Search Results (154)

Background/Objectives: Detecting copy number variations (CNVs) from next-generation sequencing (NGS) is challenging, particularly in targeted sequencing panels, especially for cell-free DNA (cfDNA), where the signal is weak and noise is high. Methods: We present BayesCNV, a Bayesian hierarchical model for gene-level copy ratio estimation from targeted amplicon read depths compared to a CNV-neutral reference sample. The model provides posterior uncertainty for each gene and supports interpretable calling based on effect size and posterior confidence. The model also provides a principled quality-control strategy based on the marginal log likelihood of each sample, with low values indicating low confidence in the calls. BayesCNV uses thermodynamic integration, a technique to reliably estimate this quantity. We benchmark our method against two publicly available CNV callers using Seracare^® reference samples with known CNVs on the OncoReveal^® Core Lbx panel. Results: Our method achieves a sensitivity of 0.87 and specificity of 0.996, dramatically outperforming two competitor methods, IonCopy and DeviCNV. In a separate FFPE dataset using the OncoReveal^® Essential Lbx panel, we show that the marginal log likelihood cleanly separates, degraded from high-quality samples, even when conventional sequencing QC metrics do not. Conclusions: BayesCNV provides accurate and interpretable gene-level CNV estimates and uncertainty quantification, along with an evidence-based quality control metric that improves robustness in targeted cfDNA workflows. Full article

Theories of emergent gravity have established a deep connection between entropy and the geometry of spacetime by looking at the latter through a thermodynamic lens. In this framework, the macroscopic properties of gravity arise in a statistical way from an effective small-scale discrete structure of spacetime and its information content. In this review, we begin by outlining how theories of quantum gravity imply the existence of a minimum length of spacetime as a general feature. We then describe how such a structure can be implemented in a way that is independent from the details of the quantum fluctuations of spacetime via a bi-tensorial quantum metric $q_{\alpha \beta }(x,x^{\prime })$ that yields a finite geodesic distance in the coincidence limit $x\to x^{\prime }$. Finally, we discuss how the entropy encoded by these microscopic degrees of freedom can give rise to the field equations for gravity through a thermodynamic variational principle. Full article

Sea surface temperature (SST) serves as a critical indicator of oceanic thermodynamic processes and climate variability, exerting essential influence on ocean fronts, typhoon tracks, and monsoon evolution. Nevertheless, owing to the highly nonlinear and complex multi-scale characteristics of SST, achieving accurate spatiotemporal forecasting remains a formidable challenge. To address this issue, we proposed an enhanced Transformer architecture that incorporates a Coordinate Attention (CA) module and an Adaptive Fusion (AD) module, enabling the joint extraction and integration of temporal and spatial features. The proposed model is evaluated through SST prediction experiments over a localized region of the South China Sea with lead times of 1, 7, 15, and 30 days. Results indicate that our approach consistently outperforms baseline models across multiple evaluation metrics. Moreover, generalization experiments conducted on datasets from regions with diverse latitudes and climate regimes further demonstrate the model’s robustness and adaptability in terms of both accuracy and stability. Full article

