Short Time Horizon Research Articles

Cost data in implementation science: categories and approaches to costing

A lack of cost information has been cited as a barrier to implementation and a limitation of implementation research. This paper explains how implementation researchers might optimize their measurement and inclusion of costs, building on traditional economic evaluations comparing costs and effectiveness of health interventions. The objective of all economic evaluation is to inform decision-making for resource allocation and to measure costs that reflect opportunity costs—the value of resource inputs in their next best alternative use, which generally vary by decision-maker perspective(s) and time horizon(s). Analyses that examine different perspectives or time horizons must consider cost estimation accuracy, because over longer time horizons, all costs are variable; however, with shorter time horizons and narrower perspectives, one must differentiate the fixed and variable costs, with fixed costs generally excluded from the evaluation. This paper defines relevant costs, identifies sources of cost data, and discusses cost relevance to potential decision-makers contemplating or implementing evidence-based interventions. Costs may come from the healthcare sector, informal healthcare sector, patient, participant or caregiver, and other sectors such as housing, criminal justice, social services, and education. Finally, we define and consider the relevance of costs by phase of implementation and time horizon, including pre-implementation and planning, implementation, intervention, downstream, and adaptation, and through replication, sustainment, de-implementation, or spread.

A simulated annealing based heuristic for a location-routing problem with two-dimensional loading constraints

A hybrid heuristic is proposed to solve a location-routing problem with two-dimensional loading constraints. This problem appears in military situations and natural disasters in such a way that decisions are taken in a short time horizon. The proposed heuristic combines the simulated annealing method and the artificial algae algorithm. Simulated annealing is used to handle the location-routing problem, while the artificial algae algorithm is used to determine the sequence in which the items will be packed. Therefore, we apply the Skyline technique to find a feasible packing of such items onto the vehicle’s rectangular surface. As there is no other work in the literature handling the location-routing problem with two-dimensional loading constraints that we can compare the results, we evaluate the heuristic performance on its subproblems: the location-routing problem and the vehicle routing problem with two-dimensional loading constraints. Although the heuristic is not designed for these subproblems, it still obtains competitive results, with an average relative difference of 1.26% and equal or better solutions for more than 90 instances. Regarding the problem under study, the heuristic obtains solutions close to an estimated lower bound for instances having more items per customer.

Attitude control of an underactuated satellite in presence of disturbance torque with optimal motion planning

The failure of mechanical components is a common phenomenon in satellites. This failure can happen in the satellite attitude control system, which causes that the control system of the satellite becomes underactuated. There have been many attempts to control the orientation of underactuated satellites. However, in most studies, the inertia matrix of the satellite is assumed to be diagonal with respect to the body coordinate system, and no limitations on the amount of torque applied by the reaction wheels have been considered. In this paper, at first, it is attempted to control the satellite using the motion planning method. The satellite control inputs are assumed to be cubic spline with some unknown parameters. The spline parameters are determined by minimizing a cost function defined such that the attitude of the satellite tracks desired preset values. Also, in the simulations, the inertia matrix of the satellite is considered non-diagonal and the maximum torques applied by the reaction wheels are considered limited. Then, the effect of disturbance on the controlled satellite is considered which is modeled by some external torques, and the proposed method is modified by repeating the control algorithm in short time horizons. Finally, the method is customized to be used in tracking an object by the satellite in the space which is applicable in time varying maneuvers such as observation and communications. The performance of the proposed method is demonstrated via numerical simulations.

