Search Results (10,103)

Traditional energy harvesting systems, such as photovoltaics and wind power, often rely on external environmental conditions and are typically associated with contact-based vibration wear and bulky structures. This study introduces light-fueled self-vibration to propose a self-harvesting system, consisting of liquid crystal elastomer fibers, two resistors, and two piezoelectric cantilever beams arranged symmetrically. Based on the photothermal temperature evolution, we derive the governing equations of the liquid crystal elastomer fiber–piezoelectric beam system. Two distinct states, namely a self-harvesting state and a static state, are revealed through numerical simulations. The self-oscillation results from light-induced cyclic contraction of the liquid crystal elastomer fibers, driving beam bending, stress generation in the piezoelectric layer, and voltage output. Additionally, the effects of various system parameters on amplitude, frequency, voltage, and power are analyzed in detail. Unlike traditional vibration energy harvesters, this light-fueled self-harvesting system features a compact structure, flexible installation, and ensures continuous and stable energy output. Furthermore, by coupling the light-responsive LCE fibers with piezoelectric transduction, the system provides a non-contact actuation mechanism that enhances durability and broadens potential application scenarios. Full article

The submandibular glands (SMGs), along with the parotid and sublingual glands, generate the majority of saliva and play critical roles in maintaining oral and systemic health. Despite their physiological importance, long-term therapeutic options for salivary gland dysfunction remain limited, highlighting the need for a deeper molecular understanding of SMG biology, particularly in humans. To address this knowledge gap, we have performed transcriptomic- and epigenomic-based analyses and molecular characterization of the human SMG. Our integrated analysis of multiorgan RNA-sequencing datasets has identified an SMG-enriched gene expression signature comprising 289 protein-coding and 75 long non-coding RNA (lncRNA) genes that include both known regulators of salivary gland function and several novel candidates ripe for future exploration. To complement these transcriptomic studies, we have generated chromatin immunoprecipitation sequencing (ChIP-seq) datasets of key histone modifications on human SMGs. Our epigenomic analyses have allowed us to identify genome-wide enhancers and super-enhancers that are likely to drive genes and regulatory pathways that are important in human SMG biology. Finally, comparative analysis with mouse and human SMG and other tissue datasets reveals evolutionary conserved gene and regulatory networks, underscoring fundamental mechanisms of salivary gland biology. Collectively, this study offers a valuable knowledge-based resource that can facilitate targeted research on salivary gland dysfunction in human patients. Full article

Single-object vision–language tracking has become an important research topic due to its potential in applications such as intelligent surveillance and autonomous driving. However, existing cross-modal alignment methods typically rely on contrastive learning and struggle to effectively address semantic ambiguity or the presence of multiple similar objects. This study aims to explore how to achieve more robust vision–language alignment under these challenging conditions, thereby achieving accurate object localization. To this end, we propose a text heatmap mapping (THM) module that enhances the spatial guidance of textual cues in tracking. The THM module integrates visual and language features and generates semantically aware heatmaps, enabling the tracker to focus on the most relevant regions while suppressing distractors. This framework, developed based on UVLTrack, combines a visual transformer with a pre-trained language encoder. The proposed method is evaluated on benchmark datasets such as OTB99, LaSOT, and TNL2K. The main contribution of this paper is the introduction of a novel spatial alignment mechanism for multimodal tracking and its effectiveness on various tracking benchmarks. Results demonstrate that the THM-based tracker improves robustness to semantic ambiguity and multi-instance interference, outperforming baseline frameworks. Full article

Wnt signaling is an ancient developmental mechanism that drives the initial specification and patterning of the primary axis in many metazoan embryos. Yet, it is unclear how exactly the various Wnt components interact in most Wnt-mediated developmental processes as well as in the molecular mechanism regulating adult tissue homeostasis. Recent work in invertebrate deuterostome sea urchin embryos indicates that three different Wnt signaling pathways (Wnt/β-catenin, Wnt/JNK, and Wnt/PKC) form an interconnected Wnt signaling network that specifies and patterns the primary anterior–posterior (AP) axis. Here, we detail our current knowledge of this critical regulatory process in sea urchin embryos. We also illustrate examples from a diverse group of metazoans, from cnidarians to vertebrates, that suggest aspects of the sea urchin AP Wnt signaling network are deeply conserved. We explore how the sea urchin is an excellent model to elucidate a detailed molecular understanding of AP axis specification and patterning that can be used for identifying unifying developmental principles across animals. Full article

