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Recent Articles  

29 pages, 2318 KiB  
Article
A Bounded Sine Skewed Model for Hydrological Data Analysis
by Tassaddaq Hussain, Mohammad Shakil, Mohammad Ahsanullah and Bhuiyan Mohammad Golam Kibria
Analytics 2025, 4(3), 19; https://doi.org/10.3390/analytics4030019 (registering DOI) - 13 Aug 2025
Abstract
Hydrological time series frequently exhibit periodic trends with variables such as rainfall, runoff, and evaporation rates often following annual cycles. Seasonal variations further contribute to the complexity of these data sets. A critical aspect of analyzing such phenomena is estimating realistic return intervals, [...] Read more.
Hydrological time series frequently exhibit periodic trends with variables such as rainfall, runoff, and evaporation rates often following annual cycles. Seasonal variations further contribute to the complexity of these data sets. A critical aspect of analyzing such phenomena is estimating realistic return intervals, making the precise determination of these values essential. Given this importance, selecting an appropriate probability distribution is paramount. To address this need, we introduce a flexible probability model specifically designed to capture periodicity in hydrological data. We thoroughly examine its fundamental mathematical and statistical properties, including the asymptotic behavior of the probability density function (PDF) and hazard rate function (HRF), to enhance predictive accuracy. Our analysis reveals that the PDF exhibits polynomial decay as x→∞, ensuring heavy-tailed behavior suitable for extreme events. The HRF demonstrates decreasing or non-monotonic trends, reflecting variable failure risks over time. Additionally, we conduct a simulation study to evaluate the performance of the estimation method. Based on these results, we refine return period estimates, providing more reliable and robust hydrological assessments. This approach ensures that the model not only fits observed data but also captures the underlying dynamics of hydrological extremes. Full article
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35 pages, 2591 KiB  
Article
Emergence and Evolution of ‘Big Data’ Research: A 30-Year Scientometric Analysis of the Knowledge Field
by Ignacio Perez Karich and Simon Joss
Metrics 2025, 2(3), 15; https://doi.org/10.3390/metrics2030015 - 13 Aug 2025
Abstract
In the ongoing ‘data revolution’, the ubiquity of digital data in society underlines a transformative era. This is mirrored in the sciences, where ‘big data’ has emerged as a major research field. This article significantly extends previous scientometric analyses by tracing the field’s [...] Read more.
In the ongoing ‘data revolution’, the ubiquity of digital data in society underlines a transformative era. This is mirrored in the sciences, where ‘big data’ has emerged as a major research field. This article significantly extends previous scientometric analyses by tracing the field’s conceptual emergence and evolution across a 30-year period (1993–2022). Bibliometric analysis is based on 17 data categories that co-constitute the conceptual network of ‘big data’ research. Using Scopus, the search query resulted in 70,163 articles and 315,235 author keywords. These are analysed aggregately regarding co-occurrences of the 17 data categories and co-occurrences of data categories with author keywords, and regarding their disciplinary distributions and interdisciplinary reach. Temporal analysis reveals two major development phases: 1993–2012 and 2013–2022. The study demonstrates: (1) the rapid expansion of the research field concentrated on seven main data categories; (2) the consolidation of keyword (co-)occurrences on ‘machine learning’, ‘deep learning’, ‘artificial intelligence’ and ‘cloud computing’; and (3) significant interdisciplinarity across four main subject areas. Scholars can use the findings to combine data categories and author keywords in ways that align scholarly work with specific thematic and disciplinary interests. The findings could also inform research funding, especially concerning opportunities for cross-disciplinary research. Full article
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24 pages, 5852 KiB  
Article
Methodologies and Criteria for Defining Areas for Forest Restoration Aiming at Water Production and Security
by Terencio Rebello de Aguiar Junior, Lafayette Dantas da Luz, Reginaldo da Silva Rangel Neto, Diogo Caribé de Sousa and Eduardo Mariano-Neto
Limnol. Rev. 2025, 25(3), 37; https://doi.org/10.3390/limnolrev25030037 - 13 Aug 2025
Abstract
This study presents a methodological framework for prioritizing areas for forest restoration with the primary objective of enhancing water provision. A multi-scale approach was employed, starting with macro-scale criteria at the river basin level, followed by more localized landscape and hydro-ecological assessments. This [...] Read more.
This study presents a methodological framework for prioritizing areas for forest restoration with the primary objective of enhancing water provision. A multi-scale approach was employed, starting with macro-scale criteria at the river basin level, followed by more localized landscape and hydro-ecological assessments. This two-stage process facilitated strategic planning for interventions aimed at restoring forest cover in permanent preservation areas (PPAs) along watercourses and springs. The methodology was applied to the Joanes and Jacuípe Rivers Permanent Protection Areas Forest Rehabilitation Project in the Salvador Metropolitan Region, Bahia. The project’s primary goal is to improve water security by restoring native vegetation across 100 springs and 100 hectares of riparian zones, which are critical to the water supply system for the Salvador Metropolitan Region. The prioritization process integrated hydrological, ecological, and socio-environmental criteria, ensuring that restoration efforts not only enhance water production but also provide long-term ecological and social benefits. Full article
16 pages, 4982 KiB  
Review
The Role of Metal Foams for Sustainability and Energy Transition
by Alessandra Ceci, Girolamo Costanza, Fabio Giudice, Andrea Sili and Maria Elisa Tata
Alloys 2025, 4(3), 16; https://doi.org/10.3390/alloys4030016 - 13 Aug 2025
Abstract
The global pursuit of a sustainable and decarbonized energy landscape requires the development of novel materials capable of supporting lightweight construction, advanced energy conversion, storage, and thermal management technologies. Among these, metal foams have emerged as a versatile class of porous materials, offering [...] Read more.
