Search Results (665)

In this paper, it is suggested that the property–energy consistent (PEC) method represents an efficient and reliable method for generating accurate contraction coefficients for the property-oriented basis sets. Due to its peculiarities, the PEC method allows the implementation of rather succinct contraction schemes, without a noticeable loss of accuracy, giving in result very compact property-oriented basis sets that are capable of providing the same or even better accuracy than that reached with considerably larger basis sets of the same kind. This idea has been demonstrated on the example of the recontraction of previously introduced spin–spin coupling constant (SSCC)-oriented pecJ-n (n = 1, 2) basis sets for H, C, N, and F atoms, whose exponents were optimized by the PEC algorithm, but the contraction coefficients were defined using the usual self-consistent field calculations of the molecular energies of the simplest hydrides. In this work, the original pecJ-n basis sets were recontacted by means of the PEC method, resulting in compact segmented–contracted basis sets being smaller in size than their previous analogies by four and seven functions for hydrogen and the 2nd-period atoms, respectively. High-quality SOPPA(CCSD) calculations of 436 SSCCs of various types involving H, C, N, and F nuclei showed the supremacy of the newly contracted pecJ-n (n = 1, 2) basis sets over their original versions and most of the other well-known SSCC-oriented basis sets. Full article

Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles (UAVs) acting as aerial base stations can be rapidly deployed to provide on-demand coverage; however, ship mobility, heterogeneous emergency priorities, and UAV endurance limitations make the joint optimization of user association and multi-UAV deployment a challenging mixed-integer, long-horizon decision problem. This paper considers a multi-UAV maritime emergency communication system where ships are categorized into multiple priority classes and served links must satisfy a minimum signal-to-noise ratio (SNR) constraint. We formulate a long-term system-utility maximization problem that jointly determines (i) per-slot association between UAVs and ships under capacity, priority, and SNR constraints, and (ii) dynamic UAV deployment under mobility, geofencing, and battery constraints. To obtain tractable and high-quality solutions, we decompose the problem into two coupled subproblems. For user association, we propose a Priority-Aware Branch-and-Cut (PA-BAC) algorithm that integrates linear programming relaxation, cutting-plane tightening, and priority-guided branching, with a priority-greedy feasible initialization to accelerate incumbent improvement. For dynamic deployment, we develop an Enhanced Multi-Agent Proximal Policy Optimization (E-MAPPO) method featuring a global value network, entropy regularization, and sequential actor updates to enhance learning stability and exploration. Importantly, the PA-BAC association is embedded into the learning loop to provide reliable, constraint-satisfying per-slot rewards and reduce the burden of end-to-end learning over hybrid-action spaces. Simulation results demonstrate that PA-BAC consistently improves normalized priority-weighted throughput over heuristic association baselines. Moreover, by mathematically enforcing priority and QoS feasibility at every slot and delegating only continuous mobility to MARL, the integrated E-MAPPO-PA-BAC framework achieves higher long-term system utility, improved energy efficiency, and strong robustness across varying ship densities—properties that are vital for time-sensitive maritime emergency communications. Additional runtime, sensitivity, and AIS-driven trace evaluations further verify the computational practicality of PA-BAC and the applicability of the proposed framework under realistic ship mobility patterns. Full article

