Opportunities For Applications Research Articles

RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension

Abstract. The reduction of in situ observations over the last few decades poses a potential risk of losing important information in regions where local effects dominate the climatology. Reanalyses face challenges in representing climatologies with highly localized effects, especially in regions with complex orography. Empirical downscaling methods offer a cost-effective and easier-to-implement alternative to dynamic downscaling methods and can partially overcome the aforementioned limitations of reanalyses by taking into account the local effects through statistical relationships. This article introduces RASCAL (Reconstruction by AnalogS of ClimatologicAL time series), an open-source Python tool designed to extend time series and fill gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features using the analog method in combination with principal component analysis (PCA). The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allow fast and reproducible downscaling. The evaluations carried out in central Spain, in mountainous and urbanized areas, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis, as expected, in terms of R2, standard deviation, and bias. When analyzing reconstructions against observations, RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations. This confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, as with any other method based on empirical training, this method requires the availability of sufficiently long-term data series. Furthermore, it is susceptible to disruption caused by changes in land use or urbanization processes that might compromise the homogeneity of the training data. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project.

AI Methods and Algorithms for Diagnosis of Intestinal Parasites: Applications, Challenges and Future Opportunities

Artificial intelligence (AI) transforms intestinal parasite diagnosis, particularly through deep learning models like convolutional neural networks (CNNs). This paper reviews the application of AI, especially CNNs, in automating parasite detection and classification from microscopic images. Integrating AI into parasitology diagnostics speeds up the process, reduces human error, and enhances treatment and patient outcomes. However, there is a need for more datasets reflecting the African context to ensure accurate ground-truthing, particularly in low- and middle-income countries (LMICs) across Africa. Most AI models for medical diagnosis are trained on datasets from high-income countries, which may not capture the unique epidemiological, genetic, and environmental factors prevalent in African populations. This can lead to less accurate diagnoses and treatment recommendations in African LMICs. For example, intestinal parasitic infections are common in many African regions, yet the datasets used to train AI models often lack sufficient representation from these areas. Developing datasets that reflect the diverse African context is crucial for improving the accuracy and reliability of AI-based diagnostic tools. Issues like overfitting, data privacy, and cost also require attention. Collaboration between researchers, healthcare professionals, and technologists is essential to address these challenges. Standardized protocols for data collection, model training, and validation are necessary for reliable AI systems. Combining AI with traditional techniques holds promise for better parasite diagnosis, ultimately improving African healthcare outcomes

Thermal stability of γ-PGA modified fish gelatin emulsion.

Emulsions are thermally unstable systems. This research aimed to investigate the thermal stability of fish gelatin (FG) oil-in-water emulsions in the presence of poly-γ-glutamic acid (γ-PGA) as an additive after heat treatment. The study assessed how γ-PGA influences the thermal stability of FG emulsions over time, focusing on their properties, structure, and food application potential. The incorporation of γ-PGA significantly enhanced the thermal stability of FG emulsions, preserving their morphology after heating. Emulsions containing 0.1% γ-PGA showed no significant changes after 24 h at 90 °C, while emulsions without γ-PGA experienced noticeable delamination. Rheological evaluations revealed that the energy storage modulus and loss modulus of FG-γ-PGA emulsions remained consistently higher than those of FG emulsions, regardless of heating duration. Particle size analysis indicated minimal changes for FG-γ-PGA emulsions (413 nm after 24 h) compared to a substantial increase for FG emulsions (1598 nm). After heating, FG-γ-PGA emulsions demonstrated significantly higher emulsifying activity index (EAI) (74 m2 g-1 versus 22.7 m2 g-1) and emulsifying stability index (ESI) (97% versus 76%). Additionally, the texture properties of meat mince formulated with FG-γ-PGA emulsions were comparable to those containing fat, showcasing their potential as a fat replacement. The study concludes that γ-PGA enhances the thermal stability of FG emulsions, maintaining their integrity and improving functional properties under heat treatment. These findings offer valuable insights for the formulation of thermally stable emulsions, presenting promising opportunities for innovative applications in the food industry. © 2024 Society of Chemical Industry.

