Search Results (454)

Anaerobic digestion of municipal wastewater sludge is often limited by slow hydrolysis rates. This study evaluated the effects of ozone pretreatment on methane production during mesophilic batch digestion. Ozone was applied at 0–10% for 30–90 s, with inoculum-to-substrate ratios of 1.0–2.0. Methane production was monitored using the AMPTS II system. The maximum methane yield (736 NmL CH₄ g⁻¹ VS; 1381 NmL total) was obtained at 10% ozone for 30 s and I/S = 1.5. Kinetic modelling showed enhanced methane production rates and reduced lag phases, with the Gompertz and Logistic models providing the best fit. Full article

In this paper, we propose two novel defective survival models within the Gamma–G family: the defective Gamma–Gompertz and the defective Gamma–Dagum distributions. In contrast to the corresponding Gamma–G mixture cure formulation, in which the Gamma–G distributional parameters are combined with an explicit cure fraction mixing parameter, the proposed defective formulation induces the cure fraction through the limiting behavior of the survival function. Thus, within the same Gamma–G baseline structure, the model avoids introducing an additional cure fraction parameter. The motivation for these new models lies in the limited set of defective distributions currently available, despite the increasing demand for flexible cure rate models in biomedical applications. By extending the defective property to the Gamma–G construction, our approach fills this methodological gap while providing models that are both interpretable and computationally efficient. We show that the Gamma–G construction preserves defectiveness whenever the baseline distribution is defective, thus establishing a coherent theoretical foundation. Both models allow covariate effects through regression structures on shape, scale, and, in the case of the Gamma–Dagum distribution, on the cure fraction parameter, resulting in flexible and interpretable specifications. Parameters are estimated via maximum likelihood, and an extensive Monte Carlo study confirms estimator consistency and accurate coverage in finite samples. The practical relevance of the models is illustrated with two large clinical datasets on melanoma and cervical cancer from the São Paulo Cancer Registry. Results reveal that the proposed models provide competitive goodness-of-fit and offer useful insights into long-term survival compared to traditional cure rate approaches. Overall, this work introduces a unifying and flexible framework for defective survival models, extending their applicability and delivering practical improvements over existing cure models. Full article

The Gompertz distribution is a well-known lifetime model in survival and reliability analysis, but its hazard rate is restricted to monotone increasing behavior, which limits its applicability to more complex data structures. In this study, we investigate the New Extended Gompertz (NEG) distribution, which is obtained by applying the existing NE-X generator framework to the classical Gompertz baseline distribution. Thus, the NEG model is a special case within an already established generator family rather than an entirely new family of distributions. The main contribution of this paper is not the introduction of a new generator, but rather a comprehensive and systematic investigation of this particular Gompertz-based extension, including its statistical properties, estimation procedures, and practical applications. The proposed model introduces an additional shape parameter that provides increased flexibility in modeling skewness, tail behavior, and hazard-rate structures, allowing for increasing, decreasing, bathtub-shaped, and unimodal hazard patterns under different parameter configurations. Several mathematical properties of the NEG distribution are derived, including explicit expressions for the density, distribution, survival, and hazard-rate functions, as well as moments, entropy measures, and series representations. Parameter estimation is performed using both maximum likelihood and Bayesian approaches, with numerical optimization and Metropolis–Hastings MCMC procedures employed due to the absence of closed-form estimators. The finite-sample behavior of the estimators is investigated through extensive Monte Carlo simulation studies under three different parameter settings. The practical usefulness of the NEG distribution is illustrated using two real datasets on carbon-fiber tensile strength. Comparative results with several competing Gompertz-type models indicate that the NEG distribution provides competitive performance. However, all comparisons should be interpreted within the context of the considered datasets and parameter settings, rather than as claims of universal superiority. The findings suggest that the NEG distribution offers a flexible and practical extension of the Gompertz model for lifetime data analysis. Full article

The canopy closure stage is a critical phase of rice (Oryza sativa L.) development that influences canopy structure and final grain yield. Accurate and continuous monitoring of canopy closure dynamics is therefore essential for variety screening and cultivation optimization. This study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics. UAV RGB images were acquired for 198 hybrid rice varieties during early growth stages and used to build a canopy segmentation dataset. Three semantic segmentation models, i.e., DeepLabv3+, U-Net, and PSPNet, were systematically evaluated. Results show that DeepLabv3+ performed the best and enabled precise extraction of rice canopy features, obtaining a mean intersection over union (mIoU) of 0.86. Based on the extracted canopy coverage, the Gompertz model was utilized to characterize temporal canopy closure trajectories for all varieties, achieving an average R² of 0.978. Subsequently, five key dynamic indicators were derived, including canopy closure limit value (K), initial growth coefficient (a), growth rate coefficient (b), maximum instantaneous growth rate (MGR), and days to maximum growth rate (Tm). K-means clustering analysis was performed on these indicators to categorize all rice varieties into three clusters, disclosing pronounced differences in early-stage canopy development characteristics. Correlation analysis further demonstrated that canopy closure dynamics were closely associated with grain yield. Overall, while acknowledging the limitations of a single-season and single-site dataset, this study provides a scalable and objective framework for quantifying rice canopy closure dynamics, offering valuable support for variety selection, cultivation optimization, and high-yield rice production. Full article

