Search Results (2,366)

This work presents integrated flux and velocity channel maps of the planetary nebula Abell 30 (A30) inner knot system. The observations were taken with the INTEGRAL spectrograph at the William Herschel Telescope (WHT), La Palma, Spain. Our IFU data cube has a field of view (FoV) of 12.3${}^{\prime \prime }$× 16${}^{\prime \prime }$ that partially covers knots J1 and J2, and completely covers knots J3 and J4 in the system. Optical Recombination Lines (ORLs) of C II, He I, He II, N III, O II and Collisionally Excited Lines (CELs) of [Ar IV], [Ar V], [N II], [Ne III], [Ne IV], and [O III] were detected. Our integrated flux maps visualise the ionisation structure and the chemical inhomogeneity in the system previously reported by other groups. We find that ORLs are concentrated in the polar region (J1, J3), whereas the equatorial knots (J2, J4) are dominated by CELs. The flux ratio map of the diagnostic [O III $\lambda$ 5007/4363 Å] lines reveals the electron temperature distribution, which shows cold cores of 15,000 K in knots J3 and J4 surrounded by a hot outer layer of above 20,000 K. Our channel maps show positive and negative velocity excursions from the systemic value among the ions. Several ions show variation in their velocity structures from their lower-energy-level counterparts, including [Ar IV] and [Ar V], [Ne III] and [Ne IV], and He I and He II. New recurrent velocity structures are identified in the low-density regions where the ions move much faster compared to their surrounding environments. The velocity dispersion measurements highlight extreme turbulence in some of the ions (${\sigma }_{{\mathrm{v}}_{\mathrm{rad}}}\approx 140$ km/s), consistent with supersonic/hypersonic motion driven by shocks. The forbidden line species [N II] exhibits lower turbulence (${\sigma }_{{\mathrm{v}}_{\mathrm{rad}}}\approx$ 50–60 km/s), tracing denser, less-turbulent gases. Based on our data, we conclude that both the ionisation and kinematic studies hint at shock heating and multiple ejection history in the evolutionary pathway of A30. Full article

Cold atmospheric plasma (CAP) has emerged as a promising anticancer approach because of its ability to selectively eliminate malignant cells. Among the proposed mechanisms of this selectivity, the Bauer theory emphasizes the synergistic action of plasma-derived hydrogen peroxide (H₂O₂) and nitrite (NO₂⁻), leading to the transient generation of primary singlet oxygen (¹O₂). This early event inactivates membrane-bound catalase, allowing tumor cell-derived H₂O₂ and peroxynitrite to initiate a self-amplifying cycle that produces secondary ¹O₂, as a hallmark of CAP selectivity. To test this hypothesis, in this work, we monitored extracellular dissolved oxygen (DO) dynamics in HT-29 colorectal cancer cells treated with an argon plasma jet using time-resolved phosphorescence lifetime spectroscopy. Temporal variations in DO likely reflect the cumulative effect of rapid ¹O₂ production and its reactions with cells. A delayed surge in extracellular ¹O₂ was observed specifically in dying cancer cells within the 10–20 min window predicted by the model. Intracellular ROS imaging confirmed a strong correlation between intracellular ROS, extracellular ¹O₂ dynamics, and viability loss. Together, these results provide mechanistic validation of Bauer’s redox model and suggest that early oxygen dynamics after CAP exposure can serve as predictive markers for treatment efficacy in plasma or photodynamic therapies. Full article

This study presents an integrated, open-source process simulation for converting agricultural biogas into high-purity liquid hydrogen using DWSIM (Distillation, Water, Separation and Inorganic Modules), an open-source sequential-modular simulator. The model simulates a farm-scale biogas feed and is optimised to enhance liquid hydrogen yield while reducing specific energy consumption under set operating conditions. The proposed model links biogas upgrading via dual pressure swing adsorption, steam–methane reforming, two-stage water–gas shift, hydrogen purification, and cryogenic liquefaction within a single optimisation framework. Using a representative farm-scale feed (103.7 kg h⁻¹ biogas containing 60 mol% CH₄), the optimised process produces 16.5 kg h⁻¹ of liquid hydrogen with 99.2% para-hydrogen purity while simultaneously capturing 104 kg h⁻¹ of CO₂ at 98% purity and 16 bar. Optimal operating conditions include SMR at 909 °C and 16 bar with a steam-to-carbon ratio of 3.0, followed by high- and low-temperature water–gas shifts at 413 °C and 210 °C, respectively. The overall cold-gas efficiency (LHV basis, excluding liquefaction electricity) reaches 78%, and the specific electricity demand for liquefaction is 32.4 kWh per kg of liquid hydrogen, which is consistent with reported values for small-scale hydrogen liquefiers. Sensitivity analysis over a methane content range of 40–75% confirms near-linear scalability of hydrogen output (R² = 0.998), demonstrating feedstock flexibility without re-parameterisation. The developed process in this work provides a transparent and extensible digital twin for early-stage design and optimisation of decentralised biogas-to-hydrogen systems. Using the open-source DWSIM platform ensures full transparency, reproducibility, and accessibility compared with proprietary simulators. Full article