Task-Relevant Encoding of Domain Knowledge in Dynamics Modeling: Application to Furnace Forecasting From Video

Waste incineration plants are complex dynamical systems that rely on expert human operators to maintain steady combustion, by observing real-time in-chamber video feeds. Real-time plant forecasting provides vital operational support in decision making, and applying machine learning to automatically learn dynamics forecast models from video feeds is an attractive means to realise this. However, learning complex dynamics in systems that requires cost-efficiency remains an open research problem. Specifically, modelling plant dynamics in real-time is challenging due to uncertainties caused by inhomogeneous waste inputs, requiring complex learning that impedes real-time modelling. To address this, this paper presents a real-time data-driven framework for generating video forecasts, by incorporating <i>task-relevant domain-knowledge</i>, during learning. Specifically, this method combines dynamics modelling and forecasting using <i>dynamic mode decomposition</i>, with <i>Fourier transformations</i> informed by expert operator heuristic knowledge for encoding task-relevant frequency information inside the learning process. Experiments in this paper demonstrate that the proposed framework captures intuitive physical aspects of the underlying physiochemical process, with a greatly reduced computational runtime in comparison to standard approaches, allowing for application in real-time domains. Forecasted video predictions are accurate over short time horizons, and capture important system characteristics over longer time periods.

Robust Predictive Energy Management of Connected Power-Split Hybrid Electric Vehicles Using Dynamic Traffic Data

Abstract This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEVs) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudo-spectral optimal controller (PSOC) with discounted cost is utilized at the high level to find an approximate optimal solution for the entire driving cycle. At the low-level, a long short-term memory neural network (NN) is developed for higher quality velocity predictions over the low-level's short time horizons. Tube-based model predictive controller is then used at the low level to ensure constraints satisfaction in the presence of velocity prediction errors. Simulation results over real-world traffic data show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

Budoucí prostředí a robotické autonomní systémy

The article deals with robotic autonomous systems in current environment, from military viewpoint. The goal of the article and supposed contribution is to present concentrated, synthetised and via experts discussed known and new data about robotic autonomous systems, which can be used as a booster for following studies. Authors selectively describe current security and operational environment, using various sources, trends, unmanned technologies and possibilities to increase military capabilities. Using deep analysis of large scale of sources and Dolphi method, they present real options to utilise robotic autonomous systems in short and medium-term time horizon according to capabilities. The motivation for creating the article was fact, that in military family are not complexly discussed arguments and circumstances, why to deal and implement robotic autonomous systems. The employment of the article is directed mostly into field of military theory and military art.

Cost-effectiveness analysis of the implementation of advanced practice nursing: how to move forward?

This essay explores possibilities of advances in cost-effectiveness analysis (CEA) in advanced practice nursing (APN). The arguments were structured according to the current health landscape, the need to evaluate APN practices as health technology and evidence and recommendations for conducting CEA. Benefits of APN were evidenced in the improvement of indicators such as mortality, hospital readmission, among others. However, the absence of a standard of care, combined with the existence of different models and short time horizon interfered with the estimation of direct costs. The studies on CEA were inconclusive, mainly due to the lack of cost per unit of success and calculation of the CEA ratio. In the context of the APN, to conduct CEA that really contributes to robust results, thus subsidizing decision-making requires a joint effort of training institutions, delimitation and standardization of practice by regulatory agencies of the profession and health services, based especially on accreditation policies.

The Bureaucratic Perils of Presidentialism: Political Impediments to Good Governance in Latin America

In the face of poorly performing bureaucracies, public management specialists and policy experts have been quick to suggest administrative reforms. Yet, since governance lies at the nexus of politics and administration, reforms limited to the administrative dimension are likely to fall short without concomitant political change. This essay argues that in contemporary Latin America, certain political-institutional arrangements or institutional traits are more likely to produce (in)effective governance than others. Specifically, Latin American presidentialism creates several “perils” for governance, including: the rigidity and short time horizons of presidentialism; multipartyism under presidentialism; weakly institutionalized party systems and non-programmatic parties; an imbalance of power between strong presidents and weak legislatures; the plebiscitarian nature of presidentialism; and democratic backsliding and the centralization of power in the presidency. These institutional traits incentivize patronage, promote myopic policymaking, and weaken bureaucratic autonomy. To illustrate how they manifest themselves in public policy, the paper uses evidence from the region’s responses to the Covid-19 pandemic, then concludes by suggesting political reforms that may help to improve governance in the region.

