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• Most U.S. Pre-K systems offer seats in public schools and CBOs. • We examined differences between public schools and CBO Pre-K settings in 5 systems. • We found evidence of substantial sorting of children and teachers by setting. • Differences in quality and children's gains tended to favor public schools. • Localities with fewer policy differences by setting showed fewer setting differences. Mixed-delivery prekindergarten (Pre-K) systems with slots in both public schools and community-based organization (CBO) settings are common in the U.S. Historically, policies and investments in many of these systems have placed CBOs and, by extension the children who attend them, at a disadvantage relative to public school programs and peers. In this descriptive study, we used secondary data to explore setting inequities in five large-scale Pre-K mixed-delivery systems (Boston, New York City, Seattle, New Jersey, and West Virginia), all of which had taken explicit steps to improve equity across settings. Our public school sample included 2,247 children in 367 classrooms in 146 schools and our CBO sample consisted of 1,700 children in 220 classrooms in 109 centers. We found evidence of substantial sorting of children and teachers by setting. Where we found differences in quality and children's gains, these tended to favor public schools. However, localities with fewer policy differences by setting showed fewer such setting differences. Findings suggest that advancing the goals of equitable, high-quality Pre-K access and narrowing opportunity gaps before kindergarten entry may require more research and policy attention to mixed-delivery setting policy equity. [ABSTRACT FROM AUTHOR]

Port digital transformation requires substantial investments. However, the benefits after transformation are difficult to accurately predict due to incomplete information available to ports. Balancing digital investments with benefits poses a key challenge for ports. We propose a game model based on revenue sharing between the port and the technology company to determine the digital level in equilibrium under incomplete information. Our study reveals that the incomplete information influences digital level of ports. When information is incomplete and ambiguous, the digital level of ports may be lower. However, significant ambiguous does not necessarily decrease the digital level due to risk pooling and agent cost effects. Additionally, revenue sharing reduces the incentive for technology company to withhold private information, and specific sharing ratios are identified. The study also finds that under incomplete information, digital level of port initially increases and then decreases with increasing optimism or the ratio of port's retained revenue. By considering these factors, our study provides guidance for ports in optimal investment strategies for digital transformation. • When information is significant ambiguous, the digital level of ports may not necessarily decrease due to risk pooling and agent cost effects. • Under incomplete information, digital level of ports initially increases and then decreases with increasing optimism or the ratio of port's retained revenue. • The revenue sharing ratio should also be carefully determined, with the port retention no less than 40%. [ABSTRACT FROM AUTHOR]

This paper reviews the basic issues involved in identifying and providing solutions to transport problems. The paper argues that solutions focusing solely on investment in capacity or second-best solutions to pricing such as road pricing and free public transport ignore the essential economic relationships involved in transport. The paper provides a critique of investment and pricing and argues for a more integrated approach in which optimal pricing is a core consideration in any investment decisions. This is seen as particularly important in a post Covid-19 world where many of the fundamental drivers of transport demand have changed, and public authorities face significant constraints on budgets whilst new pressures such as commitments to net zero in the face of global warming threats add additional pressures. The paper offers some thoughts on the principles involved in establishing a more suitable structure for dealing with the transport problem. • Identifies the core elements of the transport problem as pricing and investment. • Reviews problems in implementing first-best pricing. • Critiques investment decisions in the light of wider economic impacts. • Considers changes brought about by the Covid-19 pandemic and the move to net-zero. • Offers a set of principles for reforming transport policy. [ABSTRACT FROM AUTHOR]

This paper explores into stock market analysis using computer models to predict stock price changes, particularly focusing on the AAPL dataset from Apple Inc. Accurate stock forecasts are vital for informed investment decisions. By comparing different models—Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—the study offers a comprehensive performance analysis of deep learning approaches. The use of different deep learning architectures allowed us to capture different aspects of the stock price data and assess their effectiveness in making predictions. The project's findings would enable us to make informed decisions about which model to choose for future stock price prediction tasks, benefiting traders, investors, and other stakeholders in the financial domain. LSTM exhibited the best predictive capability, capturing intricate patterns due to its ability to handle long-term dependencies GRU also performed well. However, the MLP model didn't do as well because it couldn't grasp the complex patterns in the data as effectively as the other two models. So, for forecasting stock prices, the LSTM model proved to be the most accurate and reliable choice. These findings offer valuable insights for financial practitioners aiming to enhance stock price prediction using deep learning techniques, aiding in smarter investment strategies. [ABSTRACT FROM AUTHOR]

This paper analytically studies the optimal charging infrastructure investment decisions in parking lots with state-owned firms and private firms, which aims to alleviate range anxiety and facilitate the mass adoption of electric vehicles. The investment decisions are made based on the rational choice in the short term and are assumed to be static. However, the investment in charging infrastructure is a long-term strategy and the decisions might evolve over time. As a result, an evolutionary game theoretic approach is proposed to analytically explore the instability of the investment decisions and derive the optimal investment strategies. We first construct a basic model with a payoff matrix of public and private parking lots and formulate the dynamic replicator equations to find the equilibrium points. We further explore the evolutionary stable strategy (ESS) with its conditions, and analyze the impact of key factors on the strategy evolution. In the basic model, we find that the investment ratio of the state-owned firms and private firms is crucial to the ESS and its original parking revenue has no impact on the investment decisions. To discover the impact of the uncertainty of investments, we next reformulate the model and extend the original payoff matrix by incorporating the perceived value function based on prospect theory. The ESS and its relevant conditions are also derived. We observe that two models have the same ESS in most cases when there is a marked difference in their profitability. When the difference in their profitability is small, the perceived return on investment plays a pivotal role in their investment decisions. Finally, managerial implications are discussed. • Study EV charging infrastructure investment strategies in public and private parking lots. • Examine the decision-making process of state-owned and private firms with bounded rationality. • Quantify the benefits of the charging infrastructure investment in parking lots. • Observe the perceived ROI plays a pivotal role in their investment decisions. [ABSTRACT FROM AUTHOR]

Public subsidy, a frequently applied tool of economic development is often provided to the business sector assuming that it promotes investments, employment, and income generation. In this paper a micro economic model based empirical analysis is presented that shows the possible impact of public subsidy on economic growth. The outcomes are derived by investigating the investment decision of the enterprise with and without public subsidy. It is demonstrated that public subsidy may increase social welfare, but under certain circumstances it also may decrease it, partly because authorities deciding about them fail to consider the information on the investment projects that determines the outcome. The empirical analysis of corporate subsidies proves that both cases occur. The number of firms that performed well and presumably had not enough own resources to fund the investment was less than 10% of all subsidized firms. The programs had an overall negative impact on economic growth. We provide recommendations on how methods of evaluations, project selection and monitoring should be modified to achieve better results of subsidy programs. [ABSTRACT FROM AUTHOR]

