Search Results (51)

This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity. Full article

Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R² = 0.90) and weedy yield (MAE = 258, R² = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials. Full article

The untreated discharge of dairy industry wastewater, characterized by high organic and nutrient loads, poses a severe eutrophication threat, leading to oxygen depletion and the disruption of aquatic ecosystems, which necessitates advanced treatment strategies. Anaerobic digestion (AD) represents an effective and sustainable alternative, converting organic matter into biogas while minimizing sludge production and contributing to Circular Economy strategies. This study investigated the effects of fat concentration and operational temperature on the anaerobic digestion of dairy effluents. Three types of effluents, skimmed, semi-skimmed, and whole substrates, were evaluated under mesophilic 35 °C and thermophilic 55 °C conditions to degrade substrates with different fat content. Low-fat effluents exhibited higher COD removal, shorter lag phases, and stable activity under mesophilic conditions, while high-fat substrates delayed start-up due to accumulation of fatty acids and brief methanogen inhibition. Thermophilic digestion accelerated hydrolysis and methane production but demonstrated increased sensitivity to lipid-induced inhibition. Kinetic modeling confirmed that the modified Gompertz model accurately described mesophilic digestion with rapid microbial adaptation, while the Cone model better captured thermophilic, hydrolysis-limited kinetics. The thermophilic operation significantly enhanced methane productivity, yielding 105–191 mL CH₄ g⁻¹VS compared to 54–70 mL CH₄ g⁻¹VS under mesophilic conditions by increasing apparent hydrolysis rates and reducing lag phases. However, the mesophilic process demonstrated superior operational stability and robustness during start-up with fat-rich effluents, which otherwise suffered delayed methane formation due to lipid hydrolysis and volatile fatty acid (VFA) inhibition. Overall, the synergistic interaction between temperature and fat concentration revealed a trade-off between methane productivity and process stability, with thermophilic digestion increasing methane yields up to 191 mL CH₄ g⁻¹ VS but reducing COD removal and robustness during start-up, whereas mesophilic operation ensured more stable performance despite lower methane yields. Full article

Studies of swordfish growth provide essential biological parameters for stock assessment and fisheries management, informing both conventional population models and the evaluation of different management strategies. The present study aims to provide insight into the dynamics of the South Atlantic Ocean stock growth patterns. The sampling is the most complete to date in the literature, with a wide geographical distribution and in every month of the year. The analysis included 788 anal fins. Biometric relationships between different anal fin spine measurements and fish size were found. Some variation in the size of annulus one and vascularisation hiding some internal bands was found in larger specimens. Marginal increment ratio (MIR) and edge type analyses showed an annual band formation in the austral winter (July to September), thereby confirming the hypothesis of one annulus formation per year. Growth parameters were calculated using different growth models. The Gompertz model yielded the most reliable parameters (L_∞ = 341 cm LJFL, k = 0.13 yr⁻¹, T = 2.83 yr). The tagging and recapture data corroborated the selected model. Results were compared with other growth curves published. Full article

The growing global demand for food and energy requires land-use strategies that support agricultural production and renewable energy generation. Agrivoltaic (APV) systems allow farmland to be used for both agriculture and solar power generation. The aim of this study is to critically synthesize the interactions between the key dimensions of APV implementation—technical, agronomic, legal, and economic—in order to create a multidimensional framework for designing an APV optimization model. The analysis covers APV system topologies, appropriate types of photovoltaic modules, installation geometry, shading conditions, and micro-environmental impacts. The paper categorizes quantitative indicators and critical thresholds that define trade-offs between energy production and crop yields, including a discussion of shade-tolerant crops (such as lettuce, clover, grapevines, and hops) that are most compatible with APV. Quantitative aspects were integrated in detail through a review of mathematical approaches used to predict yields (including exponential-linear, logistic, Gompertz, and GENECROP models). These models are key to quantitatively assessing the impact of photovoltaic modules on the light balance, thus enabling the simultaneous estimation of energy efficiency and yields. Technical solutions that enhance synthesis, such as dynamic tracking systems, which can increase energy production by up to 25–30% while optimizing light availability for crops, are also discussed. Additionally, the study examines regional legal frameworks and the economic factors influencing APV deployment, highlighting key challenges such as land use classification, grid connection limitations, investment costs and the absence of harmonised APV policies in many countries. It has been shown that APV systems can increase water retention, mitigate wind erosion, strengthen crop resilience to extreme weather conditions, and reduce the levelized cost of electricity (LCOE) compared to small rooftop PV systems. A key contribution of the work is the creation of a coherent analytical design framework that integrates technical, agronomic, legal and economic requirements as the most important input parameters for the APV system optimization model. This indicates that wider implementation of APV requires clear regulatory definitions, standardized design criteria, and dedicated support mechanisms. Full article

