Search Results (4,713)

Herein, we propose a quantum circuit learning framework for modeling the realized volatility (RV) of Bitcoin and investigate the statistical properties of the predicted time series through multifractal analysis. Unlike conventional GARCH-type models, which require a pre-specified functional form for the volatility process, a parameterized quantum circuit directly approximates the volatility function from empirical data, eliminating the need for explicit model selection. Using five-minute Bitcoin price data, we construct daily RV, train a single-qubit parameterized quantum circuit, and generate a long synthetic time series from the optimized quantum circuit. Multifractal Detrended Fluctuation Analysis is applied to calculate the generalized Hurst exponent $h\left(q\right)$, the singularity spectrum $f\left(\alpha \right)$, and the multifractal scaling exponent $\tau \left(q\right)$. The predicted return series exhibits $h\left(2\right)\approx 0.5$, consistent with near-random dynamics, and both the predicted and the empirical return series display multifractality that partially persists after random shuffling. The increment series of RV shows pronounced anti-persistence with $h\left(2\right)\approx 0.05$–$0.1$, consistent with the rough volatility hypothesis. These results demonstrate that a simple single-qubit parameterized quantum circuit captures qualitatively some observed properties in Bitcoin volatility dynamics. Full article

With the increasing penetration of distributed renewable energy sources, such as photovoltaic and wind power, their strong randomness and volatility pose significant challenges to distribution network operation and control. Simultaneously, missing and noisy source-load data in practical distribution network operation further constrain the accuracy of optimization decisions. To address these issues, this paper proposes a data-driven optimization method that integrates low-rank limited-information reconstruction, WGAN-GP-based scenario generation, and source–storage–load coordinated dispatch. Firstly, a low-rank matrix completion model solved by singular value thresholding (SVT) is used to reconstruct incomplete photovoltaic and load profiles. Secondly, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is trained on the reconstructed dataset to generate renewable-output scenarios, and five representative scenarios are retained through conditional scenario matching and averaging. Finally, a mixed-integer linear programming (MILP) dispatch model is established by considering energy-storage operating constraints, demand response constraints, and time-of-use electricity prices. The numerical case uses 60 daily profiles with 24 hourly points per day and a 20% random missing-data setting. Case study results show that the proposed reconstruction method reduces the overall RMSE from 177.15 kW to 52.40 kW compared with zero-fill processing. The coordinated dispatch decreases the daily operating cost from 10,060.36 CNY to 9414.67 CNY, corresponding to a 6.42% cost reduction. The limitations of the single-test-day benchmark and simplified active-power dispatch validation are also discussed. Full article

Price volatility in fresh food commodities can weaken supply-chain coordination, disturb market expectations, and increase short-term risks to food availability and affordability. This issue is more pronounced for specialty crops with seasonal production, concentrated supply, limited storability, and high sensitivity to climate, trade, energy, and online-attention shocks. This study develops a trajectory-regularized physics-informed multi-source forecasting framework for daily wholesale prices of garlic, scallion, and ginger in China from 2014 to 2024. The framework, denoted as STL–ETO–EMA–PILSTM, integrates Seasonal-Trend decomposition using LOESS (STL), Efficient Multi-scale Attention (EMA), Long Short-Term Memory (LSTM), an economically motivated physics-informed trajectory residual constraint, and Exponential-Trigonometric Optimization (ETO), using production, climate, macroeconomic, trade, crude-oil, and online-attention indicators. In this framework, the physics-informed component is implemented as a trajectory residual constraint inspired by price-adjustment inertia and local continuity, rather than as a conventional PINN based on strict governing physical equations. In one-step-ahead forecasting, the model outperformed conventional machine learning baselines and additional time-series baselines, including naive persistence, Transformer Encoder, and PatchTST, with MAE values of 0.0853, 0.0581, and 0.1409 for garlic, scallion, and ginger, respectively, and R² values above 0.996. Leakage-prevention procedures, walk-forward validation, multi-horizon forecasting, and Diebold–Mariano tests were used to strengthen result credibility. Multi-step forecasting showed clear performance degradation as the horizon increased, supporting the positioning of the framework as a short-term market-monitoring tool rather than a long-horizon structural projection model. Permutation-based feature-importance and interaction analyses revealed crop-specific price drivers. The framework provides an interpretable tool for fresh food price forecasting, market stability monitoring, and short-term operational risk monitoring in fresh food supply chains. Full article

