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16 pages, 8596 KB

Open AccessArticle

Green Synthesis of Activated Carbons from Coconut Coir Dust via Steam Activation for Supercapacitor Electrode Applications

by Jirayu Kongtip, Natapol Kanjulkeat, Thanapol Ninneit, Norapat Phanapadipong, Nattapat Chaiammart, Apiluck Eiad-ua, Ratiporn Munprom and Gasidit Panomsuwan

Chemistry 2025, 7(6), 184; https://doi.org/10.3390/chemistry7060184 (registering DOI) - 24 Nov 2025

Abstract

Activated carbons derived from coconut coir dust were synthesized via a two-step process combining carbonization and steam activation for application as electrode materials in supercapacitors. The influence of carbonization temperature (500–700 °C) on the morphological, structural, textural, and electrochemical properties of the resulting [...] Read more.

Activated carbons derived from coconut coir dust were synthesized via a two-step process combining carbonization and steam activation for application as electrode materials in supercapacitors. The influence of carbonization temperature (500–700 °C) on the morphological, structural, textural, and electrochemical properties of the resulting activated carbons was systematically investigated. Increasing the carbonization temperature led to a progressive collapse of the cellular structure and formation of a more compact and thermally stable carbon matrix, while the overall morphology remained largely unchanged after steam activation. The steam-activated carbon prepared from the carbonized sample at 700 °C (SA-CCD-7) exhibited the highest specific surface area (889 m² g⁻¹) and a well-developed hierarchical micro–mesoporous structure. Structural analyses confirmed the amorphous nature and an increase in structural disorder after activation, consistent with the enhanced pore development. Electrochemical measurements in 6 M KOH using a three-electrode system revealed that the SA-CCD-7 displayed a typical electric double-layer capacitor (EDLC) behavior, delivering the highest specific capacitance of 86 F g⁻¹ at 1 A g⁻¹ and retaining 81% of its initial capacitance at 20 A g⁻¹, demonstrating excellent rate capability. The symmetric coin-cell supercapacitor device assembled with SA-CCD-7 as the electrodes achieved an energy density of 0.9–1.2 Wh kg⁻¹ and a power density of 50–2500 W kg⁻¹, along with remarkable cycling stability over 10,000 cycles with negligible capacitance loss. These findings highlight steam activation of coconut coir dust as a simple, scalable, and eco-friendly approach for producing biomass-derived carbon electrodes for sustainable energy storage applications. Full article

(This article belongs to the Special Issue Advanced Biomass Utilization for Sustainable Chemical Synthesis)

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17 pages, 4351 KB

Open AccessArticle

Sequential Treatment of Domestic Wastewater in Rural Zones Applying Aloe Vera Extract as Coagulant (Preliminar), E. crassipes in a Horizontal Biofilter (Secondary), and Activated Carbon from Soursop Seeds (Tertiary)

by Franco Hernan Gomez, Maria Cristina Collivignarelli, Stefano Bellazzi, Kelly Cristina Torres, Alessandro Abbà and Sabrina Sorlini

Clean Technol. 2025, 7(4), 105; https://doi.org/10.3390/cleantechnol7040105 (registering DOI) - 24 Nov 2025

Abstract

The absence of domestic wastewater (DWW) treatment in impoverished rural communities of the global south remains a pressing challenge for both public health and environmental sustainability. This study presents a simplified and decentralized treatment chain at laboratory-scale designed under the principles of nature-based [...] Read more.

The absence of domestic wastewater (DWW) treatment in impoverished rural communities of the global south remains a pressing challenge for both public health and environmental sustainability. This study presents a simplified and decentralized treatment chain at laboratory-scale designed under the principles of nature-based solutions (NBS) and the circular economy (CE), emphasizing the integration of the macrophyte Eichhornia crassipes (EC) and bioproducts derived from aloe vera waste (AVW) and soursop seed waste (SSW). The system comprises three sequential stages: (1) coagulation using AVW, which achieved up to 39.9% turbidity reduction; (2) a horizontal flow biofilter system (HFB) employing the aquatic macrophyte EC, which removed 97.9% of fecal coliforms, 82.4% of Escherichia coli, and 99.9% of heterotrophic bacteria; and (3) a tertiary treatment step employing adsorbent derived from SSW, which attained 99.7% methylene blue removal in preliminary tests and an average 97.5% turbidity reduction in DWW. The integrated configuration demonstrates a practical, effective, and replicable approach for decentralized domestic wastewater treatment, fostering local waste valorization, reducing reliance on commercial chemicals, and enhancing water quality in resource-limited rural areas, with potential for scaling to pilot applications in rural communities. Full article

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33 pages, 916 KB

Open AccessReview

The Impact of High-Intensity Interval Training on Cardiometabolic, Neurologic, Oncologic, and Pain-Related Outcomes: A Comprehensive Review of Systematic Reviews

by Dmitriy Viderman, Yeltay Rakhmanov, Mina Aubakirova, Sultan Kalikanov and Michael Fredericson

J. Clin. Med. 2025, 14(23), 8328; https://doi.org/10.3390/jcm14238328 (registering DOI) - 24 Nov 2025

Abstract

High-intensity interval training (HIIT) has gained attention for its potential to improve health outcomes across various conditions. Thus, the aim of the study was to summarize studies on HIIT to understand its effects on various health outcomes. We conducted an umbrella review of [...] Read more.

High-intensity interval training (HIIT) has gained attention for its potential to improve health outcomes across various conditions. Thus, the aim of the study was to summarize studies on HIIT to understand its effects on various health outcomes. We conducted an umbrella review of systematic reviews and meta-analyses. PubMed, Cochrane Database of Systematic Reviews, EMBASE, Scopus, CINAHL, and Web of Science were searched for relevant articles. The experimental group was subjected to HIIT with or without treatment, while the control group comprised individuals who underwent alternative forms of training or were non-exercisers. Included studies were systematically analyzed for effects of HIIT and cardiovascular, respiratory, metabolic, neurological, gastrointestinal, immunological, and survival-related outcomes. Of 336 identified systematic reviews, 133 were included in the final analysis. HIIT was found to confer significant physiological benefits, including improvements in body composition, cardiovascular and metabolic parameters, and mental health outcomes. Studies demonstrated the efficacy of HIIT across diverse patient populations, with comparable or superior effects to moderate-intensity continuous training in conditions such as diabetes, cardiovascular diseases, neurological, oncologic, and pain-related disorders. Our review highlights the potential of HIIT as a time-efficient intervention for improving health outcomes and managing chronic diseases. However, interpretation of the results should be performed cautiously due to the heterogeneity observed. High-intensity interval training shows promise as an effective strategy for managing chronic diseases among diverse patient populations. Future research should focus on refining HIIT protocols and elucidating their long-term effects and sustainability. Full article

(This article belongs to the Special Issue Physical Activity, Exercise and Health: Clinical Management and Research)

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12 pages, 3534 KB

Open AccessArticle

Characterizing the Vertical Heterogeneity in Ultra-High Bed Sintering: From Mixture Properties to Stratified Phase Composition and Sinter Strength

by Yuchao Zhao, Hongzhuang Dong, Peng Li, Wenzheng Jiang, Qiang Zhong and Mingjun Rao

Metals 2025, 15(12), 1282; https://doi.org/10.3390/met15121282 (registering DOI) - 24 Nov 2025

Abstract

With the growing demand for efficiency, low consumption, and environmental sustainability in the iron and steel industry, ultra-high bed sintering technology emerges as a research hotspot due to its advantages in significantly reducing fuel consumption and pollutant emissions. However, studies on the influence [...] Read more.

