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23 pages, 3214 KB  
Article
Enhanced GNSS Navigation Using a Centered Error Entropy Extended Kalman Filter in Non-Gaussian Noise Environments
by Yi Chang, Dah-Jing Jwo and Bo-Yang Lee
Sensors 2026, 26(4), 1148; https://doi.org/10.3390/s26041148 (registering DOI) - 10 Feb 2026
Abstract
Global Navigation Satellite Systems (GNSSs) observables, such as those of the Global Positioning System (GPS), are frequently affected by multipath effects that cause unpredictable signal interference at the receiver, posing significant challenges for accurate state estimation in complex environments with non-Gaussian noise or [...] Read more.
Global Navigation Satellite Systems (GNSSs) observables, such as those of the Global Positioning System (GPS), are frequently affected by multipath effects that cause unpredictable signal interference at the receiver, posing significant challenges for accurate state estimation in complex environments with non-Gaussian noise or outliers. The traditional extended Kalman filter (EKF), based on the minimum mean square error (MMSE) criterion, assumes Gaussian noise distributions and exhibits degraded performance under non-Gaussian conditions. To overcome this limitation, the minimum error entropy (MEE) criterion was proposed to reduce random uncertainty in estimation error distributions; however, due to its translation invariance property, MEE may inadvertently increase bias when errors contain systematic offsets, leading to poor convergence. In contrast, the maximum correntropy criterion (MCC) concentrates the error probability density function (PDF) around zero, enabling effective entropy adjustment even in the presence of bias and achieving superior error convergence. This paper presents the centered error entropy (CEE) extended Kalman filter (CEE-EKF) that integrates the complementary merits of both MEE and MCC approaches to overcome their individual limitations. Experimental validation in complex nonlinear GPS environments with non-Gaussian noise demonstrates that the CEE-EKF significantly outperforms individual algorithms in noise suppression, particularly exhibiting enhanced robustness and accuracy when handling outliers. These results offer an effective approach to enhancing the reliability of GPS navigation in challenging real-world environments, and the algorithm can be readily extended to other GNSS applications. Full article
(This article belongs to the Special Issue UAV and Sensor Applications for Navigation and Positioning—2nd Edition)
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43 pages, 7304 KB  
Article
miRNA-Based Breast Cancer Subtyping Using AHALA Multi-Stage Classification Approach
by Mohammed Qaraad, Eric P. Rahrmann and David Guinovart
Cancers 2026, 18(4), 586; https://doi.org/10.3390/cancers18040586 - 10 Feb 2026
Abstract
Background: Breast cancers are heterogeneous in nature, including many molecular subtypes, each displaying varying characteristics in clinical outcomes as well as in responses to treatments. Subtyping requires absolute precision for the application of precision medicine; however, this is not an easy task, given [...] Read more.
Background: Breast cancers are heterogeneous in nature, including many molecular subtypes, each displaying varying characteristics in clinical outcomes as well as in responses to treatments. Subtyping requires absolute precision for the application of precision medicine; however, this is not an easy task, given the dimensionality as well as noise in miRNA expression profiles. Even though miRNAs display potential as a biological marker for subtyping breast cancers, feature selection and optimizing learning algorithms would help harness their potential as a diagnostic tool. Methods: We propose the Adaptive Hill Climbing Artificial Lemming Algorithm (AHALA), a hybrid optimization framework that integrates the global search capability of the Artificial Lemming Algorithm with an adaptive hill-climbing local search strategy. Low-variance filtering and differential gene expression analysis were first applied to reduce dimensionality and enhance biological relevance. AHALA was then used to optimize deep neural network hyperparameters for miRNA-based multi-class breast cancer subtype classification. The method was validated using TCGA breast cancer miRNA expression data and benchmarked against state-of-the-art optimization algorithms using the CEC2021 test suite. Results: AHALA had a high classification performance measure for each type of breast cancer with a mean accuracy of 95.74%, precision of 95.98%, recall of 95.74%, F1 measure of 95.74%, and AUC value of 0.9682. The new algorithm had superior convergence and significance compared with other optimization algorithms. Feature selection revealed miRNAs that belong to each subtype, such as hsa-miR-190b, hsa-miR-429, hsa-miR-505-3p, hsa-miR-3614-5p, and hsa-miR-935. Conclusions: The AHALA framework offers a potent and efficient method of performing miRNA-based subtyping of breast cancer that integrates global exploration and local search to its advantage. Its high level of classification, stability, and ability to identify biologically important biomarkers mark this method as promising. Full article
(This article belongs to the Section Cancer Pathophysiology)
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19 pages, 6210 KB  
Article
Clusterin Promotes the Migration and Invasion of Highly Aggressive Breast Cancer Cells Through Molecular Mechanisms That Affect the Cell Cytoskeleton and Extracellular Matrix Dynamics
by Alessia Ciringione, Marina Marozzi, Silvana Belletti, Margot Lo Pinto, Simone Dario Scilabra, Patrizia Cancemi and Federica Rizzi
Int. J. Mol. Sci. 2026, 27(4), 1721; https://doi.org/10.3390/ijms27041721 - 10 Feb 2026
Abstract
Metastatic breast cancer (BC) remains a major clinical challenge, and identifying molecular mechanisms driving tumor cell migration and invasion is critical to develop effective therapeutic strategies. Clusterin (CLU), a secreted chaperone-like protein, is upregulated in BC and metastatic tissue; however, its functional contribution [...] Read more.
Metastatic breast cancer (BC) remains a major clinical challenge, and identifying molecular mechanisms driving tumor cell migration and invasion is critical to develop effective therapeutic strategies. Clusterin (CLU), a secreted chaperone-like protein, is upregulated in BC and metastatic tissue; however, its functional contribution to tumor aggressiveness remains unclear. Here, we silenced CLU by siRNA in two BC cell lines with distinct aggressiveness and examined its impact on migration, invasion, and associated signaling pathways. Following CLU silencing, cell migration and invasion were assessed using transwell assays. Cytoskeletal organization was evaluated by F-actin staining, while downstream signaling pathways were analyzed by RT-PCR, Western blotting, and Rho GTPase pull-down. A comparative proteomic analysis was performed in CLU-expressing and CLU-silenced MDA-MB-231 cells. CLU knockdown significantly reduced migration and invasion in MDA-MB-231, concomitantly with loss of F-actin-rich membrane protrusions, reduced expression of MMP9, COL1A1, and COL4A1, and decreased activation of Akt, NF-κB, and RhoA. Proteomic profiling revealed extensive remodeling of pathways involved in cell adhesion, cytoskeletal dynamics, and extracellular matrix interactions. Differently, no or very mild effects were observed in CLU-silenced MCF-7 cells. These findings identify CLU as an upstream regulator of a pro-metastatic adhesion–cytoskeleton signaling in BC, selectively operative in EMT-engaged, basal-like cells, highlighting the importance of patient stratification for CLU-targeted therapeutic strategies. Full article
(This article belongs to the Special Issue Advances and Mechanisms in Breast Cancer—2nd Edition)
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38 pages, 17461 KB  
Review
Nanotechnology Revolutionizing Food Processing Technology
by Zhifei Gou, Weiyun Guo, Ting Du, Sijie Liu, Yuechun Li, Jianlong Wang, Wentao Zhang and Jihong Huang
Foods 2026, 15(4), 643; https://doi.org/10.3390/foods15040643 - 10 Feb 2026
Abstract
Owing to population expansion, widespread diseases and pandemics, climate alterations, and evolving consumer preferences, the optimization of production processes and technological advancements in food processing have become imperative. The integration of nanotechnology with food processing technology, characterized by numerous advantages, holds the promise [...] Read more.
