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24 pages, 2364 KiB  
Article
Breakthrough Position and Trajectory of Sustainable Energy Technology
by Bart Bossink, Sandra Hasanefendic, Marjolein Hoogstraaten and Charusheela Ramanan
Sustainability 2025, 17(1), 313; https://doi.org/10.3390/su17010313 - 3 Jan 2025
Viewed by 275
Abstract
This research aims to determine the position and the breakthrough trajectory of sustainable energy technologies. Fine-grained insights into these breakthrough positions and trajectories are limited. This research seeks to fill this gap by analyzing sustainable energy technologies’ breakthrough positions and trajectories in terms [...] Read more.
This research aims to determine the position and the breakthrough trajectory of sustainable energy technologies. Fine-grained insights into these breakthrough positions and trajectories are limited. This research seeks to fill this gap by analyzing sustainable energy technologies’ breakthrough positions and trajectories in terms of development, application, and upscaling. To this end, the breakthrough positions and trajectories of seven sustainable energy technologies, i.e., hydrogen from seawater electrolysis, hydrogen airplanes, inland floating photovoltaics, redox flow batteries, hydrogen energy for grid balancing, hydrogen fuel cell electric vehicles, and smart sustainable energy houses, are analyzed. This is guided by an extensively researched and literature-based model that visualizes and describes these technologies’ experimentation and demonstration stages. This research identifies where these technologies are located in their breakthrough trajectory in terms of the development phase (prototyping, production process and organization, and niche market creation and sales), experiment and demonstration stage (technical, organizational, and market), the form of collaboration (public–private, private–public, and private), physical location (university and company laboratories, production sites, and marketplaces), and scale-up type (demonstrative, and first-order and second-order transformative). For scientists, this research offers the opportunity to further refine the features of sustainable energy technologies’ developmental positions and trajectories at a detailed level. For practitioners, it provides insights that help to determine investments in various sustainable energy technologies. Full article
(This article belongs to the Special Issue Sustainable Clean Energy and Green Economic Growth)
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12 pages, 944 KiB  
Systematic Review
Role of Gut and Urinary Microbiome in Children with Urinary Tract Infections: A Systematic Review
by Anjali Srivastava, Omprakash Shete, Annu Gulia, Sumit Aggarwal, Tarini Shankar Ghosh, Vineet Ahuja and Sachit Anand
Diagnostics 2025, 15(1), 93; https://doi.org/10.3390/diagnostics15010093 - 3 Jan 2025
Viewed by 297
Abstract
Background: The complex interaction between the gut and urinary microbiota underscores the importance of understanding microbial dysbiosis in pediatric urinary tract infection (UTI). However, the literature on the gut–urinary axis in pediatric UTIs is limited. This systematic review aims to summarize the [...] Read more.
Background: The complex interaction between the gut and urinary microbiota underscores the importance of understanding microbial dysbiosis in pediatric urinary tract infection (UTI). However, the literature on the gut–urinary axis in pediatric UTIs is limited. This systematic review aims to summarize the current literature on the roles of gut and urinary dysbiosis in pediatric UTIs. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive literature search was performed across four databases, including PubMed, Web of Science, Scopus, and EMBASE. All studies published between January 2003 and December 2023 utilizing 16S rRNA sequencing to profile the gut or urinary microbiome in children with UTIs were included. Heat map visualization was used to compare microbial profiles between UTI and control cohorts. The methodological quality assessment was performed using the Newcastle–Ottawa scale (NOS). Results: Eight studies were included in this review. While five studies compared the microbiota signatures between patients and controls, three studies focused solely on the UTI cohort. Also, the gut and urinary microbiome profiles were investigated by four studies each. The consistent loss of microbiome alpha-diversity with an enrichment of specific putative pathobiont microbes was observed among the included studies. Escherichia