Search Results (3,360)

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

Electricity price spikes are the most important characteristic of the electricity price time series. Operationally, they result from various stresses in the power system or the strategic bidding behavior of market participants. These high prices are important as they represent economic opportunities in the form of profits and savings. Theoretically, price spikes are defined as prices that exceed a threshold over a typically short duration. This definition serves as the basis for several established modeling approaches in the literature. In general, the threshold component determines the design of a price spike model, often overlooking the duration aspect. Therefore, this paper presents a simple yet informative model to quantify the duration of electricity price spikes using historical price data from different market jurisdictions. We approach the problem through the lens of survival analysis, a widely used technique for evaluating time-to-event data. Specifically, we use the Kaplan–Meier (KM) estimator, which enables a nonparametric evaluation of the survival (duration) of price spikes over time. We refer to this as the price spike duration model. Full article

Global modern renewable energy based on geothermal, wind, solar, and marine resources has grown rapidly over the last decades despite low energy density, intermittent supply, and other qualities inferior to those of fossil fuels. What is the explanation for this growth? The main drivers of growth are assessed using economic theories and verified with statistical data. From the neo-classic viewpoint that focuses on price substitutions, the growth can be explained by the shift from energy-intensive agriculture and industry to labour-intensive services. However, the energy resources complemented rather than substituted for each other. In the evolutionary idea, investments supported by policies enabled cost-reducing technological change. Still, policies alone are insufficient to generate the growth of modern renewable energy as they are inconsistent across countries and in time. From the behavioural perspective that is preoccupied with innovative entrepreneurs, the value addition of electrification can explain the introduction of modern renewable energy in market niches, but not its fast growth. Instead of these mono-causalities, the growth of modern renewable energy is explained by technology diffusion during the pioneering, growth, and maturation phases. Possibilities that postpone the maturation are pinpointed. Full article

The prediction of stock prices is challenging due to their volatility, irregular patterns, and complex time-series structure. Reliably forecasting stock market data plays a crucial role in minimizing financial risk and optimizing investment strategies. However, traditional models often struggle to capture temporal dependencies and extract relevant features from noisy inputs, which limits their predictive performance. To improve this, we developed an enhanced recursive feature elimination (RFE) method that blends the importance of impurity-based features from random forest and gradient boosting models with Kendall tau correlation analysis, and we applied SHapley Additive exPlanations (SHAP) analysis to externally validate the reliability of the selected features. This approach leads to more consistent and reliable feature selection for short-term stock prediction over 1-, 3-, and 7-day intervals. The proposed deep learning (DL) architecture integrates a temporal convolutional network (TCN) for long-term pattern recognition, a gated recurrent unit (GRU) for sequence capture, and multi-head attention (MHA) for focusing on critical information, thereby achieving superior predictive performance. We evaluate the proposed approach using daily stock price data from three leading companies—HDFC Bank, Tata Consultancy Services (TCS), and Tesla—and two major stock indices: Nifty 50 and S&P 500. The performance of our model is compared against five benchmark models: temporal convolutional network (TCN), long short-term memory (LSTM), GRU, Bidirectional GRU, and a hybrid TCN–GRU model. Our method consistently shows lower error rates and higher predictive accuracy across all datasets, as measured by four commonly used performance metrics. Full article

This paper analyzes high-frequency intraday Bitcoin data from 2019 to 2022. The Bitcoin market index exhibits two distinct periods, characterized by abrupt volatility shifts. Bitcoin returns can be described by anomalous diffusion processes, transitioning from subdiffusion for short intervals to weak superdiffusion at longer intervals. Heavy tails are captured well by q-Gaussian distributions, and the autocorrelation of absolute returns shows power law behavior. Both periods display multifractality, with Hurst exponents shifting toward 0.5 over time, indicating increased market efficiency. The time evolution of the empirical PDF of price return allows us to connect these stylized facts to the mathematical framework of multifractals and locally fractional porous medium equations. Full article