Background: Missing data, particularly progression-driven dropout, introduces substantial bias in longitudinal oncology studies, directly impacting response classification based on RECIST criteria. While machine learning-based imputation methods are increasingly used, their performance is rarely evaluated in a clinically interpretable framework centered on patient-level endpoints such as Best Overall Response (BOR). Methods: We propose a clinically grounded evaluation framework based on RECIST 1.1 focused on patient-level Best Overall Response classification. Longitudinal tumor trajectories were simulated for 270 patients (1:1, HER2+ and HER2−) across nine follow-up visits using both Gompertz and Stein–Fojo growth models, resulting in 2700 patient-visit observations. Realistic missingness was introduced through a combination of random mechanisms and progression-driven dropout. Three machine learning imputation models, long short-term memory (LSTM), MissForest, and Multiple Imputation (MI) were evaluated under both direct (MAR-based) and non-responder imputation strategies. Performance was assessed using BOR classification metrics, including accuracy and Cohen’s kappa. Result: Across both simulation frameworks, imputation substantially improved BOR classification performance. Under the Gompertz model, accuracy increased from 0.84–0.89 with direct imputation to 0.94–0.99 with non-responder imputation, with corresponding kappa improvements from 0.73–0.82 to 0.90–0.99. Similar trends were observed under the Stein–Fojo model (accuracy: 0.82–0.84 vs. 0.91–0.96; kappa: 0.69–0.72 vs. 0.86–0.94). Across all evaluated methods, NRI improved classification performance by approximately 10 percentage points in accuracy and up to 17 percentage points in kappa. The improvement was observed consistently across both tumor growth models and different missingness scenarios, demonstrating the robustness of the findings. Conclusions: This study demonstrates that successful handling of missing data depends not only on the imputation method itself, but also on the choice of a clinically meaningful endpoint and appropriate estimand strategies aligned with the underlying missing data assumptions. In the METABRIC-derived simulations, clinically informed handling of progression-related missingness substantially improved RECIST-based BOR classification across all evaluated methods, suggesting that appropriate endpoint selection and the corresponding estimand strategy for missing data handling may have a greater influence on classification performance than the choice among the imputation models applied. Full article

Internet of things long range (IoT/LoRa) devices emit radiofrequency electromagnetic fields (RF-EMF), ensuring long-range, low-power communication, and their use in precision agriculture continuously expands. Thus, the interest in the impact of low-intensity but long-term EMF exposure on plants has increased. In this study, maize plants were exposed to 868 MHz, 10 mW EMF for the first 28 days of their development with soil-buried antennas. Plants were divided into three groups: Control, Sham-exposed, and EMF-exposed. Biological effects were followed on morphological, physiological, and biochemical levels every week. The plant height values were fitted to a Gompertz function modeling the growth. The results showed slightly faster early development of EMF-exposed plants in about 21 days. The relative dry-leaf biomass from EMF-affected plants was a bit higher than in the Control and Sham groups until day 21. Chlorophyll fluorescence analysis (JIP-test) indicated photosynthetic stability. Antioxidant enzyme activity, antioxidant capacity, content of malondialdehyde, hydrogen peroxide, and reducing sugars were measured, and principal component analysis was done for all parameters. Overall, the developmental stage accounts for most of the observed variations in the data rather than EMF exposure. The results suggest that under the tested conditions, IoT/LoRa-emitted EMF did not provoke adverse effects in maize and acted as a modest modulator of physiological functions. Full article