coli consistently emerged as the predominant uropathogen in pediatric UTIs. In addition to this, Escherichia fergusonii, Klebsiella pneumoniae, and Shigella flexneri were isolated in the urine of children with UTIs, and enrichment of Escherichia, Enterococcus, Enterobacter, and Bacillus was demonstrated in the gut microbiota of UTI patients. On the contrary, certain genera, such as Achromobacter, Alistipes, Ezakiella, Finegoldia, Haemophilus, Lactobacillus, Massilia, Prevotella, Bacteroides, and Ureaplasma, were isolated from the controls, predominantly in the fecal samples. The methodological quality of the included studies was variable, with total scores (NOS) ranging from 5 to 8. Conclusions: The enrichment of specific pathobionts, such as Escherichia coli, in the fecal or urinary samples of the UTI cohort, along with the presence of core microbiome-associated genera in the non-UTI population, underscores the critical role of the gut–urinary axis in pediatric UTI pathogenesis. These findings highlight the potential for microbiome-based strategies in pediatric UTIs. Further studies with larger cohorts, standardized healthy controls, and longitudinal profiling are essential to validate these observations and translate them into clinical practice. Full article
(This article belongs to the Special Issue Clinical Diagnosis and Management in Pediatric Urology)
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20 pages, 7507 KiB  
Article
Sliding-Window Dissimilarity Cross-Attention for Near-Real-Time Building Change Detection
by Wen Lu and Minh Nguyen
Remote Sens. 2025, 17(1), 135; https://doi.org/10.3390/rs17010135 - 2 Jan 2025
Viewed by 353
Abstract
A near-real-time change detection network can consistently identify unauthorized construction activities over a wide area, empowering authorities to enforce regulations efficiently. Furthermore, it can promptly assess building damage, enabling expedited rescue efforts. The extensive adoption of deep learning in change detection has prompted [...] Read more.
A near-real-time change detection network can consistently identify unauthorized construction activities over a wide area, empowering authorities to enforce regulations efficiently. Furthermore, it can promptly assess building damage, enabling expedited rescue efforts. The extensive adoption of deep learning in change detection has prompted a predominant emphasis on enhancing detection performance, primarily through the expansion of the depth and width of networks, overlooking considerations regarding inference time and computational cost. To accurately represent the spatio-temporal semantic correlations between pre-change and post-change images, we create an innovative transformer attention mechanism named Sliding-Window Dissimilarity Cross-Attention (SWDCA), which detects spatio-temporal semantic discrepancies by explicitly modeling the dissimilarity of bi-temporal tokens, departing from the mono-temporal similarity attention typically used in conventional transformers. In order to fulfill the near-real-time requirement, SWDCA employs a sliding-window scheme to limit the range of the cross-attention mechanism within a predetermined window/dilated window size. This approach not only excludes distant and irrelevant information but also reduces computational cost. Furthermore, we develop a lightweight Siamese backbone for extracting building and environmental features. Subsequently, we integrate an SWDCA module into this backbone, forming an efficient change detection network. Quantitative evaluations and visual analyses of thorough experiments verify that our method achieves top-tier accuracy on two building change detection datasets of remote sensing imagery, while also achieving a real-time inference speed of 33.2 FPS on a mobile GPU. Full article
(This article belongs to the Special Issue Remote Sensing and SAR for Building Monitoring)
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11 pages, 967 KiB  
Article
Visual Noise Mask for Human Point-Light Displays: A Coding-Free Approach
by Catarina Carvalho Senra, Adriana Conceição Soares Sampaio and Olivia Morgan Lapenta
NeuroSci 2025, 6(1), 2; https://doi.org/10.3390/neurosci6010002 - 2 Jan 2025
Viewed by 214
Abstract
Human point-light displays consist of luminous dots representing human articulations, thus depicting actions without pictorial information. These stimuli are widely used in action recognition experiments. Because humans excel in decoding human motion, point-light displays (PLDs) are often masked with additional moving dots (noise [...] Read more.
