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Methyl-CpG-binding domain (MBD) proteins play vital roles in epigenetic gene regulation, and they have diverse molecular, cellular, and biological functions in plants. MBD proteins have been functionally characterized in a few plant species. However, the structure and function of MBD proteins in Arabidopsis halleri and Arabidopsis lyrata remain unknown. In this study, 12 A. halleri MBD (AhMBD) and 13 A. lyrata MBD (AlMBD) genes were identified. A phylogenetic analysis of the Arabidopsis genus showed that the MBD proteins of three species (Arabidopsis thaliana, A. helleri, and A. lyrata) could be classified into eight classes. Expression patterns suggested that the AtMBD genes were expressed in different tissues. We characterized the function of AtMBD3 and found that it was constitutively localized to the nucleus and interacted with several AtMBD protein members. Our results reveal that AtMBD3 is involved in the development of A. thaliana, which may be helpful in further studies on these genes in A. helleri and A. lyrata.

We present a case of a lung adenocarcinoma patient harboring a novel kinesin family member 5B (KIF5B)-NTRK1 gene fusion that responds well to entrectinib. Moreover, KIF5B-NTRK1 gene chimera has been shown to be an oncogene, activating both the MAPK and PI3K/AKT signaling pathways. The biopsy sample was analyzed using various methods such as hematoxylin–eosin staining (HE), immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and next-generation sequencing (NGS) based on a 1267-gene panel. Additionally, human lung adenocarcinoma cell lines A549 and H1755 were used to obtain a stable expression of chimera gene products. The cell proliferation was confirmed using CCK8 and adhesion-dependent colony formation assay. Cell invasion was confirmed using the transwell invasion assay. The protein levels of the MAPK and PI3K/AKT signaling pathways were assessed using Western blotting. The patient, a 66-year-old Chinese male, was diagnosed with adenocarcinoma (stage IVB) located in the upper lobe of the left lung. NGS analysis identified a novel KIF5B-NTRK1 fusion gene, which was further confirmed by FISH and IHC analyses. As a first-line therapy, entrectinib was administered to the patient at a dose of 600 mg once daily, resulting in a partial response. The patient’s progression-free survival (PFS) has now been more than 12 months, and no serious toxicities have been observed so far. Furthermore, stable KIF5B-NTRK1-expressing cells were generated and the experimental results demonstrate enhanced proliferation abilities, along with increased levels of proteins involved in the MAPK and PI3K/AKT signaling pathways. Our study reports a novel KIF5B-NTRK1 genetic rearrangement that supports favorable responses to entrectinib. Moreover, in vitro experiments showed that the fusion gene could exert oncogenic properties by activating the MAPK and PI3K/AKT signaling pathways. To summarize, our findings broaden the spectrum of NTRK gene fusions in the context of lung adenocarcinoma.

Large Language Models (LLMs), based on transformer architecture, have demonstrated remarkable capabilities in natural language processing tasks, enabling machines to generate human-like text and engage in meaningful dialogues. However, the exponential increase in model parameters has led to limitations in inference speed and energy efficiency. Compute-in-memory (CIM) technology offers a promising solution to accelerate AI inference by performing analog computations directly within memory, potentially reducing latency and power consumption. At the same time, CIM has been successfully applied to accelerate Convolutional Neural Networks (CNNs); however, the matrix–matrix multiplication (MatMul) operations inherent in the scaled dot-product attention of the transformer present unique challenges for direct CIM implementation. In this work, we propose InMemQK, a compute-in-memory-based attention accelerator that focuses on optimizing MatMul operations through software and hardware co-design. At the software level, InMemQK employs product quantization (PQ) to eliminate data dependencies. At the hardware level, InMemQK integrates energy-efficient time-domain MAC macros for ADC-free computations. Experimental results show InMemQK achieves 13.2×–13.9× lower power consumption than existing CIM-based accelerators.

Idesia polycarpa from Sichuan is a valuable germplasm with high economic potential, but it faces variety scarcity. To address this, this study collected 16 varieties (lines), identifying IpHT1 as a promising parent due to its high oil content (38.5%) and red fruits. Polyploid induction via adding 0.50% colchicine to Murashige and Skoog (MS) medium yielded 520 IpHT1 mutagenized seedlings. Subsequently, flow cytometry (FCM) was performed on 401 morphologically variant seedlings which had been initially screened, resulting in the identification of 15 suspected triploids, 35 suspected tetraploids, and 3 chimeras. Furthermore, fluorescence in situ hybridization (FISH) analysis found that the probe (AG3T3)3 had terminal signals at both ends of each chromosome, allowing for the counting of 42 chromosomes in diploids and 84 in tetraploids. The probe 5S rDNA showed 2, 3, and 4 hybridization signals in the interphase nuclei of diploid, triploid, and tetraploid cells, respectively, but the probe (GAA)6 failed to produce any signal on I. polycarpa chromosomes. Ultimately, 18 polyploids were selected, including 7 triploids and 11 tetraploids. Triploids and tetraploids showed significant leaf morphological and physiological differences from diploids. Consequently, this study successfully established a polyploid breeding system for I. polycarpa, thereby enhancing its genetic diversity and breeding potential.

The growing altitude is an important factor affecting the quality of coffee. We explored the flavor precursors, sensory characteristics, and cupping qualities of coffee growing at different altitudes and discussed their associated relationships. The altitude at which coffee is grown has different effects on its chemical composition. Fatty acid contents increased with increasing altitudes, whereas alkaloid and chlorogenic acids contents decreased with increasing elevation. There was no obvious trend in either organic acids or monosaccharides. Eleven of the 112 detected volatile components were significantly affected by the growing altitude. The contents of pyrazines and alcohols tended to decrease, whereas those of aldehydes tended to increase. A significantly altered composition reduces the nutty and roasted flavors of coffee, while increasing the sweet sugar and caramel aromas. The aroma and flavor tended to increase with increasing altitudes during cupping, whereas the other indicators did not change significantly. The results provide a theoretical reference for the sales and promotion of Pu’er coffee.

During roadway excavation, the presence of roof deterioration zones, such as layered spaces and weak interlayers, significantly affects the stability of the surrounding rock. To achieve timely and effective support for roadways, it is essential to utilize drilling measurement signals obtained during the construction of anchorage holes for the identification and prediction of these deterioration zones. This study systematically investigates the response characteristics of thrust, torque, and Y-direction vibration signals to different combinations of rock layers through theoretical analysis, laboratory experiments, ABAQUS dynamic numerical simulations, and field measurements. The results indicate that these drilling parameters effectively characterize variations in rock structure and strength, with distinct signal features observed particularly in roof deterioration zones. Based on these findings, this paper proposes a deep learning algorithm that employs Long Short-Term Memory (LSTM) recurrent neural networks for classification prediction, along with a random forest algorithm for regression prediction, aimed at the intelligent identification and prediction of roof deterioration zones. The algorithm demonstrates outstanding performance in both laboratory experiments and field tests, achieving a 100% recognition rate for layered spaces and a 96.6% accuracy for identifying deterioration zones, with high accuracy at lower values of mechanical specific energy (MSE). The proposed method provides significant insights for real-time monitoring and control of roof deterioration zones, enhancing the safety and stability of roadway excavations, and serves as a valuable reference for future research and practical applications.

