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The rapid evolution of wireless communication systems necessitates advanced handover mechanisms for seamless connectivity and optimal network performance. Traditional algorithms, like 3GPP Event A3, often struggle with fluctuating signal strengths and dynamic user mobility, leading to frequent handovers and suboptimal resource utilization. This study proposes a novel approach combining Federated Learning (FL) and Long Short-Term Memory (LSTM) networks to predict Reference Signal Received Power (RSRP) and the strongest nearby Reference Signal Received Power (RSRP) signals. Our method leverages FL to ensure data privacy and LSTM to capture temporal dependencies in signal data, enhancing prediction accuracy. We develop a dynamic handover algorithm that adapts to real-time conditions, adjusting thresholds based on predicted signal strengths and historical performance. Extensive experiments with real-world data show our dynamic algorithm significantly outperforms the 3GPP Event A3 algorithm, achieving higher prediction accuracy, reducing unnecessary handovers, and improving overall network performance. In conclusion, this study introduces a data-driven, privacy-preserving approach that leverages advanced machine learning techniques, providing a more efficient and reliable handover mechanism for future wireless networks.

Aging, an independent risk factor for cardiometabolic diseases, refers to a progressive deterioration in physiological function, characterized by 12 established hallmarks. Vascular aging is driven by endothelial dysfunction, telomere dysfunction, oxidative stress, and vascular inflammation. This study investigated whether aged gut microbiome promotes vascular aging and metabolic impairment. Fecal microbiome transfer (FMT) was conducted from aged (>75 weeks old) to young C57BL/6 mice (8 weeks old) for 6 weeks. Wire myography was used to evaluate endothelial function in aortas and mesenteric arteries. ROS levels were measured by dihydroethidium (DHE) staining and lucigenin-enhanced chemiluminescence. Vascular and intestinal telomere function, in terms of relative telomere length, telomerase reverse transcriptase expression and telomerase activity, were measured. Systemic inflammation, endotoxemia and intestinal integrity of mice were assessed. Gut microbiome profiles were studied by 16S rRNA sequencing. Some middle-aged mice (40–42 weeks old) were subjected to chronic metformin treatment and exercise training for 4 weeks to evaluate their anti-aging benefits. Six-week FMT impaired glucose homeostasis and caused vascular dysfunction in aortas and mesenteric arteries in young mice. FMT triggered vascular inflammation and oxidative stress, along with declined telomerase activity and shorter telomere length in aortas. Additionally, FMT impaired intestinal integrity, and triggered AMPK inactivation and telomere dysfunction in intestines, potentially attributed to the altered gut microbial profiles. Metformin treatment and moderate exercise improved integrity, AMPK activation and telomere function in mouse intestines. Our data highlight aged microbiome as a mechanism that accelerates intestinal and vascular aging, suggesting the gut-vascular connection as a potential intervention target against cardiovascular aging and complications.

Pine wilt disease, caused by Bursaphelenchus xylophilus, is a highly destructive and contagious forest affliction. Often termed the “cancer” of pine trees, it severely impacts the growth of Masson pine (Pinus massoniana). Previous studies have demonstrated that ectopic expression of the PmACRE1 gene from P. massoniana in Arabidopsis thaliana notably enhances resistance to pine wilt nematode infection. To further elucidate the transcriptional regulation and protein interactions of the PmACRE1 in P. massoniana in response to pine wilt nematode infection, we cloned a 1984 bp promoter fragment of the PmACRE1 gene, a transient expression vector was constructed by fusing this promoter with the reporter GFP gene, which successfully activated the GFP expression. DNA pull-down assays identified PmMYB8 as a trans-acting factor regulating PmACRE1 gene expression. Subsequently, we found that the PmACRE1 protein interacts with several proteins, including the ATP synthase CF1 α subunit, ATP synthase CF1 β subunit, extracellular calcium-sensing receptor (PmCAS), caffeoyl-CoA 3-O-methyltransferase (PmCCoAOMT), glutathione peroxidase, NAD+-dependent glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase 1, cinnamyl alcohol dehydrogenase, auxin response factor 16, and dehydrin 1 protein. Bimolecular fluorescence complementation (BiFC) assays confirmed the interactions between PmACRE1 and PmCCoAOMT, as well as PmCAS proteins in vitro. These findings provide preliminary insights into the regulatory role of PmACRE1 in P. massoniana’s defense against pine wilt nematode infection.

Background: In 2020, the WHO proposed the global strategic goal of accelerating the elimination of cervical cancer (CC). One of the key strategies is that, by 2030, 90% of girls will complete HPV vaccination by age 15. In 2017, HPV vaccines were first marketed in China. Objectives: This study aimed to explore the changes in parents’ knowledge about CC, HPV, HPV vaccines, and acceptance of HPV vaccination 5 years after the introduction of HPV vaccines into China. Associated factors and reasons for refusal by parents were also explored. Methods: A school-based follow-up study was conducted among parents in China from May 2018 to May 2023. Comparison using Chi-square tests was performed to measure the changes. Univariate and multivariate analyses were used to explore factors associated with HPV vaccination among teenage girls. Results: The overall cognitive level in terms of CC- and HPV-related knowledge among parents increased, and they expressed more willingness to vaccinate their daughter (35.4% in 2018 vs. 56.6% in 2023). The HPV vaccination rate among teenage girls remained at a low level (4.4% in 2018, 9.8% in 2023). The main obstacles reported by parents were limited knowledge (42.0%), scarcity of the HPV vaccine (29.2%), waiting until their daughter was older (27.5%), safety concerns (14.7%), high cost (9.0%), and effectiveness concerns (4.6%). Parents who are elderly, local residents, have received HPV vaccines themselves, have had experience of vaccinating their children with influenza vaccines, and have a higher knowledge level of CC, HPV and HPV vaccines are more likely to vaccinate their children with HPV vaccines. Conclusions: Although we observed an increment in parents’ knowledge level in terms of CC, HPV, HPV vaccines, and HPV vaccine uptake over the past 5 years, the HPV vaccine coverage among girls still falls short of the WHO’s 2030 target. Comprehensive intervention strategies, including tailored health education among adolescents and their parents, doctor recommendations, and providing financial subsidies or free HPV vaccines are needed in China.

Today’s prediction of ocean dynamics relies on numerical models. However, numerical models are often unable to accurately model and predict real ocean dynamics, leading to a lack of fulfillment of a range of services that require reliable predictions at various temporal and spatial scales. Indeed, a numerical model cannot fully resolve all the physical processes in the ocean due to various reasons, including biases in the initial field and calculation errors in the numerical solution of the model. Thus, bias-correcting methods have become crucial to improve the dynamical accuracy of numerical model predictions. In this study, we present a machine learning-based three-dimensional velocity bias correction method derived from historical observations that applies to both hindcast and forecast. Our approach is based on the modification of an existing deep learning model, called U-Net, designed specifically for image segmentation analysis in the biomedical field. U-Net was modified to create a Transform Model that retains the temporal and spatial evolution of the differences between the model and observations to produce a correction in the form of regression weights that evolves spatially and temporally with the model both forward and backward in time, beyond the observation period. Using daily ocean current observations from a 2.5-year current meter array deployment, we show that significant bias corrections can be conducted up to 50 days pre- or post-observations. Using a 3-year-long virtual array, valid bias corrections can be conducted for up to one year.