Preferential flow paths and ineffective water circulation are difficult to quantify in ultra-high water-cut reservoirs because long-term waterflooding intensifies dynamic heterogeneity and oil–water flow interactions. This study develops a displacement unit (DU)-scale method that integrates dynamic liquid-volume splitting, saturation tracking, and techno-economic water-cut evaluation while considering time-varying reservoir properties. The method was applied to a typical ultra-high water-cut block in the Daqing Oilfield to characterize the temporal evolution of preferential flow paths. A total of 902 DUs were delineated from streamline envelopes, and validation with production profile data from representative wells showed an accuracy exceeding 82%. Under an oil price of 60 USD/bbl, the proposed economic water-cut criterion identified 368 economically strong preferential-flow DUs, accounting for 40.79% of all DUs. Two indicators, the water-cut profit–loss margin (Δf_w) and oil displacement efficiency (E_d), were then used to establish a Δf_w-E_d classification matrix. The DUs were divided into four types: economically ineffective strong-channeling units, channeling units with remaining potential, mature stable production units, and homogeneous units. The results support differentiated control measures, such as channel plugging, profile control, cyclic waterflooding, and fluid-rate optimization, for improving waterflood management in mature reservoirs. Full article

This paper evaluates residential energy management systems (EMSs) that combine on-site renewable generation and battery energy storage in an all-electric house. This work compares four levels of control complexity: baseline operation, deterministic rule-based control, an optimization-based benchmark, and adaptive control using machine learning, predictive control, and a transactive framework. A calibrated gray-box house model based on the Archetype Sustainable House in Vaughan, Ontario, was used to test each strategy under the same operating assumptions. The comparison shows a clear trade-off between simplicity and performance. Deterministic load-shifting strategies are easy to implement but deliver the lowest savings. The optimized controller provides a practical upper bound on achievable performance. The machine-learning controller, trained from optimized historical operation, produced the strongest annual savings and outperformed deterministic control by a range of about 15–22%. Predictive control showed promise, but its demonstration was limited by forecast-data quality; more than 40% of collected forecast files were unusable, leaving only a 10-day continuous case study. A transactive energy management system delivered moderate direct savings, but its main value was flexibility, agent-based coordination, and future applicability to community-scale control. Experimental work further showed that 98% of an air-source heat pump peak-hour load could be shifted using battery control hardware. Despite these technical benefits, this study finds that battery-supported residential EMSs remain financially unattractive under the electricity prices and battery costs considered here. The results suggest that the most realistic path forward is not a one-size-fits-all controller, but a staged transition from simple battery logic to adaptive and transactive control as hardware prices fall, data quality improves, and homes become more connected. Full article

In the post-Olympic era, China’s ice and snow tourism is shifting toward an experience-oriented model. Taking the Chongli Ice and Snow Tourism Resort as a case study, this research applies a BERTopic-LLM framework, BERT-based sentiment analysis, and the IPA-Kano model to multi-platform user-generated content (UGC). We systematically examined tourists’ perceptual structures, spatial experiential differences, and nonlinear needs. The results indicate that while overall tourist sentiments are positive, substantial spatial and perceptual heterogeneity exists. Positive perceptions are primarily driven by high-quality core attractions (ski slopes and Olympic heritage), whereas negative perceptions stem from operational issues like peak-season congestion, inflated prices, and insufficient service. Based on these characteristics, the resort’s spatial units are categorized into resource-integrated, facility-oriented, and core-attraction mismatch areas. The findings demonstrate that tourist satisfaction is non-linearly conditioned by the quality of supporting infrastructure rather than just resource endowment. Accordingly, we propose three optimization strategies—strengthening service guarantees, enhancing experiential value, and promoting cultural transformation—to support the sustainable development of China’s ice and snow tourism destinations. Full article

