Sample-based Strategies Research Articles

Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm

Underwater autonomous path planning is a critical component of intelligent underwater vehicle system design, especially for maritime conservation and monitoring missions. Effective path planning for these robots necessitates considering various constraints related to robot kinematics, optimization objectives, and other pertinent factors. Sample-based strategies have successfully tackled this problem, particularly the rapidly exploring random tree star (RRT*) algorithm. However, conventional path-searching algorithms may face challenges in the marine environment due to unique terrain undulations, sparse and unpredictable obstacles, and inconsistent results across multiple planning iterations. To address these issues, we propose a new approach specifically tailored to the distinct features of the marine environment for navigation path planning of underwater vehicles, named bidirectional cached rapidly exploring random tree star (BCRRT*). By incorporating bidirectional path planning and caching algorithms on top of the RRT*, the search process can be expedited, and an efficient path connection can be achieved. When encountering new obstacles, ineffective portions of the cached path can be efficiently modified and severed, thus minimizing the computational workload while enhancing the algorithm’s adaptability. A certain number of simulation experiments were conducted, demonstrating that our proposed method outperformed cutting-edge techniques like the RRT* in several critical metrics such as the density of path nodes, planning time, and dynamic adaptability.

Copeptin, Natriuretic Peptides, and Cardiovascular Outcomes in Patients With CKD: The German Chronic Kidney Disease (GCKD) Study

Copeptin and Midrange pro-atrial natriuretic peptide (MR-pro-ANP) are associated with outcomes independently of N-terminal pro-brain natriuretic peptide (NT-pro-BNP) in patients with heart failure (HF). The value of these markers in patients with chronic kidney disease (CKD) has not been studied. Prospective cohort study. A total of 4,417 patients enrolled in the German Chronic Kidney Disease (GCKD) study with an estimated glomerular filtration rate of 30-60mL/min/1.73m2 or overt proteinuria (urinary albumin-creatinine ratio>300mg/g or equivalent). Copeptin, MR-pro-ANP, and NT-pro-BNP levels were measured in baseline samples. Noncardiovascular death, cardiovascular (CV) death, major adverse CV event (MACE), and hospitalization for HF. HRs for associations of Copeptin, MR-pro-ANP, and NT-pro-BNP with outcomes were estimated using Cox regression analyses adjusted for established risk factors. During a maximum follow-up of 6.5 years, 413 non-CV deaths, 179 CV deaths, 519 MACE, and 388 hospitalizations for HF were observed. In Cox regression analyses adjusted for established risk factors, each one of the 3 markers were associated with all the 4 outcomes, albeit the highest HRs were found for NT-pro-BNP. When models were extended to include all the 3 markers, NT-pro-BNP remained associated with all 4 outcomes. Conversely, from the 2 novel markers, associations remained only for Copeptin with non-CV death (HR, 1.62; 95% CI, 1.04-2.54 for highest vs lowest quintile) and with hospitalizations for HF (HR, 1.73; 95% CI, 1.08-2.75). Single-point measurements of Copeptin, MR-pro-ANP, and NT-pro-BNP. In patients with moderately severe CKD, we confirm NT-pro-BNP to be strongly associated with all outcomes examined. As the main finding, the novel marker Copeptin demonstrated independent associations with non-CV death and hospitalizations for HF, and should therefore be evaluated further for risk assessment in CKD. A blood sample-based biomarker that indicates high cardiovascular risk in a patient with kidney disease would help to guide interventions and has the potential to improve outcomes. In 4,417 patients of the German Chronic Kidney Disease study, we assessed the relationship of Copeptin, pro-atrial natriuretic peptide, and N-terminal pro-brain natriuretic peptide (NT-pro-BNP) with important outcomes over a follow-up period of 6.5 years. NT-pro-BNP was strongly associated with all of the 4 outcomes, including death unrelated to cardiovascular disease, death because of cardiovascular disease, a major cardiovascular event, and hospitalization for heart failure. Copeptin was associated with death unrelated to cardiovascular disease and hospitalization for heart failure. NT-pro-BNP and Copeptin are, therefore, promising candidates for a blood sample-based strategy to identify patients with kidney disease at high cardiovascular risk.

Fast path planning for underwater robots by combining goal-biased Gaussian sampling with focused optimal search

Autonomous path planning plays an important role in the navigation of intelligent underwater robots. Path planning is a nondeterministic polynomial hard issue in classical path planning models. This problem can be solved using various sample-based strategies. However, the effectiveness of these sample-based strategies is significantly lower in underwater environments, owing to the special undulating terrain and obstacles that are sparser compared to those in the ground. In this study, a more efficient underwater path planning method is proposed for underwater robot navigation. The method employs a goal-biased Gaussian sampling algorithm to select searching nodes optimally, and a focused optimal search algorithm is proposed to accelerate the path optimization process. Combining these two algorithms results in high-efficiency and fast autonomous underwater path planning. Experimental results demonstrate that our method can generate a shorter path and is more efficient than a rapidly exploring random tree star in underwater robot navigation.

