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As an emerging, alternative mode of transportation, an in-depth understanding of autonomous shuttle (AS) experiences among all age groups, with and without disabilities, may impact acceptance and adoption of the AS, shape industry guidelines, and impact public policy. Therefore, this study analyzed qualitative data from older (n = 104), younger, and middle-aged (n = 106) adults and people with disabilities (n = 42). The data were obtained by asking participants four open-ended questions from an Autonomous Vehicle User Perception Survey. The result revealed seven themes (Safety, Ease of Use, Cost, Availability, Aging, AS Information, and Experience with AS) for older, younger, and middle-aged adults and six themes (all of the previously mentioned except for Aging) for people with disabilities. Frequency counts indicated priority attention, among all groups, to Safety and Ease of Use. This study provides valuable information pertaining to the experiences, concerns, and motivations of all potential users across age groups and disabilities—and may inform policymakers and industry partners to address their needs more adequately. These findings may contribute to improving and enhancing AS programming, design, and deployment in a safer, accessible, affordable, and tailored way.

Acanthopagrus latus and Rhabdosargus sarba are economically important marine species along the coast of China, with similar external morphological characteristics and living habits, with wide distribution and strong adaptability. To investigate the molecular mechanisms underlying the adaptive evolution of these two species, we conducted whole-genome resequencing of 10 individuals of both species from the coastal waters of Wuyu Island, Fujian, China, using high-throughput sequencing technology. We obtained SNP, InDel, CNV, and SV variation information and annotated these variations, constructing a genomic variation database for both species. By comparing the resequencing data with reference genomes, we identified 9,829,511 SNP loci in the population of A. latus and 34,051,056 SNP loci in the population of R. sarba. Using whole-genome SNP data, we employed Fst and ROD methods to identify candidate genomic regions under selection. Functional annotation and enrichment analysis using GO and KEGG databases revealed potential adaptive evolution in R. sarba associated with immune response, feeding, growth and development, and locomotion, while A. latus showed potential adaptive evolution associated with immune response, nervous system, growth and development, and metabolism.

As the demand for power supply stability and reliability in the active distribution network (ADN) increases, the primary and secondary integrated intelligent switch (PSIIS) has become the primary choice for smart grid transformation in many weak infrastructure areas. However, the number of PSIISs that can be configured is often limited. It is necessary to comprehensively utilize the measurement data and sectional capabilities of PSIISs through the optimal configuration method to find the optimal configuration scheme. Therefore, fault observability-related indexes (FORIs) are proposed based on the functional characteristics of PSIISs. These indexes include fault type observability, fault location observability, fault current distribution characteristics observability, transition resistance observability, and weak infrastructure area observability. An optimization configuration model of PSIISs considering the comprehensive fault observability (CFO) is constructed. The adaptive genetic algorithm (AGA) is selected as the model-solving method. Subsequently, an optimal configuration method of PSIISs considering CFO is proposed. Finally, an example analysis is conducted in MATLAB to verify the effectiveness and feasibility of the proposed method. This optimal configuration method aims to maximize the use of a limited number of PSIISs by considering CFO, and the AGA proves to be an effective tool in solving the optimal configuration scheme.

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

Rock materials failures are accompanied by the co-existence of various failure mechanisms, including rock fracturing, shearing, and compaction yield. These mechanisms manifest macroscopically as multiple failure modes and nonlinear strength characteristics related to stress levels. Considering the limitations of current rock mechanics strength theories, which are primarily derived from single failure mechanisms, this study evaluates the applicability of alternative strength theories. Based on the extensional-strain criterion and the PMC (Paul-Mohr-Coulomb) model, a piecewise linear strength model was proposed that is suitable for analyzing multiple failure mechanisms in rocks, revealing the intrinsic mechanisms of multi-mechanism rock material failure. A multiple failure mechanism strength model in the form of inequalities was proposed, using the generalized shear stress, mean stress, and stress Lode angle as parameters. Strength tests conducted on sandstone and granite rock material samples under different stress conditions revealed distinct piecewise linear strength characteristics for both rock types, validating the rationality and applicability of the multiple failure mechanism model. The findings construct a multi-mechanism failure model for rocks, providing enhanced predictive capabilities and aiding in the prevention of rock structural failures.

Widespread glyphosate contamination in the environment and its endocrine-disrupting potential are concerning. However, evidence of glyphosate’s effects on glycemic health is limited. To examine the association between glyphosate and glucose homeostasis in the general US population, a total of 3038 individuals were enrolled from the 2013-2016 cycles of the National Health and Nutrition Examination Survey (NHANES). Survey-weighted linear regression and restricted cubic spline curves were used to detect the associations between glyphosate and glycemic disorders. The effects of interactions between sex hormones and glyphosate on glycemic outcomes were evaluated. The results showed that glyphosate was significantly linked to increased glycated hemoglobin A1c (HbA1c) levels (β = 0.01; 95%CI, 0.01 to 0.02; p = 0.001) and the compromised homeostatic model assessment of beta-cell function (HOMA-beta) scores (β = −0.09; 95%CI, −0.17 to −0.01; p = 0.024). More importantly, these “glyphosate–glycemic disorder” associations were significantly modified by sex hormone-binding globulin (SHBG; P for interaction < 0.05), with more pronounced relationships being identified in individuals with low SHBG levels. Our findings indicate that glyphosate is correlated with glucose dyshomeostasis. Individuals with low SHBG levels exhibited susceptibility to glyphosate-related glycemic toxicity; therefore, it might be prudent to determine glycemic health in those subjects with glyphosate exposure.

During the development of animal organs, various adverse stimuli or toxic environments can induce oxidative stress and delay ovarian development. Paeoniflorin (PF), the main active ingredient of the traditional Chinese herb Paeonia lactiflora Pall., has protective effects on various diseases by preventing oxidative stress. However, the mechanism by which PF attenuates oxidative damage in mouse ovaries remains unclear. We evaluated the protective effects of PF on ovaries in an H2O2-induced mouse oxidative stress model. The H2O2-induced mouse ovarian oxidative stress model was used to explore the protective effect of PF on ovarian development. Histology and follicular development were observed. We then detected related indicators of cell apoptosis, oxidative stress, and autophagy in mouse ovaries. We found that PF inhibited H2O2-induced ovarian cell apoptosis and ferroptosis and promoted granulosa cell proliferation. PF prevented oxidative stress by increasing nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) expression levels. In addition, the autophagic flux of ovarian cells was activated and was accompanied by increased lysosomal biogenesis. Moreover, PF-mediated autophagy was involved in clearing mitochondria damaged by H2O2. Importantly, PF administration significantly increased the number of primordial follicles, primary follicles, secondary follicles, and antral follicles. PF administration improved ovarian sizes compared with the H2O2 group. The present study suggested that PF administration reversed H2O2-induced ovarian developmental delay and promoted follicle development. PF-activated mitophagy is crucial for preventing oxidative stress and improving mitochondrial quality.