The global pursuit of a sustainable and decarbonized energy landscape requires the development of novel materials capable of supporting lightweight construction, advanced energy conversion, storage, and thermal management technologies. Among these, metal foams have emerged as a versatile class of porous materials, offering a unique combination of low density, high surface area, three-dimensional (3D) interconnected porosity, and favorable thermal and electrical conductivities. These attributes make them highly suitable for a broad range of applications critical to the ongoing energy transition, assuming an increasingly central role in enabling clean, efficient, and resilient energy infrastructures. From this key perspective, the present review highlights the relevance of the adoption of metal foams in several fields crucial for the energy transition. By presenting methodologies and outcomes of research results, mainly from the last five years, the paper underscores the potential of low-weight, high-surface, and high-performance porous materials in contemporary and future industry, supporting sustainable development and, more generally, energy transition and circular economy. The approach also aims to minimize negative impacts and promote sustainability, for example, by recycling and transforming waste materials. Full article
(This article belongs to the Special Issue Lightweight Alloys)
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27 pages, 1370 KiB  
Review
Immune Organoids: A Review of Their Applications in Cancer and Autoimmune Disease Immunotherapy
by David B. Olawade, Emmanuel O. Oisakede, Eghosasere Egbon, Saak V. Ovsepian and Stergios Boussios
Curr. Issues Mol. Biol. 2025, 47(8), 653; https://doi.org/10.3390/cimb47080653 - 13 Aug 2025
Abstract
Immune organoids have emerged as a ground-breaking platform in immunology, offering a physiologically relevant and controllable environment to model human immune responses and evaluate immunotherapeutic strategies. Derived from stem cells or primary tissues, these three-dimensional constructs recapitulate key aspects of lymphoid tissue architecture, [...] Read more.
Immune organoids have emerged as a ground-breaking platform in immunology, offering a physiologically relevant and controllable environment to model human immune responses and evaluate immunotherapeutic strategies. Derived from stem cells or primary tissues, these three-dimensional constructs recapitulate key aspects of lymphoid tissue architecture, cellular diversity, and functional dynamics, providing a more accurate alternative to traditional two-dimensional cultures and animal models. Their ability to mimic complex immune microenvironments has positioned immune organoids at the forefront of cancer immunotherapy development, autoimmune disease modeling, and personalized medicine. This narrative review highlights the advances in immune organoid technology, with a focus on their applications in testing immunotherapies, such as checkpoint inhibitors, CAR-T cells, and cancer vaccines. It also explores how immune organoids facilitate the study of autoimmune disease pathogenesis with insights into their molecular basis and support in high-throughput drug screening. Despite their transformative potential, immune organoids face significant challenges, including the replication of systemic immune interactions, standardization of fabrication protocols, scalability limitations, biological heterogeneity, and the absence of vascularization, which restricts organoid size and maturation. Future directions emphasize the integration of immune organoids with multi-organ systems to better replicate systemic physiology, the development of advanced biomaterials that closely mimic lymphoid extracellular matrices, the incorporation of artificial intelligence (AI) to optimize organoid production and data analysis, and the rigorous clinical validation of organoid-derived findings. Continued innovation and interdisciplinary collaboration will be essential to overcome existing barriers, enabling the widespread adoption of immune organoids as indispensable tools for advancing immunotherapy, vaccine development, and precision medicine. Full article
(This article belongs to the Section Molecular Medicine)
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17 pages, 1779 KiB  
Article
Removal of Fluoride from Aqueous Solution Using Biochar Derived from Brown Macroalgae (Sargassum Polycystum) Impregnated with Fe3O4 Nanoparticles
by Sania Kanwal, Satesh Kumar Devrajani and Saif Ali Khan Hashmani
Phycology 2025, 5(3), 37; https://doi.org/10.3390/phycology5030037 - 13 Aug 2025
Abstract
This study explores the enhancement of fluoride adsorption using biochar derived from the brown macroalga Sargassum polycystum, which was treated with iron oxide (Fe3O4). The macroalgal biomass underwent pyrolysis at 400 °C, followed by Fe3O4 [...] Read more.
This study explores the enhancement of fluoride adsorption using biochar derived from the brown macroalga Sargassum polycystum, which was treated with iron oxide (Fe3O4). The macroalgal biomass underwent pyrolysis at 400 °C, followed by Fe3O4 impregnation, to improve surface functionality and create active sites for fluoride ion binding. Various factors affecting fluoride removal were systematically examined. A maximum fluoride removal effectiveness of 90.2% was attained under ideal circumstances (pH 2, 60 mg adsorbent dose, 30 mg/L fluoride concentration, and 150 min contact duration). Adsorption isotherm analysis showed that the Langmuir model provided a better fit (R2 = 0.998) than the Freundlich model (R2 = 0.941), with a maximum adsorption capacity (qₘ) of 3.41 mg/g, indicating monolayer adsorption on a homogeneous surface. Kinetic modeling revealed that the pseudo-second-order model best described the adsorption process (R2 = 0.9943), suggesting chemisorption as the dominant mechanism, while the intraparticle diffusion model also showed a good fit (R2 = 0.9524), implying its role in the rate-limiting step. Surface complexation, facilitated by the enhanced surface area and porosity of the iron-modified biochar, was identified as the primary mechanism of fluoride ion interaction. This study highlights the potential of Fe3O4-modified macroalgal biochar as an effective and sustainable solution for fluoride remediation in contaminated water sources. Full article
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20 pages, 2290 KiB  
Article
Machine Learning vs. Langmuir: A Multioutput XGBoost Regressor Better Captures Soil Phosphorus Adsorption Dynamics
by Miltiadis Iatrou and Aristotelis Papadopoulos
Crops 2025, 5(4), 55; https://doi.org/10.3390/crops5040055 - 13 Aug 2025
Abstract
Accurate prediction of soil phosphorus (P) adsorption capacity is essential for efficient fertilizer management and environmental protection. Traditional isotherm models, such as the Langmuir equation, have been widely used to quantify P sorption, but they do not adequately capture the nonlinear and multivariate [...] Read more.
