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Effective safety management is vital for ensuring construction safety. Traditional safety inspections in construction heavily rely on manual labor, which is both time-consuming and labor-intensive. Extensive research has been conducted integrating computer-vision technologies to facilitate intelligent surveillance and improve safety measures. However, existing research predominantly focuses on singular tasks, while construction environments necessitate comprehensive analysis. This study introduces a multi-task computer vision technology approach for the enhanced monitoring of construction safety. The process begins with the collection and processing of multi-source video surveillance data. Subsequently, YOLOv8, a deep learning-based computer vision model, is adapted to meet specific task requirements by modifying the head component of the framework. This adaptation enables efficient detection and segmentation of construction elements, as well as the estimation of person and machine poses. Moreover, a tracking algorithm integrates these capabilities to continuously monitor detected elements, thereby facilitating the proactive identification of unsafe practices on construction sites. This paper also presents a novel Integrated Excavator Pose (IEP) dataset designed to address the common challenges associated with different single datasets, thereby ensuring accurate detection and robust application in practical scenarios.

With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we still face a series of challenges, including repetitive fragments, inverted repeats, low sequencing coverage, and the limitations of sequencing technologies. These challenges lead to unknown gaps in small genomes, hindering complete genome assembly. Although there are many existing assembly software options, they do not fully utilize the potential of artificial intelligence technologies, resulting in limited improvement in gap filling. Here, we propose a novel method, DLGapCloser, based on deep learning, aimed at assisting traditional tools in further filling gaps in small genomes. Firstly, we created four datasets based on the original genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla. To further extract effective information from the gene sequences, we also added homologous genomes to enrich the datasets. Secondly, we proposed the DGCNet model, which effectively extracts features and learns context from sequences flanking gaps. Addressing issues with early pruning and high memory usage in the Beam Search algorithm, we developed a new prediction algorithm, Wave-Beam Search. This algorithm alternates between expansion and contraction phases, enhancing efficiency and accuracy. Experimental results showed that the Wave-Beam Search algorithm improved the gap-filling performance of assembly tools by 7.35%, 28.57%, 42.85%, and 8.33% on the original results. Finally, we established new gap-filling standards and created and implemented a novel evaluation method. Validation on the genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla showed that DLGapCloser increased the number of filled gaps by 8.05%, 15.3%, 1.4%, and 7% compared to traditional assembly tools.

Within the dynamic realm of variable-geometry shock wave/boundary layer interactions, the wall parameters of the flow field undergo real-time fluctuations. The conventional approach to sensing these changes in wall pressure through sensor measurements is encumbered by a cumbersome process, leading to diminished efficiency and an inability to provide swift predictions of wall parameters. This paper introduces a data-driven methodology that leverages non-contact schlieren imaging to predict wall pressure within the flow field, a technique that holds promise for informing the optimized design of variable-geometry systems. A sophisticated deep learning framework, predicated on Convolutional Neural Networks (CNN), has been engineered to anticipate alterations in wall pressure stemming from high-speed shock wave/boundary layer interactions. Utilizing an impulsive wind tunnel with a Mach number of 6, we have procured a sequence of schlieren images and corresponding wall pressure measurements, capturing the continuous variations induced by an attack angle from a shock wave generator. These data have been instrumental in compiling a comprehensive dataset for the training and evaluation of the CNN. The CNN model, once trained, has adeptly deduced the distribution of wall pressure from the schlieren imagery. Notwithstanding, it was observed that the CNN’s predictive prowess is marginally diminished in regions where pressure variations are most pronounced. To assess the model’s generalization capabilities, we have segmented the dataset according to different temporal intervals for network training. Our findings indicate that while the generalization of all models crafted was less than optimal, Model 4 demonstrated superior generalization. It is thus suggested that augmenting the training set with additional samples and refining the network architecture will be a worthwhile endeavor in subsequent research initiatives.

Watermelon is one of the most important edible plants worldwide. Owing to its special cultivation conditions, watermelon is exposed to many biological and abiotic stresses during its development. Lectin receptor-like kinases (LecRLKs) are plant-specific membrane proteins that play important roles in sensing and responding to environmental stimuli. Although the LecRLK gene family has been identified in a variety of plants, a comprehensive analysis has not yet been undertaken in watermelon. In this study, 61 putative LecRLK genes were identified in watermelon, consisting of 36 G-type, 24 L-type, and 1 C-type LecRLK genes. They were distributed in clusters on chromosomes, and members from the same subfamily were mostly clustered together. The analysis of the phylogenetic tree and conserved motif indicated that there were obvious differences among three ClaLecRLK subfamilies, and there was also rich diversity in the C-terminal within subfamilies. A collinear analysis revealed that the evolution of the ClaLecRLK gene family in different Cucurbitaceae crops was asynchronous. Furthermore, the analysis of the ClaLecRLK protein structure showed that not all proteins contained signal peptides and a single transmembrane domain. A subcellular localization assay confirmed that the number and position of transmembrane domains did not affect ClaLecRLK protein localization in cells. Transcriptome data revealed distinct expression patterns of LecRLK genes of watermelon in various tissues, and their responses to different fungi infection were also significantly different. Finally, the potential binding sites of the ClaLecRLK genes targeted by miRNA were predicted. This study enhances the understanding of the characteristics and functions of the LecRLK gene family in watermelon and opens up the possibility of exploring the roles that LecRLK genes may play in the life cycle of Cucurbitaceae plants.

As micron-sized objects, mobile microrobots have shown significant potential for future biomedical applications, such as targeted drug delivery and minimally invasive surgery. However, to make these microrobots viable for clinical applications, several crucial aspects should be implemented, including customizability, motion-controllability, imageability, biodegradability, and biocompatibility. Developing materials to meet these requirements is of utmost importance. Here, a gelatin methacryloyl (GelMA) and (2-(4-vinylphenyl)ethene-1,1,2-triyl)tribenzene (TPEMA)-based multifunctional hydrogel with 3D printability, fluorescence imageability, biodegradability, and biocompatibility is demonstrated. By using 3D direct laser writing method, the hydrogel exhibits its versatility in the customization and fabrication of 3D microstructures. Spherical hydrogel microrobots were fabricated and decorated with magnetic nanoparticles on their surface to render them magnetically responsive, and have demonstrated excellent movement performance and motion controllability. The hydrogel microstructures also represented excellent drug loading/release capacity and degradability by using collagenase, along with stable fluorescence properties. Moreover, cytotoxicity assays showed that the hydrogel was non-toxic, as well as able to support cell attachment and growth, indicating excellent biocompatibility of the hydrogel. The developed multifunctional hydrogel exhibits great potential for biomedical microrobots that are integrated with customizability, 3D printability, motion controllability, drug delivery capacity, fluorescence imageability, degradability, and biocompatibility, thus being able to realize the real in vivo biomedical applications of microrobots.