The combination of no-till farming and green manure is key to nourishing the soil and increasing crop yields. However, it remains unclear how to enhance the efficiency of green manure under no-till conditions. We conducted a two-factor field trial of silage maize rotated with hairy vetch to test the effects of tillage methods and returning. Factor 1 is the type of tillage, which is divided into conventional ploughing and no-tillage; factor 2 is the different ways of returning hairy vetch as green manure, which were also compared: no return (NM), stubble return (H), mulching (HM), turnover (HR, for CT only), and live coverage (LM, for NT only). Our findings indicate that different methods of returning hairy vetch to the field will improve maize yield and quality. The best results were obtained in CT and NT in HM and LM, respectively. Specifically, HM resulted in the highest dry matter quality and yield, with improvements of 35.4% and 31.9% over NM under CT, respectively. It also demonstrated the best economic and net energy performance. However, other treatments had no significant effect on the beneficial utilization and return of nutrients. The LM improved yields under NT by boosting soil enzyme activity, promoting nitrogen transformation and accumulation, and increasing nitrogen use efficiency for better kernel development. Overall, NTLM is best at utilizing and distributing soil nutrients and increasing silage maize yield. This finding supports the eco-efficient cultivation approach in silage maize production in the region.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with poor survival rates, primarily due to the limited effectiveness of gemcitabine (Gem)-based chemotherapy, as well as the acquisition of chemotherapeutic resistance. Aronia berry extracts (ABEs), abundant in phenolic constituents, have been recently recognized for their anticancer properties as well as their encouraging potential to help overcome chemoresistance in various cancers. In the present study, we explored ABE’s potential to overcome Gem resistance in PDAC and identify specific growth regulatory pathways responsible for its anticancer activity. Through a series of in vitro experiments in gemcitabine-resistant (Gem-R) cells, we elucidated the synergistic interactions between Gem and ABE treatments. Using advanced transcriptomic analysis and network pharmacology, we revealed key molecular pathways linked to chemoresistance and potential therapeutic targets of ABE in Gem-R PDAC cells. Subsequently, the findings from cell culture studies were validated in patient-derived 3D tumor organoids (PDOs). The combination treatment of ABE and Gem demonstrated significant synergism and anticancer effects on cell viability, proliferation, migration, and invasion in Gem-R cells. Transcriptomic analysis revealed a correlation between the NF-Κb signaling pathway and Gem-R (p < 0.05), exhibiting a marked upregulation of MYD88. Additionally, MYD88 exhibited a significant correlation with the overall survival rates in patients with PDAC patients in the TCGA cohort (HR = 1.58, p < 0.05). The MYD88/NF-Κb pathway contributes to chemoresistance by potentially upregulating efflux transporters like P-glycoprotein (P-gp). Our findings revealed that the combined treatment with ABE suppressed the NF-Κb pathway by targeting MYD88 and reducing P-gp expression to overcome Gem resistance. Lastly, the combination therapy proved highly effective in PDOs in reducing both their number and size (p < 0.05). Our study offers previously unrecognized insights into the ability of ABE to overcome Gem resistance in PDAC cells through its targeting of the MYD88/NF-κb/P-gp axis, hence providing a safe and cost-effective adjunctive therapeutic strategy to improve treatment outcomes in PDAC.

The deteriorating soil health under continuous monoculture is commonly found across various cropping systems. This study evaluated the effects of different tillage practices (conventional tillage and no till) and species mixtures (legumes and grasses) on arbuscular mycorrhizal fungi (AMF) community properties, soil nutrients, and enzyme activity in a 3-year experiment. Compared with traditional tillage, the number of AMF species under no-till conditions was increased, with the Glomus group being dominant. Under different tillage conditions, TN (total N) and AN (available N) contents under no till were significantly higher than those under conventional tillage, while no significant differences among other nutrients were found. The activities of soil acid phosphatase (S-ACP), soil dehydrogenase (S-DHA), and soil sucrose (S-SC) under conventional tillage were significantly higher than those under no till, and the cover crop mixtures also had an exclusive advantage in yield. Soil organic matter (SOM) indicated a significant negative correlation with glomalin-related soil protein (GRSP). The increase in diversity associated with the AMF species community was strongly correlated with the increase in three enzyme activities, and AN was negatively correlated with all species. Tillage did not significantly change soil chemistry, except for AN, and the high concentration of AN led to a decrease in AMF species. The results of this study showed that no till was an effective measure for enriching soil micro-organism population. Additionally, soil AMF diversity was improved by cover crop mixtures, and microbial diversity was higher than that under monoculture cover crops. Different AMF groups responded differently to tillage and cover crop mixtures. Across all mixtures, the combination of hairy vetch (Vicia villosa R.) and ryegrass (Lolium perenne L.) performed the best.

Deep neural networks have played a crucial role in the field of deep learning, achieving significant success in practical applications. The architecture of neural networks is key to their performance. In the past few years, these architectures have been manually designed by experts with rich domain knowledge. Additionally, the optimal neural network architecture can vary depending on specific tasks and data distributions. Neural Architecture Search (NAS) is a class of techniques aimed at automatically searching for and designing neural network architectures according to the given tasks and data. Specifically, evolutionary-computation-based NAS methods are known for their strong global search capability and have aroused widespread interest in recent years. Although evolutionary-computation-based NAS has achieved success in a wide range of research and applications, it still faces bottlenecks in training and evaluating a large number of individuals during optimization. In this study, we first devise a multi-objective evolutionary NAS framework based on a weight-sharing supernet to improve the search efficiency of traditional evolutionary-computation-based NAS. This framework combines the population optimization characteristic of evolutionary algorithms with the weight-sharing ideas in one-shot models. We then design a bi-population MOEA/D algorithm based on the proposed framework to effectively solve the NAS problem. By constructing two sub-populations with different optimization objectives, the algorithm can effectively explore network architectures of various sizes in complex search spaces. An inter-population communication mechanism further enhances the algorithm’s exploratory capability, enabling it to find network architectures with uniform distribution and high diversity. Finally, we conduct performance comparison experiments on image classification datasets of different scales and complexities. Experimental results demonstrate the effectiveness of the proposed multi-objective evolutionary NAS framework and the practicality and transferability of the introduced bi-population MOEA/D-based NAS method compared to existing state-of-the-art NAS methods.

We proposed a novel method for predicting the service life of optical cables based on the Autoformer model combined with the calculation method. Leveraging historical weather data from Guangzhou and employing specific cable length calculation techniques, our study comprehensively considers factors impacting cable lifespan. Moreover, through comparative analysis with alternative deep learning models and parameter assessments, our method validates the superiority and stability of the Autoformer model in predicting cable lifespan, which can offer a more reliable approach for ensuring cable technology reliability and the management of associated industries.

With the development of natural gas pipelines, the proportion of aged pipelines in service has been increasing, and corrosion remains a primary cause of pipeline failure. Regular inspections and reliability assessments are crucial to ensure the safe operation of pipelines. This study investigated an efficient reliability assessment method for corroded pipelines that considers in-line inspection intervals. First, this study compared the commonly used limit state equations for corrosion defects to select one suitable for X80-grade steel pipelines. Additionally, a Tail-Fit Monte Carlo Simulation (TF-MCS) algorithm was proposed to improve the computational speed by 30 times compared to traditional Monte Carlo simulations. Then, this study explored the inspection intervals used for reliability assessments of corroded pipelines. Finally, the parameter sensitivity was analyzed considering the yield strength, maximum operating pressure, and pipe diameter. This study ensures the reliable operation of corroded gas pipelines.