In recent decades, conservation of local poultry breeds has gained relevance to preserve genetic resources adapted to low-input systems and to enhance their valorization. This study addresses a key knowledge gap by providing a comprehensive characterization of the endangered Murciana chicken breed, native to southeastern Spain. We jointly evaluate recent population dynamics, conservation framework, morphology and morphometrics, growth patterns, and reproductive and productive traits. Data includes census and pedigree records, standardized morphological assessments, growth modeling, and production data from the conservation nucleus. The population increased from fewer than 150 registered animals in 2017 to more than 550 in 2024, indicating stabilization. The breed showed characteristics of slow-growing dual-purpose Mediterranean genotypes, with marked sexual dimorphism, Gompertz relative growth rates of 0.020 d⁻¹ (males) and 0.023 d⁻¹ (females), and adult weights of 3.2 kg and 2.4 kg, respectively. Carcass yield was moderate (61.9%), with higher leg (36.7%) than breast proportion (16.9%). Reproductive (fertility 88.6%, hatchability 80.6%) and laying performance (116.6 eggs/hen/year) were consistent with local extensive systems. These results provide a robust baseline to support conservation, genetic management, and sustainable use of the Murciana chicken breed, contributing to its long-term preservation and valorization. Full article

Anaerobic digestion (AD) of agro-industrial residues supports the green energy transition by converting organic matter into renewable biogas. Sugar beet pulp is a highly fermentable feedstock, although its process response may vary with chemical composition. This study examined how chemical composition affects mesophilic biogas-production kinetics of sugar beet pulp prepared under laboratory conditions from surplus sugar beet roots. The roots represented ten sugar beet varieties (A–J), and the prepared pulp was characterised for pH, dry matter, organic dry matter, mineral composition, and the relative shares of simple sugars, polysaccharides, protein, and fibre. Batch digestion tests were performed at 39 °C for 30 days. Production curves were analysed using complementary kinetic models (modified Gompertz and a two-fraction first-order model) to capture the lag phase and the contributions of rapidly and slowly degradable substrate pools. Biogas yields ranged from 126 to 141 m³ Mg⁻¹ fresh matter with 50–55% CH₄, corresponding to 64.3–76.1 m³ CH₄ Mg⁻¹ organic dry matter, while organic matter conversion reached 71.2–82.4%. Varieties enriched in simple sugars exhibited a higher share of the fast-degradable fraction and shorter lag phases, indicating faster onset and stronger methane formation. In contrast, higher fibre contents reduced the slow-fraction rate constant and lowered overall conversion, consistent with hydrolysis-limited degradation of the structural carbohydrate matrix. The mineral ion background, particularly K and Na, indicated moderate ionic buffering and stable operation without inhibition. The novelty of this work lies in integrating detailed compositional profiling with dual kinetic modelling to translate chemical fingerprints into tentative process-relevant implications. These implications include feeding strategy, organic loading control and hydraulic retention time selection, and they require further validation in continuous or semi-continuous AD systems. Full article

This study presents a new version of the ZLindly (ZL) model that improves modeling flexibility while maintaining ease of analysis, allowing for the simultaneous accommodation of redundant zeros, thick-tailed behavior, and complex failure rate dynamics within a unified probabilistic framework. Marshall–Olkin (MO) theory facilitates this advancement. The MOZL hazard rate can exhibit several patterns, including increasing, decreasing, bathtub, or upside-down bathtub-shaped. These features enable the model to capture diverse reliability phenomena such as early-life failures, random shocks, and wear-out effects. Comprehensive theoretical investigations were conducted and shown to be governed by an interpretable dual-parameter mechanism, where the Marshall–Olkin parameter controls tail behavior and dispersion, while the scale parameter regulates skewness and hazard evolution. A likelihood-based approach was developed under Type-II censoring conditions, and rigorous evidence is provided for the existence and uniqueness. To address inferential uncertainty, both classical asymptotic confidence intervals and log-normal approximations were constructed. Within a Bayesian framework, independent gamma priors were assumed, and posterior inference was performed via an efficient Metropolis–Hastings algorithm. Bayesian point and credible estimators were obtained and compared with their classical counterparts. An extensive simulation study demonstrates that Bayesian estimators, particularly with informative priors, consistently outperform likelihood-based estimators in terms of bias, mean squared error, interval length, and coverage probability, especially for moderate sample sizes and higher censoring levels. Three engineering applications are provided to assess the practical utility of the MOZL model, where it provides superior goodness-of-fit relative to 15 competing models, including MO–Exponential, MO–Gompertz, MO–Nadarajah–Haghighi, MO–Exponentiated Weibull, and Birnbaum–Saunders, among others. Overall, the proposed MOZL distribution emerges as a flexible, interpretable, and computationally efficient lifetime model whose structurally meaningful parameter interactions enhance distributional balance and flexible hazard behavior, thereby contributing to modern symmetry-oriented distribution theory while offering valuable applications in reliability engineering, survival analysis, and applied statistical modeling. Full article

