Browse Articles

Green approach for improving functionality of medical protective textiles using ZnO NPs

Abstract The article explores the possibility of applying enzymatic treatment as a green approach for fabric surface activation that can facilitate loading polyester containing cotton fabrics zinc oxide nanoparticles (ZnO NPs) prepared by sol–gel method. Cotton and PET/C fabrics treated by neutral (Cellu GN 50) and acid (Producto EAPS 55), cellulase enzymes before and after loading with ZnO NPs were investigated by the use of Scanning Electron Microscopy (SEM), Electron Dispersion Emission X-Ray (EDX) and Fourier Transformed Infrared Spectroscopy (FT-IR).The functionality of activated fabrics loaded by ZnO NPs was evaluated by analyzing their antimicrobial activity and UV protection efficacy. Antimicrobial activity of activated fabrics and loaded by sol ZnO NPs was tested against Gram-positive ( Bacillus mycoides ), Gram-negative ( Escherichia coli ), and nonfilamentous fungus ( Candida albicans ). The level of UV protection was verified by the UV Protection factor (UPF) of fabrics. Activated and loaded fabrics with sol ZnO NPs showed outstanding antimicrobial and UV protection efficiency. The achieved antimicrobial function and UV protection on the fabrics are durable with repeated laundering processes even after five washing cycles.

Impact of random spatial truncation and reciprocal-space binning on the detection of hyperuniformity in disordered systems

We study how finite-window sampling (random spatial truncation) and reciprocal-space radial binning influence the detection of hyperuniformity in disordered systems. Starting from thirteen representative two-dimensional simulation systems and two experimental biological systems, we apply random spatial truncation and then rescale the cropped systems to a fixed number density. We then compute the structure factor S(k) and the local number variance σN2R to determine whether cropped configurations preserve the salient structural properties of the original ones. We find that moderate random spatial truncation does not change qualitatively the hyperuniformity classification of the systems, despite a reduction in measured hyperuniformity exponent α for α>1. Since spatial truncations exacerbate fluctuations in measured S(k) at small k, we show that modest reciprocal-space radial binning (controlled by a binning parameter m) effectively smooths out such fluctuations without changing the hyperuniformity class. Practical guidelines for choosing m and cross-checking S(k) fits with σN2R scaling are provided. Our research provides a concrete, low-cost, and effective methodology for robust detection and classification of hyperuniformity in finite and truncated datasets, which are prevalent in experimental systems.

Exploring the perspectives of pulmonologists on referrals for pulmonary rehabilitation in India: insights into current practices

Abstract Pulmonary rehabilitation (PR) is an evidence-based treatment for chronic respiratory diseases that improves physical and psychosocial health. However, global referral rates remain low, especially in resource-limited settings where unique barriers complicate the referral process. This survey examined the facilitators of and barriers to PR referral among pulmonologists in India. A cross-sectional survey of pulmonologists in India was conducted via a validated questionnaire (content validation index of 0.93) with open- and closed-ended questions on facilitators and barriers to the referral of an individual to a PR. A total of 114 pulmonologists participated in the survey. Among the pulmonologists, facilitators of PR referrals included the availability of a PR centre within the hospital or nearby ( n  = 71, 81%), the availability of trained professionals at the PR centre ( n  = 67, 76%), positive beliefs about PR ( n  = 64, 73%) and major barriers were limited centres offering PR ( n  = 70, 80%), and financial constraints for patients ( n  = 58, 66%). According to the pulmonologists, the factors that facilitated patients’ participation in the PR program were good family support ( n  = 73, 86%), patients’ level of motivation ( n  = 68, 80%), easy accessibility to the rehabilitation centre ( n  = 66, 78%), and the barriers were a lack of awareness of the benefits of the PR ( n  = 59, 69%), and inaccessibility to the rehabilitation program ( n  = 55, 65%). Our survey identified various facilitators and barriers encountered by pulmonologists while referring patients to PRs. These factors can inform the development of context-specific referral pathways to improve PR access in resource-limited settings.