Artificial intelligence (AI) and machine learning (ML) are increasingly important in financial technology (FinTech) applications involving large datasets, uncertainty, and complex decision-making. First, this paper presents a review of AI- and ML-based approaches in FinTech from 2010 to 2025, with particular emphasis on the relationship between predictive analytics and optimization-based decision-making. The review identifies two major research streams: (i) predictive AI/ML models for financial forecasting, stock price prediction, risk management, and fraud detection and (ii) optimization approaches for constrained financial decision problems, including portfolio optimization, asset–liability management, and risk-based decision-making. These two streams have largely evolved independently, which creates challenges in real financial environments, where uncertainty in predictions directly affects decision quality. Secondly, the paper also provides a decision-oriented perspective on how AI/ML-based predictions can support optimization under uncertainty and practical financial constraints. It highlights the role of uncertainty-aware optimization, simulation-based methods, and hybrid approaches such as simheuristics in improving the robustness of financial decision-making. Finally, the paper identifies open research directions toward integrated financial decision-support frameworks that combine predictive analytics, optimization, and simulation to address dynamic and uncertain FinTech environments. Full article

Accurately assessing the multifaceted performance of urban commercial land is essential for steering consumption upgrading and refining urban spatial planning. However, existing scholarship continues to treat commercial land through a predominantly economic lens; non-economic indicators have rarely been compared with price-based metrics within a unified analytical framework. To bridge this gap, this study constructs a multidimensional benefit evaluation framework encompassing four indicators, namely, land values, shop rent, online reviews, and customer ratings, across 10,766 commercial land parcels in Chengdu, China, and applies XGBoost-SHAP methods to examine their predictive associations and spatial patterns. This study reveals the differences and commonalities among the four benefit indicators in terms of statistical characteristics, spatial distribution, and matching patterns, and further examines the ranking of their key predictors, as well as the nonlinear effects, threshold effects, and interaction effects with key factors. These findings corroborate the distinctiveness of the four benefit variables and the significance of comprehensively evaluating commercial land-use benefits. This study provides novel perspectives and empirical evidence for assessing commercial land-use benefits and their predictive mechanisms, and offers actionable guidance for formulating targeted consumption development, spatial layout, and business format optimization strategies, thereby contributing to the sustainable vitality of urban economic spaces. Full article

With the increasing adoption of the Engineering, Procurement, and Construction (EPC) contracts in government-funded and large-scale infrastructure projects, bill-of-quantities bidding based on preliminary design has emerged as a new procurement approach. Although this approach improves early-stage investment control, it also imposes higher requirements on contractors’ cost-management capabilities. Based on whole-process cost-control theory, this study develops a design-driven full-process cost-control framework for EPC projects using a reclaimed water plant project in northwest China as a case study. The model comprises three layers: a design-driven decision-making layer, a whole-process cost-control layer, and a collaborative management support layer. It covers the key stages of bidding, design, procurement, construction, and final settlement, and integrates design, cost, and procurement management with Building Information Modeling (BIM) and dynamic monitoring based on Earned Value Management (EVM). The case results show that the model can effectively identify and control cost risks, promote the integration of design optimization and cost control, and improve cost management performance. The final settlement price was 0.93% below the contractual settlement ceiling and about 6.6% below the initial investment estimate. This study provides both theoretical support and practical guidance for enhancing full-process cost control in EPC projects under preliminary-design bill-of-quantities bidding. Full article

Clay, as a sediment material, is an attractive option for the production of porous ceramics due to its low price and high abundance. Porous ceramics possess a combination of essential properties of clay-based materials, including high porosity and thermal and chemical stability, making them suitable for various industrial applications, such as filters, heat insulators, and absorbents. In this study, thermally and chemically purified clay was mixed with boric acid as a pore-forming agent. Obtained results reveal that different contents of boric acid (2 wt.% and 0.5 wt.%) and variations in synthesis conditions, including low pressing pressures up to 60 MPa and low sintering temperatures of 1150 °C and 1300 °C, optimize the production of a filter medium with good separation and mechanical properties. Further, these findings indicate that an adequate combination of boric acid content and synthesis conditions positively affects mechanical properties, including values of hardness, Young’s modulus, compressive and tensile strength of clay-based filters. The clay-based filter with 2 wt.% boric acid exhibited a larger maximum pore diameter of nearly 0.2 mm, compared to the one with 0.5 wt.% boric acid. The filtering efficiencies of both filters were tested on pharmaceutical-grade ciprofloxacin with removal efficiency above 80% for two tested concentrations (6 μM and 9 μM). Full article