Mycorrhizal fungi, a category of fungi that form symbiotic relationships with plant roots, can participate in the induction of plant disease resistance by secreting phosphatase enzymes. While extensive research exists on the mechanisms by which mycorrhizal fungi induce resistance, the specific contributions of phosphatases to these processes require further elucidation. This article reviews the spectrum of mycorrhizal fungi-induced resistance mechanisms and synthesizes a current understanding of how phosphatases mediate these effects, such as the induction of defense structures in plants, the negative regulation of plant immune responses, and the limitation of pathogen invasion and spread. It explores the role of phosphatases in the resistance induced by mycorrhizal fungi and provides prospective future research directions in this field.

The measurement precision of a line-structured light measurement system is directly affected by the accuracy of extracting the center points of the laser stripes. When the measured object’s surface has significant undulations and severe reflections, existing algorithms are prone to issues such as significant susceptibility to noise and the extraction of false center points. To address these issues, an improved unilateral tracing-based structured light centerline extraction algorithm is proposed. The algorithm first performs unilateral and bidirectional tracing on the upper boundary of the preprocessed laser stripes, then uses the grayscale centroid method to extract the initial coordinates of the center points, and finally corrects them by calculating the stripe’s normal direction using the Hessian matrix. Experimental results show that the proposed algorithm can still extract the stripe center points well under strong interference, with the RMSE reduced by 37% compared to the Steger method and the running speed increased by almost 4 times compared to the grayscale centroid method. The algorithm’s strong robustness, high accuracy, and efficiency provide a viable solution for real-time measurement of line-structured light and high-precision three-dimensional reconstruction.

Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs‘ low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs’ response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid’s regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments.

The hydrological monitoring of flow data is important for flood prevention and modern river management. However, traditional contact methods are increasingly struggling to meet the requirements of simplicity, accuracy, and continuity. The video-based river discharge measurement is a technique to monitor flow velocity without contacting the water body by using the image-recognition algorithms, which has been verified to have the advantages of full coverage and full automation compared with the traditional contact technique. In order to provide a timely summary of the available results and to inform further research and applications, this paper reviews and synthesizes the literature on the general implementation routes of the video-based river discharge measurement technique and the principles and advances of today’s popular image-recognition algorithms for velocity detection. Then, it discusses the challenges of image-recognition algorithms in terms of image acquisition conditions, parameter uncertainties, and complex meteorological and water environments. It is concluded that the performance of this technique can be improved by enhancing the robustness and accuracy of video-based discharge measurement algorithms, minimizing weather effects, and improving computational efficiency. Finally, future development directions for further perfecting this technique are outlined.

The utilization of baffle structures as a highly effective strategy for mitigating debris flow has attracted significant scholarly attention in recent years. Although the predominant focus of existing research has been on augmenting the energy dissipation capabilities of baffle structures, their deformation behavior under impact load has not been extensively investigated. Addressing this research gap, the current study systematically designs a series of physical model experiments, incorporating variables such as baffle height, shape, and various combinations of baffle types to comprehensively analyze the deformation characteristics of baffles subjected to debris flow impact. The experimental results reveal that the deformation of baffle group structures demonstrates a marked non-uniform spatial distribution and exhibits a latency effect. Additionally, distinct baffle configurations show considerable variations in peak strain, suggesting that combining different baffle shapes can not only optimize energy dissipation but also enhance resistance to deformation. Moreover, the relationship between baffle height and the development of deformation in relation to energy dissipation capacity is inconsistent, indicating that deformation must be a key consideration in the design of baffle structures. Consequently, this paper advocates for the formulation of a deformation-based design strategy for baffle structures, with the findings presented herein providing a foundational reference for future studies.

The cutting of thick glass is extensively employed in aerospace, optical, and other fields. Although ultrafast laser Bessel beams are heavily used for glass cutting, the cutting thickness and cutting quality need to be further improved. In this research, the high-quality cutting of thick glass was realized for the first time using ultrafast laser perforation assisted by CO2 laser separation. Initially, an infrared picosecond laser Bessel beam was employed to ablate the soda-lime glass and generate a perforated structure. Subsequently, a CO2 laser was employed to induce crack propagation along the path of the perforated structure, resulting in the separation of the glass. This study investigates the influence of hole spacing, pulse energy, and the defocusing distance of the picosecond laser Bessel beam on the average surface roughness of the glass sample cutting surface. The optimal combination of cutting parameters for 6 mm thick glass results in a minimum surface roughness of 343 nm in the cross-section.

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users’ travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications.

Accurate quantification of forest biomass (FB) is the key to assessing the carbon budget of terrestrial ecosystems. Using remote sensing to apply inversion techniques to the estimation of FBs has recently become a research trend. However, the limitations of vertical scale analysis methods and the nonlinear distribution of forest biomass stratification have led to significant uncertainties in FB estimation. In this study, the biomass characteristics of forest vertical stratification were considered, and based on the integration of random forest and least squares (RF-LS) models, the FB prediction potential improved. The results indicated that compared with traditional biomass estimation methods, the overall R2 of FB retrieval increased by 12.01%, and the root mean square error (RMSE) decreased by 7.50 Mg·hm−2. The RF-LS model we established exhibited better performance in FB inversion and simulation assessments. The indicators of forest canopy height, soil organic matter content, and red-edge chlorophyll vegetation index had greater impacts on FB estimation. These indexes could be the focus of consideration in FB estimation using the integrated RF-LS model. Overall, this study provided an optimization method to map and evaluate FB by fine stratification of above-ground forest and reveals important indicators for FB inversion and the applicability of the RF-LS model. The results could be used as a reference for the accurate inversion of subtropical forest biomass parameters and estimation of carbon storage.

Bacterial diversity and its distribution characteristics in sediments are critical to understanding and revealing biogeochemical cycles in sediments. However, little is known about the relationship between biogeochemistry processes and vertical spatial distribution of bacterial communities in sandy sediments. In this study, we used fluorescence quantitative PCR, high-throughput sequencing technology and statistical analysis to explore the vertical distribution pattern of bacterial community diversity and its influencing factors in sandy sediments of the Yangtze River Basin. The aim is to enrich the understanding of the ecological characteristics and functions of bacteria in river ecosystems. The results showed that both sediment bacterial abundance and diversity showed a gradual decrease from surface to bottom in the vertical distribution. The main environmental factors that influenced the bacterial distribution pattern were pore water dissolved oxygen (DO), total nitrogen (TN) concentration and sediment nitrogen (N) content. The dominant bacterial species, Massilia and Flavobacterium, are suitable for growth and reproduction in high oxygen and nutrient-richer environments, while Limnobacter prefers low oxygen or anaerobic conditions. The vertical distribution pattern of bacteria and its influencing factors in river sandy sediment found in this study differ from the results in mud sediment, which may be related to the larger granular gap between sandy sediment and the lower content of organic matter. The findings of this study further our understanding of the distribution patterns and ecological preferences of microbial communities in river sediments, providing insights into how these communities may adapt to varying environmental conditions.

