Search Results (2,240)

Lane detection is a key technology in automatic driving environment perception, and its accuracy directly affects vehicle positioning, path planning, and driving safety. In this study, an enhanced real-time model for lane detection based on an improved DeepLabV3+ architecture is proposed to address the challenges posed by complex dynamic backgrounds and blurred road boundaries in suburban road scenarios. To address the lack of feature correlation in the traditional Atrous Spatial Pyramid Pooling (ASPP) module of the DeepLabV3+ model, we propose an improved LC-DenseASPP module. First, inspired by DenseASPP, the number of dilated convolution layers is reduced from six to three by adopting a dense connection to enhance feature reuse, significantly reducing computational complexity. Second, the convolutional block attention module (CBAM) attention mechanism is embedded after the LC-DenseASPP dilated convolution operation. This effectively improves the model’s ability to focus on key features through the adaptive refinement of channel and spatial attention features. Finally, an image-pooling operation is introduced in the last layer of the LC-DenseASPP to further enhance the ability to capture global context information. DySample is introduced to replace bilinear upsampling in the decoder, ensuring model performance while reducing computational resource consumption. The experimental results show that the model achieves a good balance between segmentation accuracy and computational efficiency, with a mean intersection over union (mIoU) of 95.48% and an inference speed of 128 frames per second (FPS). Additionally, a new lane-detection dataset, SubLane, is constructed to fill the gap in the research field of lane detection in suburban road scenarios. Full article

The rise of algorithm-driven virtual museums presents a philosophical challenge for how cultural meaning is constructed and critiqued in digital curation. Prevailing approaches highlight important but partial aspects: the loss of aura and authenticity in digital reproductions, efforts to maintain semiotic continuity with physical exhibits, optimistic narratives of technological democratisation, and critical technopessimist warnings about commodification and bias. Yet none provides a unified theoretical model of meaning-making under algorithmic curation. This paper proposes a dialectical-semiotic framework to synthesise and transcend these positions. The Dialectical Modelling Nexus (DMN) is a new conceptual structure that views meaning in virtual museums as emerging from the dynamic interplay of original and reproduced contexts, human and algorithmic sign systems, personal interpretation, and ideological framing. Through a critique of prior theories and a synthesis of their insights, the DMN offers a comprehensive model to diagnose how algorithms mediate museum content and to guide critical curatorial practice. The framework illuminates the dialectical tensions at the heart of algorithmic cultural mediation and suggests principles for preserving authentic, multi-layered meaning in the digital museum milieu. Full article

Numerous rural residential buildings exhibit inadequate seismic performance when subjected to blast-induced vibrations, which poses potential threats to their overall stability and structural integrity when in proximity to blasting project sites. The investigation conducted in conjunction with the Qianshi Mountain blasting operations along the Wenzhou segment of the Hangzhou–Wenzhou High-Speed Railway integrates household field surveys and empirical measurements to perform modal analysis of rural residential buildings through finite element simulation. Adhering to the principle of stratified arrangement and composite measurement point configuration, an effective and reasonable experimental observation framework was established. In this investigation, the seven-story rural residential building in adjacent villages was selected as the research object. Strong-motion seismographs were strategically positioned adjacent to frame columns on critical stories (ground, fourth, seventh, and top floors) within the observational system to acquire test data. Methodical signal processing techniques, including effective signal extraction, baseline correction, and schedule conversion, were employed to derive temporal dynamic characteristics for each story. Combined with the Fourier transform, the frequency–domain distribution patterns of different floors are subsequently obtained. Leveraging the structural dynamic theory, time–domain records were mathematically converted to establish the structure’s maximum response spectra under blast-induced loading conditions. Through the analysis of characteristic curves, including floor acceleration response spectra, dynamic amplification coefficients, and spectral ratios, the dynamic response patterns of rural residential buildings subjected to blast-induced vibrations have been elucidated. Following the normalization of peak acceleration and velocity parameters, the mechanisms underlying differential floor-specific dynamic responses were examined, and the layout principles of measurement points were subsequently formulated and summarized. These findings offer valuable insights for enhancing the seismic resilience and structural safety of rural residential buildings exposed to blast-induced vibrations, with implications for both theoretical advancements and practical engineering applications. Full article

