Search Results (1,571)

Siponimod, a selective sphingosine 1-phosphate (S1P) receptor modulator, represents a next-generation therapeutic drug for active secondary progressive multiple sclerosis. This study conducted in-depth forced degradation studies of siponimod in solid state subjected to acidic, alkaline, oxidative, photolytic, and thermal conditions, in compliance with ICH guidelines Q1A (R2) and Q3A (R2). An HPLC method was developed to quantify siponimod and separate its degradation products (DPs). The DPs were characterized using LC-HRMS/MS and LC-MSⁿ techniques. Moreover, the toxicological profiles of siponimod and its DPs were evaluated through the in silico tools ProTox 3.0 and ADMETlab 3.0, with molecular docking and dynamics simulations assessing their binding to the S1P1 receptor. Siponimod was stable to light but degraded under acidic, alkaline, oxidative, and thermal stress, producing five products: DP-1 (acidic), DP-2/3 (oxidative), DP-4 (hydrolytic), and DP-5 (thermal). The toxicity prediction suggested that neither siponimod nor its DPs exhibited carcinogenic or mutagenic potential, and the molecular modeling analysis revealed that DP-2 and DP-3 demonstrated favorable binding affinities, with stable dynamic profiles and thermodynamic properties that closely resembled those of siponimod. As far as we know, this is the first study on the structural elucidation of the DPs of siponimod by LC-HRMS/MS and LC-MSⁿ. Full article

In-cylinder pressure is a key parameter for evaluating combustion processes and engine performance in spark-ignition engines. However, acquiring high-resolution pressure data over a wide range of operating conditions, particularly under varying spark advance (SA), is costly and technically challenging, which limits its practical application. To address this issue, this study proposes two artificial neural network (ANN)-based methods for in-cylinder pressure reconstruction using data from a three-cylinder gasoline engine under different spark advance conditions. Both methods employ crank angle and spark advance as input features. The first method (ANN-P) directly predicts the in-cylinder pressure profile, achieving a coefficient of determination (R²) exceeding 0.99 on both training and validation datasets, with a root mean square error (RMSE) below 0.13 bar. The model accurately reproduces the pressure evolution throughout the compression, combustion, and expansion processes and enables reliable estimation of indicated mean effective pressure (IMEP). The second method (ANN-HRR) adopts an indirect strategy by first predicting the heat release rate (HRR) and subsequently reconstructing the pressure trace through thermodynamic integration based on a single-zone model. This approach avoids error amplification associated with numerical differentiation and demonstrates improved accuracy in predicting combustion phasing metrics, such as CA10 and CA50. The results indicate that both methods effectively capture the influence of spark timing on combustion characteristics and peak pressure. While ANN-P provides higher accuracy in pressure reconstruction, ANN-HRR offers superior performance in characterizing combustion features. Overall, this study presents a cost-effective and accurate framework for combustion diagnostics, performance calibration, and control optimization of gasoline engines. Full article

This study investigates the phase formation and smelting process of a complex Al-Si-Mn alloy based on thermodynamic diagram analysis (TDA). The Fe-Si-Mn-Al system was analyzed considering binary and ternary subsystems, and the standard Gibbs free energy of formation of selected ternary compounds was calculated using the additive method. Based on these results, phase equilibrium diagrams were constructed, and the system was tetrahedralized, leading to the identification of 15 thermodynamically stable tetrahedra. It was established that compositions of industrial interest are predominantly localized within tetrahedra enriched in silicide and aluminosilicide phases, particularly FeSi-Fe₂Al₂Si-Fe₃Al₁₁Si₆-Mn₅Si₃. Experimental verification was carried out in a 250 kVA ore-thermal furnace using manganese ore, high-ash coal, and quartzite. The smelting process was conducted under slag-free conditions with stable electrical operation. The obtained alloy had the following composition (wt.%): Fe ~ 12.1, Si ~ 44.7, Mn ~ 34.5, and Al ~ 5.1, with low impurity levels (C < 0.5%, S < 0.02%, p < 0.09%). Microstructural analysis using SEM-EDS confirmed the formation of silicide (FeSi, Mn₅Si₃) and aluminosilicide phases, which ensure the structural stability of the alloy. It is shown that the localization of alloy compositions within specific tetrahedra of the Fe-Si-Mn-Al system prevents self-disintegration. The results demonstrate that TDA is an effective tool for predicting phase composition and optimizing the production technology of complex ferroalloys. Full article

