Cold stress is a major environmental factor affecting the growth, development, and productivity of various crop species. With the current trajectory of global climate change, low temperatures are becoming more frequent and can significantly decrease crop yield. Wheat (Triticum aestivum L.) is the first crop to be domesticated and is the most popular cereal crop in the world. Due to a lack of systematic research on cold response pathways and gene regulatory networks, the underlying molecular mechanisms of cold signal transduction in wheat are poorly understood. This study reviews recent progress in wheat, including the ICE-CBF-COR signaling pathway in cold stress and the effects of cold stress on hormonal pathways, reactive oxygen species (ROS), and epigenetic regulation. This review also highlights outstanding issues that are crucial for understanding the interactions between wheat and low-temperature conditions.

The shape of the knee is a chain-like structure of an ellipsoid. This suggests that, in the latest study, in addition to rolling, there is slippage in the motion of the knee. This paper selects a mechanical braking model for cattle based on two mechanical structures in different directions at the joints. The experimental results show that the modified dynamical system has strong chaotic properties. This can be one of the basis for judging various health states.

Our understanding of phylogenetic relationships among Gadiformes fish is obtained through the analysis of a small number of genes, but uncertainty remains around critical nodes. A series of phylogenetic controversial exists at the suborder, family, subfamily, and species levels. A total of 1105 orthologous exon sequences and translated amino acid sequences from 36 genomes and 12 transcriptomes covering 33 species were applied to investigate the phylogenetic relationships within Gadiformes and address these problems. Phylogenetic trees reconstructed with the amino acid data set using different tree-building methods (RAxML and MrBayes) showed consistent topology. The monophyly of Gadifromes was confirmed in our study. However, the three suborders Muraenolepidoidei, Macrouroidei, and Gadoidei were not well recovered by our phy-logenomic study, rejecting the validity of suborder Muraenolepidoidei. Four major lineages were revealed in this study. The family Bregmacerotidae forming clade I was the basal lineage of Gadiformes. The family Merluciidae formed clade II. Clade III contained families Melanonidae, Muraenolepididae, Macrouridae (with subfamilies Trachyrincinae, Macrourinae, and Bathygadinae), and Moridae. Clade IV contained at least three families of suborder Gadoidei, i.e., Gadidae, Phycidae, and Ranicipitidae. The subspecies of Lota lota from Amur River were confirmed, indicating that exon markers were a valid high-resolution method for delimiting subspecies or distinct lineages within species level. The PSMC analysis of different populations of L. lota suggests a continuous decline since 2 Myr.

The pile foundation in the permafrost region is in a negative temperature environment, so the concrete is affected by the negative temperature of the surrounding soil.It not only affects the formation of concrete strength, but also leads to engineering quality accidents in serious cases.Based on the actual measurement of temperature at different strata depths and the comprehensive consideration of surface temperature, terrestrial heat flux and other parameters, the law curve of temperature change along depth in Greater Khingan is established.The calculated results of the curve are consistent with the measured results of ground temperature.The results show that the variation trend of ground temperature along the strata depth at different monitoring sites is basically the same. From June to November, the ground temperature at different depths tends to be constant.From December to May, the ground temperature at any depth within the depth range of 0 to 5.5m follows the law of the cosine function.Below 5.5m, the earth temperature no longer varies with depth.The research results can be used as reference for pile foundation construction under negative temperature environment.

How to achieve sustainable development and protection of historical district is a worthwhile research topic. As a vital way to update urban space, tourism development in historical district is an effective solution to redistribute urban functions and increase urban vitality. This paper takes the Insadong in South Korea as a case to carry out the evaluation of tourist satisfaction in historical districts. The research finds that: 1) The tourist satisfaction evaluation of Insadong includes 6 dimensions in total, namely “Embodiment of historical elements”, “The blend of tradition and modernity”, Industry distribution and type", "Consumer demand", "Street layout and function", "Landscaping". The most satisfying for tourists is "Landscaping", and the most dissatisfying is "Street layout and function". 2) “The blend of tradition and modernity” has the highest weight while “Industry distribution and type” has the lowest one in the analysis of influencing factors on overall satisfaction. 3) The analysis of the common factor weight and the common factor satisfaction shows that “The blend of tradition and modernity” and “Street layout and function” are the parts that need to be improved. “Consumer demand” also has a lot of room for promotion. The research results will benefit to enhance the tourist experiences of historical district and provide theoretical basis and practical experience reference for effective protection and sustainable development of historical district.

This paper evaluates the effect of China’s New Rural Pension Scheme (NRPS) on the retirement sustainability in forms of both formal labor supply and informal labor supply, using data from China Health and Retirement Longitudinal Study (CHARLS). We explore the regional differences of the NRPS effect on labor supply between the Western regions and the other regions of China. Our analysis shows that western rural China has a more severe problem of “ceaseless toil” compared to the rest of the country. We find that NRPS improves the “ceaseless toil” situation of the Chinese rural elderly, and the results show a very different pattern between western China and other parts of the country.

Increased attention has been paid to the influence of coal mining subsidence on ecological environment. Restoration of ecosystem in damaged mining area is critical for restoring disturbed environment. The comparing of plant communities and microbial communities in the artificial restoration and natural restoration areas provides an effective method for evaluating the restoration effects. However, such studies are limited in coal mining subsidence restoration areas. Subsidence area in Shendong mining area, located in the semi-arid region of Western China, was restored from 2003 with 5 ecological restoration plant species. In July 2017, the comparison and analysis of plant and microbial communities were conducted at the artificial restoration areas (AR) and the natural remediation areas (NR). The results showed that the artificial ecological restoration in Shendong mining area has achieved some success, but it has not recovered to a similar ecosystem before the destruction. A higher plant species, coverage and bacterial community diversity were observed in AR. However, these features have lower similarity compared with those in NR sites. Potential soil factors, such as pH, moisture content, total carbon content, organic matter, nitrogen and bulk density, have a greater impact on soil bacterial community structure and diversity. In the ecological restoration of the mining area, attention should be paid to the restoration of soil properties in the mining area. This study can provide theoretical guidance for more scientific ecological restoration in the damaged mining area.

A double Roman dominating function on a graph G is a function f : V(G) → {0, 1, 2, 3} 2 with the condition that every vertex u for which f(u) = 0 is adjacent to at least one vertex v for 3 which f(v) = 3 or two vertices v1 and v2 for which f(v1) = f(v2) = 2, and every vertex u for which 4 f(u) = 1 is adjacent to at least one vertex v for which f(v) ≥ 2. The weight of a double Roman dominating function f is the value w(f) = ∑u∈V(G) 5 f(u). The minimum weight over all double 6 Roman dominating functions on a graph G is called the double Roman domination number γdR(G) 7 of G. In this paper we determine the exact value of the double Roman domination number of the 8 generalized Petersen graphs P(n, 2) by using a discharging approach.

An immiscible blend of poly(hydroxy ether of bisphenol-A) (phenoxy) and poly(1,4-butylene terephthalate) (PBT) with phase separation was observed in as-blended samples. However, compatibilization of the phenoxy/PBT blends can be promoted through chemical exchange reactions of phenoxy with PBT upon annealing. In contrast to the as-blended samples, the annealed phenoxy/PBT blends had a homogeneous phase with a single T_g that could be enhanced by annealing at 260°C. Infrared (IR) spectroscopy demonstrated that phase homogenization could be promoted by annealing of the phenoxy/PBT blend, where alcoholytic exchange occurred between the dangling hydroxyl group in phenoxy and the carbonyl group in PBT in the heated blends. The alcoholysis reaction changes the aromatic linkages to aliphatic linkages in carbonyl groups, which initially led to the formation of a graft copolymer of phenoxy and PBT with an aliphatic/aliphatic carbonyl link. The progressive alcoholysis reaction resulted in the transformation of the initial homopolymers into block copolymers and finally into random copolymers, which promoted phase compatibilization in blends of phenoxy with PBT. Due to the fact that the amount of copolymers increased upon annealing, crystallization of PBT was inhibited by alcoholytic exchange in the blends.

This paper considers a two-way relay network, where two source nodes exchange messages through several relays in the presence of an eavesdropper, and the channel state information (CSI) of the eavesdropper is imperfectly known. The amplify-and-forward relay protocol is used and the relay beamforming weights are designed. The model is built up to minimize the total relay transmit power while guaranteeing the quality of service at users and preventing the eavesdropper from decoding the signals. Due to the imperfect CSI, a semi-infinite programming problem is obtained. An algorithm is proposed to solve the problem, and the iterative points are updated through the linesearch technique, where the feasibility are preserved during iterations. The optimality property is analyzed. The obtained subproblems are quadratic constrained quadratic programming problems, either with less than $4$ constraints or with only one variable, which are solved optimally. Simulation results demonstrate the importance of the proposed model, and imply that the proposed algorithm is efficient and converges very fast, where more than 85% of the problems are solved optimally.

The majority of existing numerical simulations of the effect of bridge piers on water movement are based on a limited number of bridge piers at a laboratory scale. Furthermore, some 2D numerical simulations for actual bridge projects have deficiencies, including the use of overly large mesh sizes and an inadequate treatment of bridge piers. In this study, we compare three methods of structural mesh encryption, suspension mesh, and nonstructural mesh based on Delft3D, and apply the optimization scheme to a real bridge project. It is demonstrated that optimal results can be achieved by utilising a grid size comparable to the pier diameter (Dp) in the region away from the piers. In the vicinity of the piers, the grid cell size should be no larger than 1/9 Dp. The suspended grid technique (DD Boundary) can yield results consistent with those obtained using a full-area high-resolution grid, provided that the total number of grids can be reduced and the computational time is considerably reduced. In this study, the unstructured mesh (Deflt3D Flexible Mesh) scheme was unable to capture the oscillations of the wake flow behind the bridge piers. However, the application of the optimized scheme in bridge engineering has demonstrated its practical value. The findings of this study on mesh resolution and suspension mesh scheme can be applied to the Delft3D software and are also useful for other numerical simulation work.

The gut has been a focus of chronic disease research. The gut microbiota produces metabolites that act as signaling molecules and substrates, closely influencing host health. Indigestible oli-gosaccharides (NDOs), as a common dietary fiber, play an important role in regulating the structure and function of gut microbiota. Their mechanism of action is mainly attributed to their carbon source as specific probiotics, producing related metabolites, and regulating the gut mi-crobial community. However, due to the selective utilization of oligosaccharides, some factors such as the types and structures of oligosaccharides have different impacts on the composition of microbial populations and the production of metabolites in the colon ecosystem. This review systematically describes the key factors influencing the selective utilization of oligosaccharides in microorganisms and elaborates how oligosaccharides affect the host's immune system, inflam-mation levels and energy metabolism by regulating microbial diversity and metabolic function, which in turn will affect the onset and progress of chronic diseases, especially diabetes, obesity, depression and intestinal inflammatory diseases, constipation. In this review, we re-examine the interaction mechanisms between gut microbiota and its associated metabolites and diseases, and explore new strategies for promoting human health and combating chronic diseases through di-etary interventions.

