Search Results (636)

The aim of this study was to comprehensively characterize lavender pellets produced from post-distillation residues and evaluate their multifunctional valorization potential. Physicochemical properties, including moisture, ash, heating value, organic matter, total and organic carbon, macro- and micronutrients, potentially toxic heavy metals, polyphenols, microbiological safety, and nutritive composition, were assessed. The pellets demonstrated an energy content comparable to other agricultural residues, with a higher heating value of 18,900 kJ/kg and a lower heating value of 16,603 kJ/kg. High organic matter (87%) and a slightly acidic pH support soil moisture retention, while favorable macronutrient levels enhance their suitability as a soil amendment. Water-based extractions (infusion and decoction) achieved higher yields (15.60–21.66%) than ethanol (13.04%) and more effectively recovered bioactive polyphenols, particularly rosmarinic and chlorogenic acids. Low moisture and water activity ensured storage stability and minimal microbial growth, which was confirmed by microbiological safety tests. Nutritionally, pellets contained moderate protein (9.38%), high cellulose (33.38%), and low fat (2.18%), with total amino acids of 8.91 g/100 g and 36.7% essential amino acids, along with a favorable fatty acid profile rich in polyunsaturated fractions. Overall, these findings highlight lavender pellets as a sustainable resource for energy, soil improvement, bioactive compound recovery, and complementary animal feed within circular economy frameworks. However, future research should focus on investigating whether residual compounds remain in lavender residues that could exert antifeedant or phytotoxic effects. Additionally, the potential for the sequential valorization of lavender residues should be explored, initially through the extraction of bioactive phenols, followed by pellet production for use as fuel or soil amendments. This approach would enable multiple cascading uses and maximize their contribution to comprehensive circular economy strategies. Full article

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector. Full article

Drought will likely become more frequent and intense in Europe due to climate change, which may worsen Mn²⁺ and P deficiencies found in high pH soils. In this context, research investigating the effectiveness of ammonium-based nitrogen fertilizers treated with nitrification inhibitors (NIs) in alleviating Mn²⁺ and P deficiencies in such soils has been done. However, studies considering the impact of drought periods and soil texture on this topic are lacking. Therefore, we carried out a study addressing this research gap. Maize plants were grown in a greenhouse experiment, and the experimental setup comprised three factors consisting of soil texture (sand and silt loam), soil moisture (sufficient and drought), and DMPP application (with and without DMPP). The measured variables were bulk and rhizosphere soil pH, Mn²⁺ availability, maize biomass yield, and shoot concentration of selected macro- and micronutrients. DMPP increased shoot biomass production by 60% in silt loam under drought but not in sand soil texture. In addition, DMPP increased Mn²⁺ and P shoot concentrations by 38% and 21%, respectively, in the silt loam soil texture under drought. In contrast, DMPP did not alleviate the negative impact of drought on plant biomass production, Mn²⁺ and P shoot concentration in the sand soil texture. In conclusion, DMPP application is effective in alleviating Mn²⁺ and P deprivation in high pH soil subjected to drought. However, this effect was soil texture-dependent and observed in the silt loam rather than in the sand soil texture. Full article

This study rigorously evaluates 13 Convolutional Neural Network (CNN) architectures for Thai dialect recognition. By treating Automatic Speech Recognition (ASR) as a computer vision texture classification task, we processed an extensive 840-h dataset from the Spoken Language Systems, Chulalongkorn University (SLSCU) corpus. Raw audio from four major dialects—Central, Northern (Khummuang), Northeastern (Korat), and Southern (Pat-tani)—was transformed into 2D Mel-spectrograms using the Short-Time Fourier Transform (STFT). We analyzed a diverse range of architectures, including the VGG, Inception, ResNet, DenseNet, and MobileNet families, to establish the optimal trade-off between mathematical complexity and spectral feature extraction. Our experimental results identify NASNet-Mobile as the most effective model, achieving a macro-average F1-score of 0.9425. The analysis suggests that NASNet’s search-optimized cell structure is uniquely capable of capturing the multiscale texture of phonetic formants. In contrast, we observed a catastrophic mode collapse in VGG16 (32.97% accuracy), likely due to excessive parameter bloat, while Xception and MobileNetV2 maintained robust generalization. Confusion matrix analysis reveals high acoustic distinctiveness for Southern Thai (96.7% recall), whereas Northern Thai exhibits significant spectral overlap with Central Thai. These results support the hypothesis that CNNs interpret spectrograms as textures rather than discrete objects, positioning NASNet-Mobile as a high-performance, low-latency baseline for edge-device deployment in resource-constrained environments. Full article

