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24 pages, 5249 KB

Open AccessArticle

Transcriptome and Hormone Analysis Revealed Jasmonic Acid-Mediated Immune Responses of Potato (Solanum tuberosum) to Potato Spindle Tuber Viroid Infection

by Iva Marković, Bernard Jarić, Jana Oklešťková, Jitka Široká, Kristina Majsec, Jasna Milanović, Snježana Kereša, Ivanka Habuš Jerčić, Ondřej Novák and Snježana Mihaljević

Antioxidants 2026, 15(1), 86; https://doi.org/10.3390/antiox15010086 (registering DOI) - 8 Jan 2026

Abstract

Potato is a globally important non-cereal crop in which infection with potato spindle tuber viroid (PSTVd) can cause stunted growth and significantly reduce tuber yield. We previously showed that PSTVd induces accumulation of the plant hormone jasmonic acid (JA) and alters antioxidant responses [...] Read more.

Potato is a globally important non-cereal crop in which infection with potato spindle tuber viroid (PSTVd) can cause stunted growth and significantly reduce tuber yield. We previously showed that PSTVd induces accumulation of the plant hormone jasmonic acid (JA) and alters antioxidant responses in potato plants. To clarify the role of JA in response to PSTVd, we analyzed disease development in transgenic JA-deficient opr3 and JA-insensitive coi1 lines compared to the wild-type. Transcriptomic analysis using RNA-Seq revealed that most genotype-specific differentially expressed genes (DEGs) in all comparisons were enriched in plant hormone signal transduction, plant-pathogen interaction, and MAPK signaling pathways, although the number of DEGs varied. These differences were confirmed by independent data from RT-qPCR, hormone, and hydrogen peroxide (H₂O₂) analyses. After PSTVd infection, opr3 plants showed enhanced JA signaling and increased abscisic acid (ABA) and auxin (AUX) content. In contrast, coi1 plants showed reduced ABA, AUX, and salicylic acid content. Both opr3 and coi1 plants showed reduced JA and H₂O₂ content and lower expression of defense-related genes, resulting in milder symptoms but increased viroid accumulation. In addition, treatment with methyl jasmonate alleviated symptoms in infected wild-type plants. Together, these results indicate a modulatory role for JA and JA signaling in basal immune responses and symptom development in the potato-PSTVd interaction. Full article

(This article belongs to the Special Issue Oxidative Stress and Antioxidant Defense in Crop Plants, 2nd Edition)

21 pages, 1139 KB

Open AccessArticle

The Bright Future of Online Programming for Girls’ STEM Identity Development

by Roxanne Hughes, Rachael Dominguez, Kata Lucas, Sharon Ndubuisi, Brenda Britsch, Sheri Levinsky-Raskin, Abi Olukeye, Amanda Sullivan and Khadija Zogheib

Educ. Sci. 2026, 16(1), 98; https://doi.org/10.3390/educsci16010098 (registering DOI) - 8 Jan 2026

Abstract

Informal STEM education programs (ISEs) can be a successful vehicle for addressing the underrepresentation of girls in STEM by expanding their views of what constitutes science and debunking stereotypes related to who succeeds in STEM careers. Research has demonstrated how in-person ISEs provide [...] Read more.

Informal STEM education programs (ISEs) can be a successful vehicle for addressing the underrepresentation of girls in STEM by expanding their views of what constitutes science and debunking stereotypes related to who succeeds in STEM careers. Research has demonstrated how in-person ISEs provide opportunities for girls to engage in hands-on, authentic science experiences, interact with diverse women role models, and understand the real-world application of STEM to improve their STEM identity development (i.e., STEM competence, performance, self and external recognition, and sense of belonging within STEM). But few studies have focused on STEM identity development in online spaces. Our study addresses this gap through a mixed methods study that investigates how an online program (Brite), held in 2023, influenced the STEM identities of the participating girls. Our results highlight the aspects of the online program that improved the STEM identity for participants as well as lessons learned for future programs. The influential programmatic pieces were role model interactions and the supportive Brite community that included program educators, the other girls, and the Brite facilitators, which helped girls feel inspired and motivated to continue along their STEM identity trajectories. Full article

(This article belongs to the Section STEM Education)

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19 pages, 1159 KB

Open AccessArticle

Unveiling the Genomic Landscape of Yan Goose (Anser cygnoides): Insights into Population History and Selection Signatures for Growth and Adaptation

by Shangzong Qi, Zhenkang Ai, Yuchun Cai, Yang Zhang, Wenming Zhao and Guohong Chen

Animals 2026, 16(2), 194; https://doi.org/10.3390/ani16020194 (registering DOI) - 8 Jan 2026

Abstract

The Yan goose (YE, Anser cygnoides) is a valuable indigenous poultry genetic resource, renowned for its superior meat quality and environmental adaptability. Despite its economic importance, the genetic basis underlying these adaptive traits remains unclear. In this study, we employed whole-genome resequencing [...] Read more.

The Yan goose (YE, Anser cygnoides) is a valuable indigenous poultry genetic resource, renowned for its superior meat quality and environmental adaptability. Despite its economic importance, the genetic basis underlying these adaptive traits remains unclear. In this study, we employed whole-genome resequencing (WGS) to perform high-throughput sequencing on a conserved population of 15 samples. Bioinformatic analyses were conducted to systematically evaluate the population’s genetic structure, and a genome-wide scan for selection signals related to economically significant traits was performed using the integrated haplotype score (iHS) method. An average of 4.43 million high-quality SNPs were identified, which were predominantly located in intergenic and intronic regions. Population structure analysis revealed a close genetic relationship within the conserved population of YE, with no significant lineage stratification observed. Pairwise sequentially Markovian coalescent (PSMC) analysis indicated that the YE underwent a severe genetic bottleneck during the Last Glacial Maximum (LGM), followed by gradual population recovery in the early Neolithic period. Genome-wide selection signal scanning identified multiple genomic regions under strong selection, annotating key genes associated with growth and development (e.g., GHRL, AKT1, and MAPK3), lipid deposition (e.g., PLPP4, SAMD8, and LPIN1), and disease resistance and stress resilience (e.g., TP53, STAT3). Functional enrichment analysis revealed significant enrichment of these genes in pathways related to glycerophospholipid metabolism (p < 0.01), purine metabolism (p < 0.01), and immune response (p < 0.01). This study not only provides a theoretical foundation for the scientific conservation of the YE germplasm resources but also offers valuable genomic resources for identifying functional genes underlying important economic traits and advancing molecular breeding strategies. Full article

