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First exploration of Pr6O11 nanoparticle integration in borotellurite glasses: synthesis, characterization, and performance for enhanced mechanical strength and radiation shielding
No full textThis study investigates the incorporation of Pr6O11 nanoparticles into lithium borotellurite glass matrices to enhance their mechanical and radiation shielding properties. Glass compositions, synthesized with varying Pr6O11 concentrations from 0 to 8 mol%, exhibited increasing densities from 4.00783 g cm−3 to 4.94440 g cm−3 and reduced molar volumes, confirming nanoparticle-induced densification. X-ray diffraction analysis revealed amorphous structures with shifts in the hollow band indicating compact network rearrangements. Scanning electron microscopy and energy-dispersive X-ray analyses confirmed homogeneous Pr distribution up to 6 mol%, with clustering observed in 8 mol% samples. Vickers’ microhardness values progressively increased, highlighting enhanced mechanical strength due to reduced non-bridging oxygen ions and network cross-linking. Gamma-ray shielding experiments demonstrated superior performance of the 8 mol% sample (Pr8), with the highest mass attenuation coefficients, effective atomic number, and reduced half-value layer. Neutron attenuation assessments further confirmed improved shielding capabilities, with Pr8 achieving the highest effective removal cross-section. In conclusion, Pr6O11-doped lithium borotellurite glasses demonstrate significant potential for advanced radiation shielding applications
A combinatorial interpretation of the series for Rogers–Ramanujan–Gordon identities when k=3
No full textIn this paper, we give a new combinatorial interpretation for the Rogers–Ramanujan–Gordon partitions for k=3. Our interpretation is given by base partition and moves ideas. We conclude the paper with some research questions related to the generalization of this approach
0-3 piezocomposites with anisometric single-crystalline filler particles crystallographically oriented by dielectrophoretic alignment method
No full textThis study investigates the synthesis, structural characterization, and dielectrophoretic alignment of potassium strontium niobate—KSN (KSr2Nb5O15) particles to develop textured 0-3 piezocomposites with enhanced dielectric and piezoelectric properties for tactile sensor applications. KSN particles were synthesized using a molten salt process. Anisometric needle-like morphology of the particles were confirmed by electron microscopy and their single crystalline nature by electron diffraction techniques. Dielectrophoretic alignment under an alternating current (AC) electric field facilitated particle orientation along the [001] c-axis of the tetragonal structure, as confirmed by x-ray diffraction and quantified using the Lotgering factor (f(00l)). An f(00l) = 0.83 was achieved for piezocomposite containing 5 vol% KSN particles and cured under an AC field of 2 kV mm−1 at 200 Hz. Electrical characterization revealed a strong correlation between particle alignment and properties. Compared to random piezocomposites prepared without AC field, increase in dielectric constant of up to two-folds, in polarization of up to ten folds, and in piezoelectric charge coefficient of up to 3 folds were observed in textured piezocomposites. A tactile sensor prototype developed using these textured piezocomposites exhibited voltage output proportional to particle orientation, demonstrating the importance of particle alignment in enhancing the functional properties of piezocomposites
Development of a mucoadhesive drug delivery system and its interaction with gastric cells
No full textDrugs that are designed for local treatment of gastric diseases require increased gastric residence time for prolonged action and increased efficacy. In this study, we report a mucoadhesive drug delivery system that was developed to fulfill these requirements. Alginate nanoparticles were synthesized by water-in-oil emulsification followed by external gelation and then coated with the mucoadhesive polymer Eudragit RS100. The formulated nanoparticles had a mean size of 219 nm and positive charge. A peptide, as a model drug, was loaded onto the nanoparticles with an encapsulation efficiency of 58%. The release of the model drug from the delivery system was pH-independent and lasted for 7 days. The periodic acid–Schiff stain assay indicated 69% mucin interaction for the nanoparticles, which were also capable of diffusion through artificial mucus. The nanoparticles were not toxic to gastric epithelial cells and can be internalized by the cells within 4 h. The adsorption of nanoparticles onto mucus-secreting gastric cells was found to be correlated with cell number. The delivery system developed in this study is intended to be loaded with active therapeutic agents and has the potential to be used as an alternative drug delivery strategy for the treatment of gastric related diseases
Optimizing strategic and operational decisions of car sharing systems under demand uncertainty and substitution
