Search Results (35)

Cervical spine (c-spine) injuries are a prominent concern in sporting activities, and dynamic axial (i.e., head-first) impacts are associated with a high risk of c-spine trauma. This methodology study implanted pressure sensors in post-mortem human subject (PMHS) cervical intervertebral discs (CIVDs) to assess biomechanical response and disc pressure changes during dynamic injurious axial impacts. Two fresh frozen male head–neck PMHS (cephalus with complete c-spine) were instrumented with miniature pressure sensors (Model 060S, Precision Measurement Company, Ann Arbor, MI, USA) at three CIVD levels (upper, middle, and lower c-spine). Experiments replicated the Nightingale et al. studies, simulating a rigid unconstrained head vertex (0°) axial impact. PMHS were raised to a drop height of 0.53 m to reach the desired impact velocity of ~3.2 m/s and were allowed to drop vertically. Results showed characteristic c-spine deformations/buckling motion patterns and marked CIVD pressure differences between CIVD levels. The more cranial (C2–C4) and caudal (C6–T1) CIVD exhibited greater and more comparable pressure values than those of the mid-spine (C4–C6), and the pressure in upper/lower levels was at least ~four to six times higher than that of the middle. This study establishes the feasibility and assesses the potential of CIVD pressure as a biomechanical metric for assessing injurious axial loading and contributes a novel experimental framework for future injury tolerance research and model validation. Full article

This work presents the design, modeling, and experimental validation of an innovative, highly autonomous, and economically viable photovoltaic solar cooker, integrating a robust battery storage system. The system combines 1200 Wp photovoltaic panels, a control block with DC/DC power converters and digital control for intelligent energy management, and a thermally insulated heating plate equipped with two resistors. The objective of the system is to reduce dependence on conventional fuels while overcoming the limitations of existing solar cookers, particularly insufficient cooking temperatures, the need for continuous solar orientation, and significant thermal losses. The optimization of thermal insulation using a ceramic fiber and glass wool configuration significantly reduces heat losses and increases the thermal efficiency to 64%, nearly double that of the non-insulated case (34%). This improvement enables cooking temperatures of 100–122 °C, heating element surface temperatures of 185–464 °C, and fast cooking times ranging from 20 to 58 min, depending on the prepared dish. Thermal modeling takes into account sheet metal, strengths, and food. The experimental results show excellent agreement between simulation and measurements (deviation < 5%), and high converter efficiencies (84–97%). The integration of the batteries guarantees an autonomy of 6 to 12 days and a very low depth of discharge (1–3%), allowing continuous cooking even without direct solar radiation. Crucially, the techno-economic analysis confirmed the system’s strong market competitiveness. Despite an Initial Investment Cost (CAPEX) of USD 1141.2, the high performance and low operational expenditure lead to a highly favorable Return on Investment (ROI) of only 4.31 years. Compared to existing conventional and solar cookers, the developed system offers superior energy efficiency and optimized cooking times, and demonstrates rapid profitability. This makes it a sustainable, reliable, and energy-efficient home solution, representing a major technological leap for domestic cooking in rural areas. Full article

Background: Chromobacterium violaceum is an emerging multidrug-resistant (MDR) Gram-negative bacterium associated with severe septicemia, abscess formation, and high mortality, particularly in immunocompromised individuals. Increasing antimicrobial resistance and the absence of approved vaccines underscore the urgent need for alternative preventive strategies. Traditional vaccine approaches are often inadequate against genetically diverse MDR pathogens, prompting the use of computational immunology and reverse vaccinology for vaccine design. Objectives: This study aimed to design and characterize a novel multi-epitope subunit vaccine (MEV) candidate against C. violaceum using a comprehensive pangenome-guided subtractive proteomics and immunoinformatics pipeline to identify conserved antigenic targets capable of eliciting strong immune responses. Methods: Comparative genomic analysis across eight C. violaceum strains identified 3144 core genes. Subtractive proteomics filtering yielded two essential, non-homologous, surface-accessible, and antigenic proteins—penicillin-binding protein 1A (Pbp1A) and organic solvent tolerance protein (LptD)—as vaccine targets. Cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes were predicted and integrated into a 272-amino-acid MEV construct adjuvanted with human β-defensin-4A using optimal linkers. The construct was evaluated through structural modeling, molecular docking with TLR4, molecular dynamics simulation, immune simulation, and in silico cloning into the pET-28a(+) vector. Results: The MEV construct exhibited strong antigenicity, non-allergenicity, and non-toxicity, with stable tertiary structure and favorable physicochemical properties. Docking and dynamics simulations demonstrated high binding affinity and stability with TLR4 (ΔG = −16.2 kcal/mol), while immune simulations predicted durable humoral and cellular immune responses with broad population coverage (≈89%). Codon optimization confirmed high expression potential in E. coli K12. Conclusions: The pangenome-guided immunoinformatics approach enabled the identification of conserved antigenic proteins and rational design of a promising multi-epitope vaccine candidate against MDR C. violaceum. The construct exhibits favorable immunogenic and structural features, supporting its potential for experimental validation and future development as a preventive immunotherapeutic against emerging MDR pathogens. Full article