The purpose of the research is to study the cryptocurrency data listed on Binance, and design a profitable strategy based on the findings. The data covers over 150 selected cryptocurrencies. The study aims to detect anomalies in the volume and number of transactions and apply an investment strategy based on deviations and sudden price fluctuations. An autoencoder and LSTM-based neural network have been used. Based on the results of the present research, it can be concluded that the model successfully identified anomalies in the data regarding the volume and number of transactions carried out. I it was also observed that price volatility in the period close to the detected anomaly was significantly higher than average volatility for the sample. [ABSTRACT FROM AUTHOR]

• This paper develops an integrated economic model to examine two competing ports' adaptation to climate-change disasters, when they have asymmetric information. • Under publicity policy, the better-informed port initiates a "signaling game". • Under confidentiality policy, both ports make their decisions simultaneously. • The government needs to promote inter-port competition (when they have different information structure) if it decides to publicize port adaptation information. • A joint implementation of the publicity and subsidy policies may result in excessive adaptation investment, leading to social welfare loss. This paper develops an integrated economic model to examine two competing ports' investment in adaptation to climate-change disasters. The ports have asymmetric information on the actual disaster damage. In deciding on adaptation investment, the "leader" port is a better-informed first mover and the "follower" port is a less-informed follower. The government is able to acquire and verify port adaptation information and chooses whether to publicize it (publicity policy) or keep it confidential (confidentiality policy). If the information is publicized, the leader port initiates a signaling game in which it makes a decision first, and the follower acts after observing the leader's decision. Under the confidentiality policy, both ports make their decisions simultaneously. Our analysis shows the following. 1) Under the publicity policy, the leader port does not adopt a pooling strategy; rather, it determines its adaptation and output based on true information on disaster damage (i.e., it implements a separating equilibrium). 2) The publicity policy has two effects: it increases the leader's throughput while decreasing the follower's throughput (i.e., the throughput effect), and it helps rationalize the follower's adaptation investment by making more up-to-date disaster information available (i.e., the adaptation investment effect). 3) When the degree of inter-port competition is high, the publicity policy improves social welfare by increasing the total throughput of the two ports (i.e., an overall positive throughput effect) and rationalizes the follower's adaptation investment (i.e., a positive adaptation investment effect). Otherwise, it reduces the total throughput of the ports (i.e., an overall negative throughput effect). This negative throughput effect may outweigh the positive adaptation investment effect, thereby reducing social welfare. These analytical results suggest that the government needs to promote inter-port competition if it decides to publicize port adaptation information. Finally, we find that the joint implementation of publicity and subsidy policies could result in excessive adaptation investment, leading to social welfare loss. Our study illustrates the important effects of information asymmetry on adaptation investment and has policy implications in the presence of port competition and government subsidy. [ABSTRACT FROM AUTHOR]

Talent Management (TM) has become a strategic priority for companies looking to identify high potential individuals for future positions, because investments only in Talent Acquisition proved to be inefficient over time, due to the "talent war". Given the scenario, investments in strategies to retain and develop high potential talents in the company increase competitiveness in the long term. The professional potential evaluation is an important TM practice, however there is no exact formula to identify an individual's potential, due to the different dimensions required. With this, the present article aims to contributes for fill a gap in the literature of multicriteria frameworks for potential evaluation, considering expert's behaviour and knowledge. The methodology was divided in three main points: 1) identify attributes and design a criteria structure for the potential evaluation with experts; 2) criteria evaluation by the experts, through the Analytic Hierarchy Process (AHP) method; and 3) alternatives evaluation through the ELECTRE-TRI method, using the expert's knowledge defining the method's parameters. The results indicated a structure with 6 main criteria, its importance weights, and a professional potential evaluation, considering 3 classes: a) Hight Potential; b) Potential to Test; and c) Professional Maintainer. For the future of the research, one of the steps in the roadmap is consider other methos to do the evaluation and compare them. [ABSTRACT FROM AUTHOR]

In line with the losses brought about by the recent pandemic of the new coronavirus, the oil market, as well as other segments, has been facing economic difficulties to recover. Seeking to bring a practical and efficient strategy to the corporate environment, in this area, this study demonstrates a new multi-criteria method based on multi-objectives, capable of calculating the importance of each criterion, to encourage more assertive and objective decision-making to evaluate different situations in the oil industry, such as the application of resources, investments and other strategies. As a case study, a group of AHTS (Anchor Handling Tug Supply) vessels was used to supply offshore platforms and application of the CRITIC-MOORA-3N method, which allows a logical analysis of the relevant criteria and presents an objective solution, as result for the scenario that applies. [ABSTRACT FROM AUTHOR]

This paper uses the bootstrap rolling-window Granger causality test to investigate the relationship between U.S. monetary policy uncertainty and green bond. The method addresses the limitations of ignoring the instability of coefficients and the time-varying relationship between the variables in previous literature. The results find both insignificant and significant relationship in the full period. One explanation of the insignificant relationship is that the changes of investor sentiment influence their judgment regarding to the monetary policy uncertainty and green bond, and thus the significance of the relationship between the variables. Besides, this paper finds the significant inter-relationship is time-varying. Specifically, in the sub-period when the market sentiment is low, the rising monetary policy uncertainty influences green bond price negatively. Meanwhile, the unexpected changes of green bond may also have both positive and negative effects on monetary policy uncertainty in different sub-periods. Thus, this paper proposes that the impact mechanism between the variables is effective when the investors are irrational. Furthermore, the findings may provide valuable implications for investors and governments, including adjusting investment strategy when the monetary policy is unclear, increasing monetary policy transparency, and monitoring speculative activity in green bond market. In addition, it is necessary to emphasize that the validity of the findings is limited to the U.S. and the period when investors' sentiment experience large fluctuations. Finally, future research may consider expand the analysis to the green stock, as it is another important channel for renewable energy investment. • The inter-relationships between monetary policy uncertainty and prices of green bonds are examined. • The rolling-window Granger causality method is applied. • The results find both insignificant and significant relationship. • The rising monetary policy uncertainty may influence green bond prices negatively. • The green bond may have a time-varying impact on monetary policy uncertainty. [ABSTRACT FROM AUTHOR]

Due to the impact of COVID-19, China's civil airlines face an urgent imperative: to devise strategies that reconcile their development goals with the imperative of reducing emissions. Consequently, we have developed a multi-objective model that assesses the trade-offs between mitigation efforts and developmental pursuits. Furthermore, we have established future recovery and development scenarios for the post-epidemic era, with a specific focus on two major Chinese civil airlines selected for comprehensive case studies. Results show that: Our model can be used as a reference for airlines when deciding to introduce aircraft types and to purchase SAF fuel, and it can also offset the impact of rapidly rising airline costs due to fuel and aircraft type prices; In the scenario of considerable development of the operational level, the optimal investment strategy of the case companies would introduce mainly two types of aircraft, the A320NEO and the B737MAX8, which account for about 28.2 percent and 32.6 percent of the proportion of the types of aircraft introduced in the year. The results of the sensitivity analysis show that the main factor affecting the use of sustainable aviation fuel (SAF) by civil aviation enterprises is the SAF supply cap. Only when the supply ceiling is increased by 90 % does the SAF usage rate change significantly with the SAF price. Finally, policy proposals are made on the use of clean fuels, the introduction of aircraft types and the resumption of the development of aviation companies. • A multi-objective model is constructed on the trade-off between mitigation and development. • Recovery and development scenarios of China's airlines in the post-epidemic era are established. • Propose strategies for the introduction of aircraft types and the procurement of SAF. [ABSTRACT FROM AUTHOR]

