Saved Queries

by Eugenia Accorsi Buttini, Alberto Zucchelli, Paolo Tura, Gianluca Bianco, Daniele Avenoso, Giovanni Campisi, Mirko Farina, Gabriele Magliano, Enrico Morello, Vera Radici, Nicola Polverelli, Domenico Russo, Alessandra Marengoni and Michele Malagola

Cancers 2025, 17(20), 3278; https://doi.org/10.3390/cancers17203278 (registering DOI) - 10 Oct 2025

Abstract

Background: The Comprehensive Geriatric Assessment (CGA) has proven to be a valuable tool for providing a more comprehensive health evaluation of allogeneic stem cell transplantation (allo-SCT) recipients. Methods: We prospectively developed and tested a new Simplified Geriatric Score-4 (SGS-4) on 135 consecutive patients aged ≥50 years who underwent allo-SCT between 2020 and 2023. Each CGA component was individually analyzed for its association with overall survival (OS), non-relapse mortality (NRM), and cumulative incidence of relapse (CIR). Then, we performed a two-factor analysis (FA) using oblimin rotation and Bartlett estimation on all CGA components and sex. Based on component weights, a simplified geriatric score-4 score (SGS-4) was created: [Gait Speed] + 2 × [Hand Grip] + Geriatric 8 + 1.5 × [Sex]. ROC analysis defined three fitness groups, frail (≤13), prefrail (>13–22.5), and fit (>22.5). Results: Reduced hand grip strength and impaired mini mental state examination (MMSE) were associated with worse OS and higher NRM. Vulnerable Elders Survey (VES-13) and Fondazione Italiana Linfomi (FIL) scores also indicated poorer OS, though with uneven group sizes. Other CGA domains and the Hematopoietic Cell Transplantation–Comorbidity Index (HCT-CI) showed no significant prognostic value. The SGS-4 effectively stratified patients into three fitness groups, with those in the frail category experiencing lower OS and an increased risk of relapse. Conclusions: The new Simplified Geriatric Score-4 (SGS-4) based on three CGA domains (gait speed, hand grip, Geriatric 8) and sex effectively predicts OS and CIR risk in patients aged ≥50 years undergoing allo-SCT. The study’s small sample size and disease heterogeneity warrant further validation in larger cohorts. Full article

(This article belongs to the Special Issue New Challenges in Allogeneic Hematopoietic Stem Cell Transplantation for Acute Myeloid Leukemia)

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16 pages, 456 KB

Open AccessReview

Forensic Odontology in the Digital Era: A Narrative Review of Current Methods and Emerging Trends

by Carmen Corina Radu, Timur Hogea, Cosmin Carașca and Casandra-Maria Radu

Diagnostics 2025, 15(20), 2550; https://doi.org/10.3390/diagnostics15202550 (registering DOI) - 10 Oct 2025

Abstract

Background/Objectives: Forensic dental determination plays a central role in human identification, age estimation, and trauma analysis in medico-legal contexts. Traditional approaches—including clinical examination, odontometric analysis, and radiographic comparison—remain essential but are constrained by examiner subjectivity, population variability, and reduced applicability in fragmented or degraded remains. Recent advances in cone-beam computed tomography (CBCT), three-dimensional surface scanning, intraoral imaging, and artificial intelligence (AI) offer promising opportunities to enhance accuracy, reproducibility, and integration with multidisciplinary forensic evidence. The aim of this review is to synthesize conventional and emerging approaches in forensic odontology, critically evaluate their strengths and limitations, and highlight areas requiring validation. Methods: A structured literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2015 and 2025. Search terms combined forensic odontology, dental identification, CBCT, 3D scanning, intraoral imaging, and AI methodologies. From 108 records identified, 81 peer-reviewed articles met eligibility criteria and were included for analysis. Results: Digital methods such as CBCT, 3D scanning, and intraoral imaging demonstrated improved diagnostic consistency compared with conventional techniques. AI-driven tools—including automated age and sex estimation, bite mark analysis, and restorative pattern recognition—showed potential to enhance objectivity and efficiency, particularly in disaster victim identification. Persistent challenges include methodological heterogeneity, limited dataset diversity, ethical concerns, and issues of legal admissibility. Conclusions: Digital and AI-based approaches should complement, not replace, the expertise of forensic odontologists. Standardization, validation across diverse populations, ethical safeguards, and supportive legal frameworks are necessary to ensure global reliability and medico-legal applicability. Full article

