Search Results (8)

This study contributes to the modeling and optimization model for reverse osmosis water treatment (ROWT) due to a lack of economic and operational aspects. This study proposes a hybrid modeling and optimization framework using a hybrid artificial neural network (ANN) and genetic algorithm (GA) to enhance the accuracy of economic and operational predictions for ROWT. The ANN model is developed using seventeen key process parameters extracted from various ROWT plants, including flow rate, pH, conductivity, and turbidity. The GA is employed to optimize the network architecture and learning parameters based on the mean absolute percentage error (MAPE) as the fitness function. The findings of this study indicate that the GA-optimized model significantly outperforms the baseline model, reducing MAPE for the economic aspect (84.9% improvement) and the operational aspect (32.2% improvement). The findings from this study indicate that the hybrid ANN–GA approach is a management decision-making tool for reducing expenses without compromising water quality in ROWT management. The practical implications of this study are that predictions not only meet operational parameters but also predict expenses incurred, allowing managers to plan future budgets by optimizing ROWT resources and maintenance activities. Full article

Hyperparameter optimization for cryptocurrency trading strategies encounters distinct challenges owing to continuous operation, volatility rates 3–4 times higher than equity indices, and price dynamics influenced by market sentiment. Bayesian optimization (Tree-Structured Parzen Estimator, TPE) and evolutionary algorithms (Differential Evolution, DE) are great for machine learning, but there are not many systematic comparisons for trading cryptocurrencies. This research evaluates Random Sampling, TPE, and DE through 36 factorial experiments, comprising 3 trading strategies (3, 4, and 5 hyperparameters) × 3 optimizers × 4 cryptocurrency pairs (BTC/USDT, ETH/USDT, INJ/USDT, SOL/USDT), resulting in 14,400 backtesting trials with walk-forward validation. TPE won 75% of strategy–asset pairs (9 of 12), reaching 90% of optimal performance within 13–17% of trial budgets. We find strategy-specific optimizer compatibility: mean-reversion strategies show DE underperformance independent of topology (−1% to −8%), whereas trend-following strategies show consistent DE competitiveness across assets (+13% to +37%). Most notably, for the same strategy, parameter space topology differs significantly between assets (trend following: 4.6% viable on BTC to 82% on ETH = 17.8×; mean reversion: 10.8% on ETH to 92% on SOL = 8.5×), indicating that topology results from strategy–asset interaction rather than intrinsic properties. Complete testing failures and widespread severe overfitting point to regime non-stationarity as a fundamental problem. Among the contributions are: (1) evidence shows that topological effects are dominated by optimizer–strategy compatibility (DE fails on mean-reversion strategies even in 92% viable spaces, but succeeds on trend-following strategies regardless of topology, spanning 13.6–82% viable spaces); (2) this is the first systematic Bayesian versus evolutionary comparison across 4 cryptocurrency assets; (3) parameter space topology emerges from strategy–asset interaction, varying up to 17.8-fold; and (4) single-period backtests inadequately identify parameter instability. Full article

This study uses high-frequency monitoring across a river–barrier lake–reservoir continuum in the upper Minjiang River, southwestern China, to quantify the spatiotemporal dynamics and drivers of aquatic CO₂ partial pressure (pCO₂) and to identify the dominant controls under contrasting lotic and lentic conditions. River reaches were CO₂-supersaturated throughout the year, with higher pCO₂ in the wet season (mean 521 ppm) than in the dry season (421 ppm), indicating persistent CO₂ evasion to the atmosphere. In contrast, the downstream canyon-type reservoir showed a pronounced seasonal reversal. During the wet season, surface-water pCO₂ averaged 395 ppm, about 24% lower than that of the river and below atmospheric levels (~419 ppm); more than 55% of observations were undersaturated, with minima as low as 141–185 ppm, indicating temporary CO₂-sink behavior. In the dry season, mean pCO₂ increased to 563 ppm, exceeding both riverine and atmospheric levels and returning the reservoir to a CO₂ source. The reservoir pCO₂ variability was governed by the interaction of hydrology and metabolism: rising water levels and longer residence times likely enhanced CO₂ accumulation from the decomposition of inundated organic matter, while warm temperatures, high light and monsoon-driven nutrient inputs promoted phytoplankton growth that removed dissolved CO₂ and elevated dissolved oxygen, producing temporary sink behavior. In the river, short residence time and strong turbulence limited in-stream biological regulation, and pCO₂ variability was mainly driven by catchment-scale carbon inputs along the elevation gradient. Overall, our results demonstrate that dam construction and impoundment can substantially modify carbon cycling in high-mountain rivers. Under specific conditions (warm water, sufficient nutrients, high algal biomass), lentic environments may strengthen photosynthetic CO₂ uptake and temporarily transform typical riverine CO₂ sources into sinks, with important implications for carbon-budget assessments and reservoir management in mountainous basins. Full article