Velocity Optimization and Robust Energy Management of Connected Power-Split Hybrid Electric Vehicles

AbstractThis paper focuses on an eco-driving-based hierarchical robust energy management strategy for connected and automated hybrid electric vehicles in the presence of uncertainty. The proposed control strategy includes a velocity optimizer, which evaluates the optimal vehicle velocity, and a powertrain energy manager, which evaluates the optimal power split between the engine and the battery in a hierarchical framework. The velocity optimizer accounts for regenerative braking and minimizes the total driving power and friction braking over a short control horizon. The hierarchical powertrain energy manager employs a long- and short-term strategy where it first approximately solves its problem over a long time horizon (the whole trip time in this paper) using the traffic data obtained from vehicle-to-infrastructure (V2I) connectivity. This is followed by a short-term decision maker that utilizes the velocity optimizer and long-term solution, and solves the energy management problem over a relatively short time horizon using robust model predictive control (MPC) methods to factor in any uncertainty in the velocity profile due to uncertain traffic. We solve the long-term energy management problem using pseudo-spectral optimal control method, and the short-term problem using robust tube-based MPC method. Simulation results with standard driving cycle velocity profile and real-world traffic data show the competence of our proposed approach. Our proposed co-optimization approach with long- and short-term solution results in ≈12% more energy efficiency than a baseline co-optimization approach.

How does the Chinese bureaucracy sustain economic growth without stable local political leaders? Stratified spatial mobility and the role of stable political elites in local governments

ABSTRACT The economic influence of local political leaders, i.e. heads of local governments and party committees in subnational jurisdictions, has been highly emphasized and widely studied in China. Yet studies have also documented that these leaders experienced frequent spatial transfers and had short time horizons in each jurisdiction, which should have perverse effects on their incentives and capacities to promote the regional economy. What, then, explains the continued economic growth in China without stable local political leaders? Drawing on the stratified spatial mobility model, we emphasize the importance of the more stable political elites, who constitute a crucial buffer zone to alleviate the perverse economic effects of leaders’ frequent turnover, in sustaining long-term local development. Our empirical analyses in Jiangsu province provide evidence that supports this proposition. This study deepens our understanding of the institutional logic underlying China’s economic growth and political stability.

Purpose in Life: A Reconceptualization for Very Late Life

Across disciplines, we have long sought to understand the factors that contribute to purpose in life. Theorists have posited that having life goals, feeling productive, and remaining active are essential contributing elements to purpose in life (Crumbaugh & Maholick, 1969; Rowe & Kahn, 1997; Ryff, 1989). While these factors can undoubtedly contribute to purpose in life, they may not fully explain purpose in life for older adults in very late life (85 years old and older) who have long past and short future time horizons. In this presentation, we explore the concept of purpose in life for older adults in very late life and how current measures may not fully or accurately apply to this group. We examine the two most commonly used measures of purpose in life, the Purpose in Life Test (Crumbaugh & Maholick, 1964, 1969) and the Ryff Purpose Subscale (Ryff, 1989; Ryff & Keyes, 1995), and identify specific items that should be reconsidered for use with older adults in very late life. We then reconceptualize purpose in life for the oldest old based on several foundational theories, including Socioemotional Selectivity Theory, the Theory of Gerotranscendence, and Terror Management Theory. Stemming from this analysis, we posit that purpose in life in very life consists of three domains – the very long past, the very near future, and the transcendental post-mortem. Based upon this reconceptualization, we recommend the development of new measures of purpose of life in very late life that capture these three domains.

