Search Results (4,617)

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions. Full article

This study emphasizes overcoming the challenges of targeted drug delivery in colon cancer therapy by developing gastrointestinal (GI) stable, pectin-coated nanoliposomes for the oral delivery of Apigenin-Cyclodextrin Complex as an in situ gel formation. Initially, the formulation was strategically designed using design expert software for formulation optimization. FTIR and XRD studies were conducted to ensure physical compatibility and to confirm the encapsulation of apigenin within the formulation. In process optimization, among all seventeen formulations run tested, PNL (Api-Cy)-13 was identified for the highest drug loading, favourable size dimension of particle with zeta potential, and spherical external morphology through SEM analysis. The metered drug release during an in vitro study for PNL (Api-Cy)-13 was remarkably high (more than 75% of drug availability in the colonic environment, precisely in contrast to only 20% in the gastric phase in a sustained release manner), focused on colon drug targeting as an in situ gel. Furthermore, apigenin release from PNL (Api-Cy)-13 in an ex vivo chick ileum permeability study was observed both in the absence and presence of 1% vancomycin. An incremental apigenin release in the absence of the antibiotic (1% vancomycin) indicated gut microbial-associated and pectinase-mediated drug release. Here, pectin degradation materializes by the colonic microbial environment, which facilitates desirable incremental colonic drug permeation. Finally, an in vitro MTT assay and a competitive flowcytometric cell uptake study with PNL (Api-Cy)-13 using HCT-116 cells proved significant superiority in cytotoxicity profile for apigenin when delivered as an optimized coated nanoliposome in comparison to free apigenin or other non-modified nano-formulation. Also, the inhibition of the cell efflux process was validated by Multidrug Resistance 1 (MDR1) gene regulation. These observations establish an undoubted promise for the novel biopolymer, pectin-based apigenin-cyclodextrin nanoliposomes as targeted therapy in colon cancer with significant in vivo pharmacokinetics and safety profile. Full article

Pediatric obesity is rising globally, and emerging evidence suggests that sleep timing may influence metabolic health through epigenetic mechanisms. This study investigated epigenome-wide DNA methylation patterns associated with bedtime in children and explored their biological relevance. Children aged 6–10 years were classified as early (≤8:30 PM) or late (>8:30 PM) bedtime groups. Saliva-derived DNA was analyzed using the Illumina Infinium MethylationEPIC BeadChip Array, and the Sparse Wrapper Algorithm (SWAG) was applied to identify differentially methylated loci. A total of 1006 CpG sites, representing 571 unique genes, were significantly associated with bedtime (p < 0.001). Significant methylation differences were observed between early and late bedtime groups, with ABCG2, ABHD4, MOBKL1A, AK3, SDE2, PRAMEF4, CREM, CDH4, BRAT1, and SDK1 showing the most consistent variation. Functional enrichment analyses (Gene Ontology, KEGG, and DisGeNET) conducted on the SWAG-identified gene set revealed enrichment in biological processes including peptidyl-lysin demethylation, regulation of sodium ion transport, DNA repair, and lipo-protein particle assembly. Key KEGG pathways included circadian entrainment, neurotransmission (GABAergic, dopaminergic, and glutamatergic), growth hormone synthesis, and insulin secretion. DisGeNET analysis identified associations with neurodevelopmental disorders and cognitive impairment. Cross-comparison with established sleep and obesity gene sets identified ten overlapping genes(CDH4, NR3C2, ACTG1, COG5, CAT, HDAC4, FTO, DOK7, OCLN, and ATXN1). These findings suggest that variations in bedtime during childhood may epigenetically modify genes regulating circadian rhythm, metabolism, neuronal connectivity, and stress response, potentially predisposing to later-life developmental, and metabolic challenges. Full article

