Search Results (111,850)

Terminalia catappa L. (Family: Combretaceae) is used globally to treat various diseases, including bacterial infections. Whilst the antibacterial activity of T. catappa has previously been tested against antibiotic-sensitive bacterial strains, the antimicrobial activity against methicillin and β-lactam-resistant pathogens has been relatively ignored. The antibacterial activity of T. catappa extracts, both alone and combined with selected clinical antibiotics, was evaluated in this study. The inhibition of bacterial growth by the extracts was determined using agar diffusion and broth micro-dilution assays. Combinations of the extracts and several clinical antibiotics were also examined and the ∑FICs were calculated to determine the interaction class. Synergistic combinations were further evaluated by isobologram analysis. The T. catappa leaf extracts were screened for toxicity using Artemia franciscana lethality bioassays (ALA). Orbitrap liquid chromatography–mass spectrometry (LC-MS) profiling analysis was undertaken to identify flavonoid components of the extracts, putatively. The T. catappa methanolic extract inhibited all the tested bacterial strains. It displayed especially good inhibitory activity against E. coli (MIC = 130 µg/mL). Combining the T. catappa extracts with some conventional antibiotics potentiated the inhibitory activity of the combinations compared to the activity of individual components. LC-MS profiling analysis identified multiple flavonoid components, including rutin, quercitin, orientin, the tannin component, and ellagic acid in the extracts. All extracts were non-toxic against Artemia nauplii. The phytochemical constituents present in the T. catappa leaf extracts warrant future investigation as potential antibacterial agents. Full article

Cultural heritage is framed as a living resource for citizenship and education, although evidence on how in situ augmented reality can cultivate sustainability competences remains limited. This study examines the Art Nouveau Path, a location-based mobile augmented reality game across eight points of interest in Aveiro, Portugal, aligned with the GreenComp framework. Within a design-based research case study, the analysis integrates repeated cross-sectional student questionnaires (S1-PRE N = 221; S2-POST N = 439; S3-FU N = 434), anonymized gameplay logs from 118 collaborative groups, and 24 teacher field observations (T2-OBS), using quantitative summaries with reflexive thematic analysis. References to heritage preservation in students’ sustainability conceptions rose from 28.96% at baseline to 61.05% immediately after gameplay, remaining above baseline at follow-up (47.93%). Augmented reality items were answered more accurately than non- augmented reality items (81% vs. 73%) and involved longer on-site exploration (+10.17 min). Triangulated evidence indicates that augmented reality and multimodality amplified attention to architectural details and prompted debates about authenticity. Built heritage, mobilized through lightweight augmented reality within a digital teaching and learning ecosystem, can serve as an effective context for Education for Sustainable Development, strengthening preservation literacy and civic responsibility and generating interoperable cultural traces for future reuse. Full article

Fused deposition modeling of polyether ether ketone offers distinct advantages for fabricating complex and lightweight structures. Although three principal build orientations theoretically exist for practical 3D engineering components, research on their effects remains limited, especially regarding the influence of the interaction between build orientation and printing path on mechanical performance. This study investigated the tensile and shear properties, as well as the failure mechanisms, of FDM-fabricated PEEK under the coupled effects of build orientation and printing path through mechanical testing, fracture morphology analysis, and statistical methods. The results indicate that the printing path exerts a dominant influence on anisotropic behavior, while the interaction between printing path and build orientation jointly governs the shear failure modes. Under identical printing paths, the elongation at break varied by up to twofold across different build orientations, reaching a maximum of 96%, whereas samples printed with W or T paths exhibited elongations at break below 5%. Although shear and tensile moduli remained largely consistent across build orientations, other mechanical properties demonstrated significant differences. Variations in cross-sectional dimensions induced by build orientation markedly affected tensile performance: the coupled effect of build orientation and printing path was found to render the path repetition frequency a critical factor in determining temperature uniformity within the printed region and the quality of interlayer interfaces, thereby constituting the core mechanism underlying anisotropic behavior. Furthermore, larger cross-sections re-duced tensile modulus but enhanced yield strength and elongation at break, highlight-ing the regulatory role of cross-sectional geometry on mechanical response. Based on these findings, a synergistic optimization strategy integrating printing path, build orientation, and tensile–shear performance is proposed to achieve tailored mechanical properties in FDM-fabricated PEEK components. This approach enables controlled enhancement of structural performance to meet diverse application requirements. Full article

