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20 pages, 4029 KB

Open AccessArticle

RIPK2 Inhibition Blocks NOD2-Mediated IL-1β Production by Macrophages In Vitro but Exacerbates Crohn’s Disease-like Ileitis in SHIP–/– Mice

by Yvonne C. F. Pang, Wei Jen Ma, Susan C. Menzies and Laura M. Sly

Immuno 2025, 5(3), 37; https://doi.org/10.3390/immuno5030037 (registering DOI) - 29 Aug 2025

Abstract

Crohn’s disease is a chronic, idiopathic inflammatory bowel disease characterized by patchy, transmural inflammation that is influenced by genetic, environmental, and microbial factors. The NOD2 pathway mediates NFκB activation and pro-inflammatory cytokine production. In the SHIP–/– murine model of Crohn’s disease-like ileitis, macrophage-derived [...] Read more.

Crohn’s disease is a chronic, idiopathic inflammatory bowel disease characterized by patchy, transmural inflammation that is influenced by genetic, environmental, and microbial factors. The NOD2 pathway mediates NFκB activation and pro-inflammatory cytokine production. In the SHIP–/– murine model of Crohn’s disease-like ileitis, macrophage-derived IL-1β production drives intestinal inflammation. SHIP reduces NOD2 signaling by preventing downstream interaction between RIPK2 and XIAP, leading us to hypothesize that blocking RIPK2 in SHIP–/– mice would ameliorate intestinal inflammation. We examined the effects of RIPK2 inhibition on pro-inflammatory cytokine production in SHIP+/+ and SHIP–/– macrophages and in mice, using the RIPK2 inhibitor, GSK2983559. We found that GSK2983559 blocked RIPK2 activation in SHIP+/+ and SHIP–/– bone marrow-derived macrophages (BMDMs), and reduced Il1b transcription and IL-1β production in (MDP+LPS)-stimulated SHIP–/– BMDMs. Despite the reduction of IL-1β production in BMDMs, in vivo treatment with GSK2983559 worsened intestinal inflammation and increased IL-1β concentrations in the ileal tissues of SHIP–/– mice. GSK2983559 only modestly reduced IL-1β in (MDP+LPS)-stimulated SHIP–/– peritoneal macrophages, and did not suppress pro-inflammatory cytokine production in response to TLR ligands in peritoneal macrophages from either SHIP+/+ or SHIP–/– mice. Taken together, our data suggest that although RIPK2 inhibition can block IL-1β production by (MDP+LPS)-stimulated macrophages in vitro, it is not an effective anti-inflammatory strategy in vivo, highlighting the limitations of targeting RIPK2 to treat intestinal inflammation in the context of SHIP deficiency. Full article

(This article belongs to the Section Innate Immunity and Inflammation)

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22 pages, 3246 KB

Open AccessArticle

Effects of Recycled and Supplemented Cementitious Materials on Corrosion Resistance and Mechanical Properties in Reinforced Concrete

by Abdulrahman Al Fuhaid

J. Compos. Sci. 2025, 9(9), 457; https://doi.org/10.3390/jcs9090457 - 29 Aug 2025

Abstract

Reinforced concrete is the most widely utilized building material for bridges, buildings, and other infrastructure components, and its longevity is significantly influenced by corrosion or rust. Corrosion shortens reinforced concrete’s service life and safety, which raises maintenance expenses. Concrete is a porous material [...] Read more.

Reinforced concrete is the most widely utilized building material for bridges, buildings, and other infrastructure components, and its longevity is significantly influenced by corrosion or rust. Corrosion shortens reinforced concrete’s service life and safety, which raises maintenance expenses. Concrete is a porous material that allows air and water to pass through, and corrosion begins when the air and water reach the steel. This study evaluated the mechanical and corrosion resistance properties of reinforced concrete containing recycled and supplemented cementitious materials. The results showed that mixtures containing fine glass aggregate, glass powder, slag, fly ash, or silica fume significantly improved the compressive, tensile, and flexural strengths, but the 10% slag mix, and 5% glass aggregate with 10% glass powder with 10% fly ash mix produced the best results overall. In addition, the mixture containing 15% fly ash produced the best result against corrosion. The corrosion tests revealed that mixtures with 10% slag and 20% glass powder also significantly enhanced the corrosion resistance of steel with the same results, confirming their effectiveness in reducing the permeability and increasing the durability of reinforced concrete. Full article

(This article belongs to the Special Issue Advancements in Sustainable Cement-Based Composites: Innovations in Performance, Durability, and Environmental Impact)

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32 pages, 1829 KB

Open AccessEssay

Research on Thermal Characteristics and Algorithm Prediction Analysis of Liquid Cooling System for Leaf Vein Structure Power Battery

by Mingfei Yang, Shanhua Zhang, Han Tian, Li Lv and Jiqing Han

Batteries 2025, 11(9), 326; https://doi.org/10.3390/batteries11090326 (registering DOI) - 29 Aug 2025

Abstract

With the increase in energy density of power batteries, the risk of thermal runaway significantly increases under extreme working conditions. Therefore, this article proposes a biomimetic liquid cooling plate design based on the fractal structure of fir needle leaf veins, combined with Murray’s [...] Read more.

With the increase in energy density of power batteries, the risk of thermal runaway significantly increases under extreme working conditions. Therefore, this article proposes a biomimetic liquid cooling plate design based on the fractal structure of fir needle leaf veins, combined with Murray’s mass transfer law, which has significantly improved the heat dissipation performance under extreme working conditions. A multi-field coupling model of electrochemistry fluid heat transfer was established using ANSYS 2022 Fluent, and the synergistic mechanism of environmental temperature, coolant parameters, and heating power was systematically analyzed. Research has found that compared to traditional serpentine channels, leaf vein biomimetic structures can reduce the maximum temperature of batteries by 11.78 °C at a flow rate of 4 m/s and 5000 W/m³. Further analysis reveals that there is a critical flow rate threshold of 2.5 m/s for cooling efficiency (beyond which the effectiveness of temperature reduction decreases by 86%), as well as a thermal saturation temperature of 28 °C (with a sudden increase in temperature rise slope by 284%). Under low-load conditions of 2600 W/m ³, the system exhibits a thermal hysteresis plateau of 40.29 °C. To predict the battery temperature in advance and actively intervene in cooling the battery pack, based on the experimental data and thermodynamic laws of the biomimetic liquid cooling system mentioned above, this study further constructed a support vector machine (SVM) prediction model to achieve real-time and accurate prediction of the highest temperature of the battery pack (validation set average relative error 1.57%), providing new ideas for intelligent optimization of biomimetic liquid cooling systems. Full article

(This article belongs to the Section Battery Modelling, Simulation, Management and Application)

19 pages, 704 KB

Open AccessFeature PaperArticle

Robust Tail Risk Estimation in Cryptocurrency Markets: Addressing GARCH Misspecification with Block Bootstrapping

by Christos Christodoulou-Volos

Risks 2025, 13(9), 166; https://doi.org/10.3390/risks13090166 (registering DOI) - 29 Aug 2025

Abstract

This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in [...] Read more.

