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Intramolecular charge transfer (ICT) of confined 1-tert-butyl-6-cyano-1, 2, 3, 4-tetrahydroquinoline (NTC6) is determined with a single time constant of ∼1 ps regardless of solvents, although ICT of 4-(dimethylamino)benzonitrile (DMABN) shows dispersive dynamics in solvents.

Nuclear microreactors (MRs) have attracted significant attention for their versatility in installation locations, especially with the growing demand for sustainable green energy in large data processing facilities. However, ensuring their safe operation necessitates frequent inspections, which can be challenging for practical efficient management. This study proposes a novel approach using direct energy deposition to incorporate an optical fiber sensor into MR components, enabling real-time monitoring with artificial intelligence. The embedded optical fiber generates real-time data that allows for AI-driven in-vivo thermal deformation analysis. Our smart MR component can detect structural anomalies, identify abnormal operations, and assess operational conditions through augmented reality interfaces and AI technology.

Abstract One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be misdiagnosed as a normal lesion by both primary care physicians and orthopedic surgeons; expert consultation is needed for accurate diagnosis. To overcome this limitation, we developed a transfer learning-based ensemble model to detect and localize fractures. A total of 1050 radiographs, including 100 incomplete fractures, were preprocessed by applying a Sobel filter. Six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) were selected for transfer learning. We then composed two ensemble models; the first was based on the three models having the highest accuracy, and the second was based on the five models having the highest accuracy. The area under the curve (AUC) of the case that used the three most accurate models was the highest at 0.998. This study demonstrates that an ensemble of transfer-learning-based models can accurately classify and detect fractures, even in an imbalanced dataset. This artificial intelligence (AI)-assisted diagnostic application could support decision-making and reduce the workload of clinicians with its high speed and accuracy.

David S Im, Carrie M Tamarelli Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan, USACorrespondence: David S Im, University of Michigan Hospital, Department of Psychiatry, University of Michigan Medical School, 1500 East Medical Center Drive, UH 9C 9150, Ann Arbor, MI, 48109-5120, USA, Tel +1 734 764-0227, Fax +1 734 936 9983, Email dvsm@med.umich.eduPurpose: Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by functionally impairing levels of inattention and/or hyperactive-impulsive behavior. It affects 3% to 5% of adults. This perspective piece aims to highlight the occurrence of ADHD in medical learners and physicians, including its reported prevalence in these groups, why reported rates may reflect underestimates, consequences of untreated symptoms, and a potentially helpful innovative educational tool to assist these individuals with a critical aspect of medical training and practice.Results: Despite recent attention being paid to concerning levels of depression, anxiety, and burnout in medical learners and physicians, comparatively little attention has been devoted to the occurrence of ADHD in these groups. Reported rates of ADHD in medical learners and physicians, though low compared to rates of other mental health conditions and compared to rates of ADHD in the general population, may represent underestimates for a variety of reasons. The consequences of untreated ADHD symptoms are likely numerous and significant for these groups. Research has shown that about half of adults with ADHD discontinue prescribed treatment (stimulant medication) over time due to lack of perceived effectiveness, highlighting the need for durable, effective interventions to help medical learners and physicians with ADHD during and after their training. An innovative educational tool to assist medical learners and physicians with ADHD with a critical facet of their training and practice – the reading of scientific articles – is proposed, including a description of the tool, rationale for its design, practical considerations around implementation, and proposed directions for future research.Conclusion: Untreated ADHD in medical learners and physicians may have numerous and significant consequences that can adversely impact training, practice, and ultimately patient care. These challenges warrant proper support for medical learners and physicians with ADHD via evidence-based treatments, program-based accommodations, and innovative educational tools.Keywords: ADHD, medical student, resident, learner, physician, education

Fused in sarcoma (FUS) is a DNA/RNA‐binding protein associated with amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration. The exact molecular mechanisms by which FUS results in neurotoxicity have not yet been fully elucidated. Here, we found that parkin is a genetic suppressor of defective phenotypes induced by exogenous human wild type FUS in Drosophila. Although parkin overexpression did not modulate the FUS protein expression level, the locomotive defects in FUS‐expressing larvae and adult flies were rescued by parkin expression. We found that FUS expression in muscle tissues resulted in a reduction of the levels and assembly of mitochondrial complex I and III subunits, as well as decreased ATP. Remarkably, expression of parkin suppressed these mitochondrial dysfunctions. Our results indicate parkin as a neuroprotective regulator of FUS‐induced proteinopathy by recovering the protein levels of mitochondrial complexes I and III. Our findings on parkin‐mediated neuroprotection may expand our understanding of FUS‐induced ALS pathogenesis. [ABSTRACT FROM AUTHOR]

