Search Results (5,812)

This study examined the thermal degradation of pulverized Musa sapientum (banana) peel waste through thermogravimetric measurements and thermokinetic modelling. For the first time, it also incorporated backpropagation deep learning to model pyrolysis traces, enabling the prediction and optimization of the process. Physicochemical characterization confirmed the material’s lignocellulosic composition. TGA was performed between 30 and 950 °C at heating rates of 5, 10, 20, and 40 °C min⁻¹, identifying a primary devolatilization range of 190 to 660 °C. The application of a backpropagation machine learning technique to the processed TGA data enabled the estimation of arbitrary constants that accurately captured the characteristic behaviour of the experimental data (). This modelling and simulation approach achieved a significant reduction in training loss—decreasing from 35.9 to 0.07—over 47,688 epochs and 1.4 computational hours. Sensitivity analysis identified degradation temperature as the primary parameter influencing the thermochemical conversion of BP biomass. Furthermore, analyzing deconvoluted DTG traces via Criado master plots revealed that the 3D diffusion model (Jander [D3]) is the most suitable reaction model for the hemicellulose, cellulose, and lignin components, followed by the R2 and R3 geometrical contraction models. The estimated overall activation energy values obtained through the Starink (STK) and Friedman (FR) model-free isoconversional kinetic methods were 82.8 ± 3.3 kJ.mol⁻¹ and 97.6 ± 3.9 kJ.mol⁻¹, respectively. The thermodynamic parameters estimated for the pyrolysis of BP indicate that the formation of activated complexes is endothermic, endergonic, and characterized by reduced disorder, thereby establishing BP as a potential candidate material for bioenergy generation. Full article

In order to study the impacts of the carbon-green certificate trading mechanism and the fluctuation of feed-in tariffs on the low-carbon and economic aspects of the investment and operation of the integrated energy system, and to transform the system carbon emission into a low-carbon economic indicator, a two-layer capacity optimization allocation model is established with the objectives of the investment, operation, and maintenance cost and the operation cost, respectively. For the source-load uncertainty, the scenario reduction theory based on Monte Carlo simulation and Wasserstein distance is used to obtain the per-unit value of wind and photovoltaic output, and the K-means clustering method is used to obtain the typical day of electric-heat-cold multi-energy load. Based on the geometric Brownian motion in finance to simulate the feed-in tariffs under different volatilities, the multidimensional analysis scenarios are constructed according to different combinations of carbon emission reduction policies and tariff volatilities. The model is solved using the non-dominated sorting genetic algorithm (NSGA-II) with mixed integer linear programming (MILP) method. Case study results show that under the optimal scenario considering policy interaction and price volatility (δ = 1.0), the total annual operating cost is reduced by approximately 17.9% (from 2.80 million CNY to 2.30 million CNY) compared to the baseline with no carbon policy. The levelized cost of the energy system reaches 0.2042 CNY/kWh, and carbon-green certificate trading synergies contribute about 70% of the operational cost reduction. The findings demonstrate that carbon reduction policies and electricity price volatility significantly affect system configuration and operational economy, providing a new perspective and decision-making basis for integrated energy system planning. Full article

Oversampling is common and effective in resolving the classification problem of imbalanced data. Traditional oversampling methods are prone to generating overlapping or noisy samples. Clustering can effectively alleviate the above problems to a certain extent. However, the quality of clustering results has a significant impact on the final classification performance. To address this problem, an oversampling algorithm based on the Gaussian distribution oversampling algorithm and the K-means clustering algorithm combining compactness and separateness (CSKGO) is proposed in this paper. The algorithm first uses the K-means clustering algorithm, combining compactness and separateness to cluster the minority samples, constructs the cluster compactness index and inter-cluster separateness index to obtain the optimal number of clusters and the clustering results, and obtains the local distribution characteristics of the minority samples through clustering. Secondly, the sampling ratio for each cluster is assigned based on the compactness of the clustering results to determine the number of samples for each cluster in the minority class. Then, the mean vectors and covariance matrices of each cluster are calculated, and the Gaussian distribution oversampling algorithm is used to generate new samples that match the distribution of characteristics of the real minority samples, which are combined with the majority samples to form balanced data. To verify the effectiveness of the proposed algorithm, 24 datasets were selected from the University of California Irvine (UCI) Repository, and they were oversampled using the CSKGO algorithm proposed in this paper and other oversampling algorithms, respectively. Finally, these datasets were classified using Random Forest, Support Vector Machine, and K-Nearest Neighbor Classifiers. The results indicate that the algorithm proposed in this paper has higher accuracy, F-measure, G-mean, and AUC values, which can effectively improve the classification performance of the imbalanced datasets. Full article