The established imitation protocol mandates that participants observe and learn strategies from the most successful individuals in their vicinity. Previous studies have highlighted the critical role of this protocol in understanding the mechanisms that drive the evolution of cooperation. Nevertheless, individuals often exhibit a natural reluctance to adopt substantial changes rapidly. In this context, we integrate the concept of investment trustworthiness into the strategy updating process and explore the outcomes of dynamic beliefs through numerical simulations. Our results demonstrate that dynamic trustworthiness significantly supports cooperation. Cooperators impede the incursion of defectors by progressively reducing their willingness to invest, thus fostering the formation of cooperative clusters. Our findings suggest that while sensitivity to higher payoffs bolsters cooperation, a tempered response to negative payoffs is advantageous. Additionally, the initial distribution of willingness is pivotal for the success of this dynamic. Our study expands the repertoire of strategy update techniques in investigating the evolution of cooperation and offers profound insights into the complexities of human cooperative behaviour. • Dynamic trustworthiness aids in strategy updates. • Sensitivity to payoffs enhances cooperation. • Moderate response to losses promotes success. • Initial willingness distribution is key for dynamics. • Formation of large clusters critical for cooperative success. [ABSTRACT FROM AUTHOR]

To improve energy efficiency and mitigate environmental impact, the iron and steel industry (ISI) is responsible for implementing a low-carbon transition. The renewable hydrogen-based steelmaking system (RHSS) has emerged as an innovative energy system in the pathway of reducing the carbon footprint of ISI. Nonetheless, the sustainable development requirements of RHSS emphasize the critical need for well-planned capacity investments to boost its economic viability. This study introduces a novel planning strategy for RHSS referred to as a multi-stage expansion planning (MSEP) model, which is aimed to minimize the levelized cost of crude steel over the planning horizon from 2025 to 2050 (EU climate neutrality year). The MSEP model incorporates the construction sequence and dynamic techno-economic parameters to formulate investment strategies that adapt to changing system requirements and external conditions over the planning horizon. To address the complexity of the MSEP model, a rolling-horizon approach (RHA) is proposed to solve the model sequentially while integrating updated information. The proposed approach is benchmarked against both the single-stage approach and the perfect-sight approach through case studies. The numerical results show that the proposed RHA (i) guarantees a rational capacity investment strategy for RHSS considering the construction sequence and dynamic techno-economic parameters over multiple planning stages; (ii) achieves a reduction in the levelized cost of crude steel by approximately 50% relative to the single-stage approach; (iii) resolves the convergence issues encountered with the perfect-sight approach while achieving an optimal solution in a reasonable time frame. Additionally, sensitivity analysis evaluates the uncertainties of multiple factors on the planning results, while the robustness analysis confirms the applicability and adaptability of RHA across various scenarios. • Rational capacity investments for RHSS from 2025 to 2050. • Develop a multi-stage expansion planning model for RHSS. • RHA can improve computational efficiency and achieve an optimal solution in a reasonable time frame. • Sensitivity analysis shows the discount rate as the key factor in the planning results. • The adaptability of RHA is evaluated through robustness analysis. [ABSTRACT FROM AUTHOR]

Throughout the years, the European Commission has allocated significant financial resources to road infrastructure construction, based on the belief that it would boost, socioeconomic development. However, literature evidence shows it is not consensual whether those investment policies will yield the expected outcomes. The main goal of this work is to shed light on where, and to what extent, accessibility changes due to road infrastructure construction might contribute to boost socioeconomic development at the municipal scale and regional cohesion, resorting to a case study consisting of the municipalities located in the Center Region of Portugal. The analysis uses spatial statistical methods and considers both space and time-lagged variables simultaneously, while controlling for endogeneity, spatial autocorrelation and demographic variables. Results show that accessibility changes induced by 20 years of road infrastructure construction had, in general, a positive impact on the development of the Center Region municipalities. However, depending on how accessibility is defined, negative impacts were also reported, as well as impacts that depend on geographic location. It is suggested that these latter impacts might be related to population migration within the Center Region. Concerning cohesion, no significant impacts of accessibility changes were found. • A methodology to assess the impact of accessibility changes, due to road infrastructure construction, on development at a municipal scale is presented. • The methodology was applied to the NUTS2 Center Region of Portugal, disaggregated at the local level (municipality), whose highway network has grown >100% in the last two decades. • Results show that in the same region is possible to identify municipalities where accessibility changes didn't have a significant impact on development and others where the impact was positive. Negative impacts were also reported. • Geographic location and scale analysis play a key role in the assessment of the impact of accessibility changes on development. [ABSTRACT FROM AUTHOR]

Stock prediction is a prominent and challenging topic of interest for both investors and researchers. With the introduction of Graph Convolutional Networks (GCNs) to financial data analysis, many researchers have attempted to use GCNs to exploit the interrelationships among stock data. However, a notable research gap remains in the application of GCNs to establish associations among transaction data, which inherently exhibit more stable and well-defined correlations. Hence, this study proposes a regularized ensemble framework with graph convolutional networks for stock price prediction. The framework employs multiple multi-graph convolutional networks to construct an ensemble graph convolutional network, elucidating spatiotemporal relationships within transaction data across various time scales. Additionally, the Variational Modal Decomposition (VMD) method is utilized to extract features from each dimensional sequence of the transaction data, enabling the capture of features across different time scales and mitigating non-stationarity and noise in the data. Furthermore, a regularization term incorporating trend accuracy is introduced to strike a balance between numerical precision and trend accuracy, thereby enhancing the overall model performance. Finally, this study presents a specialized investment strategy designed for stock price prediction. Our approach yields substantial improvements on SSE and DJIA datasets, with ACC and R 2 improving by over 15% and 10%, respectively, accompanied by reductions of more than 40% in RMSE, MAE, and MAPE compared to the baseline. These results underscore the effectiveness, robustness, and adaptability of our proposed methodology. Our codes and data can be accessed at https://github.com/WonderNothing/REGCN/. [Display omitted] • Predicting stock price using a regularized ensemble framework. • The ensemble framework can capture data relationships across different time scales. • Utilizing spatiotemporal correlations in trading data for stock price prediction. • Introducing a regularization term to balance numerical precision and trend accuracy. • Proposing a trading strategy to assess stock price prediction model performance. [ABSTRACT FROM AUTHOR]

