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24 pages, 3841 KB

Open AccessArticle

The Neural Network Fitting Method for Green’s Function of Finite Water Depth

by Wenhui Xiong, Zhinan Mi, Yu Liu and Lunwei Zhang

J. Mar. Sci. Eng. 2026, 14(2), 203; https://doi.org/10.3390/jmse14020203 (registering DOI) - 19 Jan 2026

In marine hydrodynamics, the core of the boundary element method (BEM) lies in the numerical calculation of the free-surface Green’s function. With the rise of artificial intelligence, using neural networks to fit Green’s function has become a new trend, yet most existing studies [...] Read more.

In marine hydrodynamics, the core of the boundary element method (BEM) lies in the numerical calculation of the free-surface Green’s function. With the rise of artificial intelligence, using neural networks to fit Green’s function has become a new trend, yet most existing studies are confined to fitting Green’s function in infinite water depth. In this paper, a neural network fitting method for a finite-depth Green’s function is proposed. The classical Multilayer Perceptron (MLP) network and the emerging Kolmogorov–Arnold Network (KAN) are employed to conduct global and partition-based fitting experiments. Experiments indicate that the partition-based KAN fitting model achieves higher fitting accuracy, with most regions reaching 4D fitting precision. For large-scale data input, the average time for the model to calculate a single Green’s function value is 0.0868 microseconds, which is significantly faster than the 0.1120 s required by the traditional numerical integration method. These results demonstrate that the KAN can serve as an accurate and efficient model for finite-depth Green’s functions. The proposed KAN-based fitting method not only reduces the computational cost of numerical evaluation of Green’s functions but also maintains high prediction precision, providing an alternative approach to accelerate BEM calculations for floating body hydrodynamic analysis. Full article

(This article belongs to the Section Ocean Engineering)

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16 pages, 1020 KB

Open AccessArticle

In Vivo Determination of Skin Absorption Coefficient in a Mexican Cohort

by Erick Enrique Amezcua-López, Luis Francisco Corral-Martínez, Gerardo Trujillo-Schiaffino, Didia Patricia Salas-Peimbert, Marcelino Anguiano-Morales and Juan Alberto Ramírez-Quintana

Appl. Sci. 2026, 16(2), 1021; https://doi.org/10.3390/app16021021 (registering DOI) - 19 Jan 2026

Abstract

We determined the in vivo absorption coefficient (μ_a) for 82 test subjects, all classified as Fitzpatrick skin phototypes II, III, IV, and V. Measurements were conducted using the integrating-sphere technique on the dorsal and palmar surfaces of the hand and [...] Read more.

We determined the in vivo absorption coefficient (μ_a) for 82 test subjects, all classified as Fitzpatrick skin phototypes II, III, IV, and V. Measurements were conducted using the integrating-sphere technique on the dorsal and palmar surfaces of the hand and the forearm. The reflectance data obtained were processed using the Inverse Adding Doubling algorithm to calculate the absorption coefficient. The mean values for this parameter ranged from 0.0132 mm⁻¹ to 0.1021 mm⁻¹ at a central wavelength of 624 nm. It was found that these parameters may be grouped into a distinct cohort, paving the way for studies and the design of light-based diagnostics and treatments better suited to the population in Mexico and Latin America. Full article

(This article belongs to the Special Issue Advanced Devices, Biomedical Technologies and Their Applications in Dermatology)

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19 pages, 4111 KB

Open AccessArticle

The Effects of Chinese Dwarf Cherry (Cerasus humilis) Kernel Oil on Defecation and the Gut Microbiota in Constipated Mice

by Jingyu Gao, Yumin Dai, Zhe Liang, Nan Chen, Xilong Li, Xin Wen, Yuanying Ni and Mo Li

Nutrients 2026, 18(2), 319; https://doi.org/10.3390/nu18020319 (registering DOI) - 19 Jan 2026

Abstract

Background: The Chinese dwarf cherry (CDC) has been valued for over 2000 years for its medicinal and nutritional properties, particularly its kernels. Despite its recognition as a rich source of oil, the potential health benefits of CDC kernel oil remain unclear. Method: Initially, [...] Read more.

Background: The Chinese dwarf cherry (CDC) has been valued for over 2000 years for its medicinal and nutritional properties, particularly its kernels. Despite its recognition as a rich source of oil, the potential health benefits of CDC kernel oil remain unclear. Method: Initially, we evaluated the preventive effectiveness of CDC in a mouse model of constipation induced by loperamide. Results: The findings indicated that CDC kernel oil alleviated constipation by reducing the first black fecal defecation time and increasing the fecal number, wet weight, water content and gastrointestinal transit rate in model mice. Additionally, CDC kernel oil reduced inhibitory neurotransmitters and increased excitability neurotransmitters, two anti-oxidases’ activity and fecal short-chain fatty acid (SCFA) content. Histological analysis revealed an improved mucus cell morphology in the intestinal tract. Furthermore, CDC kernel oil increased the abundance of some beneficial bacteria. It was identified that the gut microbiota was associated with neurotransmitters, mediators of inflammation and SCFAs. Conclusion: The findings offer a scientific foundation for considering CDC kernel oil as a potential functional food for the alleviation of constipation. Full article

(This article belongs to the Section Phytochemicals and Human Health)

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17 pages, 5352 KB

Open AccessArticle

Preliminary Study on Mechanism of Cold Stress on Dendroctonus valens Larvae

by Debin Li, Shisong Lu, Shiyu Kuang, Bo Dong, Hongna Chen, Yijing Wang and Shengwei Jiang

Forests 2026, 17(1), 136; https://doi.org/10.3390/f17010136 (registering DOI) - 19 Jan 2026

Abstract

To elucidate the effect of cold stress on Dendroctonus valens larvae, a study was conducted under controlled laboratory conditions to examine the physiological and biochemical mechanisms associated with cold stress, coupled with transcriptome sequencing. Physiological and biochemical assessments indicated stable water content in [...] Read more.

To elucidate the effect of cold stress on Dendroctonus valens larvae, a study was conducted under controlled laboratory conditions to examine the physiological and biochemical mechanisms associated with cold stress, coupled with transcriptome sequencing. Physiological and biochemical assessments indicated stable water content in larvae during cold stress initiation, with triglycerides and fats serving as primary energy reserves that decreased over cold stress progression. Glycogen and trehalose were identified as energy sources for larval energy metabolism, with their levels increasing as cold stress duration extended. Superoxide dismutase (SOD) activity exhibited an initial decline followed by an increase, while peroxidase (POD) activity initially rose before decreasing over induction time, and catalase (CAT) activity decreased during cold stress induction. Transcriptome sequencing at various time points revealed 4630 upregulated and 1554 downregulated genes, predominantly involved in metabolic pathways such as carbohydrate, amino acid, and lipid metabolism. Quantitative polymerase chain reaction (qPCR) results validated the transcriptome data accuracy. This investigation delineated the physiological, biochemical, and transcriptome alterations during cold stress, offering a theoretical framework for the rational prediction of Dendroctonus valens outbreaks. Full article

(This article belongs to the Section Forest Health)

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19 pages, 2065 KB

Open AccessArticle

Multiscale Wind Forecasting Using Explainable-Adaptive Hybrid Deep Learning

by Fatih Serttas

Appl. Sci. 2026, 16(2), 1020; https://doi.org/10.3390/app16021020 (registering DOI) - 19 Jan 2026

Abstract

This study presents a multiscale, uncertainty-aware hybrid deep learning approach addressing the short-term wind speed prediction problem, which is critical for the reliable planning and operation of wind energy systems. Wind signals are decomposed using adaptive variational mode decomposition (VMD), and the resulting [...] Read more.