Owing to population expansion, widespread diseases and pandemics, climate alterations, and evolving consumer preferences, the optimization of production processes and technological advancements in food processing have become imperative. The integration of nanotechnology with food processing technology, characterized by numerous advantages, holds the promise to establish a secure, efficient, and sustainable food supply system. Nanoparticles can mitigate the risk of microbial contamination through the generation of reactive oxygen species and by leveraging their electrical charge properties to exert antibacterial effects or detoxify; they can serve as an energy transfer medium to enhance food quality; or utilize its high catalytic efficiency for the recycling and decomposition of food waste. When integrated with food processing technologies, they demonstrate a synergistic or additive effect. This paper reviews representative instances of the convergence between nanotechnology and food processing technologies, elucidates the practical application effects and underlying mechanisms, aims to inform the development of more advantageous application strategies for nanotechnology in the realm of food processing. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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Graphical abstract

23 pages, 1536 KB  
Article
Optimal Control of a Genotype-Structured Prey–Predator Model: Strategies for Ecological Rescue and Oscillatory Dynamics Restoration
by Preet Mishra, Shyam Kumar, Sorokhaibam Cha Captain Vyom and R. K. Brojen Singh
AppliedMath 2026, 6(2), 29; https://doi.org/10.3390/appliedmath6020029 - 10 Feb 2026
Abstract
Evolutionary changes can significantly impact interactions among populations and disrupt ecosystems by driving extinctions or collapsing population oscillations, posing substantial challenges to biodiversity conservation. This study addresses the ecological rescue of a predator population threatened by a mutant prey population using the optimal [...] Read more.
Evolutionary changes can significantly impact interactions among populations and disrupt ecosystems by driving extinctions or collapsing population oscillations, posing substantial challenges to biodiversity conservation. This study addresses the ecological rescue of a predator population threatened by a mutant prey population using the optimal control method. To study this, we study a model that incorporates a genotypically structured prey population comprising wild-type, heterozygous, and mutant prey types, as well as the predator population. We prove that this model has both local and global existence and uniqueness of solutions, ensuring the model’s robustness. Then, we applied the optimal control method, incorporating Pontryagin’s Maximum Principle, to introduce a control input into the model and minimize the mutant population, thereby stabilizing the ecosystem. We utilize a reproduction number and a control efficacy measure to numerically demonstrate that the undesired dynamics of the model can be controlled, leading to the suppression of the mutant and the restoration of the oscillatory dynamics of the system. These findings demonstrate the applicability of optimal control strategies and provide a mathematical framework for managing such ecological disruptions. Full article
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28 pages, 1110 KB  
Review
Application of Prenatal Whole Exome Sequencing for Congenital Heart Anomalies
by Threebhorn Kamlungkuea, Fuanglada Tongprasert, Duangrurdee Wattanasirichaigoon, Sirinart Kumfu, Siriporn C. Chattipakorn, Nipon Chattipakorn and Theera Tongsong
Int. J. Mol. Sci. 2026, 27(4), 1720; https://doi.org/10.3390/ijms27041720 - 10 Feb 2026
Abstract
 Congenital heart disease (CHD) is the most common congenital anomaly worldwide and poses significant diagnostic challenges due to its structural complexity and frequent association with extracardiac anomalies and genetic abnormalities. While conventional tests such as karyotyping, quantitative fluorescent polymerase chain reaction (QF-PCR), [...] Read more.
 Congenital heart disease (CHD) is the most common congenital anomaly worldwide and poses significant diagnostic challenges due to its structural complexity and frequent association with extracardiac anomalies and genetic abnormalities. While conventional tests such as karyotyping, quantitative fluorescent polymerase chain reaction (QF-PCR), and chromosomal microarray analysis (CMA) are standard first-tier investigations, many cases remain genetically unexplained. Prenatal whole exome sequencing (WES) has emerged as a valuable tool to detect pathogenic single gene variants underlying CHD. This narrative review synthesizes findings from 28 studies involving over 2000 WES-tested fetuses and more than 10,000 CHD cases. The additional diagnostic yield of WES over CMA ranged from 8.0% to 66.7%, with higher yields in syndromic or non-isolated CHD (10–50%) compared to isolated cases (7.1–27.8%). Trio-based WES outperformed proband-only sequencing by improving accuracy, reducing turnaround time, and lowering the rate of variant of uncertain significance (VUS). Prenatal WES not only clarifies genetic etiology but also reveals syndromic diagnoses, allowing CHD to be interpreted within broader multisystem contexts. Integration of phenotypic and genomic data enhances prenatal counseling, prognostication, delivery planning, and postnatal care—advancing precision medicine in fetal cardiology.  Full article
(This article belongs to the Section Molecular Genetics and Genomics)
27 pages, 768 KB  
Systematic Review
Sexual Violence Against Mental Health Nurses in Inpatient Psychiatric Settings: A Systematic Review of Prevalence, Outcomes, and Risk Factors
by Giuliano Anastasi, Marika Lo Monaco, Mariachiara Figura, Daniela D’Amico, Emanuele Amodio, Alessandro Stievano, Ippolito Notarnicola and Roberto Latina
Nurs. Rep. 2026, 16(2), 59; https://doi.org/10.3390/nursrep16020059 - 10 Feb 2026
Abstract
Background/Objectives: Workplace violence (WPV) is a major occupational concern in psychiatric settings, where mental health nurses (MHNs) are consistently identified as a high-risk professional group. Within this context, sexual violence (SV) remains understudied as a distinct phenomenon and is often embedded within [...] Read more.