This work presents optimized decision tree-based ensemble machine learning models for predicting and quantifying the effects of greenhouse gas (GHG) emissions in Bangladesh. It aims to identify policy implications in response to significant environmental changes. The models analyze the emissions of CO₂, N₂O, and CH₄ from sectors including energy, industry, agriculture, and waste. We consider many parameters, including energy consumption, population, urbanization, gross domestic products, foreign direct investment, and per capita income. The data covers the period from 1971 to 2019. The model is trained using 80% of the dataset and validated using the remaining 20%. The hyperparameters, such as the number of estimators, maximum samples, maximum depth, learning rate, and minimum samples leaf, were optimized via particle swarm optimization. The models were tested, and their forecasts were extended till 2041. An examination of feature importance has identified energy consumption as a critical factor in greenhouse gas emissions, acknowledging the positive effects of clean energy in accordance with the clean development mechanism. The results demonstrate a robust model performance, with an R² score of approximately 0.90 for both the training and testing datasets. The bagging decision tree model showed the lowest mean squared error of 151.3453 and the lowest mean absolute percentage error of 0.1686. The findings of this study will help decision-makers understand the complex connections between socioeconomic conditions and the elements that contribute to greenhouse gas emissions. The discoveries will enable more precise monitoring of national greenhouse gas (GHG) inventories, allowing for focused efforts to mitigate climate change in Bangladesh. Full article

This study investigates the control of the phase-structural state in Ni–45Ti–xCu (x = 5, 7 at.%) shape memory alloys fabricated via a shortened powder metallurgy route: mechanical activation → spark plasma sintering (SPS) → heat treatment. Compact samples were produced from mechanically alloyed powders (650–750 rpm, up to 5 h) and sintered at 900 °C. The structure and microstructure were characterized using X-ray diffraction (to identify B2/B19′/Ni₄Ti₃ phases and assess ordering) and SEM–BSE/EDS (to analyze morphology, porosity, and Ni-rich precipitates). Two post-processing treatments were applied: single-stage annealing (500 °C, 2 h) and a three-stage treatment (900 °C/30 min → water quenching → 300 °C/20 min). Mechanical alloying transformed the initial elemental powder mixture (fcc-Ni, hcp-Ti, fcc-Cu) into a supersaturated fcc-(Ni, Cu, Ti) solid solution with emerging NiTi phases, with a minimum particle size achieved after ~300 min at 750 rpm. SPS compaction yielded a high-density matrix consisting predominantly of the B2 phase. Single-stage annealing preserved B19′ martensite and Ni₄Ti₃ precipitates, particularly in the 5 at.% Cu alloy. In contrast, the three-stage treatment dissolved the Ni₄Ti₃ precipitates, suppressed the formation of B19′ and R phases, and stabilized a highly ordered B2 matrix. Increasing the Cu content from 5 to 7 at.% significantly enhanced the B2 phase fraction, reduced secondary nickel-rich phases, and improved structural homogeneity, evidenced by a continuous neck network and closed porosity. The optimized condition—7 at.% Cu combined with the three-stage annealing—produced a microstructure with >95% B2 phase, <1% Ni₄Ti₃, and ~98% relative density. This forms the prerequisite microstructural state for a narrow transformation hysteresis and high functional cyclic stability. Full article

Purple waxy corn cobs (PWCCs) represent an underutilized agricultural waste rich in anthocyanins with promising cosmeceutical potential. This study investigated niosome-based encapsulation to enhance the stability and bioactivity of PWCC anthocyanin extracts. PWCC extract was macerated in 50% ethanol. The extract exhibited a high total anthocyanin content (3.02 ± 0.81 mg C3GE/L), while cyanidin-3-glucoside identified as the major anthocyanin (1.17 ± 0.02 mg/g dry weight). Furthermore, the extracts showed strong antioxidant activities as evidence by DPPH, ABTS, and FRAP assays. The optimized niosome preparations synthesized by the probe sonication method exhibited better entrapment efficiency (80–85%), nanoscale particle size (185–296 nm), and stable zeta potential (−29 to −32 mV). TEM verification of the spherical morphology and FT-IR spectra confirmed the successful loading of anthocyanins. The thermal stability test exhibited negligible changes in the particle size and zeta potential. Furthermore, in vitro release profile followed the Higuchi model, indicating enhanced release kinetics. Biological assays demonstrated moderate UVB protection effects and potent anti-melanogenesis activity in B16F10 cells. Notably, formulation N5 exhibited the highest tyrosinase inhibition and melanin synthesis suppression. These findings indicate that niosome-based encapsulation represents a promising strategy for enhancing the stability, bioavailability, and biological efficacy of anthocyanin extracts, especially in the cosmetic and pharmaceutical industries. Full article

