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In the late 1980s and 1990s, the advanced industrial countries considered replacing the existing analogue television infrastructure with a new digital one. A key common feature to the debates over digital TV (DTV) in the United States, Western Europe and Japan was the eventual victory of the ideas of digitalism (the superiority of everything digital over everything analogue) and of digital convergence (the merging of computing, telecommunications and broadcasting infrastructures made possible by digitalization) in public debates over standards. Jeffrey Hart's book shows how nationalism and regionalism combined with digitalism to produce three different and incompatible DTV standards in the three regions, an outcome which has led to missed opportunities in developing the new technologies. Hart's book contributes to our understanding of relations between business and government, and of competition between the world's great economic powers.

This work deals with the structure of TV channels that are distributed from satellite positions where DTH platforms offer their services in European countries. The structure is being considered via service availability (FTA and PAY TV), resolution (SDTV, HDTV and UHDTV), standards of broadcasting (DVB-S and DVB-S2), satellites and satellite positions, as well as market share of leading satellite operators at European market through which DTH providers do their services to the ultimate users. We also represent the market of TV channel distribution through the number of household which use cable, satellite, terrestrial and IPTV. Collected data are represented as a table and graph for the period from 1996 to 2020.

The authors report on a real‐time 48‐Gbit/s terahertz band wireless communication experiment employing a pair of resonant tunnelling diodes at the receiver and uni‐travelling carrier photodiodes at the transmitter. The wireless transmission of uncompressed full‐resolution 8K ultra high‐definition television video signal is successfully demonstrated in the 300‐GHz band for the first time.

High-definition (HD) maps aim to provide detailed road information with centimeter-level accuracy, essential for enabling precise navigation and safe operation of autonomous vehicles (AVs). Traditional offline construction methods involve several complex steps, such as data collection, point cloud generation, and feature extraction, but these methods are resource-intensive and struggle to keep pace with the rapidly changing road environments. In contrast, online HD map construction leverages onboard sensor data to dynamically generate local HD maps, offering a bird’s-eye view (BEV) representation of the surrounding road environment. This approach has the potential to improve adaptability to spatial and temporal changes in road conditions while enhancing cost-efficiency by reducing the dependency on frequent map updates and expensive survey fleets. This survey provides a comprehensive analysis of online HD map construction, including the task background, high-level motivations, research methodology, key advancements, existing challenges, and future trends. We systematically review the latest advancements in three key sub-tasks: map segmentation, map element detection, and lane graph construction, aiming to bridge gaps in the current literature. We also discuss existing challenges and future trends, covering standardized map representation design, multitask learning, and multi-modality fusion, while offering suggestions for potential improvements.

Despite significant progress has been made in image and video deblurring, much less attention has been paid to process ultra high-definition (UHD) videos (e.g., 4K resolution). In this work, we propose a novel deep model for fast and accurate UHD video deblurring (UHDVD). The proposed UHDVD is achieved by a depth-wise separable-patch architecture, which operates with a multi-scale integration scheme to achieve a large receptive field without adding the number of generic convolutional layers and kernels. Additionally, we adopt the temporal feature attention module to effectively exploit the temporal correlation between video frames to obtain clearer recovered images. We design an asymmetrical encoder–decoder architecture with residual channel-spatial attention blocks to improve accuracy and reduce the depth of the network appropriately. Consequently, the proposed UHDVD achieves real-time performance on 4K videos at 30 fps. To train the proposed model, we build a new dataset comprised of 4K blurry videos and corresponding sharp frames using three different smartphones. Extensive experimental results show that our network performs favorably against the state-of-the-art methods on the proposed 4K dataset and existing 720p and 2K benchmarks in terms of accuracy, speed, and model size.

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps.

Plant-microbe interactions are a phenomenal display of symbiotic/parasitic relationships between living organisms. Plant growth-promoting rhizobacteria (PGPR) are some of the most widely investigated plant-beneficial microbes due to their capabilities in stimulating plant growth and development and conferring protection to plants against biotic and abiotic stresses. As such, PGPR-mediated plant priming/induced systemic resistance (ISR) has become a hot topic among researchers, particularly with prospects of applications in sustainable agriculture. The current study applies untargeted ultra-high performance liquid chromatography-high-definition mass spectrometry (UHPLC-HDMS) to investigate PGPR-based metabolic reconfigurations in the metabolome of primed wheat plants against Puccinia striiformis f. sp. tricti ( Pst ). A seed bio-priming approach was adopted, where seeds were coated with two PGPR strains namely Bacillus subtilis and Paenibacillus alvei (T22) and grown under controlled conditions in a glasshouse. The plants were infected with Pst one-week post-germination, followed by weekly harvesting of leaf material. Subsequent metabolite extraction was carried out for analysis on a UHPLC-HDMS system for data acquisition. The data was chemometrically processed to reveal the underlying trends and data structures as well as potential signatory biomarkers for priming against Pst . Results showed notable metabolic reprogramming in primary and secondary metabolism, where the amino acid and organic acid content of primed-control, primed-challenged and non-primed-challenged plants were differentially reprogrammed. Similar trends were observed from the secondary metabolism, in which primed plants (particularly primed-challenged) showed an up-regulation of phenolic compounds (flavonoids, hydroxycinnamic acids-HCAs- and HCA amides) compared to the non-primed plants. The metabolomics-based semi-quantitative and qualitative assessment of the plant metabolomes revealed a time-dependent metabolic reprogramming in primed-challenged and primed-unchallenged plants, indicating the metabolic adaptations of the plants to stripe rust infection over time.