(This article belongs to the Special Issue Genetic Diversity and Conservation of Local Poultry Breeds)

22 pages, 1587 KB

Open AccessArticle

Characterization of Novel Sigma Receptor Ligands Derived from Multicomponent Reactions as Efficacious Treatments for Neuropathic Pain

by Ryosuke Shinouchi, Bengisu Turgutalp, Rohini S. Ople, Shainnel O. Eans, Ashai K. Williams, Haylee R. Hammond, Andras Varadi, Rebecca Notis Dardashti, Susruta Majumdar and Jay P. McLaughlin

Pharmaceuticals 2026, 19(1), 117; https://doi.org/10.3390/ph19010117 (registering DOI) - 8 Jan 2026

Abstract

Background/Objectives: Neuropathic pain remains a significant clinical challenge, with current treatments often providing inadequate relief and adverse effects. Sigma receptors (SRs) modulate nociception and have emerged as potential therapeutic targets for neuropathic pain. Although putative sigma-1 receptor (S1R) ligands have demonstrated analgesic [...] Read more.

Background/Objectives: Neuropathic pain remains a significant clinical challenge, with current treatments often providing inadequate relief and adverse effects. Sigma receptors (SRs) modulate nociception and have emerged as potential therapeutic targets for neuropathic pain. Although putative sigma-1 receptor (S1R) ligands have demonstrated analgesic efficacy in preclinical models, their in vivo efficacy and safety profiles require further clarification. Methods: Analogs of well-known selective S1R ligand UVM147 were synthesized using 3-component Ugi reactions and examined in vitro for receptor affinity in radioligand competition binding assays and in vivo with mouse models of neuropathic and inflammatory pain and adverse effects. Results: Three novel heterocyclic compounds (RO-4-3, RO-5-3, and RO-7-3) displayed in vitro nanomolar affinity with varying selectivity for both SR subtypes (S1R and S2R). When screened in vivo at a dose of 30 mg/kg s.c. in mice first subjected to chronic constriction injury (CCI), RO-5-3 and RO-7-3 possessed anti-allodynic potential, while UVM147 was inactive. Upon full characterization, RO-5-3 significantly attenuated mechanical allodynia in a dose-dependent manner, while RO-7-3 was ineffective at higher doses. Both compounds dose-dependently attenuated nociceptive behaviors in the mouse formalin assay. RO-5-3 induced mild respiratory depression without impairing locomotor activity, whereas RO-7-3 caused transient respiratory depression and locomotor impairment. Additionally, RO-5-3, but not RO-7-3, induced conditioned place aversion consistent with potential S2R involvement. Conclusions: RO-5-3 exerts antinociceptive and anti-allodynic effects with minimal adverse behavioral effects, supporting the role of SRs in pain modulation. These results add to growing evidence supporting the development of SR ligands as efficacious therapeutics for neuropathic pain with fewer clinical liabilities. Full article

(This article belongs to the Special Issue Current Advances in Therapeutic Potential of Sigma Receptor Ligands)

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21 pages, 3352 KB

Open AccessArticle

DHAG-Net: A Small Object Semantic Segmentation Network Integrating Edge Supervision and Dense Hybrid Dilated Convolution

by Qin Qin, Huyuan Shen, Qing Wang, Qun Yang and Xin Wang

Appl. Sci. 2026, 16(2), 684; https://doi.org/10.3390/app16020684 (registering DOI) - 8 Jan 2026

Abstract

Small-object semantic segmentation remains challenging in urban driving scenes due to limited pixel occupancy, blurred boundaries, and the constraints imposed by lightweight deployment. To address these issues, this paper presents a lightweight semantic segmentation framework that enhances boundary awareness and contextual representation while [...] Read more.

Small-object semantic segmentation remains challenging in urban driving scenes due to limited pixel occupancy, blurred boundaries, and the constraints imposed by lightweight deployment. To address these issues, this paper presents a lightweight semantic segmentation framework that enhances boundary awareness and contextual representation while maintaining computational efficiency. The proposed method integrates an edge-supervised boundary gating module to emphasize object boundaries, an efficient multi-scale context aggregation strategy to mitigate scale variation, and a lightweight feature enhancement mechanism for effective feature fusion. Edge supervision is introduced as an auxiliary regularization signal and does not require additional manual annotations. Extensive experiments conducted on multiple benchmark datasets, including Cityscapes, CamVid, PASCAL VOC 2012, and IDDA, demonstrate that the proposed framework consistently improves segmentation performance, particularly for small-object categories, while preserving a favorable balance between accuracy and efficiency. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

17 pages, 329 KB

Open AccessArticle

Multivalued Fixed Point Results in Rectangular m-Metric Spaces

by Safeer Hussain Khan, Muhammad Zahid and Ali Raza

Axioms 2026, 15(1), 48; https://doi.org/10.3390/axioms15010048 (registering DOI) - 8 Jan 2026

Abstract

In this paper, we initiate the study of multivalued fixed point results in the framework of rectangular m-metric spaces. We establish fixed point theorems for Reich–Rus–Ćirić-type contractions and analyze two distinct cases based on the sum of the interpolative exponents: when the [...] Read more.

In this paper, we initiate the study of multivalued fixed point results in the framework of rectangular m-metric spaces. We establish fixed point theorems for Reich–Rus–Ćirić-type contractions and analyze two distinct cases based on the sum of the interpolative exponents: when the sum is less than one and when it is greater than one. Furthermore, by introducing the Hausdorff metric structure induced by rectangular m-metrics, our results generalize and extend various existing results in the literature. Illustrative examples are also provided to support and validate the obtained results. Full article

22 pages, 660 KB

Open AccessArticle

From People to Performance: Factors Driving Sustainable Family Business Success in Lebanon

by Jean Elia, Najib Bou Zakhem, Joseph Serghani, Mireille Karam and Chadia Sawaya

Sustainability 2026, 18(2), 669; https://doi.org/10.3390/su18020669 (registering DOI) - 8 Jan 2026

Abstract

This research examines the impact of five crucial factors underlying human resource management (HRM), namely, compensation, transformational leadership, motivation, and job satisfaction on sustainable employees’ performance in Lebanese family companies. The research is founded on Social Exchange Theory, Maslow’s Hierarchy of Needs, and [...] Read more.

