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Facial expression recognition (FER) utilizes artificial intelligence for the detection and analysis of human faces, with significant applications across various scenarios. Our objective is to deploy the facial emotion recognition network on mobile devices and extend its application to diverse areas, including classroom effect monitoring, human–computer interaction, specialized training for athletes (such as in figure skating and rhythmic gymnastics), and actor emotion training. Recent studies have employed advanced deep learning models to address this task, though these models often encounter challenges like subpar performance and an excessive number of parameters that do not align with the requirements of FER for embedded devices. To tackle this issue, we have devised a lightweight network structure named RS-Xception, which is straightforward yet highly effective. Drawing on the strengths of ResNet and SENet, this network integrates elements from the Xception architecture. Our models have been trained on FER2013 datasets and demonstrate superior efficiency compared to conventional network models. Furthermore, we have assessed the model's performance on the CK+, FER2013, and Bigfer2013 datasets, achieving accuracy rates of 97.13%, 69.02%, and 72.06%, respectively. Evaluation on the complex RAF-DB dataset yielded an accuracy rate of 82.98%. The incorporation of transfer learning notably enhanced the model's accuracy, with a performance of 75.38% on the Bigfer2013 dataset, underscoring its significance in our research. In conclusion, our proposed model proves to be a viable solution for precise sentiment detection and estimation. In the future, our lightweight model may be deployed on embedded devices for research purposes. [ABSTRACT FROM AUTHOR]

Convolutional neural networks have made significant progress in human Facial Expression Recognition (FER). However, they still face challenges in effectively focusing on and extracting facial features. Recent research has turned to attention mechanisms to address this issue, focusing primarily on local feature details rather than overall facial features. Building upon the classical Convolutional Block Attention Module (CBAM), this paper introduces a novel Parallel Hybrid Attention Model, termed PH-CBAM. This model employs split-channel attention to enhance the extraction of key features while maintaining a minimal parameter count. The proposed model enables the network to emphasize relevant details during expression classification. Heatmap analysis demonstrates that PH-CBAM effectively highlights key facial information. By employing a multimodal extraction approach in the initial image feature extraction phase, the network structure captures various facial features. The algorithm integrates a residual network and the MISH activation function to create a multi-feature extraction network, addressing issues such as gradient vanishing and negative gradient zero point in residual transmission. This enhances the retention of valuable information and facilitates information flow between key image details and target images. Evaluation on benchmark datasets FER2013, CK+, and Bigfer2013 yielded accuracies of 68.82%, 97.13%, and 72.31%, respectively. Comparison with mainstream network models on FER2013 and CK+ datasets demonstrates the efficiency of the PH-CBAM model, with comparable accuracy to current advanced models, showcasing its effectiveness in emotion detection. [ABSTRACT FROM AUTHOR]

A thermal dynamic disaster in the goaf is one of the most serious coal mine disasters formed by coal spontaneous combustion and gas interweaving. However, the influence of the high-temperature hidden fire source formed in the goaf on the evolution law of thermal dynamic disasters is not clear, and effective prevention and control measures cannot be taken. Therefore, this paper uses the experimental platform of thermal dynamic disaster in the goaf to study the influence of different fire point positions on the development of thermal dynamic disaster in the goaf through a similar simulation experiment of thermal dynamic disaster evolution in the goaf and analyzes the corresponding relationship between temperature and CO concentration in the upper corner. The results show that under different locations of heat source, the high-temperature heat source of coal spontaneous combustion migrates to the air leakage side with sufficient oxygen supply, and an oxygen-poor circle is formed near the ignition point. Under the action of air leakage flow, CH4 accumulates in the deep part of the goaf on the return air side. Due to the increase in coal, part of CH4 is produced, which leads to the increase in concentration of CH4 at the ignition point. Under the action of different heat sources, the changing trend of concentration of CO and temperature in the return air corner is the same, but the temperature change in the return air corner shows a lag compared with the change in the concentration of CO, so concentration monitoring of CO can reflect the evolution process of the fire field in the goaf more quickly than temperature monitoring. [ABSTRACT FROM AUTHOR]

Hypophthalmichthys nobilis are widely distributed in the Yangtze River basin and its related lakes. They are an important economic fish species and are a famous cultured species known as the "Four Famous Domestic Fishes" in China. Currently, with the fishing ban in the Yangtze River basin, fishing for H. nobilis in the natural water bodies of the Yangtze River basin has been completely prohibited. In order to identify the sources of H. nobilis appearing in the market, further control and accountability is necessary to trace the sources of H. nobilis in the Yangtze River basin and its related water bodies. Therefore, this study identified and traced different sources of H. nobilis through muscle element fingerprint analysis (EFA). The results show that H. nobilis from different stations have characteristic element compositions. The characteristic element of H. nobilis from Wuhan (WH) is Pb, which is significantly higher than that in other stations; the characteristic element from Anqing (AQ) is Hg, which is significantly higher than that in other stations; and the characteristic element from Taihu (TH) is Al, which is significantly higher than that in other water areas. Multivariate analysis selected different spatial distribution patterns in four discriminative element ratios (Pb/Ca, Cr/Ca, Na/Ca, and Al/Ca) in the muscle of H. nobilis in the Yangtze River basin and its related lakes. This study suggests that the screened discriminative elements can be used to visually distinguish different sources of H. nobilis and to quickly trace and verify the origin of newly emerging samples. Therefore, the use of selected discriminative element fingerprint features to trace the origin of new samples has been proven to be feasible. By further discriminating and verifying the muscle element fingerprints of new samples, the discrimination rate is high. Therefore, a multivariate analysis of muscle element fingerprints can be used for tracing the origins of samples of unknown origin in market supervision. [ABSTRACT FROM AUTHOR]

A room-temperature photoluminescence (PL) study of amorphous Si/amorphous silicon oxynitride multilayer films prepared by plasma-enhanced chemical vapor deposition is reported. The PL peak position can be tuned from 800 nm to 660 nm by adjusting the oxygen/nitride ratio in the a-SiOxNy:H sublayer. The Fourier transform infrared (FTIR) absorption spectra indicate that the shift of the PL peak position is accompanied by an increase in the Si-O-Si absorption peak's intensity, which induces the structural disorder at the interface, resulting in an increase in band gap energy. The effects of size on the photoluminescence spectrum have been studied. As a result, it has been observed that the addition of oxygen atoms introduces a large number of localized states at the interface, causing a blue shift in the emission peak position. With an increase in oxygen atoms, the localized states tend to saturate, and the quantum phenomenon caused by the a-Si sublayer becomes more pronounced. It is found that, as the thickness of the a-Si sublayer decreases, the increase in the [O/N] ratio is more likely to cause an increase in disordered states, leading to a decrease in luminescence intensity. For a-Si/a-SiOxNy:H samples with thinner a-Si sublayers, an appropriate value of [O/N] is required to achieve luminescence enhancement. When the value of [O/N] is one, the enhanced luminescence is obtained. It is also suggested that the PL originates from the radiative recombination in the localized states' T3- level-related negatively charged silicon dangling bond in the band tail of the a-Si:H sublayer embedded in an a-Si/a-SiOxNy:H multilayer structure. [ABSTRACT FROM AUTHOR]

The nutritional status of preschool children in economically underdeveloped multi-ethnic areas is a global concern. This study aimed to examine the effect of a 2.2-year cluster randomized clinical trial that provided customized nutritious breakfast and nutrition education to preschool children in Linxia County, China. A total of 578 children aged 3 to 6 years were enrolled. After the intervention, the incidence of undernourishment was significantly lower in the intervention group compared to the control group (8.73% vs. 9.92%, OR = 0.01 [95%CI 0.00, 0.39], p = 0.014). Additionally, children with non-Muslim dietary habits had a lower incidence of undernourishment compared to those with Muslim dietary habits (OR = 0.05 [95%CI 0.00, 0.88]; p = 0.010). The intervention group also had a lower prevalence rate of wasting (OR = 0.02 [95%CI 0.00, 0.40]; p = 0.011) and a higher mean BMI-for-age Z-score (β = 1.05 [95%CI 0.32, 1.77]; p = 0.005) compared to the control group. These findings suggest that providing nutritious breakfast and nutrition education is an effective strategy to improve the nutrition and health of preschool children, particularly in economically disadvantaged regions and among children with Muslim dietary habits. [ABSTRACT FROM AUTHOR]