Understanding the regulation of autotetraploid sterility is essential for harnessing the strong advantages in genomic buffer capacity, biodiversity, and heterosis of autotetraploid rice. miRNAs play crucial roles in fertility regulation, yet information about their reproductive roles and target genes in tetraploid rice remains limited. Here, we used three tetraploid lines, H1 (fertile), HF (fertile), and LF (sterile), to investigate cytological features and identify factors associated with autotetraploid sterility. LF showed abnormal meiosis, resulting in low pollen fertility and viability, ultimately leading to scarce fertilization and a low-seed setting compared to H1 and HF. RNA-seq revealed 30 miRNA-candidate target pairs related to autotetraploid pollen sterility. These pairs showed opposite expression patterns, with differential expression between fertile lines (H1 and HF) and the sterile line (LF). qRT-PCR confirmed that miR9564, miR528, and miR27874 were highly expressed in the anthers of H1 and HF but not in LF, while opposite results were obtained in their targets (ARPS, M2T, and OsRPC53). Haplotype and expression pattern analyses revealed that ARPS was specifically expressed in lines with the same haplotype of MIR9564 (the precursor of miR9564) as LF. Furthermore, the Dual-GFP assay verified that miR9564 inhibited the fluorescence signal of ARPS-GFP. The over-expression of ARPS significantly decreased the seed setting rate (59.10%) and pollen fertility (50.44%) of neo-tetraploid rice, suggesting that ARPS plays important roles in autotetraploid pollen sterility. This study provides insights into the cytological characteristic and miRNA expression profiles of tetraploid lines with different fertility, shedding light on the role of miRNAs in polyploid rice.

The study of extreme events (EEs) in photonics has expanded significantly due to straightforward implementation conditions. EEs have not been discussed systematically, to the best of our knowledge, in the chaotic dynamics of a Fabry–Perot laser with optical feedback, so we address this in the current contribution. Herein, we not only find EEs in all modes but also divide the EEs in total output into two categories for further discussion. The two types of EEs have similar statistical features to conventional rogue waves. The occurrence probability of EEs undergoes a saturation effect as the feedback strength increases. Additionally, we analyze the influence of feedback strength, feedback delay, and pump current on the probability of EEs defined by two criteria of EEs and find similar trends. We hope that this work contributes to a deep understanding and serves as inspiration for further research into various multimode semiconductor laser systems.

In this study, a constructed wetland–Fe3O4/granular activated carbon anode microbial electrolysis cell (CW-FMEC) was constructed to enhance denitrification in low COD/N ratio wastewater. The introduction of Fe3O4 boosted the expression of functional genes involved in the denitrification pathway, and the abundance of narG, nirS, and nosZ increased by 99.29%, 70.54%, and 132.18%, respectively, compared to CW. In addition, the content of c-type cytochromes (c-Cyts) and EPS were also enhanced in the CW-FMEC. The microbial communities study displayed that Thauera, Dechloromonas, and Arenimonas became the main genera for denitrification. The denitrification performance at different COD/N ratios was investigated in depth. Under optimal working circumstances, the CW-FMEC had an excellent nitrate removal rate (88.9% ± 1.12%) while accumulating nearly no NO2−-N or NH4+-N in the effluent. This study provides a new direction for the development of CW-MEC and accelerates its implementation.

Childhood obesity might threaten children’s current and adulthood health outcomes. Previous studies have illustrated the positive effects of physical activity on weight control; however, there is a lack of evidence on the optimal dose of physical activity. Therefore, we aimed to explore the relationship between physical activity and overweight and obesity, as well as the optimal threshold for physical activity. The median (interquartile range) and number (proportion) were used to describe the statistics. The Mann–Whitney U test and chi-square test were used for an univariable analysis. The generalized additive model with a smooth function was used to depict the preliminary relationship between physical activity and overweight and obesity. The cut-off level of physical activity was identified using AddFor algorithms, and a logistic regression model was applied to explore the multivariable relationship between physical activity and overweight and obesity after adjusting for control variables. According to the statistical analyses, 24.3% of 842 children and adolescents had overweight and obesity. The average number of days that the participants engaged in more than one hour of physical activity was three days a week. The optimal cut-off level of physical activity for the prevention of childhood overweight and obesity was 4 times a week. The participants who exercised more than four times a week (AOR(95% CI) = 0.56(0.38, 0.83), p = 0.004) were less likely to be overweight and obese. In the context of the general lack of physical activity among children and adolescents, we recommend that children and adolescents engage in 60 min of physical activity more than four times a week for a healthy weight.

Double-wall steel sheet piles (DSSPs) are widely used in large-span cofferdams for docks due to their good performance against wave action during storm surges. This paper describes a study of the dynamic behavior of a DSSP cofferdam under wave action through flume tests and a numerical simulation that combined computational fluid dynamics (CFD) and the finite element method. The influences of the water level and wave height on the DSSP cofferdam were investigated experimentally and numerically. Tall waves in shallow water broke upon and impacted the seaside pile with large dynamic wave pressure, dramatically increasing the stress and displacement of the seaside pile. The overlap of the traveling and reflected waves increased the excess pore water pressure near the seaside pile due to taller overlapped waves and higher wave frequency. The DSSP cofferdam failed under the combined actions of the dynamic wave pressure and erosion of the landside seabed. The leakage and overflow of the breaking waves resulted in significant erosion of the landside seabed and greatly weakened the support of the seabed. The dynamic wave pressure then pushed the DSSP cofferdam until it failed. The simulation with the combined methods of CFD and FEM resulted in trends that were similar to those of the test measurements. Compared to the quasi-static method and pseudo-dynamic method, the results of the simulation via the present method were much closer to the test results because the simulation included the effects of breaking waves. The reinforced measure worked well to prevent the DSSP cofferdam in a sandy seabed foundation from continuous failures of deformation–leakage–erosion–tilting. However, it failed in a clay interlayer seabed foundation due to the large settlement.

