Search Results (15,245)

The transformation of the energy market toward renewable finance is achieved primarily through green innovations, which require sources of funding. The aim of this article is to show the state of research on green credit and green bonds in the context of their role in financing the green transition of the energy market. The article uses a critical literature review supported by VoSviewer software and quantitative analysis. While most literature reviews cover green finance or sustainable finance in the energy transition process (macro level), our attempt refers to the level of financial instruments (micro level); we demonstrate the significant role of green credit and green bonds in financing energy transition and the high degree of differentiation in the functionality of both instruments. Specifically, green bonds strongly accelerate green innovation in the energy sector and stimulate energy transition through green credits. Articles from the second typological group identified in the study clearly emphasize this relationship. The remaining key findings highlight the dominant share of developed countries as a base for research and analysis, as well as large enterprises, which were the primary focus of the study. Full article

Cellular senescence can occur with similar phenotypes in normal cells, during aging, and in tumor cells, spontaneously or after cytostasis. The fall or increase in proliferative activity are key aspects of the respective conditions, in which the levels of reactive oxygen species can vary, affecting the cellular redox homeostasis. This work aimed to study the relationships between senescence and transformation by comparing cells with different proliferative activities and phenotypes attributable to transformation (NIHs cultures) or senescence (NIHv cultures), before and after incubation with hydrogen peroxide. Both cultures were derived from the NIH/3T3 cell line, which was used here as a reference (NIHb), after the serum starvation. Our experimental model can be representative of the heterogeneity of cell subpopulations, with different degrees of transformation and senescence, found in some tumors. The characterization of the functional properties of NIHb, NIHs, and NIHv cells was performed by a morphocytometric analysis of the cell cycle progression, mitochondrial and lysosomal content/activity, and superoxide anion production. The efficiency of the lysosomal compartment was also assessed by estimating the autophagic activity and measuring lipofuscin autofluorescence. Comparisons of nuclear and cytoplasmic parameters before and after the incubation with hydrogen peroxide revealed differences in the expression and modulation of cellular senescence patterns. The treatment effects were very limited in the NIHb culture; the senescence condition was essentially maintained in the NIHv cells, while the most relevant changes were found in the NIHs cells. In the latter, the acquisition of the senescent phenotype, also demonstrated by the positivity of SA-β-galactosidase, was correlated with a decrease in proliferative activity and a change in the content/activity of the mitochondria and lysosomes, which showed similarities with the basal senescence conditions of NIHv cells. In NIHs cells, increased autophagy events and lipofuscin accumulation also indicate the establishment of cytoplasmic dynamics typical of senescence. The variable responses to hydrogen peroxide, besides depending on the different basal cytokinetic activity of the cultures examined, appeared to be related to the specific cell redox state resulting from the balance between endogenous ROS and those produced after treatment. Especially in NIHs cells, the slowing down of the cell cycle was linked to dynamic interconnections between the mitochondrial and lysosomal compartments. This would indicate that transformed cells, such as NIHs, may express morpho-functional aspects and markers typical of cellular senescence, as a consequence of the modulation of their redox state. Full article

Multimaterial 3D printing is transforming the landscape of additive manufacturing, enabling the production of advanced, functional parts with tailored properties for sectors like automotive, aerospace, and engineering. However, achieving strong interlayer adhesion between different polymers remains a significant challenge, limiting the mechanical reliability. This study investigates adhesion properties of widely used materials—polycarbonate (PC), acrylonitrile styrene acrylate (ASA), polylactic acid (PLA), and polyethylene terephthalate glycol (PETG)—and enhances mechanical performance of structural joints through optimized interlayer bonding techniques. Using the Material Extrusion (MEX) method, tensile testing was employed to evaluate the mechanical strength of joints by co-depositing and bonding material layers during the printing process. The results demonstrate that specific material combinations and joint design strategies, particularly increasing the interfacial contact area and applying interlayer bonding pressure, significantly enhance tensile strength. For instance, the strength of PC/PTEG composite joints increased from 15.2 MPa (standard joint) to 29.9 MPa (interlayer bonding strategy), nearly doubling the bond strength. These findings provide valuable insights into the behavior of multimaterial joints and propose practical approaches for improving the durability and functionality of 3D-printed structures. This research lays the groundwork for advancing multimaterial additive manufacturing, with implications for high-performance applications in engineering, aerospace, and beyond. Full article