Sleep deprivation (SD) disrupts circadian rhythms; however, its effects on SD and the mechanisms involved require further investigation. Previous studies on SD were mainly conducted on rodents, such as mice, with few studies on its effects on the liver of large diurnal animals, such as sheep. In this study, we used a Tibetan sheep model for the first time to investigate the effects of SD on the liver by exposing Tibetan sheep (Ovis aries) to 7 days of SD (6 h/day) and performed transcriptome sequencing analysis on liver samples taken at 4 h intervals over 24 h. The results revealed that SD significantly altered the circadian expression of genes and their expression patterns in the liver of Tibetan sheep. Enrichment analysis of the circadian rhythm-altered genes revealed changes in the pathways related to lipid metabolism in the liver. Further evidence from serum markers and gene expression analyses using qualitative real-time polymerase chain reaction and Oil Red O and apoptosis staining indicated that SD leads to abnormal lipid metabolism in the liver, potentially causing liver damage. Therefore, our results suggest that SD disrupts the circadian rhythms of metabolism-related genes in the Tibetan sheep liver, thereby affecting metabolic homeostasis.

In the prokaryotic kingdom, protein phosphorylation serves as one of the most important posttranslational modifications (PTMs) and is involved in orchestrating a broad spectrum of biological processes. Here, we report an updated online server named the group-based prediction system for prokaryotic phosphorylation language model (GPS-pPLM), used for predicting phosphorylation sites (p-sites) in prokaryotes. For model training, two deep learning methods, a transformer and a deep neural network, were employed, and a total of 10 sequence features and contextual features were integrated. Using 44,839 nonredundant p-sites in 16,041 proteins from 95 prokaryotes, two general models for the prediction of O-phosphorylation and N-phosphorylation were first pretrained and then fine-tuned to construct 6 predictors specific for each phosphorylatable residue type as well as 134 species-specific predictors. Compared with other existing tools, the GPS-pPLM exhibits higher accuracy in predicting prokaryotic O-phosphorylation p-sites. Protein sequences in FASTA format or UniProt accession numbers can be submitted by users, and the predicted results are displayed in tabular form. In addition, we annotate the predicted p-sites with knowledge from 22 public resources, including experimental evidence, 3D structures, and disorder tendencies. The online service of the GPS-pPLM is freely accessible for academic research.

As the main method of secondary development, water injection has been widely used in fractured vuggy carbonate reservoirs. By using an evaluation index system combined with an integrated approach to objectively evaluate and support targeted and operational adjustments of water injection development effects in fractured vuggy carbonate reservoirs, a comprehensive evaluation method is established in this study. The CRITIC method is used as the main approach, and the analytic hierarchy process, the entropy weight method, and the coefficient of variation method are used as sub-methods. Additionally, the clustering centres are divided using the clustering method to reduce the error caused by the irrational distribution of the relevant evaluation index data. The method is used to evaluate and analyse the effects of water injection development, using the FI7 fault zone of the Halahatang oilfield as an example. The application results show that the evaluation method is feasible and effective for cases with small data volume, fewer computational resources, and less time. This study has a certain reference significance for the evaluation of the effect of water injection development in similar fractured vuggy reservoirs.

Taro is a tuber crop that is used for nutritional and medicinal purposes due to its abundance of non-starch polysaccharides (NSPs). Red Sprout taro is a local variety in Southern China, but the characteristics and bioactivities of its NSPs are currently unknown. In this study, NSPs were isolated from the corms of Red Sprout taro using hot-water extraction, ion-exchange chromatography, and ethanol precipitation; their molecular weight, monosaccharide composition, structural formulae, and immunomodulatory effects were examined. A novel NSP named Colocasia esculenta polysaccharide 1 (CEP1) was purified and characterized and was shown to mainly consist of glucose (60.49%) and galactose (25.92%) and have a molecular weight of 4556.272 kDa. The backbone of CEP1 consisted primarily of →4)-α-D-Glcp-(1→, →4,6)-β-D-Galp-(1→, and →3)-β-D-Galp-(1→ residues, with a branch consisting of the β-D-Glcp-(1→ residue. In addition, 25–400 µg/mL CEP1 was shown to have immunomodulatory effects on RAW264.7 macrophages. CEP1 not only increased cell viability, phagocytic capacity, inducible nitric oxide synthase secretion, and nitric oxide generation in RAW264.7 cells, but it also activated M1 and M2 macrophages to generate tumor necrosis factor α, interleukin 6, transforming growth factor β, and interleukin 10. These findings could lead to the use of CEP1 from Red Sprout taro as a possible immunomodulatory polysaccharide in functional foods.

Ganoderma is the most important genus in the family Ganodermataceae; many species have attracted much attention and widely cultivated because of their medicinal values, but so far, not a sequenced mitogenome derived from dikaryon strains has been explicitly recorded. Herein, four novel mitogenomes of commonly cultivated Ganoderma (G. leucocontextum H4, G. lucidum G6, G. sinense MZ96 and G. tsugae SS) were de novo assembled and given detail functional annotations. Collinearity analysis revealed that the four mitogenomes shared 82.93–92.02% similarity with their corresponding reference mitogenomes at the nucleotide level. A total of 15 core protein-coding genes (PCGs), along with rrnL and rrnS (mtLSU and mtSSU) were chosen as potential candidates for constructing their individual phylogenetic trees. These trees were compared with those derived from the concatenated sequences of 15 core PCGs. And finally, we found that the atp9 and nad4L were the most reliable markers for the phylogenetic analysis of Ganoderma and chosen as standard sequences to generate new DNA barcodes. This finding was further verified by comparing it against almost all available Ganoderma mitogenomes in the NCBI, with Trametes versicolor (Polyporaceae) and Rigidoporus microporus (Meripilaceae) as two outgroups. A total of 52 mitogenomes from three families were highly conserved, with identical gene lengths for atp9 (222 bp) and nad4L (267 bp). These genes were capable of distinguish distinctly different various species, which are grouped into separate clades within the phylogenetic trees. The closest related clades (I and II), including at least 30 samples of the three classical taxonomic species (G. lingzhi, G. sichuanense and G. lucidum), differed in only one SNP. The single base mutation rate increased with the evolutionary divergence of the phylogenetic clades, from two to three SNPs in earlier clades (e.g., clade IV containing G. leucocontextum) to five to six SNPs in later clades (e.g., clade X containing G. sinense). Despite these variations between species, the atp9 and nad4L genes of Ganoderma mitogenomes consistently encoded the same ATP synthase F0 subunit c (73 aa) and NADH dehydrogenase subunit 4L (88 aa). These two genes have been identified as reliable markers of new DNA barcodes, offering valuable insights and contributing significantly to understanding the evolutionary relationships and phylogeny of the Ganoderma genus and even the Ganodermataceae family.

Alginate oligosaccharides (AOs), derived from alginate degradation, exhibit diverse biological activities and hold significant promise in various fields. The enzymatic preparation of AOs relies on alginate lyases, which offers distinct advantages. In contrast to the conventional use of sodium alginate derived from brown algae as the substrate for the enzymatic preparation of AOs, AO preparation directly from brown algae is more appealing due to its time and energy efficiency. Thus, the identification of potent alginate lyases and cost-effective brown algae substrates is crucial for optimizing AO production. Herein, we identified and characterized an alginate lyase, AlyP18, capable of efficiently decomposing algae, from a marine bacterium Pseudoalteromonas agarivorans A3 based on secretome analysis. AlyP18 is a mesothermal, endo-type and bifunctional alginate lyase with high enzymatic activity. Two brown algae substrates, Laminaria japonica roots and Macrocystis pyrifera, were used for the AO preparation by AlyP18. Upon optimization of AlyP18 hydrolysis parameters, the substrate degradation efficiency and AO production reached 53% and ~32% for L. japonica roots, respectively, and 77% and ~46.5% for M. pyrifera. The generated AOs primarily consisted of dimers to pentamers, with trimers and tetramers being dominant. This study provides an efficient alginate lyase and alternative brown algal feedstock for the bioconversion of high-value AOs from brown algae.

