Search Results (162,304)

This study addresses the dual challenges of low copper recovery and persistent arsenic pollution in the bioleaching of low-grade, high-arsenic copper ores containing enargite (Cu₃AsS₄). Through integrated electrochemical, chemical, and biological investigations, a selective and environmentally sustainable two-stage hybrid leaching process was developed. Electrochemical analysis identified a critical oxidation threshold of ~750 mV governing enargite dissolution. Chemical leaching and X-ray Photoelectron Spectroscopy (XPS) analysis revealed a temperature-dependent sulfur transformation pathway, enabling a staged thermal strategy: flotation below 40 °C to maximize hydrophobic elemental sulfur (S⁰) formation, and bioleaching at 40–55 °C to promote complete sulfur oxidation to sulfate. Optimization produced a two-stage process comprising 10-day chemical pre-leaching with FeSO₄ (10.0 g/L Fe²⁺) followed by bioleaching, achieving 78.3% copper extraction while suppressing arsenic dissolution to approximately 10%. The use of FeSO₄ instead of Fe₂(SO₄)₃ reduces reagent costs by ~70%, saving an estimated CNY 47,250 daily at 1000 t/d scale. Leaching toxicity tests confirm residue As < 0.10 mg/L, meeting non-hazardous waste standards (GB5085.3-2007). This work provides the first integrated demonstration of electrochemical threshold control combined with temperature-dependent sulfur speciation for selective copper extraction from arsenic-bearing enargite ores, offering a scalable, reagent-economical, and environmentally sustainable metallurgical route. Full article

To facilitate the next generation of renewable energy devices, it is important to engineer oxygen reduction reaction (ORR) catalysts that balance efficiency and production costs. This work examines oxygen adsorption on the WC (0001) surface as a function of electrode potential, utilizing DFT simulations with an implicit solvent environment. The results demonstrate that electrode potential significantly influences oxygen adsorption energy and electronic structure. Among the adsorption sites examined, the top site exhibits the highest stability across the entire potential range. The observed reduction in adsorption energy at lower potentials is attributed to the d-band center moving further from the Fermi energy, which weakens C–O orbital interactions, as revealed by DOS and COHP analyses. Our results demonstrate the crucial role of electrochemical conditions in modulating catalytic behavior and provide valuable insights for optimizing tungsten carbide (WC)-based electrocatalysts for ORR applications. Full article

Zoonotic diseases account for approximately one billion cases of illness and millions of deaths globally each year. Increasing contact between humans and competent wildlife hosts elevates the risk of zoonotic spillover. Synanthropic bird species are key players in the transmission of zoonotic pathogens, including flaviviruses such as West Nile virus (WNV) and influenza A viruses like Avian Influenza Virus (AIV). Active surveillance of sentinel birds inhabiting urban areas allows for early detection of emerging pathogens before they cause zoonotic outbreaks. Despite nesting in close proximity to humans, the role of the house martin (Delichon urbicum) in the circulation of flaviviruses and AIV remains poorly understood. Here, we analyzed the presence of antibodies against flaviviruses and AIV in a colony of house martins from southwestern Spain. In addition, we aimed to detect amplicons of the matrix and nucleoprotein genes of AIV using RT-qPCR. While none of the samples tested positive for AIV by RT-qPCR, we observed an AIV seroprevalence of 2.13% based on non-subtyped ELISA. Notably, this is the first report of AIV-seropositive D. urbicum individuals captured in Spain. Moreover, we detected a flavivirus-group seroprevalence of 24.34%, similar to rates reported in the same house martin population between 2018 and 2020, suggesting widespread circulation of flaviviruses within this synanthropic species. These results support the hypothesis that house martins may participate in the transmission of these viruses between wild bird populations and humans in urban environments. Full article

