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21 pages, 6711 KB

Open AccessArticle

Legume-Based Rotations Reduce Cereal Yield Loss and Water Use to Enhance System Yield Resilience in Response to Climate Change

by Bo Wang, Xiaolin Yang, Jos van Dam, Tiegui Nan, Taisheng Du, Shaozhong Kang and Coen Ritsema

Agriculture 2026, 16(3), 335; https://doi.org/10.3390/agriculture16030335 (registering DOI) - 29 Jan 2026

Abstract

Climate change significantly challenges efforts to maintain and improve crop production worldwide. Diversified crop rotations have emerged as a promising way to adapt cropping systems and bolster food security under changing climate conditions; however, robust empirical evidence remains limited. This study evaluates the [...] Read more.

Climate change significantly challenges efforts to maintain and improve crop production worldwide. Diversified crop rotations have emerged as a promising way to adapt cropping systems and bolster food security under changing climate conditions; however, robust empirical evidence remains limited. This study evaluates the long-term performance of diversified crop rotations under future climate scenarios in the North China Plain via an 80-year scenario analysis (2020–2100) spanning three shared socioeconomic pathways (SSPs:126, 370, 585). The calibrated and validated SWAP (Soil–Water–Atmosphere–Plant)–WOFOST (WOrld FOod STudies) model simulated water consumption and yield. Sustainability indices were employed to assess the cereal yield stability and compensation effect to yield loss caused by climate change. The study compares the conventional winter wheat–summer maize rotation (WM) with two legume-based rotations: soybean–WM (S–WM) and peanut–WM (P–WM). The results indicate that, under all three climate scenarios, the two legume-based rotations reduced annual water consumption by 7–9%, maintained system economic equivalent yields with one crop less, and improved water productivity by up to 10%. Future climate change decreased cereal yields by 9–26% across all rotations compared to historical baselines. However, the two legume-based rotations showed a significant residual effect, increasing subsequent cereal yields by 9–14% over the conventional WM under all scenarios. Consequently, the legume-based rotations provided a 25–51% yield compensation. Additionally, these rotations improved the sustainable yield index and system resilience and reduced cereal yield variance under future climate scenarios compared to the more vulnerable WM. This study demonstrates that diversified crop rotations are a viable strategy to mitigate negative climate impacts. The study provides critical insights for policy-makers, supporting crop-rotation diversification as a core component of risk-reduction strategies to mitigate future climate change impacts. Full article

(This article belongs to the Section Agricultural Systems and Management)

18 pages, 3622 KB

Open AccessArticle

Data-Driven Optimization and Modelling of the Gap Bridgeability Performance of Multi-Pin Friction Stir Welded EN AW 7020-T651 Joints

by Ramin Delir Nazarlou, Pouya Zarei, Samita Salim, Michael Wiegand, Martin Kahlmeyer and Stefan Böhm

Materials 2026, 19(3), 544; https://doi.org/10.3390/ma19030544 (registering DOI) - 29 Jan 2026

Abstract

Friction stir welding (FSW) of high-strength aluminum alloys, including EN AW 7020-T651, encounters significant challenges under weld line gap conditions, leading to compromised joint integrity. This study develops a predictive, data-driven framework to assess and optimize the gap bridgeability performance of FSW joints [...] Read more.

Friction stir welding (FSW) of high-strength aluminum alloys, including EN AW 7020-T651, encounters significant challenges under weld line gap conditions, leading to compromised joint integrity. This study develops a predictive, data-driven framework to assess and optimize the gap bridgeability performance of FSW joints with weld line gaps ranging from 0 to 4 mm in 2 mm thick plates. A structured experimental matrix was implemented, systematically varying rotational speed, welding speed, axial force, and tool shoulder diameter. To promote stable material flow and consistent weld quality under varying gap conditions, a multi-pin tool was employed throughout the welding trials. This configuration supported defect-free weld formation across a broad process window and contributed to improved weld soundness under gap conditions. Weld quality was evaluated using a comprehensive, multi-criteria approach that required (i) defect-free joints verified by visual and cross-sectional (metallographic) inspection, (ii) an ultimate tensile strength of at least 230 MPa, and (iii) a novel metric termed weak area percentage (WAP). Derived from micro-hardness mapping, WAP quantified the proportion of the heat-affected zone (HAZ) exhibiting hardness below 96 HV, providing a more robust and spatially sensitive measure of mechanical integrity than conventional average hardness values. Two machine learning models, Logistic Regression and Random Forest, were trained to classify weld acceptability. The Random Forest model demonstrated superior performance, achieving 92.5% classification accuracy and an F1-score of 0.90. Feature importance analysis identified the interaction terms “welding speed × gap size” and “rotational speed × gap size” as the most influential predictors of weld quality. Full article

(This article belongs to the Special Issue Evaluation of Mechanical Properties and Microstructure of Lightweight Alloys)

22 pages, 45752 KB

Open AccessArticle

Chrominance-Aware Multi-Resolution Network for Aerial Remote Sensing Image Fusion

by Shuying Li, Jiaxin Cheng, San Zhang and Wuwei Wang

Remote Sens. 2026, 18(3), 431; https://doi.org/10.3390/rs18030431 (registering DOI) - 29 Jan 2026

Abstract

Spectral data obtained from upstream remote sensing tasks contain abundant complementary information. Infrared images are rich in radiative information, and visible images provide spatial details. Effective fusion of these two modalities improves the utilization of remote sensing data and provides a more comprehensive [...] Read more.

Spectral data obtained from upstream remote sensing tasks contain abundant complementary information. Infrared images are rich in radiative information, and visible images provide spatial details. Effective fusion of these two modalities improves the utilization of remote sensing data and provides a more comprehensive representation of target characteristics and texture details. The majority of current fusion methods focus primarily on intensity fusion between infrared and visible images. These methods ignore the chrominance information present in visible images and the interference introduced by infrared images on the color of fusion results. Consequently, the fused images exhibit inadequate color representation. To address these challenges, an infrared and visible image fusion method named Chrominance-Aware Multi-Resolution Network (CMNet) is proposed. CMNet integrates the Mamba module, which offers linear complexity and global awareness, into a U-Net framework to form the Multi-scale Spatial State Attention (MSSA) framework. Furthermore, the enhancement of the Mamba module through the design of the Chrominance-Enhanced Fusion (CEF) module leads to better color and detail representation in the fused image. Extensive experimental results show that the CMNet method delivers better performance compared to existing fusion methods across various evaluation metrics. Full article

(This article belongs to the Section Remote Sensing Image Processing)

15 pages, 286 KB

Open AccessArticle

Stability of Volume Inequalities Associated with L_p Zonoids

by Ai-Jun Li and Siyao Sun

Axioms 2026, 15(2), 98; https://doi.org/10.3390/axioms15020098 (registering DOI) - 29 Jan 2026

Abstract

In this paper, we establish stability versions of the volume inequalities associated with

L_{p}

zonoids. These results, particularly for the case

1 \leq p \leq 2

, extend the case

p = 1

previously obtained by Brazitikos and Giannopoulos. As applications, we [...] Read more.