Rapid urbanization has affected ecosystem stability, and the construction of ecological security patterns (ESPs) can rationally allocate resources and achieve ecological protection. Priority evaluation of critical areas can maximize the benefits of ecological protection, which is crucial for sustainable urban development. However, most prior studies have focused on assessing individual elements of the ESP, rarely considering both the protection priority of ecological sources and corridors. We constructed ESPs for the Wuhan Metropolitan Area (WMA) from 2000 to 2020 and evaluated the priority of ecological sources and corridors for protection. The findings indicated that high-level ecological sources exhibited higher overall landscape connectivity and ecosystem service values with lower patch fragmentation. The average area proportions of primary, secondary, and tertiary ecological sources in 2000, 2010, and 2020 were 41.11%, 23.03%, and 29.86%, respectively. High-level ecological corridors had shorter lengths and offered higher comprehensive ecosystem service values. The total length of secondary corridors exceeded that of primary corridors by 1951.19 km, 650.39 km, and 2238.18 km in 2000, 2010, and 2020, respectively. Primary corridors, which connected fragmented and isolated sources, should have their ecological land percentage increased to enhance connectivity. Secondary corridors connected two independent and distant sources, providing the basis for ecological protection in the intervening area, whose surrounding habitats should be protected. This study identifies the ecological protection priority and offers a theoretical basis and practical reference for balancing urban development with ecological protection.

Hand, foot, and mouth disease (HFMD), a common childhood infection caused by enterovirus, poses a serious public health concern in China. We collected and analyzed epidemiological data on 62,133 HFMD cases in Shenyang City, Liaoning Province, from 2013 to 2023. The average annual incidence was 76.12 per 100,000 person-years; 99.45% of cases were mild, while 0.55% were severe. Only one patient died. HFMD infections peaked annually in July. Children in kindergartens and scattered children accounted for 44.6% and 42.2% of cases, respectively. Real-time RT-PCR detection of enteroviruses in 5534 patient samples revealed 3780 positives, of which 25.1% were CVA16-positive. Positives were randomly sampled, yielding 240 VP1 sequences of CVA16. Phylogenetic tree results showed that all VP1 sequences belonged to the B1 sub-genogroup. However, the sub-genogroup prevalence varied over time: from 2013 to 2014 and 2019 to 2021, the predominant sub-genogroup was B1a, while it was B1b from 2015 to 2018. Further phylogenetic analyses showed substantial divergence between B1a branches in CVA16, suggesting possible turnover of the B1a sub-genogroup in CVA16 due to evolution. This study provides epidemiological data on HFMD in Shenyang, and provides a phylogenetic analysis of CVA16, offering a theoretical basis for preventing and controlling HFMD in Shenyang City.

With the popularity of High Dynamic Range (HDR) display technology, consumer demand for HDR images is increasing. Since HDR cameras are expensive, reconstructing High Dynamic Range (HDR) images from traditional Low Dynamic Range (LDR) images is crucial. However, existing HDR image reconstruction algorithms often fail to recover fine details and do not adequately address the fundamental principles of the LDR imaging pipeline. To overcome these limitations, the Reversing Lossy UNet (RLUNet) has been proposed, aiming to effectively balance dynamic range expansion and recover overexposed areas through a deeper understanding of LDR image pipeline principles. The RLUNet model comprises the Reverse Lossy Network, which is designed according to the LDR–HDR framework and focuses on reconstructing HDR images by recovering overexposed regions, dequantizing, linearizing the mapping, and suppressing compression artifacts. This framework, grounded in the principles of the LDR imaging pipeline, is designed to reverse the operations involved in lossy image operations. Furthermore, the integration of the Texture Filling Module (TFM) block with the Recovery of Overexposed Regions (ROR) module in the RLUNet model enhances the visual performance and detail texture of the overexposed areas in the reconstructed HDR image. The experiments demonstrate that the proposed RLUNet model outperforms various state-of-the-art methods on different testsets.

Gravity data inversion is of critical importance in geophysics, encompassing a range of applications, such as the exploration of geological resources, the identification of geological structures, and the detection of groundwater resources. This study proposes a three-dimensional (3D) machine learning approach to enhance the efficiency of the aforementioned exploration tasks by leveraging gravity data. The mapping relationship between gravity data and subsurface density structures is modeled by the broad learning network, distinguished by its high training efficiency and robust modeling capability. Notably, the proposed inversion method obviates the constraints on the number of anomalies prior to the inversion process. This is achieved by setting one anomaly with varied locations for different training samples. Numerical and field data applications demonstrate the efficiency of the proposed 3D machine learning gravity data inversion method, especially in automatically determining the number of anomalies. In particular, the proposed method produced accurate density inversion results in the field application, aiding in the identification of potential oil and gas reservoirs in the target region and offering the potential for broader application in other resource exploration. The proposed inversion method can promote the construction of density structures of subsurfaces based on gravity data.

Corneal endothelium cells (CECs) regulate corneal hydration between the leaky barrier of the corneal endothelium and the ionic pumps on the surface of CECs. As CECs do not regenerate, loss of CECs leads to poor vision and corneal blindness. Corneal transplant is the only treatment option; however, there is a severe shortage of donor corneas globally. Cell therapy using propagated primary human CECs is an alternative approach to corneal transplantations, and proof of functionality is crucial for validating such CECs. Expression markers like Na-K-ATPase and ZO-1 are typical but not specific to CECs. Assessing the barrier function of the expanded CECs via electrical resistance (i.e., TEER and Ussing’s chamber) involves difficult techniques and is thus impractical for clinical application. Calcium has been demonstrated to affect the paracellular permeability of the corneal endothelium. Its absence alters morphology and disrupts apical junctions in bovine CECs, underscoring its importance. Calcium signaling patterns such as calcium waves affect the rate of wound healing in bovine CECs. Therefore, observing calcium waves in expanded CECs could provide valuable insights into their health and functional integrity. Mechanical or chemical stimulations, combined with Ca2+-sensitive fluorescent dyes and time-lapse imaging, can be used to visualize these waves, which could potentially be used to qualify expanded CECs.

Background: The mechanism of aneurysm wall enhancement (AWE) in middle cerebral artery (MCA) bifurcation aneurysms on vessel wall magnetic resonance imaging (VW-MRI) remains unclear. We aimed to explore the morphologically related hemodynamic mechanism for the AWE of MCA bifurcation aneurysms. Methods: Patients with unruptured MCA bifurcation aneurysms undergoing VW-MRI were enrolled. Logistic regression analyses were performed to determine the risk factors for AWE. Based on the results of retrospective analyses, bifurcation aneurysm silicone models with a specific aspect ratio (AR) were designed and underwent VW-MRI with different inlet velocities. Computational fluid dynamics (CFD) analyses were conducted on both silicone models and patients’ aneurysms. Results: A total of 104 aneurysms in 95 patients (mean age 60; 34 males) were included for baseline analysis and morphological analysis. Logistic regression analysis indicated AR (OR, 5.92; 95% CI, 2.00–17.55; p = 0.001) was associated with AWE. In the high-AR group of 45 aneurysms with AWE, the aneurysm sac exhibited lower blood flow velocity, lower wall shear stress, a larger proportion of low-flow regions and higher wall enhancement values. In total, 15 silicone models were analyzed, divided into three subgroups based on neck width (4 mm, 6 mm, and 8 mm). Each subgroup contained aneurysms with five different ARs: 1.0, 1.25, 1.5, 1.75, and 2.0. In silicone models, contrast enhancement (CE) was mainly located beneath the dome of the aneurysm wall. With the same inlet velocity, CE gradually increased as the AR increased. Similarly, at the same AR, CE increased as the inlet velocity decreased. CFD demonstrated a moderate positive correlation between the near-wall enhancement index and the ratio of the low-velocity area (r = 0.6672, p < 0.001). Conclusions: The AR is associated with the AWE of MCA bifurcation aneurysms. A high AR may promote wall enhancement by causing near-wall slow flow.