Over the past two decades, several add-on modules for computational fluid dynamics (CFD) software focusing on modelling electrochemical processes and two-phase effects within fuel cells have been described in the literature. Most of these models are based on custom-written code that is not openly accessible to everyone. Furthermore, several commercial CFD codes offer specific modules for modelling fuel cells. Here, code modification is difficult to achieve. This work analyses and further develops the PEMFC toolbox of Kone et al. for use in OpenFOAM to simulate low temperature (LT-)PEM fuel cells. This model is freely available under the GNU GPLv3 licence. The present work focuses on enhanced physical and electrochemical modelling and improved user-friendliness. The major improvements compared to the original toolbox will be detailed in the article, together with the first results obtained. The improved PEMFC toolbox is validated using experimental data from an automotive fuel cell stack design. Furthermore, these results are compared to the original Kone model, and a commercially available CFD model. The improved toolbox reproduces both the experimentally measured polarisation curve and the current density distribution quite accurately, producing results that are fairly comparable to the more sophisticated commercial model. Full article

The regularization parameter plays an important role in regularization-based electrical tomography (ET) algorithms, but the existing methods generally cannot determine the parameter. Moreover, these methods are not real-time since a thorough search must be performed for the best parameter. To address the issue, a reproducing kernel-based interpolation approximation method is proposed to efficiently estimate the best regularization parameter from a group of representative samples. The optimization and generation of the new method have been verified by theoretical analysis and experimental demonstration. The theoretical evaluation is conducted in a Hilbert space with a known reproducing kernel, and its symmetry ensures the uniqueness of the interpolation. And experimental validation is carried out using both simulated and actual models, each with a range of distinct features. Results indicate that the new method can approximately find the best regularization parameter. Consequently, when using the regularization parameter, the new method can effectively improve both the spatial resolution and steadiness of ET imaging process. Full article