Non-Markovian dynamics in ice nucleation

In simulation studies of crystallization, the size of the largest crystalline nucleus is often used as a reaction coordinate to monitor the progress of the nucleation process. Here, we investigate, for the case of homogeneous ice nucleation, whether the nucleus size exhibits Markovian dynamics, as assumed in classical nucleation theory. Using 300 independent nucleation trajectories generated by molecular dynamics, we evaluate the mean recurrence time required to reach selected values of the largest nucleus size. Early recurrences consistently take longer than later ones, revealing a clear history dependence and thus non-Markovian dynamics. To identify the slow modes underlying this behavior, we analyze several structural descriptors of the nucleus, observing subtle but systematic differences between nuclei at early and late recurrences. By training a neural network on 2700 short trajectories to learn the committor, we identify relevant collective variables. Based on these features, symbolic regression provides a compact approximation of the committor, that is, an improved reaction coordinate, which we subsequently test for Markovian dynamics.

Long-term remote sensing assessment of Natura 2000 protected areas in Poland (2004–2023)

Abstract Protected areas are essential for safeguarding biodiversity, yet their long-term environmental trajectories and stability remain insufficiently quantified at the national scale. This study evaluates two decades (2004–2023) of satellite-derived environmental dynamics within Poland’s Natura 2000 Special Areas of Conservation (SACs) using Landsat-based indicators: NDVI (vegetation greenness), TC Wetness (surface moisture), NDBSI (surface exposure related to land transformation), and LST (land surface temperature). Non-parametric trend analyses (Mann–Kendall and Sen’s slope) reveal that SACs maintained consistently high vegetation greenness with slower rates of increase than surrounding landscapes, consistent with relatively stable, long-established vegetation states. TC Wetness showed no consistent long-term directional trend, however, SACs exhibited slightly smaller declines during extreme drought periods, indicating greater persistence of moisture-related surface conditions. NDBSI declined more rapidly within SACs, suggesting lower levels of exposure surfaces commonly associated with anthropogenic land transformation, while LST remained statistically flat but consistently lower than in surrounding areas, indicating more stable thermal surface conditions. Taken together, these results indicate that areas currently designated as Natura 2000 sites follow more stable and internally consistent long-term environmental trajectories than the surrounding landscape, under shared climatic and land-use pressures. Our findings underline the importance of long-term remote sensing for assessing environmental trajectories and relative stability within protected area network.

Systematic selection of symmetry functions for transferable neural network potentials in coarse-grained molecular modeling

Developing transferable coarse-grained (CG) models is a major challenge in molecular simulations, as conventional potentials are often state-point and system composition dependent. While neural network (NN) potentials offer a promising path to transferability, their development is hampered by training instabilities and the complex, often heuristic, selection of model parameters. This work presents a systematic methodology to address these issues, focusing on the rational selection of descriptors for NN-based CG potentials. We propose a systematic workflow for parameterizing Behler–Parrinello symmetry functions (G2) by analyzing their resolving power and response to changes in system conditions, such as concentration. This allows for the selection of an optimized set of descriptors that captures essential structural features, handles short-range repulsions efficiently, and includes descriptors sensitive to the thermodynamic state to ensure transferability. Furthermore, we adopt a network extension technique that enables iterative improvement of model accuracy by expanding the network architecture and descriptor set without discarding prior training. The methodology is demonstrated on a CG model of methanol–water mixtures, with the NN trained to reproduce radial distribution functions derived from atomistic simulations across a wide range of methanol concentrations (10%–100%). The resulting NN potential, built using the systematic approach, demonstrates significantly improved accuracy and transferability, particularly at high concentrations, outperforming models developed previously with empirically chosen parameters. Our findings provide practical guidelines and a robust workflow for developing accurate and transferable NN potentials for CG simulations, paving the way for more reliable mesoscale modeling of complex systems.

Inhibitory control training and unhealthy behaviours: a meta-analysis testing short and long- term effects in clinical and at-risk populations

High-resolution optical spectroscopy of buffer-gas-cooled silicon monofluoride (28Si19F)

Precise measurement of high-resolution molecular spectroscopy plays an important role in elucidating the quantum nature governing the molecular properties and in exploring fundamental physics and chemistry. Here, we report the high-resolution optical spectroscopy of buffer-gas-cooled silicon monofluoride (28Si19F, hereafter SiF) molecules in the A2Σ+ (υ′ = 0) ← X2Π1/2 (υ = 0) transition. We measured a total of 94 hyperfine-resolved transitions with an uncertainty of 13.8 MHz via the laser-induced fluorescence technique, enabling the first determination of the hyperfine constant b and dipole–dipole interaction constant c of the A2Σ+ state arising from the 19F. By employing an effective Hamiltonian analysis, we reconstructed the hyperfine energy level structures for both ground and excited states, providing a comprehensive spectroscopic framework for SiF. These results establish SiF as a promising candidate for laser cooling, though the measured hyperfine splitting suggests that multiple frequency components will be required to achieve efficient optical cycling. Our findings enrich the spectroscopic database for cold SiF molecules, advancing their potential applications in quantum control, precision measurement, astrophysics, and plasma physics.

