yield (28.92%). The net income of the optimum scheme was 102.95% higher compared to the traditional scheme. The economic analysis also revealed that the benefit–cost ratio of the optimum scheme was high (48.86%) compared with the traditional scheme. This study concludes that the use of the optimum scheme can better utilize the wheat straw and cow manure waste for substrate preparation and reducing nutrient losses. In addition, the final mushroom residue can also be used as a leftover substrate for further utilization.

Actinidia arguta is a cold-resistant fruit tree but intolerant to waterlogging. Waterlogging stress is the major abiotic stress in A. arguta growth, and several pathways are involved in the response mechanisms. Fifteen physiological indices and transcriptome data of two A. arguta cultivars, which showed two forms under waterlogging, were used to identify the major factor following the leaf senescence in waterlogging. Through principal component analysis (PCA) of 15 physiological indices in ‘Kuilv’ and ‘Lvwang’, the hormone contents were selected as the most important principal component (PCA 2) out of four components in response to waterlogging stress. According to the analysis of transcriptome data, 21,750 differentially expressed genes were identified and 10 genes through WGCNA, including hormone metabolism and sucrose metabolism, were screened out on the 6th day of waterlogging. In particular, the ABA signal transduction pathway was found to be closely related to the response to waterlogging based on the correlation analysis between gene expression level and plant hormone content, which may have regulated physiological indicators and morphological changes together with other hormones. Overall, the phenomenon of leaves falling induced by ABA might be a protective mechanism. The results provided more insights into the response mechanism of coping with waterlogging stress in A. arguta.

The authors regret to pinpoint two editorial errors in [...]

Prefabricated construction (PC) is increasingly promoted in the construction sector for its potential benefits, including reduced resource assumption and improved quality. Accordingly, Lean methods are popularly applied to PC projects for optimizing operational processes and enhancing their performance in line with strategic objectives. A key factor in effectively implementing Lean to improve strategic control is developing specific strategies and planning that consider their complex interactions. Thus, this paper aims to propose a quantitative network-based model by integrating Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) under complex network theory to develop a Lean implementation framework for effective strategy formulation. Specifically, 17 Lean implementation strategies for PC in the context of the Chinese prefabrication industry were identified via an extensive literature review and expert interviews. Then, ISM-MICMAC quantitatively identifies the direct and indirect relationships among strategies, while subsequent analysis of Topological Structure Weight (TSW) and Structural Degree Weight (SDW), as complex network parameters, is used to evaluate the importance of each strategy. The findings show that the strategic planning for Lean implementation in PC consists of four levels, i.e., foundation, organizational, technical, and control. Selecting appropriate Lean tools and technologies is crucial for PC implementation, which must be built on a top-level management team and foster a Lean culture. Moreover, it involves building a standardized system of processes and activities, enhancing both internal and external collaboration, and continuously improving processes in response to changes. On one hand, this in-depth network-based analysis offers practical insights for PC stakeholders, particularly in China, on Lean implementation in line with PC performance and strategic control and objectives. On the other hand, the network-based model can be future-implemented globally. Additionally, this study expands the current body of knowledge on Lean in PC by exploring the interrelationships of Lean implementation strategies.

Waste slag and rubber particles are commonly used to modify concrete, offering benefits such as reduced cement consumption and lower greenhouse gas emissions during cement production. In this study, these two environmentally friendly, sustainable waste materials were proposed for the preparation of mortar intended for snow-melting pavements. A series of experiments were conducted to evaluate the performance of the material and to determine whether its compressive and flexural strengths meet the requirements of pavement specifications. The mortar’s suitability for snow-melting pavements was assessed based on its thermal conductivity, impermeability, and freeze–thaw resistance. The results indicate that slag, when used in different volume fractions, can enhance the compressive and flexural strength of the mortar. Slag also provides excellent thermal conductivity, impermeability, and resistance to freeze–thaw cycles, contributing to the overall performance of snow-melting pavements. When the slag content was 20%, the performance was optimal, with the compressive strength and flexural strength reaching 58.5 MPa and 8.1 MPa, respectively. The strength loss rate under freeze–thaw cycles was 8.03%, the thermal conductivity reached 2.2895 W/(m * K), and the impermeability pressure value reached 0.5 MPa. Conversely, the addition of rubber particles was found to decrease the material’s mechanical and thermal properties. However, when used in small amounts, rubber particles improved the mortar’s impermeability and resistance to freeze–thaw cycles. When the rubber content was 5% by volume, the impermeability pressure value reached 0.5 MPa, which was 166.7% lower than that of ordinary cement mortar. Under freeze–thaw cycles, the strength loss rate of the test block with a rubber content of 25% volume fraction was 9.83% lower than that of ordinary cement mortar.

