Entire Horizon Research Articles

The differential pricing of cash flows and total accruals

PurposeRecent studies on the securities market's differential pricing of earnings components indicate that cash flows from operations are valued more highly than extreme total accruals. However, no previous study has examined whether cash flows from operations have a higher valuation than moderate total accruals. Therefore, this study examines the securities market's differential pricing of cash flows from operations and both moderate and extreme total accruals.Design/methodology/approachThe study's sample is divided into two sub-samples: a moderate total accruals sub-sample; and an extreme total accruals sub-sample. To evaluate whether cash flows have a higher valuation when compared to total accruals, for the entire sample and for each of the two sub-samples, the study examines the statistical significance of the difference between slope coefficients of cash flows and total accruals for regression of returns on both unexpected cash flows from operations and unexpected total accruals.FindingsConsistent with prior research, results from the entire sample show a differential higher valuation of cash flows when compared to total accruals. Another finding, consistent with recent studies, is that cash flows from operations have a higher valuation when compared to extreme total accruals. However, there is no higher differential valuation of cash flows over moderate total accruals. These findings support the decomposition of earnings into the components of cash flows from operations and total accruals only when total accruals are extreme (rather than moderate).Practical implicationsA possible explanation for these results is that since accruals predict cash flows, total accruals – when moderate (i.e. not extreme) – are priced similarly to cash flows. These results reveal that when total accruals are moderate, earnings are a better proxy for the underlying cash flows (over the entire future horizon, not just the current period) than is cash flows. However, since total accruals are unlikely to persist in a permanent way over the years, these results indicate that the decomposition of earnings into the components of cash flows from operations and total accruals is consistent with the information set used to value equity securities. Therefore, separate disclosure of cash flows is value relevant. In addition, users of financial statements certainly need the cash flows information as an ex-post validation of the prior earnings.Originality/valueThis study's contribution stems from its determination of the preferred level of disaggregation of earnings components (i.e. operating cash flows and total accruals). This is expected to help investors in their attempt to enhance the outcome of their informed investment and credit decisions.

Data-Driven Multiple Shooting for Stochastic Optimal Control

The implementation of data-driven predictive control schemes based on Willems’ fundamental lemma often relies on a single-shooting approach, i.e., it uses one large Hankel matrix to cover the entire optimization horizon. However, the numerical solution is fostered by the use of multiple segmented horizons which require less data in smaller Hankel matrices. This letter extends the segmentation idea towards multiple shooting for data-driven optimal control of stochastic LTI systems. Using a stochastic variant of the fundamental lemma and polynomial chaos expansions, we propose a multiple-shooting formulation which combines trajectory segmentation and moment matching. We show that, for LTI systems subject to Gaussian noise of finite variance, our formulation is without loss of optimality while it allows for a significant reduction of the problem dimension in Gaussian and non-Gaussian settings. We draw upon a numerical example to compare the proposed framework to the usual single-shooting approach.

A temporal–spatial charging coordination scheme incorporating probability of EV charging availability

The charging coordination of electric vehicle (EV) fleets in both temporal domain and spatial domain has attracted growing attention in recent years. Meanwhile, the uncertainties in EV arrival time and total available charging power from charging stations make the coordination problem highly dynamic and challenging. This paper develops a new temporal–spatial EV charging coordination scheme that jointly considers the above two major uncertainties. Firstly, EV charging scheduling (i.e., temporal coordination) is treated as a generalized Nash equilibrium game, in which each EV (including an upcoming EV) prefers to meet its own charging demand with minimized charging cost. The probability of EV charging availability is especially proposed to incorporate the charging demands of the upcoming EVs into the coordination scheme. In order to provide flexibility and private information protection, a distributed receding horizon optimization-based solution is developed, through which the Lagrange multipliers to reach the social equilibrium are determined via an iterative manner. The charging station selection is then recommended that minimizes the objective function over the entire optimization horizon. Finally, simulations under both small-scale and large-scale scenarios effectively demonstrate improved service quality of the EV charging, both in temporal and spatial domains, and avoidance of overload in charging stations. Results in a 150-EV scenario show that, averagely, the proposed method reduces battery SoC mismatch by 43% and increases degree of consistency by 5.9%.