This paper presents a complete planner and controller scheme to achieve balance and walking for a biped robot, which does not need to distinguish the robot's dynamic model parameters. The high-level planner utilizes model predictive control to optimize both the foothold location and step duration based on the Divergent Component of Motion (DCM) model to enhance the robustness of generated gaits. For low-level control, we use quadratic programming (QP) to optimize the contact force distribution under the contact constraints to achieve the virtual wrench exerted on the base of the robot. Then, the joint torques sent to the robot are derived from three parts: first, the torques mapped from the contact force; second, the swing leg tracking; and third, the stance foot stabilization. The simulation and experiment on BRUCE, a miniature bipedal robot from Westwood Robotics (Los Angeles, CA, USA), testify to the performance of the control scheme, including push recovery, Center of Mass (CoM) tracking, and omnidirectional walking. [ABSTRACT FROM AUTHOR]

The widespread use of carbamate pesticides has raised significant environmental and health concerns, particularly regarding water contamination and the disruption of defense systems in organisms. Despite these concerns, research on the differential impacts of pesticides on male and female organisms remains limited. This study focused on methomyl, investigating sex-specific differences in liver antioxidant defenses and inflammatory response indices in male and female zebrafish after 56 days of exposure to environmentally relevant concentrations (0, 0.05, 0.10, and 0.20 mg/L). Our findings indicate that methomyl exposure significantly increased ROS content in zebrafish livers, inducing oxidative stress and activating enzymatic antioxidant defenses such as SOD, CAT, and GSH-Px activities. Sub-chronic exposure altered the expression of apoptosis-related genes (Bax/Bcl2a and Caspases3a), resulting in liver cell apoptosis in a concentration-dependent manner, with the 0.20 mg/L concentration causing the most severe damage. Additionally, methomyl exposure at environmentally relevant concentrations triggered persistent inflammatory responses in liver tissues, evidenced by increased transcription levels of inflammatory factor genes and the activation of toll-like receptors, heightening susceptibility to exogenous allergens. It is noteworthy that oxidative damage indicators (AST, ROS, MDA) and inflammatory gene expressions (IL-1β, TNF-α) were significantly higher in female livers compared to male livers at 0.10–0.20 mg/L methomyl exposure. Consequently, our study underscores the potential adverse effects of environmental methomyl exposure on aquatic organisms and highlights the need for heightened consideration of the risks posed by environmental endocrine disruptors to female health and safety. [ABSTRACT FROM AUTHOR]

To investigate the activities of intestinal digestive enzymes, liver antioxidant enzymes, immunological enzymes, and glucometabolic enzymes in largemouth bass (Micropterus salmoides) under the biofloc model, an experiment was conducted in 300-liter glass tanks. The experiment comprised a control group, which was fed a basal diet, and a biofloc group, where glucose was added to maintain a C/N ratio of 15. Each group had three parallel setups, with a stocking density of 20 fish per tank. The experiment ran for 60 days, employing a zero-water exchange aquaculture model. The results showed that at the end of the culture period, there were no significant differences between the initial weight, final weight, WGR, SGR, and SR of the biofloc group and the control group of largemouth bass (p > 0.05), whereas the lower FCR and the higher PER in the biofloc group were significant (p < 0.05); intestinal α-amylase, trypsin, and lipase activities of largemouth bass in the biofloc group were significantly increased by 37.20%, 64.11%, and 51.69%, respectively, compared with the control group (p < 0.05); liver superoxide dismutase and catalase activities, and total antioxidant capacity of largemouth bass in the biofloc group were significantly increased by 49.26%, 46.87%, and 98.94% (p < 0.05), while the malondialdehyde content was significantly reduced by 19.91% (p < 0.05); liver lysozyme, alkaline phosphatase, and acid phosphatase activities of largemouth bass in the biofloc group were significantly increased by 62.66%, 41.22%, and 29.66%, respectively (p < 0.05); liver glucokinase, pyruvate kinase, glucose-6-phosphate kinase, pyruvate kinase, glucose-6-phosphatase, and glycogen synthase activities were significantly increased by 46.29%, 99.33%, 32.54%, and 26.89%, respectively (p < 0.05). The study showed that the biofloc model of culturing largemouth bass can not only enhance digestive enzyme activities, antioxidant capacity, and immune response but can also promote the process of glucose metabolism and reduce feeding costs. This study provides data support for healthy culturing of largemouth bass in future production, provides a theoretical reference for optimizing the biofloc technology culture model, and is crucial for promoting the healthy and green development of aquaculture. [ABSTRACT FROM AUTHOR]

The Huangling region is located in the central part of the Chinese Loess Plateau, which is sensitive to climate change due to the transitional characteristics of the natural environmental zone in which it is located. In this study, we utilized a spore–pollen analysis of the Tianjiahe (TJH) profile in Huangling to apply the pollen–climate factor conversion function method. This approach allowed us to quantitatively reconstruct the paleotemperature and paleoprecipitation of the Huangling area during the Middle and Late Holocene. The results show that the Huangling area experienced four climatic stages during the Middle and Late Holocene, including mild and slightly humid → warm and humid → warm and slightly humid → warm and humid. Except for the period of 5.3–4.72 kaBP, during which the climate was relatively cool and dry compared to the present, the climate in the remaining period (4.72–0.03 kaBP) was warmer and more humid than that of the present. The above results provide an important insight for further exploring the mechanism of paleoclimate change and predicting future climate change. [ABSTRACT FROM AUTHOR]

Simple Summary: The Chironomidae family stands out as the most widely dispersed and often the most abundant insect group in freshwater habitats. The significance of color patterns is well-recognized, as they fulfill multiple roles such as communication, camouflage, mimicking, and defense. However, the taxonomy of species within this family, relying on color patterns, as well as the shape and distribution of thoracic pigmentation, wing markings, and leg pigmentation, remains controversial and unstable. Here, we conduct a comprehensive review on the taxonomy of a Chiromidae genus, Stictochironomus from China, which is characterized by a combination of distinctive wing and leg markings. Using DNA barcode data and morphological data, two new species to science from China are well supported. Species delimitation analyses performed with distance-based approach and coalescent tree-based approaches also support them as distinct species. Therefore, color patterns should be a good diagnostic characteristic for species delimitation in Stictochironomus. Furthermore, we provided an up-to-date taxonomic key for male adults of Stictochironomus from China. The genus Stictochironomus (Diptera: Chironomidae) has an almost worldwide distribution, with more than 30 species. However, species delimitation and identification based on the markings on the wings and legs are controversial and uncertain. In this study, we focused on color patterns to review the adults of the genus from China, and two new species (S. trifuscipes sp. nov. and S. quadrimaculatus sp. nov.) are described and figured. DNA barcodes can accurately separate the two new species with specific color patterns. However, heterospecific individuals form a monophyletic cluster in the phylogeny tree. For example, S. maculipennis (Meigen) and S. pictulus (Meigen), which have a lower interspecific genetic divergence, form a single clade. Sequences with the same species name but with high intraspecific distance form more than one phylogenetic clade, such as S. sticticus (Fabricius) of three clades, S. pictulus of four clades, S. akizukii (Tokunaga) and S. juncaii Qi, Shi, and Wang of two clades, might have potential cryptic species diversity. Species delimitation analysis using ASAP, PTP, and GMYC clearly delineated them as separate species. Consequently, color patterns are a good diagnostic characteristic for species delimitation in Stictochironomus. The distance-based analysis shows that a threshold of 4.5–7.7% is appropriate for species delimitation in Stictochironomus. Additionally, an updated key including color pattern variation for male adults of known Stictochironomus species from China is provided. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) reconstruction from RGB input has drawn much attention recently and plays a crucial role in further vision tasks. However, current sparse coding algorithms often take each single pixel as the basic processing unit during the reconstruction process, which ignores the strong similarity and relation between adjacent pixels within an image or scene, leading to an inadequate learning of spectral and spatial features in the target hyperspectral domain. In this paper, a novel tensor-based sparse coding method is proposed to integrate both spectral and spatial information represented in tensor forms, which is capable of taking all the neighboring pixels into account during the spectral super-resolution (SSR) process without breaking the semantic structures, thus improving the accuracy of the final results. Specifically, the proposed method recovers the unknown HSI signals using sparse coding on the learned dictionary pairs. Firstly, the spatial information of pixels is used to constrain the sparse reconstruction process, which effectively improves the spectral reconstruction accuracy of pixels. In addition, the traditional two-dimensional dictionary learning is further extended to the tensor domain, by which the structure of inputs can be processed in a more flexible way, thus enhancing the spatial contextual relations. To this end, a rudimentary HSI estimation acquired in the sparse reconstruction stage is further enhanced by introducing the regression method, aiming to eliminate the spectral distortion to some extent. Abundant experiments are conducted on two public datasets, indicating the considerable availability of the proposed framework. [ABSTRACT FROM AUTHOR]