This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in order to compare the performance of classical and block bootstrap procedures in residual risk. Diagnostic tests indicate that standardized residuals are dependent, contrary to the independent and identically distributed (i.i.d.) assumption of conventional FHS. Comparing the block and ordinary bootstrapping approaches, we find that block bootstrap produces wider, more conservative confidence intervals, particularly in extreme tails (e.g., 0.1% and 99.9% percentiles). The findings suggest that block bootstrapping can be employed as a correction instrument in risk modeling where the standard volatility filters do not work. The article highlights the necessity to account for remaining dependencies and offers practical recommendations for more robust tail risk estimation during volatile markets. Full article

(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)

26 pages, 5439 KB

Open AccessArticle

Methods for Evaluating the Effects of 2D and 3D Culture Environment on Macrophage Response to Mycobacterium Infection

by Danielle L. Stolley, Komal S. Rasaputra and Elebeoba E. May

Microorganisms 2025, 13(9), 2026; https://doi.org/10.3390/microorganisms13092026 - 29 Aug 2025

Abstract

Macrophages are critical to the formation of infection- and non-infection-associated immune structures such as cancer spheroids, pathogen-, and non-pathogen-associated granulomas, contributing to the spatiotemporal and chemical immune response and eventual outcome of disease. While well established in cancer immunology, the prevalence of using [...] Read more.

Macrophages are critical to the formation of infection- and non-infection-associated immune structures such as cancer spheroids, pathogen-, and non-pathogen-associated granulomas, contributing to the spatiotemporal and chemical immune response and eventual outcome of disease. While well established in cancer immunology, the prevalence of using three-dimensional (3D) cultures to characterize later-stage structural immune response in pathogen-associated granulomas continues to increase, generating valuable insights for empirical and computational analysis. To enable integration of data from 3D in vitro studies with the vast bibliome of standard two-dimensional (2D) tissue culture data, methods that determine concordance between 2D and 3D immune response need to be established. Focusing on macrophage migration and oxidative species production, we develop experimental and computational methods to enable concurrent spatiotemporal and biochemical characterization of 2D versus 3D macrophage–mycobacterium interaction. We integrate standard biological sampling methods, time-lapse confocal imaging, and 4D quantitative image analysis to develop a 3D ex vivo model of Mycobacterium smegmatis infection using bone-marrow-derived macrophages (BMDMs) embedded in reconstituted basement membrane (RBM). Comparing features of 2D to 3D macrophage response that contribute to control and resolution of bacteria infection, we determined that macrophages in 3D environments increased production of reactive species, motility, and differed in cellular volume. Results demonstrate a viable and extensible approach for comparison of 2D and 3D datasets and concurrent biochemical plus spatiotemporal characterization of initial macrophage structural response during infection. Full article

(This article belongs to the Section Molecular Microbiology and Immunology)

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18 pages, 967 KB

Open AccessArticle

GAN-AHR: A GAN-Based Adaptive Hybrid Resampling Algorithm for Imbalanced Intrusion Detection

by Monirah Al-Ajlan and Mourad Ykhlef

Electronics 2025, 14(17), 3476; https://doi.org/10.3390/electronics14173476 (registering DOI) - 29 Aug 2025

Abstract

With the recent proliferation of the Internet and the ever-evolving threat landscape, developing a reliable and effective intrusion detection system (IDS) has become an urgent need. However, one of the key challenges hindering the success of IDS development is class imbalance, which often [...] Read more.

With the recent proliferation of the Internet and the ever-evolving threat landscape, developing a reliable and effective intrusion detection system (IDS) has become an urgent need. However, one of the key challenges hindering the success of IDS development is class imbalance, which often leads to biased models and poor detection rates. To address this challenge, this paper proposes a GAN-AHR algorithm which adaptively balances the dataset by augmenting minority classes using CGAN or BSMOTE, based on class-specific characteristics such as compactness and density. By leveraging BSMOTE to oversample classes with high compactness and high density, we can exploit its simplicity and effectiveness. However, the quality of BSMOTE-generated data is significantly lower when the classes are sparse and lacking clear boundaries. In such cases, CGAN is better suited in this scenario given its ability to capture complex data distributions. We present empirical results on the NF-UNSW-NB15 dataset using a Random Forest (RF) classifier, reporting a significant improvement in the precision, recall, and F1-score of several minority classes. Specifically, a remarkable increase in the F1-score for the Shellcode and DoS classes was reported, reaching 0.90 and 0.51, respectively. Full article

(This article belongs to the Special Issue New Trends in Cryptography, Authentication and Information Security)

28 pages, 8440 KB

Open AccessArticle

Multi-Omics Platforms Reveal Synergistic Intestinal Toxicity in Tilapia from Acute Co-Exposure to Polystyrene Microplastics, Sulfamethoxazole, and BDE153

by Yao Zheng, Jiajia Li, Lihong Li and Gangchun Xu

Int. J. Mol. Sci. 2025, 26(17), 8441; https://doi.org/10.3390/ijms26178441 (registering DOI) - 29 Aug 2025

Abstract

Polystyrene microplastic (MP) and its co-existing contaminants may exert different toxic effects on its surrounding aquatic organisms. In order to detect the intestinal harmful responses, tilapia were subjected to exposure with 75 nm of MPs, 100 ng·L⁻¹ of sulfamethoxazole (SMZ), 5 ng·L [...] Read more.