Mathematical modeling is a key tool for describing and predicting the dynamic behavior of anaerobic digestion. Studies combining the co-digestion of aquaponics effluent (AE) and cattle manure (CM) with kinetic modeling remain scarce, particularly regarding the estimation of the apparent kinetic constant of hydrolysis constants and energy conversion indicators. Accordingly, this study aimed to evaluate the bioenergy potential of co-digesting aquaponics effluent (AE) and cattle manure (CM), with an emphasis on kinetic modeling and energy conversion. The experiments were carried out in a bench-scale Indian-type anaerobic biodigester. Different AE, CM, and water (W) (0:1, 1:0, 1:1, 1:3, 3:1 W:CM, and 1:1, 1:3, and 3:1 AE:CM) ratios were tested to identify the most efficient substrate combination for biogas production. The 1:3 AE:CM ratio achieved the best performance, with the Gompertz model providing the best fit for cumulative production and the first-order model accurately estimating k. This ratio yielded the highest cumulative biogas production (72.2 L kg⁻¹ substrate), shorter lag phase, higher production rate, and greater energy conversion efficiency. Comparative analysis revealed that 1:3 AE:CM outperformed both 1:3 A:CM and CM alone, highlighting the positive influence of aquaponics effluent on microbial activity and process stability. These results demonstrate that anaerobic co-digestion of AE and CM, particularly at the 1:3 ratio, is a viable and efficient strategy for renewable energy generation in rural areas, while promoting waste valorization and enhancing environmental and energy sustainability. Full article

This study explores the sequential valorization of orange peel waste (OPW) through photo-fermentation using real dark fermentation effluents (DFE) as substrates for hydrogen production using Rhodobacter capsulatus B10. Three DFE types—differing in prior biocompound extraction method—and their concentrations at three levels (25, 35, and 45%) were evaluated. The highest hydrogen yield (126.5 mL H₂ g⁻¹ VFA) was achieved with DFE derived from essential oil-extracted OPW at a concentration of 25%. The highest DFE concentration reduced the hydrogen yield due to intensified medium opacity and potential substrate inhibition. Kinetic modeling revealed that the Modified Gompertz and T_i-Gompertz models best described hydrogen production dynamics. This study presents the first evidence of hydrogen production via photo-fermentation using real effluents derived from OPW processing, demonstrating a novel route for citrus waste reuse within a biorefinery framework. These findings underscore the innovation and relevance of integrating waste valorization with clean energy production, while also identifying key operational challenges to be addressed. Full article

This study introduces an inverted version of the three-parameter Hjorth lifespan model, characterized by one scale parameter and two shape parameters, referred to as the inverted Hjorth (IH) distribution. This asymmetric distribution can fit various positively skewed datasets more accurately than several existing models in the literature, as it can accommodate data exhibiting an inverted (upside-down) bathtub-shaped hazard rate. We derive key properties of the model, including quantiles, moments, reliability measures, stress–strength reliability, and order statistics. Point estimation of the IH model parameters is performed using maximum likelihood and Bayesian approaches. Moreover, for interval estimation, two types of asymptotic confidence intervals and two types of Bayesian credible intervals are obtained using the same estimation methodologies. As an extension to a complete sampling plan, Type-II censoring is employed to examine the impact of data incompleteness on IH parameter estimation. Monte Carlo simulation results indicate that Bayesian point and credible estimates outperform those obtained via classical estimation methods across several precision metrics, including mean squared error, average absolute bias, average interval length, and coverage probability. To further assess its performance, two real datasets are analyzed: one from the environmental domain (minimum monthly water flows of the Piracicaba River) and another from the pharmacological domain (plasma indomethacin concentrations). The superiority and flexibility of the inverted Hjorth model are evaluated and compared with several competing models. The results confirm that the IH distribution provides a better fit than several existing lifetime models—such as the inverted Gompertz, inverted log-logistic, inverted Lomax, and inverted Nadarajah–Haghighi distributions—making it a valuable tool for reliability and survival data analysis. Full article