The detection of the moisture content of wheat is an important index used to measure the quality and preservation of wheat. In order to rapidly and non-destructively detect the moisture content of wheat, in this study, we designed a stripline detection device that measures 151 frequency points in the 50–200 MHz frequency range with a vector network analyzer. Random forest (RF), extreme learning machine (ELM), and BP neural network prediction models were established, using the frequency, temperature, volume density and dielectric constant as input and the water content as output. It was shown that, in the frequency range 50–200 MHz, the permittivity of wheat decreases as the frequency increases, and that this is negatively correlated. The dielectric constant of wheat increases as the moisture content, temperature, and bulk density increase, and these are positively correlated. The random forest (RF) prediction model, which uses the frequency, temperature, effective dielectric constant ε e f f . and volume density as inputs and the wheat moisture content as the output, demonstrates the best performance. The determination coefficient (R2) = 0.99977, the mean absolute error (MAE) = 0.044368, the mean square error (MAE) = 0.0053011, and the root mean square error (RMSE) = 0.072809. This study provides a new device and method for the detection of the moisture content of wheat. The device is small and is not easily disturbed by the external environment. It can be measured in a variety of conditions and is important for the development of low-cost, high-precision, and portable devices for the detection of the moisture content of wheat. [ABSTRACT FROM AUTHOR]

Recently, researchers have extensively explored various methods for electronic medical record named entity recognition, including character-based, word-based, and hybrid methods. Nonetheless, these methods frequently disregard the semantic context of entities within electronic medical records, leading to the creation of subpar-quality clinical knowledge bases and obstructing the discovery of clinical knowledge. In response to these challenges, we propose a novel purpose-driven SoftLexicon-RoBERTa-BiLSTM-CRF (SLRBC) model for electronic medical records named entity recognition. SLRBC leverages the fusion of SoftLexicon and RoBERTa to incorporate the word lexicon information from electronic medical records into the character representations, enhancing the model's semantic embedding representations. This purpose-driven approach helps achieve a more comprehensive representation and avoid common segmentation errors, consequently boosting the accuracy of entity recognition. Furthermore, we employ the classical BiLSTM-CRF framework to capture contextual information of entities more effectively. In order to assess the performance of SLRBC, a series of experiments on the public datasets of CCKS2018 and CCKS2019 were conducted. The experimental results demonstrate that SLRBC can efficiently extract entities from Chinese electronic medical records. The model attains F1 scores of 94.97% and 85.40% on CCKS2018 and CCKS2019, respectively, exhibiting outstanding performance in the extraction and utilization efficiency of clinical information. [ABSTRACT FROM AUTHOR]

In order to make the identification and utilization of nutritional quality components in Chinese cabbage more predictive, to obtain ideal raw materials, and to help screen functional Chinese cabbage varieties that have high nutritional value, we conducted a comprehensive evaluation of the nutritional quality of different Chinese cabbage varieties. In this study, 17 nutritional quality indexes of 35 Chinese cabbage varieties, including crude fiber (CF), crude protein (CP), vitamin C (VC), glucose (Glc), fructose (Fru), malic acid (MA), citric acid (CA), oxalic acid (OA), total amino acid (TAA) and CA, K, Mg, P, Cu, Fe, Mn and Zn, were analyzed using diversity analysis, correlation analysis, principal component analysis, membership function analysis and cluster analysis. The results showed that there were different degrees of variation in the 17 nutritional quality indexes, and the coefficients of variation ranged from 11.45% to 91.47%. The correlation analysis found that there were significant or extremely significant correlations between different nutrient elements of Chinese cabbage, which indicated that principal component analysis could be carried out, and the comprehensive score (D value) of different materials could be obtained using principal component analysis and the membership function method. The nutritional quality of Chinese cabbage was classified into five grades by cluster analysis. Finally, a mathematical model for evaluating the nutritional quality of Chinese cabbage was established using the D value and multiple stepwise regression methods, and 10 key indexes were selected from the 17 indexes, which could be used for the rapid identification of the nutritional quality of Chinese cabbage. This study provided a theoretical basis for the nutritional quality evaluation and variety breeding of Chinese cabbage. [ABSTRACT FROM AUTHOR]

Simple Summary: Many obligate endosymbionts colonize an invertebrate host and are directly transferred maternally to the host's embryo. Consequently, the symbiotic bacteria from diverse host species evolve independently, reflecting the host's phylogeny. Whiteflies, which consist of thousands of species, harbor obligate endosymbionts of the Portiera genus. The divergence status of these bacteria, after a long history of coevolution with their hosts, remains ambiguous. In the present study, we aim to unravel the divergence of obligate endosymbionts from different whitefly species through genome comparison. Our findings indicate that these endosymbionts have diverged into at least three disparate genetic groups. Such findings underscore the divergence of whitefly obligate endosymbionts and provide a cue for investigation into the co-divergence between obligate endosymbionts and their hosts. Maternally inherited obligate endosymbionts codiverge with their invertebrate hosts and reflect their host's evolutionary history. Whiteflies (Hemiptera: Aleyrodidae) harbor one obligate endosymbiont, Candidatus Portiera aleyrodidarum (hereafter Portiera). Portiera was anciently acquired by whitefly and has been coevolving with its host ever since. Uncovering the divergence of endosymbionts provides a fundamental basis for inspecting the coevolutionary processes between the bacteria and their hosts. To illustrate the divergence of Portiera lineages across different whitefly species, we sequenced the Portiera genome from Aleyrodes shizuokensis and conducted a comparative analysis on the basic features and gene evolution with bacterial genomes from five whitefly genera, namely Aleurodicus, Aleyrodes, Bemisia, Pealius, and Trialeurodes. The results indicated that Portiera from Bemisia possessed significantly larger genomes, fewer coding sequences (CDSs), and a lower coding density. Their gene arrangement differed notably from those of other genera. The phylogeny of the nine Portiera lineages resembled that of their hosts. Moreover, the lineages were classified into three distinct genetic groups based on the genetic distance, one from Aleurodicus (Aleurodicinae), one from Bemisia (Aleyrodinae), and another from Aleyrodes, Pealius, and Trialeurrodes (Aleyrodinae). Synonymous and nonsynonymous rate analyses, parity rule 2 plot analyses, neutrality plot analyses, and effective number of codons analyses supported the distinction of the three genetic groups. Our results indicated that Portiera from distant hosts exhibit distinct genomic contents, implying codivergence between hosts and their endosymbionts. This work will enhance our understanding of coevolution between hosts and their endosymbionts. [ABSTRACT FROM AUTHOR]