Accurate prediction of soil phosphorus (P) adsorption capacity is essential for efficient fertilizer management and environmental protection. Traditional isotherm models, such as the Langmuir equation, have been widely used to quantify P sorption, but they do not adequately capture the nonlinear and multivariate nature of soil systems. This study evaluates the performance of a multi-output XGBoost regression model trained on laboratory-measured P adsorption data from 147 soils, representing a wide range of textures, pH levels, and CaCO3 contents. The model was developed to simultaneously predict P adsorption at five different equilibrium concentrations (1, 2, 4, 6, and 10 mg/L). SHAP analysis and causal discovery via DirectLiNGAM revealed that initial Olsen P concentration and sand content are the primary factors reducing P adsorption. The multi-output XGBoost model was compared against classical Langmuir isotherms using an extended dataset of 10,389 soil samples. The extended dataset was binned into four groups based on Olsen P concentrations and four groups based on sand content. This binning was based on the identification of these variables as highly influential by the XGBoost model, and on their demonstrated causal relationship with soil P sorption capacity through causal inference analysis. The XGBoost model outperformed the Langmuir model in capturing the effect of Olsen P and sand content, as it predicted a 12.6% drop in P adsorption in the very high Olsen P group and a 19.2% drop in the very high sand content groups, which are substantially higher than the reductions estimated by Langmuir isotherms. These results demonstrate that machine learning models, trained on well-designed experimental data, offer a superior alternative to classical isotherms for modeling P sorption dynamics. Full article
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38 pages, 751 KiB  
Article
Machine Learning and Feature Selection in Pediatric Appendicitis
by John Kendall, Gabriel Gaspar, Derek Berger and Jacob Levman
Tomography 2025, 11(8), 90; https://doi.org/10.3390/tomography11080090 - 13 Aug 2025
Abstract
Background/Objectives: Accurate prediction of pediatric appendicitis diagnosis, management, and severity is critical for clinical decision-making. We aimed to evaluate the predictive performance of a wide range of machine learning models, combined with various feature selection techniques, on a pediatric appendicitis dataset. A particular [...] Read more.
Background/Objectives: Accurate prediction of pediatric appendicitis diagnosis, management, and severity is critical for clinical decision-making. We aimed to evaluate the predictive performance of a wide range of machine learning models, combined with various feature selection techniques, on a pediatric appendicitis dataset. A particular focus was placed on the role of ultrasound (US) image-descriptive features in model performance and explainability. Methods: We conducted a retrospective cohort study on a dataset of 781 pediatric patients aged 0–18 presenting to Children’s Hospital St. Hedwig in Regensburg, Germany, between January 2016 and February 2023. We developed and validated predictive models; machine learning algorithms included the random forest, logistic regression, stochastic gradient descent, and the light gradient boosting machine (LGBM). These were paired exhaustively with feature selection methods spanning filter-based (association and prediction), embedded (LGBM and linear), and a novel redundancy-aware step-up wrapper approach. We employed a machine learning benchmarking study design where AI models were trained to predict diagnosis, management, and severity outcomes, both with and without US image-descriptive features, and evaluated on held-out testing samples. Model performance was assessed using overall accuracy and area under the receiver operating characteristic curve (AUROC). A deep learner optimized for tabular data, GANDALF, was also evaluated in these applications. Results: US features significantly improved diagnostic accuracy, supporting their use in reducing model bias. However, they were not essential for maximizing accuracy in predicting management or severity. In summary, our best-performing models were, for diagnosis, the random forest with embedded LGBM feature selection (98.1% accuracy, AUROC: 0.993), for management, the random forest without feature selection (93.9% accuracy, AUROC: 0.980), and for severity, the LGBM with filter-based association feature selection (90.1% accuracy, AUROC: 0.931). Conclusions: Our results demonstrate that high-performing, interpretable machine learning models can predict key clinical outcomes in pediatric appendicitis. US image features improve diagnostic accuracy but are not critical for predicting management or severity. Full article
(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
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35 pages, 1612 KiB  
Article
The Development of the Relate-Know-Respond Model to Enhance Family-Centred Care
by Lizz Carrington, Leigh Hale, Claire Freeman and Meredith Perry
Disabilities 2025, 5(3), 71; https://doi.org/10.3390/disabilities5030071 - 13 Aug 2025
Abstract
Disabled children often experience limited access to Family-Centred Care and social participation, while their families face significant caregiving responsibilities. Healthcare providers have a vital role in providing paediatric rehabilitation and support, yet access to and quality of services remain a key concern. To [...] Read more.
Disabled children often experience limited access to Family-Centred Care and social participation, while their families face significant caregiving responsibilities. Healthcare providers have a vital role in providing paediatric rehabilitation and support, yet access to and quality of services remain a key concern. To improve quality healthcare delivery, it is essential to understand stakeholder experiences of Family-Centred Care. Using an interpretive paradigm, semi-structured qualitative interviews and focus groups were conducted with paediatric healthcare service providers and service users, followed by reflexive thematic analysis. A key theme of ‘relationships enhance knowingness’ was identified. This theme highlights relationships as critical in meaningful service delivery and emphasises that mutual understanding, or knowingness, between service providers and service users is essential for success. Both service providers and service users identified subthemes related to ‘individual characteristics’ and ‘perceptions of families’, shaped by their differing positions within the care relationship. A distinct subtheme concerning ‘experiences of therapy’ was described by service users but was absent from service provider accounts. Fluctuating family capacity was identified as an influential factor affecting service engagement. A new service delivery model is presented to guide providers in delivering tailored, Family-Centred responsive Care. Full article
11 pages, 346 KiB  
Article
Proposal of a Cephalometric Method in Computed Tomography to Mandibular Analysis in Infants with Pierre Robin Sequence Treated by Fast and Early Mandibular Osteo-Distraction: Pilot Study
by Francesca Imondi, Adriana Assunta De Stefano, Rachele Podda, Martina Horodynski, Roberto Antonio Vernucci, Valentina Mazzoli, Piero Cascone and Gabriella Galluccio
Oral 2025, 5(3), 58; https://doi.org/10.3390/oral5030058 - 13 Aug 2025
Abstract
Background: Newborns with Pierre Robin Sequence (PRS) usually show varying degrees of upper airway obstruction and difficulty feeding due to severe micrognatia. Mandibular distraction osteogenesis has become popular as an alternative treatment option when other medical or surgical techniques are unsatisfactory. The [...] Read more.