With the growing demand for efficiency, low consumption, and environmental sustainability in the iron and steel industry, ultra-high bed sintering technology emerges as a research hotspot due to its advantages in significantly reducing fuel consumption and pollutant emissions. However, studies on the influence of fuel on mineralization behavior under ultra-high bed sintering conditions remained limited. This study systematically analyzes the effects of particle size, chemical composition, alkalinity, and MgO/Al₂O₃ ratio on mineralization behavior using a 500 m² sintering machine, while evaluating the tumbler strength and phase composition of the sinter. The results reveal that particle size segregation in the mixture was primarily caused by the upper layer, with the lower layer having a lesser impact on overall segregation. Chemical composition also exhibited significant segregation, particularly in TFe and fuel distribution along the bed height. Fuel segregation was pronounced vertically but negligible horizontally. Under the current fuel distribution, uneven heat distribution was observed, with excessive heat in the lower layer leading to increased liquid phase formation, reduced porosity, and improved sinter strength downward along the bed. Additionally, the phase composition varied markedly across layers: hematite content gradually increases from top to bottom, calcium ferrite (SFCA) content peaks in the middle layers, and magnetite decreases with bed depth. Full article

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19 pages, 5123 KB

Open AccessArticle

Additive Manufacturing of a PA11 Prototype Fabricated via Selective Laser Sintering for Advanced Industrial Applications

by Giovanna Colucci, Domenico Riccardi, Alberto Giubilini and Massimo Messori

Polymers 2025, 17(23), 3111; https://doi.org/10.3390/polym17233111 (registering DOI) - 24 Nov 2025

Abstract

Selective Laser Sintering (SLS) is an Additive Manufacturing (AM) technology that is receiving considerable attention in the scientific and industrial communities due to its great ability to efficiently produce functional and complex parts. The present work aims to fabricate a real prototype via [...] Read more.

Selective Laser Sintering (SLS) is an Additive Manufacturing (AM) technology that is receiving considerable attention in the scientific and industrial communities due to its great ability to efficiently produce functional and complex parts. The present work aims to fabricate a real prototype via SLS, such as a hose reel for industrial applications, using polyamide 11 (PA11) as a starting material. Characterization of the PA11 powder properties was first carried out from a thermal and morphological viewpoint to determine the powder’s thermal stability by TGA, the sintering window and degree of crystallinity by DSC, and the microstructure by SEM, PSD, and XRD analyses. The results revealed that PA11 has a 45-micron average particle size, circularity close to 1, and a Hausner ratio of 1.17. Together, these parameters ensure that PA11 powder flows smoothly, packs uniformly, and forms dense and defect-free layers during the SLS process, directly contributing to high part quality, dimensional precision, and stable process performance. The printability of the PA11 was optimized for the realization of 3D-printed parts for industrial applications. Finally, the quality of the printed samples and the mechanical and thermal performance were investigated. Several PA11-based parts were fabricated via SLS, showing a high level of complexity and definition, ideal for industrial applications, as confirmed by the predominantly green areas of the colored maps of X-CT. A complete prototypal case for a hose reel was assembled by using the parts realized, and it was chosen as a technological demonstrator to verify the feasibility of PA11 powder in the production of industrial professional components. Full article

(This article belongs to the Special Issue Polymer Materials for Application in Additive Manufacturing, 2nd Edition)

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22 pages, 4835 KB

Open AccessArticle

Effect of Metal Oxide Nanoparticles on the Breakdown Voltage of Transformer Oil Containing Cellulose Particles

by Tarek S. Negm, Diaa-Eldin A. Mansour and Ahmed A. Hossam-Eldin

Nanomaterials 2025, 15(23), 1758; https://doi.org/10.3390/nano15231758 (registering DOI) - 24 Nov 2025

Abstract

Failures are sometimes attributed to the deterioration of insulating oil, with contamination by cellulose particles. Such contamination lowers the dielectric strength of the oil. This study investigates the effect of cellulose contamination on the impulse breakdown voltage of transformer oil and evaluates the [...] Read more.

Failures are sometimes attributed to the deterioration of insulating oil, with contamination by cellulose particles. Such contamination lowers the dielectric strength of the oil. This study investigates the effect of cellulose contamination on the impulse breakdown voltage of transformer oil and evaluates the potential of nanofluids as a remediation strategy. A controlled amount of cellulose particles is added and dispersed into mineral oil at a concentration of 0.02 g/L to simulate a contaminated oil sample. Titanium dioxide (TiO₂) and aluminum oxide (Al₂O₃) nanoparticles are then dispersed into the contaminated oil at concentrations of 0.02 and 0.04 g/L. Impulse breakdown voltage is measured under both positive and negative polarities using electrode gaps of 1 mm and 2.5 mm, while dielectric permittivity is also measured to assess polarization effects. The influence of nanoparticle type and concentration is analyzed considering relaxation time and electron scavenging mechanisms. The results show that cellulose contamination markedly reduces dielectric strength, whereas the addition of nanoparticles effectively restores and, in several cases, enhances the insulating properties beyond those of uncontaminated oil. Full article

(This article belongs to the Section Nanocomposite Materials)

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21 pages, 1959 KB

Open AccessArticle

Cultivating Expertise: The Impact of Lesson Study on the Topic-Specific Pedagogical Content Knowledge of Grade 11 Life Sciences Educators in South Africa

by Steven Zuzidlelenhle Motaung and Moses Sibusiso Mtshali

Educ. Sci. 2025, 15(12), 1577; https://doi.org/10.3390/educsci15121577 (registering DOI) - 24 Nov 2025

Abstract

This study investigated the enhancement of topic-specific pedagogical content knowledge (TSPCK) among life science educators, with a particular focus on cellular respiration. The research identified a lack of studies on this topic, especially within South African educational settings, thereby emphasizing the challenges educators [...] Read more.

