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This review critically examines the integration of Virtual Reality (VR) and Augmented Reality (AR) in medical training across Low- and Middle-Income Countries (LMICs), offering a novel perspective by combining quantitative analysis with qualitative insights from medical students in Egypt and Ghana. Through a systematic review process, 17 peer-reviewed studies published between 2010 and 2023 were analysed. Altogether, these studies involved a total of 887 participants. The analysis reveals a growing interest in VR and AR applications for medical training in LMICs with a peak in published articles in 2023, indicating an expanding research landscape. A unique contribution of this review is the integration of feedback from 35 medical students assessed through questionnaires, which demonstrates the perceived effectiveness of immersive technologies over traditional 2D illustrations in understanding complex medical concepts. Key findings highlight that VR and AR technologies in medical training within LMICs predominantly focus on surgical skills. The majority of studies focus on enhancing surgical training, particularly general surgery. This emphasis reflects the technology’s strong alignment with the needs of LMICs, where surgical skills training is often a priority. Despite the promising applications and expanding interest in VR and AR, significant challenges such as accessibility and device limitations remain, demonstrating the need for ongoing research and integration with traditional methods to fully leverage these technologies for effective medical education. Therefore, this review provides a comprehensive analysis of existing VR and AR applications, their evaluation methodologies, and student perspectives to address educational challenges and enhance healthcare outcomes in LMICs.

In this article, a small-aperture, low-profile, and omnidirectional conformal antenna is proposed which can be utilized on space-limited equipment platforms such as airplanes, ships, and vehicles. The antenna consists of an open metal cavity, a discone antenna, a parasitic structure, and a radome. The small aperture and low-profile design of the metal cavity result in a rapid narrowing of the bandwidth of the discone antenna. Therefore, we introduce a parasitic structure that not only enlarges the impedance bandwidth by adding a resonant point, but can also be used to adjust the unroundness of the horizontal pattern. Meanwhile, the conformal design of the antenna with four surfaces of different curvatures is presented. The simulation and testing results demonstrate that the antenna can achieve a VSWR of less than 2 within a bandwidth of 1.95–2.62 GHz (29.3%), with a minimum aperture of 0.43 omnidirectional radiation pattern, with a gain exceeding −2.2 dBi in the azimuthal plane. This antenna offers the advantages of a small aperture, low profile, and conformal capability. Furthermore, the resonances of high and low frequencies can be adjusted through two different structures, enhancing the flexibility of antenna design.

The objectives of the study were to design, synthesize, and evaluate the antibacterial activity of a series of novel aminoguanidine-indole derivatives. Thirty-seven new compounds were effectively synthesized through nucleophilic substitution reaction and guanidinylation reaction. Chemical structures of all the desired compounds were identified by NMR and HR-MS spectroscopy. Most of the synthesized compounds showed significant antibacterial activity against ESKAPE pathogens and clinical resistant Klebsiella pneumoniae (K. pneumoniae) isolates. K. pneumoniae is an important opportunistic pathogen that often threatens the health of immunocompromised people such as the elderly, children, and ICU patients. The most active compound 4P showed rapid bactericidal activity against resistant K. pneumoniae 2108 with MIC and MBC values that were 4 and 8 µg/mL, respectively. The hemolytic activity of 4P was low, with an HC50 value of 123.6 µg/mL. Compound 4P induced the depolarization of the bacterial membrane and disrupted bacterial membrane integrity and was not prone to antibiotic resistance. The dihydrofolate reductase (DHFR) activity was also notably inhibited by 4P in vitro. Molecular docking revealed that the aminoguanidine moiety and indole structure of 4P played an important role in binding to the target site of the K. pneumoniae dihydrofolate reductase (DHFR) receptor. In the mouse pneumonia model caused by K. pneumoniae, 4P improved the survival rate of mice, reduced bacterial loads, and alleviated tissues’ pathological injuries at a dosage of 4 mg/kg. Therefore, compound 4P may be a promising lead compound or drug candidate for antibacterial purposes against K. pneumoniae.

This study investigates speech intelligibility and its influencing factors within pier-style airport lounges and assesses the applicability of the Speech Transmission Index (STI) in these large, elongated spaces. Field impulse response measurements were conducted in two pier-style departure lounges with volumes of 98,099 m3 and 60,414 m3, respectively, complemented by simulated binaural room impulse responses for subjective speech intelligibility testing in Mandarin. The research explores the correlations between various acoustic parameters—Early Decay Time (EDT), Reverberation Time (T30), and Definition(D50)—and speech intelligibility scores under different Signal-to-Noise Ratios (SNRs). Findings indicate a significant impact of SNR on speech intelligibility, with a coefficient of determination (R2) of 0.849, suggesting substantial variability explained by SNR. As SNR increases to 10 dB(A), speech intelligibility scores improve significantly; however, further enhancements in clarity diminish beyond this threshold. Additionally, the study reveals a significant relationship between room acoustic parameters, particularly EDT and D50, and speech intelligibility scores, with EDT having a negative impact and D50 a positive impact on speech clarity. The results confirm the suitability of STI in evaluating speech intelligibility in these specific architectural contexts. This study recommends maintaining an SNR of 10 dB(A) and a minimum STI of 0.45 for public address broadcasts in pier-style departure lounges to ensure that announcements are clearly audible to passengers.

Skeletal muscle atrophy, which is induced by factors such as disuse, spaceflight, certain medications, neurological disorders, and malnutrition, is a global health issue that lacks effective treatment. Hindlimb unloading is a commonly used model of muscle atrophy. However, the underlying mechanism of muscle atrophy induced by hindlimb unloading remains unclear, particularly from the perspective of the myocyte proteome and metabolism. We first used mass spectrometry for proteomic sequencing and untargeted metabolomics to analyze soleus muscle changes in rats with hindlimb unloading. The study found 1052 proteins and 377 metabolites (with the MS2 name) that were differentially expressed between the hindlimb unloading group and the control group. Proteins like ACTN3, MYH4, MYBPC2, and MYOZ1, typically found in fast-twitch muscles, were upregulated, along with metabolism-related proteins GLUL, GSTM4, and NDUFS4. Metabolites arachidylcarnitine and 7,8-dihydrobiopterin, as well as pathways like histidine, taurine, and hypotaurine metabolism, were linked to muscle atrophy. Protein and metabolism joint analyses revealed that some pathways, such as glutathione metabolism, ferroptosis, and lysosome pathways, were likely to be involved in soleus atrophy. In this study, we have applied integrated deep proteomic and metabolomic analyses. The upregulation of proteins that are expressed in fast-twitch fibers indicates the conversion of slow-twitch fibers to fast-twitch fibers under hindlimb unloading. In addition, some differentially abundant metabolites and pathways revealed the important role of metabolism in muscle atrophy of the soleus. As shown in the graphical abstract, our study provides insights into the pathogenesis and treatment of muscle atrophy that results from unloading by integrating proteomics and metabolomics of the soleus muscles.