Ceramic Matrix Composites (CMCs) have emerged as a structural material alternative to nickel superalloys for high-pressure turbines (HPT) components operating at high temperature, like shrouds. Despite the outstanding thermal stability of the CMCs, limited cooling is still necessary due to the extreme thermal operating conditions necessary to maximize engine performance and minimize fuel consumption. The design of CMC components, indeed, must consider a maximum service temperature that should not be exceeded to avoid damage and accelerated oxidation. The cooling, on the other hand, may induce the formation of thermal gradients and thermal stresses. In this work, different design options for the cooling system are investigated to minimize the thermal stresses of an HPT shroud-like geometry subjected to maximum temperature constraints on the material. Cooling is obtained via colder air jet streams (air taken from the compressor), whose impact position (the surface where the cold air impacts the component) has a different effect on the temperature field and on the induced stress field. Besides stress evaluation with different cooling systems, an ONERA damage model is investigated at a key location to potentially take into account stress components acting simultaneously and potential stiffness degradation of the CMC. Finally, the design evaluation of potential discrete crack propagation is discussed. A standard cohesive elements approach has been compared with a brittle element death approach. The results showed that the cohesive element approach resulted in shorter crack propagation, underestimating the actual crack behavior due to the embedded stiffness degradation method, while the element death returned encouraging results as a quicker, less complex, but still accurate design evaluation. Full article

This study presents a quantitative, cross-sectional analysis of work-related musculoskeletal disorders (WRMSDs) among sick leave recipients in Brazil’s meat production chain, using official surveillance data. A marked temporal shift was observed; women remained more affected by upper limb injuries, such as shoulder and wrist disorders. In 2022, male notifications surpassed female ones, marking a turning point linked to improved reporting and the inclusion of WRMSDs in Brazil’s compulsory notification list. Workers aged 20–49 were the most impacted group, with diagnoses including shoulder lesions, tenosynovitis, carpal tunnel syndrome, back pain, and occupational risk exposure. The findings highlight systemic barriers, including underreporting, inadequate protection, and weak return-to-work protocols. Implementing gender-differentiated ergonomic protocols is crucial, as it requires reducing repetitive strain for women in line-feeding/cutting roles, and mitigating environmental hazards (such as cold, vibration, and chemical exposure) for men in farming/slaughtering. These results underscore the urgent need for gender-sensitive preventive strategies and occupational health policies tailored to the meat processing industry. Full article

The problem of the post-harvest loss of perishable products has been a loss facing food security, especially in areas that lack adequate cold chain facilities. This issue is directly connected with sustainability objectives because post-harvest losses are the major source of food wastage, unneeded energy use, and related greenhouse gas emissions. Cold storage with phase-change material (PCM) is a promising alternative, as it aims at stabilizing temperatures and enhancing energy consumption, but current analyses of performance have been conducted through experimental testing and computational fluid dynamic (CFD) simulations, which are precise but computationally expensive. To handle this drawback, the current work constructs a machine learning predictive model to predict the dynamics of charging and discharging temperature of PCM cold storage systems. Four regression models, namely Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and K-Nearest Neighbors (KNNs), were trained and tested on experimental datasets that were obtained for varying storage layouts. The various error and accuracy measures used to determine model performance comprised MSE, MAE, R², MAPE, and percentage accuracy. The findings suggest that Random Forest provides the best accuracy during both the charging and the discharging process, with the highest R² values of over 0.98 and with minimal mean absolute errors. The KNN model was competitive in the discharge process, especially in cases of consistent thermal recovery patterns, and XGBoost was consistent in layout accuracy. However, SVR had relatively lower robustness, particularly when using nonlinear charged dynamics. Among the evaluated models, the Random Forest algorithm demonstrated the highest predictive accuracy, achieving coefficients of determination (R²) exceeding 0.98 for both charging and discharging processes, with mean absolute errors below 0.6 °C during charging and 0.3 °C during discharging. This paper has proven that machine learning is an efficient surrogate to CFD and experimental-only methods and can be used to predict the thermal behavior of PCM quickly and precisely. The proposed framework will allow for developing cold storage systems based on energy efficiency, low costs, and sustainability, especially in the context of decentralized and resource-limited agricultural supply chains, with the help of quick and data-focused forecasting of PCM thermal behavior. Full article

