Integrated gasification combined cycle (IGCC) systems have a significant role in advancing the transition toward cleaner energy production by enabling the efficient and environmentally responsible use of coal and other carbon-based feedstocks. IGCC systems not only reduce emissions and improve thermal efficiency but also offer integration potential with emerging low-carbon technologies. Integrated gasification combined cycle with syngas storage (IGCC-SS) offers greater operational flexibility, particularly during maintenance periods or sudden power outages, and enhances the ability of the plant to respond to variable energy demands. This review focuses on analyzing syngas storage technologies and their operational considerations to enhance the flexibility, reliability, and scalability of IGCC systems. The feasibility of syngas storage is highly influenced by the physicochemical properties of syngas, which depend on the gasification operating conditions and the type of feedstock. These factors affect syngas composition, energy density, and flammability limits, all of which are critical for safe and effective storage. The integration of syngas storage in IGCC systems provides enhanced operational flexibility, enabling the system to adapt to a broader range of duty cycles and variable energy demands. However, it also has trade-offs, including reduced overall efficiency and increased energy production costs. Therefore, the process design configuration and operational variables must be carefully developed to achieve the optimal balance between performance, reliability, and economic viability. Future research may focus on optimizing syngas storage parameters and the syngas distribution ratio to minimize efficiency losses and enhance the adaptability of IGCC-SS systems to dynamic power grid requirements.
Thomas P. McAuliffe, Samuel Liew, Yuchao Li
et al.
We address the problem of executing large client orders in continuous double-auction markets under time and liquidity constraints. We propose a model predictive control (MPC) framework that balances three competing objectives: order completion, market impact, and opportunity cost. Our algorithm is guided by a trading schedule (such as time-weighted average price or volume-weighted average price) but allows for deviations to reduce the expected execution cost, with due regard to risk. Our MPC algorithm executes the order progressively, and at each decision step it solves a fast quadratic program that trades off expected transaction cost against schedule deviation, while incorporating a residual cost term derived from a simple base policy. Approximate schedule adherence is maintained through explicit bounds, while variance constraints on deviation provide direct risk control. The resulting system is modular, data-driven, and suitable for deployment in production trading infrastructure. Using six months of NASDAQ 'level 3' data and simulated orders, we show that our MPC approach reduces schedule shortfall by approximately 40-50% relative to spread-crossing benchmarks and achieves significant reductions in slippage. Moreover, augmenting the base policy with predictive price information further enhances performance, highlighting the framework's flexibility for integration with forecasting components.
With the advancement of video generation foundation models (VGFMs), customized generation, particularly subject-to-video (S2V), has attracted growing attention. However, a key challenge lies in balancing the intrinsic priors of a VGFM, such as motion coherence, visual aesthetics, and prompt alignment, with its newly derived S2V capability. Existing methods often neglect this balance by enhancing one aspect at the expense of others. To address this, we propose LibraGen, a novel framework that views extending foundation models for S2V generation as a balance game between intrinsic VGFM strengths and S2V capability. Specifically, guided by the core philosophy of "Raising the Fulcrum, Tuning to Balance," we identify data quality as the fulcrum and advocate a quality-over-quantity approach. We construct a hybrid pipeline that combines automated and manual data filtering to improve overall data quality. To further harmonize the VGFM's native capabilities with its S2V extension, we introduce a Tune-to-Balance post-training paradigm. During supervised fine-tuning, both cross-pair and in-pair data are incorporated, and model merging is employed to achieve an effective trade-off. Subsequently, two tailored direct preference optimization (DPO) pipelines, namely Consis-DPO and Real-Fake DPO, are designed and merged to consolidate this balance. During inference, we introduce a time-dependent dynamic classifier-free guidance scheme to enable flexible and fine-grained control. Experimental results demonstrate that LibraGen outperforms both open-source and commercial S2V models using only thousand-scale training data.
Kholidiyah Masykuroh, Hendrawan, Eueung Mulyana
et al.