A community landscape is intricately tied to the daily lives and livelihoods of its residents, and it significantly influences human behavior. Community co-management serves as a pivotal approach to fostering the harmonious coexistence between national parks and community dwellers, and it requires the extensive participation of community residents. Accordingly, this study aims to clarify the mechanism by which community landscape perception (LP) affects community participation (CP), with the ultimate goal of promoting widespread engagement. After conducting a survey of 564 residents from three entrance communities in the Sichuan region of the Giant Panda National Park, the data were analyzed using structural equation modeling. The results suggest the following: (1) Cultural landscape and living landscape have a direct impact on CP. The natural landscape can stimulate CP through fostering place attachment (PA) or a sense of responsibility (SR), while the production landscape only influences CP through PA. (2) The cultural landscape plays a crucial role in shaping a sense of belonging and responsibility within a community, while the production landscape is not effective in cultivating an SR. (3) PA can awaken residents’ SR and affect CP, playing a chain mediating role between community LP and CP.

In this work, a hydrophobically associative polymer (HAP) was mixed with β-cyclodextrin and epichlorohydrin polycondensate (β-CDP) in an aqueous solution to enhance the intermolecular interaction through host–guest inclusion between hydrophobes and cyclodextrins. Results showed that the host–guest interaction improved the thickening ability and viscoelasticity of the HAP solution and maintained its shear thinning behavior. The host–guest inclusion system demonstrated special viscosity–temperature curves and variable activation energy. Enhanced oil recovery (EOR) performance tests showed that the oil increment produced by the host–guest inclusion system was 5.5% and 9.3% higher than that produced by the HAP and the partially hydrolyzed polyacrylamide solution, respectively. Compared with pure HAP, β-CDP/HAP has a better comprehensive performance and is more attractive for EOR in high-temperature reservoirs.

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. This study focuses on predicting hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions and improving air quality. We developed two models using artificial neural networks (ANNs) and support vector machine (SVM) to predict hydrogen production. The input variables are current, voltage, communication delay time, and real-time measured hydrogen production, while the output variable is hydrogen production at the next sampling time. Both models address the critical issue of production hysteresis. Using 50 h of SOEC system data, we evaluated the effectiveness of the ANN and SVM methods, incorporating hydrogen production time as an input variable. The results show that the ANN model is superior to the SVM model in terms of hydrogen production prediction performance. Specifically, the ANN model shows strong predictive performance at a communication delay time ε = 0.01–0.02 h, with RMSE = 2.59 × 10−2, MAPE = 33.34 × 10−2%, MAE = 1.70 × 10−2 Nm3/h, and R2 = 99.76 × 10−2. At delay time ε = 0.03 h, the ANN model yields RMSE = 2.74 × 10−2 Nm3/h, MAPE = 34.43 × 10−2%, MAE = 1.73 × 10−2 Nm3/h, and R2 = 99.73 × 10−2. Using the SVM model, the prediction error values at delay time ε = 0.01–0.02 h are RMSE = 2.70 × 10−2 Nm3/h, MAPE = 44.01 × 10−2%, MAE = 2.24 × 10−2 Nm3/h, and R2 = 99.74 × 10−2, while at delay time ε = 0.03 h they become RMSE = 2.67 × 10−2 Nm3/h, MAPE = 43.44 × 10−2%, MAE = 2.11 × 10−2 Nm3/h, and R2 = 99.75 × 10−2. With this precision, the ANN model for SOEC hydrogen production prediction has positive implications for air pollution control strategies and the development of cleaner energy technologies, contributing to overall improvements in air quality and the reduction of atmospheric pollutants.

Sweet potato (Ipomoea batatas (L.) is regarded among the most crucial crops globally because it is abundant in essential nutrients vital for human health. However, limited comprehensive information is available regarding the nutritional composition of sweet potato, which hinders its optimal utilization. This study investigated the nutritional and chemical composition of sweet potato roots and explored their interrelationships. In total, 86 sweet potato accessions, comprising white, yellow, orange, and purple flesh-colored varieties, were used. A total of 34 components, including nutrients, phytochemicals, and minerals, were identified. Multivariate analysis was performed to assess the relationships among these components. The sweet potato roots were rich in carbohydrates, polyphenols, and minerals. Carbohydrates were primarily composed of total starch (22.6–69.7 g/100 g DW), total soluble sugar (TSS) (10.3–40.0 g/100 g DW), and total dietary fiber (TDF) (7.99–26.0 g/100 g DW). Polyphenols included total caffeoylquinic acids (CQAs) (0.478–14.2 g/kg DW), total anthocyanins (0–2003 mg/kg DW), and β-carotene (0–133 mg/kg DW). The mineral content followed the order: potassium > calcium > phosphorus > sodium > magnesium > iron > manganese > zinc > copper > selenium. White-fleshed sweet potato exhibited high total starch levels (50.4 g/100 g DW) but low TSS levels (21.1 g/100 g DW). Orange-fleshed sweet potato contained high levels of TSS (26.5 g/100 g DW), TDF (17.9 g/100 g DW), and β-carotene (61.4 mg/100 g DW) but low levels of protein (2.99 g/100 g DW) and total starch (43.0 g/100 g DW). Purple-fleshed sweet potato had high levels of phytochemicals, particularly total CQAs (8.17 g/kg DW) and anthocyanins (904 mg/kg DW). Cluster analysis categorized sweet potato accessions into six clusters with unique characteristics. Furthermore, principal component analysis identified accessions with exceptionally high nutritional content. The correlation analysis indicated that starch was negatively correlated with soluble sugar and TDF, whereas CQAs and anthocyanins were highly positively correlated. These findings offer a solid theoretical foundation for sweet potato breeding and utilization.

The timely and accurate monitoring of high-seas fisheries is essential for effective management. However, efforts to monitor industry fishing vessels in the central-eastern North Pacific have been hampered by frequent cloud cover and solar illumination interference. In this study, enhanced fishing extraction algorithms based on computer vision were developed and tested. The results showed that YOLO-based computer vision models effectively detected dense small fishing targets, with original YOLOv8 achieving a precision (P) of 89% and a recall (R) of 79%, while refined versions improved these metrics to 93% and 99%, respectively. Compared with traditional threshold methods, the YOLO-based enhanced models showed significantly higher accuracy. While the threshold method could identify similar trend changes, it lacked precision in detecting individual targets, especially in blurry scenarios. Using our trained computer vision model, we established a dataset of dynamic changes in fishing vessels over the past decade. This research provides an accurate and reproducible process for precise monitoring of lit fisheries in the North Pacific, leveraging the operational and near-real-time capabilities of Google Earth Engine and computer vision. The approach can also be applied to dynamic monitoring of industrial lit fishing vessels in other regions.

Nightly images offer a special data source for monitoring fishing activities. This study used images from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) to analyze the early development of lit fisheries in the open South China Sea (SCS), which mainly occurred around the Zhong Sha and Xi Sha Islands. Based on peak detection and a fixed threshold, lit fishing positions were extracted well from filtered, high-quality DMSP/OLS images. The results indicated that fisheries experienced an apparent rise and fall from 2005 to 2012, with the numbers of lit fishing boats rising to a maximum of ~60 from 2005 to 2008, almost disappearing in 2009, peaking at ~130 from 2010 to 2011, and starting to decline in 2012. The fish price of major fishing targets declined by ~60% in 2009, which obviously impacted the year’s fishing operations. The reason for declined fishing operations in 2012 was that most of the lit fishing operations shifted farther south to fishing grounds around the Nan Sha Islands. We also explored factors shaping the distribution patterns of lit fisheries by using MaxEnt models to relate fishing positions to environmental variables. Major environmental factors influencing the distribution of lit fishing boats varied with years, of which water depth was the most important factor across years, with an optimal depth range of 1000–2000 m. In addition to depth, the distribution of lit fisheries was also influenced by SST, especially for the years 2005–2008, and a suitable SST was found between 26 and 28 °C. This study fills the knowledge gaps of the inception of lit fisheries and their dynamic changes in the SCS.