Polystyrene microplastic (MP) and its co-existing contaminants may exert different toxic effects on its surrounding aquatic organisms. In order to detect the intestinal harmful responses, tilapia were subjected to exposure with 75 nm of MPs, 100 ng·L⁻¹ of sulfamethoxazole (SMZ), 5 ng·L⁻¹ of BDE153, and combinations thereof over periods of 2, 4, and 8 days. Enzymatic assays, transcriptomics, proteomics, and metabolomics were employed to evaluate intestinal histopathological effects. Results showed that significant reductions were observed in ATP, ROS, SOD, EROD, lipid metabolism-related enzymes, pro-inflammatory cytokines (TNFα and IL-1β), and apoptosis marker caspase 3 across all groups at day 8. Histological evaluation revealed diminished goblet cell density, with distinct vacuole formation in the BDE153+MPs group. KEGG pathway analysis highlighted disruptions in endocytosis, MAPK signaling, phagosome formation, and actin cytoskeleton regulation. Proteomic findings indicated notable enrichment in endocytosis (decreased sorting nexin-2; increased Si:dkey-13a21.4), MAPK/PPAR signaling, protein processing in the endoplasmic reticulum (Sec61 subunit gamma), and cytoskeletal modulation (reduced fibronectin; elevated activation peptide fragment 1), with or without SMZ and BDE153. Metabolomic profiling showed significant alterations in ABC transporters, aminoacyl-tRNA biosynthesis, protein digestion and absorption, and linoleic acid metabolism. In summary, these findings suggest that BDE153 and MPs synergistically exacerbate intestinal damage and gene/protein expression over time, while SMZ appears to exert an antagonistic, mitigating effect. Full article

(This article belongs to the Section Molecular Toxicology)

22 pages, 3114 KB

Open AccessArticle

Simulative Investigation and Optimization of a Rolling Moment Compensation in a Range-Extender Powertrain

by Oliver Bertrams, Sebastian Sonnen, Martin Pischinger, Matthias Thewes and Stefan Pischinger

Vehicles 2025, 7(3), 92; https://doi.org/10.3390/vehicles7030092 (registering DOI) - 29 Aug 2025

Abstract

Battery electric vehicles (BEVs) are gaining market share, yet range anxiety and sparse charging still create demand for hybrids with combustion-engine range extenders. Range-extender vehicles face high customer expectations for noise, vibration, and harshness (NVH) due to their direct comparability with fully electric [...] Read more.

Battery electric vehicles (BEVs) are gaining market share, yet range anxiety and sparse charging still create demand for hybrids with combustion-engine range extenders. Range-extender vehicles face high customer expectations for noise, vibration, and harshness (NVH) due to their direct comparability with fully electric vehicles. Key challenges include the vibrations of the internal combustion engine, especially from vehicle-induced starts, and the discontinuous operating principle. A technological concept to reduce vibrations in the drivetrain and on the engine mounts, called “FEVcom,” relies on rolling moment compensation. In this concept, a counter-rotating electric machine is coupled to the internal combustion engine via a gear stage to minimize external mount forces. However, due to high speed fluctuations of the crankshaft, the gear drive tends to rattle, which is perceived as disturbing and must be avoided. As part of this work, the rolling moment compensation system was examined regarding its vibration excitation, and an extension to prevent gear rattling was simulated and optimized. For the simulation, the extension, based on a chain or belt drive, was set up as a multi-body simulation model in combination with the range extender and examined dynamically at different speeds. Variations of the extended system were simulated, and recommendations for an optimized layout were derived. This work demonstrates the feasibility of successful rattling avoidance in a range-extender drivetrain with full utilization of the rolling moment compensation. It also provides a solid foundation for further detailed investigations and for developing a prototype for experimental validation based on the understanding gained of the system. Full article

34 pages, 9634 KB

Open AccessArticle

Placemaking and the Complexities of Measuring Impact in Aotearoa New Zealand’s Public and Community Housing: From Theory to Practice and Lived Experience

by Crystal Victoria Olin, Karen Witten, Edward Randal, Elinor Chisholm, Amber Logan, Philippa Howden-Chapman and Lori Leigh

Architecture 2025, 5(3), 69; https://doi.org/10.3390/architecture5030069 - 29 Aug 2025

Abstract

This paper explores the complexities of measuring impact from placemaking in the context of public and community housing (sometimes known as social or subsidised housing) in Aotearoa New Zealand. Placemaking refers to a range of practices and interventions—including the provision or facilitation of [...] Read more.

This paper explores the complexities of measuring impact from placemaking in the context of public and community housing (sometimes known as social or subsidised housing) in Aotearoa New Zealand. Placemaking refers to a range of practices and interventions—including the provision or facilitation of access to community infrastructure—that seek to cultivate a positive sense of place through everyday experiences, spaces, relationships, and rituals. Drawing on interviews with four community housing providers (CHPs), analysis of their documentation, and tenant survey and interview data from two of those CHPs, this research examines providers’ change theories about placemaking in relation to tenants’ experiences of safety, belonging and connectedness, including access to local amenities, ease of getting around, and a sense of neighbourhood and community affiliation. Based on the importance of these variables to wellbeing outcomes, the study highlights the potential of placemaking to support tenant wellbeing, while also recognising that providers must navigate trade-offs and co-benefits, limited resources, and varying levels of tenant engagement. While placemaking can help to foster feelings of connection, belonging and safety, its impact depends on providers’ capacity to initiate and sustain such efforts amidst competing demands and constraints. The study offers indicative findings and recommendations for future research. Although the impacts of placemaking and community infrastructure provision are difficult to quantify, research findings are synthesised into a prototype framework to support housing providers in their decision-making and housing development processes. The framework, which should be adapted and evaluated in situ, potentially also informs other actors in the built environment—including architects, landscape architects, urban designers, planners, developers and government agencies. In Aotearoa New Zealand, where housing provision occurs within a colonial context, government agencies have obligations under Te Tiriti o Waitangi to actively protect Māori rights and to work in partnership with Māori in housing policy and delivery. This underscores the importance of placemaking practices and interventions that are culturally and contextually responsive. Full article

(This article belongs to the Special Issue Healthy Habitats—Innovative Approaches to Creating Built Environments That Support Health and Wellbeing)

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32 pages, 2277 KB

Open AccessHypothesis

POLETicians in the Mud: Preprokaryotic Organismal Lifeforms Existing Today (POLET) Hypothesis

by Douglas M. Ruden and Glen Ray Hood

Bacteria 2025, 4(3), 42; https://doi.org/10.3390/bacteria4030042 - 29 Aug 2025

Abstract

The discovery of Asgard archaea has reshaped our understanding of eukaryotic origins, supporting a two-domain tree of life in which eukaryotes emerged from Archaea. Building on this revised framework, we propose the Pre-prokaryotic Organismal Lifeforms Existing Today (POLET) hypothesis, which suggests that relic [...] Read more.