Quantifying the trade-off between cost and precision in estimating area of forest loss and degradation using probability sampling in Guyana

Guyana is a high forest cover, low deforestation country. Since 2011–2014 the Guyana Forestry Commission (GFC) has used visual interpretation of 5 m resolution RapidEye imagery to map forest loss and nearby degradation for the entire country. According to the GFC produced national map, 58% of all forest loss events cover less than 1 ha, so forest loss is very rare and occurs at a scale that can be difficult to detect and monitor. Nearly all (~97%) of the forest loss and nearby degradation is due to small-scale mining and its associated infrastructure. For any country wanting to accurately map and monitor forest loss, sample-based area estimation can be considerably less expensive than wall-to-wall mapping. To quantify the tradeoff between precision and cost, we evaluated the standard errors of area estimators for several sample-based strategies using Guyana as a case study. We partitioned Guyana into 374 blocks, with each block 24 km × 24 km to align with the image tiles provided by RapidEye's 3A product. The area of forest loss and area of degradation for each of the 374 blocks were determined from the GFC map to create the population data used to evaluate the precision of different sample-based strategies. We compared the standard errors of estimators of area of forest loss and area of degradation obtained from simple random, stratified random and systematic sampling as applied to these population data. To construct strata for the stratified design, we evaluated two forest loss maps produced from Landsat data, a 30 m global forest loss product and a 30 m national forest loss map produced specifically for Guyana. For both of these maps, several options for defining stratum boundaries and allocating sample size to strata were evaluated. All stratified design options reduced the standard error of the area estimators relative to simple random sampling. The Dalenius-Hodges and Jenks methods for choosing stratum boundaries yielded greater improvements in precision than the Equal Area method. For this Guyana case study, optimal and equal allocation of sample size to strata led to substantially better precision than proportional allocation. Only small improvements in precision were attained by increasing the number of strata from three to five. For simple random sampling, incorporating the global and national forest loss map information in a regression estimator reduced standard errors of estimated area of forest loss and area of degradation relative to the standard error of the Horvitz-Thompson estimator that does not incorporate the forest loss map information. However, the best performing stratified sampling design options had better precision than the regression estimator. RapidEye data cost 800 USD per ‘3A’ tile so the annual imagery cost for mapping Guyana would be 274,000 USD. For an investment of 75,200 USD (the cost of 94 RapidEye tiles, the largest sample size evaluated), the best performing stratified random sample achieved a relative standard error (100% times the standard error divided by area) of 7% for estimating area of forest loss and 6% for estimating area of degradation. This study demonstrates the utility of Landsat-based forest loss maps to provide effective strata to improve precision of area estimators produced from a sample of RapidEye imagery in a country for which forest loss and degradation are very rare features of the landscape.

Six strategies for generalizing software engineering theories

General theories of software engineering must balance between providing full understanding of a single case and providing partial understanding of many cases. In this paper we argue that for theories to be useful in practice, they should give sufficient understanding of a sufficiently large class of cases, without having to be universal or complete. We provide six strategies for developing such theories of the middle range.In lab-to-lab strategies, theories of laboratory phenomena are developed and generalized to other laboratory phenomena. This is a characteristic strategy for basic science. In lab-to-field strategies, theories are developed of artifacts that first operate under idealized laboratory conditions, which are then scaled up until they can operate under uncontrolled field conditions. This is the characteristic strategy for the engineering sciences.In case-based strategies, we generalize about components of real-world cases, that are supposed to exhibit less variation than the cases as a whole. In sample-based strategies, we generalize about the aggregate behavior of samples of cases, which can exhibit patterns not visible at the case level. We discuss three examples of sample-based strategies.Throughout the paper, we use examples of theories and generalization strategies from software engineering to illustrate our analysis. The paper concludes with a discussion of related work and implications for empirical software engineering research.

Stable and Efficient Portfolios

DeMiguel et al. (2009a) show that estimation errors lead to mean-variance portfolios that underperform naive diversification (1/N). In response to this finding, new studies have developed more efficient sample-based portfolio strategies that offer higher risk-adjusted returns than 1/N . In this paper, I show that the latter portfolio may still be superior in the presence of transaction costs. This is because sample-based strategies tend to be unstable over time. Instability leads to high transaction costs that significantly reduce portfolio returns. I resolve this issue by introducing a stability penalty to the standard mean-variance objective that controls the deviation from the investor’s portfolio before rebalancing. In contrast to other techniques in the literature that account for transaction costs, the stability penalty offers intuitive analytical solutions to the portfolio choice problem. As such, it allows to treat estimation errors and transactions costs in a joint manner and it can be directly applied to any portfolio strategy. I evaluate the stability approach by applying it to nine sample-based strategies from the literature and study their performance in five data sets of monthly returns. The results confirm that it leads to stable and efficient portfolios that offer higher Sharpe ratio than 1/N and equal or lower turnover.