Headspace (HS) in anaerobic batch biodigesters is a critical design parameter that modulates pressure stability, gas–liquid equilibrium, and methanogenic productivity. This systematic review, guided by PRISMA 2020, analyzed 84 studies published between 2015 and 2025, of which 64 were included in the qualitative and quantitative synthesis. The interplay between headspace volume fraction ${\mathrm{V}}_{\mathrm{H}\mathrm{S}}/{\mathrm{V}}_{\mathrm{t}\mathrm{o}\mathrm{t}}$, operating pressure, and normalized methane yield was assessed, explicitly integrating safety and instrumentation requirements. In laboratory settings, maintaining a headspace volume fraction (HSVF) of 0.30–0.50 with continuous pressure monitoring P(t) and gas chromatography reduces volumetric uncertainty to below 5–8% and establishes reference yields of 300–430 NmL CH₄ g⁻¹ VS at 35 °C. At the pilot scale, operation at 3–4 bar absolute increases the CH₄ fraction by 10–20 percentage points relative to ~1 bar, while maintaining yields of 0.28–0.35 L CH₄ g COD⁻¹ and production rates of 0.8–1.5 Nm³ CH₄ m⁻³ d⁻¹ under OLRs of 4–30 kg COD m⁻³ d⁻¹, provided pH stabilizes at 7.2–7.6 and the free NH₃ fraction remains below inhibitory thresholds. At full scale, gas domes sized to buffer pressure peaks and equipped with continuous pressure and flow monitoring feed predictive models (AUC > 0.85) that reduce the incidence of foaming and unplanned shutdowns, while the integration of desulfurization and condensate management keep corrosion at acceptable levels. Rational sizing of HS is essential to standardize BMP tests, correctly interpret the physicochemical effects of HS on CO₂ solubility, and distinguish them from intrinsic methanogenesis. We recommend explicitly reporting standardized metrics (Nm³ CH₄ m⁻³ d⁻¹, NmL CH₄ g⁻¹ VS, L CH₄ g COD⁻¹), absolute or relative pressure, HSVF, and the analytical method as a basis for comparability and coupled thermodynamic modeling. While this review primarily focuses on batch (discontinuous) anaerobic digesters, insights from semi-continuous and continuous systems are cited for context where relevant to scale-up and headspace dynamics, without expanding the main scope beyond batch systems. Full article

We investigate the thermodynamic properties of a generalized de Sitter-like configuration. This investigation proceeds in two essential steps: (1) first, we construct a spacetime whose energy–momentum tensor asymptotically reproduces quintessence while maintaining isotropic pressures, despite being fueled by a nonconstant energy–momentum tensor; (2) second, we define a finite domain of validity for the solution, within which an additional Cauchy horizon emerges. Afterwards, we analyze the thermodynamic behavior of this configuration and compare it with the standard de Sitter case. Our results indicate that the extra parameter introduced in the metric does not lead to a positive specific heat; this value remains negative, suggesting that the role of such a parameter is thermodynamically nonessential. Full article

Despite substantial advances in turbofan engineering, a crucial gap persists: there remains the need for an all-inclusive comparative analysis that includes real-world operational data and evaluates the performance of modern turbofans used in aviation. Specifically, systematic investigations that examine the exergy and efficiency of turbofan engines for takeoff and cruise remain scarce. Further, the current literature needs to address rigorous performance assessments that include simultaneous consideration of the combined effects of ambient conditions (e.g., temperature, density, relative humidity), Mach number, and turbine inlet temperature on high-bypass turbofan engines used in modern, commercial aircraft. Energetic and exergetic analyses were conducted on five commercial high-bypass turbofan engines with different configurations for both takeoff and cruise flight modes. The computational thermodynamic models developed showed strong correlation with manufacturers’ specifications. Performance evaluations included variations in ambient conditions, altitude, Mach number, and turbine inlet temperature. Results demonstrate that three-spool engine architecture exhibits 70–71% reduction in exergy destruction between flight phases compared to 62.5% for two-spool designs, indicating greater operational adaptability. The combustion chamber emerged as the dominant contributor to irreversibilities, representing approximately 55–58% of overall exergy destruction during takeoff operations. Results demonstrate that increased ambient temperature and/or humidity increase both degraded exergetic efficiency and thrust-specific fuel consumption, and that Mach number and altitude influenced efficiency metrics through ram compression and density effects, while higher turbine inlet temperatures enhanced exhaust kinetic energy via increased thermal input. We show that cruise operations demonstrated superior exergetic efficiency (68–74%) compared with takeoff (47–60%) across all engine configurations. Our results confirm the fundamental trade-off in turbofan design: for long-range applications, high-bypass engines prioritize propulsive efficiency, while for power-intensive operations, moderate-bypass configurations deliver higher specific thrust. Full article

