Search Results (3,078)

Artificial intelligence is rapidly reshaping skill expectations across media, marketing, and journalism, however, university curricula are not evolving at a comparable speed. To quantify the resulting curriculum–skill gap in communication-related programs, two synchronous corpora were assembled for the period July 2024–June 2025: 66 course descriptions from six leading UK universities and 107 graduate-to-mid-level job advertisements in communications, digital media, advertising, and public relations. Alignment around AI, datafication, and platform governance was assessed through a three-stage natural-language-processing workflow: a dual-tier AI-keyword index, comparative TF–IDF salience, and latent Dirichlet allocation topic modeling with bootstrap uncertainty. Curricula devoted 6.0% of their vocabulary to AI plus data/platform terms, whereas job ads allocated only 2.3% (χ² = 314.4, p < 0.001), indicating a conceptual-critical emphasis on ethics, power, and societal impact in the academy versus an operational focus on SEO, multichannel analytics, and campaign performance in recruitment discourse. Topic modeling corroborated this divergence: universities foregrounded themes labelled “Politics, Power & Governance”, while advertisers concentrated on “Campaign Execution & Performance”. Environmental and social externalities of AI—central to the Special Issue theme—were foregrounded in curricula but remained virtually absent from job advertisements. The findings are interpreted as an extension of technology-biased-skill-change theory to communication disciplines, and it is suggested that studio-based micro-credentials in automation workflows, dashboard visualization, and sustainable AI practice be embedded without relinquishing critical reflexivity, thereby narrowing the curriculum–skill gap and fostering environmentally, socially, and economically responsible media innovation. With respect to the novelty of this research, it constitutes the first large-scale, data-driven corpus analysis that empirically assessed the AI-related curriculum–skill gap in communication disciplines, thereby extending technology-biased-skill-change theory into this field. Full article

Background: Genetic improvement in wheat yield is the most focused research area for the breeding community to ensure sustainable production. Wheat kernel traits and biomass are considered key contributors to enhance crop yield. Methods: This study was designed to explore the genetic diversity of kernel and biomass traits in popular wheat varieties from the US Southern Great Plains using 264 doubled haploid (DH) lines mainly derived from TAM 114 or TAM 204. This population was evaluated in two field environments planted in alpha lattice design during the 2020 crop season. Kernel traits were collected using the hp Scanjet G4010 photo scanner for image capturing and GrainScan v3. software for image analysis. Biomass parameters were collected and processed manually. For genotyping genomic libraries were prepared and sequenced on Illumina NovaSeq 6000 to generate paired end reads of 150 bp. Sequences were aligned to the IWGSC RefSeq genome assembly v2.1 using the Burrows Wheeler Aligner for SNP calling. Results: A total of 59,482 polymorphic SNP markers were retained for genetic analysis after the filtration at 50% missing data and 5% minor allele frequency. To investigate the marker–trait association and the genomic regions, four genome-wide association study models were implemented using the R package GAPIT version 3.5. Based on the Bonferroni correction <8.41 × 10⁻⁷ was used as a threshold to declare marker-trait associations (MTAs) significant. The BLINK model identified 12 MTAs on chromosomes 1A, 2A, 2B, 4A, 4B, and 6B. Conclusions: The identified MTAs can be used to develop diagnostic markers for efficient selection and utilization in recombination breeding and cultivar development process. Full article

The development of algal bioplastics offers a promising pathway toward sustainable materials that can mitigate reliance on fossil fuel-derived plastics. This article reviews recent advances in algal cultivation, strain optimization, biopolymer extraction, and processing technologies, alongside techno-economic and life cycle assessments. Special emphasis is placed on integrated biorefinery models, innovative processing techniques, and the role of government–industry–academia partnerships in accelerating commercialization. The analysis incorporates both demonstrated algal systems and theoretical applications derived from established microbial processes, reflecting the emerging nature of this field. The environmental advantages, market readiness, and scalability challenges of algal bioplastics are critically evaluated, with reference to peer-reviewed studies and industrial pilot projects. The analysis underscores that while technical feasibility has been demonstrated, economic viability and large-scale adoption depend on optimizing yield, reducing production costs, and fostering collaborative frameworks. Future research priorities include enhancing strain performance via AI-enabled screening, expanding product valorization streams, and aligning regulatory standards to support global market integration. Full article

