Search Results (4,958)

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have become widely used in many fields, ranging from agriculture to military operations, due to recent advances in technology and decreases in costs. Quadrotors are particularly important UAVs, but their complex, coupled dynamics and sensitivity to outside disturbances make them challenging to control. This paper introduces a new control method for quadrotors called Backstepping Sliding Mode Control (BSMC), which combines the strengths of two established techniques: Backstepping Control (BC) and Sliding Mode Control (SMC). Its primary goal is to improve trajectory tracking while also reducing chattering, a common problem with SMC that causes rapid, high-frequency oscillations. The BSMC method achieves this by integrating the SMC switching gain directly into the BC through a process of differential iteration. Herein, a Lyapunov stability analysis confirms the system’s asymptotic stability; a genetic algorithm is used to optimize controller parameters; and the proposed control strategy is evaluated under diverse payload conditions and dynamic wind disturbances. The simulation results demonstrated its capability to handle payload variations ranging from 0.5 kg to 18 kg in normal environments, and up to 12 kg during gusty wind scenarios. Furthermore, the BSMC effectively minimized chattering and achieved a superior performance in tracking accuracy and robustness compared to the traditional SMC and BC. Full article

The study of tropical landscape dynamics is of critical importance, particularly within protected areas, for evaluating ecosystem functioning and the effectiveness of natural conservation efforts. This study aims to identify landscape dynamics within the Dong Nai Biosphere Reserve (including Cat Tien National Park and the Ma Da Forest) using remote sensing (Landsat and others) and geographic information system methods. The analysis is based on changes in the Enhanced Vegetation Index (EVI), land cover transformations, landscape metrics (Class area, Percentage of Landscape and others), and natural landscape fragmentation, as well as a spatio-temporal assessment of anthropogenic impacts on the area. The results revealed structural changes in the landscapes of the Dong Nai Biosphere Reserve between 2000 and 2024. According to Sen’s slope estimates, a generally EVI growth was observed in both the core and buffer zones of the reserve. This trend was evident in forested areas as well as in regions of the buffer zone that were previously occupied by highly productive agricultural land. An analysis of Environmental Systems Research Institute (ESRI) Land Cover and Land Cover Climate Change Initiative (CCI) data confirms the relative stability of land cover in the core zone, while anthropogenic pressure has increased due to the expansion of agricultural lands, mosaic landscapes, and urban development. The calculation of landscape metrics revealed the growing isolation of natural forests and the dominance of artificial plantations, forming transitional zones between natural and anthropogenically modified landscapes. The human disturbance index, calculated for the years 2000 and 2024, shows only a slight change in the average value across the territory. However, the coefficient of variation increased significantly by 2024, indicating a localized rise in anthropogenic pressure within the buffer zone, while a reduction was observed in the core zone. The practical significance of the results obtained lies in the possibility of their use for the management of the Dongnai biosphere Reserve based on a differentiated approach: for the core and the buffer zone. There should be a ban on agriculture and development in the core zone, and restrictions on urbanized areas in the buffer zone. Full article

Traditionally, retrieving both temperature and CO₂ concentration in atmospheric remote sensing has relied on two independent lidar systems, leading to increased system complexity and limited coordination. To address this challenge, we propose a coordinated retrieval approach using a three-wavelength differential absorption lidar (DIAL) system. A temperature-sensitive wavelength is selected to distinguish strong absorption from weak absorption, forming the tri-wavelength configuration. By exploiting the different sensitivities of absorption cross-sections to thermal and molecular variations, simultaneous retrieval of both parameters is achieved. A standard atmospheric profile under clean-air conditions is constructed. The CO₂ absorption spectrum near 1573 nm is generated using Voigt line shapes and data from the HITRAN database. Extinction and backscatter coefficients are retrieved through the Klett method. A layer-by-layer solution of the coupled differential equations is then performed to extract temperature and concentration simultaneously. Results are benchmarked against the atmospheric model, demonstrating the feasibility of the approach. This method provides a promising pathway for high-precision, multi-parameter DIAL sensing. Full article