This study investigated the enhancement of topic-specific pedagogical content knowledge (TSPCK) among life science educators, with a particular focus on cellular respiration. The research identified a lack of studies on this topic, especially within South African educational settings, thereby emphasizing the challenges educators and learners face. With an emphasis on lesson planning, teaching, and reflections, this study used a qualitative research methodology to investigate how the lesson study approach enhances Grade 11 educators’ TSPCK in cellular respiration. The design used was lesson study. The lesson study approach has been identified as an effective strategy for enhancing educators’ TSPCK. Six educators from secondary schools participated. Data came from field notes and observations made while the lessons were being taught, as well as from educators’ reflective diaries and questionnaires. The findings revealed that educators utilize contextualization, differentiated teaching, collaborative planning, and a focus on conceptual understanding. Through the improvement of lesson designs, assessment methodologies, technological integration, and an emphasis on critical thinking and problem-solving, educators refined their cellular respiration TSPCK components. The results of this study will enhance educator professional development, impact curriculum design, and promote teaching and learning methodologies. This research will contribute to developing effective teaching approaches for cellular respiration and offer insights for initiatives aimed at advancing Life Sciences Education in South Africa. Full article

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17 pages, 1876 KB

Open AccessArticle

Parameter Optimization of Wet Stirred Media Milling Using an Intelligent Algorithm-Based Stressing Model

by Kang He, Bo Wu, Fei Sun, Xiaobiao Li and Chengcai Xi

Processes 2025, 13(12), 3785; https://doi.org/10.3390/pr13123785 (registering DOI) - 24 Nov 2025

Abstract

Wet stirred media milling (WSMM) is a popular grinding method used to produce important ultrafine-particle materials, such as pigments, pharmaceuticals, and pesticides. Therefore, it is crucial to improve the process capability and quality of WSMM by setting optimal parameters. This study proposes a [...] Read more.

Wet stirred media milling (WSMM) is a popular grinding method used to produce important ultrafine-particle materials, such as pigments, pharmaceuticals, and pesticides. Therefore, it is crucial to improve the process capability and quality of WSMM by setting optimal parameters. This study proposes a multi-objective optimization methodology based on an intelligent algorithm to optimize the ultra-fine grinding parameters; this can mitigate the issue whereby grinding parameters are difficult to determine during wet grinding industrial production. A mechanistic model is proposed based on the analysis of energy dissipation mechanisms. The specific energy in the WSMM process is quantified using a stressing model. A shuffled frog leaping algorithm (SFLA)-based stressing model is proposed to maximize the specific stress intensity and specific stress number of the entire system under the constraint of the product particle size and grinding time, which provides the optimal process parameters. The performance of the proposed strategy is validated using two case studies in different industrial optimization scenarios. The result of the first case study illustrates that, in comparison to a quadratic programming-based response surface methodology, the proposed SFLA-based stressing model greatly enhances the wet grinding efficiency (decreasing P₈₀ from 3.28 μm to 2.88 μm). In the second case study, the parameter optimization under different feed particle sizes and different productivities was discussed. The results confirmed that the optimized parameters can achieve the minimum particle size (P₅₀ = 1.78 μm) and maximum solid concentration (C_v = 120 g/L) within the minimum grinding time (t_g = 5 min). The contribution of our work lies in the fact that the proposed SFLA-based stressing model can direct multiple-objective decision-making in a more efficient way without requiring costly experimental procedures to acquire the optimized parameters in WSMM. The proposed approach is systematic and robust and can be integrated into WSMM architectures for parameter optimization in other complex wet grinding systems. Full article

(This article belongs to the Special Issue Multi Dimensional Value Driven Sustainable Manufacturing Processes and Innovation)

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19 pages, 402 KB

Open AccessArticle

Tourism, Energy Consumption and Environmental Quality: Role of Financial Development and Technological Innovations in the Ten Most-Visited Countries

by Xu Yang, Quan Qi and Zihan An

Sustainability 2025, 17(23), 10496; https://doi.org/10.3390/su172310496 (registering DOI) - 24 Nov 2025

Abstract

This study investigates whether tourism and energy consumption degrade or improve environmental quality in the world’s ten most-visited nations over 2000–2023 and whether financial development, trade openness, and technological innovation moderate these effects. Using complementary panel estimators—Driscoll–Kraay fixed effects for cross-sectionally robust inference, [...] Read more.

This study investigates whether tourism and energy consumption degrade or improve environmental quality in the world’s ten most-visited nations over 2000–2023 and whether financial development, trade openness, and technological innovation moderate these effects. Using complementary panel estimators—Driscoll–Kraay fixed effects for cross-sectionally robust inference, two-step feasible GLS for efficiency under heteroskedasticity and autocorrelation, and Lewbel IV–2SLS to address potential endogeneity—the analysis yields three consistent patterns. The study employed three models to investigate these associations. The results show that renewable energy consumption consistently reduces emissions, while trade openness is strongly associated with lower CO₂. Financial development becomes emission-reducing when paired with technological innovation. Tourism intensity is neutral to modestly negative once controls are applied, and urbanization is weakly negative or statistically insignificant. The study formulated well-coordinated policies based on these findings. Full article

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43 pages, 1006 KB

Open AccessSystematic Review

Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances

by Dragoș-Claudiu Popescu and Mihnea-Alexandru Găman

Med. Sci. 2025, 13(4), 280; https://doi.org/10.3390/medsci13040280 (registering DOI) - 24 Nov 2025

Abstract

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous malignancy, with various outcomes despite significant advances in therapeutic options. Current conventional prognostic tools, e.g., the International Prognostic Index (IPI), lack sufficient precision at an individual patient level. However, artificial intelligence (AI), [...] Read more.

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous malignancy, with various outcomes despite significant advances in therapeutic options. Current conventional prognostic tools, e.g., the International Prognostic Index (IPI), lack sufficient precision at an individual patient level. However, artificial intelligence (AI), including machine learning (ML) and deep learning (DL), can enable specialists to navigate complex datasets, with the final aim of improving prognostic models for DLBCL. Objectives: This scoping review aims to systematically map the current literature regarding the use of AI/ML techniques in DLBCL outcome prediction and risk stratification. We categorized studies by data modality and computational approach to identify key trends, knowledge gaps, and opportunities for their translation into current practice. Methods: We conducted a structured search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases through July 2025 using terms related to DLBCL, prognosis, and AI/ML. Eligible studies included original papers applying AI/ML to predict survival outcomes, classify risk groups, or identify prognostic subtypes. Studies were categorized based on input modality: clinical, positron emission tomography/computed tomography (PET/CT) imaging, histopathology, transcriptomics, genomics, circulating tumor DNA (ctDNA), and multi-omics data. Narrative synthesis was performed in line with PRISMA-ScR guidelines. Results: From the 215 records screened, 91 studies met the inclusion criteria. Group-wise we report the following categories: clinical risk features (n = 8), PET/CT imaging (n = 30), CT (n = 1), digital pathology (n = 3), conventional histopathology (n = 2), gene expression profiling (n = 19), specific mutational signatures (n = 18), ctDNA (n = 3), microRNA (n = 2), and multi-omics integration (n = 5). The most common techniques reported amongst the papers included ensemble learning, convolutional neural networks (CNNs), and LASSO-based Cox models. Several AI techniques demonstrated superior predictive performance over IPI, with area under the curve (AUC) values frequently exceeding 0.80. Multi-omics models and ctDNA-based predictors showed strong potential for clinical translation, a perspective worth considering in further studies. Conclusions: AI/ML methods are increasingly used in DLBCL to improve prognostic accuracy by leveraging data types with diverse inputs. These approaches allow an enhanced stratification, superior to traditional indices, and support the early identification of high-risk patients, earlier guidance for therapy tailoring, and early trial enrollment for flagged cases. Future investigations should focus on external validation and improvement of model interpretability, with tangible perspectives of integration into real-world workflows and translation from bench to bedside. Full article

(This article belongs to the Section Cancer and Cancer-Related Research)

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19 pages, 1587 KB

Open AccessArticle

Transformer Attention-Guided Dual-Path Framework for Bearing Fault Diagnosis

by Saif Ullah, Wasim Zaman and Jong-Myon Kim

Appl. Sci. 2025, 15(23), 12431; https://doi.org/10.3390/app152312431 (registering DOI) - 23 Nov 2025

Abstract

Reliable bearing fault diagnosis plays an important role in maintaining the safety and performance of rotating machinery in industrial systems. Although deep learning models have achieved remarkable success in this field, their dependence on a single feature-extraction approach often restricts the diversity of [...] Read more.