Autonomous commercial vehicles often mount multiple LiDARs to enlarge their field of view, but conventional calibration is labor-intensive and prone to drift during long-term operation. We present an online self-calibration method that combines a ground plane motion constraint with a virtual RGB–D projection, mapping 3D point clouds to 2D feature/depth images to reduce feature extraction cost while preserving 3D structure. Motion consistency across consecutive frames enables a reduced-dimension hand–eye formulation. Within this formulation, the estimation integrates geometric constraints on $SE\left(3\right)$ using Lagrange multiplier aggregation and quasi-Newton refinement. This approach highlights key aspects of identifiability, conditioning, and convergence. An online monitor evaluates plane alignment and LiDAR–INS odometry consistency to detect degradation and trigger recalibration. Tests on a commercial vehicle with six LiDARs and on nuScenes demonstrate accuracy comparable to offline, target-based methods while supporting practical online use. On the vehicle, maximum errors are 6.058 cm (translation) and 4.768° (rotation); on nuScenes, 2.916 cm and 5.386°. The approach streamlines calibration, enables online monitoring, and remains robust in real-world settings. Full article

The rapid integration of renewable energy sources into modern power systems has exacerbated power quality challenges, particularly broadband oscillation phenomena that threaten grid symmetry and stability. The proposed symplectic geometric mode decomposition (SGMD) method advances the field; however, issues like mode aliasing and over-decomposition are unresolved within the symplectic geometric paradigm. To resolve these limitations in existing methods, this paper proposes a novel time-frequency-coupled symmetry mode decomposition technique. The approach first applies symplectic symmetry geometric mode in the time domain, then iteratively refines the modes using frequency-domain Local Outlier Factor (LOF) detection to suppress aliasing. Final mode integration employs Dynamic Time Warping (DTW) for optimal alignment, enabling accurate extraction of oscillation characteristics. Comparative evaluations demonstrate that the average error of the amplitude and frequency identification of the proposed method are 1.39% and 0.029%, which are lower than the results of SVD at 5.09% and 0.043%. Full article

Understanding flow dynamics in open-channel node systems is crucial for designing effective hydraulic engineering solutions and minimizing energy losses. This study investigates how junction geometry—specifically the lateral inflow angle (α = 45° and 60°) and the longitudinal bed slope (I = 0.0011 to 0.0051)—influences the water velocity distribution and hydraulic losses in a rigid-bed Y-shaped open-channel junction. Experiments were performed in a 0.3 m wide and 0.5 m deep rectangular flume, with controlled inflow conditions simulating steady-state discharge scenarios. Flow velocity measurements were obtained using a PEMS 30 electromagnetic velocity probe, which is capable of recording three-dimensional velocity components at a high spatial resolution, and electromagnetic flow meters for discharge control. The results show that a lateral inflow angle of 45° induces stronger flow disturbances and higher local loss coefficients, especially under steeper slope conditions. In contrast, an angle of 60° generates more symmetric velocity fields and reduces energy dissipation at the junction. These findings align with the existing literature and highlight the significance of junction design in hydraulic structures, particularly under high-flow conditions. The experimental data may be used for calibrating one-dimensional hydrodynamic models and optimizing the hydraulic performance of engineered channel outlets, such as those found in hydropower discharge systems or irrigation networks. Full article

Antiviral peptides (AVPs) are promising therapeutic candidates, yet the rapid growth of sequence data and the field’s emphasis on predictors have left a gap: the lack of an integrated view linking peptide chemistry with biological context. Here, we map the AVP landscape through interactive data mining using Half-Space Proximal Networks (HSPNs) and Metadata Networks (MNs) in the StarPep toolbox. HSPNs minimize edges and avoid fixed thresholds, reducing computational cost while enabling high-resolution analysis. A threshold-free HSPN resolved eight chemically and biologically distinct communities, while MNs contextualized AVPs by source, function, and target, revealing structural–functional relationships. To capture diversity compactly, we applied centrality-guided scaffold extraction with redundancy removal (90–50% identity), producing four representative subsets suitable for modeling and similarity searches. Alignment-free motif discovery yielded 33 validated motifs, including 10 overlapping with reported AVP signatures and 23 apparently novel. Motifs displayed category-specific enrichment across antimicrobial classes, and sequences carrying multiple motifs (≥4–5) consistently showed higher predicted antiviral probabilities. Beyond computational insights, scaffolds provide representative “entry points” into AVP chemical space, while motifs serve as modular building blocks for rational design. Together, these resources provide an integrated framework that may inform AVP discovery and support scaffold- and motif-guided therapeutic design. Full article