Growing pressure on electricity grids and the increasing availability of smart meter data have intensified the need for accurate, interpretable, and scalable methods to analyze and forecast household electricity consumption. In this context, this study presents a general, data-agnostic methodology for predicting and classifying household energy consumption. The proposed workflow unifies data preparation, feature engineering, and machine learning techniques (including clustering, classification, regression, and time series forecasting) within a single interpretable pipeline that supports actionable insights. Rather than proposing new prediction algorithms, this work contributes a fully reproducible, end-to-end methodological pipeline that enables the controlled evaluation of the impact of contextual variables, customer segmentation, and cold-start conditions on household energy forecasting. A distinctive aspect of the pipeline is the explicit use of household- and dwelling-level contextual variables to derive customer typologies via clustering and to enrich forecasting models. The models are evaluated for predictive accuracy, reliability under varying conditions, and suitability for operational use. The results show that incorporating contextual variables and clustering significantly improves forecasting accuracy, particularly in cold-start scenarios where no historical consumption data are available. Although numerous public datasets of residential electricity consumption exist, they rarely provide, in an openly accessible form, both detailed load histories and rich contextual attributes, while many are subject to privacy or licensing restrictions. To ensure full reproducibility and to enable controlled experiments where contextual variables can be switched on and off, the experiments are conducted on a synthetically generated dataset that reproduces realistic behavior and seasonal usage patterns. However, the proposed methodology is independent of the specific data source and can be directly applied to any real or synthetic dataset with similar structure. The approach enables applications such as short- and long-term demand forecasting, estimation of household energy costs, and forecasting demand for new customers. These findings demonstrate that the proposed pipeline provides a transparent and effective framework for end-to-end analysis of household electricity consumption. Full article

Esophageal squamous cell carcinoma (ESCC) represents a major therapeutic challenge due to the rapid development of resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Recent evidence highlights that this resistance is driven not only by genetic mutations but also by spatial heterogeneity of tumor microenvironments and compensatory signaling mechanisms. In this review, we propose a “spatial-signaling-intervention” framework with a particular focus on the NTRK2/MAPK signaling axis, which plays dual roles in signaling compensation and immune evasion. By integrating spatial multi-omics, proteomics, and AI-assisted topological modeling, three resistant niches are identified: (1) cancer stemness-enriched zones, (2) MAPK hyperactive islands, and (3) immune-cold regions. Based on this atlas, we design precision nanotherapeutic platforms, including responsive, dual-target, and feedback-loop nanocarriers, to selectively modulate resistant spatial niches. Preclinical validation in patient-derived xenografts and organoid models further demonstrates the translational potential of these strategies. This work provides a conceptual and technological roadmap for overcoming EGFR-TKI resistance in ESCC. Atlas-guided nanocarrier systems offer a promising avenue for spatially targeted and feedback-responsive therapy, highlighting the role of pharmaceutics in advancing precision oncology. Full article

We present a unified description of dense matter and neutron star structure based on simple but physically motivated models. Starting from the thermodynamics of degenerate Fermi gases, we construct an equation of state for cold, catalyzed matter by combining relativistic fermion statistics with the liquid drop model of nuclear binding. The internal stratification of matter in the outer crust is described by the $\beta$-equilibrium, neutron drip and a gradual transition to supranuclear matter. Short-range repulsive interactions inspired by Quantum Hadrodynamics are incorporated at high densities in order to ensure stability and causality. The resulting equation of state is used as input in the Tolman–Oppenheimer–Volkoff equations, yielding self-consistent neutron star models. We compute macroscopic stellar properties including the mass–radius relation, compactness and surface redshift that can be compared with recent observational data. Despite the simplicity of the underlying microphysics, the model produces neutron star masses and radii compatible with current observational constraints from X-ray timing and gravitational-wave measurements. This work demonstrates that physically transparent models can capture the essential features of neutron star structure and provide valuable insight into the connection between dense-matter physics and astrophysical observables; they can also be used as easy-to-handle models to test the impact of more complicated phenomena and variations in neutron stars. Full article