Human point-light displays consist of luminous dots representing human articulations, thus depicting actions without pictorial information. These stimuli are widely used in action recognition experiments. Because humans excel in decoding human motion, point-light displays (PLDs) are often masked with additional moving dots (noise masks), thereby challenging stimulus recognition. These noise masks are typically found within proprietary programming software, entail file format restrictions, and demand extensive programming skills. To address these limitations, we present the first user-friendly step-by-step guide to develop visual noise to mask PLDs using free, open-source software that offers compatibility with various file formats, features a graphical interface, and facilitates the manipulation of both 2D and 3D videos. Further, to validate our approach, we tested two generated masks in a pilot experiment with 12 subjects and demonstrated that they effectively jeopardised human agent recognition and, therefore, action visibility. In sum, the main advantages of the presented methodology are its cost-effectiveness and ease of use, making it appealing to novices in programming. This advancement holds the potential to stimulate young researchers’ use of PLDs, fostering further exploration and understanding of human motion perception. Full article
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25 pages, 5124 KiB  
Article
Visual System Inspired Algorithm for Enhanced Visibility in Coronary Angiograms (VIAEVCA)
by Hedva Spitzer, Yosef Shai Kashi, Morris Mosseri and Jacob Erel
Biomimetics 2025, 10(1), 18; https://doi.org/10.3390/biomimetics10010018 - 1 Jan 2025
Viewed by 344
Abstract
Numerous efforts have been invested in previous algorithms to expose and enhance blood vessel (BV) visibility derived from clinical coronary angiography (CAG) procedures, such as noise reduction, segmentation, and background subtraction. Yet, the visibility of the BVs and their luminal content, particularly the [...] Read more.
Numerous efforts have been invested in previous algorithms to expose and enhance blood vessel (BV) visibility derived from clinical coronary angiography (CAG) procedures, such as noise reduction, segmentation, and background subtraction. Yet, the visibility of the BVs and their luminal content, particularly the small ones, is still limited. We propose a novel visibility enhancement algorithm, whose main body is inspired by a line completion mechanism of the visual system, i.e., lateral interactions. It facilitates the enhancement of the BVs along with simultaneous noise reduction. In addition, we developed a specific algorithm component that allows better visibility of small BVs and the various CAG tools utilized during the procedure. It is accomplished by enhancing the BVs’ fine resolutions, located in the coarse resolutions at the BV zone. The visibility of the most significant clinical features during the CAG procedure was evaluated and qualitatively compared by the consensus of two cardiologists (MM and JE) to the algorithm’s results. These included the visibility of the whole frame, the coronary BVs as well as the small ones, the main obstructive lesions within the BVs, and the various angiography interventional tools utilized during the procedure. The algorithm succeeded in producing better visibility of all these features, even under low-contrast or low-radiation conditions. Despite its major advantages, the algorithm also caused the appearance of disturbing vertebral and bony artifacts, which could somewhat lower diagnostic accuracy. Yet, viewing the processed images from multiple angles and not just from a single one and evaluating the cine mode usually overcomes this drawback. Thus, our novel algorithm potentially leads to a better clinical diagnosis, improved procedural capabilities, and a successful outcome. Full article
(This article belongs to the Special Issue Advanced Biologically Inspired Vision and Its Application)
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16 pages, 1949 KiB  
Article
Learning Transversus Abdominis Activation in Older Adults with Chronic Low Back Pain Using an Ultrasound-Based Wearable: A Randomized Controlled Pilot Study
by Luis Perotti, Oskar Stamm, Hannah Strohm, Jürgen Jenne, Marc Fournelle, Nils Lahmann and Ursula Müller-Werdan
J. Funct. Morphol. Kinesiol. 2025, 10(1), 14; https://doi.org/10.3390/jfmk10010014 - 1 Jan 2025
Viewed by 249
Abstract
Background/Objectives: Chronic low back pain (CLBP) is prevalent among older adults and leads to significant functional limitations and reduced quality of life. Segmental stabilization exercises (SSEs) are commonly used to treat CLBP, but the selective activation of deep abdominal muscles during these [...] Read more.