(This article belongs to the Special Issue Advances in Dental Imaging, Oral Diagnosis, and Forensic Dentistry)

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17 pages, 4819 KB

Open AccessArticle

A Novel Continuous Ultrasound-Assisted Leaching Process for Rare Earth Element Extraction: Environmental and Economic Assessment

by Rebecca M. Brown, Ethan Struhs, Amin Mirkouei and David Reed

Sustain. Chem. 2025, 6(4), 33; https://doi.org/10.3390/suschem6040033 (registering DOI) - 10 Oct 2025

Abstract

Rare earth elements (REEs) make up integral components in personal electronics, healthcare instrumentation, and modern energy technologies. REE leaching with organic acids is an environmentally friendly alternative to traditional extraction methods. Our previous study demonstrated that batch ultrasound-assisted organic acid leaching of REEs can significantly decrease environmental impacts compared to traditional bioleaching. The batch method is limited to small volumes and is unsuitable for industrial implementation. This study proposes a novel approach to increase reaction volume using a continuous ultrasound-assisted organic acid leaching method. Laboratory experiments showed that continuous ultrasound-assisted leaching increased the leaching rate (µg/h) 11.3–24.5 times compared to our previously reported batch method. Techno-economic analysis estimates the cost of the continuous approach using commercially purchased organic acids is $9465/kg of extracted REEs and $4325/kg of extracted REEs, using gluconic acid and citric acid, respectively. The sensitivity analysis reveals that substituting commercially purchased organic acids with microbially produced biolixiviant can reduce the process cost by approximately 99% while minimally increasing energy consumption. Environmental assessment shows that most of the emissions stemmed from the energy required to power the ultrasound reactor. We concluded that increased leaching capacity using a continuous ultrasound-assisted approach is feasible, but process modifications are needed to reduce the environmental impact. Full article

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29 pages, 2241 KB

Open AccessArticle

Mathematical Development for the Minimum Cost of Elliptical Combined Footings

by Griselda Santiago-Hurtado, Arnulfo Luévanos-Rojas, Victor Manuel Moreno-Landeros, Eyran Roberto Diaz-Gurrola, Rajeswari Narayanasamy, Facundo Cortés-Martínez and Luis Daimir López-León

Buildings 2025, 15(19), 3633; https://doi.org/10.3390/buildings15193633 - 9 Oct 2025

Abstract

This work shows the mathematical development for the minimum cost of ECF (elliptical combined footings) subjected to biaxial bending due to the two columns, assuming that the distribution of soil pressure below the footing is linear and that the footing rests on elastic soil. There are no similar contributions on the subject of this article, as it is an innovative contribution in terms of its form. This work is developed in two parts: first, determine the minimum area in contact with the soil below the footing, and then the minimum cost is obtained. The formulation of the development by integration is shown to determine the moments, unidirectional shears, and punching shears acting on the critical sections, according to the ACI (American Concrete Institute) design code, and then the flowchart algorithm is applied to determine the solution using Maple Software, which is the main contribution of this article. Some authors show studies on the combined footings of various shapes such as rectangular, trapezoidal, strap, corner or L, and T, but there are none for ECF. Two numerical studies are shown with different length: the first with free ends in the longitudinal direction and the second with ends limited in the longitudinal direction to estimate the minimum cost of ECF under biaxial bending. A third numerical study is shown, with different allowable bearing capacities of the ground and with free ends in the longitudinal direction. Also, a comparison is developed between ECF and RCF (rectangular combined footings). The model for the design of ECF shows a savings of 7.17% with limited ends and a savings of 1.67% with free ends for the minimum area, and for the minimum cost, it shows a savings of 23.95% with limited ends and a savings of 9.14% with free ends rather than RCF. Therefore, the proposed development will be of great help to structural engineers specializing in foundations, as it represents significant savings. Full article