This study presents a multiscale, uncertainty-aware hybrid deep learning approach addressing the short-term wind speed prediction problem, which is critical for the reliable planning and operation of wind energy systems. Wind signals are decomposed using adaptive variational mode decomposition (VMD), and the resulting wind components are processed together with meteorological data through a dual-stream CNN–BiLSTM architecture. Based on this multiscale representation, probabilistic forecasts are generated using quantile regression to capture best- and worst-case scenarios for decision-making purposes. Unlike fixed prediction intervals, the proposed approach produces adaptive prediction bands that expand during unstable wind conditions and contract during calm periods. The developed model is evaluated using four years of meteorological data from the Afyonkarahisar region of Türkiye. While the proposed model achieves competitive point forecasting performance (RMSE = 0.700 m/s and MAE = 0.54 m/s), its main contribution lies in providing reliable probabilistic forecasts through well-calibrated uncertainty quantification, offering decision-relevant information beyond single-point predictions. The proposed method is compared with a classical CNN–LSTM and several structural variants. Furthermore, SHAP-based explainability analysis indicates that seasonal and solar-related variables play a dominant role in the forecasting process. Full article

(This article belongs to the Topic Advances in Wind Energy Technology: 2nd Edition)

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12 pages, 2318 KB

Open AccessArticle

Enhanced Room-Temperature Optoelectronic NO₂ Sensing Performance of Ultrathin Non-Layered Indium Oxysulfide via In Situ Sulfurization

by Yinfen Cheng, Nianzhong Ma, Zhong Li, Dengwen Hu, Zhentao Ji, Lieqi Liu, Rui Ou, Zhikang Shen and Jianzhen Ou

Sensors 2026, 26(2), 670; https://doi.org/10.3390/s26020670 (registering DOI) - 19 Jan 2026

Abstract

The detection of trace nitrogen dioxide (NO₂) is critical for environmental monitoring and industrial safety. Among various sensing technologies, chemiresistive sensors based on semiconducting metal oxides are prominent due to their high sensitivity and fast response. However, their application is hindered [...] Read more.

The detection of trace nitrogen dioxide (NO₂) is critical for environmental monitoring and industrial safety. Among various sensing technologies, chemiresistive sensors based on semiconducting metal oxides are prominent due to their high sensitivity and fast response. However, their application is hindered by inherent limitations, including low selectivity and elevated operating temperatures, which increase power consumption. Two-dimensional metal oxysulfides have recently attracted attention as room-temperature sensing materials due to their unique electronic properties and fully reversible sensing performance. Meanwhile, their combination with optoelectronic gas sensing has emerged as a promising solution, combining higher efficiency with minimal energy requirements. In this work, we introduce non-layered 2D indium oxysulfide (In₂S_xO_3−x) synthesized via a two-step process: liquid metal printing of indium followed by thermal annealing of the resulting In₂O₃ in a H₂S atmosphere at 300 °C. The synthesized material is characterized by a micrometer-scale lateral dimension with 6.3 nm thickness and remaining n-type semiconducting behavior with a bandgap of 2.53 eV. It demonstrates a significant response factor of 1.2 toward 10 ppm NO₂ under blue light illumination at room temperature. The sensor exhibits a linear response across a low concentration range of 0.1 to 10 ppm, alongside greatly improved reversibility, selectivity, and sensitivity. This study successfully optimizes the application of 2D metal oxysulfide and presents its potential for the development of energy-efficient NO₂ sensing systems. Full article

(This article belongs to the Special Issue Gas Sensing for Air Quality Monitoring)

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16 pages, 819 KB

Open AccessArticle

Differences in Management of Neonates with Hypoxic–Ischemic Encephalopathy (HIE) by Level of Neonatal Care Provided at Birth: Insights from a Referral-Based Cohort in the Canton of Zurich, Switzerland

by Ladina Erni, Ariane Pfister, Christian Haslinger, Michael Kleber, Barbara Brotschi, Dirk Bassler, Vinzenz Boos and Beate Grass

Children 2026, 13(1), 142; https://doi.org/10.3390/children13010142 (registering DOI) - 19 Jan 2026

Abstract

Background/Objectives: Neonates with hypoxic–ischemic encephalopathy (HIE) are born in delivery facilities with different levels of neonatal care. The objective of this study was to investigate differences in the incidence of HIE and postnatal management between different levels of neonatal care in delivery [...] Read more.

Background/Objectives: Neonates with hypoxic–ischemic encephalopathy (HIE) are born in delivery facilities with different levels of neonatal care. The objective of this study was to investigate differences in the incidence of HIE and postnatal management between different levels of neonatal care in delivery facilities. Methods: This is a retrospective, multi-center cohort study of neonates with moderate-to-severe HIE receiving therapeutic hypothermia (TH) in the Canton of Zurich, Switzerland, registered in the Swiss National Asphyxia and Cooling Register between 2015 and 2023. Incidences of HIE receiving TH were calculated for all delivery facilities according to the national levels of neonatal care on site (Level I—basic; Level IIB—intermediate (no Level IIA facility in the Canton of Zurich); Level III—intensive neonatal care). Perinatal characteristics and variables on transport and outcomes were compared between neonates born in Level I and Level IIB facilities (the majority of the HIE population) and reported for neonates born in all other facilities (for completeness). Results: A total of 173 neonates (79 (45.7%) born in Level I; 80 (46.2%) in Level IIB; 9 (5.2%) in Level III; 5 (2.9%) in birthing centers) were admitted to a neonatal cooling center to receive TH. The average number of annual cases of HIE receiving TH per facility was 0.67 (0.11–1.50) in Level I and 2.22 (0.22–3.11) in Level IIB facilities (p = 0.088), respectively. There was no difference in Apgar score, worst pH (within 60 min after birth) and the severity of encephalopathy between neonates born in Level I and Level IIB facilities. Neonatal transport team requests were initiated earlier in Level I facilities (median 12 vs. 34 min of life, p < 0.001). There was no difference in age at initiation of TH (median 3 vs. 3 h, p = 0.431) and the time when target temperature was reached (median 4 vs. 4 h, p = 0.431) between neonates born in Level I and Level IIB facilities. Conclusions: The level of neonatal care available in delivery facilities influenced the management of neonates with HIE receiving TH. Full article

(This article belongs to the Special Issue Hypoxic–Ischemic Brain Injury in Newborn: The Present Status and Challenges in the Future)

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28 pages, 7850 KB

Open AccessArticle

A Systematic Approach for the Conservation and Sustainable Activation of Traditional Military Settlements Using TRIZ Theory: A Case Study of Zhenjing Village, Arid Northern China

by Hubing Li, Feng Zhao and Haitao Ren

Buildings 2026, 16(2), 420; https://doi.org/10.3390/buildings16020420 (registering DOI) - 19 Jan 2026

Abstract

This study aims to examine the methodological applicability of the Theory of Inventive Problem Solving (TRIZ) in the conservation and revitalization of traditional military settlements. Using Zhenjing Village in Jingbian County as a case, the research constructs a systematic framework for contradiction identification [...] Read more.