Reliable bearing fault diagnosis plays an important role in maintaining the safety and performance of rotating machinery in industrial systems. Although deep learning models have achieved remarkable success in this field, their dependence on a single feature-extraction approach often restricts the diversity of learned representations and limits diagnostic accuracy. To overcome this limitation, this study proposes an attention-guided dual-path framework that integrates spatial and time–frequency feature learning with transformer-based classification for precise fault identification. In the proposed framework, vibration signals collected from an experimental bearing test rig are simultaneously processed through two complementary pipelines: one converts the signals into two-dimensional matrix images to extract spatial features, while the other transforms them into continuous wavelet transform (CWT) scalograms to capture fine-grained temporal and spectral information. The extracted features are fused through a lightweight transformer encoder with an attention mechanism that dynamically emphasizes the most informative representations. This fusion enables the model to effectively capture cross-domain dependencies and enhance discriminative capability. Experimental validation on an industrial vibration dataset demonstrates that the proposed model achieves 99.87% classification accuracy, outperforming conventional CNN and transformer-based approaches. The results confirm that integrating multi-domain features with attention-driven fusion significantly improves the robustness and generalization of deep learning models for intelligent bearing fault diagnosis. Full article

(This article belongs to the Special Issue Sustainable Reliability, Maintenance, and Fault Diagnosis Strategies for Mechanical and Manufacturing Systems)

38 pages, 25106 KB

Open AccessArticle

A Two-Stage End-to-End Framework for Robust Scene Text Spotting with Self-Calibrated Detection and Contextual Recognition

by Yuning Cheng, Jinhong Huang, Io San Tai, Subrota Kumar Mondal, Tianqi Wang and Hussain Mohammed Dipu Kabir

Electronics 2025, 14(23), 4594; https://doi.org/10.3390/electronics14234594 (registering DOI) - 23 Nov 2025

Abstract

End-to-end scene text detection and recognition, which involves detecting and recognizing text in natural images, still faces significant challenges, particularly in handling text of arbitrary shapes, complex backgrounds, and computational efficiency requirements. This paper proposes a novel and viable end-to-end OCR framework that [...] Read more.

End-to-end scene text detection and recognition, which involves detecting and recognizing text in natural images, still faces significant challenges, particularly in handling text of arbitrary shapes, complex backgrounds, and computational efficiency requirements. This paper proposes a novel and viable end-to-end OCR framework that synergistically combines a powerful detection network with advanced recognition models. For text detection, we develop a method called Text Contrast Self-Calibrated Network (TextCSCN), which employs pixel-wise supervised contrastive learning to extract more discriminative features. TextCSCN addresses long-range dependency modeling and limited receptive field issues through self-calibrated convolutions and Global Convolutional Networks (GCNs). We further introduce an efficient Mamba-based bidirectional module for boundary refinement, enhancing both accuracy and speed. For text recognition, our framework employs a Swin Transformer backbone with Bidirectional Feature Pyramid Networks (BiFPNs) for optimized multi-scale feature extraction. We propose a Pre-Gated Contextual Attention Gate (PCAG) mechanism to effectively fuse visual and linguistic information while minimizing noise and uncertainty in multi-modal integration. Experiments on challenging benchmarks including TotalText and CTW1500 demonstrate the effectiveness of our approach. Our detection module achieves state-of-the-art performance with an F-score of 88.21% on TotalText, and the complete end-to-end system shows comparable improvements in recognition accuracy, establishing new benchmarks for scene text spotting. Full article

(This article belongs to the Special Issue Exploring Edge AI: Architectures, Algorithms, and the Role of Edge–Cloud Cooperation for Scalable AI Systems)

19 pages, 5316 KB

Open AccessArticle

Disturbance Characteristics of Subsoiling in Paddy Soil Based on Smoothed Particle Hydrodynamics (SPH)

by Lei Liang, Qishuo Ding, Haiyan Zhang and Qi Liu

Agronomy 2025, 15(12), 2695; https://doi.org/10.3390/agronomy15122695 (registering DOI) - 23 Nov 2025

Abstract

Subsoiling is an important technology in conservation tillage. The disturbance characteristics of paddy soil were simulated by smoothed particle hydrodynamics (SPH) in this paper in order to explore the optimal tillage depth of paddy soil in a rice–wheat rotation area. Firstly, a subsoiling [...] Read more.

Subsoiling is an important technology in conservation tillage. The disturbance characteristics of paddy soil were simulated by smoothed particle hydrodynamics (SPH) in this paper in order to explore the optimal tillage depth of paddy soil in a rice–wheat rotation area. Firstly, a subsoiling experiment with five tillage depths was carried out by a self-made multi-functional in situ test-rig facility. Then, a three-layer-soil subsoiling model of a cultivated layer, plow pan, and subsoil layer was established based on the SPH method. Finally, the soil disturbance characteristics were analyzed from macroscopic and microscopic perspectives. The results showed that the average draft force in simulation was consistently lower than in the field, with a maximum error of 18.71%, and the field draft force fluctuated greatly. The soil block above the tine was not lifted up as a big block but broken into many small soil blocks and then lifted up, resulting in different displacements of the soil particles, but the relative position was unchanged from top to bottom. The particle displacements were concentrated above the tine, the stress was concentrated around the tine, while the velocity and acceleration were closely attached to the subsoiler. A “mole cavity” at 25 and 30 cm tillage depths existed at the bottom of the disturbance, which was consistent with the finding in the field. The disturbance area and specific draft were maximum and minimum at 20 cm tillage depth, respectively. These findings suggest that the optimal tillage depth was 20 cm for the rice–wheat rotation area. The results of the analysis provide a theoretical basis for the optimal design of subsequent subsoiling. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

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15 pages, 1071 KB

Open AccessArticle

Exploring the Role of CT-Based Delta-Radiomics in Unresectable Vulvar Cancer

by Abdulla Alzibdeh, Bara M. Hammadeh, Rahaf Alnajjar, Mohammad Abd Al-Raheem, Rima Mheidat, Alzahra’a Al Matairi, Mohamed Qamber, Hanan Almasri, Bayan Altalla’, Amal Al-Omari and Fawzi Abuhijla

Diagnostics 2025, 15(23), 2972; https://doi.org/10.3390/diagnostics15232972 (registering DOI) - 23 Nov 2025

Abstract

Background/Objectives: To explore the prognostic potential of gross tumor volume (GTV)-based delta-radiomic features from CT simulation scans in patients with locally advanced unresectable vulvar cancer. Methods: A total of 21 patients (between 2019 and 2024) undergoing definitive radiotherapy were included, with baseline and [...] Read more.