In this paper, we establish stability versions of the volume inequalities associated with

L_{p}

zonoids. These results, particularly for the case

1 \leq p \leq 2

, extend the case

p = 1

previously obtained by Brazitikos and Giannopoulos. As applications, we derive several stability inequalities for

L_{p}

isotropic convex bodies and for bodies with minimal p-mean width and minimal

L_{p}

surface area. Full article

(This article belongs to the Special Issue Advances in Functional Analysis and Banach Space)

13 pages, 237 KB

Open AccessArticle

by Marcelina Porożyńska and Anna Weronika Szablewska

Nutrients 2026, 18(3), 447; https://doi.org/10.3390/nu18030447 (registering DOI) - 29 Jan 2026

Abstract

Background/Objectives: The World Health Organisation (WHO) recommends exclusive breastfeeding for the first six months of life, but exclusive breastfeeding (EBF) rates remain low in many countries, including Poland. Factors related to pregnancy, childbirth and the organisation of care can significantly affect the maintenance [...] Read more.

Background/Objectives: The World Health Organisation (WHO) recommends exclusive breastfeeding for the first six months of life, but exclusive breastfeeding (EBF) rates remain low in many countries, including Poland. Factors related to pregnancy, childbirth and the organisation of care can significantly affect the maintenance of lactation. There is a lack of representative data on these relationships in Poland, which makes it difficult to plan effective support measures. The aim of this study is to analyse the relationship between pregnancy and perinatal factors and exclusive breastfeeding in infants aged 6 to 12 months. Methods: This cross-sectional online survey was conducted between April and October 2025. A total of 557 women aged ≥18 years with infants aged 6–12 months participated in the research. Data were collected using a structured questionnaire covering sociodemographic characteristics, the course of pregnancy and childbirth, postpartum complications, early breastfeeding experiences and maternal birth satisfaction assessed using the Polish version of the Birth Satisfaction Scale—Revised (BSS-R). Multivariable logistic regression analysis was used to identify factors associated with exclusive breastfeeding up to six months. Results: Exclusive breastfeeding was significantly associated with vaginal delivery, the absence of postpartum complications and a lack of early breastfeeding problems in the first days postpartum, with initial lactation difficulties emerging as its strongest predictor. Most specific pregnancy-related conditions, maternal birth satisfaction and selected recommended hospital practices, including early skin-to-skin contact, were not independently associated with exclusive breastfeeding in the model. Conclusions: Exclusive breastfeeding up to six months is primarily determined by factors operating in the immediate perinatal and early postpartum period, particularly postpartum clinical stability and successful early lactation. Targeted support during this critical window may be key to improving exclusive breastfeeding outcomes. Full article

(This article belongs to the Special Issue Infant and Toddler Feeding and Development)

17 pages, 7114 KB

Open AccessArticle

A Water Extract of Mixed Mushroom Mycelia Mitigates Cognitive Deficit and Oxidative Stress After Global Cerebral Ischemia–Reperfusion Injury

by Hyeon-Jeong Noh, Ji-Hyun Moon, Hye Jeong Ahn, Ah La Choi, Nam Seob Lee, Young Gil Jeong, Sang Seop Lee, Yung Choon Yoo, Ji-Min Lee, Do-Eun Kim, Jaeku Kang, Jong Yea Park, Hyun Min Kim, Sung Baek Kim and Seung Yun Han

Curr. Issues Mol. Biol. 2026, 48(2), 151; https://doi.org/10.3390/cimb48020151 (registering DOI) - 29 Jan 2026

Abstract

Background: GMK is a bioactive material newly identified from a water extract of mixed mushroom mycelia (Phellinus linteus, Inonotus obliquus, and Ganoderma lucidum). It has shown protective effects against glutamate-induced excitotoxicity and lipopolysaccharide-triggered neuroinflammation. However, whether GMK can ameliorate [...] Read more.

Background: GMK is a bioactive material newly identified from a water extract of mixed mushroom mycelia (Phellinus linteus, Inonotus obliquus, and Ganoderma lucidum). It has shown protective effects against glutamate-induced excitotoxicity and lipopolysaccharide-triggered neuroinflammation. However, whether GMK can ameliorate global cerebral ischemia–reperfusion injury (GCIRI) and its associated cognitive deficit remains to be elucidated. Methods: GCIRI was induced in male Sprague–Dawley rats by bilateral common carotid artery occlusion with hypovolemia (BCCAO/H). GMK (30 or 90 mg/kg, p.o.) was administered once daily for 14 days before surgery. Cognitive functions were evaluated using the Y-maze, Barnes maze, and passive avoidance tests. Hippocampal CA1 neuronal survival and glial activation were analyzed by cresyl violet staining and Iba1/GFAP immunohistochemistry. In parallel, PC12 cells were pretreated with GMK (100 or 200 μg/mL, 24 h) before oxygen–glucose deprivation and reoxygenation (OGD/R), and apoptosis (TUNEL, Bax/Bcl-2), oxidative stress markers (ROS, MDA, and NO), antioxidant enzymes including glutathione peroxidase (GPX) and catalase (CAT), and signaling proteins (p-ERK/ERK, iNOS) were examined. Results: GMK significantly ameliorated GCIRI-induced learning and memory impairments, protected CA1 pyramidal neurons, and reduced microglial and astrocytic activation. In OGD/R-challenged PC12 cells, GMK attenuated apoptosis, suppressed ROS, MDA, and NO production, normalized GPX and CAT activities, and favorably regulated p-ERK and iNOS pathways. Conclusions: These findings suggest that GMK confers dose-dependent behavioral and histopathological protection against GCIRI, potentially by modulating redox- and apoptosis-related signaling (Bax/Bcl-2, GPX/CAT, and ERK/iNOS pathways), with more consistent effects at a higher dose. Full article

(This article belongs to the Special Issue Advances in Phytochemical Research: Molecular Pathways in Health and Disease)

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23 pages, 3973 KB

Open AccessArticle

Amagmatic Mylonitic Deformation of Mantle Peridotites from the Tosa Megamullion in the Shikoku Basin, Philippine Sea

by Katsuyoshi Michibayashi, So Inoue, Valentin Basch, Marco Cuffaro, Yumiko Harigane, Seira Katagiri, Takaaki Moriguchi, Itsuki Natsume, Kohei Nimura, Kyoko Okino, Takeo Okuwaki, Ryosuke Oyanagi, Alessio Sanfilippo, Jonathan E. Snow, Hiroyuki Yamashita and Yasuhiko Ohara

Minerals 2026, 16(2), 154; https://doi.org/10.3390/min16020154 (registering DOI) - 29 Jan 2026

Abstract

Mylonitic mantle peridotites exposed at the Tosa Megamullion in the Shikoku Basin, Philippine Sea, provide direct evidence for amagmatic ductile shear deformation of the upper mantle beneath a back-arc spreading center. Oceanic core complexes (OCCs), or megamullions, are dome-shaped structures formed by detachment [...] Read more.