Alkaline coal mine drainage represents one of the most critical issues in the coal industry, driven by complex hydro-biogeochemical processes. However, the interplay of hydrogeochemical and biogeochemical interactions in alkaline coal mine drainage is still poorly understood. To this end, water samples were systematically collected from alkaline coal mine drainage sites from five coal mining areas in Chongqing coal mining district, located in southwestern China. Hydrogeochemical analyses showed that the main water type of the coal mine drainage sample was HCO3-SO4~K-Na, which primarily originated from local meteoric water. The microbial community compositions in the studied alkaline coal drainage were critically associated with sulfate, bicarbonate, DOC, nitrate, and pH, and linked to three putative keystone genera via network analysis (Thiothrix, Methylophilaceae_MM1, and an unclassified genus from Comamonadaceae family). Functional predictions from FAPROTAX suggested a high abundance of metabolic pathways involving the oxidation of sulfide and sulfur compounds, potentially underscoring their importance in controlling sulfate enrichment in alkaline coal mine drainage. Interestingly, members of the Methylomonadaceae family (methanotrophs) and the Methylotenera genus (methylotrophs) had positive Spearman correlations with both ammonium and sulfate, potentially inferring that the enhanced activities of methanotrophs might help capture methane in the alkaline coal mine drainage. This study further enhances our comprehension of the intricate interplay between hydrogeochemical and biogeochemical interactions in alkaline coal mine drainage, contributing to the carbon budget.

With increasing age, dyslipidemia becomes a common health problem in the middle-aged and elderly population, posing a significant risk of cardiovascular disease. Aerobic exercise, as a non-pharmacological intervention, is considered to be effective in improving blood lipid levels, but the extent to which different types of aerobic exercise affect blood lipids is not clear. This study aims to investigate the effects of 12 different aerobic exercises on total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol in middle-aged and elderly people aged 45 years and over through systematic review and Bayesian network Meta-analysis of randomized controlled trials. We systematically searched relevant databases and included eligible randomized controlled trials. Bayesian network meta-analysis was used to compare the effects of 12 types of aerobic exercise on lipid levels. A total of 487 randomized controlled trials involving middle-aged and elderly people over 45 years old were included. The results of the network meta-analysis showed that all types of aerobic exercise could reduce blood lipid levels compared with no intervention in middle-aged and elderly people. In terms of total cholesterol, triglyceride, and low-density lipoprotein cholesterol, swimming had the most significant effect. For HDL cholesterol, dance showed a better effect. Studies have shown that swimming and dancing have a positive effect on improving blood lipid levels in middle-aged and elderly people. It is recommended to choose the appropriate type of exercise according to personal preference and physical condition.

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

As the third-largest maize-producing province in China and a typical arid and semi-arid region, quantitatively evaluating the carbon and nitrogen footprints of maize production and their dominant factors is of great significance in guiding the high-yield, low-carbon, and sustainable development of maize production in the Inner Mongolia Autonomous Region. This study quantitatively evaluated the interannual evolution characteristics of the carbon and nitrogen footprints in maize production and their dominant factors in Inner Mongolia from 2003 to 2022 based on statistical yearbook data and emission parameter models. The results showed that from 2003 to 2022, the maize planting area, yield, and total yield in Inner Mongolia all increased, with an average annual increase of 97 kg ha−1 in yield and 1.23 × 106 Mg in total yield. The carbon and nitrogen footprints of Inner Mongolia maize production over the past 20 years had overall decreasing trends, while the nitrogen fertilizer bias productivity, net ecosystem carbon balance, and sustainability index had increasing trends. Carbon footprint reduced by an average of 5.2 kg CO2 eq Mg−1 per year, and nitrogen footprint reduced by an average of 0.21 g N eq kg−1 per year. Currently, the transportation and production of fertilizer and field application of N fertilizer are the main controlling factors of GHG emissions from maize production. NO3−-N, NH4+-N leaching, and NH3 volatilization from field application of N fertilizer are the main sources of reactive N losses. The application of simplified processes, such as phased regulation of nitrogen and controlled-release fertilizers, as well as conservation tillage, have broad prospects for emission reduction in maize production in Inner Mongolia.

This study introduces a novel approach to landslide detection by incorporating the Spatial and Band Refinement Convolution (SBConv) module into the U-Net architecture, to extract features more efficiently. The original U-Net architecture employs convolutional layers for feature extraction, during which it may capture some redundant or less relevant features. Although this approach aids in building rich feature representations, it can also lead to an increased consumption of computational resources. To tackle this challenge, we propose the SBConv module, an efficient convolutional unit designed to reduce redundant computing and enhance representative feature learning. SBConv consists of two key components: the Spatial Refined Unit (SRU) and the Band Refined Unit (BRU). The SRU adopts a separate-and-reconstruct approach to mitigate spatial redundancy, while the BRU employs a split-transform-and-fuse strategy to decrease band redundancy. Empirical evaluation reveals that models equipped with SBConv not only show a reduction in redundant features but also achieve significant improvements in performance metrics. Notably, SBConv-embedded models demonstrate a marked increase in Recall and F1 Score, outperforming the standard U-Net model. For instance, the SBConvU-Net variant achieves a Recall of 75.74% and an F1 Score of 73.89%, while the SBConvResU-Net records a Recall of 70.98% and an F1 Score of 73.78%, compared to the standard U-Net’s Recall of 60.59% and F1 Score of 70.91%, and the ResU-Net’s Recall of 54.75% and F1 Score of 66.86%. These enhancements in detection accuracy underscore the efficacy of the SBConv module in refining the capabilities of U-Net architectures for landslide detection of multisource remote sensing data. This research contributes to the field of landslide detection based on remote sensing technology, providing a more effective and efficient solution. It highlights the potential of the improved U-Net architecture in environmental monitoring and also provides assistance in disaster prevention and mitigation efforts.

Kinematic calibration plays a pivotal role in enhancing the absolute positioning accuracy of industrial robots, with parameter identification and error compensation constituting its core components. While the conventional parameter identification method, based on linearization, has shown promise, it suffers from the loss of high-order system information. To address this issue, we propose an unscented Kalman filter (UKF) with adaptive process noise covariance for robot kinematic parameter identification. The kinematic model of a typical 6-degree-of-freedom industrial robot is established. The UKF is introduced to identify the unknown constant parameters within this model. To mitigate the reliance of the UKF on the process noise covariance, an adaptive process noise covariance strategy is proposed to adjust and correct this covariance. The effectiveness of the proposed algorithm is then demonstrated through identification and error compensation experiments for the industrial robot. Results indicate its superior stability and accuracy across various initial conditions. Compared to the conventional UKF algorithm, the proposed approach enhances the robot’s accuracy stability by 25% under differing initial conditions. Moreover, compared to alternative methods such as the extended Kalman algorithm, particle swarm optimization algorithm, and grey wolf algorithm, the proposed approach yields average improvements of 4.13%, 26.47%, and 41.59%, respectively.

The combustion characteristics of micro-combustors significantly impact the performance of micro-thermophotovoltaic (MTPV) systems. This study aims to investigate the effects of sinusoidal-shaped walls and catalyst segmentation on flame stability and combustion performance in a micro-combustor by using numerical methods. The numerical simulation with detailed gas-phase and surface reaction mechanisms is reliable, as the results of numerical simulation align with experimental data. The results show that the interplay between flame stability and sinusoidal-shaped walls is crucial, particularly because of the cavities formed by the sinusoidal-shaped walls of the micro-combustor. The gas-phase ignition position of the sinusoidal-shaped wall combustor moves upstream by 0.050 m compared to the planar-wall combustor, but the flame is stretched. The catalyst segments coated on the crest can shorten the flame length and increase the average temperature by a maximum 62 K, but delay the gas-phase ignition. Conversely, catalyst segments coated on the trough can advance ignition, but this results in flame elongation and a decrease in the average temperature. The rational combination of catalyst segmentation and sinusoidal-shaped walls facilitates moving the ignition position upstream by a maximum of 0.065 m while substantially reducing the length of the combustor required for complete fuel conversion by more than 60%. These attributes are highly beneficial for improving efficiency and minimizing the length of the micro-combustor for MTPV application.