No full textCar sharing is an efficient way to improve mobility, reduce the use of personal vehicles, and lessen the associated carbon emissions. Due to increasing environmental awareness of customers and government regulations, car sharing providers must be careful about the composition of their vehicle fleet to meet diverse customer demand through vehicle types with different carbon emission levels. In this study, for a car sharing company, we consider the problems of determining service regions and purchasing decisions with a mixed fleet of vehicles under budget and carbon emission constraints, and the deployment of these vehicles to service regions under uncertain one-way and round-trip rental requests over a multi-period planning horizon. We further introduce the concept of “substitution” to the car sharing operations that provides customers with alternative vehicle options when their preferred type is unavailable. To address this complex problem, we propose a novel two-stage stochastic mixed-integer program leveraging spatial–temporal networks and multicommodity flows to capture these strategic and operational decisions of this system over the planning horizon while allowing substitution in operations. We further prove that the corresponding second-stage problem of the proposed program has a totally unimodular constraint matrix. Taking advantage of this result, we develop a branch-and-cut-based decomposition algorithm with various computational enhancements. We present an extensive computational study that highlights the value of the proposed models from different perspectives and demonstrates the performance of the proposed solution algorithm with significant speedups. Our case study provides insights for region opening and fleet allocation plans under demand uncertainty and demonstrates the value of introducing substitution to car sharing operations and the importance of integrating strategic and operational decisions and obtaining stochastic solutions
SCITUNA: single-cell data integration tool using network alignment
No full textBackground: As single-cell genomics experiments increase in complexity and scale, the need to integrate multiple datasets has grown. Such integration enhances cellular feature identification by leveraging larger data volumes. However, batch effects-technical variations arising from differences in labs, times, or protocols-pose a significant challenge. Despite numerous proposed batch correction methods, many still have limitations, such as outputting only dimension-reduced data, relying on computationally intensive models, or resulting in overcorrection for batches with diverse cell type composition. Results: We introduce a novel method for batch effect correction named SCITUNA, a Single-Cell data Integration Tool Using Network Alignment. We perform evaluations on 39 individual batches from four real datasets and a simulated dataset, which include both scRNA-seq and scATAC-seq datasets, spanning multiple organisms and tissues. A thorough comparison of existing batch correction methods using 13 metrics reveals that SCITUNA outperforms current approaches and is successful at preserving biological signals present in the original data. In particular, SCITUNA shows a better performance than the current methods in all the comparisons except for the multiple batch integration of the lung dataset where the difference is 0.004. Conclusion: SCITUNA effectively removes batch effects while retaining the biological signals present in the data. Our extensive experiments reveal that SCITUNA will be a valuable tool for diverse integration tasks
Predicting energy consumption in Mexico: integrating environmental, economic, and energy data with machine learning techniques for sustainable development
No full textAccurate energy consumption prediction is crucial for developing effective government energy policies. This study introduces a novel machine learning-based approach for reliable energy consumption prediction to provide stability in forecasting energy usage. This study aims to investigate the predictive capabilities of advanced hybrid machine learning models, specifically CatBoost and SVR hybrids, in accurately forecasting energy consumption based on key variables such as carbon dioxide emissions, global temperature, and oil production. This study investigates the prediction of energy consumption in Mexico, considering its significance for environmental conservation and economic development. Accurate prediction models are developed by using advanced machine learning algorithms such as CatBoost and SVR, along with optimization techniques including Arch-OA, PSO, and ALO. The analysis reveals strong correlations between energy consumption and various factors, emphasizing the importance of considering parameters such as carbon dioxide emissions and GDP per capita. Results demonstrate the superior performance of CatBoost over SVR, with hybrid models incorporating SVR showing notable improvements in accuracy. These findings highlight the efficacy of advanced machine learning and optimization algorithms in accurately forecasting energy demand, thereby facilitating informed policymaking for sustainable development