In recent years, China’s hilly and mountainous areas have faced widespread farmland abandonment. However, research on farmland abandonment and its driving mechanisms in hilly and mountainous regions is limited. This study proposes a transferable methodological framework that integrates Landsat data, Google Earth Engine, a time sliding-window algorithm, and the interpretable XGBoost–Shapley Additive explanation (SHAP) model. The time sliding-window algorithm is used to robustly detect long-term land cover changes across the entire study period. The SHAP quantifies the contributions of key drivers to farmland abandonment, providing transparent insights into the driving mechanisms. Applying this framework, we systematically analyzed the spatiotemporal evolution patterns and driving factors of farmland abandonment in Ji’an City, a typical city located in the hilly and mountainous areas of southern China and ultimately developed a farmland abandonment probability distribution map. The findings demonstrate the following. (1) Methodological validation showed that the random forest classifier achieved a mean overall accuracy (OA) of 91.05% (Kappa = 0.88) and the abandonment maps achieved OA of 91.58% (Kappa = 0.83). (2) Spatiotemporal analysis revealed that farmland area increased by 13.26% over 1990–2023, evolving through three stages: fluctuation (1990–2005), growth (2006–2015), and stability (2016–2023). The abandonment rate showed a long-term decreasing trend, peaking in 1998, whereas the abandoned area reached its minimum in 2007. From a spatial perspective, abandonment was more pronounced in mountainous and hilly regions of the study areas. (3) The XGBoost–SHAP model (R² > 0.85) identified key driving factors, including the potential crop yield, soil properties, mean annual precipitation, population density, and terrain features. By offering an interpretable and transferable monitoring framework, this study not only advances farmland abandonment research in complex terrains but also provides concrete policy implications. The results can guide targeted protection of high-risk abandonment zones, promote sustainable land-use planning, and support adaptive agricultural policies in hilly and mountainous regions. Full article

The increasing demand for reliable, sensitive, and cost-effective gas sensors drives ongoing research in this field. Ideal gas sensors must demonstrate high sensitivity and selectivity, stability, rapid response and recovery times, energy efficiency, and affordability. One-dimensional (1D) metal oxide semiconductors (MOSs) are prominent candidates due to their excellent sensing properties and straightforward fabrication processes. The sensing efficacy of 1D MOSs is heavily dependent on their surface area and porosity, which influence gas interaction and detection efficiency. Polymeric templates serve as effective tools for enhancing these properties by enabling the creation of uniform, porous nanostructures with high surface area, thereby improving gas adsorption, sensitivity, and dynamic response characteristics. This review systematically examines the role of polymeric templates in the construction of 1D MOSs for gas sensing applications. It discusses critical factors influencing polymer template selection and how this choice affects key microstructural parameters, such as grain size, pore distribution, and defect density, essential to sensor performance. The recent literature highlights the mechanisms through which polymer templates facilitate the fine-tuning of nanostructures. Future research directions include exploring novel polymer architectures, developing scalable synthesis methods, and integrating these sensors with emerging technologies. Full article

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI. Full article

The gut microbiome possesses a diverse range of biological properties that play a role in maintaining host health and preventing disease. Gut microbial metabolites, including short-chain fatty acids, natural purine nucleosides, ellagic acid derivatives, and tryptophan metabolites, have been observed to have complex and multifaceted roles in the gut and in wider body systems in the management of disease, including cancer. Triple-negative breast cancer is the most aggressive subtype of breast cancer, with restricted treatment options and poor prognoses. Recently, preclinical research has investigated the antiproliferative potential of gut microbial metabolites against this type of breast cancer with promising results. However, little is understood about the mechanisms of action and molecular pathways driving this antiproliferative potential. Understanding the complex mechanisms of action of gut microbial metabolites on triple-negative breast cancer will be instrumental in the investigation of the combined administration with standard chemotherapeutic drugs. To date, there is a paucity of research studies investigating the potential synergistic interactions between gut microbial metabolites and standard chemotherapeutic drugs. The identification of synergistic potential between these compounds may provide alternate and more effective therapeutic options in the treatment and management of triple-negative breast cancer. Further investigation into the mechanistic action of gut microbial metabolites against this breast cancer subtype may support the administration of more cost-effective treatment options for breast cancer, with an aim to reduce side effects associated with standard treatments. Additionally, future research will aim to identify more potent metabolite–drug combinations in the mitigation of triple-negative breast cancer progression and metastasis. Full article