The diesel substitution ratio is a key parameter influencing the combustion characteristics and energy conversion efficiency of hydrogen diesel dual-fuel free-piston engines. This study develops a thermodynamic hydrodynamic coupled model for a dual-fuel free engine to investigate the effects of five substitution ratios (15%, 20%, 25%, 30%, and 35%) on in-cylinder mixture formation, combustion characteristics, and emission performance. The key novelty of this work lies in employing this fully coupled combustion-dynamics model to systematically optimize the hydrogen–diesel substitution ratio, which explicitly captures the critical feedback between combustion and the piston’s unique motion. The cumulative heat release served as the key quantitative metric. The analyzed parameters included the gas mixture fraction, turbulent kinetic energy, flow trajectories, in-cylinder pressure and temperature, combustion reaction rate, unburned equivalent ratio, cumulative heat release and its rate, heat release rate, and emission mass. The results demonstrate that the engine’s overall performance is optimal at a substitution ratio of 25%. At this ratio, a peak volumetric mixture fraction of 0.0088 was achieved with a broad distribution range, indicating significantly improved spatial fuel uniformity. The flow field exhibited organized swirl patterns that enhanced fuel dispersion. The peak in-cylinder pressure reached 7.2 MPa, which was 0.044 MPa higher than that of the 20% group. The combustion temperature remained stable, with a peak value of 1606 K, exceeding the 20% and 30% groups by 7 K and 16 K, respectively. The heat release phase was well-synchronized with the piston motion, ensuring a high proportion of premixed combustion for thorough fuel oxidation. Although nitrogen oxide (NOx) emissions were slightly higher, the reduction in soot was substantially greater than in the 20% group, leading to overall superior performance compared to the other substitution ratios. This study develops a thermodynamic hydrodynamic coupled model for a dual-fuel free-piston engine by leveraging the interaction between piston motion and combustion. This paper presents a novel strategy for optimizing the substitution ratio in a free piston engine via a fully coupled combustion-dynamics model. Full article

Current CO₂-based energy storage systems still face several unsolved technical challenges, including strong thermal destruction between the multi-stage compression and expansion processes, significant exergy destruction in heat exchange units, limited utilization of low-grade heat, and the lack of an integrated comprehensive performance framework capable of simultaneously evaluating thermodynamic, economic, and environmental performance. Although previous studies have explored various compressed CO₂ energy storage (CCES) configurations and CCES–Organic Rankine Cycle (ORC) couplings, most works treat the two subsystems separately, neglect interactions between the heat exchange loops, or overlook the combined effects of exergy losses, cost trade-offs, and CO₂-emission reduction. These gaps hinder the identification of optimal operating conditions and limit the system-level understanding needed for practical application. To address these challenges, this study proposes an innovative system that integrates a multi-stage CCES system with ORC. The system introduces ethylene glycol as a dual thermal carrier, coupling waste-heat recovery in the CCES with low-temperature energy utilization in the ORC, while liquefied natural gas (LNG) provides cold energy to improve cycle efficiency. A comprehensive 4E (energy, exergy, economic, and environmental) assessment framework is developed, incorporating thermodynamic modeling, exergy destruction analysis, CEPCI-based cost estimation, and environmental metrics including primary energy saved (PES) and CO₂ emission reduction. Sensitivity analyses on the high-pressure tank (HPT) pressure, heat exchanger pinch temperature difference, and pre-expansion pressure of propane (P₃₀) reveal strong nonlinear effects on system performance. A multi-objective optimization combining NSGA-II and TOPSIS identifies the optimal operating condition, achieving 69.6% system exergy efficiency, a 2.07-year payback period, and 1087.3 kWh of primary energy savings. The ORC subsystem attains 49.02% thermal and 62.27% exergy efficiency, demonstrating synergistic effect between the CCES and ORC. The results highlight the proposed CCES–ORC system as a technically and economically feasible approach for high-efficiency, low-carbon energy storage and conversion. Full article