Background: Perinatal research faces significant challenges in understanding placental biology and maternal–fetal interactions due to limited access to human tissues and the lack of reliable models. Emerging technologies, such as liquid biopsy and single-cell analysis, offer novel, non-invasive approaches to investigate these processes. This scoping review explores the current applications of these technologies in placental development and the diagnosis of pregnancy complications, identifying research gaps and providing recommendations for future studies. Methods: This review adhered to PRISMA-ScR guidelines. Studies were selected based on their focus on liquid biopsy or single-cell analysis in perinatal research, particularly related to placental development and pregnancy complications such as preeclampsia, preterm birth, and fetal growth restriction. A systematic search was conducted in PubMed, Scopus, and Web of Science for studies published in the last ten years. Data extraction and thematic synthesis were performed to identify diagnostic applications, monitoring strategies, and biomarker identification. Results: Twelve studies were included, highlighting the transformative potential of liquid biopsy and single-cell analysis in perinatal research. Liquid biopsy technologies, such as cfDNA and cfRNA analysis, provided non-invasive methods for real-time monitoring of placental function and early identification of complications. Extracellular vesicles (EVs) emerged as biomarkers for conditions like preeclampsia. Single-cell RNA sequencing (scRNA-seq) revealed cellular diversity and pathways critical to placental health, offering insights into processes such as vascular remodeling and trophoblast invasion. While promising, challenges such as high costs, technical complexity, and the need for standardization limit their clinical integration. Conclusion: Liquid biopsy and single-cell analysis are revolutionizing perinatal research, offering non-invasive tools to understand and manage complications like preeclampsia. Overcoming challenges in accessibility and standardization will be key to unlocking their potential for personalized care, enabling better outcomes for mothers and children worldwide. Full article

The emergence of the Internet of Things (IoT) has brought about a significant technological shift, coupled with the rise of intelligent computing. IoT integrates various digital and analogue devices with the Internet, enabling advanced communication between devices and humans.The pervasive adoption of IoT has transformed urban infrastructures into interconnected smart cities. Here, we propose a framework that mathematically models and automates power consumption management for IoT devices in smart city environments ranging from residential buildings to healthcare settings. The proposed framework utilises set theoretic association-rule mining and combines unsupervised preprocessing with frequent-item set mining and iterative numerical optimisation to reduce non-critical energy consumption. Readings are first converted into binary transaction matrices; then a modified Apriori algorithm is applied to extract high-confidence usage patterns and association rules. Dimensionality reduction techniques compress these transaction profiles, while the Gauss–Seidel method computes control set points that balance energy efficiency. The resulting rule set is deployed through a web portal that provides real-time device status, remote actuation, and automated billing. These associative rules generate predictive control functions, optimise the response of the framework, and prepare the framework for future events. A web portal is introduced that enables remote control of IoT devices and facilitates power usage monitoring, as well as automated billing. Full article