Trichoderma species have been reported as masters in producing cellulolytic enzymes for the biodegradation of lignocellulolytic biomass and biocontrol agents against plant pathogens and pests. In our previous study, a novel Trichoderma strain LZ117, which shows potent capability in cellulase production, was isolated. Herein, we conducted multilocus phylogenetic analyses based on DNA barcodes and performed time-scaled phylogenomic analyses using the whole genome sequences of the strain, annotated by integrating transcriptome data. Our results suggest that this strain represents a new species closely related to T. atrobrunneum (Harzianum clade). Genes encoding carbohydrate-active enzymes (CAZymes), transporters, and secondary metabolites were annotated and predicted secretome in Trichoderma sp. LZ117 was also presented. Furthermore, genetic manipulation of this strain was successfully achieved using PEG-mediated protoplast transformation. A putative transporter gene encoding maltose permease (Mal1) was overexpressed, which proved that this transporter does not affect cellulase production. Moreover, overexpressing the native Cre1 homolog in LZ117 demonstrated a more pronounced impact of glucose-caused carbon catabolite repression (CCR), suggesting the importance of Cre1-mediated CCR in cellulase production of Trichoderma sp. LZ117. The results of this study will benefit further exploration of the strain LZ117 and related species for their applications in bioproduction.

Feature selection (FS) is a classic and challenging optimization task in most machine learning and data mining projects. Recently, researchers have attempted to develop more effective methods by using metaheuristic methods in FS. To increase population diversity and further improve the effectiveness of the beluga whale optimization (BWO) algorithm, in this paper, we propose a multi-strategies improved BWO (MSBWO), which incorporates improved circle mapping and dynamic opposition-based learning (ICMDOBL) population initialization as well as elite pool (EP), step-adaptive Lévy flight and spiral updating position (SLFSUP), and golden sine algorithm (Gold-SA) strategies. Among them, ICMDOBL contributes to increasing the diversity during the search process and reducing the risk of falling into local optima. The EP technique also enhances the algorithm′s ability to escape from local optima. The SLFSUP, which is distinguished from the original BWO, aims to increase the rigor and accuracy of the development of local spaces. Gold-SA is introduced to improve the quality of the solutions. The hybrid performance of MSBWO was evaluated comprehensively on IEEE CEC2005 test functions, including a qualitative analysis and comparisons with other conventional methods as well as state-of-the-art (SOTA) metaheuristic approaches that were introduced in 2024. The results demonstrate that MSBWO is superior to other algorithms in terms of accuracy and maintains a better balance between exploration and exploitation. Moreover, according to the proposed continuous MSBWO, the binary MSBWO variant (BMSBWO) and other binary optimizers obtained by the mapping function were evaluated on ten UCI datasets with a random forest (RF) classifier. Consequently, BMSBWO has proven very competitive in terms of classification precision and feature reduction.

Joint deflection during curved pipe jacking in power tunnels poses a significant risk of structural failure due to the resulting eccentric and diagonal loading on the pipes. This study investigated the axial stress and strain characteristics of reinforced-concrete pipes under varying joint deflection angles and jacking forces, using a combined approach of experimental model testing and finite element method (FEM) numerical simulations. The experimental setup replicated curved pipe jacking conditions, allowing for the measurement of strains and deformation under controlled loading. Numerical simulations, validated against experimental data, provided detailed insights into the stress distribution patterns. The results revealed distinct stress states in different pipe sections. The pipe closest to the jacking force (3# pipe) experienced eccentric loading, leading to localized stress concentrations and inelastic strain on the inner wall at the point of eccentricity, indicating vulnerability to compressive failure. The middle pipe section (2# pipe) underwent complex diagonal loading, resulting in the development of inelastic strain on both the inner and outer walls at specific orientations, highlighting a risk of both compressive and shear failure modes. The study also demonstrated that the magnitude of the axial jacking force and the degree of joint deflection significantly influence the stress distribution and the extent of inelastic strain. These findings provide important information for optimizing the design and construction of curved pipe jacking projects in power tunnels. The identified failure mechanisms and the influence of key parameters on pipe behavior can inform strategies to mitigate the risk of structural failure, improve the resilience of pipe systems, and enhance the overall safety and reliability of underground power tunnel infrastructure.

Uncrewed aerial vehicles (UAVs) for plant protection play a vital role in modern agricultural operations. In recent years, advancements in UAVs and pest control technologies have significantly enhanced operational efficiency. These innovations have addressed historical challenges in agricultural practices by improving automation and precision in managing insect pests, diseases, and weeds. UAVs offer high operational efficiency, wide adaptability to different terrain, and safe applications. The development and demand for these technologies have increased to boost agricultural production. In agricultural settings where conventional machinery struggles to carry out farming operations, UAVs have transformed farming practices by providing high operational efficiency and significant profitability. The integration of UAVs and other smart technologies has driven advancements. The UAV sector has received substantial attention as a convergence of production, service, and delivery, introducing synergy through the presence of several developing areas. The market for this technology is expected to grow in the future. In this comprehensive review, we analyzed an overview of historical research, diverse techniques, the transition from conventional to advanced application, development trends, and operational milestones across diverse cropping systems. We also discussed adoption and subsidy policies. In order to properly understand UAV operational efficiency, we also analyzed and discussed smart atomization systems, spray drift, droplet deposition detection technologies, and the capabilities of related technologies. Additionally, we reviewed the role of software programs, data-driven tools, biodegradable materials, payloads, batteries, sensing technologies, weather, and operational and spraying factors. Regulatory limitations, operating and farmer’s training, economic effects, and guidelines were also acknowledged in this review. This review highlights deficiencies and provides essential knowledge of the use of UAVs for agriculture tasks in different regions. Finally, we examine the urgency of UAV technology implementations in the agricultural sector. In conclusion, we summarize the integration of UAVs and their related technologies with applications and future research prospects, offering directions for follow-up research on the key technologies of UAVs and encouraging the enhancement of agricultural production management in terms of efficiency, accuracy, and sustainability.

To alleviate the shortage of natural gas supply, the in-situ conversion of coal to natural gas is more beneficial for advancing the clean and efficient use of energy. Since in-situ coal gas contains complex components, such as H2, CH4, and CO, their leakage poses a serious risk to human life and property. Currently, the area of consequence of the harm caused by a leak in a gathering pipeline transporting in-situ coal gas has not been clarified. Therefore, this paper adopted the method of numerical simulation to pre-study the concentration distribution of each component and determined that the main components of concern are CO and H2 components. Afterward, the diffusion law of in-situ coal gas is analyzed and studied under different working conditions, such as wind speed, temperature, pipe diameter, leakage direction, and leakage aperture ratio. The results indicate that when a pipeline leak occurs, the CO component has the largest influence range. With increasing wind speed, the warning boundary of CO rapidly expands downwind, then gradually diminishes, reaching a peak value of 231.62 m at 7 m/s. The range of influence of the leaked gas is inversely proportional to temperature and directly proportional to pipe diameter and leakage aperture ratio. When the gas leaks laterally, the diffusion early warning boundary value of each component is maximal. Among them, the leakage aperture ratio has a significant impact on the concentration distribution of in-situ coal gas, whereas the effect of temperature is relatively minor. This study contributes to an understanding of the leakage and diffusion characteristics of in-situ coal gas-gathering pipelines.