Elastic yarns are the key component of high-performance compression garments. However, it remains a challenge to fabricate anti-fatigue yarns with high mechanical force and long elongation for generating compression garments with prolonged wear. In this paper, we report the development of anti-fatigue double-wrapped yarns with excellent mechanical properties by wrapping high-denier Spandex with nylon filaments in opposite twists. In particular, 560D high-denier Spandex as the core was untwisted which can maximumly reduce the interaction between the core and wrapping filaments, enabling high elongation of the double-wrapped yarns. In addition, we chose 70D nylon filaments with a tensile force of 387.40 ± 8.82 cN as the wrapping materials to provide sufficient force for double-wrapped yarns. Notably, opposite twists were induced for the inner and outer wrapping filaments to achieve a balanced stable yarn structure. By systematically optimizing manufacturing parameters including inner wrapping density, outer wrapping density, taking-up ratio, and drafting ratio, we obtained double-wrapped yarn with excellent tensile force (952.00 ± 24.03 cN) and elongation (357.28% ± 9.10%). Notably, the stress decay rate of optimized yarns was only 12.0% ± 2.2%. In addition, the optimized yarn was used as the weft-lining yarn for generating weft-lined fabrics, the elastic recovery rate of the obtained fabric was only 2.6% after cyclic stretching, much lower than the control fabric. Our design of anti-fatigue double-wrapped yarns could be widely used for fabricating high-performance compression garments.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

Although crop conversion from annual to perennial crops has been considered as one path towards climate-smart and resource-efficient agriculture, the effects of this conversion on soil multifunctionality and biomass yields remain unclear. Here, we investigated the effects of one annual and three perennial crops (a grass (Lolium perenne), a legume (Medicago sativa), and their mixture) on soil multifunctionality and biomass yield on the Yellow River floodplain. Soil multifunctionality was assessed by the capacity of water regulation and the multifunctionality of carbon (C), nitrogen (N), and phosphorus (P) cycles. Results showed that perennial crops enhanced soil multifunctionality by 207% for L. perenne, 311% for M. sativa, and 438% for L. perenne + M. sativa, compared with annual winter wheat (Triticum aestivum). The effect of perennial crops on soil multifunctionality increased with infiltration rate, dissolved organic C, microbial biomass C, and extracellular enzymatic activities for both C and N acquisition. However, we observed that perennial crops had lower biomass yield than annual crop. Therefore, the transition of agricultural landscapes to perennials needs to take into account the balance between environmental protection and food security, as well as environmental heterogeneity, to promote sustainable agricultural development.

Limited studies have shown that exposure to arsenic is associated with serum vitamin D levels, but the results are still inconsistent. A cross-sectional study of 762 participants was conducted in Wenshui Country, Shanxi Province, which was identified as an area of water-borne arsenicosis. The results showed positive relationship between arsenic species (iAs, MMA, DMA) and serum 25(OH)D. Binary logistic regression analysis showed that the significant increases of 0.4% and 0.6% in the risk of vitamin D excess for every 1-unit increment in the Box-Cox transformed urinary tAs and DMA, respectively. After stratifying populations based on arsenic methylation metabolic capacity, each one-unit increase in the Box-Cox transformed tAs level was associated with increases of 0.064 (95%CI: 0.032 to 0.096) in serum 25(OH)D in the populations with iAs% above the median. In the populations with skin hyperkeratosis, urinary iAs was positively associated with serum 25(OH)D (β=0.592, 95%CI: 0.041 to 1.143). Overall, our findings support the positive relationship between urinary arsenic and serum 25(OH)D.

Both ricin and R. communis agglutinin (RCA120), belonging to the type II ribosome-inactivating proteins (RIPs-Ⅱ), are derived from the seeds of castor bean plant. They share very similar amino acid sequences, but ricin is much more toxic than RCA120. It is urgently necessary to distinguish ricin and RCA120 in response to public safety. Currently, mass spectrometric assays are well established for unambiguous identification of ricin by accurate analysis of differentiated amino acid residues after trypsin digestion. However, diagnostic peptides are relatively limited for unambiguous identification of trace ricin, especially in complex matrices. Here, we demonstrate a digestion strategy of multiple proteinases to produce novel peptide markers for unambiguous identification of ricin. LC-HRMS was used to verified the resulting peptides, among which only the peptides with uniqueness and good MS response were selected as peptide markers. Seven novel peptide markers were obtained from tandem digestion of trypsin and endoproteinase Glu-C in PBS buffer. From the chymotrypsin digestion under reduction and non-reduction conditions, eight and seven novel peptides were selected respectively. Using pepsin under pH 1~2 and proteinase K digestion, 6 and 5 peptides were selected as novel peptide markers. In conclusion, the obtained novel peptides from the established digestion methods can be recommended for the unambiguous identification of ricin during the investigation of illegal use of the toxin.

Heavy metals are compounds that can be hazardous and impair growth of living organisms. Bacteria have evolved the capability not only to cope with heavy metals but also to detoxify polluted environments. Three heavy metal-resistant strains of Mucilaginibacer rubeus and one of Mucilaginibacter kameinonensis were isolated from the gold/copper Zijin mining site, Longyan, Fujian, China. These strains were shown to exhibit high resistance to heavy metals with minimal inhibitory concentration reaching up to 3.5 mM Cu(II), 21 mM Zn(II), 1.2 mM Cd(II), and 10.0 mM As(III). Genomes of the four strains were sequenced by Illumina. Sequence analyses revealed the presence of a high abundance of heavy metal resistance (HMR) determinants. One of the strain, M. rubeus P2, carried genes encoding 6 putative P1B-1-ATPase, 5 putative P1B-3-ATPase and 4 putative Zn(II)/Cd(II) P1B-4 type ATPase, and 16 putative RND-type metal transporter systems. Moreover, the four genomes carry a high abundance of genes coding for putative metal binding chaperones. Analysis of the close vicinity of these HMR determinants uncovered the presence of clusters of genes potentially associated with mobile genetic elements. These loci include genes coding for tyrosine recombinases (integrases) and subunits of mating pore (type 4 secretion system) respectively allowing integration/excision and conjugative transfer of numerous genomic islands. Further in silico analyses revealed that their genetic organization and gene products resemble the Bacteroides integrative and conjugative element CTnDOT. These results highlight the pivotal role of genomic islands in the acquisition and dissemination of adaptive traits, allowing for rapid adaption of bacteria and colonization of hostile environments.

GaN ultraviolet photodetector with metal-semiconductor-metal structure is achieved by growing on a periodic trapezoid column-shape patterned sapphire substrate using metalorganic chemical vapor deposition. Under 5-V reverse bias, the photodetector fabricated on such patterned sapphire substrate exhibits a lower dark current, a higher photocurrent, and a 476 % enhancement in the maximum responsivity as compare with those of the photodetector fabricated on conventional flat sapphire substrate. It is also found that the much larger UV-to-visible rejection ratio and the fact that responsivity drops in a smaller cut-off region are observed from photodetector fabricated by using a periodic trapezoid column-shape patterned sapphire substrate. These phenomena may all be attributed to the reduction of threading dislocation density and the improved quality of GaN film, as well as the internal reflection and/or scattering effect on the interface between GaN film and the periodic trapezoid column-shape pattern of the substrate.

Cellular senescence has recently been recognized as a hallmark of cancer, reflecting its association with aging and inflammation, its role as a response to deregulated proliferation and oncogenic stress, and its induction by cancer therapies such as radiation, chemotherapy, and targeted agents. While such therapy-induced senescence (TIS) has been linked to resistance, recurrence, metastasis and normal tissue toxicity, TIS has the potential to enhance therapy response and stimulate anti-tumor immunity. In this review, we examine the Jekyll and Hyde nature of senescent cells (SnCs), with a particular focus on how their persistence while expressing the senescence-associated secretory phenotype (SASP) modulates the tumor microenvironment through autocrine and paracrine mechanisms. Via the SASP, the formation of SnCs may alternately mediate resistance or response to conventional therapy. Further, toward fulfilling the unmet potential of cancer immunotherapy, we consider how SnCs may influence tumor inflammation and serve as a source of antigen to potentiate anti-tumor immunity. This new view suggests treatment strategies based on inducing TIS to enhance immune checkpoint blockade. Finally, we describe strategies for mitigating the detrimental effects of senescence, such as modulating the SASP or targeting SnC persistence, toward enhancing the benefits of cancer treatment.

With much more challenging scenarios encountered in engineering, traditional quadrotors couldn’t accomplish all desired missions perfectly. Task-driven based reconfigurable quadrotors have more latent capacity in environment with dense obstacles and narrow corridors. The translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed in this presentation, assumptions, mathematical models and related motion control laws are given subsequently, in order to cope with hazard missions, model reference adaptive control laws are proposed, sufficient numerical simulations demonstrate the validity of the proposed control laws.

Extreme-mass-ratio inspirals (EMRIs) are significant observational targets for spaceborne gravitational wave detectors, namely, LISA, Taiji, and Tianqin, which involve the inspiral of stellar-mass compact objects into massive black holes with a mass range of approximately 104∼107M⊙. EMRIs are estimated to produce long lived gravitational wave signals with more than 105 cycles before plunge, making them an ideal laboratory for exploring the strong-gravity properties of the spacetimes around the MBHs, stellar dynamics in galactic nuclei, and properties of the MBHs itself. However, the complexity of the waveform model, which involves the superposition of multiple harmonics, as well as the high-dimensional and large-volume parameter space, make the fully coherent search challenging. In our previous work, we proposed a 10-dimensional search using Particle Swarm Optimization (PSO) with local maximization over the three initial angles. In this study, we extend the search to an 8-dimensional PSO with local maximization over both the three initial angles and the angles of spin direction of the MBH where the latter contribute a time independent amplitude to the waveforms. Additionally we propose a 7-dimensional PSO search by using a fiducial value for the initial orbital frequency and shifting the corresponding 8-dimensional Time Delay Interferometry responses until a certain lag returns the corresponding 8-dimensional log-likelihood ratio maximum. The reduced dimensionality likelihoods enable us to successfully search for EMRI signals with duration of 0.5 years and signal-to-noise ratio of 50 within a wider search range than our previous study. We discuss further developments, such as using a hierarchical search to narrow down the search ranges of certain parameters, and applying Graphics Processing Units to speed up the code. These advances aim to improve the efficiency and accuracy of the EMRI search algorithm.

Xanthogranulomatous orchitis is a chronic, non-neoplastic inflammation that is quite rare. In this paper, we discuss the case of a 79-year-old male patient who presented with bilateral scrotum swelling and dysuria that persisted for two weeks. Despite two weeks of anti-infection treatment, the patient's symptoms persisted, leading to a decision to perform transurethral prostate resection and bilateral orchiectomy after careful consideration. After the surgery, the postoperative pathology indicated that the patient had xanthogranulomatous orchitis on the left side and acute positive orchitis with hemorrhage on the right side. Fortunately, three months after the procedure, the patient experienced no negative events and was pleased with the postoperative results.

Information is uploaded and downloaded through the Internet day in and day out ever since we immersed ourselves in the Internet. Data security becomes an area demanding high attention and one of the efficient techniques to protect data is data hiding. Among various data hiding schemes, more applications prefer the schemes with reversibility. In recent reversible data hiding studies, the indices of a codebook can be reordered to hide secret bits. The hiding capacity of the codeword index reordering scheme increases when the size of the codebook gets larger. Since the codewords in the codebook are not modified, the visual performance of compressed images is retained. We propose a novel scheme making use of the fundamental principle of codeword index reordering technique to hide secret data in encrypted images. By observing our experimental results, the embedding capacity is significantly larger. Secret data can be extracted when a receiver owns a data hiding key and image can be recovered when a receiver owns an encryption key.

Hepatocellular carcinoma (HCC) is characterized by uncontrolled cellular proliferation driven by aberrant signal transduction. Cells respond to potential carcinogenic signals by inducing cellular senescence (CS) as a defense mechanism. However, assessing CS levels in various tumors is challenging due to heterogeneity and the lack of universal CS markers. This study integrates single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from HCC samples to analyze gene expression profiles. HCC-specific senescence-related pathways are identified, and genes from these pathways distinguish molecular subtypes associated with senescence in HCC. These subtypes exhibit significant variations in biological and clinical attributes. A specific CS risk model is developed, revealing associations between CS scores and prognosis, clinical staging, immune infiltration, immunotherapy response, and drug sensitivity. The key gene G6PD is identified as a potential senescence-related target in liver cancer. Molecular experiments confirm the effective suppression of HepG2 cell proliferation, invasion, and migration upon G6PD knockdown. In summary, this analysis enhances our understanding of HCC-specific senescence molecular features and introduces a novel senescence-related biomarker, offering promising implications for future research.