Mylonitic mantle peridotites exposed at the Tosa Megamullion in the Shikoku Basin, Philippine Sea, provide direct evidence for amagmatic ductile shear deformation of the upper mantle beneath a back-arc spreading center. Oceanic core complexes (OCCs), or megamullions, are dome-shaped structures formed by detachment faulting and occur locally along slow-spreading mid-ocean ridges and back-arc basins, where they expose fault rocks derived from ductile shear zones in the lower crust and upper mantle. The Shikoku Basin hosts several OCCs, including the Tosa Megamullion, which formed during the early stage of back-arc spreading. In this study, nine ultramafic rocks were collected from the Tosa Megamullion using the submersible Shinkai6500 during cruise YK23-05S. Although all samples were highly serpentinized, several preserved primary peridotitic textures were composed mainly of olivine, orthopyroxene, with subordinate clinopyroxene, plagioclase, and spinel. Seven samples exhibit well-developed foliation and porphyroclastic textures dominated by orthopyroxene porphyroclasts, ranging from rounded to strongly elongated forms, commonly showing microkinks and undulose extinction. Crystallographic preferred orientations (CPOs) of three representative samples, analyzed using SEM-EBSD, reveal E-type-dominant olivine fabrics characterized by the (001)[¹⁰⁰] slip system, with a subordinate contribution from C-type (100)[⁰⁰¹] slip. These CPOs suggest deformation under non-dry conditions involving moderate hydration and/or elevated differential stress. These results indicate that the ultramafic rocks from the Tosa Megamullion represent mantle-derived mylonitic peridotites formed by ductile shear beneath the spreading axis and subsequently exhumed under strongly magma-poor, amagmatic conditions. The Tosa Megamullion thus represents an amagmatic end-member of the OCC formation in back-arc basins, dominated by tectonic strain localization rather than by magmatic accretion. Full article

(This article belongs to the Section Crystallography and Physical Chemistry of Minerals & Nanominerals)

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13 pages, 1857 KB

Open AccessArticle

Age-Dependent Dynamics of the Biliary Microbiome in Children with Choledochal Cysts: Functional Remodeling Underlying Taxonomic Conservation

by Xueqi Wang, Ran Duan, Anxiao Ming, Yifan Zhang, Tiezhu Liu, Xin Wang and Mei Diao

Pathogens 2026, 15(2), 147; https://doi.org/10.3390/pathogens15020147 (registering DOI) - 29 Jan 2026

Abstract

Choledochal cyst (CC), a congenital biliary anomaly, is associated with recurrent infections, chronic inflammation, and an increased risk of malignancy. Although emerging evidence implicates the biliary microbiome in disease pathophysiology, its developmental dynamics in pediatric CC remain unclear. Using deep metagenomic sequencing and [...] Read more.

Choledochal cyst (CC), a congenital biliary anomaly, is associated with recurrent infections, chronic inflammation, and an increased risk of malignancy. Although emerging evidence implicates the biliary microbiome in disease pathophysiology, its developmental dynamics in pediatric CC remain unclear. Using deep metagenomic sequencing and comprehensive functional annotation, this study characterized age-dependent changes in the biliary microbiome of 201 pediatric CC patients stratified into infancy (<1 year), early childhood (1–5 years), and later childhood (5–12 years). We found that while the taxonomic composition and alpha diversity of the microbiota remained conserved across age groups, profound functional remodeling occurred with host development. A core set of microbial species(Bacteroidota, Actinomycetota, Bacillota, and Pseudomonadota) and functional pathways was shared across all ages; however, early childhood (1–5 years) exhibited the greatest number of unique functional genes, metabolic pathways, and carbohydrate-active enzymes, identifying this period as a critical window for microbial metabolic adaptation. Age-specific patterns were also evident in clinically relevant traits: infants (<1 year) harbored the most unique antibiotic resistance and virulence factor genes, whereas the resistome and virulome became more streamlined in older children. These findings establish a paradigm of “taxonomic conservation coupled with functional remodeling” in the CC microbiome and highlight age as a key determinant of microbial community function. This study offers novel insights into the microbial dynamics underlying CC progression and suggests potential age-specific targets for future therapeutic strategies. Full article

(This article belongs to the Section Bacterial Pathogens)

17 pages, 3788 KB

Open AccessArticle

Morphology and Surface Reconstruction-Driven Catalytic Enhancement in CoMn₂O₄ for Efficient OER Application

by Abu Talha Aqueel Ahmed, Abu Saad Ansari, Sangeun Cho and Atanu Jana

Materials 2026, 19(3), 542; https://doi.org/10.3390/ma19030542 (registering DOI) - 29 Jan 2026

Abstract

The development of efficient and durable oxygen evolution reaction (OER) catalysts from earth-abundant materials is essential for advancing alkaline water electrolysis. Herein, nanograss-like CoMn₂O₄ electrode films are directly grown on stainless-steel substrates via a temperature-controlled hydrothermal approach, and their OER [...] Read more.