Human cytomegalovirus (HCMV) remains a significant cause of morbidity in immunocompromised patients, necessitating the development of improved antivirals. Using an integrated in silico and in vitro approach, we identified a novel ligand (NL) as a letermovir analog with enhanced binding affinity and reduced cytotoxicity. A pUL56 terminase subunit model generated with AlphaFold 3 was used for the virtual screening of a 15,000-compound library. Among the 73 candidates with structural similarity to letermovir (Tanimoto ≥ 0.6), NL exhibited superior predicted binding affinity (ΔG_bind = −10.7 kcal/mol). In silico toxicity prediction (ProTox 3.0) classified NL as having low toxicity (class 4, LD50 ≈ 1000 mg/kg), which was confirmed in vitro, where NL demonstrated 158-fold less toxic (CC50 = 2.69 mg/mL) in MRC-5 cells than letermovir (0.017 mg/mL). Molecular dynamics simulations over 500 ns revealed that the pUL56-NL complex forms a more thermodynamically stable interaction, with a lower calculated free energy of binding (MMGBSA: −40.89 ± 7.40 kcal/mol vs. −32.76 ± 4.96 kcal/mol) and a narrower free energy landscape. These results establish NL as a promising, low-cytotoxicity candidate with enhanced target engagement, warranting further investigation as a potential anti-HCMV therapeutic. Full article

Efficient design of desalination and brine management systems, which are central to a water circular economy, requires accurate thermodynamic data such as the osmotic coefficient. This property is key to understanding salt behavior in aqueous solutions, directly impacting the energy efficiency and sustainability of treatment processes. This study presents a predictive framework that combines machine learning with the Gazelle Optimization Algorithm (GOA) to accurately estimate osmotic coefficients for various inorganic salt solutions. The GOA was employed to automatically tune the hyperparameters of two models: Decision Tree (DT) and Gradient Boosting Machine (GBM). Using a comprehensive dataset of 893 samples with 27 salt-specific parameters, the GOA-GBM hybrid model delivered the highest predictive accuracy, achieving an R² of 0.9734 on test data. The GOA-DT model also performed robustly (R² = 0.9260), providing a more interpretable alternative. By creating a reliable tool for predicting osmotic coefficients, this methodology enables more precise process simulation and optimization. This directly supports the development of energy-efficient desalination technologies and informed decision-making for water reuse and resource recovery. The integration of advanced digital tools like GOA with machine learning offers a powerful approach to enhancing process efficiency and environmental safety, contributing directly to the design of sustainable, circular economy-based water treatment solutions for industrial and municipal applications. Full article

The 5′-proximal region of the citrus tristeza virus (CTV) RNA genome is a hub where several elements involved in different facets of the virus cycle reside, including the sequences driving the production of the viral long non-coding RNA (lncRNA) LMT1. The sequence of this region is one of the most divergent genome areas, allowing for strain differentiation. Beyond its use in assessing viral population diversity, the region provides a valuable model for studying the conservation of RNA structure and function despite sequence variation. Here, we integrated comparative in silico analysis of the LMT1 region from variants of eight CTV strains with selective 2′-hydroxyl acylation, analyzed by primer extension and mutational profiling (SHAPE-MaP) probing of in vitro–generated LMT1 RNAs from two divergent strains, T36 and T68. The predicted consensus structures revealed 19 putative, conserved stem-loops. The SHAPE-MaP reactivity data supported and substantiated the thermodynamics-based predictions for the 15 previously uncharacterized stem-loops and two functional elements identified earlier. The strong structural conservation across strains highlights that the LMT1 RNA structure contributes to its function during CTV infection. These results provide the first experimentally supported structure of this viral lncRNA and lay the foundation for defining how individual RNA motifs influence CTV biology. Full article

