Complementary Nature Research Articles

Theoretical foundations for service innovation research in sport management – insights from nonprofit sport organisations

ABSTRACT Research question: Research on service innovation in sport is not theoretically aligned with service innovation theory. To address this, we conduct an empirical study utilising major service innovation theoretical approaches. We address the question: How do nonprofit sport organisations engage in service innovation? Research methods: We combine existing service innovation frameworks to guide service innovation research in the nonprofit sport context. We apply it in a qualitative study, collecting data via 20 semi-structured interviews with Australian golf club managers. Data analysis relied on an inductive–deductive strategy. Results and findings: We found evidence for practices following service innovation archetypes, with a propensity for the output-based and process-based archetypes, and less evidence for the systemic and experiential service innovation archetypes. Service innovation practices centred on provision and representational practices and were less dependent on management and organisational practices. Implications: First, we demonstrate the complementary nature of the dominant and diverging theoretical approaches to service innovation. Second, we propose and provide empirical support for a combined conceptual framework for analysing service innovation. Finally, our research provides concrete managerial recommendations for stimulating service innovation within organisations and beyond.

An Autoethnography of an Islamic Teacher Education Programme

This article explores Islamic Teacher Education through an autoethnographic account of the author’s experience with the Graduate Certificate of Education (Islamic Pedagogy) at the University of South Australia. It addresses the lack of research on how Islamic Pedagogy is taught, contributing to the growing scholarship on faith-based teacher education. Autoethnography is a qualitative research method that combines autobiography and ethnography, emphasising personal experiences to explore cultural communities. It is especially useful in studying emerging concepts like Islamic Pedagogy and Faithful Praxis. This approach challenges Western positivism, promoting epistemic reflexivity, and offering critical insights into marginalised perspectives and educational practices. This paper employs autoethnography to present the author’s Faithful Praxis journey as a transformative pedagogical shift, shaped by their experiences with Western and Islamic epistemologies, aiming to empower Muslim voices in education and challenge marginalization, with the Graduate Certificate fostering epistemic reflexivity and providing a platform to reconcile Islamic and Western knowledge in the classroom. This paper also clarifies the distinction between Islamic Pedagogy and Islamic Integration through autoethnography by highlighting their complementary nature as opposed to the author’s initial assumptions around their interchangeability. Whilst this article contributes to the growing Islamic Teacher Education scholarship through an autoethnographic perspective, further research to assess broader program efficacy is still needed.

Enhancing corrosion resistance in HCl environments: Comparative efficacy of a conjugated 3ph-Schiff base versus benzotriazole

This research addresses the need for effective corrosion inhibitors in industrial applications, evaluating the performance of 4-nitro-2-(((1E,2E)-3-phenylallylidene)amino)aniline (noted as 3ph-Schiff base) as a corrosion inhibitor in comparison to benzotriazole (BTA) in a high-concentration 3 M HCl environment. Unlike studies conducted in lower concentrations (e.g. 0.1 and 1 M), this study aims to assess inhibitor performance under more aggressive conditions. Rigorous methods, including Tafel polarisation, electrochemical impedance spectroscopy (EIS), and mass loss measurements, revealed variations in inhibition efficiency (IE). Polarisation data indicated that 3ph-Schiff base at 0.3% (approximately 8.96 mM) achieved a maximum IE of 99.5%, surpassing BTA at the same concentration (approximately 25.2 mM), which recorded an IE of 96.9%. Meanwhile, EIS analysis yielded slightly lower IE values, with 3ph-Schiff base reaching 90.21% and BTA 67.39%, reflecting the complementary nature of these methods. The corrosion inhibition mechanism of 3ph-Schiff base is attributed to strong adsorption on the metal surface, where amino (NH2) and nitro (NO2) groups promote stable complex formation with metal ions, enhancing electron donation and forming a protective layer that blocks corrosive agents. Scanning electron microscopy and elemental mapping confirm uniform nitrogen and carbon surface coverage, integral to the protective layer. This layer acts as both a physical barrier and an electrochemical passivator, effectively reducing anodic and cathodic reactions. The combined effects of adsorption, physical blocking and passivation underscore the 3ph-Schiff base's superior performance as a corrosion inhibitor in acidic environments.