The discovery of Asgard archaea has reshaped our understanding of eukaryotic origins, supporting a two-domain tree of life in which eukaryotes emerged from Archaea. Building on this revised framework, we propose the Pre-prokaryotic Organismal Lifeforms Existing Today (POLET) hypothesis, which suggests that relic pre-prokaryotic life forms—termed POLETicians—may persist in deep, anoxic, energy-limited environments. These organisms could represent a living bridge to the RNA world and other origin-of-life models, utilizing racemic oligoribonucleotides and peptides, non-enzymatic catalysis, and mineral-assisted compartmentalization. POLETicians might instead rely on radical-based redox chemistry or radiolysis for energy and maintenance. These biomolecules may be racemic or noncanonical, eluding conventional detection. New detection methods are required to determine such life. We propose generalized nanopore sequencing of any linear polymer—including mirror RNAs, mirror DNAs, or any novel genetic material—as a potential strategy to overcome chirality bias in modern sequencing technologies. These approaches, combined with chiral mass spectrometry and stereoisomer-resolved analytics, may enable the detection of molecular signatures from non-phylogenetic primitive lineages. POLETicians challenge the assumption that all life must follow familiar biochemical constraints and offer a compelling extension to our search for both ancient and extant forms of life hidden within Earth’s most extreme environments. Full article

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16 pages, 1199 KB

Open AccessReview

The Rethinking Clinical Trials (REaCT) Program: A Pragmatic Research Strategy to Improve Cancer Care for Patients, Caregivers, and Healthcare Systems

by Marie-France Savard, Mark Clemons and Sharon F. McGee

Curr. Oncol. 2025, 32(9), 484; https://doi.org/10.3390/curroncol32090484 - 29 Aug 2025

Abstract

Cancer care has become increasingly complex, expensive, and inaccessible, with patients often exposed to increased treatment-related harms for marginal benefits. Pragmatic clinical trials offer a solution by conducting real-world studies that evaluate dose optimization, toxicity, quality of life, and resource utilization. Pragmatic trials [...] Read more.

Cancer care has become increasingly complex, expensive, and inaccessible, with patients often exposed to increased treatment-related harms for marginal benefits. Pragmatic clinical trials offer a solution by conducting real-world studies that evaluate dose optimization, toxicity, quality of life, and resource utilization. Pragmatic trials can also address the efficacy–effectiveness gap: the poorer outcomes and greater toxicity observed in everyday practice compared to those reported in many clinical trials. The Rethinking Clinical Trials (REaCT) program was designed to conduct patient-centered practice-changing research by involving patients, their families, and healthcare providers in the design of inclusive, real-world clinical trials. The REaCT process starts with surveys and systematic reviews to identify knowledge gaps and uses this information to design pragmatic clinical trials that address these deficits. Since 2014, the program has conducted 17 patient and 17 healthcare provider surveys with 2298 and 1033 responses, respectively. With these results, the program has performed 22 systematic reviews. These surveys and systematic reviews have resulted in 19 completed and 8 ongoing REaCT clinical trials that have recruited over 5000 patients from across Canada. Here, we present some of the practice-changing research conducted by the REaCT program and address challenges facing the growth of pragmatic research. Full article

9 pages, 205 KB

Open AccessArticle

Real-World Selection of Patients for Allogeneic HCT at a Single Centre: Lack of a Suitable Donor and Other Reasons for Not Proceeding

by Madeline Monaghan, An Duong, Kalina Abrol, Trang Doan, Carolina Cieniak, Harold Atkins, Natasha Kekre, Ashish Masurekar, Ram Vasudevan Nampoothiri, Santhosh Thyagu, Christopher N. Bredeson, Michael Kennah and David S. Allan

Curr. Oncol. 2025, 32(9), 483; https://doi.org/10.3390/curroncol32090483 - 29 Aug 2025

Abstract

The reasons why patients cannot proceed with HCT, including cases where no suitable donor is identified, remain poorly described. We reviewed all referrals for allogeneic HCT to our programme between 1 January 2019 and 31 December 2023. Of 880 patients referred for allogeneic [...] Read more.

The reasons why patients cannot proceed with HCT, including cases where no suitable donor is identified, remain poorly described. We reviewed all referrals for allogeneic HCT to our programme between 1 January 2019 and 31 December 2023. Of 880 patients referred for allogeneic HCT, 494 (61.8%) proceeded to transplant (mean 52 ± 14.8 years, 61.5% male) using HLA-matched unrelated (64.2%) or related (19.4%) donors and HLA-mismatched (13%) or haploidentical (3%) donors. Of patients that did not proceed with HCT (386, 38.2%), disease-related causes (54.2%), patient preference (15.8%), and significant patient comorbidity (11.4%) were the most common reasons. Eleven patients (2.9% of transplants that did not proceed; 1.3% of all referrals) lacked a suitable donor and had HLA phenotypes most associated with Caucasian (six patients, 55%), First Nations, Inuit or Metis (two patients, 18%), Black African, Caribbean or African American (one patient, 9%), Asian or Pacific Islander (9%), or unknown ethnicity (one patient, 9%). Very few patients were unable to proceed with transplant due to lack of a suitable donor; however, those cases are overrepresented by non-Caucasian ethnicity relative to the population. Full article

(This article belongs to the Section Cell Therapy)

27 pages, 3393 KB

Open AccessArticle

A Novel Spectral Vegetation Index for Improved Detection of Soybean Cyst Nematode (SCN) Infestation Using Hyperspectral Data

by Yuhua Wang, Ruopu Li, Jason Bond, Ahmad Fakhoury and Justin Schoof

Crops 2025, 5(5), 58; https://doi.org/10.3390/crops5050058 - 29 Aug 2025

Abstract

Soybean cyst nematode (SCN) is a pathogen with serious impacts on soybean yields, yet traditional field-based assessment is labor-intensive and often ineffective for early interventions, and the existing spectral vegetation indices (VIs) also lack the ability to accurately detect SCN infested plants. This [...] Read more.