A proof of concept study on the diagnostic utility of in vivo expressed mycobacterial transcripts in tuberculous pleuritis

Surface morphology control of the Cassie–Wenzel transition: An energy landscape perspective

Surface morphology is widely recognized to influence wetting behavior; however, a comprehensive understanding of how specific morphological factors govern the Cassie–Wenzel transition remains incomplete. In this work, building on a bivariate energy-minimization framework, we focus on a key structural design parameter characterizing individual surface defects and systematically investigate its effect—both independently and in conjunction with surface defect density—on three critical aspects of the Cassie–Wenzel transition: wettability, energy barrier, and static friction. Our results demonstrate that this structural design parameter exerts distinct influences on the Cassie–Wenzel transition, depending on the wetting type: in type A, the Wenzel state is energetically favored, while in type B, the Cassie state represents the global energy minimum. These findings reveal that this parameter modulates the stability and reversibility of wetting states, as well as droplet mobility, through nontrivial energy landscapes. Moreover, we uncover a previously unreported non-monotonic dependence of static friction on the morphological factors, which we attribute to a geometric constraint on the contact angle of the transition state. We anticipate that our findings can offer quantitative design guidelines for engineering surfaces with tunable wettability and droplet transport properties.

Strategies and recommendations for embedding sustainability in innovation and design processes

Abstract The current environmental issues we face are largely due to harmful economic practices. Designing sustainable products and services is crucial for reducing future emissions. Hence, adopting system-oriented models is is essential for encouraging a rethink of current economic and consumption patterns. One important aspect of these approaches is the behavior of consumers, which can be influenced through sustainable product design. For instance, altering the default option to a sustainable alternative can stimulate more environmentally friendly choices by ensuring accessibility and enhancing consumer appeal. However, implementing such design elements requires new structures for the design and innovation process. Therefore, it is important to investigate the most effective design elements for changing behavior and to consider the processes that incorporate these elements. This research is crucial for guiding the development of sustainable product design strategies to address environmental challenges effectively. In our study, we conducted semi-structured interviews ( n = 6) with industry experts to gain insights into the barriers and motivators of innovation and design for sustainable behavior. Building on these insights, we carried out a quantitative survey with a larger sample of industry experts ( n = 79) to delve deeper into the identified topics such as process integration or lack of knowledge. Results highlight the importance of integrating sustainability considerations into design and innovation processes to promote sustainable outcomes. Companies are increasingly building internal sustainability structures, yet gaps remain in the use of behavioral interventions such as incentives and choice architecture. Effective strategies, such as training designers and innovators in behavioral change techniques and improving existing processes and guidelines, are crucial. Practitioners favor early and continuous integration of sustainability initiatives. Overall, the results underscore the need to embed sustainability and behavioral insights systematically to support long-term environmental and social responsibility.

Quantifying electron correlation effects in ethanol decomposition pathways

Ethanol decomposition is a prototypical multichannel organic reaction in which electron correlation plays a decisive role in determining activation barriers and reaction selectivity. We use fixed-node diffusion Monte Carlo (FN-DMC) to investigate three principal decomposition pathways: dehydration, C–C bond cleavage, and H2 elimination. The obtained results are compared with those from Hartree–Fock (HF), hybrid density functional theory (B3LYP), modified Gaussian-2 composite theory, and experimental kinetic data. By recovering the missing many-body correlation, FN-DMC lowers the HF forward activation barriers by 4–15 kcal mol−1 and yields barrier heights that are consistent with available Arrhenius activation parameters within expected thermal corrections. A correlation-energy analysis along the intrinsic reaction coordinate reveals a pathway-dependent modulation of dynamical correlation near the transition state, with the largest stabilization observed for the dehydration channel. The results demonstrate that FN-DMC provides a robust description of static activation barriers and offers mechanistic insight into the evolution of electron-correlation effects in complex bond-breaking reactions.