Background/Objectives: To explore the prognostic potential of gross tumor volume (GTV)-based delta-radiomic features from CT simulation scans in patients with locally advanced unresectable vulvar cancer. Methods: A total of 21 patients (between 2019 and 2024) undergoing definitive radiotherapy were included, with baseline and post-phase I (after 25 fractions) CT simulation scans analyzed. Radiomic features (n = 107) were extracted from GTVs using PyRadiomics, and delta features were calculated as the relative change between scans. A multi-step selection pipeline (univariable Cox screening (p < 0.10), correlation filtering, and Lasso–Cox) was applied for each endpoint: local control (LC), regional control, distant metastasis-free survival, progression-free survival, and overall survival (OS). Model discrimination was assessed via 500-iteration bootstrapped concordance index (C-index), and calibration was plotted at 24 months. Results: Median follow-up was 50.0 months. The 2-year LC and OS rates were 56.2% and 55.9%, respectively. Final multivariable models retained a sole texture Δ feature for LC (HR = 2.62, 95% CI = 1.05–6.52, p = 0.039; C-index = 0.748) and six Δ features for OS (C-index = 0.864). No features were retained for other endpoints. For LC, increased run-length non-uniformity after phase I predicted poorer control. For OS, increased texture/shape complexity predicted worse survival, whereas increased uniformity predicted better survival. Conclusions: CT-based delta-radiomic features, particularly shape and texture metrics, may predict LC and OS in unresectable vulvar cancer. Despite the small sample size, these findings highlight the potential for delta-radiomics as a noninvasive biomarker for risk stratification. Validation in larger cohorts and exploring potential in adaptive radiotherapy are warranted. Full article

(This article belongs to the Special Issue Medical Image Analysis and Machine Learning)

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18 pages, 1342 KB

Open AccessArticle

Kinematic Upper-Bound Analysis of Safety Performance for Precast 3D Composite Concrete Structure with Extended Mohr–Coulomb Criterion

by Taoxiang Feng, De Zhou and Qiang Chen

Appl. Sci. 2025, 15(23), 12429; https://doi.org/10.3390/app152312429 (registering DOI) - 23 Nov 2025

Abstract

This study develops a systematic kinematic upper-bound framework to evaluate the ultimate bearing capacity and failure mechanisms of prefabricated cast-in-place slab–wall joints in overlapped metro stations. Recognizing the complex shear–compression interaction in these critical structural nodes, a novel three-dimensional short-block shear failure model [...] Read more.

This study develops a systematic kinematic upper-bound framework to evaluate the ultimate bearing capacity and failure mechanisms of prefabricated cast-in-place slab–wall joints in overlapped metro stations. Recognizing the complex shear–compression interaction in these critical structural nodes, a novel three-dimensional short-block shear failure model is established based on the principle of energy balance. The analysis employs a modified Mohr–Coulomb strength criterion incorporating a finite tensile strength cut-off, enabling more accurate representation of cracking and tensile resistance effects. Analytical solutions are derived to predict the ultimate capacity and critical failure angle, followed by a comprehensive parametric analysis. The results reveal that cross-sectional dimensions dominate the bearing capacity, while the internal friction angle and tensile-to-compressive strength ratio significantly influence both the magnitude and mode of failure. A narrower load distribution width enhances capacity and reduces the optimal failure angle. Overall, the proposed 3D model provides a rigorous and efficient theoretical tool for the design optimization and safety assessment of prefabricated underground structures. Full article

(This article belongs to the Special Issue Slope Stability and Earth Retaining Structures—2nd Edition)

25 pages, 43287 KB

Open AccessArticle

Document Image Verification Based on Paragraph Alignment and Subtle Change Detection

by Daoquan Li, Weifei Jia, Quanlin Yu and Zhaoxu Hu

Appl. Sci. 2025, 15(23), 12430; https://doi.org/10.3390/app152312430 (registering DOI) - 23 Nov 2025

Abstract

The digitization of paper documents enables rapid sharing and long-term preservation of information, making it a widely adopted approach for efficient document storage and management across various domains. However, the recent advances in image editing software pose an increasing threat to the integrity [...] Read more.

The digitization of paper documents enables rapid sharing and long-term preservation of information, making it a widely adopted approach for efficient document storage and management across various domains. However, the recent advances in image editing software pose an increasing threat to the integrity of document images. Comparing the input with the corresponding reference document image is a direct and effective approach to verification. Nevertheless, this task is challenging due to two key factors, namely, the need for efficient retrieval of the reference document images and the difficulty of detecting subtle content changes under the print–scan (PS) distortions. To address these challenges, this work proposes a document image verification scheme based on paragraph alignment and subtle change detection. It first extracts paragraph structural features from both input and reference document images to achieve efficient image retrieval and accurate paragraph alignment. Based on the alignment results, the proposed scheme employs contrastive learning to reduce the effect of PS distortions in extracting features from the input and reference document images. Finally, an additional verification step is introduced that significantly reduces the false positive detection by addressing the feature misalignment within the extracted paragraphs. To evaluate the proposed scheme, extensive experiments were conducted on databases constructed from public datasets, and various benchmark methods were compared. Experimental results show that the proposed scheme outperforms benchmark methods, achieving an accuracy score of

0.963

. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

15 pages, 7465 KB

Open AccessArticle

Sensorless Payload Estimation of Serial Robots Using an Improved Disturbance Kalman Filter with a Variable-Parameter Noise Model

by Ruiqing Luo, Jianjun Yuan, Yimin He, Sheng Bao, Liang Du and Zhengtao Hu

Actuators 2025, 14(12), 568; https://doi.org/10.3390/act14120568 (registering DOI) - 23 Nov 2025

Abstract

The accurate estimation of the end-effector load force is essential in dynamic robotic scenarios, especially when the end-effector payload varies, to ensure safe and stable physical interaction among humans, robots, and environments. Currently, most applications still rely on payload calibration schemes, but existing [...] Read more.