Changes in the Carbon Stocks, Microbial and Enzyme Activities of Retic Albic Podzol in Southern Taiga during Postagrogenic Evolution

Soil organic matter largely controls the ecosystem functions of soil and is a source of energy and a nutrient substrate for the soil microbial community as well. The postagrogenic dynamics of organic matter stock (content of organic carbon, Corg; total nitrogen, Ntot; and their ratio, C/N), basic soil properties, and indicators of soil microbial and enzyme activities are analyzed for the chronosequence of Retic Albic Podzol (southern taiga zone, Kostroma oblast, Russia). Different plots of the chronosequence have been withdrawn from agricultural use 11, 16, and 40 years ago. The stratification ratios (SRs) 0–5 : 5–10, 0–10 : 10–20, and 0–10 : 20–30 cm layers for Corg and Ntot contents are also estimated. A statistically significant increase in the contents of Corg and Ntot during the postagrogenic succession is observed in the topsoil layer of the old arable horizon (0–5 and 5–10 cm) as well as an increase in the stratification of their distribution, especially after a closed tree canopy has developed on the former arable land. Postagrogenic succession promotes an increase in the soil basal respiration rate and the content of microbial biomass, the maximum values of which are most often recordable in the soil under a 40-year-old secondary small-leaved forest. During the natural reforestation of the former arable lands, a statistically significant increase in the phosphatase activity is observed within the entire old arable horizon. The peaks of oxidoreductase activity correspond to the stages of succession with the maximum uptake of easily decomposable plant litter. Over the 40 years of postagrogenic succession, all analyzed parameters (except for pHKCl) have increased in a statistically significant manner at all considered depths of the old arable layer, with the highest annual increase in 0–5-cm layer, most enriched for organic matter.

The generalized cash balance problem: optimization‐based one step ahead optimal control

AbstractThis paper generalizes the Sethi–Thompson model of cash balance dynamics to the case of multiple current and investment accounts, with the cash flows or demands incident on the current accounts. Cash transfers can occur between any pair of current–current or current–investment accounts, with transaction costs proportional to the amount transferred, always deducted from current accounts. Performance indices are defined and maximization of an index for the next instant (day), by suitable choice of cash transfers that satisfy desired constraints on account balances, defines the basic optimal control problem referred to as one step ahead optimal control. It is shown that this one step ahead optimal control can be written as a mixed integer linear program of small size, leading to an implementable real time strategy for the generalized cash balance problem. In addition, for the simple case of a single cash and investment account, the proposed strategy is derived analytically and compared with a strategy of Miller–Orr type, called a three‐level strategy. Omniscient optimal control over a horizon is defined as the globally optimal control strategy, assuming the demands over the entire horizon to be known: although computable a posteriori, it is not implementable a priori, and only serves as a benchmark for comparison. The proposed one step ahead optimal or myopic approach makes no assumptions about the distribution of cash demands, and examples show that it attains performance indices which are superior to those attained by three‐level control strategies, and compare favorably with the omniscient benchmark performance indices.

Online Assortment and Market Segmentation under Bertrand Competition with Set-Dependent Revenues

