Search Results (122)

Thermodynamic fault diagnosis of marine steam turbines remains challenging due to non-stationary multivariate sensor data under stochastic loads and transient conditions. While conventional threshold-based methods lack the sophistication for such dynamics, existing data-driven Transformers struggle with inherent non-stationarity. To address this, we propose a hybrid DLinear–Transformer framework that synergistically integrates localized trend decomposition with global feature extraction. The model employs a dual-branch architecture with adaptive positional encoding and a gated fusion mechanism to enhance robustness. Extensive evaluations demonstrate the framework’s superiority: on public benchmarks (SMD, SWaT), it achieves statistically significant F1-score improvements of 2.7% and 0.3% over the state-of-the-art TranAD model under a controlled, reproducible setup. Most importantly, validation on a real-world marine steam turbine dataset confirms a leading fault detection accuracy of 94.6% under variable conditions. By providing a reliable foundation for identifying precursor anomalies, this work establishes a robust offline benchmark that paves the way for practical predictive maintenance in marine engineering. Full article

The Technology-Enhanced Learning (TEL) Marketplace was a joint initiative by the vice rectorate for academic affairs and the vice rectorate for digitization and change management at Graz University of Technology to modernize lectures. As part of this initiative, an exercise course on chemical thermodynamics was redesigned as a learner-centered course and enriched with interactive learning materials designed to promote self-directed learning. The core of the method used to implement this redesign is interactive notebooks created in Wolfram Mathematica to enable students to work through the examples independently, in depth, and irrespective of time, with the required theoretical background integrated into the notebooks. In this paper, we ask the following questions: RQ1: How did students use and accept the interactive notebooks? RQ2: What was the impact of the interactive notebooks and the corresponding course design as perceived by the students? To answer these questions, we conducted a questionnaire-based survey with 45 course students and statistically analyzed the results. Key results for RQ1 show that $93.33\%$ of the participating students reported using the interactive notebooks, and technology acceptance (1 = low TA, 5 = high TA) was high in both the dimensions of perceived usefulness ($m=3.88$) and attitude ($m=4.24$). Regarding RQ2, our key results show that students perceived the notebooks to have a positive impact on their learning experience, especially regarding their self-directed learning. The results of this work are in alignment with observations by lecturers, which showed that this more student-centric course design and the integration of the interactive learning materials made it possible to clarify detailed questions during the independent learning phase, allowing the interactive part of the course to focus on the tactical approaches, solutions, and problems that arose during the calculations, which raised the overall level of content teaching. Full article

The variational method holds considerable significance within mathematical and theoretical physics. Its importance stems from its capacity to characterise natural systems through physical quantities, irrespective of the chosen frame of reference. This characteristic makes it a powerful tool for understanding the behaviour of diverse physical phenomena. A global and statistical approach originating from the principles of non-equilibrium thermodynamics has been developed. This approach culminates in the principle of maximum entropy generation, specifically tailored for open systems. The principle itself arises as a direct consequence of applying the Lagrangian approach to open systems. The work focuses on a generalised method for deriving the thermodynamic Hamiltonian. This Hamiltonian is essential to the dynamical analysis of open systems, allowing for a detailed examination of their time evolution. The analysis suggests that irreversibility appears to be a fundamental process related to the evolution of states within open systems. Full article

Water pollution from pharmaceutical and textile industries urgently requires effective treatment solutions due to environmental and health risks. Effective treatment methods are desperately needed for water pollution from the textile and pharmaceutical industries because of the dangers to the environment and human health. To treat these micropollutants, the optimized granular activated carbon (OGAC) produced from olive fruit stones was utilized as an adsorbent in this study. The central composite design (CCD) of response surface methodology (RSM) was statistically used to optimize the operating factors for rhodamine B (RhB) and thiamphenicol (THI) removal efficiency on the optimized granular activated carbon. This study evaluated the influence of factors such as the solution’s pH, initial RhB and THI concentration, and OGAC dose, along with their interactions to model outcomes and determined optimal adsorption conditions on OGAC. The adsorption kinetic data will be analyzed using the intra-particle diffusion, pseudo-second-order, and pseudo-first-order models. Equilibrium data will be analyzed using the Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich isotherms. The adsorption thermodynamics of the various systems under investigation will also be examined. Finally, a study on OGAC regeneration has been conducted. Results showed that THI and RhB removal is primarily influenced by pH, initial pollutant concentration, and dose. RSM indicated the optimal adsorption parameters for THI and RhB on OGAC as pH = 5.7, an initial concentration of C₀ = 2.5 mg/L, and a dose of 6 g/L. The kinetic study revealed that THI and RhB retention on OGAC generally follows a pseudo-second-order kinetic model, indicating chemisorption as the primary mechanism controlling adsorption. The adsorption isotherm data analysis showed that chemisorption has a significant role in the THI and RhB adsorption process on OGAC. Furthermore, thermodynamic parameters suggest that THI adsorption on OGAC is exothermic, while RhB adsorption is endothermic. Activated carbon regeneration tests demonstrated its cost-effectiveness, and activated carbon was successfully regenerated over three cycles, achieving efficiencies of 62.39% for RhB and 59.6% for THI. These results demonstrate that the studied OGAC is an effective adsorbent for THI and RhB removal. Full article