The accurate estimation of the end-effector load force is essential in dynamic robotic scenarios, especially when the end-effector payload varies, to ensure safe and stable physical interaction among humans, robots, and environments. Currently, most applications still rely on payload calibration schemes, but existing calibration techniques often struggle to balance efficiency and accuracy. Moreover, current-based payload estimation methods, which are a commonly used and low-cost technique, face practical challenges such as non-negligible noise. To handle these issues, we propose a sensorless scheme based on a modified disturbance Kalman filter for accurately estimating the load force exerted on robots. Specifically, we introduce the dynamic model of robots that incorporates the nonlinear friction related to velocity and load. Subsequently, a generalized disturbance observer for the robot dynamics is adopted to avoid the measurement noise of joint acceleration. Considering the influence of friction and velocity on the noise parameters in the Kalman filter, a variable-parameter noise model is established. Finally, experimental results demonstrate that the proposed method achieves better performance in terms of accuracy, response, and overshoot suppression compared to the existing methods. Full article

(This article belongs to the Section Actuators for Robotics)

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14 pages, 2958 KB

Open AccessArticle

Dynamic Imprint and Recovery Mechanisms in Hf_0.2Zr_0.8O₂ Anti-Ferroelectric Capacitors with FORC Characterization

by Yuetong Huo, Jianguo Li, Zeping Weng, Yaru Ding, Lijian Chen, Jiabin Qi, Yiming Qu and Yi Zhao

Electronics 2025, 14(23), 4593; https://doi.org/10.3390/electronics14234593 (registering DOI) - 23 Nov 2025

Abstract

The conventional static imprint effect in Hf_xZr_1−xO₂ (HZO) ferroelectric (FE) devices, which degrades data retention, is generally characterized by a shift in the hysteresis loop along the electric field axis. Unlike the static imprint effect, the dynamic imprint [...] Read more.

The conventional static imprint effect in Hf_xZr_1−xO₂ (HZO) ferroelectric (FE) devices, which degrades data retention, is generally characterized by a shift in the hysteresis loop along the electric field axis. Unlike the static imprint effect, the dynamic imprint effect emerges under dynamic electric fields or actual operating conditions, making the FE film exceptionally sensitive to switching pulse parameters and domain history. In HZO anti-ferroelectric (AFE) devices, this dynamic imprint effect alters the coercive field distribution associated with domain switching and poses a significant challenge to long-term stable device operation. This study systematically investigates the dynamic imprint effect and its recovery process using a comprehensive integration of first-order reversal curve (FORC) analysis, transient current-voltage (I-V), and polarization-voltage (P-V) characterization. By analyzing localized imprint behavior under sub-cycling conditions, mechanisms and recovery pathways of imprint in AFE devices are proposed. Finally, possible physics-based mechanisms describing imprint behaviors and recovery behaviors are discussed, providing insights for optimizing AFE memory technology performance and reliability. Full article

(This article belongs to the Special Issue Integration of Emerging Memory and Neuromorphic Architecture Chips)

11 pages, 1913 KB

Open AccessArticle

The Frictional Impact with Rebound for 3D Printed Surfaces

by Ahmet Faruk Akhan and Dan Marghitu

Appl. Sci. 2025, 15(23), 12427; https://doi.org/10.3390/app152312427 (registering DOI) - 23 Nov 2025

Abstract

This research explores the oblique impact with rebound of a rigid rod and 3D-printed surfaces with varying infill ratios. A visco-elastic contact model is developed using normal impact experiments and then confirmed with oblique impact experiments. The force coefficients are determined using a [...] Read more.

This research explores the oblique impact with rebound of a rigid rod and 3D-printed surfaces with varying infill ratios. A visco-elastic contact model is developed using normal impact experiments and then confirmed with oblique impact experiments. The force coefficients are determined using a genetic algorithm. The impact on the polylactic acid surface is defined by a coefficient of restitution, coefficient of friction, and the coefficients of the contact force. The simulations demonstrate good compatibility with the experimental data. Full article

(This article belongs to the Special Issue Nonlinear Dynamics in Mechanical Engineering and Thermal Engineering)

13 pages, 2150 KB

Open AccessArticle

Study on Atmospheric Boundary Layer Retrieval Method and Observation Data Analysis Based on Aerosol Lidar

by Chao Chen, Bingao Sui, Zhangjun Wang, Baoqing Sun, Hui Li, Xin Pan, Guoliang Shentu, Quanfeng Zhuang, Xianxin Li, Hao Chen and Wenbo Jiang

Atmosphere 2025, 16(12), 1323; https://doi.org/10.3390/atmos16121323 (registering DOI) - 23 Nov 2025

Abstract

The atmospheric boundary layer is the lowest part of the troposphere, directly influenced by the Earth’s surface. The boundary layer’s height is a critical parameter for weather forecasting, air quality monitoring, and climate modeling. Lidar has become a premier tool for continuous boundary [...] Read more.

The atmospheric boundary layer is the lowest part of the troposphere, directly influenced by the Earth’s surface. The boundary layer’s height is a critical parameter for weather forecasting, air quality monitoring, and climate modeling. Lidar has become a premier tool for continuous boundary layer height detection with its high spatial–temporal resolution. A multi-wavelength aerosol lidar with 355 nm, 532 nm, and 1064 nm has been developed and deployed for operational observations at the Haidian District Meteorological Service of Beijing. The structure design, specifications, observation campaign, and detection principle of the multi-wavelength aerosol lidar are presented and the retrieval method of the boundary layer’s height is introduced. By comparing it with the data of the digital radiosonde, it is verified that the first normalized gradient of the range-corrected signal can more accurately retrieve the boundary layer’s height. The typical daily variation characteristics and influencing factors of urban boundary layer height are analyzed through observational examples and the monthly mean value of the boundary layer’s height in 2019 is acquired and analyzed. Full article

(This article belongs to the Special Issue Data Analysis and Algorithms for Aerosols Remote Sensing)

29 pages, 2593 KB

Open AccessArticle

Ensemble Transfer Learning for Gastric Cancer Prediction Using Electronic Health Records in a Data-Scarce Single-Hospital Setting

by Hyon Hee Kim, Ji Yeon Han, Yae Bin Lim, Young Seo Lim, Seung-In Seo, Kyung Joo Lee and Woon Geon Shin

Appl. Sci. 2025, 15(23), 12428; https://doi.org/10.3390/app152312428 (registering DOI) - 23 Nov 2025

Abstract

Gastric cancer is a significant health concern in East Asia, where early risk prediction is critical for prevention. However, the scarcity of single-hospital electronic health records (EHRs) data limits the applicability and generalizability of machine learning models. To address this challenge, we propose [...] Read more.

Gastric cancer is a significant health concern in East Asia, where early risk prediction is critical for prevention. However, the scarcity of single-hospital electronic health records (EHRs) data limits the applicability and generalizability of machine learning models. To address this challenge, we propose an ensemble transfer learning framework for gastric cancer prediction using structured EHRs in a data-scarce single-hospital setting. Three base models, Support Vector Machine (SVM), Random Forest, and Deep Neural Network (DNN), were pretrained on a large-scale national dataset from the Republic of Korean National Health Insurance Service (NHIS) and fine-tuned on a smaller institutional dataset from Kangdong Sacred Heart Hospital (KSHH). These fine-tuned models were combined via stacking ensemble learning with logistic regression as a meta-learner. The proposed model achieved strong performance with precision 0.78, recall 0.92, F1-score 0.83, accuracy 0.91, and AUC 0.93. For interpretability, permutation feature importance and Shapley Additive Explanations (SHAP) were applied. Smoking status, gender, and hypertensive disorder were identified as key predictors consistent with previous studies. This study demonstrates the successful application of transfer learning to overcome data scarcity in single-hospital structured EHRs. Furthermore, our stacking ensemble strategy outperformed the individual fine-tuned models, offering a generalizable framework for gastric cancer prediction in data-scarce clinical settings. Full article

(This article belongs to the Special Issue Advances in Machine Learning for Healthcare Applications)

24 pages, 8438 KB

Open AccessArticle

Cooling Performance of Night Ventilation and Climate Adaptation of Vernacular Buildings in the Turpan Basin with an Extremely Hot–Arid Climate

by Qingqing Han, Lei Zhang, Wuxing Zheng, Guochen Sang and Yiyun Zhu

Energies 2025, 18(23), 6135; https://doi.org/10.3390/en18236135 (registering DOI) - 23 Nov 2025

Abstract

This study investigates the cooling potential of night ventilation and the climate adaptability of local vernacular buildings in the Turpan basin, aiming to identify passive energy-saving design strategies. A rural building with an air-drying shelter was selected for summer indoor environment measurements (two [...] Read more.