An Integrated Segmentation Framework Utilizing Mamba Fusion for Optical and SAR Images

ABSTRACTSemantic segmentation of multimodal remote sensing images is an effective approach to enhancing segmentation accuracy. Optical and synthetic aperture radar (SAR) images capture ground features from distinct perspectives, offering varied information for ground observation. Effectively fusing these two modalities information and performing multimodal segmentation remains a promising yet challenging task due to their complementary nature and significant differences. The existing methods have the problem of ignoring spatial dimension information and being unable to bridge semantic gaps. However, the proposal of Selective Structured State Space Models (Mamba) provides opportunities for multimodal fusion. Therefore, we propose a segmentation framework based on Mamba fusion of optical and SAR images. The framework introduces a novel fusion module inspired by the principle of Mamba. This module selects effective features from different modalities for cross‐fusion within a global sensing field, facilitating the mutual compensation of optical and SAR image features and reducing the semantic gap. The fused features are then accurately segmented using a decoder that incorporates the Atrous Spatial Pyramid Pooling (ASPP) technique. On the WHU‐OPT‐SAR dataset, this method outperforms other state‐of‐the‐art deep learning approaches, achieving an overall accuracy (OA) of 84.13%, compared to 76.29% for the Kappa statistic.

The collaborative role of K-Shell and PageRank for identifying influential nodes in complex networks

Finding the most influential nodes in complex networks is a significant challenge with applications in various fields, including social networks, biology, and transportation systems. Many existing methods rely on different structural properties but often overlook complementary features. This paper highlights the complementary nature of K-Shell and PageRank and proposes a novel linear metric that combines them. Through extensive comparisons of 19 real-world and several artificial networks, the proposed method demonstrates superior accuracy, resolution, and computational efficiency. Evaluations against 11 state-of-the-art methods, including IDME, HGSM, and DNC, underscore the superiority of the proposed approach. Notably, the average accuracy has increased by 33.3% compared to PageRank and 23.1% compared to K-Shell, emphasizing the importance of integrating these two features.

Corrosion State Monitoring Based on Multi-Granularity Synergistic Learning of Acoustic Emission and Electrochemical Noise Signals

Corrosion monitoring is crucial for ensuring the structural integrity of equipment. Acoustic emission (AE) and electrochemical noise (EN) have been proven to be highly effective for the detection of corrosion. Due to the complementary nature of these two techniques, previous studies have demonstrated that combining both signals can facilitate research on corrosion monitoring. However, current machine learning models have not yet been able to effectively integrate these two different modal types of signals. Therefore, a new deep learning framework, CorroNet, is designed to synergistically integrate AE and EN signals at the algorithmic level for the first time. The CorroNet leverages multimodal learning, enhances accuracy, and automates the monitoring process. During training, paired AE-EN data and unpaired EN data are used, with AE signals serving as anchors to help the model better align EN signals with the same corrosion stage. A new feature alignment loss function and a probability distribution consistency loss function are designed to facilitate more effective feature learning to improve classification performance. Experimental results demonstrate that CorroNet achieves superior accuracy in corrosion stage classification compared to other state-of-the-art models, with an overall accuracy of 97.01%. Importantly, CorroNet requires only EN signals during the testing phase, making it suitable for stable and continuous monitoring applications. This framework offers a promising solution for real-time corrosion detection and structural health monitoring.