Skeletal muscle, constituting ~40% of body mass, serves as a primary effector for movement and a key metabolic regulator through myokine secretion. Hereditary myopathies, including dystrophinopathies (DMD/BMD), limb–girdle muscular dystrophies (LGMD), and metabolic disorders like Pompe disease, arise from pathogenic mutations in structural, metabolic, or ion channel genes, leading to progressive weakness and multi-organ dysfunction. Gene therapy has emerged as a transformative strategy, leveraging viral and non-viral vectors to deliver therapeutic nucleic acids. Adeno-associated virus (AAV) vectors dominate clinical applications due to their efficient transduction of post-mitotic myofibers and sustained transgene expression. Innovations in AAV engineering, such as capsid modification (chemical conjugation, rational design, directed evolution), self-complementary genomes, and tissue-specific promoters (e.g., MHCK7), enhance muscle tropism while mitigating immunogenicity and off-target effects. Non-viral vectors (liposomes, polymers, exosomes) offer advantages in cargo capacity (delivering full-length dystrophin), biocompatibility, and scalable production but face challenges in transduction efficiency and endosomal escape. Clinically, AAV-based therapies (e.g., Elevidys^® for DMD, Zolgensma^® for SMA) demonstrate functional improvements, though immune responses and hepatotoxicity remain concerns. Future directions focus on AI-driven vector design, hybrid systems (AAV–exosomes), and standardized manufacturing to achieve “single-dose, lifelong cure” paradigms for muscular disorders. Full article

The aim of this study was to address the issue of significant performance degradation in existing defogging algorithms under extreme fog conditions. Traditional Taylor series-based deformable convolutions are limited by local approximation errors, while the heavy-tailed characteristics of the Cauchy distribution can more successfully model outliers in fog images. The following improvements are made: (1) A displacement generator based on the inverse cumulative distribution function (ICDF) of the Cauchy distribution is designed to transform uniform noise into sampling points with a long-tailed distribution. A novel double-peak Cauchy ICDF is proposed to dynamically balance the heavy-tailed characteristics of the Cauchy ICDF, enhancing the modeling capability for sudden changes in fog concentration. (2) An innovative Cauchy–Gaussian fusion module is proposed to dynamically learn and generate hybrid coefficients, combining the complementary advantages of the two distributions to dynamically balance the representation of smooth regions and edge details. (3) Tree-based multi-path and cross-resolution feature aggregation is introduced, achieving local–global feature adaptive fusion through adjustable window sizes (3/5/7/11) for parallel paths. Experiments on the RESIDE dataset demonstrate that the proposed method achieves a 2.26 dB improvement in the peak signal-to-noise ratio compared to that obtained with the TaylorV2 expansion attention mechanism, with an improvement of 0.88 dB in heavily hazy regions (fog concentration > 0.8). Ablation studies validate the effectiveness of Cauchy distribution convolution in handling dense fog and conventional lighting conditions. This study provides a new theoretical perspective for modeling in computer vision tasks, introducing a novel attention mechanism and multi-path encoding approach. Full article

Novel polyester–polyurethane polymeric materials were formulated by combining a natural aliphatic polyester, poly(3-hydroxybutyrate) (P3HB), with a synthetic aliphatic polyurethane via melt blending. The resulting fully biodegradable compositions were functionally modified through the incorporation of urea, with the aim of enabling post-consumer utilization of the material residues as nitrogen-rich fertilizers. The fabrication process was systematically established and optimized, focusing on homogeneous blending and processability. Comprehensive mechanical characterization—including tensile strength, impact resistance, and Shore hardness—was performed. Among the tested formulations, composites containing 1 wt.% urea demonstrated superior mechanical performance and optimal processing behavior. Fourier-transform infrared (FTIR) spectroscopy was employed to investigate molecular-level interactions between polymeric phases and urea, while scanning electron microscopy (SEM) was utilized to assess the morphological characteristics of the resulting biocompositions. Comparative analyses of the physico-mechanical properties and biodegradability were conducted among the urea-modified compositions, binary P3HB–polyurethane blends, and neat P3HB. The observed improvements in mechanical integrity and functional biodegradability suggest that the developed urea-enriched compositions are promising candidates for the fabrication of eco-friendly seedling pots via injection molding technology. Full article