In this study, inter-brand variations in volatile flavor compound profiles of four lager beers were systematically investigated by integrating sensory evaluation with GC-MS, GC×GC-TOF-MS, and GC-O-MS. A total of 594 volatile compounds were identified, of which 71 with odor activity values (OAV) ≥ 1 were found to contribute directly to aroma expression. Additionally, 59 compounds with taste activity values (TAV) ≥ 1 were identified and may also contribute to taste perception. Furthermore, 53 aroma-active compounds were confirmed through GC-O-MS, providing additional evidence for their sensory contribution. Partial least squares discriminant analysis (PLS-DA), correlation analysis, and flavor addition experiments revealed brand-specific differential flavor compounds. Ultimately, twenty key differential flavor compounds, encompassing esters, alcohols, aromatic compounds, acids, lactones, and others, were confirmed to contribute to fruity, floral, burnt, and sweet notes. Phenethyl alcohol, with concentrations varying from 1377.1 mg/L in QD to 3297.5 mg/L in HR, showed a more than 2.4-fold difference across brands and was strongly associated with fruity (r = 0.553) and floral (r = 0.564) aroma. These compounds acted in combination to shape distinct aroma profiles. This study provides a molecular-level basis for understanding lager beer flavor and offers practical guidance for targeted flavor modulation in brewing. Full article

This study examines the genetic diversity and population structure of silver carp (Hypophthalmichthys molitrix), an ecologically and economically important freshwater species. Samples were collected from 17 sites along the Yangtze River, including LCH, LCS, LJHK, and LXZX, as well as one population from the United States (SV). Restriction-site associated DNA sequencing (RAD-seq) generated 759,453 high-quality single-nucleotide polymorphisms (SNPs) for population genomic analyses, including genetic differentiation (F_ST), population structure, and linkage disequilibrium (LD) decay. Genetic variation was primarily found within populations (78.05%), with 21.94% among populations. Most sites exhibited low genetic differentiation (F_ST < 0.05), suggesting high admixture along the river, although a few sites displayed elevated values (F_ST > 0.15). Rapid LD decay in LCH, LCS, and LJZ indicated frequent recombination and moderate to large effective population sizes. These patterns reflect the influence of geographic and ecological factors on population structure. Conservation strategies should maintain genetic connectivity while protecting distinct genetic resources. Populations with high differentiation, such as LXZX and LWZ, warrant targeted management to preserve unique genetic diversity. Full article

Coptis trifolia (threeleaf goldthread) offers a valuable comparative system for investigating the evolution and regulation of benzylisoquinoline alkaloid (BIA) synthesis. In this study, we analyzed the leaf and root transcriptomes of C. trifolia using both long-read and short-read RNA-Sequencing. We assembled 41,926 unigenes (≥500 bp) and identified 37 genes related to BIA biosynthesis, including two transcription factors, bHLH1 and WRKY1. The number of BIA genes identified in C. trifolia was comparable to that in other Coptis species. Transcriptome analysis revealed that most of these genes were more highly expressed in roots than leaves. Consistent with previous studies, C. trifolia contained a single (S)-stylopine synthase (SPS) gene homolog, potentially multifunctional for (S)-canadine synthase (CAS), (S)-cheilanthifoline synthase (CFS), and SPS. Transcriptome and untargeted metabolomic data indicated greater variation in root samples than leaf samples, although slightly more differentially expressed transcripts and metabolites were observed in leaves. Targeted metabolite profiling showed higher BIA accumulation in roots, with epiberberine being the most abundant, followed by coptisine, berberine, and columbamine. These results provide essential genomic resources for comparative analysis of the BIA pathway across Ranunculaceae, targeted gene function studies for metabolic bioengineering, and conservation strategies for C. trifolia, a member of an early-diverging clade within the genus with limited genetic resources. Full article