Double stator cylindrical linear oscillating generators (DSCLOGs) have been widely used in renewable energy power generation systems due to their higher power density, higher reliability, and low-noise characteristics. However, the detent force of a DSCLOG is an inevitable problem, which causes oscillations in the generator and leads to system instability. Conventionally, auxiliary teeth and skewed pole methods are employed to mitigate detent force, but these approaches often increase the overall machine size and the complexity of the manufacturing process. To solve this issue, an improved DSCLOG with curved teeth (CT-DSCLOG) is proposed to minimize the detent force. First, the structural characteristics and working principle of CT-DSCLOG are introduced. Then, to achieve a rapid and accurate analysis of the magnetic field in the irregular air gap, a 2D magnetic equivalent circuit (MEC) model is established by introducing Schwarz–Christoffel (S-C) mapping. And key structural parameters are identified through variance sensitivity analysis. Subsequently, a multi-objective optimization of the linear generator is performed using the Taguchi method combined with 3D finite element analysis (3D-FEA) to obtain the optimal structural parameters of CT-DSCLOG. Finally, the proposed structure is validated through prototype experiments. The results are provided to validate the effectiveness of the proposed structure. Full article

Background/Objectives: The development of stimuli-responsive hydrogels for biomedical uses is an intense field of research. The use of dendritic crosslinkers can enhance the control over the structure and properties of the networks. This work presents a comparative study on the design and evaluation of Pluronic L35 thermogels, incorporating either hydrophobic carbosilane dendrimers (CBS, generations 1 to 3) or hydrophilic dendritic polyglycerols (dPG, 10 k) as crosslinkers. Methods: The thermogels were synthesized via UV-initiated thiol–ene click chemistry. Additionally, they were characterized through swelling studies, mechanical properties, degradation kinetics as well as loading and release studies of the antitumor drug doxorubicin as poorly soluble model cargo. Results: The incorporation of dendritic crosslinkers allowed higher control over the crosslinking process, while the amphiphilic polymer imparted temperature-responsive properties to the resulting networks. Remarkable differences were observed in swelling behavior, mechanical properties and degradation kinetics, depending on the nature of the dendritic crosslinker. Additionally, regarding doxorubicin loading and release in water, CBS hydrogels produced a sustained release over one week, led by network swelling, while dPG hydrogels exhibited a burst release in 4–24 h but were limited by the stronger interaction of DOX with the dPG scaffold. Conclusions: The study provided useful insight for the tailoring of dendritic thermogels for specific biomedical uses such as controlled drug delivery. Full article

Background/Objectives: Patients with pre-existing heart failure (HF) represent a clinically vulnerable population with increased susceptibility to adverse outcomes during acute systemic illnesses, including coronavirus disease 2019 (COVID-19). Systemic inflammation is increasingly recognized as a central pathophysiological mechanism linking cardiovascular vulnerability with infection-related organ dysfunction. However, the prognostic role of inflammatory biomarkers in hospitalized COVID-19 patients with pre-existing HF remains incompletely defined. This study aimed to evaluate the association between inflammatory biomarkers and clinical outcomes in this high-risk population. Methods: This retrospective single-center cohort study included 395 consecutive adult patients hospitalized with confirmed COVID-19 between March 2020 and December 2024 at a tertiary referral center. Pre-existing HF was documented in 143 patients (36.2%). Inflammatory biomarkers, including C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin, and D-dimer, were measured at admission. The primary outcomes were development of sepsis and in-hospital mortality. Multivariable logistic regression models were constructed to identify independent predictors of adverse outcomes after adjustment for demographic characteristics, comorbidities, disease severity, and cardiac biomarkers. Results: Patients with pre-existing HF had significantly higher in-hospital mortality compared with those without HF (11.9% vs. 4.8%, p = 0.016) and showed a trend toward increased intensive care unit admission. HF patients exhibited higher admission IL-6 levels, indicating enhanced inflammatory activation. In univariable analysis, HF was associated with mortality (OR 2.67, 95% CI 1.22–5.83, p = 0.014). After multivariable adjustment, the association between HF and mortality was attenuated, whereas IL-6 remained an independent predictor of mortality (adjusted OR 1.38, 95% CI 1.04–1.82, p = 0.021). Elevated IL-6 and procalcitonin levels were also independently associated with sepsis development. Conclusions: Pre-existing heart failure identifies a population at increased risk of adverse outcomes in hospitalized COVID-19 patients, and this excess risk appears to be partly mediated by systemic inflammatory activation. Interleukin-6 emerged as a key biomarker linking cardiovascular vulnerability, immune dysregulation, and clinical deterioration. These findings support the potential role of inflammation-based risk stratification to improve prognostic assessment and guide personalized management in high-risk patients with underlying cardiovascular disease. Full article

