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Polarization imaging technology can be applied to unveil the interaction between light and matter by harnessing the transverse vector wave attributes of light, thus to accentuate target characteristics amidst complex weather conditions. This technology has the potential to be widely used in road target detection. However, polarization detection is significantly affected by illumination and detection angles, as well as the considerable variation in the scale of road targets. The optimal polarization parameters should be adaptively adjusted to weather conditions, angles and target features, whereas most existing research employs handcrafted polarization parameters without considering actual complex detection requirements, which are unable to adaptively adjust the polarization feature enhancement methods. In this paper, we propose a road target detection algorithm based on an end-to-end adaptive polarization coding method, named YOLO-Polarization of Road Target Detection (YOLO-PRTD). To enhance the polarized features of targets under complex weather conditions, an Adaptive Polarization Coding Module (APCM) is designed. This module integrates channel-wise global self-attention and small kernel convolution to adaptively adjust the polarization enhancement method using dynamically extracted global and local polarization feature information. A multi-scale detection network is also designed to fully extract and fuse multi-scale feature information from receptive fields, channels, and spaces in different dimensions. Additionally, a dataset of Polarized Images of Road Targets in Complex Weather conditions (PIRT-CW) is proposed for training and evaluation. Experimental results on the PIRT-CW show that the YOLO-PRTD algorithm achieves a mAP0.5 of 89.83%, reducing the error rate by 15.54% compared to the baseline network YOLOX. [ABSTRACT FROM AUTHOR]

To address the fatigue damage issues in continuous beam bridges under vehicle loads, a method using vibration-mixed steel fiber-reinforced concrete to improve critical vulnerable areas of the bridge is proposed, thereby enhancing the bridge’s fatigue resistance. Fatigue performance and micro electron microscopy tests were designed for vibration-mixed steel fiber-reinforced concrete, analyzing its damage conditions and microstructural changes under 0 to 2 million cyclic loads, and the key mechanical parameters of the concrete were determined. Based on this, a numerical analysis model was established to simulate the fatigue damage of continuous beam bridges under moving vehicle loads. The results show that piers made with vibration-mixed steel fiber-reinforced concrete exhibit a 56.86% reduction in compressive damage compared to conventional piers, a reduction in stiffness damage range, and a 29.35% increase in fatigue life. [ABSTRACT FROM AUTHOR]

Alkaline phosphatase (ALP) is a reliable biomarker for various diseases, making the development of new detection methods highly significant. Herein, we report a simple and accurate dual-signal optical detection strategy for measuring ALP activity across the entire clinical range in adults. Based on this, we designed a smartphone app to eliminate the limitations of large instruments, enabling real-time field detection. In this work, ALP catalyzes the conversion of the phosphate monoester of 2-phospho-L-ascorbic acid (PAA) into ascorbic acid (AA), a compound with strong reducing properties. AA facilitates the in-situ reduction of Ag+ to form Au@Ag core-shell nanoparticles, accompanied by significant changes in the ultraviolet-visible absorption spectra and color. These changes allow for the qualitative and quantitative detection of ALP activity. In UV absorption spectroscopy and colorimetric detection, the signal shows good linearity with ALP activity concentrations, with detection ranges of 0 U/L to 200 U/L (R2=0.9961) and 25 U/L to 250 U/L (R2=0.9771), respectively. Subsequent selective analysis and serum sample analysis demonstrated the high selectivity and accuracy of the proposed method, highlighting its potential for field application. [ABSTRACT FROM AUTHOR]

Pollen coat proteins (PCPs) are cysteine-rich small-molecule proteins, which exhibit high levels of polymorphism and are expressed in gametocytes. Previous investigations have revealed that PCP genes are involved in pollen wall synthesis, pollen-stigma recognition, and pollen development and germination. However, gene expression and function of PCP family in pollen development is not well understood in Wucai (Brassica campestris L.). In this study, genome-wide identification and expression analysis of the BcPCP gene family members were conducted, including their physical and chemical properties, chromosome localization, phylogenetic relationships, gene structure, and tertiary structure. A total of 20 BcPCP genes were identified and classified into three subfamilies showing high homology to Arabidopsis thaliana. Expression pattern analysis indicated that the BcPCP gene family exhibits higher expression levels in reproductive organs, suggesting their potential involvement in the reproductive development. Notably, BraA02g002400.3 C, potentially associated with male sterility, was identified through multiple transcriptomic and proteomic datasets. Subsequently, sequence analysis revealed its homology with the Arabidopsis GRP20 gene, and thus it was named BcGRP20. Functional analysis of this gene showed that overexpression of BcGRP20 gene in the Arabidopsis grp20 mutant could restore anther fertility. Overall, our findings indicate that BcGRP20 plays a critical role in pollen development and may be the causative gene for male sterility in Wucai. This study provides candidate genes for further functional identification of BcPCP genes in Wucai, which are crucial for anther development. [ABSTRACT FROM AUTHOR]

This study presents an innovative enhancement to retrieval-augmented generation (RAG) systems by seamlessly integrating fine-tuned large language models (LLMs) with vector databases. This integration capitalizes on the combined strengths of structured data retrieval and the nuanced comprehension provided by advanced LLMs. Central to our approach are the LoRA and QLoRA methodologies, which stand at the forefront of model refinement through parameter-efficient fine-tuning and memory optimization. A novel feature of our research is the incorporation of user feedback directly into the training process, ensuring the model's continuous adaptation to user expectations and thus, improving its performance and applicability. Additionally, we introduce a Quantized Influence Measure (QIM) as an innovative "AI Judge" mechanism to enhance the precision of result selection, further refining the system's accuracy. Accompanied by an executive diagram and a detailed algorithm for fine-tuning QLoRA, our work provides a comprehensive framework for implementing these advancements within chatbot technologies. This research contributes significant insights into LLM optimization for specific uses and heralds new directions for further development in retrieval-augmented models. Through extensive experimentation and analysis, our findings lay a robust foundation for future advancements in chatbot technology and retrieval systems, marking a significant step forward in the creation of more sophisticated, precise, and user-centric conversational AI systems. We make the dataset, the data processing package huggify-data, the model, and the app publicly available for the community. [ABSTRACT FROM AUTHOR]

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation. [ABSTRACT FROM AUTHOR]

