Search Results (429)

Fish processing and aquaculture wastes are protein- and lipid-rich by-products that can be used to produce renewable energy. However, evidence on their anaerobic valorization is limited. This bibliometric and systematic review maps the field and synthesizes evidence on the anaerobic digestion and dark fermentation of fish-derived waste. Scopus records from 2000 to 2025 were screened according to the PRISMA 2020 guidelines. A total of 164 articles comprised the bibliometric corpus and 120 research articles informed the qualitative synthesis. The annual publication growth rate was 13.29%, with 65% of publications occurring between 2019 and 2025. Most described experiments employed laboratory-scale batch assays, mesophilic conditions and co-digestion. Methane yields from fish offal, silage and recirculating aquaculture system sludge ranged from 48 to 1174 mL CH₄/g VS. These differences reflect variations in feedstock composition and preparation, proportion of fish waste, selection of co-substrates and operating conditions. The process performance was mainly constrained by ammonia, volatile fatty acids and long-chain fatty acids. The modified Gompertz model predominated, whereas multi-step dynamic modeling remained rare. Microbial studies, primarily 16S rRNA gene surveys conducted in a batch-based manner, linked fish waste digestion to bacteria that degrade proteins and lipids, as well as hydrogenotrophic methanogens. However, community responses depended on the composition of the feedstock, inoculum and loading rate. Only three dark fermentation studies were identified, two of which used fish-derived substrates. Overall, progress toward industrial implementation requires fraction-specific characterization, validation in continuous systems, integration of hydrogen and methane production, dynamic modeling, multi-omics, digestate-safety assessment and integrated techno-economic and life cycle assessment based on pilot- and industrial-scale data. To assess potential database coverage bias, the search was repeated in Scopus and an equivalent search was run in Web of Science. Five additional eligible studies were identified. Full article

Floricultural residues represent a substantial and largely unvalorised organic stream in Europe, yet their anaerobic digestion has received limited attention. This study benchmarked the biochemical methane potential and degradation kinetics of Gerbera spp. flower residues (FRs), non-marketable apples (AW) and pig slurry (PS) under mesophilic batch conditions in mono-digestion and in five co-digestion mixtures. Mono-digestion yielded 362.5 ± 4.1 mL_CH4·g_VS⁻¹ for FR, constrained by lignocellulosic recalcitrance and a high extractive content, and 320.9 ± 39.2 mL_CH4·g_VS⁻¹ for AW, whose dispersion reflects the heterogeneity of non-marketable fruit; PS yielded 437.7 ± 2.6 mL_CH4·g_VS⁻¹ and supplied the alkalinity that both plant residues lacked. The highest yield was obtained for the ternary mixture COAD_5 (0.30 PS, 0.45 AW, 0.25 FR on a VS basis), reaching 472.8 ± 8.6 mL_CH4·g_VS⁻¹. This corresponds to a 29.1% increase over the theoretical additive potential (synergy index: 1.29 ± 0.07) and a gain of 8.0% over pig slurry mono-digestion. Once uncertainties were propagated, demonstrable synergy was confined to COAD_5 and to the binary mixture COAD_4. All treatments remained stable, with final pH between 7.40 and 7.86 and VFA-to-alkalinity ratios below 0.11. Kinetic modelling distinguished two regimes: first-order behaviour for the hydrolysis-limited and slurry-dominated substrates, and Modified Gompertz behaviour for the sugar-rich mixtures. Applied to the project site, these yields correspond to a preliminary gross scenario of the order of 481,000 Nm³ CH₄·yr⁻¹. The results establish that floricultural residues can be integrated into livestock-based anaerobic digestion without compromising process stability. Full article

This research proposes an approach based on a circular economy principle for the integral valorization of Sargassum from the Mexican Caribbean. The biomass was characterized through proximal and elemental analyses, and then an oxidative pretreatment was carried out to enhance a sequential processing scheme to extract sodium alginate and use the solid waste as a substrate for biogas production via anaerobic digestion. The oxidative pretreatment successfully reduces the recalcitrant content and the concentration of heavy metals. The sodium alginate extracted from treated biomass achieves a yield higher than 20%; the characterization of the polymer via nuclear magnetic resonance showed that the mannuronic-to-guluronic ratio was between 0.34 and 0.62, indicating the potential for its use for environmental and biomedical applications. The highest yield in methane production was 328 mL CH₄/g of volatile solids, with a purity of 90%, and was achieved using the waste from alginate extraction with an inoculum-to-substrate ratio of 1:1. The experimental data presented an excellent fit to a Gompertz model (R² > 0.99). The proposed valorization pathway improves the sustainability of Sargassum management, prioritizing the recovery of high-value compounds before energy production. This circular approach provides a framework for converting environmental challenges into opportunities in the Caribbean. Full article