This study investigates the cooling potential of night ventilation and the climate adaptability of local vernacular buildings in the Turpan basin, aiming to identify passive energy-saving design strategies. A rural building with an air-drying shelter was selected for summer indoor environment measurements (two stages: all-day window closure vs. night ventilation), and a numerical model was established to simulate the impacts of window-to-wall ratio and window shading projection factor on the indoor environment. Results indicate that night ventilation introduces cool outdoor air to replace indoor hot air, cools building components, improves thermal comfort, and reduces cooling energy demand. Without additional cooling technology, increasing the window-to-wall ratio lowers nighttime temperatures but increases Degree Discomfort Hours, while appropriately sized shading devices mitigate daytime overheating from larger windows. Benefiting from the high thermal storage capacity of earth-appressed walls, semi-underground rooms offer better comfort with lower temperatures and higher humidity; for aboveground rooms, orientation is critical due to intense solar radiation. The air-drying shelter reduces solar radiant heat absorption and inhibits convective/radiative heat transfer on the roof’s external surface, significantly lowering its temperature from noon to midnight. This leads to notable reductions in the roof’s internal surface temperature (1.02 °C in the sealed stage, 2.09 °C during night ventilation) and the average indoor temperature (1.70 °C). Full article

(This article belongs to the Special Issue Energy Efficiency and Thermal Performance in Buildings)

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27 pages, 487 KB

Open AccessArticle

Imperfect Demand Information Sharing Under Manufacturer Encroachment

by Beifen Wang and Zhibao Li

Systems 2025, 13(12), 1060; https://doi.org/10.3390/systems13121060 (registering DOI) - 23 Nov 2025

Abstract

The dual-channel structure resulted from manufacturer encroachment could alter the incentives of downstream retailer to ex ante communicate demand forecast. And different types of channel competition need to be investigated in this dual-channel information sharing scenario. This paper aims to investigate retailer’s ex [...] Read more.

The dual-channel structure resulted from manufacturer encroachment could alter the incentives of downstream retailer to ex ante communicate demand forecast. And different types of channel competition need to be investigated in this dual-channel information sharing scenario. This paper aims to investigate retailer’s ex ante imperfect demand information sharing strategy given that upstream manufacturer has set up direct sales channel (manufacturer encroachment). The imperfect information sharing means the demand information shared is uncertain and has some error relative to the real-world demand condition. It examines two types of channel competition: quantity competition and price competition. Additionally, this study discusses the encroaching manufacturer’s incentives for adjusting channel substitution. The paper adopts a stylized game theoretic model to describe interactions between retailer and the encroaching manufacturer. Contrary to conventional wisdom, the paper shows that under manufacturer encroachment, it is always possible for ex ante demand information sharing. Specifically, in the Cournot competition scenario where retailer channel and the encroaching manufacturer direct channel compete in quantity, the encroaching manufacturer could encourage demand information communication through side payment. Furthermore, in the Bertrand competition scenario, retailer may voluntarily share demand information. In addition, in either quantity or price competition, the encroaching manufacturer has incentives to adjust channel substitution for profit maximization. Full article

(This article belongs to the Section Supply Chain Management)

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16 pages, 2690 KB

Open AccessArticle

Silencing the Circadian Clock Genes Cycle and Clock Disrupts Reproductive–Metabolic Homeostasis but Does Not Induce Reproductive Diapause in Arma chinensis

by Junjie Chen, Qiaozhi Luo, Maosen Zhang, Zhuoling Lv, Meng Liu, Xiangchao Huang, Yuyan Li and Lisheng Zhang

Insects 2025, 16(12), 1192; https://doi.org/10.3390/insects16121192 (registering DOI) - 23 Nov 2025

Abstract

The circadian clock is a conserved timekeeping mechanism that enables organisms to anicipate and adapt to daily environmental cycles. While its role in photoperiodic diapause has been documented, its fundamental function in maintaining reproductive and metabolic homeostasis under favorable conditions remains less explored, [...] Read more.

The circadian clock is a conserved timekeeping mechanism that enables organisms to anicipate and adapt to daily environmental cycles. While its role in photoperiodic diapause has been documented, its fundamental function in maintaining reproductive and metabolic homeostasis under favorable conditions remains less explored, especially in biological control agents. This study investigates the functional roles of the core circadian clock genes Cycle (AcCyc) and Clock (AcClk) in the predatory bug Arma chinensis, focusing on their regulation of reproduction and metabolism under non-diapause conditions. We characterized these genes and analyzed their spatiotemporal expression under diapause and non-diapause conditions. Using RNA interference, we knocked down AcCyc and AcClk in non-diapausing females and evaluated phenotypic impacts on ovarian development, fecundity, and energy reserves. qPCR analyses delineated downstream effects on juvenile hormone (JH) signaling. Results showed that diapause altered AcCyc and AcClk expression rhythms. Their knockdown severely impaired reproduction, reducing ovarian size, vitellogenin expression, and egg production, while concurrently decreasing triglyceride levels indicating disrupted energy homeostasis. Mechanistically, gene silencing downregulated key JH pathway components, Methoprene-tolerant (Met) and Krueppel homolog 1 (Kr-h1). We conclude that AcCyc and AcClk are essential maintainers of reproductive–metabolic homeostasis, not merely diapause regulators. This reframes the clock’s role from a seasonal timekeeper to a central hub for daily physiological coordination, offering new insights for improving biocontrol agent production. Full article

(This article belongs to the Section Insect Molecular Biology and Genomics)

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29 pages, 8374 KB

Open AccessArticle

Cross-Domain Land Surface Temperature Retrieval via Strategic Fine-Tuning-Based Transfer Learning: Application to GF5-02 VIMI Imagery

by Peyman Heidarian, Hua Li, Zelin Zhang, Yumin Tan, Feng Zhao, Biao Cao, Yongming Du and Qinhuo Liu

Remote Sens. 2025, 17(23), 3803; https://doi.org/10.3390/rs17233803 (registering DOI) - 23 Nov 2025

Abstract

Accurate prediction of land surface temperature (LST) is critical for remote sensing applications, yet remains hindered by in situ data scarcity, limited input variables, and regional variability. To address these limitations, we introduce a three-stage strategic fine-tuning-based transfer learning (SFTL) framework that integrates [...] Read more.