Grid-connected photovoltaic–battery energy storage–electric vehicle (PV-BESS-EV) charging stations require supervisory energy management that can coordinate tariff response, carbon-intensity signals, peak constraints, storage utilization, and converter-level operability within a transparent evidential framework. This study develops a bounded-reference rule-based supervisory energy management system (RB-SEMS) that preserves lower-level local converter controllers while generating operating modes and saturated reference commands for BESS power, grid exchange, and EV charging limits. A dual-time-scale evaluation framework is established by combining short-time switching/control simulations for dynamic traceability and SOC-sensitive protection with 24 h, 15 min EMS-level energy-balance simulations for cost, carbon, peak, PV utilization, EV service, and storage throughput assessment. Selected daily reference-injection cases are retained as copied-model diagnostic checks rather than as full-day switching-level validation. Under the D4-LSOC condition, RB-SEMS reduces the reported post-startup DC-bus deviation from 46.13 V to 40.60 V and the filtered BESS peak from 269.18 kW to 84.42 kW. In the E1-TOU scenario, E1-TOU-cost reduces daily total cost from 623.57 CNY to 564.05 CNY, lowers peak-period grid import from 183.75 kWh to 126.75 kWh, and increases local PV utilization from 71.13% to 78.71%; E1-PC66 further reduces the maximum 15 min grid import from 77.88 kW to 66.00 kW. Under the prescribed E2-PCC scenario, E2-CP reduces the calculated grid-related CO₂ emissions from 550.29 kg to 500.42 kg, whereas the price-only diagnostic increases them to 572.29 kg. Same-metric PV-SC and MILP comparisons, tested-range sensitivity analysis, and a throughput-based degradation proxy clarify that RB-SEMS is an interpretable supervisory baseline for cost–carbon–peak–cycling trade-off analysis rather than a cost-optimal controller or regionally validated proof of carbon reduction. Full article

Road construction contributes to embodied carbon in infrastructure, with asphalt-bound layers often dominating construction-stage greenhouse gas emissions in flexible pavements. Reclaimed asphalt pavement (RAP) and high-modulus asphalt concrete can reduce virgin material demand and improve structural efficiency, but their sustainability benefit depends on maintaining equivalent pavement performance. This study develops a climate-informed, mechanistic, environmental, and economic Pareto optimization framework for RAP-modified high-performance asphalt concrete (RAP-HPAC) pavement sections in Alberta. The framework couples fitted dynamic modulus master curves, monthly pavement temperature inputs, ALVA layered elastic analysis, Asphalt Institute fatigue and rutting criteria, A1–A5 global warming potential (GWP), and Alberta 2026 installed unit-price cost data. The RAP-HPAC mixture contains 50% RAP and was designed through a balanced mix design to target approximately 80% effective RAP binder activation. Three traffic classes were evaluated: 731, 1300, and 5426 ESAL/day/direction, each with 2% annual compound growth over a 20-year design period. Relative to independently optimized conventional HMA controls, Pareto-selected RAP-HPAC sections reduced P50 construction-stage GWP by approximately 19–30% and first cost by approximately 6–11% at a conservative 0.90× RAP-HPAC cost multiplier. The results show that RAP-HPAC is most beneficial when used as a structural-bound base that replaces conventional asphalt-bound capacity while preserving sufficient granular support. The framework provides a reproducible design-stage approach for comparing recycled high-modulus asphalt mixtures using performance, carbon, and cost criteria simultaneously. Full article