To address the electromechanical coupling and multi-source disturbance problems of the seeker stabilized platform, this paper constructs an electromechanical coupling model of the seeker stabilized platform based on the Lagrange-Maxwell equation. To mitigate the influence of electromechanical coupling on the control performance of the seeker, a super-twisting controller based on a fractional-order terminal sliding mode surface (FOSTSMC) is proposed. Additionally, to handle various disturbances in the system, this paper introduces a method that combines the extended state observer (ESO) with the proposed controller to enhance the system's stability and anti-disturbance performance. The Lyapunov function is designed to prove that the proposed controller can reach a convergence state within finite time. Finally, the proposed control method is compared with PID control, fuzzy PID control, linear sliding mode control, and super spiral control combined with a disturbance observer (DOB). Multiple simulation experiments demonstrate that, under the influence of electromechanical coupling and multi-source disturbance, the FOSTSMC-ESO significantly improves the stability and anti-disturbance performance of the seeker stabilization platform. [ABSTRACT FROM AUTHOR]

Type 2 diabetes is one of the most prevalent chronic diseases in the world, and more people than ever before have impaired glucose tolereance, or prediabetes. Many patients with impaired glucose tolerance and undiagnosed diabetes do not know that their glucose metabolism system has been in a state of disorder. Every year, about 5-10% of prediabetics develop diabetes. One of the important achieving factors may be the increase in blood lipids. However, it is not clear whether triglyceride is associated with impaired glucose tolerance and prediabetes in the Qatari population. Therefore, we investigated the relationship between the first several clinical variables and prediabetes status in normal and overweight populations. We conducted a cross-sectional study using data from the Qatar Biobank program. The study included 5,996 participants who were adults over the age of 20. We collected information about participants' fasting blood glucose levels with other clinical measurements and used various machine learning models and logistic regression to study the association between the clinical measurements and prediabetes for normal and overobese weight groups. The use of several machine learning models showed that, after adjusting the potential confounding factors such as age and sex, Triglyceride has been demonstrated to be positively correlated with prediabetes, and there was a special population dependence phenomenon. Among them, nonobese people (p < 0.05). The effect value and 95% confidence interval and OR of triglyceride on prediabetes was 2.79 and (e0.78, e1.28), respectively. [ABSTRACT FROM AUTHOR]

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance. [ABSTRACT FROM AUTHOR]

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods. [ABSTRACT FROM AUTHOR]

In-vehicle information system (IVIS) use is prevalent among young adults. However, their interaction with IVIS needs to be better understood. Therefore, an on-road study aims to explore the effects of input modalities and secondary task types on young drivers' secondary task performance, driving performance, and visual glance behavior. A 2 × 4 within-subject design was undertaken. The independent variables are input modalities (auditory-speech and visual-manual) and secondary task types (calls, music, navigation, and radio). The dependent variables include secondary task performance (task completion time, number of errors, and SUS), driving performance (average speed, number of lane departure warnings, and NASA-TLX), and visual glance behavior (average glance duration, number of glances, total glance duration, and number of glances over 1.6 s). The statistical analysis result showed that the main effect of input modalities is significant, with more distraction during visual-manual than auditory-speech. The main impact of secondary task types was also substantial across most metrics, aside from average speed and average glance duration. Navigation and music were the most distracting, followed by calls, and radio came in last. The distracting effect of input modalities is relatively stable and generally not moderated by the secondary task types, except radio tasks. The findings practically benefit the driver-friendly human–machine interface design, preventing IVIS-related distraction. [ABSTRACT FROM AUTHOR]

Prostate cancer, one of the most prevalent malignancies among men worldwide, is intricately linked with androgen signaling, a key driver of its pathogenesis and progression. Understanding the diverse expression patterns of androgen-responsive genes holds paramount importance in unraveling the biological intricacies of this disease and prognosticating patient outcomes. In this study, utilizing consensus clustering analysis based on the expression profiles of androgen-responsive genes, prostate cancer patients from the TCGA database were stratified into two distinct subtypes, denoted as C1 and C2. Notably, the C1 subtype demonstrates a significant upregulation of certain genes, such as CGA and HSD17B12, along with a shorter progression-free survival duration, indicating a potentially unfavorable prognosis. Further analyses elucidated the immune infiltration disparities, mutation landscapes, and gene functional pathways characteristic of each subtype. Through integrated bioinformatics approaches and machine learning techniques, key genes such as BIRC5, CENPA, and MMP11 were identified as potential therapeutic targets, providing novel insights into tailored treatment strategies. Additionally, single-cell transcriptome analysis shed light on the heterogeneous expression patterns of these genes across different cell types within the tumor microenvironment. Furthermore, virtual screening identified candidate drugs targeting the BIRC5 receptor, offering promising avenues for drug development. Collectively, these findings deepen our understanding of prostate cancer biology, paving the way for personalized therapeutic interventions and advancing the quest for more effective treatments in prostate cancer management. [ABSTRACT FROM AUTHOR]

The market value of vanilla beans (Vanilla planifolia) is constantly increasing due to their natural aroma and flavor properties that improve after a curing process, where bacteria colonization plays a critical role. However, a few publications suggest that bacteria play a role in the curing process. Hence, this study aimed to isolate Bacillus sp. that could be used for fermenting V. planifolia while analyzing their role in the curing process. Bacillus velezensis ZN-S10 identified with 16S rRNA sequencing was isolated from conventionally cured V. planifolia beans. A bacteria culture solution of B. velezensis ZN-S10 (1 mL of 1 × 107 CFU mL−1) was then coated on 1 kg of non-cured vanilla pods that was found to ferment and colonize vanilla. PCA results revealed distinguished bacterial communities of fermented vanilla and the control group, suggesting colonization of vanilla. Phylogenetic analysis showed that ZN-S10 was the dominant Bacillus genus member and narrowly correlated to B. velezensis EM-1 and B. velezensis PMC206-1, with 78% and 73% similarity, respectively. The bacterial taxonomic profiling of cured V. planifolia had a significant relative abundance of Firmicutes, Proteobacteria, Cyanobacteria, Planctomycetes, and Bacteroidetes phyla according to the predominance. Firmicutes accounted for 55% of the total bacterial sequences, suggesting their colonization and effective fermentation roles in curing vanilla. [ABSTRACT FROM AUTHOR]

Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details, adopting a multi-directional difference constrained global gradient sparsity decomposition method, which provides a guidance image with weaker texture detail gradients. Meanwhile, using the luminance channel as a reference, edge-aware operator is constructed based on local gradient constraints. This operator weakens the gradients of repetitive and similar texture details, enabling it to obtain more accurate structural information for guiding global optimization of the image. By projecting multi-directional differences onto the horizontal and vertical directions, a mapping from multi-directional differences to bi-directional gradients is achieved. Additionally, to ensure the consistency of measurement results, a multi-directional gradient normalization method is designed. Through experiments, we demonstrate that our method exhibits significant advantages in preserving image edges compared to current advanced smoothing methods. [ABSTRACT FROM AUTHOR]