Background: Newborns with Pierre Robin Sequence (PRS) usually show varying degrees of upper airway obstruction and difficulty feeding due to severe micrognatia. Mandibular distraction osteogenesis has become popular as an alternative treatment option when other medical or surgical techniques are unsatisfactory. The aim of this study is to test a three-dimensional (3D) cephalometric method in computed tomography (CT) to measure effective mandibular and midface length, and maxillomandibular ratio (Md/Mx ratio), as a mode of growth normalization expression in PRS patients before and after Fast and Early Mandibular Osteo-distraction (FEMOD), for assessing the diagnostic method and the efficacy of surgical treatment. Methods: In this retrospective pilot study, six PRS patients treated via the FEMOD surgical protocol were included. The measurements of effective maxillary and mandibular length were performed on 3D reconstructions from pre-surgical (T1) and post-surgical CT (T2). The growth disparity between the mandible and the maxilla was verified in T1 and was compared with the measurements obtained from the adaptation of the McNamara Norms; the correction of growth disproportion after FEMOD was assessed. Results: In T1, the PRS patients’ mandibular length and the Md/Mx ratio were smaller than the expected mandibular length (p = 0.029) and the expected Md/Mx ratio (p = 0.028). In T2, the PRS patients’ mandibular length and the Md/Mx ratio did not show significant differences from the expected results (p = 0.461 and p = 0.400). Conclusions: The 3D cephalometric analysis identifies the disproportion in pre-surgical maxillomandibular growth between PRS and reference measurements, and demonstrates that FEMOD allows the achievement of proportionality in the growth of the maxillomandibular complex in PRS patients. Full article
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12 pages, 4698 KiB  
Article
Use of Electrochemical Impedance Spectroscopy, Capacity, and Electrochemical Noise Measurements to Study Aging of Lithium-Ion Batteries
by Abdelfattah Boukhssim, Hassan Yassine, Gérard Leroy, Jean-Claude Carru, Manuel Mascot, Christophe Poupin and Mohammad Kassem
Solids 2025, 6(3), 44; https://doi.org/10.3390/solids6030044 - 13 Aug 2025
Abstract
Aging studies of lithium-ion batteries are essential for understanding material degradation, which impacts performance and, consequently, battery lifespan. In this paper, we propose the use of electrochemical impedance spectroscopy, differential capacity analysis, and electrochemical noise measurements to evaluate the effects of different C-rates [...] Read more.
Aging studies of lithium-ion batteries are essential for understanding material degradation, which impacts performance and, consequently, battery lifespan. In this paper, we propose the use of electrochemical impedance spectroscopy, differential capacity analysis, and electrochemical noise measurements to evaluate the effects of different C-rates (2C, C/2, and C/20) on a cell. We study aging up to 800 charge/discharge cycles. We demonstrate that aging is associated with a linear increase in electrode resistance, which correlates with capacity fading. Additionally, noise measurements indicate a rise in noise levels at low frequencies following a 1/fγ trend with 1<γ<2. Full article
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19 pages, 3411 KiB  
Article
Insulinotropic and Beta-Cell Proliferative Effects of Unripe Artocarpus heterophyllus Extract Ameliorate Glucose Dysregulation in High-Fat-Fed Diet-Induced Obese Mice
by Prawej Ansari, Sara S. Islam, Asif Ali, Md. Samim R. Masud, Alexa D. Reberio, Joyeeta T. Khan, J. M. A. Hannan, Peter R. Flatt and Yasser H. A. Abdel-Wahab
Diabetology 2025, 6(8), 83; https://doi.org/10.3390/diabetology6080083 - 13 Aug 2025
Abstract
Background: Artocarpus heterophyllus, familiar as jackfruit, is a tropical fruit highly valued not only for its nutritional content but also for its medicinal properties, including potential antidiabetic effects. Objectives: This study aimed to evaluate the insulinotropic, β-cell proliferative and anti-hyperlipidaemic properties of [...] Read more.
Background: Artocarpus heterophyllus, familiar as jackfruit, is a tropical fruit highly valued not only for its nutritional content but also for its medicinal properties, including potential antidiabetic effects. Objectives: This study aimed to evaluate the insulinotropic, β-cell proliferative and anti-hyperlipidaemic properties of the ethanol extract of unripe Artocarpus heterophyllus (EEAH) in high-fat-fed (HFF) diet-induced obese mice. Method: We evaluated acute insulin secretion and β-cell proliferation in BRIN-BD11 cells, and assessed in vitro glucose diffusion and starch digestion. In vivo, acute and chronic studies in HFF induced obese mice measured glucose tolerance, body weight, food and fluid intake, and lipid profiles. A preliminary phytochemical screening was also performed. Results: In this study, EEAH exhibited significant antidiabetic activity through multiple mechanisms. EEAH enhanced glucose-stimulated insulin secretion in BRIN-BD11 β-cells via KATP channel modulation and cAMP-mediated pathways, with partial dependence on extracellular calcium, and it also promoted β-cell proliferation. In vitro assays revealed its ability to inhibit starch digestion and glucose diffusion, indicating delayed carbohydrate digestion and absorption. In high-fat-fed (HFF) obese mice, the acute and chronic oral administration of EEAH improved oral glucose tolerance, reduced fasting blood glucose, decreased body weight, and normalized food and fluid intake. Lipid profile analysis showed increased HDL and reduced total cholesterol, LDL, and triglycerides, while higher doses of EEAH also enhanced gut motility. Phytochemical screening revealed the presence of bioactive compounds such as alkaloids, tannins, flavonoids, saponins, steroids, and terpenoids, which are likely responsible for these therapeutic effects. Conclusion: These findings highlight EEAH as a promising natural candidate for adjunctive therapy in managing type 2 diabetes and associated metabolic disorders and emphasize the importance of future multi-omics studies to elucidate its molecular targets and pathways. Full article
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26 pages, 891 KiB  
Review
The Evolution of Landscape Ecology in the Democratic Republic of the Congo (2005–2025): Scientific Advances, Methodological Challenges, and Future Directions
by Yannick Useni Sikuzani and Jan Bogaert
Earth 2025, 6(3), 97; https://doi.org/10.3390/earth6030097 - 13 Aug 2025
Abstract
Since 2005, landscape ecology has emerged as a structured scientific field in the Democratic Republic of Congo, notably shaped by the contributions of Professor Jan Bogaert. The evolution of research in this field can be divided into three main phases. The first phase [...] Read more.