What motivates industrial companies to decarbonise? While climate policy has intensified, the specific factors driving corporate decisions remain underexplored. This article addresses that gap through a mixed-methods study combining qualitative insights from a leading automotive supplier with quantitative data from over 800 manufacturing companies in Germany. The study distinguishes between internal motivators—such as risk reduction, future-proofing, and competitive positioning—and external drivers like regulation, supply chain pressure, and investor expectations. Results show that internal economic logic is the strongest trigger: companies act more ambitiously when decarbonisation aligns with their strategic interests. Positive motivators outperform external drivers in both influence and impact on ambition levels. For instance, long-term cost risks were rated more relevant than reputational gains or regulatory compliance. The analysis also reveals how company size, energy intensity, and supply chain position shape motivation patterns. The findings suggest a new framing for climate policy: rather than relying solely on mandates, policies should strengthen intrinsic motivators. Aligning business interests with societal goals is not only possible—it is a pathway to more ambitious, resilient, and timely decarbonisation. By turning external pressure into internal logic, companies can move from compliance to leadership in the climate transition. Full article

As a commercially valuable aquaculture species, rainbow trout (Oncorhynchus mykiss) urgently require solutions to growth inhibition associated with reproductive development. To elucidate the function of the cell cycle regulator Cyclin E genes (CCNE1 and CCNE2) in this process, we cloned the genes and analyzed their relative expression across various tissues and gonadal developmental stages. Using RNA interference (RNAi) and overexpression in RTG2 cells, we examined the effects of CCNE on cell viability, proliferation, and meiotic gene expression. Results showed that the open reading frame lengths of CCNE1 and CCNE2 were 1230 bp and 1188 bp, encoding 408 and 395 amino acids, respectively. Both proteins contain two conserved cyclin boxes, exhibit high structural similarity, and are phylogenetically most closely related to Oncorhynchus tshawytscha and Oncorhynchus kisutch. Expression and localization analyses revealed that CCNE1 was highly expressed in the ovary, while CCNE2 was highly expressed in the testis. Both proteins were expressed during fertilized egg development and key gonadal stages (at 13, 21, and 35 months post-fertilization). CCNE expression positively correlated with RTG2 cell viability and proliferation, with immunofluorescence confirming that CCNE is localized in the nucleus. Knockdown or overexpression of CCNE induced the differential expression of reproductive-related genes and key meiotic regulators. These findings suggest that CCNE1 and CCNE2 balance meiosis and gamete development through specific regulatory mechanisms, and their dysregulation may be a key factor underlying meiosis inhibition and reproductive development abnormalities. Full article

Phytoene synthase (PSY) is a multimeric enzyme that serves as the first enzyme in carotenoid synthesis within plant tissues and plays a crucial role in the production of carotenoids in plants. To understand the function of the PSY gene in Panax japonicus C. A. Meyer. fruit, the gene’s transcript was obtained by analyzing the transcriptome sequencing data of Panax japonicus fruit. The CDS sequence of the gene was cloned from Panax japonicus fruit using the RT-PCR cloning technique and named PjPSY1, which was then subjected to biosynthetic analysis and functional verification. The results showed that the open reading frame of the gene was 1269 bp, encoding 423 amino acids, with a protein molecular mass of 47,654.67 KDa and an isoelectric point (pI) of 8.63; the protein encoded by these amino acids was hydrophilic and localized in chloroplasts, and its three-dimensional structure was predicted by combining the pymol software to annotate the N site of action and active centre of the protein. Phylogenetic analysis demonstrated that PjPSY1 had the closest affinity to DcPSY from Daucus carota. Overexpression of PjPSY1 led to a significant increase in the content of carotenoid-related monomers in Arabidopsis thaliana, with Violaxanthin being synthesized in transgenic Arabidopsis thaliana but not in wild-type Arabidopsis thaliana. The PjPSY1 clone obtained in this study was able to promote carotenoid synthesis in the fruits of Panax japonicus, revealing that the mode of action of PjPSY1 in the carotenoid biosynthesis pathway of Panax japonicus fruits has a positive regulatory effect. Full article

Background/Objectives: The global food system faces mounting pressures from population growth, dietary transitions, and resource and environmental constraints. Hybrid foods, which combine nutritional, environmental, and economic advantages, are increasingly regarded as a promising solution. This study examined consumer acceptance and willingness to pay (WTP) for a novel hybrid food product—beef rice. Methods: Based on online survey data collected from 1536 Chinese consumers, this study measured food neophobia and investigated its influence on WTP for beef rice. In addition, it explored the moderating effects of four distinct types of information interventions. Results: More than 80% of respondents expressed a willingness to purchase beef rice. Food neophobia exerted a significant negative effect on WTP (β = –1.538, p < 0.001). Among the information treatments, environmental information significantly mitigated the negative impact of food neophobia on WTP (β = 0.573, p < 0.01), while health-related and combined framings did not show significant effects. Conclusions: Chinese consumers generally hold a positive attitude toward hybrid foods such as beef rice. However, food neophobia significantly reduces their WTP. Environmental information shows a significant moderating effect and may serve as an effective strategy to enhance consumer acceptance of novel hybrid food products. Full article