The purpose of this paper is to present the development of an engineering level code for calculating hypersonic aerodynamics and convective heating, HI-Mach. Novel to this paper are the use of analytic methods for streamline tracing and the direct differentiation of geometric sensitivities for both forces and heat load. Independent panel inclination methods calculate the pressure distribution on the surface of a hypersonic vehicle. Normal shock relations provide the thermodynamic state on each panel. Streamlines are integrated using closed-form streamline equations. Flat plate formulas corrected for compressibility calculate the skin friction coefficient and acreage heat flux on each panel. Formulas for heating on stagnation points and lines, including effects of ellipticity and sweep, are used to calculate stagnation region heating. A method for obtaining the sensitivities of a quantity of interest with respect to the geometry in a hypersonic panel code is described. These are obtained using direct analytical derivatives. The approach is precise and has been thoroughly tested against finite differencing. HI-Mach provides results orders of magnitude faster than would be obtained by CFD. Results from HI-Mach are analyzed and compared to experimental results for the HL-20 lifting body geometry. For the aerodynamic characteristics, HI-Mach predicted force coefficients within 12% of experimental results at $M\infty =4.5$ and 21% at $M_{\infty }=1.6$. Heating results for the HL-20 match experimental and CFD results to within 20% over a wide range of operating conditions. Full article

Constant composition expansion (CCE) experiments provide critical relative-volume and density information describing the thermodynamic behavior of reservoir oils and gases under varying pressure. These properties are vital inputs for hydrocarbon reservoir engineering, as they impact how oil and gas move through the reservoir during production. However, the need for specialized personnel, high-end equipment and measures taken to ensure safety in handling high pressure fluids often render the CCE experiments expensive and slow. This work introduces a Local Interpolation Method (LIM), a proximity-informed, end-to-end CCE fluid properties prediction Artificial Intelligence (AI) model that leverages domain expertise and synthetic Pressure–Volume–Temperature (PVT) data archives that mimics the actual data. The AI model generates surrogate CCE behavior for new fluids, thereby reducing the need for completing laboratory CCE measurements when sufficiently similar fluids exist in the available archive and neighborhood support is strong. Each new fluid is embedded in a compositional–thermodynamic descriptor space, and its response is inferred from a small neighborhood of thermodynamically similar fluids. Within this locality, the LIM combines hybrid local interpolation for key scalar properties (such as saturation-point quantities and expansion endpoints) with shape-preserving reconstruction of monophasic and diphasic relative-volume curves, enforcing continuity at saturation and consistency between relative volume, density and compressibility. The workflow operates purely at inference time and does not require case-specific retraining. Application to a curated archive of CCE tests shows that LIM reproduces key CCE features with very good agreement to existing data across a range of fluid types, indicating that proximity-based AI modeling can substantially reduce reliance on new CCE experiments while maintaining engineering-useful agreement for compositional simulation workflows. Under leave-one-out evaluation on 488 CCE tests, mean curve-level Mean Absolute Percentage Error (MAPE) is 0.07% for monophasic relative volume and 0.07% for monophasic density. For well-supported neighborhoods (Tiers 1–3, n = 376), mean MAPE is 0.04% for both, with 2.65% for derived compressibility and 1.78% for diphasic relative volume. The workflow is automated in software to facilitate reproducible inference on operator-owned archives and can reduce turnaround time and laboratory burden in well-supported neighborhoods. The proposed AI model uses available experimental data owned by each operator and does not use others’ data while respecting the data privacy and data ownership. Full article