Purpose To compare dosimetric characteristics and treatment efficiency between the high‐definition multileaf collimator (HD‐MLC) system and robotic radiosurgery (RRS) for brain metastases, with stratified analysis by lesion count. Methods Twenty‐nine patients (12 single [SBM], 17 multiple [MBM] brain metastases) treated with RRS (CyberKnife) were re‐planned using a linear accelerator (LINAC) with 2.5‐mm micro‐MLC (D2SRS system). Key indices included target parameters (RTOG conformity index [RTOG CI], Paddick conformity index [Paddick CI], homogeneity index [HI], gradient index [GI], dose at 2 cm outside the target [D2cm]) and normal brain doses (V12Gy, V20Gy, V30Gy). The Wilcoxon signed‐rank test was used (p < 0.05). Results In SBM, RTOG CI, Paddick CI, and GI showed no significant differences, but Paddick CI favored RRS for tumor volume (TV) < 80 cc and D2SRS for TV > 80 cc. D2SRS had better HI (0.08 ± 0.02 vs. 0.12 ± 0.03, p < 0.01); RRS had lower D2cm (18.2 ± 5.5 Gy vs. 23.8 ± 6.7 Gy, p = 0.008), V12Gy (5.3 ± 2.1 cc vs. 8.7 ± 3.5 cc, p < 0.01), and V20Gy (18.47 ± 21.17 cc vs. 35.30 ± 53.85 cc, p = 0.015), with similar V30Gy. D2SRS had shorter treatment time (TT) (8.2 ± 2.1 min vs. 26.8 ± 7.8 min, p < 0.01). In MBM, D2SRS outperformed RRS in RTOG CI (1.12 ± 0.21 vs. 1.45 ± 0.33, p = 0.007), Paddick CI (0.85 ± 0.11 vs. 0.68 ± 0.14, p = 0.007), and HI (0.07 ± 0.02 vs. 0.13 ± 0.03, p = 0.03). RRS had lower V12Gy (38.5 ± 12.6 cc vs. 48.1 ± 15.3 cc, p = 0.017), V20Gy (14.5 ± 4.2 cc vs. 21.3 ± 6.8 cc, p = 0.04), and V30Gy (14.99 ± 20.81 cc vs. 18.49 ± 23.19 cc, p = 0.026). D2SRS reduced TT by 77.3% (12.4 ± 3.5 min vs. 54.6 ± 15.2 min, p < 0.01). No significant differences in Dmax of the brainstem, optic nerves, or lenses were found. Conclusion RRS provides better normal brain protection in both groups, while D2SRS shows superior conformity and efficiency in MBM. Clinical selection should depend on lesion burden.

This study aimed to evaluate the effect of combining high-definition transcranial direct current stimulation (HD-tDCS) with foot core exercise (FCE) on dynamic postural stability and to determine whether the improvement achieved through this mix-type intervention outperforms the intervention of HD-tDCS and FCE alone. Sixty healthy males were recruited and randomly divided into four groups: (1) HD-tDCS + FCE group (HD-tDCS combined with FCE intervention); (2) s-tDCS + FCE (sham tDCS combined with FCE intervention); (3) HD-tDCS group which only received HD-tDCS; (4) FCE group which only performed FCE. All participants received a four-week intervention (3 times a week, 20 min each time). The Y-balance task was completed before and after the intervention. The maximum reaching distance was recorded, and the data of the center of pressure (COP) were collected by a three-dimensional force plate to calculate COP displacement and velocity. No significant change in COP displacement was found among the four groups. However, the COP velocity decreased significantly in the posteromedial direction after HD-tDCS + FCE intervention compared with the baseline. The maximum reach distance was significantly increased after HD-tDCS + FCE intervention in the posteromedial (p < 0.001) and posterolateral (p < 0.001) directions of the Y balance task compared with the baseline, and the extent of increase was greater than that in the three other groups. The intervention of HD-tDCS combined with FCE may exert a synergistic effect and more effectively improve dynamic postural stability.

The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in mapping the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field.