The goal of this paper is to fabricate innovative diaphragm headphones using graphene oxide paper (GOP) and GOP/epoxy nanocomposites (GOPC). Initially, graphene oxide suspension is fabricated, and the vacuum filtration method is adopted to make GOP. Then vacuum bag molding is used to fabricate GOPC from GOP. Hot pressing and associated molds are adopted to fabricate line-indented (GOPC-L) or curve-indented patterns (GOPC-C) on the GOPC. The performance of one kind of GOP and three kinds of GOPC diaphragm headphones is analyzed based on their sound pressure level (SPL) curves achieved by the Soundcheck measurement system. There are four important processing parameters that will influence the performance of the diaphragm, including material type GOP versus GOPC, indented pattern type, sonication time on suspension, and graphene weight fraction in suspension. Compliances of various diaphragms are measured by the Klippel LPM laser measurement system. The results indicate that effects of sonication time and graphene weight fraction on SPL of GOP and GOPC headphones are in reverse, and this is associated with their difference on compliance (modulus), mass, damping ratio, and microstructure uniformity. Either GOPC-L or GOPC-C seems to improve the microstructure of the GOPC, and leads to better SPL performance. The correlation between the previous four factors and SPLs of four kinds of diaphragm headphones is proposed by using scanning electron microscope (SEM) to examine the microstructure of these diaphragms.

Generally, when cognitive radio users are in Underlay mode, through the analysis of the electromagnetic spectrum in time, frequency, space, and field of multiple dimensional data accounts for judging the inherent correlation of the radio spectrum used/idle state, this is the realization of cognitive radio users (secondary users, SUs) efficient access to the foundation of the limited spectrum resources. Therefore, how to efficiently use of spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper addressing the problem. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results show that our model achieved better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirmed the feasibility of the model.

With the rapid and continuous advancement and deployment of advanced energy storage de-vices, there has been significant interest in aqueous capacitors that possess non-flammable properties and high safety features. Consequently, extensive research efforts have focused on investigating zinc anodes and low-cost carbonaceous cathode materials. Despite these efforts, the development of high-performance zinc-ion capacitors (ZICs) still faces challenges such as limited cycling stability and low energy densities. In this study, we present a novel approach to address these challenges. We demonstrate the utilization of a three-dimensionally (3D) grown conductive porous carbon framework cathode coupled with zinc anode cells, which exhibit exceptional sta-bility and durability in ZICs. Our experiments reveal a remarkable cycling performance, with a capacity retention of approximately 97.3% and a coulombic efficiency of nearly 100% even after 10,000 charge-discharge cycles. These findings signify significant progress in enhancing the performance of ZICs.

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small-field in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement of detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi frame projection. We use bad point repair and background suppression algorithms to preprocess star image. Afterwards, we analyze and enhance the target signal-to-noise ratio(SNR). Then, we use multi frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescope. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has magnitudes about 12 and SNR about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as real time target recognition algorithm for space-based synchronous orbit detection payload.

Zearalenone is one of the most prevalent estrogenic mycotoxins produced mainly by Fusarium family fungi, and harmed the heath of animals. Zearalenone hydrolase is an important enzyme capable of degrading zearalenone into a non-toxic compound. Although previous research has investigated the catalytic mechanism of zearalenone hydrolase, information on its dynamic interaction with zearalenone remains unknown. This study aimed to develop a pipeline for identifying the allosteric pathway of zearalenone hydrolase. Using an identity analysis, we identified hub genes whose sequences can generalize a set of sequences in a protein family. We then utilized a neural relational inference (NRI) model to identify the allosteric pathway of the protein throughout the entire molecular dynamics simulation. The production run lasted 1 microsecond, and we analyzed residues 139-222 for the allosteric pathway using the NRI model. We found that the cap domain of the protein opened up during catalysis, resembling a hemostatic tape. We used umbrella sampling to simulate the dynamic docking phase of the ligand-protein complex and found that the protein took on a square sandwich shape. Our energy analysis using both MMPBSA and PMF analysis showed discrepancies, with scores of -8.45 kcal/mol and -1.95 kcal/mol, respectively. MMPBSA, however, obtained a similar score to that of a previous report.

F-box protein is a key protein of the SCF E3 ubiquitin ligase complex, responsible for substrate recognition and degradation through specific interactions. Previous studies have shown that F-box proteins play crucial roles in Cryptococcus sexual reproduction. However, the molecular mechanism by which F-box proteins regulate sexual reproduction in C. neoformans is unclear. In the study, we discovered the AICAR transformylase/IMP cyclohydrolase Ade16 as a substrate of Fbp1. Through protein interaction, protein stability, and in vivo ubiquitination experiments, we demonstrated that Ade16 is a substrate for Fbp1. To examine the role of ADE16 in C. neoformans, we constructed the iADE16 strains and ADE16OE strains to analyze the function of Ade16. Our results revealed that the iADE16 strains had a smaller capsule and showed growth defects under NaCl, while the ADE16OE strains were sensitive to SDS but not to Congo red, which is consistent with the stress phenotype of the fbp1Δ strains, indicating that the intracellular protein expression level after ADE16 overexpression was similar to that after FBP1 deletion. Interestingly, although iADE16 strains can produce basidiospores normally, ADE16OE strains can produce mating mycelia but not basidiospores after mating, which is consistent with the fbp1Δmutant strains, suggesting that Fbp1 is likely to regulate the sexual reproduction of C. neoformans through the modulation of Ade16. Fungal nuclei development assay showed that the nuclei of the ADE16OE strains failed to fuse in the bilateral mating, indicating that Ade16 plays a crucial role in the regulation of meiosis during mating. In summary, our findings have revealed a new determinant factor involved in fungal development related to the post-translational regulation of AICAR transformylase/IMP cyclohydrolase.

Following the occurrence of a typhoon, quick damage assessment related to residents can facilitate quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following the Typhoon Faxai in 2019 to automatically detect and evaluate the roof damage. This study comprised three parts: training deep learning model, detecting the roof damage using trained model, and classifying the level of roof damage. The detection object comprised roof outline, blue tarps, and roof completely destroyed. The roofs were divided into three categories: roof without damage, roof with blue tarps and roof completely destroyed. the F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into 5 levels from level 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide certain reference for the real-time detection of the roof damage after the occurrence of a typhoon.

In this study, a composite flame retardant was created by combining fish scale (FS), fishery waste, and ammonium polyphosphate (APP), a commercially available flame retardant. The composite flame retardant was added to epoxy resin (EP) to form an EP/APP/FS composite that prevents burns and is environmentally friendly. The use of FS conforms to the concept of circular economy and lowers costs by reducing the consumption of APP. Thermogravimetric analysis (TGA), integral procedural decomposition temperature (IPDT), pyrolysis kinetics, limiting oxygen index (LOI), the Underwriters Laboratories 94 (UL94) flammability test, scanning election microscopy, Raman spectroscopy, and energy-dispersive X-ray spectroscopy were used to determine the thermal properties, flame retardant properties, flame retardant mechanism, char morphology, and composition of the composites. The TGA results indicated that the addition of 40% flame retardant raised the char residue from 16.45 wt% (pure EP) to 36.07 wt%, IPDT from 685.6 °C (pure EP) to 1143.1°C, LOI from 21% (pure EP) to 30%, and UL94 classification from fail (pure EP) to V-0. These results suggest an increase in char residue, which indicates better protection of the polymer matrix material. The improvements in IPDT, LOI, and UL94 classification, which indicate greater thermal stability, lower flammability (from flammable to fireproof), and higher flammability rating (from fail to V-0), respectively, suggest that the composite material has favorable thermal properties and is less inflammable.

Purpose - Soil aggregates are of great significance to soil and water conservation and ecological environment construction in arid area of northwest district．Methods - Exploring the effects of different vegetation includes types and land use methods on the stability of soil aggregates in the Loess Plateau, and provide reference for the rational use and management of land, also the improvement of soil structure in the region. Select 9 types of samples of 0-30 cm of typical soil plots as the research objects, compare and analyze the particle size index, stability differences and anti-erodibility of soil aggregates under various vegetation cover. Results - The results show that P value, MWD value, GMD value, D value, and AI value of the 0-10cm surface soil all show the maximum value. As the depth increases, the size distribution of the above index values of each soil sample in the 10-20cm and 20-30cm layers is different; P value in the 0-30cm depth layer is linearly positively correlated with the AI value and MWD value, and linearly negatively correlated with the D value. The correlation coefficient R between each variable is in the range of 0.78-0.97, and the D value reflects the Loess Plateau area stability and erosion resistance of soil aggregates better. GMD and MWD value show an exponential relationship, the correlation coefficient R value of 10-20cm height layer is 0.46; AI and MWD value in 0-10cm, 20-30cm height layer have a power function relationship, 10-20cm height layer has a polynomial function, the correlation coefficient R value is 0.97. The scour coefficient of different soil samples has a high degree of dispersion, the maximum CV value is 1.92, and the minimum value is 0.49. Conclusions - The results of this study can provide a theoretical basis for the ecological and hydrological benefit evaluation of slope erosion control and vegetation restoration on the Loess Plateau.

Age-related sarcopenia meaningfully increases the risks of functional limitations and mortality in the elderly. Although circulating microRNAs (c-miRNAs) are associated with aging-related cellular senescence and inflammation, the relationships between c-miRNAs and sarcopenia in the elderly remain unclear. This study investigates whether circulating myo-miRNAs and inflammation-related miRNAs are associated with sarcopenia in the elderly. This study recruited 77 eligible subjects (41 males and 36 females) from 597 community-dwelling older adults, and then divided into normal (n=24), dynapenic (loss of muscular function without mass, n=35), and sarcopenic groups (loss of muscular function with mass, n=18). Moreover, myo-miRNAs (c-miRNA-133a and c-miRNA-486), inflammation-related miRNAs (c-miRNA-21 and c-miRNA-146a), and inflammatory-related cytokine levels in plasma were determined using quantitative polymerase chain reaction and ELISA, respectively. The results demonstrated that sarcopenic group exhibited lesser skeletal muscle mass index (SMI), handgrip strength, and gait speed, as well as, lower c-miR-486 and c-miR-146a levels, compared to those of normal and dynapenic groups. Moreover, c-miR-486 level was positively related to SMI (r=0.334, P=0.003), whereas c-miR-146a level was positively associated with SMI (r=0.240, P=0.035) and handgrip strength (r=0.253, P=0.027). In the receiver operating characteristic analysis for predicting sarcopenia, the area under the curve in c-miR-486 was 0.708 (95% confidence interval: 0.561-0.855, P=0.008) and c-miR-146a was 0.676 (95% CI: 0.551-0.801, P=0.024). However, no significant relationships were observed between SMI/ handgrip strength/gait speed and plasma myeloperoxidase/interleukin-1?/interleukin-6 levels. In conclusion, myo-miRNA (c-miR-486) and inflammation-related miRNA (c-miR-146a) are superior to inflammatory peroxide/cytokines in plasma for serving as critical biomarkers of age-related sarcopenia.

Copper-based nanoparticles were synthesized using the glycine–nitrate process (GNP) by using copper nitrate trihydrate [Cu(NO₃)₂‧3H₂O] as the main starting material and glycine [C₂H₅NO₂] as the complexing and incendiary agent. The as-prepared powders were characterized through X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) analysis, X-ray photoelectron spectroscopy, and scanning electron microscopy. Using Cu(NO₃)₂.3H₂O as the oxidizer (N) and glycine as fuel (G), we obtained CuO, mixed-valence copper oxides (CuO + Cu₂O, G/N = 0.3–0.5), and metallic Cu (G/N = 0.7). The XRD and BET results indicated that increasing the glycine concentration (G/N = 0.7) and reducing particle surface area increased the yield of metallic Cu. The effects of varying reaction parameters such as catalyst activity, catalyst dose, and H₂O₂ concentration on nonylphenol-9-polyethoxylate (NP9EO) degradation were assessed. With a copper‐based catalyst in a heterogeneous system, the NP9EO and total organic carbon removal efficiencies were 83.1% and 70.6%, respectively, under optimum operating conditions (pH, 6.0; catalyst dose, 0.3 g/L; H₂O₂ concentration, 0.05 mM). The results suggested that removal efficiency increased with an increase in H₂O₂ concentration but decreased when the H₂O₂ concentration exceeded 0.0.5 mM. Furthermore, the trend of photocatalytic activity was as follows: G/N = 0.5 > G/N = 0.7 > G/N = 0.3. The G/N = 0.5 catalysts showed the highest photocatalytic activity and resulted in 94.6% NP9EO degradation in 600 min.