The development of efficient and durable oxygen evolution reaction (OER) catalysts from earth-abundant materials is essential for advancing alkaline water electrolysis. Herein, nanograss-like CoMn₂O₄ electrode films are directly grown on stainless-steel substrates via a temperature-controlled hydrothermal approach, and their OER performance is systematically investigated. The CoMn₂O₄ obtained at 120

^{\circ}

C (CMO-120) delivers the best catalytic activity in 1.0 M KOH, requiring an overpotential of 292 mV at 10 mA cm⁻², which is lower than those synthesized at 150 (CMO-150) and 90

^{\circ}

C (CMO-90). Notably, activity of CMO-120 becomes even more pronounced at elevated current densities, achieving the low overpotential of 434 mV even at 300 mA cm⁻², substantially outperforming both CMO-90 and CMO-150 electrodes. The enhanced activity is attributed to an interconnected nanograss architecture with mixed Co²⁺/Co³⁺ and Mn²⁺/Mn³⁺ redox couples and abundant defect-related oxygen species, which result in increased electrochemically active surface area and improved charge transportation throughout the nanograss architecture that facilitate OH⁻ adsorption and OER intermediate transformation. Furthermore, CMO-120 demonstrates excellent durability (100 h) after electro-oxidation-induced surface activation. These findings highlight precise temperature regulation as an effective strategy for optimizing Mn-Co spinel for efficient alkaline OER applications. Full article

(This article belongs to the Special Issue Developments and Applications of Nanotechnologies in Surface/Interface, Catalysis and Fuel Cells)

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17 pages, 839 KB

Open AccessArticle

Green Hydrogen Production for Decarbonizing the Steel Industry: Energy and Economic Assessment of Electrolysis and Ammonia Cracking Systems

by Elvira Spatolisano, Antonio Trinca, Domenico Flagiello and Giorgio Vilardi

Energies 2026, 19(3), 717; https://doi.org/10.3390/en19030717 (registering DOI) - 29 Jan 2026

Abstract

The global transition toward a low-carbon economy has intensified the interest in green hydrogen as a key enabler of industrial decarbonization. In particular, the steel sector, one of the most carbon-intensive industries, offers significant opportunities for emissions reduction through H₂-based technologies. [...] Read more.

The global transition toward a low-carbon economy has intensified the interest in green hydrogen as a key enabler of industrial decarbonization. In particular, the steel sector, one of the most carbon-intensive industries, offers significant opportunities for emissions reduction through H₂-based technologies. This study presents a techno-economic assessment of alternative green hydrogen supply pathways, namely alkaline electrolysis and ammonia cracking, and evaluates their integration into hydrogen-based direct reduction (HyDR) routes. Process simulations are performed using Aspen Plus^® V14 to quantify the energy consumption, hydrogen demand, and associated CO₂ emissions across multiple configurations and case studies. A comprehensive 3E (energy, economics, and environmental) evaluation framework is applied to compare system performance and assess the suitability of each pathway for large-scale deployment. The results indicate that ammonia cracking represents a technically viable and potentially competitive hydrogen supply option for steel decarbonization under the assumed operating conditions, highlighting its relevance as a transitional pathway toward low-carbon steel production. Full article

(This article belongs to the Special Issue Techno-Economic Evaluation of Hydrogen Energy)

15 pages, 20413 KB

Open AccessArticle

Optimization of Reserve Capacity for New Energy Participating in Primary Frequency Regulation of the Power System

by Yichao Jia, Ning Chen, Lei Zhang, Minhui Qian, Bingjie Tang, Yanzhang Liu, Chang Zhou and Peipei Peng

Energies 2026, 19(3), 718; https://doi.org/10.3390/en19030718 (registering DOI) - 29 Jan 2026

Abstract

The frequency regulation problem of the power system under the scenario of a high proportion of new energy access has attracted attention. It has become an important technical means for new energy to reserve a certain amount to participate in system frequency regulation. [...] Read more.

The frequency regulation problem of the power system under the scenario of a high proportion of new energy access has attracted attention. It has become an important technical means for new energy to reserve a certain amount to participate in system frequency regulation. Reserve capacity, response speed, and regulation rate jointly determine the post-disturbance frequency trajectory of the system. This paper briefly compares the primary frequency regulation control performances of wind power generation, photovoltaic power generation and thermal power generation, analyzes the influence of factors such as frequency distribution, regulation rate, frequency regulation capacity and frequency deviation on primary frequency regulation, and, considering the need for rapid frequency response in power systems with a high share of new energy, taking into account the system frequency response performance and new energy consumption demand, a method for optimizing the reserve configuration of new energy power generation for primary frequency regulation is proposed. Simulation analysis is carried out using a simplified actual power system, and the results show that an appropriate reserve provided by new energy helps the system frequency recover quickly. Full article

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26 pages, 1063 KB

Open AccessArticle

Feature Selection Using Nearest Neighbor Gaussian Processes

by Konstantin Posch, Maximilian Arbeiter, Christian Truden, Martin Pleschberger and Jürgen Pilz

Mathematics 2026, 14(3), 476; https://doi.org/10.3390/math14030476 (registering DOI) - 29 Jan 2026

Abstract

We introduce a novel Bayesian approach for feature (variable) selection using Gaussian process regression, which is crucial for enhancing interpretability and model regularization. Our method employs nearest neighbor Gaussian processes as scalable approximations to classical Gaussian processes. Feature selection is performed by conditioning [...] Read more.

We introduce a novel Bayesian approach for feature (variable) selection using Gaussian process regression, which is crucial for enhancing interpretability and model regularization. Our method employs nearest neighbor Gaussian processes as scalable approximations to classical Gaussian processes. Feature selection is performed by conditioning the process mean and covariance function on a random set representing the indices of relevant variables. A priori beliefs regarding this set control the feature selection, while reference priors are assigned to the remaining model parameters, ensuring numerical robustness in the process covariance matrix. For model inference, we propose a Metropolis-within-Gibbs algorithm. The effectiveness of the proposed feature selection approach is demonstrated through evaluation on simulated data, a computer experiment approximation, and two real-world data sets. Full article

(This article belongs to the Special Issue Evolutionary Computation for Feature Selection and Dimensionality Reduction)

29 pages, 37667 KB

Open AccessArticle

First Agriculture Land Use Map in Vietnam Using an Adaptive Weighted Combined Loss Function for UNET⁺⁺

by Ta Hoang Trung, Nguyen Vu Ky, Duong Cao Phan, Duong Binh Minh, Ho Nguyen and Kenlo Nishida Nasahara

Remote Sens. 2026, 18(3), 430; https://doi.org/10.3390/rs18030430 (registering DOI) - 29 Jan 2026

Abstract

Accurate and timely agricultural mapping is essential for supporting sustainable agricultural development, resource management, and food security. Despite its importance, Vietnam lacks detailed and consistent large-scale agricultural maps. In this study, we produced the first national-scale agricultural map of Vietnam for 2024 using [...] Read more.