Cellulose nanofibrils/poly(N-Isopropylacrylamide) semi-interpenetrating networks (MMCNF-PNAs) were synthesized using an in situ fabrication (semi-IPN). The polymerization of N-isopropylacrylamide (NIPAM) (free radical) was conducted in the presence of magnetic modified cellulose nanofibrils (MMCNFs). The adsorption behaviors and surface morphology of the synthesized adsorbents were investigated systematically. The adsorption behaviors of the as-prepared MMCNF-PNA towards methylene blue (MB, as the model contaminant) dye was studied, and the optimal adsorption conditions were also studied. The adsorption processes could be well fitted using pseudo-second-order and Elovich kinetic models. Meanwhile, Langmuir and Freundlich isotherm models were used to fit the adsorption which occurred at 25, 37 and 65 °C. The corresponding results showed that the Freundlich isotherm model fitted the adsorption process better, indicating that the dye’s adsorption happened via heterogeneous adsorptive energies on the prepared MMCNF-PNAs. Their desorption and reusability were also studied to verify magnetic responsivity. To sum up, MMCNF-PNAs are promising magnetic and thermal stimuli-responsive adsorbents, showing a controlled adsorption/desorption process.

Dongba characters are ancient ideographic scripts with abstract expressions that differ greatly from modern Chinese characters; directly applying existing methods cannot achieve the font style transfer of Dongba characters. This paper proposes an Attention-based Font style transfer Generative Adversarial Network (AFGAN) method. Based on the characteristics of Dongba character images, two core modules are set up in the proposed AFGAN, namely void constraint and font stroke constraint. In addition, in order to enhance the feature learning ability of the network and improve the style transfer effect, the Convolutional Block Attention Module (CBAM) mechanism is added in the down-sampling stage to help the network better adapt to input font images with different styles. The quantitative and qualitative analyses of the generated font and the real font were conducted by consulting with professional artists based on the newly built small seal script, slender gold script, and Dongba character dataset, and the styles of the small seal script and slender gold script were transferred to Dongba characters. The results indicate that the proposed AFGAN method has advantages in evaluation indexes and visual quality compared to existing networks. At the same time, this method can effectively learn the style features of small seal script and slender gold script, and transfer them to Dongba characters, indicating the effectiveness of this method.

Although XLPE cables are widely used in power transmission and distribution systems, their insulating properties are susceptible to degradation due to thermal aging. In order to clarify the influence law of the thermal aging process on the structural and dielectric properties of XLPE cables, this paper investigates the thermal aging characteristics of XLPE cables by using polarization and depolarization current measurement. Results show that when the XLPE cable is aged at 140 °C, the crystallinity of the insulation layer appears to increase and then decrease. With the increase in aging time, micron-sized microvoids appear on the surface of the XLPE. At the same time, the DC conductivity and 0.1 Hz dielectric loss factor of the insulating layer increase with the aging time. The average DC conductivity increased from 2.26 × 10−16 S/m for new cables to 4.47 × 10−16 S/m after aging for 432 h, while the dielectric loss increased from 0.11% to 0.42%. The polarization characteristics of thermal-aged cables were further analyzed using the extended Debye model. Results indicate that the time constant of the third branch of the model increased significantly with increasing aging time. A correspondence between this parameter and the thermal aging time of the cable was established. Thermal aging can damage the crystalline structure of XLPE, so that the number of interfaces between the crystalline and amorphous regions of the material increases, resulting in structural damages and a decline in the dielectric properties of the cable insulation.

It has been shown that there are thresholds of influence on the response of ecosystem services to their drivers, and the range of drivers that provide high levels of ecosystem services can be delineated through thresholds. However, due to the spatial heterogeneity of landscapes in karst regions, the results of ecosystem service threshold studies in non-karst regions may not be applicable to karst regions. This study explores the threshold effects between ecosystem services in karst landscapes and their natural and social drivers. It is shown that there are nonlinear constraints between them, and different critical thresholds exist for different kinds of ecosystem services. The main thresholds for water supply services include the slope (43.64°) and relief amplitude (331.60 m); for water purification services, they include relief amplitude (147.05 m) and distance to urban land (DTUL) (32.30 km); for soil conservation services, they include the normalized difference vegetation index (NDVI) (0.80) and nighttime light intensity (43.58 nW∙cm−2∙sr−1); the main thresholds for biodiversity maintenance services include population density (1481.06 person∙km−2) and distance to urban land (DTUL) (32.80 km). This enables regional ecological conservation planning based on different threshold ranges corresponding to different ecosystem services to meet the different needs of different decision makers.

Fertilization has a significant impact on soil nutrients and microbiological properties, which, in turn, affect crop yield. However, the specific effects of organic and inorganic fertilizers on soil fertility and microbial characteristics in maize fields, as well as the key soil factors influencing changes in crop yield, remain largely unknown. A study was conducted over two years (2021–2022) to investigate the impact of various nitrogen fertilization rates and types on maize yield and soil properties in the Loess Plateau. Field experiments with five nitrogen levels (60, 90, 120, 150, and 180 kg N ha−1) and two fertilizer types (chemical and organic) were conducted. The optimal yield was achieved with 150 kg N ha−1, significantly surpassing that of other treatments by 4.5−45.7%. Compared with the organic fertilizers, the chemical fertilizers increased soil salt and catalase levels, with soil nitrate and ammonium content being higher at the jointing stage but lower at maturity. Organic fertilization improved soil potassium, organic matter, urease and phosphatase activities, and microbial populations. Yield correlated with several soil indicators, including salt content, nitrate, available potassium, and enzyme activities. Notably, soil nitrate also correlated with actinomyces quantity. A principal component analysis showed that the organic fertilizer was more beneficial to soil health than the chemical fertilizer. Consequently, this study recommends 150 kg N ha−1 of organic fertilizer for sustainable maize farming and soil health in China’s northwest arid region, providing a theoretical framework for agricultural practices.

The effect of UV-B radiation exposure on transgenerational plasticity, the phenomenon whereby the parental environment influences both the parent’s and the offspring’s phenotype, is poorly understood. To investigate the impact of exposing successive generations of rice plants to UV-B radiation on seed morphology and proanthocyanidin content, the local traditional rice variety ‘Baijiaolaojing’ was planted on terraces in Yuanyang county and subjected to enhanced UV-B radiation treatments. The radiation intensity that caused the maximum phenotypic plasticity (7.5 kJ·m−2) was selected for further study, and the rice crops were cultivated for four successive generations. The results show that in the same generation, enhanced UV-B radiation resulted in significant decreases in grain length, grain width, spike weight, and thousand-grain weight, as well as significant increases in empty grain percentage and proanthocyanidin content, compared with crops grown under natural light conditions. Proanthocyanidin content increased as the number of generations of rice exposed to radiation increased, but in generation G3, it decreased, along with the empty grain ratio. At the same time, biomass, tiller number, and thousand-grain weight increased, and rice growth returned to control levels. When the offspring’s radiation memory and growth environment did not match, rice growth was negatively affected, and seed proanthocyanidin content was increased to maintain seed activity. The correlation analysis results show that phenylalanine ammonialyase (PAL), cinnamate-4-hydroxylase (C4H), dihydroflavonol 4-reductase (DFR), and 4-coumarate:CoA ligase (4CL) enzyme activity positively influenced proanthocyanidin content. Overall, UV-B radiation affected transgenerational plasticity in seed morphology and proanthocyanidin content, showing that rice was able to adapt to this stressor if previous generations had been continuously exposed to treatment.

Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity.

