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Obtaining multiple series of magnetic resonance (MR) images with different contrasts is useful for accurate diagnosis of human spinal conditions. However, this can be time consuming and a burden on both the patient and the hospital. We propose a Bloch equation-based autoencoder regularization generative adversarial network (BlochGAN) to generate a fat saturation T2-weighted (T2 FS) image from T1-weighted (T1-w) and T2-weighted (T2-w) images of human spine. To achieve this, our approach was to utilize the relationship between the contrasts using Bloch equation since it is a fundamental principle of MR physics and serves as a physical basis of each contrasts. BlochGAN properly generated the target-contrast images using the autoencoder regularization based on the Bloch equation to identify the physical basis of the contrasts. BlochGAN consists of four sub-networks: an encoder, a decoder, a generator, and a discriminator. The encoder extracts features from the multi-contrast input images, and the generator creates target T2 FS images using the features extracted from the encoder. The discriminator assists network learning by providing adversarial loss, and the decoder reconstructs the input multi-contrast images and regularizes the learning process by providing reconstruction loss. The discriminator and the decoder are only used in the training process. Our results demonstrate that BlochGAN achieved quantitatively and qualitatively superior performance compared to conventional medical image synthesis methods in generating spine T2 FS images from T1-w, and T2-w images.

Purpose: This study aims to identify people's perceptions of what behaviourally differentiates effective managers from ineffective managers within a South Korean (SK) public sector organization, and the extent to which the findings are similar or different to those of an equivalent previous study in the SK private sector. Design/methodology/approach: Adopting the "pragmatic approach" and assuming a post-positivist ontology and constructivist-interpretivist epistemology, examples of "effective" and "ineffective" managerial behaviour were collected from managers and non-managerial employees in an SK central government Ministry using the critical incident technique. The collected critical incidents were coded, classified and reduced to a smaller number of behavioural categories. These were then compared against equivalent findings from a previous SK private sector replication study using open, axial and selective coding to identify generic behavioural criteria (GBCs). Findings: High degrees of convergence point towards the emergence of a "two-factor" SK behavioural taxonomy of perceived managerial and leadership effectiveness comprised of positive (n = 11) and negative (n = 4) GBCs of effective and ineffective managerial behaviour. Practical implications: The GBCs constituting the deduced SK behavioural taxonomy could be used by HRD practitioners to critically evaluate the efficacy of extant management and leadership development (MLD) programmes, or to inform/shape the creation of new MLD programmes. Additionally, they could be used by other HR professionals to critically evaluate the relevance and efficacy of the assessment criteria used for existing management selection, 360-degree feedback and formal performance appraisal systems. Originality/value: The emergence of an SK behavioural taxonomy through Type 3 (emic-as-emic) and Type 4 (emic-and-etic) indigenous research is a rare example of Eastern mid-range theory development.

Purpose: The purpose of this study was to apply social exchange and organizational support theories to examine the relationship between mid-level strategic enrollment managers' perceptions of managerial coaching behaviors enacted by their senior managers and their own reported job engagement, as mediated by perceived organizational support (POS) within the US higher education context. Design/methodology/approach: A quantitative survey-based half-longitudinal design, which used the latent marker variable technique, was conducted with a sponsoring professional organization in the strategic enrollment management (SEM) field in the USA. A total of 301 usable surveys were analyzed using structural equation modeling. Findings: The results of this study indicate that SEM managers' job engagement and the perceived managerial coaching behaviors provided to them by their senior managers were positively correlated, and that POS fully mediated this relationship. These findings highlight how coaching behaviors may allow managers to elicit positive emotional responses and, by fostering enhanced POS, ultimately enhance job engagement among their team members. Originality/value: This study addresses several calls for research on managerial coaching, job engagement and POS in an under-examined higher education context within the human resource development field.

Wheat (Triticum aestivum L.) omega-5 gliadin, a major allergen responsible for wheat-dependent exercise-induced anaphylaxis in humans, is encoded by genes located at the Gli-B1 locus on chromosome 1B, which exhibits genetic polymorphism. Gli-B1 alleles have generally been identified based on the electrophoretic mobilities of the encoded gamma-, omega-1,2, and omega-5 gliadins in acid polyacrylamide gel electrophoresis. However, the similar mobilities of omega-5 gliadin variants make it difficult to distinguish them among different wheat varieties. In this study, we optimized reverse-phase–ultra-performance liquid chromatography (RP-UPLC) conditions to separate omega-5 gliadins in the reference wheat cultivar Chinese Spring and its nullisomic–tetrasomic lines for chromosome 1B. Five chromatographic peaks corresponded to omega-5 gliadin, and the average relative standard deviation to each peak retention time ranged from 0.31% to 0.93%, indicating that the method is accurate and reproducible for fractionating omega-5 gliadins in gliadin extracts from wheat flour. Using the optimized RP-UPLC method, we analyzed omega-5 gliadins in 24 wheat varieties with the Gli-B1f allele. The result showed that the wheat varieties were sorted into eight groups according to the composition of omega-5 gliadin, indicating that the classification of Gli-B1 alleles based on A-PAGE could not explain the composition of omega-5 gliadin in wheat. We reclassified 73 wheat varieties containing 16 unique Gli-B1 alleles into 31 groups based on the chromatographic patterns of their omega-5 gliadins. Our results provide information on the specific Gli-B1 alleles of wheat varieties belonging to each group and demonstrate the potential for RP-UPLC to facilitate genetic studies of wheat varieties. [ABSTRACT FROM AUTHOR]