As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented. [ABSTRACT FROM AUTHOR]

This study aims to explore the research methodology of applying the Transformer model algorithm to Chinese word sense disambiguation, seeking to resolve word sense ambiguity in the Chinese language. The study introduces deep learning and designs a Chinese word sense disambiguation model based on the fusion of the Transformer with the Bi-directional Long Short-Term Memory (BiLSTM) algorithm. By utilizing the self-attention mechanism of Transformer and the sequence modeling capability of BiLSTM, this model efficiently captures semantic information and context relationships in Chinese sentences, leading to accurate word sense disambiguation. The model's evaluation is conducted using the PKU Paraphrase Bank, a Chinese text paraphrase dataset. The results demonstrate that the model achieves a precision rate of 83.71% in Chinese word sense disambiguation, significantly outperforming the Long Short-Term Memory algorithm. Additionally, the root mean squared error of this algorithm is less than 17, with a loss function value remaining around 0.14. Thus, this study validates that the constructed Transformer-fused BiLSTM-based Chinese word sense disambiguation model algorithm exhibits both high accuracy and robustness in identifying word senses in the Chinese language. The findings of this study provide valuable insights for advancing the intelligent development of word senses in Chinese language applications. [ABSTRACT FROM AUTHOR]

Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process. [ABSTRACT FROM AUTHOR]

The investigation focused on creating and studying a new 2D–2D S-scheme CdS/g-C3N4 heterojunction photocatalyst. Various techniques examined its structure, composition, and optical properties. This included XRD, XPS, EDS, SEM, TEM, HRTEM, DRS, and PL. The heterojunction showed a reduced charge recombination rate and more excellent stability, helping to lessen photocorrosion. This was due to photogenerated holes moving more quickly out of the CdS valence band. The interface between g-C3N4 and CdS favored a synergistic charge transfer. A suitable flat band potential measurement supported enhanced reactive oxygen species (ROS) generation in degrading 4-nitrophenol and 2-nitrophenol. This resulted in remarkable degradation efficiency of up to 99% and mineralization of up to 79%. The findings highlighted the practical design of the new 2D–2D S-scheme CdS/g-C3N4 heterojunction photocatalyst and its potential application in various energy and environmental settings, such as pollutant removal, hydrogen production, and CO2 conversion. [ABSTRACT FROM AUTHOR]

Plant stem cell cultures have so far been established in only a few plant species using cambial meristematic cells. The presence of stem cells or stem cell-like cells in other organs and tissues of the plant body, as well as the possibility of de novo generation of meristematic cells from differentiated cells, allow to consider the establishment of stem cell cultures in a broader range of species. This study aimed to establish a stem cell culture of the medicinal plant Calendula officinalis L. Callus tissues were induced from leaf and root explants, and already at this stage, stem and dedifferentiated cells could be identified. The cell suspension cultures established both from the root- and leaf-derived calli contained a high proportion of stem cells (92–93% and 72–73%, respectively). The most effective combination of growth regulators for the development of stem cells in calli as well as cell cultures was 1.0 mg/L 2,4-D and 0.5 mg/L BAP. The highest amount of stem cells (5.60–5.72 × 105) was in cell suspension derived from the roots. An effective protocol for the establishment of marigold stem cell suspension culture was developed. The ratio of root-derived stem cells against dedifferentiated cells exceeded 90%. [ABSTRACT FROM AUTHOR]

Extracting useful features at multiple scales is a crucial task in computer vision. The emergence of deep-learning techniques and the advancements in convolutional neural networks (CNNs) have facilitated effective multiscale feature extraction that results in stable performance improvements in numerous real-life applications. However, currently available state-of-the-art methods primarily rely on a parallel multiscale feature extraction approach, and despite exhibiting competitive accuracy, the models lead to poor results in efficient computation and low generalization on small-scale images. Moreover, efficient and lightweight networks cannot appropriately learn useful features, and this causes underfitting when training with small-scale images or datasets with a limited number of samples. To address these problems, we propose a novel image classification system based on elaborate data preprocessing steps and a carefully designed CNN model architecture. Specifically, we present a consecutive multiscale feature-learning network (CMSFL-Net) that employs a consecutive feature-learning approach based on the usage of various feature maps with different receptive fields to achieve faster training/inference and higher accuracy. In the conducted experiments using six real-life image classification datasets, including small-scale, large-scale, and limited data, the CMSFL-Net exhibits an accuracy comparable with those of existing state-of-the-art efficient networks. Moreover, the proposed system outperforms them in terms of efficiency and speed and achieves the best results in accuracy-efficiency trade-off. [ABSTRACT FROM AUTHOR]

The m6A methylation of mRNA has been demonstrated to interact with the "Reader". YTH domain family is one of the readers containing five members involved in the progression of multiple tumors. The present study aimed to explore the YTH family's role in seventeen cancer types. Data were downloaded from The Cancer Genome Atlas (TCGA) dataset and analyzed by Software R 3.6.3. Using different bioinformatics methods, including analyses of the overall survival (OS) and disease-free survival (DFS), Gene Set Variation Analysis (GSVA) enrichment. Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT algorithm, multivariate and lasso cox regression analysis our results reveal that, while the expression of the YTH domain family varies distinctively in different cancer types the expression of YTH family is upregulated in most cancer types, especially in liver cancer, and the liver cancer prediction model established herein includes YTHDF1 and YTHDF2. Therefore, the results of the present study have demonstrated that the YTH domain family has the potential to predict the prognosis of cancer and the sensitivity to immunotherapy. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology can be used in many areas of everyday life. The objective of this paper is to obtain physiological functions in a non-invasive manner using commonly available IoT devices. The aim of the research is to point out the possibility of using physiological functions as an identifier of changes in students' level of arousal during the teaching process. The motivation of the work is to find a correlation between the change in heart rate, the student's level of arousal and the student's partial and final learning results. The research was focused on the collection of physiological data, namely heart rate and the evaluation of these data in the context of identification of arousal during individual teaching activities of the teaching process. The experiment was carried out during the COVID-19 pandemic via distance learning. During the teaching process, individual activities were recorded in time and HR was assigned to them. The benefit of the research is the proposed methodology of the system, which can identify changes in students' arousal in order to increase the efficiency of the teaching process. Based on the results of the designed system, they could also alert teachers who should be able to modify their teaching style in specific situations so that it is suitable for students and provides a basis for better teaching and understanding of educational materials. The presented methodology will be able to guarantee an increase in the success of the teaching process itself in terms of students' understanding of the teaching materials. [ABSTRACT FROM AUTHOR]