This research examines the impact of five crucial factors underlying human resource management (HRM), namely, compensation, transformational leadership, motivation, and job satisfaction on sustainable employees’ performance in Lebanese family companies. The research is founded on Social Exchange Theory, Maslow’s Hierarchy of Needs, and Transformational Leadership Theory. Based on a cross-sectional design and quantitative approach, data were collected from 511 full-time employees working for family-owned businesses in Lebanon via structured questionnaires. Structural equation modeling (SEM) using SmartPLS-4 was used to analyze the relationships among the variables. The results point out that job satisfaction, motivation, and the transformational leadership style meaningfully impact employees’ performance. Compensation had a slight yet statistically significant effect. Furthermore, the work environment was found to have both a direct influence on performance and a moderating effect on the relationships between job satisfaction, transformational leadership style, and employees’ outcomes. These outcomes provide theoretical contributions to the literature on HRM in family-owned enterprises and deliver practical insights for improving employees’ performance through targeted HR strategies in emerging economies. The present study concludes by highlighting the role of a supportive environment at work and participative leadership in enhancing performance outcomes, mostly in culturally complex and intergenerational business settings. Full article

(This article belongs to the Special Issue Sustainable Leadership and Strategic Management in SMEs)

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17 pages, 2233 KB

Open AccessArticle

Self-Templated Highly Porous Gold Electrodes for Antibiofouling Electrochemical (Bio)Sensors

by Anisa Degjoni, Cristina Tortolini, Daniele Passeri, Andrea Lenzi and Riccarda Antiochia

Nanomaterials 2026, 16(2), 87; https://doi.org/10.3390/nano16020087 (registering DOI) - 8 Jan 2026

Abstract

Biofouling arises from non-specific adsorption of several components present in complex biofluids, such as full blood, on the surface of electrochemical biosensors, with a resulting loss of functionality. Most biomarkers of clinical relevance are present in biological fluids at extremely low concentrations, making [...] Read more.

Biofouling arises from non-specific adsorption of several components present in complex biofluids, such as full blood, on the surface of electrochemical biosensors, with a resulting loss of functionality. Most biomarkers of clinical relevance are present in biological fluids at extremely low concentrations, making antibiofouling strategies necessary in electrochemical biosensing. Here, we demonstrate the effect of a highly porous gold (h-PG) film electrodeposited on a gold screen-printed electrode (AuSPE) using a self-templated method via hydrogen bubbling as an antibiofouling strategy in electrochemical biosensor development following exposure of the electrode to bovine serum albumin (BSA) at two different concentrations (2 and 32 mg/mL). The h-PG film has a high electrochemically active surface area, 88 times higher than the AuSPE electrode, with a pore size ranging from 2 to 50 μm. A rapid decrease in the Faradaic current was observed with the unmodified AuSPE, attesting to the strong biofouling effect of BSA at both concentrations tested. Notably, the h-PG-modified electrode showed an initial peak current decline, more evident at a higher BSA concentration, followed by rapid electrode regeneration when the electrode was left idle in the biofouling solution. Similar results were obtained for unmodified and modified electrodes in real serum and plasma samples. The regeneration process, explained in terms of balance between h-PG pore size and protein size, the nanoscale architecture of the h-PG electrodes, and repulsive electrostatic forces, indicates the huge potential of the h-PG film for use in biomedical electrochemical sensing. Full article

(This article belongs to the Special Issue Nanotechnology-Based Electrochemical Biosensors)

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17 pages, 4683 KB

Open AccessArticle

Investigation on Wake Characteristics of Two Tidal Stream Turbines in Tandem Using a Mobile Submerged PIV System

by Sejin Jung, Heebum Lee, In Sung Jang, Seong Min Moon, Heungchan Kim, Chang Hyeon Seo, Jihoon Kim and Jin Hwan Ko

J. Mar. Sci. Eng. 2026, 14(2), 135; https://doi.org/10.3390/jmse14020135 (registering DOI) - 8 Jan 2026

Abstract

Understanding wake interactions between multiple tidal stream turbines is essential for optimizing the performance and layout of tidal energy farms. This study investigates the hydrodynamic behavior of two horizontal-axis tidal turbines arranged in tandem under simplified inflow conditions, where the incoming flow was [...] Read more.

Understanding wake interactions between multiple tidal stream turbines is essential for optimizing the performance and layout of tidal energy farms. This study investigates the hydrodynamic behavior of two horizontal-axis tidal turbines arranged in tandem under simplified inflow conditions, where the incoming flow was dominated by the streamwise velocity component without imposed external disturbances. Wake measurements were conducted in a large circulating water tunnel using a mobile, submerged particle image velocimetry (PIV) system capable of long-range, high-resolution measurements. Performance tests showed that the downstream turbine experienced a decrease of approximately 9% in maximum power coefficient compared to the upstream turbine due to reduced inflow velocity and increased turbulence generated by the upstream wake. Phase-averaged PIV results revealed the detailed evolution of velocity deficit, turbulence intensity, turbulent kinetic energy, and tip vortex structures. The tip vortices shed from the upstream turbine persisted over a long downstream distance, remaining coherent up to 10D and merging with those generated by the downstream turbine. These merged vortex structures produced elevated turbulence and complex flow patterns that significantly influenced the downstream turbine’s operating conditions. The results provide experimentally validated insight into turbine-to-turbine wake interactions and highlight the need for high-fidelity wake data to support array optimization and numerical model development for tidal stream turbine array. Full article

(This article belongs to the Special Issue Hydrodynamic Performance, Optimization, and Design of Marine Turbines)

17 pages, 3322 KB

Open AccessArticle

Global Warming Drives the Adaptive Distribution and Landscape Fragmentation of Neosinocalamus affinis Forests in China

by Huayong Zhang, Junwei Liu, Yihe Zhang, Zhongyu Wang and Zhao Liu

Forests 2026, 17(1), 84; https://doi.org/10.3390/f17010084 (registering DOI) - 8 Jan 2026

Abstract

Compared with other forest vegetation, bamboo forests have a stronger carbon sequestration capacity, which plays a vital role in achieving the national goals of carbon peak and carbon neutrality. Global warming has profoundly impacted the adaptive distribution and landscape fragmentation of bamboo forests. [...] Read more.