Background/Objectives: Chronic low back pain (CLBP) is prevalent among older adults and leads to significant functional limitations and reduced quality of life. Segmental stabilization exercises (SSEs) are commonly used to treat CLBP, but the selective activation of deep abdominal muscles during these exercises can be challenging for patients. To support muscle activation, physiotherapists use biofeedback methods such as palpation and ultrasound imaging. This randomized controlled pilot study aimed to compare the effectiveness of these two biofeedback techniques in older adults with CLBP. Methods: A total of 24 participants aged 65 years or older with CLBP were randomly assigned to one of two groups: one group performed self-palpation biofeedback, while the other group used real-time ultrasound imaging to visualize abdominal muscle activation. Muscle activation and thickness were continuously tracked using a semi-automated algorithm. The preferential activation ratio (PAR) was calculated to measure muscle activation, and statistical comparisons between groups were made using ANOVA. Results: Both groups achieved positive PAR values during all repetitions of the abdominal-draw-in maneuver (ADIM) and abdominal bracing (AB). Statistical analysis revealed no significant differences between the groups in terms of PAR during ADIM (F(2, 42) = 0.548, p = 0.58, partial η2 = 0.025) or AB (F(2, 36) = 0.812, p = 0.45, partial η2 = 0.043). Both groups reported high levels of exercise enjoyment and low task load. Conclusions: In conclusion, both palpation and ultrasound biofeedback appear to be effective for guiding older adults with CLBP during SSE. Larger studies are needed to confirm these results and examine the long-term effectiveness of these biofeedback methods. Full article
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16 pages, 3265 KiB  
Article
EMNet: A Novel Few-Shot Image Classification Model with Enhanced Self-Correlation Attention and Multi-Branch Joint Module
by Fufang Li, Weixiang Zhang and Yi Shang
Biomimetics 2025, 10(1), 16; https://doi.org/10.3390/biomimetics10010016 - 1 Jan 2025
Viewed by 277
Abstract
In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. Few-shot image classification aims to address [...] Read more.
In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. Few-shot image classification aims to address the problem of image classification when data are limited. Traditional models require a large amount of labeled data for training, while few-shot learning trains models using only a small number of samples (just a few samples per class) to recognize new categories. EMNet shows its potential for bio-inspired algorithms in optimizing feature extraction and enhancing generalization capabilities. It features two key modules: Enhanced Self-Correlated Attention (ESCA) and Multi-Branch Joint Module (MBJ Module). EMNet tackles two main challenges in few-shot learning: how to make an effective important feature extraction and enhancement in images, and improving generalization to new categories. The ESCA module boosts the precision in extracting crucial local features, enhancing classification accuracy. The MBJ module focuses on shared features across images, emphasizing similarities within classes and subtle differences between them. This enhances model adaptability and generalization to new categories. Experimental results show that our model performs better than existing models in one-shot and five-shot tasks on mini-ImageNet, CUB-200, and CIFAR-FS datasets, which proves the proposed model to be an efficient end-to-end solution for few-shot image classification. In the five-way one-shot and five-way five-shot experiments on the CUB-200-2011 dataset, EMNet achieved classification accuracies that were 1.27 and 0.54 percentage points higher than those of RENet, respectively. In the five-way one-shot and five-way five-shot experiments on the miniImageNet dataset, EMNet’s classification accuracies were 0.02 and 0.48 percentage points higher than those of RENet, respectively. In the five-way one-shot and five-way five-shot experiments on the CIFAR-FS dataset, EMNet’s classification accuracies were 0.19 and 0.18 percentage points higher than those of RENet. Full article
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25 pages, 11184 KiB  
Article
A Novel Virtual Reality-Based Simulator for Maxillofacial Reconstruction Surgery: Development and Validation Study
by Jun-Seong Kim, Kun-Woo Kim, Hyo-Joon Kim and Seong-Yong Moon
Appl. Sci. 2025, 15(1), 332; https://doi.org/10.3390/app15010332 - 31 Dec 2024
Viewed by 416
Abstract
Maxillofacial reconstruction surgery involves restoring bones or skeletal structures in areas such as the mouth, jaw, and face using bones like the iliac crest and fibula. This surgery requires a high level of difficulty and precision, necessitating extensive practice and accurate 3D model [...] Read more.