We aimed to investigate the species composition of a small mammal community and the prevalence of Echinococcus spp. in a typical endemic area of the Tibetan Plateau. One pika and five rodent species were identified based on the morphological characteristics of 1278 small mammal specimens collected during 2014–2019. Detection of Echinococcus DNA in tissue samples from small mammal specimens revealed that Ochotona curzoniae (pika, total prevalence: 6.02%, 26/432), Neodon fuscus (5.91%, 38/643), N. leucurus (2.50%, 3/120), and Alexandromys limnophilus (21.74%, 10/46) were infected by both E. multilocularis and E. shiquicus; Cricetulus longicaudatus (16.67%, 1/6) was infected by E. shiquicus; and no infection was detected in N. irene (0/15). Neodon fuscus and O. curzoniae were the two most abundant small mammal species. There was no significant difference in the prevalence of pika and the overall rodent species assemblage (6.26%, 53/846); however, the larger rodent populations suggested that more attention should be paid to their role in the transmission of echinococcosis in the wildlife reservoir, which has long been underestimated. Moreover, although DNA barcoding provides a more efficient method than traditional morphological methods for identifying large numbers of small mammal samples, commonly used barcodes failed to distinguish the three Neodon species in this study. The close genetic relationships between these species suggest the need to develop more powerful molecular taxonomic tools.

Yam (Dioscorea spp.) is an important tuber crop consumed globally. However, stable yam production faces challenges from a variety of diseases caused by fungi, nematodes, viruses, and bacteria. Prominent diseases such as anthracnose, leaf spot, yam wilt, dry rot, and crazy root syndrome, currently pose serious threats to yam yields. These diseases not only result in quality degradation but also cause great economic losses. This review summarizes the damages, symptoms, causal agents, and epidemic factors of major yam diseases. It also outlines a comprehensive disease control strategy that includes the use of resistant varieties, proper crop rotation, sanitation measures, and the application of agrochemicals and biocontrol agents. Additionally, this review addresses future perspectives on risk factors and knowledge gaps, aiming to serve as a reference for in-depth research into advanced disease monitoring and control technologies for yams.

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10−3 V2/Hz to 4.04 × 10−6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.

With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7.

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers’ life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

Aluminum alloy frame is a novel structure system developed in recent years. In this article, the load-bearing performance of the beam-beam joint of the aluminum alloy frame is studied through numerical analysis and experiment and a safety monitoring method is developed. The impacts of the arch angle and bolt diameter on the beam-beam joint mechanical characteristics are explored through experiments under vertical load. When the diameter of the bolt was increased, the load-bearing performance of such joint displayed a pattern of first increasing and then decreasing. As the arch angle increased, the load-bearing performance on the joint gradually improved. Based on the experiments, numerical analysis models varying in arch angle were established, and the impacts of arch angles on the stiffness during the yield stage, ultimate load, and elastic stiffness of the aluminum alloy portal frame (AAPF) beam-beam joints were further explored through numerical simulation of the structure under vertical and horizontal loads. When the arching angle was increased, the elastic stiffness and yield stiffness of the beam-beam joint under vertical load showed a pattern of first increasing and then decreasing. When the arching angle was increased, the elastic stiffness and yield stiffness of the beam-beam joint under horizontal load significantly decreased. Based on the mechanical properties, a safety monitoring method for AAPF beam-beam joints based on displacement monitoring and frequency monitoring is proposed.

The Hepatitis E virus (HEV) causes acute and chronic Hepatitis E and is a global public health concern. HEV genotypes 3 (HEV-3) and 4 (HEV-4) are common to humans and animals, and domestic pigs and wild boars have been identified as the main reservoirs. However, limited information is available on the status of HEV infection in pigs, particularly in the Guangdong Province, China. This study aimed to investigate the seroprevalence of HEV in pig farms within the Guangdong Province. A total of 1568 serum samples were collected from 25 farms and tested for anti-HEV IgG antibodies. Enzyme-linked immunosorbent assay (ELISA) results revealed that 57.53% (902/1568) of serum samples from 24 farms (24/25, 96%) were positive for anti-HEV IgG antibodies. Year, season, region, and age were all linked risk factors for HEV in Guangdong, with season and region showing more significant impacts. The results showing a high seroprevalence of HEV confirmed its circulation among domestic pigs in the Guangdong Province, China. The presence of this antibody indicates that HEV infection was or is present on farms, posing a risk of zoonotic transmission of HEV from pigs to exposed workers and from pork or organs to consumption.

Currently, most assumptions in nonholonomic mobile robot controllers indicate that the robot velocity can become significantly large and that the robot actuators are able to generate the necessary level of torque input. Based on the sliding mode control theory, this paper develops a new framework to handle the control problem of a wheeled robot dynamics model with constrained velocity and torque. Through rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed controller guarantees asymptotic convergence of tracking or stabilization errors and boundedness of closed-loop signals. The advantages of the developed controller include the ability to simultaneously achieve tracking and stabilization control of nonholonomic mobile robots and the ability to ensure that the prescribed velocity and torque constraints are not breached by simply tuning the design parameters a priori, even in the presence of uncertain disturbances.

The water diversion project in Central Yunnan Province (WDP-YN) is the largest water diversion project under construction in China. However, the ecological effects of this water diversion project are still unclear. This study utilized Sentinel-2 remote sensing data to estimate fractional vegetation cover (FVC), maps spatiotemporal variations of FVC in construction areas from 2017 to 2022, and evaluates the impact of the WDP-YN on regional vegetation coverage using buffer analysis and vegetation type transition matrix methods. The study led to the following findings: (1) From 2017 to 2022, FVC within 10 km of the tunnel construction route showed a slightly downward trend or remained relatively stable with no significant changes in the spatial pattern of FVC. (2) Before and after the construction of WDP-YN, over 60% of the area within 10 km of the tunnel construction route showed no change in FVC. On Construction Route Section I (CRS-I), vegetation improved and/or degraded within 12.90% (14.10%) of the area and the regions with degraded FVC concentrated in the northern CRS-I. For Construction Route Section II (CRS-II), 11.96% and 27.51% of the regions were dominated by improved and/or degraded FVC. Vegetation changes near Groundwater Monitoring Point a (GMPa) were relatively stable. (3) The WDP-YN degraded vegetation within 2 km of both sides of CRS-I, slowing down the increase in FVC, while the WDP-YN improved vegetation within 2–6 km of both sides of CRS-II, the closer the distance to CRS-II, the faster the increase in FVC and the decrease in FVC slowed down within 0–2 km of both sides of CRS-II. This study sheds light on the impacts of water diversion infrastructure on vegetation coverage and provides practical guidance and reference for eco-environment protection and ecological restoration given water conservancy projects in China and other regions of the world.

Amphibians serve as reliable indicators of ecosystem health and are the most threatened group of vertebrates. Studies on their spatial distribution pattern and threats are crucial to formulate conservation strategies. Gongga Mountains, with a peak at 7509 m a.s.l. and running latitudinally, are in the center of the Hengduan Mountains Range and at the eastern steep edge of the Qinghai–Xizang Plateau, providing heterogeneous habitats and varied niches for amphibians. In this study, we combined 83 days of field work with information from 3894 museum specimens that were collected over the past 80 years, and identified twenty amphibian species belonging to seven families and twelve genera by morphology. Of these species, seven were listed in the threatened categories of the Red List of China’s Biodiversity and thirteen were endemic to China. Ten species were found on the plateau side (western slope) and eleven species were found on the other side close to the Sichuan Basin (eastern slope). Only one species was found on both sides, indicating different community structures horizontally. The species richness was unimodal vertically and peaking at mid elevation on both sides, with the maximum number (ten vs. nine) of species occurring at 3300–3700 vs. 1700–1900 m a.s.l. and in different types of vegetation. The elevation span and body length of species distributed on both slopes did not show significant differences. These findings help to understand the horizontal and vertical distribution pattern of amphibian diversity, laying a foundation for future biogeographical and conservation research in this area.