To reliably operate anode-less solid-state Li metal batteries, wherein precipitated Li acts as the anode, stabilizing the interface between the solid electrolyte and electrode is crucial. The interface can be controlled by a metal interlayer on the electrolyte to form a Li alloy buffer that facilitates stable Li plating/stripping, thereby mitigating the loss of physical contact and preventing short circuits. However, the mechanism governing stable Li plating/stripping in the metal interlayer without degrading battery materials remains unclear owing to an incomplete understanding of the dynamic and complex electrochemical reactions in the solid state. Through multiple operando and post-mortem analyses of the Li deposition behavior in the morphology, chemistry and microstructure, a close correlation is found between the Li-metal alloying process with the microstructural evolution and electrochemical performance when Ag, Au, Zn, and Cu interlayers were adopted on the garnet-type solid electrolyte Li6.5La3Zr1.5Ta0.5O12. The Ag interlayer improved the interfacial stability enabled by Ag-dissolved Li, which inhibited dendritic growth, passing through the phase-separated Li-Ag alloy microstructure, while the other metals did not because of the Li plating at the Li-metal alloy/solid electrolyte interface. This work provides fundamental guidance for material selection and interface design, advancing the development of anode-less solid-state batteries. To ensure the reliable operation of anode-less solid-state lithium metal battery, herein, the authors report the role of metal interlayer as the interface control strategy for achieving the stable lithium plating and stripping using the multiple operando and in-situ analysis. [ABSTRACT FROM AUTHOR]

The article discusses the evolving field of Human Resource Development (HRD) and its connection to public policy frameworks at national, state/provincial, and local levels. It highlights the importance of integrating public policy into HRD practices, emphasizing the need for HRD researchers and practitioners to develop strategies aligned with national priorities. The text also explores how public policies shape HRD frameworks, drive program design and implementation, and influence workforce sustainability in the technological era. Additionally, it underscores the role of HRD professionals in contributing to policy discussions and workforce development reforms, particularly in promoting social equity goals. [Extracted from the article]

We propose a new deep learning network capable of successfully segmenting intervertebral discs and their complex boundaries from magnetic resonance (MR) spine images. The existing U-network (U-net) is known to perform well in various segmentation tasks in medical images; however, its performance with respect to details of segmentation such as boundaries is limited by the structural limitations of a max-pooling layer that plays a key role in feature extraction process in the U-net. We designed a modified convolutional and pooling layer scheme and applied a cascaded learning method to overcome these structural limitations of the max-pooling layer of a conventional U-net. The proposed network achieved 3% higher Dice similarity coefficient (DSC) than conventional U-net for intervertebral disc segmentation (89.44% vs. 86.44%, respectively; p < 0.001). For intervertebral disc boundary segmentation, the proposed network achieved 10.46% higher DSC than conventional U-net (54.62% vs. 44.16%, respectively; p < 0.001).

We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user's role while achieving good segmentation accuracy.

This study reports a facile and reliable operando X‐ray photoelectron spectroscopy (XPS) system for the characterization of the chemical state and electronic structure during Li‐plating/stripping in all‐solid‐state batteries (ASSBs). The measurement conditions (including manufacturing an in situ sample holder) and experimental parameters have been optimized through experimentation. The proposed system accurately defines the status of every metal interlayer component during real‐time battery operation (with simultaneous Li+‐ion in/out migration) under a repeating Li‐plating/stripping process. Moreover, it enables elucidation of ambiguous reaction mechanisms depending on the battery performance, such as the interlayer‐material effect on the efficiency and reversibility of the Li‐plating/stripping process, and changes in the oxygen‐containing bonds due to reaction with Li. The significance of the developed system is further validated by the usefulness and reliability of the valence band structure as a criterion for the analysis of lithium‐metal alloy formation and the progress of de‐alloying reactions. [ABSTRACT FROM AUTHOR]

