Search Results (1,534)

Groundwater depletion in arid regions poses a significant threat to agricultural sustainability and rural livelihoods. This study employs geospatial analysis and economic modeling to address groundwater depletion in the arid Barind region of Northwest Bangladesh, where 84% of the rural population depends on agriculture. Using remote sensing and GIS, we developed an elevation map revealing areas up to 60 m above sea level, exacerbating evaporation and aquifer dryness. Field data collected through Participatory Rural Appraisal tools showed farmers exhibiting “ignorant myopic” behavior, prioritizing short-term profits over resource conservation. To address this, an Increasing Block Tariff (IBT) water pricing model was developed, dividing water usage into three blocks based on irrigation hours: 1–275 h, 276–550 h, and 551+ h. The proposed IBT model significantly increases water prices across the three blocks: 117 BDT/hour for the first block (from current 100–110 BDT/hour), 120 BDT/hour for the second block, and 138 BDT/hour for the third block. A demand function (y = −0.1178x + 241.8) was formulated to evaluate the model’s impact. The results show potential reductions in groundwater consumption: 59 h in the first block, 26 h in the second block, and 158 h in the third block. These reductions align with the principles of integrated water resource management (IWRM): social equity, economic efficiency, and environmental integration. The model incorporates economic externalities (e.g., well lifting costs) and environmental externalities (e.g., crop pattern shifts), with total costs reaching 92,709,049 BDT for environmental factors. This research provides a framework for sustainable groundwater management in arid regions, potentially reducing overextraction while maintaining agricultural productivity. The proposed IBT model offers a locally driven solution to balance resource conservation with the livelihood needs of farming communities in the Barind tract. By combining remote sensing, GIS, and economic modeling, this research provides a framework for sustainable groundwater management in arid regions, demonstrating the power of geospatial technologies in addressing complex water resource challenges. Full article

It is well established that financial development and innovation promote economic growth through improving the allocation of capital, enhancing risk management, contributing to price discovery, and increasing market efficiencies. While a vast empirical literature is devoted to the nexus between financial development and economic growth, however, substantially less research has been done on the relationship between derivatives and growth, especially in the emerging-market context. Derivatives can be viewed as a specific category of financial innovation, which may advance economic growth through its specialised functions of risk management and price discovery. This paper contributes to bridging this gap in the literature by exploring the impact of exchange-traded futures derivatives on South African economic growth, output, and economic growth volatility. It employs ARDL bounds tests, Granger causality tests and GARCH volatility modeling to analyse the effects of exchange-traded futures derivatives on various measures of South African economic activity. The main result is that exchange-traded futures derivatives contribute positively to South African economic growth and economic activity. This may suggest that opportunities might exist in other emerging economies, with financial structures comparable to that of South Africa, to encourage the development of organised and well-regulated derivatives markets to unlock economic growth in these economies. Full article

Hydrogen has the potential to decarbonize the energy and industrial sectors in the future, mainly if it is generated by water electrolysis. The proton-exchange membrane water electrolysis (PEMWE) system is regarded as a propitious technology to produce green hydrogen from water using power supplied by renewable energy sources. It offers many benefits, such as high performance, high proton conductibility, quick response, compact size, and low working temperature. Many conceptual and functional parameters influence the effectiveness of PEM, including temperature, pressure of anode and cathode regions, water content and wideness of the layer, and cathode and anode exchange current density. In addition, the anodic half-reaction (known as the oxygen evolution reaction (OER)) and cathodic half-reaction (known as the hydrogen evolution reaction (HER)) perform an important function in the development of PEMWE. The current study aims to present these parameters and discuss their impacts on the performance of PEM. Also, the PEM efficiency is presented. The different methods used to enhance the scattering of OER electrocatalysts and minimize catalyst loading to minimize the price of PEMWE are also highlighted. Moreover, the alternative noble metals that could be used as electrocatalysts in HER and OER to minimize the cost of PEM are reviewed and presented. Full article