Spurs, which mainly appear in roosters, are protrusions near the tarsometatarsus on both sides of the calves of chickens, and are connected to the tarsometatarsus by a bony core. As a male-biased morphological characteristic, the diameter and length of spurs vary significantly between different individuals, mainly related to genetics and age. As a specific behavior of hens, egg-laying also varies greatly between individuals in terms of traits such as age at first egg (AFE), egg weight (EW), and so on. At present, there are few studies on chicken spurs. In this study, we investigated the inheritance pattern of the spur trait in roosters with different phenotypes and the correlations between spur length, body weight at 18 weeks of age (BW18), shank length at 18 weeks of age (SL18), and the egg-laying trait in hens (both hens and roosters were from the same population and were grouped according to their family). These traits related to egg production included AFE, body weight at first egg (BWA), and first egg weight (FEW). We estimated genetic parameters based on pedigree and phenotype data, and used variance analysis to calculate broad-sense heritability for correcting the parameter estimation results. The results showed that the heritability of male left and right spurs ranged from 0.6 to 0.7. There were significant positive correlations between left and right spur length, BW18, SL18, and BWA, as well as between left and right spur length and AFE. We selected 35 males with the longest spurs and 35 males with the shortest spurs in the population, and pooled them into two sets to obtain the pooled genome sequencing data. After genome-wide association and genome divergency analysis by FST, allele frequency differences (AFDs), and XPEHH methods, we identified 7 overlapping genes (CENPE, FAT1, FAM149A, MANBA, NFKB1, SORBS2, UBE2D3) and 14 peak genes (SAMD12, TSPAN5, ENSGALG00000050071, ENSGALG00000053133, ENSGALG00000050348, CNTN5, TRPC6, ENSGALG00000047655,TMSB4X, LIX1, CKB, NEBL, PRTFDC1, MLLT10) related to left and right spur length through genome-wide selection signature analysis and a genome-wide association approach. Our results identified candidate genes associated with chicken spurs, which helps to understand the genetic mechanism of this trait and carry out subsequent research around it.

To explore the dynamics of flow and heat transfer behaviors associated with bubbles during solution absorption in a vacuum environment, we present the design of an experimental setup for measuring the absorption and transport properties of bubbles in a pressurized vertical tube. The structure and operational principle of the setup are detailed. The reliability and accuracy of the system are validated through a series of experiments, including vacuum level maintenance, bubble flow verification, and energy checks. The findings reveal that the supercharging technology effectively facilitates bubble absorption under negative pressure. Over a 12 h period, the system vacuum level elevates by only 2.33%, indicating a minimal gas leakage rate of 2.4 mL/h and affirming the device’s exceptional reliability. The observed bubble formation, rise, collision, coalescence, and rupture behaviors in the experiment are consistent with previous studies on bubble flow. The maximum relative deviations of temperature and concentration at the solution and cooling water outlets are 0.08%, 0.02%, and 0.01%, respectively, validating the device’s excellent accuracy. Additionally, the energy check experiments, performed with varying solution inlet temperature and flow rate, reveal the maximum errors of 10.4 J and 12.5 J, respectively, demonstrating the device’s satisfactory accuracy. In summary, this work lays a robust experimental foundation for subsequent investigations into the transport properties and transfer mechanisms of bubble absorption in a vacuum environment.

Microbial fuel cells (MFCs) have the potential to directly convert the chemical energy in organic matter into electrical energy, making them a promising technology for achieving sustainable energy production alongside wastewater treatment. However, the low extracellular electron transfer (EET) rates and limited bacteria loading capacity of MFCs anode materials present challenges in achieving high power output. In this study, three-dimensionally heteroatom-doped carbonized grape (CG) monoliths with a macroporous structure were successfully fabricated using a facile and low-cost route and employed as independent anodes in MFCs for treating brewery wastewater. The CG obtained at 900 °C (CG-900) exhibited excellent biocompatibility. When integrated into MFCs, these units initiated electricity generation a mere 1.8 days after inoculation and swiftly reached a peak output voltage of 658 mV, demonstrating an exceptional areal power density of 3.71 W m−2. The porous structure of the CG-900 anode facilitated efficient ion transport and microbial community succession, ensuring sustained operational excellence. Remarkably, even when nutrition was interrupted for 30 days, the voltage swiftly returned to its original level. Moreover, the CG-900 anode exhibited a superior capacity for accommodating electricigens, boasting a notably higher abundance of Geobacter spp. (87.1%) compared to carbon cloth (CC, 63.0%). Most notably, when treating brewery wastewater, the CG-900 anode achieved a maximum power density of 3.52 W m−2, accompanied by remarkable treatment efficiency, with a COD removal rate of 85.5%. This study provides a facile and low-cost synthesis technique for fabricating high-performance MFC anodes for use in microbial energy harvesting.

The highly pathogenic coronaviruses SARS-CoV-2 and SARS-CoV have led to the COVID-19 pandemic and SARS outbreak, respectively. The receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2, particularly the Omicron variant, has frequent mutations, resulting in the reduced efficiency of current COVID-19 vaccines against new variants. Here, we designed two lipid nanoparticle-encapsulated mRNA vaccines by deleting the mutant RBD of the SARS-CoV-2 Omicron variant (SARS2-S (RBD-del)) or by replacing this mutant RBD with the conserved and potent RBD of SARS-CoV (SARS2-S (SARS-RBD)). Both mRNA vaccines were stable at various temperatures for different time periods. Unlike SARS2-S (RBD-del) mRNA, SARS2-S (SARS-RBD) mRNA elicited effective T-cell responses and potent antibodies specific to both SARS-CoV-2 S and SARS-CoV RBD proteins. It induced strong neutralizing antibodies against pseudotyped SARS-CoV-2 and SARS-CoV infections and protected immunized mice from the challenge of the SARS-CoV-2 Omicron variant and SARS-CoV by significantly reducing the viral titers in the lungs after Omicron challenge and by completely preventing SARS-CoV-induced weight loss and death. SARS2-S (SARS-RBD)-immunized serum antibodies protected naïve mice from the SARS-CoV challenge, with its protective efficacy positively correlating with the neutralizing antibody titers. These findings indicate that this mRNA vaccine has the potential for development as an effective vaccine against current and future SARS-CoV-2 variants and SARS-CoV.