This work aims to study the conditions of topological phase transition (TPT) between the topological and trivial states in the antiferromagnetic topological insulator (AFM TI) MnBi 2 Te 4 and propose some theory about the relationship of this TPT with the possibility of axion-like state realization in this material. Using the density functional approach we have analyzed the changes in the electronic and spin structure of topological surface states (TSSs) and the nearest conduction and valence bands (CB and VB) including the changes in the bulk band gap as well as the Dirac point (DP) gap in TSSs under variation of the spin-orbit coupling strength in the region of the TPT for infinite crystal and slab with a surface both. We have shown that in both cases the TPT occurs with inversion of the contributions of the Bi- p z and Te- p z states of different parity at the gap edges related to change in the gap sign. In the case of surface calculations, the Bi- p z and Te- p z states at the edges of the bulk band gap and their inversion at the TPT point are transformed into the TSSs with an energy gap at the DP. In this case the TPT takes place without closing the band gap, i.e. with a "jump" through zero and the formation of the nonzero gap during such a transition. Our calculations show that the TPT point is also characterized by an inversion of the out-of-plane spin polarization s z at the Γ point for lower and upper parts of the Dirac cone and a significant spatial redistribution of the TSSs between the surface and the bulk. We suppose that the nonzero Dirac gap can have some relationship with the formation of the axion-like state, which presumably couples nonmagnetic spin-orbit and magnetic contributions at the boundary between the topological and trivial phases for a system with parameters close to the TPT conditions. A practically realized system is proposed - the AFM TI with a stoichiometry close to that of MnBi 2 Te 2 Se 2 with partial (about 50%) substitution of Te atoms for Se atoms in MnBi 2 Te 4 which can be an experimental platform for the implementation and experimental analysis of the TPT and the corresponding possibility of the axion-like state realization in Condensed Matter. Besides, such system could serve as a good platform for studying the dynamic axion effect, where the axion field fluctuations are maximised when a small external field is applied to the system which state is close to the TPT. [ABSTRACT FROM AUTHOR]

In this work, we present the results of the ortho-positronium (o-Ps) annihilation lifetimes and nitrogen adsorption measurements for different porous materials and an approach for describing the annihilation of o-Ps in a pore, which results in a surface-volume formula (SVF) for calculating the pore-related o-Ps lifetime. This proposed formula gives the relationship between the o-Ps annihilation rate and the effective pore radius, bulk composition, and pore structure, including pore geometry and topology. The pore-related o-Ps lifetimes of different materials calculated by the SVF are consistent with experimental results for both micro- and mesopores (and macropores) with different geometries and topologies. The SVF is convenient for calculations of pore dimensions for many cases of metal organic frameworks and zeolites. This approach enables us to fully explain the temperature dependence of the o-Ps annihilation lifetime over a wide temperature range, 20–700 K. [ABSTRACT FROM AUTHOR]

The current climate change setting necessitates the development of methods to mitigate the effects of water scarcity to ensure the sustainability of agricultural activities.f Hydrogen peroxide (H2O2) is a plant signaling molecule that can trigger metabolic defense mechanisms in response to adverse environmental circumstances like as drought. The purpose of this study was to investigate if foliar application of H2O2 stimulates modifications in photosynthetic metabolism for adaptation of tomato plants to a period of water deficit and recovery. The study, which was carried out in a factorial scheme, tested plants subjected to two water conditions (well-watered plants and plants subjected to water deficit), as well as foliar application of 1 mM H2O2 (zero, one, or two applications, 24 h after the first), and was evaluated in two moments, during the deficit period and after recovery. Foliar application of 1 mM H2O2 resulted in a 69% increase in the maximum rate of RuBisCO carboxylation in well-watered plants, contributing to tomato photosynthetic adjustment. H2O2 treatment resulted in a 37% increase in dry mass in these plants. In plants subjected to water deficiency, 2× H2O2 increased stress tolerance by reducing the maximal rate of RuBisCO carboxylation by only 18%, but in plants that did not receive H2O2 treatment, the reduction was 86% in comparison to the wet plants. Plants exposed to a water shortage and given 2× H2O2 stored sucrose in the leaves and had a 17% higher relative water content than plants not given H2O2. Thus, H2O2 foliar treatment can be used in tomato management to induce drought tolerance or to boost photosynthetic activity and dry mass formation in well-watered plants. [ABSTRACT FROM AUTHOR]

Rapid and accurate bioburden detection has become increasingly necessary for food, health, pharmaceutical and environmental applications. To detect bioburden accurately, and in a highly sensitive manner, we have fabricated a novel microfluidic device with an integrated filter to trap the cells. Bioburden is detected on the filter paper in situ using the redox reaction of fluorescent label resorufin and a portable multichannel fluorometer is used for fluorescence measurement. The microfluidic device was fabricated in a facile, low-cost, and rapid way with microwave-induced thermally assisted bonding. To characterize the bonding quality of the microfluidic cassettes, different tests were performed, and the filter paper material and size were optimized. Primary Bacillus subtilis culture bacterial samples were filtered through the device to validate and investigate the performance parameters. Our results show that a limit of detection (LOD) of 0.037 CFU/mL can be achieved through this microfluidic device whereas the LOD in a normal microfluidic cassette in the fluorometer and the golden standard spectrophotometer are 0.378 and 0.128 CFU/mL respectively. The results depict that three to ten times LOD improvement is possible through this microfluidic cassette and more sensitive detection is possible depending on the volume filtered within a rapid 3 min. This novel microfluidic device along with the fluorometer can be used as a rapid portable tool for highly sensitive, accurate and high-throughput bacterial detection for different applications. [ABSTRACT FROM AUTHOR]

Amorphous carbon (a-C) has attracted considerable interest due to its desirable properties, which are strongly dependent on its structure, density and impurities. Using ab initio molecular dynamics simulations we show that the sp2/sp3 content and underlying structural order of a-C produced via liquid quenching evolve at high temperatures and pressures on sub-nanosecond timescales. Graphite-like densities (≲ 2.7 g/cc) favor the formation of layered arrangements characterized by sp2 disordered bonding resembling recently synthesized monolayer amorphous carbon (MAC), while at diamond-like densities (≳ 3.3 g/cc) the resulting structures are dominated by disordered tetrahedral sp3 hybridization typical of diamond-like amorphous carbon (DLC). At intermediate densities the system is a highly compressible mixture of coexisting sp2 and sp3 regions that continue to segregate over 10's of picoseconds. The addition of nitrogen (20.3%) (a-CN) generates major system features similar with those of a-C, but has the unexpected effect of reinforcing the thermodynamically disfavored carbon structural motifs at low and high densities, while inhibiting phase separation in the intermediate region. At the same time, no nitrogen elimination from the carbon framework is observed above ≃ 2.8 g/cc, suggesting that nitrogen impurities are likely to remain embedded in the carbon structures during fast temperature quenches at high pressures. [ABSTRACT FROM AUTHOR]