The polysilicon production process has significant potential to be made adjustable, and actively changing its production schedule to participate in grid dispatch can effectively alleviate the pressure on the power supply and balance demand while promoting renewable energy consumption. Considering the complex inter-coupling relationship between the sequential requirements and adjustable potential of polysilicon production, this paper analyzes the electricity consumption characteristics of various stages of a polysilicon production process that uses the improved Siemens method as its primary approach to production. The interaction between the polysilicon production process, equipment, and materials is modeled through the state–task network method, and the production timing requirements are transformed into constraint expressions. An optimized scheduling model that includes the production’s sequential requirements within a time-of-use electricity pricing context is established. Our analysis shows that the proposed model can formulate a feasible production plan with the lowest power purchase cost for polysilicon plants while meeting the production’s sequential requirements and product order demands.

Autonomous electric vehicles (AEVs) can potentially revolutionize the transportation landscape, offering a safer, contact-free, easily accessible, and more eco-friendly mode of travel. Prior to the market uptake of AEVs, it is critical to understand the consumer segments that are most likely to adopt these vehicles. Beyond market adoption, it is also important to quantify the impact of AEVs on broader transportation systems and the environment, such as impacts on the annual vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. In this pilot study, using survey data, a statistical model correlating AEV adoption intention and socioeconomic and built environment attributes was estimated, and a sensitivity analysis was conducted to understand the importance of factors impacting AEV adoption. We found that the market segments range from early adopters who are wealthy, technologically savvy, and relatively young to non-adopters who are more cautious to new technologies. This is followed by a synthetic population microsimulation of market penetration for the San Francisco Bay Area. With five household vehicle replacement scenarios, we assessed the annual VMT and tailpipe carbon dioxide (CO2) emissions change associated with vehicle replacement. It is found that adopting AEVs can potentially reduce more than 5 megatons of CO2 yearly, which is approximately 30% of the total CO2 emitted by internal combustion engine (ICE) cars in the region.

Lithium, a crucial raw material for new energy vehicles, is experiencing significant market price fluctuations due to escalating geopolitical conflicts, periodic mismatches in supply and demand, and increased attention to lithium resources from countries around the world. These factors may adversely affect the development of the new energy vehicle industry. This paper adopts the TVP-VAR-DY model, which measures dynamic spillover effects by allowing for variance changes through the estimation of a stochastic Kalman filter, thereby measuring risk spillover among upstream and downstream firms in the lithium industry chain. We selected 16 listed companies and six regional financial markets as the research sample, with the sample period from 4 July 2018, to 30 June 2023. The main conclusions are as follows: Between 2018 and 2020, the overall risk spillover in the lithium industry chain demonstrated a declining trend, though it experienced a sudden surge in 2020 as a result of the COVID-19 pandemic. This increase was followed by a gradual decline as the global economy improved and market stability was restored, leading to a reduction in risk aversion. Regarding the reception of risk spillovers, upstream firms exhibited a generally consistent level of directional risk spillovers, whereas downstream firms experienced more significant fluctuations. Chinese firms exhibited a higher level of received risk spillovers compared to their international counterparts, with less variation in these spillovers. From the perspective of risk spillover effects, significant variations were observed between firms in both the upstream and downstream markets. Chinese firms exhibited a higher level of risk inflow than international firms, with more pronounced changes in risk spillovers. Upstream enterprises should enhance their market competitiveness to mitigate the adverse effects of economic uncertainty. Downstream enterprises can alleviate the rise in raw material costs resulting from market price fluctuations through strategic cooperation. Additionally, the government should increase the market supply of resources, which will contribute to the establishment of a more robust lithium industry chain system.

Hydrogen production technology is becoming increasingly important with the rapid development of hydrogen energy. Among existing hydrogen production technologies, alkaline water electrolysis (AWE) is drawing wide attention due to its advantages such as high maturity and low cost, and its performance analysis and optimization are important for applications. However, the AWE system contains processes with different physical and mathematical properties such as electrochemical reaction and heat transport processes, bringing difficulties to the system modeling. Moreover, the electrical and thermal processes have different characteristic time scales, and the system shows a sophisticated dynamic behavior, which has not been well studied yet. Here, a homomorphic dynamic model of the AWE system in the form of electrical circuit is built to describe the thermal and electrochemical processes uniformly, where the two parts are integrated via the energy conservation seamlessly. The model is verified by comparing with the experimental data and shows a high accuracy. The dynamic simulation analysis is conducted to investigate the dynamic response characteristics of the system under current step changes and fluctuations. The temperature overshoot and oscillation phenomena caused by delays in heat transport processes are studied. Results show that the time delay yields a maximum temperature overshoot of 10 °C, which would reduce the lifespan of the stack. This also highlights the importance of dynamic system analysis.

Consumer behavior is one of the key factors influencing product sales, especially in food packaging design, where green, organic, sustainable, and human-centered designs are more effective in promoting food sales. This paper aims to develop a sustainability evaluation method for children’s food packaging. The study first explores the theoretical foundations of sustainability, establishing a systematic set of quantitative indicators and evaluation criteria. Based on this framework, the research gathers consumption behavior, rating data from 250 parents of various ages, professions, and income backgrounds. Using the CRITIC model, the study performs dimensionless processing and detailed quantitative evaluation of the indicators’ comparability, contradictions, and information content to allocate weights for the sustainability evaluation metrics. Furthermore, the MABAC model is applied to construct a weighted decision matrix and boundary approximation area, ranking the sustainability of 20 representative children’s food packaging design schemes (S1–S20). The results show that Scheme S1, after calculation using the CRITIC–MABAC model, has a total distance of 0.214 from the boundary approximation area, exhibiting the smallest deviation from the ideal solution across multiple evaluation criteria and achieving the best overall performance. Building on the optimal Scheme S1, this study comprehensively considers key elements such as eco-friendliness, safety, functionality, and educational value in the optimization of a sustainable design for children’s fruit puree packaging. The research validates the practicality and effectiveness of the quantitative model through the sustainable design and evaluation of children’s food packaging from a consumer behavior perspective, promoting sustainability design and optimization in the children’s food packaging sector.

Discussing evolutionary network models and corresponding degree distributions under different mechanisms is applied basic research in network science. This study proposes a new evolutionary network model, which integrates node preference deletion and edge reconnection mechanisms and is also an extension of the existing evolutionary network model. In order to analyze the key statistical property of the model, the steady-state distribution, we propose a Markov chain method based on the enhanced stochastic process rule (ESPR). The ESPR method makes the evolving network’s topological structure and statistical properties consistent with those observed in the natural evolution process, ensures the theoretical results of the degree distribution of the evolving network model, and overcomes the limitations of using empirical methods for approximate analysis. Finally, we verify the accuracy of the steady-state distribution and tail feature estimation of the model through Monte Carlo simulation. This work has laid a solid theoretical foundation for the future development of evolutionary network models and the study of more complex network statistical properties.