Improving learning experience through implementing standardized team-based learning process in undergraduate medical education

IntroductionTeam-Based Learning (TBL) is a highly structured active learning methodology shown to have positive learning outcomes in undergraduate medical education (UME). While many medical schools have increasingly adopted TBL, there is considerable variation reported in the literature in the implementation of the standard sequence and key components of TBL. The extent to which this variation affects the benefits of TBL is not well understood. This study aims to assess the impact of implementing the standardized framework of the TBL process on student perception of the benefits of TBL.MethodsFaculty at our institute were surveyed to determine the frequency of implementation of TBL standards, as described in the literature. Second-year osteopathic medical students (OMS II) were surveyed for their perception of the experience of TBL at the beginning of the academic year. Curricular interventions targeting the enforcement of the standard steps and key components of TBL were implemented in a particular 6-week system course. The students were re-surveyed at the end of the course. Descriptive and thematic analyses were performed on quantitative and qualitative data.ResultsTwenty four of 31 (77.4%) faculty participated in the faculty survey, 53 of 105 OMS II students (50.4%) participated in the pre-intervention survey, and 72 of 104 OMS II students (69.2%) participated in the post-intervention survey. Faculty survey results indicated inconsistent implementation of key steps and components of the TBL process. Comparisons of student pre- and post-survey means of the perceived value of various aspects of TBL showed significant improvement in multiple aspects of the TBL process, including readiness assurance tests, immediate feedback, and application activities. Both before and after the intervention, students highly rated the value of teamwork and opportunities for practical application of concepts within TBL.ConclusionWhile medical students value teamwork in UME and professional development, their perception of TBL can be less than positive if not conducted deliberatively. Assessment of the implementation of the standard TBL steps and components and development of corresponding targeted interventions may improve the TBL experience of medical students. Our process could be generalized to help educators interested in improving TBL in their home institutes.

The application of curriculum analytics for improving assessments and quality assurance in higher education

Curriculum mapping is a necessary process for establishing evidence of where learning outcomes are taught and assessed in higher education programmes. Mapping ensures the credibility of the institution and programme offerings and provides students with a clear understanding of what they can expect to learn and achieve during their academic studies. Well-mapped curricula that reference relevant standards and articulate aligned assessments are foundational for effective teaching and learning. Curriculum quality assurance measures are commonly accepted as a means of assuring accurate mapping. Assurance processes are, however, resource-intensive, error-prone and reflect human biases. Few studies have adopted an analytical approach to ensure programme quality assurance steps are traceable to a fundamental level of these maps: single courses and their assessment items. In this study, we adopted a novel curriculum analytics methodology to validate the mapping of professional standards to course-based assessments in a higher education degree program. Using an Australian initial teacher education programme as a case study, we applied and analysed results from a structured methodology that may be implemented across other disciplinary programs. Practical implications for quality assurance in higher education and the methodological contributions of this novel approach are further discussed. Implications for practice or policy: Quality assurance mechanisms in higher education serve to demonstrate that specific accreditation standards have been attained or maintained. An evidence-based approach is needed to ensure curriculum mapping and quality assurance. This study's novel curriculum analytics approach provides a robust foundation for demonstrating assessments’ compliance with accreditation standards at the degree program level. A data-driven approach for quality assurance presents opportunities for wider and scalable applications across other institutions.

Application scenarios and opportunities of autonomous pass-by measurements

Traditionally, pass-by measurements of vehicles were performed manually along the roadside. Recent developments allow for such measurements to be performed autonomously. Without the labour-intensive need of a measurement engineer at the spot, a variety of new applications for pass-by measurements become apparent. Beyond the application of the standardized SPB-measurement, a continuous and autonomous measurement of single vehicles can be used to detect high noise emitters. E.g., so-called posers or motorcycles increase the annoyance of inhabitants significantly. The detection of these single events is an important step to advance noise protection. Furthermore, the steady collection of data leads to numerous opportunities to describe and categorize vehicle and road noise. It can be used to further investigate the change of the acoustic properties of the roads in detail as well as those of the vehicles. The ongoing electrification of the vehicle fleet and the resulting changes in the noise behaviour can be scientifically tracked in real time. This progression affects next to the engine type (and sound) also the driving behaviour, the used tyres on the vehicle, and more. A continuous collection of pass-by measurements will deliver an increased understanding of the different influential factors of the vehicle-road-interaction.