Body measurements provide a non-invasive basis for estimating live weight, yet their predictive value and age-related variation remain poorly characterized in female Jiangyue donkeys. Body weight (BW) and 11 morphometric traits were recorded in 484 females: withers height (WH), body length (BL), chest circumference (CC), cannon circumference (CAC), head length (HL), neck length (NL), chest width (CW), chest depth (CD), rump height (RH), rump length (RL), and rump width (RW). Descriptive statistics, correlation analyses, and regression analyses were performed using SPSS 27.0; stepwise regression was then applied to derive and validate BW prediction equations. Additionally, four nonlinear functions—Logistic, Gompertz, Brody, and von Bertalanffy—were fitted to cross-sectional BW data from 241 females sampled at different ages to characterize the population-level relationship between age and weight. BW was positively correlated with all morphometric traits (p < 0.01), with CC showing the strongest association. The optimal equations explained 91.8% and 84.4% of the variation in BW among growing and adult donkeys, respectively (both p < 0.01). Of the four nonlinear functions evaluated, the Brody model provided the closest fit (R² = 0.99960) and yielded an estimated asymptotic mature weight of 191.049 kg. Because the age–weight analysis was based on cross-sectional observations, the resulting curve represents population-level variation rather than within-animal growth trajectories. Collectively, these models offer quantitative tools for live-weight estimation, growth assessment, and herd management in female Jiangyue donkeys. Full article

Interval censoring commonly arises in clinical trials, screening studies, and longitudinal medical investigations in which event status is assessed only at scheduled examination times. Rank-based procedures provide flexible tools for comparing interval-censored (IC) event-time distributions, but inference is usually based on first-order normal approximations that may be inaccurate in small or moderately sized samples and under substantial censoring. We develop a saddlepoint approximation (SPA) to the conditional permutation distribution of a linear rank statistic derived from the proportional reversed-hazard model with IC data. Conditional on the observed group size, the permutation distribution is represented through a bivariate cumulant generating function, and Skovgaard’s approximation is used to obtain computationally efficient tail probabilities without exhaustive permutation enumeration. The finite-sample performance of the proposed method is evaluated under log-normal, Weibull, and Gompertz event-time distributions and under monitoring schemes producing predominantly left, interval, or right-censored observations. Monte Carlo (MC) permutation p-values based on ${10}^6$ random permutations are used as a numerical benchmark (not the exact permutation distribution). Across the evaluated simulation scenarios, the SPA generally produces p-values that are closer to the MC permutation benchmark than those obtained from the standard normal approximation (NA). Applications to lung tumor, HIV drug-resistance, and breast-cosmesis data illustrate the relevance of the method to biomedical event-time studies. The proposed approximation provides an accurate and computationally efficient approach to rank-based inference for IC medical data. Full article

This study addresses the inverse problem of parameter identification in mathematical models of tumor growth under limited and noisy experimental data. Three classical growth models—logistic, Richards, and Gompertz—are investigated in the context of structural and practical identifiability. It is demonstrated that, despite structural identifiability, parameter estimation remains highly unstable due to the ill-posed nature of the inverse problem. A comparative analysis of the Levenberg–Marquardt method and a genetic algorithm shows that improvements in optimization strategies alone do not resolve this instability and may lead to overfitting. To overcome this limitation, a Tikhonov regularization framework is introduced for the Gompertz model, ensuring stable and physically interpretable parameter estimates. The regularized formulation provides a balance between data fidelity and parameter stability, resulting in improved agreement with experimental observations. To further validate the identified parameters, a reaction–diffusion partial differential equation model is employed. Numerical simulations demonstrate that regularized parameters lead to significantly different spatial tumor morphologies, including more compact structures with sharper interfaces, highlighting the impact of inverse problem regularization on forward model predictions. The results confirm that the primary limitation in tumor growth modeling lies in the ill-posedness of the inverse problem rather than in the choice of optimization algorithm. The proposed framework provides a robust approach for parameter identification and improves the reliability of predictive tumor growth models. Full article