Accurate and timely agricultural mapping is essential for supporting sustainable agricultural development, resource management, and food security. Despite its importance, Vietnam lacks detailed and consistent large-scale agricultural maps. In this study, we produced the first national-scale agricultural map of Vietnam for 2024 using a UNet++ deep learning architecture that integrates multi-temporal Sentinel-1 and Sentinel-2 imagery with Global-30 DEM data. The resulting product includes 15 land-cover categories, eight of which represent the most popular agricultural types in Vietnam. We further evaluate the model’s transferability by applying the 2024 trained model to generate a corresponding map for 2020. The approach achieves overall classification accuracies of

83.01 % \pm 1.37 %

(2020) and

80.09 % \pm 0.76 %

(2024). To address class imbalance within the training dataset, we introduced an adaptive weight combined loss function that automatically adjusts the weight of dice loss and cross-entropy loss within a combined loss function during the model training process. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

28 pages, 799 KB

Open AccessReview

Animation in Speech, Language, and Communication Assessment of Children: A Scoping Review

by Triantafyllia I. Vlachou, Maria Kambanaros, Arhonto Terzi and Voula C. Georgopoulos

Languages 2026, 11(2), 24; https://doi.org/10.3390/languages11020024 (registering DOI) - 29 Jan 2026

Abstract

Animation has been used to assess speech, language, and communication skills in children. We aimed to map and synthesize relevant research addressing how and when animation is used for assessment purposes in speech–language pathology practice. Four databases were searched, yielding 18 studies that [...] Read more.

Animation has been used to assess speech, language, and communication skills in children. We aimed to map and synthesize relevant research addressing how and when animation is used for assessment purposes in speech–language pathology practice. Four databases were searched, yielding 18 studies that met the inclusion criteria. Data was extracted on study design, objectives, participant characteristics, results, assessment areas, purposes of animation use, underlying theoretical and research bases, and technical features. Theoretical grounding for children’s perception of animation was not evident in the studies, while several studies showed research foundations for its use in speech–language pathology assessment. Various animations were used for diverse purposes and research goals, primarily involving typically developing children and fewer clinical samples. All studies focused on language assessment. The diversity in animation research precludes conclusions regarding best practices in use of animation in speech–language pathology assessment. An initial evidence base was established, documenting research approaches, the effects of animation on language and cognition, observed behaviors, the performance of clinical samples, and psychometric properties of the assessment tools. Limitations, knowledge gaps, and future research are discussed. Full article

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18 pages, 1262 KB

Open AccessArticle

Atorvastatin Protects Against Deleterious Carfilzomib-Induced Transcriptional Changes in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

by Marwa Tantawy, Danxin Wang, Mohammed Gbadamosi, Fahong Yu, Yanping Zhang, Mohammed E. Alomar, Kenneth H. Shain, Rachid C. Baz, Katelyn A. Bruno and Yan Gong

Int. J. Mol. Sci. 2026, 27(3), 1358; https://doi.org/10.3390/ijms27031358 (registering DOI) - 29 Jan 2026

Abstract

The mechanisms underlying carfilzomib (CFZ)-induced cardiotoxicity remain incompletely elucidated. In this study, we used human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to characterize the transcriptional impact of CFZ and to evaluate whether atorvastatin could prevent these deleterious transcriptional changes. hiPSC-CMs were treated with [...] Read more.

The mechanisms underlying carfilzomib (CFZ)-induced cardiotoxicity remain incompletely elucidated. In this study, we used human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to characterize the transcriptional impact of CFZ and to evaluate whether atorvastatin could prevent these deleterious transcriptional changes. hiPSC-CMs were treated with 1 µM CFZ, CFZ + atorvastatin, atorvastatin, or vehicle control, followed by RNA sequencing, differential expression analyses, and pathway analyses. Transcriptomic profiling revealed a marked upregulation of genes in multiple proteasome subunits, including ATPase components (PSMC1, PSMC4, PSMC5, PSMC6) and non-ATPase regulatory subunits (PSMD1, PSMD2, PSMD12), suggesting a strong compensatory activation of proteostasis and protein quality-control pathways in response to CFZ exposure. In addition, several of the most significantly altered genes were those implicated in cardiomyopathy and heart failure, such as BAG3 and FLNC, and many heat-shock proteins, indicating the activation of cardiac stress–response pathways relevant to CFZ-associated cardiotoxicity. Atorvastatin co-treatment partially reversed a subset of CFZ-induced transcriptional changes, particularly within cholesterol biosynthesis and lipid-regulatory pathways (e.g., ACAT2 and ACTA1) but did not restore the CFZ-mediated downregulation of sarcomeric genes. Together, these findings define a multifactorial signature of deleterious CFZ-induced transcriptional changes and suggest that atorvastatin may provide partial metabolic, but not structural, cardio protection. Full article

(This article belongs to the Special Issue Cardiomyocytes Among Physiology and Pathophysiology: Molecular Insights and Therapeutic Implications)

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24 pages, 7205 KB

Open AccessArticle

Influence of Donor Age, Donor Body Mass Index, and Harvesting Site on Cell Preparations from Human Adipose Tissue

by Olga Hahn, Philipp-Kjell Ficht, Wendy Bergmann-Ewert, Juliane Meyer, Anne Wolff and Kirsten Peters

Int. J. Mol. Sci. 2026, 27(3), 1351; https://doi.org/10.3390/ijms27031351 (registering DOI) - 29 Jan 2026

Abstract

Adipose tissue includes various cell types beyond the typical adipocytes. The stromal vascular fraction (SVF) contains mesenchymal stem cells (MSCs), pericytes, and endothelial cells, which can be isolated from adipose tissue by mechanical and enzymatic methods. The composition of the SVF is heterogeneous, [...] Read more.

Adipose tissue includes various cell types beyond the typical adipocytes. The stromal vascular fraction (SVF) contains mesenchymal stem cells (MSCs), pericytes, and endothelial cells, which can be isolated from adipose tissue by mechanical and enzymatic methods. The composition of the SVF is heterogeneous, and donor factors such as sex, age, body mass index (BMI), and harvesting site are associated with variations in cellular composition and viability. The expression of specific surface markers, which determine the immunophenotype of the cells, can also vary. In this study, we investigated the effects of donor age, BMI, and harvesting site on cell yield, viability, and size. Our results showed that BMI significantly influenced cell yield and size, with overweight and obese donors yielding more cells than normal-weight donors. Additionally, cells isolated from the adipose tissue of the thighs/legs were larger than those from other areas. Flow cytometry showed considerable variability in SVF composition among donors. These results emphasize that SVF donor characteristics have a significant impact on cell yield, viability, and cell size, with the immunophenotype being highly donor-dependent. Understanding these factors is crucial for optimizing cell yield and defining populations for therapeutic applications of SVF cells. Full article