In flooded soils, the concentrations of exchangeable Mn²⁺ and, especially, Fe²⁺ can be high and must be considered when determining the cation exchange capacity (CEC) of the soil under flooded conditions. However, these reduced forms of Mn and Fe are oxidized and precipitated during the extraction process used in traditional CEC methods. This procedure underestimates the exchangeable portion of these cations and, consequently, the CEC value of the flooded soil. We introduce a pH-gradient-based model to predict ECEC and exchangeable Fe²⁺ in flooded soils, circumventing oxidation artifacts inherent in conventional methods. The objective of this study is to propose an alternative to estimate the exchangeable Fe²⁺ and the effective CEC (ECEC) of flooded soils. To achieve this goal, 21 surface samples (0–20 cm) of soil from rice fields were collected and distributed in the cultivation regions of southern Brazil. The soils were flooded for 50 days. The soil solution was collected on the first day and after 50 days of flooding and pH, Na, K, Ca, Mg, Fe and Mn were determined. In these samples, exchangeable cations (K, Na, Ca, Mg, Mn, Al and H + Al) were determined to calculate ECEC and CEC at pH 7 of unflooded soil and after 50 days of flooding. There was a wide range of variation in the exchangeable cation contents among the soil samples. The K contents ranged from 0.12 to 0.54 cmol_c kg⁻¹, the Na contents from 0.00 to 1.18 cmol_c kg⁻¹, the Ca contents from 0.48 to 37.31 cmol_c kg⁻¹, the Mg contents from 0.10 to 15.53 cmol_c kg⁻¹, the Mn contents from 0.01 to 0.36 cmol_c kg⁻¹, the Al contents from 0.10 to 1.74 cmol_c kg⁻¹ and the H + Al contents from 2.01 to 8.42 cmol_c kg⁻¹. The results were used to develop models to predict ECEC and exchangeable Fe content after 50 days of flooding. Estimating the ECEC after flooding using the pH gradient before and after flooding yielded values closer to CEC pH 7.0, correcting for the possible underestimation of the ECEC during flooding. The amount of exchangeable Fe estimated was higher than the exchangeable Fe determined, correcting the possible underestimation of these quantities determined during flooding. It is concluded that the estimations of ECEC after flooding through the equation ${ECEC}_{after}=ECEC+\left({pH}_{sol.after}-{pH}_{sol.before}\right)\times {\displaystyle \frac{({CEC}_{pH7}-ECEC)}{(7-{pH}_{sol.before})}}$, where pH_sol.before is pre-flooding soil pH, pH_sol.after is after flooding pH, ECEC_after is effective CEC after flooding and the exchangeable Fe²⁺ after flooding through the equation ${Fe}_{exc.after.estimated}={ECEC}_{after}-\left(Ca+Mg+K+Na+Mn\right)$ where Fe_{exc.after.estimated} is estimated exchangeable Fe²⁺ after flooding corrected the problem of underestimating the values of these variables by analytical methods, demonstrating its viability for use in flood-prone soils. Full article

Background: The transition from the term non-alcoholic fatty liver disease (NAFLD) to steatotic liver disease (SLD), an umbrella term for several related conditions, offers benefits, particularly in identifying cardiometabolic risk factors more effectively. However, the impact of alcohol consumption on liver disease progression remains significant, leading to the recognition of a new entity: MetALD (metabolic dysfunction-associated steatotic liver disease with moderate alcohol intake). Aim: This study aimed to compare characteristics associated with liver disease progression in diabetic patients diagnosed with metabolic dysfunction-associated steatotic liver disease (MASLD) versus those with MetALD. Materials and Methods: In this prospective study, 286 diabetic patients were followed for 12 months. All patients underwent transient elastography (TE) and ultrasound to assess hepatic steatosis. Participants were classified into MASLD and MetALD groups. The performance of fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) were also evaluated. Results: MASLD was diagnosed in 58.2% (167 patients), of whom 4.9% (7 patients) had TE values suggestive for liver cirrhosis. Among those with MetALD, 17.6% (21 patients) had TE values compatible with advanced fibrosis. MASLD subjects presented a slight decrease in liver fibrosis values from 6.58 ± 2.27 kPa to 6.03 ± 1.57 kPa in the 12 months. On the contrary, MetALD subjects had an increase of liver stiffness measurements (LSM) values from 11.83 ± 6.27 kPa to 12.24 ± 8.66 kPa. Conclusions: in diabetic patients, the coexistence of moderate alcohol intake and cardiometabolic risk factors (MetALD) is associated with more advanced liver fibrosis and impaired long-term glycemic control, compared to MASLD alone. Full article