Background/Objectives: Workplace violence (WPV) is a major occupational concern in psychiatric settings, where mental health nurses (MHNs) are consistently identified as a high-risk professional group. Within this context, sexual violence (SV) remains understudied as a distinct phenomenon and is often embedded within aggregated measures of WPV. This systematic review aimed to synthesize the available evidence on SV against MHNs working in inpatient settings by: (1) describing its prevalence, forms, and characteristics; (2) examining psychological, occupational, and physical outcomes; and (3) identifying associated risk factors. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251103606). A literature search was performed across PubMed, CINAHL, Scopus, Web of Science, and PsycInfo, supplemented by reference list checking and citation tracking. Peer-reviewed quantitative and qualitative studies published in English or Italian were eligible if they involved MHNs working in inpatient settings and addressed SV. Study selection, data extraction, and risk-of-bias assessment were conducted independently by two reviewers. A narrative synthesis following SWiM guidance was undertaken, and the certainty of evidence for statistically significant outcomes was assessed using the GRADE approach. Results: Twenty-five studies published between 2003 and 2025 were included. Definitions of SV varied substantially. Reported prevalence ranged from 0% to 68%, with verbal sexual harassment ranging from 19.5% to 53.4%, physical sexual harassment ranging from 14% to 42.9%, and sexual assault up to 18.6%. Evidence indicated associations between SV exposure and poorer quality of life, burnout, and days lost from work. The main risk factors included gender, age, education, work experience, employment type, acute psychiatric settings, night shifts, normalization of violence, and history of physical and sexual violence. Conclusions: SV against MHNs represents a relevant issue in psychiatric settings. Findings suggest significant psychological and occupational consequences. Standardized definitions and measurement, longitudinal research, and intervention studies are needed to inform effective prevention strategies and organizational responses. Full article
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21 pages, 1400 KB  
Article
A Geological Modeling Workflow for Shale Reservoirs: A Case Study of the F2 Member in the Qintong Sag
by Maozhou Han, Siyu Yu, Shaohua Li, Changsheng Lu, Chijun Huang, Kailong Wei and Shengze Li
Appl. Sci. 2026, 16(4), 1759; https://doi.org/10.3390/app16041759 - 10 Feb 2026
Abstract
Shale reservoirs provide critical storage space for unconventional oil and gas, yet their frequent vertical facies alternations and complex spatial architectures make it difficult for conventional two-point geostatistical methods to reproduce thin interbedding and reservoir-scale continuity. Multiple-point geostatistics can incorporate structural information through [...] Read more.
Shale reservoirs provide critical storage space for unconventional oil and gas, yet their frequent vertical facies alternations and complex spatial architectures make it difficult for conventional two-point geostatistical methods to reproduce thin interbedding and reservoir-scale continuity. Multiple-point geostatistics can incorporate structural information through training images (TIs), but practical 3D shale modeling is often hindered by the limited availability of representative 3D TIs. Using the F2 Member in the Qintong Sag, Subei Basin, eastern China, as a case study, we propose a hierarchical 2D-to-3D geological modeling workflow that combines mixed-point geostatistical simulation (MIXSIM) for generating vertical 2D facies sections and a sequential 2D simulation strategy with conditioning data (s2Dcd) for propagating section-based patterns into 3D space under hard well constraints. In the workflow, vertical sections serve as TI carriers to explicitly capture bedding-scale alternations, while well data are imposed as hard conditioning information during 3D simulation. Quantitative evaluation is performed in terms of (i) conditioning-data consistency, (ii) vertical facies-transition statistics quantified by transition counts and Markov transition probability matrices, (iii) global facies proportions summarized as the mean of 10 realizations, and (iv) connectivity characterized by connected geobody analysis. The realizations honor the conditioning data exactly, reproduce vertical transition behavior with a transition-matrix discrepancy of DMAE = 0.0396, and maintain global facies proportions close to well-based estimates with a maximum deviation of 2.36%. These results demonstrate that the proposed MIXSIM–s2Dcd workflow provides a practical solution for well-data-driven, high-resolution 3D shale facies modeling when 3D training images are unavailable. Full article
(This article belongs to the Section Earth Sciences)
16 pages, 4404 KB  
Article
Taming the Fine Particulate–Mortality Curve
by Richard Thomas Burnett
Atmosphere 2026, 17(2), 185; https://doi.org/10.3390/atmos17020185 - 10 Feb 2026
Abstract
Estimating the population-level mortality burden attributable to exposure to outdoor fine articulate matter (PM2.5) requires characterizing both the magnitude and shape of the relative risk function that mathematically models how exposure affects response. The relationship can be derived using cohort [...] Read more.
Estimating the population-level mortality burden attributable to exposure to outdoor fine articulate matter (PM2.5) requires characterizing both the magnitude and shape of the relative risk function that mathematically models how exposure affects response. The relationship can be derived using cohort studies where the association between (PM2.5) exposure and mortality is directly observed. As policy issues of interest can involve exposures that exceed by several-fold those observed in cohort studies, how the association is extrapolated to these high concentrations is a major source of uncertainty. To address this issue, we suggest the extrapolation criterium that the estimated proportion of deaths attributed to exposure above the observed cohort exposure range is no more than that below the range. This criterion implies that the relative risk function must be bounded from above with marginal changes in risk, rapidly declining with increasing concentration. We illustrated the approach with the use of meta-data from 44 cohorts conducted in North America, Western Europe, Asia, and China, examining the association between long-term exposure to outdoor (PM2.5) and non-accidental mortality. Full article
(This article belongs to the Section Air Quality and Health)
33 pages, 744 KB  
Article
XAI-Driven Malware Detection from Memory Artifacts: An Alert-Driven AI Framework with TabNet and Ensemble Classification
by Aristeidis Mystakidis, Grigorios Kalogiannnis, Nikolaos Vakakis, Nikolaos Altanis, Konstantina Milousi, Iason Somarakis, Gabriela Mihalachi, Mariana S. Mazi, Dimitris Sotos, Antonis Voulgaridis, Christos Tjortjis, Konstantinos Votis and Dimitrios Tzovaras
AI 2026, 7(2), 66; https://doi.org/10.3390/ai7020066 - 10 Feb 2026
Abstract
Modern malware presents significant challenges to traditional detection methods, often leveraging fileless techniques, in-memory execution, and process injection to evade antivirus and signature-based systems. To address these challenges, alert-driven memory forensics has emerged as a critical capability for uncovering stealthy, persistent, and zero-day [...] Read more.