This study aims to examine the methodological applicability of the Theory of Inventive Problem Solving (TRIZ) in the conservation and revitalization of traditional military settlements. Using Zhenjing Village in Jingbian County as a case, the research constructs a systematic framework for contradiction identification and strategy generation. Methods: Through preliminary surveys, data integration, and system modeling, the study identifies major conflicts among authenticity preservation, ecological carrying capacity, and community vitality in Zhenjing Village. Technical contradiction matrices, separation principles, and the Algorithm of Inventive Problem Solving (ARIZ) are employed for structured analysis. Further, system dynamics modeling is used to simulate the effectiveness of strategies and to evaluate the dynamic impacts of various conservation interventions on authenticity maintenance, ecological stress, and community vitality. The research identifies three categories of core technical contradictions and translates the 39 engineering parameters into an indicator system adapted to the cultural heritage conservation context. ARIZ is used to derive the Ideal Final Result (IFR) for Zhenjing Village, which includes self-maintaining authenticity, self-regulating ecology, and self-activating community development, forming a systematic strategy. System dynamics simulations indicate that, compared with “inertial development,” TRIZ-oriented strategies reduce the decline in heritage authenticity by approximately 40%, keep ecological pressure indices below threshold levels, and significantly enhance the sustainability of community vitality. TRIZ enables a shift in the conservation of traditional military settlements from experience-driven approaches toward systematic problem solving. It strengthens conflict-identification capacity and improves the logical rigor of strategy generation, providing a structured and scalable innovative method for heritage conservation in arid and ecologically fragile regions in northern China and similar contexts worldwide. Full article

(This article belongs to the Special Issue Built Heritage Conservation in the Twenty-First Century: 2nd Edition)

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32 pages, 122293 KB

Open AccessArticle

Hybrid Negation: Enhancing Sentiment Analysis for Complex Sentences

by Miftahul Qorib and Paul Cotae

Appl. Sci. 2026, 16(2), 1000; https://doi.org/10.3390/app16021000 (registering DOI) - 19 Jan 2026

Abstract

Numerous valuable information is available on the Internet, and many individuals rely on mass media as their primary source of information. Various views, comments, expressions, and opinions on social networks have been a tremendous source of information. Harvesting free, resourceful information through social [...] Read more.

Numerous valuable information is available on the Internet, and many individuals rely on mass media as their primary source of information. Various views, comments, expressions, and opinions on social networks have been a tremendous source of information. Harvesting free, resourceful information through social media makes text mining a powerful tool for analyzing public opinions on various issues across diverse social networks. Various research projects have implemented text sentiment analysis through machine and deep learning approaches. Social media text often expresses sentiment through complex syntax and negation (e.g., implicit and double negation and nested clauses), which many classifiers mishandle. We propose hybrid negation, a clause-aware approach that combines (i) explicit/implicit/double-negation rules, (ii) dependency-based scope detection, (iii) a TextBlob back-off for phrase polarity, and (iv) an MLP-learned clause-weighting module that aggregates clause-level scores. Across 156,539 tweets (three-class sentiment), we evaluate six negation strategies and 228 model configurations with and without SMOTE (applied strictly within training folds). Hybrid Negation achieves 98.582% accuracy, 98.196% precision, 98.189% recall, and 98.193% F1 with BERT, outperforming rule-only and antonym/synonym baselines. Ablations show each component contributes to the model’s performance, with dependency scope and double negations offering the largest gains. Per-class results, confidence intervals, and paired tests with multiple-comparison control confirm statistically significant improvements. We release code and preprocessing scripts to support reproducibility. Full article

(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)

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38 pages, 2784 KB

Open AccessFeature PaperArticle

Neurodegenerative Disease–Specific Relations Between Temporal and Kinetic Gait Features Identified Using InterCriteria Analysis

by Irena Jekova, Vessela Krasteva and Todor Stoyanov

Mathematics 2026, 14(2), 340; https://doi.org/10.3390/math14020340 (registering DOI) - 19 Jan 2026

Abstract

Gait analysis is a non-invasive, cost-effective method for detecting subtle motor changes in neurodegenerative disorders. This study uses an exploratory approach to identify temporal–kinetic gait feature relationships specific to amyotrophic lateral sclerosis (ALS) and Huntington (HUNT) and Parkinson (PARK) disease versus healthy controls [...] Read more.

Gait analysis is a non-invasive, cost-effective method for detecting subtle motor changes in neurodegenerative disorders. This study uses an exploratory approach to identify temporal–kinetic gait feature relationships specific to amyotrophic lateral sclerosis (ALS) and Huntington (HUNT) and Parkinson (PARK) disease versus healthy controls (CONTROL) using recent advances in InterCriteria Analysis (ICrA). The novelty lies in the (i) comprehensive temporal–kinetic feature set, (ii) use of ICrA to characterize inter-feature coordination patterns at population and disease-group levels and (iii) interpretation in a neuromechanical context. Forty-one temporal/kinetic features were extracted from left/right leg ground reaction force and rate-of-force-development signals, considering laterality, gait phase (stance, swing, double support), magnitudes, waveform correlations, and inter-/intra-limb asymmetries. The analysis included 14,580 steps from 64 recordings in the Gait in Neurodegenerative Disease Database: 16 CONTROL (4054 steps), 13 ALS (2465), 20 HUNT (4730), 15 PARK (3331). Sensitivity analysis identified strict consonance thresholds (μ ≥ 0.75, ν ≤ 0.25), selecting <5% strongest inter-feature relations from 820 feature pairs: population level (16 positive, 14 negative), group-level (15–25 positive, 9–14 negative). ICrA identified group-specific consonances—present in one group but absent in others—highlighting disease-related alterations in gait coordination: ALS (15/11 positive/negative, disrupted bilateral stride coordination, prolonged stance/double-support, decoupled stride/cadence, desynchronized force-generation patterns—reflecting compensatory adaptations to muscle weakness and instability), HUNT (11/7, severe temporal–kinetic breakdown consistent with gait instability—loss of bilateral coordination, reduced swing time, slowed force development), PARK (1/2, subtle localized disruptions—prolonged stance and double-support intervals, reduced force during weight transfer, overall coordination remained largely preserved). Benchmarking vs. Pearson correlation showed strong linear agreement (R² = 0.847, p < 0.001), confirming that ICrA captures dominant dependencies while moderating the correlation via uncertainty. These results demonstrate that ICrA provides a quantitative, interpretable framework for characterizing gait coordination patterns and can guide principled feature selection in future predictive modeling. Full article