This work presents a proof of concept including simulation and experimental validations of acoustic gas sensor prototypes for trace CO₂ detection up to 1 ppm. For the detection of lower gas concentrations especially, the dependency of acoustic resonances on the molecular weights and, consequently, the speed of sound of the gas mixture, is exploited. We explored two resonator types: a cylindrical acoustic resonator and a Helmholtz resonator intrinsic to the MEMS microphone’s geometry. Both systems utilized mass flow controllers (MFCs) for precise gas mixing and were also modeled in COMSOL Multiphysics 6.2 to simulate resonance shifts based on thermodynamic properties of binary gas mixtures, in this case, N₂-CO₂. We performed experimental tracking using Zurich Instruments MFIA, with high-resolution frequency shifts observed in µHz and mHz ranges in both setups. A compact and geometry-independent nature of MEMS-based Helmholtz tracking showed clear potential for scalable sensor designs. Multiple experimental trials confirmed the reproducibility and stability of both configurations, thus providing a robust basis for statistical validation and system reliability assessment. The good simulation experiment agreement, especially in frequency shift trends and gas density, supports the method’s viability for scalable environmental and industrial gas sensing applications. This resonance tracking system offers high sensitivity and flexibility, allowing selective detection of low CO₂ concentrations down to 1 ppm. By further exploiting both external and intrinsic acoustic resonances, the system enables highly sensitive, multi-modal sensing with minimal hardware modifications. At microscopic scales, gas detection is influenced by ambient factors like temperature and humidity, which are monitored here in a laboratory setting via NDIR sensors. A key challenge is that different gas mixtures with similar sound speeds can cause indistinguishable frequency shifts. To address this, machine learning-based multivariate gas analysis can be employed. This would, in addition to the acoustic properties of the gases as one of the variables, also consider other gas-specific variables such as absorption, molecular properties, and spectroscopic signatures, reducing cross-sensitivity and improving selectivity. This multivariate sensing approach holds potential for future application and validation with more critical gas species. Full article

Controlling dissolved nitrogen is critical to meeting increasingly stringent steel quality targets, yet the variable kinetics of gas absorption and removal across production stages complicate real-time decision-making. Leveraging a total of 291 metal samples, the research applied ordinary least squares (OLS) regression, enhanced by cointegration diagnostics, to develop four stage-specific models covering pig iron after desulfurization, crude steel in the basic oxygen furnace (BOF) before tapping, steel at the beginning and end of secondary metallurgy processing. Predictor selection combined thermodynamic reasoning and correlation analysis to produce prediction equations that passed heteroscedasticity, normality, autocorrelation, collinearity, and graphical residual distribution tests. The k-fold cross-validation method was also used to evaluate models’ performance. The models achieved an adequate accuracy of 77.23–83.46% for their respective stages. These findings demonstrate that statistically robust and physically interpretable regressions can capture the complex interplay between kinetics and the various processes that govern nitrogen pick-up and removal. All data are from U. S. Steel Košice, Slovakia; thus, the models capture specific setup, raw materials, and production practices. After adaptation within the knowledge transfer, implementing these models in process control systems could enable proactive parameter optimization and reduce laboratory delays, ultimately minimizing excessive nitrogenation in finished steel. Full article

This article investigates the mechanism of self-organized DNA criticality with open states, which plays a key role in the regulation of gene expression and consequently in cell fate determination. Based on a mechanobiological model developed using methods of statistical physics and thermodynamics, we demonstrate that the collective behavior of DNA open-state ensembles governs transitions between bistable, metastable, and critical genomic states. These states correspond to different gene expression scenarios involved in cell fate determination. Through simulation results, we introduce the concept of a criticality cascade, linking the dynamics of the DNA molecule structural parameter χ with global changes in cellular processes. The findings align with experimental data and offer new perspectives for studying genome regulation mechanisms, including pathological conditions such as cancer. Full article