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD. [ABSTRACT FROM AUTHOR]

Generating fluent, coherent, and informative text from structured data is called table-to-text generation. Copying words from the table is a common method to solve the "out-of-vocabulary" problem, but it's difficult to achieve accurate copying. In order to overcome this problem, we invent an auto-regressive framework based on the transformer that combines a copying mechanism and language modeling to generate target texts. Firstly, to make the model better learn the semantic relevance between table and text, we apply a word transformation method, which incorporates the field and position information into the target text to acquire the position of where to copy. Then we propose two auxiliary learning objectives, namely table-text constraint loss and copy loss. Table-text constraint loss is used to effectively model table inputs, whereas copy loss is exploited to precisely copy word fragments from a table. Furthermore, we improve the text search strategy to reduce the probability of generating incoherent and repetitive sentences. The model is verified by experiments on two datasets and better results are obtained than the baseline model. On WIKIBIO, the result is improved from 45.47 to 46.87 on BLEU and from 41.54 to 42.28 on ROUGE. On ROTOWIRE, the result is increased by 4.29% on CO metric, and 1.93 points higher on BLEU., (© 2021. The Author(s).)

Methane is the main component of shale gas and is adsorbed in shale pores. Methane adsorption not only affects the estimation of shale gas reserves but also reduces extraction efficiency. Therefore, investigating the behavior of methane adsorption in shale reservoirs is important for evaluating shale gas resources, as well as understanding its desorption and displacement from the nanochannels of shale gas reservoirs. In this research, molecular dynamics simulations were used to investigate the adsorption behavior of methane gas in organic shale pores made of graphenylene, followed by its displacement by CO2 and N2 injection gases. The effects of pore size, pressure, and temperature on adsorption were examined. It was observed that increasing the pore size at a constant pressure led to a decrease in the density of adsorbed methane molecules near the pore surface, while a stable free phase with constant density formed in the central region of the nanopore. Moreover, adsorption increased with increasing pressure, and at pressures ranging from 0 to 3 MPa, 15 and 20 Å pores exhibited lower methane adsorption compared to other pores. The amount of adsorption decreased with increasing temperature, and the observed adsorption isotherm followed the Langmuir adsorption isotherm. The mechanism of methane displacement by the two injected gases differed. Carbon dioxide filled both vacant adsorption sites and directly replaced the adsorbed methane. On the other hand, nitrogen only adsorbed onto the vacant sites and, by reducing the partial pressure of methane, facilitated the displacement of methane. [ABSTRACT FROM AUTHOR]

CRISPR/Cas9-based genome editing represents an unprecedented potential for plant breeding. Unlike animal cells, plant cells contain a rigid cell wall, genome editing tool delivery into plant cells is thus challenging. In particular, the delivery of the Cas9-gRNA ribonucleoprotein (RNP) into plant cells is desired since the transgene insertion into the genome should be avoided for industrial applications in plants. In this study, we present a novel RNP delivery approach in rice. We applied the sonication-assisted whisker method, conventionally developed for DNA delivery in plants, for RNP delivery in rice. Combined with marker gene delivery, we successfully isolated OsLCYβ genome-edited lines generated by RNPs. The calli and regenerated shoot of the OsLCYβ mutant showed abnormal carotenoid accumulation. In addition, we also detected, although at a low frequency, genome editing events in rice calli cells by RNP delivery using the sonication-assisted whisker method without any additional. Therefore, the sonication-assisted whisker method could be an attractive way to create RNP-based genome-edited lines in plants. [ABSTRACT FROM AUTHOR]

Thermal conductivity is a basic parameter of soil heat transferring, playing an important role in many fields including groundwater withdrawal, ground source heat pump, and heat storage in soils. However, it usually requires a lot of time and efforts to obtain soil thermal conductivity. To conveniently obtain accurate soil thermal conductivity, a new model describes the relationship between soil thermal conductivity (λ) and degree of saturation (Sr) was proposed in this study. Dry soil thermal conductivity (λdry) and saturated soil thermal conductivity (λsat) were described using a linear expression and a geometric mean model, respectively. A quadratic function with one constant was added to calculate λ beyond the lower λdry and upper λsat limit conditions. The proposed model is compared with five other frequently used models and measured data for 51 soil samples ranging from sand to silty clay loam. Results show that the proposed model match the measured data well. The proposed model can be used to determine soil thermal conductivity of a variety of soil textures over a wide range of water content. [ABSTRACT FROM AUTHOR]

Mastering the creep characteristics of artificial frozen soil and scientifically evaluating the creep model is an important guarantee for the safety of subway tunnel freezing construction. Base on the construction of Nantong metro tunnel, the uniaxial compressive strength tests of the artificially frozen soft soil were carried out to obtain the influence law of temperature on the uniaxial compressive strength, and the uniaxial creep tests were carried out to obtain the influence law of temperature and stress grade on creep, at − 5, − 10 and − 15 °C. The experimental results show that the creep characteristics of frozen soft soil specimens have obvious fuzzy randomness. The traditional ant colony algorithm is improved by optimizing the pheromone fuzzification coefficient, which improves the search efficiency and avoids the local optimum effectively. Subsequently, the improved fuzzy ant colony algorithm is used to invert the flexibility parameters of commonly used permafrost creep models. The fuzzy weight of evaluation index and the fuzzy random evaluation matrix were determined to evaluate the optimal creep model under three different stress levels of frozen soft soil. Finally, the reliability of the fuzzy random evaluation method was verified by engineering measured data. [ABSTRACT FROM AUTHOR]

In order to explore the performance evolution of prestressed anchor cables in corrosive environment, the corrosion immersion and corrosion damage performance tests of prestressed anchor bars in corrosive environment were carried out indoors. Based on the experimental results, the effects of stress level, pH and time on the corrosion process of prestressing anchor bars, the amount of corrosion per unit length and the change of mechanical properties were analyzed. It was found that the greater the stress level in the three corrosive media, the more severe the corrosion of the anchor bars, especially in acidic solutions; at the beginning of corrosion, prestressing anchor bars corrode fastest in neutral solution, followed by acidic and slowest in alkaline, with the continuation of corrosion, the degree of corrosion damage to anchor bars by acidic solution is more obvious; The corrosion damage of anchor tendons reflected by the corrosion amount per unit length is uniform corrosion, and local corrosion will aggravate the degree of corrosion damage of prestressing anchor tendons; The longer the corrosion time, the more serious the corrosion of anchor bars, and the 2 mechanical indexes of anchor bar elongation and fracture load have a significant decreasing trend. [ABSTRACT FROM AUTHOR]