This study proposes stock portfolio optimization models for risk-averse investors under uncertain conditions. To accomplish this, the study measures risk based on the conditional drawdown-at-risk (CDaR) measure, which can prevent major declines in investment as a conservative investment strategy. Given the uncertain character of the input parameters of the problem, the study first proposes a hybrid possibilistic and flexible model by considering the CDaR measure (CDaR-HPFM) to handle uncertainty. Furthermore, to offer more robust outcomes, the study constructs a hybrid possibilistic and flexible robust model by considering the CDaR measure (CDaR-HPFRM), in the forms of a non-linear and a linear mathematical programming problem. The CDaR-HPFRM can process the robustness of output decisions while dealing with uncertain parameters. The real stock exchange data of 100 companies registered on the Tehran Stock Exchange are investigated to validate the functionality of the proposed models. The results reveal that, when various penalty costs are factored in, the CDaR-HPFRM yields more efficient and more robust results than the CDaR-HPFM. Comparative results also indicate that the proposed models almost always outperform existing methods, in terms of both CDaR and rate of return measures. Proposed models can be used for all types of stock market investments (including micro-investing and investment funds) and can handle portfolio optimization and selection processes in project-based organizations. [ABSTRACT FROM AUTHOR]

In this paper, a generalized interval vector space is investigated and defined as an ordered relation in the form of a bijective linear transformation of its onto a real vector space. The ordered relation is utilized to formulate an interval optimization problem in the same manner as a classical multi-objective programming problem. A methodology that addresses the existence of efficient solutions for the multi-objective interval optimization problem has also been discussed. Various numerical examples are described to illustrate and substantiate all developed concepts. Furthermore, the multi-objective portfolio rebalancing problem for a time horizon is designed based on the developed interval vector optimization. An algorithm using exhaustive solution technology has been proposed to achieve an efficient investment strategy. Finally, its applicability and efficacy are analyzed using Bombay Stock Exchange India data sets. • Bijective relation for an interval is created using interval vector space. • The solution technique is developed for interval optimization. • Introduction and existence of the interval vector optimization problem. • The Multi-objective Interval Portfolio Rebalancing Problem is designed. • Evaluating the model's applicability and efficacy using historical data. [ABSTRACT FROM AUTHOR]

This study establishes a low-carbon construction supply chain (CSC) composed of a developer and a contractor. The developer invests in low-carbon promotion (LCP) to give consumers more accurate information about the project's low-carbon aspects. The contractor invests in low-carbon construction technology (LCT) with the intention of reducing carbon emissions. This paper explores the investment strategies and cooperative behaviors of developers and contractors from the perspective of government subsidies and further studies the conditions that affect the successful implementation of cost-sharing contracts between the developer and contractor. This study considers four decision-making scenarios: decentralized decision-making without government subsidies, decentralized decision-making with government subsidies, unilateral cost-sharing contract (UCC) with government subsidies, and bilateral cost-sharing contract (BCC) with government subsidies. The findings indicate that: (1) The contractor's LCT investment has a better optimization effect on CSC than the developer's LCP investment. (2) When only government subsidies are implemented, subsidizing contractors is more conducive to the optimization of CSC. When UCC and BCC are further introduced, subsidizing developers becomes more beneficial. Moreover, excessively high subsidy standards are not conducive to the sustainable development of CSC. (3) UCC and BCC both hold the potential for Pareto improvements within CSC, with success contingent upon specific conditions, and BCC tends to achieve better optimization than UCC. (4) The marginal profits of developers and contractors primarily drive the effectiveness of contracts. CSC's sustainability is jeopardized by cost-sharing ratios that are either excessively high or too low. This study provides a robust theoretical foundation for promoting low-carbon cooperation among members of CSC and formulating government subsidy policies. [Display omitted] • The dynamic change of carbon reduction is considered. • The investment efficiency of developers and contractors is studied. • Two types of cost-sharing contracts are proposed. • The government subsidy standards and subsidy coefficients are analyzed. [ABSTRACT FROM AUTHOR]

• Efficient integrated cascading failure analysis for enhanced investment planning. • Quantifying cascading failure impact using spatio-temporal event simulator. • ML algorithm to determine CVaR confidence level for tailored risk assessment. • Comparing cascade quantification metrics for investment strategies. Critical component outages can lead to widespread cascading propagation, which is however typically ignored in existing investment planning approaches. To address this gap, this paper seamlessly integrates advanced cascading failure analysis into resilient investment planning. It first deploys a stochastic simulator to generate spatiotemporal high-impact low-probability (HILP) events, which are then assessed using a cascading failure model, generating various cascading quantification metrics (CQMs). The framework explicitly quantifies tail risks (i.e., HILP events) using Conditional Value-at-Risk (CVaR) with a confidence level determined by unsupervised clustering, instead of using a predetermined confidence level. This enables the more tailored identification of a set of worst-case scenarios for the system under investigation, improving its practicality. An optimization model then utilizes the outputs of the cascading analysis and the defined CVaR confidence level to identify investment portfolios that provide a hedge against cascading failures. The proposed work is demonstrated on the IEEE 39-bus system, revealing reduced cascading propagation. [ABSTRACT FROM AUTHOR]

Understanding the relationship between air pollution and corporate investment strategy has significant implications for both environmental sustainability and economic development. Utilizing data from China's A-share listed companies spanning from 2014 through 2022, we investigate the causal relationship between air pollution and corporate allocation of financial assets. Using an instrumental variable approach, we find that worse air quality has a positive effect on corporate financial asset allocation. Specifically, a 1% increase in the number of air pollution days tends to result in a 0.002% increase in the share of financial assets held by firms. This effect prevails through diverse robustness tests. Furthermore, as companies encounter more stringent financing constraints and environmental regulations, the trend of air pollution promoting the increased financialization of assets becomes more pronounced. Based on the findings, this paper suggests providing more support for enterprises to facilitate the upgrading of production and operation to meet environmental standards. [ABSTRACT FROM AUTHOR]

Bitcoin market crashes bring huge economic losses and weaken the global financial system. Thus, predicting Bitcoin market crashes is very important for investors. However, due to the high volatility of Bitcoin prices, accurate prediction of crash events is difficult. This study proposes three components within a hybrid prediction approach to improve Bitcoin market crash prediction for better investment decision-making. First, we offer a hybrid prediction approach that combines various machine learning models with knowledge modeling of bubble phenomenon through the Generalized Supremum Augmented Dickey-Fuller (GSADF) test. Second, we employed the Synthetic Minority Oversampling Technique to address the loss of predictive power due to the severe imbalance between crash and no-crash periods in real-world data. Each machine learning model was trained using an effective oversampling strategy to determine the best predictive performance. Finally, SHapley Additive exPlanations interpretation of outcomes from the predictive model provides practical information for establishing efficient investment strategies. As a result, our crash prediction model based on four long-term bubble cycle information captured via the GSADF test showed significantly improved predictive performance. In particular, the precision-recall area under the curve performance of our prediction model after 7 and 14 days increased by 9.9% and 6.2% compared to original models, which proves that it is effective for mid-to long-term prediction. In addition, factors such as increased interest rates and the continuation of the bubble greatly increased the probability of future crashes in the Bitcoin market. We found that bubble phenomena lasting longer than 14 days significantly increase the probability of a crash. • Hybrid prediction for better Bitcoin crash event forecast and decision-making. • Machine learning models are combined with a multiple bubble detection model. • SMOTE is applied to balance imbalanced real-world data with skewed class distribution. • Interpretation of the outcomes using SHAP proposes efficient investment strategies. [ABSTRACT FROM AUTHOR]