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

AbstractConnectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation

Forecasting Photovoltaic (PV) energy production, based on the last weather and power data only, can obtain acceptable prediction accuracy in short-time horizons. Numerical Weather Prediction (NWP) systems usually produce free forecasts of the local cloud amount each 6 h. These are considerably delayed by several hours and do not provide sufficient quality. A Differential Polynomial Neural Network (D-PNN) is a recent unconventional soft-computing technique that can model complex weather patterns. D-PNN expands the n-variable kth order Partial Differential Equation (PDE) into selected two-variable node PDEs of the first or second order. Their derivatives are easy to convert into the Laplace transforms and substitute using Operator Calculus (OC). D-PNN proves two-input nodes to insert their PDE components into its gradually expanded sum model. Its PDE representation allows for the variability and uncertainty of specific patterns in the surface layer. The proposed all-day single-model and intra-day several-step PV prediction schemes are compared and interpreted with differential and stochastic machine learning. The statistical models are evolved for the specific data time delay to predict the PV output in complete day sequences or specific hours. Spatial data from a larger territory and the initially recognized daily periods enable models to compute accurate predictions each day and compensate for unexpected pattern variations and different initial conditions. The optimal data samples, determined by the particular time shifts between the model inputs and output, are trained to predict the Clear Sky Index in the defined horizon.

The Impact of Poland’s Energy Transition on the Strategies of Fossil Fuel Sector Companies—The Example of PKN Orlen Group

Sustainability and decarbonisation are buzzwords in today’s economy. The fossil fuel sector has had a great impact on development of many countries and it is interesting how this sector is going to survive in the era of climate neutrality imposed by the European and national decarbonisation policies. PKN Orlen is one of the largest industrial corporations in Poland and Central Europe representing the energy and fossil fuel sector. The article sets out to identify the main areas of PKN Orlen’s strategic plans resulting from the energy transition process in Poland. For this reason, the research question posed in this study is: to what extent and in what direction has Poland’s energy policy influenced the new strategy of PKN Orlen? Poland’s most recent energy policy was adopted in 2021, therefore an attempt to assess the impact of this policy on the strategy of a large player in the fossil fuel sector in such a short time horizon proves the originality of the study. The applied methodological approach, including case study, comparative analysis, and in-depth interview, was determined by the specific nature of the research problem and the fact that the analyzed sector and entity are strategic from the point of view of the country’s economy, which imposes the confidentiality of some data related to the study and determines the possibility of making them public. As shown by the research, PEP2040 had an indirect impact on shaping the strategy of PKN Orlen, while the main source of knowledge about the current and future trends is the analysis, evaluation and synthesis of various sources of information, including those from consulting companies.

Short-Term Nacelle Orientation Forecasting Using Bilinear Transformation and ICEEMDAN Framework

To maximize energy extraction, the nacelle of a wind turbine follows the wind direction. Accurate prediction of wind direction is vital for yaw control. A tandem hybrid approach to improve the prediction accuracy of the wind direction data is developed. The proposed approach in this paper includes the bilinear transformation, effective data decomposition techniques, long-short-term-memory recurrent neural networks (LSTM-RNNs), and error decomposition correction methods. In the proposed approach, the angular wind direction data is firstly transformed into time-series to accommodate the full range of yaw motion. Then, the continuous transformed series are decomposed into a group of subseries using a novel decomposition technique. Next, for each subseries, the wind directions are predicted using LSTM-RNNs. In the final step, it decomposed the errors for each predicted subseries to correct the predicted wind direction and then perform inverse bilinear transformation to obtain the final wind direction forecasting. The robustness and effectiveness of the proposed approach are verified using data collected from a wind farm located in Huitengxile, Inner Mongolia, China. Computational results indicate that the proposed hybrid approach outperforms the other single approaches tested to predict the nacelle direction over short-time horizons. The proposed approach can be useful for practical wind farm operations.

Intraday Market Predictability: A Machine Learning Approach

AbstractConducting, to our knowledge, the largest study ever of 5-min equity market returns using state-of-the-art machine learning models trained on the cross-section of lagged market index constituent returns, we show that regularized linear models and nonlinear tree-based models yield significant market return predictability. Ensemble models perform the best across time and their predictability translates into economically significant Sharpe ratios of 0.98 after transaction costs. These results provide strong evidence that intraday market returns are predictable during short time horizons, beyond what can be explained by transaction costs. Furthermore, we show that constituent returns hold significant predictive information that is not contained in market returns or in price trend and liquidity characteristics. Consistent with the hypothesis that predictability is driven by slow-moving trader capital, predictability decreased post-decimalization, and market returns are more predictable during the middle of the day, on days with high volatility or illiquidity, and in financial crisis periods.