Acoustic emission monitoring of a laser powder bed fusion process

Additive manufacturing (AM) metal parts offers opportunities for various industrial applications. From individual production of spare parts for unique mechanical components to prototyping of complex structures, the possibilities of production using the additive manufacturing process are manifold. One common AM technique is the Laser Powder Bed Fusion (PBF-LB/M) process, where a laser is used to selectively melt metal powder and create the parts layer wise as designed in a model. During manufacturing certain defects like pores, cracks and lack of fusion may be created in the built parts. As AM parts often have complex geometries, a postprocess non-destructive testing is difficult or even not possible. Thus, different optical monitoring techniques are applied to detect flaws during the build process with the aim to detect and repair defects right away during the manufacturing process. However, optical monitoring system require a clear view of the melt pool and quite expensive equipment. Acoustic monitoring by AE sensors attached to the built plate would be a cheaper alternative which also can be used without visual contact to the building chamber. This paper shows a first approach of an AE based process monitoring. The build plate of a PBF-LB/M machine therefore was adapted to hold four AE sensors that are then used to monitor the process. The build process itself creates AE signals caused by the melting and cooling and the mechanical application of powder. The formation of pores and cracks is expected to create additional acoustic emissions that will be distinct from the AE pattern from the printing process. This AE signals can be linked to the different layers of the printed part or in the long run to the laser position. Results from the first tests in a customized LPBF machine will be shown as well as the implementation of AE into the PBF-LB/M machine.

Evaluating Flood Early Warning System and Public Preparedness and Knowledge in Urban and Semi-urban Areas of Johor, Malaysia: Challenges and Opportunities

Flood Early Warning System (FEWS) play a crucial role in mitigating the impacts of floods by providing timely information to the public, which is particularly important for flood-prone areas in the Malaysian State of Johor that have suffer significant damages and disruptions almost every year. This research presents an assessment into the current state of FEWS through expert interviews and consultations with the concerned department to understand the management and institutional status and challenges in employing FEWS. In addition, a total of 106 structured questionnaires were administered as part of the survey research to a sample of residents in urban Johor Bahru and semi-urban Kota Tinggi, two regions vulnerable to floods, to collect data on their level of preparedness and knowledge, and their perceived effectiveness of FEWS. The analysis revealed that 69.8% of the respondents did not have any knowledge on the FEWS in their place, and 60.5% respondents turn to social media to gain information on flood warning, occurrence and forecast. This study also evaluates the opportunity for the messaging application as an additional support system for FEWS. The research highlights and identifies the challenges and barriers to accessing and utilizing FEWS. The study calls for the incorporation of messaging application, preferable WhatsApp to transmit early warnings to the end-users and to use their feedback in designing/redesigning the FEWS. The findings can inform policy and decision-making processes aimed at improving flood preparedness and response strategies, and contribute to the existing literature on FEWS in Malaysia and other similar regions.

Engineering inhibitory repeat domains of Pin-II type proteinase inhibitors indicate their high structural-functional tolerance to mutagenesis

Plant proteinase inhibitors (PIs) are critical in defending against biotic stress. Most PIs contain an inhibitory repeat domain (IRD), which serves as the functional component, displaying a high degree of sequence and structural conservation. In this study, we examined the structural and functional resilience of IRDs using a combination of computational modeling and experimental validation. We have taken an evolution-based approach to enhance the PIs effectiveness of two previously identified Capsicum annuum IRDs, IRD4 and IRD10. Through in silico site-saturation mutagenesis of IRD4 and IRD10, we identified key sites associated with enhanced PI activity for targeted mutagenesis. Binding energy predictions for a mutant IRD library, tested against target proteases, suggested that positions R11 and N32 in IRD4 and N32 and H33 in IRD10 were promising candidates for further modification to improve inhibitory potential. Subsequent experimental validation revealed that the mutant proteins IRD4_R11K and IRD4_N32S exhibited stronger chymotrypsin inhibition than the wild-type (WT) IRD4. Similarly, the mutants IRD10_N32S and IRD10_H33N demonstrated improved trypsin inhibition relative to the WT IRD10. These findings indicate that engineered IRD variants can tolerate structural changes while maintaining or enhancing their inhibitory activity against target proteases. Overall, this study demonstrates the potential of engineering PIs to increase their structural and functional resilience, offering new opportunities for biotechnological applications.