THE MADHHABİAN TENDENCY OF AHMAD YASSAWİ

Khoja Ahmad Yassawi (1041-1166), who lived in the region of Turkestan (Central Asia) in the 11th and 12th centuries, was a highly respected and influential scholar among the Turkic peoples, who formed the cornerstones of Turkish national identity, and was also the founder of Turkish Sufism. We see that the Sufi activity caused the Turkic peoples living in the Turkistan region to create a new civilisation of their own. If the jurisprudential dimension of Ahl al-Sunnah in Turkestan was formed by Hanafism and Shafi'ism, and the theological dimension was formed by Asharism and Māturīdism, the mystical dimension of Ahl al-Sunnah in the region was mainly formed by the Sufism understanding shaped around the Yassawiyya, Kubraviyya and Naqshibandiyya views. Therefore, Ahmad Yassawi's madhhabian tendency should be compatible with Hanafiism and his religious views should be compatible Maturidiism views. At the same time, Ahmad Yassawi's political perception should naturally be shaped in the Hanafi-Maturidi line of thought. There are various claims made by various researchers about Yassawi's basic religious orientation. However, in order to present the subject with an accurate opinion, the first thing we need to do is to base ourselves on the own works of the person to be investigated. Because this is the complementary nature of scientific research. For this reason, in this research, his own works, which are considered the first sources regarding Yassawi's teachings, will be taken as basis. The article reaches the conclusion that the fiqh, creedal and political views of Ahmad Yassawi originate from the knowledge of Ahl al-Sunnah wal Jama'at. In our opinion, it is possible to determine Ahmad Yassawi's madhhabian tendencies based on his works that have survived to the present day. İn the article, based on his own works, the fiqh, belief and political aspects of Ahmad Yassawi's madhhabian tendency will be determined by using the descriptive method and textual analysis methods.

MHAGuideNet: a 3D pre-trained guidance model for Alzheimer’s Disease diagnosis using 2D multi-planar sMRI images

BackgroundAlzheimer’s Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and reducing the incidence rate and morbidity. Traditional computer-aided diagnostic methods using structural MRI data often focus on capturing such features but face challenges, like overfitting with 3D image analysis and insufficient feature capture with 2D slices, potentially missing multi-planar information, and the complementary nature of features across different orientations.MethodsThe study introduces MHAGuideNet, a classification method incorporating a guidance network utilizing multi-head attention. The model utilizes a pre-trained 3D convolutional neural network to direct the feature extraction of multi-planar 2D slices, specifically targeting the detection of features like structural atrophy. Additionally, a hybrid 2D slice-level network combining 2D CNN and 2D Swin Transformer is employed to capture the interrelations between the atrophy in different brain structures associated with Alzheimer’s Disease.ResultsThe proposed MHAGuideNet is tested using two datasets: the ADNI and OASIS datasets. The model achieves an accuracy of 97.58%, specificity of 99.89%, F1 score of 93.98%, and AUC of 99.31% on the ADNI test dataset, demonstrating superior performance in distinguishing between Alzheimer’s Disease and cognitively normal subjects. Furthermore, testing on the independent OASIA test dataset yields an accuracy of 96.02%, demonstrating the model’s robust performance across different datasets.ConclusionMHAGuideNet shows great promise as an effective tool for the computer-aided diagnosis of Alzheimer’s Disease. Within the guidance of information from the 3D pre-trained CNN, the ability to leverage multi-planar information and capture subtle brain changes, including the interrelations between different structural atrophies, underscores its potential for clinical application.