Facing frequent impacts from extreme weather such as tsunamis, storms and tropical cyclones, ports suffer from crises of operation shutdowns, shipping traffic jams, commuting obstacles and even manpower shortages. This research focuses on investigating how ports can enhance resilience through forming alliance from the perspective of dynamic evolutionary games. It explores the port's investment strategy, government guidance for port alliance, and shipping companies' decision to alleviate the damages from extreme weather attack, particularly with the consideration of stochastic risks. The study examines key factors including investment in resilience enhancement, port changing, vessel detentions, government subsidy and reputation. By analyzing the evolutionary process, evolutionary rate, stable states and stochastic disturbances, the research reveals the dominant strategies under various conditions, resilience enhancement priority, threshold condition for port changing, optimal subsidy strategy, and the impacts of various risks. The findings would offer valuable theoretical insights and practical guidance to port management. [ABSTRACT FROM AUTHOR]

This article describes the idea of creating an intelligent system that could help investors. A partial implementation of the system in Java can be found at github.com. This part of the system is based on the data models presented in the article below. However, the implementation of the user interface, contrary to the suggestion below, is developed using JSF technology. This served as the basis for the development of the idea to abandon Web user interface in favor of its development in popular instant messengers. The idea of a module for calculating investment strategies is described but hasn't been implemented yet. Possible ways of its development and implementation are listed, the described intelligent system is a subject for further research. [ABSTRACT FROM AUTHOR]

Making long-term investment decisions is possible using the portfolio approach. In this respect, there are classical (e.g., Markowitz model) and non-classical (e.g., fundamental model) approaches. The key element in the construction of models is not only the measurement of risk and rate of return, but also the method of selecting assets (companies) to the portfolio. The method of selecting assets often determines the direction of the investor's investment strategy. The portfolio construction and asset selection processes remain an open question. The aim of the study is to assess the effectiveness of portfolio investment using the concept of fundamental strength. The results of the study indicate that the fundamental approach, depending on the portfolio construction procedure used, gives different effects, but allows for risk diversification in times of crisis [ABSTRACT FROM AUTHOR]

In the stock market, it can be challenging to predict stock trends and identify stocks with investment potential due to uncertainty and risk. Accurate predictive models and reliable asset preselection methods are essential for portfolio management. This paper proposes an integration of the deep learning model, the three-way decision (3WD) theory, and the Mean-Variance (MV) method for asset preselection and optimal asset allocation. In the initial stage, an attention-based Encoder–Decoder model is used to predict asset returns and calculate volatility. It outperforms other benchmark models regarding relative error, absolute error, and directional accuracy. The subsequent stage involves selecting stocks based on expected returns and volatility characteristics. The 3WD theory is then used to identify the delayed decision set for implementing additional investment strategies to hedge portfolio risk and enhance returns. Finally, the MV model is employed for investment portfolio optimization. The proposed model is validated using a substantial dataset from the A-share market's SSE 50 Index from January 2010 to December 2022. Results indicate that the proposed model outperforms benchmark models in terms of daily average returns, annualized Information ratio, Sortino ratio, and other aspects under certain trading costs. However, further exploration is required to establish a mechanism that limits the quantity of preselected assets to ensure its broad applicability. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) indices have emerged to assess countries' progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices are derived from a linear weighted sum of various criteria, employing deterministic weights. However, this approach fails to capture interactions among criteria, and the use of deterministic weights is susceptible to debate. To mitigate these issues, we conduct a methodological analysis based on Choquet integral (CI) and Stochastic Multicriteria Acceptability Analysis 2 (SMAA-2). We assess correlations between different AI dimensions and employ CI to model them. Additionally, we apply SMAA-2 to conduct a sensitivity analysis using both weighted sum and CI in order to evaluate the stability of the indices with regard to the weights. Finally, we introduce a ranking methodology based on SMAA-2, which considers several sets of weights to derive the ranking of countries. In the computational analysis, we evaluate our approach using the dataset employed in The Global AI Index, as proposed by the British news website Tortoise. The results reveal that our approach effectively mitigates bias. Furthermore, we scrutinize changes in the ranking resulting from weight adjustments and demonstrate that our proposed rankings closely align with those derived from variations in weights, indicating robustness. • Methodological analysis using Choquet integral and SMAA enhances AI index robustness. • We combine SMAA and the Condorcet method to obtain more robust AI country rankings. • Interactions among AI dimensions are considered, mitigating bias in rankings. • SMAA assesses weighting influence on AI rankings, ensuring robustness. • Robust AI rankings derived without deterministic weight definition. [ABSTRACT FROM AUTHOR]

With the advancement of prediction methods in the field of artificial intelligence, accurate price predictions can effectively support financial portfolio selection. This paper proposes an intelligent stock portfolio selection method based on a prediction neural network, incorporating signal processing and hyperparameter optimization techniques. The method is divided into two key stages: stock price prediction and portfolio selection. In the first stage, we apply Savitzky–Golay filtering to denoise price data and reveal its patterns, and optimize the hyperparameters of the long short-term memory network using the sparrow search algorithm to achieve high-precision stock price predictions. In the second stage, we use the mean-Conditional Value-at-Risk (mean-CVaR) model to select the optimal stock allocation, considering factors such as potential returns, prediction accuracy, and growth rate. Numerical comparisons based on multiple public financial datasets demonstrate that the proposed two-stage method significantly outperforms seven benchmark methods. Specifically, on the Shanghai and Shenzhen 300 (CSI 300) Index dataset, the proposed method achieves a determination coefficient of 0.9980 and an accuracy rate of 97.05%. Additionally, its cumulative returns reach 9.38%, 8.63%, and 7.54% at different confidence levels. [Display omitted] • An AI-powered technique for stock price prediction via filtering and optimization. • Two-stage portfolio decision process with the mean-conditional value-at-risk model. • Achieved high predictive ability: R-squared of 0.9980 and Accuracy Rate of 97.05%. • Outperforms traditional investment strategies: a maximal cumulative return of 9.3%. • Performance tops, compared with 7 benchmark strategies on open financial datasets. [ABSTRACT FROM AUTHOR]

Effective online portfolio selection necessitates seamless integration of three key properties: diversity, sparsity, and risk control. However, existing algorithms often prioritize one property at the expense of the others due to inherent conflicts. To address this issue, we propose an asset subset-constrained minimax (ASCM) optimization framework, which generates optimal portfolios from diverse investment strategies represented as asset subsets. ASCM consists of: (i) a minimax optimization model that focuses on risk control by considering a set of loss functions constrained by different asset subsets; (ii) the construction of asset subsets via price-feature clipping, which effectively reduces redundant assets in the portfolio; (iii) a state-based estimation of price trends that guides all ASCM loss functions, facilitating the generation of sparse solutions. We solve the ASCM minimax model using an efficient iterative updating formula derived from the projected subgradient method. Furthermore, we achieve near O (1) time complexity through a novel initialization scheme. Experimental results demonstrate that ASCM outperforms eight representative algorithms, including the best constant rebalanced portfolio in hindsight (BCRP) on five out of the six real-world financial datasets. Notably, ASCM achieves a 67-fold improvement over BCRP in cumulative wealth on the TSE dataset. • An extensible framework for integrating diversity, sparsity, and risk control. • A minimax model for risk control of loss functions constrained by asset subsets. • Construct asset subsets by price-feature clipping to reduce redundant assets. • State-based estimation of price trends to guide all loss functions. [ABSTRACT FROM AUTHOR]