The present study provides a comprehensive investigation into the hydrodynamic performance of grooved rubber journal bearings (GRJBs) employed as shaft supports in various rotating systems, with particular emphasis on marine applications. These bearings are lubricated with non-Newtonian fluids such as modern oil containing additives and viscoelastic water-based lubricant, which—owing to its complex composition including hydrocarbon chains, metal oxides, and impurity particles and contaminants such as salts, organic substances, microalgae, biopolymers, and microorganisms—deviates from the ideal Newtonian fluid model and demonstrates non-Newtonian rheological behavior. By examining various theories used in the analysis of non-Newtonian fluid behavior, the power-law model, which has a high degree of generality, has been employed in the present study. Also, to improve modeling accuracy, the elastic deformation of the rubber bush in this study is characterized using the Winkler foundation approach and analyzed via the finite element method (FEM). This advanced mechanical formulation, integrated with non-Newtonian lubrication modeling of lubricant using the power-law fluid model, and the parametric assessment of groove number and dimensions on steady-state bearing performance parameters, constitutes the core of this research. The investigation focuses on groove configurations of 4, 6, 8, and 10 channels. The findings indicate that increasing the groove count partitions the convergent pressure film zone into discrete segments, thereby reducing the maximum hydrodynamic pressure while intensifying the overall energy dissipation within the bearing. Additionally, the influences of rheological properties of the fluid—namely the power-law index (n) and the consistency index (m)—on key performance characteristics are thoroughly examined. An increase in both parameters enhances the effective viscosity and load carrying capacity; however, the exponential amplification due to the power-law index exhibits a more pronounced effect on load capacity and peak pressure compared to the consistency index. Full article

Predicting parameter estimates under item response theory (IRT) from expert-coded item features offers a scalable alternative to resource-intensive field testing. This study evaluates whether inferential feature selection can improve predictive accuracy for item difficulty and item discrimination using five filter methods: the Analysis of Variance (ANOVA) F-test, Kendall’s Tau, the Kolmogorov–Smirnov test, the Anderson–Darling test, and the Energy Distance test. Models were trained using K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) under random split and fixed-form cold-start partitioning strategies. Results show that the distributional properties of item features, rather than train–test splitting alone, drive predictive gains: distribution-based filter approaches, particularly the Kolmogorov–Smirnov test, consistently outperformed mean-based approaches by better capturing the full probability structure of the feature-parameter relationship. KNN benefited substantially from feature selection given its reliance on Euclidean distance, while SVR showed smaller gains due to its inherent regularization. Item discrimination generalized well to previously unseen test forms that share no calibration data with the training set, whereas item difficulty prediction was considerably more sensitive to distributional shifts when predicting entirely new, operationally administered forms. The main finding is that the distributional properties of item features are more important than the quantity of features for obtaining robust IRT parameter predictions. Full article

Thermal modernization of existing buildings is an important part of sustainability-oriented retrofit practice because it can reduce operational energy demand, but its environmental effect depends partly on the insulation material selected and on the thermal assumptions used in design. This study examines how the use of declared thermal conductivity (λ_decl) and design conductivity (λ_design) affects the required insulation thickness and the A1–A3 global warming potential (GWP) of alternative insulation materials for an attic ceiling separating heated space from an unheated ventilated attic in a multi-family building. This study supports product-stage sustainability assessment; it does not constitute a comparison of the full life cycle climate effect of the selected material groups. The thickness needed to achieve U_target = 0.15 W/(m²·K) was determined for scenarios based on λ_decl, temperature-corrected λ_design, and a moisture sensitivity analysis for cellulose. Environmental assessment was based on European EN 15804+A2-compliant EPDs, with separate reporting of GWP_fossil and GWP_biogenic. In the analyzed case, differences between material groups were driven mainly by EPD data, whereas conversion from declared to design thermal properties had a smaller, but not negligible, effect. This effect became more important for moisture-sensitive materials. The results show that sustainability-oriented environmental comparisons based only on declared thermal conductivity may be misleading when functionally equivalent solutions are compared. In the analyzed case, the transition from λ_decl to temperature-corrected λ_design resulted in only a small change in the required insulation thickness and the corresponding GWP result. At the same time, the scenario-based sensitivity analysis for cellulose insulation and the variability of data reported in the EPDs indicate that moisture-related assumptions and the selection of input data may be of greater importance. The results show that, when interpreting the environmental performance of insulation solutions in sustainable retrofit design, consistency should be maintained between the adopted functional unit and the method used to define the thermal properties of the material after installation in the building envelope. Full article