Prognostic impact of neurological dysfunction assessed by modified Rankin Scale in acute myocardial infarction

Toward quantum-aware machine learning: Improved prediction of quantum dissipative dynamics via complex valued neural networks

Accurately modeling quantum dissipative dynamics remains challenging due to environmental complexity and non-Markovian memory effects. Although machine learning provides a promising alternative to conventional simulation techniques, most existing models employ real-valued neural networks (RVNNs) that inherently mismatch the complex-valued nature of quantum mechanics. By decoupling the real and imaginary parts of the density matrix, RVNNs can obscure essential amplitude–phase correlations, compromising physical consistency. Here, we introduce complex-valued neural networks (CVNNs) as a physics-consistent framework for learning quantum dissipative dynamics. CVNNs operate directly on complex-valued inputs, preserve the algebraic structure of quantum states, and naturally encode quantum coherences. Through numerical benchmarks on the spin-boson model and few variants of the Fenna–Matthews–Olson complex, we demonstrate that CVNNs outperform RVNNs in convergence speed, training stability, and physical fidelity—including significantly improved trace conservation and Hermiticity. These advantages increase with system size and coherence complexity, establishing CVNNs as a robust, scalable, quantum-aware classical approach for simulating open quantum systems in the pre-fault-tolerant quantum era.

Research on hydraulic shock suppression of crawler crane slewing mechanism based on hydraulic simulation

Beyond the surface: Investigating CO2 electroreduction pathways on copper foil

Cation effects in the electrochemical CO2 reduction reaction (CO2RR) are often attributed to modifications of the electric double layer, yet their role in driving catalyst surface restructuring remains insufficiently understood. Here, we investigate CO2RR on polycrystalline Cu foil in non-buffering sulfate electrolytes containing Li+, Na+, K+, or Cs+, enabling a direct comparison of alkali cation identity under otherwise similar conditions. We observe a systematic shift in selectivity between CH4 and C2H4, with larger cations favoring C–C coupling and higher C2H4/CH4 ratios. Ex situ grazing-incidence x-ray diffraction, scanning electron microscopy, and x-ray photoelectron spectroscopy reveal pronounced cation-dependent reconstruction of the Cu surface, including changes in near-surface crystallographic texture, morphology/roughness, and surface chemical signatures. An electrolyte-exchange experiment further shows that key features of the reconstructed state can be partially retained and continue to influence selectivity after transfer between electrolytes, indicating that restructuring contributes to the observed cation trends. Together with density functional theory calculations of key intermediate stabilization on Cu facets, these results highlight that alkali cations act through multiple coupled mechanisms—including surface restructuring and interfacial effects—to govern CO2RR pathways and product selectivity.

Potential evaluation and favorable zone optimization of CO2 geological sequestration in deep coal reservoirs

Photoelectron spectroscopy of HCCS radical

Linear carbon chains are an important family of molecules detected in various astrophysical environments. In this work, we investigate the vacuum ultraviolet photoionization of HCCS, the smallest sulfur-bearing carbon chain in the HCnS family. The radical is produced from thiirane in situ in a flow-tube reactor coupled to a microwave discharge. The vibronic structures are assigned using ab initio calculations. The adiabatic ionization energies toward the ground (X+Σ−3) and first electronic excited (a+Δ1) states of the cation are measured experimentally for the first time at 9.191 ± 0.003 and 9.856 ± 0.003 eV, respectively, while a tentative value of 10.364 ± 0.006 eV is offered for the b+Σ+1 state.

The effects of normal fault movement on the failure mechanism of water conveyance tunnels considering multi-field interaction

Ultrafast and anisotropic vibrational energy transfer in a β-barrel heme protein: Orientation dependence in a cylindrical protein matrix

Vibrational energy exchange is a fundamental process that governs how proteins overcome energy barriers during their function, and heme proteins are excellent model systems for its investigation. The migration of excess energy released by the heme prosthetic group can be directly monitored using time-resolved anti-Stokes ultraviolet resonance Raman spectroscopy. Crucially, the anti-Stokes Raman intensity from a tryptophan residue acts as an exquisite probe for this excess energy, enabling its location to be mapped with single-amino-acid spatial resolution. Here, we investigated the dependence of vibrational energy transfer on the orientation of the heme and tryptophan residue within nitrobindin from Arabidopsis thaliana, a β-barrel protein containing a heme group. Tryptophan residues were systematically introduced into the protein’s cylindrical structure to sample the excess energy in the heme’s vicinity for different spatial orientations of the residues. By combining time-resolved anti-Stokes ultraviolet and visible resonance Raman spectroscopy—which selectively probe tryptophan residues and the heme group, respectively—we revealed that the vibrational energy transfer from the heme group to its immediate surroundings in nitrobindin is ultrafast and orientationally anisotropic, with atomic contacts within the protein playing a critical role.