This paper proposes an energy, exergy, economic, and exergoeconomic (4E) analysis of an Organic Rankine Cycle (ORC) enhanced by an ejector refrigeration system. The two systems are combined via an intercooler, where the unwanted heat is transferred to the ejector cooling loop. The major objective is to reduce the discharge pressure of the expander so that higher power is achieved. However, the combined system requires more equipment and energy input, and, hence, 4E analysis is an efficient tool for assessing the feasibility of it in practical use based on a comprehensive analysis. This study aims to provide a systematic 4E-based evaluation of an ORC integrated with an ejector cooling cycle under realistic tropical conditions. The innovation of this work lies in combining unified thermodynamic, economic, and exergoeconomic assessments to quantify both performance enhancement and cost interactions attributable to condenser-side cooling. The findings offer significant insights into the dominant thermal–economic trade-offs, identify key cost drivers within the ORC + ECC configuration, and highlight operating conditions that maximize the power output and minimize the electricity generation cost. These results contribute practical guidelines for improving the feasibility and deployment of ORC–ejector systems for low-grade heat recovery applications. A theoretical model is formulated to examine both energy and exergy performance indicators together with key economic metrics. Parametric investigations are conducted to investigate the effects of the intercooler temperature (16–22 °C) and generator temperature (70–85 °C) on overall system performance. It is found that the integration of an ejector cooling cycle (ORC + ECC) can significantly enhance the thermo-economic potential of waste heat power generation systems compared to a standard ORC, from both exergoeconomic and LCOE perspectives. The exergoeconomic analysis identified that, while the expander dominates the cost of the standard ORC, the condenser and cooling tower become critical components of the ORC + ECC due to their high exergy-destruction costs. At the system level, the LCOE results confirm that the ORC + ECC can achieve 37–38% lower electricity generation costs compared to the standard ORC. Full article

Enzyme kinetics is fundamental across diverse fields—from enzymology and medicine to biocatalysis and metabolic engineering. Analyses of enzyme kinetics provide insights into catalytic rates, substrate affinities, inhibition patterns, productivities and mechanistic pathways, which are critical for areas such as drug development, industrial biocatalysis and mechanistic enzymology. However, each research field emphasizes different types of kinetic parameters, leading to challenges in establishing a common ground for understanding and interpreting enzyme properties. This review covers interpretation of enzyme kinetic parameters under three main categories—steady-state, transient-state and performance metrics—in a descriptive way and discusses their relevance with respect to different scientific and applied fields that investigate and utilize enzymes. By comparatively defining key kinetic and thermodynamic parameters, the review aims to help researchers interpret and report enzyme behavior more effectively, bridging gaps across interdisciplinary fields. Full article

Advancements in deep learning have revolutionized materials discovery by enabling predictive modeling of complex material properties. However, single-modal approaches often fail to capture the intricate interplay of compositional, structural, and morphological characteristics. This study introduces a novel multimodal deep learning framework for enhanced material property prediction, integrating textual (chemical compositions), tabular (structural descriptors), and image-based (2D crystal structure visualizations) modalities. Utilizing the Alexandriadatabase, we construct a comprehensive multimodal dataset of 10,000 materials with symmetry-resolved crystallographic data. Specialized neural architectures, such as FT-Transformer for tabular data, Hugging Face Electra-based model for text, and TIMM-based MetaFormer for images, generate modality-specific embeddings, fused through a hybrid strategy into a unified latent space. The framework predicts seven critical material properties, including electronic (band gap, density of states), thermodynamic (formation energy, energy above hull, total energy), magnetic (magnetic moment per volume), and volumetric (volume per atom) features, many governed by crystallographic symmetry. Experimental results demonstrated that multimodal fusion significantly outperforms unimodal baselines. Notably, the bimodal integration of image and text data showed significant gains, reducing the Mean Absolute Error for band gap by approximately 22.7% and for volume per atom by 22.4% compared to the average unimodal models. This combination also achieved a 28.4% reduction in Root Mean Squared Error for formation energy. The full trimodal model (tabular + images + text) yielded competitive, and in several cases the lowest, error metrics, particularly for band gap, magnetic moment per volume and density of states per atom, confirming the value of integrating all three modalities. This scalable, modular framework advances materials informatics, offering a powerful tool for data-driven materials discovery and design. Full article

This work investigates the evaporation of black holes immersed in a de Sitter environment, using the Vaidya-de Sitter spacetime. The role of cosmological observers is highlighted in the development and Hayward thermodynamics for non-stationary geometries is employed in the description of the compact objects. The results of the proposed dynamical model are compared with the usual description based on stationary geometries, with specific results for primordial black holes (PBHs). The timescale of evaporation is shown to depend significantly on the choice of cosmological observer and can differ substantially from predictions based on stationary models at late times. Deviations are also shown with respect to the standard assertion that there is a fixed initial mass just below ${10}^{15}\mathrm{g}\sim {10}^{-18}{M}_{\odot }$ for the PBHs which are completing their evaporation process at the present epoch. Full article