Since 2005, landscape ecology has emerged as a structured scientific field in the Democratic Republic of Congo, notably shaped by the contributions of Professor Jan Bogaert. The evolution of research in this field can be divided into three main phases. The first phase (2005–2012) focused on the quantitative analysis of forest fragmentation using Geographic Information Systems and landscape metrics. From 2013 to 2019, research approaches broadened to include the social sciences, marking a shift toward a socio-ecological perspective on landscapes. Since 2020, the field has increasingly adopted holistic frameworks that integrate climatic factors and forward-looking modeling. Key research themes now include ecological flows across landscape mosaics, land-use dynamics, and the anthropogenic transformation of ecosystems. However, several challenges persist, including the lack of long-term temporal datasets, uneven geographic coverage, and limited integration of local knowledge systems. Notable advances have been made through high-resolution remote sensing and participatory methods, although their application is still limited by technical and financial constraints. This manuscript advocates for stronger interdisciplinary collaboration, improved field methodologies, and the development of context-appropriate tools to support sustainable and locally grounded landscape management in the Congolese context. Full article
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33 pages, 999 KiB  
Perspective
Theoretical Framework and Methodological Approach for Investigating Potential Associations Between Long COVID and Autism Spectrum Disorder Prevalence
by Thorsten Rudroff
NeuroSci 2025, 6(3), 80; https://doi.org/10.3390/neurosci6030080 - 13 Aug 2025
Abstract
This perspective paper proposes a theoretical framework for investigating potential associations between Long COVID and rising autism spectrum disorder (ASD) prevalence through established epidemiological methodologies. I propose examining temporal correlations, biological mechanisms, and rigorous methodological approaches, including Mendelian randomization, animal models, and evidence-based [...] Read more.
This perspective paper proposes a theoretical framework for investigating potential associations between Long COVID and rising autism spectrum disorder (ASD) prevalence through established epidemiological methodologies. I propose examining temporal correlations, biological mechanisms, and rigorous methodological approaches, including Mendelian randomization, animal models, and evidence-based analyses, that could distinguish association from causation. The proposed framework recognizes autism as neurodiversity while suggesting investigation of environmental factors that may influence expression of genetic predispositions. Hypothesized key mechanisms include neuroinflammation, cytokine alterations, and immune dysfunction. I emphasize the critical distinction between demonstrating statistical associations and establishing causal influence, proposing specific experimental designs that could test causality. This paper presents conceptual frameworks requiring future empirical validation and does not include original data analysis. Full article
(This article belongs to the Topic Brain Plasticity Across the Lifespan: Development, Aging, and Implications for Cerebrovascular Health)
19 pages, 1914 KiB  
Article
Assessment of Gesture Accuracy for a Multi-Electrode EMG-Sensor-Array-Based Prosthesis Control System
by Vinod Sharma, Erik Lloyd, Mike Faltys, Max Ortiz-Catalan and Connor Glass
Prosthesis 2025, 7(4), 99; https://doi.org/10.3390/prosthesis7040099 - 13 Aug 2025
Abstract
Background: Upper limb loss significantly impacts quality of life, and whereas myoelectric prostheses restore some function, conventional surface electromyography (sEMG) systems face challenges like poor signal quality, high cognitive burden, and suboptimal control. Phantom X, a novel implantable electrode-array-based system leveraging machine [...] Read more.
Background: Upper limb loss significantly impacts quality of life, and whereas myoelectric prostheses restore some function, conventional surface electromyography (sEMG) systems face challenges like poor signal quality, high cognitive burden, and suboptimal control. Phantom X, a novel implantable electrode-array-based system leveraging machine learning (ML), aims to overcome these limitations. This feasibility study assessed Phantom X’s performance using non-invasive surface EMG electrodes to approximate implantable system behavior. Methods: This single-arm, non-randomized study included 11 participants (9 able-bodied, 2 with transradial amputation) fitted with a 32-electrode cutaneous array around the forearm. EMG signals were processed through an ML algorithm to control a desk-mounted prosthesis. Performance was evaluated via gesture accuracy (GA), modified gesture accuracy (MGA), and classifier gesture accuracy (CGA) across 11 hand gestures in three arm postures. User satisfaction was also assessed among the two participants with transradial amputation. Results: Phantom X achieved an average GA of 89.0% ± 6.8%, MGA of 96.8% ± 2.0%, and CGA of 93.6% ± 4.1%. Gesture accuracy was the highest in the Arm Parallel posture and the lowest in the Arm Perpendicular posture. Thumbs Up had the highest accuracy (100%), while Index Point and Index Tap gestures showed lower performance (70% and 79% GA, respectively). The mean latency between EMG onset and gesture detection was 250.5 ± 145.9 ms, with 91% of gestures executed within 500 ms. The amputee participants reported high satisfaction. Conclusions: This study demonstrates Phantom X’s potential to enhance prosthesis control through multi-electrode EMG sensing and ML-based gesture decoding. The non-invasive evaluation suggests high accuracy and responsiveness, warranting further studies with the implantable system to assess long-term usability and real-world performance. Phantom X may offer a superior alternative to conventional sEMG-based control, potentially reducing cognitive burden and improving functional outcomes for upper limb amputees. Full article
24 pages, 766 KiB  
Article
The Spirituality–Resilience–Happiness Triad: A High-Powered Model for Understanding University Student Well-Being
by Moises David Reyes-Perez, Leticia Carreño Saucedo, María Julia Sanchez-Levano, Roxana Cabanillas-Palomino, Paola Fiorella Monje-Yovera, Johan Pablo Jaime-Rodríguez, Luz Angelica Atoche-Silva, Johannes Michael Alarcón-Bustíos and Antony Esmit Franco Fernández-Altamirano
Eur. J. Investig. Health Psychol. Educ. 2025, 15(8), 158; https://doi.org/10.3390/ejihpe15080158 - 13 Aug 2025
Abstract
This study examines the relationships between spirituality, resilience, and happiness among higher education students, exploring the moderating roles of religious belief and years of study based on developmental and religious coping theoretical frameworks. Developmental theory suggests that university students’ psychological resources evolve across [...] Read more.