We consider an online assortment problem with $[n]:=\{1,2,\ldots,n\}$ sellers, each holding exactly one item $i\in[n]$ with initial inventory $c_i\in \mathbb{Z}_+$, and a sequence of homogeneous buyers arriving over a finite time horizon $t=1,2,\ldots,m$. There is an online platform whose goal is to offer a subset $S_t\subseteq [n]$ of sellers to the arriving buyer at time $t$ to maximize the expected revenue derived over the entire horizon while respecting the inventory constraints. Given an assortment $S_t$ at time $t$, it is assumed that the buyer will select an item from $S_t$ based on the well-known multinomial logit model, a well-justified choice model from the economic literature. In this model, the revenue obtained from selling an item $i$ at a given time $t$ critically depends on the assortment $S_t$ offered at that time and is given by the Nash equilibrium of a Bertrand game among the sellers in $S_t$. This imposes a strong dependence/externality among the offered assortments, sellers' revenues, and inventory levels. Despite that challenge, we devise a constant competitive algorithm for the online assortment problem with homogeneous buyers. We also show that the online assortment problem with heterogeneous buyers does not admit a constant competitive algorithm. To compensate for that issue, we then consider the assortment problem under an offline setting with heterogeneous buyers. Under a mild market consistency assumption, we show that the generalized Bertrand game admits a pure Nash equilibrium over general buyer-seller bipartite graphs. Finally, we develop an $O(\ln m)$-approximation algorithm for optimal market segmentation of the generalized Bertrand game which allows the platform to derive higher revenues by partitioning the market into smaller pools.

A recursive time aggregation-disaggregation heuristic for the multidimensional and multiperiod precedence-constrained knapsack problem: An application to the open-pit mine block sequencing problem

A recursive time aggregation-disaggregation (RAD) heuristic is proposed to solve large-scale multidimensional and multiperiod precedence-constrained knapsack problems (MMPKP) in which a profit is maximized by filling the knapsack in multiple periods while satisfying minimum and maximum resource consumption constraints per period as well as precedence constraints between items. An important strategic planning application of the MMPKP in the mining industry is the well-known open-pit mine block sequencing problem (BSP). In the BSP, a mine is modeled as a three-dimensional grid of blocks to determine a block extraction sequence that maximizes the net present value while satisfying constraints on the shape of the mine and resource consumption over time. Large real-life instances of this problem are difficult to solve, particularly with lower bounds on resource consumption. The advantage of the time aggregation-disaggregation heuristic over a rolling-horizon-based time decomposition is twofold: first, the entire horizon is considered for the resource consumption from the first aggregation; and second, only two-period subproblems have to be solved. This method is applied to a well-known integer programming model and a variant thereof in which blocks can be extracted in parts over multiple periods. Tests on benchmark instances show that near-optimal solutions for both of the models can be obtained for extremely large instances with up to 2,340,142 blocks and 10 periods.

Prognosis of Epileptic Seizure Event Onsets Using Random Survival Forests

This article introduces a machine learning approach, based on a nonparametric, decision-tree-based random survival forest (RSF) model, for a continuous prognosis of epileptic seizure events using electroencephalogram (EEG) data. While earlier seizure prediction methods forecast seizure occurrences only at a specified future time, the RSF model allows estimation of the probability of seizure onset, in terms of a hazard function, over the entire prediction horizon. These estimates are crucial for developing individualized quantitative risk measures and management plans for epilepsy patients. Additionally, RSF can identify the key risk factors by capturing the interdependencies among the features extracted from EEG data. The performance of RSF was evaluated for prognosing seizure onsets of the rat and mice specimens in an 80 small animals cohort at the Texas A&M Department of Neuroscience and Experimental Therapeutics. The results suggest that RSF outperforms other contemporary survival models, including the popular Cox proportional hazard, with 87.5% lower integrated Brier Score (IBS) errors, and 17.5% higher concordance index (C-index). Further, a continuous seizure prediction sensitivity of 83% and specificity of 87% were obtained even over a 5-min prediction horizon (the average time between successive seizure onsets was 5 min long). These results suggest that the RSF model can be used to effectively quantify the likelihood of seizure onsets over time to the patients and caregivers, promoting informed decision making.