(This article belongs to the Special Issue Advanced Intelligent Algorithms for Decision Making Under Uncertainty)

19 pages, 593 KB

Open AccessReview

Additive Manufacturing of Ceramics Study: Sustainable Material Extrusion and Its Potential Role in Circular Economy

by Paula González-Suárez, Pedro Manuel Hernández-Castellano and Annabella Narganes-Pineda

Appl. Sci. 2026, 16(2), 1019; https://doi.org/10.3390/app16021019 (registering DOI) - 19 Jan 2026

Abstract

Additive Manufacturing (AM) has emerged as a transformative technology enabling the production of complex geometries and customized components with minimal material waste. Within this field, the processing of ceramic materials represents a rapidly expanding research area due to their exceptional mechanical, thermal, and [...] Read more.

Additive Manufacturing (AM) has emerged as a transformative technology enabling the production of complex geometries and customized components with minimal material waste. Within this field, the processing of ceramic materials represents a rapidly expanding research area due to their exceptional mechanical, thermal, and chemical properties. This work presents a comprehensive review of additive manufacturing processes applied to ceramics, such as Vat Photopolimerization, Binder Jetting and Laser Powder Bed Fusion, emphasizing their technological principles and capabilities. Particular attention is given to material extrusion-based additive manufacturing (MEX-AM) for ceramics, detailing its process mechanisms, rheological requirements, feedstock formulations and post-processing treatments necessary to achieve high-density and defect-free components. Furthermore, the study develops a sustainability-oriented evaluation of the ceramic MEX-AM process, addressing its environmental, economic, and social dimensions. Based on this assessment, several methodological approaches and tools are proposed to enhance process sustainability, as well as its alignment with Circular Economy principles. The outcomes of this research provide an integrated perspective on the sustainable development of ceramic additive manufacturing, supporting future advancements in Circular Design, process optimization, and industrial implementation. Full article

(This article belongs to the Special Issue Smart and Sustainable Additive Manufacturing: Emerging Technologies and Applications)

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18 pages, 1153 KB

Open AccessArticle

Fixed-Time Event-Triggered Control for Distributed Unmanned Underwater Vehicles

by Xiaoling Liang, Jie Li and Dan Bao

J. Mar. Sci. Eng. 2026, 14(2), 202; https://doi.org/10.3390/jmse14020202 (registering DOI) - 19 Jan 2026

Abstract

This paper investigates the problem of fixed-time event-triggered consensus control for distributed unmanned underwater vehicle systems subject to communication and energy constraints. The systematic integration control framework is developed, where each unmanned underwater vehicles updates their control inputs only at event-triggered instants instead [...] Read more.

This paper investigates the problem of fixed-time event-triggered consensus control for distributed unmanned underwater vehicle systems subject to communication and energy constraints. The systematic integration control framework is developed, where each unmanned underwater vehicles updates their control inputs only at event-triggered instants instead of continuously, thereby reducing unnecessary communication and actuation efforts. By designing a fixed-time consensus protocol, it is guaranteed that the group of unmanned underwater vehicles achieves time-synchronized consensus within the convergence time, independent of the initial conditions. The stability and convergence of the proposed scheme are rigorously proved using Lyapunov theory and fixed-time stability analysis. Furthermore, a zeno-free triggering condition is established to ensure the feasibility of practical implementation. Numerical simulations are carried out on a team of unmanned underwater vehicles to demonstrate the effectiveness of the proposed method in achieving precise coordination, reducing communication burden, and enhancing energy efficiency in distributed marine operations. Full article

(This article belongs to the Special Issue Advances in Underwater Positioning and Navigation Technology)

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16 pages, 1229 KB

Open AccessArticle

Myeloperoxidase (MPO) Enzymatic Activity, but Not Its Protein Concentration, Is Associated with the Risk of Type 2 Diabetes in Females, Regardless of Obesity Status

by Alessandro Trentini, Raffaella Riccetti, Domenico Sergi, Juana Maria Sanz, Riccardo Spaggiari, Valentina Rosta, Gianmarco Mola, Angelina Passaro, MEDIA HDL Research Group and Carlo Cervellati

Antioxidants 2026, 15(1), 130; https://doi.org/10.3390/antiox15010130 (registering DOI) - 19 Jan 2026

Abstract

To date, neutrophil-derived myeloperoxidase (MPO), a key mediator of inflammation and oxidative stress, has predominantly been assessed in peripheral fluids by protein concentration rather than enzymatic activity, mainly due to methodological limitations. However, MPO activity directly reflects the enzyme’s cytotoxic potential and pathogenic [...] Read more.

To date, neutrophil-derived myeloperoxidase (MPO), a key mediator of inflammation and oxidative stress, has predominantly been assessed in peripheral fluids by protein concentration rather than enzymatic activity, mainly due to methodological limitations. However, MPO activity directly reflects the enzyme’s cytotoxic potential and pathogenic role in inflammatory diseases. To address this gap, we employed an optimized immunocapture assay to evaluate MPO activity, specific activity, and protein concentration in females with type 2 diabetes mellitus (T2DM), a condition tightly linked to chronic low-grade inflammation and obesity. Our findings revealed that females with T2DM exhibited nearly three-fold higher serum MPO activity and more than two-fold greater specific activity compared to controls with no differences in MPO protein concentration. Notably, MPO-specific activity remained significantly associated with T2DM (p < 0.01 to p < 0.001 across multivariate models), even after adjusting for age and dual-energy X-ray absorptiometry-derived measures of total and regional fat mass. Only android/gynoid fat distribution retained marginal significance in these models. This study is the first demonstration that MPO enzymatic activity, rather than protein concentration, is independently linked to T2DM in females. These findings underscore the importance of assessing functional MPO activity in the context of metabolic disease and support its potential role as a pathophysiological marker. Full article

(This article belongs to the Section Antioxidant Enzyme Systems)

13 pages, 2721 KB

Open AccessArticle

Analysis of Interrupting Energy Variations in MCCBs Under Repetitive Fault Conditions in Accelerator Environments

by Young-Maan Cho, Houng-Kun Joung and Kun-A Lee

Actuators 2026, 15(1), 65; https://doi.org/10.3390/act15010065 (registering DOI) - 19 Jan 2026

Abstract

This study quantitatively analyzed the effects of repetitive fault currents occurring in an accelerator environment on the breaking performance of molded-case circuit breakers (MCCBs). To this purpose, four MCCB samples are subjected to one, two, and three repeated fault tests. The interrupting process [...] Read more.