Vanadium borate (V₃BO₆) has recently been synthesized and identified as a promising material for use in energy storage applications, particularly as a potential anode for lithium-ion batteries. However, despite previous studies highlighting its electrochemical performance, a comprehensive understanding of its intrinsic mechanical, thermal, and vibrational properties remains limited. The compound crystallizes in an orthorhombic phase with the Pnma (No. 62) space group. To explore its intrinsic physical characteristics, full geometry optimization of the unit cell and atomic positions was performed using density functional theory (DFT) within the CASTEP framework. The Perdew–Burke–Ernzerhof (PBE) functional under the generalized gradient approximation (GGA) was used to model exchange–correlation effects. A plane-wave cut-off of 408 eV and a 6 × 6 × 13 Monkhorst–Pack grid were employed to ensure numerical convergence. The optimized lattice constants (a = 9.9025 Å, b = 8.4751 Å and c = 4.5354 Å) are highly consistent with experimental data, which confirms the reliability of the computational approach adopted. The elastic behaviour was further investigated using the first-principles strain-energy method, yielding nine independent elastic constants consistent with orthorhombic symmetry. The calculated bulk and shear moduli, along with the anisotropy parameters, suggest that V₃BO₆ has a favourable balance of mechanical robustness and moderate ductility. A Vickers hardness of 10.95 GPa and a B/G ratio of approximately 1.93 corroborate these findings. Additional parameters, such as Poisson’s ratio, Debye temperature and average sound velocities, were derived to gain deeper insight into the material’s thermomechanical performance. These results provide a solid theoretical foundation for understanding the mechanical stability and potential anode suitability of V₃BO₆ in lithium-ion battery systems. Full article

To address issues such as thermal stress concentration in metal bone implants produced via high-energy beam direct additive manufacturing, a method was proposed to fabricate porous titanium scaffolds. This approach combined Fused Deposition Modeling (FDM) with a debinding–sintering process. Ti/ABS composite filaments with titanium volume fractions of 35%, 40%, and 45% were successfully developed via a single-screw extrusion process. Their feasibility in the FDM process was subsequently verified. The effects of different processing parameters on the forming quality and dimensional accuracy of the green bodies were investigated. After debinding and sintering the composite scaffolds prepared with optimized parameters, structurally intact porous titanium scaffolds were obtained. Microscopic characterization shows that the scaffold surface consists primarily of titanium, and the pore structure remains intact. Furthermore, compression tests were performed on three types of porous titanium scaffolds with different porosities. The results indicate that the combination of ABS/titanium alloy composite filaments, FDM technology, and debinding–sintering post-processing enables the high-quality and efficient production of porous titanium scaffolds. The elastic modulus of the resulting scaffolds ranges from 1.2 to 1.6 GPa, and the compressive strength is between 25.7 and 68.3 MPa. The elastic modulus matches that of human cancellous bone. Meanwhile, the compressive strength is significantly higher than that of cancellous bone and falls between the values for cancellous and cortical bone. These mechanical properties meet the requirements for human bone, providing a new approach for the manufacture of orthopedic implants. Full article

