A comparative benchmarking study of multi-storey residential buildings in two periods of energy saving efforts: the 1980s and the 2010s

In 1990, Technological Institute (TI) in Denmark made a benchmarking study of 92 typical multi-storey buildings covering 23 000 dwellings. The study included measurement data from the 1970s and the years after the energy crises. This study showed that over a period of less than 20 years a significant reduction in energy consumption took place. In a new similar study, TI and Aalborg University have analysed 62 buildings covering 18 000 dwellings including measurement data from the last 20 years. This time, the data covers a period with an increasing focus on the carbon-emission impacts of energy consumption. As opposed to the first benchmarking study, the new 20-years study shows that the heat consumption has been almost constant over the last 20 years. This paper presents a comparative study of the two sets of measurements and evaluates energy saving efforts and individual building energy performance. Furthermore, the paper compares two different ways of deriving benchmarks from the data and demonstrates how utilizing change-point models/energy signature as opposed to the more traditional mean annual values per heated area, significantly increases the usability.

To what degree is late life cognitive decline driven by age-related neuropathologies?

The aging brain is vulnerable to a wide array of neuropathologies. Prior work estimated that the three most studied of these, Alzheimer’s disease (AD), infarcts, and Lewy bodies, account for about 40% of the variation in late life cognitive decline. However, that estimate did not incorporate many other diseases that are now recognized as potent drivers of cognitive decline (e.g. limbic predominant age-related TDP-43 encephalopathy [LATE-NC], hippocampal sclerosis, other cerebrovascular conditions). We examined the degree to which person-specific cognitive decline in old age is driven by a wide array of neuropathologies. 1,164 deceased participants from two longitudinal clinical-pathologic studies, the Rush Memory and Aging Project and Religious Orders Study, completed up to 24 annual evaluations including 17 cognitive performance tests and underwent brain autopsy. Neuropathologic examinations provided 11 pathologic indices, including markers of AD, non-AD neurodegenerative diseases (i.e. LATE-NC, hippocampal sclerosis, Lewy bodies), and cerebrovascular conditions (i.e. macroscopic infarcts, microinfarcts, cerebral amyloid angiopathy, atherosclerosis, and arteriolosclerosis). Mixed effects models examined the linear relation of pathologic indices with global cognitive decline, and random change point models examined the relation of the pathologic indices with the onset of terminal decline and rates of preterminal and terminal decline. Cognition declined an average of about 0.10 unit per year (estimate = -0.101, SE = 0.003, p < 0.001) with considerable heterogeneity in rates of decline (variance estimate for the person-specific slope of decline was 0.0094, p < 0.001). When considered separately, 10 of the 11 pathologic indices were associated with faster decline and accounted for between 2 and 34% of the variation in decline, respectively. When considered simultaneously, the 11 pathologic indices together accounted for a total of 43% of the variation in decline; AD-related indices accounted for 30–36% of the variation, non-AD neurodegenerative indices 4–10%, and cerebrovascular indices 3–8%. Finally, the 11 pathologic indices combined accounted for less than a third of the variation in the onset of terminal decline (28%) and rates of preterminal (32%) and terminal decline (19%). Although age-related neuropathologies account for a large proportion of the variation in late life cognitive decline, considerable variation remains unexplained even after considering a wide array of neuropathologies. These findings highlight the complexity of cognitive aging and have important implications for the ongoing effort to develop effective therapeutics and identify novel treatment targets.

Northern Hemisphere Glaciation, African Climate and Evolution

<p>The hypothesis of a connection between the onset (or intensification) of Northern Hemisphere Glaciation (NHG), the stepwise increase in African aridity (and climate variability) and an important mammalian (including hominin) species turnover is a textbook example of the initiation of a scientific idea and its propagation in science. It is, however, also an example of the persistent popularity of a hypothesis despite mounting evidence against it. The first part of our work analyzes of the history of the scientific idea by seeking its roots, including coincidental meetings and exchanges between of scientists, at project meetings, conferences and workshops. The consequences of this idea are examined and its influence on subsequent scientific investigations both before and after it has been falsified. In the second part of our investigation, we examine why the idea that the high latitudes have a major control on the climate of the low latitudes and thus early human evolution persists. For this purpose, an attempt is made to understand the original interpretation of the data, with special consideration of the composition of the scientific team and their scientific backgrounds and persuasions. Some of the key records in support of the hypothesis of a step-wise transition will be statistically re-analyzed by fitting change-point models to the time series to determine the midpoint and duration of the transition – in case such a transition is found in the data. A critical review of key publications in support of such a connection and a statistical re-analysis of key data sets leads to three conclusions: (1) Northern Hemisphere Glaciation is a gradual process between ~3.5–2.5 Ma, not an abrupt onset, either at ~2.5 Ma, nor at ~2.8 Ma, or any other time in the Late Cenozoic Era, (2) the trend towards greater aridity in Africa during this period was also gradual, not stepwise in the sense of a consistent transition of a duration of ≤0.2 Ma, and (3) accordingly, a step-wise change in environmental conditions cannot be used to explain an important mammalian (including hominin) species turnover.</p>

Accurate models vs. accurate estimates: A simulation study of Bayesian single-case experimental designs

Although statistical practices to evaluate intervention effects in single-case experimental design (SCEDs) have gained prominence in recent times, models are yet to incorporate and investigate all their analytic complexities. Most of these statistical models incorporate slopes and autocorrelations, both of which contribute to trend in the data. The question that arises is whether in SCED data that show trend, there is indeterminacy between estimating slope and autocorrelation, because both contribute to trend, and the data have a limited number of observations. Using Monte Carlo simulation, we compared the performance of four Bayesian change-point models: (a) intercepts only (IO), (b) slopes but no autocorrelations (SI), (c) autocorrelations but no slopes (NS), and (d) both autocorrelations and slopes (SA). Weakly informative priors were used to remain agnostic about the parameters. Coverage rates showed that for the SA model, either the slope effect size or the autocorrelation credible interval almost always erroneously contained 0, and the type II errors were prohibitively large. Considering the 0-coverage and coverage rates of slope effect size, intercept effect size, mean relative bias, and second-phase intercept relative bias, the SI model outperformed all other models. Therefore, it is recommended that researchers favor the SI model over the other three models. Research studies that develop slope effect sizes for SCEDs should consider the performance of the statistic by taking into account coverage and 0-coverage rates. These helped uncover patterns that were not realized in other simulation studies. We underline the need for investigating the use of informative priors in SCEDs.

The Consistency of the CUSUM-Type Estimator of the Change-Point and Its Application

In this paper, we investigate the CUSUM-type estimator of mean change-point models based on m-asymptotically almost negatively associated (m-AANA) sequences. The family of m-AANA sequences contains AANA, NA, m-NA, and independent sequences as special cases. Under some weak conditions, some convergence rates are obtained such as OP(n1/p−1), OP(n1/p−1log1/pn) and OP(nα−1), where 0≤α<1 and 1<p≤2. Our rates are better than the ones obtained by Kokoszka and Leipus (Stat. Probab. Lett., 1998, 40, 385–393). In order to illustrate our results, we do perform simulations based on m-AANA sequences. As important applications, we use the CUSUM-type estimator to do the change-point analysis based on three real data such as Quebec temperature, Nile flow, and stock returns for Tesla. Some potential applications to change-point models in finance and economics are also discussed in this paper.