Polyhalite, K₂SO₄•MgSO₄•2CaSO₄•2H₂O, a ternary evaporite mineral, is commonly found in evaporitic rock salt strata, where it acts as an indicator mineral for potash evaporite deposits. As a directly exploitable mineral potash fertilizer, polyhalite serves as an important substitute for potassium resources. The thermodynamic properties of polyhalite remain poorly characterized experimentally; consequently, current estimates predominantly rely on predictive modeling and indirect experimental approaches. The Raman spectra of free SO₄²⁻ vibrational modes in various sulfate minerals are sensitive to the local symmetry and hydrogen-bonding environment within crystal hydrates, and are directly influenced by the surrounding crystal field. This sensitivity makes Raman spectroscopy a powerful tool for investigating and identifying the crystal structures of sulfate minerals. In this work, the thermodynamic and Raman vibrational properties of polyhalite were investigated using density functional theory (DFT). Phonon calculations at the optimized geometry were employed to compute polyhalite’s key thermodynamic properties—specific heat, entropy, enthalpy, Gibbs free energy, and Debye temperature—over a temperature range of 0–1000 K. The results showed that: (1) the computed volume exhibited minimal error, approximately 0.87%, compared to experimental data; (2) the calculated values for the isobaric heat capacity and entropy were 420.72 and 531.39 J·mol⁻¹·K⁻¹ at 298.15 K, respectively; and (3) the calculated value for the free energy of formation at 298.15 K was −5670 kJ·mol⁻¹. The computed Raman spectrum results showed that the typical spectral features of polyhalite are: (1) ν1 for 1024 cm⁻¹, symmetric stretching mode; (2) ν2 for 464 cm⁻¹, symmetry bending mode; and (3) ν4 for 627 cm⁻¹, anti-symmetry bending mode. Full article

The emergence of classicality through quantum decoherence is commonly described from complementary perspectives emphasizing stability (environment-induced superselection), objectivity (Quantum Darwinism), or physical feasibility (information thermodynamics). In realistic open quantum systems, however, these aspects coexist and compete under finite physical resources. In this work we argue that classical structure selection is most naturally understood as a resource-constrained, multi-objective process. We introduce the Informational Economy Functional (IEF), an effective accounting framework that places loss of distinguishability, energetic dissipation, and the generation of redundantly accessible records on equal footing. The associated Principle of Informational Economy characterizes emergent classical structures as those achieving an optimal compromise among stability, objectivity, and energetic feasibility. Classicality is thus neither maximally stable, nor maximally redundant, nor maximally energy-efficient, but instead reflects a Pareto-optimal balance shaped by environmental constraints. The IEF yields falsifiable predictions concerning pointer-structure variability, redundancy deformation, and resource-sensitive trade-offs, and suggests concrete experimental tests in continuously monitored quantum platforms. Classical reality is thereby reinterpreted as the most economical configuration in which information can stably form, propagate, and persist. Full article

Wax deposition occurs to varying degrees in most oil and gas wells. The basic data of existing wax precipitation prediction models are mainly single-component wax experimental data based on the melting process of wax crystals during heating, which is quite different from the cooling crystallization process of wax in oil and gas production. Moreover, the published solubility test data of binary n-alkanes are mainly concentrated in the range of nC10–nC36, leaving existing thermodynamic models without available data for predicting the behavior of high-carbon alkanes. Based on the idea of wax crystallization and precipitation during cooling, this study experimentally determined the solid–liquid equilibrium solubilities of high-carbon n-alkanes (nC38, nC40, nC44, nC48 and nC50) with different concentrations in n-heptane, n-nonane and n-dodecane, as well as the crystallization parameters of pure substances, by using a DSC instrument. This effectively fills the gap in the basic physical property data of long-chain alkanes (more than nC36) and the cooling process in existing studies. In addition, we measured the crystallization parameters of pure high-carbon n-alkanes (nC38, nC40, nC44, nC48 and nC50) during cooling, including crystallization temperature, transition temperature, crystallization enthalpy and transition enthalpy under cooling conditions. The experimental data are in good agreement with the solubility predicted by the ideal solution model for the cooling process, with an average absolute percentage error of less than 10% and average solubility deviation generally within 0.078 mol%. This indicates that the ideal solution model has good accuracy for predicting the precipitation of n-alkane wax and n-alkane solvents. This study provides basic data for the prediction theory of paraffin precipitation. Full article