Video analytics is essential in smart city management, traffic monitoring, and security surveillance, where real-time decision-making is critical. However, the efficiency of these applications depends on optimizing video compression parameters to maintain high detection accuracy while minimizing bandwidth usage and computational costs. This paper presents a comprehensive survey of video compression optimization techniques, focusing on Quantization Parameter (QP), Frames per Second (FPS), Entropy Coding, and Motion Estimation for video analytics tasks. We examine traditional compression algorithms, machine learning-based approaches, dynamic parameter adjustment strategies, and hybrid models, each offering unique strengths and limitations. Our findings highlight that adaptive compression techniques improve the trade-off between detection accuracy and efficiency. However, challenges remain, particularly in dynamic and bandwidth-constrained environments. To evaluate these techniques, we employ AccMPEG. This video compression framework dynamically adjusts compression settings based on real-time inference feedback, ensuring an optimal balance between detection accuracy and bitrate efficiency. In addition, we assess perceptual quality using Just Noticeable Distortion (JND) and Mean Opinion Score (MOS). The results indicate higher QP values lead to noticeable quality degradation, particularly at lower bitrates. Motion estimation techniques influence perceived quality, and TESA achieves MOS ratings higher than alternative methods. Furthermore, H.265 demonstrates superior MOS scores compared to H.264 at lower bitrates, reinforcing its higher compression efficiency while preserving visual clarity. Despite these advancements, the generalization of findings is limited, as the dataset consists mainly of traffic videos, which may not fully represent other video analytics applications. Future research should explore a broader range of datasets and develop adaptive compression frameworks that integrate real-time inference feedback and advanced machine learning models.
Noon Hussein, Ayesha Khan, Ijaz Haider Naqvi
et al.
Community energy storage systems must balance equitable energy sharing among prosumers with long-term battery health. While forecast-driven allocation strategies improve fairness and operational efficiency of such systems, their impact on battery degradation remains underexplored. This study integrates supply-demand forecasting with a comprehensive battery aging model to examine the trade-offs between system performance and asset longevity in community storage applications. An extended power-law degradation model is used to capture the combined effects of state-of-charge variability, C-rate fluctuations, and thermal conditions on capacity fade mechanisms. To address these dynamics, a multi-objective optimization framework with special ordered sets linearization is proposed, balancing degradation minimization with renewable self-consumption maximization under adaptive power constraints. Validation using a 10-year dataset of five residential prosumers sharing a 20 kWh system shows that forecast-driven control enhances utilization while reducing capacity retention from 93.88% to 85.45% due to intensified cycling. The proposed degradation-aware optimization mitigates this penalty, retaining 91.01% capacity—representing a 6.5% improvement over the base forecast approach—while preserving efficiency gains. Results highlight that intelligent state-of-charge management with adaptive power limiting can reduce stress-induced aging while maintaining predictive scheduling advantages, particularly during periods of renewable energy surpluses when aggressive charging strategies become acceptable from a degradation perspective. The proposed framework demonstrates that sustainability and equity in community energy systems need not be mutually exclusive objectives.
Efficient deployment of deep learning models on resource-constrained devices requires balancing accuracy with energy consumption and/or latency. Quantization is a proven method to achieve this balance by reducing the precision of neural network weights and activations. However, simply changing the precision does not enable direct iso-accuracy and iso-energy comparisons. To address this, we combine a realistic processor energy model with a network filter multiplier that scales the number of channels, thereby enabling such comparisons. This work presents a Pareto-Optimal Quantization (POQ) methodology aimed at mapping a neural network architecture to a specific hardware platform while systematically exploring the design space in between to identify the most effective quantization strategy. Our approach evaluates how different design choices impact the accuracy-energy trade-off. Using detailed energy modeling instead of proxy metrics, our results reveal that 8-bit integer (<monospace>int8</monospace>) quantization is Pareto-Optimal for MobileNetV2, providing up to <inline-formula> <tex-math notation="LaTeX">$2.8\times $ </tex-math></inline-formula> energy savings or 10% higher accuracy compared to 16-bit floating-point (<monospace>fp16</monospace>). Furthermore, employing high-precision residuals shifts the Pareto frontier, making 4-bit integer (<monospace>int4</monospace>) quantization optimal, achieving up to <inline-formula> <tex-math notation="LaTeX">$1.9\times $ </tex-math></inline-formula> additional energy reduction or 2% additional accuracy gains. Moreover, our findings emphasize the role of DRAM energy in certain model configurations and highlight the importance of precise energy modeling. These results reflect the application of our POQ methodology to the practical deployment of energy-efficient deep learning models on constrained hardware.