Escalating requirements for smart energy management are driving advances in functional electrochromic devices (ECDs), which are pivotal for the regulation of light, heat, and reduction in energy consumption in buildings, transportation, and smart devices. However, the commercialization of ECDs is hindered by com plex designs, high fabrication costs, and slow switching speeds. Additive manufacturing (AM, 3D-printing) emerges as a promising approach to overcome these limitations, as it enables the creation of complex structures, enhances design flexibility, and can reduce production costs. For such printed devices, materials combining poly(ionic liquids) (PILs) with ionogels—an emerging and promising class of materials known for their high ionic conductivity, stability, and tunable properties—are particularly suitable for integration with 3D printing. Comparing previous reviews that address PILs, ionogels, or AM modalities in isolation, this work uniquely combines the structure–property–processing relationships specific to the synergistic integration of these fields. Current work highlights recent progress in PIL/ionogel-based ECDs and distills specific design guidelines for optimizing ink rheology, balancing ionic conductivity with mechanical integrity, and selecting appropriate printing modalities. These insights provide a roadmap for overcoming current fabrication challenges and scaling up next-generation smart devices. Full article

We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as a computational cost mitigation approach. Given modern seismic acquisition systems, the data (as an input for FWI) required for an industrial-strength case is in the teraflop level of storage; therefore, solving complex subsurface cases or exploring multiple scenarios with FWI becomes prohibitive. The proposed method utilizes a deep neural network with a binarized sensing layer that learns by compressed learning seismic acquisition layouts from a large corpus of subsurface models. Thus, given a large seismic data set to invert, the trained network selects a smaller subset of the data, then by using representation learning,an autoencoder computes latent representations of the shot gathers, followed by K-means clustering of the latent representations to further select the most relevant shot gathers for FWI. This approach can effectively be seen as a hierarchical selection. The proposed approach consistently outperforms random data sampling, even when utilizing only 10% of the data for 2D FWI, and these results pave the way to accelerating FWI in large scale 3D inversion. Full article

The adhesion G protein-coupled receptor F5 (ADGRF5) has been implicated in modulating immune responses in cancer; however, its role in inflammatory bowel diseases (IBDs), particularly colitis, remains largely unexplored. In this study, we aimed to design and characterize novel peptide agonists derived from the ADGRF5 Stachel sequence, as well as to evaluate their therapeutic potential in preclinical colitis models. In silico analysis and single amino acid substitutions within the ADGRF5 tethered agonist sequence, combined with functional assays in ADGRF5-overexpressing cells, including calcium mobilization and inositol phosphate production, were employed to assess the activity of novel ADGRF5 agonists. Western blot technique and murine model of colitis were used to evaluate downstream signaling pathways and immunomodulatory effects of ADGRF5 ligands. We identified a series of peptides exhibiting significantly enhanced ADGRF5 agonist activity, achieving up to a 6-fold increase in potency over the wild-type version. We identified critical substitutions within the Stachel sequence, namely S11N and D13S, as essential for improving agonistic activity. Finally, using these novel ADGRF5 agonists, we demonstrated their potent anti-inflammatory effects in vivo, showing that ADGRF5 activation ameliorates experimental colitis, as evidenced by reduced macroscopic damage scores and improved colon architecture. These findings establish ADGRF5 as a potential therapeutic target for colitis and highlight the promise of Stachel-derived peptide agonists for the development of novel anti-inflammatory therapies. Full article