The photoreduction of CO2 to other products containing carbon through simulated photosynthesis is a promising area of research. However, given the complexity of the CO2 photocatalytic reduction reaction, it is crucial to adjust the structure of the photocatalysts. The focus of this study was on creating NaOH-modified Bi2WO6 nanosheet photocatalysts via a one-step hydrothermal route and using them to convert CO2 into CO through photocatalytic reduction under the condition of not using an electron sacrifice agent. The results of characterizations and activity data showed that adding an appropriate amount of NaOH significantly improved the photoreduction activity of CO2, as seen in the BWO-2 catalyst. The efficiency of photocatalysts could be improved by tuning the band structure through the addition of an appropriate amount of alkali. This adjustment improves the separation of photogenerated carriers and controls the concentration of oxygen vacancy to reduce recombination. As a result, the photocurrent activity is highly enhanced, leading to better reduction performance compared to unmodified photocatalysts. In experiments, the CO yield of the modified photocatalyst BWO-2 remained above 90 μmol/g after four trials, indicating its effectiveness in reducing CO2. This study offers insights into the regulation of band structure in bismuth-based photocatalysts for efficient CO2 reduction. [ABSTRACT FROM AUTHOR]

The vehicle routing problem (VRP) is a common problem in logistics and transportation with high application value. In the past, many methods have been proposed to solve the vehicle routing problem and achieved good results, but with the development of neural network technology, solving the VRP through neural combinatorial optimization has attracted more and more attention by researchers because of its short inference time and high parallelism. PMOCO is the most state-of-the-art multi-objective vehicle routing optimization algorithm. However, in PMOCO, preferences are often uniformly selected, which may lead to uneven Pareto sets and may reduce the quality of solutions. To solve this problem, we propose a multi-objective vehicle routing optimization algorithm based on preference adjustment, which is improved from PMOCO. We incorporate the weight adjustment method in PMOCO that is able to adapt to different approximate Pareto fronts and to find solutions with better quality. We treat the weight adjustment as a sequential decision process and train it through deep reinforcement learning. We find that our method could adaptively search for a better combination of preferences and have strong robustness. Our method is experimented on multi-objective vehicle routing problems and obtained good results (about 6% improvement compared with PMOCO with 20 preferences). [ABSTRACT FROM AUTHOR]

With the rapid development of society and the economy, human activities are increasing, which often brings potential threats such as a decline in forest resource quality and ecological function. In order to investigate the change in forest resource quality and human activity intensity, this study constructed a calculation model for a forest resource quality index and a human activity intensity index and conducted a quantitative analysis of the temporal and spatial changes in forest resource quality and human activity intensity in Yulin City based on sub-compartment data in 2017 and 2020. By combining spatial autocorrelation analysis, the changes in human activity intensity and spatial forest resource quality were explored, and key areas such as the prominent contradictions between humans and the land were superimposed and coupled as potential areas of concern. The results show the following: From 2017 to 2020, the forest resource quality in Yulin City improved as a whole, especially in Zizhou County, but there were increases and decreases in other regions. Human activity intensity increased as a whole, and the most obvious increase was in Hengshan District. Both the forest resource quality and human activity intensity indexes had spatial aggregation, the differences in forest resource quality between regions were reduced, and human activity intensity showed a trend towards aggregated development. The high–high cluster area for human activity intensity showed a decreasing trend, but it expanded outward in urban areas and other human-gathering areas, such as the surrounding area of Yulin City, Jingbian County, and Shenmu City. The high–high cluster area for forest resource quality showed a shrinking trend. Four specific regions were identified through a spatial coupled superposition analysis to reveal the dynamic relationship between forest resource quality and human activity intensity. The most obvious region was the Yuyang District, where the forest resource quality improved because of a reduction in the pressure of human activities on the natural environment. [ABSTRACT FROM AUTHOR]

With the popularization of the network and the expansion of its application scope, the problem of abnormal network traffic caused by network attacks, malicious software, traffic peaks, or network device failures is becoming increasingly prominent. This problem not only leads to a decline in network performance and service quality but also may pose a serious threat to network security. This paper proposes a hybrid data processing model based on deep learning for network anomaly detection to improve anomaly detection performance. First, the Grey Wolf optimization algorithm is improved to select high-quality data features, which are then converted to RGB images and input into an anomaly detection model. An anomaly detection model of network dataflow based on a convolutional neural network is designed to recognize network anomalies, including DoS (Denial of Service), R2L (Remote to Local), U2R (User to Root), and Probe (Probing). To verify the effectiveness of the improved Grey Wolf algorithm and the anomaly detection model, we conducted experiments on the KDD99 and UNSW-NB15 datasets. The proposed method achieves an average detection rate of 0.986, which is much higher than all the counterparts. Experimental results show that the accuracy and the detection rates of our method were improved, while the false alarm rate has been reduced, proving the effectiveness of our approach in network anomaly classification tasks. [ABSTRACT FROM AUTHOR]

During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

Simple Summary: Microorganisms are important components of biological communities in the aquaculture ecosystems, which play fundamental roles in regulating the water quality and maintaining the health of cultured animals. Microalgae addition as a common aquaculture practice has been reported to have positive effects on both the aquaculture environment and organisms. However, the ecological effects of microalgae addition on the aquaculture ecosystems from the perspective of microbial community ecology are still unclear. In the present study, we analyzed the characteristics of microbial communities in various microhabitats in tilapia aquaculture systems with different microalgae additions. It was found that microalgae addition exhibited greater impacts on the diversity, compositions, and co-occurrence patterns of the gill-associated bacterial communities than those of water and fish gut. The mucosal surface of the fish gill represented an important barrier essential for fish health. Microalgae addition altered the composition of gill-associated bacterial communities, which could be important for mucosal immunity and thereby influence fish health. These findings provided a new insight into the mechanisms underlying the impacts of microalgae addition on aquaculture. Microalgae has been widely used in aquaculture to improve both the water environment and fish growth; however, the current understanding of the effects of microalgae addition on the key players involved in regulating the water environment and fish health, such as microorganisms, remains limited. Here, a 50-day mesocosm experiment was set up to simulate the culture of Genetic Improvement of Farmed Tilapia (GIFT, Oreochromis niloticus) with an average weight of 14.18 ± 0.93 g and an average length of 82.77 ± 2.80 mm. Different amounts of Chlorella pyrenoidosa were added into these artificial systems to investigate dynamics of bacterial communities in aquaculture water, fish gill, and gut using amplicon-based high-throughput sequencing technology. Our results showed that Chlorella pyrenoidosa addition increased diversity and network complexity of gill-associated bacterial communities rather than those of the water and gut. Furthermore, more biomarkers in the gill-associated bacterial communities were detected in response to Chlorella pyrenoidosa addition than the water and fish gut samples. These findings highlighted the high sensitivity of gill-associated bacterial communities in response to the Chlorella pyrenoidosa addition, implying Chlorella pyrenoidosa addition could play important roles in regulating the fish mucosal immunity by altering the gill-associated microbiota. [ABSTRACT FROM AUTHOR]

A novel fatigue evolution model considering the effect of defect size and additive manufacturing building direction based on the theories of continuum damage mechanics and its numerical implementation in ABAQUS is proposed in this paper. First, the constitutive model, fatigue damage evolution model and their parameter calibration methods are presented. Second, using the ABAQUS platform, the proposed model is implemented with user-defined subroutines. After that, based on the proposed model and its numerical implementation, the fatigue life of additively manufactured AlSi10Mg is predicted and its applicability is verified through experimental results. Finally, a support vector regression model is established to predict the fatigue life, and its results are compared to those of the numerical finite element method. The results show that the support vector regression model makes better predictions than the finite element method. [ABSTRACT FROM AUTHOR]