Automatic speech recognition (ASR) has been widely used to realize daily human–machine interactions. Face masks have become everyday wear in our post-pandemic life, and speech through masks may have impaired the ASR. This study explored the effects of different kinds of face masks (e.g., surgical mask, KN95 mask, and cloth mask) on the Mandarin word accuracy of two ASR systems with or without noises. A mouth simulator was used to play speech audio with or without wearing a mask. Acoustic signals were recorded at distances of 0.2 m and 0.6 m. Recordings were mixed with two noises at a signal-to-noise ratio of +3 dB: restaurant noise and speech-shaped noise. Results showed that masks did not affect ASR accuracy without noise. Under noises, masks did not significantly influence ASR accuracy at 0.2 m but had significant effects at 0.6 m. The activated-carbon mask had the most significant impact on ASR accuracy at 0.6 m, reducing the accuracy by 18.5 percentage points compared to that without a mask, whereas the cloth mask had the least effect on ASR accuracy at 0.6 m, reducing the accuracy by 0.9 percentage points. The acoustic attenuation of masks on the high-frequency band at around 3.15 kHz of the speech signal attributed to the effects of masks on ASR accuracy. When training ASR models, it may be important to consider mask robustness.

In this study, oxidized single-walled carbon nanohorns (oxSWCNHs) were prepared using nitric acid oxidation and subsequently combined with 3′6-carboxyfluorescein through charge transfer to prepare fluorescent probes. These oxSWCNHs were used to quench fluorogen signals at short distances and dissociate ssDNA using cryonase enzymes. We established a method for rapidly detecting tetracycline (TC) in complex samples based on the amplification of cryonase enzyme signals. After optimizing the experimental conditions, our method showed a detection limit of 5.05 ng/mL, with good specificity. This method was used to determine the TC content in complex samples, yielding a recovery rate of 90.0–103.3%. This result validated the efficacy of our method in detecting TC content within complex samples.

Vegetation plays a crucial role in maintaining the balance between nature, water and soil resources. However, understanding its impact mechanisms in arid and semi-arid areas remains limited. This study aims to analyze the spatial–temporal characteristics of the vegetation leaf area index (LAI) and climate elements in typical regions of northwest China and the correlations between LAI and climate elements; it also aims to explore the influence of regional vegetation growth on climate change. The results reveal significant correlations between LAI and various climate elements. Specifically, within the same region, surface temperature, precipitation, vegetation transpiration, and total evaporation show positive correlations with the LAI, whereas surface albedo shows a negative correlation. Vegetation may affect climate through both heat and water exchange between the land and atmosphere. Increased vegetation leads to the enhanced absorption of solar radiation by the land surface, elevating surface temperature. Increased levels of vegetation also increase vegetation transpiration and total evaporation, increasing the water vapor content in the atmosphere and thus leading to increased surface precipitation. Therefore, vegetation distribution plays a role in climate change, and ecological restoration projects in the northwest region hold significant potential for addressing ecological challenges in its arid and semi-arid areas.

A digital control system (DCS), based on the Mitsubishi MELTAC + HOLLIAS platform, has been developed in ACPR1000 nuclear power plants. To fully utilize the superiority of digitalized technology, an analysis of fallback value setting in a DCS was conducted. Fallback values, as the substitution of invalid signals when they are detected in a DCS, are in a uniquely dominant position to determine the system behavior and consequences of a nuclear power plant’s operation, as they participate in the control logic in place of invalid signals. After briefly introducing both the architecture of the ACPR1000 DCS and the invalidity management mechanism of signals, the failure mode of signals from sensors to DCS cabinets, the analysis range for fallback values, analysis principles a nd method, the implementation of modes for various signals, and engineering applications are summarized in this paper. This study is significant for enhancing the reliability of the instrument and control system itself and guaranteeing the safety level of nuclear power plants.

The K-feldspar mineral is an insoluble potassium resource with a high potassium content and the most extensive and abundant reserves. To address the insufficient supply of soluble potassium fertilizers in China, the application of appropriate processing methods to extract potassium from K-feldspar and transform it into a soluble potassium fertilizer is of great significance. To date, various techniques have been developed to extract potassium from K-feldspar and produce a soluble potassium fertilizer. This review summarizes the main methods, i.e., the hydrothermal, high-temperature pyrolysis, microbial decomposition, and low-temperature methods, for potassium extraction from K-feldspar. The mechanisms, efficiencies, impact parameters, and research progress of each potassium extraction method are comprehensively discussed. This study also compares the merits and drawbacks of the individual methods in terms of potassium extraction efficiency and practical operating conditions. The species of additives, reaction temperature, reaction time, particle size of K-feldspar, and dosage of additives significantly affected the potassium extraction efficiency. Moreover, the combination of different methods was very effective in improving the potassium extraction efficiency. This review elaborates the research prospects and potential strategies for the efficient utilization of the K-feldspar mineral as a fertilizer resource.

Geometric complexities of a fault system have a significant impact on the rupture behavior of the fault. The 2021 Mw7.4 Maduo earthquake occurred on a multi-segmented complex sinistral fault in the interior of the Bayan-Har block in the northern Tibetan Plateau. Here, we integrate centimeter-resolution surface rupture zones and Sentinel-2 optical displacement fields to accurately determine the geometric parameters of the causative fault in detail. An adaptive quadtree down-sampling method for interferograms was employed to enhance the reliability of the coseismic slip model inversion for interferograms. The optimal coseismic slip model indicated a complex non-planar structure with varying strike and dip angles. The largest slip of ~6 m, at a depth of ~7 km, occurred near a 6 km-wide stepover (a geometric complexity area) to the east of the epicenter, which occurred at the transition zone from sub-shear to super-shear rupture suggested by seismological studies. Optical and SAR displacement fields consistently indicated the local minimization of effective normal stress on releasing stepovers, which facilitated rupture through them. Moreover, connecting intermediate structures contributes to maintaining the rupture propagation through wide stepovers and may even facilitate the transition from subshear to supershear. Our study provides more evidence of the reactivation of a branched fault at the western end during the mainshock, which was previously under-appreciated. Furthermore, we found that a strong asymmetry in slip depth, stress drop, and rupture velocity east and west of the epicenter was coupled with variations in geometric and structural characteristics of fault segments along the strike. Our findings highlight the sensitivity of rupture behaviors to small-scale details of fault geometry.

Examining fuel droplet evaporation is crucial for enhancing fuel engine efficiency, conserving energy, and reducing emissions. This study utilizes experimental methods involving ultrasonic standing wave levitation and high-speed cameras to investigate the impact of temperatures and droplet properties, including initial diameter and composition, on the evaporation process. The evaporation behaviors of fuel droplets, like hexadecane and diesel, are documented across a temperature spectrum spanning 150 °C to 550 °C, with an initial droplet equivalent diameter ranging from 0.10 to 0.30 mm. The evaporation rate positively correlates with temperature and may vary by 15% to 71% between hexadecane and diesel droplets.