With the rapid growth in the penetration of Electric Vehicles (EVs), their large-scale uncoordinated charging behavior presents significant challenges to the hosting capacity of traditional distribution networks (DNs). The novelty of this paper lies in its methodology, which integrates a Markov Chain Monte Carlo (MCMC) method for realistic load profiling with a bi-level optimization framework for Time-of-Use (TOU) pricing, whose effectiveness is then rigorously evaluated through an Optimal Power Flow (OPF)-based assessment of the grid’s hosting capacity. First, to compensate for the limitations of historical data, the MCMC method is employed to simulate the uncoordinated charging process of a large-scale EV fleet. Second, the bi-level optimization model is constructed to formulate a globally optimal TOU tariff that maximizes charging cost savings for EV users. At the same time, its lower-level simulates the optimal economic response of the EV user population. Finally, the change in the minimum daily hosting capacity is calculated based on the OPF. Case study simulations for IEEE 33-bus and IEEE 69-bus systems demonstrate that the proposed model effectively shifts charging loads to off-peak hours, achieving stable user cost savings of 20.95%. More importantly, the findings reveal substantial security benefits from this economic strategy, validated across diverse network topologies. In the 33-bus system, the minimum daily capacity enhancement ranged from 174.63% for the most vulnerable node to 2.44% for the strongest node. In the 69-bus system, vulnerable nodes still achieved a significant 78.62% improvement. This finding highlights the limitations of purely economic assessments and underscores the necessity of the proposed integrated framework for achieving precise, location-dependent security planning. Full article

Stated preference surveys have been utilized in the field of sustainable multimodal transport planning for some time. Stated preference, which relies on hypothetical scenarios to determine user preferences, offers critical insights into travelers’ choices between different transport modes. The stated preference method is used in a wide range of transport studies, such as the mode choice, route choice, service attribute analysis, pricing and fare policies, and technical innovations. On the basis of the collected data in stated preference studies, it is possible to optimize current services, forecast future demand, or analyze the possibilities of nonexistent services. A literature review reveals that there are certain gaps regarding the calibration of utility functions in multimodal and new services. Full article

In fresh produce cold chain last-mile delivery, the highly dispersed customer base leads to exorbitant delivery costs, posing the greatest challenge for cold chain enterprises. Achieving a symmetrical balance between cost-efficiency, environmental sustainability, and service quality is a fundamental pursuit in logistics system optimization. This paper proposes integrating the crowd-shipping logistics model—characterized by internet platform sharing and flexibility—into the delivery service. It incorporates and extends features such as cold chain delivery, mixed fleets using gasoline and diesel vehicles (GDVs), electric vehicles (EVs), partial charging strategies for EVs, and time-of-use electricity pricing into the crowd-shipping model. A joint delivery mode combining traditional professional delivery (using GDVs and EVs) with crowd-shipping is proposed, creating a symmetrical collaboration between centralized fleet management and distributed social resources. The challenges associated with utilizing occasional drivers (ODs) are analyzed, along with the corresponding compensation decisions and allocation-related constraints. A route optimization model is constructed with the objective of minimizing total cost. To solve this model, an Improved Whale Optimization Algorithm (IWOA) is proposed. To further enhance the algorithm’s performance, an adaptive variable neighborhood search is embedded within the proposed algorithm, and four local search operators are applied. Using a case study of 100 customer nodes, the joint delivery mode with OD participation reduces total delivery costs by an average of 24.94% compared to the traditional professional vehicle delivery mode, demonstrating a more symmetrical allocation of logistical resources. The experiments fully demonstrate the effectiveness of the joint delivery model and the proposed algorithm. Full article