High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning

Accurate mapping of tea plantations is crucial for agricultural management and economic planning, yet it poses a significant challenge due to the complex and variable nature of tea cultivation landscapes. This study presents a high-precision approach to mapping tea plantations in Anji County, Zhejiang Province, China, utilizing multi-source remote sensing data and advanced deep learning models. We employed a combination of Sentinel-2 optical imagery, Sentinel-1 synthetic aperture radar imagery, and digital elevation models to capture the rich spatial, spectral, and temporal characteristics of tea plantations. Three deep learning models, namely U-Net, SE-UNet, and Swin-UNet, were constructed and trained for the semantic segmentation of tea plantations. Cross-validation and point-based accuracy assessment methods were used to evaluate the performance of the models. The results demonstrated that the Swin-UNet model, a transformer-based approach capturing long-range dependencies and global context for superior feature extraction, outperformed the others, achieving an overall accuracy of 0.993 and an F1-score of 0.977 when using multi-temporal Sentinel-2 data. The integration of Sentinel-1 data with optical data slightly improved the classification accuracy, particularly in areas affected by cloud cover, highlighting the complementary nature of Sentinel-1 imagery for all-weather monitoring. The study also analyzed the influence of terrain factors, such as elevation, slope, and aspect, on the accuracy of tea plantation mapping. It was found that tea plantations at higher altitudes or on north-facing slopes exhibited higher classification accuracy, and that accuracy improves with increasing slope, likely due to simpler land cover types and tea’s preference for shade. The findings of this research not only provide valuable insights into the precision mapping of tea plantations but also contribute to the broader application of deep learning in remote sensing for agricultural monitoring.

Joint neural denoising and consolidation for portable handheld laser scan

Abstract. Mobile and handheld laser scanners document scenes in an economical manner, but the data they acquire are often noisy, of low resolution, unevenly distributed, and feature voids within the scanned scene. These characteristics challenge such applications as feature extraction and 3D modeling when processing the raw pointset. To date, point cloud denoising and consolidation (address of distribution and void regions) have been treated independently despite their complementary nature and their mutual dependence on the underlying surface representation. We argue that if treated jointly, richer shape context features can be learned and an improved enhancement framework can be derived. Accordingly, we formulate the shape context description as a joint contribution by both denoising and consolidation, within an end-to-end framework. To this end, we introduce densely packed graph convolution layers to extract contextual information, allowing to query points offset to the underlying surface and to compensate for the structural loss. We demonstrate how the commonly used L2-driven loss functions generate non-smooth output and volume shrinkage, and alleviate this by ones that mitigate the noisy outcome, repair voids, and improve point density distributions. Performance analysis on benchmark datasets demonstrates how we outperform state-of-the-art solutions, produce high-fidelity outcomes, and improve reconstruction-based tasks in real-world setups.

GNSS and accelerometer data fusion by variational Bayesian adaptive multi-rate Kalman filtering for dynamic displacement estimation of super high-rise buildings

The integration of Global Navigation Satellite Systems (GNSS) into Structural Health Monitoring (SHM) systems of super high-rise buildings is becoming increasingly prevalent, with a notable trend of combining GNSS with accelerometers to leverage the complementary nature of both technologies for estimating dynamic displacements. However, the multipath errors between the GNSS reference and monitoring stations give rise to the time-varying noises into the process of solving position, resulting in considerable fluctuations in the accuracy of displacement monitoring in complex environments, which impedes the development of GNSS-accelerometer integration techniques in the field of SHM. To address the issue, a variational Bayesian adaptive multi-rate algorithm (VBMRKF) is proposed to rapidly respond the time-varying noise for the GNSS system, thereby facilitating the fusion of GNSS and accelerometer data with greater accuracy. In this algorithm, a convergence criterion is established for approximation of posterior distribution in multi-rate process based on Evidence Lower Bound (ELBO) theory, which reduces the effects of noise accumulation on the accuracy of the approximation processes for the posterior distribution. Furthermore, a noise-sensitive factor is also heuristically proposed, which enables the algorithm to quickly adapt to fluctuations of noise characteristics during multi-rate processes. The numerical simulation results reveal that the algorithm has a higher estimation accuracy for dynamic displacements than the conventional algorithm, and it has a superior adaptability to systems with different frequencies. In instances where the initial settings of noise parameters are highly unreasonable, traditional algorithms even demonstrated a lack of convergence. Conversely, the normalized root mean square errors (NRMSE) of the displacement estimation results obtained by VBMRKF are consistently below 16 %, which also substantiates the robustness of this algorithm. In the presence of time-varying noise, the algorithm demonstrates the capacity to rapidly converge upon the distribution of the noise. Compared to the multi-rate Kalman filtering (MRKF) algorithm, the proposed algorithm achieves the highest reduction of 27.02 % in NRMSE, and compared to around 13.97 % NRMSE of the recent adaptive algorithm, the proposed algorithm also demonstrates significantly superior performance, reducing NRMSE by at least 5.13 %. In addition, regardless of whether the signal is time-varying or not, the algorithm is able to quickly approximate the distribution of noise and produces acceptable displacement estimation results with unreasonable initial settings of the noise parameters, demonstrating its robustness. Finally, the measured accelerometer and GNSS data are employed to successfully determine the two-dimensional dynamic displacement of the 632-metre-high Shanghai Tower at a distance of only 2.1 km from the eye of Typhoon Muifa (202212). A further analysis of the results demonstrates that the estimated displacements are more comprehensive in both time and frequency domains.