Future humanoid robots will be widely deployed in our daily lives. Motion planning and control in an unstructured, confined, and human-centered environment utilizing dexterity and a cooperative ability of dual-arm robots is still an open issue. We propose a globally guided dual-arm reactive motion controller (GGDRC) that combines the strengths of global planning and reactive methods. In this framework, a global planner module with a prospective task horizon provides feasible guidance in a Cartesian space, and a local reactive controller module addresses real-time collision avoidance and coordinated task constraints through the exploitation of dual-arm redundancy. GGDRC extends the start-of-the-art optimization-based reactive method for motion-restricted dynamic scenarios requiring dual-arm cooperation. We design a pick–handover–place task to compare the performances of these two methods. Results demonstrate that GGDRC exhibits accurate collision avoidance precision (5 mm) and a high success rate (84.5%).

Melting point depressants (MPDs) are required to lower the melting point of filler for brazing. In this study, Zr was used as the MPD, and powder filler was prepared by adjusting the Zr and Mo content referring to Thermo-Calc calculations. The prepared filler was used to braze a high-Mo Ni3Al-based single crystal superalloy, IC21, for 1200 °C/30 min. The effects of adjusting the Zr and Mo content on the microstructure and tensile properties of the joint were investigated. The increase in Zr content promotes the formation of Ni7Zr2 in the joint, leading to a decrease in the tensile strength of the joint. The increase in Mo content forms diffusion barriers between the BM and filler, resulting in an enhancement in the tensile strength of the joint. However, continued increases in Mo content leads to an increase in the P-topologically close packed phase, causing a decline in the tensile strength of the joint. When the Zr content was (11.8–12.2) wt.% and the Mo content was (7.3–7.7) wt.%, the tensile strength of the joint at 980 °C reached a maximum of 550 MPa. This study provides a potential direction for the design of brazing filler composition for high-Mo Ni3Al-based superalloys.

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

The MADS-box gene family constitutes a vital group of transcription factors that play significant roles in regulating plant growth, development, and signal transduction processes. However, research on the MADS-box genes in cassava (Manihot esculenta) has been relatively limited. To gain deeper insights into the functions of the MADS-box genes in cassava development, in this study, we undertook a comprehensive genome-wide identification of the MADS-box gene family in cassava. We identified a total of 86 MADS-box genes with complete domains in the cassava genome, designated as MeMADS01 to MeMADS86. Through bioinformatic analyses, we investigated the basic physicochemical properties, conserved motifs, chromosomal locations, and phylogenetic relationships of the cassava MADS-box genes. The MADS-box gene family of cassava exhibited conservation, as well as species-specific characteristics, with intron loss being a predominant mode of evolution for the MADS-box genes. Expression pattern variations in the MeMADS genes across different tissues offer insights into their potential functions. Time-ordered gene co-expression network (TO-GCN), transcriptome data, and RT-qPCR analysis suggested the responsiveness of the MADS-box genes to drought stress. Meanwhile, MeMADS12 might be involved in regulating flowering under drought conditions via an ABA (abscisic acid)-dependent pathway. These findings provide valuable resources for a deeper understanding of the biological roles of the MADS-box genes in cassava.

This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target’s posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring.

This study was to investigate the effects of the polysaccharides (PPM60−III) and sulfated polysaccharides (SPPM60−III) of pine pollen on the Th17/Treg balance, inflammatory cytokines, intestinal microbiota, and metabolite distribution in 3% DSS drinking water-induced UC mice. First of all, the physiological results showed that PPM60−III and SPPM60−III could alleviate UC, which was shown by the reduction in liver Treg cells, the rebalance of Th17/Treg, and the modulation of inflammatory cytokines. In addition, the 16S rRNA results showed that PPM60−III and SPPM60−III could decrease Beijerinck and Bifidobacterium, and increase Akkermansia, Escherichia coli, and Fidobacteria. Finally, the metabonomics results showed that PPM60−III and SPPM60−III also restored purine and glycerolipid metabolism, up-regulated nicotinate and nicotinamide metabolism and caffeine metabolism to inhibit inflammation. In conclusion, PPM60−III and SPPM60−III could inhibit UC by regulating gut bacteria composition and metabolite distribution; SPPM60−III showed better anti-colitis activity.