Soybean cyst nematode (SCN) is a pathogen with serious impacts on soybean yields, yet traditional field-based assessment is labor-intensive and often ineffective for early interventions, and the existing spectral vegetation indices (VIs) also lack the ability to accurately detect SCN infested plants. This study aimed to develop an improved detection method using hyperspectral data. A greenhouse-based experiment was designed to collect 100 hyperspectral datasets from 20 soybean plants inoculated with four SCN egg levels (0–10,000) from the 68th to 97th day after planting. Based on spectral similarity and inoculation levels, three stress classes were defined as proxies for actual plant stress: healthy (0 egg), moderate (1000 and 5000 eggs), and severe (10,000 eggs). These classifications are based on predefined inoculation thresholds and spectral trends, which may not fully align with direct physiological stress measurements due to inherent variability in individual plant responses. Through analysis of variance (ANOVA), principal component analysis (PCA), feature selection, and classification comparison, a new spectral VI, called SCNVI, was proposed using bands 338 nm and 665 nm. The SCNVI coupled with eXtreme Gradient Boosting (XGBoost) achieved an accurate classification of 70% for three classes and outperformed the 12 traditional VIs. These findings suggest that integrating the SCNVI and XGBoost algorithm provides the potential for improving the detection of SCN infestation, though further validation in field environments is required to confirm its practical applicability. Full article

(This article belongs to the Topic Advances in Integrated Pest Management: New Tools and Tactics for Pest Control)

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20 pages, 1725 KB

Open AccessArticle

Living Cultural Infrastructure as a Model for Biocultural Conservation: A Case Study of the Maekha Canal, Chiang Mai, Thailand

by Warong Wonglangka, Samart Suwannarat and Sudarat Auttarat

Conservation 2025, 5(3), 45; https://doi.org/10.3390/conservation5030045 - 29 Aug 2025

Abstract

This paper introduces and defines ‘Living Cultural Infrastructure’ as dynamic social-ecological systems where plant heritage and community knowledge are co-produced to reclaim degraded urban landscapes. Addressing the dual challenges of ecological degradation and cultural erosion, we demonstrate this concept through a case study [...] Read more.

This paper introduces and defines ‘Living Cultural Infrastructure’ as dynamic social-ecological systems where plant heritage and community knowledge are co-produced to reclaim degraded urban landscapes. Addressing the dual challenges of ecological degradation and cultural erosion, we demonstrate this concept through a case study on the Maekha Canal in Chiang Mai, Thailand, employing Participatory Landscape Architecture integrated with urban ethnobotany. Through co-design workshops, biocultural spatial analysis, and ethnobotanical surveys involving 20 key community members, the project engaged residents to reclaim the canal as a functional biocultural corridor. The research documented 149 culturally significant plant species and resulted in a co-created trail system that embodies the principles of a living infrastructure, fostering intergenerational knowledge exchange and strengthening community stewardship. This study demonstrates how a participatory, ethnobotany-informed process can regenerate degraded urban waterways into Living Cultural Infrastructure. The research advances a new paradigm for landscape architecture by providing replicable governance and design tools. Full article

59 pages, 4527 KB

Open AccessReview

Energy-Efficient Strategies in Wireless Body Area Networks: A Comprehensive Survey

by Marwa Boumaiz, Mohammed El Ghazi, Anas Bouayad, Younes Balboul and Moulhime El Bekkali

IoT 2025, 6(3), 49; https://doi.org/10.3390/iot6030049 - 29 Aug 2025

Abstract

Wireless body area networks (WBANs) are a pivotal solution for continuous health monitoring, but their energy constraints pose a significant challenge for long-term operation. This paper provides a comprehensive review of state-of-the-art energy-efficient mechanisms, critically evaluating solutions across various network layers. We focus [...] Read more.

Wireless body area networks (WBANs) are a pivotal solution for continuous health monitoring, but their energy constraints pose a significant challenge for long-term operation. This paper provides a comprehensive review of state-of-the-art energy-efficient mechanisms, critically evaluating solutions across various network layers. We focus on three key approaches: energy-aware MAC protocols that reduce idle listening and optimize duty cycling; energy-efficient routing protocols that enhance data transmission and network longevity; and emerging energy harvesting techniques that offer a path toward energy-autonomous WBANs. Furthermore, the paper provides a detailed analysis of the inherent trade-offs between energy efficiency and other critical performance metrics, such as latency, reliability, and security. It also explores the transformative potential of emerging technologies, such as AI and blockchain, for dynamic energy management and secure data handling. By synthesizing these findings, this work contributes to the development of sustainable WBAN solutions and outlines clear directions for future research. Full article

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56 pages, 7375 KB

Open AccessArticle

A Two-Stage Hybrid Federated Learning Framework for Privacy-Preserving IoT Anomaly Detection and Classification

by Mohammad Shahin, Ali Hosseinzadeh and F. Frank Chen

IoT 2025, 6(3), 48; https://doi.org/10.3390/iot6030048 - 29 Aug 2025

Abstract

The rapid surge of Artificial Internet-of-Things (AIoT) devices has outpaced the deployment of robust, privacy-preserving anomaly detection solutions suitable for resource-constrained edge environments. This paper presents a two-stage hybrid Federated Learning (FL) framework for IoT anomaly detection and classification, validated on the real-world [...] Read more.

The rapid surge of Artificial Internet-of-Things (AIoT) devices has outpaced the deployment of robust, privacy-preserving anomaly detection solutions suitable for resource-constrained edge environments. This paper presents a two-stage hybrid Federated Learning (FL) framework for IoT anomaly detection and classification, validated on the real-world N-BaIoT dataset. In the first stage, each device trains a generative Artificial Intelligence (AI) model on benign traffic only, and in the second stage a Histogram-based Gradient-Boosting (HGB) classifier labels flagged traffic. All models operate under a synchronous, collaborative FL architecture across nine commercial IoT devices, thus preserving data privacy and minimizing communication. Through both inter- and intra-benchmarking against state-of-the-art baselines, the Variational Autoencoder–HGB (VAE-HGB) pipeline emerges as the top performer, achieving an average end-to-end accuracy of 99.14% across all classes. These results demonstrate that reconstruction-driven generative AI models, when combined with federated averaging and efficient classification, deliver a highly scalable, accurate, and privacy-preserving solution for securing resource-constrained IoT environments. Full article

(This article belongs to the Special Issue AIoT-Enabled Sustainable Smart Manufacturing)

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27 pages, 3885 KB

Open AccessArticle

Research on Energy Saving for Hybrid Tractor Based on Working Condition Prediction and DDPG-Fuzzy Control

by Shilong Fan, Xianghai Yan, Shuaishuai Ge, Junjiang Zhang and Mengnan Liu

World Electr. Veh. J. 2025, 16(9), 490; https://doi.org/10.3390/wevj16090490 - 29 Aug 2025

Abstract

To significantly reduce fuel consumption and improve fuel economy in hybrid tractor under complex working conditions, an energy—saving strategy based on working condition prediction and Deep Deterministic Policy Gradient and Fuzzy control (DDPG-Fuzzy) was proposed. Firstly, a hybrid tractor system dynamics model containing [...] Read more.