Cecina de León is a traditional Spanish dry-cured beef product whose surface, as in other similar meat products, becomes heavily colonised by fungi during ripening, raising concerns related to possible mycotoxin contamination. This study aimed to characterise the mycobiota associated with cecina and its production environment, with particular emphasis on mycotoxigenic Penicillium species. Seventy-eight cecina samples and 26 air samples were collected from meat-processing plants and local markets in the province of León (Spain) and analysed for fungal counts, water activity and pH. A total of 101 mould isolates and 16 yeasts were recovered, with Penicillium accounting for 88% of all moulds. Sixteen Penicillium species were identified using a polyphasic approach integrating macro- and micromorphological analysis, extrolite production, molecular markers (BenA, CaM and ITS), and MALDI-TOF MS. Mycotoxin screening by HPTLC and HPLC-PDA targeted cyclopiazonic acid, ochratoxin A, patulin, citrinin, griseofulvin and mycophenolic acid, revealing that 51% of the Penicillium isolates were mycotoxin producers, mainly P. commune. The proposed polyphasic strategy, including MALDI-TOF MS as a rapid complementary tool, offers a practical framework for the surveillance of fungal communities and mycotoxin risk in meat-processing environments. Full article

This work presents the synthesis, characterization, and application of zirconium oxide (ZrO₂)-based catalysts, modified with macro (silica nanospheres, NSP-SiO₂) and mesopore templates (Pluronic 123), impregnated with tungstophosphoric acid (TPA), in the catalytic pyrolysis of tomato agro-industrial residues. The NSP-SiO₂ (SXX) and P123 (PYY) amount mainly influences the ZrO₂SXXPYY-specific surface area (S_BET) and average pore diameter (D_p). ³¹P MAS NMR and FT-IR characterization results show that TPA (H₃PW₁₂O₄₀) was partially transformed into [P₂W₂₁O₇₁]⁶⁻ and [PW₁₁O₃₉]⁷⁻ during the synthesis steps. The acidic properties of ZrO₂SXXPYY samples containing 25 and 50 wt% of TPA (ZrO₂SXXPYYT25 and ZrO₂SXXPYYT50, respectively) are dependent on both the TPA content and the support nature. Bio-oil composition and product selectivity were strongly influenced by the textural and acid-based properties of the catalysts. Notably, non-catalytic pyrolysis favored pathways leading to C2 compounds, with a high content of acetic acid and hydroxyacetone. In contrast, the use of catalysts promoted the formation of higher molecular weight oxygenated compounds (C5–C6), specifically furans, aldehydes, and ketones. Full article

Expanded polystyrene (EPS) foam concrete is attractive for lightweight building applications, yet its practical use is often limited by weak EPS–cement interfacial bonding, which promotes interfacial debonding and crack propagation and thereby compromises mechanical performance. Although nano-SiO₂ (NS) has been reported to improve EPS–cement compatibility, the interfacial strengthening mechanism is still not fully clarified across scales, especially the molecular-level interactions that govern the formation of a robust interfacial transition zone (ITZ). Herein, EPS particles were modified with NS and a multi-scale framework (macro tests, micro-characterization, and molecular dynamics (MD) simulations) was employed to establish a mechanistic linkage between interfacial chemistry/structure and macroscopic performance. The results show that an optimal NS dosage of 9% (by cement mass) increases the 28-day compressive strength and flexural strength of EPS concrete by up to 18.3% and 11.2%, respectively, compared with the unmodified system. SEM, XRD, and FTIR collectively indicate a denser interfacial microstructure, increased hydration-product accumulation near the EPS surface, refined interfacial porosity, and the occurrence of condensation-related reactions involving NS. MD simulations further reveal that NS facilitates the formation of molecular bridges between EPS and C–S–H through hydrogen bonding and ionic interactions, which enhances interfacial adhesion and contributes to improved ITZ thermal stability. This study provides a cross-scale mechanistic understanding for designing high-performance EPS foam concrete via targeted interfacial engineering. MD simulations further suggest that NS enhances interfacial bonding by increasing the occurrence of hydrogen-bond networks and ionic associations at the EPS/C–S–H interface, as evidenced by the intensified interaction-related distributions and peaks in the simulation outputs. Full article