The diffusion of trace elements in mining wastelands has attracted widespread attention in recent years. Vegetation restoration is an effective measure for controlling the surface migration of trace elements. However, there is no field evidence of the effective riparian zone width in mining wastelands. Three widths (5 m, 7.5 m, and 10 m) of Rhododendron simsii/Lolium perenne L. riparian zones were constructed in lead–zinc mining wastelands to investigate the loss of soil, cadmium (Cd), copper (Cu), arsenic (As), lead (Pb), and zinc (Zn). Asbestos tiles were used to cut off connections between adjacent plots to avoid hydrological interference. Plastic pipes and containers were used to collect runoff water. Results showed that more than 90% of trace elements were lost in sediment during low coverage and heavy rainfall periods. Compared with the 5 m riparian zone, the total trace element loss was reduced by 69–85% during the whole observation period in the 10 m riparian zone and by 86–99% during heavy rain periods in the 10 m riparian zone, which was due to reduction in runoff and concentrations of sediment and trace elements in the 10 m riparian zone. Indirect negative effects of riparian zone width on trace element loss through runoff and sediment concentration were found. These results indicated that the wide riparian zone promoted water infiltration, filtered soil particles, and reduced soil erosion and trace element loss. Riparian zones can be used as environmental management measures after mining areas are closed to reduce the spread of environmental risks in mining wastelands, although the long-term effects remain to be determined.

The exploration into nanomaterial-based nonenzymatic biosensors with superb performance in terms of good sensitivity and anti-interference ability in disease marker monitoring has always attained undoubted priority in sensing systems. In this work, we report the design and synthesis of a highly active nanocatalyst, i.e., palladium and platinum nanoparticles (Pt&Pd-NPs) decorated ultrathin nanoporous gold (NPG) film, which is modified on a homemade graphene paper (GP) to develop a high-performance freestanding and flexible nanohybrid electrode. Owing to the structural characteristics the robust GP electrode substrate, and high electrochemically catalytic activities and durability of the permeable NPG support and ultrafine and high-density Pt&Pd-NPs on it, the resultant Pt&Pd-NPs–NPG/GP electrode exhibits excellent sensing performance of low detection limitation, high sensitivity and anti-interference capability, good reproducibility and long-term stability for the detection of small molecular biomarkers hydrogen peroxide (H2O2) and glucose (Glu), and has been applied to the monitoring of H2O2 in different types of live cells and Glu in body fluids such as urine and fingertip blood, which is of great significance for the clinical diagnosis and prognosis in point-of-care testing.

In order to evaluate the risk of transmission line tripping due to wildfires, it is necessary to predict the breakdown voltage of the insulation gap under the flame. Firstly, this paper studies the breakdown prediction of rod–plane gaps under the full-flame bridging of wooden cribs; it then obtains the breakdown voltage and the leakage current values of full-flame bridging considering different sizes of wooden cribs and different gap distances. Then, a multi-physical field simulation is carried out to obtain the flame gap characteristic parameters, such as spatial temperature. The feature quantity is normalized and reduced in dimension, and a prediction model for gap breakdown voltage under flame conditions based on a support vector machine (SVM) is established. Finally, the DC withstand voltage values and corresponding characteristic quantities under different flame gap conditions are used as sample sets to test the prediction model. The results show that the prediction error for small gap breakdown voltage is less than 2.6%. The samples were tested under different flame intensities for training and prediction, and the error was less than 3.3%. The small gap data for 30~60 cm is used to predict the breakdown voltage of the long gap for 100~140 cm, and the error is less than 3.2%. Compared with the fitting correction formula method proposed in existing research, the error is reduced by 11.5% and 4.4%, respectively, which verifies the effectiveness of the SVM prediction model.

Breast cancer is the most commonly diagnosed female cancer and the leading cause of cancer death. Early detection and treatment are important to reduce the number of deaths. Japan recommends mammography every two years for women over 40 years of age. However, in recent years, an increasing number of younger women have been undergoing breast cancer screening (BCS). To reduce the harms of BCS among young adults, our study extracted data from an online survey conducted in 2018 and applied χ2 tests and logistic analysis to identify the influencing factors regarding interest in undergoing BCS. The results of our analysis support the need for a reduction in the BCS rate through awareness regarding the harms of health screening among young people. In particular, for those who receive BCS through occupational screening, we believe that improving education on breast awareness, the accuracy of occupational screening, and breast self-examination methods could reduce the harms from BCS in younger age groups.

Background: The drug selection of radical surgery (RS), with hyperthermic intraperitoneal chemotherapy (HIPEC), in pT4 colorectal cancer (CRC) remains controversial. Methods: Adverse events after HIPEC were estimated by common terminology criteria for adverse events version 5.0. The efficacy was evaluated using overall survival (OS) and recurrence-free rate (RFR). Propensity score matching (PSM) was used to reduce the influence of confounders between Mitomycin and Lobaplatin groups. Results: Of the 146 patients, from April 2020 to March 2021, 47 were managed with mitomycin and 99 with lobaplatin. There was no significant difference in the incidence of all adverse events between the two groups after PSM. OS and RFR were not significantly different between the two groups at 22 months (p = 0.410; p = 0.310). OS and RFR of the two groups also showed no significant difference for patients with T4a or T4b stage, tumor size < or ≥ 5 cm. Among patients with colon cancer, RFR at 22 months of the two groups was significantly different (100.0% vs. 63.2%, p = 0.028). Conclusions: In summary, the safety of mitomycin and lobaplatin for HIPEC was not different. Compared with lobaplatin, mitomycin for HIPEC after RS could benefit patients with colon cancer in RFR.

The tensile mechanical behavior of water-saturated earlywood (EW) and latewood (LW) within the same growth ring of Masson pine (Pinus massoniana) was investigated in the hydrothermal environment and discussed with respect to the density and microfibril angle (MFA) of the wood specimens. The tensile modulus, tensile strength, and strain at failure of EW and LW in the longitudinal (L) and tangential (T) directions were determined at different temperature levels ranging from 30 °C to 80 °C. Major differences in the tensile mechanical properties were found between EW and LW in the L and T directions. Compared to LW, EW showed a smaller density and a larger MFA, resulting in a lower tensile modulus, lower tensile strength, and higher strain at failure. Compared to the L specimens, the T specimens showed lower tensile modulus, lower tensile strength, and higher strain at failure. As the hygrothermal temperature increased, the MFAs, tensile modulus, and tensile strength of EW and LW specimens decreased, except for the MFAs of LW, while the strain at failure of the specimens showed the opposite trend. Variations in the tensile mechanical behavior between EW and LW were mainly influenced by the density and MFA of the specimens, and are closely associated with the hydrothermal softening properties of wood. These findings contribute to a further understanding of the structural–mechanical relationships of Masson pine wood at the cell wall level, and provide a scientific basis for the better utilization of plantation softwood in the hydrothermal environment.

Aspect-level sentiment analysis is a research focal point for natural language comprehension. An attention mechanism is a very important approach for aspect-level sentiment analysis, but it only fuses sentences from a semantic perspective and ignores grammatical information in the sentences. Graph convolutional networks (GCNs) are a better method for processing syntactic information; however, they still face problems in effectively combining semantic and syntactic information. This paper presents a sentiment-supported graph convolutional network (SSGCN). This SSGCN first obtains the semantic information of the text through aspect-aware attention and self-attention; then, a grammar mask matrix and a GCN are applied to preliminarily combine semantic information with grammatical information. Afterward, the processing of these information features is divided into three steps. To begin with, features related to the semantics and grammatical features of aspect words are extracted. The second step obtains the enhanced features of the semantic and grammatical information through sentiment support words. Finally, it concatenates the two features, thus enhancing the effectiveness of the attention mechanism formed from the combination of semantic and grammatical information. The experimental results show that compared with benchmark models, the SSGCN had an improved accuracy of 6.33–0.5%. In macro F1 evaluation, its improvement range was 11.68–0.5%.