Rouxiella badensis DSM 100043^T had been previously proven to produce a novel glucoselipid biosurfactant which has a very low critical micelle concentration (CMC) as well as very good stability against a wide range of pH, temperature, and salinity. In this study, we performed a function-based library screening from a R. badensis DSM 100043^T genome library to identify responsible genes for biosynthesis of this glucoselipid. The identified open reading frames (ORFs) were cloned into several constructs in Escherichia coli for gene permutation analysis and the individual products were analyzed using high-performance thin-layer chromatography (HPTLC). Products of interest from positive expression strains were purified and analyzed by liquid chromatography/electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) and nuclear magnetic resonance (NMR) for further structure elucidation. Function-based screening of 5400 clones led to the identification of an operon containing three ORFs encoding acetyltransferase GlcA (ORF1), acyltransferase GlcB (ORF2), and phosphatase/HAD GlcC (ORF3). E. coli pCAT2, with all three ORFs, resulted in the production of identical R. badensis DSM 100043^T glucosedilipid with Glu-C_10:0-C_12:1 as the main congener. ORF2-deletion strain E. coli pAFP1 primarily produced glucosemonolipids, with Glu-C_10:0,3OH and Glu-C_12:0 as the major congeners, predominantly esterified at the C-2 position of the glucose moiety. Furthermore, fed-batch bioreactor cultivation of E. coli pCAT2 using glucose as the carbon source yielded a maximum glucosedilipid titer of 2.34 g/L after 25 h of fermentation, which is 55-fold higher than that produced by batch cultivation of R. badensis DSM 100043^T in the previous study. Full article

Scholars and journalists typically trace the diffusion of breed-specific legislation (BSL) in the U.S. to the surge in negative media portraits of pit bull-type dogs (PBTDs) during the late twentieth century. Yet, while news coverage still portrays these dogs unfavorably, we document a sharp rise in countervailing sources of “pit bull positivity” over the past two decades. Drawing on insights from the respective social science research on changes in attitudes and public policy, we argue that this influx of positivity should powerfully impact opinions and policies towards PBTDs. Our data and analyses consistently support that argument. We analyze two different series of repeated cross-sectional surveys to show that public support for “pit bulls” grew considerably from 2014 to 2024. We also show that voters’ support for ballot measures overturning local “pit bull bans” increased substantially during that same ten-year period. Finally, our analysis of the frames and narratives deployed in recent state and local policy debates shows how this growing pit bull positivity has helped overturn over 300 discriminatory laws against these dogs since 2012. We conclude with a discussion of how shifts in portraits and opinions of PBTDs will likely continue eroding breed-specific legislation going forward. Full article

In recent years, deep learning-based end-to-end Text-To-Speech (TTS) models have made significant progress in enhancing speech naturalness and fluency. However, existing Variational Inference Text-to-Speech (VITS) models still face challenges such as insufficient pitch modeling, inadequate contextual dependency capture, and low inference efficiency in the decoder. To address these issues, this paper proposes an improved TTS framework named Q-VITS. Q-VITS incorporates Rotary Position Embedding (RoPE) into the text encoder to enhance long-sequence modeling, adopts a frame-level prior modeling strategy to optimize one-to-many mappings, and designs a style extractor based on a diffusion model for controllable style rendering. Additionally, the proposed decoder ConfoGAN integrates explicit F0 modeling, Pseudo-Quadrature Mirror Filter (PQMF) multi-band synthesis and Conformer structure. The experimental results demonstrate that Q-VITS outperforms the VITS in terms of speech quality, pitch accuracy, and inference efficiency in both subjective Mean Opinion Score (MOS) and objective Mel-Cepstral Distortion (MCD) and Root Mean Square Error (RMSE) evaluations on a single-speaker dataset, achieving performance close to ground-truth audio. These improvements provide an effective solution for efficient and controllable speech synthesis. Full article