The use of traditional optimization methods in engineering design problems, specifically in aerodynamic and infrared stealth optimization for engine nozzles, requires a large number of objective function evaluations, therefore introducing a considerable challenge in terms of time constraints. In this paper, this limitation is addressed by using a sample-efficient multi-objective Bayesian optimization that takes Kriging as a surrogate model and Expected Hypervolume Improvement as the infill criterion. Using this approach, the probabilistic model is continuously established and updated, and the approximate Pareto front is obtained at a relatively small computational budget. The objective of this work is to evaluate the applicability of employing a multi-objective Bayesian optimization framework for the aerodynamic-infrared shape optimization of an elliptical double serpentine nozzle at 6 km flight condition, where the objective functions are evaluated by means of high-fidelity computational fluid dynamics and reversed Monte Carlo ray tracing simulations. We achieve good results in both infrared radiation signature reduction and aerodynamic performance improvement with a reasonable number of evaluations, indicating that the proposed method is effective and efficient for tackling the computationally intensive optimization challenges in the aircraft design. Full article

We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows us to further consider various portfolio designs and rebalancing strategies. Working with empirical data, our experiments suggest the significant benefits of diversification within our proposed framework. Full article

We simulate the future performance of the Norwegian Government Pension Fund Global as a leading example of sovereign wealth funds intended both to preserve wealth and provide regular budget contributions. Withdrawals are limited to the fund’s expected real returns; however, deviations are allowed to fund automatic stabilizers as well as discretionary fiscal policy. It also allows smoothing to avoid abrupt changes. Except for the fiscal policy part, many endowment funds are subject to similar rules. The main findings are: (i) Even if withdrawals matching expected returns maintain the fund’s value in expectation, the fund will be depleted eventually. (ii) Because the fund is invested in foreign currency, long-run purchasing power parity introduces an element of mean reversion and hence a negative serial correlation in the rates of return, so that the fund’s value is not even preserved in expectation. (iii) The use of the fund for fiscal policy introduces a substantial risk of depletion in finite time. (iv) Smoothing raises the risk of depletion after large negative financial returns, though only modestly so. Risk reduction and withdrawal rates below expected returns are explored as remedies. Risk reduction postpones the eventual depletion but does not eliminate it. Lower withdrawal rates help sustainability more fundamentally, but bounds on fiscal policy are needed for depletion risk to be eliminated. Full article

Following potential reforestation in the Amazon Basin, changes in the biophysical characteristics of the land surface may affect the fluxes of heat and moisture behavior. This research examines the impacts of potential tropical reforestation on surface energy and moisture budgets, including precipitation and temperature. The study is novel in that while most studies look at the opposite driver (deforestation), this one examines the impact of potential forest rehabilitation on atmospheric behavior using WRF.V3.9 (weather research and forecast model). We found that forest rehabilitation across the Amazon Basin can make the atmosphere cooler with more moisture and latent heat (LH), especially during May-November. For instance, the mean seasonal temperature decreased significantly by about 1.2 °C, indicating the cooling effects of reforestation. Also, the seasonal precipitation increased by 5 mm/day in reforested areas. By reforestation, the mean monthly LH also increased as much as 50 W m⁻² in August in certain areas, while available moisture to the atmosphere increased by 27%, indicating possible causal mechanisms between increased LH and precipitation and emphasizing the mechanisms that were identified between the onset of the wet season and forest cover. Therefore, it is likely that forest regrowth across the basin leads to, if not reverses regional climate change, at least slowing down the rate of changes in the climate. Full article

Building on the agri-environment framework in Central and Eastern Europe, the article emphasizes the role and the use of the agri-environment in provision of different ecosystem services. It shows that relevant conservation measures with regard to ameliorating soil degradation contribute to the existence of sustainable land systems. In our study, we (i) identified what the soil water aggregate means, (ii) reviewed how agri-environment schemes (AES) function to support soil water requirements, and (iii) how appropriate soils are identified with regard to the implementation of soil conservation under the agri-environment. Empirical data were surveyed to assess AES as the pivotal subsidy in four countries: the Czech Republic, Hungary, Poland, and Slovakia. Quantitative data were assessed to contribute to evidence on and the expenditure effect of the measures. This review found that AES schemes in arable land systems implement several approaches such as cover crops and the reversion of arable land systems to grassland. The costs of AE measures reflect the costs of the particular agri-environmental practice and its constraints on commercial performance by the farmer. The AES budget analysis showed that subsidization moderately increased over the 2000–2020 time frame. However, the magnitude of the AES budget is still largely overshadowed by generic subsidies at farm level. Full article