Systematic spatiotemporal records of the International Roughness Index (IRI) for South American Andean rural corridors remain scarce, and available deterioration models, calibrated mostly under temperate or arid conditions, transfer to Andean tropical contexts with considerable uncertainty. This exploratory baseline study addresses that gap on the 36.50 km Loja–Catamayo corridor in southern Ecuador under three a priori constraints: eleven IRI campaigns, one meteorological station whose record starts ten months after the first campaign, and a traffic series anchored on a base-year count conducted ten years before the monitoring window. The campaigns, conducted with a Roughometer III between 2023 and 2025, were integrated with daily climate records from the INAMHI Villonaco station, a yearly AADT series cross-validated against a contemporary classified count, and the as-designed pavement structural section. The non-parametric framework combined the Mann–Kendall trend test with a 25-cell Antecedent Moisture Index sensitivity grid, AASHTO 1993 Structural Number computation, Sayers-derived Present Serviceability Index, and linear, exponential, and Gompertz modelling. The results revealed a statistically significant positive monotonic trend robust to post-peak truncation (H₁ supported) and no detectable short-term climate–IRI association under any of the twenty-five AMI specifications tested (H₂ not supported at the available resolution). The corridor exhibits a structural reserve exceeding projected cumulative ESAL demand by an order of magnitude yet reached the functional intervention threshold at one-third of its design service life. This decoupling between structural adequacy and functional decay locates the dominant deterioration mechanism in the bituminous surface and the drainage regime, supporting surface preservation interventions as the operationally appropriate response. Full article

The circular valorization of biomass for sustainable energy recovery is a strategic priority in the transition toward low-carbon systems. In the last decade, anaerobic digestion (AD) has emerged as an efficient technology to produce an energetic vector to replace natural gas with biomethane and reduce waste; however, the hydrolysis of refractory fractions remains the main rate-limiting step. This study investigates an innovative electro-assisted pretreatment of biomass to promote the first rate-limiting hydrolysis step of refractory compounds in biomethane production. Lignocellulosic residues are employed not only as feedstock for the AD process but also as substrates in electrohydrolysis (EH) pretreatment using an Ir-Ta mixed metal oxide (MMO) anode coupled with advanced biomass-derived carbon felt cathodes. Two cathodes were functionalized with Phragmites Australis (PhA) hydrochars, untreated (PA) and KOH-activated (PA-KOH), to enhance the in situ generation of reactive oxygen species (ROS). Brassica napus (Bn) was chosen as the other biomass selected as a feedstock of AD, and was subjected to EH at varying energy inputs (500–5000 kJ kg⁻¹), evaluating structural and biochemical shifts. The results demonstrate that EH effectively modifies the biomass matrix; the PA-KOH-CF cathode exhibited good selectivity to degrade lignocellulosic structures, but higher biomethane production was achieved at 2500 kJ·kg⁻¹ TS using PA-CF, reaching an increase of 52% compared with untreated samples. Kinetic analysis of the biomethane potential was performed using the modified Gompertz model. The model accurately captured the asymmetric sigmoidal transitions of methane production with different electrode configurations, and finally, energy balance assessment identified 2500 kJ·kg⁻¹ TS as the optimal operational threshold. These findings suggest that an excess of applied energy is critical to the availability of soluble organic matter and the presence of refractory compounds that reduce efficiency. This electro-assisted approach offers a robust strategy for intensifying AD, aligning with circular bioenergy objectives. Full article

The present study aimed to characterize the vegetative growth and phenology of hop cultivars grown in successive seasons with artificial supplementation in a subtropical region. The experiment was conducted in Palotina, Paraná, Brazil (24° S) during the summer 2023–2024, winter 2024, and fall 2024–2025 growing seasons. LED lamps were used to extend the daily photoperiod to 17 h during the vegetative phase. The following hop cultivars were assessed: (a) Alpharoma; (b) Cascade; (c) Chinook; (d) Comet; (e) Dr. Rudi; (f) Hallertau Magnum; (g) Hallertau Mittelfruher; (h) Nugget; (i) Saaz; (j) Smooth; (k) Sorachi Ace; (l) Southern Cross; (m) Triple Pearl; (n) Yakima Gold; (o) Zeus. The assessed variables included plant height (Ht), hop growth rate (HGR), classification of four growth stages, number of lateral shoots, plant fresh mass, and phenology. Ht and HGR were analyzed by means of Gompertz and Gaussian regression models, respectively. The number of lateral shoots per plant and fresh mass were subjected to analysis of variance (ANOVA), and means were grouped using the Scott-Knott test (p < 0.01). Seasonal temperature fluctuations, associated with advancing age and plant establishment throughout successive cycles, acted as important modulating factors in vegetative growth and phenology. In the summer season (2023–2024), Cascade and Hallertau Magnum were characterized as early cultivars. In the winter season (2024), Chinook, Nugget, Saaz, and Zeus were classified as early cultivars, while in the fall season (2024–2025), Dr. Rudi, Sorachi Ace, and Zeus were also considered early hops. The vegetative growth Stage I was found to be critical for earliness classification. The phenological cycle variability was amplified during seasons with higher temperatures. The ‘Sorachi Ace’, ‘Triple Pearl’, and ‘Zeus’ hops were the only ones capable of completing the phenological cycle in all three harvest seasons, with ‘Sorachi Ace’ standing out due to its uniform, stable growth pattern regardless of the season. It is concluded that successive hop cultivation is technically viable for specific hop cultivars grown under subtropical conditions with supplemental lighting. Full article