The kinetics and modeling of dual-wavelength controlled photopolymerization confinement (PC) are presented and measured data are analyzed by analytic formulas and numerical data. The UV-light initiated inhibition effect is strongly monomer-dependent and different monomers have different C=C bond rate constants and conversion efficacy. Without the UV-light, for a given blue-light intensity, higher initiator concentration (C₁₀) and rate constant (k’) lead to higher conversion, as also predicted by analytic formulas, in which the total conversion rate (R_T) is an increasing function of k’R, which is proportional to k[gB₁C₁]^0.5. However, the coupling factor b₁ plays a different role that higher b₁ leads to higher conversion only in the transient regime; whereas higher b₁ leads lower steady-state conversion. For a fixed initiator concentration C₁₀, higher inhibitor concentration (C₂₀) leads to lower conversion due to stronger inhibition effect. However, same conversion reduction was found for the same H-factor of H₀ = [b₁C₁₀ - b₂C₂₀]. Conversion of blue-only are much higher than that of UV-only and UV-blue combined, in which high C₂₀ results a strong reduction of blue-only-conversion, such that the UV-light serves as the turn-off (trigger) mechanism for the purpose of spatial confirmation within the overlap area of UV and blue light. For example, UV-light controlled methacrylate conversion of a glycidyl dimethacrylate resin formulated with a tertiary amine co-initiator, and butyl nitrite, subject to a continuous exposure of a blue light, but an on-off exposure of a UV-light. Finally, we developed a theoretical new finding for the criterion of a good material/candidate governed by a double ratio of light-intensity and concentration, [I₂₀C₂₀.]/[I₁₀C₁₀].

This paper focused on the oxidative leaching process of vanadium from vanadium-chromium reducing residue in alkaline medium with MnO₂. The effect of experimental parameters including reaction time, reaction temperature, dosage of MnO₂, dosage of NaOH, and liquid-to-solid ratio on the leaching efficiency of vanadium had been studied. The results indicated that MnO₂ was an efficient oxidant for leaching out of vanadium. The leaching efficiency of vanadium was up to 97.25% under optimal reaction conditions: reaction temperature of 90 ℃, reaction time of 60 min, dosage of MnO₂ at 50 wt.%, concentration of NaOH at 30 wt.% and liquid-to-solid at 5:1 mL/g.

Hepatocellular carcinoma (HCC) is the fifth most common type of cancer, and is the third leading cause of cancer-related deaths each year. It involves a multi-step progression and is strongly associated with chronic inflammation induced by the intake of environmental toxins and/or viral infections (i.e., hepatitis B and C viruses). Although several genetic dysregulations are considered to be involved in disease progression, the detailed regulatory mechanisms are not well defined. Homeobox (Hox) genes that encode transcription factors with homeodomains control cell growth, differentiation, and morphogenesis in embryonic development. Recently, more aberrant expressions of Hox genes were found in a wide variety of human cancer, including HCC. In this review, we summarize the currently available evidence related to the role of Hox genes in the development of HCC. The objective is to determine the roles of this conserved transcription factor family and its potential use as a therapeutic target in future investigations.

Optimal conditions for maximum efficacy of photoinitiated polymerization are theoretically presented. Analytic formulas are shown for the crosslink time, crosslink depth and efficacy function. The roles of photoinitiator (PI) concentration, diffusion depth and light intensity on the polymerization spatial and temporal profiles, for both uniform and non-uniform cases, are presented. For optimal efficacy, a strategy via controlled PI concentration is proposed, where re-supply of PI in high light intensity may achieve a combined-efficacy similar to low light intensity, but has a much faster procedure. A new criterion of efficacy based on the polymerization (crosslink) [strength] and [depth] is introduced. Experimental data are analyzed for the role of PI concentration and light intensity on the gelation time and efficacy.

The kinetics and efficacy profiles of photoinitiated polymerization are theoretically presented. For the same dose, lower light intensity achieves a higher steady-state-efficacy (SSE) in type-I; in contrast, type-II has an equal SSE. Higher light intensity has a faster rising efficacy, due to faster depletion of photoinitiator (PS) concentration. However, type-II process is also affected by the available oxygen. Higher light intensity produces more efficient singlet oxygen, resulting a higher transient efficacy, in which all intensities reach the same SSE when oxygen is completely depleted. With external oxygen, type-II efficacy increases with time, otherwise, it is governed only by the light dose, i.e., same dose achieves same efficacy. Moreover, type-II has an efficacy follows Bunsen Roscoe law (BRL), whereas type-I follows non-BRL. The measured type-I efficacy and gelation profile are analyzed by our analytic formulas. Schematics of the photocrosslinking stage defined by the availability of oxygen is developed, where both type-I and –II coexist until the oxygen is depleted. The overall efficacy may be enhanced by resupply of PS or oxygen during the light exposure. The roles of light dose and PS concentration on the efficacy of photoinitiated polymerization should be are governed a new concept of a volume efficacy (Ve), defined by the product of the crosslink (or gelation) depth (CD) and local [efficacy].

The purpose of this paper is to evaluate the timing of innovative investment in technology product life cycles using a compound binomial option with management flexibility. Considering the business cycles changes in the macroeconomic will affect consumer purchasing power. The focus is how to evaluate the optimal investment strategy and the project value. It was applied to different product stages (three stages including production innovation, manufacture innovation, and operation innovation) and factored to different risks to build a technology innovation strategy model. An aim of this study is the options premium of the best strategy timing for each innovation stage. Its application of the compound binomial options for the manufacture innovation will only be considered after the execution of the production innovation. The same condition is applied to the operation innovation, which will only be considered after the execution of the manufacture innovation. Then, this paper constructs the dynamic investment sequential decision model, assesses the feasibility of an investment strategy, and makes a decision on the appropriate project value and options premium for each stage under the possible change of Gross Domestic Product (GDP). This paper investigates the product life cycle innovation investment topic by using the compound binomial options method and will provide a more flexible strategy decision compared with other trend forecast criteria.

The present study examined and compared the effects of high- and low-molecular weight (MW) chitosan, a nutraceutical, on intestinal and liver lipid metabolism in rats fed with high-fat diet. Both high- and low-MW chitosan decreased liver weight, elongated small intestine, improved the dysregulation of blood lipids and liver fat accumulation, and increased fecal lipid excretion in high-fat diet-fed rats. Supplementation of both high- and low-MW chitosan significantly inhibited the decreased phosphorylated AMP-activated protein kinase (AMPK)α and peroxisome proliferator-activated receptor (PPAR)α protein expressions and the increased lipogenesis/cholesterogenesis-associated protein expressions (sterol regulatory element binding protein (SREBP)1c, SREBP2, and PPARγ) and the decreased apolipoprotein (Apo)E and microsomal triglyceride transfer protein (MTTP) protein expressions in the livers of high-fat diet-fed rats. Both high and low-MW chitosan supplementation could also suppress the increased MTTP protein expression and the decreased angiopoietin-like protein (Angptl)4 protein expression in the intestines of high-fat diet-fed rats. Comparison between high and low-MW chitosan, high-MW chitosan has a higher efficiency than low-MW chitosan on the inhibition of intestinal lipid absorption and the increase of hepatic fatty acid oxidation, which can improve liver lipid biosynthesis and accumulation.

In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literatur A key finding of this study is that risk preferences at individual bank level can lead to unique interbank market structures which are suggestive of the capacity that the market responds to surprising

Potential permanent magnet (Fe0.7Co0.3)2B has large saturation magnetization and high Curie temperature but a moderate magnetocrystalline anisotropy (MCA) and displays relatively low coercivity. One way to improve coercivity is to combine the contribution from magnetocrystalline- and magnetic shape anisotropy by preparing (Fe0.7Co0.3)2B nanowires. We study the effect of size, morphology, and surface defect on the hard magnetic properties of nanowires using micromagnetic simulation. The hard magnetic properties of (Fe0.7Co0.3)2B nanowire bonded magnet have been estimated, including the role of inter-wire magnetostatic interaction. By considering the existence of local reduction of MCA energy up to 30% on the surface layer of nanowires, the anisotropic bonded magnet with 65% vol. of (Fe0.7Co0.3)2B nanowires displays typical magnetic properties of remanence Br=8.4 kG, coercivity Hci=9.9 kOe, and maximum energy product (BH)m=17.8 MGOe. Developing effective technology for synthesizing nanowires and fabricating corresponding bonded magnets is promising for manufacturing practical magnets based on magnetic phase with relatively low or moderate MCA, such as (Fe0.7Co0.3)2B.

In order to solve the problem of many constraints and complex navigation environment in the path planning of unmanned surface vehicle(USV), an improved Sparrow search algorithm com-bining Cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialization stage, Cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimize the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late it-eration of the algorithm, the standard deviation of fitness is introduced to determine whether the population is trapped in the local optimum. If true, the Gaussian random walk strategy is used to perturb the optimal individual and assist the algorithm to escape the local optimum. Thirdly, the chosen water environment is modeled, and the navigation information of the original inland electronic navigation chart(ENC) is preprocessed, gridized, and the obstacle swelling is processed. Finally, the path planning experiments of USV are carried out in inland ENC grid environment. The experimental results show that, compared with the traditional sparrow search algorithm, the average fitness value of the path planned by improved sparrow search algorithm is reduced by 14.8%, and the variance is reduced by 49.9%. The path planned by the algorithm is of good quality and high stability, and combined with ENC, it can provide a reliable path for USV.

With the deepening of the diversification and openness of financial system, financial vulnerability, as an endogenous attribute of financial system, becomes an important measurement of financial security. Based on network analysis, we introduce network curvature indicator improved by Copula entropy as an innovative metric of financial vulnerability. Compared with previous network curvature analysis method, CE-based curvature proposed in this paper can measure market vulnerability and systematic risk with significant advantages.

The objective of this investigation was to enhance the sensory appeal of Cerasus humilis (C. humilis) juice and ascertain potential interactions among bioactive compounds within a functional juice matrix. Sensory attributes, color, antioxidant potential, polyphenol and vitamin C (VC) concentrations, and sugar levels were assessed. Diverse dosage combinations were examined to determine their impact on the quality of both C. humilis and Green grape juices. Among the formulations, CG5 exhibited elevated antioxidant activit y and polyphenol content (455.02 mg TE/100 ml and 173.1 mg/100 ml, respectively), coupled with favorable consumer preference. Notable variations in compound content were observed across the seven analyzed juices. In the blended juices (CG1–CG5), interactions between distinct juices influenced VC and sugar levels, with other bioactives contributing to beneficial modifications, displaying a progressive rise. This study innovatively crafted delectable and convenient C. humilis juice, upholding its intrinsic high antioxidant activity, thereby presenting a novel avenue for marketable functional juice offerings.

To study the influence of electrothermal effect of ESD protection device on the reliability of inte-grated circuit,a theoretical model of ESD protection device thermal resistance is constructed in the paper based on the Conservation of Energy Principle. By analyzing the temperature relativity between electricity parameter and thermology parameter of ESD protection device characterized by self-heating thermal resistance, the quantitative relationship expression of physical parameter and geometric parameter is obtained, which can be used to improve ESD protection device elec-tro-thermal effect. The simulation testing and the real tape-out testing of ESD protection device self-heating thermal resistance based on ggNMOS are shown and compared. It is concluded that self-heating thermal resistance is an effective index for the protection device electrothermal char-acterization. Based on the established model, the self-heating thermal resistance could be decreased to improve the homogeneity of the heat transference. The uneven distribution of device temperature can be reduced, and the overall reliability of chips can be improved.