(This article belongs to the Special Issue Adipose Tissue and Adipose Tissue-Derived Stem Cells in Multidisciplinary Research)

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13 pages, 945 KB

Open AccessArticle

Fusion Between Control Mesoangioblasts and mtDNA-Mutant Myotubes Preserves Myotube Morphology and Mitochondrial Network Organization

by Somaieh Ahmadian, Patrick J. Lindsey, Monique Ummelen, Anton Hopman, Marc A. M. J. van Zandvoort, Hubert J. M. Smeets and Florence H. J. van Tienen

Int. J. Mol. Sci. 2026, 27(3), 1357; https://doi.org/10.3390/ijms27031357 (registering DOI) - 29 Jan 2026

Abstract

Mitochondria are the energy factories of a cell and mitochondrial morphology, quantity, membrane potential, and DNA copy number can change depending on metabolic requirements and/or genetic defects. Different mutations in mitochondrial DNA might affect mitochondrial morphology and membrane potential differently. In this study [...] Read more.

Mitochondria are the energy factories of a cell and mitochondrial morphology, quantity, membrane potential, and DNA copy number can change depending on metabolic requirements and/or genetic defects. Different mutations in mitochondrial DNA might affect mitochondrial morphology and membrane potential differently. In this study we investigated mitochondrial morphology and membrane potential in vitro in mesoangioblast-derived human myotubes harboring a pathogenic mtDNA mutation and analyzed mitochondrial behavior following fusion with healthy mesoangioblasts. Myotubes were differentiated in vitro from mesoangioblasts obtained from two mitochondrial myopathy patients, M02 (96% m.3271T>C) and M11 (73% m.3291T>C), and from a functionally healthy male control, M06 (3% m.3243A>G). On day 5 of differentiation, healthy male mesoangioblasts (mM06) were added to mutant myotube cultures to allow cell fusion. On day 11, mitochondrial morphology and membrane potential were assessed by three-dimensional live-cell imaging using spinning disk confocal microscopy with tetramethylrhodamine methyl ester (TMRM). Following live imaging, cells were fixed and subjected to Y-chromosome fluorescence in situ hybridization (FISH), enabling identification and retrospective analysis of hybrid (i.e., fused with male control mesoangioblasts) and non-hybrid (i.e., not fused with these control mesoangioblasts) myotubes within the same imaging fields. Quantitative image analysis at the level of individual myotubes revealed that, when normalized to sarcoplasmic volume, mitochondrial volume, object number, and membrane potential did not differ between mutant and control myotubes despite heteroplasmy levels exceeding 70%. Fusion of healthy mM06 mesoangioblasts did not impair myotube formation and resulted in redistribution of mitochondrial content without an increase in mitochondrial object number, consistent with integration of donor mitochondria into the existing mitochondrial network. Across conditions, mitochondrial parameters were strongly influenced by myotube size, underscoring the importance of accounting for biological variation when quantifying mitochondrial features. Together, these findings demonstrate that high mtDNA mutation loads do not necessarily alter mitochondrial morphology or membrane potential under standard in vitro differentiation conditions and provide mechanistic insight into mitochondrial behavior following mesoangioblast fusion in human myotubes. Fusion of healthy mesoangioblasts supports integration of donor mitochondria into the existing network without compromising myogenesis, consistent with mitochondrial mixing rather than replacement. Full article

(This article belongs to the Special Issue Mitochondrial Function in Health and Diseases)

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16 pages, 1713 KB

Open AccessArticle

QoS and Grid-Shifting Ability Guaranteed Optimal Capacity Sizing Method of Battery Swapping Station Considering Seasonal Characteristics

by Jingruo Hu, Jiawei Chen, Xi Chen, Yuan Jin and Zhuoqun Li

Electronics 2026, 15(3), 600; https://doi.org/10.3390/electronics15030600 (registering DOI) - 29 Jan 2026

Abstract

A battery swapping station (BSS) is an enabling facility for battery swapping electric vehicles (EVs). To ensure the high quality of service (QoS) provided for EV customers while providing new batteries, the capacities of batteries and chargers in a BSS should be optimized. [...] Read more.

A battery swapping station (BSS) is an enabling facility for battery swapping electric vehicles (EVs). To ensure the high quality of service (QoS) provided for EV customers while providing new batteries, the capacities of batteries and chargers in a BSS should be optimized. To achieve that, an EV battery swapping demand prediction model that specially considers the influences of different seasons, the output of which is the key data for capacity sizing, is firstly developed based on Monte Carlo algorithm. Then, an optimal capacity sizing model targeted at both minimizing the construction and operation cost of the BSS and maximizing the grid-shifting ability is proposed under a proposed optimal battery swapping and charging algorithm. The optimal capacity sizing for the batteries and chargers is finally obtained using the NSGA-II algorithm to solve the developed model with all operation constraints. Case studies based on the real data provided by BSS operation companies in China are done to verify the validity of the proposed method. The results show that the cost of the BSS can be reduced while peak-shifting can be enabled with the proposed capacity sizing and battery charging/discharging algorithm. Full article

(This article belongs to the Special Issue Optimal Design and Advanced Control and Management for Electric Vehicle Battery Swapping Stations)

21 pages, 607 KB

Open AccessArticle

Thermodynamic Assessment of a Cogeneration System Based on Aluminium–Water Reaction for Hydrogen and Power Production

by Lisa Branchini, Andrea De Pascale, Lorenzini Elena and Mariucci Giorgio

Energies 2026, 19(3), 715; https://doi.org/10.3390/en19030715 (registering DOI) - 29 Jan 2026

Abstract

This paper presents a conceptual and thermodynamic assessment of an innovative cogeneration system based on the aluminium–water reaction, designed to simultaneously produce hydrogen and electricity. The proposed layout integrates a liquid aluminium combustion chamber with a dual-stage heat recovery section and a steam [...] Read more.