This study quantitatively analyzed the effects of repetitive fault currents occurring in an accelerator environment on the breaking performance of molded-case circuit breakers (MCCBs). To this purpose, four MCCB samples are subjected to one, two, and three repeated fault tests. The interrupting process is divided into the arc stretch and moving (t₁–t₂) section and the absorption in the splitter plate (t₂–t₃) section, and the energy and time are analyzed. The experimental results show that the total energy consumption increased by an average of 1.8–1.9 times in the second and third tests compared to the first test, and the interruption time is also extended by 1.6–2.0 times. In particular, the energy increase rate in the t₂–t₃ section is the highest, at an average of 220%, indicating that the splitter plate is thermally saturated and significantly affected by hot gas due to repeated breaking. These results imply that the thermal and electrical performances of MCCBs deteriorates in a repetitive fault environment, with the interrupting speed delayed and internal energy loss increased. This study suggests the possibility of energy-based condition diagnosis using the energy consumption ratio of each section. Furthermore, the ratios can be used as basic data for evaluating the reliability of circuit breakers under repetitive failure conditions and building predictive maintenance models. Full article

(This article belongs to the Special Issue Intelligent Actuation and Control Systems for Electrified Mobility and Robotics)

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21 pages, 3116 KB

Open AccessReview

The Role of Cancer-Associated Fibroblasts and Tumor-Associated Macrophages in the Tumor Microenvironment and Their Impact on Ovarian Cancer Survival and Therapy

by Alena A. McQuarter, Joseph Cruz, Celina R. Yamauchi, Mariem Chouchen, Cody S. Carter, Tonya J. Webb and Salma Khan

Curr. Oncol. 2026, 33(1), 59; https://doi.org/10.3390/curroncol33010059 (registering DOI) - 19 Jan 2026

Abstract

Ovarian cancer is the deadliest gynecologic cancer, mainly because it is often diagnosed late and resists standard treatments. The tumor microenvironment (TME) plays a major role in disease progression and therapy failure. Two key components of the TME, cancer-associated fibroblasts (CAFs) and tumor-associated [...] Read more.

Ovarian cancer is the deadliest gynecologic cancer, mainly because it is often diagnosed late and resists standard treatments. The tumor microenvironment (TME) plays a major role in disease progression and therapy failure. Two key components of the TME, cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs), create conditions that facilitate tumor growth and immune evasion. CAFs are highly diverse and originate from sources like fibroblasts and stem cells. They support cancer by remodeling the extracellular matrix, promoting angiogenesis, and releasing cytokines and growth factors that aid tumor survival. TAMs, which are usually in an M2 state, also promote metastasis and suppress immune responses by secreting immunosuppressive molecules. Together, CAFs and TAMs interact with cancer cells to activate pathways such as the TGF-β, IL-6, and PI3K/AKT pathways, which drive resistance to therapy. New treatments aim to block these interactions by targeting CAFs and TAMs through depletion, reprogramming, or pathway inhibition, often combined with immunotherapy. Advances such as single-cell sequencing and spatial transcriptomics now enable more precise identification of CAF and TAM subtypes, enabling more targeted therapies. This review summarizes their roles in epithelial ovarian cancer and explores how targeting these cells could improve outcomes. Full article

(This article belongs to the Topic Antibody-Mediated Therapy and Other Emerging Therapies in Cancer Treatment)

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53 pages, 2229 KB

Open AccessReview

Progress in Aero-Engine Fault Signal Recognition and Intelligent Diagnosis

by Shunming Li, Wenbei Shi, Jiantao Lu, Haibo Zhang, Yanfeng Wang, Peng Zhang, Mengqi Feng and Yan Wang

Machines 2026, 14(1), 118; https://doi.org/10.3390/machines14010118 (registering DOI) - 19 Jan 2026

Abstract

Accurate diagnosis of aero-engine faults and precise signal characterization are crucial to ensuring operational reliability and service life prediction. The structural complexity of engines and the variability of operating conditions pose significant challenges for fault diagnosis and identification. Based on an analysis and [...] Read more.

Accurate diagnosis of aero-engine faults and precise signal characterization are crucial to ensuring operational reliability and service life prediction. The structural complexity of engines and the variability of operating conditions pose significant challenges for fault diagnosis and identification. Based on an analysis and emphasis on the critical importance of aero-engine fault signal recognition and diagnosis, this paper comprehensively reviews and discusses the classification and evolution of aero-engine fault signal recognition techniques. The review traces this evolution along its developmental trajectory, from classical methods to emerging approaches such as quantum signal processing for weak feature extraction. It also examines characteristics of different types of aviation engine failures and the progression of diagnostic research over time. This review provides multiple tables to compare the applicability, advantages, and limitations of various signal recognition methods and deep learning diagnostic architectures. Detailed discussions synthesize the relative merits of different approaches and their selection trade-offs. Based on this overview, the paper outlines the complexity of real aero-engine faults and key research directions. Building on these developments in fault signal recognition and diagnosis, the paper addresses the complexity and the research areas receiving particular attention within real aero-engine faults. It highlights key research areas, including handling data imbalance, adapting to variable and cross-domain conditions, and advancing diagnostic and data enhancement methods for weak composite faults. Finally, the paper analyzes the multifaceted challenges in the field and identifies future trends in aero-engine fault signal recognition and intelligent diagnosis. Full article

(This article belongs to the Special Issue Recent Advances in Machinery Condition Monitoring and Fault Diagnosis: From Typical Algorithms to the Era of AI Blooming)

30 pages, 731 KB

Open AccessReview

Bacteriocins, a New Generation of Sustainable Alternatives to Antibacterial Agents in Primary Food Production Systems

by Besarion Meskhi, Svetoslav Dimitrov Todorov, Dmitry Rudoy, Anastasiya Olshevskaya, Victoria Shevchenko, Tatiana Maltseva, Arkady Mirzoyan, Denis Kozyrev, Mary Odabashyan, Svetlana Teplyakova and Maria Mazanko

Molecules 2026, 31(2), 356; https://doi.org/10.3390/molecules31020356 (registering DOI) - 19 Jan 2026

Abstract

Modern agriculture faces the critical need to develop sustainable, safe, and effective strategies for enhancing productivity, protecting plants and animals, and ensuring food security. Challenges posed by antibiotic resistance and the adverse environmental and consumer health impacts of chemical agents are driving the [...] Read more.