Background: hMPXV poses a major public health risk due to its human-to-human transmissibility, severe complications, especially in immunocompromised individuals, and global spread, necessitating effective surveillance and stringent prophylactic measures to mitigate its colossal impact. Objective: The study aimed to annotate hMPXV(IMV) proteins to propose a potential reverse vaccinology-based vaccine against hMPXV. Methods: The target MPXV(IMV) protein’s sequences, formatted in FASTA, were sourced from genome/proteome databases (BV-BRC and UniProt) (accessed on 6 November 2024), followed by CD-Hit-based redundancy removal. Epitope prediction for B-cells (lymphocytes), cytotoxic T-cells or cytotoxic T-lymphocytes (CTLs), and helper T-cells (HTLs) was executed using ABCpred, IEDB’s ANNs 4.0, and an artificial neural network-based alignment tool (NN-align 2.3)/ML-based tool (NetMHCII 2.3). Various immunoinformatics filters (antigenicity, toxicity, and allergenicity) were applied to substantiate the potency and safety of the formulated vaccine candidate. The constructed vaccine’s physiochemical and structural features (secondary and tertiary), with structural stability (confirmed by molecular docking followed by dynamic simulation with TLRs (TLR4 & TLR2) and MHCs), were determined. Additionally, cloning (using pET-28a(+) vector) was conducted to verify the vaccine’s expression potential and translation efficiency. The construct’s population coverage was also ascertained. Results: The MPXV-2-Beta vaccine constructs, of the six initially designed constructs, was identified as the most promising candidate, signifying nonallergenic profile and nontoxic features, with a predicted antigenicity score (PAS) = 0.7202, 407 residues, a molecular weight of 43,102.1 Da, pI of 9.2, and favorable stability parameters (AI: 65.65, GRAVY: −0.597, I-i: 25.92). It showed high solubility (score: 0.942). The ProSA Z-score of −9.38 confirmed the structural stability, reliability, and precision of the MPXV-2-Beta 3D model, which is comparable to experimental structures. Furthermore, 98.8% of all the residues nested within favored or allowed regions in a critical Ramachandran plot signified the model’s exceptional structural integrity and quality. Docking and dynamic simulation of MPXV-2-Beta with TLRs (TLR4 & TLR2) and MHCs demonstrated stiffer docking stability (strong polar and nonpolar interaction) and negative eigenvalue value (during dynamic simulation), suggesting its ability to enhance immune receptor activation under physiological conditions. MPXV-2-Beta was predicted to trigger a robust immune response (IR) with comprehensive world population coverage (98.55%, SD = 10.41). Conclusions: Based on the evaluated parameters, the MPXV-2-Beta designed in this study exhibited significant potential as an effective candidate against hMPXV. This study establishes a foundation for developing an efficient vaccine against hMPXV, requiring further experimental and clinical validation to confirm computational findings. Full article

The effectiveness of the use of PhET educational simulators as didactic tools in the teaching of physics concepts was evaluated, comparing their effects on motivation and self-efficacy in engineering students when performing traditional laboratory practices since their impact on STEM disciplines, commitment, and understanding are crucial for academic success. The sample consisted of 236 first-year students divided into an experimental group using simulators and a control group that performed the practices in the physics laboratory. The Reduced Instructional Materials Motivation Scale (RIMMS) and the self-efficacy subscale of the MSLQ, translated and validated in Spanish, were used. The results showed that the experimental group exhibited a significant increase in motivation. No statistically significant differences were found in self-efficacy, suggesting that its impact on this aspect is limited and possibly requires more activities with simulators. This study concludes that simulators can complement and improve traditional practices, especially to increase student motivation. Full article

A jet flame occurs when the release of flammable gas or liquid ignites, resulting in a long, intense, and highly directional flame. This type of fire is commonly associated with industrial incidents involving pipelines, storage tanks, and other pressurized equipment. Jet fires are a significant concern in the oil and gas industry due to the handling and processing of large volumes of flammable hydrocarbons under pressure. The new computational method presented here includes several aspects of hydrogen jet flame accidents and their mitigation: the CFD simulation of a hydrogen jet flame using the HyRAM code and Fire Dynamics Simulator (FDS) software 5.0 using a large eddy simulation (LES) turbulence model; the calculation of the gaseous mixture’s thermo-physical properties using the GASEQ thermochemical code; the calculation of convective and radiative heat fluxes using empirical correlation; and a heat transfer simulation on the pipe thermal barrier coating (TBC) using COMSOL Multiphysics software 4.2a during the heating phase. This method developed for the ceramic blanket was validated successfully against the previous experimental results obtained by Gravit et al. It was shown that a jet fire’s maximum temperature obtained using FDS software was similar to that obtained using GASEQ thermochemical software 0.79 and HyRAM software. The TBC’s surface temperature reached 1945 °C. The stainless steel’s maximal temperature reached 165.5 °C. There was a slight decrease in UTS at this temperature. Full article