(This article belongs to the Section Building Structures)

17 pages, 724 KB

Open AccessArticle

Balancing Privacy and Utility in Artificial Intelligence-Based Clinical Decision Support: Empirical Evaluation Using De-Identified Electronic Health Record Data

by Jungwoo Lee and Kyu Hee Lee

Appl. Sci. 2025, 15(19), 10857; https://doi.org/10.3390/app151910857 - 9 Oct 2025

Abstract

The secondary use of electronic health records is essential for developing artificial intelligence-based clinical decision support systems. However, even after direct identifiers are removed, de-identified electronic health records remain vulnerable to re-identification, membership inference attacks, and model extraction attacks. This study examined the balance between privacy protection and model utility by evaluating de-identification strategies and differentially private learning in large-scale electronic health records. De-identified records from a tertiary medical center were analyzed and compared with three strategies—baseline generalization, enhanced generalization, and enhanced generalization with suppression—together with differentially private stochastic gradient descent. Privacy risks were assessed through k-anonymity distributions, membership inference attacks, and model extraction attacks. Model performance was evaluated using standard predictive metrics, and privacy budgets were estimated for differentially private stochastic gradient descent. Enhanced generalization with suppression consistently improved k-anonymity distributions by reducing small, high-risk classes. Membership inference attacks remained at the chance level under all conditions, indicating that patient participation could not be inferred. Model extraction attacks closely replicated victim model outputs under baseline training but were substantially curtailed once differentially private stochastic gradient descent was applied. Notably, privacy-preserving learning maintained clinically relevant performance while mitigating privacy risks. Combining suppression with differentially private stochastic gradient descent reduced re-identification risk and markedly limited model extraction while sustaining predictive accuracy. These findings provide empirical evidence that a privacy–utility balance is achievable in clinical applications. Full article

(This article belongs to the Special Issue Digital Innovations in Healthcare)

27 pages, 1341 KB

Open AccessArticle

The Impact of R&D Investment on Economic Growth: Evidence from Panama Using Elastic Net and Bootstrap Techniques

by Gresky Gutiérrez-Sánchez and Enrique Benéitez-Andrés

Economies 2025, 13(10), 293; https://doi.org/10.3390/economies13100293 - 9 Oct 2025

Abstract

This study analyzes the impact of research and development (R&D) investment on economic growth in Panama, an emerging economy with structural challenges in its innovation system. Using a multivariate econometric approach that included elastic net regularization and fixed-effect panel data estimation, the analysis incorporated key explanatory variables such as public education expenditure, inflation, infrastructure investment, population growth, and exports. The results indicated that both R&D and education spending have a positive and statistically significant effect on GDP growth, while inflation has a negative impact and exports show no significant effect. To ensure robustness, the study applied the augmented Dickey–Fuller test for stationarity, nonparametric bootstrapping (1000 replications), and multiple diagnostic tests, including RMSE, adjusted R², Durbin–Watson statistic, and White’s test. Scenario-based projections suggest that gradual and sustained increases in R&D investment, supported by stronger institutional coordination and absorptive capacity, could enhance Panama’s long-term productivity and innovation outcomes. The findings underscore that improving R&D funding alone is not sufficient; effective governance and coherent science, technology, and innovation (STI) policies are essential. This research contributes empirical evidence to a relatively underexplored area in the development literature and offers strategic insights for policymakers seeking to build more integrated and sustainable STI ecosystems in emerging economies. Full article