Maxillofacial reconstruction surgery involves restoring bones or skeletal structures in areas such as the mouth, jaw, and face using bones like the iliac crest and fibula. This surgery requires a high level of difficulty and precision, necessitating extensive practice and accurate 3D model simulations. However, due to limitations in training environments, opportunities for sufficient practice are restricted, and the precision of simulations may be compromised by the limitations of existing tools. To address these challenges, this paper proposes a maxillofacial reconstruction surgery simulator utilizing virtual reality technology. The proposed method allows users to explore a virtual space through a head-mounted display, where they can visualize, navigate, and manipulate bone models (move and rotate) using the joystick and buttons of a controller, as well as perform resection operations. Additionally, to verify the effectiveness of the simulator, performance evaluation is conducted through frame per second and resource usage analysis, usability testing is performed via questionnaires with dental students, and accuracy validation is carried out for the reconstruction models. The results of each evaluation method are analyzed to confirm the utility and potential of the proposed simulator. Full article
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24 pages, 4196 KiB  
Article
Impact of Physical Features on Visual Walkability Perception in Urban Commercial Streets by Using Street-View Images and Deep Learning
by Gonghu Huang, Yiqing Yu, Mei Lyu, Dong Sun, Bart Dewancker and Weijun Gao
Buildings 2025, 15(1), 113; https://doi.org/10.3390/buildings15010113 - 31 Dec 2024
Viewed by 315
Abstract
Urban commercial streets are a crucial component of urban life, serving as the central hubs of commercial activity and providing vital spaces for both residents and visitors to engage in various activities. Walkability is commonly used as a key indicator of environmental quality, [...] Read more.
Urban commercial streets are a crucial component of urban life, serving as the central hubs of commercial activity and providing vital spaces for both residents and visitors to engage in various activities. Walkability is commonly used as a key indicator of environmental quality, playing a significant role in improving residents’ health, community interaction, and environmental quality of life. Therefore, promoting the development of a high-quality walking environment in commercial districts is crucial for fostering urban economic growth and the creation of livable cities. However, existing studies predominantly focus on the impact of the built environment on walkability at the urban scale, with limited attention given to commercial streets, particularly the influence of their physical features on walking-need perceptions. In this study, we utilized Google Street-View Panorama (GSVP) images of the Tenjin commercial district and applied the Semantic Differential (SD) method to assess four walking-need perceptions of visual walkability perception, including usefulness, comfort, safety, and attractiveness. Additionally, deep-learning-based semantic segmentation was employed to extract and calculate the physical features of the Tenjin commercial district. Correlation and regression analysis were used to investigate the impact of these physical features on the four walking-need perceptions. The results showed that the different walking-need perceptions in the Tenjin commercial district are attractiveness > safety > comfort > usefulness. Furthermore, the results show that there are significant spatial distribution differences in walking-need perceptions in the Tenjin commercial district. Safety perception is more prominent on primary roads, all four walking-need perceptions in the secondary roads at a high level, and the tertiary roads have generally lower scores for all walking-need perceptions. The regression analysis indicates that walkable space and the landmark visibility index have a significant impact on usefulness, street cleanliness emerges as the most influential factor affecting safety, greenness is identified as the primary determinant of comfort, while the landmark visibility index exerts the greatest influence on attractiveness. This study expands the existing perspectives on urban street walkability by focusing on street-level analysis and proposes strategies to enhance the visual walkability perception of commercial streets. These findings aim to better meet pedestrian needs and provide valuable insights for future urban planning efforts. Full article
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24 pages, 2509 KiB  
Article
Unified Design Methodology for a Polycentric Transfemoral Knee Prosthesis Using Kinematic Synthesis
by Mertcan Koçak and Erkin Gezgin
Machines 2025, 13(1), 20; https://doi.org/10.3390/machines13010020 - 31 Dec 2024
Viewed by 354
Abstract
This study introduces a novel single-degree-of-freedom polycentric knee mechanism specifically designed for transfemoral prostheses to address dual challenges of stability during the stance phase and biomimetic motion during the swing phase. Leveraging analytical kinematic synthesis, the proposed mechanism integrates separate kinematic designs for [...] Read more.