Investment in distributed energy resources (DERs) is crucial for the expansion of distribution networks. However, traditional methods often lack comprehensive project management throughout their entire lifecycle and precise matching between distribution network operator (DNO) and energy consumer (EC) at the investment level. To address these issues, this paper proposes a novel investment model based on energy performance contracts (EPCs). Three EPC mechanisms are introduced: energy shared savings model (ESSM), energy guaranteed saving model (EGSM), and energy cost trust model (ECTM). Additionally, DERs investment strategies under EPC mechanisms are proposed to satisfy full-process management of project and precise matching between EC and DNO. The investment and operation problem is solved by an evolutionary game theory that ensures bounded rational agent to achieve satisfactory expected returns through repeated dynamic games. Results from the case study of an actual urban 37-bus system demonstrate that different EPC mechanisms are suitable for various scenarios depending on the EC's profit demand and project scale. The proposed model provides a comprehensive framework for DERs investment, considering the interests of both DNO and EC, and promotes the optimal expansion of distribution networks in a more practical and effective manner. [ABSTRACT FROM AUTHOR]

• A framework to assess equilibrium accessibility for import/export containers in the hub-and-spoke transport network is introduced. • Optimal investment and operational strategies for the port and shipping under the International Land-Sea Trade Corridor are determined. • Potential markets for the port and shipping and optimal routing choices for shippers are identified. • Computational errors are reduced by transitioning from a multi-parameter to a single-parameter gravity model. In Asia, the international land-sea trade corridor (ILSTC) is undergoing significant expansion, notably through the China Railway Express (CR-Express) and the export tax rebate at the departure port with Port of Yangpu as gateway. This expansion plays a pivotal role in shaping the accessibility for import/export containers in the hub-and-spoke transport network (A-IEC-HSTN). This paper delves into analyzing the ILSTC's impacts and introduces an approach assessing the equilibrium accessibility for import/export containers in the hub-and-spoke transport network (EA-IEC-HSTN), tailored to consider the impact of emerging policies like ILSTC. To examine how ILSTC affects transport accessibility and shapes stakeholder behaviors, a tri-level model is developed which synergistically combines the port investment decision model, liner shipping schedule design model, and multinomial logit model (MNL), all enriched by the incorporation of a gravity model. This tri-level model is combined with a classic transport accessibility framework to quantify EA-IEC-HSTN in the context of ILSTC when no stakeholders can gain more profit through changing their activities. This study offers a comprehensive examination of the EA-IEC-HSTN, the strategic investment of the Port of Yangpu, the liner shipping schedule and the associated incremental export freight demand. This paper markedly advances the understanding of trade transport network dynamics, offering critical insights that are invaluable for stakeholders and policymakers in the realm of international trade and transport policy. [ABSTRACT FROM AUTHOR]

Supply chain disruptions can be costly and disruptive, and reducing supplier uncertainty through investments can be an effective strategy for mitigating these risks. In this study, we explore ordering and investment strategies in a downstream competitive environment, considering supply uncertainty. We consider two buyers ordering from a single supplier and competing in a Cournot market. Buyers must balance investment benefits against costs when making investment decisions. We find that without downstream competition, a relatively low investment proportion can yield a "win-win" outcome. Under downstream competition, an increased number of investors does not necessarily lead to higher supplier reliability. When both buyers invest at a high ratio, the significant decline in order quantities may results in lower supplier reliability. When there is only one investing buyer in the market, even though the competitor may benefit from spillover reliability enhancement — free-riding, the buyer may also be willing to provide an investment subsidy to improve the supplier's reliability. When both buyers invest at a moderate ratio, downstream competition may benefit all supply chain members. Moreover, increasing wholesale price differentiation and endogenous investment ratios can effectively reduce free-riding behavior of the non-investing buyer. These findings have practical implications for supply chain managers aiming to reduce supply risks and allocate investment resources efficiently. • Buyers invest in suppliers to improve reliability under downstream competition. • Without competition, lower investment proportion leads to a win-win outcome. • Buyers may invest in suppliers despite the potential for competitor free-riding. • All supply chain members may benefit from the investment with downstream competition. • Differential wholesale prices and endogenous investment ratios reduce free-riding. [ABSTRACT FROM AUTHOR]

Yield gap analyses for livestock systems may utilize different approaches, including mathematical models based on pasture carrying capacity concepts. Protocols typically used to estimate pasture carrying capacity in Brazil do not consider adequately the seasonal variability of forage production, mismatching the estimates of demand and supply. This study aimed to develop a protocol based on the concept of pasture carrying capacity for yield gap analysis of pasture-based beef cattle production systems on a regional scale and apply the protocol to estimate yield gap on pasture-based beef cattle systems in Central Brazil. The framework gathered techniques such as homogeneous climatic zones definition; systematization of primary data with weather and soil information to run the models; scenario definition and assumptions; adaptation of the forage model to run long-term simulations; use of a pasture carrying capacity model, which estimates maximum (carrying capacity of systems with variable stocking rate) and critical stocking rates (carrying capacity limited by seasonal and interannual variability of forage production); and the use of national agricultural census databases to estimate actual stocking rate and calculate yield gaps. The protocol was applied to pasture-based beef cattle production systems under different management scenarios in Central Brazil. The maximum forage production and stocking rate increased with nitrogen fertilization and water availability. However, under cooler temperatures during winter months, forage production and critical stocking rates were less responsive to these factors. Mean yield gap for maximum stocking rate (difference between maximum and actual stocking rate) ranged from 5.81 to 5.12 animal units per hectare (AU ha−1) in the potential scenario, 4.18–2.9 AU ha−1 in the water-limited, and 2.73–1.43 AU ha−1 in the attainable scenario, while mean yield gap for critical stocking rate (difference between critical and actual stocking rate) varied from 5.44 AU ha−1 to 2.91 AU ha−1 in the potential scenario, 1.21–0 AU ha−1 in the water-limited, and 1.04–0 AU ha−1 in the attainable scenario. Yield gap analysis of pasture-based beef cattle systems can be performed using long-term forage production simulations coupled with a model based on cumulative forage deficits to determine stocking rate metrics. The protocol allowed the identification and quantification of the gap size due to interaction of the main factors influencing forage production and pasture carrying capacity under several environmental and management conditions, and may be applied to support policy and investment decisions. [Display omitted] • A improved framework for yield-gap analysis was developed for pasture-based beef cattle systems. • The forage model and cumulative forage deficits coupling allowed to estimate stocking rate metrics. • We found yield gaps associated with both primary plant production and pasture carrying capacity. • Pasture carrying capacity and seasonal production are mainly affected by rainfall and temperature. • The protocol may be applied to support policies and investment decisions on a regional scale. [ABSTRACT FROM AUTHOR]

