Scientific Applications Research Articles

Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.

Pure shift NMR spectroscopy enables the robust probing on molecular structure and dynamics, benefiting from great resolution enhancements. Despite extensive application landscapes in various branches of chemistry, the long experimental times induced by the additional time dimension generally hinder its further developments and practical deployments, especially for multi-dimensional pure shift NMR. Herein, this study proposes and implements the fast, reliable, and robust reconstruction for accelerated pure shift NMR spectroscopy with lightweight attention-assisted deep neural network. This deep learning protocol allows one to regain high-resolution signals and suppress undersampling artifacts, as well as furnish high-fidelity signal intensities along with the accelerated pure shift acquisition, benefitting from the introduction of the attention mechanism to highlight the spectral feature and information of interest. Extensive results of simulated and experimental NMR data demonstrate that this attention-assisted deep learning protocol enables the effective recovery of weak signals that are almost drown in the serious undersampling artifacts, and the distinction and recognition of close chemical shifts even though using merely 5.4% data, highlighting its huge potentials on fast pure shift NMR spectroscopy. As a result, this study affords a promising paradigm for the AI-assisted NMR protocols toward broader applications in chemistry, biology, materials, and life sciences, and among others.

Dynamic simulation of unsteady Casson–Carreau fluid flow considering temperature-dependent variable-properties and thermo-solutal mixed convection over flat and slendering sheets

The present investigation delves into the intricate interplay between surface effects and fluid dynamics, focusing on the flow characteristics of Casson and Carreau fluids endowed with temperature-dependent thermophysical properties. Specifically, the study explores the ramifications of radiative, mixed convection, and magnetized processes on heat and mass transfer phenomena. A comprehensive mathematical model is developed to analyze the behavior of Casson–Carreau fluids in conjunction with microscopic particles and gyrotactic microorganisms over both flat and slender surfaces. Leveraging similarity transformations, the governing equations are transformed into dimensionless form, facilitating numerical solutions using the bvp5c solver in MATLAB. The ensuing numerical and graphical analyses elucidate key physical quantities, including local skin friction coefficients, gyrotactic microorganism density, Nusselt number, and Sherwood number, across a spectrum of physical parameters. Notably, the investigation underscores the profound influence of flat surfaces on flow patterns and highlights the heightened consumption of fluid particles with increasing variable diffusivity and thermal conductivity. Moreover, enhanced values of parameters such as variable motile microorganism diffusion coefficient, Brownian motion parameter, and temperature-dependent thermal conductivity parameter are found to augment the Nusselt number, indicative of intensified heat transfer rates. This research underscores the imperative of comprehending the intricate dynamics of fluid flow and heat transfer, offering actionable insights for enhancing engineering designs and addressing multifaceted challenges across domains encompassing chemical engineering, environmental science, and biomedical applications.

The mechanochemistry of lanthanum dihydride (LaH\(_{2}\)) with hydrogen (H\(_{2}\)) using the ball-mill process and the effect of oxidation on the resulting products

The complex behavior of LaH2 during ball milling was investigated in this study, with its mechanical, chemical, and morphological changes explored. The relationship between milling time and hydrogen pressure reduction was uncovered through detailed experiments, reflecting the dynamic nature of the process. A transient yet significant event was observed upon unsealing the milling jar post-milling: the emergence of a minor fire ember, indicative of the interplay between mechanical forces and chemical reactivity within the LaH2 powder. Profound changes in the structure, composition, and shape were unraveled using advanced techniques such as X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDX), and particle size distribution analysis. The resulting powder exhibited a dual-phase composition of lanthanum dihydride (LaH2, 68.1% to 71.5%) and lanthanum oxide (La2O3, 28.5% to 31.9%), reflecting a dynamic chemical equilibrium during milling. Particle size distribution analysis revealed a notable increase in average diameter to 6420 nm, accompanied by a polydispersity index (PDI) of 0.831, signifying a broadening compared to the initial LaH2 powder. The morphological evolution of the powder was elucidated through SEM imaging, showing predominantly spherical and rounded forms, indicating extensive particle agglomeration and plastic deformation during milling. Additionally, the formation of oxide layers on the powder surface, intertwined with pronounced particle agglomeration, was highlighted through EDX mapping, shedding light on the mechanical aspects of morphological evolution during milling. These findings contribute to our understanding of LaH2 behavior under extreme mechanical and chemical conditions and have implications for materials processing, hydrogen storage technologies, and broader applications in materials science and engineering.

Artificial intelligence in forensic medicine and related sciences – selected issues = Sztuczna inteligencja w medycynie sądowej i naukach pokrewnych – wybrane zagadnienia

Aim. The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. Materials and methods. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as „artificial intelligence,” „deep learning,” „machine learning,” „forensic medicine,” „legal medicine,” „forensic pathology” and „medicine” were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Conclusions. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions.