Multi‐hour ahead forecasting of building energy through a new integrated model

AbstractThe increase in electricity demand requires improved energy planning programs, which involve better energy distribution. Therefore, precise energy demand forecasting is of great importance for optimizing energy distribution. In this respect, a new hybrid forecasting model was proposed in this study for multi‐hour forecasting of total energy requirement (cooling and heating load of buildings) in three different regions in Algeria with different climate conditions. The proposed models are based on two main steps: In the first step, time‐series data were decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) into a different intrinsic function (IMFs), and then extreme learning machine (ELM) is employed as essence predictors. The proposed CEEMDAN‐ELM model uses the decomposed IMFs as input data, and the total energy concern as a desired output. The integrated CEEMDAN‐ELM model was evaluated and validated on three different databases each of which had 2 years of measurement on an hourly scale. Experimental results show that the hybridization mechanism CEEMDAN‐ELM outperform the stand‐alone model (ELM) in terms of forecasting errors over the entire forecasting horizon. Forecasting results of CEEMDAN‐ELM led to a normalized root mean square error (nRMSE) in the range of [0.71–4.66] for all studied regions and horizons whereas conventional ELM provides an nRMSE in the range of [0.93–16.89].

The role of afforestation species as a driver of Hg accumulation in organic horizons of forest soils from a Mediterranean mountain area in SW Europe

Forest areas are a primary sink of atmospheric mercury (Hg) within terrestrial ecosystems, whereas forest vegetation plays a key role in atmospheric Hg transfer to soil horizons. This study assessed variations in total Hg contents (HgT) and accumulation (HgRes) in the soil organic horizons of a forest area in NE Portugal, where post-wildfire afforestation led to the substitution of the native deciduous species (Quercus pyrenaica) by fast-growing coniferous species (Pseudotsuga menziesii and Pinus nigra). The study also evaluated, for each species, the links between Hg contents and other biophilic elements of soil organic matter (C, N, S) present in organic subhorizons (OL, OF, OH). Mean HgT in the organic horizons of the different tree species follow the sequence: P. nigra (88 μg kg−1) < Q.pyrenaica (101 μg kg−1) <P. menziesii (141 μg kg−1). The highest HgRes for the entire organic horizon was found under P. menziesii (471 μg m−2), followed by P. nigra (253 μg m−2) and Q. pyrenaica (189 μg m−2). Among the organic subhorizons, values of HgT and HgRes follow the sequence OL < OF < OH, which is consistent with the degree of organic matter humification. Indeed, HgT and HgRes correlated significantly with the C/N and C/S ratios for all species and organic subhorizons, suggesting that the quality of organic matter may influence strongly the Hg fate in these forest soils. Soils from P. menziesii plots have shown an HgRes 2.5 times higher than in plots dominated by the native Q. pyrenaica. Hg accumulation in the organic horizons, promoted in the coniferous species, may increase the risk of Hg mobilization due to wildfires and forest management practices. Therefore, forest management plans should select cautiously the tree species for afforestation in order to minimize adverse environmental effects caused by changes in the biogeochemical cycle of contaminants such as Hg.

Stochastic model predictive control for linear systems with unbounded additive uncertainties

This paper presents two stochastic model predictive control methods for linear time-invariant systems subject to unbounded additive uncertainties. The new methods are developed by formulating the chance constraints into deterministic form, which are treated in analogy with robust constraints, by using the probabilistic reachable set. The first one is the time-varying tube-based stochastic model predictive control algorithm, which is designed by employing the time-varying probabilistic reachable sets as tubes. The second one is the constant tube-based stochastic model predictive control algorithm, which is developed by enforcing a constant tightened constraint in the entire prediction horizon. In addition, the soft constraints are proposed to associate with the state initialization in the algorithms to enhance the feasibility. The algorithm feasibility and closed-loop stability results are provided. The efficacy of the approaches is demonstrated by means of numerical simulations.

Time consistent in efficiency dynamic mean–variance policy

The dynamic mean–variance formulation for a market with all risky assets is not time consistent in efficiency (TCIE), in the sense that the pre-committed policy, which is optimal for the entire investment horizon, could become mean–variance inefficient when facing a truncated time-horizon problem at an intermediate time instant. By removing the self-financing restriction and fully avoiding the irrational investment behaviours in intermediate time periods, we develop the TCIE dynamic mean–variance policy, which is TCIE. Comparing to the polices in the literature, such as the strictly dominating policy, the pre-committed policy and the time consistent policy, the TCIE policy can achieve better global investment performance.

Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model of an Overhead Traveling Crane.