Modern malware presents significant challenges to traditional detection methods, often leveraging fileless techniques, in-memory execution, and process injection to evade antivirus and signature-based systems. To address these challenges, alert-driven memory forensics has emerged as a critical capability for uncovering stealthy, persistent, and zero-day threats. This study presents a two-stage host-based malware detection framework, that integrates memory forensics, explainable machine learning, and ensemble classification, designed as a post-alert asynchronous SOC workflow balancing forensic depth and operational efficiency. Utilizing the MemMal-D2024 dataset—comprising rich memory forensic artifacts from Windows systems infected with malware samples whose creation metadata spans 2006–2021—the system performs malware detection, using features extracted from volatile memory. In the first stage, an Attentive and Interpretable Learning for structured Tabular data (TabNet) model is used for binary classification (benign vs. malware), leveraging its sequential attention mechanism and built-in explainability. In the second stage, a Voting Classifier ensemble, composed of Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting (XGB), and Histogram Gradient Boosting (HGB) models, is used to identify the specific malware family (Trojan, Ransomware, Spyware). To reduce memory dump extraction and analysis time without compromising detection performance, only a curated subset of 24 memory features—operationally selected to reduce acquisition/extraction time and validated via redundancy inspection, model explainability (SHAP/TabNet), and training data correlation analysis —was used during training and runtime, identifying the best trade-off between memory analysis and detection accuracy. The pipeline, which is triggered from host-based Wazuh Security Information and Event Management (SIEM) alerts, achieved 99.97% accuracy in binary detection and 70.17% multiclass accuracy, resulting in an overall performance of 87.02%, including both global and local explainability, ensuring operational transparency and forensic interpretability. This approach provides an efficient and interpretable detection solution used in combination with conventional security tools as an extra layer of defense suitable for modern threat landscapes. Full article
28 pages, 1152 KB  
Article
Recycling-Based STEM Education for Sustainability: Effects on Secondary School Students’ STEAM Attitudes, Recycling Behaviours and Design Thinking Skills
by Akın Karakuyu and Burcu Karafil
Sustainability 2026, 18(4), 1820; https://doi.org/10.3390/su18041820 - 10 Feb 2026
Abstract
This study examines the associations between participation in recycling-based STEM activities and secondary school students’ STEAM attitudes, recycling-related behaviours, and design thinking skills. A nested mixed-methods design was employed. The quantitative part used a one-group pre-test–post-test experimental design with 32 students, while the [...] Read more.
This study examines the associations between participation in recycling-based STEM activities and secondary school students’ STEAM attitudes, recycling-related behaviours, and design thinking skills. A nested mixed-methods design was employed. The quantitative part used a one-group pre-test–post-test experimental design with 32 students, while the qualitative part included semi-structured interviews with 7 students selected through criterion sampling. Data were collected using a STEAM attitude scale, an attitude towards recycling scale, a design thinking scale and an interview form. Paired-samples t-tests were conducted for quantitative analyses, and the interview data were examined using content analysis. Statistically significant increases were observed from pre-test to post-test in students’ STEAM attitudes, recycling-related behaviours, and design thinking skills following participation in the recycling-based STEM activities. Qualitative findings indicated that students described coping with challenges in the design process by using problem-solving strategies and collaborating with peers. They also reported perceived increases in self-efficacy, creativity, and understanding of interdisciplinary (STEM) approaches. In addition, students reported greater awareness and described changes in recycling-related behaviours. Overall, the findings suggest that integrating recycling into STEM education may be associated with sustainability-oriented behaviours and higher-order thinking skills among secondary school students. Full article
(This article belongs to the Section Sustainable Education and Approaches)
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25 pages, 1687 KB  
Review
Unraveling GDAP1: Bridging Mitochondrial Biology and Peripheral Neuropathy
by Lara Cantarero, Janet Hoenicka and Francesc Palau
Biomolecules 2026, 16(2), 280; https://doi.org/10.3390/biom16020280 - 10 Feb 2026
Abstract
The mitochondrial outer membrane (OMM) plays a crucial role in maintaining cellular homeostasis by regulating mitochondrial dynamics, organelle interactions, and stress responses. In peripheral neurons—cells with high metabolic demands and long axons—the OMM acts as a vital platform for coordinating bioenergetics, calcium signaling, [...] Read more.
The mitochondrial outer membrane (OMM) plays a crucial role in maintaining cellular homeostasis by regulating mitochondrial dynamics, organelle interactions, and stress responses. In peripheral neurons—cells with high metabolic demands and long axons—the OMM acts as a vital platform for coordinating bioenergetics, calcium signaling, and redox balance. Ganglioside-induced differentiation-associated protein 1 (GDAP1), an OMM-anchored protein, has emerged as a key regulator of mitochondrial fission and transport, redox homeostasis, and mitochondrial membrane contact sites (MCSs). Genetic variants in GDAP1 cause Charcot–Marie–Tooth disease (CMT), emphasizing its essential role in peripheral nerve function. This review highlights the multifaceted functions of GDAP1 in neuronal physiology and as a model protein that integrates organelle communication and mitochondrial biology. We further discuss how GDAP1 dysfunction leads to structural and functional impairments in peripheral neurons, proposing the OMM and its microenvironment as critical targets for therapeutic intervention in inherited neuropathies. Full article
(This article belongs to the Special Issue Mitochondrial Proteins: From Import and Biogenesis to Complex Functional Network)
17 pages, 549 KB  
Article
Economic Globalization and Environmental Technology: Implications for Environmental Degradation in G7 Countries
by Mehdi Seraj
Sustainability 2026, 18(4), 1819; https://doi.org/10.3390/su18041819 - 10 Feb 2026
Abstract
The global ecological conditions are degrading rapidly, and even after the commitment to achieve Sustainable Development Goals by 2030, G7 countries still struggle to meet basic environmental standards. This study offers a novel marginal contribution by analyzing how economic globalization (EG), economic growth [...] Read more.
The global ecological conditions are degrading rapidly, and even after the commitment to achieve Sustainable Development Goals by 2030, G7 countries still struggle to meet basic environmental standards. This study offers a novel marginal contribution by analyzing how economic globalization (EG), economic growth intensity (EGI), financial innovation (FI), and environmental technology (ET) influenced environmental degradation (ED) in the G7 countries from 2000 to 2021. Using the Method of Moments Quantile Regression (MMQR) alongside Driscoll and Kraay Standard Error (DKSE), this research provides a first-of-its-kind distributional mapping that accounts for cross-sectional dependency and heterogeneous slopes. Crucially, the findings reveal a “decoupling failure” in advanced economies, where the existing treatment mechanism for ET is insufficient to separate industrial growth from emissions due to institutional discrepancies. While FI is often viewed as a green catalyst, this study identifies it as a “double-edged sword,” showing that it significantly increases environmental degradation in higher quantiles due to carbon-intensive global supply chains. Conversely, EGI is discovered to be mitigatory, suggesting that enhancing financial efficiency and growth soundness can diminish ecological damage. This research fills a critical literature gap by reconciling the Pollution Haven Hypothesis and Green Finance Theory, providing empirical evidence that developed financial systems may inadvertently exacerbate damage if not specifically aligned with green mandates. Full article
(This article belongs to the Special Issue Innovation and Strategic Management in Business)
15 pages, 394 KB  
Article
Staying Despite the Intention to Leave: Insights from Frontline Nurses and Nurse Managers from a Qualitative Descriptive Study
by Martina Falomo, Stefania Chiappinotto, Giovanni Napoli, Anna Inserra, Maura Mesaglio and Alvisa Palese
Nurs. Rep. 2026, 16(2), 58; https://doi.org/10.3390/nursrep16020058 - 10 Feb 2026
Abstract
Background/Objectives: The global nursing workforce shortage has heightened concerns about burnout, workload, and nurse retention, with an increasing intention to leave the profession and the unit, especially in the post-pandemic context. Although intention to leave has been widely studied, limited attention has been [...] Read more.