Enterohemorrhagic Escherichia coli (EHEC) is an important foodborne pathogen that infects humans by colonizing the large intestine. The genome of EHEC O157:H7 contains 177 unique O islands (OIs). Certain OIs significantly contribute to the heightened virulence and pathogenicity exhibited by EHEC O157:H7. However, the function of most OI genes remains unknown. We demonstrated here that EHEC O157:H7 adherence to and colonization of the mouse large intestine are both dependent on OI-97. Z3495, which is annotated as a LysR-type transcriptional regulator and encoded in OI-97, contributes to this phenotype. Z3495 activated the locus of enterocyte effacement (LEE) gene expression, promoting bacterial adherence. Deletion of z3495 significantly decreased the transcription of ler and other LEE genes, the ability to adhere to the host cells, and colonization in the mouse large intestine. Furthermore, the ChIP-seq results confirmed that Z3495 can directly bind to the promoter region of rcsF, which is a well-known activator of Ler, and increase LEE gene expression. Finally, phylogenetic analysis revealed that Z3495 is a widespread transcriptional regulator in enterohemorrhagic and enteropathogenic Escherichia coli. As a result of this study, we have gained a deeper understanding of how bacteria control their virulence and provide another example of a laterally acquired regulator that regulates LEE gene expression in bacteria.

Marine polychaetes represent an extremely rich and underexplored source of novel families of antimicrobial peptides (AMPs). The rapid development of next generation sequencing technologies and modern bioinformatics approaches allows us to apply them for characterization of AMP-derived genes and the identification of encoded immune-related peptides with the aid of genome and transcriptome mining. Here, we describe a universal bioinformatic approach based on the conserved BRICHOS domain as a search query for the identification of novel structurally unique AMP families in annelids. In this paper, we report the discovery of 13 novel BRICHOS-related peptides, ranging from 18 to 91 amino acid residues in length, in the cosmopolitan marine worm Heteromastus filiformis with the assistance of transcriptome mining. Two characteristic peptides with a low homology in relation to known AMPs—the α-helical amphiphilic linear peptide, consisting of 28 amino acid residues and designated as HfBRI-28, and the 25-mer β-hairpin peptide, specified as HfBRI-25 and having a unique structure stabilized by two disulfide bonds—were obtained and analyzed as potential antimicrobials. Interestingly, both peptides showed the ability to kill bacteria via membrane damage, but mechanisms of their action and spectra of their activity differed significantly. Being non-cytotoxic towards mammalian cells and stable to proteolysis in the blood serum, HfBRI-25 was selected for further in vivo studies in a lethal murine model of the Escherichia coli infection, where the peptide contributed to the 100% survival rate in animals. A high activity against uropathogenic strains of E. coli (UPEC) as well as a strong ability to kill bacteria within biofilms allow us to consider the novel peptide HfBRI-25 as a promising candidate for the clinical therapy of urinary tract infections (UTI) associated with UPEC.

Microbially induced calcite precipitation (MICP) is a promising, more eco-friendly alternative method for landslide prevention and foundation reinforcement. In this study, we investigated the optimization of injection methods within the MICP process in porous media to enhance calcite mass and consolidation effect. The results demonstrated that staged injections with considerable advantages significantly improved precipitated calcite mass by 23.55% compared with continuous injection methods. However, extended retention times in staged injections reduced reinforcement effects. Moreover, setting the additional time in all injection methods can improve the consolidation area and effect without added injections. Apart from the injection methods, the changes in porosity and substance concentration also directly affected calcite masses and the reinforcement effect. Both the total calcite mass and the reinforcement effect should be taken into account when selecting appropriate injection methods. In terms of influencing factors on the total calcite mass, substance concentration ≫ average porosity ≫ additional time > retention time in staged injection. For the consolidation effect, substance concentration ≫ retention time in staged injection > average porosity ≫ additional time. The 5 h retention time in staged injections was recommended as the optimum injection method in the geotechnical conditions for average porosity from 0.25 to 0.45, with the changes in different reactant concentrations.

Cassava (Manihot esculenta Crantz) is a major tuber crop worldwide, but its mechanized harvesting is inefficient. The digging–pulling cassava harvester is the primary development direction of the cassava harvester. However, the harvester clamping–pulling mechanism cannot automatically adjust its position relative to the stalks in forward movement, which results in clamping stalks with a large off-center distance difficulty, causing large harvest losses. Thus, solving the device’s clamping location problem is the key to loss reduction in the harvester. To this end, this paper proposes a real-time detection method for field stalks based on YOLOv4. First, K-means clustering is applied to improve the consistency of cassava stalk detection boxes. Next, the improved YOLOv4 network’s backbone is replaced with MobileNetV2 + CA, resulting in the KMC-YOLO network. Then, the proposed model’s validity is demonstrated using ablation studies and comparison tests. Finally, the improved network is embedded into the NVIDIA Jetson AGX Xavier, and the model is accelerated using TensorRT, before conducting field trials. The results indicate that the KMC-YOLO achieves average precision (AP) values of 98.2%, with detection speeds of 33.6 fps. The model size is reduced by 53.08% compared with the original YOLOv4 model. The detection speed after TensorRT acceleration is 39.3 fps, which is 83.64% faster than before acceleration. Field experiments show that the embedded model detects more than 95% of the time at all three harvest illumination levels. This research contributes significantly to the development of cassava harvesters with intelligent harvesting operations.

Photocatalytic NO removal is usually accompanied by the generation of NO2, an intermediate with a higher toxicity than NO. Therefore, it is critically important to develop new photocatalysts capable of NO selective conversion. Herein, we report on the synergistic roles of cyano and hydroxyl functional groups in photocatalytic NO removal. According to the results, the NO2 production efficiencies on cyano/hydroxyl-group-modified g-C3N4 (DCN-O-R) was limited to 4.8%, which was lower than that of cyano-group-modified g-C3N4 (DCN, 38.6%) and pure g-C3N4 (CN, 50.0%). Meanwhile, the photocatalytic NO conversion efficiency over DCN-O-R was higher than that of DCN and g-C3N4. It was found that the insertion of cyano groups favorably changes the energy band of g-C3N4 towards the generation of •O2−. NO can only be oxidized to NO2 by the photogenerated holes. When NO2 is adsorbed on the surface of hydroxyl groups, it can be further oxidized to the product NO3− by •O2−. The synergistic effect of bifunctional groups regulates the conversion pathway from NO→NO2 to NO→NO2→NO3−. This work provides a strategy to abate toxic intermediates during the NO removal process, underlining the importance of surface/interface molecular engineering in regulating catalytic reaction pathways.

The semiconductor industry is one of the most technology-evolving and capital-intensive market sectors. Effective inspection and metrology are necessary to improve product yield, increase product quality and reduce costs. In recent years, many types of semiconductor manufacturing equipments have been equipped with sensors to facilitate real-time monitoring of the production processes. These production-state and equipment-state sensor data provide an opportunity to practice machine-learning technologies in various domains, such as anomaly/fault detection, maintenance scheduling, quality prediction, etc. In this work, we focus on the soft-sensing regression problem in metrology systems, which uses sensor data collected during wafer processing steps to predict impending inspection measurements that used to be measured in wafer inspection and metrology systems. We proposed a regressor based on Long Short-term Memory network and devised two distinct loss functions for the purpose of the training model. Although the assessment of our prediction errors by engineers is subjective, a novel piece-wise evaluation metric was introduced to evaluate model accuracy in a mathematical way. Our experimental results showcased that the proposed model is capable of achieving both accurate and early prediction across various types of inspections in complicated manufacturing processes.