This study investigates the impact of TiO₂ incorporation (0, 2, 4, 6, 8 wt.%) on the structural, optical, electrical, mechanical, and antibacterial properties of electrospun PVA/PVP nanofibers. FESEM observations revealed continuous, randomly oriented nanofibrous films with an average diameter in the 77–96 nm range, depending on TiO₂ content. FTIR and XRD analyses confirmed successful nanoparticle integration, showing effective interfacial interactions and the presence of crystalline TiO₂ phases within the semi-crystalline PVA/PVP matrix. Optical studies demonstrated a progressive decrease in the indirect band gap with increasing TiO₂ loading, decreasing from 3.75 to 3.54 eV according to the Tauc method and from 3.70 to 3.43 eV according to the ASF method, accompanied by an increase in Urbach energy from 0.43 to 0.64 eV, indicating enhanced structural disorder and tail state formation. The optical dispersion parameters obtained from the Wemple−DiDomenico model were consistent with these trends. Electrical characterization showed enhanced DC conductivity with increasing TiO₂ content and a marked reduction in thermal activation energy from 2.54 eV for the neat blend to 0.98 eV at higher TiO₂ loading, confirming facilitated charge transport in nanocomposite system. Mechanical characterization indicated that TiO₂ reinforcement improved both stiffness and strength, with the 6 wt.% sample achieving an optimal strength–ductility synergy (8.9 MPa and 121.5% elongation). Additionally, TiO₂ loading significantly boosted antibacterial performance, particularly against Escherichia coli and Staphylococcus aureus at 8 wt.%. These multifunctional properties position PVA/PVP:TiO₂ nanofibers as highly promising candidates for flexible biomedical coatings, optoelectronic devices, and advanced functional surfaces. Full article

Accurate thermal characterization of building insulation materials is essential for reliable energy performance assessment, regulatory compliance, and the development of high-performance envelopes. On one hand, the growing adoption of innovative insulating products, such as nanoporous materials, aerogel-based composites, bio-based panels, and thin insulating coatings, helps to enhance buildings’ energy efficiency by means of sustainable raw materials. On the other hand, conventional measurement techniques encounter significant challenges, due to their heterogeneity, reduced thickness, and unconventional geometries. In this study, an intra-laboratory comparison of three widely used methods for thermal conductivity determination is presented: the Transient Plane Source (TPS, Hot Disk) method, the Guarded Hot Plate (GHP) method, and the Heat Flow Meter (HFM) method. A total of twelve insulating materials, spanning super-insulating cores, insulating renders, bio-based panels, and nanocomposite coatings, were experimentally characterized under controlled laboratory conditions. A view on the analyzed insulating materials’ cradle-to-grave environmental impact is also given, to enhance the users’ awareness for the highly informed choice. The results highlight systematic differences between transient and steady-state approaches, with TPS measurements generally exhibiting larger deviations for materials characterized by surface roughness, limited thickness, or strong internal heterogeneity. In contrast, GHP and HFM methods show closer agreement when specimen geometry and stabilization requirements are satisfied. The influence of contact resistance, probing depth, specimen preparation, and uncertainty propagation is critically analyzed for each technique. The study provides practical insights into the applicability limits of commonly used thermal characterization methods and emphasizes the importance of selecting measurement techniques in relation to material morphology and testing constraints. These findings support more reliable thermal property assessment of emerging insulation materials and contribute to improved consistency between laboratory measurements and energy performance evaluations for buildings. Full article

In this paper, the effects of 4 wt.% of silicon on the microstructure and mechanical properties of AM50 magnesium alloys fabricated by the casting method are presented. New AM50/Si material and the base AM50 alloy were gravity cast into a metal mould under the same conditions for comparison. Analyses of the alloys’ microstructures were carried out by light microscopy (with differential interface contrast), scanning electron microscopy (with an energy dispersive X-ray spectrometer), as well as X-ray diffraction (XRD). In as-cast conditions, both materials were composed of α-Mg solid solution, α + γ eutectic (where γ is Al₁₂Mg₁₇), Al₈Mn₅ intermetallic phases and discontinuous γ precipitates. The AM50/Si material also consisted of the Mg₂Si phase. This structural constituent appeared in the form of primary crystals with regular polygonal morphology and an α + Mg₂Si eutectic in the form of “Chinese script”. In the microstructure of the AM50/Si material, the Mn₃SiAl₉ ternary phase was also identified. The detailed analyses presented in this paper revealed that the new ternary Mn₃SiAl₉ structural compound caused a reduction in the volume fraction of the Al₈Mn₅ phase but did not completely replace it. These two phases formed competitively. The fabricated material exhibited higher tensile and compression strength as well as yield strength in comparison with the AM50 alloy. Additionally, analyses of the fracture surfaces of the AM50/Si material carried out using scanning electron microscopy (SEM) were presented. Full article