Sustainable management of vineyard residues through biochar production requires balancing carbon stability with agronomically relevant nutrient functionality. Pyrolysis temperature controls this trade-off by affecting carbon condensation and micronutrient availability. This study aimed to determine how pyrolysis temperatures (300 and 600 °C) govern this trade-off in vineyard-trimming biochars. The motivation focuses on optimizing carbon storage while maintaining micronutrient availability. Biochars were produced by slow pyrolysis at 300 and 600 °C for 1 h and characterized using proximate and elemental analyses, total macro- and micronutrient determination, and DTPA extraction to evaluate potentially bioavailable trace elements. The results showed that increasing temperature from 300 to 600 °C reduced yield (45.15 to 32.30%) and volatile matter (40.33 to 16.50%), while increasing fixed carbon from 55.37 to 77.33% and total carbon from 66.49 to 77.89%. Atomic ratios (H/C: 0.67 to 0.31; O/C: 0.32 to 0.18) confirmed enhanced carbon condensation at 600 °C. Regarding nutrients, although total Mn, Fe, Cu, and Zn concentrations declined at higher temperatures, their potentially bioavailable fractions (operationally defined as extractable with the chelating agent DTPA showed element-specific redistribution; Fe, Cu, and Zn extractability increased, while Mn decreased. These findings reveal a temperature-driven trade-off between carbon sequestration and micronutrient release. Full article

This study addresses the brittleness, poor bonding, and low crack resistance of ordinary Portland cement (OPC) grouting materials by incorporating an ethylene-vinyl acetate (EVA) copolymer. The enhancement mechanisms and engineering applicability of EVA-modified cement grouts were systematically investigated. Using EVA contents from 0% to 20%, macro-scale tests covering fluidity, rheology, bleeding rate, and compressive strength were conducted, along with microstructural analyses (SEM, XRD, FT-IR). Results indicate that with 12% EVA, the 28-day compressive strength reached 21.03 MPa, reflecting a 68% increase over the unmodified grout. Most favorable amount of EVA promoted the formation of C–S–H gel, filled microcracks, and enhanced structural densification, whereas excessive EVA content led to the formation of a polymer film that hindered hydration and reduced strength. Furthermore, EVA effectively improved the rheological behavior of the grout, with the Vipulanandan model demonstrating superior accuracy over the Bingham model in characterizing its non-Newtonian flow. This study systematically established a quantitative–qualitative correlation between EVA content, nonlinear rheological behavior (characterized by advanced models), microstructure evolution (porosity, C–S–H, polymer film) and final macromechanics and durability. Full article

Accurate engine fault classification with limited labeled data is critical for the safety and reliability of rotating machinery. This task is challenging because operating regimes are time-varying, and key variables must satisfy physical constraints, under which traditional feature classifier pipelines degrade and deep networks tend to overfit. We propose PhysGMM-MoE, a physics-aware Gaussian Mixture Model (GMM)-Mixture-of-Experts (MoE) framework for small-sample engine fault classification. At the data level, PhysGMM-MoE fits class-conditional, regime-aware GMMs and performs physically constrained, distance-based quality control to selectively augment minority classes while preserving engine operating semantics. At the model level, a heterogeneous pool of lightweight statistical experts and a lightweight Transformer-based deep expert (ECFT-Transformer) capture complementary neighborhood cues and high order multi-sensor correlations, and an L2-regularized logistic regression meta-learner fuses expert outputs via stacking. We evaluate fault classification on the 3500-DEFault diesel-engine dataset using the adopted eight-class cylinder-fault labeling (H, F1–F7) built from in-cylinder pressure statistics and torsional-vibration harmonics; although severity levels exist in the dataset, this study focuses on classification rather than severity estimation. With 40 training samples per class, PhysGMM-MoE achieves a mean accuracy of 0.9875, exceeding SMOTE+XGBoost by 0.0086, and attains the best macro precision/recall/F1 of 0.9878/0.9826/0.9889, demonstrating strong performance under the adopted small-sample setting. Full article