On the processability and antibacterial activity of silver nitrate nanocomposites manufactured by stereolithography
No full textThis study investigates the level of multifunctionality that might be achieved with the implementation of silver nitrate (AgNO3) to UV-curable resins during stereolithography. To achieve that it sets improved mechanical response and antibacterial activity of the additively manufactured nanocomposites as two objectives to be achieved without disturbing the printing quality. The resins containing 0.1, 0.3, 0.5, 1, 3, and 5 wt % AgNO3 were mixed via high shear mixing under 6000 rpm. A desktop-scale SLA machine and a postcure UV device were employed for the manufacturing of nanocomposite specimens. The level of printing quality was evaluated by SEM analysis focusing on the layer-by-layer printing marks that were highly disturbed after 1 wt % particle loading. Potential chemical effects causing this disappearance were investigated with UV-visible spectroscopy and FTIR analysis. Results suggested that some of the provided UV energy was used for the conversion of silver cations of silver nitrate to AgNPs during the polymerization reaction. Such a reduction reaction was found to decrease the degree of monomer conversion with increasing particle amount, which was confirmed by the FTIR analyses. Results of mechanical tests assisted by a detailed fractographic analysis also confirmed that 1 wt % was a particle agglomeration threshold above which the mechanical response was highly deteriorated. The maximum mechanical performance with a 20% improvement in elastic modulus and yield strength values was noted for 0.5 wt % case. The antibacterial activity tests were then performed on 0.3, 0.5, and 1 wt % samples according to ISO 22196. The results suggested that antibacterial activity was maximum for 1 wt % particle containing nanocomposites. Hence, the aim of a stronger, antibacterial yet adaptable nanocomposite material design was achieved at 0.5 wt % silver nitrate loading
Graphene nanoplatelets enhance neuronal differentiation of human bone marrow mesenchymal stem cells
No full textStem cell technology plays a key role in advancing the understanding of neurological treatments and developing disease models that mimic human conditions. Differentiation of human bone marrow mesenchymal stem cells (hBMSCs) into neurons shows promise for treating neurodegenerative diseases. However, improving the functionality of these nerve cells remains a challenge. Graphene nanoplatelets (GNPs), with their excellent conductivity and biocompatibility, can enhance neuronal differentiation. This study examines the effect of GNPs on hBMSC differentiation. Cells cultured with varying GNP concentrations were assessed at 4 and 7 days using RT-qPCR and immunocytochemistry for neuronal markers MAP2, Nestin, and Tuj1. Results show that GNPs enhance marker expression and promote differentiation. Lower GNP concentrations maintained viability, while higher concentrations were detrimental. Morphological changes and increased fluorescence were observed with a 0.4 µg/ml GNP coating. Calcium imaging with Fluo4-AM indicated increased neuronal activity, underscoring GNPs’ role in neuronal maturation. These findings suggest GNPs can drive stem cell differentiation toward neurons, offering new therapeutic potential for neurodegenerative diseases
Novel concept of hydroxyl radical generation in hydrodynamic cavitation on a chip
Get PDFIn this study, the hydrodynamic cavitation on a chip reactor (HC on a chip), facilitated by two configurations: a micro-orifice and a long diaphragm, was utilized to produce the reactive species, particularly hydroxyl radicals ([rad]OH). The micro-orifice configuration allowing intense localized pressure gradients promotes intense cavitation events, leading to higher yields of [rad]OH per unit energy input (0.6 × 10−6 g/J in comparison to 3.0 × 10−8 g/J for the long diaphragm configuration in the cavitation inception regime). This is advantageous for applications requiring concentrated [rad]OH production, such as advanced oxidation processes. In contrast, the long diaphragm reactor, despite a larger power input (6.4 W at 2.9 MPa compared to 1.5 W for the micro-orifice configuration), provides a more uniform distribution of radicals along its length, based on the temporal trend of I3− formation. This shows a gradual and sustained rate compared to the sharp, early peak in the micro-orifice reactor. The lower reaction in Reactor 2 indicates that radical formation occurs over a wider area rather than being localized. This aligns with the geometry and flow pattern of the diaphragm which allows for a larger zone of cavitation and longer bubble activity. Comparative analysis reveals that microscale reactors demonstrate higher reaction rates and sharper peaks in I3− production than macroscale reactors, which show higher chemical activity. The orifice possesses the highest maximum peak in the microreactors, and the long diaphragm releases more uniformly distributed radicals, with microscale systems overall having higher energy efficiency and lower energy costs