We present fully relativistic calculations of integral cross sections and swarm transport properties for positron–radon scattering over a wide energy range (0–1000 eV) and reduced electric field range (0.01–1000 Td). Elastic (total, momentum-transfer and viscosity-transfer), discrete excitation, direct annihilation, positronium formation and positron-impact ionization cross sections are obtained using a complex relativistic optical potential method. Owing to the large atomic number of radon and the absence of experimental scattering data, a consistent relativistic treatment is essential. The present work provides the first fully relativistic, internally consistent cross-section dataset for positron swarms in radon gas. Using a multi-term solution of Boltzmann’s equation, steady-state transport coefficients are calculated and found to be strongly influenced by energy-dependent reactive loss, particularly positronium formation. Significant divergence between bulk and flux transport coefficients is observed, including non-monotonic bulk drift velocities and pronounced suppression of longitudinal bulk diffusion at intermediate fields (0.3–1000 Td). Time-dependent field-free calculations further quantify thermalization and annihilation dynamics through the evolution of the mean energy and $⟨Z_{\mathrm{eff}}⟩\left(t\right)$. These results provide a robust theoretical foundation for modelling positron transport and annihilation in radon and other heavy noble gases where relativistic and reactive effects are crucial. Full article

Traditional concrete faces challenges such as low energy absorption, brittleness and major environmental impacts, attributed to its dependence on natural resources. Integrating natural fibers with recycled coarse aggregates into concrete presents a promising method of enhancing concrete’s sustainability and mechanical performance. Still, accurately predicting the mechanical properties of these innovative concrete mixes remains complex. This research investigates the predictive abilities of two machine learning (ML) models, classification and regression trees (CART) and stepwise polynomial regression (SPR), for estimating the compressive and splitting tensile strengths of NF-reinforced concrete containing recycled coarse aggregates. The CART model showed greater predictive accuracy, reaching R² = 0.91 for compressive strength and R² = 0.89 for splitting tensile strength. Additionally, the model demonstrated consistently lower error metrics (RMSE, MAD, MAPE, MSE) than comparable approaches. For compressive strength, CART achieved R² = 0.91, RMSE = 5.5686, MSE = 31.0098, MAD = 4.1076, and MAPE = 0.1055, while for splitting tensile strength, it achieved R² = 0.89, RMSE = 0.3954, MSE = 0.1563, MAD = 0.2996, and MAPE = 0.0939. These results emphasize the significant potential of ML, particularly CART, to optimize the design of sustainable concrete mixtures, enabling more accurate and effective strength predictions and finally contributing to more resilient and sustainable infrastructure. Full article

Water exhibits unique interfacial properties that arise from the collective organization of its hydrogen-bond network. Establishing clear links between molecular-scale interactions and macroscopic observables remains a central challenge in understanding the behavior of liquid water. In this work, we combine experimental measurements of the contact angle of sessile water drops with quantum-chemical modeling of small water clusters (H₂O)_n (n = 2–6) to explore multiscale effects of hydrogen-bond cooperativity. The cluster calculations reveal a nonlinear, saturating evolution of hydrogen-bond geometries with increasing cluster size, reflecting the onset of cooperative many-body effects. Experimentally, the evolution of the apparent contact angle during evaporation is quantified using both conventional geometry and a non-invasive geometrical-optical method based on analysis of the dark refractive ring, which provides independent validation against conventional goniometric measurements. The evaporation dynamics are further interpreted within the diffusion-limited framework of the Popov model, indicating that the temporal evolution of the apparent contact angle is primarily consistent with geometry-controlled mass loss under diffusion-limited conditions, rather than requiring variations in intrinsic surface energy. By combining macroscopic contact-angle measurements with molecular-level cluster analysis, this study offers a qualitative multiscale perspective in which minimal cooperative hydrogen-bond motifs provide molecular context for interpreting interfacial behavior, without implying direct quantitative prediction of macroscopic interfacial observables. Full article