Hypersaline semi-enclosed seas impose physiological constraints on marine bivalves by increasing osmotic regulation costs and limiting somatic growth. This study examined life-history variation in natural populations of the pearl oyster Pinctada radiata across contrasting salinity regimes along the eastern and western coasts of Bahrain in the Arabian Gulf. Environmental conditions, shell morphometrics, growth modeling, oocyte abundance, physiological condition indicators, and the production-to-biomass ratio (P:B) were assessed to determine whether high salinity constrains somatic growth while reproductive persistence is maintained. Monthly surveys were conducted from June 2021 to May 2022 at one pearl oyster bed on each coast, comprising 12 surveys per coast. The two coasts formed a salinity gradient, averaging approximately 42‰ in the east and 55‰ in the west. Oysters from the western coast were smaller, with a mean shell height decreasing from 63.12 ± 6.47 mm in the east to 45.88 ± 3.41 mm in the west. The Gompertz model indicated a lower asymptotic shell height in the more saline population. The population-level P:B ratio was 0.23 yr⁻¹ on the eastern coast and 7.46 yr⁻¹ on the western coast. Despite reduced shell size, western oysters maintained strong seasonal oocyte abundance. The higher biomass turnover in the west partly reflected greater oyster density. This pattern is consistent with osmoregulatory costs and constrained shell biomineralization under chronic hypersalinity. Reproductive activity followed a bimodal pattern on both coasts. These findings are consistent with a life-history trade-off in which pearl oysters exposed to chronic salinity stress maintain reproductive activity despite constrained growth. Overall, this study links environmental forcing, growth limitation, population-level biomass turnover, and reproductive activity, providing field-based evidence that chronic hypersalinity constrains somatic growth while reproductive persistence is maintained in a benthic bivalve inhabiting hypersaline coastal systems under climate-related stress. Full article

This study, conducted in the Ecuadorian Andes, evaluated the anaerobic co-digestion of local crop residues (amaranth and quinoa) with llama, vicuña, and pig manure to analyze methane production kinetics. The raw materials were characterized by proximate, elemental, and structural analyses, and biogas volume and the CH₄ fraction were monitored daily. The Amaranth-vicuña and Amaranth-llama treatments reached 77.29 ± 5.63 and 64.62 ± 3.62 mL biogas/g VS and 36.20 ± 7.29 and 31.78 ± 3.62 mL CH₄/g VS, respectively; in contrast, Quinoa-vicuña and Quinoa-llama produced only 1.04 ± 0.25 and 0.24 ± 0.03 mL CH₄/g VS. Monod-model parameters were estimated using an apparent formulation based on the methane production rate, and the kinetic behavior was compared with first-order, modified Gompertz, and modified logistic models. In addition, artificial neural networks (ANNs) were evaluated to predict the methane production curve from substrate characterization. Network 44, with a 13-15-10-1 architecture, yielded an overall R² = 0.998, validation R² = 0.997, and validation MSE = 0.415. Ten-times repeated five-fold cross-validation of the same architecture yielded R² = 0.985 ± 0.007 and RMSE = 1.21 ± 0.28 mL CH₄/g VS, supporting its interpolation capability within the experimental domain, although this does not demonstrate extrapolation to new substrate combinations. Overall, the proposed approach combines interpretable kinetic parameters with ANN-based prediction, but external validation with independent datasets is still required. The reported yields correspond to the specific production achieved in a low-cost batch system operated at room temperature and should not be interpreted as standardized biochemical methane potential (BMP) values. Full article

With the acceleration of global urbanization, neighborhood green spaces have emerged as important carbon sinks. However, current urban carbon inventories frequently neglect the understory shrub layer owing to morphological heterogeneity and a lack of standardized allometric models. To address this limitation, we analyzed 13 major shrub species (n = 665) through whole-plant excavation. Hierarchical cluster analysis and linear discriminant analysis classified the 13 species into three functional morphological groups based on intrinsic morphological traits (basal stem density, root-to-shoot biomass allocation, and secondary radial growth capacity) (p < 0.001): small shrubs (Type I), large shrubs with high root-to-shoot allocation (Type II), and multi-stemmed sprouting shrubs (Type III). Standard models accurately estimated biomass for Type I species, whereas symbolic regression improved the prediction of the complex non-linear biomass allocation of Type II species. For Type III species, characterized by multi-stemmed growth and anthropogenic management, robust regression provided stable biomass estimates. Gompertz growth models predicted carbon sequestration trajectories, indicating that urban shrubs function as rapid carbon sinks during the early establishment stage. To facilitate practical application, we developed the Urban Forest Carbon Storage Calculator, which integrates Monte Carlo simulation and bootstrapping to generate 95% confidence intervals for species-specific biomass estimation. This study quantifies the overlooked carbon value of the urban shrub layer and provides a morphology-driven methodological approach and a practical tool for sustainable urban forest management. Full article