The tuning of fractional-order proportional-integral-derivative (FOPID) controllers for automatic voltage regulator (AVR) systems presents a complex challenge, necessitating the solution of real-order integral and differential equations. This study introduces the Dumbo Octopus Algorithm (DOA), a novel metaheuristic inspired by machine learning with animal behaviors, as an innovative approach to address this issue. For the first time, the DOA is invented and employed to optimize FOPID parameters, and its performance is rigorously evaluated against 11 existing metaheuristic algorithms using 23 classical benchmark functions and CEC2019 test sets. The evaluation includes a comprehensive quantitative analysis and qualitative analysis. Statistical significance was assessed using the Friedman’s test, highlighting the superior performance of the DOA compared to competing algorithms. To further validate its effectiveness, the DOA was applied to the FOPID parameter tuning of an AVR system, demonstrating exceptional performance in practical engineering applications. The results indicate that the DOA outperforms other algorithms in terms of convergence accuracy, robustness, and practical problem-solving capability. This establishes the DOA as a superior and promising solution for complex optimization problems, offering significant advancements in the tuning of FOPID for AVR systems.

As an extension of quantitative temporal logic, uncertain temporal logic essentially describes the temporal behavior of uncertain and incomplete systems, thus better solving search and decision-making problems in such systems. Fuzzy linear temporal logic (FLTL) is a focal point in uncertain temporal logic research. However, there are evident shortcomings in the current research outcomes. First, in previous FLTL studies, the practice of obtaining path reachability and formula satisfaction values independently and subsequently selecting the smaller of the two as the satisfaction value metric led to information loss. Furthermore, this simplistic information fusion approach fails to reflect the varying importance of these two types of information to the requirements. Second, computing path reachability and temporal logic formula satisfaction values separately may result in a mismatch between the two pieces of information with respect to the same path segment. Thus, the primary challenge lies in accurately integrating the satisfaction values of temporal logic formulas with the path reachability of the segments that yields these satisfaction values, utilizing various reasonable information synthesis methods, to ensure synchronization between path reachability and formula satisfaction values without incurring information loss. Additionally, it is crucial to reflect the different preference requirements for these two types of information. Moreover, the temporal logic formula characterizes system properties, with its sub-formulas delineating distinct sub-properties. Consequently, considering the varying importance preferences of sub-formulas is also significant. To address these deficiencies, we introduced quality constraint operators into FLTL, resulting in quality-constrained fuzzy linear temporal logic (QFLTL). This incorporation enables the synchronization and comprehensive fusion of path-reachability information and formula satisfaction values within the final semantic metric, thereby resolving the issues related to information synchronization and loss. Furthermore, it can accommodate the differing preference requirements between the two types of information and sub-properties during the information synthesis process. We defined the syntax and semantics of QFLTL and examined its expressive power and properties. Notably, we investigated the decidability of logical decision problems in QFLTL, encompassing validity, satisfiability, and model-checking issues. We proposed corresponding solution algorithms and analyzed their complexities.

The remarkable efficacy of COVID-19 vaccines has established mRNA as a highly promising biomedical technology. However, the adequate application of mRNA therapeutics necessitates additional measures to mitigate the inherent immunogenicity, which is predominantly caused by dsRNA. As a byproduct of the in vitro transcription of mRNA, dsRNA was reported to be originated through several distinct mechanisms, including the extension of 3′ loop-back hairpins, the extension of hybridized abortive transcripts, and promoter-independent transcription. The intricate mechanisms involved pose a dilemma as the reduction in dsRNA results in a concomitant decrease in other critical quality attributes of mRNA. Here, we demonstrate that the promoter binding motifs of T7 RNA polymerase directly impact the production of promoter-independent transcription-based dsRNA. Specifically, the G753A mutation significantly reduces the formation of dsRNA byproducts, which can further combine with modified nucleotides to enhance the effectiveness of dsRNA mitigation and with previously reported high-integrity mutation K389A to minimize side effects. Accordingly, the present study reports a cost-effective approach to synthesize high-purity, less immunostimulatory mRNA by using an engineered T7 RNA polymerase mutant.

