Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of the electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and can not outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with rolling 3-year window and compared the results with the RNNs. In our experiments, 3-layered GRUs outperformed all other neural network structures and state of the art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

Stock price prediction has long been a critical area of research in financial modeling. The inherent complexity of financial markets, characterized by both short-term fluctuations and long-term trends, poses significant challenges in accurately capturing underlying patterns. While Long Short-Term Memory (LSTM) networks have shown strong performance in short-term stock price prediction, they struggle with effectively modeling long-term dependencies. In this paper, we propose an advanced stock price prediction model based on the Extended Long Short-Term Memory (xLSTM) algorithm, designed to enhance predictive accuracy over both short and long-term periods. We conduct extensive experiments by building and evaluating models based on xLSTM and LSTM architectures for multiple stocks. Our results demonstrate that the xLSTM model consistently outperforms the LSTM model across all stocks and time horizons, with the performance gap widening as the prediction period extends. The observations underscore the superior capability of the xLSTM-based model to capture long-term patterns in financial data, offering a promising approach for more accurate and reliable stock price predictions.

The renewable energy sector plays a pivotal role in addressing climate change and achieving sustainable development goals. However, the sector faces challenges related to price inflation and supply chain disruptions, which can impede its growth and resilience. This qualitative research explores supply chain resilience strategies adopted by companies in the renewable energy sector to mitigate the impact of price inflation and disruptions. Through semi-structured interviews with key industry stakeholders, the study identifies several key themes. Supplier diversification emerges as a crucial strategy for mitigating risks associated with geopolitical tensions and trade restrictions. Inventory management plays a pivotal role in ensuring continuity in operations amidst supply chain disruptions, with a focus on optimizing inventory levels of critical components. The adoption of digital technologies offers significant potential for enhancing supply chain visibility, transparency, and responsiveness, although integration challenges remain. Collaboration and partnerships within the industry are essential for sharing resources, knowledge, and best practices, fostering innovation and collective problem-solving. Sustainable practices are integral to supply chain resilience, aligning with broader industry trends towards environmental stewardship and social responsibility. Overall, the findings highlight the multifaceted nature of supply chain resilience in the renewable energy sector and the interconnectedness of various strategies and practices. Moving forward, companies must adopt a proactive and adaptive approach to supply chain management, integrating these strategies and practices into their operations to navigate uncertainties and drive sustainable growth. This study contributes to the understanding of supply chain resilience in the renewable energy sector and provides valuable insights for industry stakeholders, policymakers, and researchers.

Forage fish are crucial to human nutrition, especially in developing countries. However, in these countries, most forage fish catches are used to make aquafeeds for aquaculture, which leaves only a small quantity for direct human consumption. This practice increases fish prices in local markets and undermines people's nutrition. Overfishing forage fish for industrial purposes harms the ecosystem, reduces the catch of larger fish, and marginalizes local fishermen. Although previous discussions have touched qualitatively on the ethics and injustice of this practice, this article provides quantitative scales to measure the impact of the practice on the finances and forage fish consumption of low-income consumers using reasonable assumptions and literature values. Increasing the annual supply of forage fish by 25% to the 5 million MT of fresh fish markets globally could save consumers several billion US dollars annually. Families could consume a higher percentage of nutritious fish for less money. Furthermore, leaving forage fish in the sea could benefit wildlife and increase the capture of larger fish, such as mackerel, which are in demand in local markets and fished by local fishermen. The unintentional and involuntary contributions of low-income societies to enable the growth of high-value fish and shrimp for affluent societies seem unjust and warrant further investigation.