This study introduces a novel single-degree-of-freedom polycentric knee mechanism specifically designed for transfemoral prostheses to address dual challenges of stability during the stance phase and biomimetic motion during the swing phase. Leveraging analytical kinematic synthesis, the proposed mechanism integrates separate kinematic designs for each of the gait phases into a combined structure that prevents singularity issues during full knee flexion, which is a significant limitation in conventional active designs. The stance phase mechanism emphasizes stability through precise control of the instantaneous center of rotation (ICR) and weight-bearing support, while the swing phase mechanism adopts a biomimetic motion trajectory. In order to validate the proposed methodology, kinematic synthesis, numerical simulations, and visual analyses were conducted. Incorporating insights from polycentric prostheses and orthotic applications, the proposed mechanism achieves a seamless transition between two different configurations by keeping its overall mobility. Additionally, its possible compatibility with motorized actuation offers a foundation for active prosthesis systems, paving the way for adapting the advantages of polycentric prosthesis to active devices. This innovative approach offers a scientifically grounded pathway for improving transfemoral prosthetic systems, advancing both their biomechanical utility and user comfort. Full article
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21 pages, 8944 KiB  
Article
BiST-SA-LSTM: A Deep Learning Framework for End-to-End Prediction of Mesoscale Eddy Distribution in Ocean
by Yaoran Chen, Zijian Zhao, Yaojun Yang, Xiaowei Li, Yan Peng, Hao Wu, Xi Zhou, Dan Zhang and Hongyu Wei
J. Mar. Sci. Eng. 2025, 13(1), 52; https://doi.org/10.3390/jmse13010052 - 31 Dec 2024
Viewed by 323
Abstract
Mesoscale eddies play a critical role in sea navigation and route planning, yet traditional prediction methods have often overlooked their spatial relationships, relying on indirect approaches to capture their distribution across extensive maps. To address this limitation, we present BiST-SA-LSTM, an end-to-end prediction [...] Read more.
Mesoscale eddies play a critical role in sea navigation and route planning, yet traditional prediction methods have often overlooked their spatial relationships, relying on indirect approaches to capture their distribution across extensive maps. To address this limitation, we present BiST-SA-LSTM, an end-to-end prediction framework that combines Bidirectional Spatial Temporal LSTM and Self-Attention mechanisms. Utilizing data sourced from the South China Sea and its surrounding regions, which are renowned for their intricate maritime dynamics, our methodology outperforms similar models across a range of evaluation metrics and visual assessments. This is particularly evident in our ability to provide accurate long-term forecasts that extend for up to 10 days. Furthermore, integrating sea surface variables enhances forecasting accuracy, contributing to advancements in oceanic physics. Full article
(This article belongs to the Section Ocean Engineering)
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17 pages, 2430 KiB  
Article
ADL+: A Digital Toolkit for Multidomain Cognitive, Physical, and Nutritional Interventions to Prevent Cognitive Decline in Community-Dwelling Older Adults
by Justin Chew, Zhiwei Zeng, Toh Hsiang Benny Tan, Pamela Chew, Noorhazlina Ali, Hao Wang, Melissa Ong, Roslyn Raymond, Kalene Pek, Di Wang, Liang Zhang, Zhiqi Shen, Cyril Leung, Jing Jih Chin, Wee Shiong Lim and Chunyan Miao
Int. J. Environ. Res. Public Health 2025, 22(1), 42; https://doi.org/10.3390/ijerph22010042 - 31 Dec 2024
Viewed by 396
Abstract
Background: Current research highlights the importance of addressing multiple risk factors concurrently to tackle the complex etiology of dementia. However, limited evidence exists on the efficacy of technology-driven, multidomain community-based interventions for preventing cognitive decline. Objectives: To evaluate the efficacy of ADL+, an [...] Read more.