Hydraulic concrete is prone to cracking and interactive propagation under complex stress, threatening its structural integrity and service life. To address limitations of traditional numerical methods (e.g., mesh dependency in FEM) and imprecision of existing meshless methods for characterizing multi-fissure interactions, this study improved SPH to model double-crack interactions in hydraulic concrete under three-point bending and clarify the underlying mechanisms. A modified SPH framework was developed by introducing a failure parameter (ξ) to refine the kernel function, enabling simulation of particle progressive failure via the Mohr–Coulomb criterion; a three-point bending numerical model of concrete beams containing double precast fissures (induced and obstacle) was established, with simulations under varying obstacle fissure angles (α = 0–75°) and distances (d = 0.02–0.06 m). The results show that the obstacle fissure angles significantly regulate the crack paths: as the α increases, the tensile stress concentration shifts from the obstacle fissure’s middle to its ends, causing cracks to deflect toward the lower end, with a reduced propagation length and lapping time; at an α = 75°, the obstacle fissure’s lower tip dominates failure, forming an “induced fissure–lower end of obstacle fissure–top” penetration mode. The fissure distances affect the stress superposition: a smaller d (e.g., 0.02 m) induces vertical propagation and rapid lapping with the obstacle fissure’s lower end, while a larger d (e.g., 0.06 m) weakens the stress at the induced fissure tip, promoting horizontal deflection toward the obstacle fissure’s upper end and transforming the failure into “upper-end dominated.” This confirms that the improved SPH method effectively simulates crack behaviors, providing insights into multi-fissure failure mechanisms and theoretical support for hydraulic structure crack control and safety evaluation. Full article

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article

Microorganisms exhibit remarkable diversity, making their comprehensive characterization essential for understanding ecosystem functioning and safeguarding human health. However, traditional culture-based methods entail inherent limitations for resolving microbial heterogeneity, isolating slow-growing microorganisms, and accessing uncultivated microbes. Conversely, droplet-based microfluidics enables a high-throughput and precise platform for single-bacterium manipulation by physically isolating individual cells within microdroplets. This technology presents a transformative approach to overcoming the constraints of conventional techniques. This review outlines the fundamental principles, recent research advances, and key application domains of droplet-based microfluidics, with a particular focus on innovations in single-bacterium encapsulation, sorting, cultivation, and functional analysis. Applications such as antibiotic susceptibility testing, enzyme-directed evolution screening, microbial interaction studies, and the cultivation of novel bacterial species are discussed, underscoring the technology’s broad potential in microbiological research and biotechnology. Full article

Quantum computing is a groundbreaking innovation that can resolve complex supply chain problems that traditional computing techniques are unable to manage. Given a focus on information flow, optimization, and potential future applications, this study explores how supply chain management could utilize quantum computing. The study used a mixed-methods approach, including scenario modeling, case studies of prominent companies, and literature reviews. The study intends to evaluate the function of quantum computing in dynamic route optimization, investigate how it can enhance supply chain resilience, and examine how it could optimize the flow of information for decision-making processes. Findings demonstrate that quantum computing offers unprecedented computational power for scenario analysis and decision-making and operates exceptionally well in activities like dynamic route optimization, parcel packaging, and reorganization during disruptions. For instance, companies like DHL and FedEx utilize quantum systems to improve efficiency substantially. However, constraints like high implementation costs, cybersecurity weaknesses, and technological infancy prevent widespread acceptance. Further research should investigate hybrid solutions that integrate quantum and classical computing while addressing these obstacles. This paper concludes that although quantum computing has the potential to transform supply chains by improving information flow, resilience, and efficiency, its wider adoption will require overcoming current financial and technological challenges. Full article