This paper investigates the energy efficiency and positional stability of two types of ultralight unmanned aerial vehicles (UAVs)—bicopter and quadcopter—both with mass below 250 g, under varying flight conditions. The study is motivated by increasing interest in low-weight drones due to their regulatory flexibility and application potential in constrained environments. A comparative methodology was adopted, involving the construction of both UAV types using identical components where possible, including motors, sensors, and power supply, differing only in propulsion configuration. Experimental tests were conducted in wind-free and wind-induced environments to assess power consumption and stability. The data were collected through onboard blackbox logging, and positional deviation was tracked via video analysis. Results show that while the quadcopter consistently demonstrated lower energy consumption (by 6–22%) and higher positional stability, the bicopter offered advantages in simplicity of frame design and reduced component count. However, the bicopter required extensive manual tuning of PID parameters due to the inherent instability introduced by servo-based control. The findings highlight the potential of bicopters in constrained applications, though they emphasize the need for precise control strategies and high-performance servos. The study fills a gap in empirical analysis of energy consumption in lightweight bicopter UAVs. Full article

This study examines the long-term financial sustainability of commercial banks, emphasizing the crucial role of credit risk management. Given that the core function of credit creation inherently exposes banks to credit risk, this analysis evaluates how five key bank-specific risk variables, namely expected credit losses (ECL_BS), impairment gains or losses (ECL_IS), non-performing loans (NPLs), common equity tier 1 capital (CET1), and leverage (LEV) affect long-term financial sustainability. Applying a panel error correction model on data from listed South African banks spanning 2006 to 2023, the study reveals a stable long-term relationship, with approximately 74% of short-term deviations corrected over time, indicating convergence towards equilibrium. By taking into account the significance of major exogeneous shocks such as the 2009–2010 global financial crisis and the COVID-19 pandemic, as well as regulatory framework changes, the results reveal persistent relationships between credit risk factors and banks’ long-term financial sustainability in both short and long horizons. Notably, expected credit losses, and impairment gains and losses exert significant negative influence on long-term financial sustainability, while higher CET1 and NPLs exhibit positive effects. The study findings are framed within four complementary theoretical perspectives—the resource-based view, institutional theory, industrial organisation, and the dynamic capabilities framework—highlighting the multidimensional drivers of financial resilience. Thus, the study’s originality lies in its integrated approach to assessing credit risk, offering a holistic model for evaluating its influence on long-term financial sustainability. This integrated framework provides valuable, actionable insights for financial regulators, bank executives, policymakers, and banking practitioners committed to strengthening credit risk frameworks and aligning banking sector stability with broader sustainable development goals. Full article

The performance of visual SLAM in complex, real-world scenarios is often compromised by unreliable feature extraction and matching when using handcrafted features. Although deep learning-based local features excel at capturing high-level information and perform well on matching benchmarks, they struggle with generalization in continuous motion scenes, adversely affecting loop detection accuracy. Our system employs a Model-Agnostic Meta-Learning (MAML) strategy to optimize the training of keypoint extraction networks, enhancing their adaptability to diverse environments. Additionally, we introduce a coarse-to-fine feature tracking mechanism for learned keypoints. It begins with a direct method to approximate the relative pose between consecutive frames, followed by a feature matching method for refined pose estimation. To mitigate cumulative positioning errors, DK-SLAM incorporates a novel online learning module that utilizes binary features for loop closure detection. This module dynamically identifies loop nodes within a sequence, ensuring accurate and efficient localization. Experimental evaluations on publicly available datasets demonstrate that DK-SLAM outperforms leading traditional and learning-based SLAM systems, such as ORB-SLAM3 and LIFT-SLAM. DK-SLAM achieves 17.7% better translation accuracy and 24.2% better rotation accuracy than ORB-SLAM3 on KITTI and 34.2% better translation accuracy on EuRoC. These results underscore the efficacy and robustness of our DK-SLAM in varied and challenging real-world environments. Full article

Indonesia’s commitment to digital transformation is exemplified by the Gerakan 100 Smart City program, aiming to enhance public sector performance through technology integration. This study examines how information technology capability and 21st century digital skills influence public sector performance, mediated by managerial digital readiness and moderated by digital leadership. Grounded in Dynamic Capability Theory and Upper Echelon Theory, data from 1380 civil servants were analyzed using PLS-SEM via SmartPLS 4.1.0.9. Results show that both IT capability and digital skills significantly improve managerial digital readiness, which in turn positively impacts public sector performance. Managerial readiness mediates the effect of both predictors on performance, while digital leadership strengthens these relationships. Theoretically, this study frames managerial digital readiness as a dynamic capability shaped by leadership cognition. Practically, it highlights the importance of aligning infrastructure, skills, and leadership development to advance digital governance. Future research should consider longitudinal, multilevel, and qualitative designs to deepen insights. Full article

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article