In the era of growing global economic integration, international transactions such as trade, investment, and financial activities have become inevitable for every country, and central to these activities is the exchange rate. Nowadays, the Indian economy is more open, and hence, the stability of the rupee-dollar exchange rate (₹/$ ER) has become a crucial issue for the economy. Despite the historic 1993 reform in the Indian exchange rate management system, the rupee remained highly volatile, especially against the dollar, raising questions about the factors driving its fluctuations. The present study aims to identify these factors. The study employs the ARDL model, using annual data from reliable sources such as the Reserve Bank of India (RBI) and the World Bank for the post-reform period (1993-2023). The analysis reveals a long-run cointegrating relationship among the variables in the reference period. The estimated long-run ARDL model suggests that, among the selected macroeconomic factors, inflation and the interest rate are significant determinants of the ₹/$ ER in the post-reform period, and that, among the selected external factors, foreign portfolio investment (FPI), the current account balance, and oil prices are significant determinants of the ₹/$ ER in the post-reform period. However, the short-run dynamics highlight the role of interest rates and FPI alone, while the error-correction term confirms a moderate adjustment toward equilibrium. Identification of the significant factors affecting the ₹/$ ER would help in implementing policies to control these factors, at least to some extent, which may help to keep the ₹/$ ER more or less stable. The findings underscore the interconnectedness of monetary policy, trade dynamics, and global financial flows in shaping exchange rate behaviour.
Among CSP technologies, the linear Fresnel reflector (LFR) can provide reliable carbon-neutral electricity for large-scale applications. In this study, the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications, such as solar multiple and full-load thermal storage hours, were examined. Next, artificial neural network (ANN) surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology. Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg–Marquardt training algorithm was adopted. To overcome overfitting, validation and Bayesian Regularization approaches were compared. As training and testing data, 36 geographical sites with various combinations of design parameters were used. Through multi-objective optimization techniques, including the Multi-Objective Particle-Swarm Optimizer and Multi-Objective Grey Wolf Optimizer coupled with ANN surrogate modeling, this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria. The study also identified Site 4 (S4) as a promising candidate for optimal balance between the capacity factor (51.05%) and specific cost (5246.71$/kW), showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.
We consider a Kyle-type model where insider trading takes place among a potentially large population of liquidity traders and is subject to legal penalties. Insiders exploit the liquidity provided by the trading masses to "camouflage" their actions and balance expected wealth with the necessary stealth to avoid detection. Under a diverse spectrum of prosecution schemes, we establish the existence of equilibria for arbitrary population sizes and a unique limiting equilibrium. A convergence analysis determines the scale of insider trading by a stealth index $γ$, revealing that the equilibrium can be closely approximated by a simple limit due to diminished price informativeness. Empirical aspects are derived from two calibration experiments using non-overlapping data sets spanning from 1980 to 2018, which underline the indispensable role of a large population in insider trading models with legal risk, along with important implications for the incidence of stealth trading and the deterrent effect of legal enforcement.