This paper proposes a multi-gene genetic programming (MGGP) approach to identifying the dynamic prediction model for an overhead crane. The proposed method does not rely on expert knowledge of the system and therefore does not require a compromise between accuracy and complex, time-consuming modeling of nonlinear dynamics. MGGP is a multi-objective optimization problem, and both the mean square error (MSE) over the entire prediction horizon as well as the function complexity are minimized. In order to minimize the MSE an initial estimate of the gene weights is obtained by using the least squares approach, after which the Levenberg–Marquardt algorithm is used to find the local optimum for a k-step ahead predictor. The method was tested on both a simulation model obtained from the Euler–Lagrange equation with friction and the experimental stand. The simulation and the experimental stand were trained with varying control inputs, rope lengths and payload masses. The resulting predictor model was then validated on a testing set, and the results show the effectiveness of the proposed method.

Tube-based model predictive full containment control for stochastic multi-agent systems

In this paper, we develop a tube-based distributed model predictive full containment control (MPFCC) algorithm for leader-following systems with bounded disturbance under dynamic leaders. The proposed algorithm employs knowledge regarding the constraints on states and control inputs to extrapolate their admissible values in the entire predictive horizon. The Kalman filter is utilized to estimate the system states, introducing estimated error. The error and disturbance are both involved in the time-varying tubes to construct the tightened constraints. For each follower, by penalizing the control input difference from its neighbors&#x0027; control inputs and the deviation of the states from the convex hull produced by its neighbors&#x0027; states, the containment problem is optimized only by utilizing the local nominal state and control sequences. To overcome the problem that followers may fall slightly out of the convex hull of the leaders caused by disturbance, a full containment control algorithm is introduced by designing a tightened convex hull. Then the recursive feasibility and robust stability are proved through the design of proper distributed terminal controllers and constraints. Finally, the effectiveness and robustness of the proposed method are illustrated for both linear and nonlinear multi-agent systems by simulation examples.

Multi-stage stochastic optimization of islanded utility-microgrids design after natural disasters

Natural disasters (e.g., hurricanes) can cause widespread power outages within distribution networks and interrupted power supply to critical loads (e.g., grocery stores, hospitals, gas, fire, and police stations) that provide utility services. Microgrids are localized power grids that can incorporate solar/photovoltaic (PV) distributed generators (PV-DGs) and energy storage systems (ESSs) for stand-alone system operations independent of the main grid, known as the island mode. This study investigates a microgrid design problem using PV-DGs and ESSs when facing prolonged power outages in the main grid. We propose a multi-stage stochastic program that holistically considers the techno-economics of microgrid investment and daily operations by optimizing the reliability and resilience of the microgrid during a week-long power outage. The model is designed from a utility perspective that includes budget constraints for investment. Due to the large model size, we develop a nested L-shaped algorithm that solves the problem exactly and analyzes the microgrid’s reliability across different weather scenarios in the entire decision-making horizon. Results from a case study using real-world data show that an islanded utility-scale microgrid can effectively provide uninterrupted power supply to a network of 5 and 10 critical loads, covering 100% and 97% of the demand in all possible future scenarios, with potential investments of $8 million and $15 million, respectively.

Sustainable development of countries all over the world and the impact of renewable energy

This study assesses the sustainable development of countries worldwide and the impact of renewable energy on country-level sustainability for the period 1990–2014. The nonparametric method “data envelopment analysis” is employed to measure the performance of the countries by merging environmental and economic elements into unified sustainability measures. Reflecting the different attitudes with which the countries treat environmental and economic factors in pursuing sustainability, the assessment procedure assigns priority to either economic or environmental course. We find that if economic criterion is prioritized over environmental criterion, then the developed countries significantly outperform the developing countries until early 2000s. However, the gap quickly diminishes after the period. If environmental criterion takes precedence, then developed and developing countries have similar performance over the entire horizon. Moreover, no matter which criterion is prioritized, dispersion in sustainability levels for countries in different regions has shrunk over time. Panel analysis shows that the share of renewable energy in total energy consumption has positive and strongly significant impact on sustainability, no matter whether economic prosperity or environmental protection is the priority. This implies that despite legitimate concerns about its cost efficiency, renewable energy has shown a strong positive effect in enhancing sustainability of the world.