Prussian blue analogs (PBAs) are appealing cathode materials for sodium-ion batteries because of their low material cost, facile synthesis methods, rigid open framework, and high theoretical capacity. However, the poor electrical conductivity, unavoidable presence of [Fe(CN)6] vacancies and crystalline water within the framework, and phase transition during charge–discharge result in inferior electrochemical performance, particularly in terms of rate capability and cycling stability. Here, cobalt-free PBAs are synthesized using a facile and economic co-precipitation method at room temperature, and their sodium-ion storage performance is boosted due to the reduced crystalline water content and improved electrical conductivity via the high-entropy and component stoichiometry tuning strategies, leading to enhanced initial Coulombic efficiency (ICE), specific capacity, cycling stability, and rate capability. The optimized HE-HCF of Fe0.60Mn0.10-hexacyanoferrate (referred to as Fe0.60Mn0.10-HCF), with the chemical formula Na1.156Fe0.599Mn0.095Ni0.092Cu0.109Zn0.105 [Fe(CN)6]0.724·3.11H2O, displays the most appealing electrochemical performance of an ICE of 100%, a specific capacity of around 115 and 90 mAh·g−1 at 0.1 and 1.0 A·g−1, with 66.7% capacity retention observed after 1000 cycles and around 61.4% capacity retention with a 40-fold increase in specific current. We expect that our findings could provide reference strategies for the design of SIB cathode materials with superior electrochemical performance.

Ecosystems and their services to society have exhibited dramatic degradation all over the world, and landscape planning based on ecosystem service (ES) science is a promising way to mitigate ES loss and improve human well-being. However, ecosystem health, which is crucial for intrinsic ecosystem values, may be overlooked in ES-based landscape planning. Therefore, we proposed a landscape functional zoning method by combining the ES and EH using the Jinan Southern Mountainous Area as a case study. Specifically, we first quantified and mapped six ESs (including regulating, cultural, and provisioning services) and three EH properties (ecosystem vigor, organization, and resilience). Then, we used coupling coordination analysis to determine the coordination of the ES and EH, and adopted bundle analysis to reveal ES-EH compositions. Finally, landscape functional zones were delineated by spatially overlapping the maps of ES-EH bundle types and coupling coordination degrees. The results show that the different ESs and EH properties exhibited uneven spatial distributions. In terms of the ES-EH coupling coordination degree, high values were located along the mountains and aggregated in the eastern part of the study area, and the Caishi town had the highest coupling coordination degree on the town scale. Furthermore, five ES-EH bundle types were identified, i.e., bundles of multifunctionality, subordinate multifunctionality, the highest crop production, ESs and EH properties of medium levels, and ESs and EH properties of low levels. Finally, nine landscape functional zones based on the ES-EH bundle and coupling coordination were identified, and the grids within one zone were homogeneous in terms of their ES-EH compositions and coupling coordination. This study can contribute to the integration of ES and EH into landscape planning and provides a zoning method as a spatial instrument to achieve synergic ES-EH management.

Solid oxide fuel cells (SOFCs) are an efficient, reliable and clean source of energy. Predictive modeling and analysis of their performance is becoming increasingly important, especially with the growing emphasis on sustainable development’s requirements. However, mathematical modeling is difficult due to the complexity of its internal structure. In this study, the system’s electricity generating performance and operational characteristics were analyzed using recent on-site monitoring data first. Then, based on Pearson’s correlation coefficient, some of the variables were selected to build two prediction models: an artificial neural network (ANN) model and a multiple linear regression (MLR) model. The models were evaluated on the basis of the normalized mean square error (NRMSE), which was 1.89% for the MLR model and 0.66% for the ANN model, with no overall bias. They were also compared with other existing models, and it was found that the two models used in this study have the advantage of high accuracy and low difficulty. Therefore, the models developed in this study can more accurately and effectively assess the SOFC system’s state and can better support work to improve the thermoelectric performance of SOFC systems.

Photoactive artificial nanocatalysts that mimic natural photoenergy systems can yield clean and renewable energy. However, their poor photoabsorption capability and disfavored photogenic electron–hole recombination hinder their production. Herein, we designed two nanocatalysts with various microstructures by combining the tailored self-assembly of the meso-tetra(p-hydroxyphenyl) porphine photosensitizer with the growth of titanium dioxide (TiO2). The porphyrin photoabsorption antenna efficiently extended the absorption range of TiO2 in the visible region, while anatase TiO2 promoted the efficient electron–hole separation of porphyrin. The photo-induced electrons were transferred to the surface of the Pt co-catalyst for the generation of hydrogen via water splitting, and the hole was utilized for the decomposition of methyl orange dye. The hybrid structure showed greatly increased photocatalytic performance compared to the core@shell structure due to massive active sites and increased photo-generated electron output. This controlled assembly regulation provides a new approach for the fabrication of advanced, structure-dependent photocatalysts.

This article demonstrates the integration of sensor fusion for pose estimation and data collection in tennis balls, aiming to create a smaller, less intrusive form factor for use in progressive learning during tennis practice. The study outlines the design and implementation of the Bosch BNO055 smart sensor, which features built-in managed sensor fusion capabilities. The article also discusses deriving additional data using various mathematical and simulation methods to present relevant orientation information from the sensor in Unity. Embedded within a Vermont practice foam tennis ball, the final prototype product communicates with Unity on a laptop via Bluetooth. The Unity interface effectively visualizes the ball’s rotation, the resultant acceleration direction, rotations per minute (RPM), and the orientation relative to gravity. The system successfully demonstrates accurate RPM measurement, provides real-time visualization of ball spin and offers a pathway for innovative applications in tennis training technology.

Measles is a highly contagious disease and remains a major cause of child mortality worldwide. While measles vaccine is highly effective, high levels of population immunity are needed to prevent outbreaks. Simple but accurate tools are needed to estimate the profile of population measles immunity by age to identify and fill immunity gaps caused by low levels of vaccination coverage. The measles immunity profile estimates and visualizes the percentage of each birth cohort immune or susceptible to measles based on measles vaccination coverage. Several tools that employed this approach have been developed in the past, including informal unpublished versions. However, these tools used varying assumptions and produced inconsistent results. We updated the measles population immunity profile methodology to standardize and better document the assumptions and methods; provide timely estimates of measles population immunity; and facilitate prompt actions to close immunity gaps and prevent outbreaks. We recommend assuming that the second dose of the measles-containing vaccine (MCV2) and doses given during supplementary immunization activities (SIAs) first reach children who have been previously vaccinated against measles, so that previously unvaccinated children are reached only when the coverage of MCV2 or SIA is higher than the coverage achieved by all previous measles vaccination opportunities. This updated method provides a conservative estimate of immunization program impact to assess measles outbreak risk and to facilitate early planning of timely preventive SIAs to close population immunity gaps.