This paper introduces a zero-carbon heating solution called High-Performance Heat-Powered Heat Pumps (HP³), which combine the best attributes of hydrogen boilers and electric heat pumps. HP³ systems allow us to continue using the existing gas infrastructure, offer higher efficiencies than hydrogen boilers, and avoid overwhelming the electricity grid. An HP³ blends a heat engine and a heat pump into a single, fully integrated system sharing a common working fluid. This differentiates HP³ systems from gas-engine-driven heat pumps (GEHP), where the integration between subsystems is limited to a mechanical shaft. A parametric analysis of a propane-based system is presented. The heat engine section has two main design variables: the working fluid’s temperature ($T_{max}$) and pressure ($P_{high}$) after collecting high-grade heat from hydrogen combustion. Typical GEHPs achieve CoPs of around 1.8. The HP³ concept achieves a CoP of 2.59 considering a $T_{max}$ of 650 °C, $P_{high}$ of 250 bar, and an ambient temperature of −9 °C. The paper presents a model for the expander’s efficiency, which indicates that increasing the system’s output makes it possible to achieve a higher expansion efficiency with a lower rotational speed. Results show that HP³ is a promising concept for larger applications such as commercial buildings or district heating systems. Full article

Crowd counting is a significant task in computer vision. By combining the rich texture information from RGB images with the insensitivity to illumination changes offered by thermal imaging, the applicability of models in real-world complex scenarios can be enhanced. Current research on RGB-T crowd counting primarily focuses on feature fusion strategies, multi-scale structures, and the exploration of novel network architectures such as Vision Transformer and Mamba. However, existing approaches face two key challenges: limited robustness to illumination shifts and insufficient handling of scale discrepancies. To address these challenges, this study aims to develop a robust RGB-T crowd counting framework that remains stable under illumination shifts, through introduces two key innovations beyond existing fusion and multi-scale approaches: (1) a cross-modal adaptive fusion module (CMAFM) that actively evaluates and fuses reliable cross-modal features under varying scenarios by simulating a dynamic feature selection and trust allocation mechanism; and (2) a multi-scale aggregation module (MSAM) that unifies features with different receptive fields to an intermediate scale and performs weighted fusion to enhance modeling capability for cross-modal scale variations. The proposed method achieves relative improvements of 1.57% in GAME(0) and 0.78% in RMSE on the DroneRGBT dataset compared to existing methods, and improvements of 2.48% and 1.59% on the RGBT-CC dataset, respectively. It also demonstrates higher stability and robustness under varying lighting conditions. This research provides an effective solution for building stable and reliable all-weather crowd counting systems, with significant application prospects in smart city security and management. Full article

This paper presents a comprehensive study on the feasibility and implications of a CO₂ injection simulation in the Syderiai deep saline aquifer of Lithuania, focusing on leakage, geo-mechanical aspects, and geo-chemical aspects. The Syderiai aquifer, characterized by its sandstone formation covered by shaly rocks, is considered a potential site for CO₂ geological storage in Lithuania. Using 3D mechanistic models developed in T-navigator software, we conducted extensive simulations to analyze CO₂ storage behavior and associated impacts. The leakage study examines various scenarios to assess the impact of fracture permeability, layer-wise heterogeneity, and fracture position on CO₂ injection and leakage volumes. Results indicate that while fracture permeability influences CO₂ migration dynamics, its impact on both free and dissolved CO₂ leakage volumes is minimal, highlighting that leakage behavior is more dependent on the presence of fractures than their permeability. Geo-mechanical analysis reveals the effects of CO₂ injection on the bulk modulus and shear modulus of sandstone and shale formations, highlighting changes in compaction and cementation. The geo-chemical study was performed using TOUGHREACT software to investigate the distribution of pH, porosity change, and free CO₂ over 1000-years following 10-year CO₂ injection. Results demonstrate the acidifying effect of CO₂ injection and its implications for the caprock–reservoir interface over time. The findings offer valuable perspectives on the feasibility and consequences of CO₂ geological storage in the Syderiai deep saline aquifer, highlighting the importance of incorporating leakage, geo-mechanical aspects, and geo-chemical aspects for implementing efficient CO₂ storage. Full article