This study aims to examine the relationship between customs policy and the economic blocs of which Egypt is a member, with a focus on the theory of New Regionalism and modern trends in customs policies. Egypt joined numerous economic blocs following its accession to the world trade organization (WTO) in 1995, yet this membership has not yielded significant positive impacts on the performance of Egyptian exports or the trade balance. The study utilized panel data analysis of Egypt’s international trade from 2001 to 2023. The results indicate that, despite Egypt’s limited success in reaping the benefits of most economic blocs, largely due to the concentration of Egyptian exports in primary and agricultural products and the low tariff rates, factors such as customs clearance processes, tariff barriers, non-tariff barriers, regional trade agreements, and technology adoption still play a crucial role in influencing trade volume among member countries. The findings highlight the significance of effective customs procedures and the reduction of trade barriers in boosting trade volumes within regional trade agreements. The study proposes a strategy for Egypt’s customs policy to maximize benefits from economic blocs, focusing on four key areas: aligning customs policy planning with targeted export sectors to realize trade creation and trade diversion effects; fully implementing trade facilitation programs and liberalizing customs policy procedures; adopting a national strategy to stimulate high value-added export industries as a long-term solution; and adopting regional trade agreements that support cumulative origin as a short-term solution.
This paper explores the intricate interplay of socio-economic drivers influencing environmental sustainability in oil-importing countries, examining CO2 emissions and greenhouse gas concentrations over the period from 2000 to 2021. Employing the Feasible Generalized Least Squares (FGLS) and Prais-Winsten regression method (PCSE), data from fifteen major oil-importing nations are scrutinized. The study identifies pivotal socio-economic factors, including environmental innovations, green energy adoption, and energy prices, as key drivers in mitigating emissions. Environmental innovations, catalyzing cleaner technologies, emerge as effective tools in curbing hazardous emissions and promoting green energy sources. The study underscores the trade-off between energy use and prices, advocating a strategic shift towards green energy adoption. It also highlights the adverse association between industrial and agricultural production with emissions. Policy interventions are recommended, emphasizing the need for cleaner technologies and the adoption of renewable energy practices, thus achieving a harmonious balance between economic development and environmental quality. The research offers valuable insights into the socio-economic dimensions of environmental sustainability, emphasizing the importance of well-balanced policies that align environmental innovations, environmental conservation, and energy transition in oil-importing nations within the broader context of human-environment interactions.
This paper presents an innovative Comprehensive Growth Dynamic Model (CGDM). CGDM is designed to simulate the temporal evolution of an event, incorporating economic and social factors. CGDM is a regression of logistic regression, power law regression, and Gaussian perturbation term. CGDM is comprised of logistic regression, power law regression, and Gaussian perturbation term. CGDM can effectively forecast the temporal evolution of an event, incorporating economic and social factors. The illicit trade in wildlife has a deleterious impact on the ecological environment. In this paper, we employ CGDM to forecast the trajectory of illegal wildlife trade from 2024 to 2034 in China. The mean square error is utilized as the loss function. The model illuminates the future trajectory of illegal wildlife trade, with a minimum point occurring in 2027 and a maximum point occurring in 2029. The stability of contemporary society can be inferred. CGDM's robust and generalizable nature is also evident.
Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can enhance decision-making in cryptocurrency algorithmic trading compared to traditional methods. In order to address this question, we combined reinforcement learning with a statistical arbitrage trading technique, pair trading, which exploits the price difference between statistically correlated assets. We constructed RL environments and trained RL agents to determine when and how to trade pairs of cryptocurrencies. We developed new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1 min intervals (n=263,520). The traditional non-RL pair trading technique achieved an annualized profit of 8.33%, while the proposed RL-based pair trading technique achieved annualized profits from 9.94% to 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as~cryptocurrencies.