Japan has the world’s largest old population ratio, and aging is an urgent societal issue. As global trends seem to be following Japan’s social changes, there is an emphasis on municipalities becoming more age-friendly. Hence, we examine the age-friendliness of 135 Japanese municipalities, selecting 240 resident architectural designers and constructors to assess their municipalities, using the Age-Friendly Cities and Communities Questionnaire (AFCCQ). The findings indicate that Japan lacks “outdoor spaces and buildings.” Additionally, the evaluation of “housing,” “community support and health services,” and “transportation” in populated municipalities in the past five years is significantly higher than those in depopulated ones. Age-friendliness is significantly affected in the AFCCQ total score (hereafter, Score) based on “housing,” “social participation,” “community support and health services,” “transportation” and “financial situation” evaluations. High specificity (0.939) was found when the Score was treated as a marker of depopulation and, therefore, an age-friendly approach is a necessary condition to prevent depopulation. Furthermore, a lack of “communication and information” is observed in municipalities with a higher rate of single-person households aged 65 years and above. Therefore, resident architectural designers’ and constructors’ assessments, combined with the AFCCQ can be a powerful tool in evaluating the age-friendliness of municipalities.

The In this paper, a universal method has been developed, which is a combination of an optimization method and a method for assessing the marginal utility. At present, the problem of optimal load distribution in the power system between a hydropower plant (HPP) and thermal power plants (TPP) is solved using the equality of the differential incremental rate characteristics of fuel consumption at TPP and water consumption at HPP by the Lagrangian multiplier method. In this case, the number of iterations can be five or more. The proposed approach is based, first of all, on the correct representation of the differential characteristics and calculation of a hydro resource price for the operational control of the HPP. Based on the comparison of water volume at a HPP and fuel amount at combined heat and power plants (CHPP) used for generation of 1 kW power, it is possible to determine a water price for a HPP. As a result of implementing the developed method for the HPP, a price of sold electricity in the flexible energy market will be comparable with the price for sold electricity produced at CHPPs, being equal to approximately 120 rubles/MW∙h.

DC residential distribution system (RDS) consisted by DC living home will be a significant integral part in the future green transmission. Meanwhile, the increasing number of distributed resources and intelligent devices will change the power flow between main grid and demand sides. The utilization of distributed generations (DGs) requires an economic operation, stability, environmentally friendly in the whole DC system. This paper not only presents an optimization schedule and transactive energy (TE) approach through centralized energy management system (CEMS), but a control approach to implement and ensure DG output voltages to various DC buses in DC RDS. Based on data collection, prediction and a certain objection, the expert system in CEMS can work out the optimization schedule, after this, the voltage droop control for steady voltage is aligned with the command of unit power schedule. In this work, a DC RDS is as a case study to demonstrate the process, the RDS is associated with unit economic models, cost minimization objective is proposed to achieve based on real-time electrical price. The results show that the proposed framework and methods will help the targeted DC residential system to reduce the total cost and reach stability and efficiency.

The forage fish, a crucial source of nutrition in developing countries, are unfortunately primarily used for fishmeal and aquafeed production for aquaculture that mainly serves consumers in developed countries. Industrial fish use leaves a meager portion of the catch for direct human consumption in the fish-producing countries, leading to inflated fish prices locally. Overfishing forage fish necessary to support the fish reduction industry disrupts the ecosystem, diminishes the catch of larger fish, such as mackerel, and marginalizes local artisanal fishermen. This article appraises roughly the financial and nutritional impacts of elevated fish prices on low-income consumers in forage fish-producing countries, drawing attention to the ethical implications of this situation. A hypothetical 10% boost in the annual supply of fresh forage fish to the current global supply of 5 million MT markets could save consumers hundreds of millions of US dollars globally, tens of mil-lions of US dollars nationally, and several dollars for a family while improving the nutrition of families that depend on forage fish. The numbers suggest that even a modest boost to the supply of forage fish in fresh fish markets could significantly benefit the fish-producing nations' consumers and the fish-producing nations. In some countries, such as South Africa, the orders of magnitude of these sums approach that of the entire local fish reduction industries' value. The increased fish prices could be considered involuntary subsidies by low-income consumers to the aquafeed and aquaculture industries. In summary, the reduction industry's and aquaculture's current use of captured forage fish warrants further scrutiny, as it inadvertently burdens disadvantaged societies financially and nutritionally. The article proposes alternative protein sources and cultivating non-carnivorous fish among several optional measures to ensure equitable distribution of forage fish resources.