Accurate prediction of micro-pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pKa prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pKa units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pKa prediction models. QupKake represents a significant advancement in micro-pKa prediction, offering a powerful tool for various applications in chemistry and beyond.

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm.

Exploring Clique Transversal Problems for d-degenerate Graphs with Fixed d: From Polynomial-Time Solvability to Parameterized Complexity

This paper explores the computational challenges of clique transversal problems in d-degenerate graphs, which are commonly encountered across theoretical computer science and various network applications. We examine d-degenerate graphs to highlight their utility in representing sparse structures and assess several variations of clique transversal problems, including the b-fold and {b}-clique transversal problems, focusing on their computational complexities for different graph categories. Our analysis identifies that certain instances of these problems are polynomial-time solvable in specific graph classes, such as 1-degenerate or 2-degenerate graphs. However, for d-degenerate graphs where d≥2, these problems generally escalate to NP-completeness. We also explore the parameterized complexity, pinpointing specific conditions that render these problems fixed-parameter tractable.

Arabic Music Genre Identification

Music Information Retrieval (MIR) is one data science application crucial for different tasks such as recommendation systems, genre identification, fingerprinting, and novelty assessment. Different Machine Learning techniques are utilised to analyse digital music records, such as clustering, classification, similarity scoring, and identifying various properties for the different tasks. Music is represented digitally using diverse transformations and is clustered and classified successfully for Western Music. However, Eastern Music poses a challenge, and some techniques have achieved success in clustering and classifying Turkish and Persian Music. This research presents an evaluation of machine learning algorithms' performance on pre-labelled Arabic Music with their Arabic genre (Maqam). The study introduced new data representations of the Arabic music dataset and identified the most suitable machine-learning methods and future enhancements.

Using Smartphones for English Speaking Skill Development from the Omani EFL Learners' Perspectives

The widespread use of hand-held devices, including smartphones, has been invested in language learning education around the world. This study intends to inspect Omani EFL learners' perspectives on using smartphones to develop their speaking skills. To answer the research questions, the researchers used a 5-point Likert questionnaire to collect data from 259 Omani EFL students from the English Language Unit, Preparatory Studies Center, University of Technology and Applied Sciences (UTAS) in Salalah, Sultanate of Oman. Findings showed positive beliefs about using smartphones. The participants stated that they sometimes use smartphones for oral skills. In the study, a significant difference between female and male students in terms of perceptions towards smartphones was evident, in addition to a positive correlation between the students' levels of study, perceptions, and uses of smartphones. In light of the results, the study provides invaluable recommendations and pedagogical implications for students, teachers, and syllabi designers.

Omani Students’ Familiarity with IELTS-based Reading Exam Format and Strategies: A Qualitative Phenomenological Study

This study aims to investigate Omani students’ familiarity with the IELTS-based reading exam’s time limit, format, and reading strategies. It also aims to explore the extent to which Omani students adopt independent learning strategies and the challenges they face while taking the IELTS-based reading exams. Omani students need help with their reading skills, as is shown in their below-average scores in IELTS or IELTS-based reading exams conducted by the British Council and the University of Technology and Applied Sciences (UTAS-Ibra). Hence, this study seeks to answer the research questions of whether Omani students studying at level four are familiar with the IELTS-based reading exam’s time limit, format, and reading strategies, and adopt independent learning strategies. The study employed a qualitative research method, and data was collected from eight students (N=8) studying at level four in the preparatory studies center of UTAS-Ibra. This study is significant because it has implications for the stakeholders. The study results show that participants generally possessed a positive comprehension of the time constraints and overall structure of the Level 4 IELTS-based reading exam. Nevertheless, a notable distinction emerged between high and moderate achievers in utilizing reading strategies, proficiency in comprehending complex vocabularies, and sustained engagement in independent learning. To address the challenges high and moderate achievers face, the study suggests implementing specific interventions tailored to their needs.

Enhanced trapping properties of coupled plasmonic tweezers via plasmon-exciton interaction

Excited plasmonic nanoantennas enable the manipulation of photons coupled with quantum emitters or the trapping of particles as plasmonic tweezers, leveraging the strong evanescent gradient fields at the nanoscale. However, the ohmic loss of metals presents a significant challenge for the stable and high-precision manipulation of nanoparticles without causing damage. In this study, we investigated the enhanced trapping properties induced by plasmon-exciton interaction for coupled plasmonic tweezers. Through the coupling between plasmons and excitons, dynamic particle trapping is achievable under low excitation power conditions of 0.45 mW, with the trapping stiffness increasing by nearly 20 times. Furthermore, the trapping stiffness can be fine-tuned by modulating the quantity of excitons to regulate the coupling strength. Coupled plasmonic tweezers offer an effective strategy to mitigate the influence of ohmic loss on trapping performance, by manipulating particles with minimal laser power. These findings provide insights into enhancing trapping performance through plasmon-exciton coupling, with potential applications in biomedicine and quantum information science.