Cultural diversity and leadership styles in Malaysia’s corporate sector

In an era marked by globalization and heightened competition, this study delves into how sociodemographic factors such as age, gender, education, and work experience influence leadership styles, crucial for achieving organizational objectives. Employing the leadership behaviour questionnaire on 336 predominantly educated, male professionals of Indian descent in Malaysia's corporate sector, the research evaluates task and relationship orientations in leadership. The findings highlight a significant prevalence of both task-oriented and relationship-oriented leadership behaviours. Intriguingly, there is a positive correlation between higher education, age, work experience, and relationship orientation, while education and salary predominantly affect task-oriented leadership. A notable discovery is the positive interplay between task and relationship orientations, suggesting their complementary nature. The study concludes that a balanced, multifaceted leadership approach, fostering both efficiency and employee welfare, is vital. It underscores the impact of educational and professional growth on leadership skills and the necessity of diverse leadership styles for optimal organizational performance, offering key insights for leadership development in varied settings.

The Joint Impact of Organizational and Individual Factors on Employees' Green Behavior

ABSTRACTEmployee green behavior is a necessary condition for organizations to achieve sustainable success. Drawing upon self‐determination theory, this paper thoroughly investigates the mechanism through which a synergistic combination of multiple antecedents’ influences employee green behavior via qualitative comparative analysis. The findings reveal that no single factor constitutes a necessary condition for high employees' green behavior. However, organizational identification and green self‐efficacy play crucial roles in fostering such behaviors. We identify three equivalent pathways for promoting employees' green behavior, namely, “synergistic internal and external environmental orientation,” “psychological climate driven by organizational identity,” and “internal environmental orientation under environmental regulation.” Furthermore, this study uncovers the complementary nature of organizational identity and employee self‐efficacy, as well as the substitutability of internal and external environmental orientation. These findings suggest that organizations should focus on fostering a strong organizational identity and enhancing employees' self‐efficacy while maintaining a balance between internal and external environmental orientations to effectively promote green behavior among employees.

Not Another Dual Attention UNet Transformer (NNDA-UNETR): a plug-and-play parallel dual attention block in U-Net with enhanced residual blocks for medical image segmentation.

Medical image segmentation is crucial for clinical diagnostics and treatment planning. Recently, hybrid models often neglect the local modeling capabilities of Transformers for medical image segmentation, despite the complementary nature of local information from both convolutional neural networks (CNNs) and transformers. This limitation is particularly problematic in multi-organ segmentation, where organs are closely adhered, and accurate delineation is essential. This study aims to develop a novel method that leverages the strengths of both CNNs and transformers to address the challenges of segmenting multiple closely adhered organs, improving accuracy and robustness in multi-organ segmentation. In this study, we present the Not Another Dual Attention block (NNDA-block), a versatile, plug-and-play module that seamlessly integrates channel and spatial attention mechanisms. This block can be incorporated at any stage within the Not Another Dual Attention UNet Transformer (NNDA-UNETR) framework. Our novel approach to spatial attention uniquely combines local modeling, significantly reducing detail and texture loss during the up- and down-sampling processes typical in U-shaped architectures. Evaluations on the Beyond the Cranial Vault (BTCV) and Automatic Cardiac Diagnosis Challenge benchmark datasets demonstrate our method's effectiveness in balancing model complexity with accuracy. On the BTCV dataset, our model sets new state-of-the-art benchmarks, achieving a Dice similarity coefficient of 84.13%, normalized surface dice of 86.96%, mean average surface distance of 3.46 mm, and an Hausdorff distance at 95% of 17.76 mm. Moreover, it reduces the parameter count by 34.2% compared to leading contemporary methods, all while maintaining low computational costs [measured in floating point operations per second (FLOPs)]. NNDA-UNETR offers a robust solution for accurate segmentation in multi-organ tasks, particularly where organ adhesion poses challenges. Its lightweight design also makes it well-suited for deployment in real-world medical environments with limited computational resources.