This paper reports the efforts made and experiences gained in developing the Flood Prevention and Emergency Response System (FPERS) powered by Google Earth Engine, with focus on its applications at the three stages of floods. At the post-flood stage, FPERS integrates various remote sensing imageries, including Formosat-2 optical imagery, to detect and monitor barrier lakes, synthetic aperture radar imagery to derive an inundation map, and high-spatial-resolution photographs taken by unmanned aerial vehicles to evaluate damage to river channels and structures. At the pre-flood stage, a huge amount of geospatial data are integrated in FPERS and are categorized as typhoon forecast and archive, disaster prevention and warning, disaster events and analysis, or basic data and layers. At the during-flood stage, three strategies are implemented to facilitate the access of the real-time data: presenting the key information, making a sound recommendation, and supporting the decision-making. The example of Typhoon Soudelor in August of 2015 is used to demonstrate how FPERS was employed to support the work of flood prevention and emergency response from 2013 to 2016. The capability of switching among different topographic models and the flexibility of managing and searching data through a geospatial database are also explained, and suggestions are made for future works.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

The Dongtangzi large Zn-Pb deposit is located in the southwest of the Fengxian-Taibai (abbr. Fengtai) ore cluster in the west Qinling orogen, whose origin has been controversial, positing diverse genesis mechanisms such as sedimentary-exhalative (SEDEX), sedimentary-reformed, and epigenetic hydrothermal type. In this work, based on detailed observation and systematic study on ore geology, the results of high-precision Rb-Sr isotopic dating on sulfides of the main orebody and Sm–Nd isotopic dating on associated carbonates have been carried out, which yield isochron ages of 211.6 ± 2.6 Ma and 211 ± 4 Ma, respectively. In-situ S-Pb isotopic studies show that the ore related sulfides display a narrow range of δ³⁴S values from 1.1 ‰ to 10.2 ‰, with ²⁰⁶Pb/²⁰⁴Pb, ²⁰⁷Pb/²⁰⁴Pb and ²⁰⁸Pb/²⁰⁴Pb ratios of 18.07 to 18.27, 15.64 to 15.66, and 38.22 to 38.76, respectively. While the pyrite of sedimentary period and the granite porphyry dike have δ³⁴S values vary from 15.8 to 21.4‰, and from 2.1 to 4.3‰ (with ²⁰⁶Pb/²⁰⁴Pb ratios of 18.09 to 18.10, ²⁰⁷Pb/²⁰⁴Pb ratios of 15.59 to 15.61, and ²⁰⁸Pb/²⁰⁴Pb ratios of 38.17 to 38.24) respectively. Combining the regional geology, we propose that the Dongtangzi Zn-Pb deposit is the product of multi-stage epigenetic hydrothermal fluid processes, driven by late Triassic regional tectono-magmatic activities in regional extensional regime, with metallic materials originate from a mixture of Triassic magmatic processes and metamorphic basement.

Banana is one of the most important fruits in the world due to its status as a major food source for more than 400 million people. Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) causes substantial losses of banana crops every year, and molecular host resistance mechanisms are currently unknown. We here performed a genome-wide analysis of the autophagy related protein 8 (ATG8) family in a wild banana species. The banana genome was found to contain 10 MaATG8 genes. Four MaATG8s formed a gene cluster in the distal part of chromosome 4. Phylogenetic analysis of ATG8 families in banana, Arabidopsis thaliana, Citrus, rice, and ginger revealed five major phylogenetic clades shared by all of these plant species, demonstrating evolutionary conservation of the MaATG8 families. The transcriptomic analysis of plants infected with Foc TR4 showed that almost all of the MaATG8 genes were more highly induced in resistant cultivars than in susceptible cultivars. Finally, MaATG8F was found to interact with MaATG4B in vitro (with yeast two-hybrid assays), and MaATG8F and MaATG4B all positively regulated banana resistance to Foc TR4. Our study provides novel insights into the structure, distribution, evolution, and expression of the MaATG8 family in bananas. Furthermore, the discovery of interactions between MaATG8F and MaATG4 could facilitate future researches of disease resistance genes for genetic improvement of bananas.

The Cosmonaut Sea is an under-studied area and a “blank canvas” for macrobenthos research. Here, we report on the species diversity and community structure of macrobenthos collected using the tringle trawl on the 38th Chinese National Antarctic Research Expedition (CHINARE) in the Cosmonaut Sea, East Antarctica. A total of 11 tringle trawls were deployed at different depths across the shelf, slope and seamount of the Cosmonaut Sea. A total of 275 macrobenthic species were found from 207-1994 m. The species richness per station varied from 23 to 89. Echinoderms (100 species), arthropods (48 species) and mollusks (36 species) were the most dominant groups. Echinoderms and arthropods dominated in abundance at seamount stations, and echinoderms, arthropods and polychaetes dominated in abundance at slope stations, while bryozoans, corals, ascidians and sponges were abundant on the Cosmonaut Sea Shelf. Depth was the major driving force influencing the distribution of macrobenthos. The main components were two core communities. One was dominated by sessile suspension feeders and associated fauna. Variants of this community include sponges and bryozoans. The other core community was dominated by mobile deposit feeders, infauna and grazers–epifauna, which included arthropods and echinoderms. The results showed that the slope (40-50° E, 65-67° S) of the Cosmonaut Sea may be an important area with complex ecological processes. The results of this study contribute to the knowledge of species diversity and communities of macrobenthos in the Cosmonaut Sea and provide monitoring data for future ecosystem health assessments and better protection.

The internal wave recognition algorithm in an ocean data buoy system can be used to realize the real-time and flexible observation of internal waves, but there is no accurate automatic recognition method. To meet the need for automatic, real-time, and reliable internal wave recognition, an automatic internal wave recognition algorithm has been proposed for a tightly profiled intelligent buoy system. The sea profile temperature data collected by the Bailong buoy system in the Andaman Sea in 2018 were used to train and test the internal wave recognition neural network model, which consists of two parts: feature extraction and feature classification. The experiment compares the long short-term memory network (LSTM), convolutional neural network (CNN) with different layers, and deep neural network (DNN) without a feature extraction network and adjusts the number of convolutional nuclei and convolutional strides to improve the feature extraction efficiency. Experiments show that the best results can be obtained when a CNN layer is used as the feature extraction network, the convolutional step length is 4, the number of convolutional kernels is 5. The recall reaches 95.31% and the precision is 97.53%. The internal wave identification delay of the algorithm is 5.0862 minutes, the number of parameters is 1593, and the number of calculations is 3024. The algorithm can be directly deployed to the ocean data buoy system to realize the demand for automatic, real-time and reliable internal wave identification at the buoy end.

Three carbon-chain extension genes associated with fatty acid synthesis in cotton, namely GhKAR, GhHAD, and GhENR, play important roles in oil accumulation in cotton seeds. In the present study, these three genes were cloned and characterized. The expression patterns of GhKAR, GhHAD and GhENR in the high seed oil content cultivars 10H1007, 10H1014, and 10H1041 differed somewhat compared with those of 2074B with low seed oil content at different stages of seed development. GhKAR showed all three cultivars showed higher transcript levels than that of 2074B at 10, 40 and 45 DPA. The expression pattern of GhHAD showed a lower transcript level than that of 2074B at both 10 and 30 DPA but higher transcript level than that of 2074B at 40 DPA. GhENR showed a lower transcript level than that of 2074B at both 15 and 30 DPA. The highest transcript levels of GhKAR and GhENR were detected at 15 DPA in 10H1007, 10H1014, and 10H1041 compared with 2074B. From 5 to 45 days post anthesis (DPA) cotton seed, the oil content accumulated continuously in the developing seed. Oil accumulation reached the peak at 40 DPA to 45 DPA and slightly decreased in mature seed. In addition, GhKAR and GhENR showed different expression patterns in fiber and ovule development processes, in which they showed high expression levels at 20 DPA during fiber elongation stage, but their expression level peaked at 15 DPA during ovule development processes. These two genes showed the lowest expression levels at the late seed maturation stage, while GhHAD showed peak at 10 DPA of fiber development. Compared to 2074B, the oil contents of GhKAR and GhENR overexpression lines increased 1.05~1.08 folds. These results indicated that GhHAD, GhENR and GhKAR were involved in both seed oil synthesis and fiber elongation with dual biological functions in cotton.

Background: The major histocompatibility complex (MHC) plays a key role in the adaptive immune response to pathogens due to its extraordinary polymorphism. However, the spatial patterns of MHC variation in the striped hamster remain unclear, particularly regarding the relative contribution of balancing selection in shaping MHC population and diversity compared to neutral forces. Methods: In this study, we investigated the immunogenic variation of the striped hamster in four wild populations in Inner Mongolia, which experience heterogeneous parasitic stress. Our goal was to identify local adaptation by comparing the genetic structure at MHC with that at seven microsatellite loci, taking into account neutral processes. Results: We observed significant variation in parasite pressure among sites, with parasite burden showing a correlation with temperature and precipitation. Molecular analysis revealed a similar co-structure between MHC and microsatellite loci. We observed lower genetic differentiation at MHC loci compared to microsatellite loci, and no correlation was found between the two. Conclusions: Overall, these results suggest a complex interplay between neutral evolutionary forces and balancing selection in shaping the spatial patterns of MHC variation. Local adaptation was not detected at a small scale but may be applicable at a larger scale.

Habitat quality has great significance for regional ecological conservation and human welfare. In this study, we evaluated the spatial and temporal characteristics of land use and habitat quality in the temperate desert sub-region of Ordos Plateau using patch-generating land use simulation (PLUS) and integrated valuation of ecosystem services and trade-offs (InVEST) models. From 2000 to 2020, the areas of grassland, cropland, and unused land in the study area increased significantly; the areas of water bodies and woodland increased slightly; and the area of wasteland decreased significantly. Moreover, the habitat quality in the temperate desert subzone of the Ordos Plateau showed a trend of increasing and then decreasing from 2000 to 2020. The areas of lower and low habitat quality first decreased and then increased, and the overall area decreased over time. Conversely, the areas of high and higher habitat quality initially increased and then decreased, and the overall area increased over time. The area of medium habitat quality first decreased and then increased, although the overall change was minimal. Based on the PLUS model, the habitat quality of the study area in 2025 predicted under the natural development scenario was compared with that predicted under the ecological conservation scenario, showing higher habitat quality and lower habitat degradation under the ecological conservation development scenario. These results can be used to provide a scientific basis and decision reference for the sustainable use of land resources and high-quality socio-economic development in the temperate desert sub-region of the Ordos Plateau.

Abnormality of fibroblast growth factor receptors (FGFRs) mediated signaling pathways were frequently found in various human malignancies, making FGFRs hot targets for cancer treatment. To address the consistent need of new chemotype of FGFR inhibitors, here, we started with a hit structure identified from our internal c-Met inhibitor project, and conducted a chemical optimization. After exploring three parts of the hit compound, we finally discovered a new series of pyrrolo[2,3-b] pyrazine FGFR inhibitors, which contain a novel scaffold and unique molecular shape. We believed that our findings can benefit others to further develop selective FGFR inhibitors.