The prediction of crop yield plays a crucial role in national economic development, encompassing grain storage, processing, and grain price trends. Employing multiple sensors to acquire remote sensing data and utilizing machine learning algorithms can enable accurate, fast, and nondestructive yield prediction for maize crops. However, current research heavily relies on single-type remote sensing data and traditional machine learning methods, resulting in the limited robustness of yield prediction models. To address these limitations, this study introduces a field-scale maize yield prediction model named the convolutional neural network–attention–long short-term memory network (CNN-attention-LSTM) model, which utilizes multimodal remote sensing data collected by multispectral and light detection and ranging (LIDAR) sensors mounted on unmanned aerial vehicles (UAVs). The model incorporates meteorological data throughout the crop reproductive stages and employs the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) for the initial part of the vegetative stage (initial part of the V period), the later part of the vegetative stage (later part of the V period), the reproductive stage (R period), and the maturity stage (M period), along with LIDAR data for Point75–100 in the later part of the V period, Point80–100 in the R period, and Point50–100 in the M period, complemented by corresponding meteorological data as inputs. The resulting yield estimation demonstrates exceptional performance, with an R2 value of 0.78 and an rRMSE of 8.27%. These results surpass previous research and validate the effectiveness of multimodal data in enhancing yield prediction models. Furthermore, to assess the superiority of the proposed model, four machine learning algorithms—multiple linear regression (MLR), random forest regression (RF), support vector machine (SVM), and backpropagation (BP)—are compared to the CNN-attention-LSTM model through experimental analysis. The outcomes indicate that all alternative models exhibit inferior prediction accuracy compared to the CNN-attention-LSTM model. Across the test dataset within the study area, the R2 values for various nitrogen fertilizer levels consistently exceed 0.75, illustrating the robustness of the proposed model. This study introduces a novel approach for assessing maize crop yield and provides valuable insights for estimating the yield of other crops. [ABSTRACT FROM AUTHOR]

As a popular technique, additive manufacturing (AM) has garnered extensive utilization in various engineering domains. Given that numerous AM metal components are exposed to fatigue loads, it is of significant importance to investigate the life prediction methodology. This study aims to investigate the high-cycle fatigue (HCF) behavior of AM AlSi10Mg, taking into account the influences of powder size and fatigue damage, and a novel ML-based approach for life prediction is presented. First, the damage-coupled constitutive model and fatigue damage model are derived, and the Particle Swarm Optimization method is employed for the material parameters' calibration of M AlSi10Mg. Second, the numerical implementation of theoretical models is carried out via the development of a user-defined material subroutine. The predicted fatigue lives of AM AlSi10Mg with varying powder sizes fall within the triple error band, which verifies the numerical method and the calibrated material parameters. After that, the machine learning approach for HCF life prediction is presented, and the Random Forest (RF) and K-Nearest Neighbor (KNN) models are employed to predict the fatigue lives of AM AlSi10Mg. The RF model achieves a smaller MSE and a larger R2 value compared to the KNN model, signifying its superior performance in predicting the overall behavior of AM AlSi10Mg. Under the same maximum stress, a decrease in the stress ratio from 0.5 to −1 leads to a reduction in fatigue life for both powder sizes. As the powder size decreases, the rate of damage evolution accelerates, leading to shorter fatigue life. [ABSTRACT FROM AUTHOR]

Paleoclimate studies of loess in China have focused mostly on the time series of a single borehole or profile. However, research on loess strata and regional paleoenvironmental patterns could facilitate a deeper understanding of loess as a paleoenvironmental indicator and provide new insights into interpreting loess in sedimentary records of the paleoclimate. In this study, we determined the spatial pattern and regional characteristics of the paleoclimate during the Last Interglacial period in the Luohe River Basin, Shaanxi Province, China. We selected four representative boreholes in the study area (ZK04, ZK18, ZK13, and ZK19) from different landforms and zones, distributed from the northwest to the southeast, as well as three classic profiles (JB, JD, and LC). From north to south, comparative analyses were conducted of the loess strata, magnetic susceptibility, and grain size, and we analyzed the distribution characteristics of loess and paleosols in different geomorphological regions. The results showed that both the thickness and the sedimentation rate of loess in this river basin decreased from north to south. There were few paleosol horizons in the northern Liangmao area, but numerous such horizons in the southern plateau, and the degree of paleosol development increased from north to south. The magnetic susceptibility increased, whereas the particle composition tended to become thinner from north to south. The climate fluctuations of the Last Glacial recorded by the loess and paleosols in different regions were inconsistent. [ABSTRACT FROM AUTHOR]

The rapid and nondestructive detection of tea leaf moisture content (MC) is of great significance to processing tea with an automatic assembly line. This study proposes an MC detection method based on microwave scattering parameters (SPs). Through the established free-space electromagnetic measurement device, 901 different frequency points are taken between 2.45 and 6 GHz using a vector network analyzer (VNA). The SPs of tea leaves with different moisture contents (5.72–55.26%) at different bulk density and different sample thicknesses were measured. The relationship between frequency, S21 amplitude and moisture content, thickness, and bulk density of tea was analyzed using correlation coefficients, significance analysis, and model construction. Back propagation (BP) neural network, decision tree (DT), and random forest (RF) MC prediction models were established with the frequency, amplitude, and phase of the SPs, thickness, and bulk density of the samples as inputs. The results showed that the RF-based model had the best performance, with determination coefficient (R2) = 0.998, mean absolute error (MAE) = 0.242, and root mean square error (RMSE) = 0.614. Compared to other nondestructive testing processes for tea, this method is simpler and more accurate. This study provides a new method for the detection of tea MC, which may have potential applications in tea processing. [ABSTRACT FROM AUTHOR]

Women aged 18–49 years are women of childbearing age. Their nutrition knowledge (NK) is not only related to their physical health but also affects the diet quality of the entire family and the health status of the next generation. Studies that assess the NK level of women of childbearing age using representative data in China are lacking. This study aimed to evaluate the NK level of Chinese women aged 18–49 years and explore influencing factors at both the individual and provincial levels. Data were from the Chinese Nutrition and Health Knowledge Survey 2021. A total of 38,065 females aged 18–49 years were included in the analysis. A face-to-face validated standard questionnaire was used to collect NK from respondents. The full score of the questionnaire was 100. Respondents who scored higher than 75 were considered to have NK. The Rao-Scott chi-square test was used to compare the differences in the NK awareness rate between or among the subgroups. A multilevel logistic regression model was used to explore influencing factors at the individual and provincial levels. All analyses accounted for complex sample design including clustering, stratification, and sample weights. The mean score of NK among Chinese women aged 18–49 years was 65.1 ± 11.8, and the awareness rate was 20.9% (95% CI: 19.6−22.3%). Among the five dimensions, the awareness rate of food safety was the highest (74.0%, 95% CI: 72.8−75.3%), and the dietary recommendation was the lowest (16.4%, 95% CI: 15.3−17.5%). The two-level logistic regression model analysis revealed that at the individual level, age, education level, occupation, chronic disease status and residence were the main influencing factors of the NK level. At the provincial level, the region and the female illiteracy rate were associated with the NK level. Dependent children ratio, per capita income, and health expenditure were not significantly associated with the NK level. The model accounted for 58.8% of the variance in the NK level. The NK level of Chinese women aged 18–49 years was low. Knowledge of dietary recommendations was weakest. Both individual and provincial-level factors were associated with the NK level. There is an urgent need to strengthen nutrition education actions for enhancing the NK of reproductive-age women effectively to improve their dietary behaviors. [ABSTRACT FROM AUTHOR]

In the protection of migratory species, Coilia nasus has always been a research topic of very high interest in various countries because of the high economic value and the serious decline of resources. In this study, C. nasus were collected from the Shengsi Sea area, the Yangtze River estuary, the Taizhou section of the Yangtze River and Poyang Lake. By using 2b-RAD simplified genome sequencing technology, 63,110 SNP sites were screened, and the genetic diversity of each population was analyzed with SNP markers. The results showed that (1) the genetic purity of the four groups of populations was not high, with the need to further increase levels of genetic purity; (2) the genetic diversity in the four populations was high, indicating that they had strong adaptability to environmental changes and could easily expand their distribution and resource growth; (3) the FST values (0.112–0.142) of the four populations were higher, indicating that there was moderate genetic differentiation, but no independent population was formed. This study speculated that these migratory species may have the same spawning grounds and pointed out that the gene purity of C. nasus may have been polluted, and it is urgent to improve the purity in the protection of migratory C. nasus. [ABSTRACT FROM AUTHOR]