Modern agriculture faces the critical need to develop sustainable, safe, and effective strategies for enhancing productivity, protecting plants and animals, and ensuring food security. Challenges posed by antibiotic resistance and the adverse environmental and consumer health impacts of chemical agents are driving the search for eco-friendly alternatives. In this context, bacteriocins—naturally occurring antimicrobial peptides synthesized by diverse bacteria—represent a promising alternative to traditional chemical compounds. This article reviews the potential and current advances in bacteriocin applications across agricultural sectors, with particular focus on their targeted antagonistic activity, structural diversity, commercial bacteriocin-based products, and their utilization in livestock farming, crop production, poultry farming, and aquaculture. Key findings demonstrate that bacteriocins, particularly nisin and pediocin PA-1, exhibit potent activity against major agricultural pathogens including Listeria monocytogenes, Staphylococcus aureus, Clostridium perfringens, and Escherichia coli, with efficacy rates reaching 90% in mastitis treatment and significantly reducing pathogen loads in poultry and aquaculture systems. Commercial products such as Nisaplin, Wipe Out, and ALTA 2431 have been successfully implemented in veterinary medicine and food production. In aquaculture, bacteriocins effectively control Lactococcus garvieae, Aeromonas spp., Vibrio spp., and Pseudomonas aeruginosa, contributing to sustainable disease management with minimal environmental impact. It can be suggested that bacteriocins may play an essential role in combating pathogens and offer viable alternatives to conventional antibiotics across primary food production systems, though optimization of production methods and regulatory frameworks remains essential for broader commercial adoption. Full article

(This article belongs to the Special Issue Green Chemistry and Molecular Tools in Agriculture)

20 pages, 1210 KB

Open AccessArticle

From Establishment to Expansion: Changing Drivers of Acacia spp. Invasion in Mainland Central Portugal

by Matilde Salgueiro, Carla Mora and César Capinha

Forests 2026, 17(1), 135; https://doi.org/10.3390/f17010135 (registering DOI) - 19 Jan 2026

Abstract

Land abandonment and recurrent wildfires are major drivers of landscape transformation in Mediterranean Europe, creating favorable conditions for the spread of non-native invasive woody species. Among these, Australian wattles (genus Acacia) are particularly widespread and problematic in Portugal. This work analyzed the [...] Read more.

Land abandonment and recurrent wildfires are major drivers of landscape transformation in Mediterranean Europe, creating favorable conditions for the spread of non-native invasive woody species. Among these, Australian wattles (genus Acacia) are particularly widespread and problematic in Portugal. This work analyzed the spatiotemporal dynamics of Acacia spp. in two municipalities of central Portugal (Sertã and Pedrógão-Grande) by combining multitemporal photointerpretation of aerial imagery (2004–2021), generalized additive models (GAMs), and local perception surveys. Results reveal a 417% increase in occupied area over the last two decades. Modeling outcomes indicate a temporal shift in invasion drivers: from an establishment phase (2004–2010), mainly constrained by altitude and proximity to primary introduction sites, to a disturbance-driven expansion phase (2010–2021), influenced by fire recurrence, slope, and land-use context. Spatial clustering persisted throughout, underscoring the role of founder populations. Surveys confirmed high public awareness of Acacia invasiveness and identified abandonment and wildfire as the main perceived triggers of spread. By integrating ecological and social dimensions, this study provides a socioecological perspective on Acacia spp. expansion in Mediterranean rural landscapes and highlights the urgent need for integrated, landscape-scale management strategies. Full article

(This article belongs to the Section Forest Ecology and Management)

21 pages, 1012 KB

Open AccessArticle

Comparative Evaluation of Deep Learning Architectures for Non-Destructive Estimation of Carotenoid Content from Visible–Near-Infrared (400–850 nm) Spectral Reflectance Data

by Yuta Tsuchiya, Yuhei Hirono and Rei Sonobe

AgriEngineering 2026, 8(1), 36; https://doi.org/10.3390/agriengineering8010036 (registering DOI) - 19 Jan 2026

Abstract

This study compared three deep learning architectures—one-dimensional convolutional neural network (1D-CNN), self-supervised learning (SSL), and Vision Transformer (ViT)—to evaluate their ability to predict carotenoid content from visible–near-infrared (VIS–NIR) spectral reflectance data (400–850 nm) acquired non-destructively from tea leaves. Model performance was evaluated using [...] Read more.

This study compared three deep learning architectures—one-dimensional convolutional neural network (1D-CNN), self-supervised learning (SSL), and Vision Transformer (ViT)—to evaluate their ability to predict carotenoid content from visible–near-infrared (VIS–NIR) spectral reflectance data (400–850 nm) acquired non-destructively from tea leaves. Model performance was evaluated using 10-fold cross-validation and analyzed through the mean SHapley Additive exPlanations values to identify key spectral features. The ViT model achieved the highest predictive accuracy (coefficient of determination [R²] = 0.81, root mean square error [RMSE] = 1.04, ratio of performance to deviation [RPD] = 2.32), followed by 1D-CNN (R² = 0.75, RMSE = 1.21, RPD = 1.99), whereas SSL showed substantially lower predictive performance (R² = 0.30, RMSE = 2.01, RPD = 1.20). Feature importance analysis revealed that ViT focused strongly on the red-edge region around 720 nm, which corresponds to spectral features associated with carotenoids and chlorophyll. The 1D-CNN relied mainly on blue (450–480 nm) and red (670–700 nm) regions, while SSL exhibited a broadly distributed importance pattern across wavelengths. These results indicate that ViT’s self-attention mechanism captures long-range spectral dependencies more effectively than conventional convolutional or self-supervised models. Overall, the study demonstrates that transformer-based architectures provide a powerful and interpretable framework for non-destructive estimation of carotenoid content from VIS–NIR reflectance spectroscopy. Full article

(This article belongs to the Special Issue The Future of Artificial Intelligence in Agriculture, 2nd Edition)

22 pages, 3006 KB

Open AccessReview

Molecular Crosstalk Underlying Pre-Colonization Signaling and Recognition in Ectomycorrhizal Symbiosis

by Rosario Ramírez-Mendoza, Magdalena Martínez-Reyes, Yanliang Wang, Yunchao Zhou, Arturo Galvis-Spinola, Juan José Almaraz-Suárez, Fuqiang Yu and Jesus Perez-Moreno

Forests 2026, 17(1), 134; https://doi.org/10.3390/f17010134 (registering DOI) - 19 Jan 2026

Abstract

Ectomycorrhizal (ECM) symbiosis is a fundamental mutualism crucial for forest eco-system health. Its establishment is governed by sophisticated molecular dialogue preceding physical colonization. This review synthesizes this pre-colonization crosstalk, beginning with reciprocal signal exchange where root exudates trigger fungal growth, and fungal lipochitooligosaccharides [...] Read more.