To significantly reduce fuel consumption and improve fuel economy in hybrid tractor under complex working conditions, an energy—saving strategy based on working condition prediction and Deep Deterministic Policy Gradient and Fuzzy control (DDPG-Fuzzy) was proposed. Firstly, a hybrid tractor system dynamics model containing diesel, motor, and power battery was established. Secondly, a working condition prediction model for plowing velocity and resistance was constructed based on the adaptive cubic exponential smoothing method. Finally, a two-layer control architecture was designed. The upper layer adopted the DDPG algorithm, which takes demand torque, equivalent fuel consumption, and the State of Charge (SOC) as state inputs to optimize energy consumption by generating the diesel benchmark torque through the policy network. The lower layer introduced a fuzzy control compensation mechanism that calculates the torque correction based on the plowing velocity error and the plowing resistance deviation to adjust the power allocation. In light of on this, an energy—saving strategy for hybrid tractor based on working condition prediction and DDPG-Fuzzy control was proposed. Under a standard 140 s plowing cycle, the results showed that the working condition prediction model achieved mean prediction accuracies of 97% for plowing velocity and 96.8% for plowing resistance. Under plowing conditions, the proposed strategy reduced the equivalent fuel consumption by 9.7% compared to the power-following strategy, and reduced SOC by 4.4% while maintaining it within a reasonable range. By coordinating the operation of the diesel and motor within high-efficiency regions, this approach enhances fuel economy under complex working conditions. Full article

15 pages, 3141 KB

Open AccessArticle

Gravity Data-Driven Machine Learning: A Novel Approach for Predicting Volcanic Vent Locations in Geohazard Investigation

by Murad Abdulfarraj, Ema Abraham, Faisal Alqahtani and Essam Aboud

GeoHazards 2025, 6(3), 49; https://doi.org/10.3390/geohazards6030049 - 29 Aug 2025

Abstract

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed [...] Read more.

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed a predictive framework that integrates high-resolution gravity data with multiple machine learning algorithms. Logistic Regression, Gradient Boosting Machine (GBM), Decision Tree, Support Vector Machine (SVM), and Random Forest models were applied to analyze the gravitational characteristics of known volcanic vents and predict the likelihood of undiscovered vents at other locations. The problem was formulated as a binary classification task, and model performance was assessed using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The Random Forest algorithm yielded optimal outcomes: 95% classification accuracy, AUC-ROC score of 0.99, 75% geographic correspondence between real and modeled vent sites, and a 95% certainty degree. Spatial density analysis showed that the distribution patterns of predicted and actual vents are highly similar, underscoring the model’s reliability in identifying vent-prone areas. The proposed method offers a valuable tool for geoscientists and disaster management authorities to improve volcanic hazard evaluation and implement effective mitigation strategies. These results represent a significant step forward in our ability to model volcanic dynamics and enhance predictive capabilities for volcanic hazard assessment. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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25 pages, 6151 KB

Open AccessArticle

Identification of Sparse Interdependent Edges in Heterogeneous Network Models via Greedy Module Matching

by Qingyu Zou and Yue Gong

Modelling 2025, 6(3), 92; https://doi.org/10.3390/modelling6030092 - 29 Aug 2025

Abstract

The identification of interdependent edges plays a critical role in improving information propagation efficiency and enhancing network robustness in interdependent networks. However, existing methods exhibit significant limitations when identifying interdependent edges between networks with substantial differences in edge density. This paper proposes a [...] Read more.

The identification of interdependent edges plays a critical role in improving information propagation efficiency and enhancing network robustness in interdependent networks. However, existing methods exhibit significant limitations when identifying interdependent edges between networks with substantial differences in edge density. This paper proposes a greedy module matching-based method for sparse interdependent edge identification in similar-order heterogeneous networks. The method utilizes degree entropy and betweenness centrality as node characteristic values for sparse and dense networks, respectively. It first leverages structural differences between sparse and dense networks to determine the upper bound of interdependent edges. Then, a clustering algorithm is employed to identify modules in both networks that align with the estimated number of interdependent edges. Finally, a greedy algorithm is applied to infer interdependent edges between sparse and dense networks. The proposed method is validated using synthetic networks and power-communication networks, with network robustness and connection efficiency as evaluation metrics. Additionally, further validation is conducted through applications in problem–answer networks. Experimental results demonstrate that the proposed approach significantly improves the prediction of sparse interdependent relationships in heterogeneous complex networks and has broad applicability across multiple domains. Full article

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19 pages, 3130 KB

Open AccessArticle

Numerical Analysis and Experimental Verification of Radial Shear Rolling of Titanium Alloy

by Abdullah Mahmoud Alhaj Ali, Anna Khakimova, Yury Gamin, Tatiana Kin, Nikolay Letyagin and Dmitry Demin

Modelling 2025, 6(3), 93; https://doi.org/10.3390/modelling6030093 - 29 Aug 2025

Abstract

Numerical simulation of metal forming processes is finding increasingly wide applications in advanced industry for the optimization of material processing conditions and prediction of process parameters, finally delivering a reduction of production costs. This work presents a comparison between simulation results of radial [...] Read more.

Numerical simulation of metal forming processes is finding increasingly wide applications in advanced industry for the optimization of material processing conditions and prediction of process parameters, finally delivering a reduction of production costs. This work presents a comparison between simulation results of radial shear rolling (RSR) of VT3-1 titanium alloy (Ti-Al-Mo-Cr-Fe-Si) and results of experimental RSR at 1060 °C, 980 °C, and 900 °C in one, three, and five passes, respectively. The digital model (DM) demonstrates a high convergence of the calculation results (calculation error of less than 5%) with the actual geometric parameters of the experimental bars, their surface temperature, and rolling time during the experiment, which indicates a good potential for its application in the selection of deformation modes. Based on the simulation and experimental data, the conditions providing for the formation of differently sized grains in the bar cross-section have been identified. All of the as-rolled bars exhibit a gradient distribution of macrostructure grain size number (GSN), from the smallest one at the bar surface (2–4) to the greatest one in the center (4–6). The macrostructure GSN correlates with the workpiece temperature, which is the highest in the axial zone of the bars, and with the experimentally observed high plastic strain figures in the surface layers. It was found that, depending on the temperature conditions and reduction ratio per pass, any minor change in the values of process parameters can lead to the formation of macrostructures with different grain size numbers. Full article

(This article belongs to the Special Issue Finite Element Simulation and Analysis)

16 pages, 1166 KB

Open AccessArticle

Preservation of Rabbit Meat in High-Density Polyethylene Packaging Bags Reinforced with Ethyl Cellulose Nanoparticles Loaded with Rosemary Extract

by Brenda Sánchez-Camacho, María de la Luz Zambrano-Zaragoza, José Eleazar Aguilar-Toalá, Rosy Gabriela Cruz-Monterrosa, Monzerrat Rosas-Espejel and Jorge L. Mejía-Méndez

Polysaccharides 2025, 6(3), 76; https://doi.org/10.3390/polysaccharides6030076 - 29 Aug 2025

Abstract

In this work, ethyl cellulose nanoparticles loaded with rosemary extract (RCL-NPs) were synthesized and utilized to reinforce high-density polyethylene (HDPE) packaging bags as a nanotechnological alternative for rabbit meat preservation. The synthesized RCL-NPs were characterized by DLS and for their stability. The analyzed [...] Read more.