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected photovoltaic (PV) system integrated with pumped hydro storage (PHS) and battery storage for residential applications. A novel optimization algorithm is employed to achieve techno-economic optimization of the hybrid system. The results indicate a remarkably short payback period of about 5 years, significantly outperforming previous studies. Additionally, a threshold is introduced to activate pumping and water storage during off-peak nighttime electricity hours, strategically directing surplus power to either the pump or battery according to system operation principles. This nighttime water storage strategy not only promises considerable cost savings for residents, but also helps to mitigate grid stress under time-of-use pricing schemes. Overall, this study demonstrates that, through optimized system sizing, costs can be substantially reduced. Importantly, with the nighttime storage strategy, the payback period can be shortened even further, underscoring the novelty and practical relevance of this research. Full article

Background/Objectives: The rapid identification of carbapenemase genes directly from positive blood culture (BC) samples shortens the time needed to initiate optimal antimicrobial therapy for Carbapenemase-Producing Enterobacterales (CPE) infections. Several commercial automated PCR systems are available for detecting CPE resistance genes but are expensive. The Xpert^® Carba-R assay (Cepheid GeneXpert System) has high sensitivity and specificity for the detection of carbapenamase genes from bacterial colonies or rectal swabs, with an affordable price. This assay was not used for positive BC testing of CPE resistance genes. Whole-Genome Sequencing (WGS) for resistance genes can be used as the gold standard at a research level. In this study, we evaluated the performance of the Xpert^® Carba-R assay for the early detection of carbapenamase genes directly from positive BCs, using WGS as the gold standard. Methods: A prospective observational study was conducted at Children’s Cancer Hospital-Egypt (CCHE-57357). All positive BCs underwent direct gram staining and conventional cultures. A total of 590 positive BCs containing Gram-negative rods (GNRs) were identified. The Xpert^® Carba-R assay was used to detect carbapenemase genes directly from the positive BC bottle compared with WGS results. Results: Among the 590 GNR specimens, 178 were found to carry carbapenemase genes using the Xpert^® Carba-R assay, with results obtained in approximately one hour. The main genotypes detected were bla_NDM, bla_OXA-48-like, and dual bla_NDM/bla_OXA-48-like at 27%, 29%, and 33%, respectively. The agreement between Xpert^® Carba-R assay and WGS results was almost perfect for the genotype resistance pattern of isolates and individual gene detection. Conclusions: The use of the Xpert^® Carba-R assay directly from BC bottles was an easy-to-use, time-saving, affordable tool with high accuracy in identifying carbapenemase genes and, thus, shortens the time needed to initiate optimal antimicrobial therapy for CPE infections. Full article

At the international level, new measures, policies, and technologies are being developed to reduce greenhouse gas emissions and, more broadly, air pollutants. Road transportation is one of the main contributors to such emissions, as vehicles are extensively used in logistics operations, and many fleet owners of fossil-fueled trucks are adopting new technologies such as electric, hybrid, and hydrogen-based vehicles. This paper addresses the Hybrid Fleet Capacitated Vehicle Routing Problem with Time Windows (HF-CVRPTW), with the objectives of minimizing costs and mitigating environmental impacts. A mixed-integer linear programming model is developed, incorporating split deliveries, scheduled arrival times at stores, and a carbon cap-and-trade mechanism. The model is tested on a real case study provided by Decathlon, evaluating the performance of internal combustion engine (ICE), electric (EV), and hydrogen fuel cell (HV) vehicles. Results show that when considering economic and emission trading costs, the optimal fleet deployment priority is to use ICE vehicles first, followed by EVs and then HVs, but considering only total emissions, the result is the reverse. Further analysis explores the conditions under which alternative fuel, electricity, or hydrogen prices can achieve competitiveness, and a further analysis investigates the impact of different electricity generation and hydrogen production pathways on overall indirect emissions. Full article