As a common engineering practice, the buildings are usually evaluated under the Typical Meteorological Year (TMY), which represents the common weather situation. The warm and cool conditions, however, can affect the building performance considerably, yet building performances under such conditions cannot fully be given by the conventional TMY. This paper gives approaches to constructing the weather data that represents several warm and cool conditions and compares their differences by studying the cumulative cooling demands of a typical building in a hot and humid climate. Apart from the Extreme Weather Year (EWY), the Near-Extreme Weather Year (NEWY) and Common warm/cool Years (CY) data are proposed according to the occurrence distributions of the weather over the long term. It was found that the cooling demands of NEWY and EWY differ by 4.8% from the cooling needs of TMY. The difference between the cooling demands of NEWY and CY for most calendar months can be 20% and 15%, respectively. For the hot months, the cooling demands under NEWY and CY take 7.4–11.6% and 2.3–5.6% differences from those under TMY. The uncertainties of building performance due to the ever-changing weather conditions can be essential to the robustness of building performance evaluations.

The magnetic domain structures and martensite microstructures of porous Ni2MnGa Heusler alloys with various circle-shaped and ellipse-shaped pores were systematically studied by the phase field method. The magnetization curves and magnetic field-induced strains (MFIS) at the external field were determined. A mesoscopic mechanism was proposed for simulation to reveal the influence of the pores on the microstructures and the MFIS of porous magnetic shape memory alloy. The stress concentration effect and the recovery strain of the porous alloy are studied. The results indicate the MFIS value increases when ellipse-shaped pores elongate along the twin boundary. The effects of porosity and pore size on MFIS for porous Ni-Mn-Ga alloys with randomly distributed pores were also explored. The present study is of guiding significance for understanding the role played by pores on the MFIS and may provide a possible way to adjust the functional properties of ferromagnetic shape memory alloys.

As pioneering non-contact water level measurement technologies, both computer vision and radar have effectively addressed challenges posed by traditional water level sensors in terms of maintenance cost, real-time responsiveness, and operational complexity. Moreover, they ensure high-precision measurements in appropriate conditions. These techniques can be seamlessly integrated into unmanned aerial vehicle (UAV) systems, significantly enhancing the spatiotemporal granularity of water level data. However, computer-vision-based water level measurement methods face the core problems of accurately identifying water level lines and elevation calculations, which can lead to measurement errors due to lighting variations and camera position offsets. Although deep learning has received much attention in improving the generation, the effectiveness of the models is limited by the diversity of the datasets. For the radar water level sensor, the hardware structure and signal processing algorithms have to be further improved. In the future, by constructing more comprehensive datasets, developing fast calibration algorithms, and implementing multi-sensor data fusion, it is expected that the robustness, accuracy, and computational efficiency of water level monitoring will be significantly improved, laying a solid foundation for further innovations and developments of hydrological monitoring.

Hyperglycemia is a crucial risk factor for cardiovascular diseases. Chronic inflammation is a central characteristic of obesity, leading to many of its complications. Recent studies have shown that high glucose activates Yes-associated protein 1 (YAP) by suppressing AMPK activity in breast cancer cells. Metformin is a commonly prescribed anti-diabetic drug best known for its AMPK-activating effect. However, the role of YAP in the vasoprotective effect of metformin in diabetic endothelial cell dysfunction is still unknown. The present study aimed to investigate whether YAP activation plays a role in obesity-associated endothelial dysfunction and inflammation and examine whether the vasoprotective effect of metformin is related to YAP inhibition. Reanalysis of the clinical sequencing data revealed YAP signaling, and the YAP target genes CTGF and CYR61 were upregulated in aortic endothelial cells and retinal fibrovascular membranes from diabetic patients. YAP overexpression impaired endothelium-dependent relaxations (EDRs) in isolated mouse aortas and increased the expression of YAP target genes and inflammatory markers in human umbilical vein endothelial cells (HUVECs). High glucose-activated YAP in HUVECs and aortas was accompanied by increased production of oxygen-reactive species. AMPK inhibition was found to induce YAP activation, resulting in increased JNK activity. Metformin activated AMPK and promoted YAP phosphorylation, ultimately improving EDRs and suppressing the JNK activity. Targeting the AMPK–YAP–JNK axis could become a therapeutic strategy for alleviating vascular dysfunction in obesity and diabetes.

Ovarian cancer is the deadliest gynecological malignancy of the reproductive organs in the United States. Cyclin-dependent kinase 1 (CDK1) is an important cell cycle regulatory protein that specifically controls the G2/M phase transition of the cell cycle. RO-3306 is a selective, ATP-competitive, and cell-permeable CDK1 inhibitor that shows potent anti-tumor activity in multiple pre-clinical models. In this study, we investigated the effect of CDK1 expression on the prognosis of patients with ovarian cancer and the anti-tumorigenic effect of RO-3306 in both ovarian cancer cell lines and a genetically engineered mouse model of high-grade serous ovarian cancer (KpB model). In 147 patients with epithelial ovarian cancer, the overexpression of CDK1 was significantly associated with poor prognosis compared with a low expression group. RO-3306 significantly inhibited cellular proliferation, induced apoptosis, caused cellular stress, and reduced cell migration. The treatment of KpB mice with RO-3306 for four weeks showed a significant decrease in tumor weight under obese and lean conditions without obvious side effects. Overall, our results demonstrate that the inhibition of CDK1 activity by RO-3306 effectively reduces cell proliferation and tumor growth, providing biological evidence for future clinical trials of CDK1 inhibitors in ovarian cancer.

Endogenous single-stranded DNA (essDNA) can form in a mammalian genome as the result of a variety of molecular processes and can both play important roles inside the cell as well as have detrimental consequences to genome integrity, much of which remains to be fully understood. Here, we established the SSiNGLe-P1 approach based on limited digestion by P1 endonuclease for high-throughput genome-wide identification of essDNA regions. We applied this method to profile essDNA in both human mitochondrial and nuclear genomes. In the mitochondrial genome, the profiles of essDNA provide new evidence to support the strand-displacement model of mitochondrial DNA replication. In the nuclear genome, essDNA regions were found to be enriched in certain types of functional genomic elements, particularly, the origins of DNA replication, R-loops, and to a lesser degree, in promoters. Furthermore, interestingly, many of the essDNA regions identified by SSiNGLe-P1 have not been annotated and thus could represent yet unknown functional elements.