Structural variation (SV) serves as a fundamental driver of phenotypic diversity and environmental adaptation in plants and animals, significantly influencing key agronomic traits in crops. Common wheat (Triticum aestivum L.), an allohexaploid species, harbors extensive chromosomal SVs and distant hybridization-induced recombination events that provide critical resources for genetic improvement. This study utilizes non-denaturing fluorescence in situ hybridization (ND-FISH) and oligonucleotide multiplex probe-based FISH (ONPM-FISH) to analyze the karyotypes of 153 BC₁F₄–BC₁F₆ lines derived from the hybrid line Xiaoyan 7430 and common wheat Yannong 1212. The results revealed that Xiaoyan 7430 carries 8 alien chromosome pairs and 20 wheat chromosome pairs (lacking 6B), and Yannong 1212 contains 21 pairs of wheat chromosomes. The parental lines exhibited presence/absence variations (PAVs) on chromosomes 2A, 6A, 5B, 1D, and 2D. Chromosomal variations, including numerical chromosomal variation (NCV), structural chromosomal variation (SCV), and complex chromosomal variation (CCV), were detected in the progeny lines through ONPM-FISH analysis. The tracking of alien chromosomes over three consecutive generations revealed a significant decrease in transmission frequency, declining from 61.82% in BC₁F₄ to 26.83% in BC₁F₆. Telosomes were also lost during transmission, declining from 21.82% in BC₁F₄ to 9.76% in BC₁F₆. Alien chromosome 1J^S, 4J, and 6J exhibited the highest transmission stability and were detected across all three generations. Association analysis showed that YN-PAV.2A significantly affected the length/width ratio (LWR) and grain diameter (GD); YN-PAV.6A, XY-PAV.6A, and PAV.5B increased six grain traits (+2.25%~15.36%); YN-PAV.1D negatively affected grain length (GL) and grain circumference (GC); and XY-PAV.2D exerted positive effects on thousand-grain weight (TGW). Alien chromosomes differentially modulated grain characteristics: 1J^S and 6J both reduced grain length and grain circumference; 1J^S increased LWR; and 4J negatively impacted TGW, grain width (GW), GD, and grain area (GA). Meanwhile, increasing alien chromosome numbers correlated with progressively stronger negative effects on grain traits. These findings elucidate the genetic mechanisms underlying wheat chromosomal variations induced by distant hybridization and their impact on wheat grain traits, and provide critical intermediate materials for genome design breeding and marker-assisted selection in wheat improvement. Full article

Generating non-standard fonts, such as running script (e.g., XingShu), poses significant challenges due to their high stroke continuity, structural flexibility, and stylistic diversity, which traditional component-based prior knowledge methods struggle to model effectively. While diffusion models excel at capturing continuous feature spaces and stroke variations through iterative denoising, they face critical limitations: (1) style leakage, where large stylistic differences lead to inconsistent outputs due to noise interference; (2) structural distortion, caused by the absence of explicit structural guidance, resulting in broken strokes or deformed glyphs; and (3) style confusion, where similar font styles are inadequately distinguished, producing ambiguous results. To address these issues, we propose a novel skeleton-guided diffusion model with three key innovations: (1) a skeleton-constrained style rendering module that enforces semantic alignment and balanced energy constraints to amplify critical skeletal features, mitigating style leakage and ensuring stylistic consistency; (2) a cross-scale skeleton preservation module that integrates multi-scale glyph skeleton information through cross-dimensional interactions, effectively modeling macro-level layouts and micro-level stroke details to prevent structural distortions; (3) a contrastive style refinement module that leverages skeleton decomposition and recombination strategies, coupled with contrastive learning on positive and negative samples, to establish robust style representations and disambiguate similar styles. Extensive experiments on diverse font datasets demonstrate that our approach significantly improves the generation quality, achieving superior style fidelity, structural integrity, and style differentiation compared to state-of-the-art diffusion-based font generation methods. Full article

Non-invasive glucose monitoring remains a persistent challenge in the scientific literature due to the complexity of biological samples and the limitations of traditional optical methods. Although advances have been made in the use of near-infrared (NIR) spectrophotometry, the direct application of the Lambert–Beer Law (LBL) to such systems has proven problematic, particularly due to the non-linear behavior observed in complex organic solutions. In this context, the objective of this work is to propose and validate a methodology for the determination of the extinction coefficient of glucose in blood, taking into account the limitations of the LBL and the specificities of molecular interactions. The method was optimized through an iterative process to provide consistent results over multiple replicates. Whole blood and plasma samples from two individuals were analyzed using spectrophotometry in the 700 nm to 1400 nm. The results showed that glucose has a high spectral sensitivity close to 975 nm.The extinction coefficients obtained for glucose (${\alpha }_g$) ranged from −0.0045 to −0.0053, and for insulin (${\alpha }_i$) from 0.000075 to 0.000078, with small inter-individual variations, indicating strong stability of these parameters. The non-linear behaviour observed in the relationship between absorbance, glucose and insulin concentrations might be explained by the changes imposed by both s and p orbitals of organic molecules. In order to make the LBL valid in this context, the extinction coefficients must be functions of the analyte concentrations, and the insulin concentration must also be a function of glucose. A regression model was found which allows to differentiate glucose from insulin concentration, by considering the cuvette thickness and sample absorbance at 965, 975, and 985 nm. It can also be concluded from experiments that wavelength of approximately 975 nm is more suitable for blood glucose calculation by using photometry. The final spectra are consistent with those reported in mid-infrared validation studies, suggesting that the proposed model encompasses the key aspects of glucose behavior in biological media. Full article