Simple Summary: Non-biting midges are the most widely distributed, and frequently the most abundant, insect family in freshwater environments. Species delimitation concerning color patterns and the shape or distribution of thorax pigmentation, wing spots, abdomen pigmentation, and leg pigmentation are disputable and unstable in the family. This research focuses on a genus that shares the general appearance of the hypopygium, but with variations in coloration of the antennae, thorax, wings, and legs. In this study, we analyzed collected species along with public sequences, resulting in a preliminary DNA library including 21 morphospecies. DNA barcodes can successfully delimit Microtendipes species and showed deep intraspecific divergence in some species. We also confirmed that color patterns can be important diagnostic characters. As a result of this analysis, five species new to science are identified and described, and an updated key to male adults of known Microtendipes species from China is provided. The genus Microtendipes Kieffer (Diptera: Chironomidae) has a nearly worldwide distribution, comprising more than 60 species, which are further divided into two species groups based on larval stage. However, species delimitation and identification among the adults of this genus are controversial and uncertain. For instance, previous studies have provided many synonymies based on conspecific color pattern variations in Microtendipes species. Here, we used DNA barcode data to address Microtendipes species delimitation as well as to test whether color pattern variations can be diagnostic characters for interspecific identification. The 151 DNA barcodes used, 51 of which were contributed by our laboratory, represent 21 morphospecies. Species with specific color patterns could be accurately separated based on DNA barcodes. Consequently, the color patterns of adult males could be important diagnostic characters. The average intraspecific and interspecific sequence divergences were 2.8% and 12.5%, respectively, and several species exhibited deep intraspecific divergences higher than 5%. Molecular operational taxonomic units (OTUs) ranged from 21 to 73, based on methods including phylogenetic trees, the assemble species by automatic partitioning method, the Poisson tree process (PTP), and the general mixed Yule-coalescent (GMYC) method. As a result of these analyses, five new species were recognized (M. baishanzuensis sp. nov., M. bimaculatus sp. nov., M. nigrithorax sp. nov., M. robustus sp. nov., and M. wuyiensis sp. nov.). [ABSTRACT FROM AUTHOR]

Crystallization often occurs in the processing of amorphous alloys, causing the materials lose their excellent properties. The study adopts chemical mechanical polishing of amorphous alloys, presenting the effect of the rotational speed of the polishing turntable, size of abrasive, polishing pressure, and oxidant concentration. The Taguchi method is used to find the best processing parameters, and AFM is used to characterize the machined material surface. At the same time, XPS is used to detect the change of oxide film composition with the addition of oxidant. The results indicate the optimum process parameters: rotational speed of the polishing turntable is 75 r/min, polishing pressure is 28.3 kPa, the size of abrasive is 0.5 μm, and the size of abrasive is a significant factor affecting surface roughness Sa. In addition, as the size of abrasive increases, the material removal rate increases while the surface roughness Sa increases. At pH 10, with an abrasive particle size of 0.5 μm, as the H2O2 concentration increases, the MRR first rapidly decreases at 0.21 wt.% H2O2, and then gradually increases, while the Sa decreases. Furthermore, with the addition of oxidant, the main composition of the surface oxide film changes from oxide to hydroxide, and the contents of Zr4+ and Cu0/Cu1+ elements increase. The findings can provide a feasible chemical mechanical polishing process for zirconium-based amorphous alloys to obtain a satisfactory polishing effect. [ABSTRACT FROM AUTHOR]

Carrots are a type of vegetable with high nutrition. Before entering the market, the surface defect detection and sorting of carrots can greatly improve food safety and quality. To detect defects on the surfaces of carrots during combine harvest stage, this study proposed an improved knowledge distillation network structure that took yolo-v5s as the teacher network and a lightweight network that replaced the backbone network with mobilenetv2 and completed channel pruning as a student network (mobile-slimv5s). To make the improved student network adapt to the image blur caused by the vibration of the carrot combine harvester, we put the ordinary dataset Dataset (T) and dataset Dataset (S), which contains motion blurring treatment, into the teacher network and the improved lightweight network, respectively, for learning. By connecting multi-stage features of the teacher network, knowledge distillation was carried out, and different weight values were set for each feature to realize that the multi-stage features of the teacher network guide the single-layer output of the student network. Finally, the optimal lightweight network mobile-slimv5s was established, with a network model size of 5.37 MB. The experimental results show that when the learning rate is set to 0.0001, the batch size is set to 64, and the dropout is set to 0.65, the model accuracy of mobile-slimv5s is 90.7%, which is significantly higher than other algorithms. It can synchronously realize carrot harvesting and surface defect detection. This study laid a theoretical foundation for applying knowledge distillation structures to the simultaneous operations of crop combine harvesting and surface defect detection in a field environment. This study effectively improves the accuracy of crop sorting in the field and contributes to the development of smart agriculture. [ABSTRACT FROM AUTHOR]

Tai Lake, an important biodiversity hotspot of the lower reaches of the Yangtze River in China, possesses its characteristic fish fauna. Barcoding on native species is important for species identification and biodiversity assessment with molecular-based methods, such as environmental DNA (eDNA) metabarcoding. Here, DNA-barcoding coupled with high-throughput sequencing (HTS) and traditional Sanger sequencing was introduced to barcoding 180 specimens belonging to 33 prior morphological species, including the most majority of fish fauna in Tai Lake. HTS technology, on the one hand, significantly enhances the capture of barcode sequences of fish. The successful rate of fish barcoding was 74% and 91% in Sanger and HTS, respectively. On the other hand, the HTS output has a large number (64%) of insertions and deletions, which require strict bioinformatics processing to ensure that the "true" barcode sequence is captured. Cross-contamination and parasites were the primary error sources that compromised attempts at the DNA barcoding of fish species. The barcode gap analysis was 100% successful at delimiting species in all specimens. The automatic barcode gap discovery (ABGD) method grouped barcode sequences into 34 OTUs, and some deep divergence and closed species failed to obtain corresponding OTUs. Overall, the local species barcode library established by HTS barcoding here is anticipated to shed new light on conserving fish diversity in Tai Lake. [ABSTRACT FROM AUTHOR]

To clarify the effect of monochromatic light on circadian rhythms of plasma insulin level and pancreatic clock gene expression and its mechanism, 216 newly hatched chicks were divided into three groups (intact, sham operation and pinealectomy) and were raised under white (WL), red (RL), green (GL) or blue (BL) light for 21 days. Their plasma and pancreas were sampled at six four-hour intervals. For circadian rhythm analysis, measurements of plasma melatonin, insulin, and clock gene expression (cClock, cBmal1, cBmal2, cCry1, cCry2, cPer2, and cPer3) were made. Plasma melatonin, insulin, and the pancreatic clock gene all expressed rhythmically in the presence of monochromatic light. Red light reduced the mesor and amplitude of plasma melatonin in comparison to green light. The mesor and amplitude of the pancreatic clock gene in chickens exposed to red light were dramatically reduced, which is consistent with the drop in plasma melatonin levels. Red light, on the other hand, clearly raised the level of plasma insulin via raising the expression of cVamp2, but not cInsulin. After the pineal gland was removed, the circadian expressions of plasma melatonin and pancreatic clock gene were significantly reduced, but the plasma insulin level and the pancreatic cVamp2 expression were obviously increased, resulting in the disappearance of differences in insulin level and cVamp2 expression in the monochromatic light groups. Therefore, we hypothesize that melatonin may be crucial in the effect of monochromatic light on the circadian rhythm of plasma insulin level by influencing the expression of clock gene in chicken pancreas. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed numerous challenges to human society. Previous studies explored multiple factors in virus transmission. Yet, their impacts on COVID-19 are not universal and vary across geographical regions. In this study, we thoroughly quantified the spatiotemporal associations of 49 health, socioeconomic, demographic, and environmental factors with COVID-19 at the county level in Arkansas, US. To identify the associations, we applied the ordinary least squares (OLS) linear regression, spatial lag model (SLM), spatial error model (SEM), and multiscale geographically weighted regression (MGWR) model. To reveal how such associations change across different COVID-19 times, we conducted the analyses for each season (i.e., spring, summer, fall, and winter) from 2020 to 2021. We demonstrate that there are different driving factors along with different COVID-19 variants, and their magnitudes change spatiotemporally. However, our results identify that adult obesity has a positive association with the COVID-19 incidence rate over entire Arkansas, thus confirming that people with obesity are vulnerable to COVID-19. Humidity consistently negatively affects COVID-19 across all seasons, denoting that increasing humidity could reduce the risk of COVID-19 infection. In addition, diabetes shows roles in the spread of both early COVID-19 variants and Delta, while humidity plays roles in the spread of Delta and Omicron. Our study highlights the complexity of how multifactor affect COVID-19 in different seasons and counties in Arkansas. These findings are useful for informing local health planning (e.g., vaccine rollout, mask regulation, and testing/tracing) for the residents in Arkansas. [ABSTRACT FROM AUTHOR]