Microgrid is a promising way to integrate renewable distributed generation into the electric power sector, which has received widespread attention. In this paper, a uniform capacity-constrained optimization model under different power purchase and sale price contracts is developed to explore the optimal green investment strategy for grid-connected microgrid (GCM) with three configuration types. The impact of renewable energy source endowment and incentive policy is focused when macrogrid's generators emit more pollution than GCM's non-renewable generators, such as in China and India. The results show that: (1) GCM with renewable generation can only be invested in a site with relatively abundant renewable endowment; (2) carbon tax effectively promotes the deployment of GCM with renewable generation and achieves carbon emission abatement, but excessive carbon tax cannot incentive investment in the most desired GCM type; (3) a subsidy trap is found under feed-in tariff (FIT) subsidy, which implies that the fade-out or exit of FIT subsidy is beneficial for microgrid's healthy development; (4) and the comparisons under different price contracts emphasize the importance of macrogrid providing non-discriminatory access to feed-in and feed-out services to microgrid. • Investment strategy is explored for grid-connected microgrid (GCM) with three types. • Renewable source endowment and incentive policy determine the investment for GCM. • Excessive carbon tax cannot incentive investment in the most desired renewable GCM. • The fade-out or exit of FIT subsidy is beneficial for healthy GCM deployment due to the subsidy trap. • Emphasizing the importance of macrogrid providing non-discriminatory services to GCM. [ABSTRACT FROM AUTHOR]

Hydrocarbons combustion is one of the primary sources of greenhouse gas emissions, causing climate change. As large volumes of gas are burned to power the energy-consuming oil and gas production on offshore platforms, the Norwegian petroleum industry contributes significantly to the country's emissions, making it a target for environmental regulations. Hence, Norwegian petroleum companies are facing the challenge of finding solutions to comply with increasing environmental requirements while being economically feasible. Offshore platforms electrification helps to reduce carbon emissions, but relevant investment decisions are complicated by uncertainty related to market fluctuations and regulations. In this study, we analyze how considering environmental and economic objectives simultaneously affects optimal investment strategies under uncertainty. We develop a novel methodology for determining the project value and the best investment policy for field development, considering several objectives and uncertain factors. By introducing multi-objective optimization into a real options investment valuation model, economic and environmental objectives are combined using the weighted sum method. The optimization model allows to determine trade-offs between the objectives of the project along with a flexible investment policy. Such optimal solutions result in a significant decrease in carbon emissions at only a marginal reduction in economic value. • Novel investment valuation approach: multi-objective optimization & real options. • Specific weights yield improved environmental impact with minimal project value loss. • Opportunity to postpone investment decision increases share of projects undertaken. • Subsidies might be more efficient in achieving emissions reduction than taxes. [ABSTRACT FROM AUTHOR]

Energy communities based on joint investments of energy assets might enable the democratisation of the power system by enabling all individuals to become active participants. Adopting this investment strategy requires ensuring fair economic value distribution among investors. Some studies propose using the Shapley value, given its uniqueness in fairly allocating value. However, this method suffers from scalability issues due to its computational demands. This paper introduces the Nested Shapley value as a new sharing mechanism to allocate profits to members of large coalitions fairly, thereby addressing the fairness-scalability dilemma. In conjunction with a cooperative investment model for communities with multi-dwelling buildings, the method is applied to assess individual preferences toward investment strategies in a real-world case study. The Nested Shapley value is demonstrated to encourage residents to opt for joint investments over individual strategies. Also, if combined with adequate governance structures, this payoff allocation could lead to the selection of resource-efficient investment strategies within residential communities. Furthermore, the Nested Shapley value is proven to satisfy two of the four fairness axioms defined for the Shapley value. A third one is also satisfied, albeit under specific conditions. • Design of an investment model to distribute value in multi-dwelling neighbourhoods. • Jointly investing leads to better use of resources in multi-dwelling neighbourhoods. • Formulation of the Nested Shapley value for value sharing in large coalitions. • The Nested Shapley value encourages most members to opt for joint investing. • Decision-making analysis based on governance structures and the Nested Shapley value. [ABSTRACT FROM AUTHOR]

Iceland features a high share of renewable energy supply but remains largely dependent on fossil fuels for transport and is relatively car dependent for mobility. In the context of a public transport system perceived by users and non-users as inadequate, we document individuals' corresponding micro-stories and show how these constrain their 'functionings' within a mobility capabilities perspective. We then show how this relates to a set of mobility justice principles, informing inferences for a more just and inclusive transition to decarbonised mobility. While acknowledging that the country has a pattern of urbanisation and challenging terrain that help to explain the limitations of its public transport system, we nonetheless argue that additional investment and policy measures will be required to address transport poverty. For data, we draw on analysis of Icelandic bus routes, focus groups with representative public, and interviews with vulnerable individuals. We find that transport decarbonisation policy needs to account for existing material poverty and be far more equitable if a just transition to low-carbon mobility is achieved. We also reveal how mobility justice issues interact with other dimensions of justice such as distributive, procedural, cosmopolitan, restorative, and recognition. [Display omitted] • In Iceland, most of the population live in a small number of urban areas. • This distributed urbanisation and limited public transport exacerbate car dependence. • Poverty limits the mobility options of vulnerable rural and urban residents. • Micro-stories illustrate individual experiences and point to policy deficits. • Transport decarbonisation policy needs to account for existing material poverty. [ABSTRACT FROM AUTHOR]

This paper investigates Spanish drivers' perceptions of the main barriers existing in Spain to the purchase of zero-emission vehicles (ZEVs). Following a comprehensive literature review in this field, this paper quantifies, by means of a survey conducted in Spain of 1474 Spanish drivers, the drivers' desired levels for each barrier to consider ZEVs in their next purchase decision to replace their current usually-used car. The analysis of these reported levels with latent class cluster models revealed the existence, in the sample, of groups of consumers with homogeneous preferences regarding the barriers. These groups differ in terms of individuals' characteristics, the car to be replaced, and journeys made with it. The most flexible groups comprise individuals with a significant knowledge of ZEVs, which underscores the importance of educational policies for the promotion of the use of ZEVs. The desired levels of the barriers for each group are confronted with the current status of the barriers for certain ZEVs. This comparison reveals that Fuel Cell Electric Vehicles (FCEVs) would have great potential if they received government support, because their only barriers are economic (purchase price and fuel availability). This paper also quantifies the effects that purchase incentives and infrastructure investment policies could have in terms of higher FCEV penetration rates. [ABSTRACT FROM AUTHOR]