Background: Cysteinyl leukotrienes are components of slow-reacting substances of anaphylactic shock (SRS-A) and play a key role in asthma and inflammatory responses. Although chromone-2-carboxylic acids and substituted (aryloxy)alkanoic acids have the potential to be SRS-A antagonists, their comprehensive structure–activity relationships and pharmacokinetic characteristics remain understudied. Objective: This study integrated computational and experimental approaches, including QSAR modeling, molecular docking, ADMET analysis, molecular dynamics (MD) simulations, and antioxidant evaluation to identify and prioritize bifunctional compounds with anti-inflammatory and free radical-scavenging properties. Methods: A set of 68 compounds was analyzed using 2D and 3D quantitative structure–activity relationships (QSAR) (MLR, MNLR, SVR, ANN, and atom-based partial least squares). Molecular docking and 100 ns MD simulations were performed against the CysLT₁ receptor (PDB ID: 6RZ5). ADMET and drug-like properties of the compounds were predicted using ADMETlab 2.0 and SwissADME, and the in vitro antioxidant activity of the top-ranked compounds was evaluated using the DPPH method. Results: The atom-based 3D-QSAR model showed strong predictive power (R² = 0.9524, Q² = 0.5382). Compounds 25, 41, and 47 stood out with the most significant binding energies: −9.5 kcal/mol for 25, −10.0 kcal/mol for 41, and −9.4 kcal/mol for 47. MD simulations confirmed the structural stability and consistent interactions of the protein-compound 47 complex. ADMET analysis showed that compounds 25 and 41 had good pharmacokinetic properties, and in vitro antioxidant assays verified their free radical-scavenging efficacy. Conclusion: Our results highlight the utility of an integrated computational–experimental strategy for the discovery of dual-acting SRS-A antagonists. Compound 25 is highlighted as a promising lead compound for further preclinical development, which effectively combines leukotriene receptor antagonism and antioxidant activity. This framework provides an effective strategy for prioritizing lead compounds in anti-inflammatory drug development. Full article

With the growing demand for advanced passive cooling technologies in fields such as building energy efficiency, thermal protection of electronic devices, and personal thermal comfort, radiative cooling materials have garnered considerable attention due to their ability to achieve cooling without external energy input. In this study, TiO₂ hollow microspheres with a core–shell structure were successfully synthesized via a solvothermal method using TiCl₄ as the titanium source and (NH₄)₂SO₄ and CO(NH₂)₂ as structure-directing agents. The effects of reaction temperature (120–200 °C) and reaction time (0.5–36 h) on the morphology, crystal phase, specific surface area, pore structure, and infrared optical properties of the microspheres were systematically investigated. The results indicate that all prepared samples consisted of anatase-phase TiO₂, with the microstructure significantly influenced by the synthesis conditions. An increase in reaction temperature promoted the transition from solid to hollow structures; the microspheres exhibited the most regular morphology and the largest specific surface area at 180 °C. Prolonging the reaction time facilitated the Ostwald ripening process, leading to a more complete hollow structure at 24 h. Infrared optical performance analysis revealed that all samples exhibited high emissivity approaching 100% in the 8–15 μm wavelength range, attributed to the intrinsic lattice vibration absorption of TiO₂. In the 3–8 μm range, however, the emissivity was strongly modulated by the microstructure. Samples synthesized at 180 °C for 12–24 h demonstrated stable emissivity characteristics owing to their dense shells, uniform particle size, and well-defined hollow structures. This study elucidates the intrinsic relationship between microstructural evolution and infrared emission performance in TiO₂ hollow microspheres, providing a theoretical foundation and process optimization strategy for their application in radiative cooling coatings, device thermal protection, and personal thermal management textiles. Full article