Compared with other forest vegetation, bamboo forests have a stronger carbon sequestration capacity, which plays a vital role in achieving the national goals of carbon peak and carbon neutrality. Global warming has profoundly impacted the adaptive distribution and landscape fragmentation of bamboo forests. This study utilized an optimized MaxEnt model to calculate the current habitat range of Neosinocalamus affinis (Rendle) Keng f. forests across China and project their potential distribution under three future climate scenarios (SSP126, SSP370, SSP585) for the 2050s and 2090s and analyzed the landscape fragmentation of their land use using landscape indices. The results reveal that Neosinocalamus affinis forests are currently primarily distributed in Chongqing Municipality, eastern and southeastern Sichuan Province, and northern Guizhou Province. The key environmental factors influencing their distribution are identified as: mean diurnal temperature range (Bio2), precipitation of warmest quarter (Bio18), and precipitation of wettest quarter (Bio16). Across the three future scenarios, the suitable habitat area for Neosinocalamus affinis forests demonstrates an overall expanding trend. Rising CO₂ concentrations correlate with a reduction in suitable habitat. The habitat centroid shifts southward in the 2050s and northeastward in the 2090s. In the future, the fragmentation degree of highly suitable areas for Neosinocalamus affinis forests will be higher than at present and show an increasing trend, with forest fragmentation significantly intensifying and overall landscape quality further declining. The predictive results of this study provide a scientific basis for the effective conservation and management of Neosinocalamus affinis forests, thereby contributing to the sustainable utilization of bamboo forest resources. Full article

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

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41 pages, 11911 KB

Open AccessArticle

Polydopamine-Coated Surfaces Promote Adhesion, Migration, Proliferation, Chemoresistance, Stemness, and Epithelial–Mesenchymal Transition of Human Prostate Cancer Cell Lines In Vitro via Integrin α₂β₁–FAK–JNK Signaling

by Won Hoon Song, Ji-Eun Kim, Lata Rajbongshi, Su-Rin Lee, Yuna Kim, Seon Yeong Hwang, Sae-Ock Oh, Byoung Soo Kim, Dongjun Lee and Sik Yoon

Int. J. Mol. Sci. 2026, 27(2), 655; https://doi.org/10.3390/ijms27020655 (registering DOI) - 8 Jan 2026

Abstract

Polydopamine (PDA) surface coatings are widely used in biomedical engineering to enhance cell–substrate interactions; however, their effects on cancer-cell behavior remain unclear. In this study, we investigated how PDA-coated two-dimensional (2D) culture surfaces influence oncogenic traits of human prostate cancer (PC) cells in [...] Read more.

Polydopamine (PDA) surface coatings are widely used in biomedical engineering to enhance cell–substrate interactions; however, their effects on cancer-cell behavior remain unclear. In this study, we investigated how PDA-coated two-dimensional (2D) culture surfaces influence oncogenic traits of human prostate cancer (PC) cells in vitro. Using LNCaP, DU145, and PC3 cell lines, we found that PDA-coated substrates markedly increased the adhesion, migration, invasion, proliferation, and colony formation in a dose- and time-dependent manner. PDA exposure also induced epithelial–mesenchymal transition (EMT), upregulated cancer stem cell markers (CD44, CD117, CD133, Sox2, Oct4, and Nanog), and elevated expression of metastasis- and chemoresistance-associated molecules (MMP-2, MMP-9, MDR1, and MRP1). Mechanistically, PDA coatings enhanced integrin α₂β₁-associated cell adhesion, accompanied by increased focal adhesion kinase (FAK) phosphorylation and downstream activation of JNK signaling. Pharmacological inhibition of integrin α₂β₁ (BTT-3033), FAK (PF573228) and JNK (SP600125) effectively abrogated PDA-induced malignant phenotypes and restored chemosensitivity to cabazitaxel, cisplatin, docetaxel, curcumin, and enzalutamide. Collectively, these findings identify PDA-coated surfaces as a simple, efficient, and reductionist in vitro platform for studying adhesion-mediated signaling and phenotypic plasticity in PC cells, while acknowledging that further validation in three-dimensional (3D) and patient-derived models will be required to establish in vivo relevance. Full article

(This article belongs to the Special Issue Breakthroughs in Anti-Cancer Agents Discovery)

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27 pages, 2659 KB

Open AccessArticle

Technological Triangle—Making Public Transport Sustainable and More Accessible

by Petr Nachtigall, Marek Vyhnanovský, Lukáš Křižan, Jaromír Široký and Jozef Gašparík

Sustainability 2026, 18(2), 670; https://doi.org/10.3390/su18020670 (registering DOI) - 8 Jan 2026

Abstract

The technological triangle is a non-mathematical representation of the relationship between the characteristics of transport infrastructure, modes of transport, and the operational concept in a specific region. It is only through the synergistic effect of these three vertices that the railway undertaking, infrastructure [...] Read more.

The technological triangle is a non-mathematical representation of the relationship between the characteristics of transport infrastructure, modes of transport, and the operational concept in a specific region. It is only through the synergistic effect of these three vertices that the railway undertaking, infrastructure manager, and authority can achieve optimal resource utilisation. Concurrently, it is imperative to exert pressure on the authorities to implement conceptual, systematic, and predictable measures. The process of implementing changes to transport infrastructure is a protracted one, typically spanning several years from the initial stages of preparation through to the project’s execution. The application of the technological triangle is possible on various parts of the infrastructure. Based on previous research, the authors prepared this Article to address intermediate stations, which were identified as the key focus of this article. Therefore, the authors in this article answer the question of what typical solutions exist for intermediate station configurations in relation to the operational concept and financial costs. Twenty different configurations were selected, and each was examined from the perspectives of financial, operational, planning, automation, and user pillars. The weights of the individual pillars were then assessed from the perspective of the infrastructure manager, the carrier, and the customer. The result is a comprehensive assessment of all wayside station configurations from different perspectives. Each user of this workflow can determine the weights of the individual pillars according to their needs and financial capabilities. This also gives the article a general use. The final part of the article presents specific examples of existing structures in the Czech Republic, which were not built with the perspective of this article in mind. The authors point out that if our method were applied, not only would large platform stations be built, which is the case for many intermediate stations in the Czech Republic; instead, more efficient solutions would be developed and adapted to the specific case. Full article

(This article belongs to the Special Issue Rethinking the Accessibility and Sustainability of Public Transportation)

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27 pages, 7553 KB

Open AccessArticle

Deep Learning Applied to Spaceborne SAR Interferometry for Detecting Sinkhole-Induced Land Subsidence Along the Dead Sea

by Gali Dekel, Ran Novitsky Nof, Ron Sarafian and Yinon Rudich

Remote Sens. 2026, 18(2), 211; https://doi.org/10.3390/rs18020211 (registering DOI) - 8 Jan 2026

Abstract

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along [...] Read more.