Accurate prediction of land surface temperature (LST) is critical for remote sensing applications, yet remains hindered by in situ data scarcity, limited input variables, and regional variability. To address these limitations, we introduce a three-stage strategic fine-tuning-based transfer learning (SFTL) framework that integrates a large simulated dataset (430 K samples), in situ measurements from the Heihe and Huailai regions in China, and high-resolution imagery from the GF5-02 Visible and Infrared Multispectral Imager (VIMI). The key novelty of this study is the combination of large-scale simulation, an engineered humidity-sensitive feature, and multiple parameter-efficient tuning strategies—full, head, gradual, adapter, and low-rank adaptation (LoRA)—within a unified transfer-learning framework for cross-site LST estimation. In Stage 1, pre-training with 5-fold cross-validation on the simulated dataset produced strong baseline models, including Random Forest (RF), Light Gradient Boosting Machine (LGBM), Deep Neural Network (DNN), Transformer (TrF), and Convolutional Neural Network (CNN). In Stage 2, strategic fine-tuning was conducted under two cross-regional scenarios—Heihe-to-Huailai and Huailai-to-Heihe—and model transfer for tree-based learners. Fine-tuning achieved competitive in-domain performance while materially improving cross-site transfer. When trained on Huailai and tested on Heihe, DNN-gradual attained RMSE 2.89 K (R² ≈ 0.96); when trained on Heihe and tested on Huailai, TrF-head achieved RMSE 3.34 K (R² ≈ 0.94). In Stage 3, sensitivity analyses confirmed stability across IQR multipliers of 1.0–1.5, with <1% RMSE variation across models and sites, indicating robustness against outliers. Additionally, application to real GF5-02 VIMI imagery demonstrated that the best SFTL configurations aligned with spatiotemporal in situ observations at both sites, capturing the expected spatial gradients. Overall, the proposed SFTL framework—anchored in cross-validation, strategic fine-tuning, and large-scale simulation—outperforms the widely used Split-Window (SW) algorithm (Huailai: RMSE = 3.64 K; Heihe: RMSE = 4.22 K) as well as direct-training Machine Learning (ML) models, underscoring their limitations in modeling complex regional variability. Full article

(This article belongs to the Special Issue Spatio-Temporal Land Surface Temperature Retrieval Based on Ground-Based, Satellite Observations and Artificial Intelligence)

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20 pages, 862 KB

Open AccessReview

Heparin Resistance in Cardiac Surgery with Cardiopulmonary Bypass: Mechanisms, Clinical Implications, and Evidence-Based Management

by Karina E. Rivera Jiménez, Yahaira M. Mamani Ticona, Giancarlo Gutierrez-Chavez, Cristian O. Astudillo, Edisson Calle, Giancarlo A. Torres Heredia, Dario S. Lopez Delgado, Oriana Rivera-Lozada and Joshuan J. Barboza

Medicina 2025, 61(12), 2088; https://doi.org/10.3390/medicina61122088 (registering DOI) - 23 Nov 2025

Abstract

Background: Unfractionated heparin (UFH) is the standard anticoagulant during cardiopulmonary bypass (CPB). A clinically relevant subset develops heparin resistance (HR)—failure to reach adequate anticoagulation with usual UFH—raising thrombotic risk and complicating perioperative care. Objectives: To synthesize contemporary evidence on the mechanisms, [...] Read more.

Background: Unfractionated heparin (UFH) is the standard anticoagulant during cardiopulmonary bypass (CPB). A clinically relevant subset develops heparin resistance (HR)—failure to reach adequate anticoagulation with usual UFH—raising thrombotic risk and complicating perioperative care. Objectives: To synthesize contemporary evidence on the mechanisms, clinical implications, and perioperative management of HR in adult cardiac surgery with CPB. Methods: This narrative review synthesizes contemporary evidence on the epidemiology, mechanisms, recognition, and management of HR in adult cardiac surgery with CPB, emphasizing clinically actionable points. Results: Incidence varies across centers and definitions. Mechanisms include antithrombin (AT) deficiency or consumption and AT-independent drivers such as systemic inflammation or sepsis, protein-loss states, thrombocytosis, hyperfibrinogenemia, obesity, prior heparin exposure, and drug interactions. Sole reliance on activated clotting time (ACT) may misestimate anticoagulant effect; anti–factor Xa (anti-Xa) assays or heparin titration systems improve assessment when available. Management is stepwise: UFH dose escalation; targeted AT supplementation (or fresh frozen plasma where concentrates are unavailable); and transition to direct thrombin inhibitors when HR persists or UFH is contraindicated. Protocolized pathways and multidisciplinary coordination reduce delays and adverse events. Conclusions: HR is a multifactorial, common challenge in CPB. Pre-bypass risk assessment, multimodal monitoring, and an algorithm prioritizing UFH optimization, AT repletion, and timely use of direct thrombin inhibitors provide a pragmatic framework to limit thrombosis and bleeding. Harmonized definitions and comparative trials remain priorities. Full article

(This article belongs to the Special Issue Recent Advances in Cardiovascular Surgery)

12 pages, 2004 KB

Open AccessArticle

Fire-Enhanced Soil Carbon Sequestration in Wetlands: A 5000-Year Record from the Ussuri River, Northeast China

by Yan Zhao, Xinyuan He and Zhenqing Zhang

Atmosphere 2025, 16(12), 1322; https://doi.org/10.3390/atmos16121322 (registering DOI) - 23 Nov 2025

Abstract

Using high-resolution charcoal and TOC records from a sediment core collected in a coastal wetland along the middle reaches of the Ussuri River, the local fire history and carbon accumulation patterns were reconstructed for the past 5000 years. Results indicate that fire intensity [...] Read more.

Using high-resolution charcoal and TOC records from a sediment core collected in a coastal wetland along the middle reaches of the Ussuri River, the local fire history and carbon accumulation patterns were reconstructed for the past 5000 years. Results indicate that fire intensity remained relatively low and stable from 5000 to 1500 cal. yr BP, after which it increased markedly. This trend intensified over the past 400 years, likely due to rapid population growth and heightened anthropogenic disturbance. Regional fire frequency averaged approximately 3.1 fires per 1500 years, with notable peaks during 5000–4600 cal. yr BP, 3400–2400 cal. yr BP, and 1500 cal. yr BP to present. These high-fire intervals correspond closely to regional warm and dry climatic conditions, underscoring the strong influence of climate variability on fire activity. Carbon accumulation rates also showed a significant increase, rising from 0.11 g·kg⁻¹·a⁻¹ around 5000 years ago to 1.60 g·kg⁻¹·a⁻¹ in recent centuries. Importantly, a significant positive correlation was observed between fire regimes and carbon accumulation rates, suggesting that fires have potentially played a key role in enhancing long-term carbon sequestration in wetlands of this region. These findings highlight the complex interplay between fire, climate, and carbon dynamics in wetland ecosystems. Full article

(This article belongs to the Special Issue The Evolution of Climate and Environment in the Holocene)

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