Seawater intrusion into coastal river systems poses increasing challenges for freshwater availability and estuarine ecosystem integrity, especially under evolving climatic and anthropogenic pressures. This study presents a multidisciplinary investigation of marine intrusion dynamics within the Magra River estuary (Northwest Italy), integrating field monitoring, isotopic tracing (δ¹⁸O; δD), and multivariate statistical modeling. Over an 18-month period, 11 fixed stations were monitored across six seasonal campaigns, yielding a comprehensive dataset of water electrical conductivity (EC) and stable isotope measurements from fresh water to salty water. EC and oxygen isotopic ratios displayed strong spatial and temporal coherence (R² = 0.99), confirming their combined effectiveness in identifying intrusion patterns. The mass-balance model based on δ¹⁸O revealed that marine water fractions exceeded 50% in the lower estuary for up to eight months annually, reaching as far as 8.5 km inland during dry periods. Complementary δD measurements provided additional insight into water origin and fractionation processes, revealing a slight excess relative to the local meteoric water line (LMWL), indicative of evaporative enrichment during anomalously warm periods. Multivariate regression models (PLS, Ridge, LASSO, and Elastic Net) identified river discharge as the primary limiting factor of intrusion, while wind intensity emerged as a key promoting variable, particularly when aligned with the valley axis. Tidal effects were marginal under standard conditions, except during anomalous events such as tidal surges. The results demonstrate that marine intrusion is governed by complex and interacting environmental drivers. Combined isotopic and machine learning approaches can offer high-resolution insights for environmental monitoring, early-warning systems, and adaptive resource management under climate-change scenarios. Full article

Fujian Tulou, a UNESCO World Heritage Site, is highly vulnerable to environmental and anthropogenic stresses, with its earthen walls prone to surface cracking that threatens both structural stability and cultural value. Traditional manual inspection is inefficient, subjective, and may disturb fragile surfaces, highlighting the need for non-destructive and automated solutions. This study proposes a dual-path framework that integrates lightweight crack detection with independent physical simulation. On the detection side, an improved YOLOv12 model is developed to achieve lightweight and accurate recognition of multiple crack types under complex wall textures. On the simulation side, a two-layer RFPA^3D model was employed to parameterize loading conditions and material thickness, reproducing the four-stage crack evolution process, and aligning well with field observations. Quantitative validation across paired samples demonstrates improved consistency in morphology, geometry, and topology compared with baseline models. Overall, the framework offers an effective and interpretable solution for standardized crack documentation and mechanistic interpretation, providing practical benefits for the preventive conservation and sustainable management of Fujian Tulou. Full article

Patient-specific technologies within the field of adult spinal deformity (ASD) aid surgeons in pre-surgical planning, accurately help identify anatomical landmarks, and can project optimal post-surgical sagittal alignment. This narrative review aims to discuss the current uses of patient-specific technologies in ASD and identify new innovations that may very soon be integrated into patient care. Pre-operatively, machine learning or artificial intelligence helps surgeons to simulate post-operative alignment and provide information for the 3D-printing of pre-contoured rods and patient-specific cages. Intraoperatively, robotic surgery and intraoperative guides allow for more accurate positioning of implants. Implant materials are being developed to allow for better osseointegration and patient outcome monitoring. Despite the significant promise of these technologies, work still needs to be performed to ensure their accuracy, safety, and cost efficacy. Full article