In this study, one-year-old seedlings of Paeonia ostii ‘Fengdan’ were used as materials. The method of spraying exogenous salicylic acid (SA), calcium chloride (CaCl2) and abscisic acid (ABA) was used. The effects of different concentrations of SA, CaCl2 and ABA on the heat tolerance of peony seedlings under high temperature stress were studied. The optimum concentration and mechanism of SA-, CaCl2- and ABA-induced heat tolerance of peony seedlings under high temperature stress were discussed. The results showed that 100 μmol/L SA, 40 mmol/L CaCl2 and 40 mg/L ABA had the best induction effect on the heat tolerance of peony. The effects of three exogenous substances on the heat resistance of peony seedlings were ranked as SA > CaCl2 > ABA. The three exogenous substances mainly alleviated high temperature damage by alleviating the degradation of chlorophyll (Chl), relative electrical conductivity (Rec) content, increasing superoxide dismutase (SOD) activity, soluble proteins (SPs), free proline (Pro) and soluble sugars (SSs) content, and reducing SSs content under high temperature stress. The five indices had the most significant effect on the three exogenous substances in improving the heat resistance of peony seedlings. These could be used as an index to identify the heat resistance.

A common infectious disease, otitis media (OM) has a low rate of early diagnosis, which significantly increases the difficulty of treating the disease and the likelihood of serious complications developing including hearing loss, speech impairment, and even intracranial infection. Several areas of healthcare have shown great promise in the application of artificial intelligence (AI) systems, such as the accurate detection of diseases, the automated interpretation of images, and the prediction of patient outcomes. Several articles have reported some machine learning (ML) algorithms such as ResNet, InceptionV3 and Unet, were applied to the diagnosis of OM successfully. The use of these techniques in the OM is still in its infancy, but their potential is enormous. We present in this review important concepts related to ML and AI, describe how these technologies are currently being applied to diagnosing, treating, and managing OM, and discuss the challenges associated with developing AI-assisted OM technologies in the future.

At present, solar remote sensing instruments face problems regarding the short detection time for solar activity and the need to preheat the electronics before detection. The accurate orbit prediction function ensures that the maximum error of the payload electronics system preheating time is less than 8 s, enabling the electronics to obtain stable and reliable solar remote sensing data, and allowing the prediction function to adapt to the changes in preheating time and the limit position of turntable rotation. To achieve the complete detection of solar activity by a payload in orbit, a turntable with two directions of rotation was accurately controlled to achieve rapid capture and track the Sun’s activity, and the delay time for tracking the Sun successfully was less than 8 s to ensure that the payload made full use of the solar detection time for full detection. The turntable’s tracking error was less than 0.002°, allowing the payload to obtain high-precision solar remote sensing data in orbit. The above methods enable the payload to realize the complete, stable, and high-precision detection of solar activity in orbit for wavelengths ranging approximately from 165 nm to 2400 nm.

The proper replication of mitochondrial DNA is key to the maintenance of this crucial organelle. Multiple studies aimed at understanding the mechanisms of replication of the mitochondrial genome have been conducted in the past several decades; however, while highly informative, they were conducted using relatively low-sensitivity techniques. Here, we established a high-throughput approach based on next-generation sequencing to identify replication start sites with nucleotide-level resolution and applied it to the genome of mitochondria from different human and mouse cell types. We found complex and highly reproducible patterns of mitochondrial initiation sites, both previously annotated and newly discovered in this work, that showed differences among different cell types and species. These results suggest that the patterns of the replication initiation sites are dynamic and might reflect, in some yet unknown ways, the complexities of mitochondrial and cellular physiology. Overall, this work suggests that much remains unknown about the details of mitochondrial DNA replication in different biological states, and the method established here opens up a new avenue in the study of the replication of mitochondrial and potentially other genomes.

The strip crown in hot rolling has the characteristics of multivariablity, strong coupling and, nonlinearity. It is difficult to describe accurately using a traditional mechanism model. In this paper, based on the industrial data of a hot continuous rolling field, the modeling dataset of a strip steel prediction model is constructed through the collection and collation of the on-site data. According to the classical strip crown control theory, the important process parameters that affect the strip crown are determined as input variables for the data-driven model. Some new intelligent strip crown prediction models integrating the shape control mechanism model, artificial intelligence algorithm, and production data are constructed using four machine learning algorithms, including XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). The overall performance of the models is evaluated using error indicators, such as Mean Absolute Percentage Error (MAPE), Root Mean square Error (RMSE), and Mean Absolute Error (MAE). The research results showed that, for the test set, the determination coefficient (R2) of the predicted value of the strip crown model based on the XGBoost algorithm reached 0.971, and the three error indexes are at the lowest level, meaning that the overall model has the optimal generalization performance, which can realize the accurate prediction of the outlet strip crown in the hot rolling process. The research results can promote the application of industrial data and machine learning modeling to the actual strip shape control process of hot rolling, and also have important practical value for the intelligent preparation of the whole process of steel.

As an emerging non-contact method of flow monitoring, radar technology compensates for the shortcomings of traditional methods in terms of the efficiency, timeliness, and difficulty in monitoring high floods, and can provide accurate measurement results, making it one of the most promising flow monitoring methods in the future. This paper describes in detail the workflow from radar data acquisition to flow calculation; compares and analyzes the current state-of-the-art signal sampling and its limitations, Doppler spectrum estimation, signal processing and flow inversion; points out the challenges that these technologies may face in the future; and provides corresponding solutions in order to improve the real-time and accuracy of hydrometric as well as promote the development of non-contact flow monitoring technology.

The accuracy and consistency of Earth observation (EO) instrument radiometric calibration is a fundamental prerequisite for achieving accurate results and delivering reliable predictions. Frequent calibration and validation (Cal/Val) activities are needed during the instrument’s lifetime, and this procedure is often extended to historical archives. Numerous satellites in orbit and proposed future missions have incorporated lunar observation into their vicarious calibration components over recent years, facilitated by the extreme long-term photometric stability of the Moon. Since the birth of the first lunar calibration reference model, lunar-dependent calibration techniques have developed rapidly, and the application and refinement of the lunar radiometric model have become a welcome research focus in the calibration community. Within the context of the development of lunar observation activities and calibration systems globally, we provide a comprehensive review of the activities and results spawned by treating the Moon as a reference for instrument response and categorize them against the understanding of lunar radiometric reference. In general, this appears to be a process of moving from data to instruments, then back into data, working towards a stated goal. Here we highlight lunar radiometric models developed by different institutions or agencies over the last two decades while reporting on the known limitations of these solutions, with unresolved challenges remaining and multiple lunar observation plans and concepts attempting to address them from various perspectives, presenting a temporal development. We also observe that the methods seeking uncertainty reduction at this stage are rather homogeneous, lacking the combination of approaches or results from lunar surface studies conducted by many spacecraft missions, and joint deep learning methods to extract information. The factors that influence the accuracy of the measurement irradiance may be regulated when practical models arrive. As a central element in lunar calibration, the development of an absolute radiometric datum helps to better understand the Earth system.