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds. Full article

(This article belongs to the Special Issue Remote Sensing Applications Along the Dead Sea Fault: From the Red Sea to Anatolia)

24 pages, 2366 KB

Open AccessArticle

Hybrid Modeling of Wave Propagation in a 1D Bar: Integrating Peridynamics and Finite Element Methods for Enhanced Dynamic Analysis

by Laxman Khanal, Mijia Yang and Evan J. Pineda

Appl. Sci. 2026, 16(2), 686; https://doi.org/10.3390/app16020686 (registering DOI) - 8 Jan 2026

Abstract

This study analyzes a hybrid computational framework that combines peridynamics (PD) and the finite element (FE) method to model wave propagation in a one-dimensional bar, focusing on their integration for enhanced accuracy and efficiency. The analysis investigates PD’s ability to capture non-local interactions [...] Read more.

This study analyzes a hybrid computational framework that combines peridynamics (PD) and the finite element (FE) method to model wave propagation in a one-dimensional bar, focusing on their integration for enhanced accuracy and efficiency. The analysis investigates PD’s ability to capture non-local interactions in regions near loading points, with computationally efficient coarse discretization in other areas through finite element methods. The dynamic response to symmetric and asymmetric axial loading, including loading and unloading phases, is analyzed through time-dependent external forces, solving displacement, velocity, and acceleration fields at each time step. The effects of PD-specific parameters, such as the horizon size, and the FE–PD node spacing size ratios on the performance of the hybrid model in wave propagation are investigated. Additionally, the study examines the von Neumann stability for PD to ensure stability and reliability, offering a robust framework for integrating PD and FE in dynamic analyses. Full article

(This article belongs to the Special Issue Advances in AI and Multiphysics Modelling)

29 pages, 4039 KB

Open AccessReview

Targeting Mesenchymal-Epidermal Transition (MET) Aberrations in Non-Small Cell Lung Cancer: Current Challenges and Therapeutic Advances

by Fahua Deng, Weijie Ma and Sixi Wei

Cancers 2026, 18(2), 207; https://doi.org/10.3390/cancers18020207 (registering DOI) - 8 Jan 2026

Abstract

The mesenchymal–epithelial transition (MET) receptor is a tyrosine kinase activated by its sole known ligand, hepatocyte growth factor (HGF). MET signaling regulates key cellular processes, including proliferation, survival, migration, motility, and angiogenesis. Dysregulation and hyperactivation of this pathway are implicated in multiple malignancies, [...] Read more.

The mesenchymal–epithelial transition (MET) receptor is a tyrosine kinase activated by its sole known ligand, hepatocyte growth factor (HGF). MET signaling regulates key cellular processes, including proliferation, survival, migration, motility, and angiogenesis. Dysregulation and hyperactivation of this pathway are implicated in multiple malignancies, including lung, breast, colorectal, and gastrointestinal cancers. In non–small cell lung cancer (NSCLC), aberrant activation of the MET proto-oncogene contributes to 1% of known oncogenic drivers and is associated with poor clinical outcomes. Several mechanisms can induce MET hyperactivation, including MET gene amplification, transcriptional upregulation of MET or HGF, MET fusion genes, and MET exon 14 skipping mutations. Furthermore, MET pathway activation represents a frequent mechanism of acquired resistance to EGFR- and ALK-targeted tyrosine kinase inhibitors (TKIs) in EGFR- and ALK-driven NSCLCs. Although MET has long been recognized as a promising therapeutic target in NSCLC, the clinical efficacy of MET-targeted therapies has historically lagged behind that of EGFR and ALK inhibitors. Encouragingly, several MET TKIs such as capmatinib, tepotinib, and savolitinib have been approved for the treatment of MET exon 14 skipping mutations. They have also demonstrated potential in overcoming MET-driven resistance to EGFR TKIs or ALK TKIs. On 14 May 2025, the U.S. Food and Drug Administration granted accelerated approval to telisotuzumab vedotin-tllv for adult patients with locally advanced or metastatic non-squamous NSCLC whose tumors exhibit high c-Met protein overexpression and who have already received prior systemic therapy. In this review, we summarize the structure and physiological role of the MET receptor, the molecular mechanisms underlying aberrant MET activation, its contribution to acquired resistance against targeted therapies, and emerging strategies for effectively targeting MET alterations in NSCLC. Full article

(This article belongs to the Special Issue Advances in Precision Medicine: Targeting Known and Emerging Oncogenic Targets in Lung Cancer (2nd Edition))

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15 pages, 6088 KB

Open AccessArticle

A Multi-Scale Soft-Thresholding Attention Network for Diabetic Retinopathy Recognition

by Xin Ma, Linfeng Sui, Ruixuan Chen, Taiyo Maeda and Jianting Cao

Appl. Sci. 2026, 16(2), 685; https://doi.org/10.3390/app16020685 (registering DOI) - 8 Jan 2026

Abstract

Diabetic retinopathy (DR) is a major cause of preventable vision loss, and its early detection is essential for timely clinical intervention. However, existing deep learning-based DR recognition methods still face two fundamental challenges: substantial lesion-scale variability and significant background noise in retinal fundus [...] Read more.

Diabetic retinopathy (DR) is a major cause of preventable vision loss, and its early detection is essential for timely clinical intervention. However, existing deep learning-based DR recognition methods still face two fundamental challenges: substantial lesion-scale variability and significant background noise in retinal fundus images. To address these issues, we propose a lightweight framework named Multi-Scale Soft-Thresholding Attention Network (MSA-Net). The model integrates three components: (1) parallel multi-scale convolutional branches to capture lesions of different spatial sizes; (2) a soft-thresholding attention module to suppress noise-dominated responses; and (3) hierarchical feature fusion to enhance cross-layer representation consistency. A squeeze-and-excitation module is further incorporated for channel recalibration. On the APTOS 2019 dataset, MSA-Net achieves 97.54% accuracy and 0.991 AUC-ROC for binary DR recognition. We further evaluate five-class DR grading on APTOS2019 with 5-fold stratified cross-validation, achieving 82.71 ± 1.25% accuracy and 0.8937 ± 0.0142 QWK, indicating stable performance for ordinal severity classification. With only 4.54 M parameters, MSA-Net remains lightweight and suitable for deployment in resource-constrained DR screening environments. Full article

22 pages, 1710 KB

Open AccessArticle

Shape Parameterization and Efficient Optimization Design Method for the Ray-like Underwater Gliders

by Daiyu Zhang, Daxing Zeng, Heng Zhou, Chaoming Bao and Qian Liu

Biomimetics 2026, 11(1), 58; https://doi.org/10.3390/biomimetics11010058 (registering DOI) - 8 Jan 2026

Abstract

To address the challenges of high computational cost and lengthy design cycles in the high-precision optimization of ray-like underwater gliders, this study proposes a high-accuracy, low-cost parametric modeling and optimization method. The proposed framework begins by extracting the characteristic contours of the manta [...] Read more.

