Nanoarchitectonics, like architectonics, allows the design and building of structures, but at the nanoscale. Unlike those in architectonics, and even macro-, micro-, and atomic-scale architectonics, the assembled structures at the nanoscale do not always follow the projected design. In fact, they do follow the projected design but only for self-assembly processes producing structures with perfect order. Here, we look at nanoarchitectonics allowing the building of nanostructures without a perfect arrangement of building blocks. Here, fabrication of structures from molecules, polymers, nanoparticles, and nanosheets to polymer brushes, layer-by-layer assembly structures, and hydrogels through self-assembly processes is discussed, where perfect order is not necessarily the aim to be achieved. Both planar substrate and spherical template-based assemblies are discussed, showing the challenging nature of research in this field and the usefulness of such structures for numerous applications, which are also discussed here.
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http://dx.doi.org/10.1039/d2nr02537j | DOI Listing |
We propose a conformal vision transformer (CViT)-based demodulation for the perfect optical vortices shift keying (POV-SK) signal in the low-density parity check (LDPC) coded free-space optical (FSO) link. Despite the growing interest in POV for FSO links, atmospheric turbulence (AT) induces phase distortions, resulting in POV-SK demodulation errors and degrading POV-SK FSO communication performance. The CViT demodulator utilizes conformal mapping to reshape the circular POV-SK patterns into rectangles, enabling more efficient feature learning.
View Article and Find Full Text PDFInt J Environ Res Public Health
January 2025
Facultad de Deportes, Universidad Autónoma de Baja California, Mexicali 21100, Mexico.
Based on the theory of planned behavior, the objective was to test a theoretical model that explains the intention to continue practicing sports among adolescents currently involved in sports practice in Mexicali based on factors that generate perceived social pressure to be perfect (perceived descriptive norm) and that lead to internal factors of perceived control (perceived competence, general self-concept, and enjoyment). A battery of questionnaires that measured the study variables was applied to 195 adolescent athletes of both sexes. The causal model with observed variables rejected part of the hypothesis since the athletes' perception that their parents impose high performance expectations on them and that they criticize them when these expectations are not achieved was not associated with the athletes' perceived competence.
View Article and Find Full Text PDFEmerg Med J
January 2025
Department of Emergency Medicine, Medisch Centrum Leeuwarden, Leeuwarden, Fryslân, The Netherlands.
Background: Point-of-care ultrasound (POCUS) can potentially be used in the triage of patients with elbow injuries. However, the diagnostic accuracy of POCUS performed by non-radiologists for the exclusion of elbow fractures is yet unknown. This study aimed to investigate the diagnostic potential of POCUS of the posterior fatpad performed by non-radiologists in the workup of adult patients presenting with elbow injuries.
View Article and Find Full Text PDFOrthop Surg
January 2025
Department of Spine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
Objective: Coronal malalignment is a common feature of adult spinal deformity, and accurate classification is essential for diagnosis and treatment planning. However, variations in interpretation among clinicians can impact classification consistency. By assessing the reliability and applicability of these systems across different medical experts, this study seeks to establish a standardized approach to enhance clinical outcomes.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Kenya Medical Research Institute- Center for Global Health Research (KEMRI-CGHR), P.O Box 1578-40100, Kisumu, Kenya.
Background: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities.
Methods: LDD was defined as a diarrhea episode lasting ≥ 7 days.
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