Background/Objectives: The global nursing workforce shortage has heightened concerns about burnout, workload, and nurse retention, with an increasing intention to leave the profession and the unit, especially in the post-pandemic context. Although intention to leave has been widely studied, limited attention has been paid to nurses who continue to provide high-quality care and persist despite expressing a desire to leave. This study aimed to explore the reasons for persistence among nurses who intend to leave the organization and the profession. Methods: A descriptive qualitative study was conducted involving frontline nurses and nurse managers working in a large university healthcare trust in Northern Italy. Data were collected through three focus groups, using a semi-structured interview, until data saturation was achieved. Data were analyzed using inductive content analysis. Findings were reported in accordance with COnsolidated criteria for REporting Qualitative research guidelines. Results: Thirty-two participants were included. Overall, two main themes emerged: ‘Reasons that are inside of me’ and ‘Reasons that are outside of me but influence my decisions to stay’, with eight and six subthemes respectively. Internal reasons included professional passion, commitment, autonomy, perceived usefulness, and supportive collegial relationships. External reasons included organizational flexibility, opportunities for internal mobility and professional development, responsiveness to nurses’ expectations, and, in some cases, limited external employment alternatives. Conclusions: Persistence represents a distinct and underexplored dimension within the intention-to-leave continuum. While internal reasons reflect deeply rooted professional identity, external organizational reasons are modifiable and play a critical role in promoting retention. Organizational strategies aligned with nurses’ values, expectations, and professional development needs may enhance workforce stability and inform more targeted retention interventions. Full article
(This article belongs to the Special Issue Transforming Healthcare Delivery: Advances in Organizational Models of Nursing Care)
22 pages, 3072 KB  
Article
Research on the Mechanisms and Influencing Factors of Sediment Accumulation in Mountain Tunnel Drainage Trenches
by Yichen Peng, Jinhui Jing, Yimin Wu, Shuai Yang, Haiping Wu, Yangqi Xiang, Delei Jing and Hongshan Yin
Appl. Sci. 2026, 16(4), 1758; https://doi.org/10.3390/app16041758 - 10 Feb 2026
Abstract
Sediment accumulation in the drainage systems of mountain tunnels is a typical issue threatening operational safety. To explore the sedimentation behavior under the coupling of multiple factors, this study systematically analyzes the coupled effects of sediment content, flow rate, slope, and cross-sectional shape [...] Read more.
Sediment accumulation in the drainage systems of mountain tunnels is a typical issue threatening operational safety. To explore the sedimentation behavior under the coupling of multiple factors, this study systematically analyzes the coupled effects of sediment content, flow rate, slope, and cross-sectional shape on sedimentation through full-scale experiments and numerical simulations. The results indicate that: (1) the sediment accumulation is linearly positively correlated with sediment concentration (fitting slope of 0.87) and exponentially negatively correlated with flow rate and slope (R2 > 0.90); (2) for drainage trenches with different cross-sectional shapes under the same boundary conditions, the maximum flow velocity and anti-sedimentation capacity rank as narrow rectangular > semi-circular ≈ inverted trapezoidal > rectangular; (3) the study proposes engineering anti-sedimentation strategies, such as moderately increasing the slope and adopting a periodic concentrated discharge model to enhance sediment transport capacity using peak flow; (4) under the premise of meeting drainage and flood control standards, the inverted trapezoidal or semi-circular cross-sections are preferred. The bottom waterway width can be reduced to increase flow velocity, thereby achieving a synergistic optimization of drainage efficiency and operational reliability. This provides a quantitative basis for the structural selection and anti-sedimentation design of tunnel drainage systems. Full article
(This article belongs to the Special Issue Tunnel Construction and Underground Engineering)
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23 pages, 8836 KB  
Article
Driving Mechanisms and Multi-Scenario Simulation of Spatiotemporal Evolution in Habitat Quality Within Core Region of Anyang, Yellow River Basin
by Junfeng Zhang and Yue Shen
Sustainability 2026, 18(4), 1818; https://doi.org/10.3390/su18041818 - 10 Feb 2026
Abstract
In the context of intensifying industrial emissions and rapid urban sprawl, balancing environmental sustainability and economic expansion has grown more pronounced in Anyang. Drawing on land-use data spanning the years 2000, 2010, and 2020, this research employed the InVEST model to examine the [...] Read more.
In the context of intensifying industrial emissions and rapid urban sprawl, balancing environmental sustainability and economic expansion has grown more pronounced in Anyang. Drawing on land-use data spanning the years 2000, 2010, and 2020, this research employed the InVEST model to examine the spatiotemporal dynamics of habitat quality across the region. To uncover key influencing factors, this study integrated the GeoDetector method, while future habitat trends were projected to 2030 using a coupled intPLUS-InVEST simulation framework. The analysis revealed that over the past two decades, habitat quality remained consistently low, displaying a geographic gradient associated with elevated levels in western mountainous zones, along with lower levels across eastern plains. The evolution of habitat conditions appears to result from the intricate interdependencies between environmental variables, anthropogenic pressures, and industrial expansion. Projections in various development scenarios point to stark contrasts in future habitat outcomes: notably, the scenario combining industrial transformation with ecological rehabilitation fosters moderate gains and stabilizes declining trends in habitat quality. These insights emphasize the urgency of implementing robust policy mechanisms and spatially nuanced land-use strategies to facilitate Anyang’s ecological transition and ensure long-term regional sustainability. Full article
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26 pages, 3971 KB  
Article
Short-Term Forecasting of the Total Power Generation from Wind Farms and Solar Power Plants in the National Power System Using Advanced Ensemble Machine Learning Models
by Paweł Piotrowski
Energies 2026, 19(4), 930; https://doi.org/10.3390/en19040930 - 10 Feb 2026
Abstract
The introduction of the article presents the state of renewable energy development in Poland and statistical information on its dynamics in the context of sustainable development, highlighting both the positive aspects of this situation and the potential risks to the national power system. [...] Read more.