Leucine-rich repeat-containing 8A (LRRC8A; also known as SWELL1), the essential subunit of volume-regulated anion channels (VRACs), is amplified in multiple malignancies and has been implicated in tumor progression and therapeutic resistance. Three-dimensional (3D) cancer spheroids have been well-established as in vitro models that recapitulate characteristics of tumor stemness and intrinsic drug resistance. In the present study, spheroid formation in human breast cancer cell lines, YMB-1 and MDA-MB-468, conferred resistance to multiple anticancer drugs, including doxorubicin (DOX), gemcitabine (GEM), and 5-fluorouracil (5-FU), thereby mimicking the characteristic properties of breast cancer stem-like cells. LRRC8A expression was upregulated in 3D spheroids compared with adherent 2D monolayers, and its pharmacological inhibition induced membrane hyperpolarization accompanied by intracellular Cl⁻ accumulation. Inhibition of LRRC8A significantly sensitized spheroids to DOX, GEM, and 5-FU. Spheroid formation increased the expression of multidrug resistance-related protein 3 (MRP3) and the drug-metabolizing enzyme cytochrome P450 3A4 (CYP3A4), whereas LRRC8A inhibition suppressed their expression. The transcriptional upregulation of MRP3 and CYP3A4 was mediated through the NRF2–CEBPB/D transcriptional axis. Collectively, these findings suggest that LRRC8A inhibition may represent a therapeutic strategy to overcome chemoresistance by repressing MRP3 and/or CYP3A4 expression in breast cancer stem cells. Full article

Background/Objectives: Endometrial cancer frequently develops resistance to therapy, partly due to the ability of tumor cells to adapt to cellular stress through non-apoptotic mechanisms. Mitochondrial dysfunction and cytoskeletal remodeling are increasingly recognized as key components of stress adaptation; however, their structural relationship under pharmacological stress in three-dimensional (3D) tumor models remains poorly characterized. The present study aimed to investigate the ultrastructural and phenotypic effects of lithium chloride (LiCl)-induced stress in 3D endometrial cancer spheroids, with a particular focus on mitochondrial alterations and intermediate filament organization. Methods: Three-dimensional spheroids generated from Ishikawa endometrial cancer cells were exposed to lithium chloride at concentrations of 1, 10, or 50 mM for defined time periods. Cell viability, proliferative activity, and clonogenic capacity were assessed using Trypan Blue exclusion, BrdU incorporation, and soft agar assays. Ultrastructural changes were examined by transmission electron microscopy to evaluate mitochondrial morphology, cytoplasmic organization, and intermediate filament distribution. Results: LiCl exposure resulted in a dose- and time-dependent reduction in cell viability, proliferation, and clonogenic potential in 3D spheroids. Ultrastructural analysis revealed pronounced mitochondrial swelling, cristae disorganization, and membrane-associated mitochondrial alterations. These changes were consistently accompanied by conspicuous accumulation and reorganization of intermediate filaments in close spatial proximity to damaged mitochondria, suggesting a structural association between cytoskeletal remodeling and mitochondrial injury. Across all experimental conditions, classical apoptotic ultrastructural features, including chromatin condensation and apoptotic body formation, were not observed. Conclusions: Together, these observations indicate that lithium chloride elicits a stress phenotype in 3D endometrial cancer spheroids that primarily manifests at the organelle and cytoskeletal levels, rather than through classical apoptotic execution. Although descriptive in nature, the present study highlights intermediate filament accumulation as a prominent structural feature of lithium-induced mitochondrial stress and establishes a structural reference point for future studies aimed at further investigating mitochondrial–cytoskeletal relationships during pharmacological stress in endometrial cancer. Full article

Additive manufacturing technologies such as Multi-Jet Fusion (MJF) enable the production of polymer parts with relatively isotropic mechanical properties; however, their surface condition often limits direct functional application. This study investigates the feasibility of selected surface treatments applied to PA12GB (glass bead-filled PA12) parts manufactured by MJF, with the aim of improving abrasion resistance and temperature-related performance through the modification of surface properties. Five surface treatments were evaluated: base coating (BC), acrylic coating (AC), chemical vapor smoothing (PostPro3D), glasscoat (epoxy-based SiO₂ system), and a ceramic-filled 2K epoxy coating. Untreated samples served as a reference. Surface layer thickness, roughness (ISO 21920-2:2021), coefficient of friction (ASTM G99-23), and Shore D hardness (ASTM D2240-15R21) were measured. The results showed significant differences among treatments. Glasscoat and ceramic coatings formed the thickest and hardest layers (≈265 μm and ≈409 μm; Shore D ≈ 84) but exhibited substantially increased friction coefficients. Vapor smoothing and BC produced thinner layers with properties comparable to untreated samples. Acrylic coating reduced surface roughness while moderately increasing hardness. The findings demonstrate that surface treatments substantially alter the tribological and mechanical surface behavior of MJF-printed PA12GB parts. The suitability of a given treatment strongly depends on the intended functional requirements, particularly with respect to friction and surface hardness. Full article