We investigate the statistical entropy of black holes within the framework of the generalized uncertainty principle (GUP) by employing effective metrics that incorporate leading-order and all-order quantum gravitational corrections. We construct three distinct effective metrics induced by the GUP, which are derived from the GUP-corrected temperature, entropy, and all-order GUP corrections, and analyze their impact on black hole entropy using ’t Hooft’s brick wall method. Our results show that, despite the differences in the effective metrics and the corresponding ultraviolet cutoffs, the statistical entropy consistently satisfies the Bekenstein–Hawking area law when expressed in terms of an invariant (coordinate-independent) distance near the horizon. Furthermore, we demonstrate that the GUP naturally regularizes the ultraviolet divergence in the density of states, eliminating the need for artificial cutoffs and yielding finite entropy even when counting quantum states only in the vicinity of the event horizon. These findings highlight the universality and robustness of the area law under GUP modifications and provide new insights into the interplay between quantum gravity effects and black hole thermodynamics. Full article

The separation of three proton pump inhibitors (omeprazole, lansoprazole, and rabeprazole) as exemplified molecules containing chiral sulfoxide groups was investigated in polar organic liquid chromatographic mode on seven different polysaccharide stationary phases (Chiralcel OD and OJ; Chiralpak AD, AS, and IA; Lux Cellulose-2 and -4). Different alcohols, such as methanol, ethanol, 1-propanol, 2-propanol, and their combinations, were used as eluents. After method optimization, semi-preparative enantioseparation was successfully applied for the three proton pump inhibitors to collect the individual enantiomers. A detailed investigation was conducted into elution order reversal, thermodynamic parameters, the effect of eluent mixtures, and the hysteresis of retention time and selectivity. Using Chiralpak AS, containing the amylose tris[(S)-α-methylbenzylcarbamate] chiral selector, the separation of the investigated enantiomers was achieved in all four neat eluents, with methanol providing the best results. In many cases, a reversal of the enantiomer elution order was observed. In addition to chiral-selector-dependent reversal, eluent-dependent reversal was also observed. Notably, even replacing methanol with ethanol altered the enantiomer elution order. Both enthalpy- and entropy-controlled enantioseparation were also observed in several cases; however, temperature-dependent elution order reversal was not. The hysteresis of retention and selectivity was further investigated on amylose-type columns in methanol–2-propanol and methanol–ethanol eluent mixtures. The phenomenon was observed on all amylose columns regardless of the eluent mixtures employed. Hystereticity ratios were calculated and used to compare the hysteresis behaviors of different systems. Multivariate statistical analysis revealed that Chiralpak AS exhibited the most distinct enantioselective behavior among the tested columns, likely due to the absence of a direct connection between the carbamate moiety and the aromatic substituent. The present study aided in understanding the mechanisms leading to enantiomer recognition, which is crucial for developing new chiral stationary phases and chiral HPLC method development in general. Full article

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials. This formulation allowed us to employ analytical tools from statistical mechanics to identify optimal ordering strategies via the minimization of a free energy functional. Our approach not only sheds new light on the structural characteristics of optimal planning but also introduces the concept of configurational entropy as a measure of decision variability and robustness. Numerical simulations and analytical approximations demonstrate the efficacy of the lattice gas model in capturing key features of the problem and suggest promising avenues for extending the framework to more complex settings, including multi-item systems and time-varying demand. This work represents a significant step toward bridging physical sciences with supply chain optimization, offering a robust theoretical foundation for both future research and practical applications. Full article

We present novel proofs of the Riemann hypothesis by extending the standard complex Riemann zeta function into a quaternionic algebraic framework. Utilizing λ-regularization, we construct a symmetrized form that ensures analytic continuation and restores critical-line reflection symmetry, a key structural property of the Riemann ξ(s) function. This formulation reveals that all nontrivial zeros of the zeta function must lie along the critical line Re(s) = 1/2, offering a constructive and algebraic resolution to this fundamental conjecture. Our method is built on convexity and symmetrical principles that generalize naturally to higher-dimensional hypercomplex spaces. We also explore the broader implications of this framework in quantum statistical physics. In particular, the λ-regularized quaternionic zeta function governs thermodynamic properties and phase transitions in Bose–Einstein condensates. This quaternionic extension of the zeta function encodes oscillatory behavior and introduces critical hypersurfaces that serve as higher-dimensional analogues of the classical critical line. By linking the spectral features of the zeta function to measurable physical phenomena, our work uncovers a profound connection between analytic number theory, hypercomplex geometry, and quantum field theory, suggesting a unified structure underlying prime distributions and quantum coherence. Full article