The International Agreement on the Carriage of Perishable Foodstuffs and on the Special Equipment to Be Used for Such Carriage (usually known as ATP Treaty) defines a standardized isothermal test for qualifying refrigerated containers, but its current protocol is lengthy, costly and lacks scientific justification. This paper presents a combined theoretical and experimental study aimed at optimizing this procedure. First, a heat-transfer framework based on transient conduction and thermal diffusivity is developed to estimate stabilization times using dimensionless criteria. Then, extensive experimental tests on ATP containers validate these predictions and reveal additional phenomena such as air leakage and chimney effects. Based on these findings, a revised protocol is proposed that reduces the test duration from more than 18 h to approximately 2 h while preserving the thermal stabilization conditions required by ATP. Experimental results show that the uncertainty in the determination of the global heat-transfer coefficient K is reduced from about 2–$2.3\%$ in the classical ATP procedure to roughly $0.7$–$1.0\%$ with the new protocol. In addition, the method suppresses secondary physical effects—such as chimney-driven air leakage and latent-heat losses due to water evaporation—thus improving the physical representativeness of the measured K value. The proposed accelerated protocol offers a scientifically grounded, cost-effective alternative for future ATP standards. Full article

The article presents a comparative analysis of mechanical properties of M8 threaded joints produced using three different methods, in rectangular nylon (PA 12) specimens manufactured in SLS technology. Threaded holes in specimens were made by direct thread printing (specimens marked PT), thread reinforcement with Helicoil inserts (HT), and the use of heat-set inserts (IT). The specimens were subjected to a tensile testing at a constant displacement rate of 2 mm/min. The maximum force and the displacement at failure were recorded. The results indicated that the lowest load-bearing capacity F_MF was observed in the printed thread specimens, with an average value of 3.41 kN. The use of heat-set inserts increased F_MF to 3.83 kN, representing a 12% improvement. The highest load-bearing capacity was achieved in specimens reinforced with Helicoil inserts, which enhanced joint strength by 40% compared to printed thread specimens, reaching an average F_MF of 4.78 kN. In all cases, failure occurred due to the thread or insert pull-out from the specimen material. Studies have shown that the use of metal inserts significantly enhances the strength of threaded joints in SLS-printed PA12 components. Helicoil inserts provide the highest F_MF load capacity, while heat-set inserts offer better technological advantages. Although printed threads are easier to manufacture, their applicability is limited to larger thread sizes and lower mechanical loads. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

Coarctation of the aorta is a localized narrowing of the aortic lumen. This pathology leads to hypertension in upper extremity vessels, left ventricular hypertrophy and to impaired perfusion of the abdominal cavity and lower extremities. Along with traditional diagnostic methods, mathematical modeling is used for risk assessment and the prediction of disease outcomes. However, when applying numerical models to describe hemodynamic parameters, the choice of turbulence model to describe swirling flow occurring in the aorta in this pathology must be justified. Thus, three turbulence models, namely k-ε, k-ω, and SST were analyzed for the description of swirling flows in the study of coarctation’s effect on hemodynamic parameters and analysis of the mechanisms leading to various cardiovascular diseases caused by altered hemodynamics. The results revealed significant differences in swirling flow patterns between the k-ε and k-ω models, while the k-ω and SST models showed consistent results over the cardiac cycle. In the peak systolic phase, average velocity rises to 1.07–1.98 m·s⁻¹ for the k-ε model, 0.82–2.12 m·s⁻¹ for the k-ω model, 1.22–2.12 m·s⁻¹ for the SST model and 0.8–2.12 m·s⁻¹ for laminar flow. WSS values increase rapidly to 11–22 Pa in k-ε, 25–50 Pa in k-ω and SST models of turbulence, and 30–55 Pa for laminar flow. Significant differences were also evident in the prediction of wall shear stress, with the k-ε model giving values more than twice as high as the k-ω and SST models. The data obtained confirm the necessity of careful model selection for accurate hemodynamic parameter estimation, especially in coarctation. The findings of this study can be used for further physics-informed neural network analysis of evaluation of treatment evaluations for congenital heart disease patients. Full article