Indoor point clouds often present significant challenges due to the complexity and variety of structures and high object similarity. The local geometric structure helps the model learn the shape features of objects at the detail level, while the global context provides overall scene semantics and spatial relationship information between objects. To address these challenges, we propose a novel network architecture, PointMSGT, which includes a multi-scale geometric feature extraction (MSGFE) module and a global Transformer (GT) module. The MSGFE module consists of a geometric feature extraction (GFE) module and a multi-scale attention (MSA) module. The GFE module reconstructs triangles through each point’s two neighbors and extracts detailed local geometric relationships by the triangle’s centroid, normal vector, and plane constant. The MSA module extracts features through multi-scale convolutions and adaptively aggregates features, focusing on both local geometric details and global semantic information at different scale levels, enhancing the understanding of complex scenes. The global Transformer employs a self-attention mechanism to capture long-range dependencies across the entire point cloud. The proposed method demonstrates competitive performance in real-world indoor scenarios, with a mIoU of 68.6% in semantic segmentation on S3DIS and OA of 86.4% in classification on ScanObjectNN.

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm.

Piezocatalytic materials have attracted widespread attention in the fields of clean energy and water treatment because of their ability to convert mechanical energy directly into chemical energy. In this study, γ-AlON particles synthesised using carbothermal reduction and nitridation (CRN) were used for the first time as a novel piezocatalytic material to degrade dye solutions under ultrasonic vibration. The γ-AlON particles exhibited good performance as a piezocatalytic material for the degradation of organic pollutants. After 120 min under ultrasonic vibration, 40 mg portions of γ-AlON particles in 50 mL dye solutions (10 mg/L) achieved 78.06%, 67.74%, 74.29% and 64.62% decomposition rates for rhodamine B (RhB), methyl orange (MO), methylene blue (MB) and crystal violet (CV) solutions, respectively; the fitted k values were 13.35 × 10−3, 10.79 × 10−3, 12.09 × 10−3 and 8.00 × 10−3 min−1, respectively. The piezocatalytic mechanism of γ-AlON particles in the selective degradation of MO was further analysed in free-radical scavenging activity experiments. Hydroxyl radicals (•OH), superoxide radicals (•O2−), holes (h+) and electrons (e−) were found to be the main active substances in the degradation process. Therefore, γ-AlON particles are an efficient and promising piezocatalytic material for the treatment of dye pollutants.

It is a great challenge to prepare efficient and stable electrocatalysts for hydrogen evolution (HER) using non-precious metals. In this study, a series of PtCo/Ti3C2Tx-Y (Y: 16, 32, and 320, Y indicates the quality of Co(NO3)2) catalysts were synthesized by loading PtCo alloy on Ti3C2Tx. The PtCo/Ti3C2Tx-32 catalyst showed the best HER performance, reaching a current density of 10 mA cm−2 with low overpotential (36 and 101 mV) and small Tafel slopes (66.37 and 105.17 mV dec−1) in 0.5 mol L−1 H2SO4 and 1 mol L−1 KOH solution. The excellent HER activity of PtCo/Ti3C2Tx-32 can be attributed as follows: Ti3C2Tx has a unique two-dimensional structure, which can provide a large number of attachment sites for the PtCo alloy, hence exposing more active sites; on the other hand, it can also provide a fast and efficient conductive network for electron transport during electrocatalytic processes, thus enhancing the connectivity between electrolyte and catalyst. PtCo alloy makes the PtCo/Ti3C2Tx catalyst more hydrophilic, accelerating the release rate of bubbles. The DFT calculation results showed that the energy barrier of HER is reduced because the charge around Pt is redistributed by Co after alloying Pt and Co, so that the PtCo/Ti3C2Tx catalyst has a suitable ΔGH* value. This study can be expected to provide some references for the design and synthesis of Ti3C2Tx-supported alloy electrocatalysts with high activity and stability for HER.

Magnaporthe oryzae causes devastating rice blast disease, significantly impacting rice production in many countries. Among the many known resistance (R) genes, Piz-t confers broad-spectrum resistance to M. oryzae isolates and encodes a nucleotide-binding site leucine-rich repeat receptor (NLR). Although Piz-t-interacting proteins and those in the signal transduction pathway have been identified over the last decade, the Piz-t-mediated resistance has not been fully understood at the transcriptomic and metabolomic levels. In this study, we performed transcriptomic and metabolomic analyses in the Piz-t plants after inoculation with M. oryzae. The transcriptomic analysis identified a total of 15,571 differentially expressed genes (DEGs) from infected Piz-t and wild-type plants, with 2791 being Piz-t-specific. K-means clustering, GO term analysis, and KEGG enrichment pathway analyses of the total DEGs identified five groups of DEGs with distinct gene expression patterns at different time points post inoculation. GO term analysis of the 2791 Piz-t-specific DEGs revealed that pathways related to DNA organization, gene expression regulation, and cell division were highly enriched in the group, especially at early infection stages. The gene expression patterns in the transcriptomic datasets were well correlated with the metabolomic profiling. Broad-spectrum “pathway-level” metabolomic analyses indicated that terpenoid, phenylpropanoid, flavonoid, fatty acid, amino acid, glycolysis/TCA, and phenylalanine pathways were altered in the Piz-t plants after M. oryzae infection. This study offers new insights into the molecular dynamics of transcripts and metabolites in R-gene-mediated resistance against M. oryzae and provides candidates for enhancing rice blast resistance through the engineering of metabolic pathways.

Chloroplasts are not only places for photosynthesis, but also participate in plant immunity and are important targets of pathogens. Pathogens secrete chloroplast-targeted proteins (CTPs) that disrupt host immunity and promote infection. Sclerotinia sclerotiorum (Lib.) de Bary is a phytopathogenic fungus with a broad host range. However, little is known about the pathogenic mechanisms underlying this wide host range. In this study, we investigated the role of Chloroplast-Targeted Protein 1 (SsCTP1) secreted by S. sclerotiorum in pathogenesis, which inhibits plant immunity and promotes pathogen infections. SsCTP1 was highly up-regulated during the early stages of S. sclerotiorum infection in various hosts, and its transient expression in Nicotiana benthamiana revealed that it was predominantly localized within chloroplasts. Mutants with SsCTP1 deletion exhibited a similar growth rate and colony morphology to the wild type, but significantly reduced pathogenicity in various hosts. Moreover, SsCTP1 inhibited chitin-induced callose deposition and defense gene expression, and enhanced sensitivity to S. sclerotiorum in N. benthamiana. Similarly, transgenic Arabidopsis thaliana overexpressing SsCTP1 displayed an increased susceptibility to S. sclerotiorum. Furthermore, two host proteins that interact with SsCTP1, Coproporphyrinogen-III oxidase (GmCPX), and shikimate kinase 2 (GmSKL2) were identified by screening the soybean cDNA library, and these interactions were confirmed in vivo. Importantly, the silencing of NbCPX by virus-induced gene silencing enhanced N. benthamiana resistance to S. sclerotiorum. Our results indicate that SsCTP1 is an important pathogenic factor that contributes to the wide host range of S. sclerotiorum and may inhibit plant immunity by targeting the chloroplast proteins GmCPX and GmSKL2, which are ubiquitous in host plants.