This study explores Industry 4.0 in the Australian context, where manufacturing is dominated by smaller firms servicing the domestic market. We interviewed 20 advanced Australian manufacturers from diverse fields, capturing detailed descriptions of the Industry 4.0 implementation process. We compared the themes that emerged from their descriptions with the factors typically assessed in Industry 4.0 frameworks to draw out differences in emphasis. Consistent with these frameworks, Australian manufacturers were focused on using Industry 4.0 technologies to automate the capture, integration and analysis of data. To enable this activity, they were reorganising workforce roles and developing new skill sets. Knowledge sharing and collaborations within and across the organisation were seen to be especially important for small Australian manufacturers (with limited funding for technology investment and research and development) to maintain global competitiveness. However, while most Industry 4.0 frameworks describe supply chain applications, the development of smart products and services, and the need to adopt a strategy-led approach, relatively few participants spoke about these opportunities. Even fewer addressed the need for improved governance, standards and data security in the context of Industry 4.0. We argue that these gaps are best addressed through government policy and investment focusing beyond manufacturers, to support Industry 4.0 uplift across key domestic supply chains. [ABSTRACT FROM AUTHOR]

Portfolio optimization is one of the most important investment strategies in financial markets. It is practically desirable for investors, especially high-frequency traders, to consider cardinality constraints in portfolio selection, to avoid odd lots and excessive costs such as transaction fees. In this paper, a collaborative neurodynamic optimization approach is presented for cardinality-constrained portfolio selection. The expected return and investment risk in the Markowitz framework are scalarized as a weighted Chebyshev function and the cardinality constraints are equivalently represented using introduced binary variables as an upper bound. Then cardinality-constrained portfolio selection is formulated as a mixed-integer optimization problem and solved by means of collaborative neurodynamic optimization with multiple recurrent neural networks repeatedly repositioned using a particle swarm optimization rule. The distribution of resulting Pareto-optimal solutions is also iteratively perfected by optimizing the weights in the scalarized objective functions based on particle swarm optimization. Experimental results with stock data from four major world markets are discussed to substantiate the superior performance of the collaborative neurodynamic approach to several exact and metaheuristic methods. [ABSTRACT FROM AUTHOR]

As one of the main engines driving economic growth, investment has made an important contribution to China's economy. However, in recent years, the growth rate of China's fixed asset investment has shown a downward trend. Therefore, it is vital to determine the main influencing factors of China's investment for formulating stable investment policies. Using the nonlinear autoregressive distributed lag (NARDL) model, this study finds that economic policy uncertainty, capital cost, and raw material cost have asymmetrical impacts on China's fixed asset investment. Further research finds that the above variables have asymmetrical impacts on the two major components of aggregate investment, private investment and public investment, but the impact mechanism for each is completely different. When facing the positive or negative impacts from different factors, different investment entities will adopt different directions and magnitudes of investment decisions, ultimately leading to asymmetric relationships between various variables and aggregate investment. [ABSTRACT FROM AUTHOR]

• New RSV prevention products can substantially reduce disease burden. • Longer-acting monoclonal antibodies, priced affordably, are likely cost-effective. • Maternal vaccines meeting preferred product characteristics would be cost-effective. • RSV prevention products can provide good value in low-income countries. Low- and middle-income countries have a high burden of respiratory syncytial virus lower respiratory tract infections. A monoclonal antibody administered monthly is licensed to prevent these infections, but it is cost-prohibitive for most low- and middle-income countries. Long-acting monoclonal antibodies and maternal vaccines against respiratory syncytial virus are under development. We estimated the likelihood of respiratory syncytial virus preventive interventions (current monoclonal antibody, long-acting monoclonal antibody, and maternal vaccine) being cost-effective in Mali. We modeled age-specific and season-specific risks of respiratory syncytial virus lower respiratory tract infections within monthly cohorts of infants from birth to six months. We parameterized with respiratory syncytial virus data from Malian cohort studies, as well as product efficacy from clinical trials. Integrating parameter uncertainty, we simulated health and economic outcomes for status quo without prevention, intra-seasonal monthly administration of licensed monoclonal antibody, pre-seasonal birth dose administration of a long-acting monoclonal antibody, and maternal vaccination. We then calculated the incremental cost-effectiveness ratio of each intervention compared to status quo from the perspectives of the government, donor, and society. At a price of $3 per dose and from the societal perspective, current monoclonal antibody, long-acting monoclonal antibody, and maternal vaccine would have incremental cost-effectiveness ratios of $4280 (95% CI $1892 to $122,434), $1656 (95% CI $734 to $9091), and $8020 (95% CI $3501 to $47,047) per disability-adjusted life-year averted, respectively. In Mali, long-acting monoclonal antibody is likely to be cost-effective from both the government and donor perspectives at $3 per dose. Maternal vaccine would need higher efficacy over that measured by a recent trial in order to be considered cost-effective. [ABSTRACT FROM AUTHOR]

• A dimension reduction approach is employed to model the reverse commute. • Greater distances between origin and destination predict the reverse commute. • Higher percent of population African American at origin predict reverse commute. • Lower incomes at origin and higher incomes at destination predict reverse commute. • Effect of metropolitan fragmentation on reverse commute is unclear. This paper measures a) whether the reverse commute – a commute where a worker travels from a home location in the city to a workplace location in the suburbs – is characterized by a uniquely disparate socioeconomic and demographic space between home and workplace, and b) what role greater fragmentation between municipalities in a region might play in this theoretically inequitable circumstance. We employ administrative origin–destination data from the metropolitan Philadelphia, PA area and contemporary dimension reduction methodologies to model the likelihood of an origin destination pair being classified as a reverse commute. Particular attention is given to interactions between socioeconomic and metropolitan fragmentation terms and non-linear specifications of predictor variables. Greater socioeconomic and demographic disparity between home and work geographies is found to uniquely predict the reverse commute. We also find that fragmentation at the metropolitan scale has a multidimensional impact in relation to this socio-economic difference. Together, our findings appear to signal that, while greater regional socioeconomic disparity does indeed correlate with the reverse commute, greater metropolitan fragmentation may not. This portrait highlights new areas for transport research around political fragmentation and makes the case for renewed, equity-oriented policy investment in the reverse commuting problem. [ABSTRACT FROM AUTHOR]

We study the consumption and portfolio selection problem of a finitely lived agent who derives utility from the stock of durable goods. We show that the agent's effective relative risk aversion implied by the optimal portfolio tends to decline and approaches zero, as the planning horizon approaches, whereas the agent exhibits constant effective relative risk aversion in the infinite horizon model of Hindy and Huang (1993). The existence of the stock of durable goods acts as buffer stock and induces the highly risk-tolerant attitude. We approach this problem using successive transformations. We transform our problem by applying an isomorphism proposed by Schroder and Skiadas (2002) to a singular control problem involving the choice of a monotone increasing consumption process. We next transform the problem into a dual singular control problem using the dual martingale method. We then transform the dual singular control problem into an optimal stopping problem. We analyze the variational inequality arising from the optimal stopping problem and provide an integral representation of optimal strategies. • We study portfolio selection with durable goods in a finite horizon. • We derive optimal strategies with an integral equation representation. • Effective risk aversion tends to decline and approaches 0 as planning horizon comes. • Durable goods act as a buffer and induce a highly risk-tolerant investment policy. [ABSTRACT FROM AUTHOR]