This paper presents a unified framework for controllability and minimum-energy control of linear fractional differential systems with Caputo derivative order $\gamma \in (0,1)$ and fully time-varying state and control delays. An explicit mild solution representation is derived using the fractional fundamental matrix, and a new controllability Gramian is introduced. Using analytic properties of the matrix-valued Mittag-Leffler function, we prove a fractional Kalman-type theorem showing that bounded time-varying delays do not change the algebraic controllability structure determined by $(F,G,K)$. The minimum-energy control problem is solved in closed form through Hilbert space methods. Efficient numerical strategies and several examples—including delayed viscoelastic, neural, and robotic models—demonstrate practical applicability and computational feasibility. Full article

Bismuth–telluride-based alloys are excellent thermoelectric materials for Peltier modules and thermoelectric generators (TEGs). Owing to the emergence of the Internet of Things (IoT), the demand for sensors has increased considerably and self-power supplies to sensors using TEGs are garnering attention. To apply TEGs to IoT sensors, the thermoelectric materials used must be sufficiently small and thin while exhibiting high thermoelectric performance. In this study, Bi_0.5Sb_1.5Te₃ thin films were prepared using a pressure-gradient sputtering system. The obtained films exhibit a nanocrystalline structure with a significantly smooth surface and no preferred crystal orientation. Because the Bi_0.5Sb_1.5Te₃ thin films exhibit a high Seebeck coefficient and low thermal conductivity, the in-plane dimensionless figure of merit is 0.98, which is one of the highest values reported for thermoelectric materials measured near 300 K. Furthermore, the phonon mean-free path is 0.19 nm, as estimated using the 3ω method and nanoindentation. This value is significantly smaller than the average crystallite size of the thin film, thus indicating that phonon scattering occurs more frequently via ternary-alloy scattering inside the crystallites than via boundary scattering at the crystallite boundaries. The results of this study can advance thin-film TEGs as a source of self-sustaining power for IoT systems. Full article

Wind power forecasting is critical to renewable energy generation, as accurate predictions are essential for the efficient and reliable operation of power systems. However, wind power output is inherently unstable and is strongly affected by meteorological factors such as wind speed, wind direction, and atmospheric pressure. Weather conditions and wind power data are recorded by sensors installed in wind turbines, which may be damaged or malfunction during extreme or sudden weather events. Such failures can lead to inaccurate, incomplete, or missing data, thereby degrading data quality and, consequently, forecasting performance. To address these challenges, we propose a method that integrates a pre-trained large-scale language model (LLM) with the spatiotemporal characteristics of wind power networks, aiming to capture both meteorological variability and the complexity of wind farm terrain. Specifically, we design a spatiotemporal graph neural network based on multi-view maps as an encoder. The resulting embedded spatiotemporal map sequences are aligned with textual representations, concatenated with prompt embeddings, and then fed into a frozen LLM to predict future wind turbine power generation sequences. In addition, to mitigate anomalies and missing values caused by sensor malfunctions, we introduce a novel frequency-domain learning-based interpolation method that enhances data correlations and effectively reconstructs missing observations. Experiments conducted on real-world wind power datasets demonstrate that the proposed approach outperforms state-of-the-art methods, achieving root mean square errors of 17.776 kW and 50.029 kW for 24-h and 48-h forecasts, respectively. These results indicate substantial improvements in both accuracy and robustness, highlighting the strong practical potential of the proposed method for wind power forecasting in the renewable energy industry. Full article

Profiling the blood–brain barrier (BBB) permeability of bioactive molecules during early drug development is critical for optimizing their pharmacokinetic profile. The in vivo ability of a compound to cross the BBB is measured by the log BB parameter; however, its determination requires costly and time-consuming animal experiments. This study aimed to develop a novel in vitro method for high-throughput prediction of log BB values. The approach combines experimental data from open-tubular capillary electrochromatography (CEC) and automated potentiometric titrations, including the CEC retention factor (k′), electropherograms, and physicochemical parameters pK_a and log D_7.4. The k′ parameter reflects BBB permeability using a capillary internally coated with liposomes that mimic a biological membrane. Preliminary CEC analyses were conducted for 25 neutral drugs at pH 7.4, revealing a promising correlation between the permeability parameters log k and log BB. The validation was extended to 57 ionized drugs, with additional determination of pK_a and log D_7.4. A regression model was developed: log BB = −2.45 + 0.1k′ + 0.3logD_7.4 + 0.27pK_a (R² = 0.64). Furthermore, the analysis of CEC electropherograms enabled the machine learning-based rapid classification of compounds using Dynamic Time Warping, k-Nearest Neighbors, and the Bag-of-SFA-Symbols in Vector Space model, yielding an accuracy of 0.81 and an F1_weighted score of 0.8. Full article