The introduction of the article presents the state of renewable energy development in Poland and statistical information on its dynamics in the context of sustainable development, highlighting both the positive aspects of this situation and the potential risks to the national power system. These risks stem from the inherent instability of renewable energy generation and the seasonal variability of production. The main part of the article provides a comprehensive statistical analysis of time series data (wind energy generation and solar energy generation) aimed at identifying the appropriate input variables for forecasting models. In addition to the two time series of electricity generation, other exogenous variables and feature engineering techniques were incorporated. In the forecasting section, short-term forecasts of energy generation in the national power system from wind farms and solar power plants were developed. The forecasts for both types of renewable energy sources (RESs) were conducted separately and then integrated into a single time series to assess which forecasting approach is more effective. A detailed analysis was carried out to determine the optimal hyperparameters for individual machine learning models. Subsequently, an ensemble model was developed, integrating multiple single models. The article concludes with final insights and practical recommendations regarding the selection of preferred models and input variables that ensure the highest forecast accuracy. Additionally, potential future developments of the models and further research in this field are discussed in the context of sustainable development. Full article
(This article belongs to the Special Issue Combined Application of Solar Power Generation and Energy Storage Technology)
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21 pages, 2021 KB  
Article
The SMART‒P Algorithm for Aerosol and Ocean Properties Part 1: Algorithm Theoretical Basis and Expected Accuracy of PolCube Aerosol Products
by Subin Lee, Ukkyo Jeong, Hyunkwang Lim, Robert J. D. Spurr and Youn-Chul Ryu
Remote Sens. 2026, 18(4), 560; https://doi.org/10.3390/rs18040560 - 10 Feb 2026
Abstract
The SMART–P (Spectral Measurements for Atmospheric Radiative Transfer–Polarimeter) algorithm was developed to retrieve aerosol and ocean parameters from PolCube measurements. The PolCube is a multi-angular polarimeter (MAP) aboard the BusanSat-B CubeSat scheduled for launch in 2026, which measures polarized radiances at 410, 555, [...] Read more.
The SMART–P (Spectral Measurements for Atmospheric Radiative Transfer–Polarimeter) algorithm was developed to retrieve aerosol and ocean parameters from PolCube measurements. The PolCube is a multi-angular polarimeter (MAP) aboard the BusanSat-B CubeSat scheduled for launch in 2026, which measures polarized radiances at 410, 555, 670, and 865 nm from four viewing angles. This study presents the theoretical basis of the algorithm and conducts a sensitivity analysis of aerosol inversions over the ocean processed by SMART-P under the expected measurement conditions for PolCube observations. The results indicate that the degree of linear polarization (DoLP) significantly increases the information content of the real part of the refractive index and of the fine-mode particle-size parameters relative to radiance-only measurements. Enhanced measurement sensitivity enables more accurate retrieval of fine-dominated aerosol properties, such as smoke and sulfate. The sensitivity analysis also shows that the ocean surface reflectivity is the most critical forward-model parameter affecting aerosol-property retrievals. The SMART-P algorithm will support the BusanSat-B mission to understand the role of aerosol particles in the climate system and air quality. Full article
(This article belongs to the Special Issue Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition))
11 pages, 612 KB  
Article
Prognostic Value of Charlson Comorbidity Index in Patients with COVID-19
by Iliyan Todorov, Margarita Gospodinova and Kalina Stoyanova
Trop. Med. Infect. Dis. 2026, 11(2), 49; https://doi.org/10.3390/tropicalmed11020049 - 10 Feb 2026
Abstract
COVID-19, caused by SARS-CoV-2, is a highly contagious disease with variable clinical presentation. Severe forms are more common in patients with pre-existing chronic conditions. The objective of this study is to evaluate the prognostic value of the Charlson Comorbidity Index (CCI) in relation [...] Read more.
COVID-19, caused by SARS-CoV-2, is a highly contagious disease with variable clinical presentation. Severe forms are more common in patients with pre-existing chronic conditions. The objective of this study is to evaluate the prognostic value of the Charlson Comorbidity Index (CCI) in relation to disease severity and outcome in hospitalized COVID-19 patients with comorbidities. A retrospective analysis was conducted on 558 patients, hospitalized at the Infectious Diseases Clinic of “St. Marina” University Hospital, Varna, Bulgaria, between March 2020 and March 2021. CCI score was calculated to estimate 10-year survival probabilities. Prevalent comorbidities were arterial hypertension (78.55%), type 2 diabetes (16.09%), and ischemic heart disease (5.82%). A higher number of comorbidities was associated with increased rates of bilateral pulmonary consolidation (χ2 = 6.63, p = 0.010), oxygen therapy needs (χ2 = 5.41, p = 0.020), and mortality (χ2 = 7.88, p = 0.005). Patients with higher CCI scores had worse outcomes. A CCI score above 5 was common among non-survivors and those with pulmonary consolidation and respiratory failure. The findings confirm that advanced age and multiple comorbidities are strong predictors of poor COVID-19 prognosis. Early CCI calculation at hospital admission would help identify high-risk patients and support timely, targeted medical interventions. Full article
(This article belongs to the Special Issue Post-Pandemic Challenges: Endemic COVID-19, Vaccine Hesitancy, and Viral Resurgence)
18 pages, 5114 KB  
Article
Therapeutic Window of Morphine on Cardiac and Respiratory Parameters of Juvenile Tambaqui, Colossoma macropomum
by Brenda Maria Pereira Alho da Costa, Joelson da Silva Farias, Rodrigo Gonçalves dos Santos, Clarissa Araújo da Paz, Luana Vasconcelos de Souza, Luciana Eiró Quirino, Murilo Farias dos Santos, Marcelo Victor dos Santos Brito, Marcelo Ferreira Torres, Moisés Hamoy and Luis André Luz Barbas
Fishes 2026, 11(2), 109; https://doi.org/10.3390/fishes11020109 - 10 Feb 2026
Abstract
Morphine is widely used as an analgesic in vertebrates, yet its cardiorespiratory safety and effective therapeutic range remain poorly explored in fish. This study investigated the dose-dependent effects of morphine on cardiac and respiratory parameters of juvenile tambaqui (Colossoma macropomum). Juveniles [...] Read more.