Long-term tillage management fundamentally reshapes soil’s physical and chemical environment, yet an integrated, predictive characterization of the distinct chemical signatures induced by no-tillage (NT) versus chisel tillage (CT) remains limited. We analyzed an eight-year dataset (2010–2017) from a long-term experiment in Iowa, USA, focusing on pH, available phosphorus (Bray1-P), and macro- and micronutrients (K, Ca, Mg, Cu, Fe, Zn) at two depths (0–5 and 5–15 cm). A convergent multi-method framework combined robust univariate statistics, multivariate ordination (PCA, PERMANOVA), linear mixed-effects models, and machine learning (Random Forest and Firth-penalized logistic regression). Results reveal a clear stratification–homogenization pattern. NT is associated with surface accumulation of Zn (+14%), Fe (+16%), and Cu (+5%), with mild acidification (−0.4 pH units) and high temporal stability. CT favored vertical nutrient redistribution, marked by subsurface K enrichment (up to 6% higher than NT), progressive alkalinization, and greater temporal variability. Predictive modeling highlighted subsurface K and surface Zn/Fe as key discriminators, with Firth regression confirming their complementary effects. These findings indicate that long-term NT and CT are associated with distinct, depth-specific chemical configurations—integrated systems defined by concentration gradients, temporal stability, and element covariation—rather than isolated element changes. This work provides a robust, quantitative framework for diagnosing soil management history and characterizing the pedochemical imprint of tillage. Full article

Despite global efforts to reduce landfill use for municipal waste, many sites remain active, and older closed sites still require management, particularly regarding leachate. Landfill leachate contains varying levels of organic and inorganic pollutants, generated through biological and physicochemical processes following water infiltration. Its complex composition—including COD, inorganic macro-components, heavy metals, and xenobiotics—necessitates effective treatment technologies to enable safe discharge into surface waters. This study compares low-cost, eco-sustainable adsorbents for the removal of ammonium, trace elements (Cd, Be, Fe, Cu, Ni, Pb, Cr, As, Sn, Sb, Se), and color (as an indirect measure of organic compounds) from urban landfill leachate. In more detail, six biochars from different biomass feedstocks and pyro-gasification conditions as well as natural chabazite and synthetic zeolite 13X (FAU-type) were investigated. After characterization, biochars were characterized and adsorption performance was assessed. Removal performance was comparatively evaluated after 24 h batch contact under fixed experimental conditions. Results showed that gasified biochars achieved high removal efficiency for metals and color but were ineffective for ammonium. Instead, both zeolites demonstrated efficient ammonium removal (~50%) but were less efficient for metals, reflecting the mechanism-driven selectivity of the adsorbents studied. Finally, a principal component analysis (PCA) revealed correlations between biochar physicochemical properties and contaminant retention, providing insight into key factors governing adsorption and informing the design of sustainable leachate treatment strategies. Full article