Background: Breast cancer is the most prevalent cancer in women, and metastatic breast cancer remains a major cause of cancer-related deaths. Resveratrol (RSV) is a natural compound found in various plants and is known to exhibit various anti-cancer effects. The present study aims to investigate the therapeutic effects and mechanisms of RSV in inhibiting breast cancer metastasis in a murine model of 4T1 breast tumor that shares close molecular features with human triple negative breast cancer. Methods: Murine breast cancer 4T1 cells were used to examine the effects of RSV on breast cancer metastasis and epithelial–mesenchymal transition (EMT). In vitro cell proliferation and Transwell migration assays and in vivo 4T1 tumor transplantation models were established in female Balb/c mice to determine the anti-metastatic effects of RSV and its mechanism of action. Results: RSV significantly inhibited 4T1 tumor cell migration and significantly decreased expression levels of EMT markers Snail and Vimentin, as well as the nuclear translocation of β-catenin both in vitro and in vivo. Knockdown of β-catenin similarly reduced the expression levels of EMT markers. RSV significantly decreased the number of lung metastases in 4T1-implanted mice by inhibiting Wnt/β-catenin signaling pathway activation. RSV (150 mg/kg/day) reduced the number of visible tumor metastatic nodules and the histological count of metastatic lung carcinomas by 51.82% and 62.58%, respectively, compared to vehicle administration. Conclusions: Our study provides important new mechanistic insight into the strong anti-cancer effects of RSV in inhibiting 4T1 breast cancer metastasis by preventing Wnt/β-catenin signaling pathway-mediated epithelial–mesenchymal transition. These findings suggest the therapeutic potential of RSV as a promising drug in the treatment of metastatic breast cancer. Full article

Intelligent ventilation is positioned as a key axis for reconciling energy efficiency and indoor air quality (IAQ) in residential and non-residential buildings. This review synthesizes 51 recent publications covering control strategies (DCV, MPC, reinforcement learning), IoT architectures and sensor validation, energy recovery (HRV/ERV, anti-frost strategies, low-loss exchangers, PCM-air), active envelope solutions (thermochromic windows) and passive solutions (EAHE), as well as evaluation methodologies (uncertainty, LCA, LCC, digital twin) and smart readiness indicator (SRI) frameworks. Evidence shows ventilation energy savings of up to 60% without degrading IAQ when control is well-designed, but also possible overconsumption when poorly parameterized or contextualized. Performance uncertainty is strongly influenced by occupant emissions and pollutant sources (bioeffluents, formaldehyde, PM2.5). The integration of predictive control, scalable IoT networks, and robust energy recovery, together with life-cycle evaluation and uncertainty analysis, enables more reliable IAQ-energy balances. Gaps are identified in VOC exposure under DCV, robustness to sensor failures, generalization of ML/RL models, and standardization of ventilation effectiveness metrics in natural/mixed modes. Full article

As the last critical hydrological control station on the Lancang River before it flows out of China, the daily runoff variations at the Yunjinghong Hydrological Station are directly linked to agricultural irrigation, hydropower development, and ecological security in downstream Mekong River riparian countries such as Laos, Myanmar, and Thailand. Aiming at the core issues of the runoff sequence in the Lancang–Mekong Basin, which is characterized by prominent nonlinearity, non-stationarity, and coupling of multi-scale features, this study proposes a synergistic prediction framework of “multi-scale decomposition-model improvement-parameter optimization”. Firstly, Regenerated Phase-Shifted Sine-Assisted Empirical Mode Decomposition (RPSEMD) is adopted to adaptively decompose the daily runoff data. On this basis, a Convolutional Sparse Attention Transformer (CSAT) model is constructed. A one-dimensional convolutional neural network (1D-CNN) module is embedded in the input layer to enhance local feature perception, making up for the deficiency of traditional Transformers in capturing detailed information. Meanwhile, the sparse attention mechanism replaces the multi-head attention, realizing efficient focusing on key time-step correlations and reducing computational costs. Additionally, an Improved Multi-Verse Optimizer (IMVO) is introduced, which optimizes the hyperparameters of CSAT through a spiral update mechanism, exponential Travel Distance Rate (T_DR), and adaptive compression factor, thereby improving the model’s accuracy in capturing short-term abrupt patterns such as flood peaks and drought transition points. Experiments are conducted using measured daily runoff data from 2010 to 2022, and the proposed model is compared with mainstream models such as LSTM, GRU, and standard Transformer. The results show that the RPSEMD-IMVO-CSAT model reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 15.3–28.7% and 18.6–32.4%, respectively, compared with the comparative models. Full article