Salinity stress influences plants throughout their entire life cycle. However, little is known about the response of plants to long-term salinity stress (LSS). In this study, Puccinellia tenuiflora, a perennial halophyte grass, was exposed to 300 mM NaCl for two years (completely randomized experiment design with three biological replicates). We measured the photosynthetic parameters and plant hormones and employed a widely targeted metabolomics approach to quantify metabolites. Our results revealed that LSS induced significant metabolic changes in P. tenuiflora, inhibiting the accumulation of 11 organic acids in the leaves and 24 organic acids in the roots and enhancing the accumulation of 15 flavonoids in the leaves and 11 phenolamides in the roots. The elevated accumulation of the flavonoids and phenolamides increased the ability of P. tenuiflora to scavenge reactive oxygen species. A comparative analysis with short-term salinity stress revealed that the specific responses to long-term salinity stress (LSS) included enhanced flavonoid accumulation and reduced amino acid accumulation, which contributed to the adaptation of P. tenuiflora to LSS. LSS upregulated the levels of abscisic acid in the leaves and ACC (a direct precursor of ethylene) in the roots, while it downregulated the levels of cytokinins and jasmonic acids in both the organs. These tolerance-associated changes in plant hormones would be expected to reprogram the energy allocation among growth, pathogen defense, and salinity stress response. We propose that abscisic acid, ethylene, cytokinins, and jasmonic acids may interact with each other to construct a salinity stress response network during the adaptation of P. tenuiflora to LSS, which mediates salinity stress response and significant metabolic changes. Our results provided novel insights into the plant hormone-regulated metabolic response of the plants under LSS, which can enhance our understanding of plant salinity tolerance.

River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. To address this issue, we aimed to develop an automatic framework specifically for delineating river channels and measuring the bankfull widths at small spatial intervals along the channel. The DeepLabV3+ Convolutional Neural Network (CNN) model was employed to accurately delineate channel boundaries and a Voronoi Diagram approach was complemented as the river width algorithm (RWA) to calculate river bankfull widths. The CNN model was trained by images across four river types and performed well with all the evaluating metrics (mIoU, Accuracy, F1-score, and Recall) higher than 0.97, referring to the accuracy over 97% in prediction. The RWA outperformed other existing river width calculation methods by showing lower errors. The application of the framework in the Lillooet River, Canada, presented the capacity of this methodology to obtain detailed distributions of hydraulic and hydrological parameters, including flow resistance, flow energy, and sediment transport capacity, based on high-resolution channel widths. Our work highlights the significant potential of the newly developed framework in acquiring high-resolution channel width information and characterizing fluvial dynamics based on these widths along river channels, which contributes to facilitating cost-effective integrated river management.

As a weak high-frequency signal embedded in digital images, Photo Response Non-Uniformity (PRNU) is particularly vulnerable to interference from low-frequency components during the extraction process, which affects its reliability in real-world forensic applications. Previous studies have not successfully identified the effective frequency band of PRNU, leaving low-frequency interference insufficiently suppressed and impacting PRNU’s utility in scenarios such as source camera identification, image integrity verification, and identity verification. Additionally, due to differing operational mechanisms, current mainstream PRNU enhancement algorithms cannot be integrated to improve their performance further. To address these issues, we conducted a frequency-by-frequency analysis of the estimated PRNU and discovered that it predominantly resides in the frequency band above 10 Hz. Based on this finding, we propose a guided-filtering PRNU enhancement algorithm. This algorithm can function as a plug-and-play module, seamlessly integrating with existing mainstream enhancement techniques to further boost PRNU performance. Specifically, we use the PRNU components below 10 Hz as a guide image and apply guided filtering to reconstruct the low-frequency interference components. By filtering out these low-frequency components, we retain and enhance the high-frequency PRNU signal. By setting appropriate enhancement coefficients, the low-frequency interference is suppressed, and the high-frequency components are further amplified. Extensive experiments on publicly available Dresden and Daxing digital device forensics datasets confirm the efficiency and robustness of the proposed method, making it highly suitable for reliable forensic analysis in practical settings.

The unique magnetothermal properties of magnetic nanoparticles enable the development of a high-precision, real-time, noninvasive temperature measurement method with significant potential in the biomedical field. Based on a low-frequency alternating magnetic field excitation model, we construct two additional magnetic field excitation models—alternating current–direct current superposition and dual-frequency superposition—to extract harmonic amplitude components from the magnetization response. To increase the accuracy of harmonic information acquisition, the effects of the truncation error, excitation magnetic field frequency, and amplitude are thoroughly analyzed, and optimal parameter values are selected to minimize the error. A single algorithm is designed for temperature inversion, and a joint algorithm is proposed to optimize the performance of the single algorithm. Under low-frequency alternating-current magnetic field excitation, the autonomous group particle swarm optimization method achieves superior real-time performance in terms of temperature inversion and running time. Compared with the opposition learning gray wolf optimizer and particle swarm optimization–gray wolf optimization, the proposed method achieves reductions of 52% and 68%, respectively. Additionally, under dual-frequency superimposed magnetic field excitation, a higher temperature inversion accuracy is achieved compared with that of the particle swarm optimization–gray wolf optimization algorithm, reducing the error from 0.237 K to 0.094 K.

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made.

Effective adaptive grasping capability is regarded as crucial for climbing robots. However, many dry adhesion legged climbing robots are primarily focused on mobility and load capacity to perform various climbing tasks, often overlooking their operational grasping abilities. Furthermore, flexible grippers designed for adaptive grasping are typically not capable of supporting autonomous climbing or perching motions; they must be rigidly integrated with legged climbing robots, which results in increased weight and reduced load capacity. To address this challenge, a novel dry adhesion climbing robot, MST-G, is proposed, featuring autonomous climbing, perching, and flexible adaptive grasping capabilities. During operation, MST-G is integrated with a legged climbing robot to perform tasks, but can autonomously climb when no task is present, thereby reducing load and ensuring stable motion. Additionally, a robust controller based on prescribed performance is introduced and tested on MST-G, which limits the joint tracking error to a prescribed safety limit, ensuring that motion trajectories can be executed safely and reliably.

A highly sensitive sensor, which can detect the temperature and strain simultaneously, is proposed using a hollow-core anti-resonant fiber with composite nested tubes. The sensing fiber contains two kinds of nested tubes, and two different sensing mechanisms, the resonance coupling effect and the intermodal interference, are realized in the same section of a hollow-core anti-resonant fiber fully filled with ethanol. Five conjoined nested anti-resonant tubes are introduced to suppress the confinement loss of the higher-order mode LP02. One hybrid conjoined nested tube, which consists of a half-circular anti-resonant tube and a half-circular resonant tube, is introduced to induce a resonant coupling between the LP02 mode in the core and the dielectric mode in the nested resonant tubes. Numerical investigations demonstrate the shifts of the feature wavelengths of the resonance coupling effect, and the intermodal interference shows different velocities with temperature and strain, while a simultaneous measurement of temperature and strain can be realized with high sensitivities (3.36 nm/°C and −0.003 nm/με to temperature and strain, respectively). Since the sensor can be fabricated by full infiltration with liquid into the large-size core and cladding tubes of hollow-core anti-resonant fibers, and special post-processing, such as selective infiltration or coating, is notneeded. The proposed sensors based on hollow-core anti-resonant fibers with functional liquid infiltration provide a more efficient and versatile platform for the temperature and strain sensing.

Gravel is the most frequently used material in infrastructure construction. However, the irregular shape of the gravel pile makes it challenging for the loader to predict a stable shoveling position, which can easily result in partial collapse or a complete landslide, thereby posing a serious threat to the equipment. In view of the imperfect method of determining the shoveling position of the pile by the current unmanned loader and the high hardware requirements for the deployment of the identification model, this paper first establishes a mathematical model of the loader, and preliminarily determines the influence of the concave and convex edges of the gravel pile on the shoveling position selection through discrete element joint simulation; secondly, the influence of the pile with different edge curvatures on the loader operation process is analyzed in the simulation software, and the radar map is used to further identify the superior position features; finally, Ghost Net is used as the backbone network, the RFB module is introduced into the Backbone, and the CBAM attention mechanism is integrated into the C3 module to identify the lightweight YOLOv5s shoveling position. Discrete element analysis and a lightweight network model were used in the above study to find the safest and most effective shoveling positions. During the test that mimicked how the loader would actually shovel, the number of parameters in the improved model was cut down to 32.5% of the original, the number of calculations was cut down to about 55.2% of the original, and the average accuracy of finding the shoveling position of the gravel pile reached 98%.