This study examines the relationships between spirituality, resilience, and happiness among higher education students, exploring the moderating roles of religious belief and years of study based on developmental and religious coping theoretical frameworks. Developmental theory suggests that university students’ psychological resources evolve across academic years, while religious coping theory posits that individual differences in religious commitment may buffer spirituality’s protective effects on well-being outcomes. Using a quantitative cross-sectional approach, data were collected from 459 university students from environmental science programs across public and private universities in northern Peru. Participants were predominantly female (59.04%) and aged 18–24 years (73%). Three validated instruments were administered: the Personal Spirituality Scale, Connor–Davidson Brief Resilience Scale, and Subjective Happiness Scale. Religious beliefs were measured on a 5-point scale, while years of study was categorized by academic year. Results from partial least squares structural equation modeling revealed significant direct effects of spirituality on both happiness (β = 0.256, p < 0.001) and resilience (β = 0.274, p < 0.001), with resilience also significantly influencing happiness (β = 0.162, p < 0.05). The structural model demonstrated exceptional explanatory power, with spirituality explaining 97.1% of variance in resilience, while spirituality and resilience together accounted for 86.2% of variance in happiness. Contrary to theoretical expectations, neither religious beliefs (β = 0.032, p = 0.489) nor years of study (β = −0.047, p = 0.443) showed significant moderating effects. These results suggest that spirituality and resilience serve as universal contributors to student well-being, operating independently of specific religious orientations and academic progression. The findings support integrating spiritual development and resilience-building components into inclusive university student support programs. Full article
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13 pages, 276 KiB  
Article
New Conformally Invariant Born–Infeld Models and Geometrical Currents
by Diego Julio Cirilo-Lombardo
Physics 2025, 7(3), 36; https://doi.org/10.3390/physics7030036 - 13 Aug 2025
Abstract
A new conformally invariant gravitational generalization of the Born–Infeld (BI) model is proposed and analyzed from the point of view of symmetries. Taking a geometric identity involving the determinant functions detfBμν, Fμν with the Bach [...] Read more.
A new conformally invariant gravitational generalization of the Born–Infeld (BI) model is proposed and analyzed from the point of view of symmetries. Taking a geometric identity involving the determinant functions detfBμν, Fμν with the Bach Bμν and the electromagnetic field Fμν tensors (with the 4-dimensional Greek letter indexes), two characteristic geometrical Lagrangian densities (Lagrangians) are derived: the first Lagrangian being the square root of the determinant function detBμν+Fμν (reminiscent of the standard BI model) and the second Lagrangian being the fourth root gdetBαγBβγ+FαγFβγ4. It is shown, after explicit computation of the gravitational equations, that the square-root model is incompatible with the inclusion of the electromagnetic tensor, consequently forcing the nullity of Fμν. In sharp contrast, the traceless fourth-root model is fully compatible and a natural ansatz of the type BμρBνρ∝Ωxgμν (conformal-Killing), with Ω the conformal factor and x the 4-coordinate, can be considered. Among other essential properties, the geometrical conformal Lagrangian of the fourth-root type is self-similar with respect to the determinant g of the metric tensor gμν and can be extended to non-Abelian fields in a way similar to the model developed by the author earlier. This self-similarity is related to the conformal properties of the model, such as the Bach currents or flows presumably of a topological origin. Possible applications and comparisons with other models are briefly discussed. Full article
(This article belongs to the Special Issue Beyond the Standard Models of Physics and Cosmology: 2nd Edition)
18 pages, 2056 KiB  
Article
Change Characteristics and Driving Factors of Molybdenum Content in Purple Soil from Southwestern China
by Xueqin Li, Tao Zhou, Chunpei Li, Xuan Wang, Limei Deng, Rongyang Cui, Xiaolin Sun and Gangcai Liu
Soil Syst. 2025, 9(3), 91; https://doi.org/10.3390/soilsystems9030091 - 13 Aug 2025
Abstract
Molybdenum (Mo) is an important trace nutrient element in the soil and plays a significant role in maintaining plant growth. However, there are scarce studies on soil Mo content change and its driving factors based on historical soil samples. This paper studied the [...] Read more.
Molybdenum (Mo) is an important trace nutrient element in the soil and plays a significant role in maintaining plant growth. However, there are scarce studies on soil Mo content change and its driving factors based on historical soil samples. This paper studied the characteristics of Mo content in three different parent rock types (PRTs) and different eras. The findings indicated that the available Mo (AMo) and total Mo (TMo) in the purple soil were 0.087–0.131 mg/kg and 0.488–0.903 mg/kg, respectively, which were considered deficient. The TMo of J3p was higher than those of J2s and K2j, but the AMo was slightly lower than those of K2j and J2s. Compared with the old samples, the AMo of K2j, J2s and J3p has increased by 35.58%, 120% and 30.86%, respectively, and their TMo has increased by 29.37%, 25.21% and 11.97%, respectively. Our studies showed that PRTs directly impacted AMo, and indirectly influenced TMo and AMo through soil pH and organic matter. Organic matter and pH positively affected TMo, while pH negatively affected AMo. Overall, soil molybdenum content in the study area was generally insufficient, and local governments should comprehensively consider the molybdenum content and its main constraints for scientific fertilisation. Full article
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22 pages, 4020 KiB  
Article
Visual Heritage and Motion Design: The Graphic-Cultural Legacy of Saul Bass’s Title Sequences
by Vincenzo Maselli and Giulia Panadisi
Heritage 2025, 8(8), 329; https://doi.org/10.3390/heritage8080329 - 13 Aug 2025
Abstract
Opening titles are more than introductory devices supporting the film they have been produced for; they are artistic and cultural artefacts with a powerful visual identity. Among the most emblematic figures in this design field, the graphic and motion designer Saul Bass (1920–1996) [...] Read more.