Ethics appraisal procedure in 79,670 Marie Skłodowska-Curie proposals from the entire European HORIZON 2020 research and innovation program (2014-2020): A retrospective analysis.

Horizon 2020 was the most significant EU Research and Innovation programme ever implemented and included the Marie Skłodowska-Curie Actions (MSCA). Proposals submitted to the MSCA actions awere subject to the Ethics Appraisal Procedure. In this work we explored the ethics appraisal procedure in MSCA H2020. Using a retrospective analysis of pooled anonymized data, we explored the ethics appraisal procedure on proposals submitted to Marie Skłodowska-Curie Actions (MSCA) during the entire Horizon 2020 program period (N = 79,670). Our results showed that one of the most frequently identified ethics categories was Data protection. We also detected slight differences between applicants' and the ethics reviewers' awareness of ethical issues. Trajectory analysis of all ethics screened proposals appearing on main lists showed that a minimal portion of all screened submissions required additional ethics checks in the project implementation phase. Personal data protection is one of the most represented ethics categories indicated among MSCA actions which exhaust ethics assessment efforts and may lead to "overkills" in ethics requirements. Excluding the majority of personal data protection assessment from the ethics assessment, except for parts which are directly related to ethics like "Informed consent procedures", might be necessary in the future. A gap in understanding of ethics issues between applicants and reviewers' points to the necessity to further educate researchers on research ethics issues.

A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission delay causes mismatches between feedback and real displacement, further increasing the challenge in tracking control. In this article, a neural network-based model predictive controller (MPC) is proposed for precise tracking control of piezoelectric actuator’s displacement in situation where feedback is slow and delayed. The prediction model is based on a nonlinear-autoregressive-moving-average-with-exogenous-inputs framework, which outputs entire prediction horizon of future displacement in a single time, and is fulfilled by a multilayer feedforward neural network. An extended Kalman filter-based estimation for displacement is introduced to relieve the influence of feedback delays so as to improve dynamic performance of the controller. Another neural network is trained to provide initial values for MPC to reduce computation costs and improve performance in dynamic tracking. In a series of tracking experiments, the effectiveness of proposed controller is verified.

Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources

In this paper, a robust scheduling model is proposed for combined heat and power (CHP)-based microgrids using information gap decision theory (IGDT). The microgrid under study consists of conventional power generation as well as boiler units, fuel cells, CHPs, wind turbines, solar PVs, heat storage units, and battery energy storage systems (BESS) as the set of distributed energy resources (DERs). Additionally, a demand response program (DRP) model is considered which has a successful performance in the microgrid hourly scheduling. One of the goals of CHP-based microgrid scheduling is to provide both thermal and electrical energy demands of the consumers. Additionally, the other objective is to benefit from the revenues obtained by selling the surplus electricity to the main grid during the high energy price intervals or purchasing it from the grid when the price of electricity is low at the electric market. Hence, in this paper, a robust scheduling approach is developed with the aim of maximizing the total profit of different energy suppliers in the entire scheduling horizon. The employed IGDT technique aims to handle the impact of uncertainties in the power output of wind and solar PV units on the overall profit.

Offset-free ARX-based adaptive model predictive control applied to a nonlinear process

An adaptive steady state constrained autoregressive with extra input (SSARX) model is developed and embedded into a model predictive control (MPC) framework to regulate a nonlinear process for setpoint tracking. The resulting linear MPC can minimize the deviation of output from the setpoint along the entire prediction horizon and finally achieve zero-offset control even with model mismatch. Our contributions lie in three aspects: First, we develop a steady state constrained ARX model identification method. Second, we design an information matrix based index for model re-identification using online closed-loop data. Third, when steady state input values are unknown, our algorithm can adaptively adjust parameters in the objective function and model to track the setpoint crossing a wide range of operating conditions. A fermenter control problem is studied to demonstrate the effectiveness of the proposed approach.