Mulberries (Morus alba L.) contain rich and beneficial nutrients for human health. However, as a temperate adaptive species, high-temperature and high-humidity climate conditions may alter the main nutritional value of mulberries after their intended arrival in tropical regions, which has not yet been reported on. In this study, we analyzed the differences in morphology, sugars, organic acids, free amino acids, and aroma contents of five mulberry varieties in the tropics between two harvesting periods. The results show that the full-ripe fruits of M. laevigata W (TLM) have the longest fruit length (83.67 mm) and highest brix (25.90); meanwhile, full-ripe fruits of M. atropurpurea R (D10M) have the longest fruit transverse stem (20.00 ± 0.577 mm) and single-fruit weight (9.63 ± 0.033 g). Fructose, glucose, and sucrose were the main sugars, and oxalic acid, quinic acid, malic acid, and citric acid were the main organic acids in all varieties; in addition, the sucrose content in mature fruits of M. laevigata W. (BLM) and M. alba L. BZZ (BZM) was significantly higher than other sugars. Twenty free amino acids were detected in all five varieties and asparagine was the main free amino acid. A total of 100 volatile compounds were identified, including 31 esters, 20 aldehydes, 14 hydrocarbons, 11 alcohols, 10 acids, 6 ketones, and 8 others. Although the main components of five mulberry full-ripe fruits were significantly higher than the green-ripe fruits, gamma-amino butyric acid and a few other components were otherwise. The research results show that the tropical climate conditions could increase the main nutritional components of mulberries, but the specific molecular regulatory mechanisms need to be further analyzed.

Oil and gas pipelines are subject to soil corrosion and medium pressure factors, resulting in stress concentration and pipe rupture and explosion. Non-destructive testing technology can identify the stress concentration and defect corrosion area of the pipeline to ensure the safety of pipeline transportation. In view of the problem that the traditional pipeline inspection cannot identify the stress signal at the defect, this paper proposes a detection method using strong and weak magnetic coupling technology. Based on the traditional J-A (Jiles–Atherton) model, the pinning coefficient is optimized and the stress demagnetization factor is added to establish the defect of the ferromagnetic material. The force-magnetic relationship optimization model is used to calculate the best detection magnetic field strength. The force-magnetic coupling simulation of Q235 steel material is carried out by ANSYS 2019 R1 software based on the improved J-A force-magnetic model. The results show that the effect of the stress on the pipe on the magnetic induction increases first and then decreases with the increase in the excitation magnetic field strength, and the magnetic signal has the maximum proportion of the stress signal during the excitation process; the magnetic induction at the pipe defect increases linearly with the increase in the stress trend. Through the strong and weak magnetic scanning detection of cracked pipeline materials, the correctness of the theoretical analysis and the validity of the engineering application of the strong and weak magnetic detection method are verified.

The effects of solid-solution and ageing treatments on the microstructure and mechanical properties of a GH4169 alloy made by wire arc additive manufacturing, micro-casting, and forging were researched. The microstructure, along with the size and type of precipitated phase, were analysed using an optical microscope, scanning electron microscope, and transmission electron microscope. The strength and toughness were tested using a tensile testing machine. The results show that a polygonal austenite microstructure was obtained for the GH4169 alloy prepared through wire arc additive manufacturing, micro-casting, and forging, followed by solid-solution and double-ageing treatments at different times. There were a few twins in the austenite matrix. A large number of nano-sized γ″- and γ′-precipitated phases and a small number of Laves phases and MX phases were found in the matrix. The tensile strength and yield strength of the GH4169 alloy increased first and then decreased with the ageing time. After ageing for 16 h, the maximum yield strength was 1287 ± 22 MPa, the maximum tensile strength was 1447 ± 19 MPa, and the elongation was about 19.5%. The main strength mechanism is precipitated phase strength and solid-solution strength. The fracture exhibited obvious ductile fracture characteristics.