Artificial intelligence (AI) applications in healthcare and considerations for nursing education

Aim/objectiveTo review the current AI applications in healthcare and explore the implications for nurse educators in innovative integration of this technology in nursing education and training programs. BackgroundThere are a variety of Artificial Intelligence (AI) applications currently supporting patient care in many healthcare settings. A nursing workforce that leverages healthcare technology to enhance efficiency and accuracy of patient health outcomes is necessary. Nurse educators must understand the various uses of AI applications in healthcare to equip themselves to effectively prepare students to use the applications. DesignQualitative synthesis and analysis of existing literature. MethodsGenerative AI (ChatGPT) was used to support the development of a list of the current AI applications in healthcare. Each application was evaluated for relevance and accuracy. A literature review to define and understand the use of each application in clinical practice was completed. The search terms “AI” and “Health Education” were used to review the literature for evidence on educational programs used for training learners. ResultsTen current applications of AI in healthcare were identified and explored. There is little evidence that outlines how to integrate AI education into educational training for nurses. ConclusionA comprehensive multimodal educational approach that uses innovative learning strategies has potential to support the integration of AI concepts into nursing curriculum. The use of simulation and clinical practicum experiences to support experiential learning and to offer opportunities for practical application and training. Considerations for ethical use and appropriate critical evaluation of AI applications are necessary.

A space-adiabatic theorem for longitudinal and transversal wave motion analysis of graded metamaterials

The use of graded metamaterials is widespread in the fields of wave manipulation and energy harvesting. However, they have the drawback of causing energy scattering after a certain period due to wave reflection or trapping, known as the rainbow effect, in some cases. To address this issue, we extend the adiabatic theorem to the spatial modulation field to mitigate the impact of the rainbow effect, thereby enhancing transmission at the interface of graded metamaterials. Firstly, an analytical expression for the limiting gradient in the space-adiabatic theorem is derived. Then, it is applied to study the longitudinal wave motion in a spring-mass with graded resonators (SMGR) system and the transversal wave motion in a beam with graded resonators (BGR) system. The numerical study demonstrates that if the spatial gradient of system properties is small enough to satisfy the adiabatic theorem, wave scattering can almost be neglected, and transmission will experience a significant increase compared to the non-adiabatic case. Finally, the critical boundary between adiabatic and non-adiabatic cases is determined using the fitting method. This study introduces a design strategy for graded metamaterials, offering opportunities for communication, sensing, energy harvesting, and various other applications.

Nano-theranostics: A novel platform in drug therapy

A fundamental component of customized medicine, theranostics is a therapy approach that combines therapeutics and diagnostics with the goal of increasing drug efficacy and safety while also monitoring treatment response. It will require significant advancements in predictive medicine. Theranostics is linked to tailored drug administration based on test results as well as a diagnostic that screens patients for potential adverse drug reactions. Early diagnosis and treatment have benefited from the designed nanoparticles with unique physicochemical characteristics. This allows treatments to be delivered and the detection method to be employed concurrently, not just before or after treatment but throughout the entire regimen. The development of nanotechnology presents more opportunities for theranostics applications using nanoparticle engineering, and it has demonstrated encouraging outcomes, particularly in cancer therapy when compared with conventional treatments.

Applying the Panarchy Framework to Examining Post-Pandemic Adaptation in the Undergraduate Medical Education Environment: A Qualitative Study

Phenomenon: The COVID-19 pandemic necessitated an abrupt shift to online medical education, disrupting learning across knowledge, skills, and social connections. Post-pandemic, medical schools must evaluate how these disruptions shaped student experiences to optimize the return to in-person learning. Approach: This cross-sectional qualitative study explored medical students’ perceptions of their learning environment during the post-pandemic reintegration period in Oman. Fifty-four preclinical and clinical students participated in six focus group interviews. Content analysis identified key topics characterizing students’ perceptions of change and change processes in the post-pandemic learning environment. The panarchy framework, developed to characterize complex adaptive systems in nature, was used to frame the results. Findings: The return to in-person environments presented a mix of renewed connectivity, involving collaborative benefits alongside transitional adjustment strains. Five major topics characterizing student perceptions of change and change processes in their post-pandemic learning environment were identified: learning skills, developing clinical competence, faculty interactions, physical atmosphere, and social connections. Managing academic schedules and cognitive load as the learning environment opened challenged students’ learning skills. Learners valued a renewed opportunity for interactive application of knowledge through collaboration, patient contact, and empathy skill-building to feel prepared for future practice. Returning to in-person instruction renewed a sense of community and peer support networks disrupted by pandemic isolation. Some students continued to struggle with study-life imbalance and felt ill-equipped to handle post-pandemic demands. Improved access to student support and wellness services was emphasized to ease transitional stresses. Students defined an ideal learning climate as supportive, active, personalized, relevant, challenging, accessible, and collaborative. Insights: While pandemic disruptions posed challenges, they provide opportunities to strengthen the educational system’s resilience moving forward. Our findings highlight an opportunity for medical educators and learners to capitalize on the innovations that emerged during this period, integrating technology with interactive learning activities and reconnecting students with the core values of the medical profession. Applying the panarchy framework to frame this adaptive process could enable the tracking of multi-level interactions within the medical education environment and the evaluation of interventions targeted at identified areas of concern. Further exploration to achieve complete mapping of specific environmental domains onto the panarchical cycles merits future investigation to build integrated resilience frameworks.