Uncaria species are used in traditional medicine and are considered of high therapeutic value and economic importance. This work describes the assembly and annotation of the chloroplast genomes of U. guianensis and U. tomentosa, as well as a comparative analysis. The genomes were sequenced on MiSeq Illumina, assembled with NovoPlasty, and annotated using CHLOROBOX GeSeq. Addictionaly, comparative analysis were performed with six species from NCBI databases and primers were designed in Primer3 for hypervariable regions based on the consensus sequence of 16 species of the Rubiaceae family and validated on an in-silico PCR in OpenPrimeR. The genome size of U. guianensis and U. tomentosa was 155,505 bp and 156,390 bp, respectively. Both Species have 131 genes and GC content of 37.50%. The regions rpl32-ccsA, ycf1, and ndhF-ccsA showed the three highest values of nucleotide diversity within the species of the Rubiaceae family and within the Uncaria genus, these regions were trnH-psbA, psbM-trnY, and rps16-psbK. Our results indicates that the primer of the region ndhA had an amplification success for all species tested and can be promising for usage in the Rubiaceae family. The phylogenetic analysis recovered a congruent topology to APG IV. The gene content and the chloroplast genome structure of the analyzed species are conserved and most of the genes are under negative selection. We provide the cpDNA of Neotropical Uncaria species, an important genomic resource for evolutionary studies of the group. [ABSTRACT FROM AUTHOR]

April 15th, 2019: Notre-Dame Cathedral in Paris was burning, the spire collapsed on the nave, vaults crumbled and most of the timber roof was gone. In the post-disaster context, the authenticity and the monitoring of the archaeological remains are crucial for their potential reuse during reconstruction. This paper analyzes the collapsed transverse arch from the nave of Notre-Dame as a case study of reconstruction, using the digital twin framework. We propose four facets for the digital twin experiment—physical anastylosis, reverse engineering, spatio-temporal tracking of assets, and operational research—that are described in detail, while being assembled to support a hybrid reconstruction hypothesis. The digital twin can realize the parallel unfolding of physical-native and digital-native processes, while acquiring and storing heterogeneous information as semantically structured data. The results demonstrate that the proposed modeling method facilitates the formalization and validation of the reconstruction problem and increases solutions performances. As result, we present a digital twin framework application ranging from acquisition to data processing that informs a successful hybrid reconstruction hypothesis. [ABSTRACT FROM AUTHOR]

Pulmonary fibrosis (PF) is a special type of pulmonary parenchymal disease, with chronic, progressive, fibrosis, and high mortality. There is a lack of safe, effective, and affordable treatment methods. Qilongtian (QLT) is a traditional Chinese prescription that is composed of Panax notoginseng, Earthworm, and Rhodiola, and shows the remarkable clinical curative effect of PF. However, the mechanism of QLT remains to be clarified. Therefore, we studied the effectivity of QLT in treating Bleomycin (BLM) induced PF mice. 36 C57BL/6 J mice were randomized into the control group, the model group, the low-, medium- and high-dose QLT group, and Pirfenidone group. After establishing a model of pulmonary fibrosis in mice, the control and model groups were infused with a normal saline solution, and the delivery group was infused with QLT. Pulmonary function in the mice from each group was detected. Pulmonary tissue morphologies and collagen deposition were stained by HE and Masson. The content of hydroxyproline (HYP) was detected by alkaline hydrolysis and the mRNA and protein expression of related genes in pulmonary tissues were detected by using q-PCR, ELISA, and Western blot. Our studies have shown that QLT significantly reduced the inflammatory injury, hydroxy-proline content, and collagen deposition of pulmonary tissue in BLM-induced PF mice and down-regulated the cytokine related to inflammation and fibrosis and PF expression on the mRNA and protein level in PF mice. To identify the mechanism of QLT, the Transcriptome was measured and the IL-17 signal pathway was screened out for further research. Further studies indicated that QLT reduced the mRNAs and protein levels of interleukin 17 (IL-17), c–c motif chemokine ligand 12 (CCL12), c-x-c motif chemokine ligand 5 (CXCL5), fos-like antigen 1 (FOSL1), matrix metalloproteinase-9 (MMP9), and amphiregulin (AREG), which are inflammation and fibrosis-related genes in the IL-17 signal pathway. The results indicated that the potential mechanism for QLT in the prevention of PF progression was by inhibiting inflammation resulting in the IL-17 signal pathway. Our study provides the novel scientific basis of QLT and represents new therapeutics for PF in clinical. [ABSTRACT FROM AUTHOR]

All the diagnostic criteria of autoimmune hepatitis (AIH) include histopathology. However, some patients may delay getting this examination due to concerns about the risks of liver biopsy. Therefore, we aimed to develop a predictive model of AIH diagnostic that does not require a liver biopsy. We collected demographic, blood, and liver histological data of unknown liver injury patients. First, we conducted a retrospective cohort study in two independent adult cohorts. In the training cohort (n = 127), we used logistic regression to develop a nomogram according to the Akaike information criterion. Second, we validated the model in a separate cohort (n = 125) using the receiver operating characteristic curve, decision curve analysis, and calibration plot to externally evaluate the performance of this model. We calculated the optimal cutoff value of diagnosis using Youden's index and presented the sensitivity, specificity, and accuracy to evaluate the model in the validation cohort compared with the 2008 International Autoimmune Hepatitis Group simplified scoring system. In the training cohort, we developed a model to predict the risk of AIH using four risk factors—The percentage of gamma globulin, fibrinogen, age, and AIH-related autoantibodies. In the validation cohort, the areas under the curve for the validation cohort were 0.796. The calibration plot suggested that the model had an acceptable accuracy (p > 0.05). The decision curve analysis suggested that the model had great clinical utility if the value of probability was 0.45. Based on the cutoff value, the model had a sensitivity of 68.75%, a specificity of 76.62%, and an accuracy of 73.60% in the validation cohort. While we diagnosed the validated population by using the 2008 diagnostic criteria, the sensitivity of prediction results was 77.77%, the specificity was 89.61% and the accuracy was 83.20%. Our new model can predict AIH without a liver biopsy. It is an objective, simple and reliable method that can effectively be applied in the clinic. [ABSTRACT FROM AUTHOR]