Zirconia (ZrO2) nanoparticles were synthesized using a solvothermal method under varying synthesis conditions, namely acidic, neutral, and alkaline. X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM) were leveraged to investigate the phase evolution and topographical features in detail. The resulting crystal phase structures and grain sizes exhibited substantial variation based on these conditions. Notably, the acidic condition fostered a monoclinic phase in ZrO2, while the alkaline condition yielded a combination of tetragonal and monoclinic phases. In contrast, ZrO2 obtained under neutral conditions demonstrated a refinement in grain sizes, constrained within a 1 nm scale upon an 800 °C thermal treatment. This was accompanied by an important transformation from a monoclinic phase to tetragonal phase in the ZrO2. Furthermore, a rigorous examination of XPS data and a UV-visible spectrometer (UV-vis) analysis revealed the significant role of oxygen vacancies in phase stabilization. The notable emergence of new energy bands in ZrO2, in stark contrast to the intrinsic bands observed in a pure monoclinic sample, are attributed to these oxygen vacancies. This research offers valuable insights into the novel energy bands, phase stability, and optical absorption properties influenced by oxygen vacancies in ZrO2. Moreover, it proposes an innovative energy level model for zirconia, underpinning its applicability in diverse technological areas.

The lack of efficient ways to dispose of lignocellulosic agricultural residues is a serious environmental issue. Low temperatures greatly impact the ability of organisms to degrade these wastes and convert them into nutrients. Here, we report the isolation and genomic characterization of a microbial consortium capable of degrading corn straw at low temperatures. The microorganisms isolated showed fast cellulose-degrading capabilities, as confirmed by scanning electron microscopy and the weight loss in corn straw. Bacteria in the consortium behaved as three diverse and functionally distinct populations, while fungi behaved as a single population in both diversity and functions overtime. The bacterial genus Pseudomonas and the fungal genus Thermoascus had prominent roles in the microbial consortium, showing significant lignocellulose waste-degrading functions. Bacteria and fungi present in the consortium contained high relative abundance of genes for membrane components, with amino acid breakdown and carbohydrate degradation being the most important metabolic pathways for bacteria, while fungi contained more genes involved in energy use, carbohydrate degradation, lipid and fatty acid decomposition, and biosynthesis.

This study aimed to develop and validate an improved sparrow search algorithm (ISSA)-optimized Least Squares Support Vector Machine (LSSVM) model for accurately predicting the tooth profile deviation of rigid gears produced by wire electrical discharge machining (WEDM). The ISSA was obtained by optimizing the sparrow search algorithm (SSA) using Tent chaotic mapping, adaptive adjustment strategy, dynamic inertia weights, and grey wolf hierarchy strategy. The effectiveness of the ISSA was verified using four different classes of benchmark test functions. Four main process parameters (peak current, pulse width, pulse interval, and tracking) were taken as inputs and the tooth profile deviations of rigid gears were considered as outputs to develop an ISSA-LSSVM-based profile deviation prediction model. The prediction performance of the ISSA-LSSVM model was evaluated by comparing it with the LSSVM model optimized by three standard algorithms. The prediction results of the ISSA-LSSVM model were R2 = 0.9828, RMSE = 0.0029, and MAPE = 0.0156. The results showed that the established model exhibits high prediction accuracy and can provide reliable theoretical guidance for predicting the tooth profile deviation of rigid gears.

In complex environments, users frequently need to manage multiple tasks simultaneously, which poses significant challenges for user interface design. For instance, when driving, users must maintain continuous visual attention on the road ahead while also monitoring rearview mirrors and performing shoulder checks. These multitasking scenarios present substantial design challenges in effectively guiding users. To address these challenges, we focus on investigating and designing visual and haptic guidance systems to augment users’ performance. We initially propose the use of visual guidance, specifically employing a dynamic arrow as a guidance technique. Our evaluation shows that dynamic arrows significantly expedite both reaction and selection times. We further introduce and evaluate haptic feedback, which users perceive as more salient than visual guidance, leading to quicker responses when switching from primary to secondary tasks. This allows users to maintain visual attention on the primary task while simultaneously responding effectively to haptic cues. Our findings suggest that multimodal guidance, especially haptic guidance, can enhance both reaction time and user experience in dual-task environments, offering promising practical implications and guidelines for designing more user-friendly interfaces and systems.

Unbonded flexible risers consist of several helical and cylindrical layers, which can undergo large bending deformation and can be installed in different configurations to adapt to harsh marine environments; thus, they can be applied to transport oil and gas resources from ultra-deep waters (UDW). Due to their special geometric characteristics, they can ensure sufficient axial tensile stiffness while having small bending stiffness, which can undergo large deflection bending deformation. In recent years, the development of unbonded flexible risers has been moving in an intelligent, integrated direction. This paper presents a review of unbonded flexible risers. Firstly, the form and properties of each interlayer of an unbonded flexible riser are introduced, as well as the corresponding performance and configuration characteristics. In recent years, the development of unbonded flexible risers has been evolving, and the development of machine learning on unbonded flexible risers is discussed. Finally, with emphasis on exploring the design characteristics and working principles, three new types of unbonded flexible risers, an integrated production bundle, an unbonded flexible riser with an anti-H2S layer, and an unbonded flexible riser with a composite armor layer, are presented. The research results show that: (1) the analytical methods of cross-sectional properties of unbonded flexible risers are solved based on ideal assumptions, and the computational accuracy needs to be improved. (2) Numerical methods have evolved from equivalent simplified models to models that account for detailed geometric properties. (3) Compared with ordinary steel risers, the unbonded flexible riser is more suitable for deep-sea resource development, and the structure of each layer can be designed according to the requirements of the actual environment.

Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well after processing. Some quantitative assessment indicators have been presented to evaluate the edge-preservation capability of single-polarization SAR filters, among which the non-clean-reference-based (i.e., blind) ones are attractive. However, most of these indicators are derived based only on the basic fact that the speckle is a kind of multiplicative noise, and they do not take into account the detailed statistical distribution traits of SAR data, making the assessment not robust enough. Moreover, to our knowledge, there are no specific blind assessment indicators for fully Polarimetric SAR (PolSAR) filters up to now. In this paper, a blind assessment indicator based on an SAR Ratio Gradient Operator (RGO) and Confidence Interval Estimation (CIE) is proposed. The RGO is employed to quantify the edge gradient between two neighboring image patches in both the speckled and filtered data. A decision is then made as to whether the ratio gradient value in the filtered image is close to that in the unobserved clean image by considering the statistical traits of speckle and a CIE method. The proposed indicator is also extended to assess the PolSAR filters by transforming the polarimetric scattering matrix into a scalar which follows a Gamma distribution. Experiments on the simulated SAR dataset and three real-world SAR images acquired by ALOS-PALSAR, AirSAR, and TerraSAR-X validate the robustness and reliability of the proposed indicator.

The unbonded flexible riser has been increasingly applied in the ocean engineering industry to transport oil and gas resources from the seabed to offshore platforms. The slippage of helical layers, especially the tensile armor layers of unbonded flexible risers, contribute to the nonlinear hysteresis phenomenon, which is a research hotspot and difficulty. In this paper, on the basis of a typical eight-layer unbonded flexible riser model, the nonlinear slippage of a tensile armor layer and the corresponding nonlinear behavior of an unbonded flexible riser subjected to irregular external loads are studied by numerical modeling with detailed cross-sectional properties of the helical layers, and are verified through a theoretical method considering the coupled effect of the external loads on the unbonded flexible riser. Firstly, the balance equation of each layer considering the effect of external loads is established based on functional principles, and the overall theoretical model of the unbonded flexible riser is set up in consideration of the contact between adjacent layers. Secondly, the numerical modeling of each separate layer within the unbonded flexible riser, including the actual geometry of the carcass and pressure armor layer, is established, and solid elements are applied to all the interlayers, thus simulating the nonlinear contact and friction between and within interlayers. Finally, after verification through test data, the behavior of the unbonded flexible riser under the cyclic axial force, torsion, bending moment, and irregular external and internal pressure is studied. The results show that the tensile armor layer can slip under irregular loads. Additionally, some suggestions related to the analysis of unbonded flexible risers under irregular loads are drawn in the end.