To address the insufficient capability of YOLO-series models in representing structural information for foreign objects with diverse scales and morphologies, an improved algorithm named SSFE-YOLO is proposed. First, the Space-to-Depth Convolution (SPDConv) is adopted into the backbone network to preserve edge and texture details in shallow features during downsampling, thereby maintaining the integrity of critical target structures at the feature generation stage. Second, an adaptive receptive field enhancement module (ARFE) is designed by introducing parallel feature branches with varying receptive fields. This module performs adaptive fusion to bolster the structural perception of the network towards polymorphic foreign objects. Furthermore, a distribution-feature stable compensation module (DFSC) is designed to suppress feature distribution shifts caused by illumination variations and noise interference through structural consistency enhancement and stable distribution constraints, which significantly improves the stability of feature representation in complex environments. Finally, a dual-dimension optimized loss function (D${}^2$-OL) is constructed to achieve differentiated supervision for samples of varying quality and balanced optimization for multi-scale target detection by modulating the supervisory weights of feature layers and filtering effective training samples. Experimental results on a self-built mine conveyor belt dataset demonstrate that the proposed method achieves an mAP@0.5 of 90.5% and an mAP@0.5:0.95 of 59.1%, consistently outperforming mainstream models such as YOLOv8, YOLOv11, and YOLOv13. Simulation results indicate that the proposed approach effectively enhances the detection accuracy and robustness of foreign objects in mining environments, showcasing substantial potential for engineering applications. Full article

In the European Union, decarbonization has progressed unevenly across sectors and member states. This study examines sectoral CO₂ trajectories in the EU-27 during 2000–2022 using a harmonized annual panel built primarily from the European Commission’s Energy Statistical Country Datasheets and complemented with EDGAR/JRC sectoral emissions data. The empirical strategy combines descriptive analysis with OLS, fixed-effects, log-linear, and exploratory difference-in-differences specifications to assess conditional associations among per capita CO₂ emissions, the renewable energy share, GDP per capita, and the carbon price. EU-wide CO₂ emissions declined by 26.4% over the study period, with the largest contraction in the energy sector, while transport emissions remained comparatively stable. Across specifications, renewable energy share is consistently associated with lower emissions, although its magnitude weakens after controlling for time-invariant country heterogeneity. Carbon price is negatively associated with emissions in the baseline and log-linear models. In contrast, the exploratory DiD interaction is not statistically informative in the main treatment specification and yields negligible effect sizes in regional split models. The sign reversal in GDP between the pooled and within-country models indicates that cross-country differences and within-country dynamics should not be treated as equivalent. Overall, the findings support a heterogeneous and multi-speed decarbonization pattern and suggest that carbon pricing is better understood as part of a broader policy mix rather than as a stand-alone causal driver. Full article

Video-based photogrammetric reconstruction is increasingly used when high-resolution still images are unavailable. However, limited spatial resolution, compression artifacts, and motion blur often reduce geometric accuracy. Recent advances in learning-based image super-resolution (SR) offer a promising preprocessing method, but their geometric reliability within photogrammetric workflows remains not well understood. This study provides a controlled quantitative evaluation of learning-based super-resolution for video-based 3D reconstruction. Low-resolution video frames are enhanced using two representative methods: an open-source real-world SR model (Real-ESRGAN ×4) and a commercial solution (Topaz Video AI ×4). All datasets are processed with the same Structure-from-Motion and Multi-View Stereo pipelines and tested against terrestrial laser scanning (TLS) reference data. Results show that super-resolution significantly increases reconstruction density and improves the recovery of fine-scale surface details, while also leading to greater local surface variability compared with reconstructions from the original video; photogrammetric stability remains consistent despite these changes. The findings highlight a fundamental trade-off between reconstruction completeness and local geometric accuracy and clarify when enhanced video imagery via super-resolution can be a reliable source for 3D reconstruction. These results are especially important for spatial data science workflows and AI-powered 3D modeling and digital twin applications. Full article