To address the challenges of high computational cost and lengthy design cycles in the high-precision optimization of ray-like underwater gliders, this study proposes a high-accuracy, low-cost parametric modeling and optimization method. The proposed framework begins by extracting the characteristic contours of the manta ray and reconstructing the airfoil sections using the Class-Shape Transformation (CST) method, resulting in a flexible parametric geometry capable of smooth deformation. High-fidelity Computational Fluid Dynamics (CFD) simulations are employed to evaluate the hydrodynamic characteristics, and detailed flow field analyses are conducted to identify the most influential geometric features affecting lift and drag performance. On this basis, a Kriging-based sequential optimization framework is developed. The surrogate model is adaptively refined through dynamic infilling of sample points based on combined Mean Squared Prediction (MSP) and Expected Improvement (EI) criteria, thus improving optimization efficiency while maintaining predictive accuracy. Comparative case studies demonstrate that the proposed method achieves a 116% improvement in lift-to-drag ratio and a more uniform flow distribution, confirming its effectiveness in enhancing both design accuracy and computational efficiency. The results indicate that this approach provides a practical and efficient tool for the parametric design and hydrodynamic optimization of bio-inspired underwater vehicles. Full article

(This article belongs to the Special Issue Advances in Computational Methods for Biomechanics and Biomimetics)

24 pages, 4090 KB

Open AccessArticle

Dynamic Cooperative Control Method for Highly Maneuverable Unmanned Vehicle Formations Based on Adaptive Multi-Mode Steering

by Yongshuo Li, Huijun Yue, Hongjun Yu, Jie Gu, Zheng Li and Jicheng Fan

Machines 2026, 14(1), 80; https://doi.org/10.3390/machines14010080 (registering DOI) - 8 Jan 2026

Abstract

Traditional front-wheel-steering (FWS) unmanned vehicles frequently encounter maneuverability bottlenecks in confined spaces or during rapid formation changes due to inherent kinematic limitations. To mitigate these constraints, this study proposes an adaptive multi-mode (AMM) cooperative formation control framework tailored for four-wheel independent drive and [...] Read more.

Traditional front-wheel-steering (FWS) unmanned vehicles frequently encounter maneuverability bottlenecks in confined spaces or during rapid formation changes due to inherent kinematic limitations. To mitigate these constraints, this study proposes an adaptive multi-mode (AMM) cooperative formation control framework tailored for four-wheel independent drive and steering (4WIDS) platforms. The methodology constructs a unified planner based on the virtual structure concept, integrated with an autonomous steering-mode selector. By synthesizing real-time mission requirements with longitudinal and lateral tracking errors, the system dynamically switches between crab steering, four-wheel counter-steering (4WCS), and conventional FWS modes to optimize spatial utilization. Validated within a seven-vehicle MATLAB/Simulink environment, simulation results demonstrate that the crab-steering mode significantly reduces relocation time for small lateral adjustments by eliminating redundant heading changes, whereas FWS and 4WCS modes are preferentially selected to ensure stability during high-speed or large-span maneuvers. These findings confirm that the proposed AMM strategy effectively reconciles the trade-off between agility and stability, providing a robust solution for complex cooperative maneuvering tasks. Full article

(This article belongs to the Section Vehicle Engineering)

23 pages, 1122 KB

Open AccessArticle

A Hybrid Genetic Algorithm for Sustainable Multi-Site Logistics: Integrating Production, Inventory, and Distribution Planning with Proactive CO₂ Emission Forecasting

by Nejah Jemal, Imen Raies, Amira Sellami, Zied Hajej and Kamar Diaz

Sustainability 2026, 18(2), 671; https://doi.org/10.3390/su18020671 (registering DOI) - 8 Jan 2026

Abstract

This paper introduces a novel, integrated optimization framework for sustainable multi-site logistics planning, which simultaneously addresses production, inventory, and distribution decisions. The proposed hybrid methodology combines a Genetic Algorithm (GA) with Linear Programming (LP) to minimize total logistics costs while proactively integrating environmental [...] Read more.

This paper introduces a novel, integrated optimization framework for sustainable multi-site logistics planning, which simultaneously addresses production, inventory, and distribution decisions. The proposed hybrid methodology combines a Genetic Algorithm (GA) with Linear Programming (LP) to minimize total logistics costs while proactively integrating environmental impact assessment. The model determines optimal production schedules across multiple facilities, manages inventory levels, and solves the Vehicle Routing Problem (VRP) for distribution. A key innovation is the incorporation of a CO₂ emission forecasting module directly into the optimization loop, allowing the algorithm to anticipate and mitigate the environmental consequences of logistics decisions during the planning phase, rather than performing a post-hoc evaluation. The framework was implemented in Python 3.13.4, utilizing the PuLP library for LP components and custom-developed GA routines. Its performance was validated through a numerical case study and a series of sensitivity analyses, which investigated the effects of fluctuating demand and key cost parameters. The results demonstrate that the inclusion of emission forecasting enables the identification of solutions that achieve a superior balance between economic and environmental objectives, leading to significant reductions in both total costs and predicted CO₂ emissions. This work provides practitioners with a scalable and practical decision-support tool for designing more sustainable and resilient multi-echelon supply chains. Full article

(This article belongs to the Special Issue Green Development, Digital Ecosystem and Sustainability in Industry 4.0)

22 pages, 1194 KB

Open AccessArticle

Magnesian Calcite and Dolomite in the Krečana Marble (Bukulja–Venčac Area, Central Serbia): A Possible Modification for Geothermometry Application Purposes?

by Pavle Tančić, Željko Cvetković, Ivana Jovanić and Darko Spahić

Geosciences 2026, 16(1), 35; https://doi.org/10.3390/geosciences16010035 (registering DOI) - 8 Jan 2026

Abstract

The chemical compositions and formation temperatures of magnesian calcite and dolomite were estimated by using the combination of chemical analysis, crystallographic parameters, and a plethora of various diagrams and mathematical calculations. This study presents an example of the calculated crystallo-chemical formula (Ca_0.960 [...] Read more.