Maize silage can play a key role in policies aimed at stabilising local energy systems, as it constitutes a critical renewable feedstock for European biogas plants. By providing a dense and predictable source of chemical energy, it supports balance and reliability in the agricultural energy sector. To convert this potential into stable energy production, operators require kinetic models that translate routine silage quality indicators into concrete guidance for digester operation and control. Therefore, the aim of this article was to evaluate the batch kinetics of anaerobic digestion (AD) of maize silage and to select an adequate model for describing biochemical methane potential (BMP) profiles and associated energy recovery in the context of start-up, organic loading rate (OLR), hydraulic retention time (HRT) and feedstock preparation. Ten batches of silage (A–J) were examined, covering a realistic range of pH, electrical conductivity (EC), dry and volatile solids, ash, protein–fat–fibre fractions, fibre composition (NDF, ADF and ADL), derived fractions (hemicellulose, cellulose, and residual organic matter (OM)), C/N ratio and macro-/micronutrient profiles, including trace elements relevant to methanogenesis (Ni, Co, Mo, and Se). BMP tests were carried out in batch mode, and the resulting curves were fitted using the modified Gompertz and a first-order kinetic model. Methane yields of approx. 100–120 m³ CH₄/Mg fresh matter (FM) and 336–402 m³ CH₄/Mg volatile solids (VS), with CH₄ contents of 52–57% v/v, were typical for energy-grade maize silage. Kinetic and energetic behaviours were governed mainly by residual OM and hemicellulose (shortening the lag phase and increasing the maximum methane production rate), the ADL/cellulose ratio (controlling the slower hydrolytic tail), EC and Na/Cl/S (extending the lag phase), and C/N together with Ni/Co/Mo/Se (stabilising methanogenesis). The modified Gompertz model reproduced BMP curves with a pronounced lag phase and asymmetry more accurately (lower error and better information criterion values), and its parameters directly support start-up design, OLR ramp-up and energetic performance optimisation in bioenergy reactors. The novelty of this work lies in combining batch BMP tests, comparative kinetic modelling and detailed silage characterisation to establish quantitative links between kinetic parameters and routine maize silage quality indicators that are directly relevant for biogas plant operation and renewable energy production. Full article

Soil fertility is a critical factor for sustainable agricultural production, yet traditional soil analysis methods are often time-consuming and costly. As such, the aim of our research was to evaluate the potential of near-infrared (NIR) spectroscopy as a rapid, cost-effective, and non-destructive tool to assess soil fertility properties in agricultural soils. The soil samples were collected from the Quinta da Foz located in Benavente (Portugal) across agricultural regions including rice paddies, tomato fields, and Montado ecosystems. The sampling locations were guided by the Normalized Difference Vegetation Index (NDVI) to capture spatial variability, and soil analyses included near-infrared (NIR) spectral measurements and laboratory-based chemical determinations of soil fertility parameters (pH, electrical conductivity, total carbon and nitrogen, organic matter, macro- and micronutrients) as well as multiple soil carbon fractions. Two predictive modeling approaches (Random Forest (RF) and Partial Least Squares Regression (PLSR)) were developed to estimate soil chemical properties from spectral data. The RF models consistently outperformed PLSR, achieving high accuracy (R² = 0.85) for nutrients such as Mg, Fe, Ni, Ca, and Na and organic matter. A moderate predictive performance (R² between 0.70 and 0.80) was observed for different elements, namely, K and Mn. On the other hand, P, S, Zn, electrical conductivity, pH, total N, and various carbon fractions were poorly predicted. The spatial interpolation of predicted values enabled the generation of soil fertility maps that informed site-specific nutrient management. The results indicate that NIR spectroscopy combined with robust modeling offers a promising approach for rapid spatial assessment of selected soil nutrients, supporting precision agriculture and sustainable land management. Full article

With the exploitation of tight sandstone gas, the corrosion problem of wellbores in the X block of the Ordos Basin has become increasingly severe, necessitating the implementation of effective measures to mitigate tubing corrosion and enhance corrosion inhibition efficiency. This study conducted field corrosion monitoring in conjunction with laboratory experiments, employing weight loss method, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) to comprehensively characterize the corrosion of gas wells from both macro and micro perspectives. The results show that the gas wells in the X block of the Ordos Basin are exposed to a complex corrosion environment, where the electrochemical corrosion risk in the aqueous phase and the acidic gas corrosion risk in the gas phase coexist, posing a potential threat to wellbore integrity. Corrosion in X-1 and X-2 wells is mainly attributed to CO₂, while corrosion in X-3 well is primarily caused by sulfides. The field application of corrosion inhibitor M exhibited significant corrosion inhibition effects on steels, with the best performance at a dosage of 2000 mg/L. Based on experimental data, a corrosion inhibitor dosage prediction model for the X block gas wells was constructed. By increasing the dosing frequency and reducing the dosing concentration, the optimized dosing scheme can annually save approximately 566.4 L of corrosion inhibitor per well, providing a scientific basis for extending the service life of the gas well tubing. Given the prevalence of CO₂- and H₂S-induced corrosion in many global reservoirs, these findings provide valuable insights for corrosion management in similar international oil and gas fields, enhancing the broader applicability of the study. Full article