Low-energy cosmic rays (LECRs) play a crucial role in the formation of planetary systems, and detecting and reconstructing the properties of early LECRs is essential for understanding the mechanisms of planetary system formation. Given that LECRs interact with the surrounding medium to produce nuclear de-excitation line emissions with energy mainly within 0.1–10 MeV, which are unaffected by stellar wind modulation, these gamma-ray emissions can accurately reflect the properties of LECRs. This study introduces an innovative method for using gamma-ray emissions to infer LECR properties. We employed the Parker transport equation to simulate the propagation and spectral evolution of LECRs in a protoplanetary disk and calculated the characteristic gamma-ray emissions resulting from interactions between LECRs and disk material. These gamma-ray emissions encapsulate the spectral information of LECRs, providing a powerful tool to reconstruct the cosmic ray environment at that time. This method, supported by further theoretical developments and observations, will fundamentally enhance our understanding of the impact of CRs on the origin and evolution of planetary systems and address significant scientific questions regarding the cosmic ray environment at the origin of life.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

Digital-Radio-Frequency-Memory (DRFM) has emerged as an advanced technique to achieve diverse jamming signals, due to its capability of intercepting waveforms within a short time. Multiple-Input Multiple-Output (MIMO) radars can transmit agile orthogonal waveform sets for different pulses to combat the DRFM-based jamming, where any two groups of waveform sets are also orthogonal. In order to design multiple groups of orthogonal waveform sets at the same time, this article formulates a group orthogonal waveforms optimal design model, whose objec-tive function is the weighted sum of the intra- and inter-group orthogonal performances metrics. To solve this optimization problem, this article proposes a group orthogonal waveforms design algorithm. Based on a primal-dual type method and proper relaxations, the proposed algorithm transforms the original problem into a series of simple sub-problems. Numerical results demonstrate that the proposed algorithm has the ability to balance the intra- and inter-group orthogonal performances by adjusting the weighting factors, which is not available using other state-of-the-art orthogonal waveform design algorithms.

The earth pressure acting on soilbag reinforced retaining structures subjected to surcharge loads under non-limited states, is crucial for designing these structures. In this study, the earth pressures are investigated through model tests of the soilbag reinforced retaining wall. Findings from these tests reveal a non-linear distribution of lateral earth pressure on the wall when subjected to surcharge loads in non-limited states, with an observed escalation in pressure corresponding to increased surcharge loads. Compression tests on soilbags were conducted to analyze their lateral stress-strain behavior. Insights from these tests facilitated the development of a predictive method for estimating lateral pressure on soilbag reinforced retaining walls under similar conditions. This method, rooted in the principle of force equilibrium in differential elements, has its accuracy and reliability substantiated by the alignment of its predictions with the model test outcomes.

This paper presents an efficient topology optimization approach considering fail-safe requirements in the framework of the solid isotropic material with penalization method. Existing methods either are computationally extremely expensive or do not consider fail-safe requirements in the optimization. The current approach not only obtains the fail-safe optimization structure, but also significantly improves the computational efficiency of fail-safe topology optimization. In this method, the worst-case compliance is considered to minimize as the optimization objective, using the Kreisselmeier–Steinhauser function to approximate the non-differentiable max-operator. The sensitivity is derived by the adjoint method, and a general fail-safe optimality criteria method is developed to update design variables. The damage scenarios greatly reduces in the process of failsafe strategy optimization by employing the density-based filter method. Finally, the effectiveness and computational efficiency of this method for fail-safe optimization is verified by several numerical examples.

This review centers on the pivotal role of the oncoprotein FOSL1, within the dimeric AP-1 transcription factor consisting of FOS-related elements. FOSL1 emerges as a key regulator governing invasion and metastatic dissemination, therefore making it a promising target for therapeutic intervention in cancer patients. The review provides a comprehensive survey of the regulatory mechanisms that exert influence over FOSL1. These encompass a spectrum of transcriptional and post-translational modifications. Notably, we unveiled the intricate involvement of diverse non-coding RNAs, specifically microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), in the modulation of FOSL1 expression through targeted interactions with its mRNA transcripts. Furthermore, an exploration into the functional dimensions of FOSL1 unfolds, encompassing apoptosis, proliferation, and migration. This investigation is accompanied by a thorough examination of the elaborate signaling pathways that underpin these multifaced roles. Additionally, emphasis is placed on the role of FOSL1 in orchestrating the stimulation of various cytokines, including TGF-beta, and the initiation of IL-6 and VEGF production in tumor-associated macrophages (TAMs) that transverse into the tumor microenvironment. A distinct focus is dedicated to appraising the prognostic implications associated with FOSL1. Finally, insights emerge into the evolving roles of FOSL1 in the context of mechanisms governing resistance and dependency on targeted therapeutic modalities.

This paper focuses on improving the tracking accuracy for servo systems and increasing the contouring performance of precision machining. The dynamic friction during precision machining is analyzed using the LuGre model. The dynamic and static parameters in the friction model are efficiently and accurately identified using the improved Drosophila Swarm Algorithm based on cross-mutation. The friction tracking error can be deduced by the friction state space and an expression is derived. To compensate for the tracking error caused by friction, a feedforward compensation control is designed to avoid signal lag in traditional friction controllers. Furthermore, the factors of multi-axis parameter mismatching that impact the machining profile accuracy are analyzed for multi-axis control. An adaptive cross-coupled control-based pre-compensation strategy of contour error is designed to reduce both the tracking error and the contour error. The effectiveness of the proposed method is validated through several experiments, which demonstrate a remarkable improvement in tracking performance and contour accuracy.

This pioneering study presents an in-depth biomechanical examination of soccer's diving header, aiming to quantify its impact on ball speed enhancement (BSE) and effective offensive range (EOR). Despite the diving header's widespread acclaim and historical significance, there remains a dearth of scientific scrutiny into its biomechanical intricacies. Employing an innovative research design featuring a static hanging ball at varied offensive distances and heights, the study replicates diverse header scenarios. The results of 3D motion quantification have shown that a physically excellent player (identified through the maximal standing long jump test) could reach an EOR around 2.64 times his body height. Furthermore, the study unveils that proficient players could attain BSE surpassing 9 m/s, signifying the diving header's heightened efficacy compared to traditional heading techniques, which could only result in 4.5 m/s. Correlation analyses unveil noteworthy relationships, highlighting the pivotal role of head speed at impact and the influence of minimizing speed-drop and temporal disparities for amplified effectiveness. Considerations for optimizing diving header execution are introduced, emphasizing the necessity for targeted training programs. Despite acknowledged limitations inherent to its pilot nature, this exploration furnishes foundational knowledge to guide subsequent research and practical applications, providing valuable insights into soccer training and skill development through a biomechanical lens.

We aim to examine how happiness modifies and mediates the impact of physical activity and nu-trition on cognitive function among 699 seniors aged 60 and above in Shanghai, China. Linear regression models were used to validate the effects of nutrition and exercise on cognitive function, and to test their interaction effects with happiness. When the interactions were significant, strati-fied analyses in sub-groups were conducted. Mediation effects of happiness were examined using two-step causal mediation models. We confirmed that better nutrition (p < 0.001) and exercise (p = 0.009) were significantly associated with less cognitive decline. Furthermore, the effects of nu-trition and exercise on cognitive decline were significant in the unhappy (happiness < 20) (p <0.001) and younger (age < 74) sub-groups (p = 0.015). Happiness partially mediated 11.5% of the negative association of cognitive decline with nutrition (p = 0.015) and 23.0% of that with exercise (p = 0.017). This study suggests that happiness moderates and also partially mediates the effects of exercise and nutrition on cognitive status. The beneficial effects of exercise and nutrition were stronger in less happy or younger seniors. Future intervention studies are required to confirm this path relationship.

Herpesvirus is a prevalent pathogen that primarily infects human epithelial cells and has the ability to reside in neurons. In the field of otolaryngology, herpesvirus infection primarily leads to hearing loss and vestibular neuritis, and is considered the primary hypothesis regarding the pathogenesis of vestibular neuritis. In this review, we provide a summary of the effects of herpes virus on cellular processes in both host cells and immune cells, with a focus on HSV-1 as illus-trative examples.

2-Pyridone ligand-facilitated palladium-catalyzed direct C–H bond functionalization via the transient directing group strategy has become an attractive topic. Here we report a Pd-catalyzed direct β-C(sp3) –H arylation reaction of tertiary aliphatic aldehydes by using an α-amino acid as a transient directing group in combination with a 2-pyridone ligand.

Epitaxial lithium ferrite (LiFe5O8) films with different thicknesses have been successfully fabricated on SrTiO3 (001) substrates by magnetron sputtering deposition technique. The microstructural and magnetic properties are characterized by advanced transmission electron microscope and magnetic measurement device. It was found that the formation of structural defects can be influenced by the thickness of the film. Apart from the misfit dislocations, the orientation domains form in thinner film and twin boundaries appear in thicker film, respectively, contributing to the misfit strain relaxation in the heterosystem. The magnetic measurement shows that the thinner films have enhanced magnetization and a relatively lower coercive field compared with the thicker films containing the antiferromagnetic twin boundaries. Our results provide a way for tuning the microstructure and magnetic properties of lithium ferrite films by changing the film thickness.

Autonomous target recognition (ATR) plays a crucial role in maintaining situational awareness during environmental monitoring. Unmanned aerial vehicles (UAVs) equipped with autonomous target recognition technology can gather and analyze real-time information about targets, including their locations, sizes, and types. However, UAV-captured images in complex real-world environments often display significant variations in perspective and scale due to changes in UAV altitude and distance. Existing methods for autonomous target recognition on UAVs struggle to capture targets from large field-of-view and multi-scale images, resulting in low recognition accuracy and high false-positive rates. This paper introduces two novel Slimmable neural network models, namely SE-YOLOv5s and ST-YOLOv5s, which are based on the YOLOv5s architecture. These models incorporate the Squeeze and Excitation and Swin-Transformer mechanisms to enhance the ability to extract features from large field-of-view and multi-scale images. To evaluate their performance, experiments were conducted on the Visdrone19 aerial dataset. Compared to the state-of-the-art YOLOv5s algorithm, the utilization of SE-YOLOv5s and ST-YOLOv5s for autonomous target recognition on low-altitude drones resulted in improvements in both accuracy and false-positive rates. These proposed methods combine Slimmable neural network design with feature enhancement mechanisms, addressing the challenges posed by complex real-world environments in UAV missions. The advancements in autonomous target recognition on low-altitude drones will significantly contribute to enhancing situational awareness in future environmental monitoring.

Curriculum teaching is the basic unit to realize the cultivation and achievement of students’ ability in undergraduate colleges, and it is very important to investigate the influence mechanism of the curriculum system on the achievement states of students' ability. This paper first puts forward the theoretical system and framework of curriculum chains, and constructs a “double-energy and double-space” (DEDS) which consist of the curriculum energy efficiency space and student ability space respectively. Investigating the space-time transformation relationship of curriculum chains in the DEDS, the forward transformation model, inverse transformation model and the rheological model of the achievement states in the curriculum energy efficiency space and student ability space are established. Studying the dynamic evolution mechanism of curriculum chains within the DEDS, the dynamic evolution model, which presents the relationship between achievement states of curriculum energy efficiency/students' ability and curriculum/students' dynamic potential, are established; the control methods of courses/students' dynamic potential based on the proposed dynamic evolution model are developed to ensure the achievements of the graduates’ social ability; the evaluation and detection methods of achievement states of curriculum chains in DEDS are proposed to obtain the actual achievement states of curriculum energy efficiency and students’ ability, and this will provide effective input for the control of curriculums/students’ dynamic potential; the planning and design methods of curriculum chains facing social needs are presented, which can provide theoretical basis for the formulation of the talent training scheme and curriculum system in colleges and universities.

Micro-nanoparticles' characteristics in geothermal fluids can be applied to detect of deep hidden geothermal resources. Observations using a nanoparticle tracking analyzer (NTA) indicated that the karst geothermal water collected in the central area of Shandong Province (Jinan and Zibo) contains many natural micro-nanoparticles with sizes primarily ranging between 100 nm and 5 μm. The micro-nanoparticles’ type, shape, crystal form, and chemical composition in the samples were analyzed using transmission electron microscopy (TEM). TEM images and energy dispersive spectroscopy showed that the micro-nanoparticles in geothermal water samples were mostly amorphous, irregular, or nearly spherical, with rough edges. The micro-nanoparticles were mainly carbonates, sulfates, and chlorine-containing Fe, Ca, Na, and Mg. The characteristics of the particles can reveal the properties of the deep, hot reservoirs and aquifers from where they originate. Therefore, we believe that natural micro-nanoparticles can be essential to detecting and studying deep, hidden geothermal resources, which is a novel approach to exploring deep, hidden geothermal resources.

The Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and ensure that the predictions are consistent and stable with the physical laws. PINNs opens up a new approach to address inverse problems in fluid mechanics. Based on the single-relaxation-time lattice Boltzmann method (SRT-LBM) with the Bhatnagar-Gross-Krook (BGK) collision operator, the PINN-SRT-LBM model is proposed in this paper for solving the inverse problem in fluid mechanics. The PINN-SRT-LBM model consists of three components. The first component involves a deep neural network that predicts the equilibrium control equations in different discrete velocity directions within SRT-LBM. The second component employs another deep neural network to predict non-equilibrium control equations, enabling inference of the fluid's non-equilibrium characteristics. The third component, a physics informed function translates the outputs of the first two networks into physical infor-mation. By minimizing the residuals of the physical partial differential equations (PDEs), the physics informed function infers relevant macroscopic quantities of the flow. The model evolves two sub-models applicable to different dimensions, named PINN-SRT-LBM-I and PINN-SRT-LBM-II models according to the construction of the physical informed function. The innovation of this work is the introduction of SRT-LBM and discrete velocity models as physical drivers into the neural network through the interpretation function. Therefore, PINN-SRT-LBM allows the neural network to handle inverse problems of various dimensions and focus on problem-specific solving. Experimental results confirm the accurate prediction of flow infor-mation at different Reynolds numbers within the computational domain. Relying on the PINN-SRT-LBM models, inverse problems in fluid mechanics can be solved efficiently.

As a high-quality forestry product with excellent performance, particleboard is widely used in many fields and has great market potential. At present, it is a research difficulty to accurately identify small, fine and different shapes of defects on the whole particleboard through machine vision technology. The application of image super-resolution reconstruction technology on particleboard can improve the surface image quality of particleboard, which is conducive to the subsequent improvement of defect detection accuracy. In this study, super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct dense block, and the dense blocks were constructed by densely skip connection. Then, the corresponding 52400 image blocks with high resolution and low resolution were trained, verified and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the two indexes of PSNR and SSIM. It was found that compared with BICUBIC and SRGAN, PSNR index of SRDAGAN increased by 4.88dB and 3.25dB respectively, and SSIM increased by 0.0507 and 0.1122 respectively. The reconstructed images not only had clearer texture, but also had more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed on the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of particleboard images with high quality. In the future, it can also be further combined with defect detection for actual production to improve the quality of forestry products and increase economic benefits.

Consumer preference for products made from transgenic technology has been widely studied, yet few studies exist exploring the factors influencing producers’ adoption of transgenic technology. Based on field surveys in Chinese provinces of Shanxi, Henan and Shandong, we employed a gambling experiment to capture producers’ risk preferences by estimating their risk aversion coefficients. We further estimated producers’ risk amplification and risk perception of GM technology. Using ordered logit model and Poisson model we identified the major factors influencing producers’ adoption of transgenic technology. We found the factors impacted the decision of producers from different regions in different ways. The results showed that over 60% of participants amplified the risk of transgenic crops. When there was potential risk, producers might not be rational even if they had high level of knowledge and cognition about the technology. Our results shed light on government policies aiming to increase the adoption of new technologies by producers.

Software testing is an integral yet time-consuming component of software development, and even with capable software testing resources (technology and people), it is still regarded as problematic. Test automation is widely being utilised within the software industry to provide increased testing capacity to ensure high product quality and reliability. In this paper, we are specifically addressing automated testing whereby test cases are manually written. Test automation has its benefits, drawbacks, and impacts on different stages of development. Furthermore, there is often a disconnect between non-technical and technical roles, where non-technical roles (e.g., management) predominantly strive to reduce costs and delivery time whereas technical roles are often driven by quality and completeness. Although it is widely understood that there are challenges with adopting and using automated testing, there is a lack of evidence to understand the different attitudes toward automated testing (AtAT), focusing specifically on why it is not adopted. In this paper, the authors have surveyed practitioners within the software industry from different roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principle Component Analysis. In total, 81 participants were provided with a series of 22 questions and their responses are compared against job role types and experience levels. In summary, 6 key findings are presented covering expertise, time, cost, tools and techniques, utilisation, organisation, and capacity

Due to the complex underground environment, pumping machines are prone to produce numerous failures. The indicator diagrams of faults are similar in a certain degree, which produces indistinguishable samples. As the samples increases, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. For accurately and quickly judging the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the ReLU activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared with the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity, can improve the generalization performance of deep learning model, the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well embedded in other neural networks.

Organ transplantation remains the only treatment option for patients with end-stage organ dysfunction. However, there are numerous limitations that challenge its clinical application, including the shortage of organ donations, the quality of donated organs, injury during organ preservation and reperfusion, primary and chronic graft dysfunction, acute and chronic rejection, infection, and carcinogenesis in post-transplantation patients. Acute and chronic inflammation and cell death are two major underlying mechanisms for graft injury. Necroptosis is a type of programmed cell death involved in many diseases and has been studied in the setting of all major solid organ transplants, including the kidney, heart, liver, and lung. It is determined by the underlying donor organ conditions (e.g., age, alcohol consumption, fatty liver, hemorrhage shock, donation after circulatory death, etc.), preservation conditions and reperfusion, as well as allograft rejection. Specific molecular mechanisms of necroptosis have been uncovered in the organ transplantation setting, and potential targeting drugs have been identified. We hope this review article will promote more clinical research to determine the role of necroptosis and other types of programmed cell death in solid organ transplantation to alleviate the clinical burden of ischemia-reperfusion injury and graft rejection.

Vanadium is a well-known essential trace element, which usually exists in oxidation states in form of vanadate cation intracellularly. The pharmacological study of vanadium begins at the discovery of its unexpected inhibitory effect on ATPase. Thereafter, the protective effects on cells and the abilities in glucose metabolism regulation were observed from vanadium compound, leading to the application of vanadium compounds in clinical trials for curing diabetes. Alzheimer’s dis-ease (AD) is the most common dementia disease in elderly people. However, there is still no efficient agents for treating AD safely to date. This is mainly because of the complexity of the pathology, which are characterized by the senile plaques composed by amyloid-beta (Aβ) protein in the parenchyma of brain and the neurofibrillary tangles (NFTs) derived from hyperphosphorylated tau protein in neurocyte, along with mitochondrial damage, and eventually the central nervous system (CNS) atrophy. AD was also illustrated as type-3 diabetes, because of the observations of insulin deficiency and the high level of glucose in cerebrospinal fluid (CSF), as well as the im-paired insulin signaling in brain. In this review, we summarized the advance of applicating vanadium compound on AD treatment in experimental research and pointed out the limitation of the current study on using vanadium compounds in AD treatment. We hope it will help the future study in this field.

Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area ratio of ship targets is small and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed detection and false detection. In this study, we propose an improved-YOLOv5 algorithm. The improvement strategies mainly include: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce cross-layer connection channel and lightweight GSConv structure into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) The Wise-IoU loss function is used to cal-culate the localization loss in the Output, and assign reasonable gradient gains to cope with dif-ferences in image quality. In addition, during the preprocessing stage of experimental data, a me-dian and bilateral filter method is used for noise reduction to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Im-proved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; Compared to the original YOLOv5s, the mean Average Precision (mAP) has improved by 3.2% and the Frames Per Second (FPN) has accelerated by 8.7%.

The integration of transportation systems with land use through the Transit-Oriented Development (TOD) model can effectively promote sustainable development in cities. By increasing the intensive use of land within the supply capacity of urban public transportation, this model can comprehensively improve the efficiency of urban transportation and land use, while also addressing problems such as traffic congestion and environmental pollution. To implement this model, a "Node-Place-Function" (NPF) framework is proposed based on the coordination degree between rail transit and land use in Qingdao. A comprehensive evaluation system is constructed to quantitatively assess the TOD level and comprehensive development level of land use around rail transit stations from three dimensions. By applying the NSGA-II genetic algorithm, a land use allocation optimization model is developed to maximize rail transit station accessibility, land economic benefits, and environmental comfort. The research results guide other cities in China to implement TOD in land use planning and promote coordinated development between urban rail transit and land use.

This study aimed to evaluate the effects of a palygorskite-based antibacterial agent (PAA) as an alternative to antibiotics on growth performance, blood parameters, rumen fermentation, and rumen microbiota in sheep. A total of 120 sheep were randomly divided into five groups of six replicates with four sheep each. Sheep were fed a basal diet, an antibiotic diet supplemented with 500 g/t chlortetracycline (CTC), and a basal diet supplemented with 500, 1,000, and 2,000 g/t PAA for 80 d, respectively. Supplementation with 2,000 g/t PAA and 500 g/t CTC increased the average daily gain (ADG) of sheep compared with the control group (P < 0.05). Diets supplemented with 2,000 g/t PAA and 500 g/t CTC reduced (P < 0.05) the feed: gain (F/G ratio) in the overall periods. Dietary Supplementation with 1,000 g/t PAA significantly increased albumin and total protein (P < 0.05). A significant positive correlation was found between growth hormone concentration and PAA supplementation (P < 0.05). In addition, compared to the control group, the CTC group had higher growth hormone concentration and lower lipopolysaccharide concentration (P < 0.05). No dif-ference was observed between the five groups in terms of rumen fermentation characteristics (P > 0.05). At the phylum level, the relative abundance of Proteobacteria was lower in the PAA 2000 and CTC 500 groups than that in the control and PAA 500 groups (P < 0.05). At the genus level, a sig-nificant decrease (P < 0.05) in the relative abundance of RuminococcaceaeUCG-010 was observed in the PAA 1000, PAA 2000, and CTC 500 groups compared with that in the control group. In addition, the relative abundance of Prevotella1 (P < 0.05) was higher in the PAA 2000 group than that in the control group. These findings indicate that dietary supplementation with PAA has ameliorative effects on growth performance, blood parameters, and rumen microbiota, with an optimal dosage of 2,000 g/t for sheep.

Being able to complete the production of the product project on time is the prerequisite to win more tasks, many enterprises have begun to study intelligent scheduling algorithms to achieve dynamic control of the total project duration. However, the production operation time of products is highly volatile, and it is impossible to predict the possible operation time of future products. In the actual production environment, we often encounter extreme situations such as mixed production lines, multiple varieties, variable batches, etc. A dynamic optimization CPM model in the ERP/MES system with the attention mechanism (EMA-DCPM) will collect, extract and predict the operation time through artificial intelligence technology, and it can accurately predict the exact operation time of a batch of products that have not yet been produced. Through experiments, our algorithm can realize the production scheduling and get more than 200% better precision than former algorithms and realize dynamic optimization of complex projects.

Abstract. Natural Thermoluminescence (TL) from the core of MD81349 marine calcareous biological ooze samples in the ninetyeast ridge of the equatorial northeast Indian Ocean and samples from the core of IODP306-U1312B in the high latitudes of the North Atlantic Ocean was studied. The spurious TL intensity of 395 °C peak is dose independent when the heating rate is 6 °C/s in a nitrogen atmosphere . TL signals exhibit a significant correlation on the marine isotope stages (MIS) at the two oceans since the mid-Pleistocene. High TL intensity corresponds to a negative δ18O in the interglacial stages, and low TL intensity corresponds to a positive δ18O in the glacial stages. TL of both cores at the two oceans reveal that the global climate has experienced eight cold and warm cycles since the mid-Pleistocene. Single-frequency spectrum analysis of MD81349 and U1312 TL in the last 300 ka was performed.Near the equator of the Northeast Indian Ocean，it is more significant that the short cycle of 38 ka and 5 ka while it is more significant that the cycle of 8 ka at the North Atlantic.In addition, correlation analysis shows that the TL has a significant positive correlation with trace elements 135Ba, a significant negative correlation with 47Ti. The impurity ions (e.g. Ba2+, Mn2+ and Ti2+) doped in carbonate are acting as activators and suppressants respectively.Time series of the TL of calcareous biological ooze tests provide an important record of climate change. The source of TL signal is also discussed.