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28 pages, 986 KB

Open AccessArticle

Unlocking Carbon Emissions and Total Factor Productivity Nexus: Causal Moderation of Ownership Structures via Entropy Methods in Chinese Enterprises

by Ruize Cai, Jie You and Minho Kim

Entropy 2025, 27(10), 1048; https://doi.org/10.3390/e27101048 - 9 Oct 2025

Abstract

Amidst global imperatives for environmental sustainability, this study investigates the nexus between carbon emissions reduction (CER), ownership structures, and total factor productivity (TFP) in Chinese enterprises—recognized as vital economic drivers facing carbon emissions pressures. Based on the theoretical frameworks of innovation offsets, agency cost theory, and upper echelons theory, with data from CSMAR (2009–2023), we proposed a positive effect of CER on TFP while examining the moderating roles of ownership structure metrics: chairman shareholding ratio, manager shareholding ratio, and ownership–control separation ratio. TFP estimation employed dual approaches: mean consolidation (TFP-Mean) and entropy weighting (TFP-Entropy) methods. The results confirmed CER exerts significantly positive impacts on TFP, with ownership structures demonstrating statistically significant yet directionally heterogeneous moderation effects. Heterogeneity analysis reveals heightened TFP sensitivity to carbon emission initiatives among private enterprises, foreign-owned enterprises, and small enterprises. Notably, the entropy weighting method exhibits substantial comparative advantages in TFP measurement. These findings underscore that advancing TFP necessitates simultaneously optimizing carbon emissions efficiency and ownership governance. Full article

(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management, 4th Edition)

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14 pages, 438 KB

Open AccessArticle

CGM-Based Glycemic Metrics Support Estimating Nutritional Risk After Total Pancreatectomy: An Exploratory Retrospective Study

by Ryoma Nakamura, Miyuki Yanagimachi, Kento Mitsuhashi, Masato Yamaichi, Wataru Onodera, Atsufumi Matsumoto, Eri Sato, Yusuke Tando and Yukihiro Fujita

J. Clin. Med. 2025, 14(19), 7124; https://doi.org/10.3390/jcm14197124 (registering DOI) - 9 Oct 2025

Abstract

Introduction: After total pancreatectomy, patients inevitably develop pancreatogenic diabetes with marked glycemic variability and high risk of malnutrition due to both endocrine and exocrine insufficiency. Weight loss and malnutrition can occur even in those with adequate dietary intake and plausible pancreatic enzyme replacement. We hypothesized that glycemic variability is associated with nutritional decline. Methods: We retrospectively analyzed 14 patients who underwent continuous glucose monitoring (CGM) after total pancreatectomy. Nutritional status was assessed using the Geriatric Nutritional Risk Index (GNRI), and patients were classified into malnutrition-risk progression or nutrition-maintaining groups. Then, we evaluated glycemic indices, dietary intake, anthropometry, and pancreatic enzyme replacement therapy (PERT). Results: Insulin use, PERT dose, and dietary intake were approximately comparable between groups. In contrast, the malnutrition-risk progression group showed significantly higher mean glucose and time above range, and lower time in range (TIR). Importantly, TIR consistently showed an inverse association with malnutrition-risk progression across models adjusted for clinical covariates, including time since pancreatectomy, primary diagnosis, insulin regimen, and pancrelipase dose. These findings indicate that the observed relationship between lower TIR and worsening GNRI was independent of dietary intake and adequacy of enzyme replacement therapy, underscoring TIR as a clinically meaningful indicator of nutritional decline in this population. Conclusions: Hyperglycemia and reduced TIR were significantly associated with worsening GNRI after total pancreatectomy, independent of dietary intake or PERT. CGM-based glycemic metrics may help identify patients at risk of malnutrition and guide postoperative management. Full article

(This article belongs to the Section Endocrinology & Metabolism)

12 pages, 457 KB

Open AccessArticle

Impaired Kidney Function, Subclinical Myocardial Injury, and Their Joint Associations with Cardiovascular Mortality in the General Population

by Ahmed E. Shatta, Mohamed A. Mostafa, Mohamed A. Attia, Tarek Ahmad Zaho, Richard Kazibwe and Elsayed Z. Soliman