(1) Background: Previous studies have compared research into ski jumping in different motor processes, but there is a lack of comparative analysis of the biomechanical research methods used to investigate different ski jumping sports. (2) Content: Our study compared the advantages and disadvantages of six research methods and proposes future research directions. Motion video collection and analysis show that controlling angular momentum and achieving stable flight attitude in the take-off process are the most critical factors in ski jumping performance. Most research on force platforms focuses on dynamic performance at the time of take-off, but there are few training sites with an embedded force platform, and so, more empirical research is required. Wearable inertial measurement units, including gyroscopes and accelerometers, can be used to determine a series of forces, calculate the joint angle, and speculate the position of the centroid during motion. Surface EMG studies are primarily used to compare the activity characteristics of the lower limb muscles in the actual field of the jump, the exercise simulation, and the lack of complete training process data. Wind tunnel measurement can satisfy fluid mechanics simulation experiments and provide theoretical support for optimizing special ski jumping technology. Based on the theory of computational fluid dynamics, the optimal drag reduction posture data of ski jumpers can be derived using computer simulations. (3) Conclusions: Due to the wide range of ski jumping sports, the present research focused on the kinematics and dynamics of different movement stages, lacking the study of the complete exercise training process. The range of wearable inertial measurement and sensor equipment can cover the whole process of ski jumping, including kinematics and dynamics data, and is a feasible and reliable test method for monitoring ski jump training in natural environments. The simultaneous testing of surface electromyography, kinematics, and dynamics requires further exploration. (4) Future direction of development: Under computational fluid dynamics, wearable inertial measurement units and global navigation satellite systems (GNSSs), intelligent wind tunnel experimental training areas will become essential tools for ski jumping research.

Social trust is derived from the interaction of environmental and social factors, which has important significance for the sustainable development of society and social governance. In particular, in the post-pandemic era, tourist activity will receive special attention in terms of its role in the development of the public’s social trust. On the basis of the sample of big data, this research takes China as an example to study the influences of different geographical and environmental elements on individuals’ social trust as well as the common role played by the tourist activity. The research showed that the geographical environment and tourism activities have interacting effects on public social trust. This influencing mechanism is specifically manifested as the rice-growing ratio and tourist reception level can have interacting effects on the social trust of the residents in a tourist destination; pathogen stress and tourist supply level can exert interacting effects on the social trust of the residents in an area from which tourists originate; and economic development and tourist reception level can have interacting effects on the social trust of the residents in a tourist destination. By doing so, this research provides theoretical support and practical suggestions for the recovery of the public’s social trust from the perspective of tourism geography in the post-pandemic era.

Phytophthora blight of pepper is a notorious disease caused by the oomycete pathogen Phytophthora capsici, which poses a great threat to global pepper production. MicroRNA (miRNA) is a class of non-coding small RNAs that regulate gene expressions by altering the translation efficiency or stability of targeted mRNAs, which play important roles in the regulation of a plant’s response to pathogens. Herein, time-series mRNA-seq libraries and small RNA-seq libraries were constructed using pepper roots from the resistant line CM334 and the susceptible line EC01 inoculated with P. capsici at 0, 6, 24, and 48 h post-inoculation, respectively. For mRNA-seq analysis, a total of 2159 and 2971 differentially expressed genes (DEGs) were identified in CM334 and EC01, respectively. For miRNA-seq analysis, 491 pepper miRNAs were identified, including 330 known miRNAs and 161 novel miRNAs. Among them, 69 and 88 differentially expressed miRNAs (DEMs) were identified in CM334 and EC01, respectively. Examination of DEMs and their targets revealed 22 regulatory networks, predominantly featuring up-regulated miRNAs corresponding to down-regulated target genes. Notably, these DEM-DEG regulatory networks exhibited significant overlap between CM334 and EC01, suggesting that they might contribute to pepper’s basal defense against P. capsici. Furthermore, five selected DEMs (miR166, miR1171, miR395, miR530 and miRN2) and their target genes underwent qRT-PCR validation, confirming a consistent negative correlation in the expression patterns of miRNAs and their targets. This comprehensive analysis provides novel insights into the regulatory networks of miRNAs and their targets, offering valuable contributions to our understanding of pepper’s defense mechanisms against P. capsici.

OVATE family proteins (OFPs) play important roles in plant growth and development, hormone signaling, and stress response pathways. However, the functions of OsOFPs in rice are largely unknown. In this study, a novel gain-of-function rice mutant, Osofp6-D, was identified. This mutant exhibited decreased plant height, erect leaves, reduced panicle size, short and wide seeds, delayed seed germination time, and reduced fertility. These phenotypic changes were attributed to the increased expression of OsOFP6, which was caused by a T-DNA insertion. Complementation of the Osofp6-D phenotype by knockout of OsOFP6 using the CRISPR/Cas9 system confirmed that the Osofp6-D phenotype was caused by OsOFP6 overexpression. In addition, transgenic plants overexpressing OsOFP6 with the 35S promoter mimicked the Osofp6-D phenotype. Cytological observations of the glumes showed that OsOFP6 overexpression altered the grain shape, mainly by altering the cell shape. Hormone response experiments showed that OsOFP6 was involved in the gibberellin (GA) and brassinolide (BR) signaling responses. Further studies revealed that OsOFP6 interacts with E3BB, which is orthologous to the Arabidopsis central organ size-control protein BIG BROTHER (BB). This study further elucidates the regulation mechanism of the rice OFP family on plant architecture and grain shape.

The impaired invasion ability of trophoblast cells is related to the occurrence of preeclampsia (PE). We previously found that pregnancy-specific beta-1-glycoprotein 1 (PSG1) levels were decreased in the serum of individuals with early-onset preeclampsia (EOPE). This study investigated the effect of PSG1 on Orai1-mediated store-operated calcium entry (SOCE) and the Akt signaling pathway in human trophoblast cell migration. An enzyme-linked immunosorbent assay (ELISA) was used to determine the level of PSG1 in the serum of pregnant women with EOPE. The effects of PSG1 on trophoblast proliferation and migration were examined using cell counting kit-8 (CCK8) and wound healing experiments, respectively. The expression levels of Orai1, Akt, and phosphorylated Akt (p-Akt) were determined through Western blotting. The results confirmed that the serum PSG1 levels were lower in EOPE women than in healthy pregnant women. The PSG1 treatment upregulated the protein expression of Orai1 and p-Akt. The selective inhibitor of Orai1 (MRS1845) weakened the migration-promoting effect mediated by PSG1 via suppressing the Akt signaling pathway. Our findings revealed one of the mechanisms possibly involved in EOPE pathophysiology, which was that downregulated PSG1 may reduce the Orai1/Akt signaling pathway, thereby inhibiting trophoblast migration. PSG1 may serve as a potential target for the treatment and diagnosis of EOPE.