This paper presents a conceptual and thermodynamic assessment of an innovative cogeneration system based on the aluminium–water reaction, designed to simultaneously produce hydrogen and electricity. The proposed layout integrates a liquid aluminium combustion chamber with a dual-stage heat recovery section and a steam turbine cycle, enabling the valorisation of industrial aluminium scraps within a circular-economy framework. A steady-state thermodynamic model was developed in Aspen Plus to evaluate system performance under different operating conditions, with a sensitivity analysis on key parameters such as the aluminium-to-water ratio (2.4–4), combustion efficiency, and steam generation cycle parameters. The system performance is investigated in terms of useful output (i.e., hydrogen and electricity production), including a simplified economic evaluation for the assessment of sustainability. Results indicate that, for equivalence ratios ensuring acceptable peak temperatures (≤1700 °C), the system can deliver 2–3 MW of electric power per kg/s of aluminium and achieve cogeneration efficiencies up to 83–87%, assuming a high conversion rate of water into hydrogen (roughly 0.106 kg of produced H₂ per kg of inlet Al, if 95% of mole conversion is considered). The minimum break-even levelized cost of hydrogen is estimated to be 15.7 EUR/kg under current economic conditions. Full article

22 pages, 1360 KB

Open AccessArticle

A Data-Driven Approach to Estimating Passenger Boarding in Bus Networks

by Gustavo Bongiovi, Teresa Galvão Dias, Jose Nauri Junior and Marta Campos Ferreira

Appl. Sci. 2026, 16(3), 1384; https://doi.org/10.3390/app16031384 (registering DOI) - 29 Jan 2026

Abstract

This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is essential for optimizing service planning, improving passenger comfort, and enhancing operational efficiency. [...] Read more.

This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is essential for optimizing service planning, improving passenger comfort, and enhancing operational efficiency. This research evaluates a range of predictive models to identify the most effective techniques for forecasting demand across different routes and times. Two modeling strategies were implemented: a generalistic approach and a specialized one. The latter was designed to capture route-specific characteristics and variability. A real-world case study from a medium-sized metropolitan region in Brazil was used to assess model performance. Results indicate that ensemble-tree-based models, particularly XGBoost, achieved the highest accuracy and robustness in handling nonlinear relationships and complex interactions within the data. Compared to the generalistic approach, the specialized approach demonstrated superior adaptability and precision, making it especially suitable for long-term and strategic planning applications. It reduced the average RMSE by 19.46% (from 13.84 to 11.15) and the MAE by 17.36% (from 9.60 to 7.93), while increasing the average R² from 0.289 to 0.344. However, these gains came with higher computational demands and mean Forecast Bias (from 0.002 to 0.560), indicating a need for bias correction before operational deployment. The findings highlight the practical value of predictive modeling for transit authorities, enabling data-driven decision making in fleet allocation, route planning, and service frequency adjustment. Moreover, accurate demand forecasting contributes to cost reduction, improved passenger satisfaction, and environmental sustainability through optimized operations. Full article

(This article belongs to the Special Issue Data Science and Machine Learning in Logistics and Transport, 2nd Edition)

26 pages, 2826 KB

Open AccessReview

Research Progress of Robotic Technologies and Applications in Smart Pig Farms

by Luyang Zhou, Linqiu Hao, Yingjun Xiong, Huanhuan Qin, Aoran Bao and Zikang Chen

Agriculture 2026, 16(3), 334; https://doi.org/10.3390/agriculture16030334 (registering DOI) - 29 Jan 2026

Abstract

With the rapid development of artificial intelligence (AI), the Internet of Things (IoT), and robotics technology, the intelligent transformation of pig farms has become an inevitable trend in the livestock industry. In today’s large-scale pig farms, the traditional breeding methods are undergoing significant [...] Read more.

With the rapid development of artificial intelligence (AI), the Internet of Things (IoT), and robotics technology, the intelligent transformation of pig farms has become an inevitable trend in the livestock industry. In today’s large-scale pig farms, the traditional breeding methods are undergoing significant transformation due to the application of intelligent robotics technology. The robotic system is capable of performing autonomous inspection, precise feeding and environmental cleaning, which can effectively alleviate labor shortages on farms. It also shows great advantages in strengthening biosecurity, optimizing management processes and ensuring animal welfare. This paper systematically constructs the key technical framework of pig farm robots, including the basic support layer, the perception and execution layer, the intelligent processing layer, and the integrated application layer. On this basis, further analysis is conducted on the current application of robots in intelligent pig farms, covering the functional characteristics and technical implementations of inspection robots, cleaning robots, and feeding robots. Full article

(This article belongs to the Special Issue Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control)

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39 pages, 1649 KB

Open AccessReview

The Network and Information Systems 2 Directive: Toward Scalable Cyber Risk Management in the Remote Patient Monitoring Domain: A Systematic Review

by Brian Mulhern, Chitra Balakrishna and Jan Collie

IoT 2026, 7(1), 14; https://doi.org/10.3390/iot7010014 (registering DOI) - 29 Jan 2026

Abstract

Healthcare 5.0 and the Internet of Medical Things (IoMT) is emerging as a scalable model for the delivery of customised healthcare and chronic disease management, through Remote Patient Monitoring (RPM) in patient smart home environments. Large-scale RPM initiatives are being rolled out by [...] Read more.

Healthcare 5.0 and the Internet of Medical Things (IoMT) is emerging as a scalable model for the delivery of customised healthcare and chronic disease management, through Remote Patient Monitoring (RPM) in patient smart home environments. Large-scale RPM initiatives are being rolled out by healthcare providers (HCPs); however, the constrained nature of IoMT devices and proximity to poorly administered smart home technologies create a cyber risk for highly personalised patient data. The recent Network and Information Systems (NIS 2) directive requires HCPs to improve their cyber risk management approaches, mandating heavy penalties for non-compliance. Current research into cyber risk management in smart home-based RPM does not address scalability. This research examines scalability through the lens of the Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework and develops a novel Scalability Index (SI), informed by a PRISMA guided systematic literature review. Our search strategy identified 57 studies across major databases including ACM, IEEE, MDPI, Elsevier, and Springer, authored between January 2016 and March 2025 (final search 21 March 2025), which focussed on cyber security risk management in the RPM context. Studies focussing solely on healthcare institutional settings were excluded. To mitigate bias, a sample of the papers (30/57) were assessed by two other raters; the resulting Cohen’s Kappa inter-rater agreement statistic (0.8) indicating strong agreement on study selection. The results, presented in graphical and tabular format, provide evidence that most cyber risk approaches do not consider scalability from the HCP perspective. Applying the SI to the 57 studies in our review resulted in a low to medium scalability potential of most cyber risk management proposals, indicating that they would not support the requirements of NIS 2 in the RPM context. A limitation of our work is that it was not tested in a live large-scale setting. However, future research could validate the proposed SI, providing guidance for researchers and practitioners in enhancing cyber risk management of large-scale RPM initiatives. Full article

(This article belongs to the Topic Applications of IoT in Multidisciplinary Areas)

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Graphical abstract

16 pages, 1632 KB

Open AccessArticle

Mechanistic Analysis of Physiological and Metabolic Responses in Non-Jointed Water Dropwort Under Phosphorus Stress

by Bingqing Lu, Zhengnan Cen, Xiyu Zhang, Ting Xue and Yu Guo

Metabolites 2026, 16(2), 101; https://doi.org/10.3390/metabo16020101 (registering DOI) - 29 Jan 2026

Abstract

Background: Non-jointed water dropwort (Oenanthe javanica (Blume) DC.) is a widely cultivated aquatic vegetable with notable nutritional and pharmacological properties. Phosphorus (P) is a key nutrient affecting plant growth, photosynthesis, and metabolic balance, yet its role in water dropwort remains understudied. Methods: [...] Read more.