This study investigated the effect of the silver nanoparticles (AgNPs) and platelet-rich fibrin (PRF) in the healing of the severed superficial digital flexor tendon in donkeys (SDFT). Twenty-seven adult donkeys were used in the study. The animals were divided into three equal groups. The first group (control group) in which the severed SDFT was sutured without the addition of any adjuvant. In the second group, there was a suture of severed SDFT with the addition of 1 ml of 1 mM silver nanoparticles (AgNPs group). The third group was subjected to the cutting of SDFT and then the addition of PRF after its suture. Each group of animals was divided into three equal subgroups that were examined after 1, 2, and 3 months. Each group of animals was clinically evaluated by assessing lameness. Gross and microscopic examinations of the healed tendons were performed after 1, 2, and 3 months of surgery. In comparison to the control group, the lameness degree decreased in the PRF and AgNPs groups, particularly in the third month after surgery. Furthermore, the lameness decreased significantly after the 3rd month relative to the 1st-month lameness in the AgNPs group. Interestingly, it was found that the PRF and AgNPs enhanced cell alignment and collagen deposition at the site of tendon injury, particularly among third-month subgroups. Therefore, it could be concluded that the PRF and AgNPs are effective materials for enhancing SDFT healing in donkeys. [ABSTRACT FROM AUTHOR]

In order to classify and analyze the parameters of upper body posture in clinical or physiotherapeutic settings, a baseline in the form of standard values with special regard to age, sex and BMI is required. Thus, subjectively healthy men and women aged 21–60 years were measured in this project. The postural parameters of 800 symptom-free male (n = 397) and female (n = 407) volunteers aged 21–60 years (Ø♀: 39.7 ± 11.6, Ø ♂: 40.7 ± 11.5 y) were studied. The mean height of the men was 1.8 ± 0.07 m, with a mean body weight of 84.8 ± 13.1 kg and an average BMI of 26.0 ± 3.534 kg/m2. In contrast, the mean height of the women was 1.67 ± 0.06 m, with a mean body weight of 66.5 ± 12.7 kg and an average BMI of 23.9 ± 4.6 kg/m2. By means of video rasterstereography, a 3-dimensional scan of the upper back surface was measured when in a habitual standing position. The means or medians, confidence intervals, tolerance ranges, the minimum, 2.5, 25, 50, 75, 97.5 percentiles and the maximum, plus the kurtosis and skewness of the distribution, were calculated for all parameters. Additionally, ANOVA and a factor analyses (sex, BMI, age) were conducted. In both sexes across all age groups, balanced, symmetrical upper body statics were evident. Most strikingly, the females showed greater thoracic kyphosis and lumbar lordosis angles (kyphosis: Ø ♀ 56°, Ø♂ 51°; lordosis: Ø ♀ 49°, Ø♂ 32°) and lumbar bending angles (Ø ♀ 14°, Ø♂ 11°) than the males. The distance between the scapulae was more pronounced in men. These parameters also show an increase with age and BMI, respectively. Pelvic parameters were independent of age and sex. The upper body postures of women and men between the ages of 21 and 60 years were found to be almost symmetrical and axis-conforming with a positive correlation for BMI or age. Consequently, the present body posture parameters allow for comparisons with other studies, as well as for the evaluation of clinical (interim) diagnostics and applications. [ABSTRACT FROM AUTHOR]

Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas. [ABSTRACT FROM AUTHOR]

We used smartphone technology to differentiate the gait characteristics of older adults with osteoporosis with falls from those without falls. We assessed gait mannerism and obtained activities of daily living (ADLs) with wearable sensor systems (smartphones and inertial measurement units [IMUs]) to identify fall-risk characteristics. We recruited 49 persons with osteoporosis: 14 who had a fall within a year before recruitment and 35 without falls. IMU sensor signals were sampled at 50 Hz using a customized smartphone app (Lockhart Monitor) attached at the pelvic region. Longitudinal data was collected using MoveMonitor+ (DynaPort) IMU over three consecutive days. Given the close association between serum calcium, albumin, PTH, Vitamin D, and musculoskeletal health, we compared these markers in individuals with history of falls as compared to nonfallers. For the biochemical parameters fall group had significantly lower calcium (P = 0.01*) and albumin (P = 0.05*) and higher parathyroid hormone levels (P = 0.002**) than nonfall group. In addition, persons with falls had higher sway area (P = 0.031*), lower dynamic stability (P < 0.001***), gait velocity (P = 0.012*), and were less able to perform ADLs (P = 0.002**). Thus, persons with osteoporosis with a history of falls can be differentiated by using dynamic real-time measurements that can be easily captured by a smartphone app, thus avoiding traditional postural sway and gait measures that require individuals to be tested in a laboratory setting. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new priority multi-objective optimization strategy of system output variables in cutting tobacco process. The proposed strategy focuses on the cutting tobacco moisture-controlled output variables optimization in feasible regions with two levels according to the priority. This study aims to provide a novel technical support for the chemical industry contained drying process. In order to alleviate the lack of degree of freedom of the system, strict set-point control is given, meanwhile, other output variables adopt zone control. Firstly, the system control output variables are optimized in ascending order of priority. Secondly, the specific lower-level target constraints are first relaxed. Finally, the relaxation of other high-priority target constraints is stopped when the optimization is feasible. Thus, the system control output variables move along the optimal target trajectory. From the perspective of practical application of engineering, under the condition of disturbance existing in the cutting tobacco drying process, the simulation shows that the proposed approach has good robustness when there is disturbance, and the previous method cannot meet the control requirement. The proposed strategy meanwhile has better tracking effect through single and multiple output variables simulation, which compared with traditional predictive control in real cutting tobacco drying process. [ABSTRACT FROM AUTHOR]

Plant growth-promoting rhizobacteria (PGPR) influence plant health. However, the genotypic variations in host organisms affect their response to PGPR. To understand the genotypic effect, we screened four diverse B. distachyon genotypes at varying growth stages for their ability to be colonized by B. velezensis strain B26. We reasoned that B26 may have an impact on the phenological growth stages of B. distachyon genotypes. Phenotypic data suggested the role of B26 in increasing the number of awns and root weight in wild type genotypes and overexpressing transgenic lines. Thus, we characterized the expression patterns of flowering pathway genes in inoculated plants and found that strain B26 modulates the transcript abundance of flowering genes. An increased root volume of inoculated plants was estimated by CT-scanning which suggests the role of B26 in altering the root architecture. B26 also modulated plant hormone homeostasis. A differential response was observed in the transcript abundance of auxin and gibberellins biosynthesis genes in inoculated roots. Our results reveal that B. distachyon plant genotype is an essential determinant of whether a PGPR provides benefit or harm to the host and shed new insight into the involvement of B. velezensis in the expression of flowering genes. [ABSTRACT FROM AUTHOR]