Exploring DNA Analysis Methods and Genetic Research Applications in Low and Middle-Income Nations: A Study of Sri Lanka

Using Sri Lanka as a case study, the paper explores how DNA analysis has transformed genetic research, particularly in low and middle-income countries (LMICs). It discusses various DNA analyzing techniques, from traditional methods like Sanger sequencing to advanced techniques such as next-generation sequencing (NGS) and CRISPR-Cas9, highlighting their applications in disease research, population genetics, and forensic science. Sri Lanka's advancements in genetic research, including DNA sequencing, typing, and recent developments in X-chromosome-based DNA typing, are emphasized. The paper also examines challenges and opportunities in LMICs regarding genetic research and underscores the importance of DNA analysis in advancing personalized medicine and understanding genetic diversity. Additionally, it discusses Sri Lanka's efforts in education and training in molecular biology. It explores the country's rich genetic diversity and demographic history, focusing on ethnic studies and historical interactions among different population groups. Overall, the paper highlights the significance of DNA analysis in genetic research and its potential implications for LMICs. Sri Lanka is a notable example of progress in the field.

Research on the Current Situation and Calculation Method of Carbon Emissions Assessment for Building Curtain Walls

Curtain wall systems stand out as a pivotal domain within the construction sector’s endeavors towards energy efficiency and carbon mitigation. To refine the evaluation framework for carbon emissions within this industry, this paper explores the calculation and assessment method for building curtain walls. The article first reviews the current research status regarding carbon emissions from materials and the impact of curtain walls on buildings in the operational stage. Based on lifecycle theory, the carbon emissions from building curtain walls are divided into six stages: material acquisition, processing and production, installation and construction, transportation, use and maintenance, and dismantling. On this basis, this paper proposes a method for calculating carbon emissions from building curtain walls. Following that, a case study is conducted using a specific glass curtain wall project for illustrative analysis. The results indicate that the carbon emissions from the material acquisition stage constitute approximately 90% of the total, serving as the primary source of carbon emissions for glass curtain walls. Furthermore, the scientific application of photovoltaics can significantly reduce the carbon emission levels of building curtain walls. Finally, an analysis was conducted on the current issues existing in the evaluation of carbon emissions.

Assessment of Accuracy of Moderate-Resolution Imaging Spectroradiometer Sea Surface Temperature at High Latitudes Using Saildrone Data

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

Reducing training data needs with minimal multilevel machine learning (M3L)

For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating them with basis sets TZ, cc-pVTZ, and AVTZ-F12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of ∼1.01, 1.1, 3.8, 13.8, and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9 LCCSD(T) , Electrolyte Genome Project, QM9 AECCSD(T) , and QM9 EACCSD(T) , respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder: LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and τ-HCTH, B3LYP ∗ (VWN5), and TPSSH.

Unfolding of shocked hydrodynamic instability at SF[formula omitted] elliptical interface: Physical insights from numerical simulations

The Richtmyer–Meshkov (RM) instability widely exists in variety of industrial and scientific applications, such as in inertial confinement fusion, astrophysics explosions, supersonic combustion, bubble dynamics and cavitation. This study investigates the unfolding physical phenomena of RM instability at SF6 elliptical bubbles using numerical simulations. In contrast to cylindrical bubble, both shocked horizontal/vertical-aligned elliptical bubbles with different aspect ratios are considered, emphasizing the aspect ratio effects on flow morphology, complex wave patterns, interface deformation, vorticity generation, and interface features. For this purpose, a two-dimensional system of compressible Navier–Stokes-Fourier equations for multicomponent flows are solved using by a mixed modal discontinuous Galerkin method. Numerical simulations illustrate that the aspect ratio of elliptical bubbles have a significant influence on the vortex dynamics and the associated RM instability flows in contrast to the cylindrical bubble. In horizontal-aligned elliptical bubbles, some additional complex waves pattern, including inward jet and secondary vortex rings are observed. While, in vertical-aligned elliptical bubbles, the interface between two primary vortex rings is always vertical and has a “hippocampus” appearance due to the enhancement of downstream velocity. The baroclinic vorticity due to misalignment of density and pressure gradients at interface affects the bubble deformation and induces the Kelvin–Helmholtz instability, which leads to turbulent mixing. It is observed that the complexity of the vorticity fields is enhanced with increasing aspect ratios. Further, these aspect ratio effects result in integral diagnostic of important spatially integrated fields, and various interface features.

