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Objectives: This study aimed to evaluate the 6-month real-world outcomes of switching to faricimab in patients with aflibercept-resistant neovascular age-related macular degeneration (nAMD). Methods: A retrospective review was conducted on the eyes of 60 patients with aflibercept-resistant nAMD that were switched to faricimab. Best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) parameters, including central subfield thickness (CST), subfoveal choroidal thickness (SFCT), and both the maximum height and width of pigment epithelial detachment (PED), at baseline and 1, 3, and 6 months after switching were evaluated. The type of PED and retinal fluid were also analyzed. Results: The results showed that BCVA remained stable at month 6 (p = 0.150), while CST significantly decreased (p = 0.020), and SFCT remained unchanged (p = 0.072). The maximum PED height significantly decreased (p = 0.030), while the maximum PED width did not change (p = 0.07). The mean injection interval significantly increased from 6.8 ± 2.4 weeks before switching to 11.2 ± 1.7 weeks after switching (p = 0.068). Furthermore, the dry macula rate was 43.3% at month 6. Conclusions: Switching to faricimab in aflibercept-resistant nAMD patients showed stable visual outcomes, significant anatomical improvements, and reduced treatment burden over 6 months in real-world clinical settings. Full article

(This article belongs to the Section Ophthalmology)

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27 pages, 10150 KiB

Open AccessFeature PaperArticle

Numerical Simulation and Experimental Study of the Thermal Wick-Debinding Used in Low-Pressure Powder Injection Molding

by Mohamed Amine Turki, Dorian Delbergue, Gabriel Marcil-St-Onge and Vincent Demers

Powders 2025, 4(3), 22; https://doi.org/10.3390/powders4030022 (registering DOI) - 1 Aug 2025

Abstract

Thermal wick-debinding, commonly used in low-pressure injection molding, remains challenging due to complex interactions between binder transport, capillary forces, and thermal effects. This study presents a numerical simulation of binder removal kinetics by coupling Darcy’s law with the Phase Transport in Porous Media interface in COMSOL Multiphysics. The model was validated and subsequently used to study the influence of key debinding parameters. Contrary to the Level Set method, which predicts isolated binder clusters, the Multiphase Flow in Porous Media method proposed in this work more accurately reflects the physical behavior of the process, capturing a continuous binder extraction throughout the green part and a uniform binder distribution within the wicking medium. The model successfully predicted the experimentally observed decrease in binder saturation with increasing debinding temperature or time, with deviation limited 3–10 vol. % (attributed to a mandatory brushing operation, which may underestimate the residual binder mass). The model was then used to optimize the debinding process: for a temperature of 100 °C and an inter-part gap distance of 5 mm, the debinding time was minimized to 7 h. These findings highlight the model’s practical utility for process design, offering a valuable tool for determining optimal debinding parameters and improving productivity. Full article

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22 pages, 5581 KiB

Open AccessArticle

PruneEnergyAnalyzer: An Open-Source Toolkit for Evaluating Energy Consumption in Pruned Deep Learning Models

by Cesar Pachon, Cesar Pedraza and Dora Ballesteros

Big Data Cogn. Comput. 2025, 9(8), 200; https://doi.org/10.3390/bdcc9080200 (registering DOI) - 1 Aug 2025

Abstract

Currently, various pruning strategies including different methods and distribution types are commonly used to reduce the number of FLOPs and parameters in deep learning models. However, their impact on actual energy savings remains insufficiently studied, particularly in resource-constrained settings. To address this, we introduce PruneEnergyAnalyzer, an open-source Python tool designed to evaluate the energy efficiency of pruned models. Starting from the unpruned model, the tool calculates the energy savings achieved by pruned versions provided by the user, and generates comparative visualizations based on previously applied pruning hyperparameters such as method, distribution type (PD), compression ratio (CR), and batch size. These visual outputs enable the identification of the most favorable pruning configurations in terms of FLOPs, parameter count, and energy consumption. As a demonstration, we evaluated the tool with 180 models generated from three architectures, five pruning distributions, three pruning methods, and four batch sizes, using another previous library (e.g. FlexiPrune). This experiment revealed the significant impact of the network architecture on Energy Reduction, the non-linearity between FLOPs savings and energy savings, as well as between parameter reduction and energy efficiency. It also showed that the batch size strongly influences the energy consumption of the pruned model. Therefore, this tool can support researchers in making pruning policy decisions that also take into account the energy efficiency of the pruned model. Full article