This paper presents a measurement method that utilizes object recognition technology for continuous and quantitative real‐time monitoring of water levels in industrial boilers. Real‐time videos of water levels were monitored using a small camera, and the YOLO algorithm, a single‐stage detector, was employed to use the bounding boxes of detected objects within the video as variables, directly measuring the length ratio for each frame. The method demonstrated a high level of accuracy in water‐level measurement, with an average of 99.02%, and a stable performance, with a fluctuation of 0.13% in continuous measurements. Consequently, the proposed measurement method proves feasible for quantifying continuous water levels in industrial inspection systems even in low‐resource environments. These results demonstrate a new mechanism for monitoring technology, without requiring text detection, showing the potential for improving efficiency in complex boiler systems and the feasibility of reliable water‐level measurement and control. [ABSTRACT FROM AUTHOR]

This editorial article examines how generative Artificial Intelligence (GAI) can bridge various human resource development (HRD) processes. As GAI adoption increases in human resources practices, understanding its potential to integrate different HRD activities becomes more important. The article reviews recent literature on Artificial Intelligence (AI) applications in HRD and explores GAI‐enabled links between key HRD processes. The linkages include data‐driven decision‐making, real‐time skill gap analysis, job crafting with GAI, GAI‐supported personalized development plans, GAI‐powered employee sentiment analysis, GAI chatbots, GAI‐enabled virtual reality simulations, and GAI‐supported social network analysis in talent and organization development contexts. By highlighting these GAI‐enabled interconnections, the article provides insights into a more integrated approach to HRD. It also discusses implications for HRD practitioners and researchers, analyzing specific applications of GAI in HRD and recommending future research. [ABSTRACT FROM AUTHOR]

Human lumbar spine is composed of multiple tissue components that serve to provide structural stability and proper nutrition. Conventional magnetic resonance (MR) imaging techniques have been useful for evaluation of IVD, but inadequate at imaging the discovertebral junction and ligamentous tissues due primarily to their short T2 nature. Ultrashort time to echo (UTE) MR techniques acquire sufficient MR signal from these short T2 tissues, thereby allowing direct and quantitative evaluation. This article discusses the anatomy of the lumbar spine, MR techniques available for morphologic and quantitative MR evaluation of long and short T2 tissues of the lumbar spine, considerations for T2 relaxation modeling and fitting, and existing and new techniques for spine image post-processing, focusing on segmentation. This article will be of interest to radiologic and orthopaedic researchers performing lumbar spine imaging.

BackgroundAtopic dermatitis (AD) is characterized by pruritic and eczematous skin lesions, which often cause depressive symptoms, anxiety, stress, sleep disturbances, social withdrawal, and stigmatization.MethodsIn total, 23,442 subjects (434 AD patients and 23,008 control subjects) aged 19 years or older and without a history of major medical illness or depressive disorders were selected from The Fifth Korea National Health and Nutrition Examination Survey 2007-2012. Following the initial selection, 2,170 age- and sex-matched control subjects were selected using 1:5 propensity score matching. Multiple logistic regression analysis was performed to identify the presence of depressive symptoms of at least 2 weeks in duration.ResultsThe demographic, socioeconomic, and clinical characteristics of AD patients and control subjects were presented and compared, and some variables differed significantly between groups. Presence of depressive symptoms was set as dependent variable, and multiple logistic regression analysis was performed as follows: (1) unadjusted; (2) with alcohol use, exercise status, smoking status, and body mass index (BMI) adjusted for; and (3) with alcohol use, exercise status, smoking status, marital status, occupation, BMI, total caloric intake, history of hypertension, and history of diabetes mellitus adjusted for. Depressive symptoms were significantly higher (odds ratios, 1.46, 1.40, and 1.36; 95% confidence intervals, 1.09-1.95, 1.0.4-1.88, and 1.01-1.85, respectively) in AD patients relative to those of matched controls.ConclusionAD and clinical depression interact closely, and causal relationships between the two conditions have frequently been observed. Physicians should consider mental health interventions cautiously. It is particularly important that primary care physicians provide comprehensive, continuous long-term care.

As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at the same time, the problem arises that the AI-based models cannot explain the rationale of their predictions to users. To address this issue, we propose a time-series explanatory fault prediction framework to provide an explainability even when using AI-based fault prediction models. It consists of a data feature reduction process, a fault prediction model training process using long short-term memory, and an interpretation process of the fault prediction model via an explainable AI method. In particular, the proposed framework can explain a fault prediction based on time-series data. Therefore, it indicates which part of the data was significant for the fault prediction not only in terms of sensor type but also in terms of time. Through extensive experiments, we evaluate the proposed framework using various fault data by comparing the prediction performance of fault prediction and by assessing how well the main pre-symptoms of the fault are extracted when predicting a fault. [ABSTRACT FROM AUTHOR]