This study explores the selection of a heat recovery system for cogeneration units based on gas engines supplying the district heating system in Opole in order to enhance the efficiency and sustainability of the system. The proposed modifications focus on utilizing low-temperature (LT) waste heat from engine cooling circuits and improving exhaust heat recovery. The research examines retrofitting three cogeneration engines (total thermal capacity of 7.6 MW) by integrating water-to-water heat pumps to upgrade low-temperature waste heat (55–45 °C up to 700 kW), enhancing heat supply to the district heating network. Additionally, a second stage of economizers is evaluated to maximize condensation-based exhaust heat recovery from the existing 95–135 °C system. These system modifications increase the overall thermal capacity up to 9–9.1 MW. To maintain heat supply during cogeneration unit shutdowns (due to failures or electricity price fluctuations), an auxiliary air-to-water cascade heat pump provides an additional 0.8–1 MW. With increasing electricity price volatility, these system modifications provide crucial operational flexibility. Computational simulations confirm that the hybrid configuration successfully upgrades waste heat while strictly maintaining the existing engine return water safety limit. The evaluation demonstrates high economic profitability alongside stable emission reductions. This research presents a case study in optimizing heat recovery in cogeneration-based district heating networks, demonstrating practical and scalable applications for sustainable energy systems. Full article

This study addresses the multi-objective optimization of characterizing a flat glass processing plant. To assess the operational conditions required for a flat glass processing small and medium-sized enterprise (SME) to become a prosumer compatible with renewable energy community (REC) participation. This work is motivated by the presence of more than 300 SMEs in Italy, like this, where RECs represent one of the few viable strategies for achieving the European Union’s 2050 decarbonization targets. The research is carried out in two scenarios; Scenario-I includes Stage-i and Stage-ii with the mutual goal of forecasting and optimizing. Forecasting is used in Stage-i to optimize the factory load, and in Stage-ii to shift and curtail energy loads based on the forecast, considering the Italian national energy price and the regional price bands (“fasce orarie”) F1, F2, and F3. Forecasting and the indicators of environmental and social performance are the means to ensure the best energy utilization and management, as they prove that the reduction in CO₂ emissions and benefits on the community level can be both obtainable. Subsequently, the techno-economic analysis and evaluation of prosumer-readiness conditions are carried out through the optimization of industrial energy demand: three optimization objectives are assessed in this study (i) energy cost, (ii) carbon emission, and (iii) load curtailment. Four algorithms are put into effect to solve the tri-objective optimization: multi-objective particle swarm optimization (MOPSO), multi-objective ant nesting algorithm (MOANA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimization (MOGWO). The algorithms are validated in Stage-ii to find the desired optimum in the cost of energy, reduce peak formation, and carbon emissions. To achieve this goal, a stochastic approach based on Monte Carlo simulations and VIKOR is used to optimally select the results. The findings show that the NSGA-II, MOPSO, and MOANA are more effective in solving the problem, while the MOGWO algorithm more quickly finds the optimal solution. Based on the defined objectives, a new configuration for the energy community is introduced, together with a community well-being index and an evaluation of the resulting benefits for the factory. In Scenario-II, the PV plants’ installation on the factory is sized, and the excess energy shared with the grid is evaluated. The Scenario-II results show that 497.184 MWh (33.9%) of energy is shared with the grid. Both results suggest how optimized industrial demand profiles improve SME participation in future RECs. Full article

After the completion of temporary repair of naval ship equipment, cost settlement has long relied on an ex post auditing model, which results in long cycles and a lack of immediate pricing references for the military. To address this issue, a comprehensive unit price estimation method based on Proximal Policy Optimization (PPO) is proposed, which rapidly generates reasonable unit prices for each process after the repair is completed, thereby providing a quantitative benchmark for negotiation. The unit price estimation problem is formulated as a Markov decision process, and a multi-objective reward function combining range reward, compliance penalty, and final accuracy reward is designed. To alleviate the sparse reward problem, potential-based reward shaping using the Critic network is introduced, which decomposes the final accuracy signal into each pricing step. The clipping mechanism of PPO is adopted to limit the policy update amplitude, thereby improving training stability. Experimental results on 12,000 desensitized real repair records show that the proposed method achieves a mean absolute percentage error (MAPE) of 11.3%, a coefficient of determination (R²) of 0.913, and an abnormal estimation rate (AER) of 3.5%. Compared with standard PPO, the AER is reduced by 59%. The proposed method can sequentially output reasonable unit prices after repair completion, exploring a technical pathway for transforming temporary repair funding from ex post auditing to immediate verification. Full article