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20 pages, 2054 KiB

Open AccessArticle

Change Management in Aviation Organizations: A Multi-Method Theoretical Framework for External Environmental Uncertainty

by Ilona Skačkauskienė and Virginija Leonavičiūtė

Sustainability 2025, 17(15), 6994; https://doi.org/10.3390/su17156994 (registering DOI) - 1 Aug 2025

Abstract

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article

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22 pages, 9122 KiB

Open AccessArticle

Computational Mechanics of Polymeric Materials PEEK and PEKK Compared to Ti Implants for Marginal Bone Loss Around Oral Implants

by Mohammad Afazal, Saba Afreen, Vaibhav Anand and Arnab Chanda

Prosthesis 2025, 7(4), 93; https://doi.org/10.3390/prosthesis7040093 (registering DOI) - 1 Aug 2025

Abstract

Background/Objectives: Dental practitioners widely use dental implants to treat traumatic cases. Titanium implants are currently the most popular choice among dental practitioners and surgeons. The discovery of newer polymeric materials is also influencing the interest of dental professionals in alternative options. A comparative study between existing titanium implants and newer polymeric materials can enhance professionals’ ability to select the most suitable implant for a patient’s treatment. This study aimed to investigate material property advantages of high-performance thermoplastic biopolymers such as PEEK and PEKK, as compared to the time-tested titanium implants, and to find the most suitable and economically fit implant material. Methods: Three distinct implant material properties were assigned—PEEK, PEKK, and commercially pure titanium (CP Ti-55)—to dental implants measuring 5.5 mm by 9 mm, along with two distinct titanium (TI6AL4V) abutments. Twelve three-dimensional (3D) models of bone blocks, representing the mandibular right molar area with Osseo-integrated implants were created. The implant, abutment, and screw were assumed to be linear; elastic, isotropic, and orthotropic properties were attributed to the cancellous and cortical bone. Twelve model sets underwent a three-dimensional finite element analysis to evaluate von Mises stress and total deformation under 250 N vertical and oblique (30 degree) loads on the top surface of each abutment. Results: The study revealed that the time-tested titanium implant outperforms PEEK and PEKK in terms of marginal bone preservation, while PEEK outperforms PEKK. Conclusions: This study will assist dental practitioners in selecting implants from a variety of available materials and will aid researchers in their future research. Full article

(This article belongs to the Special Issue Advances in Digital Design for Dental and Maxillofacial Prosthodontics)

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24 pages, 5578 KiB

Open AccessArticle

Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas

by Yangzi Cong, Wenbin Su, Nan Jiang, Wenpeng Zong, Long Li, Yan Xu, Tianhe Xu and Paipai Wu

Sensors 2025, 25(15), 4745; https://doi.org/10.3390/s25154745 (registering DOI) - 1 Aug 2025

Abstract

In a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitation becomes especially acute during high-speed drone operations, where motion blur and fluctuating image clarity can significantly compromise navigation accuracy and system robustness. To address these issues, we propose an innovative adaptive covariance matrix estimation method for UAV-based VINS using Gaussian formulas. Our approach enhances the accuracy and robustness of the navigation system by dynamically adjusting the covariance matrix according to the quality of the images. Leveraging the advanced Laplacian operator, detailed assessments of image blur are performed, thereby achieving precise perception of image quality. Based on these assessments, a novel mechanism is introduced for dynamically adjusting the visual covariance matrix using a Gaussian model according to the clarity of images in the current environment. Extensive simulation experiments across the EuRoC and TUM VI datasets, as well as the field tests, have validated our method, demonstrating significant improvements in navigation accuracy of drones in scenarios with motion blur. Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors. Full article

(This article belongs to the Section Navigation and Positioning)