Assessing eDNA capture method from aquatic environment to optimise recovery of human mt-eDNA

Previous studies have shown that environmental DNA (eDNA) from human sources can be recovered from natural bodies of water, and the generation of DNA profiles from such environmental samples may assist in forensic investigations. However, fundamental knowledge gaps exist around the factors influencing the probability of detecting human eDNA and the design of optimal sampling protocols. One of these is understanding the particle sizes eDNA signals are most strongly associated with and the most appropriate filter size needed for efficiently capturing eDNA particles. This study assessed the amount of mitochondrial eDNA associated with different particle sizes from human blood and skin cells recovered from freshwater samples. Samples (300 mL) were taken from experimental 10 L tanks of freshwater spiked with 50 µL of human blood or skin cells deposited by vigorously rubbing hands together for two minutes in freshwater. Subsamples were collected by passing 250 mL of experimental water sample through six different filter pore sizes (from 0.1 to 8 µm). This process was repeated at four time intervals after spiking over 72 hours to assess if the particle size of the amount of eDNA recovered changes as the eDNA degrades. Using a human-specific quantitative polymerase chain reaction (qPCR) assay targeting the HV1 mitochondrial gene region, the total amount of mitochondrial eDNA associated with different particle size fractions was determined. In the case of human blood, at 0 h, the 0.45 µm filter pore size captured the greatest amount of mitochondrial eDNA, capturing 42 % of the eDNA detected. The pattern then changed after 48 h, with the 5 µm filter pore size capturing the greatest amount of eDNA (67 %), and 81 % of eDNA at 72 h. Notably, a ten-fold dilution proved to be a valuable strategy for enhancing eDNA recovery from the 8 µm filter at all time points, primarily due to the PCR inhibition observed in hemoglobin. For human skin cells, the greatest amounts of eDNA were recovered from the 8 µm filter pore size and were consistent through time (capturing 37 %, 56 %, and 88 % of eDNA at 0 hours, 48 hours, and 72 hours respectively). There is a clear variation in the amount of eDNA recovered between different cell types, and in some forensic scenarios, there is likely to be a mix of cell types present. These results suggest it would be best to use a 5 µm filter pore size to capture human blood and an 8 µm filter pore size to capture human skin cells to maximize DNA recovery from freshwater samples. Depending on the cell type contributing to the eDNA, a combination of different filter pore sizes may be employed to optimize the recovery of human DNA from water samples. This study provides the groundwork for optimizing a strategy for the efficient recovery of human eDNA from aquatic environments, paving the way for its broader application in forensic and environmental sciences.

TikTok for learning through movement, dramatization, and music: Insights for higher education applications in sport sciences

Social media has emerged as one of the most transformative digital phenomena that has reshaped education over the past two decades. New generations of students, often referred to as digital natives, increasingly utilise these platforms for socialization and interaction with their surrounding world. The pandemic prompted by the SARS-CoV-2 virus has further accelerated the adoption of online and hybrid teaching methodologies, wherein social media can assume a central role in creating dynamic teaching-learning environments. While the existing literature has highlighted the advantageous aspects of social media within the educational sphere, the landscape constantly evolves, witnessing the ongoing integration of new functionalities into existing platforms or the emergence of novel social media. TikTok serves as a prime illustration of this phenomenon, a social media platform centered on short videos complemented by music and its effects. Despite TikTok's burgeoning global user base, especially among younger people, its potential in the realm of higher education remains relatively unexplored. This pedagogical paper aims to address this gap in the literature by elucidating an experience involving TikTok within the context of sport sciences. Furthermore, it provides a series of guidelines and recommendations for university professors to incorporate TikTok into their instructional practices and delineates the potential applications of TikTok in various other contexts. These insights may serve as stepping stones for future investigation and educational undertakings.

Implementation science and psycho-oncology: Advancing the translation of evidence into practice.

This Special issue of Psycho-Oncology highlights examples of the application of implementation science to research in psycho-oncology. The aim is to demonstrate the different ways that implementation science is being used to generate evidence that can more readily translate evidence into changes in clinical practice. We hope this issue fosters greater interest in using the tools of implementation science to improve the lives of people affected by cancer. The papers in the issue were selected from among those that responded to a call for submissions on the application of implementation science frameworks and methods to issues in psycho-oncology. The focus included but was not limited to research on: understanding barriers and facilitators of intervention/practice adoption; assessing implementation outcomes, evaluating implementation strategies, and improving behavioural and/or clinical outcomes. The 11 papers in this issue were grouped for presentation purposes into four common topics: barriers and facilitators to implementation; feasibility as a key implementation outcome; the design, selection and adaptation of implementation strategies; and building the foundation for psycho-oncology research translation via systematic reviews that focus on implementation strategy design. These papers demonstrate the breadth of current applications of implementation science to research in psycho-oncology. Alongside the studies featured in this issue, including cost-effectiveness analyses, tests of nationally-focused strategies and proactive planning for adaptation, we look forward to other innovations that will promote further growth of both disciplines to improve the integration of psycho-oncology interventions across healthcare systems.