In this study, the changes in the DNA native conformation induced by pH changes in the alkaline and acidic regions were examined. It was shown by the methods of low gradient viscometry and flow birefringence that protonation and deprotonation of nitrogen bases inside the double helix cause a change in the persistent length of DNA. The pK values shift with the change in the ionic strength of the solution (NaCl concentration). The additional charges appearing on the DNA bases are not shielded by counterions from the solution. The increase and decrease in the volume of the DNA coil in solution resulting from protonation and deprotonation of base pairs, respectively, are mainly determined by changes in the persistent length of the macromolecule. The stability of the double-helical conformation of DNA ensures the steadiness of the equilibrium rigidity of this macromolecule. The emergence of charges on the bases, resulting from DNA protonation or deprotonation, weakens and even disrupts the hydrogen bonds between complementary bases. However, at the first stage, this occurs without altering the stacking interactions of base pairs, as reflected in the absorption spectra of DNA and in the stability of the DNA persistent length at different pH levels. Full article

Background: Optical coherence tomography (OCT) can quantify the morphology and dimensions of a macular hole for diagnosis and treatment planning. Objective: The aim of this study was to perform automatic segmentation of macular holes (MHs) and cysts from OCT macular volumes using a deep learning-based model and to quantitatively evaluate decision reliability using the model’s focus regions and GradientSHAP-based explainability. Methods: In this study, we automatically segmented MHs and cysts in OCT images from the open-access OIMHS dataset. The dataset comprises 125 eyes from 119 patients and 3859 OCT B-scans. OCT B-scan slices were input to a UNet-48-based model with a 2.5D stacking strategy. Performance was evaluated using Dice and intersection-over-union (IoU), boundary accuracy was evaluated using the 95th-percentile Hausdorff distance (HD95), and calibration was evaluated using the expected calibration error (ECE). Explainability was quantified from GradientSHAP maps using lesion coverage and spatial focus metrics: Attribution Precision in Lesion (APILτ), which is the proportion of attributions (SHAP contributions) falling inside the lesion; Attribution Recall in Lesion (ARILτ), which is the proportion of the true lesion covered by the attributions; and leakage (Leakτ = 1 − APILτ), which is the proportion of attributions falling outside the lesion. Spatial focus was monitored using the center-of-mass distance (COM-dist), which is the Euclidean distance between the attribution center and the segmentation center. All metrics were calculated using the top τ% of the pixels with the highest SHAP values. SHAP features were clustered using PCA and k-means. Explanations were calculated using the clinical mask in ground truth (GT) mode and the model segmentation in prediction (Pred) mode. Results: The Dice/IoU values for holes and cysts were 0.94/0.91 and 0.87/0.81, respectively. Across lesion classes, HD95 = 6 px and ECE = 0.008, indicating good boundary accuracy and calibration. In GT mode (τ = 20), three regimes were observed: (i) retina-dominant: high ARIL (hole: 0.659; cyst: 0.654), high Leak (hole: 0.983; cyst: 0.988), and low COM-dist (hole: 7.84 px; cyst: 6.91 px), with the focus lying within the retina and largely confined to the retinal tissue; (ii) peri-lesional: highest ARIL (hole: 0.684; cyst: 0.719), relatively lower Leak (hole: 0.917; cyst: 0.940), and medium/high COM-dist (hole: 16.22 px; cyst: 10.17 px), with the focus located around the lesion; (iii) narrow-coverage: primarily seen for cysts in GT mode (ARIL: 0.494; Leak: 1.000; COM-dist: 52.02 px), with markedly reduced coverage. In Pred mode, the ARIL20 for holes increased in the retina-dominant cluster (0.758) and COM-dist decreased (6.24 px), indicating better agreement with the model segmentation. Conclusions: The model exhibited high accuracy and good calibration for MH and cyst segmentation in OCT images. Quantitative characterization of SHAP validated the model results. In the clinic, peri-lesion and narrow-coverage conditions are the key situations that require careful interpretation. Full article