Objective: We examine whether a finite-range scalar–tensor modification of gravity can be simultaneously compatible with cosmological background data, galaxy rotation curves, and local/astrophysical consistency tests, while satisfying the luminal gravitational-wave propagation constraint ($c_T=1$) implied by GW170817 at low redshifts. Methods: We formulate the model at the level of an explicit covariant action and derive the corresponding field equations; for cosmological inferences, we adopt an effective background closure in which the late-time dark-energy density is modulated by a smooth activation function characterized by a length scale $\lambda$ and amplitude $ϵ$. We constrain this background model using Pantheon+, DESI Gaussian Baryon Acoustic Oscillations (BAOs), and a Planck acoustic-scale prior, including an explicit $\Lambda$CDM comparison. We then propagate the inferred characteristic length by fixing $\lambda$ in the weak-field Yukawa kernel used to model 175 SPARC galaxy rotation curves with standard baryonic components and a controlled spherical approximation for the scalar response. Results: The joint background fit yields ${\Omega }_m=0.293\pm 0.007$, $\lambda =7.{69}_{-1.71}^{+1.85}$$\mathrm{Mpc}$, and $H_0=72.33\pm 0.50$$\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$. With $\lambda$ fixed, the baryons + scalar model describes the SPARC sample with a median reduced chi-square of ${\chi }_{\nu }^2=1.07$; for a 14-galaxy subset, this model is moderately preferred over the standard baryons + NFW halo description in the finite-sample information criteria, with a mean $\Delta$AICc outcome in favor of the baryons + scalar model (≈2.8). A Vainshtein-type screening completion with $\Lambda =1.3\times {10}^{-8}$ eV satisfies Cassini, Lunar Laser Ranging, and binary pulsar bounds while keeping the kpc scales effectively unscreened. For linear growth observables, we adopt a conservative General Relativity-like baseline (${\mu }_0=0$) and show that current $f{\sigma }_8$ data are consistent with ${\mu }_0\simeq 0$ for our best-fit background; the model predicts $S_8=0.791$, consistent with representative cosmic-shear constraints. Conclusions: Within the present scope (action-level weak-field dynamics for galaxy modeling plus an explicitly stated effective closure for background inference), the results support a mutually compatible characteristic length at the Mpc scale; however, a full perturbation-level implementation of the covariant theory remains an issue for future work, and the role of cold dark matter beyond galaxy scales is not ruled out. Full article

Chatter is a self-excited vibration that limits productivity, accelerates roll wear and compromises strip surface quality in high-speed tandem cold-rolling. This work presents a predictive hybrid model that couples the strip-deformation physics to the structural dynamics of a five-stand, 4-high mill, providing a fast decision tool for process optimization and real-time control. The model represents each stand as a four-degree-of-freedom mass–spring–damper system whose parameters are extracted from manufacturing automation datasheets and roll-gap sensing. Linearization about the nominal point yields analytical sensitivity matrices that close the electromechanical loop; the delay between stands is also included in the model. Implemented in MATLAB/Simulink, the computational model, based on data provided by Danieli & C. Officine Meccaniche S.p.A., reproduces the onset of chatter for two types of steel. The framework therefore supports automation-ready scheduling, active vibration mitigation and design-space exploration for next-generation mechatronic cold-rolling systems. Full article

Background: Ambient yoghurt, also known as room-temperature yoghurt, has gained increasing attention due to its convenience in distribution and consumption without needing cold storage. To ensure extended shelf life, ambient yoghurt normally undergoes an additional heat treatment during manufacturing, the post-fermentation sterilisation process (typically at 65–85 °C), which may induce the formation of fine particle aggregates and result in undesirable textural attributes, particularly graininess. Assessing textural attributes of such products remains a challenge. Methods: By mimicking the oral behaviour of ambient yoghurt, this study uses rheological as well as tribological techniques for objective assessment of the textural sensations of slipperiness and graininess. Various experimental conditions, including the amount of saliva incorporation, sliding speed, and ball-contact and plate-contact lubrication, were examined, and results were analysed against perceived texture by panellists. Main findings: The results indicate that viscosity changes are closely associated with perceived slipperiness under the tested conditions. The friction coefficient obtained from a plate-contact tribometer shows a positive correlation with the sensation of graininess (Pearson’s r was 0.74, p < 0.05, N = 8). It was also observed that a 20% saliva incorporation showed the closest agreement with sensory perception, although this observation should be interpreted cautiously due to the limited sample size. Implications: Results obtained from this work indicate the feasibility of using rheology and tribology techniques for texture prediction in ambient yoghurt. The findings are exploratory in nature, and further studies with larger sample sets are required to validate the proposed approach. The methodology presented here may serve as a reference framework for investigating texture perception in other dairy systems. Full article