In this work, ethyl cellulose nanoparticles loaded with rosemary extract (RCL-NPs) were synthesized and utilized to reinforce high-density polyethylene (HDPE) packaging bags as a nanotechnological alternative for rabbit meat preservation. The synthesized RCL-NPs were characterized by DLS and for their stability. The analyzed variables of rabbit meat packaged samples included drained liquid, weight loss, color, pH, texture, and hardness. The total phenolic content (TPC) and antioxidant capacity of rosemary extract were also investigated. The results demonstrated that RCL-NPs were 117.30 nm in size with a negative surface charge (−24.59 mV) and low PDI (0.12). According to the Higuchi model, the release rate of RCL-NPs was sustained from 0 to 24 h. The encapsulation efficiency of the implemented synthesis route was 99.97%. The TPC of rosemary extract was 566.13 ± 1.72 mg GAE/L, whereas their antioxidant activity utilizing the DPPH and FRAP assays was 27.86 ± 0.32 mM Trolox/L and 0.31 mM Trolox/L, respectively. Contrary to control samples, rabbit meat samples conserved in HDPE packaging bags reinforced with RCL-NPs prevent drained liquid and weight loss, while preserving *L (60 ± 2.5–66.10 ± 2.0) and *b (10.67 ± 2.28–11.62 ± 2.39), pH (5.22 ± 0.05–5.80 ± 0.03), and texture (10.37 ± 0.82–0.70 ± 0.50). In the same regard, the developed material conserved the hardness of rabbit meat samples, exhibiting values that ranged from 27.79 ± 7.23 to 27.60 ± 3.05 N during the evaluated period (0–13 days). The retrieved data demonstrate the efficacy of RCL in preserving the quality of rabbit meat when integrated with additional food packaging materials. Full article

(This article belongs to the Collection Bioactive Polysaccharides)

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25 pages, 6573 KB

Open AccessArticle

Improving Dry-Bulb Air Temperature Prediction Using a Hybrid Model Integrating Genetic Algorithms with a Fourier–Bessel Series Expansion-Based LSTM Model

by Hussein Alabdally, Mumtaz Ali, Mohammad Diykh, Ravinesh C. Deo, Anwar Ali Aldhafeeri, Shahab Abdulla and Aitazaz Ahsan Farooque

Forecasting 2025, 7(3), 46; https://doi.org/10.3390/forecast7030046 - 29 Aug 2025

Abstract

The dry-bulb temperature is a critical parameter in weather forecasting, agriculture, energy management, and climate research. This work proposes a new hybrid prediction model (FBSE-GA-LSTM) that integrates the Fourier–Bessel series expansion (FBSE), genetic algorithm (GA), and long short-term memory (LSTM) networks together to [...] Read more.

The dry-bulb temperature is a critical parameter in weather forecasting, agriculture, energy management, and climate research. This work proposes a new hybrid prediction model (FBSE-GA-LSTM) that integrates the Fourier–Bessel series expansion (FBSE), genetic algorithm (GA), and long short-term memory (LSTM) networks together to predict the dry-bulb air temperature. The hybrid model FBSE-GA-LSTM utilises the FBSE to decompose time series data of interest into an attempt to remove the noise level for capturing the dominant predictive patterns. Then, the FBSE is embedded into the GA method for the best feature selection and dimension reduction. To predict the dry-bulb temperature, a new model (FBSE-GA-LSTM) was used by hybridising a proposed model FBSE-GA with the LSTM model on the time series dataset of two different regions in Saudi Arabia. For comparison, the FBSE and GA models were hybridised with a bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional gated recurrent unit (BiGRU) models to obtain the hybrid FBSE-GA-BiLSTM, FBSE-GA-GRU, and FBSE-GA-BiGRU models along with their standalone versions. In addition, benchmark models, including the climatic average and persistence approaches, were employed to demonstrate that the proposed model outperforms simple baseline predictors. The experimental results indicated that the proposed hybrid FBSE-GA-LSTM model achieved improved prediction performance compared with the contrastive models for the Jazan region, with a mean absolute error (MAE) of 1.458 °C, a correlation coefficient (R) of 0.954, and a root mean squared error (RMSE) of 1.780 °C, and for the Jeddah region, with an MAE of 1.459 °C, an R of 0.952, and an RMSE of 1.782 °C, between the predicted and observed values of dry-bulb air temperature. Full article

(This article belongs to the Section Environmental Forecasting)

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11 pages, 200 KB

Open AccessArticle

Liver Cysts and Artificial Intelligence: Is AI Really a Patient-Friendly Support?

by Enrico Spalice, Chiara D’Alterio, Maria Lanzone, Immacolata Iannone, Cristina De Padua, Matteo De Pastena and Alessandro Coppola

Surgeries 2025, 6(3), 73; https://doi.org/10.3390/surgeries6030073 - 29 Aug 2025

Abstract

Background: With the advancement of AI-powered online tools, patients are increasingly turning to AI for guidance on healthcare-related issues. Methods: Acting as patients, we posed eight direct questions concerning a common clinical condition—liver cysts—to four AI chatbots: ChatGPT, Perplexity, Copilot, and Gemini. The [...] Read more.