Background: Current research highlights the importance of addressing multiple risk factors concurrently to tackle the complex etiology of dementia. However, limited evidence exists on the efficacy of technology-driven, multidomain community-based interventions for preventing cognitive decline. Objectives: To evaluate the efficacy of ADL+, an artificial intelligence (AI)-enabled digital toolkit integrating cognitive assessments and multidomain interventions, on outcomes of cognitive function, activity levels, and quality of life in older adults at risk of cognitive decline. Adherence and usability were also evaluated. Methods: We conducted a quasi-experimental study including community-dwelling older adults aged 60 years and above without dementia, but with subjective memory complaints (AD8 score ≥ 2). Participants received a six-month intervention (app-based cognitive training, personalized nutritional, physical, and social activities recommendations) or a control group treatment (cognitive health educational package). The primary outcome was a change in neuropsychological test battery (NTB) Z-scores (NTB composite and its individual domains: attention, processing speed, memory, and executive function). Secondary outcomes were activity levels (Frenchay Activities Index, FAI), and quality of life (EQ-5D). Outcomes were assessed at the end of the intervention and three months post-intervention using linear mixed-effects models. Results: 96% of participants in the intervention and 89% in the control group completed the study. At six months, the intervention group showed a significant NTB composite score improvement (mean change: 0.086 (95% CI 0.020 to 0.15)), with a between-group difference of 0.17 (95% CI 0.071 to 0.27). Significant differences in attention, processing speed, and memory domains were observed, with benefits sustained in the processing speed domain at nine months. The control group’s FAI scores declined at six months (mean change: −1.04 (95% CI −1.83 to −0.26)), while the intervention group’s scores remained stable. The intervention group’s EQ-5D visual analogue scale (VAS) scores improved at both six and nine months, with between-group differences of 4.06 (95% CI 0.23 to 7.90) at six months and 5.12 (95% CI 0.81 to 9.43) at nine months. Adherence was high, while average usability scores were obtained. Conclusions: The ADL+ toolkit shows potential beneficial effects on cognitive function, activity levels, and quality of life for older adults at risk of cognitive decline. Findings will guide future randomized controlled trials and implementation efforts. Full article
(This article belongs to the Special Issue New Advances in Health of Older Adults)
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11 pages, 1626 KiB  
Article
Graph Neural Networks for Analyzing Trauma-Related Brain Structure in Children and Adolescents: A Pilot Study
by Harim Jeong, Minjoo Kang, Shanon McLeay, R. J. R. Blair, Unsun Chung and Soonjo Hwang
Appl. Sci. 2025, 15(1), 277; https://doi.org/10.3390/app15010277 - 31 Dec 2024
Viewed by 425
Abstract
This study explores the potential of graph neural networks (GNNs) in analyzing brain networks of children and adolescents exposed to trauma, addressing limitations in traditional neuroimaging approaches. MRI-based brain data from trauma-exposed and control groups were modeled as whole-brain networks using regions-of-interest (ROIs), [...] Read more.
This study explores the potential of graph neural networks (GNNs) in analyzing brain networks of children and adolescents exposed to trauma, addressing limitations in traditional neuroimaging approaches. MRI-based brain data from trauma-exposed and control groups were modeled as whole-brain networks using regions-of-interest (ROIs), with GNNs applied to capture complex, non-linear connectivity patterns. Results revealed that the trauma-exposed group exhibited simplified network structures with higher importance in regions associated with cognitive and emotional regulation, such as the posterior cerebellum. In contrast, the control group demonstrated richer connectivity patterns, emphasizing regions related to motor and visual processing, such as the Right Lingual Gyrus. Compared to traditional t-test results highlighting regional density differences, the GNN approach uncovered deeper, network-level insights into the relationships between brain regions. These findings demonstrate the utility of GNNs in advancing neuroimaging research, offering new perspectives on trauma’s impact on brain connectivity and paving the way for future applications in understanding neural mechanisms and interventions. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare System)
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12 pages, 894 KiB  
Systematic Review
The Effect of High-Intensity Interval Training (HIIT) on Brain-Derived Neurotrophic Factor Levels (BNDF): A Systematic Review
by Milosz Mielniczek and Tore Kristian Aune
Brain Sci. 2025, 15(1), 34; https://doi.org/10.3390/brainsci15010034 - 30 Dec 2024
Viewed by 444
Abstract
Background/Objectives: High-intensity interval training (HIIT) alternates short periods of intense exercise with recovery, effectively enhancing cardiorespiratory fitness, endurance, and strength in various populations. Concurrently, brain-derived neurotrophic factor (BDNF) supports neuronal resilience and activity-dependent plasticity, which are vital for learning and memory. This study [...] Read more.