Cancer continues to pose a major global health burden, with conventional therapeutic modalities such as surgical resection, chemotherapy, radiotherapy, and immunotherapy often hindered by limited tumor specificity, substantial systemic toxicity, and the emergence of multidrug resistance. The rapid advancement of nanotechnology has introduced functionalized nanomaterials as innovative tools in the realm of precision oncology. These nanoplatforms possess desirable physicochemical properties, including tunable particle size, favorable biocompatibility, and programmable surface chemistry, which collectively enable enhanced tumor targeting and reduced off-target effects. This review systematically examines recent developments in the application of nanomaterials for cancer therapy, with a focus on several representative nanocarrier systems. These include lipid-based formulations, synthetic polymeric nanoparticles, inorganic nanostructures composed of metallic or non-metallic elements, and carbon-based nanomaterials. In addition, the article outlines key strategies for functionalization, such as ligand-mediated targeting, stimulus-responsive drug release mechanisms, and biomimetic surface engineering to improve in vivo stability and immune evasion. These multifunctional nanocarriers have demonstrated significant potential across a range of therapeutic applications, including targeted drug delivery, photothermal therapy, photodynamic therapy, and cancer immunotherapy. When integrated into combinatorial treatment regimens, they have exhibited synergistic therapeutic effects, contributing to improved efficacy by overcoming tumor heterogeneity and resistance mechanisms. A growing body of preclinical evidence supports their ability to suppress tumor progression, minimize systemic toxicity, and enhance antitumor immune responses. This review further explores the design principles of multifunctional nanoplatforms and their comprehensive application in combination therapies, highlighting their preclinical efficacy. In addition, it critically examines major challenges impeding the clinical translation of nanomedicine. By identifying these obstacles, the review provides a valuable roadmap to guide future research and development. Overall, this work serves as an important reference for researchers, clinicians, and regulatory bodies aiming to advance the safe, effective, and personalized application of nanotechnology in cancer treatment. Full article

Metal–organic frameworks (MOFs) have been increasingly recognized as promising platforms for enzyme modulation, owing to their tunable porosity, high surface area, and versatile chemical functionality. In this review, the potential of MOFs for the inhibition and modulation of protein kinases and phosphatases—key regulators of cellular signaling and disease progression—is examined. The structural fundamentals of MOFs are outlined, followed by a discussion of common synthesis strategies, including solvothermal, microwave-assisted, sonochemical, and mechanochemical methods. Emphasis is placed on how synthesis conditions influence critical features such as particle size, crystallinity, surface chemistry, and functional group accessibility, all of which impact biological performance. Four primary mechanisms of MOF–enzyme interaction are discussed: surface adsorption, active site coordination, catalytic mimicry, and allosteric modulation. Each mechanism is linked to distinct physicochemical parameters, including pore size, surface charge, and metal node identity. Special focus is given to biologically relevant metal centers such as Zr⁴⁺, Ce⁴⁺, Cu²⁺, Fe³⁺, and Ti⁴⁺, which have been shown to contribute to both MOF stability and enzymatic inhibition through Lewis acid or redox-mediated mechanisms. Recent in vitro studies are reviewed, in which MOFs demonstrated selective inhibition of disease-relevant enzymes with minimal cytotoxicity. Despite these advancements, several limitations have been identified, including scalability challenges, limited physiological stability, and potential off-target effects. Strategies such as post-synthetic modification, green synthesis, and biomimetic surface functionalization are being explored to overcome these barriers. Through an integration of materials science, coordination chemistry, and molecular biology, this review aims to provide a comprehensive perspective on the rational design of MOFs for targeted enzyme inhibition in therapeutic contexts. Full article