Background: With the advancement of AI-powered online tools, patients are increasingly turning to AI for guidance on healthcare-related issues. Methods: Acting as patients, we posed eight direct questions concerning a common clinical condition—liver cysts—to four AI chatbots: ChatGPT, Perplexity, Copilot, and Gemini. The responses were collected and compared both among the chatbots and with the current literature, including the most recent guidelines. Results: Overall, the responses from the four chatbots were generally consistent with the literature, with only a few inaccuracies noted. For questions addressing “grey areas” in clinical research, all chatbots provided generalized answers. ChatGPT, Copilot, and Gemini highlighted the lack of conclusive evidence in the literature, while Perplexity offered speculative correlations not supported by data. Importantly, all chatbots recommended consulting a healthcare professional. While Perplexity, Copilot, and Gemini included references in their responses, not all cited sources were academic or of medium/high evidence quality. An analysis of Flesch Readability Ease Scores and Estimated Reading Grade Levels indicated that ChatGPT and Gemini provided the most readable and comprehensible responses. Conclusions: The integration of chatbots into real-world healthcare scenarios requires thorough testing to prevent potentially serious consequences from misuse. While undeniably innovative, this technology presents significant risks if implemented improperly. Full article

15 pages, 2912 KB

Open AccessArticle

Adsorption of Lanthanide Atoms on a Graphene Cluster Model Incorporating Stone–Wales Defect

by Vladimir A. Basiuk and Elena V. Basiuk

Surfaces 2025, 8(3), 63; https://doi.org/10.3390/surfaces8030063 - 29 Aug 2025

Abstract

To study the adsorption of lanthanide (Ln) atoms on graphene containing a Stone–Wales defect, we used a cluster model (SWG) and performed calculations at the PBE-D2/DNP level of the density functional theory. Our previous study, where the above combination was complemented with the [...] Read more.

To study the adsorption of lanthanide (Ln) atoms on graphene containing a Stone–Wales defect, we used a cluster model (SWG) and performed calculations at the PBE-D2/DNP level of the density functional theory. Our previous study, where the above combination was complemented with the ECP pseudopotentials, was only partially successful due to the impossibility of calculating terbium-containing systems and a serious error found for the SWG complex with dysprosium. In the present study we employed the DSPP pseudopotentials and completely eliminated the latter two failures. We analyzed the optimized geometries of the full series of fifteen SWG + Ln complexes, along with their formation energies and electronic parameters, such as frontier orbital energies, atomic charges, and spins. In many regards, the two series of calculations show qualitatively similar features, such as roughly M-shaped curves of the adsorption energies and trends in the changes in charge and spin of the adsorbed Ln atoms, as well as the spin density plots. However, the quantitative results can differ significantly. For most characteristics we found no evident correlation with the lanthanide contraction. The only dataset where this phenomenon apparently manifests itself (albeit to a limited and irregular degree) is the changes in the closest Ln^…C approaches. Full article

(This article belongs to the Special Issue Nanocarbons: Advances and Innovations)

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28 pages, 3780 KB

Open AccessArticle

Machine Learning Prediction Models of Beneficial and Toxicological Effects of Zinc Oxide Nanoparticles in Rat Feed

by Leonid Legashev, Ivan Khokhlov, Irina Bolodurina, Alexander Shukhman and Svetlana Kolesnik

Mach. Learn. Knowl. Extr. 2025, 7(3), 91; https://doi.org/10.3390/make7030091 - 29 Aug 2025

Abstract

Nanoparticles have found widespread application across diverse fields, including agriculture and animal husbandry. However, a persistent challenge in laboratory-based studies involving nanoparticle exposure is the limited availability of experimental data, which constrains the robustness and generalizability of findings. This study presents a comprehensive [...] Read more.

Nanoparticles have found widespread application across diverse fields, including agriculture and animal husbandry. However, a persistent challenge in laboratory-based studies involving nanoparticle exposure is the limited availability of experimental data, which constrains the robustness and generalizability of findings. This study presents a comprehensive analysis of the impact of zinc oxide nanoparticles (ZnO NPs) in feed on elemental homeostasis in male Wistar rats. Using correlation-based network analysis, a correlation graph weight value of 15.44 and a newly proposed weighted importance score of 1.319 were calculated, indicating that a dose of 3.1 mg/kg represents an optimal balance between efficacy and physiological stability. To address the issue of limited sample size, synthetic data generation was performed using generative adversarial networks, enabling data augmentation while preserving the statistical characteristics of the original dataset. Machine learning models based on fully connected neural networks and kernel ridge regression, enhanced with a custom loss function, were developed and evaluated. These models demonstrated strong predictive performance across a ZnO NP concentration range of 1–150 mg/kg, accurately capturing the dependencies of essential element, protein, and enzyme levels in blood on nanoparticle dosage. Notably, the presence of toxic elements and some other elements at ultra-low concentrations exhibited non-random patterns, suggesting potential systemic responses or early indicators of nanoparticle-induced perturbations and probable inability of synthetic data to capture the true dynamics. The integration of machine learning with synthetic data expansion provides a promising approach for analyzing complex biological responses in data-scarce experimental settings, contributing to the safer and more effective application of nanoparticles in animal nutrition. Full article

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23 pages, 5273 KB

Open AccessArticle

Federated Learning Detection of Cyberattacks on Virtual Synchronous Machines Under Grid-Forming Control Using Physics-Informed LSTM

by Ali Khaleghi, Soroush Oshnoei and Saeed Mirzajani

Fractal Fract. 2025, 9(9), 569; https://doi.org/10.3390/fractalfract9090569 - 29 Aug 2025

Abstract

The global shift toward clean production, like using renewable energy, has significantly decreased the use of synchronous machines (SMs), which help maintain stability and control, causing serious frequency stability issues in power systems with low inertia. Fractional order controller-based virtual synchronous machines (FOC-VSMs) [...] Read more.

The global shift toward clean production, like using renewable energy, has significantly decreased the use of synchronous machines (SMs), which help maintain stability and control, causing serious frequency stability issues in power systems with low inertia. Fractional order controller-based virtual synchronous machines (FOC-VSMs) have become a promising option, but they rely on communication networks to work together in real time, causing them to be at risk of cyberattacks, especially from false data injection attacks (FDIAs). This paper suggests a new way to detect FDI attacks using a federated physics-informed long short-term memory (PI-LSTM) network. Each FOC-VSM uses its data to train a PI-LSTM, which keeps the information private but still helps it learn from a common model that understands various operating conditions. The PI-LSTM incorporates physical constraints derived from the FOC-VSM swing equation, facilitating residual-based anomaly detection that is sensitive to minor deviations in control dynamics, such as altered inertia or falsified frequency signals. Unlike traditional LSTMs, the physics-informed architecture minimizes false positives arising from benign disturbances. We assessed the proposed method on an IEEE 9-bus test system featuring two FOC-VSMs. The results show that our method can successfully detect FDI attacks while handling regular changes, proving it could be a strong solution. Full article

(This article belongs to the Special Issue Fractional Order Systems with Application to Electrical Power Engineering, 3rd Edition)

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