Background: Non-jointed water dropwort (Oenanthe javanica (Blume) DC.) is a widely cultivated aquatic vegetable with notable nutritional and pharmacological properties. Phosphorus (P) is a key nutrient affecting plant growth, photosynthesis, and metabolic balance, yet its role in water dropwort remains understudied. Methods: This study investigated the physiological and metabolic responses of non-jointed water dropwort under P-deficiency treatment (0 mg·L⁻¹) and increasing P supply levels (5, 10, and 30 mg·L⁻¹). Results: Moderate P supply (10 mg·L⁻¹) significantly promoted plant growth, enhanced photosynthetic efficiency, and increased antioxidant enzyme activity, increasing by 55.9%, 20.2%, and 118%, respectively, compared with the P-deficiency treatment. High P levels (30 mg·L⁻¹) inhibited growth and induced oxidative stress. Untargeted metabolomic analysis was conducted on root samples from CK (0 mg·L⁻¹) and HP (30 mg·L⁻¹) groups using UHPLC-MS. A total of 1274 metabolites were identified, with flavonoids, phenylpropanoids, fatty acid and conjugates being predominant. A total of 842 differential metabolites were screened under HP stress, with flavonoids (e.g., narcissin) showing the most significant upregulation. KEGG enrichment revealed key pathways including biosynthesis of amino acids, ABC transporters, and aminoacyl-tRNA biosynthesis, indicating metabolic reprogramming under HP stress. Notably, flavonoid and terpenoid pathways were upregulated, while certain lipid metabolism pathways, including fatty acid conjugates and phenylpropanoids, were downregulated. These findings suggest that non-jointed water dropwort adapts to high P stress by activating defense-related secondary metabolism and adjusting carbon–nitrogen allocation. Conclusions: This study provides a theoretical basis for P management and stress-resistant cultivar selection in non-jointed water dropwort. Full article

(This article belongs to the Special Issue Metabolomics and Plant Defence, 2nd Edition)

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20 pages, 1248 KB

Open AccessArticle

Round-Trip Time Estimation Using Enhanced Regularized Extreme Learning Machine

by Hassan Rizky Putra Sailellah, Hilal Hudan Nuha and Aji Gautama Putrada

Network 2026, 6(1), 10; https://doi.org/10.3390/network6010010 (registering DOI) - 29 Jan 2026

Abstract

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or [...] Read more.

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions (

\times 10

and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons,

C = 10^{2}

) achieves

R^{2} = 0.9999

,

M A P E = 0.0018

,

M A E = 966.04

, and

R M S E = 1589.64

on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

19 pages, 4479 KB

Open AccessArticle

Pine Resin as a Natural Polymer Binder in Pine Cone-Reinforced Lightweight Concrete

by Celal Kistak, Araz Muhammed Hassan, Ayse Bicer and Nevin Celik

Polymers 2026, 18(3), 364; https://doi.org/10.3390/polym18030364 (registering DOI) - 29 Jan 2026

Abstract

The aim of this study is to investigate the potential applications of pine cones as plant-based waste material in the construction industry. In order to achieve this target, the pine cone particles (PCP) are mixed with cement to create new lightweight concretes. Furthermore, [...] Read more.

The aim of this study is to investigate the potential applications of pine cones as plant-based waste material in the construction industry. In order to achieve this target, the pine cone particles (PCP) are mixed with cement to create new lightweight concretes. Furthermore, pine tree resin (PTR), acting as a natural bio-polymer binder, is incorporated into selected samples to ascertain its potential as a binder. The pine cones are cut into particles of 2–4 cm, 0–2 cm, and ground into a powder. A series of critical tests is conducted on the novel produced samples, including thermal conductivity, specific heat, density, compressive strength, water absorption rate, and drying rate. The experiments show that thermal conductivity, specific heat capacity, and thermal expansion coefficient decrease as the weight ratio and size of PCP increase. The presence of PTR increases porosity, further decreasing thermal conductivity, specific heat, and thermal expansion coefficients for the majority of samples. The compressive strength values decrease with the presence of PTR and PCP. Regarding durability, the water absorption ratios remain below the critical 30% threshold, making the material suitable for internal applications or external facades protected by coating/plaster or as external coverings. Full article

(This article belongs to the Special Issue Application of Polymers in Cementitious Materials)

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19 pages, 374 KB

Open AccessArticle

Analysis of the Impact of Platform Economy on Residents’ Consumption of Green Agricultural Products

by Xinyao Wang and Chenyang Liu

Sustainability 2026, 18(3), 1363; https://doi.org/10.3390/su18031363 (registering DOI) - 29 Jan 2026

Abstract

The expansion of the consumption of green agricultural products is a vital direction for transforming and upgrading food consumption for China’s residents. The development of the platform economy, based on the development of electronic information technology, achieved through changing business models, economic form, [...] Read more.

The expansion of the consumption of green agricultural products is a vital direction for transforming and upgrading food consumption for China’s residents. The development of the platform economy, based on the development of electronic information technology, achieved through changing business models, economic form, and, especially, the consumption habits and channels of residents, further improves the consumption potential of green agricultural products. This paper uses micro-questionnaire survey data from 424 green agricultural product consumers nationwide and utilizes the propensity score matching (PSM) model to explore the impact of the platform economy on residents’ consumption of green agricultural products. This study found that the platform economy can significantly promote residents’ consumption of green agricultural products. In particular, the positive effect is more significant for urban residents with higher household incomes. In addition, improving residents’ health awareness and value perception is conducive to motivating residents’ green agricultural product consumption. Therefore, this paper proposes suggestions, such as focusing on strengthening the construction of platform infrastructure in rural areas, promoting the development of platform economy in urban areas through innovative application platform technologies, and cultivating residents’ basic literacy for effective participation in the platform economy, to provide new channels and ideas for expanding the consumption of green agricultural products in China. Full article

(This article belongs to the Special Issue Marketing in the Platform Economy—Performance, Value Creation, and Sustainability)

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