The aim of this study is to examine the short-run and the long-run equilibrium relationship between banking sector’s efficiency and stability and its endogenous and exogenous determinants such as inflation and international oil prices shocks in Saudi Arabia over the period 2004–2022. This study differentiates between the direct and indirect effects of international oil price changes on bank efficiency and stability to investigate the extent to which these changes can affect the banking sector through inflation. The first stage consists of using a panel Autoregressive Distributive Lag (ARDL). The empirical result confirms the existence of a long/short run relationship between oil price shocks and stability and efficiency of banks. In the long run the relationship is statistically significant and positive and is negative in the short run. On the other hand, this study finds that oil price shocks directly affect the stability and efficiency of banks. In the second stage, this study uses a nonlinear ARD (NARD) to examine the short- and long-run asymmetric impacts of oil price shocks on the stability and efficiency of banks by decomposing the oil price index into positive and negative changes. The finding confirms an asymmetric relationship between oil prices and the stability and efficiency of banks in Saudi Arabia. In addition, a positive change in oil price can affect the stability and efficiency of banks more than a negative one. Overall, the finding highlights the need for policymakers in Saudi Arabia to be vigilant in addressing the potential risks arising from oil price fluctuations and to adopt appropriate policy measures to maintain stability and efficiency in the banking sector.

Market dynamics is based on classical electrodynamics and uses quantum electrodynamics as a reference model framework. Its construction is always based on Bourbaki structuralism. Market dynamics studies the interaction between price, demand and supply. Quantum electrodynamics studies the interaction between photons, electrons and positrons. Both belong to the category of single charge dynamics and share a symmetry group. Market dynamics introduces market charge and defines demand and supply respectively. By examining the market hesitation phenomenon, spin is introduced as the intrinsic property of demand and supply. Therefore, demand and supply can be defined by Dirac spinors. By introducing the coin cone and valence shell, virtual prices are defined; demand and supply interact by exchanging virtual prices. These interactions can be described by Feynman diagrams. The cognitive field has the function of polarizing decision-making and is described by the magnetic field. Market dynamics believes that the social economy itself is an experiment and every market participant is an observer. We established the non-commutative relationship of market observation and the market version of the uncertainty principle of observation information, and defined the quantum version of the invisible hand. On the basis of classical electrodynamics, the relationship between price field and Maxwell field is discussed, and the concept of gauge potential is introduced. Market fluctuations (including mesoscopic and quantum fluctuations) caused by high-order cognition (including gaming, decision-making and reasoning). The fluctuation phenomena inherent in various markets are the basis for understanding both the cognition of market participants and the phase transition of the market. Six components of the market wave function are defined, namely the global phase factor, global gauge potential and global gauge field strength, as well as the local phase factor, local gauge potential and local gauge field strength. Gauge transformation is a integral part of quantum electrodynamics and market dynamics. The gauge principle is then given to show that global symmetry is a necessary condition for local symmetry. Four operators are introduced, namely the Newton mean operator, the Einstein cone operator, the Schrödinger evolution operator and the Dirac field theory operator. These four operators are applied to the market wave function respectively, presenting four operator phases of the market wave function. The change of operator state is called the phase transition of the market wave function.

Accurate agricultural commodity price models enable efficient allocation of limited natural resources, leading to improved sustainability in agriculture. Because of climate change, price volatility and uncertainty for the sector are expected to increase in the future, increasing the need for improved price modeling. With the emergence of machine learning (ML) algorithms, novel tools are now available to enhance the modeling of agricultural commodity prices. This research uses both univariate and multivariate ML techniques to perform probabilistic price prediction modelling for the Canadian beef industry, taking into account beef production, commodity market, and international trade features to enhance accuracy. We apply multivariate algorithms by using support vector regression (SVR), random forest (RF), and adaboost (AB) and univariate algorithms by using autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX). We apply these models to monthly fed steer price data between January 2005 and September 2023 and compare predicted prices with observed prices using several validation metrics. The outcomes indicate that both random forest (RF) and adaboost (AB) in multivariate modeling show superior performance in accurately predicting Alberta fed steer prices in comparison to other algorithms. To better account for the variance of the best model performance, we subsequently adopted a probabilistic approach by considering uncertainty in our best-selected ML model. The beef industry can use these improved price models to minimize resource waste and inefficiency in the sector and improve the long-term sustainability prospects for beef producers in Canada.