This paper analyzes the experience of first-person shooter (FPS) players when game difficulty is adjusted by personalizing their audio cue settings, considering the balance between player performance, modeled using Gaussian process regression (GPR), and prior data serving as designer preference. In addition, we investigate why player experience changes according to in-game audio cues. Previous studies have proposed various dynamic difficulty adjustment (DDA) methods for FPS games. However, few studies have considered the role of audio cues in the player experience. This paper compares the player experience of personalized enemy audio cue volume settings (GPR-DDA) with that of predetermined settings in an FPS game. Two comprehensive experimental phases, involving 80 participants, are conducted to assess the efficacy of GPR-DDA. The experience of our players is measured using questions taken from the Game User Experience Satisfaction Scale (GUESS) questionnaire and a final survey asking for their open-ended feedback. A large language model (LLM) is used to analyze the natural language expressions of the players according to their native languages. To ensure the LLM effectively assists a limited number of qualified human evaluators in classifying player responses, we have developed an original procedure for this task. The GUESS results show that GPR-DDA can improve the player experience. In addition, the high consistency in the classification results over multiple runs of the selected LLM, as well as the similarity between its results and those of our human evaluators, reflects the reliability of the proposed LLM-assisted procedure.

In recent years, the global demand for energy has been continuously increasing. Biodiesel as a replacement for fossil fuels holds strategic importance for sustainable economic development, mitigating the environmental impact, and managing air pollution. The utilization of second-generation biodiesel has garnered significant research interest due to its physical and chemical characteristics that are comparable to diesel, its elevated cetane number, and its reduced viscosity. This study will transform the TBD234v6 fuel system, transforming the original diesel fuel system into a second-generation biodiesel/diesel hybrid fuel system. This study examined the impacts of second-generation biodiesel on combustion, performance, and emissions in diesel engines, as well as the influence of the deoxygenation rate on second-generation biodiesel. Grey decision-making was used to determine the optimal mixing ratio and deoxygenation rate. The results indicated that the optimal blend comprises 10% second-generation biodiesel and 90% diesel fuel. In dual-fuel mode at this blend ratio, there is a 3% increase in maximum pressure compared to running on pure diesel. Moreover, the fuel consumption rate decreases by approximately 5.6%. Nitrogen oxide (NOx) and soot emissions decreased by 4.7% and 4.9%, respectively.

Investigating the stability of and fluctuations in the energy market has long been of interest to researchers and financial market participants. This study aimed to analyze the Chinese energy market, focusing on its volatility and response to financial tensions. For this purpose, data from eight major financial companies, which were selected based on their market share in Shanghai’s and Shenzhen’s financial markets, were collected from January 2014 to December 2023. In this study, stock prices and trading volumes were used as the key variables to build bootstrap-based minimum spanning trees (BMSTs) using ratio mutual information (RMI). Then, using the sliding window procedure, the major network characteristics were derived to create an anomaly-detection tool using the multivariate exponentially weighted moving average (MEWMA), along with the Brent crude oil price index as a benchmark and a global oil price indicator. This framework’s stability was evaluated through stress testing with five scenarios designed for this purpose. The results demonstrate that during periods of high oil price volatility, such as during the turbulence in the stock market in 2015 and the COVID-19 pandemic in 2020, the network topologies became more centralized, which shows that the market’s instability increased. This framework successfully identifies anomalies and proves to be a valuable tool for market players and policymakers in evaluating companies that are active in the energy sector and predicting possible instabilities, which could be useful in monitoring financial markets and improving decision-making processes in the energy sector. In addition, the integration of other macroeconomic factors into this field could strengthen the identification of anomalies and be considered a field for possible research.

To enhance the interests of all stakeholders in the multi-microgrid integrated energy system and to promote photovoltaic consumption, this paper proposes a master–slave game operation optimization strategy for a multi-microgrid system considering photovoltaic consumption and integrated demand response. Initially, an energy interaction model was established to delineate the relationships between each microgrid and the distribution network, as well as the interactions among the microgrids. Additionally, an integrated demand response model for end-users was developed. This framework leads to the formulation of a one-leader multi-follower interaction equilibrium model, wherein the multi-microgrid system acts as the leader and the users of the multi-microgrid serve as followers. It is proven that a unique equilibrium solution for the Stackelberg game exists. The upper level iteratively optimizes variables such as energy-selling prices, equipment output, and energy interactions among microgrids, subsequently announcing the energy-selling prices to the lower level. The lower level is responsible for optimizing energy load and returning the actual load demand to the upper level. Finally, the rationality and effectiveness of the proposed strategy are demonstrated through the case analysis. Thus, the profitability of the multi-microgrid system is enhanced, along with the overall benefits for each microgrid user, and the amount of photovoltaic curtailment is significantly reduced.

The burning chamber wall of the ramjet engine is facing an extremely thermal environment during normal conditions. Thermal protection measures must be taken on the wall surface of the combustion chamber. At the same time, the aircraft faces high-power electrical demand problems under high-speed cruising states. To address these issues, a second fluid-closed Brayton cycle system was introduced in this paper. Helium was utilized as the secondary fluid medium, while kerosene was used as the final heat sink. The ramjet engine chamber wall was cooled by the helium cycle system. At the same time, part of the heat absorbed by the helium cycle was transformed into electric power by a generator. This work proposes a new method of thermal management in a closed cycle. Unlike traditional methods, this proposal can regulate the mass flow rate of helium based on the requirement of heat load. A zero-dimensional numerical calculation method was established for thermodynamic analysis. The results show that as the equivalence ratio of 0.8~1.5 for the kerosene flow rate, the system can suffer the thermal load of 200~350 kJ/kg on the combustion chamber wall at the maximum kerosene allowable temperature. To ensure the normal operation of the circulating system, the mass flow ratio between the helium and the air changes from 0.02 to 0.045. Compared with the direct kerosene cooling method, the second fluid circulation method leads to the kerosene equivalent saving ratio by 2% to 14%; at the same time, such a system could generate 160~500 kJ/kg of electrical energy. This new thermal management method can achieve kerosene saving, electric power generating and suffering more thermal loads under the premise of satisfying normal work.

As a crucial stage in the processing of black tea, rolling plays a significant role in both the color transformation and the quality development of the tea. In this process, the production of theaflavins, thearubigins, and theabrownins is a primary factor contributing to the alteration in color of rolled leaves. Herein, tea pigments are selected as the key quality indicators during rolling of black tea, aiming to establish rapid detection methods for them. A machine vision system is employed to extract nine color feature variables from the images of samples subjected to varying rolling times. Then, the tea pigment content in the corresponding samples is determined using a UV-visible spectrophotometer. In the meantime, the correlation between color variables and tea pigments is discussed. Additionally, Z-score and PCA are used to eliminate the magnitude difference and redundant information in original data. Finally, the quantitative prediction models of tea pigments based on the images’ color features are established by using PLSR, SVR, and ELM. The data show that the Z-score–PCA–ELM model has the best prediction effect for tea pigments. The Rp values for the model prediction sets are all over 0.96, and the RPD values are all greater than 3.50. In this study, rapid determination methods for tea pigments during rolling of black tea are established. These methods offer significant technical support for the digital production of black tea.