The energy storage adjustment strategy of source and load storage in a DC microgrid is very important to the economic benefits of a power grid. Therefore, a multi-timescale energy storage optimization method for direct current (DC) microgrid source-load storage based on a virtual bus voltage control is studied. It uses a virtual damping compensation strategy to control the stability of virtual bus voltage and establishes a virtual energy storage model by combining different types of distributed capability units. The design of an optimization process for upper-level daily energy storage has the objective function of maximizing the economic benefits of microgrids to cope with unplanned fluctuations in power. A real-time energy-adjustment scheme for the lower level is introduced, and a real-time energy-adjustment scheme based on virtual energy storage for the short-term partition of the source-load storage is designed to improve the reliability of microgrid operations. The experiment shows that, in response to the constant amplitude oscillation of the power grid after a sudden increase in power, this method introduces a virtual damping compensation strategy at 20 s, which can stabilize the virtual bus voltage. From 00:00 to 09:00, the battery power remains at around 4 MW, and from 12:00 to 21:00, the battery exits the discharge state. The economic benefits from applying this method are significantly higher than before. This method can effectively adjust the source-load energy storage in real time. During peak electricity price periods, the SOC value of supercapacitors is below 0.4, and during normal electricity price periods, the SOC value of supercapacitors can reach up to 1.0, which can make the state of the charge value of supercapacitors meet economic requirements. Full article

Cocoa prices are predicted to rise continually, as demand remains high and there are supply issues caused by crop yield fluctuations. This study aimed to develop a sustainable plant-based sweet spread using functional plant-based ingredients, chickpeas and carob, as a cocoa and sugar alternative. Recipe optimisation resulted in the production of a control sample made using cocoa and three experimental samples containing varying proportions of carob (50%, 75%, and 100%). The samples were analysed for their physicochemical characteristics (water activity, pH, colour, and texture) and proximate composition (moisture, ash, carbohydrate, sugars, starch, protein, fat, and energy). Using carob as a cocoa substitute significantly decreased the pH, firmness and stickiness, fat and energy contents. On the other hand, increasing the percentage of carob led to a substantially higher sugar content in the sweet spreads. The results show the possibility of developing an innovative sustainable plant-based chocolate-flavoured spread with favourable physicochemical characteristics and nutritional profiles using carob powder and syrup as a cocoa and sugar replacement. Full article

The market for microinverters is growing, especially in Europe. Driven by rising electricity prices and an easing in legislation since 2024, the number of mini-photovoltaic energy systems (mini-PVs) being installed is increasing substantially. Indoor and outdoor studies of microinverters have been carried out at Paderborn University since 2014. In the indoor lab, conversion efficiencies as a function of load have been measured with high accuracy and ranked according to Euro and CEC weightings; the latest rankings from 2024 are included in this paper. In the outdoor lab, energy yields have been measured using identical and calibrated crystalline silicon PV modules; until 2020, measurements were carried out using 215 W_p modules. Because of increasing PV module power ratings, 360 W_p modules were used from 2020 until 2024. In 2024, the test modules were upgraded to 410 W_p modules, taking into account the increase from 600 W to 800 W of inverter power limits, which is suitable for simplified operation permission (“plug-in”) in many European countries within a homogenised legislation area for such mini-photovoltaic energy systems or “balcony power plants”. This legislation for simplified operation also covers overpowered mini-plants, although the maximum AC output remains limited to 800 W. Presently, yield assessments are being carried out in the outdoor lab, which will take at least a year to be valid and comparable. Kits consisting of PV modules, inverters, and mounting systems are also being evaluated. Yield rankings sometimes differ from efficiency rankings due to the use of different MPPT algorithms with different MPP approach speeds and accuracies. To accelerate yield assessment, we developed a novel, simple formula to determine energy yield for any module and inverter configuration, including overpowered systems. This is a linear approach, determined by just two coefficients, a and b, which are given for several inverters. To reduce costs, inverters will be integrated into the module frame or the module terminal box in the future. Full article