A new hydrazone Schiff base bridging ligand (H2LSchiff (E)-N′-((1-hydroxynaphthalen-2-yl)methylene)pyrazine-2-carbohydrazide) and L/D-proline were used to construct a pair of homochiral Dy6 cluster complexes, [Dy6(CO3)(L-Pro)6(LSchiff)4(HLSchiff)2]·5DMA·2H2O (L-1, L-HPro = L-proline; DMA = N,N-dimethylacetamide) and [Dy6(CO3)(D-Pro)6(LSchiff)4(HLSchiff)2]·5DMA·2H2O (D-1, D-HPro = D-proline), which show a novel triangular Dy6 topology. Notably, the fixation of CO2 in the air formed a carbonato central bridge, playing a key role in assembling L-1/D-1. Magnetic measurements revealed that L-1/D-1 displays intramolecular ferromagnetic coupling and magnetic relaxation behaviours. Furthermore, L-1/D-1 shows a distinct magneto-optical Faraday effect and has a second harmonic generation (SHG) response (1.0 × KDP) at room temperature. The results show that the immobilization of CO2 provides a novel pathway for homochiral multifunctional 4f cluster complexes.

The Sutra of the Buddha’s Mother (Fomu jing 佛母經), a Chinese Buddhist scripture, is closely linked to the Mahāmāyā Sutra (Mohe Moye jing 摩訶摩耶經). However, there is a significant difference between the two sutras regarding the narrative story of the Buddha’s nirvana and meeting with his mother, namely the difference in the disciple who travels to Trāyastriṃśa Heaven to inform the Buddha’s mother. The substitution of Aniruddha with Upāli could be attributed to Upāli’s inclusion in Tang dynasty commentaries on “Ānanda Asking the Buddha Four Questions,” where he is depicted as a prominent disciple in the gathering prior to the Buddha’s nirvana. This narrative preference was also reflected in the Mohe Moye jing. To a certain extent, this confusion reflects the process of ‘between translation and composition,’ or the compiling and mixing of various texts from different sutras and sources to create a new scripture in the Chinese context. In the nirvana images, which contain the inscriptions, the presence of Upāli becomes an important symbol for identifying the classical texts on which the frescoes were painted.

Testosterone (T), an environmental androgen, significantly disrupts endocrine systems in wildlife and ecosystems. Despite growing concern over its high levels in aquatic environments, the reproductive toxicity of testosterone and its mechanisms are not well understood. In this study, we investigated the reproductive toxicity and mechanisms of testosterone using Caenorhabditis elegans (C. elegans) and assessed its ecological toxicity through the benchmark dose (BMD) method. Our results indicate that T concentrations exceeding 0.01 μg/L significantly reduce the brood size, decrease germ cell counts, and prolong the generation time in C. elegans as T concentrations increase. Furthermore, to elucidate the specific mechanisms, we analyzed the expression of nhr-69, mpk-1, and other genes involved in sex determination. These findings suggest that the nhr-69-mediated reproductive toxicity of T primarily affects sperm formation and the offspring number by influencing its downstream targets, mpk-1 and fog-1/3, which are critical in the germ cell sex-determining pathway. Additionally, this study determined that the 10% lower boundary of the baseline dose (BMDL10) is 1.160 ng/L, offering a more protective reference dose for the ecological risk assessment of T. The present study suggests that nhr-69 mediates the reproductive toxicity of T by influencing mpk-1 and fog-1/3, critical genes at the end of the germ cell sex-determining pathway, thereby providing a basis for establishing reproductive toxicity thresholds for T.

An efficient and operationally simple method for the synthesis of β-keto sulfones through the BF3·OEt2-promoted reaction of alkynes and sodium sulfinates is developed. With its facile and selective access to the targets, it features good functional group compatibility, mild conditions, easily available starting materials, and good yields. Notably, the reaction does not require metal catalysts or chemical reagents with pungent odors.

Soybean, a primary vegetable protein source, boasts favorable amino acid profiles; however, its composition still falls short of meeting human nutritional demands. The soybean amino acid content is a quantitative trait controlled by multiple genes. In this study, an F2 population of 186 individual plants derived from the cross between ChangJiangChun2 and JiYu166 served as the mapping population. Based on the previously published genetic map of our lab, we increased the density of the genetic map and constructed a new genetic map containing 518 SSR (simple sequence repeats) markers and 64 InDel (insertion-deletion) markers, with an average distance of 5.27 cm and a total length of 2881.2 cm. The content of eight essential amino acids was evaluated in the F2:5, F2:6, and BLUP (best linear unbiased prediction). A total of 52 QTLs (quantitative trait loci) were identified, and 13 QTL clusters were identified, among which loci02.1 and loci11.1 emerged as stable QTL clusters, exploring candidate genes within these regions. Through GO enrichment and gene annotation, 16 candidate genes associated with soybean essential amino acid content were predicted. This study would lay the foundation for elucidating the regulatory mechanisms of essential amino acid content and contribute to germplasm innovation in soybeans.

In the power grid, inspection robots enhance operational efficiency and safety by inspecting power lines for information sharing and interaction. Edge computing improves computational efficiency by positioning resources close to the data source, supporting real-time fault detection and line monitoring. However, large data volumes and high latency pose challenges. Existing offloading strategies often neglect task divisibility and priority, resulting in low efficiency and poor system performance. This paper constructs a power grid inspection offloading scenario using Python 3.11.2 to study and improve various offloading strategies. Implementing a game-theory-based distributed computation offloading strategy, simulation analysis reveals issues with high latency and low resource utilization. To address these, an improved game-theory-based strategy is proposed, optimizing task allocation and priority settings. By integrating local and edge computing resources, resource utilization is enhanced, and latency is significantly reduced. Simulations show that the improved strategy lowers communication latency, enhances system performance, and increases resource utilization in the power grid inspection context, offering valuable insights for related research.

Ogura cytoplasmic male sterility (CMS) is considered the rapeseed (Brassica napus L.) with the most potential to be utilized as a heterosis system worldwide, but it lacks sufficient restorers. In this study, root tip cell (RTC) mitotic and pollen mother cell (PMC) meiosis observations were compared to ensure the number of chromosomes and the formation of a chromosomal bridge using restorer lines R2000, CLR650, and Zhehuhong (a new restorer) as the experimental material. Further, molecular markers of exogenous chromosomal fragments were detected and the sequence and expression differences of restorer genes in the three lines were determined to identify the distinctive characteristics of Zhehuhong. The results showed that the number of chromosomes in Zhehuhong was stable (2n = 38), indicating that the exogenous radish chromosome segment had been integrated into the chromosome of Zhehuhong. Molecular marker detection revealed that Zhehuhong was detected at most loci, with only the RMA05 locus being missed. The exogenous radish chromosome segment of Zhehuhong differed from R2000 and CLR650. The pollen mother cells of Zhehuhong showed chromosome lagging in the meiotic metaphase I, meiotic anaphase I, and meiotic anaphase II, which was consistent with R2000 and CLR650. The restorer gene PPRB in Zhehuhong had 85 SNPs compared with R2000 and 119 SNPs compared with CLR650, indicating the distinctive characteristic of PPRB in Zhehuhong. In terms of the spatial expression of PPRB, the highest level was detected in the anthers in the three restorer lines. In addition, in terms of temporal expression, the PPRB gene expression of Zhehuhong was highest at a bud length of 4 mm. Our results clearly indicated that Zhehuhong is a new restorer line for the Ogura CMS system, which can be used further in rapeseed heterosis utilization.