Accurate age determination is fundamental for investigating fish population dynamics and growth patterns. This study used the lapillus to determine age in Triplophysa strauchii populations from an oxbow lake and a stream. Growth patterns were evaluated using three models (the Von Bertalanffy, Gompertz, and Logistic models). The oxbow lake population showed faster growth and longer lifespan (6 years in Dacao Lake; 5 years in Liutiao Stream). Conversely, the stream population displayed a trend toward smaller size and younger age. Both populations exhibited higher Fulton’s condition factor in juveniles than in adults. The species exhibited a fast-growth type, with similar fitting results across models. These findings reflect subtle differentiation in life history strategies across habitats, likely related to environmental conditions and competitive pressure from invasive species. These insights into T. strauchii life history underscore the importance of further research to support conservation and sustainable management of this endemic species. Full article

Maritime transportation is responsible for most global trade and is generally considered more environmentally efficient compared to other modes of transport, particularly for long-distance trade. With increasingly stringent emission regulations, however, accurately quantifying emissions and identifying their key determinants has become essential for effective environmental management. This study introduced a structured and comparative statistical modeling framework for ship-based emission modeling using gross tonnage (GT) as the primary predictor variable, due to its strong correlation with emission levels. Emissions for hydrocarbon (HC), carbon monoxide (CO), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and volatile organic compounds (VOC) were estimated using a bottom-up approach based on emission factors and formulas defined by the U.S. Environmental Protection Agency (EPA), using data from 38,304 vessel movements through the Bosphorus in 2021. These EPA-estimated values served as dependent variables in the modeling process. The modeling framework followed a three-step strategy: (1) outlier detection using Rosner’s test to reduce the influence of outliers on model accuracy, (2) curve fitting with 12 regression models representing four curve types—polynomial (e.g., linear, quadratic), concave/convex (e.g., exponential, logarithmic), sigmoidal (e.g., logistic, Gompertz, Weibull), and spline-based (e.g., cubic spline, natural spline)—to capture diverse functional relationships between GT and emissions, and (3) model comparison using difference performance metrics to ensure a comprehensive assessment of predictive accuracy, consistency, and bias. The findings revealed that nonlinear models outperformed polynomial models, with spline-based models—particularly natural spline and cubic spline—providing superior accuracy for HC, PM₁₀, SO₂, and VOC, and the Weibull model showing strong predictive performance for CO and NO_x. These results underscore the necessity of using pollutant-specific and flexible modeling strategies to capture the intricacies of maritime emission dynamics. By demonstrating the advantages of flexible functional forms over standard regression techniques, this study highlights the need for tailored modeling strategies to better capture the complex relationships in maritime emission data and offers a scalable and transferable framework that can be extended to other vessel types, emission datasets, or maritime regions. Full article

This study explores the application of generalized conformable derivatives in modeling hotel demand dynamics in Mexico, using the Gompertz-type model. The research focuses on customizing conformable functions to fit the unique characteristics of the Mexican hotel industry, considering the Tourist Area Life Cycle (TALC) model and aiming to enhance forecasting accuracy. The parameter adjustment in all cases was made by designing a convex function, which represents the difference between the theoretical model and real data. Results demonstrate the effectiveness of the generalized conformable derivative approach in predicting hotel demand trends, showcasing its potential for improving decision-making processes in the Mexican hospitality sector. The comparison between the logistic and Gompertz models, in both integer and fractional versions, provides insights into the suitability of these modeling techniques for capturing the dynamics of hotel demand in the studied regions. Full article

Cancer cells typically divide with weaker synchronisation with the circadian clock than normal cells, with the degree of decoupling increasing with tumour maturity. Chronotherapy exploits this loss of synchronisation, using drugs with circadian-clock-dependent activity and timed infusion to balance the competing demands of reducing toxicity toward normal cells that display physiological circadian rhythms and of efficacy against the tumour. We analysed optimal chronotherapy for one-compartment nonlinear tumour growth models that were no longer synchronised with the circadian clock, minimising a cost function with a periodically driven running cost accounting for the circadian drug tolerability of normal cells. Using Pontryagin’s Minimum Principle (PMP), we show, for drugs that either increase the cell death rate or kill dividing cells, that optimal solutions are aperiodic bang–bang solutions with two switches per day, with the duration of the daily drug administration increasing as treatment progresses; for large tumours, optimal therapy can in fact switch mid treatment from aperiodic to continuous treatment. We illustrate this with tumours grown under logistic and Gompertz dynamics conditions; for logistic growth, we categorise the different types of solutions. Singular solutions can be applicable for some nonlinear tumour growth models if the per capita growth rate is convex. Direct comparison of the optimal aperiodic solution with the optimal periodic solution shows the former presents reduced toxicity whilst retaining similar efficacy against the tumour. We only found periodic solutions with a daily period in one-compartment exponential growth models, whilst models incorporating nonlinear growth had generic aperiodic solutions, and linear multi-compartments appeared to have long-period (weeks) periodic solutions. Our results suggest that chronotherapy-based optimal solutions under a harmonic running cost are not typically periodic infusion schedules with a 24 h period. Full article