Opening titles are more than introductory devices supporting the film they have been produced for; they are artistic and cultural artefacts with a powerful visual identity. Among the most emblematic figures in this design field, the graphic and motion designer Saul Bass (1920–1996) pioneered an approach that redefined the identity, the design, and the experience of cinematic title sequences, opening a path of experimentation aimed at bridging visual communication, moving images, stylistic innovation, and aesthetic synaesthesia, through a combination of sound, movement, and image into a single expressive unit. This article investigates Bass’s contribution through a historical-critical and comparative lens, reconstructing the network of artistic and technological influences that shaped his design philosophy. It analyzes a selection of Bass’s title sequences, highlights his connection to European modernism, and identifies the seeds of postmodern culture in several aspects of Bass’s work such as the merging of principles coming from design and animation studies, the ambition for technological experimentation, and the openness towards a mass audience. By framing Bass’s creative legacy as a form of visual heritage, the article examines the ways in which his kinetic typography and moving compositions can be, therefore, recognized as resources for art historians, media scholars, designers, and visual communication theorists to track down the first and impactful aesthetic and narrative experiments conducted in the postmodern and contemporary motion graphic design field. Full article
(This article belongs to the Section Cultural Heritage)
18 pages, 352 KiB  
Article
Museums as Catalysts for Creativity in Adolescence: A Review
by Ricard Huerta and Vicente Alfonso-Benlliure
Heritage 2025, 8(8), 327; https://doi.org/10.3390/heritage8080327 - 13 Aug 2025
Abstract
To adequately educate citizens on issues such as heritage and heritage education, creativity must be fostered starting in secondary education. This paper analyzes activities like museum visits to determine their modalities and scope, examining the opportunities they provide for encouraging creativity among adolescent [...] Read more.
To adequately educate citizens on issues such as heritage and heritage education, creativity must be fostered starting in secondary education. This paper analyzes activities like museum visits to determine their modalities and scope, examining the opportunities they provide for encouraging creativity among adolescent visitors. We begin with a narrative bibliographic review based on international database searches, selecting and analyzing the relevant articles. Key findings include the various ways the relationship between creativity and museums manifests: as an end goal, as a means to promote specific learning in adolescents, the most common forms of creative expression, the contexts for such expression, and the different options for evaluating existing proposals. Discussion: We explore how to clarify and structure the role of creativity in museums aimed at adolescents. Among the conclusions, we emphasize the need for in-depth investigation of this phenomenon, which can enhance creativity training among secondary school students and other personal development variables through museums. Full article
(This article belongs to the Section Museum and Heritage)
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16 pages, 4324 KiB  
Article
IDOVIR—Infrastructure for Documentation of Virtual Reconstructions: Towards a Documentation Practice for Everyone
by Markus Wacker, Marc Grellert, Wolfgang Stille, Jonas Bruschke and Daniel Beck
Heritage 2025, 8(8), 328; https://doi.org/10.3390/heritage8080328 - 13 Aug 2025
Abstract
Source-based virtual reconstructions have become essential tools for communication and research in urban and architectural studies. While these reconstructions are often showcased through exhibition visualizations, the underlying knowledge is not always apparent or even documented. This raises concerns about their sustainability. Without transparent, [...] Read more.
Source-based virtual reconstructions have become essential tools for communication and research in urban and architectural studies. While these reconstructions are often showcased through exhibition visualizations, the underlying knowledge is not always apparent or even documented. This raises concerns about their sustainability. Without transparent, publicly accessible documentation of the decision-making processes (known as paradata) that come with and support these digital reconstructions, there is a risk of losing both the knowledge embedded in them and their potential scientific value. To enhance transparency and allow for proper assessment and recognition of these reconstructions, thorough documentation and evaluation of the reconstruction processes are crucial. Although there are various approaches to documenting virtual reconstructions tailored to specific use cases, and while some focus on aspects like visualizing reliability, the overall process of documentation remains cumbersome and costly, making it an exception rather than the norm. Previous tools that claim to properly document virtual reconstructions either cover only part of the metadata and linked sources, are too complicated to use, or are no longer available. Currently, there is no universally accepted, straightforward, and easy-to-use workflow for this purpose. The IDOVIR project addresses this gap by offering a user-friendly, web-based platform designed specifically for documenting digital architectural reconstructions. We strive for achieving such a standardized workflow. To date, the platform has already been adopted by a large number of users, and many projects are publicly accessible. Full article
(This article belongs to the Special Issue Cultural Heritage and New Technologies: From NextGen Approaches to Paradigm Shifts)
24 pages, 3837 KiB  
Article
Modeling Viscoelastic Behavior of HDPE Pipes Subjected to a Diametral Load Using the Standard Linear Solid Model
by David Paniagua-Lovera, Rafael B. Carmona-Paredes and Eduardo A. Rodal-Canales
Modelling 2025, 6(3), 80; https://doi.org/10.3390/modelling6030080 - 13 Aug 2025
Abstract
This paper presents the study of the viscoelastic behavior of high-density polyethylene (HDPE) ASTM 4710 pipes under diametral loads. The experimental procedure consists of applying a displacement ramp followed by a stress relaxation stage on six ring specimens extracted from pipes with varying [...] Read more.
This paper presents the study of the viscoelastic behavior of high-density polyethylene (HDPE) ASTM 4710 pipes under diametral loads. The experimental procedure consists of applying a displacement ramp followed by a stress relaxation stage on six ring specimens extracted from pipes with varying thickness-to-diameter ratios. The proposed methodology combines the Standard Linear Solid Model (SLSM) with beam theory, introduces adjustment equations for estimating SLSM parameters, and discusses the influence of residual stresses induced during pipe manufacturing and cooling. Finally, the paper shows the validation of the modeling approach based on the results of the mechanical response of an independent test case. Full article
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9 pages, 1071 KiB  
Communication
On the Appropriateness of Fixed Correlation Assumptions in Repeated-Measures Meta-Analysis: A Monte Carlo Assessment
by Vasileios Papadopoulos
Stats 2025, 8(3), 72; https://doi.org/10.3390/stats8030072 - 13 Aug 2025
Abstract
In repeated-measures meta-analyses, raw data are often unavailable, preventing the calculation of the correlation coefficient r between pre- and post-intervention values. As a workaround, many researchers adopt a heuristic approximation of r = 0.7. However, this value lacks rigorous mathematical justification and may [...] Read more.