J. Clin. Med. 2025, 14(19), 7123; https://doi.org/10.3390/jcm14197123 (registering DOI) - 9 Oct 2025

Abstract

Background: The combined impact of impaired kidney function and subclinical myocardial injury (SCMI) on cardiovascular (CV) mortality has not been well studied. We aimed to evaluate their individual and joint associations with cardiovascular mortality. Methods: We analyzed data from 6057 participants (mean age 57.0 ± 13.0 years) in the U.S. Third National Health and Nutrition Examination Survey. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. Electrocardiographic SCMI was defined as a cardiac infarction/injury score ≥ 10. CV mortality was determined from the National Death Index. Multivariable logistic regression assessed baseline cross-sectional associations between eGFR and SCMI. Cox proportional hazards models were used to examine the individual and combined associations of eGFR and SCMI with CV mortality. Results: At baseline, 1297 participants (21.4%) had SCMI. In multivariable logistic regression analysis, eGFR < 45 mL/min/1.73 m² (vs. ≥45) was not associated with SCMI (OR [95% CI]: 1.10 [0.84–1.45]). Over a median follow-up of 18.4 years, 690 CV deaths occurred. In separate Cox models, both SCMI (vs. no SCMI) and eGFR < 45 (vs. ≥45) were associated with increased CV mortality risk (HR [95% CI]: 1.36 [1.16–1.60] and 1.56 [1.24–1.99], respectively). Compared with participants with eGFR ≥ 45 and no SCMI, those with both eGFR < 45 and SCMI had the highest CV mortality risk (HR [95% CI]: 2.36 [1.65–3.36]), followed by eGFR < 45 alone (1.47 [1.09–1.96]) and SCMI alone (1.33 [1.11–1.58]). Conclusions: Both reduced eGFR and SCMI were independently associated with CV mortality. Their coexistence showed the highest risk, but without statistical significance compared with each alone, possibly reflecting limited power and distinct mechanisms. Full article

(This article belongs to the Section Cardiovascular Medicine)

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22 pages, 4427 KB

Open AccessArticle

Higher-Order Dynamic Mode Decomposition to Identify Harmonics in Power Systems

by Aboubacar Abdou Dango, Innocent Kamwa, Himanshu Grover, Alexia N’Dori and Alireza Masoom

Energies 2025, 18(19), 5327; https://doi.org/10.3390/en18195327 (registering DOI) - 9 Oct 2025

Abstract

The proliferation of renewable energy sources and distributed generation systems interfaced to the grid by power electronics systems is forcing us to better understand the issues arising due to the quality of electrical signals generated through these devices. Understanding and monitoring these harmonics is crucial to ensure the smooth and seamless operation of these networks, as well as to protect and manage the renewable energy sources-based power system. In this paper, we propose an advanced method of dynamic modal decomposition, called Higher-Order Dynamic Mode Decomposition (HODMD), one of the recently proposed data-driven methods used to estimate the frequency/amplitude and phase with high resolution, to identify the harmonic spectrum in power systems dominated by renewable energy generation. In the proposed method, several time-shifted copies of the measured signals are integrated to create the initial data matrices. A hard thresholding technique based on singular value decomposition is applied to eliminate ambiguities in the measured signal. The proposed method is validated and compared to Synchrosqueezing Transform based on Short-Time Fourier Transform (SST-STFT) and the Concentration of Frequency and Time via Short-Time Fourier Transform (ConceFT-STFT) using synthetic signals and real measurements, demonstrating its practical effectiveness in identifying harmonics in emerging power networks. Finally, the effectiveness of the proposed methodology is analyzed on the energy storage-based laboratory-scale microgrid setup using an Opal-RT-based real-time simulator. Full article

(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 4th Edition)

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18 pages, 2584 KB

Open AccessArticle

Evaluating Factors Influencing Dynamic Modulus Prediction: GRA-MLR Compared with Sigmoidal Modelling for Asphalt Mixtures with Reclaimed Asphalt

by Majda Belhaj, Jan Valentin, Nicola Baldo and Jan B. Król

Infrastructures 2025, 10(10), 269; https://doi.org/10.3390/infrastructures10100269 (registering DOI) - 9 Oct 2025