Background/Objectives: High-intensity interval training (HIIT) alternates short periods of intense exercise with recovery, effectively enhancing cardiorespiratory fitness, endurance, and strength in various populations. Concurrently, brain-derived neurotrophic factor (BDNF) supports neuronal resilience and activity-dependent plasticity, which are vital for learning and memory. This study aims to systematically review changes in BDNF levels in response to HIIT, with three primary objectives: evaluating the benefits of HIIT for BDNF modulation, assessing methodological quality and the risk of bias in reviewed studies, and identifying patterns in BDNF response based on HIIT protocols and population characteristics. Methods: Comprehensive database searches were conducted in PubMed and SPORTDiscus to identify relevant studies published up to April 2024. Given the diversity in study designs and outcomes, a narrative synthesis was performed rather than a meta-analysis. Bias was evaluated using visualization tools such as RobVis, and the review was conducted by a single researcher, which may limit its comprehensiveness. Results: Twelve studies met the inclusion criteria, with most indicating significant increases in BDNF levels post-HIIT, suggesting HIIT’s potential to enhance neuroplasticity and cognitive functions. However, variations in BDNF responses were observed across different HIIT protocols and study populations. Some studies reported decreases or no change in BDNF levels, reflecting the complex regulation of BDNF influenced by factors such as exercise intensity, duration, and individual variability. Conclusions: HIIT shows promise as an intervention for increasing BDNF levels, with potential benefits for brain health and cognitive function. These findings underscore the need for further research to confirm the optimal conditions under which HIIT can effectively enhance neurological outcomes. Future studies should explore standardized HIIT protocols and the long-term impact of HIIT on BDNF and neuroplasticity. Full article
(This article belongs to the Section Cognitive, Social and Affective Neuroscience)
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27 pages, 6928 KiB  
Review
A Review of Supply Chain Resilience: A Network Modeling Perspective
by Chuhan Ma, Lei Zhang, Liang You and Wenjie Tian
Appl. Sci. 2025, 15(1), 265; https://doi.org/10.3390/app15010265 - 30 Dec 2024
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Abstract
Against the backdrop of globalization, the complexity of supply chains has been increasing, making supply chain resilience a critical factor in ensuring the stable operation of enterprises, national economies, and international trade. This paper adopts a network modeling perspective to systematically review the [...] Read more.
Against the backdrop of globalization, the complexity of supply chains has been increasing, making supply chain resilience a critical factor in ensuring the stable operation of enterprises, national economies, and international trade. This paper adopts a network modeling perspective to systematically review the theoretical foundations and research progress in supply chain resilience, focusing on the application of network modeling methods. First, the concept of supply chain resilience is defined, and its developmental trajectory is reviewed. Through literature visualization analysis, this study delves into the current state of research on supply chain resilience, addressing challenges and risk management, highlighting the importance of network modeling techniques in this field. Subsequently, it explores supply chain network modeling based on complex networks and agent-based modeling, analyzing their strengths and limitations in simulating the overall evolution of supply chains and the dynamic behavior of individual entities. By integrating network structural characteristics with resilience evaluation methods, this paper suggests potential directions for future research. These include enhancing the description of individual firm behavior, analyzing the dynamics of information networks, and emphasizing task-oriented model design, thereby offering new perspectives and pathways for managing supply chain resilience in a way that can generate significant positive externalities for global economies. This research also indicates that the enhanced resilience of supply chains can produce a multiplier effect, benefiting not only individual firms but also promoting economic stability and growth across multiple countries. Full article
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