Background/Objectives: The pathogenesis of ulcerative colitis (UC) is complex, and there is an urgent need for effective therapeutic agents with low side effects. Recent studies highlight the critical roles of abnormal bile acid (BA) metabolism and gut microbiota dysbiosis in UC progression. However, there is a significant knowledge gap about the relation between BA and gut microbiota. The BA derivative T3K exerts good anti-UC effect, and its mechanism is still unknown. In this study, we investigate how its anti-UC mechanism is involved in the modulation of the gut microbiota-BA axis and BA metabolism. Methods: Gene expression microarray GSE92415 of UC from the Gene Expression Omnibus was used to analyze BA metabolism. DSS-induced colitis mouse model, Caco-2 and IEC6 cells were used to confirm the anti-UC of T3K using intestinal permeability assay with FITC, Western-blot, immunohistochemical staining, immunofluorescenc and so on in vitro and in vivo. The changes in bile acid and microbiota were measured by 16S rRNA sequencing and bile acid analysis combined with pseudo-germ-free (PGF) models and fecal microbiota transplantation (FMT). Results: T3K demonstrated strong therapeutic effects, including reduced weight loss, lower disease activity index (DAI), and increased colon length. T3K also enhanced the expression of Occludin and Mucin2, and restored gut barrier integrity. Furthermore, T3K improved intestinal dysbiosis and abnormal BA metabolism in colitis mice. Through PGF models and FMT, we confirmed that T3K modulates BA metabolism via the gut microbiota. T3K specifically promotes the growth of beneficial bacteria, such as Akkermansia muciniphila, increases levels of hydrophilic BAs like muricholic acid (MCA), lithocholic acid (LCA) and its derivatives isoLCA and then repairs damaged intestinal mucosa. Conclusions: Bile acid derivative T3K, as a potential anti-UC candidate, effectively restores gut barrier integrity and then ameliorates colitis by improving gut microbiota composition and regulating BA metabolism, including increasing hydrophilic BAs. Full article

Background: Neurofibromatosis 1 is an autosomal dominant disorder accompanied by extensive early-onset spinal manifestations, with or without dystrophic scoliotic features. While non-dystrophic subtypes can often be treated similarly to idiopathic scoliosis, dystrophic scoliosis typically requires more aggressive intervention, often involving instrumentation in severely compromised pedicles or vertebrae. Purpose: This review aims to present recent advances in the surgical treatment of Neurofibromatosis 1-associated scoliosis, including surgical techniques and emerging guidance methods. Methods: An electronic literature search was conducted in Web of Science and PubMed to identify surgical techniques for scoliosis in patients with Neurofibromatosis 1. Results: Forty-one studies on the operative treatment of dystrophic scoliosis or both subtypes were retrieved. Although aggressive treatment with combined anterior and posterior fusion are widely used, posterior-only methods, which avoid plexiform tumours, present encouraging results. Recent studies highlight the effectiveness of growing rod systems in early-onset cases, enabling delayed fusion while preserving T1-S1 growth. Promising results from sectional or segmented correction techniques demonstrate better sagittal balance and Cobb angle correction, respectively. Preoperative use of halo-gravity traction, which has been extensively studied, is associated with reduced neurological impairment and encourages better correction results, avoiding autofusion. Various studies have also reported more precise pedicle screw placement with guidance of O-arm and triggered electromyography (t-EMG). Conclusions: The correction of spinal scoliotic deformities presents a significant challenge. However, recent advances in surgical techniques and intraoperative guidance offer promising strategies for more effective management. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