In repeated-measures meta-analyses, raw data are often unavailable, preventing the calculation of the correlation coefficient r between pre- and post-intervention values. As a workaround, many researchers adopt a heuristic approximation of r = 0.7. However, this value lacks rigorous mathematical justification and may introduce bias into variance estimates of pre/post-differences. We employed Monte Carlo simulations (n = 500,000 per scenario) in Fisher z-space to examine the distribution of the standard deviation of pre-/post-differences (σD) under varying assumptions of r and its uncertainty (σr). Scenarios included r = 0.5, 0.6, 0.707, 0.75, and 0.8, each tested across three levels of variance (σr = 0.05, 0.1, and 0.15). The approximation of r = 0.75 resulted in a balanced estimate of σD, corresponding to a “midway” variance attenuation due to paired data. This value more accurately offsets the deficit caused by assuming a correlation, compared to the traditional value of 0.7. While the r = 0.7 heuristic remains widely used, our results support the use of r = 0.75 as a more mathematically neutral and empirically defensible alternative in repeated-measures meta-analyses lacking raw data. Full article
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13 pages, 905 KiB  
Article
A Mixture Integer GARCH Model with Application to Modeling and Forecasting COVID-19 Counts
by Wooi Chen Khoo, Seng Huat Ong, Victor Jian Ming Low and Hari M. Srivastava
Stats 2025, 8(3), 73; https://doi.org/10.3390/stats8030073 - 13 Aug 2025
Abstract
This article introduces a flexible time series regression model known as the Mixture of Integer-Valued Generalized Autoregressive Conditional Heteroscedasticity (MINGARCH). Mixture models provide versatile frameworks for capturing heterogeneity in count data, including features such as multiple peaks, seasonality, and intervention effects. The proposed [...] Read more.
This article introduces a flexible time series regression model known as the Mixture of Integer-Valued Generalized Autoregressive Conditional Heteroscedasticity (MINGARCH). Mixture models provide versatile frameworks for capturing heterogeneity in count data, including features such as multiple peaks, seasonality, and intervention effects. The proposed model is applied to regional COVID-19 data from Malaysia. To account for geographical variability, five regions—Selangor, Kuala Lumpur, Penang, Johor, and Sarawak—were selected for analysis, covering a total of 86 weeks of data. Comparative analysis with existing time series regression models demonstrates that MINGARCH outperforms alternative approaches. Further investigation into forecasting reveals that MINGARCH yields superior performance in regions with high population density, and significant influencing factors have been identified. In low-density regions, confirmed cases peaked within three weeks, whereas high-density regions exhibited a monthly seasonal pattern. Forecasting metrics—including MAPE, MAE, and RMSE—are significantly lower for the MINGARCH model compared to other models. These results suggest that MINGARCH is well-suited for forecasting disease spread in urban and densely populated areas, offering valuable insights for policymaking. Full article
24 pages, 3617 KiB  
Article
A Comparison Between Unimodal and Multimodal Segmentation Models for Deep Brain Structures from T1- and T2-Weighted MRI
by Nicola Altini, Erica Lasaracina, Francesca Galeone, Michela Prunella, Vladimiro Suglia, Leonarda Carnimeo, Vito Triggiani, Daniele Ranieri, Gioacchino Brunetti and Vitoantonio Bevilacqua
Mach. Learn. Knowl. Extr. 2025, 7(3), 84; https://doi.org/10.3390/make7030084 - 13 Aug 2025
Abstract
Accurate segmentation of deep brain structures is critical for preoperative planning in such neurosurgical procedures as Deep Brain Stimulation (DBS). Previous research has showcased successful pipelines for segmentation from T1-weighted (T1w) Magnetic Resonance Imaging (MRI) data. Nevertheless, the role of T2-weighted (T2w) MRI [...] Read more.
Accurate segmentation of deep brain structures is critical for preoperative planning in such neurosurgical procedures as Deep Brain Stimulation (DBS). Previous research has showcased successful pipelines for segmentation from T1-weighted (T1w) Magnetic Resonance Imaging (MRI) data. Nevertheless, the role of T2-weighted (T2w) MRI data has been underexploited so far. This study proposes and evaluates a fully automated deep learning pipeline based on nnU-Net for the segmentation of eight clinically relevant deep brain structures. A heterogeneous dataset has been prepared by gathering 325 paired T1w and T2w MRI scans from eight publicly available sources, which have been annotated by means of an atlas-based registration approach. Three 3D nnU-Net models—unimodal T1w, unimodal T2w, and multimodal (encompassing both T1w and T2w)—have been trained and compared by using 5-fold cross-validation and a separate test set. The outcomes prove that the multimodal model consistently outperforms the T2w unimodal model and achieves comparable performance with the T1w unimodal model. On our dataset, all proposed models significantly exceed the performance of the state-of-the-art DBSegment tool. These findings underscore the value of multimodal MRI in enhancing deep brain segmentation and offer a robust framework for accurate delineation of subcortical targets in both research and clinical settings. Full article
(This article belongs to the Special Issue Deep Learning in Image Analysis and Pattern Recognition, 2nd Edition)
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15 pages, 2175 KiB  
Article
Thrifty World Models for Applying Machine Learning in the Design of Complex Biosocial–Technical Systems
by Stephen Fox and Vitor Fortes Rey
Mach. Learn. Knowl. Extr. 2025, 7(3), 83; https://doi.org/10.3390/make7030083 - 13 Aug 2025
Abstract
Interactions between human behavior, legal regulations, and monitoring technology in road traffic systems provide an everyday example of complex biosocial–technical systems. In this paper, a study is reported that investigated the potential for a thrifty world model to predict consequences from choices about [...] Read more.
Interactions between human behavior, legal regulations, and monitoring technology in road traffic systems provide an everyday example of complex biosocial–technical systems. In this paper, a study is reported that investigated the potential for a thrifty world model to predict consequences from choices about road traffic system design. Colloquially, the term thrifty means economical. In physics, the term thrifty is related to the principle of least action. Predictions were made with algebraic machine learning, which combines predefined embeddings with ongoing learning from data. The thrifty world model comprises three categories that encompass a total of only eight system design choice options. Results indicate that the thrifty world model is sufficient to encompass biosocial–technical complexity in predictions of where and when it is most likely that accidents will occur. Overall, it is argued that thrifty world models can provide a practical alternative to large photo-realistic world models, which can contribute to explainable artificial intelligence (AI) and to frugal AI. Full article
(This article belongs to the Special Issue Advances in Machine and Deep Learning)
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