In this paper, we designed and investigated a reduction-based method to synthesize controllably monodisperse superparamagnetic nano Fe3O4 colloidal clusters for magnetically responsive photonic crystals. It was shown that the addition of ascorbic acid (VC) to the system could synthesize monodisperse superparamagnetic nano Fe3O4 and avoided the generation of γ-Fe2O3 impurities, while the particle size and saturation magnetization intensity of nano Fe3O4 gradually decreased with the increase of VC dosage. Nano Fe3O4 could be rapidly assembled into photonic crystal dot matrix structures under a magnetic field, demonstrating tunability to various diffraction wavelengths. The nano Fe3O4 modified by polyvinylpyrrolidone (PVP) and silicon coated could be stably dispersed in a variety of organic solvents and thus diffracted different wavelengths under a magnetic field. This is expected to be applied in various scenarios in the field of optical color development.

In recent years, multi-functional lecture halls have developed rapidly and become a symbol of contemporary public spaces and places. This kind of spatial facility that brings together the advantages of land intensiveness and multi-functional integration also faces feedback such as poor acoustic effects. However, current research rarely involves the architectural design perspective, which is actually the root consideration of this problem; that is, how to set up corresponding spatial layout measures to optimize acoustic performance in a relatively economical and simple way. This study uses the academic lecture hall of the School of Architecture and Planning of Yunnan University as a case to try to solve these problems. The research is based on holistic considerations, starting from site selection, architectural design, aesthetic considerations, and environmental noise assessment, and combining simulation results with actual measurement results. Using a prediction–comparison–verification method, key acoustic parameters such as speech intelligibility, loudness, and reverberation time were calculated and evaluated to understand the acoustic design problems of the hall. The study found that the out-of-control reverberation time was the main cause of poor acoustic feedback, and based on this, optimization and transformation were carried out from an architectural perspective. Finally, a renovation suggestion was made that the application of sound-absorbing materials on the rear wall can achieve better acoustic effects inside the hall. Among the space combination methods, the combination of “rear wall, central ceiling, and front ceiling” has the best effect. Practical insights are provided for improving the acoustic performance of the multi-functional lecture halls while taking into account the acoustic design and feasible requirements.

This study introduces a novel methodology for the dynamic extraction of information on cotton growth in terms of height utilizing the DJI Zenmuse L1 LiDAR sensor mounted onto a DJI Matrice 300 RTK Unmanned Aerial Vehicle (UAV), aimed at enhancing the precision and efficiency of growth monitoring within the realm of precision agriculture. Employing the Progressive TIN Densification (PTD) and Cloth Simulation Filter (CSF) algorithms, combined with Kriging interpolation, we generated Canopy Height Models (CHMs) to extract the cotton heights at two key agricultural sites: Zengcheng and Tumxuk. Our analysis reveals that the PTD algorithm significantly outperforms the CSF method in terms of accuracy, with its R2 values indicating a superior model fit for height extraction across different growth stages (Zengcheng: 0.71, Tumxuk: 0.82). Through meticulous data processing and cluster analysis, this study not only identifies the most effective algorithm for accurate height extraction but also provides detailed insights into the dynamic growth patterns of cotton varieties across different geographical regions. The findings highlight the critical role of UAV remote sensing in enabling large-scale, high-precision monitoring of crop growth, which is essential for the optimization of agricultural practices such as precision fertilization and irrigation. Furthermore, the study demonstrates the potential of UAV technology to select superior cotton varieties by analyzing their growth dynamics, offering valuable guidance for cotton breeding and cultivation.

Obesity stands as a significant risk factor for type 2 diabetes, hyperlipidemia, and cardiovascular diseases, intertwining increased inflammation and decreased adipogenesis with metabolic disorders. Studies have highlighted the correlation between Caspase-1 and inflammation in obesity, elucidating its essential role in the biological functions of adipose tissue. However, the impact of Caspase-1 on adipogenesis and the underlying mechanisms remain largely elusive. In our study, we observed a positive correlation between Caspase-1 expression and obesity and its association with adipogenesis. In vivo experiments revealed that, under normal diet conditions, Caspase-1 deficiency improved glucose homeostasis, stimulated subcutaneous adipose tissue expansion, and enhanced adipogenesis. Furthermore, our findings indicate that Caspase-1 deficiency promotes the expression of autophagy-related proteins and inhibits autophagy with 3-MA or CQ blocked Caspase-1 deficiency-induced adipogenesis in vitro. Notably, Caspase-1 deficiency promotes adipogenesis via Atg7-mediated autophagy activation. In addition, Caspase-1 deficiency resisted against high-fat diet-induced obesity and glucose intolerance. Our study proposes the downregulation of Caspase-1 as a promising strategy for mitigating obesity and its associated metabolic disorders.

In 2014, 13.33% of youths aged 5–19 years worldwide had at least one type of behavioral problem. In China, children may be more likely to have internal and external behavioral problems, given that China has a high number of “left-behind children” and “only child of the family”. In this study, we explore the relationships between socioeconomic status (SES), social relations, and childhood behavioral problems in China. Data from 2151 children aged 10–15 years were collected from the 2018 wave of the China Family Panel Studies. We conducted structural equation modeling (SEM) using Amos (version 26) to test a theoretical model. The results showed that SES was significantly related to childhood behavioral problems, and that social relations mediated the relationship between SES and childhood behavioral problems. On the basis of these findings, we discuss relevant theoretical and practical implications.

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.

The prediction of future disease development based on past diagnosis records has gained significant attention due to the growing health awareness among individuals. Recent deep learning-based methods have successfully predicted disease development by establishing relationships for each diagnosis record and extracting features from a patient’s past diagnoses in chronological order. However, most of these models have ignored the connections between identified diseases and low-risk diseases, leading to bottlenecks and limitations. In addition, the extraction of temporal characteristics is also hindered by the problem of global feature forgetting. To address these issues, we propose a global context-aware net using disease relationship reasoning and attention-based feature fusion, abbreviated as DRR. Our model incorporates a disease relationship reasoning module that enhances the model’s attention to the relationship between confirmed diseases and low-risk diseases, thereby alleviating the current model’s bottlenecks. Moreover, we have established a global graph-based feature fusion module that integrates global graph-based features with temporal features, mitigating the issue of global feature forgetting. Extensive experiments were conducted on two publicly available datasets, and the experiments show that our method achieves advanced performance.