Diabetic wounds are characterized by a refractory healing cycle resulting from the synergistic effects of hyperglycemic microenvironment, oxidative stress, bacterial infection, and impaired angiogenesis. Conventional hydrogel dressings, with limited functionality, struggle to address the complexities of chronic diabetic ulcers. Smart hydrogels, possessing biocompatibility, porous architectures mimicking extracellular matrix, and environmental responsiveness, have emerged as promising biomaterials for diabetic wound management. This review systematically elucidates the specific response mechanisms of smart hydrogels to wound microenvironmental stimuli, including pH, matrix metalloproteinase-9 (MMP-9), reactive oxygen species (ROS), and glucose levels, enabling on-demand release of antimicrobial agents and growth factors through dynamic bond modulation or structural transformations. Subsequently, the review highlights recent advances in novel hydrogel-based sensors fabricated via optical (photonic crystal, fluorescence) and electrochemical principles for real-time monitoring of glucose levels and wound pH. Finally, critical challenges in material development and scalable manufacturing of multifunctional hydrogel components are discussed, alongside prospects for precision diagnostics and therapeutics in diabetic wound care. Full article

This study explores the synthesis, characterization, and extraction efficiency of hydrophobic natural deep eutectic solvents (NADESs), along with the allergen-modulating potential of extracted bioactive compounds. Six NADESs were synthesized using binary combinations of camphor, thymol, eugenol, and menthol (1:1 molar ratio) and characterized using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis and differential thermal analysis (TGA/DTA), density functional theory (DFT), and molecular dynamics simulations (MD simulations). Bioactive compounds were extracted from Thujopsis dolabrata wood biomass via ultrasonic-assisted extraction and analyzed using gas chromatography–mass spectrometry (GC–MS). The total essential oil yield, estimated semiquantitatively by summing the peak areas of key terpenoid compounds, ranged from 1.91% to 7.90% across different NADES systems, indicating their varied extraction capacities. Molecular docking was performed to assess their allergen-modulating interactions with Amb a 1 and Cry j 1. All NADESs exhibited single-stage decomposition (110–125 °C) except camphor–menthol, which recrystallized. FTIR and simulations confirmed strong hydrogen bonding in eugenol-based NADESs, particularly menthol–eugenol. Extraction identified 47 bioactive compounds, with 4,5α-Epoxy-3-methoxy-17-methyl-7α-(4-phenyl-1,3-butadienyl)-6β,7β-(oxymethylene) morphinan as the most abundant (9.31–11.16%). It exhibited the highest binding affinity (Cry j 1: −8.60 kcal/mol, Amb a 1: −7.40 kcal/mol) and lowest inhibition concentration (Cry j 1: 0.49 µM, Amb a 1: 3.74 µM), suggesting strong allergen-modulating potential. Hydrophobic interactions and hydrogen bonding drove protein–ligand binding. These findings highlight NADESs as effective, sustainable solvents for extracting bioactive compounds with allergen-modulating potential. Full article

DprA (also known as Smf) is a conserved RecA mediator originally characterized by its role in natural chromosomal transformation, yet its widespread presence across bacteria hints at broader DNA metabolic functions. Here, we demonstrate that Bacillus subtilis DprA enhances the frequency of Escherichia coli Hfr conjugation in vivo. In vitro, RecA·ATP binds and cooperatively polymerizes in a 50-nucleotide (nt) polydeoxy T (dT)₅₀ ssDNA to form dynamic filaments that SSB inhibits, an effect fully reversed by Bacillus subtilis DprA. Escherichia coli RecA bound to (dT)₂₁ exhibits minimal dATPase activity, but the addition of B. subtilis DprA significantly stimulates RecA dATP hydrolysis. B. subtilis RecA·dATP readily assembles on (dT)₂₀ complexes, and DprA allosterically activates RecA on even shorter (dT)₁₅ substrates. Combining biochemical assays with a fully atomic model of the RecA–DprA–ssDNA complex, we proposed that only one DNA binding site of the DprA dimer engages the ssDNA during RecA loading, owing to steric constraints. This work refines the mechanism of DprA-mediated RecA nucleation and defines the minimal ssDNA footprint required for mediator activity. Full article