Acute mountain sickness (AMS) is a common ailment in high-altitude areas caused by the body’s inadequate adaptation to low-pressure, low-oxygen environments, leading to organ edema, oxidative stress, and impaired intestinal barrier function. The gastrointestinal tract, being the first to be affected by ischemia and hypoxia, is highly susceptible to injury. This study investigates the role of Lactobacillus delbrueckii subsp. bulgaricus in alleviating acute hypoxic-induced intestinal and tissue damage from the perspective of daily consumed lactic acid bacteria. An acute hypoxia mouse model was established to evaluate tissue injury, oxidative stress, inflammatory responses, and intestinal barrier function in various groups of mice. The results indicate that strain 4L3 significantly mitigated brain and lung edema caused by hypoxia, improved colonic tissue damage, and effectively increased the content of tight junction proteins in the ileum, reducing ileal permeability and alleviating mechanical barrier damage in the intestines due to acute hypoxia. Additionally, 4L3 helped to rebalance the intestinal microbiota. In summary, this study found that Lactobacillus delbrueckii subsp. bulgaricus strain 4L3 could alleviate acute intestinal damage caused by hypoxia, thereby reducing hypoxic stress. This suggests that probiotic lactic acid bacteria that exert beneficial effects in the intestines may alleviate acute injury under hypoxic conditions in mice, offering new insights for the prevention and treatment of AMS.

Herbal medicines are widely used for clinical purposes worldwide. These herbs are susceptible to phytopathogenic fungal invasion during the culturing, harvesting, storage, and processing stages. The threat of fungal and mycotoxin contamination requires the evaluation of the health risks associated with these herbal medicines. In this study, we collected 138 samples of 23 commonly used herbs from 20 regions in China, from which we isolated a total of 200 phytopathogenic fungi. Through morphological observation and ITS sequencing, 173 fungal isolates were identified and classified into 24 genera, of which the predominant genera were Fusarium (27.74%) and Alternaria (20.81%), followed by Epicoccum (11.56%), Nigrospora (7.51%), and Trichocladium (6.84%). Quantitative analysis of the abundance of both Fusarium and Alternaria in herbal medicines via RT-qPCR revealed that the most abundant fungi were found on the herb Taraxacum mongolicum, reaching 300,000 copies/μL for Fusarium and 700 copies/μL for Alternaria. The in vitro mycotoxin productivities of the isolated Fusarium and Alternaria strains were evaluated by using liquid chromatography–tandem mass spectrometry (LC-MS/MS), and it was found that the Fusarium species mainly produced the acetyl forms of deoxynivalenol, while Alternaria species mainly produced altertoxins. These findings revealed widely distributed fungal contamination in herbal medicines and thus raise concerns for the sake of the quality and safety of herbal medicines.

Porcine reproductive and respiratory syndrome virus (PRRVS) is a major swine viral pathogen that affects the pig industry worldwide. Control of early PRRSV infection is essential, and different types of PRRSV-positive samples can reflect the time point of PRRSV infection. This study aims to investigate the epidemiological characteristics of PRRSV in China from Q4 2021 to Q4 2022, which will be beneficial for porcine reproductive and respiratory syndrome virus (PRRSV)control in the swine production industry in the future. A total of 7518 samples (of processing fluid, weaning serum, and oral fluid) were collected from 100 intensive pig farms in 21 provinces, which covered all five pig production regions in China, on a quarterly basis starting from the fourth quarter of 2021 and ending on the fourth quarter of 2022. Independent of sample type, 32.1% (2416/7518) of the total samples were PCR-positive for PRRSV, including 73.6% (1780/2416) samples that were positive for wild PRRSV, and the remaining were positive for PRRSV vaccine strains. On the basis of the time of infection, 58.9% suckling piglets (processing fluid) and 30.8% weaning piglets (weaning serum) showed PRRSV infection at an early stage (approximately 90% of the farms). The sequencing analysis results indicate a wide range of diverse PRRSV wild strains in China, with lineage 1 as the dominant strain. Our study clearly demonstrates the prevalence, infection stage, and diversity of PRRSV in China. This study provides useful data for the epidemiological understanding of PRRSV, which can contribute to the strategic and systematic prevention and control of PRRSV in China.

The Shouyang Block was selected as the research subject. Comprehensive analysis was conducted using coalbed methane (CBM) well production data, geochemical test data on water produced from the coalbed methane well, and fundamental geological information. The findings reveal the water dynamics in the Shouyang Block are characterized by weak groundwater runoff or retention in most areas. The groundwater head height exhibits a gradual decrease from the north to south, which is closely associated with the monoclinic structure of the Shouyang Block. Overall, water production is relatively high. As the average water production increases, the average gas production gradually decreases. A concentration of high water production wells is observed in the northern part of the Shouyang Block, which gradually increases towards the southeast direction. A comprehensive analysis was conducted on the factors influencing water production, including total water content of coal seams, coal seam porosity, groundwater stability index, groundwater sealing coefficient, D value of the fracture fractal dimension, fault fractal dimension, and sand–mud ratio. The correlation degree was calculated and ranked in order of magnitude through grey correlation analysis. The order of factors that influence water production, from strongest to weakest, is as follows: sand–mud ratio > porosity > fractal dimension of fault > fracture fractal dimension D value > groundwater sealing coefficient > groundwater stability index > total water content of coal seams. The dissolution amounts of carbonate and sulfate are both small, and the water source may mainly come from the sandstone aquifer. Attention should be paid to the distribution and lithological combination of sandstone aquifers in coal-bearing strata in the future exploration and development process of the Shouyang Block. This will help to avoid the potential influence of fault structures and enable the identification of favorable areas for low water and high gas production.

Local path planning, as an essential technology to ensure intelligent ships’ safe navigation, has attracted the attention of many scholars worldwide. In most existing studies, the impact of COLREGS has received limited consideration, and there is insufficient exploration of the method in complex waters with multiple interfering ships and static obstacles. Therefore, in this paper, a generation method for a time–space overlapping equivalent static obstacle line for ships in multi-ship encounter scenarios where both dynamic and static obstacles coexist is proposed. By dynamically inferring ships’ encounter situations and considering the requirements of COLREGS, the influence of interfering ships and static obstacles on the navigation of the target ship at different times in the near future is represented as static obstacle lines. These lines are then incorporated into the scene that the target ship encountered at the path planning moment. Subsequently, the existing path planning methods were extensively utilized to obtain the local path. Compared with many common path planning methods in random scenarios, the effectiveness and reliability of the method proposed are verified. It has been demonstrated by experimental results that the proposed method can offer a theoretical basis and technical support for the autonomous navigation of unmanned ships.