Mechanically adaptive and deployable intracortical probes enable long-term neural electrophysiological recordings

Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications.

All-fiber miniature non-contact photoacoustic probe based on photoacoustic remote sensing microscopy for vascular imaging in vivo.

Photoacoustic (PA) remote sensing (PARS) microscopy represents a significant advancement by eliminating the need for traditional acoustic coupling media in PA microscopy (PAM), thereby broadening its potential applications. However, current PARS microscopy setups predominantly rely on free-space optical components, which can be cumbersome to implement and limit the scope of imaging applications. In this study, we develop an all-fiber miniature non-contact PA probe based on PARS microscopy, utilizing a 532-nm excitation wavelength, and showcase its effectiveness in in vivo vascular imaging. Our approach integrates various fiber-optic components, including a wavelength division multiplexer, a mode field adaptor, a fiber lens, and an optical circulator, to streamline the implementation of the PARS microscopy system. Additionally, we have successfully developed a miniature PA probe with a diameter of 4 mm. The efficacy of our imaging setup is demonstrated through in vivo imaging of mouse brain vessels. By introducing this all-fiber miniature PA probe, our work may open up new opportunities for non-contact PAM applications.

Legal education and artificial intelligence: vectors of interaction

Objective: to develop proposals for the introduction of artificial intelligence (AI) in legal education. Methods: dialectical methods (analysis and synthesis, induction and deduction, systematization, comparison, classification, forecasting), statistical, formal-legal and comparative-legal methods. Used in combination, these methods allow comprehensive analysis of the relationship between AI and legal education. Results: the article reveals the main directions to improve legal education in the conditions of AI development: integration of information and communication technologies and training of specialists able to work effectively at the intersection of law and technology. The advantages and disadvantages of using AI in legal education are identified. Ethical aspects of AI application are outlined, as well as the need to manage the development of technologies based on the principles of fairness, transparency and consideration of human interests. The author states the importance of developing not only specialized knowledge, but also universal competencies that will help students to adapt successfully to the dynamically changing conditions of professional activity. Scientific novelty: a comprehensive analysis of the interaction between legal education and AI was carried out, including the identification of specific opportunities for the AI application in legal education, as well as related risks and problems. The author proposes a systematic approach to improving legal education in the context of AI development, focusing on theneed to revise educational programs in legal areas in terms of integrating information and communication technologies; developing universal competencies in students to adapt to a dynamically changing professional environment; introducing mandatory advanced training for judges, legal practitioners to develop skills for using AI systems. Practical significance: the study results can be used to optimize and adapt the educational programs of law schools to the digital era requirements and to develop effective approaches to the AI application in legal practice and education, taking into account ethical aspects and potential risks.

A new method for quantifying glyoxalase II activity in biological samples.

Glyoxalase II (Glo II) is a crucial enzyme in the glyoxalase system, and plays a vital role in detoxifying harmful metabolites and maintaining cellular redox balance. Dysregulation of Glo II has been linked to various health conditions, including cancer and diabetes. This study introduces a novel method using 2,4-dinitrophenylhydrazine (2,4-DNPH) to measure Glo II activity. The principle behind this approach is the formation of a colored hydrazone complex between 2,4-DNPH and pyruvate produced by the Glo II-catalyzed reaction. Glo II catalyzes the hydrolysis of S-D-lactoylglutathione (SLG), generating D-lactate and reduced glutathione (GSH). The D-lactate is then converted to pyruvate by lactate dehydrogenase, then reacting with 2,4-DNPH to form a brown-colored hydrazone product. The absorbance of this complex, measured at 430 nm, allows for the quantification of Glo II activity. The study rigorously validates the 2,4-DNPH method, demonstrating its stability, sensitivity, linearity, and resistance to interference from various biochemical substances. Compared to the existing UV method, this 2,4-DNPH-Glo II assay shows a strong correlation. The new protocol for measuring Glo II activity using 2,4-DNPH is simple, cost-effective, and accurate, making it a valuable tool for researchers and medical professionals. Its potential for widespread use in various laboratory settings, from academic research to clinical diagnostics, offers significant opportunities for future research and medical applications.