Modeling the dynamics of three tumor cell populations, i.e., sensitive, persister, and resistant tumor cells, during molecularly targeted therapy with tyrosine kinase inhibitors (TKIs) would be valuable for adjusting treatment plans for patients with epidermal growth factor receptor-mutated non-small cell lung cancer (EGFR-mt NSCLC). We hypothesized the time-variant tumor growth trajectories (TGTs) of patients with stage IV EGFR-mt NSCLC for the three tumor cell populations could be expressed using differential models after several follow-up computed tomography examinations. We aimed to propose differential models for TGTs in three cell populations from patients with EGFR-mt NSCLC treated with an EGFR-TKI (osimertinib). We selected two differential equations—Bertalanffy–Pütter (BP) and Gompertz—to develop TGT models. The parameters of the models were optimized based on a dual annealing method within parameter ranges determined using synthetic patient data. Using CT examinations that were not employed for model fitting, the mean absolute percentage errors (MAPEs) for BP-based and Gompertz-based models were 36.1 ± 40.2% and 43.9 ± 60.1%, respectively, for three follow-up computed tomography (FCT) examinations, which indicated no statistically significant difference (p = 0.61). This study suggests that the proposed BP-based and Gompertz-based differential models could have the potential to express TGTs in patients with stage IV EGFR-mt NSCLC treated with EGFR-TKIs after three follow-up CT examinations, although MAPEs should be mitigated in future works. Full article

Electro-hydraulic actuator systems are widely used in precision motion-control applications; however, their displacement regulation remains challenging because fast response, low overshoot, and high steady-state accuracy must be achieved simultaneously under strongly dynamic operating conditions. In this study, a proportional-integral-derivative (PID) controller augmented with a Gompertz function (PID-G) is proposed for the position control of a four-way valve-controlled linear actuator, and its parameters are tuned by the recently introduced dholes-inspired optimizer (DIO). First, a control-oriented mathematical model of the electro-hydraulic actuator system is established by combining the valve and actuator dynamics. Then, the PID-G structure is formulated by incorporating a nonlinear Gompertz-based term into the conventional PID framework, and the resulting seven-parameter tuning problem is cast as an optimization task using a composite objective function that accounts for overshoot, steady-state error, rise time, and settling time. The effectiveness of DIO is evaluated comparatively against flood algorithm (FLA), covariance matrix adaptation evolution strategy (CMA-ES), and particle swarm optimization (PSO) under identical simulation conditions. The results show that DIO provides the best optimization performance, yielding the lowest best, average, and standard-deviation values of the objective function among the compared algorithms. In the time domain, the DIO-based PID-G controller achieves the most favorable overall response with a rise time of 0.079511 s, a settling time of 0.099326 s, an overshoot of 0.15110%, and a steady-state error of 0.089317%. The superiority of the DIO-based design is further confirmed by lower values of error based performance metrics (IAE, ISE, ITAE, and ITSE), improved convergence characteristics, and statistically significant advantages in the Wilcoxon test. Additional comparisons with different (PI, PID, 2DOF-PID, and FOPID) controllers also demonstrate that the proposed PID-G structure provides markedly better transient and error-based performance when tuned by DIO. Frequency-domain and varying-setpoint results further indicate satisfactory stability margins, robust tracking ability, and bounded control effort. Overall, the study shows that combining DIO with a Gompertz-augmented PID structure constitutes an effective strategy for high-performance electro-hydraulic actuator displacement control. Full article

This study presents a kinetic evaluation of anaerobic co-digestion of pulsed electric field (PEF)-pretreated corn stover using three models: the Modified Gompertz (MG), Logistic, and Reaction Curve (RC). The objective was to assess model performance and improve the interpretation of biogas production dynamics beyond cumulative biogas yield analysis. Experimental data were obtained from batch mesophilic digestion of fine and coarse corn stover fractions subjected to PEF pretreatment with applied voltages of 350 V and 1 kV. All models achieved high coefficients of determination (R² ≥ 0.97), indicating good agreement between experimental and modeled biogas production curves. However, error metrics and information criteria revealed clear differences in predictive performance. The MG model provided the closest agreement with experimentally determined biogas production potential (Bmax), whereas the Logistic model consistently underestimated both Bmax and the maximum biogas production rate (Rmax). The RC model yielded the lowest prediction errors and favorable information criteria values but showed greater sensitivity to the initial phase of biogas production, occasionally overestimating Bmax and Rmax. Moderate PEF pretreatment increased both Bmax and Rmax, particularly in the fine fractions, without substantially prolonging the lag phase. In contrast, pretreatment at the higher applied voltage resulted in more heterogeneous kinetic responses, including prolonged lag phases and increased variability of kinetic parameters. Full article