In scaled laser-driven magnetized liner inertial fusion (MagLIF), externally applied magnetic fields improve energy coupling by suppressing electron thermal conduction, enhancing Joule heating, and increasing $\alpha$-particle energy deposition. However, confinement can be significantly degraded by magnetic flux transport, dominated by resistive diffusion, and more critically, the Nernst effect. One-dimensional magnetohydrodynamic simulations demonstrate that increasing the applied field generally enhances neutron yield, but when the Nernst effect is included, the benefit of stronger magnetization diminishes. Stagnation is achieved at 2.72 ns, yielding a peak temperature of 2.17 keV and a neutron production of $1.2\times {10}^{12}$. When the Nernst effect is taken into account, the neutron yield decreases by 57.3% compared with the case without it under an initial magnetic field of 10 T. During the implosion, the magnetic field in the fuel gradually diffuses outward into the outer liner. By stagnation, the magnetic flux of fuel has decreased by 33.8%. Based on the characteristics of the Nernst effect, an optimized initial magnetic field of approximately 6 T is identified, which yields an about 2.5 times higher neutron yield than the unmagnetized case. These findings emphasize the key role of magnetic–energy coupling in target performance and provide guidance for the design and scaling of magnetized targets. Full article

I will consider, first, the positive charge of energeia in Aristotle, who identified it with the actuality of the actual. Then, I will pay attention to the negative charge that re-signifies the term, bestowing on it the exact opposite sense of potentiality. Rather than a radical correction of Aristotle, this polarizing modern signification unfolds in the field prepared in, if also rejected by, Greek Antiquity and unblocks the electric current of the concept of energy, a directional flow of charged particles of meaning from the positive to the negative pole. Still, the flow does not just happen by itself: the equivalent of electromotive force (EMF) is a fresh glance at the history of philosophy, not as a field dotted with static monuments to past intellectual achievements, but as an electric, or electromagnetic, semantic field. Only by grasping the conceptual circuitry of energy as a whole is it possible to appreciate the complex relation of this concept to the history of philosophical and theological thought and to the present. Full article

The retention of polyphenols in thermally processed noodles is constrained by interactions with starch and glutenin, critically impacting functional properties (antioxidant activity, starch digestibility modulation) and quality attributes. Current understanding lacks quantitative links between initial pomace particle size, polyphenol behavior throughout processing, and the resulting noodle properties. This study systematically investigated how Rosa roxburghii pomace particle size (0.1–250 μm), fractionated into five ranges, governs polyphenol extractability, retention in fresh/boiled noodles, and their functional and quality outcomes. Mathematical modeling established quantitative particle size–property relationships. The results indicated that polyphenol release was maximized at the 1–10 μm particle size. Total phenolic retention in boiled noodles was highest with 0.1–1 μm pomace, while the retention of specific phenolics peaked with 60–80 μm pomace. Fresh noodle hardness and gumminess decreased significantly, particularly with extracts from 1 to 40 μm pomace, whereas boiled noodles showed increased chewiness/adhesiveness. All polyphenol-enriched noodles exhibited suppressed starch digestibility and enhanced antioxidant capacity. Robust quadratic regression models predicted key properties based on particle size. Molecular interactions (hydrogen bonding, hydrophobic contacts, π–cation stacking, salt bridges) between key phenolics (EGCG, hydroxybenzoic acid, gallic acid, quercetin, and isoquercitrin) and the gluten–starch matrix, critically involving residues Arg-86 and Arg-649, were identified as the underlying mechanism. These results demonstrate that precise control of pomace particle size regulates extract composition and molecular binding dynamics, providing a strategic approach to optimize functional noodle design. Full article

Regular monitoring of mineral dust is essential in order to assess its impact on air quality, human health, and climate, with satellite observations in recent decades playing a crucial role by providing consistent global coverage of various aerosol properties. In this study, the Dust Flag product of the Infrared Atmospheric Sounding Interferometer (IASI), onboard the Meteorological Operational (MetOp) satellites, is evaluated using ground-based measurements from 120 Aerosol Robotic Network (AERONET) sites worldwide. The Dust Flag serves as both an indicator of dust presence and a pseudo-indicator of dust loading. To evaluate this product, a well-established aerosol classification scheme was applied, based on AERONET Aerosol Optical Depth (AOD) and Angstrom Exponent products. Results show that the Dust Flag reliably identifies dust, achieving a 74.1% agreement score with AERONET, although some cases are misclassified. Also, this study concludes that the Dust Flag signal increases with particle load, reaching maximum values during extreme coarse dust events. Cases when IASI does not agree with AERONET are further examined and may stem either from limitations in the AERONET classification methodology or from low atmospheric particle concentrations. Finally, the spatial variability of the agreement score is examined, with the highest scores found within and near the global “dust belt”. Full article