We consider the macroscopic motion of the normal component of superfluid ³He-A in global thermodynamic equilibrium within the context of the Zubarev statistical operator method. We formulate the corresponding effective theory in the language of the functional integral. The effective Lagrangian comprising macroscopic motion of fermionic excitations is calculated explicitly for the emergent relativistic fermions of the superfluid ³He-A phase immersed in a non-trivial bosonic background due to a space- and time-dependent matrix-valued vierbein featuring nonzero torsion as well as the Nieh–Yan anomaly. We do not consider the dynamics of the superfluid component itself and thereby its backreaction effects due to normal component macroscopic flow. It is treated as an external background within which the emergent relativistic fermions of the normal component move. The matrix-valued vierbein formulation comprises an additional two-dimensional internal spin space for the two axially charged Weyl fermions living at the Fermi points, which may be replaced by one featuring a Dirac fermion doublet with a real-valued vierbein, an axial Abelian gauge field, and a spin connection gauge field mixing the Dirac and internal spin spaces. We carry out this change of description in detail and determine the constraints on the superfluid background as well as the the normal component motion as determined from the Zubarev statistical operator formalism in global thermodynamic equilibrium. As an application of the developed theory, we consider macroscopic rotation around the axis of pure integer mass vortices. The corresponding thermodynamic quantities of the normal component are analyzed. Our formulation incorporates both superfluid background flow and macroscopic motion flow of the normal component and thereby enables an analysis of their interrelation. Full article

To reduce the concentration of phosphorus, the main nutrient responsible for eutrophication, two adsorbents were tested: a commercial activated carbon and one produced from the pods of Moringa oleifera. A concentrated phosphorus solution representative of eutrophic ecosystems was produced at 0.210 mg·L⁻¹ and used as the adsorbate. Thirty-nine laboratory samples were prepared with adsorbent dosages ranging from 0.5 g∙L⁻¹ to 2.0 g∙L⁻¹, and statistical analyses were applied to evaluate the results. An increase in the concentration of phosphorus in the solution was detected in all the tests. Desorption occurred due to the presence of nutrients in the composition of the adsorbents, in addition to the mild physical activation and the use of H₃PO₄ as a chemical activator of the natural adsorbent, which further favored desorption at equilibrium, even for activated carbon. This work, therefore, highlights the limitations of using the adsorption technique to remove phosphorus from eutrophic aquatic ecosystems. It is recommended that other activation methods for M. oleifera pods be studied for phosphorus removal from water, as well as adsorption equilibrium, kinetics, and thermodynamic studies. Full article

We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materials. (1) PAFL’s primary objective is to dynamically optimize the privacy–utility trade-off in federated travel recommendation systems through phase-adaptive anonymization. The phase parameter φ ∈ [0, 1] operates as a tunable control variable that continuously adjusts the latent space geometry between differentially private (φ→1) and utility-optimized (φ→0) representations via a thermodynamic-inspired transformation. Conventional federated learning approaches often rely on static privacy-preserving techniques, which either degrade recommendation quality or inadequately protect sensitive user data; PAFL addresses this limitation through three key innovations: a latent-space phase transformer, a differential privacy-gradient inverter with mathematically provable reconstruction bounds (εt ≤ 1.0), and a lightweight sequential transformer. (2) PAFL’s core innovation lies in its phase-adaptive mechanism that dynamically balances privacy preservation through differential privacy and utility maintenance via gradient inversion, governed by the tunable phase parameter φ. Experimental results demonstrate statistically significant improvements, with 18.7% higher HR@10 (p < 0.01) and 62% lower membership inference risk compared to state-of-the-art methods, while maintaining εtotal < 2.3 over 100 training rounds. The framework advances federated learning for sensitive recommendation tasks by establishing a new paradigm for adaptive privacy–utility optimization. Full article

This study investigated methane hydrate formation and dissociation within a temperature range of 280 to 290 K and a pressure range of 5.5 to 13 MPa. These conditions are relevant to natural gas systems, where methane is the primary component of natural gas. Either experimental or thermodynamic models were used to predict the conditions of formation of gas hydrates. The Van der Waals–Platteeuw model based on statistical thermodynamics is the basis of the existing thermodynamic models for predicting the conditions of hydrate formation. In this work, the stepwise heating method was applied to determine the thermodynamic equilibrium points of methane gas in a constant volume system. The CPA (Cubic Plus Association) equation of state and the Van der Waals–Platteeuw model were employed to simulate hydrate formation conditions. Experimental equilibrium data for pure methane were compared with results from previous studies (Deaton and Frost, Nakamura, Jhaveri and Robinson, De Roo, and others). The results showed excellent agreement, with an average absolute temperature error of less than 0.1%. This high level of accuracy confirms the reliability of the experimental procedures and thermodynamic modeling approaches used in the study to accurately predict hydrate formation conditions, being critical for designing and operating natural gas systems in order to avoid hydrate accumulation. Full article