White tea quality is primarily determined by its chemical composition, which varies significantly among cultivars. This study aimed to elucidate the chemical basis underlying quality differentiation in Baimudan white tea produced from three major Fujian tea cultivars: “Zhenghe Dabaicha” (ZHDB), “Fuan Dabaicha” (FADB), and “Fuding Dahaocha” (FDDH). Headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), liquid chromatography–mass spectrometry (LC-MS), and quantitative descriptive analysis (QDA) were employed to characterize volatile compounds, amino acids, and saccharides. Odor Activity Values (OAVs) and Taste Activity Values (TAVs) were calculated to identify key contributors to sensory perception. Results showed that theanine, glutamic acid, asparagine, and serine were the primary contributors to umami taste, especially in ZHDB and FADB. Sweetness differences were largely due to sucrose, serine, and asparagine. OAV analysis further identified 22 critical aroma compounds: methyl salicylate, linalool, and β-ionone predominantly imparted floral notes, while β-ocimene, benzaldehyde, and geraniol enhanced sweet and fruity aromas. In contrast, (Z)-3-hexenol, (Z)-3-hexenal, and (E)-2-hexenal contributed grassy and refreshing characteristics, together defining the unique aroma profiles of each cultivar. This study provides an integrated chemical and sensory framework for understanding white tea quality variation, offering a theoretical basis for targeted flavor modulation. Full article

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

Rapid climate change has exacerbated global ecosystem degradation, leading to habitat fragmentation and landscape connectivity loss. Constructing ecological networks (EN) with resilient conduction functions and conservation priorities is crucial for maintaining regional ecological security and promoting sustainable development. However, the spatiotemporal modeling and dynamic resilience assessment of EN under the combined impacts of future climate and land use/land cover (LULC) changes remain underexplored. This study focuses on the Central Yunnan Urban Agglomeration (CYUA), China, and integrates landscape ecology with complex network theory to develop a dynamic resilience assessment framework that incorporates multi-scenario LULC projections, multi-temporal EN construction, and node-link disturbance simulations. Under the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCP) scenarios, we quantified spatiotemporal variations in EN resilience and identified resilience-based conservation priority areas. The results show that: (1) Future EN patterns exhibit a westward clustering trend, with expanding habitat areas and enhanced connectivity. (2) From 2000 to 2040, EN resilience remains generally stable, but diverges significantly across scenarios—showing steady increases under SSP1-2.6 and SSP5-8.5, while slightly declining under SSP2-4.5. (3) Approximately 20% of nodes and 40% of links are identified as critical components for maintaining structural-functional resilience, and are projected to form conservation priority patterns characterized by larger habitat areas and more compact connectivity under future scenarios. The multi-scenario analysis provides differentiated strategies for EN planning and ecological conservation. This framework offers adaptive and resilient solutions for regional ecosystem management under climate change. Full article

The methylation of CpG sites with 5mC mark is a dynamic epigenetic modification. However, the relationship between the methylation and the surrounding genomic sequence context remains poorly explored. Investigation of the allele methylation provides an opportunity to decipher the interplay between differences in the primary DNA sequence and epigenetic variation. Here, we performed high-coverage long-read whole-genome direct DNA sequencing of one individual using Oxford Nanopore technology. We also used Illumina whole-genome sequencing of the parental genomes in order to identify allele-specific methylation sites with a trio-binning approach. We have compared the results of the haplotype-specific methylation detection and revealed that trio binning outperformed other approaches that do not take into account parental information. Also, we analysed the cis-regulatory effects of the genomic variations for influence on CpG methylation. To this end, we have used available Deep Learning models trained on the primary DNA sequence to score the cis-regulatory potential of the genomic loci. We evaluated the functional role of the allele-specific epigenetic changes with respect to gene expression using long-read Nanopore RNA sequencing. Our analysis revealed that the frequency of SNVs near allele-specific methylation positions is approximately four times higher compared to the biallelic methylation positions. In addition, we identified that allele-specific methylation sites are more conserved and enriched at the chromatin states corresponding to bivalent promoters and enhancers. Together, these findings suggest that significant impact on methylation can be encoded in the DNA sequence context. In order to elucidate the effect of the SNVs around sites of allele-specific methylation, we applied the Deep Learning model for detection of the cis-regulatory modules and estimated the impact that a genomic variant brings with respect to changes to the regulatory activity of a DNA loci. We revealed higher cis-regulatory impact variants near differentially methylated sites that we further coupled with transcriptomic long-read sequencing results. Our investigation also highlights technical aspects of allele methylation analysis and the impact of sequencing coverage on the accuracy of genomic phasing. In particular, increasing coverage above 30X does not lead to a significant improvement in allele-specific methylation discovery, and only the addition of trio binning information significantly improves phasing. We investigated genomic variation in a single human individual and coupled computational discovery of cis-regulatory modules with allele-specific methylation (ASM) profiling. In this proof-of-concept analysis, we observed that SNPs located near methylated CpG sites on the same haplotype were enriched for sequence features suggestive of high-impact regulatory potential. This finding—derived from one deeply sequenced genome—illustrates how phased genetic and epigenetic data analyses can jointly put forward a hypotheses about the involvement of regulatory protein machinery in shaping allele-specific epigenetic states. Our investigation provides a methodological framework and candidate loci for future studies of genomic imprinting and cis-mediated epigenetic regulation in humans. Full article