Solid Oxide Fuel Cells (SOFCs) are emerging as a leading solution in sustainable power generation, boasting high power-to-energy density and minimal emissions. With efficiencies potentially exceeding 60% for electricity generation alone and up to 85% when in cogeneration applications, SOFCs significantly outperform traditional combustion-based technologies, which typically achieve efficiencies of around 35–40%. Operating effectively at elevated temperatures (600 °C to 1000 °C), SOFCs not only offer superior efficiency but also generate high-grade waste heat, making them ideal for cogeneration applications. However, these high operational temperatures pose significant thermal management challenges, necessitating innovative solutions to maintain system stability and longevity. This review aims to address these challenges by offering an exhaustive analysis of the latest advancements in SOFC thermal management. We begin by contextualizing the significance of thermal management in SOFC performance, focusing on its role in enhancing operational stability and minimizing thermal stresses. The core of this review delves into various thermal management subsystems such as afterburners, heat exchangers, and advanced thermal regulation strategies. A comprehensive examination of the recent literature is presented, highlighting innovations in subsystem design, fuel management, flow channel configuration, heat pipe integration, and efficient waste heat recovery techniques. In conclusion, we provide a forward-looking perspective on the state of research in SOFC thermal management, identifying potential avenues for future advancements and their implications for the broader field of sustainable energy technologies.

Circular RNA (circRNA) plays a very important regulatory role in a variety of human malignancies such as non-small-cell lung cancer (NSCLC). In the current study, we explored the role of hsa_circ_0092856 in the progression of NSCLC. We screened CircRNA from the eIF3a gene in the Circbase database. The biological functions of hsa_circ_0092856 in NSCLC were analyzed via qRT-PCR, a CCK-8 assay, a plate cloning experiment, scratch testing, a transwell chamber experiment, an RNA nuclear mass separation experiment, an RIP experiment, and a Western blot test. The results showed that hsa_circ_0092856 was highly expressed in NSCLC cells, and the knockdown of hsa_circ_0092856 could inhibit the proliferation, migration, and invasion of NSCLC cells. The overexpression of hsa_circ_0092856 has the opposite effect. The expression of eIF3a also changed with the change in hsa_circ_0092856. These results suggest that hsa_circ_0092856 may play a key role in the progression of NSCLC by regulating the expression of eIF3a.

Economic development and urbanization naturally give rise to expanding demand for new buildings, whose construction and operation inevitably lead to significant increases in energy consumption and greenhouse gas emissions. To better conserve resources and protect the environment, technologies for green buildings have evolved significantly in the past two decades. In this study, a scientometric analysis of green buildings research from 2003 to 2023 was performed using CiteSpace. A total of 1986 articles retrieved from the Web of Science (WoS) core collection database were used as the data source for an in-depth analysis of research trends, hotspots, and future directions, showing changes in publication numbers, core journals, key countries, and institutions that have made remarkable contributions in this field. The results showed that the field of green buildings research is in a phase of rapid growth. The current research hotspots include the adoption of the green buildings paradigm, rating systems, energy performance, greenhouse gas emissions, indoor environmental quality, and green roofs/walls. Based on the keywords citation bursts and literature review, we believe that government promotion measures, use of renewable energy, integration with plants, and application of artificial intelligence (AI) in green buildings will be the most promising development directions in the future.

Salient Object Detection (SOD) aims at identifying the most visually distinctive objects in a scene. However, learning a mapping directly from a raw image to its corresponding saliency map is still challenging. First, the binary annotations of SOD impede the model from learning the mapping smoothly. Second, the annotator’s preference introduces noisy labeling in the SOD datasets. Motivated by these, we propose a novel learning framework which consists of the Self-Improvement Training (SIT) strategy and the Augmentation-based Consistent Learning (ACL) scheme. SIT aims at reducing the learning difficulty, which provides smooth labels and improves the SOD model in a momentum-updating manner. Meanwhile, ACL focuses on improving the robustness of models by regularizing the consistency between raw images and their corresponding augmented images. Extensive experiments on five challenging benchmark datasets demonstrate that the proposed framework can play a plug-and-play role in various existing state-of-the-art SOD methods and improve their performances on multiple benchmarks without any architecture modification.

As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object Tracking (MOT) for UAVs is a key technology to fulfill this task. Traditional detection-based-tracking (DBT) methods begin by employing an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods have been dominating this area, since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of a large number of small targets. In this paper, we propose using a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Membership Filter is applied, which describes object state as a bounded set. An appearance-matching cascade is then proposed based on the target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone2019-MOT dataset certify that our proposed model, referred to as SMFMOT, outperforms the state-of-the-art models by a wide margin and achieves superior performance in the MOT tasks in UAV videos.

Bridges’ expansion joints are prone to damage during operation, and repairing them often requires interruption of traffic, the impact of which can be minimized by using fast-hardening and early-strength expansion joint materials. In this study, a fast-hardening polymer cement composite (PCC) was developed using sulfate aluminate cement and ordinary silicate cement as binding agents and polymer powder as admixture. To improve the crack resistance of the material, several types of fibers were added and the effects of different fiber types and admixtures on the crack resistance of the material were compared using SCB tests. The results showed that the best effect of improving the crack resistance of concrete was achieved with a volume fraction of 0.5% of basalt fibers. Then, a test method for the interfacial shear properties of PCC materials and ordinary concrete was established, and the cohesive force model was selected as the interface simulation parameter for finite element analysis and compared with experimental data to verify its feasibility. Finally, based on the previously obtained PCC material parameters, a solid model of the expansion joint anchorage zone was established to study the mechanical properties of the expansion joint anchorage zone with the application of fast-hardening PCC material. This research provides a new way to develop fast-hardening and early-strength expansion joint materials with high crack resistance.

Background: Copper and copper-binding proteins are key components of tumor progression as they play important roles in tumor invasion and migration, but their associations in gliomas remain unclear. Methods: Transcriptomic datasets of glioblastoma, low-grade glioma, and normal brain cortex were derived from the TCGA and GTEX databases. Differentially expressed genes (DEGs) of copper-binding proteins were screened and used to construct a prognostic model based on COX and LASSO regression, which was further validated by the CGGA datasets. The expressions of risk-model genes were selectively confirmed via anatomic feature-based expression analysis and immunohistochemistry. The risk score was stratified by age, gender, WHO grade, IDH1 mutation, MGMT promoter methylation, and 1p/19q codeletion status, and a nomogram was constructed and validated. Results: A total of 21 DEGs of copper-binding proteins were identified and a six-gene risk-score model was constructed, consisting of ANG, F5, IL1A, LOXL1, LOXL2, and STEAP3, which accurately predicted 1-, 3-, and 5-year overall survival rates, with the AUC values of 0.87, 0.88, and 0.82, respectively. The high-risk group had a significantly shorter OS (p < 0.0001) and was associated with old age, wild-type IDH1, a high WHO grade, an unmethylated MGMT promoter, and 1p/19q non-codeletion and had higher levels of immune cell infiltration, cancer-immunity suppressor, and immune checkpoint gene expression as well as a higher TMB. Conclusions: The model based on the genes of copper-binding proteins could contribute to prognosis prediction and provide potential targets against gliomas.

Deep convolution neural networks have proven their powerful ability in comparing many tasks of computer vision due to their strong data learning capacity. In this paper, we propose a novel end-to-end denoising network, termed Fourier embedded U-shaped network (FEUSNet). By analyzing the amplitude spectrum and phase spectrum of Fourier coefficients, we find that low-frequency features of an image are in the former while noise features are in the latter. To make full use of this characteristic, Fourier features are learned and are concatenated as a prior module that is embedded into a U-shaped network to reduce noise while preserving multi-scale fine details. In the experiments, we first present ablation studies on the Fourier coefficients’ learning networks and loss function. Then, we compare the proposed FEUSNet with the state-of-the-art denoising methods in quantization and qualification. The experimental results show that our FEUSNet performs well in noise suppression and preserves multi-scale enjoyable structures, even outperforming advanced denoising approaches.