Background/Objectives: Whole-body cryotherapy (WBC), a brief exposure to extreme cold (−90 °C), has been proposed to modulate immune, metabolic, and stress-related pathways. This exploratory one-armed pilot study investigated the effects of an 18-session WBC protocol on immune markers, body composition, and perceived stress in healthy adults. Methods: Nineteen participants (mean age 52.9 ± 9.8 years) completed 18 WBC sessions over 9 weeks (3–6 min each), followed by a 9-week follow-up. Assessments were performed at baseline (M1), post-intervention (M2), and follow-up (M3). Primary outcomes included immune parameters (lymphocytes, granulocytes, cytokines, soluble ACE2), body composition (waist circumference, water compartments, lean mass), and perceived stress (Trier Inventory for Chronic Stress, TICS). Results: Waist circumference decreased from 83.8 ± 5.7 cm (M1) to 80.2 ± 4.2 cm (M2) (p = 0.001; M1 vs. M2; p = 0.004). Total body water (p = 0.008), lean body mass (p = 0.008), intracellular water (p = 0.005), and extracellular water (p = 0.021) also showed time-dependent effects. Immune modulation included increased lymphocytes (25.6 ± 7.1% to 29.3 ± 8.3%, p = 0.012) and decreased granulocytes (63.5 ± 6.8% to 58.7 ± 7.9%, p = 0.011) at M2. Anti-inflammatory IL-10 (virus-stimulated) rose markedly (33.5 ± 29.3 to 63.5 ± 50.5 pg/mL, p < 0.001), while IFN-γ (virus-stimulated) increased over time (p = 0.031). Soluble ACE2 decreased at follow-up (0.5 ± 0.7 to 0.3 ± 0.4 ng/mL, p = 0.029). Perceived stress improved in several TICS domains, including Work Overload (p = 0.009) and Pressure to Succeed (p = 0.018). Conclusions: This pilot study demonstrates that repeated WBC at −90 °C induces measurable changes in immune regulation, body composition, and perceived stress. These findings support the feasibility and potential physiological relevance of WBC and providing effect-size estimates for future randomized controlled trials. Full article

The strategic balance between strength and electrical conductivity in Al-Mg-Si alloys is a critical challenge that must be overcome to enable their widespread adoption as viable alternatives to copper conductors in power transmission systems. To address this, the present study comprehensively investigates model alloys with Mg/Si ratios ranging from 1.0 to 2.0. A multi-faceted experimental approach was employed, combining tailored thermo-mechanical treatments (solution treatment, cold drawing, and isothermal annealing) with comprehensive microstructural characterization techniques, including electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM). The results elucidate a fundamental competitive mechanism governing property optimization: excess Mg atoms concurrently contribute to solid-solution strengthening via the formation of Cottrell atmospheres around dislocations, while simultaneously enhancing electron scattering, which is detrimental to conductivity. A critical synergy was identified at the Mg/Si ratio of 1.75, which promotes the dense precipitation of fine β″ phase while facilitating extensive recovery of high dislocation density. Furthermore, EBSD analysis confirmed the development of a microstructure comprising 74.1% high-angle grain boundaries alongside a low dislocation density (KAM ≤ 2°). This specific microstructural configuration effectively minimizes electron scattering while providing moderate grain boundary strengthening, thereby synergistically achieving an optimal balance between strength and electrical conductivity. Consequently, this work elucidates the key quantitative relationships and competitive mechanisms among composition (Mg/Si ratio), processing parameters, microstructure evolution, and final properties within the studied Al-xMg-0.5Si alloy system. These findings establish a clear design guideline and provide a fundamental understanding for developing high-performance aluminum-based conductor alloys with tailored Mg/Si ratios. Full article

This work investigates the effect of titanium content and the duration of mechanical alloying on the structural and phase state of powder mixtures in the Ni–Al–Ti system. The initial mixtures of Ni₆₈Al₂₅Ti₇, Ni₇₂Al₂₂Ti₆, Ni₇₀Al₂₁Ti₉, and Ni₇₅Al₂₅ were subjected to high-energy milling in a planetary ball mill for 1–6 h. It was found that the addition of titanium accelerates the dissolution of components and promotes the formation of a supersaturated fcc Ni(Al,Ti) solid solution. The most pronounced effects were observed for the Ni₇₀Al₂₁Ti₉ composition, where after 6 h of alloying, the minimum crystallite size (11.3 nm) and maximum lattice strain (1.52%) were achieved. It is shown that titanium reduces the tendency for cold welding and promotes more uniform particle refinement. The optimal conditions for synthesizing a nanocrystalline solid solution with a homogeneous structure are a titanium content of 9 at.% and a mechanical alloying duration of 6 h. The resulting powders are promising for subsequent sintering and application in structural and heat-resistant intermetallic alloys and coatings. Full article