With the development of civil engineering, lightweight and high-strength materials, as well as large-span, low-frequency structural systems, are increasingly used. However, its self-oscillation frequency is often close to the stride frequency of pedestrians, which is easily affected by human activities. To study the effect of human activities on the dynamic response of structures, it is crucial to choose an appropriate anthropogenic load model. Considering the inter-subject and intra-subject variability of pedestrian walking parameters and induced forces in a crowd, we introduce the interaction rules between pedestrians based on the floor field cellular automata (FFCA). A stochastic crowd-loading model coupling walking parameters, induced forces between pedestrians, and induced forces between pedestrians and structures is proposed for simulating crowd-walking loads. The feasibility of the model is verified by comparing the measured response of a space large-span structure with the predicted response of the proposed stochastic crowd-loading model. The comfort level of the structure under different crowd densities was also evaluated based on the model. It was found that both random combinations of walking parameters and dynamic behaviors of pedestrians can cause significant differences in the structural response. Therefore, the crowd-loading model should consider the influence of pedestrian behavioral factors on the structural response.

Soil nutrients are essential for plant growth, and it is crucial to accurately detect their levels. However, current methods for detecting soil nutrients still have low accuracy and high costs. In this study, we propose a multi-source fusion feature approach that combines multispectral image technology with electronic nose gas response technology to achieve rapid, high-precision, and cost-effective detection of soil nutrients, including soil organic matter (SOM), total nitrogen (TN), available potassium (AK), and available phosphorus (AP). To begin, a multispectral camera was used to collect spectral images and extract seven index features. Additionally, an electronic nose was used to collect soil pyrolysis gases to obtain the response curve and extract seven response features. These two sets of features were effectively fused to form a 106 × 98 fusion feature space. Secondly, principal component analysis (PCA) and Pearson correlation coefficient (PCC) algorithms were used to optimize and reduce the dimensionality of the fused feature space. Finally, a predictive model of the relationship between the feature space and the nutrient content of the soil samples was established using the random forest (RF) and partial least squares regression (PLSR) algorithms. The predictive performance of the model was used to evaluate the accuracy of soil nutrient detection. The results showed that the PLSR modeling of the optimized feature space of PCA achieved high accuracy in predicting the levels of main soil nutrients, with R2 values for SOM, TN, AK, and AP of 0.96, 0.95, 0.84, and 0.73, and the RMSE values of 0.56, 0.07, 3.21, and 3.70, respectively. Compared to using only electronic nose gas response technology, the soil nutrient detection with multi-source data features method in this study not only improved the accuracy for SOM and TN but also for metal elements AK and AP in soil. The spectral index features proposed in this study were able to compensate for the limitations of the electronic nose response features, and modeling with the fusion feature space resulted in an accurate prediction of SOM, TN, AK, and AP levels.

In certain precision work scenarios, underwater robots require the ability to adhere to surfaces in order to perform tasks effectively. An efficient and stable suction device plays a pivotal role in the functionality of such underwater robots. The vortex suction cup, distinguished by its uncomplicated design, high suction efficiency, and capability for non-contact adhesion, holds significant promise for integration into underwater robotic systems. This paper presents a novel design for a vortex suction cup and investigates its suction force and torque when encountering surfaces with varying curvature radii using Computational Fluid Dynamics (CFD) simulations and experimental testing. These findings offer valuable insights for the development of robots capable of adapting to underwater structures of different dimensions. Results from both experiments and simulations indicate that reducing the curvature radius of the adhered surface results in a decrease in suction force and an increase in torque exerted on the suction cup. As the adhered surface transitions from flat to a curvature radius of 150 mm, the adhesion force of our proposed vortex suction cup decreases by approximately 10%, while the torque increases by approximately 20% to 30%. Consequently, the adhesion efficiency of the suction cup decreases by about 25% to 30%.

Falling is inevitable for legged robots when deployed in unstructured and unpredictable real-world scenarios, such as uneven terrain in the wild. Therefore, to recover dynamically from a fall without unintended termination of locomotion, the robot must possess the complex motor skills required for recovery maneuvers. However, this is exceptionally challenging for existing methods, since it involves multiple unspecified internal and external contacts. To go beyond the limitation of existing methods, we introduced a novel deep reinforcement learning framework to train a learning-based state estimator and a proprioceptive history policy for dynamic fall recovery under external disturbances. The proposed learning-based framework applies to different fall cases indoors and outdoors. Furthermore, we show that the learned fall recovery policies are hardware-feasible and can be implemented on real robots. The performance of the proposed approach is evaluated with extensive trials using a quadruped robot, which shows good effectiveness in recovering the robot after a fall on flat surfaces and grassland.

The LD oilfield is one of the representative offshore oilfields. After weak gel flooding, the recovery rate is significantly improved. However, the oilfield is then in a medium- to high-water content stage, presenting a complex distribution of the remaining oil. The measures for further enhanced oil recovery (EOR) are uncertain. As a result, it is necessary to clarify the distribution pattern and development potential of the remaining oil during the high-water content period after weak gel flooding. In this study, an online nuclear magnetic resonance (NMR) oil displacement experiment and microscopic oil displacement experiment were conducted, and the mechanisms of weak gel flooding and the distribution pattern of the remaining oil were clarified in the LD oilfield. Additionally, high-multiple water flooding and numerical simulation experiments were conducted to analyze the development potential after weak gel flooding. The results show that the effect of weak gel flooding was more significant in the core of 1500 mD, with an increase in oil recovery of 9% compared to 500 mD. At a permeability of 500 mD, the degree of crude oil mobilization in micropores and small pores caused by weak gel flooding was improved by 29.64% and 23.48%, respectively, compared with water flooding. At 1500 mD, the degree of crude oil mobilization in small pores caused by weak gel flooding was increased by 37.79% compared to water flooding. After weak gel flooding, the remaining oil was primarily distributed in medium and large pores. Microscopically, the remaining oil was dominated by cluster residual oil, accounting for 16.49%, followed by columnar, membranous, and blind-end residual oil. High multiple water flooding experiments demonstrated that weak gel flooding could significantly reduce development time. The ultimate oil recovery efficiency of 500 mD and 1500 mD reached 71.85% and 80.69%, respectively. Numerical simulation results show that the ultimate oil recovery efficiency increased from 62.04% to 71.3% after weak gel flooding. This indicated that the LD oilfield still had certain development potential after weak gel flooding. The subsequent direction for enhanced oil recovery focuses mainly on mobilizing oil in medium pores or clustered remaining oil. This will play a crucial role in further exploring methods for utilizing the remaining oil and increasing the recovery rate.

The microscale mechanisms underlying the suction anchor–sandy soil interaction under slidable pulling actions of mooring lines remain poorly understood. This technical note addresses this knowledge gap by investigating the suction anchor–sandy soil interaction from micro to macro, with a particular emphasis on the effect of interface friction. The discrete element method (DEM) was utilized to simulate the sandy soil, while the finite element method (FEM) was employed to model the suction anchors. The peak pulling forces in numerical simulations were verified by centrifuge test results. The research findings highlight the significant influence of interface friction on the pulling force–displacement curves, as it affects the patterns of suction anchor–sandy soil interactions. Furthermore, clear relationships were established between the magnitude of interface friction, rotation angle, and pullout displacement of suction anchors. By examining the macro-to-micro behaviors of suction anchor–sandy soil interactions, this study concludes with a comprehensive understanding of failure patterns and their key characteristics under different interface friction conditions. The findings proved that the interface friction not only influences the anti-pullout capacity but also changes the failure patterns of suction anchor–soil interactions in marine engineering.