Rapeseed seeding dates are largely delayed under the rice–rape rotation system, but how rapeseeds adapt to the delayed environment remains unclear. Here, five seeding dates (20 October, 30 October, 10 November, 20 November and 30 November, T1 to T5) were set and the dynamic differences between two late-seeding-tolerant (LST) and two late-seeding-sensitive (LSS) rapeseed cultivars were investigated in a field experiment. The growth was significantly repressed and the foldchange (LST/LSS) of yield increased from 1.50-T1 to 2.64-T5 with the delay in seeding. Both LST cultivars showed higher plant coverage than the LSS cultivars according to visible/hyperspectral imaging and the vegetation index acquired from an unmanned aerial vehicle. Fluorescence imaging, DAB and NBT staining showed that the LSS cultivars suffered more stress damage than the LST cultivars. Antioxidant enzymes (SOD, POD, CAT, APX) and osmoregulation substances (proline, soluble sugar, soluble protein) were decreased with the delay in seeding, while the LST cultivar levels were higher than those of the LSS cultivars. A comparative analysis of transcriptomes and metabolomes showed that 55 pathways involving 123 differentially expressed genes (DEGs) and 107 differentially accumulated metabolites (DAMs) participated in late seeding tolerance regulation, while 39 pathways involving 60 DEGs and 68 DAMs were related to sensitivity. Levanbiose, α-isopropylmalate, s-ribosyl-L-homocysteine, lauroyl-CoA and argino-succinate were differentially accumulated in both cultivars, while genes including isocitrate dehydrogenase, pyruvate kinase, phosphoenolpyruvate carboxykinase and newgene_7532 were also largely regulated. This study revealed the dynamic regulation mechanisms of rapeseeds on late seeding conditions, which showed considerable potential for the genetic improvement of rapeseed.

Soybeans are the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) for soybean quality traits and mining related candidate genes are of great significance for the molecular breeding of soybean quality traits and understanding the genetic mechanism of protein/fat metabolism. In this study, the F2 population was derived from the high-protein material Changjiang Chun 2 and Jiyu 166. On the basis of a genetic linkage map constructed in our previous study, the QTL of crude protein content, crude oil content and fatty acid fractions were detected using the multiple-QTL model (MQM) mapping method. The results show that a total of 92 QTL were obtained affecting quality traits under three environments, including 14 QTL of crude oil content, 9 QTL of crude protein content, and 20, 20, 11, 10 and 8 QTL for the content of palmitic, stearic, oleic, linoleic and linolenic acids, respectively. Sixteen QTL clusters were identified, among which Loci01.1, Loci06.1 and Loci11.1 were identified as stable QTL clusters with phenotypic contribution rates of 16.5%, 16.4% and 12.1%, respectively, and candidate genes were mined in their regions. A total of 32 candidate genes related to soybean quality were finally screened via GO enrichment and gene annotation. The present study lies the foundations for understanding the genetic mechanism and elite germplasm innovation of seed quality in soybean.

Recent approaches for fast semantic video segmentation have reduced redundancy by warping feature maps across adjacent frames, greatly speeding up the inference phase. However, the accuracy drops seriously owing to the errors incurred by warping. In this paper, we propose a novel framework and design a simple and effective correction stage after warping. Specifically, we build a non-key-frame CNN, fusing warped context features with current spatial details. Based on the feature fusion, our context feature rectification (CFR) module learns the model’s difference from a per-frame model to correct the warped features. Furthermore, our residual-guided attention (RGA) module utilizes the residual maps in the compressed domain to help CRF focus on error-prone regions. Results on Cityscapes show that the accuracy significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS at a resolution of 1024×2048. For non-rigid categories, e.g., “human” and “object”, the improvements are even higher than 18 percentage points.

Based on the basic nonlinear parameter system of the solid oxide electrolysis cell, the data-driven method was used for system identification. The basic model of the solid oxide electrolysis cell was accomplished in Simulink and experiments were performed under a diversified input/output operating environment. The experimental results of the solid oxide electrolysis cell basic parameter system generated 15 datasets. The system identification process involved the utilization of these datasets with the application of nonlinear autoregressive-exogenous models. Initially, data identification came from the Matlab mechanism model. Then, the nonlinear autoregressive-exogenous structures were estimated and selected exploratively through an individual operating condition. In terms of fitness, we conclude that the solid oxide electrolysis cell parameter system cannot be satisfied by a solitary autoregressive-exogenous model for all datasets. Nevertheless, the nonlinear autoregressive-exogenous model utilized S-type nonlinearities to fit a total of 2 validation datasets and 15 estimated datasets. The obtained results were compared with the basic parameter system of a solid oxide electrolysis cell, and the nonlinear autoregressive-exogenous projected output demonstrated an accuracy of over 93% across diverse operational circumstances—regardless of whether there was noise interference. This result has positive significance for the future use of the solid oxide electrolysis cell to achieve the dual carbon goal in China.

In this article, we propose an effective grasp detection network based on an improved deformable convolution and spatial feature center mechanism (DCSFC-Grasp) to precisely grasp unidentified objects. DCSFC-Grasp includes three key procedures as follows. First, improved deformable convolution is introduced to adaptively adjust receptive fields for multiscale feature information extraction. Then, an efficient spatial feature center (SFC) layer is explored to capture the global remote dependencies through a lightweight multilayer perceptron (MLP) architecture. Furthermore, a learnable feature center (LFC) mechanism is reported to gather local regional features and preserve the local corner region. Finally, a lightweight CARAFE operator is developed to upsample the features. Experimental results show that DCSFC-Grasp achieves a high accuracy (99.3% and 96.1% for the Cornell and Jacquard grasp datasets, respectively) and even outperforms the existing state-of-the-art grasp detection models. The results of real-world experiments on the six-DoF Realman RM65 robotic arm further demonstrate that our DCSFC-Grasp is effective and robust for the grasping of unknown targets.

A series of vinyl versatate (VV10) modified poly(vinyl acetate) adhesive (HVPVAc) were prepared using soap-free emulsion polymerization. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was used to characterize the structure of the modified poly(vinyl acetate) latex. The effect of the VV10 content on particle size, viscosity, mechanical properties, and T-peel strength of the modified poly(vinyl acetate) was determined. No absorption peak at 1675–1500 cm−1 in the ATR-FTIR spectrum was observed as a result of the carbon-carbon double bond reacting completely. With the occurrence of -C-O-C and the disappearance of the carbon-carbon double bond in the FTIR spectrum, a more complex structure formed. The structure improves the mechanical properties. Increasing the VV10 content resulted in an increase in particle size from 63 nm to 221 nm, a steady increase in the viscosity of the HVPVAc latex, an increase in tensile strength from 7 MPa to 13.4 MPa, and a decrease in breaking elongation from 1310% to 1004%. As the VV10 content increased from 0 to 30% by weight, the T-peel strength of the HVPVAc adhesive increased from 8.35 N/mm to 18.97 N/mm, indicating improved adhesive performance.