Studies on the demersal fish composition of the East Antarctic nearshore region are very scarce due to the harsh physical geography of the East Antarctic region, which is covered in ice and snow year round, leading to a scarcity of scientific surveys. Based on the data from the bottom trawl survey conducted by the 37–38th Chinese National Antarctic Research Expedition (CHINARE) program in the Cosmonaut Sea, East Antarctica, the species of demersal fishes were identified, and the demersal fish composition and the characteristics of depth distribution were examined and analyzed. The basic biological information of highly abundant species was analyzed. The results from 97 individuals sampled within 30–60° E, south of −65° S, indicate that the fishes belong to 5 orders, 11 families, 19 genera, and 23 species. Most of the species are found in Myctophidae and Bathydraconidae, and the most common species is Macrourus whitsoni (Macrouridae). Macrourus whitsoni and Prionodraco evansii have a high abundance in the survey. Macrourus whitsoni has body lengths of 144–662 mm and body weights of 17.3–1425.1 g, and Prionodraco evansii has body lengths of 90.18–134.33 mm and body weights of 4.9–20.7 g. The length–weight relationships for Macrourus whitsoni and Prionodraco evansii are y=0.00002x2.748 and y=0.000006x3.353, respectively. All the Macrourus whitsoni samples were found in waters deeper than 1000 m, with the highest number of individuals captured at depths of 1500–2000 m. Prionodraco evansii was found only at stations less than 250 m deep. These results complement the demersal fish composition and distribution data of the Cosmonaut Sea, East Antarctica. These data can provide valuable basic information for characterizing regional assemblages and delineating zoogeographic boundaries.

Mastitis is one of the most predominant diseases with a negative impact on ranch products worldwide. It reduces milk production, damages milk quality, increases treatment costs, and even leads to the premature elimination of animals. In addition, failure to take effective measures in time will lead to widespread disease. The key to reducing the losses caused by mastitis lies in the early detection of the disease. The application of deep learning with powerful feature extraction capability in the medical field is receiving increasing attention. The main purpose of this study was to establish a deep learning network for buffalo quarter-level mastitis detection based on 3054 ultrasound images of udders from 271 buffaloes. Two data sets were generated with thresholds of somatic cell count (SCC) set as 2 × 105 cells/mL and 4 × 105 cells/mL, respectively. The udders with SCCs less than the threshold value were defined as healthy udders, and otherwise as mastitis-stricken udders. A total of 3054 udder ultrasound images were randomly divided into a training set (70%), a validation set (15%), and a test set (15%). We used the EfficientNet_b3 model with powerful learning capabilities in combination with the convolutional block attention module (CBAM) to train the mastitis detection model. To solve the problem of sample category imbalance, the PolyLoss module was used as the loss function. The training set and validation set were used to develop the mastitis detection model, and the test set was used to evaluate the network’s performance. The results showed that, when the SCC threshold was 2 × 105 cells/mL, our established network exhibited an accuracy of 70.02%, a specificity of 77.93%, a sensitivity of 63.11%, and an area under the receiver operating characteristics curve (AUC) of 0.77 on the test set. The classification effect of the model was better when the SCC threshold was 4 × 105 cells/mL than when the SCC threshold was 2 × 105 cells/mL. Therefore, when SCC ≥ 4 × 105 cells/mL was defined as mastitis, our established deep neural network was determined as the most suitable model for farm on-site mastitis detection, and this network model exhibited an accuracy of 75.93%, a specificity of 80.23%, a sensitivity of 70.35%, and AUC 0.83 on the test set. This study established a 1/4 level mastitis detection model which provides a theoretical basis for mastitis detection in buffaloes mostly raised by small farmers lacking mastitis diagnostic conditions in developing countries.

Cement-stabilization of weak and soft soils is an efficient way for ground improvement. Traditional Portland cement remains the most popular cementitious material in practice, and thus, a proper dosage design of cement-stabilized soil is of practical interest to meet the sustainable engineering requirements and to remedy environmental concerns. Based on the unconfined compression test of cement-stabilized Qiantang River silty clay, the effects of cement content, mixing moisture content, mixing-water-to-cement ratio, and curing time on the unconfined compressive strength were investigated. The results show that the mixing-water-to-cement ratio can comprehensively characterize the effects of cement content and water content on the unconfined compressive strength of the cement-stabilized clay. A prediction method for the unconfined compressive strength of cement-stabilized Qiantang River silty clay has been proposed with considerations for mixing-water-to-cement ratio and curing time. By comparing the experimental data of the present study with the existing literature data, it is found that there is a unified relationship between the unconfined compressive strength and the mixing-water-to-cement ratio of cement-stabilized Qiantang River silty clay, kaolin, Singapore marine clay, and Bangkok clay under the same curing time. The prediction method recommended by the standard may overestimate the unconfined compressive strength of cement-stabilized Qiantang River silty clay cured for 90 days.

To enhance the specificity and sensitivity, cut the cost, and realize joint detection of multiple indicators, an immunoassay system based on the technology of time-resolved fluorescence resonance energy transfer (TR-FRET) was studied. Due to the FRET of the reagent, the donor probe and acceptor probe emitted specific fluorescence to enhance specificity. Long-lifetime specific fluorescence from the acceptor probe was combined with time-resolved technology to enhance sensitivity. A xenon flash lamp and a photomultiplier tube (PMT) were selected as the light source and detector, respectively. A filter-switching mechanism was placed in the light path, so the fluorescence signal from the donor and acceptor was measured alternately. The instrument’s design is given, and some specificI parts are described in detail. Key technical specifications of the instrument and procalcitonin (PCT), C-reactive protein (CRP), and interleukin-6(IL-6) were tested, and the test results were presented subsequently. The CV value of the self-designed counting module is better than 0.01%, and the instrument noises for 620 nm and 665 nm are 41.44 and 10.59, respectively. When set at 37 °C, the temperature bias (B) is 0.06 °C, and the temperature fluctuation is 0.10 °C. The CV and bias are between ±3% and 5%, respectively, when pipetting volumes are between 10 μL and 100 μL. Within the concentration range of 0.01 nM to 10 nM, the luminescence values exhibit linear regression correlation coefficients greater than 0.999. For PCT detection, when the concentration ranges from 0.02 ng/mL to 50 ng/mL, the correlation coefficient of linear fitting exceeds 0.999, and the limit of quantification is 0.096 ng/mL. For CRP and IL-6, the detection concentration ranges from 0 ng/mL to 500 ng/mL and 0 ng/mL to 20 ng/mL, respectively, with limits of quantification of 2.70 ng/mL and 2.82 ng/mL, respectively. The experimental results confirm the feasibility of the technical and instrumental solutions.

Meloidogyne spp. are important parasitic nematodes affecting a variety of plants worldwide. We investigated the nematicidal properties of specific compounds found in bio-fumigant plants, particularly linalool, nonanal, methylene chloride, and 2-Decanal. Laboratory findings revealed that methylene chloride and 2-Decenal effectively reduced populations of second-stage juveniles (J2s). Additionally, the research explored the effects of tomato (Solanum lycopersicum L.) on M. incognita J2s, observing that tomato leaves significantly increased J2 mortality for all time measurements and different temperatures, while the opposite results were observed for root-stems. In the study, leaf treatment resulted in a maximum mortality response (MRmax) and half-maximal effective concentration (EC50) of approximately 100% and 4.0 µg/mg, respectively, at a temperature of 35 °C by week 8. In contrast, the root-stems treatment showed an MRmax of 13.5% and an EC50 of 3.0 ± 1.7 µg/mg. GC-MS analysis identified key compounds in tomato leaves and root-stems, such as α-pinene, d-limonene, and linalool. The results suggest that tomato leaves have potential as effective bio-fumigants for controlling root-knot nematodes.

