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Significant wave height prediction for the following hours is a necessity for the planning and operation of wave energy devices. For a site-specific and short-term prediction, classical numerical wave forecasting methods may not be justified as exhaustive climatological data and huge computational power are needed. In this paper, a combination of a decomposition approach and long short-term memory network was presented to forecast the significant wave heights. An improved version of complete ensemble empirical mode decomposition algorithm and recurrence quantification analysis were applied to separate the original time series into deterministic and stochastic components. Each decomposed series was forecasted by the long short-term memory network and the final predicted significant wave heights were obtained by integrating the deterministic and stochastic predictions. Wave data measured at three buoy stations along the eastern coast of the United States were utilized to verify the hybrid model. The performance of the proposed method in three different wave height ranges was evaluated. The results suggested that the hybrid model outperformed the stand-alone long short-term memory network adjusted on the unseparated signal; in particular, for longer lead times and larger wave heights. [ABSTRACT FROM AUTHOR]

• FDD tools are increasingly adopted in commercial buildings. • Using FDD tools to correct hunting faults can mitigate issues cost-effectively. • We developed and implemented automated control hunting correction in FDD tools. • The algorithm was tested in nine VAV boxes and successfully eliminated hunting faults. • We discussed challenges and future research areas for scaling the technology up. Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology. [ABSTRACT FROM AUTHOR]

Abstract Owing to its recent advance, machine learning has spawned a large collection of solar forecasting works. In particular, machine learning is currently one of the most popular approaches for hourly solar forecasting. Nevertheless, there is evidently a myth on forecast accuracy—virtually all research papers claim superiority over others. Apparently, the "best" model can only be selected with hindsight, i.e., after empirical evaluation. For any new forecasting project, it is irrational for solar forecasters to bet on a single model from the start. In this article, the hourly forecasting performance of 68 machine learning algorithms is evaluated for 3 sky conditions, 7 locations, and 5 climate zones in the continental United States. To ensure a fair comparison, no hybrid model is considered, and only off-the-shelf implementations of these algorithms are used. Moreover, all models are trained using the automatic tuning algorithm available in the R caret package. It is found that tree-based methods consistently perform well in terms of two-year overall results, however, they rarely stand out during daily evaluation. Although no universal model can be found, some preferred ones for each sky and climate condition are advised. Highlights • Hourly solar forecasting is performed using 68 machine learning models. • Models are evaluated at 7 locations in 5 climate zones for 2 years. • Tree-based methods consistently perform well in terms of 2-year average metrics. • Daily best model cannot be identified, regime-switching approach is advised. [ABSTRACT FROM AUTHOR]

To equalize differences in health plan premiums due to differences in risk pools, the German legislature introduced a simple Risk Adjustment Scheme (RAS) based on age, gender and disability status in 1994. In addition, effective 1996, consumers gained the freedom to choose among hundreds of existing health plans, across employers and state-borders. This paper (a) estimates RAS pass-through rates on premiums, financial reserves, and expenditures and assesses the overall RAS impact on market price dispersion. Moreover, it (b) characterizes health plan switchers and investigates their annual and cumulative switching rates over time. Our main findings are based on representative enrollee panel data linked to administrative RAS and health plan data. We show that sickness funds with bad risk pools and high pre-RAS premiums lowered their total premiums by 42 cents per additional euro allocated by the RAS. Consequently, post-RAS, health plan prices converged but not fully. Because switchers are more likely to be white collar, young and healthy, the new consumer choice resulted in more risk segregation and the amount of money redistributed by the RAS increased over time. [ABSTRACT FROM AUTHOR]

Medicare beneficiaries are eligible for health insurance through the public option of traditional Medicare (TM) or may join a private Medicare Advantage (MA) plan. Both are highly subsidized but in different ways. Medicare pays for most of costs directly in TM, and subsidizes MA plans based on a "benchmark" for each beneficiary choosing a private plan. The level of this benchmark is arguably the most important policy decision Medicare makes about the MA program. Many analysts recommend equalizing Medicare's subsidy across the options - referred to in policy circles as a "level playing field." This paper studies the normative question of how to set the level of the benchmark, applying the versatile model developed by Einav and Finkelstein (EF) to Medicare. The EF framework implies unequal subsidies to counteract risk selection across plan types. We also study other reasons to tilt the field: the relative efficiency of MA vs. TM, market power of MA plans, and institutional features of the way Medicare determines subsidies and premiums. After review of the empirical and policy literature, we conclude that in areas where the MA market is competitive, the benchmark should be set below average costs in TM, but in areas characterized by imperfect competition in MA, it should be raised in order to offset output (enrollment) restrictions by plans with market power. We also recommend specific modifications of Medicare rules to make demand for MA more price elastic. [ABSTRACT FROM AUTHOR]

The past two decades have witnessed an increase in ethnicity-related peer aggression, driven by the intensification of the migration phenomenon and rise of information and communication technologies. The goal of the current study is to extend the scope of previous reviews on peer aggression to examine the emerging evidence on the role of technological tools in the prevention, detection, and handling of ethnicity-related peer aggression among young people. We have identified 14 relevant papers published between 2005 and 2020 that help us answer the following research questions: What forms of aggression among young ethnic minorities do these technological interventions try to address? What types of technological interventions are being used? The results indicate that the technological tools are being used to tackle both intergroup as well as intragroup peer aggression, in which ethnic minorities youth is involved (as a victim, perpetrator or bystander). Most studies have focused on adolescents and young adults in the US or in Europe. The technological tools reported on, include: online games, videos, social media, and chat-based programmes, or machine learning algorithms aimed at tackling online and offline peer aggression. They can be used either as stand - alone tools, or as part of intervention programmes (at the school, family or community level). These findings can be useful to improve and elaborate future digital technologies developed to address ethnicity-related peer aggression. • The review maps the existing literature on the role of technological tools in tackling ethnicity-related peer aggression. • All forms of peer aggression are considered in which ethnic minorities youth was involved. • Three groups of technological interventions are identified and categorized into intra- or intergroup ethnicity-related peer aggression. • The digital tools presented include: online games, video, social media, chat-based programmes, and machine learning algorithms. • Our study reveals some follow-up research questions needed to expand current knowledge on digital technologies. [ABSTRACT FROM AUTHOR]

Privacy amid rapid digitalization of medical records is a critical ingredient to the success of electronic-based health service. This paper explores the potential roles of privacy attitudes concerning medical data, based on a large set of a national sample data (n = 2638) from the U.S. Health Information National Trend Survey. We examine the ways in which privacy concern and confidence are (a) mediated through one's interest in sharing information with health professionals and (b) moderated by one's medical condition and the reliance on Internet. Evidence from this study provides insights into the factors shaping health-related engagement in information technologies, helping us argue that privacy is a key predictor. Discussion offers interpretations of how people's perceived need of medical data will mediate privacy concern, contextualizing the affordances of health technologies in future algorithmic applications. • Tenuous behavioral effects of both privacy concern and confidence were found. • Effects of concern and confidence are mediated through interest in sharing data. • Engagement with technologies is also illustration of social contexts in sharing. [ABSTRACT FROM AUTHOR]