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21 pages, 4688 KiB

Open AccessArticle

Nondestructive Inspection of Steel Cables Based on YOLOv9 with Magnetic Flux Leakage Images

by Min Zhao, Ning Ding, Zehao Fang, Bingchun Jiang, Jiaming Zhong and Fuqin Deng

J. Sens. Actuator Netw. 2025, 14(4), 80; https://doi.org/10.3390/jsan14040080 (registering DOI) - 1 Aug 2025

Abstract

The magnetic flux leakage (MFL) method is widely acknowledged as a highly effective non-destructive evaluation (NDE) technique for detecting local damage in ferromagnetic structures such as steel wire ropes. In this study, a multi-channel MFL sensor module was developed, incorporating a purpose-designed Hall sensor array and magnetic yokes specifically shaped for steel cables. To validate the proposed damage detection method, artificial damages of varying degrees were inflicted on wire rope specimens through experimental testing. The MFL sensor module facilitated the scanning of the damaged specimens and measurement of the corresponding MFL signals. In order to improve the signal-to-noise ratio, a comprehensive set of signal processing steps, including channel equalization and normalization, was implemented. Subsequently, the detected MFL distribution surrounding wire rope defects was transformed into MFL images. These images were then analyzed and processed utilizing an object detection method, specifically employing the YOLOv9 network, which enables accurate identification and localization of defects. Furthermore, a quantitative defect detection method based on image size was introduced, which is effective for quantifying defects using the dimensions of the anchor frame. The experimental results demonstrated the effectiveness of the proposed approach in detecting and quantifying defects in steel cables, which combines deep learning-based analysis of MFL images with the non-destructive inspection of steel cables. Full article

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28 pages, 10224 KiB

Open AccessArticle

A Vulnerability Identification Method for Distribution Networks Integrating Fuzzy Local Dimension and Topological Structure

by Kangzheng Huang, Weichuan Zhang, Yongsheng Xu, Chenkai Wu and Weibo Li

Processes 2025, 13(8), 2438; https://doi.org/10.3390/pr13082438 (registering DOI) - 1 Aug 2025

Abstract

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges (2nd Edition))

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37 pages, 642 KiB

Open AccessArticle

The Goddess of the Flaming Mouth Between India and Tibet

by Arik Moran and Alexander Zorin

Religions 2025, 16(8), 1002; https://doi.org/10.3390/rel16081002 (registering DOI) - 1 Aug 2025

Abstract

This article examines the evolution and potential cross-cultural adaptations of the “Goddess of the Flaming Mouth”, Jvālāmukhī (Skt.) or Kha ‘bar ma (Tib.), in Indic and Tibetan traditions. A minor figure in medieval Hindu Tantras, Jvālāmukhī is today best known through her tangible manifestation as natural flames in a West Himalayan temple complex in the valley of Kangra, Himachal Pradesh, India. The gap between her sparse portrayal in Tantric texts and her enduring presence at this local “seat of power” (śakti pīṭha) raises questions regarding her historical development and sectarian affiliations. To address these questions, we examine mentions of Jvālāmukhī’s Tibetan counterpart, Kha ‘bar ma, across a wide range of textual sources: canonical Buddhist texts, original Tibetan works of the Bön and Buddhist traditions, and texts on sacred geography. Regarded as a queen of ghost spirits (pretas) and field protector (kṣetrapāla) in Buddhist sources, her portrayal in Bön texts contain archaic motifs that hint at autochthonous and/or non-Buddhist origins. The assessment of Indic material in conjunction with Tibetan texts point to possible transformations of the goddess across these culturally proximate Himalayan settings. In presenting and contextualizing these transitions, this article contributes critical data to ongoing efforts to map the development, adaptation, and localization of Tantric deities along the Indo-Tibetan interface. Full article

22 pages, 1289 KiB

Open AccessArticle

Assessment of Heavy Metal Contamination and Human Health Risk in Parapenaeus longirostris from Coastal Tunisian Aquatic Ecosystems

by Walid Ben Ameur, Ali Annabi, Kaddachi Rania and Mauro Marini

Pollutants 2025, 5(3), 23; https://doi.org/10.3390/pollutants5030023 (registering DOI) - 1 Aug 2025