Background: Pediatric and congenital heart surgery (PCHS) is highly risky. Complications associated with this surgical procedure are mainly caused by the severity of the disease or the unnecessary, late, or premature execution of the procedure, which can be fatal. In this context, prognostic models are crucial to reduce the uncertainty of the decision to perform surgery; however, these models alone are insufficient to maximize the probability of success or to reverse a future scenario of patient death. Method: A new approach is proposed to reverse the prognosis of death in PCHS through the use of (1) machine learning (ML) models to predict the outcome of surgery; (2) an explainability technique (ET) to determine the impact of main risk factors; and (3) a simulation method to design health scenarios that potentially reverse a negative prognosis. Results: Accuracy levels of 96% in the prediction of mortality and survival were achieved using a dataset of 565 patients undergoing PCHS and assessing 10 risk factors. Three case studies confirmed that the ET known as LIME provides explanations that are consistent with the observed results, and the simulation of one real case managed to reverse the initial prognosis of death to one of survival. Conclusions: An innovative method that integrates ML models, ETs, and Simulation has been developed to reverse the prognosis of death in patients undergoing PCHS. The experimental results validate the relevance of this approach in medical decision-making, demonstrating its ability to reverse negative prognoses and provide a solid basis for more informed and personalized medical decisions. Full article

The high penetration of renewable energy sources (RESs) and power electronics devices has led to a continuous decline in power system stability. Due to the instability of grid-following converters (GFLCs) in weak grids, the grid-forming converters (GFMCs) have gained widespread attention featuring their flexible frequency and voltage regulation capabilities, as well as the satisfactory grid-supporting services, such as inertia and damping, et al. Notably, the risk of wideband oscillations in modern power grids is increasingly exacerbated by the reduced number of synchronous generators (SGs). Thus, the wideband oscillation suppression method based on adaptive active power droop control of GFMCs is presented in this paper. First, the stability of the hybrid grid-forming and grid-following system is obtained according to the improved short circuit ratio (ISCR), where the GFMC is in parallel at the point of common coupling (PCC) of the GFLC. Then, an adaptive adjustment strategy of the active power droop control is proposed to enhance the oscillation suppression capability across the full frequency range, thereby mitigating the wideband oscillation caused by phase-locked loop (PLL) synchronization in the GFLCs. Additionally, a first-order inertia control unit is added to the active and reactive power droop controllers to mitigate frequency and voltage variations as well as suppress potential mid-to-high frequency resonance. Finally, the wideband oscillation suppression strategy is validated by the simulation and experimental results. Full article

This paper presents an innovative and efficient methodology for the determination of the solid-state diffusion coefficient in electrode materials with phase transitions for which the assumption of applying the well-known formula from the work of Weppner et al. is not satisfied. This methodology includes a k-means machine learning screening of Galvanostatic Intermittent Titration Technique (GITT) steps, whose outcomes feed a physics-informed algorithm, the latter involving a pseudo-two-dimensional (P2D) electrochemical model for carrying out the numerical simulations. This methodology enables determining, for all of the 47 steps of the GITT characterization, the dependency of the Na⁺ diffusion coefficient as well as the reaction rate constant during the sodiation of an NVPF electrode to vary between $9$ × ${10}^{-18}$ and $6.8$ × ${10}^{-16}$ m²·s⁻¹ and between $2.7$ × ${10}^{-14}$ and $1.5$ × ${10}^{-12}$ m^2.5·mol^−0.5·s⁻¹, respectively. This methodology, also validated in this paper, is (a) innovative since it presents for the first time the successful application of unsupervised machine learning via k-means clustering for the categorization of GITT steps according to their characteristics in terms of voltage; (b) efficient given the considerable reduction in the number of iterations required with an average number of iterations equal to 8, and given the fact the entire experimental duration of each step should not be simulated anymore and hence can be simply restricted to the part with current and a small part of the rest period; (c) generically applicable since the methodology and its physics-informed algorithm only rely on “if” and “else” statements, i.e., no particular module/toolbox is required, which enables its replication and implementation for electrochemical models written in any programming language. Full article