Theory - Practice integration for Chinese Postgraduates in Pathophysiology: Innovative Teaching Strategies

Background: Pathophysiology plays a pivotal role in medical education by bridging basic sciences and clinical medicine. However, traditional teaching approaches in China often lack integration between theory and clinical practice, hampering postgraduates' understanding of disease mechanisms and diagnostic abilities. This study explores innovative strategies to effectively integrate theory and practice, enhancing Chinese medical postgraduates' learning.Methodology: A comprehensive literature review analyzed the current educational landscape and identified innovative teaching strategies aligning theoretical concepts with clinical applications. The implementation methods and potential to foster autonomous and innovative learning were examined.Results: Several student-centred strategies were identified, including problem-based learning (PBL), case-based learning (CBL), flipped classrooms, and integrated teaching models. These approaches emphasize active learning, critical thinking, and applying theoretical knowledge to clinical scenarios. Adopting these strategies can improve postgraduates' learning efficiency, problem-solving abilities, and clinical reasoning skills while promoting self-directed learning and innovative thinking.Conclusion: Integrating theory and practice through innovative teaching strategies is crucial to enhance the quality of pathophysiology postgraduate education in China. Educators should implement student-centred approaches like PBL, CBL, flipped classrooms, and integrated models to stimulate autonomous learning and cultivate innovation. Providing opportunities for active learning and clinical application can significantly improve postgraduates' learning outcomes and preparedness for future practice. Further research evaluating the long-term impact on postgraduate performance and patient care is warranted.

Robust optical picometrology through data diversity

Topologically structured light contains deeply subwavelength features, such as phase singularities, and the scattering of such light can therefore be sensitive to the geometry or movement of scattering objects at such scales. Indeed, it has been shown recently that single-shot optical measurements can yield positional precision better than 100 pm (less than one five-thousandth of the wavelength λ) via a deep-learning-enabled analysis of scattering patterns. Measurement performance, and the extent to which it can be sustained, are constrained by the quality and depth of neural network training data and the stability of the experimental apparatus. Here, we show that a neural network can be trained through exposure to an extended envelope of instrumental/ambient noise conditions to robustly quantify picometric displacements of a target against orders-of-magnitude larger background fluctuations, to maintain precision and accuracy of 100–150 pm in optical measurements (at λ = 488 nm) of nanowire positional change. This capability opens up a range of application opportunities, for example in the optical study of nanostructural dynamics, stiction, material fatigue, and phase transitions.

Esterification-Induced Spontaneous Deposition of Nanoparticle Monolayers on Substrates with Percolation Network Morphology.

Nanoparticle monolayers (NPMLs) exhibit unique collective properties that are highly desirable for applications in sensors, catalysts, and optics. However, their practical use is often hindered by structural instability, especially when they are exposed to solvents. In this study, we developed a method for the spontaneous deposition of gold nanoparticle monolayers (AuNPMLs) on silicon substrates via covalent bonding interactions that provides excellent structural stability in solvents with varying polarities. The esterification reaction between carboxyl-functionalized gold nanoparticles and alkyl-chloride-functionalized silicon substrates spontaneously forms AuNPMLs on the substrate in which single-crystalline domains are interconnected, forming a percolation network morphology. The ex situ scanning electron microscopy measurements show that the surface coverage of AuNPMLs rapidly increases up to ca. 60% during the initial 10 min, followed by gradual growth and saturation at ca. 70% at 360 min of deposition time. On the other hand, the single-crystalline domain size reaches its maximum at 45 min and then gradually decreases, which may be attributed to the desorption of AuNPs by the hydrolysis of ester bonds. The reflectance spectra of AuNPMLs showed the red shift as the deposition time increases up to 45 min with a subsequent blue shift thereafter, which is consistent with the change of the single-crystalline domain size with the deposition time. The covalent bonding interaction-mediated nanoparticle deposition method can be used to form stable AuNPMLs with controlled surface coverage and domain size, allowing for fine control of the optical properties and possibly other properties. The excellent structural stability of AuNPMLs and their controlled properties may provide new opportunities for practical applications of NPMLs.