The chemical compositions and formation temperatures of magnesian calcite and dolomite were estimated by using the combination of chemical analysis, crystallographic parameters, and a plethora of various diagrams and mathematical calculations. This study presents an example of the calculated crystallo-chemical formula (Ca_0.960Mg_0.039Fe_0.001)CO₃, obtained from chemical analysis on a representative marble sample from the Bukulja–Venčac area in central Serbia. Substituting CaCO₃ with MgCO₃ and FeCO₃ in dolomite adds approximately 3–5 mol. %, enhancing the classification and indicating that it is more accurately identified as magnesium-excess dolomite. The estimated formation temperature of magnesian calcite (1) is approximately 528 °C, whereas magnesian calcite (2) forms at about 341 °C. The ~187 °C difference corresponds to ~3.28 mol. % MgCO₃ (~7.18% dolomite), reflecting the distinction between magnesian calcite (1) and magnesian calcite (2). Considering the presence of the submicroscopic intergrowth and exsolution of dolomite within magnesian calcite (1), which are further subdivided in magnesian calcite (2), the estimated formation temperature of ~341 °C appears to be more realistic. The synthesis of the results suggests that this combined method could be helpful in the geothermometry of marble samples after the treatment with acetic acid. However, despite the promising results, additional experiments are necessary to validate the proposed modified geothermometry approach. Full article

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16 pages, 7181 KB

Open AccessArticle

Statistical Study of Free-Space Optical Transmission Using Multi-Aperture Receivers Under Real-Measured Atmospheric Turbulence

by Shutong Liu, Shaoqian Tian, Baoqun Li, Zhi Liu and Haifeng Yao

Photonics 2026, 13(1), 63; https://doi.org/10.3390/photonics13010063 (registering DOI) - 8 Jan 2026

Abstract

An experimental investigation was conducted to evaluate the statistical properties and scintillation mitigation performance of multi-aperture free-space optical transmission under real-measured atmospheric turbulence. Continuous monitoring of turbulence parameters over a 24 h period showed that the atmospheric coherence length ranged from 3 to [...] Read more.

An experimental investigation was conducted to evaluate the statistical properties and scintillation mitigation performance of multi-aperture free-space optical transmission under real-measured atmospheric turbulence. Continuous monitoring of turbulence parameters over a 24 h period showed that the atmospheric coherence length ranged from 3 to 29 cm, indicating that the experimental link operated predominantly under weak-to-moderate turbulence conditions, while a limited number of measurement intervals exhibited relatively strong scintillation and were included for statistical modelling analysis. An 865 m four-channel receiving link was constructed under the measured turbulence conditions to acquire irradiance data for analysis. The results show that the multi-aperture reception significantly suppresses scintillation, reducing the scintillation index from 0.36 to 0.04 under moderate turbulence. The irradiance probability density functions were fitted using lognormal, Gamma–Gamma, exponentiated Weibull, and Málaga (M) distributions. The M distribution exhibited superior adaptability, with fitting accuracy improved by 18.75% under weak turbulence and 13.16% under moderate turbulence. Further analysis shows that the shape parameters of the M distribution vary systematically with turbulence strength, effectively capturing the turbulence-induced evolution of irradiance statistics and providing experimental support for turbulence channel modelling and the optimisation of FSO diversity reception architectures. Full article

20 pages, 1873 KB

Open AccessArticle

Nighttime Contrail Characterization from Multisource Lidar and Meteorological Observations

by Florian Mandija, Philippe Keckhut, Dunya Alraddawi, Abdanour Irbah, Alain Sarkissian, Sergey Khaykin, Frédéric Peyrin and Jean-Luc Baray

Remote Sens. 2026, 18(2), 210; https://doi.org/10.3390/rs18020210 (registering DOI) - 8 Jan 2026

Abstract

The present study provides a comprehensive nighttime contrail characterization combining Raman lidar, ADS-B flight data, and ECMWF ERA5 reanalysis over southern France. Observations of different case studies of contrail formation and development throughout their lifetimes provide valuable insights into the contrails’ morphological, microphysical, [...] Read more.

The present study provides a comprehensive nighttime contrail characterization combining Raman lidar, ADS-B flight data, and ECMWF ERA5 reanalysis over southern France. Observations of different case studies of contrail formation and development throughout their lifetimes provide valuable insights into the contrails’ morphological, microphysical, and optical properties, persistence, and dispersion. We present a multisource methodology to detect and characterize nighttime aircraft contrails over the Observatory of Haute-Provence (OHP) in France. The determination of contrail signatures was performed by applying sensitivity analyses by spatiotemporal thresholding and clustering for contrail detection. Optimizing the thresholds permits the improvement of contrail detection and the reduction of unnecessary noise. The optimal combination of these thresholds, which best reduces false positives and negatives, was SR = 2.1, time = 7.2 min, and altitude = 0.3 km. Subsequent merging of the spots produces persistent contrail signatures at altitudes of 8.7–10.3 km, with thicknesses of 0.1–1.1 km, widths of 2–2.8 km, and optical depths of 0.05–0.40. Contrail optical depth correlates significantly with geometrical thickness and width, which highlights the interplay between contrail morphology and ambient thermodynamic conditions. Our methodology demonstrates the value of combining lidar and flight data for contrail characterization using lidar measurements, flight data, and meteorological information. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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16 pages, 7979 KB

Open AccessArticle

Transfer Learning Fractional-Order Recurrent Neural Network for MPPT Under Weak PV Generation Conditions

by Umair Hussan, Mudasser Hassan, Umar Farooq, Huaizhi Wang and Muhammad Ahsan Ayub

Fractal Fract. 2026, 10(1), 41; https://doi.org/10.3390/fractalfract10010041 (registering DOI) - 8 Jan 2026

Abstract

Photovoltaic generation systems (PVGSs) face significant efficiency challenges under partial shading conditions and rapidly changing irradiance due to the limitations of conventional maximum power point tracking (MPPT) methods. To address these challenges, this paper proposes a Transfer Learning-based Fractional-Order Recurrent Neural Network (TL-FRNN) [...] Read more.