Abstract

Seafood contamination by heavy metals is a growing public health concern, particularly in regions like Tunisia where seafood is a major dietary component. This study assessed concentrations of cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) in the muscle tissue of the red shrimp Parapenaeus longirostris, collected in 2023 from four coastal regions: Bizerte, Monastir, Kerkennah, and Gabes. Metal analysis was conducted using flame atomic absorption spectroscopy. This species was chosen due to its ecological and economic importance. The study sites were chosen based on their differing levels of industrial, urban, and agricultural influence, providing a representative overview of regional contamination patterns. Mean concentrations were 1.04 µg/g for Zn, 0.59 µg/g for Cu, 1.56 µg/g for Pb, and 0.21 µg/g for Cd (dry weight). Pb was the most prevalent metal across sites. Statistically significant variation was observed only for Cu (p = 0.0334). All metal concentrations were below international safety limits set by FAO/WHO and the European Union. Compared to similar studies, the levels reported were similar or slightly lower. Human health risk was evaluated using target hazard quotient (THQ), hazard index (HI), and cancer risk (CR) values. For adults, THQ ranged from 5.44 × 10⁻⁶ to 8.43 × 10⁻⁴, while for children it ranged from 2.40 × 10⁻⁵ to 3.72 × 10⁻³. HI values were also well below 1, indicating negligible non-carcinogenic risk. CR values for Cd and Pb in both adults and children fell within the acceptable risk range (10⁻⁶ to <10⁻⁴), suggesting no significant carcinogenic concern. This study provides the first field-based dataset on metal contamination in P. longirostris from Tunisia, contributing valuable insights for seafood safety monitoring and public health protection. Full article

(This article belongs to the Special Issue Marine Pollutants: 3rd Edition)

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10 pages, 459 KiB

Open AccessArticle

Influence of Primary Care Physicians on End-of-Life Treatment Choices in Lung Cancer Diagnosed in the Emergency Department

by Tatsuyuki Kawahara, Nobuaki Ochi, Hirohito Kirishi, Yusuke Sunada, Ayaka Mimura, Naruhiko Ichiyama, Yoko Kosaka, Yasunari Nagasaki, Hidekazu Nakanishi, Hiromichi Yamane and Nagio Takigawa

J. Pers. Med. 2025, 15(8), 339; https://doi.org/10.3390/jpm15080339 (registering DOI) - 1 Aug 2025

Abstract

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. While most diagnoses occur in outpatient settings, a subset of cases are incidentally identified during emergency department (ED) visits. The clinical characteristics and treatment decisions of these patients, particularly in relation to social background factors such as living situation and access to primary care, remain poorly understood. Methods: We conducted a retrospective study of patients diagnosed with malignancies in the ED of a single institution between April 2018 and December 2021. Patients diagnosed with lung cancer within 60 days of an ED visit were included. Data on demographics, disease status, treatment decisions, and background factors—including whether patients lived alone or had a primary care physician (PCP)—were extracted and analyzed. Results: Among 32,108 patients who visited the ED, 148 were diagnosed with malignancy within 60 days; 23 had lung cancer. Of these, 69.6% had metastatic disease at diagnosis, and 60.9% received active treatment (surgery or chemotherapy). No significant associations were observed between the extent of disease and either living arrangement or PCP status. However, the presence of a PCP was significantly associated with the selection of best supportive care (p = 0.023). No significant difference in treatment decisions was observed based on age (cutoff: 75 years). Conclusions: Although social background factors such as living alone were not significantly associated with cancer stage or treatment choice, the presence of a primary care physician was associated with a higher likelihood of best supportive care being selected. This may indicate that patients with an established PCP have more clearly defined care goals at the end of life. These findings suggest that primary care access may play a role in shaping end-of-life care preferences, highlighting the importance of personalized approaches in acute oncology care. Full article

(This article belongs to the Special Issue New Insights into Personalized Care in Advance Care Planning)

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18 pages, 11340 KiB

Open AccessArticle

CLSANet: Cognitive Learning-Based Self-Adaptive Feature Fusion for Multimodal Visual Object Detection

by Han Peng, Qionglin Liu, Riqing Ruan, Shuaiqi Yuan and Qin Li

Electronics 2025, 14(15), 3082; https://doi.org/10.3390/electronics14153082 (registering DOI) - 1 Aug 2025