Morphine is widely used as an analgesic in vertebrates, yet its cardiorespiratory safety and effective therapeutic range remain poorly explored in fish. This study investigated the dose-dependent effects of morphine on cardiac and respiratory parameters of juvenile tambaqui (Colossoma macropomum). Juveniles (25.95 ± 4.08 g) were randomly assigned to control, sham (0.9% saline) or morphine groups (24, 28, 32, 36 and 40 mg kg−1, i.p.). Electrocardiogram (ECG) recordings were used to assess heart rate (HR), PQ, RR and QT intervals, and QRS amplitude, while opercular beat rate (OBR) and opercular beat intensity (OBI) were measured to evaluate respiratory responses. Morphine induced a significant dose-dependent bradycardia and QT prolongation, without affecting QRS amplitude or conduction integrity. Respiratory frequency and intensity also decreased with increasing doses, with responses plateauing above 32 mg kg−1. The EC50 for HR reduction was 27.18 mg kg−1, aligning with a safe therapeutic range of 24–32 mg kg−1. By establishing this dose–response dynamic, the study provides the first characterization of a physiologically safe therapeutic window of morphine in tambaqui and highlights its safety profile for cardiorespiratory parameters. Moreover, the present results demonstrate that the opioid system of juvenile tambaqui is functionally developed, providing a physiological basis for future studies on nociception and analgesic efficacy, with relevance to welfare-oriented practices in aquaculture. Full article
(This article belongs to the Special Issue Fish Health and Welfare in Aquaculture and Research Settings)
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14 pages, 546 KB  
Article
Maternal and Newborn Factors Associated with Meconium Metal Concentrations: A Cross-Sectional Study
by Bianka Mimica, Ajka Pribisalic, Zlatka Knezovic, Nina Knezovic and Davorka Sutlovic
Toxics 2026, 14(2), 163; https://doi.org/10.3390/toxics14020163 - 10 Feb 2026
Abstract
Prenatal exposure to essential and toxic metals may influence fetal development and birth outcomes. Meconium represents a valuable biomarker of cumulative intrauterine exposure; however, data linking maternal lifestyle and diet to meconium metal concentrations remain limited. This study included 152 mother–newborn pairs at [...] Read more.
Prenatal exposure to essential and toxic metals may influence fetal development and birth outcomes. Meconium represents a valuable biomarker of cumulative intrauterine exposure; however, data linking maternal lifestyle and diet to meconium metal concentrations remain limited. This study included 152 mother–newborn pairs at the University Hospital Center Split. Meconium samples were analyzed for essential metals (Mn, Zn, Fe, Cu) and toxic metals (Hg, Pb, Cd, Ni, Cr) using atomic absorption spectrometry. Maternal and newborn characteristics were collected via questionnaires and medical records. Associations between maternal factors and metal concentrations were assessed using multivariable regression, and inter-metal correlations were evaluated with Spearman’s rank correlation. The correlation matrix indicates positive correlations among essential metals, particularly between Fe and Cu (rs = 0.523), whereas toxic metals show mixed correlation patterns. Maternal factors were associated with several metal concentrations: zinc was positively associated with the newborn ponderal index; greater gestational weight gain and longer gestation were associated with lower iron concentrations; frequent fruit or grain consumption was associated with lower copper concentrations; frequent milk/dairy intake was associated with lower mercury; and fish consumption was associated with higher mercury and manganese. Rural residence and lower smoking intensity were associated with lower lead concentrations, while higher pre-pregnancy body mass index and frequent maternal smoking were associated with increased cadmium. No significant associations were observed for nickel or chromium. These findings highlight the influence of maternal diet, lifestyle, and environmental factors on fetal metal exposure, underscoring the need for monitoring, food safety control, and targeted education during pregnancy. Full article
(This article belongs to the Special Issue Toxicity and Safety Assessment of Exposure to Heavy Metals)
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25 pages, 4886 KB  
Article
Research on the Spatiotemporal-Coupled High-Resolution Remote Sensing Land Use Classification Method
by Jiawang Yang, Xiaodong Hu, Weifeng Ma, Jiancheng Luo, Tianjun Wu, Zhongbao Shi, Hongfeng Yu, Peijie Jin, Qirui Tan and Yufei Xu
Remote Sens. 2026, 18(4), 559; https://doi.org/10.3390/rs18040559 - 10 Feb 2026
Abstract
High-spatial-resolution remote sensing imagery provides a data foundation for fine-grained land use classification. However, due to long revisit cycles and susceptibility to cloud cover, large-area imagery often suffers from temporal inconsistency, which severely limits the classification accuracy of traditional unified models. To address [...] Read more.
High-spatial-resolution remote sensing imagery provides a data foundation for fine-grained land use classification. However, due to long revisit cycles and susceptibility to cloud cover, large-area imagery often suffers from temporal inconsistency, which severely limits the classification accuracy of traditional unified models. To address this issue, this study proposes a geographic entity-oriented, spatiotemporally coupled land use classification method for high-resolution remote sensing imagery, with agricultural land (including paddy fields, dry farmland and gardens) as an example for validation. In this method, the study area is first divided into multiple sub-regions based on image acquisition time, ensuring temporal consistency within each sub-region. A dedicated deep texture feature extraction model is then constructed for each sub-region. This model is adapted from the advanced CAPTN texture recognition network: its classification head is removed, and a multi-scale feature fusion module is introduced, transforming it into an encoder focused on extracting spatial texture feature maps. Additionally, a self-supervised loss function combining masked feature reconstruction and cross-view consistency is designed to improve the quality of the learned texture features. During the prediction stage, the corresponding feature extractor is invoked based on the temporal phase of the imagery to generate a full-region texture feature map. This feature map is then cropped using land parcel vectors, and statistical feature vectors describing the texture attributes of each parcel are formed by calculating the mean and standard deviation of the features within each parcel. Finally, a Random Forest classifier is employed to determine the land parcel categories. This study uses the Jiangjin District of Chongqing City as the experimental area. The results show that, compared to training a unified deep learning model directly on full-region multi-temporal imagery or using traditional texture features, the proposed spatiotemporally coupled classification framework achieves significant improvements in overall accuracy and Kappa coefficient, reaching 92.3% and 0.89, respectively. Full article
53 pages, 1469 KB  
Systematic Review
Passenger Car Equivalent Estimation Methods at Urban Signalized Intersections: A Systematic Review
by Sevinç Özgün and Kemal Selçuk Öğüt
Future Transp. 2026, 6(1), 41; https://doi.org/10.3390/futuretransp6010041 - 10 Feb 2026
Abstract
This study presents a systematic review of Passenger Car Equivalency (PCE) at signalized intersections. The review focuses on comparing PCE calculation methods, examining PCE values across methods, and identifying the key influencing factors. Following the PRISMA methodology, 40 relevant studies were identified. The [...] Read more.