Anaerobic digestion (AD) is a common waste management method for producing renewable energy from biogas. However, animal manures typically have low C/N ratios, which can limit biogas recovery. This study aims to optimize biogas production through the co-digestion of goat manure (GM) with high-C/N lignocellulosic rice husk (RH) and sludge as the inoculum. Characterization of the substrate and inoculum revealed a low GM C/N ratio (GM = 21.3), whereas RH has a high-C/N (RH = 107.3). The volatile solids-to-total solids (VS/TS) ratios were around 82–85% for GM and RH. Batch experiments were conducted at different organic loading rates, with an inoculum-to-substrate ratio of 2:1 (mL:g), at 36 ± 1 °C for 65 days. This study investigates optimizing biomethane recovery by using serum-bottle biomethane potential (BMP) and compares kinetic performance and yields across different GM-to-RH ratios with the characteristics of the influent and effluent. The highest BMP values (mL CH₄/g_VS) occurred at 100% GM (245.1), followed by 90% GM (233.6) and 30% GM (232.5), indicating a strong synergy between GM and RH at specific mixing ratios. Kinetic modeling using both the modified Gompertz and first-order models effectively described digestion dynamics, allowing for estimation of potential lag phases and maximum production rates. These models aligned well with experimental data across substrates, aiding process design. Overall, the results show that strategic co-digestion of GM with RH can maximize methane recovery, with defined substrate ratios and a clear understanding of the kinetics essential for scale-up and sustainable biogas production. Full article

This work addresses statistical inference for multi-level step-stress accelerated life testing. Under the assumption that product lifetime follows the Gompertz distribution at each stress level, featuring a common shape parameter and stress-varying scale parameters, the cumulative exposure model (CEM) serves to link the lifetime distributions across different stress levels. Given the physical principle that lifetime decreases with an increase in stress, order-restricted parameter estimation methods have been developed. Within the classical frequentist framework, the order restriction is converted into box-constrained optimization via parameter reparameterization, obtaining maximum likelihood estimators (MLEs) and constructing asymptotic confidence intervals (CIs). With the Bayesian approach, weakly informative priors are adopted to prevent posterior impropriety, and a Markov chain Monte Carlo (MCMC) algorithm is employed for posterior sampling to avoid weight degeneracy in importance sampling. Extensive Monte Carlo simulations demonstrate that, after incorporating the order restriction, the Bayesian estimates yield smaller values in terms of bias, mean squared error (MSE), and interval length, while the MLEs provide classical asymptotic CIs as a reference. Lastly, a real dataset on fish swimming endurance is examined to demonstrate the practicality as well as the effectiveness of these proposed approaches. Full article

The sustainability of agricultural systems is linked to soil organic carbon (SOC) and nitrogen (N) dynamics, both of which are influenced by fertilization management. To investigate the long-term effects of pig slurry, a 10-year field experiment was conducted in a Mediterranean semi-arid rainfed cereal system. Four N treatments were compared: mineral fertilizer (MN), slurry from fattening pigs (FS), slurry from gestating sows (SS), and a zero-N control. Grain and straw yields, SOC and its fractions, microbial biomass and activity, and N mineralization were measured after the final cropping season. Slurry increased grain yield by 21% compared with MN. The FS treatment also increased SOC by 24% compared with the MN treatment, mainly through the mineral-associated organic matter fraction (<0.05 mm). Soil basal respiration dynamics (Gompertz model) and the N mineralization rate (0.04382 day^–1; Stanford–Smith model) did not differ among N-fertilized treatments. However, the potentially mineralizable N pool increased under N fertilization, from 19% in MN to 51% in FS relative to the control. These results indicate that repeated applications of pig slurry can replace mineral fertilizer while maintaining or improving crop productivity and soil C storage. Incorporating N mineralization models into fertilization planning could further optimize slurry application rates and timing. Full article