The growing need to mitigate climate change has accelerated the development of Carbon Capture, Utilization, and Storage (CCUS) technologies, where the safe transport of supercritical CO₂ (sCO₂) through pipelines is a key challenge. The flow behavior in such systems is strongly influenced by phase-change processes under transient conditions such as decompression and heat transfer and is further complicated by the presence of impurities (e.g., N₂, CH₄, and Ar). These impurities modify thermodynamic properties and phase boundaries, thereby affecting the overall flow dynamics. In this study, the influence of impurities on leakage, mass flow rate, and decompression wave propagation in sCO₂ pipelines is investigated using computational fluid dynamics (CFD) simulations. A real-fluid model (RFM) implemented in the CONVERGE CFD solver is employed, with a tabulation-based approach to accurately capture thermodynamic and transport properties across multiphase regimes. The simulations were validated against available experimental data and performed for varying impurity concentrations to assess their impact on key flow variables, including pressure, temperature, and wave speed. Although simplifying assumptions were used, the results are in fairly good agreement with experimental observations and provide a better understanding of the phase behavior induced by impurities during transient decompression. Additionally, the effects of outlet geometry, pipeline configuration, and the choice of equation of state are examined, highlighting their influence on the predicted flow response. The validity of the RFM modeling framework is further demonstrated by simulations of a large-scale pipeline configuration representative of industrial conditions, which will serve as a benchmark for future improvements. Full article

A structural analysis of phenylpropanoids demonstrates that the benzene ring and the propenoic fragment act as two largely independent π-electron systems. This distinctive feature provides a theoretical basis for the rational design of novel compounds obtained through the structural integration of phenylpropanoids with polyene aldehydes and acids. These classes may be combined by elongating the carbon backbone via iterative vinyl group extension, thereby generating an expanded conjugated double-bond system. Alternatively, the structure of polyene aldehydes may be modified by replacing the unreactive methyl group with a benzene ring bearing suitable functional substituents. DFT computational studies performed at the B3LYP/QZVP level of theory indicate that the designed analogs predominantly scavenge radicals through the sequential proton loss electron transfer (SPLET) mechanism in aqueous environments. This pathway involves the initial deprotonation of carboxyl, aldehyde, or phenolic groups, with the hydroxyl moiety exhibiting the greatest propensity for proton dissociation. Carbon chain extension exerts only a minor influence on proton affinity (PA) values but significantly affects electron transfer enthalpy (ETE) parameters. Consequently, increasing the number of conjugated double bonds enhances activation of the second step of the SPLET mechanism, thereby improving overall radical-scavenging activity. Comparison of the calculated chemical reactivity parameters substantiates the conclusions drawn from the thermodynamic analysis. A pronounced enhancement in the reactivity of the modeled compounds, relative to the parent constituents, is observed. Time-dependent density functional theory (TD-DFT) calculations further predict absorption in the visible region, indicating potential applications of the modeled compounds as radical-scavenging dyes in food, pharmaceutical, cosmetic, and dietary supplement formulations. Full article

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems. Full article

Accurate prediction of CO₂ diffusion in multicomponent fluids is crucial for efficient enhanced oil and gas recovery (EGR) and carbon capture, utilization and storage (CCUS) operations. Conventional experimental methods struggle to accurately reproduce diffusion processes under reservoir conditions and provide limited insight into molecular-scale mechanisms. Therefore, a detailed microscopic understanding of CO₂ diffusion in complex fluids is urgently needed. In this study, the diffusion behavior and underlying mechanism of the CO₂-CH₄-H₂O system under reservoir temperature and pressure conditions were explored using both experimental techniques and molecular dynamics (MD) simulations. The results indicate that at 354.15 K, the diffusion coefficients follow the order D_CH4 > D_CO2 > D_Fick and decrease with increasing pressure. Higher CO₂ concentrations and water content lead to a reduction in D_CO2. Gravity exhibits a relatively minor influence, slightly enhancing D_CO2 while marginally reducing D_CH4. Near the critical point, a significant decrease in the thermodynamic factor indicates drastic changes in thermodynamic properties. Furthermore, the presence of water promotes CO₂ enrichment at the gas-water interface, consequently reducing both D_CO2 and D_CH4. This work provides valuable insights into bulk-phase transport in multicomponent aquifer systems under reservoir conditions and offers theoretical support for optimizing gas injection strategies and improving the efficiency of EGR and CCUS processes. Full article