Machine Learning Algorithms for Emotion Recognition Using Audio and Text Data

Over the years, emotion recognition has become one of the cornerstones of affective computing, enabling machines to recognize and be responsive to human emotions. The current study will present approaches to accurately classify emotions by exploiting multimodal data, namely audio and text. Challenges faced in this domain are the noisy speech signals and inherently ambiguous textual expressions that generally reduce the accuracy of unimodal systems. Classic approaches cannot make good use of the complementary nature of these modalities and, therefore, require a robust and combined framework. This study proposes a method called SVM-ERATI, Support Vector Machine (SVM) based emotion recognition (ER) approach that inputs audio and text information (ATI). Extracted audio features in this regard will include Mel-frequency cepstral coefficients (MFCCs) and prosody-like pitch and energy related to the acoustic properties of emotions. Meanwhile, semantic embeddings obtained from transformer models like BERT serve to analyze text data. A feature-level fusion scheme is then followed, whereby the feature vectors from both audio and text are combined into an integrated representation. Then, features after fusion will be classified by the multi-class SVM with a proper radial basis function (RBF) kernel function that is most appropriate to capture the non-linear relationships inherent in the multimodal emotional data. Experiments on benchmark datasets such as CMU-MOSEI demonstrate that the proposed multimodal approach using SVM significantly outperforms unimodal baselines by about 12%. The findings highlight SVM's effectiveness in combining audio and text data for emotion recognition, which has exciting implications for AI in AI-powered mental health diagnostics and AI-powered intelligent virtual assistants.

CETN: Contrast-enhanced Through Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most existing CTR Prediction models utilize explicit feature interactions to overcome the performance bottleneck of implicit feature interactions. Hence, deep CTR models based on parallel structures (e.g., DCN, FinalMLP, xDeepFM) have been proposed to obtain joint information from different semantic spaces. However, these parallel subcomponents lack effective supervision and communication signals, making it challenging to efficiently capture valuable multi-views feature interaction information in different semantic spaces. To address these issues, we propose a simple yet effective novel CTR model: Contrast-enhanced Through Network (CETN). Drawing inspiration from sociology, CETN leverages the complementary nature of diversity and homogeneity to guide the model in acquiring higher-quality feature interaction information. Specifically, CETN employs product-based feature interactions and the augmentation (perturbation) concept from contrastive learning to segment different semantic spaces, each with distinct activation functions. This improves diversity in the feature interaction information captured by the model. Additionally, we introduce self-supervised signals and through connection within each semantic space to ensure the homogeneity of the captured feature interaction information. The experiments conducted on four real datasets demonstrate that our model consistently outperforms twenty baseline models in terms of AUC and Logloss.