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies. Full article

In this study, recycled acrylonitrile-butadiene-styrene terpolymer (ABSr) was reused to produce polyamide 6 (PA6)-based blends. This was achieved through reactive compatibilization using styrene-acrylonitrile-maleic anhydride (SAN-g-MA) copolymer with a high degree of functionalization (6–10% MA). The PA6/ABSr and PA6/ABSr/SAN-g-MA blends were prepared through melt processing and injection molding and then analyzed for their rheological, mechanical, thermomechanical, thermal, and structural properties, as well as morphology. The torque rheometry revealed a maximum reactivity of the PA6/ABSr (70/30 wt%) blend with low SAN-g-MA (5 phr—parts per hundred resin) content, while above this threshold, torque began to decline, indicating compatibilizer saturation in the interface. These findings were further substantiated by the increase in complex viscosity and the lower melt flow index (MFI) of the PA6/ABSr/SAN-g-MA (5 phr) blend. The 5 phr SAN-g-MA reactive compatibilization of the PA6/ABSr blends significantly enhanced its impact strength, elongation at break, tensile strength, and heat deflection temperature (HDT) by 217%, 631%, 12.6%, and 9.5%, respectively, compared to PA6/ABSr. These findings are promising for the plastic recycling field, paving the way for the production of new tailor-made materials at a reduced price. Full article

Carefully selecting variables in problems with large volumes of data are extremely important, as it reduces the complexity of the model, improves the interpretation of the results, and increases computational efficiency, ensuring more accurate and relevant analyses. This paper presents a comprehensive approach to selecting variables in multiple regression models using the stepwise procedure. As the main contribution of this study, we present the stepwise function implemented in Python to improve the effectiveness of statistical analyses, allowing the intuitive and efficient selection of statistically significant variables. The application of the function is exemplified in a real case study of real estate pricing, validating its effectiveness in improving the fit of regression models. In addition, we presented a methodological framework for treating joint problems in data analysis, such as heteroskedasticity, multicollinearity, and nonadherence of residues to normality. This framework offers a robust computational implementation to mitigate such issues. This study aims to advance the understanding and application of statistical methods in Python, providing valuable tools for researchers, students, and professionals from various areas. Full article

Approximately one-third of the world’s food production, i.e., 1.43 billion tons, is wasted annually, resulting in economic losses of nearly USD 940 billion and undermining food system sustainability. This waste depletes resources, contributes to greenhouse gas emissions, and negatively affects food security and prices. Although traditional packaging preserves food quality, it cannot satisfy the demands of extended shelf life, safety, and sustainability. Consequently, active packaging using biopolymer matrices containing antioxidants and antimicrobials is a promising solution. This review examines the current advancements in the integration of carbon quantum dots (CQDs) into biopolymer-based active packaging, focusing on their antioxidant and antimicrobial properties. CQDs provide unique advantages over traditional nanoparticles and natural compounds, including high biocompatibility, tunable surface functionality, and environmental sustainability. This review explores the mechanisms through which CQDs impart antioxidant and antimicrobial activities, their synthesis methods, and their functionalization to optimize the efficacy of biopolymer matrices. Recent studies have highlighted that CQD-enhanced biopolymers maintain biodegradability with enhanced antioxidant and antimicrobial functions. Additionally, potential challenges, such as toxicity, regulatory considerations, and scalability are discussed, offering insights into future research directions and industrial applications. This review demonstrates the potential of CQD-incorporated biopolymer matrices to transform active packaging, aligning with sustainability goals and advancing food preservation technologies. Full article