Purpose Motivated by recent rapid exchange rate depreciations, shrank economic growth, high inflation, and persistent trade deficits, this study examines the trade balance (TB) in the face of the recent dynamics of the stated macroeconomic factors, which are also important determinants of the TB. The symmetric test of the J-curve phenomenon for the selected Sub-Saharan African (SSA) countries is revisited in this regard. The study uses panel data from 1970 to 2020 for ten of these countries for the longitudinal panel analysis with the TB as the dependent variable and the real exchange rate, foreign and domestic national incomes, and trade openness as the set of independent variables.Design/methodology/approach Because the underlying data set involves a heterogeneous panel of relatively short N and long T, the pooled mean group (PMG) and mean group (MG) heterogeneous panel models are employed based on the Hausman test for parameter consistency in heterogeneous panels.Findings The findings largely support the domestic income growth– TB worsening and the foreign income growth– TB improvement hypotheses. Trade openness is found to mostly augment the TB performance of the countries. The results also validated the J-curve effect for only 3/10 and 2/10 countries in the PMG and MG models, respectively. The divergence for most of the countries is attributed to possible import compression and institutional structure of SSA countries.Practical implications Given the favorable effects of trade openness on the TB performance of SSA countries, it is recommended that SSA countries place much emphasis on import-substitution industrialization and value addition to their natural resources as well as investment-driven growth policies to improve the competitiveness of their exports and reverse the chronic deficits in their TBs.Originality/value This paper is unique for invoking heterogeneous panel models to analyze the TB in light of recent dynamics of its determinants, as well as providing an update on the symmetric test of the J-curve phenomenon for the selected SSA countries.
Lingaraj Mallick, Smruti Ranjan Behera, Mita Bhattacharya
In this research, we investigate the dynamic relationship between the trade balance and exchange rate in the case of India using threshold cointegration and an asymmetric error-correction model. Empirical results validate that the long-run dynamic relationship between the trade balance and exchange rates is asymmetric. In the short run, the trade balance responds only due to positive deviations in the exchange rate. In contrast, in the exchange rate model, the exchange rate reacts only due to negative deviations in the trade balance. In addition, the results exhibit that the adjustment following variation in the exchange rate seems higher than the adjustment in the trade balance in the short run. Besides, the results indicate that the speed of adjustment due to the positive and negative shocks differs in the trade balance and the exchange rate models. Further, the uni- directional Granger causality result suggests that the trade balance substantially affects the exchange rate. However, the Granger causality effect of the exchange rate on the trade balance seems minimal. Finally, our results validate the impact of momentum equilibrium adjustment path asymmetric effects between the trade balance and exchange rate, indicating that the adjustment path is asymmetric in the long run. Therefore, policy planners in India should consider the asymmetric adjustment between these two drivers to overcome trade balance discrepancies in the short and long run. JEL Codes: F40, F41, C22, C32, C12
Neurological diseases are observed in approximately one billion people worldwide. A further increase is foreseen at the global level as a result of population growth and aging. Individuals with neurological disorders often experience cognitive, motor, sensory, and lower extremity dysfunctions. Thus, the possibility of falling and balance problems arise due to the postural control deficiencies that occur as a result of the deterioration in the integration of multi-sensory information. We propose a novel rehabilitation framework, Integrated Balance Rehabilitation (I-BaR), to improve the effectiveness of the rehabilitation with objective assessment, individualized therapy, convenience with different disability levels and adoption of an assist-as-needed paradigm and, with an integrated rehabilitation process as a whole, i.e., ankle-foot preparation, balance, and stepping phases, respectively. Integrated Balance Rehabilitation allows patients to improve their balance ability by providing multi-modal feedback: visual via utilization of Virtual Reality; vestibular via anteroposterior and mediolateral perturbations with the robotic platform; proprioceptive via haptic feedback.
Theodore Mariolis, Nikolaos Rodousakis, George Soklis
Using a multisectoral model and data from the Supply and Use Tables, this article estimates the COVID-19 multiplier effects of tourism on gross domestic product (GDP), total employment, and trade balance of the Greek economy. The results indicate that a—not-unexpected—decrease of international travel receipts in the range of 3.5 to 10.5 billion euros would lead to a decrease in GDP of about 2.0% to 6.0%, a decrease in the levels of employment of about 2.1% to 6.4% and an increase in the trade balance deficit of about 2.4 to 7.1 billion euros, respectively.