The numerical reservoir simulation is a valuable tool to enhance heavy oil recovery by assessing different production strategies (like SAGD and CSS) and operational scenarios. While numerous studies have developed complex models, a systematic review identifying the most critical parameters for achieving accurate production forecasts is lacking. In this work, diverse studies have been reviewed regarding the numerical models of steam injection technologies by examining various parameters (reservoir properties and operating conditions) employed and their impact on the results obtained. Additionally, the effect of using kinetic models in simulations, as well as the modeling of solvent and catalyst injection, is discussed. The outcomes highlight that oil recovery for steam injection methods requires effective steam chamber management and an understanding of geomechanical changes due to the significant role of thermal convection on energy transfer and oil displacement. Increasing steam injection pressures can enhance energy efficiency and reduce emissions, but controlling the gases generated during the reaction poses difficulties. The gas formation within the reservoir in simulations is crucial to prevent overestimating oil production and improving precision. This can be achieved using simple kinetic models, but it is essential to incorporate gas–water solubilities to mimic actual gas emissions and avoid gas buildup. Crucially, our synthesis of the literature demonstrates that incorporating gas–water solubilities and kinetic models for H₂S production can improve the prediction accuracy of gas trends by up to 20% compared to oversimplified models. Enhanced recovery methods (adding solvent and catalyst injection) provide advantages compared with conventional steam injection methods. However, suitable interaction models between oil components and solid particles are needed to improve steam displacement, decrease water production, and enhance recovery in certain circumstances. The use of complex reaction schemes in numerical modeling remarkably enhances the prediction of experimental reservoir data. Full article

The global spread of micro- and nanoplastics (MNPs) has emerged as an environmental and medical concern, with growing evidence of their role in cardiovascular disease (CVD). These particles, originating from the degradation of larger plastics and consumer products, can be ingested or inhaled, cross biological barriers, and accumulate in human tissues, including blood, myocardium, and atherosclerotic plaques. Experimental and clinical studies suggest that MNPs contribute to CVD through multiple mechanisms: activation of systemic inflammation and inflammasomes, oxidative stress, endothelial dysfunction, prothrombotic activity, and direct myocardial injury, ultimately promoting fibrosis and impaired contractility. Epidemiological data further indicate that populations exposed to higher plastic pollution or with pre-existing cardiovascular risk factors may be particularly vulnerable. Taken together, these findings identify MNPs as a potential novel environmental cardiovascular risk factor. Advancing detection methods, mechanistic research, and public health strategies will be essential to mitigate their impact and reduce plastic-related cardiovascular burden. Full article

The application of laser powder bed fusion (LPBF) to 7xxx-series aluminum alloys is fundamentally limited by hot cracking and porosity. This study demonstrates that adding 5 wt.% AlCoCrFeNi_2.1 high-entropy alloy (HEA) particles to 7075 aluminum alloy (AA7075) powder can effectively mitigate these issues. Microstructural characterization revealed that the HEA particles remained largely intact and formed a strong metallurgical bond with the α-Al matrix. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) analysis confirmed that this bonding is facilitated via the in situ formation of new intermetallic phases at the particle/matrix interface. X-ray diffraction (XRD) identified these phases as primarily Al₅Co₂ and Fe₃Ni₂. A key consequence of this reinforced interface is a significant change in cracking behavior; optical microscopy (OM) showed that long, continuous cracks typical of AA7075 were replaced by shorter, deflected cracks in the composite. While porosity was not eliminated, the addition of HEA stabilized the process, yielding a consistent density improvement of 0.5–1.5% across the processing window. This microstructural modification resulted in a substantial ~64% increase in average microhardness, which increased from 96.41 ± 9.81 HV_0.5 to 158.46 ± 11.33 HV_0.5. These results indicate that HEA reinforcement is a promising route for engineering the microstructure and improving the LPBF processability of high-strength aluminum alloys. Full article