Postannealed structure and electron transport properties of the magnetic tunnel junctions with preoxidized CoFe pinned electrode were compared with those of the conventional plasma oxidized junctions. The preoxidized junction exhibited its peak tunneling magnetoresistance ratio at 375 °C which is well above the optimal annealing of the normal junction. Using Auger electron spectroscopy and x-ray photoelectron spectroscopy of the thermally annealed junctions, structural and chemical changes after annealing were observed in the tunnel barrier as well as near the interface for both types of the junctions and these changes closely corresponded to the respective postannealed electrical properties. X-ray magnetic circular dichroism analysis indicated that the Co moment in the preoxidized CoFe electrode rose near the tunnel barrier/electrode interface as the optimal annealing temperature was reached. Our results demonstrated that the magnitude of spin polarized tunneling current is very sensitive to the interface structure and that any changes near the barrier interface during thermal annealing can greatly alter the electrical properties of the magnetic tunnel junctions. © 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to impart intelligence to metal parts using additive manufacturing. A sensor-embedded metal bracket is prototyped via a metal powder bed fusion process to recognise partial screw loosening or total screw missing or identify the source of vibration with the assistance of artificial intelligence (AI). The digital metal bracket can recognise subtle changes in the screw fixation state with 90% accuracy and identify unknown sources of vibration with 84% accuracy. The von Mises stress distribution in the prototyped metal bracket is evaluated using a finite element analysis, which is learned by AI to match the real-time deformation analysis of the metal bracket in augmented reality. The proposed prototype can contribute to hyper-connectivity for developing next-generation metal-based mechanical components.

Pipe-in-pipe (PIP) system is essential for high thermal and high pressure fluid transportation. However, in the existing PIP systems, fluid leakage between inner and outer pipe has been difficult to discover or detect, which has worked as bottle neck to utilize PIP system in high risk industries as nuclear reactor, chemical plant or oil drilling systems. Here, we propose a noble PIP leakage detection system utilizing distributed temperature sensing (DTS) with Machine Learning (ML). With the Fourier transformed spectrogram data from DTS, the ML assisted system was able to detect 0.2∼7 ml/min liquid leakage between inner and outer pipe with the accuracy of 91.67% with a single embedded optical fiber. Under varying operating temperature, the system successfully distinguished leakage and non-leakage states using the optimized convolutional neural network. Our developed PIP leakage detection system can be deployed in safety-critical industrial systems for autonomous leakage detection.

The core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, the cost and time required to optimise process parameters can be excessive in inducing good surface quality. To mitigate these challenges, we propose a novel method with artificial intelligence to generate virtual surface morphology of Ti-6Al-4V parts by given process parameters. A high-resolution surface morphology image generation system has been developed by optimising conditional generative adversarial networks. The developed virtual surface matches experimental cases well with an Fréchet inception distance score of 174, in the range of accurate matching. Microstructural analysis with parts fabricated with artificial intelligence guidance exhibited less textured microstructural behaviour on the surface which reduces the anisotropy in the columnar structure. This artificial intelligence guidance of virtual surface morphology can help to obtain high-quality parts cost-effectively.

Abstract Using energy‐saving electrochromic (EC) displays in smart devices for augmented reality makes cost‐effective, easily producible, and efficiently operable devices for specific applications possible. Prussian blue (PB) is a metal‐organic coordinated compound with unique EC properties that limit EC display applications due to the difficulty in PB micro‐patterning. This work presents a novel micro‐printing strategy for PB patterns using localized crystallization of FeFe(CN)6 on a substrate confined by the acidic‐ferric‐ferricyanide ink meniscus, followed by thermal reduction at 120 °C, thereby forming PB. Uniform PB patterns can be obtained by manipulating printing parameters, such as the concentration of FeCl3·K3Fe(CN)6, printing speed, and pipette inner diameter. Using a 0.1 M KCl (pH 4) electrolyte, the printed PB pattern is consistently and reversibly converted to Prussian white (CV potential range: −0.2–0.5 V) with 200 CV cycles. The PB‐based EC display with a navigation function integrated into a smart contact lens is able to display directions to a destination to a user by receiving GPS coordinates in real time. This facile method for forming PB micro‐patterns could be used for advanced EC displays and various functional devices.