Short- and long-term delegation: what are the effects on politicians’ sense of control?

ABSTRACT The ability of elected representatives to ensure that output is in line with political priorities is a core element of representative democracy. The article explores how delegation affects politicians’ sense of control over the administration, examining delegation practices in Norwegian municipalities and the effects of accountability mechanisms. We find no effects of long-term delegation but negative effects of short-term delegation, indicating that politicians have shorter time horizons than assumed by the NPM notion of strategic political leadership. The negative effects are exacerbated when councillors interact with citizens, suggesting that short-term delegation offers insufficient leeway for politicians when addressing citizen input.

Developing Practical Investment Resilience

While seeking practical tools for resilient investors, a myopic ability for single-period decisions to reflect outcomes at multiple time horizons without dynamic programming is developed. It captures tail risk and, with multiple overlapping observations compounded at different frequencies, captures time-series properties as well. These benefits rely on pairing multilevel representation of the utility probability distribution with the use of Rubinstein utility—logarithmic in investor surplus. Other tools demonstrated for practical use include top-down hierarchical cluster analysis to provide qualitative information, simplified shrinkage of observed return means (but not covariances or higher moments), and better measures of potential tail risk. The resulting asset allocation examples drawn from 28 mutual funds and exchange-traded funds vary by investor risk aversion, degree of anticipation of different regimes, and whether they consider short, longer, or multiple time horizons. For single-horizon problems, resilient methods appear most helpful to conservative investors or those who trade infrequently. In contrast, multiple time horizons appear potentially helpful to all investors. <b>Key Findings</b> ▪ A toolset for investors seeking greater resilience against systemic risk stemming from nontraditional sources, such as pandemics, revolutionary technology, climate change, and political upheavals, is created, with an example based on the impact of poorly understood derivatives in the financial crisis of 2007–2009. ▪ Confronting the wavelike character of systemic disruptions with time stages that may include a sudden shock, rolling disruption, or gradual recovery as winners and losers are revealed leads to a method of making single-period allocations that reflect multiple time horizon outcomes while staying within a simple and easily implemented decision-theory framework. ▪ Other tools for resilient investing include top-down hierarchical cluster analysis, measures of tail risk customized by risk aversion, preserving more useful information by shrinking means but not covariances, and transparent open-source software.

An intervention to support adherence to inhaled medication in adults with cystic fibrosis: the ACtiF research programme including RCT

An intervention to support adherence to inhaled medication in adults with cystic fibrosis: the ACtiF research programme including RCT

Incentivizing Prescription Drug Switching to Reduce Patient and Health Plan Spending: A Microsimulation Model

ObjectivesMost spending for prescription drugs is on branded drugs that do not have direct generic equivalents but many of these drugs do have therapeutic alternatives within class. We estimate the potential savings from providing patients a financial incentive to switch from a higher cost drug to a lower cost, therapeutic alternative drug. MethodsWe used individual state-transition microsimulations to model medication use and spending with and without financial incentives over a 12-month time horizon with a healthcare sector perspective. Costs and utilization inputs were from individuals on branded insulins or multiple sclerosis drugs enrolled in a regional mixed-model health maintenance organization. Base-case model used a one-time financial incentive of $83 and $250 offered to patients on higher cost insulin and multiple sclerosis treatments, respectively, to switch to lower cost drugs within class. ResultsSavings per individual offered an incentive in the insulin and multiple sclerosis classes were, respectively, $84 (95% CI $46-$122) and $2,127 (95% CI $267-$3,987). Varying the incentive size and switch probability resulted in maximum savings of $712 at elasticity of 0.2 and incentive size $250 for the insulin drug class. For the multiple sclerosis drug class, maximum savings of $5945 was at elasticity of 0.2 and incentive size of $1000. Short time horizon makes our savings estimates conservative. ConclusionsIf programs such as financial incentives could encourage even a small proportion of patients to switch among drugs within therapeutic class, then substantial savings could be generated.