The service life of Lithium-ion batteries disposed from electric vehicles, with an approximate remaining capacity of 75–80%, can be prolonged with their adoption in less demanding second life applications such as buildings. A photovoltaic energy generation system integrated with a second life battery energy storage device is modelled mathematically to assess the design’s technical characteristics. The reviewed studies in the literature assume, during the modelling process, that the second life battery packs are homogeneous in terms of their initial state of health and do not consider the module-to-module variations associated with the state of health differences. This study, therefore, conducts mathematical modelling of second life battery packs with homogenous and heterogeneous state of health in module level using second-order equivalent circuit model (ECM). The developed second-order ECM is validated against experimental data performed in the lab on 3Ah NCM batteries. The degradation parameters are also investigated using the battery cell’s first life degradation data and exponential triple smoothing (ETS) algorithm. The second-order ECM is integrated with the energy generation system to evaluate and compare the performance of the homogenous and heterogeneous battery packs during the year. Results of this study revealed that in heterogeneous packs, a lower electrical current and higher SOC is observed in modules with lower state of health due to their higher ohmic resistance and lower capacity, compared to the other modules for the specific battery pack configuration used in this study. The methodology presented in this study can be used for mathematical modelling of second life battery packs with heterogenous state of health of cells and modules, the simulation results of which can be employed for obtaining the optimum energy management strategy in battery management systems. Full article

Water scarcity threatens food security in arid areas, highlighting the importance of water-saving agriculture for food production. Agricultural management practices are developed to improve water-use efficiency, and their water-saving effects are generally evaluated at the field scale rather than the regional scale. To figure out the regional water-saving potential of irrigation methods and mulching practices, the FAO AquaCrop model was first calibrated and validated at the three experimental stations. With aggregating spatial information, a distributed model was constructed and validated in a typical arid river basin of northwest China. Twelve combinations of soil mulching (plastic and straw) and irrigation methods (basin, furrow, drip, and subsurface drip) were simulated using the model to evaluate the effect of agricultural management practices on crop evapotranspiration (ET), crop water productivity, and regional water consumption. The results showed that soil mulching, advanced irrigation methods, and their combinations reduced noneffective soil evaporation (E) and the E/ET ratios and improved crop water productivity. Plastic mulching combined with subsurface drip irrigation is the most promising practice, increasing the crop water productivity of seed maize and spring wheat by 18.2% and 11.1% on average and reducing regional crop water consumption by 7.7% (75.0 million m³) and 7.4% (72.7 million m³), respectively. The reduction in irrigation water extraction ranged from 20.6% under furrow irrigation with straw mulching to 68.7% under subsurface drip irrigation with plastic mulching. This study quantitatively assessed the water-saving potential of soil mulching, irrigation methods, and their combinations to reduce agricultural water use, offering practical implications for the management and development of water-saving agriculture in arid areas. Full article

Lyophilization process design still relies mainly on empirical studies with high experimental loads. In the regulatory demanded Quality by Design approach, process modeling is a key aspect. It allows process design, optimization and process control to ensure a safe process and product quality. A modeling approach is outlined that is able to predict the primary drying endpoint and temperature profile of distinct vials. Model parameters are determined by a reproducible determination concept. Simulated results are validated with a fractional factorial Design of Experiments (DoE) in pilot scale. The model shows higher accuracy and precision than the experiments and similar parameter interactions for both the endpoint and temperature determination. This approach can now be used to explore the primary design space in lyophilization process design. This paper proposes a distinct method for endpoint determination and product temperature prediction by a modeling approach based on Velardi et al. combined with a distinct model parameter determination according to Wegiel et al. and Tang et al. Full article

Lean premixed combustion technology became state of the art in recent heavy-duty gas turbines and aeroengines. In combustion chambers operating under fuel-lean conditions, unsteady heat release can augment pressure amplitudes, resulting in component engine damages. In order to achieve deeper knowledge concerning combustion instabilities, it is necessary to analyze in detail combustion processes. The current study supports this by conducting a numerical investigation of combustion in a premixed swirl-stabilized methane burner with operating conditions taken from experimental data that were recently published. It is a follow-up of a previous paper from Farisco et al., 2019 where a different combustion configuration was studied. The commercial code ANSYS Fluent has been used with the aim to perform steady and transient calculations via Large Eddy Simulation (LES) of the current confined methane combustor. A validation of the numerical data has been performed against the available experiments. In this study, the numerical temperature profiles have been compared with the measurements. The heat release parameter has been experimentally and numerically estimated in order to point out the position of the main reaction zone. Several turbulence and combustion models have been investigated with the aim to come into accord with the experiments. The outcome showed that the combustion model Flamelet Generated Manifold (FGM) with the k-$\omega$ turbulence model was able to correctly simulate flame lift-off. Full article