In this study, the bacterial, fungal, and amino acid compositions of the Zhuangzu acid congee (ZAC) along the Zuojiang River of Guangxi were analyzed. A total of 14 samples of ZAC were collected in four regions: Fusui (F), Ningming (N), Xiashi (X), and Suxu (S). The predominant bacterial phyla of the four groups were Firmicutes and Proteobacteria. The dominant bacterial genera were Lactobacillus, Bacillus, Schleiferitobacillus, and Acetobacter. The fungal alpha diversity in Group S was significantly lower than that in Groups F, X, and N. PCoA clearly distinguished Group S from Groups F, X, and N. The dominant fungal phylum in Group S was Ascomycota (89.12%), whereas those in Groups F, X, and N were Basidiomycota (38.07%), Ascomycota (30.65%), and Fungi (17.39%). The dominant fungal genera in Group S were Saccharomyces (42.36%) and Pichia (31.66%), whereas those in Groups F, X, and N were Mortierella (17.23%) and Russula (13.34%). The proportions of the six flavor amino acids were relatively high, and all four groups of samples were above 30.00%. PLS-DA of amino acids demonstrated that all four groups of samples could be significantly distinguished. Additionally, the concentrations of taurine, serine, leucine, phosphoserine, asparagine, 1-methylhistidine, and 3-methylhistidine in Group S were significantly greater than those in Groups F, X, and N. Correlation analysis revealed that 6 bacterial and 15 fungal genera were significantly correlated with amino acids, particularly Leuconostoc and Schleiferlactobacillus, among bacteria, as well as Saccharomyces and Russula, among fungi. In conclusion, compared with the disparity in the bacterial community, the disparity in the fungal community was more strongly correlated with the amino acid composition of ZAC. This result indicated that the difference in the fungal community might cause the variation in the amino acid composition of ZAC.

Saline soils, as a special class of soil types, have unique physicochemical properties that have far-reaching effects on crop growth and quality characteristics. In order to better develop saline soils as a reserve resource, it is particularly important to exploit the potential of saline crops. Peanut, as one of the important crops in saline soils, can have different quality characteristics depending on the differences in soil salinity and alkalinity, as well as growing conditions. In this study, we compared the nutritional quality and functional composition of five peanut varieties grown in coastal saline soils, with the same varieties grown in non-saline soils in similar areas. The results showed significant differences (p<0.05) between saline and non-saline peanuts in the contents of ash, zinc, phosphorus, β-VE, Cis-11-eicosatetraenoic acid, palmitoleic acid, linolenic acid, and total antioxidant removal capacity, whereby the former was higher than the latter by 0.12 g/100 g, 4.1 mg/kg, 321 mg/kg, 8.98 μg/g, 0.36%, 0.01%, and 0.01%, respectively, and the total antioxidant capacity was lower than that of the latter by 9.18 μg Trolxo/g of fresh weight. Sodium element and superoxide dismutase (SOD) activity contents were extremely significantly (p<0.01) different in peanuts grown in both land types, where the former was higher than the latter by 261.9 mg/kg and 285 U/g, respectively. Water, fat, protein, calcium, copper, iron, potassium, magnesium, manganese, Vc, α-VE, total VE, VB3, 5-methyl-tetrahydrofolate, 5-formyl-tetrahydrofolate, total phenols, total flavonoids, ABTS free radical scavenging capacity, DPPH free radical scavenging capacity, fatty acids (except for Cis-11-eicosapentaenoic acid, palmitoleic acid, and linolenic acid), phytosterols, and guanines showed no significant differences (p<0.01). To sum up, the origin and soil environment have an effect on the quality of peanuts. These results also provide a scientific basis for the quality assessment of peanuts in saline soil.

Utilizing Rhodopseudomonas palustris (R. pal), this study constructed a dual-chamber microbial electrosynthesis system, based on microbial electrolysis cells, that was capable of producing lycopene. Cultivation within the electrosynthesis chamber yielded a lycopene concentration of 282.3722 mg/L when the optical density (OD) reached 0.6, which was four times greater than that produced by original strains. The mutant strain showed significantly higher levels of extracted riboflavin compared to the wild-type strain, and the riboflavin content of the mutant strain was 61.081 mg/L, which was more than 10 times that of the original strain. Furthermore, sequencing and analyses were performed on the mutant strains observed during the experiment. The results indicated differences in antibiotic resistance genes, carbohydrate metabolism-related genes, and the frequencies of functional genes between the mutant and original strains. The mutant strain displayed potential advantages in specific antibiotic resistance and carbohydrate degradation capabilities, likely attributable to its adaptation to electrogenic growth conditions. Moreover, the mutant strain demonstrated an enrichment of gene frequencies associated with transcriptional regulation, signal transduction, and amino acid metabolism, suggesting a complex genetic adaptation to electrogenic environments. This study presents a novel approach for the efficient and energy-conserving production of lycopene while also providing deeper insights into the genetic basis of electro-resistance genes.

Pullulan is a high-value biopolymer synthesized by Aureobasidium pullulans through the fermentation of starch and sugars. It finds extensive applications in food, packaging, biomedicine, and other sectors. However, the high production costs significantly limit the development and application of pullulan. Consequently, there is an urgent need to identify high-quality fermentation substrates. In recent years, the rapid growth of Huangjiu industry has led to the generation of waste Huangjiu lees, which not only contribute to environmental pollution but also represent a significant waste of resources. As a result, the resource utilization of Huangjiu lees has garnered considerable attention. In this study, Huangjiu lees were employed as raw materials for fermentation to produce pullulan. Following fermentation of Huangjiu lees powder with the primary strain Aureobasidium pullulans LL1, the yield of pullulan was notably reduced. Through adaptive evolution, an evolved strain, Aureobasidium pullulans AP9, was isolated, demonstrating enhanced efficiency in producing pullulan from Huangjiu lees. The impact of Huangjiu lees on pullulan biosynthesis was elucidated via transcriptome analysis. Fermentation conditions were optimized using a single-factor approach, and a multi-strain staged fermentation strategy involving Aspergillus niger and Aureobasidium pullulans was employed to further enhance pullulan yield. Under optimal conditions, the pullulan yield reached 22.06 g/L, with a molecular weight of 1.04 × 106 Da. This study underscores the significant potential of utilizing Huangjiu lees for pullulan production and offers valuable insights for the resource utilization of this byproduct.

This paper investigated the tracking problem of mixed H∞ and L2−L∞ adaptive fault-tolerant control (FTC) for continuous-time interval type-2 fuzzy systems (IT2FSs). For the membership function mismatch and uncertainty between the modules of the nonlinear system, the IT2 fuzzy model is applied to linearly approximate it. The observer can sensitively estimate the system state, and the adaptive fault estimation functions can estimate adaptively the fault signals, which enables the designed adaptive FTC scheme to ensure the asymptotic stability of the closed-loop control system and achieve the desired mixed H∞ and L2−L∞ tracking performance. The designed adaptive control law can achieve the purpose of dynamic compensation for faults and disturbances, and the introduced lemmas further reduce the design conservatism by adjusting the slack parameters and matrices. Finally, a mass-spring-damping system is used to illustrate the effectiveness of the designed method.

Quantum heat engines (QHEs) are established by applying the principles of quantum thermodynamics to small−scale systems, which leverage quantum effects to gain certain advantages. In this study, we investigate the quantum Otto cycle by employing the dipole−dipole coupled polar molecules as the working substance of QHE. Here, the molecules are considered to be trapped within an optical lattice and located in an external electric field. We analyze the work output and the efficiency of the quantum Otto heat engine (QOHE) as a function of various physical parameters, including electric field strength, dipole−dipole interaction and temperatures of heat baths. It is found that by adjusting these physical parameters the performance of the QOHE can be optimized effectively. Moreover, we also examine the influences of the entanglement and relative entropy of coherence for the polar molecules in thermal equilibrium states on the QOHE. Our results demonstrate the potential of polar molecules in achieving QHEs.