Photovoltaic generation systems (PVGSs) face significant efficiency challenges under partial shading conditions and rapidly changing irradiance due to the limitations of conventional maximum power point tracking (MPPT) methods. To address these challenges, this paper proposes a Transfer Learning-based Fractional-Order Recurrent Neural Network (TL-FRNN) for robust global maximum power point (GMPP) tracking across diverse operating conditions. The incorporation of fractional-order dynamics introduces long-term memory and non-local behavior, enabling smoother state evolution and improved discrimination between local and global maxima, particularly under weak and partially shaded conditions. The proposed approach leverages Caputo fractional derivatives with Grünwald–Letnikov approximation to capture the history-dependent behavior of PVGSs while implementing a parameter-partitioning strategy that separates shared features from task-specific parameters. The architecture employs a multi-head design with GMPP regression and partial shading classification capabilities, trained through a two-stage process of pretraining on general PV data followed by efficient fine-tuning on target systems with limited site-specific data. The TL-FRNN achieved 99.2% tracking efficiency with 98.7% GMPP detection accuracy, reducing convergence time by 53% compared to state-of-the-art alternatives while requiring 72% less retraining time through transfer learning. This approach represents a significant advancement in adaptive, intelligent MPPT control for real-world photovoltaic energy-harvesting systems. Full article

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

16 pages, 861 KB

Open AccessReview

Emerging Oncogenic and Immunoregulatory Roles of BST2 in Human Cancers

by Chohee Kim, Seoyoon Choi and Jong-Whi Park

Biomedicines 2026, 14(1), 131; https://doi.org/10.3390/biomedicines14010131 (registering DOI) - 8 Jan 2026

Abstract

BST2 has emerged as a multifunctional molecule that bridges antiviral defense, membrane architecture, and tumor immunity. Originally characterized as an interferon-inducible restriction factor that tethers virions to the plasma membrane, BST2 is now recognized as an oncogenic driver and immunoregulatory hub in diverse [...] Read more.

BST2 has emerged as a multifunctional molecule that bridges antiviral defense, membrane architecture, and tumor immunity. Originally characterized as an interferon-inducible restriction factor that tethers virions to the plasma membrane, BST2 is now recognized as an oncogenic driver and immunoregulatory hub in diverse malignancies. In cancer, BST2 expression is frequently upregulated through promoter hypomethylation and transcriptional activation. Functionally, BST2 promotes proliferation, epithelial–mesenchymal transition, anoikis resistance, and chemoresistance, whereas its loss sensitizes tumor cells to proteotoxic and metabolic stresses. Beyond tumor cells, BST2 modulates the tumor microenvironment by promoting M2 macrophage infiltration, dendritic cell exhaustion, and natural killer (NK)-cell resistance, thereby contributing to immune evasion. Elevated BST2 expression correlates with poor prognosis in glioblastoma, breast, nasopharyngeal, and pancreatic cancers, and it serves as a circulating biomarker within small extracellular vesicles. In conclusion, BST2 is a dual-function molecule that integrates oncogenic signaling and immune regulation, making it an attractive diagnostic and therapeutic target for hematological and solid tumors. Full article

(This article belongs to the Special Issue Drug Resistance and Tumor Microenvironment in Human Cancers)

31 pages, 1781 KB

Open AccessArticle

Spatiotemporal Dynamics of Forest Biomass in the Hainan Tropical Rainforest Based on Multimodal Remote Sensing and Machine Learning

by Zhikuan Liu, Qingping Ling, Wenlu Zhao, Zhongke Feng, Huiqing Pei, Pietro Grimaldi and Zixuan Qiu

Forests 2026, 17(1), 85; https://doi.org/10.3390/f17010085 (registering DOI) - 8 Jan 2026

Abstract

Tropical rainforests play a vital role in maintaining global ecological balance, carbon cycling, and biodiversity conservation, making research on their biomass dynamics scientifically significant. This study integrates multi-source remote sensing data, including canopy height derived from GEDI and ICESat-2 satellite-borne lidar, Landsat imagery, [...] Read more.

Tropical rainforests play a vital role in maintaining global ecological balance, carbon cycling, and biodiversity conservation, making research on their biomass dynamics scientifically significant. This study integrates multi-source remote sensing data, including canopy height derived from GEDI and ICESat-2 satellite-borne lidar, Landsat imagery, and environmental variables, to estimate forest biomass dynamics in Hainan’s tropical rainforests at a 30 m spatial resolution, involving a correlation analysis of factors influencing spatiotemporal changes in Hainan Tropical Rainforest biomass. The research aims to investigate the spatiotemporal variations in forest biomass and identify key environmental drivers influencing biomass accumulation. Four machine learning algorithms—Backpropagation Neural Network (BP), Convolutional Neural Network (CNN), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were applied to estimate biomass across five forest types from 2003 to 2023. Results indicate the Random Forest model achieved the highest accuracy (R² = 0.82). Forest biomass and carbon stocks in Hainan Tropical Rainforest National Park increased significantly, with total carbon stocks rising from 29.03 million tons of carbon to 42.47 million tons of carbon—a 46.36% increase over 20 years. These findings demonstrate that integrating multimodal remote sensing data with advanced machine learning provides an effective approach for accurately assessing biomass dynamics, supporting forest management and carbon sink evaluations in tropical rainforest ecosystems. Full article

(This article belongs to the Special Issue Smart Forest Inventory, Management and Planning: Intelligent Technologies and Their Applications)

22 pages, 3264 KB

Open AccessArticle

Transition Behavior in Blended Material Large Format Additive Manufacturing

by James Brackett, Elijah Charles, Matthew Charles, Ethan Strickland, Nina Bhat, Tyler Smith, Vlastimil Kunc and Chad Duty

Polymers 2026, 18(2), 178; https://doi.org/10.3390/polym18020178 (registering DOI) - 8 Jan 2026

Abstract

Large-Format Additive Manufacturing (LFAM) offers the ability to 3D print composites at multi-meter scale and high throughput by utilizing a screw-based extrusion system that is compatible with pelletized feedstock. As such, LFAM systems like the Big Area Additive Manufacturing (BAAM) system provide a [...] Read more.

Large-Format Additive Manufacturing (LFAM) offers the ability to 3D print composites at multi-meter scale and high throughput by utilizing a screw-based extrusion system that is compatible with pelletized feedstock. As such, LFAM systems like the Big Area Additive Manufacturing (BAAM) system provide a pathway for incorporating AM techniques into industry-scale production. Despite significant growth in LFAM techniques and usage in recent years, typical Multi-Material (MM) techniques induce weak points at discrete material boundaries and encounter a higher frequency of delamination failures. A novel dual-hopper configuration was developed for the BAAM platform to enable in situ switching between material feedstocks that creates a graded transition region in the printed part. This research studied the influence of extrusion screw speed, component design, transition direction, and material viscosity on the transition behavior. Material transitions were monitored using compositional analysis as a function of extruded volume and modeled using a standard Weibull cumulative distribution function (CDF). Screw speed had a negligible influence on transition behavior, but averaging the Weibull CDF parameters of transitions printed using the same configurations demonstrated that designs intended to improve mixing increased the size of the blended material region. Further investigation showed that the relative difference and change in complex viscosity influenced the size of the blended region. These results indicate that tunable properties and material transitions can be achieved through selection and modification of composite feedstocks and their complex viscosities. Full article

(This article belongs to the Special Issue Additive Manufacturing of Polymer Based Materials)

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