In this paper, we investigate the consensus control problem of Euler–Lagrange systems which can be used to describe the motion of various mechanical systems such as manipulators and quadcopters. We focus on consensus control strategies, which are important for achieving coordinated behavior in multi-agent systems. The paper considers the key challenges posed by random communication delays and packet losses that are increasingly common in networked control systems. In addition, it is assumed that each system receives information from neighboring agents intermittently. Addressing these challenges is critical to ensure the reliability and efficiency of such systems in real-world applications. Communication delay is time-varying and can be very large, but should be smaller than some bounded constant. To decrease the frequency of control input updates, we implement an event-triggered scheme that regulates the controller’s updates for each agent. Specifically, it does not update control inputs at traditional fixed intervals, but responds to predefined conditions and introduces a dynamic consensus item to handle information irregularities caused by communication delays and intermittent information exchange. The consensus can be achieved if the communication graph of agents contains a spanning tree with the desired velocity as the root node. That is, all Euler–Lagrange systems need to obtain the desired velocity, directly or indirectly (via neighbors), to reach consensus. We establish that the Zeno behavior can be avoided, ensuring a positive minimum duration between successive event-triggered instances. Finally, we provide simulation results to show the performance of our proposed algorithm.

Global route planning has garnered global scholarly attention as a crucial technology for ensuring the safe navigation of intelligent ships. The comprehensive influence of time-varying factors such as water depth, prohibited areas, navigational tracks, and traffic separation scheme (TSS) on ship navigation in coastal global route planning has not been fully considered in existing research, and the study of route planning method from the perspective of practical application is still needed. In this paper, a global route planning method based on human-like thinking for coastal sailing scenarios is proposed. Based on the historical route’s information, and taking into full consideration those time-varying factors, an abnormal waypoint detection and correction method is proposed to make the planned route conform to relevant regulations of coastal navigation and the common practices of seafarers as much as possible, and better meet the coastal navigation needs of unmanned ships. Taking the global route planning of “ZHIFEI”, China’s first autonomous navigation container ship, as an example, the validity and reliability of the proposed method are verified. Experimental findings demonstrate the efficacy of the proposed method in global route planning for coastal navigation ships. The method offers a solid theoretical foundation and technical guidance for global route planning research of unmanned ship.

Polysaccharides extracted from Taxus media hrough an aqueous method were further refined by removing proteins via the Sevag technique and purified by dialysis. The separation of these polysaccharides was accomplished using a DEAE-cellulose chromatog-raphy column, yielding two distinct fractions, named CPTM-P1 and CPTM-P2. Notably, CPTM-P1 emerged as the primary polysaccharide component within Taxus media. Consequently, a comprehensive analysis focusing exclusively on CPTM-P1 was undertaken. The molecular weight of CPTM-P1 was established through gel permeation chromatography (GPC), and its monosaccharide composition was deciphered using HPLC-MS. The structure was further elucidated through nuclear magnetic resonance (NMR) spectroscopy. The molecular weight of CPTM-P1 was determined to be 968.7 kDa. The monosaccharide composition consisted of galactose (Gal), arabinose (Ara), galacturonic acid (Gal-UA), glucose (Glc), rhamnose (Rha), xylose (Xyl), mannose (Man), fucose (Fuc), glucuronic acid (Glc-UA), and ribose (Rib). The proportional distribution of these components was 30.53%, 22.00%, 5.63%, 11.67%, 11.93%, 1.69%, 8.50%, 1.23%, 5.63%, and 1.17%, respectively. This confirmed CPTM-P1 as an acidic heteropolysaccharide with a glycuronic acid backbone. Moreover, CPTM-P1 showed immunoenhancing properties, effectively augmenting the secretion of nitric oxide and cytokines (TNF-α, IL-1β, and IL-6). Additionally, it significantly enhances the phagocytic capacity of RAW264.7 cells. These findings underscore the potential application of these polysaccharides in functional foods and pharmaceuticals, providing a solid scientific basis for further exploration and utilization of Taxus media polysaccharides.

Field observations were combined with a coupled hydrodynamic and water quality model to investigate the spatial and temporal variation in Chlorophyll-a (Chl-a) in Jinmeng Bay, China. The relatively high Chl-a values were distributed in the inshore waters, mainly due to the abundant nutrient inflow from the Tanghe River. The model’s results indicate that the Chl-a concentration was much higher in seaweed beds surrounded by artificial islands and reefs under the southeasterly wind, largely due to the fact that pollutants are prone to accumulate in coastal areas where flow is attenuated by the presence of natural and artificial marine structures. It was also found that the southwesterly winds suppress the inflow of nutrients from the Tanghe River to the coastal areas, and, therefore, lower the Chl-a levels. River input and wind forcing are the major factors that influence Chl-a concentrations in the anthropogenically influenced bay. This finding provides useful guidance for the prediction and mitigation of green tides in Jinmeng Bay in the future.

The niobium oxide dihalides have recently been identified as a new class of van der Waals materials exhibiting exceptionally large second-order nonlinear optical responses and robust in-plane ferroelectricity. In contrast to second-order nonlinear processes, third-order optical nonlinearities can arise irrespective of whether a crystal lattice is centrosymmetric. Here, we report third harmonic generation (THG) in two-dimensional (2D) transition metal oxide iodides, namely NbOI2 and TaOI2. We observe a comparable THG intensity from both materials. By benchmarking against THG from monolayer WS2, we deduce that the third-order susceptibility is approximately on the same order. THG resonances are revealed at different excitation wavelengths, likely due to enhancement by excitonic states and band edge resonances. The THG intensity increases for material thicknesses up to 30 nm, owing to weak interlayer coupling. After this threshold, it shows saturation or a decrease, due to optical interference effects. Our results establish niobium and tantalum oxide iodides as promising 2D materials for third-order nonlinear optics, with intrinsic in-plane ferroelectricity and thickness-tunable nonlinear efficiency.

Specialized epoxy resin, capable of achieving room-temperature profound curing and sustaining prolonged exposure to high-temperature environments, stands as a pivotal material in modern high-end manufacturing sectors including aerospace, marine equipment fabrication, machinery production, and the electronics industry. Herein, a silicon-hybridized epoxy resin, amenable to room-temperature curing and designed for high-temperature applications, was synthesized using a sol–gel methodology with silicate esters and silane coupling agents serving as silicon sources. Resin characterization indicates a uniform distribution of silicon elements in the obtained silicon hybrid epoxy resin. In comparison to the non-hybridized epoxy resin, notable improvements are observed in room-temperature curing performance, heat resistance, and mechanical strength.