Abstract

The dynamic modulus of asphalt mixtures (|E*|) is a key mechanical parameter in the design of road pavements, yet direct laboratory testing is time- and resource-intensive. This study evaluates two predictive models for estimating |E*| using data from 62 asphalt mixtures containing reclaimed asphalt: a grey relational analysis–multiple linear regression (GRA-MLR) hybrid model and a mechanistic sigmoidal model. The results showed that the GRA-MLR model effectively identifies influential variables but achieved moderate predictive accuracy (R² values varying from 0.4743 to 0.6547). In contrast, the sigmoidal model outperformed across all temperature conditions (R² > 0.96) and produced predictions deviating by less than ±20% from measured values. Temperature-dependent shifts in factor influence were observed, with stiffness and gradation dominating at low temperatures and reclaimed asphalt (RA) content becoming more significant at higher temperatures. While the GRA-MLR model is advantageous, offering rapid assessments and early-stage evaluations, the sigmoidal model offers the precision suited for detailed design. Integrating both models can balance computational efficiency and provide a balanced strategy, with strong predictive reliability to advance mechanistic–empirical pavement design. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

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17 pages, 1049 KB

Open AccessArticle

AI-Based Facial Emotion Analysis in Infants During Complimentary Feeding: A Descriptive Study of Maternal and Infant Influences

by Murat Gülşen, Beril Aydın, Güliz Gürer and Sıddika Songül Yalçın

Nutrients 2025, 17(19), 3182; https://doi.org/10.3390/nu17193182 - 9 Oct 2025

Abstract

Background/Objectives: Infant emotional responses during complementary feeding offer key insights into early developmental processes and feeding behaviors. AI-driven facial emotion analysis presents a novel, objective method to quantify these subtle expressions, potentially informing interventions in early childhood nutrition. We aimed to investigate how maternal and infant traits influence infants’ emotional responses during complementary feeding using an automated facial analysis tool. Methods: This multi-center study involved 117 typically developing infants (6–11 months) and their mothers. Standardized feeding sessions were recorded, and OpenFace software quantified six emotions (surprise, sadness, fear, happiness, anger, disgust). Data were normalized and analyzed via Generalized Estimating Equations to identify associations with maternal BMI, education, work status, and infant age, sex, and complementary feeding initiation. Results: Emotional responses did not differ significantly across five food groups. Infants of mothers with BMI >30 kg/m² showed greater surprise, while those whose mothers were well-educated and not working displayed more happiness. Older infants and those introduced to complementary feeding before six months exhibited higher levels of anger. Parental or infant food selectivity did not significantly affect responses. Conclusions: The findings indicate that maternal and infant demographic factors exert a more pronounced influence on infant emotional responses during complementary feeding than the type of food provided. These results highlight the importance of integrating broader psychosocial variables into early feeding practices and underscore the potential utility of AI-driven facial emotion analysis in advancing research on infant development. Full article

(This article belongs to the Section Nutrition Methodology & Assessment)

23 pages, 4862 KB

Open AccessArticle

Rapid Temperature Prediction Model for Large-Scale Seasonal Borehole Thermal Energy Storage Unit

by Donglin Zhao, Mengying Cui, Shuchuan Yang, Xiao Li, Junqing Huo and Yonggao Yin

Energies 2025, 18(19), 5326; https://doi.org/10.3390/en18195326 (registering DOI) - 9 Oct 2025

Abstract

The temperature of the thermal energy storage unit is a critical parameter for the stable operation of seasonal borehole thermal energy storage (BTES) systems. However, existing temperature prediction models predominantly focus on estimating single-point temperatures or borehole wall temperatures, while lacking effective methods for calculating the average temperature of the storage unit. This limitation hinders accurate assessment of the thermal charging and discharging states. Furthermore, some models involve complex computations and exhibit low operational efficiency, failing to meet the practical engineering demands for rapid prediction and response. To address these challenges, this study first develops a thermal response model for the average temperature of the storage unit based on the finite line source theory and further proposes a simplified engineering algorithm for predicting the storage unit temperature. Subsequently, two-dimensional discrete convolution and Fast Fourier Transform (FFT) techniques are introduced to accelerate the solution of the storage unit temperature distribution. Finally, the model’s accuracy is validated against practical engineering cases. The results indicate that the single-point temperature engineering algorithm yields a maximum relative error of only 0.3%, while the average temperature exhibits a maximum relative error of 1.2%. After employing FFT, the computation time of both single-point and average temperature engineering algorithms over a 10-year simulation period is reduced by more than 90%. When using two-dimensional discrete convolution to calculate the temperature distribution of the storage unit, expanding the input layer from 200 × 200 to 400 × 400 and the convolution kernel from 25 × 25 to 51 × 51 reduces the time required for temperature superposition calculations to approximately 0.14–0.82% of the original time. This substantial improvement in computational efficiency is achieved without compromising accuracy. Full article