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period. Full article

This paper addresses the valuation of European options, which involves the complex and unpredictable dynamics of fractal market fluctuations. These are modeled using the $\alpha$-order time-fractional Black–Scholes equation, where the Caputo fractional derivative is applied with the parameter $\alpha$ ranging from 0 to 1. We introduce a novel, high-order numerical scheme specifically crafted to efficiently tackle the time-fractional Black–Scholes equation. The spatial discretization is handled by a tailored finite point scheme that leverages exponential basis functions, complemented by an L1-discretization technique for temporal progression. We have conducted a thorough investigation into the stability and convergence of our approach, confirming its unconditional stability and fourth-order spatial accuracy, along with $(2-\alpha )$-order temporal accuracy. To substantiate our theoretical results and showcase the precision of our method, we present numerical examples that include solutions with known exact values. We then apply our methodology to price three types of European options within the framework of the time-fractional Black–Scholes model: (i) a European double barrier knock-out call option; (ii) a standard European call option; and (iii) a European put option. These case studies not only enhance our comprehension of the fractional derivative’s order on option pricing but also stimulate discussion on how different model parameters affect option values within the fractional framework. Full article

Recreation and nature-based tourism targeting forests are important cultural services provided by forests, and are also considered non-productive social functions of forests. Many factors influence the demand for forest recreation and tourism that are worth exploring for each forest area. The main aim of our study was to compare the results of visitor surveys related to two mountainous forested areas (Börzsöny and Cserhát) that are both located near to the capital city of Budapest but have different characteristics regarding the forests, accessibility, and the level of tourism infrastructure and services. The questionnaires focused on the characteristics of the visits, perceptions of visitors regarding the values of the areas, and the development needs besides the characteristics of the respondents. In addition to basic statistics, Chi² and Fisher’s exact tests were used to detect the differences between the two sites. Despite the different characteristics of the study areas, the main results were quite similar at both sites. Most respondents came from Budapest or within a 60 km distance of the sites by car with family and friends, mainly for hiking and nature walks, and spent less than a day in the areas. The state of forests was perceived as good in both areas. Landscape and fresh air were the most attracting factors for visiting both sites. There was a demand for more guided tours, and regarding tourism infrastructure development needs, benches and toilets ranked high at both sites. However, there were also some slight differences between the sites. For example, Börzsöny was visited more frequently, and railway and bicycle were more often used to access this site. Pleasant climate, easy access and fresh air were more important reasons to visit Börzsöny, and it was more associated with wilderness. In Cserhát, low prices and cultural values seemed more important reasons to visit; Hollókő as a world heritage site was highlighted, and more nature-related and other tourism development needs were mentioned regarding this site. These differences were probably due to the different characteristics of the forests (more mosaic forests in Cserhát), the level of the public transportation network, tourist infrastructure and services (higher in Börzsöny), and cultural heritage (more important in Cserhát). Based on our results, Cserhát needs more development in tourism infrastructure and services, while in Börzsöny, the development of a visitor management plan would be useful to harmonize the different uses of the forest. Full article

This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Convolutional Long Short-Term Memory (ConvLSTM), incorporating factors like the Commodities Index and the S&P500 Index. We employed loss function metrics and various tests to assess model performance. The results indicated that for the 5-day horizon, the LSTM and ConvLSTM consistently outperformed the other models. LSTM achieved the lowest error rates and demonstrated superior capability in capturing temporal dependencies, especially in single-factor and S&P500 Index predictions. ConvLSTM also performed strongly, effectively modeling spatial and temporal data patterns. In the 10-day horizon, similar trends were observed. LSTM and ConvLSTM models had significantly lower errors and better alignment with actual values. The BPNN model performed well when all factors were included, and the SVR model maintained consistent accuracy, particularly for single-factor predictions. The Diebold–Mariano (DM) test indicated significant differences in forecasting accuracy, favoring advanced neural network models. In addition, incorporating multiple influencing factors further improved predictive performance, enhancing investment outcomes and reducing risk. Full article

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R² score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R² and RMSE (p-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector. Full article