With the rise in rural tourism, public space use has become more complex, causing significant weekday-weekend vitality imbalances. However, the factors shaping these dynamics in traditional villages remain unclear. This study uses Wi-Fi sensing method to analyze vitality variations across weekdays and weekends, and it develops a 13-metric evaluation framework to examine how built environment factors, from both internal and external dimensions, differentially influence the vitality of public spaces in traditional villages across various time periods. Using 17 public spaces in Yantou Village, Lishui, China, as a case, it finds: (1) Historical Element Proximity consistently and significantly drives public space vitality across all periods; (2) Leisure Facility Count and Decorative Element Count demonstrate strong positive effects during weekend morning peaks. (3) Retail Facility Count significantly reduces vitality during weekend morning peak but enhances it during midday off-peak, whereas Street Vendor Count shows the opposite pattern—increasing vitality in morning peak and decreasing it in midday off-peak. Using Wi-Fi sensing’s high-resolution, real-time, and non-invasive capabilities, this study provides a scientific method to accurately assess the variations in public space vitality and their impact factors between weekdays and weekends in traditional villages, offering technical support for enhancing public space vitality and sustainably revitalizing rural heritage. Full article

Immunogenic cell death (ICD) is a specialized form of cell death that triggers antitumor immune responses. In tumors, ICD promotes the release of tumor-associated and tumor-specific antigens, thereby reshaping the immune microenvironment, restoring antitumor immunity, and facilitating tumor eradication. However, the regulatory mechanisms of ICD and its immunological effects vary across tumor types, and a comprehensive understanding remains limited. We systematically analyzed the expression of 34 ICD-related regulatory genes across 33 tumor types. Differential expression at the RNA, copy number variation (CNV), and DNA methylation levels was assessed in relation to clinical features. Associations between patient survival and RNA expression, CNVs, single-nucleotide variations (SNVs), and methylation were evaluated. Patients were stratified into immunological subtypes and further divided into high- and low-risk groups based on optimal prognostic models built using a machine learning framework. We explored the relationships between ICD-related genes and immune cell infiltration, stemness, heterogeneity, immune scores, immune checkpoint and regulatory genes, and subtype-specific expression patterns. Moreover, we examined the influence of immunotherapy and anticancer immune responses, applied three machine learning algorithms to identify prognostic biomarkers, and performed drug prediction and molecular docking analyses to nominate therapeutic targets. ICD-related genes were predominantly overexpressed in ESCA, GBM, KIRC, LGG, PAAD, and STAD. RNA expression of most ICD-related genes was associated with poor prognosis, while DNA methylation of these genes showed significant survival correlations in LGG and UVM. Prognostic models were successfully established for 18 cancer types, revealing intrinsic immune regulatory mechanisms of ICD-related genes. Machine learning identified several key prognostic biomarkers across cancers, among which NT5E emerged as a predictive biomarker in head and neck squamous cell carcinoma (HNSC), mediating tumor–immune interactions through multiple ligand–receptor pairs. This study provides a comprehensive view of ICD-related genes across cancers, identifies NT5E as a potential biomarker in HNSC, and highlights novel targets for predicting immunotherapy response and improving clinical outcomes in cancer patients. Full article