Osmotically dehydrated cherry tomatoes and spinach leaves were incorporated into Greek salad-type (including OD-treated and air-dried feta cheese trimmings and air-dried olive rings) and green salad-type (including OD-treated and air-dried feta cheese trimmings and roasted ground peanuts) ready-to-eat (RTE) product prototypes, respectively. The osmotic dehydration of cherry tomatoes and spinach leaves was conducted in a pilot scale setting (100 L) in a 60% glycerol-based solution at 35 °C and 25 °C for 180 min and 60 min, respectively. To quantify the moisture transfer between the three ingredients of different moisture content (and water activity), the moisture equilibrium curves for each ingredient of the RTE product were determined. The equilibrium water activity of RTE products was 0.86 and 0.76, respectively. The quality of the RTE products (more specifically, tomato and spinach color and texture, instrumentally measured and sensorially perceived, sensory characteristics) was evaluated. The shelf life of the prototypes (from 4 °C to 20 °C) was kinetically modeled based on sensory deterioration and microbial growth, using the zero-order kinetic model and the Gompertz model, respectively. In the case of the tomato-based product, a shelf life of 54 days (based on sensory deterioration) was achieved at 4 °C, a shelf-life extension of 40 days compared to untreated, fresh-cut tomato. The shelf life of the spinach-based product (based on sensory deterioration) was 36 days at 4 °C, 30 days longer when compared to untreated spinach. Our results indicate that osmotic dehydration was successful in significantly extending the shelf life of such products, contributing to the increased temperature resilience of their keeping quality and allowing for their distribution and storage in a variable cold chain. Full article

Kombucha is a traditional, fermented beverage made with an essential biomaterial known as SCOBY (symbiotic culture of bacteria and yeast). Three different tea types, namely black, green, and oolong, were compared in kombucha fermentation in terms of pH dynamics, the formation of SCOBY biomass, and the production of acetic acid. The rational, exponential, and polynomial models described pH dynamics with good fit, R² > 0.98. The formation of SCOBY biomass and the production of acetic acid were modelled using sigmoidal functions, with three-parameter logistic and Gompertz models and four-parameter Boltzmann and Richards models. The F-test indicated that the three-parameter models were statistically adequate; thus, the Gompertz model was modified to present the biological meaning of the parameters. The SCOBY biomass formation rates ranged from 7.323 to 9.980 g/L-day, and the acetic acid production rates ranged from 0.047 to 0.049% acid (wt/vol)/day, with the highest values from the non-conventional substrate, oolong tea. The correlations between pH and SCOBY biomass or acetic acid using polynomial models enable the prediction of product formation in kombucha processing. Full article

Monitoring weight development is essential for decision-making and assessing the effectiveness of management strategies. However, this practice is often hindered by the lack of scales on farms. This study aimed to characterize the weight development and growth curves of male and female Santa Inês lambs from birth to weaning, managed on pasture with creep-fed concentrate supplementation. Data from 212 lambs during the pre-weaning phase were analyzed. The animals were weighed every seven days to evaluate total weight gain and average daily gain. Biometric measurements were taken every 28 days. Mixed models were used to assess the effects of sex and birth type on birth and weaning weights. Simple and multiple linear regression models were employed to estimate live weight using biometric measurements. The non-linear Gompertz model was utilized to describe weight development and formulate growth curves. Results were considered significant at p < 0.05. An interaction effect between birth type and sex (p < 0.05) was noted for birth weight, with the lowest weight observed in twin-birth females (2.96 kg) and the highest in single-birth males (3.73 kg) and females (3.65 kg) (p > 0.05). Birth type significantly influenced average daily gain, total weight gain, and weaning weight (p < 0.05). The Gompertz model accurately depicted the growth curves, effectively describing the weight development. Pearson’s correlation coefficients between biometric measurements and weight were positive and significant (p < 0.05), ranging from 0.599 for hip height to 0.847 for heart girth. Consequently, the simple and multiple regression equations demonstrated high precision in predicting weaning weight. In conclusion, twin-birth lambs receiving concentrate supplementation via creep-feeding and managed on pasture showed different developmental patterns compared to single-birth lambs under the same conditions. The Gompertz model proved effective for monitoring development during the pre-weaning phase. All simple and multiple linear regression models were effective in predicting weaning weight through biometric measurements. However, for practical application, the model incorporating two measurements—body length and abdominal circumference—is recommended. Full article