Infrared detection plays an important role in the military, aerospace, and other fields, which has the advantages of all-weather, high stealth, and strong anti-interference. However, infrared dim small target detection suffers from complex backgrounds, low signal-to-noise ratio, blurred targets with small area percentages, and other challenges. In this paper, we proposed a multiscale YOLOv5-AFAM algorithm to realize high-accuracy and real-time detection. Aiming at the problem of target intra-class feature difference and inter-class feature similarity, the Adaptive Fusion Attention Module - AFAM was proposed to generate feature maps that are calculated to weigh the features in the network and make the network focus on small targets. This paper proposed a multiscale fusion structure to solve the problem of small and variable detection scales in infrared vehicle targets. In addition, the downsampling layer is improved by combining Maxpool and convolutional downsampling to reduce the number of model parameters and retain the texture information. For multiple scenarios, we constructed an infrared dim and small vehicle target detection dataset, ISVD. The multiscale YOLOv5-AFAM was conducted on the ISVD dataset, compared to YOLOv7, [email protected] achieves a small improvement while the parameters are only 17.98% of it. By contrast with the YOLOv5s model, [email protected] was improved by 4.3% with a 6.6% reduction in the parameters. Experiments results demonstrate that the multiscale YOLOv5-AFAM has a higher detection accuracy and detection speed on infrared dim and small vehicles.

: Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically complex constituents in the water. The emergence and advancement of deep learn-ing-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) tech-niques. A U-Net based model, Wetland-SAV Network, was trained to identify SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha) with a significant loss of SAV habitats observed post-Hurricane Barry (~2,300 ha) in 2019. The habitat areas and habitat-specific carbon fluxes were then used to quantify net greenhouse gas (GHG) flux of the study area for with/without SAV scenarios in a Carbon Balance Model. The total net GHG flux was in the range of -0.13 ± 0.06 to -0.86 ± 0.37 ×105 tonne CO2e yr-1 and increased up to 40% (-0.23 ± 0.10 to -0.90 ± 0.39 ×105 tonne CO2e yr-1 ) when SAV was accounted for within the calculation. At the hectare scale, inclusion of SAV resulted in an increase of ~60% for net GHG sink in shallow areas adjacent to emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring of SAV in turbid sub-tropical coastal deltas as a component of increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales.

Badminton tactics refer to the techniques and strategies employed by players to win a match. Analyzing these tactics can help players improve their performance and outsmart their opponents. To study the tactics of top players, we use a gaming tree to analyze matches between two of the most powerful badminton players in history: Lin and Lee. By employing the Nash Equilibrium, we can discover the most beneficial strategies for both players, which reflect their most powerful techniques. Additionally, with the help of this gaming tree, we can precisely predict how players will implement their tactics. Empirical experimental results demonstrate that our proposed method not only evaluates and identifies each player's weaknesses and strengths but also has powerful capabilities to predict their tactics.

The heritage of ancient buildings is an important part of the world's history and culture, which has an extremely rich historical-cultural value and artistic research value. Beijing has a large number of palace ancient buildings, and because of the age of their construction, many of them have problems of varying degrees of peeling and mold on the inner surfaces of the envelope. To solve the problems of the damp and moldy interior of palace buildings, a mathematical model of indoor heat and moisture transfer was established based on a wooden palace ancient building in Beijing. Through the indoor mold distribution validation model, the effects of outdoor humidity, soil moisture, wall humidity, and other factors on the indoor heat and moisture transfer of ancient buildings were simulated and analyzed by using the control variables method. The results showed that the molds were distributed at the indoor corners and floors, and the simulation of indoor humidity match the measured humidity. Thus, the simulation results were consistent with the actual situation. The variable trend of the relative humidity of the indoor environment with the outdoor humidity is inconsistent from plane to plane, i.e. it increases or remains constant with the increase of the outdoor humidity. The indoor ambient relative humidity increased with increasing the wall humidity. And the indoor average temperature is 23.3 ℃ and indoor relative humidity ranged between 90.9 % to 92.44 %. Soil moisture and wall humidity were the main factors affecting the indoor environmental relative humidity.

This paper presents a new broadband ocean bottom seismograph (OBS) developed by the SUSTech OBS lab for passive-source seafloor seismic observations. This instrument, called Pankun, has several key features that set it apart from traditional OBS instruments. Besides the seismometer-separated scheme, these features include a unique shielding structure to minimize current-induced noise, a compact gimbal for accurate leveling, and low power consumption for extended operation on the seafloor. The design and testing of Pankun's primary components are thoroughly described in the paper. The instrument has been successfully tested in the South China Sea, demonstrating its ability to record high-quality seismic data. The anti-current shielding structure of Pankun OBS has the potential to improve low-frequency signals, particularly on the horizontal components, in seafloor seismic data.

Fast convergence routing is an important issue for LEO constellation network, due to its dynamical topology changing and time varying transmission requests. Most of existing research focus on the OSPF routing algorithm, which cannot handle the frequently links state changing of network. In this paper, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, in which the satellite gets the network links status fast and adjusts its routing strategy. In FRL-SR, each satellite node is regarded as an agent. The agent selects the port for packet forwarding according to its own routing policy. When the satellite network state changes, agent would send ’hello’ packets to the neighbor node to update the neighbor node’s routing policy. Compared with traditional reinforcement learning, FRL-SR can perceive network information faster, and then converge faster. Also, FRL-SR can mask the dynamics of satellite network topology and adaptively adjust the forwarding strategy according to the link state. Various simulation is constructed, the results show that the proposed FRL-SR algorithm out performance the Dijkstra algorithm in performance of average delay, packet arriving ratio, network load balance.

Clarifying the response of plant phenology to drought duration is helpful for accurately interpreting and predicting carbon sinks in ecosystems. Based on the response of different phenological periods of the dominant species Stipa krylovii to monthly, seasonal and semiannual time scale drought in the typical steppe of Inner Mongolia from 1983 to 2018, the results revealed that (1) the start of the growing season (SOS) was characterized by an advance-delay-advance pattern, and the heading stage (HOS), flowering stage (FOS) and end of the growing season (EOS) all showed consistent advanced trends, which provided additional insight into the conclusions of previous studies that found the SOS was advanced in arid and semiarid regions. (2) The response mechanism of the SOS to the timing of drought was not consistent. Among the response mechanism, the SOS was delayed because of January-February drought at different time scales, but advanced because of April drought at different time scales. The HOS/FOS was delayed by June-July drought at different time scales, and the EOS was advanced by August-September drought at different time scales. (3) More importantly, the SOS, HOS, FOS and EOS were affected by predrought and its persistence, and the effects were greater the closer to the phenological periods that drought occurred. (4) At the monthly scale, droughts in January, June and August were the critical drought periods affecting the SOS, HOS/FOS and EOS, while seasonal and semiannual scale droughts in February, June-July and September were the critical periods affecting the SOS, HOS/FOS and EOS. The results of this study enrich our understanding of how drought at different time scales affects different phenological periods, providing a basis for improving plant phenological models.

The upper ocean heat content in the equatorial Pacific usually serves as a primary precursor for an upcoming El Niño, while the strong atmospheric perturbations such as westerly wind burst and easterly wind surge sometimes play a decisive role in determining the final intensity of the event. The tropical Pacific Ocean has just experienced a rare 3-year La Niña, which accumulated a huge amount of warm water in the western basin by the winter of 2022. Using a state-of-the-art climate prediction system, here we show that the restored warm water is sufficient to boost a strong El Niño toward the end of 2023, and that an extreme event could take place if a few sizable westerly wind bursts would occur in spring and early summer. This prediction is not sensitive to initial errors within the tropical Pacific, but may be subject to some uncertainties brought about by influences from elsewhere.

In New Orleans, Louisiana the population’s poor baseline health led to its establishment as an early epicenter for severe acute COVID-19. Antici-pating future outbreaks of COVID-19 and other respiratory viruses, we need to identify correlates of outcome, from real clinical experience. 89 patients were recruited into the ClinSeqSer acute COVID-19 longitudinal observational study, from the beginning of the outbreak in March to July 2020. Patients admitted for acute COVID-19 were enrolled in person. The cohort is unique as it is 68% Black, 53% female, of average age of early 60s, and prevalence of obesity and hypertension respectively of 55% and 83%. The outcomes are: 53% severe (20% fatal, 33% non-fatal) and 47% non-severe, with severe defined as death or requiring mechanical ventila-tion or high flow oxygen. Obesity and admit systolic blood pressure (SBP) >140mmHg are each associated with severe outcome and, despite respective sensitivity of 71% and 76%, specificity ~66% for both, and ac-curacy of 60% and 70% by ROC analysis, would likely provide useful predictors of outcome in critically stressed health care systems. We discuss pathophysiologic hypotheses to explain why high admit SBP is observed only in half of patients with pre-COVID hypertension and is associated with severe outcome.

Nonlinear catastrophes caused by geological fluids are a fundamental issue in rock mechanics and the geoengineering hazard field. For the consideration of hydrodynamic force on red-bed mudstone softening damage, X-ray visualization test on the fissure flow in mudstone block failure under hydrodynamic force was performed in this study based on block scale and the physical phenomena of fissure seepage and nonlinear diffusion were further explored. A new method for evaluating the hydro-damage degrees of rocks using an X-ray image analysis was proposed, and the quantitative relation of diffusion coefficients of hydro-damage and seepage was established. The research results revealed that the hydrodynamic force promoted the fluid-filled fissure behavior in mudstone specimen failure. Also, the seepage and diffusion phenomena of fluid in rocks during failures were indicated using X-ray imaging. A dual mechanical behavior was presented in the nonlinear seepage and abnormal diffusion of a red mudstone geological body under hydrodynamic conditions. The damaged degree of mudstone was aggravated by the effect of hydrodynamic force, and the initial seepage–diffusion coefficient with respect to lower hydro-damage was larger than the final seepage–diffusion coefficient with respect to higher hydro-damage of rocks with a decreasing nonlinear trend.

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

To overcome the two-phase flow instability of traditional boiling heat dissipation technologies, a porous wick was used for liquid-vapor isolation, thus realizing efficient and stable boiling heat dissipation. A pump-assisted capillary phase-change loop with methanol as working medium was established to study the effect of liquid-vapor pressure difference and heating power on its start-up and steady-state characteristics. The results indicated that the evaporator undergoes four heat transfer modes including flooded, partial flooded, thin film evaporation and overheating. The thin film evaporation mode was the most efficient one with the shortest start-up period. Besides, the heat transfer modes were determined by liquid-vapor pressure difference and power. The heat transfer coefficient could be significantly improved and the thermal resistance could be reduced by increasing liquid-vapor pressure difference as long as it did not exceed 8 kPa. However, when the liquid-vapor pressure difference exceeded 8kPa, its influence on the heat transfer coefficient weakened. In addition, a two-dimensional heat transfer mode distribution diagram considering both liquid-vapor pressure difference and power was drawn through a great number of experiments. During engineering application, the liquid-vapor pressure difference can be controlled to maintain efficient thin film evaporation in order to achieve the optimum heat dissipation effect.

Copper-catalyzed and organocopper-involved reactions are of great significance in organic synthesis. To have a deep understanding of the reaction mechanisms, the structural characterizations of organocopper intermediates become indispensable. Meanwhile, the structure-function relationship of organocopper compounds would advance rational design and development of new Cu-based reactions and organocopper reagents. Compared to the mono-carbonic ligand, the C,N- and C,C-bidentate ligands better stabilize the unstable organocopper compounds. The bidentate ligands can chelate to the same copper atom via ?2-mode, forming a mono-cupra-cyclic compounds with at least one acute C-Cu-C angle. When the bidentate ligands bind to two copper atoms via ?1-mode at each coordinating site, the bimetallic macrocyclic compounds will form nearly linear C-Cu-C angles. The anionic coordinating sites of the bidentate ligand can also bridge two metals via ?2-mode, forming organocopper aggregates with Cu-Cu interactions and organocuprates with contact ion pair structures. The reaction chemistry of some selected organocopper compounds is highlighted, showing their unique structure-reactivity relationships.