The deep application of liquid fertilizer in paddy fields is a fertilization technique that applies liquid fertilizer deep near the root system of paddy field crops, which can effectively improve the absorption rate of the crops and reduce the amount of fertilizer applied. In the cold regions of China, the soil return rate of the furrowing operation of the deep application of liquid fertilizer in paddy fields is low, which can easily cause the excessive liquid leakage of fertilizer and affect crop growth. Therefore, it is difficult to popularize in large areas. According to the characteristics of paddy soil in the cold regions of China and the operating requirements of a high backfill rate and low disturbance rate of the soil of the deep application of liquid fertilizer, this paper designed a bionic liquid fertilizer deep application furrow opener based on the claw-toe structure of the muskrat. In this study, an indoor soil bin test was conducted by constructing a deep application environment for the liquid fertilizer in paddy fields. The results of the soil bin test showed the effects of the key operating parameters of the bionic design of the liquid fertilizer deep application furrow opener, spraying pressure of the liquid fertilizer and operating speed on the furrowing resistance, soil disturbance rate and the leakage amount of liquid fertilizer. The bionic design of the liquid fertilizer deep application furrow opener has a low soil disturbance rate and leakage amount of fertilizer when the operating speed is 0.8 m s−1, and the spraying pressure is 0.2 MPa. This furrow opener significantly improves the operating performance of the deep application of liquid fertilizer in the cold regions of China and is suitable for the deep application of liquid fertilizer in the paddy fields of this region. [ABSTRACT FROM AUTHOR]

All living organisms on the Earth live and evolve in the presence of the weak geomagnetic field, a quasi-uniform static magnetic field (SMF). In the meantime, although the effects of moderate and high SMFs have been investigated on multiple aspects of a living organism, a long-term SMF exposure of more than 1 year has never been reported. Here, we investigated the influence of a moderate SMF (70–220 mT head-to-toe) long-term continuous exposure (1.7 years) to two different SMF directions on healthy male C57BL/6 mice. We found that not only was the lifespan of the mice prolonged, but their healthspan was also improved. The elevated plus maze test and open field test show that SMFs could significantly improve the exploratory and locomotive activities of the aged mice. The Morris water maze test shows that SMFs could improve their spatial learning ability and spatial memory. Tissue examinations reveal that SMFs have an ameliorative effect on oxidative stress in the brain of aged mice, which was reinforced by the cellular assays, showing that SMFs could protect the PC12 cells from D-gal-induced senescence by increasing superoxide dismutase, catalase, and reducing the malonaldehyde levels. Therefore, our data show that the 1.7-year SMF exposure can improve both the lifespan and healthspan of naturally aged mice due to reduced oxidative stress, which indicates that SMFs have the potential to be used as an adjuvant physical therapy to reduce the ageing-induced health risks to benefit animals, and even humans. [ABSTRACT FROM AUTHOR]

Cisplatin is one of the most widely used anti-cancer drugs that can effectively inhibit the growth of multiple types of cancer. However, its clinical application is limited by its severe side effects, especially kidney toxicity, caused by cisplatin-induced oxidative stress, inflammation and kidney cell apoptosis. Here, we found that moderate (a few hundred mT) quasi-uniform static magnetic fields (SMFs) could inhibit cisplatin-induced renal proximal tubular cell death, especially the vertically downward direction SMF. RNA-seq experiments demonstrate that SMFs induced differential gene expressions that are closely associated with oxidative stress, apoptosis, cytokine production, transmembrane transport and DNA repair. In vivo experiments show that SMFs can reduce cisplatin-induced kidney injury in cisplatin-administrated tumor-bearing mice by reducing oxidative stress, inflammation and cell apoptosis. Furthermore, high-dose cisplatin-induced acute nephrotoxicity can be effectively alleviated by SMF treatment of as little as one day, which significantly reduced the reactive oxygen species levels in kidneys and prolonged the mice's survival. Moreover, the concentration of cisplatin in the kidney was significantly attenuated in SMF-treated mice. Therefore, our study demonstrates the effects of moderate SMFs as a novel physical method to reduce oxidative stress, and revealed their future potential to be used against cisplatin-induced kidney toxicity in cancer treatment. [ABSTRACT FROM AUTHOR]

The water conservancy scenic spot is an important part of China's water ecological civilization construction and is an important way to transform "clear water and green mountains" into "mountains of gold and silver". The current development of the water conservancy scenic spot is limited by the conditions of capital, management, and technology. The PPP model, as a means of introducing these elements, is an effective way to realize the marketization, characterization, and high-quality development of water conservancy scenic spots. Due to the particularity and complexity of water conservancy scenic spots, the PPP model, and their combination, the risks in water conservancy scenic spot PPP projects are more complicated. Identification and assessment, as well as response, are necessary ways to reduce project risks. In this paper, the risk evaluation index system and the DEMATEL-ANP-FUZZY risk evaluation model of the water conservancy scenic spot PPP project are put forward and, then, the key risk factors, causal relationship between the factors, and risk level of the project are obtained in combination with case analysis. The results show that risks in construction and operation, as well as political and economic risks, are the key risk factors in the water conservancy scenic spot PPP project, and the natural, economic and political risk factors are the main causes of project risk. Risks in construction and operation, as well as organizational and social risks, are the main affected factors. Further analysis shows that the political and economic factors are the key points to focus on when attempting to reduce the project risk, and suggestions are provided, such as improving the legal and regulatory framework, establishing a reasonable risk-sharing and social capital withdrawal mechanism, unblocking social capital participation channels, and strengthening financial support. This could provide a reference for the risk management of water conservancy scenic spot PPP projects. [ABSTRACT FROM AUTHOR]

Strontium marking technology is commonly used for extensive marking in stock enhancement and releasing activities. In order to explore the feasibility of this technology for juvenile Takifugu obscurus, five different concentrations of strontium (0, 12, 18, 36, and 72 mg/L) were set up by strontium chloride hexahydrate (SrCl2·6H2O) and T. obscurus were immersed for 7 days. Then, T. obscurus were reared in non-additive water for 25 days. The results showed that the survival rate of all groups was 100%, except the 72 mg/L group, after 7 days of immersion. Moreover, the growths of all the marked groups were better than that of the control group. There was no significant difference between the control and marked groups, except for the 18 mg/L group, indicating that the appropriate concentration of strontium might have positive effect for T. obscurus. The strontium to calcium (Sr/Ca) ratios of otoliths in the marked groups increased with increasing concentration and time, which were higher than the baseline, respectively. Then, the Sr/Ca ratios returned to the original level, but the peak value was still retained, suggesting that the success rate of the strontium marking reached 100%. Notably, the residual strontium content of 18 mg/L group was insufficient for causing negative effects for T. obscurus after 25 days. Therefore, in consideration of the growth, survival, and effect of strontium marking on otolith, 18 mg/L is an appropriate concentration for strontium marking of juvenile T. obscurus. This study confirmed the feasibility of strontium marking for T. obscurus and provided a new approach to rationally and scientifically evaluate the stock enhancement and releasing efficiency of T. obscurus. [ABSTRACT FROM AUTHOR]