Ectomycorrhizal (ECM) symbiosis is a fundamental mutualism crucial for forest eco-system health. Its establishment is governed by sophisticated molecular dialogue preceding physical colonization. This review synthesizes this pre-colonization crosstalk, beginning with reciprocal signal exchange where root exudates trigger fungal growth, and fungal lipochitooligosaccharides activate host symbiotic programming, often via the common symbiosis pathway. Successful colonization requires fungi to navigate plant immunity. They employ effectors, notably mycorrhiza-induced small secreted proteins (MiSSPs), to suppress defenses, e.g., by stabilizing jasmonate signaling repressors or inhibiting apoplastic proteases, establishing a localized “mycorrhiza-induced resistance.” Concurrent structural adaptations, including fungal hydrophobins, expansins, and cell wall-modifying enzymes like chitin deacetylase, facilitate adhesion and apoplastic penetration. While this sequential model integrates immune suppression with structural remodeling, current understanding is predominantly derived from a limited set of model systems. Significant knowledge gaps persist regarding species-specific determinants in non-model fungi and hosts, the influence of environmental variability and microbiome interactions, and methodological challenges in capturing early signaling in situ. This review’s main contributions are: providing a synthesized sequential model of molecular crosstalk; elucidating the dual fungal strategy of simultaneous immune suppression and structural remodeling; and identifying crucial knowledge gaps regarding non-model systems and species-specific determinants, establishing a research roadmap with implications for forest management and ecosystem sustainability. Full article

(This article belongs to the Section Genetics and Molecular Biology)

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24 pages, 399 KB

Open AccessReview

Extemporaneous Formulations for Pediatric Patients: Global Necessities, Challenges and Opportunities

by Vinita Balakrishna Pai and Milap Chand Nahata

Pharmaceutics 2026, 18(1), 126; https://doi.org/10.3390/pharmaceutics18010126 (registering DOI) - 19 Jan 2026

Abstract

Many commercially available medications are often unapproved or unavailable in suitable dosage forms for specific patient populations, particularly infants and children. This necessitates the use of extemporaneously compounded formulations to deliver individualized doses based on body weight or body surface area, and when [...] Read more.

Many commercially available medications are often unapproved or unavailable in suitable dosage forms for specific patient populations, particularly infants and children. This necessitates the use of extemporaneously compounded formulations to deliver individualized doses based on body weight or body surface area, and when a medication is unavailable at an appropriate concentration or contains excipients potentially unsafe for certain patients. Extemporaneous compounding is required for oral liquids when patients are unable to swallow tablets or capsules. It is also needed for topical preparations and sterile dosage forms when commercial products are unavailable. Across regions, practices follow national pharmacopeial standards for both sterile and non-sterile compounding. Stability factors influencing the safety and efficacy of compounded formulations must be carefully considered when assigning appropriate beyond-use dates. While stability information is available for some medications in monographs, peer-reviewed literature, prescribing information, and investigator’s brochures, such data is often lacking for many compounded preparations. Emerging extemporaneous formulations—such as orodispersible films, nanoparticle systems, and 3D-printed compounds—offer potential advantages over traditional compounded formulations but present unique challenges to widespread implementation. Despite the justified clinical need for extemporaneous compounding, significant barriers remain, including limited access to medications, insufficient compounding expertise or resources, gaps in pharmacokinetic and safety data, and regulatory constraints. This review critically appraises the current state of extemporaneous compounding—drawing primarily on the United States of America frameworks—and highlights its continued necessity, associated challenges, and pragmatic solutions for advancing personalized pharmacotherapy across pediatric age groups worldwide. Full article

(This article belongs to the Special Issue Extemporaneous Formulations: Filling the Gap in the Pharmaceutical Industry with Personalized Medicines)

51 pages, 4235 KB

Open AccessArticle

Intelligent Charging Reservation and Trip Planning of CAEVs and UAVs

by Palwasha W. Shaikh, Hussein T. Mouftah and Burak Kantarci

Electronics 2026, 15(2), 440; https://doi.org/10.3390/electronics15020440 (registering DOI) - 19 Jan 2026

Abstract

Connected and Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are critical components of future Intelligent Transportation Systems (ITS), yet their deployment remains constrained by fragmented charging infrastructures and the lack of coordinated reservation and trip planning across static, dynamic wireless, and [...] Read more.

Connected and Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are critical components of future Intelligent Transportation Systems (ITS), yet their deployment remains constrained by fragmented charging infrastructures and the lack of coordinated reservation and trip planning across static, dynamic wireless, and vehicle-to-vehicle (V2V) charging networks using magnetic resonance and laser-based power transfer. Existing solutions often struggle with misalignment sensitivity, unpredictable arrivals, and disconnected ground–aerial scheduling. This work introduces a three-layer architecture that integrates a handshake protocol for coordinated charging and billing, a misalignment correction algorithm for magnetic resonance and laser-based systems, and three scheduling strategies: Static Heuristic Charging Scheduling and Planning (SH-CSP), Dynamic Heuristic Charging Scheduling and Planning (DH-CSP), and the Safety, Scheduling, and Sustainability-Aware Feasibility-Enhanced Deep Deterministic Policy Gradient (SAFE-DDPG). SAFE-DDPG extends vanilla DDPG with feasibility-aware action filtering, prioritized replay, and adaptive exploration to enable real-time scheduling in heterogeneous and congested charging networks. Results show that SAFE-DDPG significantly improves scheduling efficiency, reducing average wait times by over 70% compared to DH-CSP and over 85% compared to SH-CSP, demonstrating its potential to support scalable and coordinated ground–aerial charging ecosystems. Full article

(This article belongs to the Special Issue The New Technologies and Applications on Intelligent Transportation Systems (ITS))

20 pages, 6975 KB

Open AccessReview

Logic Gates Based on Skyrmions

by Yun Shu, Qianrui Li, Wei Zhang, Yi Peng, Ping Lai and Guoping Zhao

Nanomaterials 2026, 16(2), 135; https://doi.org/10.3390/nano16020135 (registering DOI) - 19 Jan 2026

Abstract

Traditional complementary metal-oxide-semiconductor (CMOS) logic gates serve as the fundamental building blocks of modern computing, operating through the electron charge manipulation wherein binary information is encoded as distinct high- and low-voltage states. However, as physical dimensions approach the quantum limit, conventional logic gates [...] Read more.

Traditional complementary metal-oxide-semiconductor (CMOS) logic gates serve as the fundamental building blocks of modern computing, operating through the electron charge manipulation wherein binary information is encoded as distinct high- and low-voltage states. However, as physical dimensions approach the quantum limit, conventional logic gates encounter fundamental bottlenecks, including power consumption barriers, memory limitations, and a significant increase in static power dissipation. Consequently, the pursuit of novel low-power computing methodologies has emerged as a research hotspot in the post-Moore era. Logic gates based on magnetic skyrmions constitute a highly promising candidate in this context. Magnetic skyrmions, nanoscale quasiparticles endowed with topological protection, offer ideal carriers for information transmission due to their exceptional stability and mobility. In this work, we provide a concise overview of the current development status and underlying operating principles of magnetic skyrmion logic gates across various magnetic materials, including ferromagnetic, synthetic antiferromagnetic, and antiferromagnetic systems. The introduction of magnetic skyrmion-based logical operations represents a paradigm shift from traditional Boolean logic to architectures integrating memory and computation, as well as brain-inspired neuromorphic computing. Although significant challenges remain in the synthesis of materials, fabrication, and detection, magnetic skyrmion-based logic computing holds considerable potential as a future ultra-low-power computing technology. Full article

(This article belongs to the Section Theory and Simulation of Nanostructures)

22 pages, 3994 KB

Open AccessArticle

Study on Temporal Convolutional Network Rainfall Prediction Model and Its Interpretability Guided by Physical Mechanisms

by Dongfang Ma, Yunliang Wen, Chongxu Zhao and Chunjin Zhang

Hydrology 2026, 13(1), 38; https://doi.org/10.3390/hydrology13010038 (registering DOI) - 19 Jan 2026

Abstract

Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal [...] Read more.

Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal the physical mechanism of rainfall in the basin and integrate monthly scale meteorological data to achieve monthly rainfall prediction. In this paper, we propose a rainfall prediction model coupled with a physical mechanism and a temporal convolutional network (TCN) to achieve the prediction of monthly rainfall in the basin, aiming to reveal the physical mechanism between rainfall factors in the basin based on the transfer entropy and the multidimensional Copula function and based on the physical mechanism which is embedded into the TCN to construct a dual-driven prediction model with both physical knowledge and data, while the SHAP is used to analyze the interpretability of the prediction model. The results are as follows: (1) Temperature, relative humidity, and evaporation are key characteristic factors driving rainfall. (2) The physical mechanism features between temperature, relative humidity, and evaporation can be described by the three-dimensional Gumbel–Hougaard Copula function, with a more concentrated data distribution of their joint distribution probability. (3) The PHY-TCN model can accurately fit the extremes of the rainfall series, improving the model accuracy in the training set by 3.82%, 1.39%, and 9.82% compared to TCN, CNN, and LSTM, respectively, and in the test set by 6.04%, 2.55%, and 8.91%, respectively. (4) Embedding physical mechanisms enhances the contribution of individual feature variables in the PHY-TCN model and increases the persuasiveness of the model. This study provides a new research framework for rainfall prediction in the YRB and analyzes the physical relationship between the input data and output results of the deep learning model. It has important practical significance and strategic value for guiding the optimal scheduling of water resources, improving the risk management level of the basin, and promoting the ecological protection and high-quality development of the YRB. Full article

(This article belongs to the Special Issue Global Rainfall-Runoff Modelling)

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16 pages, 8045 KB

Open AccessArticle

Effect of Dietary Capsaicinoids Supplementation on Growth Performance, Intestinal Morphology, and Colon Microbiota in Weaned Piglets

by Kangwei Hou, Zhixiang Ni, Jiangdi Mao and Haifeng Wang

Antioxidants 2026, 15(1), 129; https://doi.org/10.3390/antiox15010129 (registering DOI) - 19 Jan 2026

Abstract

This study investigated the effects of encapsulated capsaicinoids (CAPs), containing 0.47% capsaicin and 0.22% dihydrocapsaicin, on growth, serum parameters, nutrient digestibility, and intestinal health in weaned piglets. A total of 168 piglets were randomly assigned to four groups: a basal diet or the [...] Read more.

This study investigated the effects of encapsulated capsaicinoids (CAPs), containing 0.47% capsaicin and 0.22% dihydrocapsaicin, on growth, serum parameters, nutrient digestibility, and intestinal health in weaned piglets. A total of 168 piglets were randomly assigned to four groups: a basal diet or the same diet supplemented with 200 (LDC), 400 (MDC), or 600 (HDC) mg/kg of CAPs. The results indicated that CAPs improved lipid metabolism, evidenced by higher crude fat digestibility in the LDC and MDC groups and reduced serum low-density lipoprotein cholesterol in all CAP groups compared to the control. Glutathione peroxidase activity was significantly higher in the MDC and HDC groups. Histological analysis showed reduced hepatic vacuolation, enlarged fungiform papillae with shallower taste pores in the tongue epithelium, and deeper ileal crypts in the LDC group. At the molecular level, ZO-1 expression in the ileum was significantly upregulated in LDC piglets. Colonic microbiota analysis revealed decreased relative abundances of Lachnospiraceae_AC2044_group, Lachnospiraceae_XPB1014_group, and Rikenellaceae_RC9_gut, while Butyricicoccus was significantly enriched in the LDC group. In conclusion, CAPs supplementation enhanced fat digestibility, lipid metabolism, antioxidant capacity, intestinal development, and colonic microbiota composition, with the 200 mg/kg dose showing the most pronounced effects. Full article

(This article belongs to the Special Issue Oxidative Stress in Animal Reproduction and Nutrition)

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22 pages, 8990 KB

Open AccessArticle

Rotor–Stator Configuration in Gas-Inducing Reactors: Effects of Blade Number and Thickness on Gas Holdup

by Ehsan Zamani Abyaneh, Farhad Ein-Mozaffari and Ali Lohi

Processes 2026, 14(2), 354; https://doi.org/10.3390/pr14020354 (registering DOI) - 19 Jan 2026

Abstract

Gas-inducing reactors (GIRs) are widely used in applications where external gas recycling is unsafe or operationally restricted, yet quantitative design guidelines for impeller–stator geometry remain scarce, despite its strong influence on gas dispersion and retention. This study investigates the effects of stator blade [...] Read more.

Gas-inducing reactors (GIRs) are widely used in applications where external gas recycling is unsafe or operationally restricted, yet quantitative design guidelines for impeller–stator geometry remain scarce, despite its strong influence on gas dispersion and retention. This study investigates the effects of stator blade number and blade thickness on gas holdup in a double-impeller GIR using a three-dimensional Euler–Euler CFD framework. Stator configurations with 12–48 blades and blade thicknesses of 1.5–45 mm were examined and validated against experimental data, with gas holdup predictions agreeing within 5–10%. The results show that the stator open-area fraction (ϕ_A) is the dominant geometric parameter governing the balance between radial dispersion and axial confinement. High-ϕ_A stators (fewer, thinner blades) enhance bulk recirculation and bubble residence time, increasing gas holdup by up to ~20% relative to dense stator designs, whereas low-ϕ_A stators suppress macro-circulation, promote axial gas transport, and reduce holdup despite higher local dissipation near the rotor–stator gap. A modified gas-holdup correlation incorporating ϕ_A is proposed, yielding strong agreement with CFD and experimental data (R² = 0.96). Torque analysis further reveals competing effects between impeller gassing, which lowers hydraulic loading, and increased flow resistance at low ϕ_A, which elevates torque. Overall, the results provide quantitative guidance on how stator blade number and thickness influence gas holdup, enabling informed stator design and optimization in GIRs to improve gas dispersion through rational geometric selection rather than trial and error approaches. Full article

(This article belongs to the Special Issue Modeling and Optimization for Multi-scale Integration)

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