Abstract

Multimodal object detection leverages the complementary characteristics of visible (RGB) and infrared (IR) imagery, making it well-suited for challenging scenarios such as low illumination, occlusion, and complex backgrounds. However, most existing fusion-based methods rely on static or heuristic strategies, limiting their adaptability to dynamic environments. To address this limitation, we propose CLSANet, a cognitive learning-based self-adaptive network that enhances detection performance by dynamically selecting and integrating modality-specific features. CLSANet consists of three key modules: (1) a Dominant Modality Identification Module that selects the most informative modality based on global scene analysis; (2) a Modality Enhancement Module that disentangles and strengthens shared and modality-specific representations; and (3) a Self-Adaptive Fusion Module that adjusts fusion weights spatially according to local scene complexity. Compared to existing methods, CLSANet achieves state-of-the-art detection performance with significantly fewer parameters and lower computational cost. Ablation studies further demonstrate the individual effectiveness of each module under different environmental conditions, particularly in low-light and occluded scenes. CLSANet offers a compact, interpretable, and practical solution for multimodal object detection in resource-constrained settings. Full article

(This article belongs to the Special Issue Digital Intelligence Technology and Applications)

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24 pages, 3553 KiB

Open AccessArticle

A Hybrid Artificial Intelligence Framework for Melanoma Diagnosis Using Histopathological Images

by Alberto Nogales, María C. Garrido, Alfredo Guitian, Jose-Luis Rodriguez-Peralto, Carlos Prados Villanueva, Delia Díaz-Prieto and Álvaro J. García-Tejedor

Technologies 2025, 13(8), 330; https://doi.org/10.3390/technologies13080330 (registering DOI) - 1 Aug 2025

Abstract

Cancer remains one of the most significant global health challenges due to its high mortality rates and the limited understanding of its progression. Early diagnosis is critical to improving patient outcomes, especially in skin cancer, where timely detection can significantly enhance recovery rates. Histopathological analysis is a widely used diagnostic method, but it is a time-consuming process that heavily depends on the expertise of highly trained specialists. Recent advances in Artificial Intelligence have shown promising results in image classification, highlighting its potential as a supportive tool for medical diagnosis. In this study, we explore the application of hybrid Artificial Intelligence models for melanoma diagnosis using histopathological images. The dataset used consisted of 506 histopathological images, from which 313 curated images were selected after quality control and preprocessing. We propose a two-step framework that employs an Autoencoder for dimensionality reduction and feature extraction of the images, followed by a classification algorithm to distinguish between melanoma and nevus, trained on the extracted feature vectors from the bottleneck of the Autoencoder. We evaluated Support Vector Machines, Random Forest, Multilayer Perceptron, and K-Nearest Neighbours as classifiers. Among these, the combinations of Autoencoder with K-Nearest Neighbours achieved the best performance and inference time, reaching an average accuracy of approximately 97.95% on the test set and requiring 3.44 min per diagnosis. The baseline comparison results were consistent, demonstrating strong generalisation and outperforming the other models by 2 to 13 percentage points. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)

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5 pages, 6475 KiB

Open AccessInteresting Images

Retractile Polyps of Soft Coral Gersemia rubiformis (Octocorallia: Alcyoniidae) Offer Protection to Developing Basket Stars (Gorgonocephalus sp.)

by Kathryn Murray, Bárbara de Moura Neves, Emmeline Broad and Vonda E. Hayes

Diversity 2025, 17(8), 543; https://doi.org/10.3390/d17080543 (registering DOI) - 1 Aug 2025

Abstract

Cold-water soft corals are a known habitat for juvenile basket stars (Gorgonocephalus sp.), but the role of this relationship in the earliest life stages of basket stars warrants further investigation. Here, basket stars and colonies of the soft coral Gersemia rubiformis were collected together from the Funk Island Deep Marine Refuge (NW Atlantic) and maintained in a laboratory setting for observation. During this time, two developing (<1 mm disc diameter) basket stars were discovered on coral colonies and could be seen retracting with the coral polyp into the colony. The basket stars were recorded unharmed once the polyps were expanded again and continued to retract within the colony over the period of observation. The results of this study show that developing basket stars can spend time inside the coral colony, which could be a form of protection. Full article

(This article belongs to the Section Marine Diversity)

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18 pages, 3318 KiB

Open AccessFeature PaperArticle

Indirect AI-Based Estimation of Cardiorespiratory Fitness from Daily Activities Using Wearables

by Laura Saldaña-Aristizábal, Jhonathan L. Rivas-Caicedo, Kevin Niño-Tejada and Juan F. Patarroyo-Montenegro

Electronics 2025, 14(15), 3081; https://doi.org/10.3390/electronics14153081 (registering DOI) - 1 Aug 2025

Abstract

Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Position, Attitude and Motion Tracking)

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