To enhance the degradability of poly(butylene adipate-co-terephthalate) (PBAT), reed fiber (RF) was blended with PBAT to create composite materials. In this study, a fifteen day degradation experiment was conducted using four different enzyme solutions containing lipase, cellulase, Proteinase K, and esterase, respectively. The degradation process of the sample films was analyzed using an analytical balance, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and differential scanning calorimetry (DSC). The PBAT/RF composites exhibited an increased surface hydrophilicity, which enhanced their degradation capacity. Among all the enzymes tested, lipase had the most significant impact on the degradation rate. The weight loss of PBAT and PBAT/RF, caused by lipase, was approximately 5.63% and 8.17%, respectively. DSC analysis revealed an increase in the melting temperature and crystallinity over time, especially in the film containing reed fibers. FTIR results indicated a significant weakening of the ester bond peak in the samples. Moreover, this article describes a biodegradation study conducted for three months under controlled composting conditions of PBAT and PBAT/RF samples. The results showed that PBAT/RF degraded more easily in compost as compared to PBAT. The lag phase of PBAT/RF was observed to decrease by 23.8%, while the biodegradation rate exhibited an increase of 11.8% over a period of 91 days. SEM analysis demonstrated the formation of more cracks and pores on the surface of PBAT/RF composites during the degradation process. This leads to an increased contact area between the composites and microorganisms, thereby accelerating the degradation of PBAT/RF. This research is significant for preparing highly degradable PBAT composites and improving the application prospects of biodegradable green materials. PBAT/RF composites are devoted to replacing petroleum-based polymer materials with sustainable, natural materials in advanced applications such as constructional design, biomedical application, and eco-environmental packaging.

Purpose: The aim of this study was to construct and validate a nomogram for preoperatively predicting perineural invasion (PNI) in gastric cancer based on machine learning, and to investigate the impact of PNI on the overall survival (OS) of gastric cancer patients. Methods: Data were collected from 162 gastric patients and analyzed retrospectively, and radiomics features were extracted from contrast-enhanced computed tomography (CECT) scans. A group of 42 patients from the Cancer Imaging Archive (TCIA) were selected as the validation set. Univariable and multivariable analyses were used to analyze the risk factors for PNI. The t-test, Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Radscores were calculated and logistic regression was applied to construct predictive models. A nomogram was developed by combining clinicopathological risk factors and the radscore. The area under the curve (AUC) values of receiver operating characteristic (ROC) curves, calibration curves and clinical decision curves were employed to evaluate the performance of the models. Kaplan–Meier analysis was used to study the impact of PNI on OS. Results: The univariable and multivariable analyses showed that the T stage, N stage and radscore were independent risk factors for PNI (p < 0.05). A nomogram based on the T stage, N stage and radscore was developed. The AUC of the combined model yielded 0.851 in the training set, 0.842 in the testing set and 0.813 in the validation set. The Kaplan–Meier analysis showed a statistically significant difference in OS between the PNI group and the non-PNI group (p < 0.05). Conclusions: A machine learning-based radiomics–clinicopathological model could effectively predict PNI in gastric cancer preoperatively through a non-invasive approach, and gastric cancer patients with PNI had relatively poor prognoses.

Forest scene 3D reconstruction serves as the fundamental basis for crucial applications such as forest resource inventory, forestry 3D visualization, and the perceptual capabilities of intelligent forestry robots in operational environments. However, traditional 3D reconstruction methods like LiDAR present challenges primarily because of their lack of portability. Additionally, they encounter complexities related to feature point extraction and matching within multi-view stereo vision sensors. In this research, we propose a new method that not only reconstructs the forest environment but also performs a more detailed tree reconstruction in the scene using conditional generative adversarial networks (CGANs) based on a single RGB image. Firstly, we introduced a depth estimation network based on a CGAN. This network aims to reconstruct forest scenes from images and has demonstrated remarkable performance in accurately reconstructing intricate outdoor environments. Subsequently, we designed a new tree silhouette depth map to represent the tree’s shape as derived from the tree prediction network. This network aims to accomplish a detailed 3D reconstruction of individual trees masked by instance segmentation. Our approach underwent validation using the Cityscapes and Make3D outdoor datasets and exhibited exceptional performance compared with state-of-the-art methods, such as GCNDepth. It achieved a relative error as low as 8% (with an absolute error of 1.76 cm) in estimating diameter at breast height (DBH). Remarkably, our method outperforms existing approaches for single-image reconstruction. It stands as a cost-effective and user-friendly alternative to conventional forest survey methods like LiDAR and SFM techniques. The significance of our method lies in its contribution to technical support, enabling the efficient and detailed utilization of 3D forest scene reconstruction for various applications.

By employing a method that combines a NaCl compacting template and supergravity infiltration, open-cell aluminum (Al) foam with varying porosities was prepared. The Al foam fabricated has a pore size of 400 µm and porosity ranging from 0.72 to 0.88. The experimental results indicate that, with an increase in compaction pressure during the NaCl compacting process, the porosity of the foam Al increases and the struts become finer. As the gravity coefficient increases, the density and integrity of the foam Al also increase. Due to the effectiveness of supergravity in overcoming the infiltration resistance between the NaCl preform and molten Al, the supergravity infiltration method holds promise as a practical new technique for fabricating high-porosity open-cell Al foam.

Background: Montelukast (MTK), a potent antagonist of cysteinyl leukotriene receptor 1, has shown therapeutic promise for the treatment of neuropsychiatric disorders. Delirium, a common complication in critically ill patients, lacks effective treatment. This study aims to explore the impact of pre-intensive care unit (ICU) MTK use on in-hospital delirium incidence and, subsequent, prognosis in critically ill patients. Methods: A retrospective cohort study (n = 6344) was conducted using the MIMIC-IV database. After propensity score matching, logistic/Cox regression, E-value sensitivity analysis, and causal mediation analysis were performed to assess associations between pre-ICU MTK exposure and delirium and prognosis in critically ill patients. Results: Pre-ICU MTK use was significantly associated with reduced in-hospital delirium (OR: 0.705; 95% CI 0.497–0.999; p = 0.049) and 90-day mortality (OR: 0.554; 95% CI 0.366–0.840; p = 0.005). The association was more significant in patients without myocardial infarction (OR: 0.856; 95% CI 0.383–0.896; p = 0.014) and could be increased by extending the duration of use. Causal mediation analysis showed that the reduction in delirium partially mediated the association between MTK and 90-day mortality (ACME: −0.053; 95% CI −0.0142 to 0.0002; p = 0.020). Conclusions: In critically ill patients, MTK has shown promising therapeutic benefits by reducing the incidence of delirium and 90-day mortality. This study highlights the potential of MTK, beyond its traditional use in respiratory disease, and may contribute to the development of novel therapeutic strategies for delirium.

Winter wheat is the main grain crop in the Yellow River Basin, and optimizing water and nitrogen management can not only improve the yield, but also reduce water and fertilizer waste and environmental pollution. Using two years of winter wheat field trials, the Aquacrop model was calibrated and validated, and 36 different water and nitrogen application scenarios were simulated and analyzed. Among them, the field trials were divided into three N application levels, and three lower limits of water control. The results showed that: (1) the model has good applicability to simulate the growth process of winter wheat in the Yellow River Basin. (2) At a constant level of irrigation or nitrogen application, crop yield initially increased and subsequently decreased with the enhancement of either irrigation or nitrogen application. Finally, through response surface analysis of regression equations, the recommended irrigation volume was determined to be between 296 and 303 mm, and the fertilizer application rate was set between 229 and 261 kg/ha. At these levels, the PFP could achieve over 60% of its maximum. This research provides a theoretical basis for the water-saving, fertilizer-saving and high-yield water and fertilizer management system of winter wheat in the irrigated area of the Yellow River Basin.