Semi-humid subtropical montane regions face the dual pressures of climate change and water scarcity, making it essential to understand how soil carbon–water coupling varies among forest types. Focusing on seven representative forest types in the central Yunnan Plateau, this study analyzes the spatial distribution, trade-offs, and drivers of soil organic carbon storage (SOCS) and soil water storage (SWS) within the 0–60 cm soil layer, using sloping rainfed farmland (SRF) as a reference. We hypothesize that, relative to SRF, both SOCS and SWS increase across forest types; however, the direction and strength of the SOCS–SWS trade-off differ among plant communities and are regulated by litter traits and soil structural properties. The results show that SOCS in all forest types exceeded that in SRF, whereas a significant increase in SWS occurred only in ACF. Broadleaf stands were particularly prominent: SOCS rose most in the 23 yr SF and the 20 yr ACF (274.44% and 256.48%, respectively), far exceeding the 9–60 yr P. yunnanensis stands (44.01%–105.32%). Carbon–water trade-offs varied by forest type and depth. In conifer stands, SWS gains outweighed SOCS and trade-off intensity increased with stand age (RMSD from 0.48 to 0.53). In broadleaf stands, SOCS gains were larger, with RMSD ranging from 0.21 to 0.45 and the weakest trade-off in SF. Across depths, SOCS gains exceeded SWS in 0–20 cm, whereas SWS gains dominated in 40–60 cm. Regression analyses indicated a significant negative SOCS–SWS relationship in conifer stands and a significant positive relationship in 0–20 cm soils (both p < 0.05), with no significant correlations in other forest types or depths (p > 0.05). Correlation results further suggest that organic matter inputs, N availability, and soil physical structure jointly regulate carbon–water trade-off intensity across forest types and soil depths. We therefore recommend prioritizing native zonal broadleaf species, as well as protecting SF and establishing mixed conifer–broadleaf stands, to achieve synergistic improvements in SOCS and SWS. Full article

Introduction: Fusobacterium nucleatum, a common oral microbe associated with periodontal disease, has emerged as a significant prognostic indicator in colorectal cancer (CRC). This organism is notably enriched in CRC tissues and is associated with reduced survival times and relapse. Fusobacterium is implicated in encouraging the development of chemoresistance through diverse tumor-promoting pathways that are increasingly being elucidated across molecular domains. Methods: This work uses a combined analysis of public data examining the role of F. nucleatum in CRC by investigating multiple transcriptomic datasets derived from co-culture infections in vitro. Results: In tandem with previously identified mechanisms known to be influenced by F. nucleatum, this analysis revealed that the bacterium activates multiple chemoresistance-associated pathways, including those driving inflammation, immune evasion, DNA damage, and metastasis. Notably, this study uncovered a novel induction of type I and type II interferon signaling, suggesting activation of a pseudo-antiviral state. Furthermore, pathway analysis (IPA) predicted altered regulation of several therapeutic agents, suggesting that F. nucleatum may compromise drug efficacy through transcriptional reprogramming. Conclusions: These findings reinforce the role of F. nucleatum in modulating host cellular pathways and support the hypothesis that bacterial association potentiates chemoresistance. Full article

In this paper, the impact of SiN passivation on dynamic-R_ON degradation of AlGaN/GaN HEMTs devices is put in evidence. To this end, samples showing different SiN passivation stoichiometry are considered, labeled as Sample A and Sample B. For dynamic-R_ON tests, two different experimental setups are employed to investigate the R_ON-drift showing up during conventional switch mode operation by driving the DUTs under both (i) resistive load and (ii) soft-switching trajectory. This allows to discern the impact of hot carriers and off-state drain voltage stress on the R_ON parameter drift. Measurements performed with both switching loci shows similar dynamic-R_ON response, indicating that hot carriers are not involved in the degradation of tested devices. Nevertheless, a significant difference was observed between Sample A and Sample B, with the former showing an additional R_ON-degradation mechanism, not present on the latter. This additional drift is totally ascribed to the SiN passivation layer and is confirmed by the different leakage current measured across the two SiN types. The mechanism is explained by the injection of negative charges from the Source Field-Plate towards the AlGaN surface that are captured by surface/dielectric states and partially depletes the 2DEG underneath. Full article