Comparative values are essential for the classification of orthopedic abnormalities and the assessment of a necessary therapy. At present, reference values for the upper body posture for healthy, male adults exist for the age groups of 18–35, 31–40 and 41–50 years. However, corresponding data on the decade of 51 to 60 year-old healthy men are still lacking. 23 parameters of the upper body posture were analyzed in 102 healthy male participants aged 51–60 (55.36 ± 2.78) years. The average height was 180.76 ± 7.81 cm with a weight of 88.22 ± 14.57 kg. The calculated BMI was 26.96 ± 3.92 kg/m2. In the habitual, upright position, the bare upper body was scanned three-dimensionally using video raster stereography. Mean or median values, confidence intervals, tolerance ranges and group comparisons, as well as correlations of BMI and physical activity, were calculated for all parameters. The spinal column parameters exhibited a good exploration of the frontal plane in the habitual standing position. In the sagittal plane, a slight, ventral inclination of the trunk with an increased kyphosis angle of the thoracic spine and increased thoracic bending angle was observed. The parameters of the pelvis showed a pronounced symmetry with deviations from the 0° axis within the measurement error margin of 1 mm/1°. The scapula height together with the scapula angles of the right and left side described a slightly elevated position of the left shoulder compared to the right side. The upper body posture is influenced by parameters of age, height, weight and BMI. Primarily there are significant correlations to measurements of trunk lengths D (age: p ≤ 0.02, rho = -0.23; height: p ≤ 0.001, rho = 0.58; weight: p ≤ 0.001, rho = 0.33), trunk lengths S (age: p ≤ 0.01, rho = -0.27; height: p ≤ 0.001, rho = 0.58; weight: p ≤ 0.001, rho = 0.32), pelvic distance (height: p ≤ 0.01, rho = 0.26; weight: p ≤ 0.001, rho = 0.32; BMI: p ≤ 0.03, rho = 0.22) and scapula distance (weight: p ≤ 0.001, rho =.32; BMI: p ≤ 0.01, rho = 0.27), but also to sagittal parameters of trunk decline (weight: p ≤ 0.001, rho = -0.29; BMI: p ≤ 0.01, rho = -0.24), thoracic bending angle (height: p ≤ 0.01, rho = 0.27) and kyphosis angle (BMI: p ≤ 0.03, rho = 0.21). The upper body posture of healthy men between the ages of 51 and 60 years was axially almost aligned and balanced. With the findings of this investigation and the reference values obtained, suitable comparative values for use in clinical practice and for further scientific studies with the same experimental set-up have been established. [ABSTRACT FROM AUTHOR]

The investigation focused on creating and studying a new 2D-2D S-scheme CdS/g-C 3 N 4 heterojunction photocatalyst. Various techniques examined its structure, composition, and optical properties. This included XRD, XPS, EDS, SEM, TEM, HRTEM, DRS, and PL. The heterojunction showed a reduced charge recombination rate and more excellent stability, helping to lessen photocorrosion. This was due to photogenerated holes moving more quickly out of the CdS valence band. The interface between g-C 3 N 4 and CdS favored a synergistic charge transfer. A suitable flat band potential measurement supported enhanced reactive oxygen species (ROS) generation in degrading 4-nitrophenol and 2-nitrophenol. This resulted in remarkable degradation efficiency of up to 99% and mineralization of up to 79%. The findings highlighted the practical design of the new 2D-2D S-scheme CdS/g-C 3 N 4 heterojunction photocatalyst and its potential application in various energy and environmental settings, such as pollutant removal, hydrogen production, and CO 2 conversion., (© 2024. The Author(s).)

Social media platforms significantly increase general information about disease severity and inform preventive measures among community members. To identify public opinion through tweets on the subject of Covid-19 and investigate public sentiment in the country over the period. This article proposed a novel method for sentiment analysis of coronavirus-related tweets using bidirectional encoder representations from transformers (BERT) bi-directional long short-term memory (Bi-LSTM) ensemble learning model. The proposed approach consists of two stages. In the first stage, the BERT model gains the domain knowledge with Covid-19 data and fine-tunes with sentiment word dictionary. The second stage is the Bi-LSTM model, which is used to process the data in a bi-directional way with context sequence dependency preserving to process the data and classify the sentiment. Finally, the ensemble technique combines both models to classify the sentiment into positive and negative categories. The result obtained by the proposed method is better than the state-of-the-art methods. Moreover, the proposed model efficiently understands the public opinion on the Twitter platform, which can aid in formulating, monitoring and regulating public health policies during a pandemic. [ABSTRACT FROM AUTHOR]

In this paper, a special ultrasonic microforming method, Micro Ultrasonic thin Sheetmetal Forming using molten plastic as a flexible punch (short as Micro-USF), was used to conduct micro-punching experiments on stainless steel sheet with thickness of 10 μm and 20 μm. The influence of ultrasonic vibration on forming force and forming quality were investigated. The experimental results showed that the forming force required for punching thin sheet metal decreased gradually as the ultrasonic time or ultrasonic power increased. By applying the ultrasonic vibration effect, the forming force could be decreased dramatically and the maximum value of forming force drop could reach 86%. Moreover, with the application of ultrasonic vibration, the size accuracy and shape accuracy of micro-holes could be increased by 36.92% and 22.65%, but the cross-section quality of micro-holes were not significantly improved. [ABSTRACT FROM AUTHOR]

Magnetic nanomaterials are increasingly impacting the field of biology and medicine. Their versatility in terms of shape, structure, composition, coating, and magnetic responsivity make them attractive for drug delivery, cell targeting and imaging. Adipose derived-mesenchymal cells (ASCs) are intensely scrutinized for tissue engineering and regenerative medicine. However, differentiation into musculoskeletal lineages can be challenging. In this paper, we show that uncoated nickel nanowires (Ni NW) partially released from their alumina membrane offer a mechanically-responsive substrate with regular topography that can be used for the delivery of magneto-mechanical stimulation. We have used a tailored protocol for improving ASCs adherence to the substrate, and showed that cells retain their characteristic fibroblastic appearance, cytoskeletal fiber distribution and good viability. We report here for the first time significant increase in osteogenic but not adipogenic differentiation of ASCs on Ni NW exposed to 4 mT magnetic field compared to non-exposed. Moreover, magnetic actuation is shown to induce ASCs osteogenesis but not adipogenesis in the absence of external biochemical cues. While these findings need to be verified in vivo, the use of Ni NW substrate for inducing osteogenesis in the absence of specific differentiation factors is attractive for bone engineering. Implant coating with similar surfaces for orthopedic and dentistry could be as well envisaged as a modality to improve osteointegration. [ABSTRACT FROM AUTHOR]