To address issues such as unstable heating loads, uneven heat consumption, and precise heating in solar heating systems, efficient and accurate heating load forecasting is essential. A suitable solar heating system model was established using the TRNSYS18 thermodynamic simulation platform. Taking a building in Alar City, Xinjiang, as the research subject, ultra-short-term prediction data parameters for the area were obtained. Using the acquired data parameters and historical heating load data as inputs, the particle swarm optimization (PSO) algorithm was employed to optimize the LSTM neural network, establishing a prediction model based on the PSO-LSTM neural network. For load forecasting in 7 min ultra-short-term time series, both the LSTM neural network model and the PSO-LSTM neural network prediction model underwent optimization. Through simulation experiments verifying indoor temperature, heat collection, and energy consumption, two model error evaluation metrics were used as results. Comparative analysis revealed that the PSO-LSTM model achieved a 3.3–86.7% increase in R² compared to the LSTM model, a 38.2–84.8% reduction in RMSE, a 57.8–91.1% decrease in MAE, and a 58–90.3% reduction in MAPE. The research results demonstrate the PSO-LSTM model’s effectiveness in southern Xinjiang, confirming its superiority as a forecasting model. This provides data support for operational adjustments and load forecasting in solar heating systems. Full article

Energy expenditure constitutes a significant portion of total operational costs in greenhouse crop production. Developing accurate energy consumption prediction models presents crucial theoretical foundations for optimizing the environmental control strategies aimed at energy efficiency enhancement. This study focuses on steel-frame solar greenhouses without back slopes in Xinjiang’s Tianshan North Slope region. A physical model was established using thermodynamic equilibrium analysis, elucidating the energy exchange mechanisms between internal and external environments. Key parameters, including outdoor temperature and solar radiation, were identified as primary input variables through systematic energy flow characterization. Building upon this theoretical framework, we developed an enhanced prediction model (WOA-ELM) by integrating the Whale Optimization Algorithm (WOA) with an Extreme Learning Machine (ELM). The WOA’s global optimization capabilities were employed to refine the connection weights between input-hidden layers and optimize hidden neuron thresholds. Comparative evaluations against conventional artificial neural networks (ANNs), radial basis function neural networks (RBFNN), and baseline ELM models were conducted under diverse meteorological conditions. Experimental results demonstrate the superior performance of WOA-ELM across multiple metrics. Under overcast conditions, the model achieved a root mean square error (RMSE) of 0.423, coefficient of determination (R²) of 0.93, and mean absolute error (MAE) of 0.252. In clear weather scenarios, performance further improved with RMSE = 0.27, R² = 0.96, and MAE = 0.063. The comprehensive evaluation ranked model effectiveness as WOA-ELM > ELM > BP > RBF. These findings substantiate that the hybrid WOA-ELM architecture, combining physical mechanism interpretation with intelligent parameter optimization, delivers enhanced prediction accuracy across varying weather patterns. This research provides valuable insights for energy load management in backslope-less steel-frame greenhouses, offering theoretical guidance for thermal environment regulation and sustainable operation. Full article

Carnot Batteries with thermal integration stand as one of the most promising approaches to tackling contemporary global energy problems. Currently, research on Carnot Battery systems utilizing the ocean thermal gradient is still in its early stages. This paper establishes a holistic thermo-economic model to assess the system’s performance. Through working fluid screening and subsequent multi-objective optimization, this study identifies the optimal working fluid and clarifies the system’s thermal economy at the optimal design point. With round-trip efficiency and total cost as metrics, a sensitivity analysis identified key parameter effects on the system. This was followed by a multi-objective optimization, where the TOPSIS method selected the optimal solution. It was found that, when Ammonia and R1234yf were used as the working fluids in the RC and ORC sub-cycles, respectively, the system can achieve peak performances of 71.79% round-trip efficiency and 36.24% exergy efficiency. Moreover, the RC evaporation temperature exerts the most significant influence on the overall thermodynamic performance. Multi-objective optimization successfully identified a balanced thermo-economic design, yielding an optimal solution with a round-trip efficiency of 65.30% at a total cost of USD 65.90 M. These results offer critical insights for the design and optimization of this promising ocean thermal-powered Carnot Battery system. Full article