Overcoming the issue of land value and cost in urban areas will not provide a miraculous solution to the problems there. Appropriate land use cost, especially for residential and commercial land, is just one of the issues to be settled in the debate. Therefore, this study aims to build a new urban land price determination model by investigating the urban syntactical analysis, street width, and their economic effects on land value. The study attempts to determine the impact of syntactic analysis of streets and street width on land prices; it also seeks to identify the factor most affected by the land cost. Ultimately, the study built a model for urban land price prediction. The case selected is evaluated and compared in three aspects of the analysis, including; the urban axial assessments and urban street width, to find out their impacts on the real estate’s land price in the context of the land use distributions, which are predominantly residential and commercial types of uses. Depth map X8, SPSS, and QGIS 3.16 were used for the study evaluations and assessments. The result showed a significant impact of the urban street network on the price of land; this result can be used to enhance future urban design regarding urban economy improvements.

This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. The study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020, in addition to macroeconomic variables such as EPU. This study investigates the moderating effects of EPU on investments in fixed and intangible assets, as well as its effect on Tobin's Q and the market price per share. The outcomes demonstrate that EPU reduces the costs of both tangible and intangible assets for businesses. In addition, companies with a greater Tobin's Q and market price per share are more impacted by uncertain corporate investment policies. However, financial leverage is found to be negatively correlated with share price and positively correlated with earnings per share and earnings per unit. Tobin's Q is positively correlated with financial leverage, indicating that firms that raise capital through debt are more likely to create value for investors. The research indicates that market-dependent enterprises are more susceptible to the unpredictability of monetary policy. Eventually, the findings suggest that consistent and transparent economic policies may increase the efficiency of corporate investment.

In this paper, we study the performance of various deep reinforcement learning algorithms to enhance the energy management system of a microgrid. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a set of thermostatically controlled loads, a set of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate among the different flexible sources by defining the priority resources, direct demand control signals, and electricity prices. Seven deep reinforcement learning algorithms were implemented and are empirically compared in this paper. The numerical results show that the deep reinforcement learning algorithms differ widely in their ability to converge to optimal policies. By adding an experience replay and a semi-deterministic training phase to the well-known asynchronous advantage actor-critic algorithm, we achieved the highest model performance as well as convergence to near-optimal policies.

In this paper, mean-field type games between two players with backward stochastic dynamics are defined and studied. They make up a class of non-zero-sum differential games where the players' state dynamics solve backward stochastic differential equations (BSDEs) that depend on the marginal distributions of player states. Players try to minimize their individual cost functionals, also depending on the marginal state distributions. Under some regularity conditions, we derive necessary and sufficient conditions for existence of Nash equilibria. Player behavior is illustrated by numerical examples, and is compared to a centrally planned solution where the social cost, the sum of player costs, is minimized. The inefficiency of a Nash equilibrium, compared to socially optimal behavior, is quantified by the so-called price of anarchy. Numerical simulations of the price of anarchy indicate how the improvement in social cost achievable by a central planner depends on problem parameters.

This article discusses the outcomes of a quantitative analysis of the competitiveness of grain and its recycling in the countries-members of the Eurasian Economic Union (EEU) through econometric panel data models. The analysis is based on the data of public authorities’ statistics of the countries-members of the EEU, as well as the United Nations Comtrade Database which is a repository of official international trade statistics. The results of the analysis allow to assess the level of competitiveness of produced agro-food products in the countries-members of the EEU and determine the extent to which various factors affect it. The research conclusions can be used to develop and adjust the agreed with the agro-food policy in the EEU.