(This article belongs to the Section G: Energy and Buildings)

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19 pages, 4854 KB

Open AccessArticle

Numerical and Experimental Assessment of Poly-Pyrrole Used in Spinal Cord Injuries

by Carlos Alberto Espinoza-Garcés, Axayácatl Morales-Guadarrama, Elliot Alonso Alcántara-Arreola, Jose Luis Torres-Ariza, Mario Alberto Grave-Capistrán and Christopher René Torres-SanMiguel

Biomimetics 2025, 10(10), 677; https://doi.org/10.3390/biomimetics10100677 (registering DOI) - 9 Oct 2025

Abstract

Some common conductive polymers are polyfuran, polyacetylene, polythiophene, and poly-pyrrole. Since their discovery, many researchers have been exploring and evaluating their conductive and electronic properties. Various applications have been developed for conductive materials. Their biocompatibility offers a new alternative for studying and solving complex problems, such as cellular activity, or, more recently, for use as neural implants and as an alternative to spinal cord regenerative tissue. This is particularly true for the use of poly pyrrole. The main obstacle lies in estimating some of the mechanical properties, such as Young’s or shear modulus values for poly pyrrole, since these vary depending on the type of synthesis used. This article outlines a composite methodology for characterizing the elastic modulus according to ASTM D882 and the shear modulus according to E143 standards. It is specifically designed and applied for 3D composite samples involving PLA and PPy, where the PPy was processed by plasma oxidation. As a result, an increase of 360.11 MPa in the modulus of elasticity is observed on samples coated with poly pyrrole. The results are evaluated through a numerical test using COMSOL Multiphysics software 6.2 version, finding a similar behavior in the elastic zone, as indicated by the stress–strain diagram. The statistical analysis yields consistent data for tensile and shear results, with low to moderate variability. Full article

(This article belongs to the Special Issue Advances in Biomaterials, Biocomposites and Biopolymers 2025)

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45 pages, 3217 KB

Open AccessSystematic Review

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology

by Luis Rojas, Sebastián Espinoza, Esteban González, Carlos Maldonado and Fei Luo

Galaxies 2025, 13(5), 114; https://doi.org/10.3390/galaxies13050114 - 9 Oct 2025

Abstract

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) Various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied to cosmological data analysis, improving parameter estimation and handling large datasets. However, models achieving significant computational speedups often exhibit worse confidence regions compared to traditional methods, emphasizing the need for future research to enhance both efficiency and measurement precision. (2) Traditional cosmological methods, such as those using Type Ia Supernovae, baryon acoustic oscillations, and cosmic microwave background data, remain fundamental, but most studies focus narrowly on specific datasets. We recommend broader dataset usage to fully validate alternative cosmological models. (3) The reviewed studies mainly address the

H_{0}

tension, leaving other cosmological challenges—such as the cosmological constant problem, warm dark matter, phantom dark energy, and others—unexplored. (4) Hybrid methodologies combining machine learning with Markov chain Monte Carlo offer promising results, particularly when machine learning techniques are used to solve differential equations, such as Einstein Boltzmann solvers, prior to Markov chain Monte Carlo models, accelerating computations while maintaining precision. (5) There is a significant need for standardized evaluation criteria and methodologies, as variability in training processes and experimental setups complicates result comparability and reproducibility. (6) Our findings confirm that deep learning models outperform traditional machine learning methods for complex, high-dimensional datasets, underscoring the importance of clear guidelines to determine when the added complexity of learning models is warranted. Full article

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