IF-USOD: Multimodal information fusion interactive feature enhancement architecture for underwater salient object detection

Underwater salient object detection (USOD) has garnered increasing attention due to its superior performance in various underwater visual tasks. Despite the growing interest, research on USOD remains in its nascent stages, with existing methods often struggling to capture long-range contextual features of salient objects. Additionally, these methods frequently overlook the complementary nature of multimodal information. The multimodal information fusion can render previously indiscernible objects more detectable, as capturing complementary features from diverse source images enables a more accurate depiction of objects. In this work, we explore an innovative approach that integrates RGB and depth information, coupled with interactive feature enhancement, to advance the detection of underwater salient objects. Our method first leverages the strengths of both transformer and convolutional neural network architectures to extract features from source images. Here, we employ a two-stage training strategy designed to optimize feature fusion. Subsequently, we utilize self-attention and cross-attention mechanisms to model the correlations among the extracted features, thereby amplifying the relevant features. Finally, to fully exploit features across different network layers, we introduce a cross-scale learning strategy to facilitate multi-scale feature fusion, which improves the detection accuracy of underwater salient objects by generating both coarse and fine salient predictions. Extensive experimental evaluations demonstrate the state-of-the-art model performance of our proposed method.

(Invited) Characterization of Catalyst Layers in Polymer Electrolyte Membrane Electrolyzers

Polymer electrolyte membrane water electrolyzers (PEMWEs) play a vital role in promoting the widespread adoption of renewable hydrogen. To successfully fabricate and integrate components, develop scalable manufacturing techniques, understand degradation mechanisms, and devise strategies to mitigate degradation, it is essential to thoroughly characterize materials and components in these devices. Due to the complexity of these systems and various interconnected problems, as well as the multitude of relevant scales, the use of multi-technique characterization and the development of novel characterization approaches and methods are crucial.This talk will highlight the complementary nature of electron and X-ray microscopy and spectroscopy methods for investigating surfaces, interfaces, and interactions with focus on catalyst layers (CLs) with an emphasis on analysis of as-prepared, tested and degraded samples. Micro X-ray computed tomography (XCT) provides both 2D and 3D analysis, but its resolution is not sufficient to identify catalyst layer features with enough detail, especially when Pt PTL coatings and low Ir-based catalyst loadings are used. On the other hand, Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-Ray Spectroscopy (EDS) provides superior resolution and enables the differentiation of PTL coatings and catalysts. Although SEM-EDS provides only 2D information, by employing a combination of top-down and cross-sectional analysis, a comprehensive understanding of catalyst distribution within the PTL can be attained, facilitating the initial assessment of PTLs and PTEs. The parameters extracted from SEM-EDS data can be used for a comprehensive quantitative assessment of the composition and distribution of catalysts, highlighting the differences between fabricated samples across a wide range of fabrication variables. Transmission Electron Microscopy (TEM) in tandem with EDS is also essential for nanoscale analysis of CLs, and PTEs. However, it is limited by its focus on small areas and necessitates complex sample preparation, especially for degraded samples. To complement these techniques, Time-of-Flight Secondary Mass Spectrometry (ToF-SIMS) has been developed as a promising complementary method to offer chemical insights, particularly valuable in analyzing protective coatings, catalyst layers, and interfaces between different layers. Furthermore, X-ray photoelectron spectroscopy (XPS) has high sensitivity in discerning catalyst evolution and subtle variations at the catalyst-ionomer interface, highlighting its complementary role in elemental and chemical composition analysis, especially in the context of tested and degraded samples.

Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.

Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.72s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

Systematic comparison of phenome-wide admixture mapping and genome-wide association in a diverse biobank.

Biobank-scale association studies that include Hispanic/Latino(a) (HL) and African American (AA) populations remain underrepresented, limiting the discovery of disease associated genetic factors in these groups. We present here a systematic comparison of phenome-wide admixture mapping (AM) and genome-wide association (GWAS) using data from the diverse Bio Me biobank in New York City. Our analysis highlights 77 genome-wide significant AM signals, 48 of which were not detected by GWAS, emphasizing the complementary nature of these two approaches. AM-tagged variants show significantly higher minor allele frequency and population differentiation (Fst) while GWAS demonstrated higher odds ratios, underscoring the distinct genetic architecture identified by each method. This study offers a comprehensive phenome-wide AM resource, demonstrating its utility in uncovering novel genetic associations in underrepresented populations, particularly for variants missed by traditional GWAS approaches.