This study aimed to model the growth trajectories of Denizli chickens under different production systems and to identify the most appropriate nonlinear growth function within a Bayesian framework. A total of 156 birds were monitored weekly from hatch to 26 weeks of age under conventional cage, conventional floor, and enriched floor systems. Eight candidate nonlinear growth models were evaluated using Bayesian model comparison criteria, including leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC). Among the evaluated models, the Gompertz function showed the best predictive performance, with the lowest LOOIC (225.16) and superior predictive accuracy across fit statistics. The selected model was subsequently extended to a Bayesian nonlinear mixed modelling framework to evaluate the effects of sex and production system on growth dynamics while accounting for between-animal variability. Males exhibited substantially higher asymptotic weights than females, whereas females showed faster early growth and earlier stabilization. Birds reared under the conventional floor system, particularly males, exhibited the highest asymptotic growth potential and later inflection ages, indicating a more prolonged growth phase. In contrast, enriched systems appeared to have promoted greater variability in growth responses, possibly due to increased behavioral activity and energy expenditure. The findings demonstrated that production system and sex jointly influenced both the scale and timing of growth in Denizli chickens. Beyond statistical model comparison, the Bayesian nonlinear mixed modelling approach provided biologically meaningful information that could support breeding, housing, and management decisions for indigenous and dual-purpose poultry production systems. Full article

The urgent need to reduce greenhouse gas emissions has driven the search for sustainable alternatives to fossil fuels. In this context, cocoa residues emerge as a promising feedstock for bioethanol production. This study evaluated the influence of a catalytic biorefinery treatment on the bioethanol production potential from cocoa pod husks. Both raw and catalytically treated biomass were characterized using SEM, pore size distribution analysis, and TGA. Subsequently, enzymatic hydrolysis was performed using various cellulase and hemicellulase loadings, followed by anaerobic fermentation with Saccharomyces cerevisiae. Bioethanol production was modeled using the modified Gompertz equation. The results evidenced changes in the structure and composition of the lignocellulosic matrix following catalytic treatment, increasing surface area and reducing hemicellulose content. Although total sugar release during hydrolysis was comparable between the two samples, the biomass processed via the catalytic biorefinery promoted higher sugar consumption and bioethanol concentration, reaching 3.36 g/L with a yield of 112 g kg⁻¹ of dry biomass. The kinetic model showed a strong fit (R² between 0.94 and 0.97). These findings demonstrate that the integration of catalytic biorefinery, enzymatic hydrolysis, and fermentation constitutes a viable alternative for the valorization of cocoa residues. Full article

This study evaluates the oscillatory frequency and amplitude in an oscillatory flow reactor with smooth periodic constrictions (OFR-SPC) for the cultivation and harvesting of Chlorella vulgaris fed with an air stream with 5% (v/v) CO₂. Their effect on biomass productivity, CO₂ capture, nutrient removal, and sedimentation kinetics was assessed. Cultures were tested at frequencies of 0.5–2.5 Hz and amplitudes of 6–18 mm. At 2.5 Hz|6 mm, the system achieved the maximum biomass concentration (592 mg_DW L⁻¹), productivity (5.36 mg_DW L⁻¹ h⁻¹), and CO₂ fixation (8.34 mg L⁻¹ h⁻¹) as well as complete nitrogen removal and near-complete phosphorus removal (100% and 91%, respectively). Complete sedimentation occurred at 0.5 Hz|6 mm, with kinetics described by the Gompertz model (k = 4.60 h⁻¹), confirming the feasibility of low-cost biomass recovery. Additionally, zeta potential positively influenced sedimentation but negatively affected productivity. Statistical analyses confirmed oscillation frequency and amplitude as key factors, establishing the OFR-SPC as a promising technology for microalgae-based efficient CO₂ capture, nutrient removal, and low-cost biomass harvesting. Full article