Simple Summary: The data show that long-term 150 mT SMF exposure enhanced the motility and alleviated the mental status of adult female mice by modulating glucose metabolism and gut microbiota, increasing the level of beneficial flora and antioxidant capacity. This certificated that moderate intensity long-term SMF exposure has the potential to ameliorate the locomotive and exploratory capacity and negative mood associated with female growth. Because of the high cost and safety of ultra-high magnetic resonance imaging (MRI), its application has certain limitations. Whereas 0.5–3 T MRI has been widely applied in hospitals, static magnetic fields (SMFs) have been shown to improve mice mental health and have anti-tumor potentials. Here, we compared the effects of the upward and downward 150 mT SMF groups with the sham group on C57BL/6J adult female mice. Locomotor and exploratory activity were also measured by behavioral tests, including the open field and elevated plus test. Additionally, physiology, pathology indicators and gut microbiota were examined. We found that 150 mT SMFs long-term exposure enhanced locomotive and exploratory activity of mice, especially the downward 150 mT SMF. Compared with the downward 150 mT SMF group, the movement speed and distance in the center area of the sham group were increased by 65.99% (p < 0.0001) and 68.58% (p = 0.0038), respectively. Moreover, compared to the sham group, downward 150 mT SMF increased the number of entrances to the center area by 67.0% (p = 0.0082) and time in the center area by 77.12% (p = 0.0054). Additionally, we observed that upward 150 mT SMF improved the number of follicles (~2.5 times, p = 0.0325) and uterine glands through increasing the total antioxidant capacity and reducing lipid peroxidation level in mice. Gut microbiome analysis showed that 150 mT SMFs long-term exposure improved the microbiota abundance (Clostridium, Bifidobacterium, Ralstonia and Yaniella) in the genus level, which may affect metabolism, anxiety and behavior in adult female mice. Our results demonstrated that 150 mT SMFs long-term exposure not only had good biosafety, but also improved athletic performance, emotion and the function of ovarian, uterine and gut microbiota abundance in adult female mice, which unraveled the potential of moderate long-term SMF exposure in clinical applications. [ABSTRACT FROM AUTHOR]

Software-defined measurement (SDM) is a simple and efficient way to deploy measurement tasks and collect measurement data. With SDM, it is convenient for operators to implement fine-grained network-wide measurements at the flow level, from which many important functions can benefit. The prior work provides mechanisms to distribute flows to monitors, such that each monitor can identify its non-overlapped subset of flows to measure, and a certain global performance criterion is optimized, such as load balance or flow coverage. Many applications of network management can benefit from a function that can find large flows efficiently, such as congestion control by dynamically scheduling large flows, caching of forwarding table entries, and network capacity planning. However, the current network-wide measurements neglect the diversity of different flows as they treat large flows and small flows equally. In this paper, we present a mechanism of accuracy-aware collaborative monitoring (ACM) to improve the measurement accuracies of large flows in network-wide measurements at the flow level. The structure of the sketch is an approximate counting algorithm, and a high-measurement accuracy can be achieved by merging the results from multiple monitors with sketches, which is termed as collaborative monitoring. The core idea of our method is to allocate more monitors to large flows and achieve the load balance to provide accuracy-aware monitoring. We modeled our problem as an integer–linear programming problem, which is NP-hard. Thus, we propose an approximation algorithm, named the improved longest processing time algorithm (iLPTA); we proved that its approximation ratio is (1 2 + n l) . We propose a two-stage online distribution algorithm (TODA). Moreover, we proved that its approximation ratio is (1 + n l − 1) . The iLPTA is an offline approximation algorithm used to assign monitors for each flow, which prove the validity and feasibility of the core idea. The TODA is an online algorithm that attempts to achieve the load balance by selecting the monitor with the smallest load to a large flow. Our extensional experiment results verify the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is an effective non-engineering landslide prevention at the regional scale. This study aims to improve the accuracy of landslide susceptibility assessment by using an optimized statistical index (SI) method. A landslide inventory containing 493 historical landslides was established, and 20 initial influencing factors were selected for modeling. First, a combination of GeoDetector and recursive feature elimination was used to eliminate the redundant factors. Then, an optimization method for weights of SI was adopted based on Gaussian process regression (GPR). Finally, the predictive abilities of the original SI model, the SI model with optimized factors (GD-SI), and the SI model with optimized factors and weights (GD-GPR-SI) were compared and evaluated by the area under the receiver operating characteristic curve (AUC) on the testing datasets. The GD-GPR-SI model has the highest AUC value (0.943), and the GD-SI model (0.936) also has a higher value than the SI model (0.931). The results highlight the necessity of factor screening and weight optimization. The factor screening method used in this study can effectively eliminate factors that negatively affect the SI model. Furthermore, by optimizing the SI weights through GPR, more reasonable weights can be obtained for model performance improvement. [ABSTRACT FROM AUTHOR]

This article mainly concentrates on the synchronization problem for a more general kind of the master–slave memristor-based neural networks with fractional derivative. By applying a continuous-frequency-distributed equivalent model tool, some new outcomes and sufficient conditions on the robust synchronization of the master–slave neural networks with uncertainty are proposed via linear matrix inequality (LMI). Finally, two memristive neural networks model with fractional derivatives are presented to validate the efficiency of the theoretical results. [ABSTRACT FROM AUTHOR]

Due to the potential economic benefits, pedestrian flow is considered an essential indication of public spaces. Pedestrian flow prediction is designed to assist operators in making decisions (such as shopping center owners). Operators hold certain events, such as sales promotions, to attract surrounding pedestrians; we refer to this type of event as a business event. Business events attract pedestrian flows, which means business opportunities for the merchants. Moreover, their placement will affect the distributions of the pedestrian flows. However, deciding which route is chosen for a specified event is difficult. To the best of our knowledge, we are the first to consider business events when predicting pedestrian flow. In this paper, we investigate two problems: one is pedestrian flow prediction with business events, and the other is route recommendation for business events. First, we propose an Attraction-Based Matrix Factorization model (ABMF) to efficiently predict the pedestrian flow with business events, which introduces the attraction index of different categories to pedestrians in matrix factorization. Second, we leverage the Skip-gram mode to learn the latent representations and improve the pair-wise ranking loss to a flow-aware-based method (SG-FWARP), which aims to learn events' latent representations for route recommendation. Compared with other state-of-the-art methods, the experimental results show ABMF can predict pedestrian flow matrix with a similarity of over 0.9 compared with the ground truth, and SG-FWARP can recommend routes for business events with high accuracy. [ABSTRACT FROM AUTHOR]

Endovascular therapy has emerged as a crucial therapeutic method for treating vascular diseases. Endovascular surgical robots have been used to enhance endovascular therapy. However, to date, there are no universal endovascular surgical robots that support molds of different types of devices for treating vascular diseases. We developed a novel endovascular surgical robotic system that can independently navigate the intravascular region, advance and retract devices, and deploy stents. This robot has four features: (1) The bionic design of the robot can fully simulate the entire grasping process; (2) the V-shaped relay gripper waived the need to redesign special guidewires and catheters for continuous rotation; (3) the handles designed based on the feedback mechanism can simulate push resistance and reduce iatrogenic damage; and (4) the detachable design of the grippers can reduce cross-infection risk and medical costs. We verified its performance by demonstrating six different types of endovascular surgeries. Early evaluation of the novel endovascular robotic system demonstrated its practicability and safety in endovascular surgeries. [ABSTRACT FROM AUTHOR]

Converting CO2 into fuels via solar-driven thermochemical cycles of metal oxides is promising to address global climate change and energy crisis challenges simultaneously. However, it suffers from low energy conversion efficiency ( η en ) due to high sensible heat losses when swinging between reduction and oxidation cycles, and a single product of fuels can hardly meet multiple kinds of energy demands. Here, we propose an alternative way to upsurge energy conversion efficiency by integrating solar thermochemical CO2 splitting with a supercritical CO2 thermodynamic cycle. When gas phase heat recovery (εgg) is equal to 0.9, the highest energy conversion efficiency of 20.4% is obtained at the optimal cycle high pressure of 260 bar. In stark contrast, the highest energy conversion efficiency is only 9.8% for conventional solar thermochemical CO2 splitting without including a supercritical CO2 cycle. The superior performance is attributed to efficient harvesting of waste heat and synergy of CO2 splitting cycles with supercritical CO2 cycles. This work provides alternative routes for promoting the development and deployment of solar thermochemical CO2 splitting techniques. [ABSTRACT FROM AUTHOR]