The recruitment of peripheral membrane proteins is tightly regulated by membrane lipid composition and local electrostatic microenvironments. Our experimental observations revealed that Vp54, a viral matrix protein, exhibited preferential binding to lipid bilayers enriched in anionic lipids such as phosphatidylglycerol (PG) and phosphatidylserine (PS), compared to neutral phosphatidylcholine/phosphatidylethanolamine liposomes, and this occurred in a curvature-dependent manner. To elucidate the molecular basis of this selective interaction, we performed a series of computational analyses including helical wheel projection, electrostatic potential calculations, electric field lines simulations, and electrostatic force analysis. Our results showed that the membrane-proximal region of Vp54 adopted an amphipathic α-helical structure with a positively charged interface. In membranes containing PG or PS, electrostatic potentials at the interface were significantly more negative, enhancing attraction with Vp54. Field line and force analyses further confirmed that both the presence and spatial clustering of anionic lipids intensify membrane–Vp54 electrostatic interactions. These computational findings align with experimental binding data, jointly demonstrating that membrane lipid composition and organization critically modulate Vp54 recruitment. Together, our findings highlight the importance of electrostatic complementarity and membrane heterogeneity in peripheral protein targeting and provide a framework applicable to broader classes of membrane-binding proteins. Full article

The accurate prediction of single-well production performance necessitates considering the multiple factors influencing the dynamic changes in coal seam permeability during deep coalbed methane (CBM) extraction. This study focuses on Block D of the Ordos Basin. The Langmuir monolayer adsorption model was selected to describe gas adsorption behavior, and a productivity prediction model for deep CBM was developed by coupling multiple dynamic effects, including stress sensitivity, matrix shrinkage, gas slippage, and coal fines production and blockage. The results indicate that the stress sensitivity coefficients of artificial fracture networks and cleat fractures are key factors affecting the accuracy of CBM productivity predictions. Under accurate stress sensitivity coefficients, the predicted daily gas production rates of the productivity model for single wells showed errors ranging from 1.89% to 14.22%, with a mean error of 8.15%, while the predicted daily water production rates had errors between 0.35% and 17.66%, with a mean error of 8.68%. This demonstrates that the established productivity prediction model for deep CBM aligns with field observations. The findings can provide valuable references for production performance analysis and development planning for deep CBM wells. Full article

Smartphone addiction has emerged as a significant public health concern, particularly among adolescents and young adults. This narrative review, conducted in line with the ANDJ checklist, synthesizes evidence from 25 systematic reviews and meta-analyses, complemented by randomized controlled trials and clinical studies, to provide a structured overview of the field. The study selection flow and publication trends reveal a rapidly expanding research landscape, with most evidence produced in the last decade, reflecting both the ubiquity of smartphones and increasing awareness of their health impacts. The synthesis highlights converging findings across reviews: excessive smartphone use is consistently associated with psychosocial, behavioral, and academic challenges, alongside sleep disturbances and mental health symptoms. Common messages include the recognition of smartphone addiction as a multidimensional phenomenon, while emerging themes point to heterogeneity in definitions, tools, and methodological approaches. Comparative analysis of reviews underscores both shared risk factors—such as emotional dysregulation and social isolation—and differences in study designs and target populations. Importantly, this review identifies critical gaps, including the lack of standardized definitions, limited longitudinal evidence, and scarce cross-cultural validation. At the same time, promising opportunities are noted, from lifestyle-based interventions (e.g., physical activity) to educational and policy-level strategies fostering digital literacy and self-regulation. The post-pandemic context further emphasizes the need for sustained monitoring and adaptive responses. Overall, this review calls for youth-centered, multi-sector interventions aligned with WHO recommendations, supporting coordinated, evidence-based action across health, education, and policy domains. Full article

Multi-modal sensor fusion has become a cornerstone of robust autonomous driving systems, enabling perception models to integrate complementary cues from cameras, LiDARs, radars, and other modalities. This survey provides a structured overview of recent advances in deep learning-based fusion methods, categorizing them by architectural paradigms (e.g., BEV-centric fusion and cross-modal attention), learning strategies, and task adaptations. We highlight two dominant architectural trends: unified BEV representation and token-level cross-modal alignment, analyzing their design trade-offs and integration challenges. Furthermore, we review a wide range of applications, from object detection and semantic segmentation to behavior prediction and planning. Despite considerable progress, real-world deployment is hindered by issues such as spatio-temporal misalignment, domain shifts, and limited interpretability. We discuss how recent developments, such as diffusion models for generative fusion, Mamba-style recurrent architectures, and large vision–language models, may unlock future directions for scalable and trustworthy perception systems. Extensive comparisons, benchmark analyses, and design insights are provided to guide future research in this rapidly evolving field. Full article

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article