Normal pregnancy involves numerous physiological changes, including changes in hormone levels, immune responses, and metabolism. Although several studies have shown that the gut microbiota may have an important role in the progression of pregnancy, these findings have been inconsistent, and the relationship between the gut microbiota and metabolites that change dynamically during and after pregnancy remains to be clarified. In this longitudinal study, we comprehensively profiled the temporal dynamics of the gut microbiota, Bifidobacterium communities, and serum and faecal metabolomes of 31 women during their pregnancies and postpartum periods. The microbial composition changed as gestation progressed, with the pregnancy and postpartum periods exhibiting distinct bacterial community characteristics, including significant alterations in the genera of the Lachnospiraceae or Ruminococcaceae families, especially the Lachnospiraceae FCS020 group and Ruminococcaceae UCG-003. Metabolic dynamics, characterised by changes in nutrients important for fetal growth (e.g., docosatrienoic acid), anti-inflammatory metabolites (e.g., trans-3-indoleacrylic acid), and steroid hormones (e.g., progesterone), were observed in both serum and faecal samples during pregnancy. Moreover, a complex correlation was identified between the pregnancy-related microbiota and metabolites, with Ruminococcus1 and Ruminococcaceae UCG-013 making important contributions to changes in faecal and serum metabolites, respectively. Overall, a highly coordinated microbiota–metabolite regulatory network may underlie the pregnancy process. These findings provide a foundation for enhancing our understanding of the molecular processes occurring during the progression of pregnancy, thereby contributing to nutrition and health management during this period.

Kiwifruit bacterial canker caused by Pseudomonas syringae pv. actinidiae (Psa) is the most serious disease threatening kiwifruit production. Our previous study found genes encoding the U-box containing proteins were significantly regulated by Psa infection. Here, we report a U-box type E3 ubiquitin ligase PUB23 in kiwifruit which acts as a negative regulator of immune responses against Psa. PUB23 was found to physically interact with GT1, a trihelix transcription factor, in vitro and in vivo. The expression of GT1 was up-regulated in PUB23-silenced plants, indicating that interacting with PUB23 may directly or indirectly suppress GT1 expression. The silencing of PUB23 led to enhanced immune responses of PAMP-triggered immunity (PTI), including a higher expression level of defense marker genes PR1 and RIN4, and increased accumulation of hydrogen peroxide and superoxide anion. Our results reveal a negative role PUB23 plays in kiwifruit immune responses against Psa and may regulate gene expression by interacting with GT1.

Aligned with global Sustainable Development Goals (SDGs) and multidisciplinary approaches integrating AI with sustainability, this research introduces an innovative AI framework for analyzing Modern French Poetry. It applies feature extraction techniques (TF-IDF and Doc2Vec) and machine learning algorithms (especially SVM) to create a model that objectively classifies poems by their stylistic and thematic attributes, transcending traditional subjective analyses. This work demonstrates AI’s potential in literary analysis and cultural exchange, highlighting the model’s capacity to facilitate cross-cultural understanding and enhance poetry education. The efficiency of the AI model, compared to traditional methods, shows promise in optimizing resources and reducing the environmental impact of education. Future research will refine the model’s technical aspects, ensuring effectiveness, equity, and personalization in education. Expanding the model’s scope to various poetic styles and genres will enhance its accuracy and generalizability. Additionally, efforts will focus on an equitable AI tool implementation for quality education access. This research offers insights into AI’s role in advancing poetry education and contributing to sustainability goals. By overcoming the outlined limitations and integrating the model into educational platforms, it sets a path for impactful developments in computational poetry and educational technology.

The application of blockchain has become a trend in the development of supply chain finance. Aiming to bridge the gap in the existing literature, this paper investigates a supply chain finance system based on blockchain technology which contains a manufacturer, a retailer and a financial institution and incorporates blockchain costs into the model. Firstly, this paper establishes a supply chain finance model based on blockchain technology and it presents a comparison with the process employed under the traditional model. Secondly, this paper establishes the revenue mathematical model of supply chain finance based on blockchain technology. Thirdly, the optimal decisions of each participant under centralized and decentralized decision-making are proved and obtained, respectively, and the influencing factors of the optimal decisions are analyzed. Finally, the conclusions are verified via simulations. This study finds that, when blockchain is used, the benefits of each participant in the chain are increased. In addition, centralized decision-making, which is more optimal in the traditional model, is also enhanced under blockchain. This paper demonstrates the superiority of blockchain-enabled supply chain finance in terms of model and revenue. This provides some suggestions for companies in the supply chain with regard to solving the problem of financing difficulties.

The impact of extreme climate events on vegetation growth and ecosystem function has garnered widespread attention, particularly in plateau regions, which are facing increasingly severe environmental pressures. This study employs the Events Coincidence Analysis (ECA) method to examine the impacts of extreme climate events on the Net Primary Productivity (NPP) of vegetation in plateau regions. Specifically, we focus on the unique phenomenon of asymmetric daytime and nighttime warming and evaluate the compounding effect of extreme precipitation with extreme temperature events. The results indicate that grassland NPP has higher overall sensitivity and Coincidence Rates (CR) to extreme precipitation events compared to extreme temperature events. Specifically, extreme drought events significantly negatively impact grassland NPP, and the sensitivity of NPP increases with the severity of extreme drought events. In lower elevations (

Tensile, compressive-shear, and tensile-shear failure are three typical failure modes of rock- and lining-support structural materials in underground engineering. Comparative studies of the acoustic emission (AE) evolution characteristics of specimens of the same shape and size under different stress states are of great significance in determining universal disaster warning guidelines. Based on a self-developed multi-functional test system, direct tensile, compressive-shear, and tensile-shear tests were conducted on intact and jointed rock-like specimens, comparing AE amplitude and peak frequency parameters under different failure modes from the perspectives of crack scale and crack type evolution. All failure tests were monitored with microcrack propagation at an early stage until ultimate rupture occurred at the peak-load moment. As a result, the “quiet-period” could only be observed from the compressive-shear test. The AE signals distributed in three bands can be used as an indicator of failure identifications. Tensile and shear cracks can be identified by strong and weak amplitudes of low- and high-frequency signals. These results enhance the knowledge of the failure modes of rock mechanics for more applications in monitoring disasters in rock engineering.

The root residual stress during gear machining has a significant impact on the bending fatigue performance of the gear. The process parameters of gear hobbing (hob speed, axial feed speed, and radial cutting depth) directly affect the residual stress of the tooth root. To investigate the relationship between the process parameters of hobbing and the residual stress of the tooth root respectively, an analysis of an orthogonal and single factor was conducted in the hobbing experiment, taking into account the interactions among factors, which revealed the influence rule and primary–secondary relationship of the process parameters on the residual stress of the tooth root. The importance coefficients of the process parameters on the residual stress of the tooth root were calculated using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The results indicate that the residual tensile stress at the tooth root increases with an increase in the hob speed and axial feed speed within the selected range but decreases with an increase in the radial cutting depth. The influence of the process parameters on the residual stress of the tooth root can be ranked as follows: hob speed (importance coefficient 0.460), axial feed speed (importance coefficient 0.278), and radial cutting depth (importance coefficient 0.262). This research provides a basis for improving the residual stress of the tooth root and enhancing the anti-fatigue manufacturing of gears, thus holding significant research value.