With the advancement of Internet of Things (IoT) technology, flexible sensors with dual optoelectronic sensing modes have emerged as a research hotspot for next-generation smart devices, further driving the urgent demand for environmentally friendly functional materials. Here, we innovatively integrated wastepaper recycling technology with a polyethyleneimine (PEI)-assisted pulping strategy to develop a novel cellulose-based sustainable photo-triboelectric hybrid nanogenerator (PT-HNG). Based on the working mechanism of a freestanding triboelectric nanogenerator (TENG), the PT-HNG can directly convert pressure stimuli into electrical energy and triboelectrification-induced electroluminescence (TIEL) signals. It achieves luminescence brightness of 0.06 mW cm⁻² (3.84 cd m⁻²) and simultaneously delivers excellent electrical output performance (172.4 V, 6.36 μA, 43.7 nC) under sliding motion. More importantly, compatible with existing industrial papermaking processes, the PT-HNG is scalable for large-scale production. By combining PT-HNG with deep learning algorithms, a handwritten e-book system based on trajectory recognition was constructed, with a recognition accuracy of up to 95.5%. In addition, real-time intelligent control of PowerPoint presentations via PT-HNG was demonstrated. This study provides a new pathway for converting wastepaper into intelligent products and presents a novel idea for the interdisciplinary integration of the circular economy and advanced electronic technology. Full article

The neuroendocrine and immune systems interact bidirectionally through shared ligands and receptors during inflammation, thereby regulating immune responses. Leptin, primarily known for its role in energy metabolism and appetite regulation, also modulates neuroinflammatory pathways. Its receptors are widely expressed on immune cells and contribute to immune mechanisms implicated in the pathogenesis of neuroinflammatory disorders such as multiple sclerosis (MS) and Alzheimer’s disease (AD). This review highlights recent advances in understanding leptin’s role in immune regulation, with a focus on its impact on MS and AD. A comprehensive literature review was conducted until October 2025, using PubMed, Google Scholar, and Scopus to identify studies investigating leptin in neuroinflammatory conditions, particularly MS and AD. Leptin exerts broad immunomodulatory effects by activating T cells, dendritic cells, and microglia, and promoting their proliferation and phagocytosis. Its elevation enhances Th1 and Th17 responses, drives pro-inflammatory macrophage phenotype polarization, and suppresses regulatory T cell and Th2 responses, immune pathways involved in MS. Peripheral leptin levels are increased in MS, especially during disease exacerbations. In contrast, in AD, they are typically reduced, particularly in patients with normal body mass index (BMI), where their decline contributes to amyloid-β and tau pathology. These divergent patterns position leptin as a bidirectional regulator at the intersection of immunity and neurodegeneration. Additionally, its protective or detrimental effects likely depend on whether it acts under physiological conditions or in the context of obesity-induced leptin resistance. Elevated leptin levels in obesity exacerbate inflammation and diminish its neuroprotective effects. In conclusion, leptin is elevated in MS patients but downregulated in AD, reflecting its bidirectional effects. In leptin resistance, peripheral proinflammatory signaling is maintained while central leptin signaling is restricted, thereby potentially promoting autoimmunity in MS and limiting neuroprotection in AD. Further mechanistic and longitudinal studies are needed to clarify the relationship between leptin dysregulation, leptin resistance, neuroinflammatory and neurodegenerative diseases. Full article

Background: Type 2 diabetes (T2D) is a growing public health problem in Mexico. Lipid profile alterations have been shown to appear years before changes in glycemic biomarkers, and some of the latter are limited in availability, especially in underserved settings. Therefore, anthropometric variables and lipids represent relevant early indicators for the early detection of the disease. This study evaluates the capacity of non-glycemic clinical data—including lipid profile and anthropometric indicators—to detect T2D using machine learning, and compares the performance of different feature engineering approaches. Methods: Using more than a thousand clinical records of Mexican adults, three experiments were developed: (1) a distribution and normality analysis to characterize the variability of lipid variables; (2) an evaluation of the predictive power of multiple atherogenic indices (Castelli I, Castelli II, TG/HDL, and AIP); and (3) the implementation of statistical transformations (logarithmic, quare-root, and Z-standardization) to stabilize variance and improve feature quality. Logistic regression, SVM-RBF, random forest, and XGBoost models were trained on each feature set and evaluated using accuracy, sensitivity, specificity, F1-score, and area under the ROC curve. Results: The AIP index showed the greatest discriminatory power among the atherogenic indices, while normality-based transformations improved the performance of distribution-sensitive models, such as SVM. In the final experiment, the SVM-RBF and XGBoost models achieved AUC values greater than 0.90, demonstrating the feasibility of a diagnostic approach based exclusively on non-glycemic data. Conclusions: The findings indicate that the transformed lipid profile and anthropometric variables can constitute a solid and accessible alternative for the early detection of T2D in clinical and public health contexts, offering a robust methodological framework for future predictive applications in the absence of traditional glycemic biomarkers. Full article