Accumulative rolling bonding (ARB) Cu/Nb nanolaminates have been widely observed to exhibit unique and large numbers of interface-based plasticity mechanisms, and these have been associated with the many extraordinary properties of the material system, especially resistances in extreme engineering environments (mechanical/pressure, thermal, irradiation, etc.) and ability to self-heal defects (microstructural, as well as radiation-induced). Recently, anisotropy in the interface shearing mechanisms in the material system has been observed and much discussed. The Cu/Nb nanolaminates appear to shear on the interface planes to a much larger extent in the transverse direction (TD) than in the rolling direction (RD). Related to that, in this present study we observe interface rotation in Cu/Nb ARB nanolaminates under constrained and unconstrained loading conditions. Although the primary driving force for interface shearing was expected only in the RD, additional shearing in the TD was observed. This is significant as it represents an interface rotation, while there was no external rotational driving force. First, we observed interface rotation in in situ rectangular micropillar compression experiments, where the interface is simply sheared in one particular direction only, i.e., in the RD. This is rather unexpected as, in rectangular micropillar compression, there is no possibility of extra shearing or driving force in the perpendicular direction due to the loading conditions. This motivated us to subsequently perform in situ microbeam bending experiments (microbeam with a pre-made notch) to verify if similar interface rotation could also be observed in other loading modes. In the beam bending mode, the notch area was primarily under tensile stress in the direction of the beam longitudinal axis, with interfacial shear also in the same direction. Hence, we expect interface shearing only in that direction. We then found that interface rotation was also evident and repeatable under certain circumstances, such as under an offset loading. As this behaviour was consistently observed under two distinct loading modes, we propose that it is an intrinsic characteristic of Cu/Nb interfaces (or FCC/BCC interfaces with specific orientation relationships). This interface rotation represents another interface-based or interface-mediated plasticity mechanism at the nanoscale with important potential implications especially for design of metallic thin films with extreme stretchability and other emerging applications. Full article

In recent years, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed to tackle constrained multi-objective optimization problems (CMOPs). However, most of them still struggle to achieve a good balance among convergence, diversity, and feasibility. To address this issue, we develop an adaptive constraint-boundary learning-based two-stage dual-population evolutionary algorithm for CMOPs, referred to as CL-TDEA. The evolutionary process of CL-TDEA is divided into two stages. In the first stage, two populations cooperate weakly through environmental selection to enhance the exploration ability of CL-TDEA under constraints. In particular, the auxiliary population employs an adaptive constraint-boundary learning mechanism to learn the constraint boundary, which in turn enables the main population to more effectively explore the constrained search space and cross infeasible regions. In the second stage, the cooperation between the two populations drives the search toward the complete constrained Pareto front (CPF) through mating selection. Here, the auxiliary population provides additional guidance to the main population, helping it escape locally feasible but suboptimal regions by means of the proposed cascaded multi-criteria hierarchical ranking strategy. Extensive experiments on 54 test problems from four benchmark suites and three real-world applications demonstrate that the proposed CL-TDEA exhibits superior performance and stronger competitiveness compared with several state-of-the-art methods. Full article

Bangkok is the largest city in Thailand and the second largest city in Southeast Asia. Due to the rapid urbanization and upgrading of economic structures, the real estate market in Bangkok is not only constrained by domestic factors but also fluctuates with international economic cycles. Bangkok’s long history, diverse culture, developed economy, and incomplete land infrastructure make the formation of housing prices particularly complex. In this study, we collected 13,175 residence transaction data from 2076 different neighborhoods in Bangkok and explored multiscale effects of various land infrastructure factors on housing prices in Bangkok at the neighborhood level. Our analysis not only supports land planning departments of Bangkok to make more reasonable facility planning but also provides new insights into driving mechanisms of housing prices in other cities of Thailand and ASEAN countries Full article

Against the backdrop of the global integration of green transformation and the digital economy, how manufacturing enterprises leverage digitalisation to drive green innovation has become a focal point for both academic and industrial sectors. Based on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study constructs a moderated mediation model to explore the internal mechanism through which digital transformation influences green innovation via green dynamic capabilities and examines the boundary role of environmental munificence. Questionnaire data, collected in two stages from 312 Chinese manufacturing enterprises using SPSS 27.0 and AMOS 24.0, was analysed, and the empirical results indicate that digital transformation not only directly promotes green innovation but also exerts an indirect influence through the three dimensions of green dynamic capabilities: insights into the capability of green opportunities, green resource integration, and green resource reconstruction. Furthermore, environmental munificence significantly and positively moderates the relationship between green dynamic capabilities and green innovation, suggesting that this relationship is strengthened in resource- and opportunity-rich environments. Path analysis of the three green dynamic capability dimensions reveals that back-end capabilities (resource integration and reconfiguration) have a more pronounced impact on green innovation than front-end capabilities (opportunity insights). From the dual perspectives of capability building and contextual fit, this study elucidates the mechanism and boundary conditions of digital transformation driving green innovation, enriches green innovation theory, and offers practical insights into the digital-green transformation of manufacturing enterprises. Full article