This study presents a stochastic Markov-based modeling framework for occupant behavior and residential lighting demand in Luxembourg. Integrating demographic data, time-use surveys, Markov chains, and dual-layer optimization, the model enhances the accuracy of non-HVAC energy demand simulations. The Harmonized European Time Use Surveys (HETUS) provide a detailed activity-based energy modeling approach, while Bayesian and constraint-based optimization improve data calibration and reduce modeling uncertainties. A Luxembourg-specific stochastic load profile generator links occupant activities to energy loads, incorporating occupancy patterns and daylight illuminance calculations. This study quantifies lighting demand variations across household types, validating results against empirical TUS data with a low mean squared error (MSE) and a minor deviation of +3.42% from EU residential lighting demand standards. Findings show that activity-aware dimming can reduce lighting demand by 30%, while price-based dimming achieves a 21.60% reduction in power demand. The proposed approach provides data-driven insights for energy-efficient residential lighting management, supporting sustainable energy policies and household-level optimization. Full article

Progressive climate change and the increasing concentration of carbon dioxide in the atmosphere represent one of the most serious challenges facing modern energy systems. At the same time, the global overproduction of plastics, particularly polyethylene terephthalate (PET), places a significant burden on the natural environment and waste management infrastructure. Electrochemical reactors offer a promising solution by enabling the simultaneous conversion of CO₂ and EG into valuable products such as carbon monoxide and glycolic acid, using electricity derived from renewable energy sources. Carbon monoxide can be further processed into high-energy synthetic fuels, such as propanol, while glycolic acid holds substantial importance in the pharmaceutical and plastics industries. An economic analysis was conducted to estimate the capital expenditures required for an electrochemical reactor and to assess the investment’s profitability based on the net present value (NPV) indicator. In addition, a Life Cycle Assessment (LCA) was carried out to evaluate the environmental impact of the proposed technology, with particular attention to its carbon footprint. The results indicate that the profitability of the system strongly depends on the market price and purity of glycolic acid, as well as on access to low-cost renewable electricity. The LCA confirms a significantly lower carbon footprint compared to conventional CO production, though further technological advancements are required for industrial deployment. Full article

This study examines the dynamic relationship between the gold markets of India and the United States from 2005 to 2025. Recognising gold’s role as a hedge and safe-haven during market uncertainty, we employ the Autoregressive Distributed Lag (ARDL) model to assess long-term co-integration and apply the Toda–Yamamoto causality test to evaluate directional influences. Additionally, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) model is applied to examine volatility spillovers. Results reveal no long-term co-integration between the two markets, suggesting they function independently over time. However, unidirectional causality is observed from the U.S. to the Indian gold market, and the GARCH model confirms bidirectional volatility transmission, indicating interconnected short-run dynamics. These findings imply that gold market shocks in one country may affect short-term pricing in the other, but not long-term trends. From a portfolio diversification and risk management perspective, investors may benefit from allocating assets across both markets. This study contributes a novel empirical framework by integrating ARDL, Toda–Yamamoto Granger causality, and GARCH(1, 1) models over a two-decade period (2005–2025), incorporating post-COVID market dynamics. The combination of these methods, applied to both an emerging (India) and developed (U.S.) economy, provides a comprehensive understanding of gold market interdependence. In doing this, the paper offers valuable insights into causality, volatility transmission, and diversification potential. The econometric rigour of the study is enhanced through residual diagnostic tests, including tests of normality, autocorrelation, and other heteroscedasticity tests, as well as VAR stability tests. These ensure strong inference and model validity; more specifically, they are pertinent to the analysis of financial time series. Full article

The objective of this research is to understand the relationship between the tone of information released in government harvest reports, in extreme market conditions (rising and falling), and the behavior of agricultural commodity prices. In the period between January/2017 and February/2023, an autoregressive model of moving averages was used with a generalized autoregressive conditional heteroscedasticity approach. The evidence allows us to infer that investors can, on some occasions, use this information to direct their portfolios in order to balance risk and return. However, the full impact of the tone is not reflected immediately, possibly requiring time to be absorbed. Depending on the informational weight, the commodity, and the market context, there may or may not be an impact. This divergent empirical evidence indicates that there is a complex relationship between tone reading and asset pricing. Full article