This study presents a systematic review of Passenger Car Equivalency (PCE) at signalized intersections. The review focuses on comparing PCE calculation methods, examining PCE values across methods, and identifying the key influencing factors. Following the PRISMA methodology, 40 relevant studies were identified. The analysis revealed several critical calculation factors, including road geometry and vehicle composition. These studies employed seven major methods for PCE estimation: (1) headway ratio, (2) regression, (3) delay-based, (4) area occupancy, (5) queue-based, (6) capacity-based, and (7) optimization (Theil’s Coefficient). The findings indicate that PCE values vary substantially across studies, with motorcycle values ranging from −1.056 to 1.02, three-wheeler values from 0.22 to 1.51, and heavy vehicle values from 1.13 to 5.06. Cross-study comparisons revealed that this variation exists not only between countries but also between cities within the same country. This variability is attributed to traffic volume, traffic composition, approach width, and differences in driver behavior. The results support treating PCE as a dynamic parameter rather than static, as fixed values from national guidelines may not adequately represent local traffic conditions. Full article
20 pages, 2126 KB  
Article
Techno-Economic and Life Cycle Assessment of Hydrogen Production from Biomass–Plastic Co-Gasification with Carbon Capture and Storage
by Mahmoud Karimi and Halis Simsek
Energies 2026, 19(4), 929; https://doi.org/10.3390/en19040929 - 10 Feb 2026
Abstract
This study evaluates the techno-economic and environmental feasibility of hydrogen (H2) production via co-gasification of woody biomass and polyethylene (PE) plastic waste, with and without carbon capture and storage (CCS), using an integrated modeling framework. Four scenarios were analyzed: (1) biomass [...] Read more.
This study evaluates the techno-economic and environmental feasibility of hydrogen (H2) production via co-gasification of woody biomass and polyethylene (PE) plastic waste, with and without carbon capture and storage (CCS), using an integrated modeling framework. Four scenarios were analyzed: (1) biomass gasification without CCS, (2) biomass with CCS, (3) co-gasification (90:10 biomass:PE) without CCS, and (4) co-gasification with CCS. Process simulations were conducted in Aspen Plus V12.1, techno-economic analysis (TEA) via NREL’s H2A model, and cradle-to-gate life cycle assessment (LCA) in OpenLCA with TRACI 2.1 and the Cumulative Energy Demand (CED) methods. The plant processes 1500 dry ton/day feedstock, yielding ~136–140 tons/day pure H2. TEA results show co-gasification without CCS achieves the lowest levelized cost of H2 (LCOH) at 2.18 USD/kg, 7% below biomass-only (2.34 USD/kg), due to reduced feedstock demand and improved efficiency. CCS increases LCOH by 30–36% (2.98–3.18 USD/kg), but 70 USD/t CO2 credits reduce it to 1.74–1.81 USD/kg, competitive with gray H2. Sensitivity and Monte Carlo analyses highlight capacity factor and feedstock as key drivers, with co-gasification narrowing uncertainties. LCA reveals co-gasification lowers most impacts by 5–10%, while CCS enables net-negative GWP (−12.3 to −14.7 kg CO2 eq/kg H2) but raises CED by 15%. Scenario 4 balances economic viability and climate mitigation, supporting circular economy principles through waste valorization. Findings affirm co-gasification with CCS as a promising pathway for low-carbon H2, with policy incentives critical for deployment. Future optimizations, like higher PE ratios, could further reduce costs below 2 USD/kg, advancing net-zero transitions. Full article
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14 pages, 2303 KB  
Article
Study on the Dynamic Mechanical Behavior of 2D C/SiC Composites at Medium Strain Rates
by Xinyue Zhang, Chao Zhang, Xiaochuan Liu, Chunyu Bai, Xulong Xi and Xiaocheng Li
Aerospace 2026, 13(2), 168; https://doi.org/10.3390/aerospace13020168 - 10 Feb 2026
Abstract
Two-dimensional C/SiC are promising candidates for use in high-temperature structures in aeroengines and thermal protection systems in the aerospace industry, which will be subjected to loads in various directions during service. In this paper, the in-plane tensile, compressive, and shear mechanical properties of [...] Read more.
Two-dimensional C/SiC are promising candidates for use in high-temperature structures in aeroengines and thermal protection systems in the aerospace industry, which will be subjected to loads in various directions during service. In this paper, the in-plane tensile, compressive, and shear mechanical properties of 2D C/SiC were investigated over a strain-rate range of 10−5 s−1 to 10 s−1. The failure mechanisms of the material under different loading conditions were analyzed. The study reveals that 2D C/SiC exhibits nonlinear stress–strain relationships under tension and shear, while it displays a linear stress–strain relationship under compression similar to the quasi-static loading state. The strain-rate strengthening effect is most pronounced under compression, whereas the effect is less significant under tensile loading. The reason for the observed increase in strength is the additional energy consumed by multiple crack initiation and propagation. A rate-dependent constitutive model was fitted, which agrees well with the experimental data for both tensile and shear loading conditions. Full article
(This article belongs to the Special Issue Advanced Aircraft Composite Structure Design)
15 pages, 1158 KB  
Review
Current Research Progress on ABHD5 in Cancers
by Huazhong Cai, Hao Chen, Jiexing Ye, Zhesi Jin and Pan Huang
Cancers 2026, 18(4), 585; https://doi.org/10.3390/cancers18040585 - 10 Feb 2026
Abstract
Lipid metabolism sits at the heart of tumor initiation, progression, metastasis, and resistance to chemotherapy. Against this background, the regulation of lipid flux has emerged as a fertile ground for anticancer strategies. Chanarin–Dorfman syndrome (CDS), a rare genetic disorder marked by massive lipid [...] Read more.
Lipid metabolism sits at the heart of tumor initiation, progression, metastasis, and resistance to chemotherapy. Against this background, the regulation of lipid flux has emerged as a fertile ground for anticancer strategies. Chanarin–Dorfman syndrome (CDS), a rare genetic disorder marked by massive lipid droplet accumulation, offers compelling human evidence that α/β-hydrolase domain-containing protein 5 (ABHD5) plays a central role in lipid droplet mobilization through the ATGL axis. This clinical insight has, perhaps unexpectedly, pushed ABHD5 into the spotlight of cancer research. ABHD5 does not behave uniformly across malignancies. In many solid tumors—such as lung, liver, and renal cell carcinoma—it restrains tumor growth. Yet in other settings, notably endometrial cancer, it appears to fuel malignant progression. Colorectal and prostate cancers occupy a more ambiguous middle ground, where ABHD5 can tip the balance in either direction depending on context. Mechanistically, ABHD5 influences lipid homeostasis and cell fate by intersecting with signaling pathways including AMPK/mTOR, AKT, and NF-κB, thereby shaping proliferation, invasion, apoptosis, immune evasion, and drug responsiveness. This review brings together experimental and clinical evidence to map the diverse, sometimes contradictory roles of ABHD5 in cancer. By tracing its context-dependent functions and molecular circuits, we also explore its emerging value as a diagnostic marker and a therapeutic target—one that demands nuance rather than blunt intervention. Full article
(This article belongs to the Section Cancer Biomarkers)
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