SNX5 is a protein that is involved in endosomal sorting, signal transduction and endocytosis pathways. However, the roles of fish SNX5 were largely unknown. In this study, we identified an SNX5 homolog (EcSNX5) from an orange-spotted grouper (E. coioides) and investigated its role during viral infection. EcSNX5 encoded 412 amino acids with a PX domain and a BAR domain. In addition, it shared high identities with other known fish SNX5. Through quantitative real-time polymerase chain reaction (qRT-PCR), the high expression of EcSNX5 was observed in the head, kidney and heart. After stimulation with the red-spotted grouper nervous necrosis virus (RGNNV) in vitro, EcSNX5 expression was significantly induced. After RGNNV infection in vitro, EcSNX5 overexpression enhanced the expression of RGNNV genes, including coat protein (CP) and RNA-dependent RNA polymerase (RdRp). EcSNX5 knockdown downregulates expression of CP and RdRp. The TCID50 assay showed a higher viral titer when EcSNX5 is over expressed. Moreover, EcSNX5 overexpression could reduce the expression of interferon genes (IRF1, IRF3, IRF7, MX1, ISG15, ISG56, MDA5 and TRIF) and inflammatory genes (IL6, IL8, IL-1β and TNF-α). EcSNX5 knockdown could promote the expression of interferon factors and inflammatory factors. Moreover, EcSNX5 overexpression suppresses the expression of autophagy genes (LC3-II, BECN1, ATG5 and ATG16L1) and upregulates the expression of apoptosis genes (Bax, BNIP3), but EcSNX5 knockdown had the opposite effect. According to the subcellular localization, EcSNX5 is localized in the cytoplasm and co-localizaed with RGNNV CP protein. The results showed EcSNX5 can influence viral infections by regulating the expression of interferon factors and inflammatory factors as well as adjusting virus-induced autophagy. These data will contribute to a better understanding of the immune response of fish during virus infection.

Reliability is the most critical characteristic of space missions, for example in capturing and tracking moving targets. To this end, two methods are designed to track sunlight using solar remote-sensing instruments (SRSIs). The primary method is to use the offset angles of the guide mirror for closed-loop tracking, while the alternative method is to use the sunlight angles, calculated from the satellite attitude, solar vector, and mechanical installation correction parameters, for open-loop tracking. By comprehensively analyzing the error and rectifying the calibration of the solar tracking system, we demonstrate that the absolute value of the azimuth tracking precision is less than 0.0121° and the pitch is less than 0.0037° with the primary method. Correspondingly, they are 0.0992° and 0.0960° with the alternative method. These precisions meet the requirements of SRSIs. In addition, recalibration due to mechanical vibration during the satellite’s launch may invalidate the above methods, leading to the failure of SRSIs. Hence, we propose a dedicated injection parameter strategy to rectify the sunlight angles to capture and track the sunlight successfully. The stable and effective results in the ultraviolet to near-infrared spectrum validate the SRSI’s high-precision sunlight tracking performance. Furthermore, the above methods can also be applied to all orbital inclinations and may provide a solution for capturing and tracking moving targets.

High temperature stress (HTS), with growth and development impairment, is one of the most important abiotic stresses frequently encountered by plants, in particular solanacaes such as pepper, that mainly distribute in tropical and subtropical regions. Plants activate thermotolerance to cope with this stress; however, the underlying mechanism is currently not fully understood. SWC4, a shared component of SWR1- and NuA4 complexes implicated in chromatin remodeling, was previously found to be involved in the regulation of pepper thermotolerance, but the underlying mechanism remains poorly understood. Herein, PMT6, a putative methyltranferase was originally found to interact with SWC4 by co-immunoprecipitation (Co-IP)-combined LC/MS assay. This interaction was further confirmed by bimolecular fluorescent complimentary (BiFC) and Co-IP assay, and PMT6 was further found to confer SWC4 methylation. By virus-induced gene silencing, it was found that PMT6 silencing significantly reduced pepper basal thermotolerance and transcription of CaHSP24 and significantly reduced the enrichment of chromatin-activation-related H3K9ac, H4K5ac, and H3K4me3 in TSS of CaHSP24, which was previously found to be positively regulated by CaSWC4. By contrast, the overexpression of PMT6 significantly enhanced basal thermotolerance of pepper plants. All these data indicate that PMT6 acts as a positive regulator in pepper thermotolerance, likely by methylating SWC4.

In large-scale air conditioning water systems, variable water flow (VWF) control strategies are frequently utilized to conserve energy. This paper presents a variable differential pressure (DP) set-point control strategy for VWF air conditioning systems based on the pipeline characteristic curve. This strategy bifurcates the most unfavorable loop into two segments: the equivalent main pipe (EMP) and the most unfavorable terminal branch pipe (MUTBP). Initially, the impedance of the EMP is obtained by curve fitting the measured values of the water supply and return main pipes (WSRMP), as well as the MUTBP. Subsequently, by calculating the disparity between the DP of the actual pipeline and the DP of the EMP, and comparing it with the DP of the MUTBP, the optimal working condition point for pipeline operation can be identified. Finally, a theoretical calculation is conducted on a typical air conditioning water system. This adjustment strategy achieves an energy-saving rate of 15.27%, 12.10%, and 11.50%, respectively, under the three adjustment conditions of closing the nearest terminal, the middle terminal, and the most unfavorable terminal, as compared with the constant DP set-point control strategy of WSRMP. This strategy boasts fewer control devices, a simple control system, and better operability and engineering applicability than other strategies.

Citrus are one of the most important fruit crops in the world, and the citrus hybrid cv. Beni-Madonna (BM) is widely cultivated in Xiangshan County in China because of its profitability. However, due to the lack of technical guidance and management, nutritional unbalances of trees occur frequently and are a major constraint for fruit quality and yield. In this study, the soil and fruit nutritional status of 62 citrus orchards in three diverse landforms (mountain, flat, and coastal saline–alkali land) located in Xiangshan County has been investigated. Higher pH, calcium, and magnesium content but lower soil organic matter, nitrogen, iron, and boron content were observed in orchards of the coastal saline–alkali land compared with the mountain and flat lands. Compared to Citrus x unshiu, another major hybrid citrus cultivar in Xiangshan county, the Fe deficiency in fruits of BM in coastal saline–alkali lands was more severe, leading to chlorosis symptoms. In addition, long-term cultivation under plastic housing increased soil salinization and affected the absorption of Fe and other nutrients by BM. In conclusion, organic fertilizers, acidic fertilizers, and micronutrient fertilizers should be applied in citrus orchards located in coastal saline–alkali lands. Especially, BM orchards should be supplemented with iron fertilizer and not rely excessively on protection with plastic films.