Input Output HMM for Indoor Temperature Prediction in Occupancy Management Under User Preferences

In this paper, a probabilistic machine learning method is proposed to predict the indoor temperature of an office environment. An IOHMM-based model is developed to represent the office environment under different circumstances of heating sources. One year of time series data is observed and studied to learn the dynamics of the indoor thermal states. The uncertainty associated with the changing aspects of the indoor temperature and its dependence on the outdoor temperature is considered in the model development. The well-known Baum Welch and forward-backward algorithms are adapted to learn the model parameters. Then, the Viterbi algorithm is used to predict the maximum path of hidden states, leading to predicting the most likely future temperatures. A numerical application is presented to demonstrate the model development steps and the training and testing results. Finally, the model's performance is validated using leave-one-out cross-validation, which shows that the model has a prediction accuracy of about 78%

Mind the gut:Genomic insights to population divergence and gut microbial composition of two marine keystone species

BACKGROUND: Deciphering the mechanisms governing population genetic divergence and local adaptation across heterogeneous environments is a central theme in marine ecology and conservation. While population divergence and ecological adaptive potential are classically viewed at the genetic level, it has recently been argued that their microbiomes may also contribute to population genetic divergence. We explored whether this might be plausible along the well-described environmental gradient of the Baltic Sea in two species of sand lance (Ammodytes tobianus and Hyperoplus lanceolatus). Specifically, we assessed both their population genetic and gut microbial composition variation and investigated not only which environmental parameters correlate with the observed variation, but whether host genome also correlates with microbiome variation. RESULTS: We found a clear genetic structure separating the high-salinity North Sea from the low-salinity Baltic Sea sand lances. The observed genetic divergence was not simply a function of isolation by distance, but correlated with environmental parameters, such as salinity, sea surface temperature, and, in the case of A. tobianus, possibly water microbiota. Furthermore, we detected two distinct genetic groups in Baltic A. tobianus that might represent sympatric spawning types. Investigation of possible drivers of gut microbiome composition variation revealed that host species identity was significantly correlated with the microbial community composition of the gut. A potential influence of host genetic factors on gut microbiome composition was further confirmed by the results of a constrained analysis of principal coordinates. The host genetic component was among the parameters that best explain observed variation in gut microbiome composition. CONCLUSIONS: Our findings have relevance for the population structure of two commercial species but also provide insights into potentially relevant genomic and microbial factors with regards to sand lance adaptation across the North Sea-Baltic Sea environmental gradient. Furthermore, our findings support the hypothesis that host genetics may play a role in regulating the gut microbiome at both the interspecific and intraspecific levels. As sequencing costs continue to drop, we anticipate that future studies that include full genome and microbiome sequencing will be able to explore the full relationship and its potential adaptive implications for these species

Nationwide Genomic Study in Denmark Reveals Remarkable Population Homogeneity

Denmark has played a substantial role in the history of Northern Europe. Through a nationwide scientific outreach initiative, we collected genetic and anthropometrical data from ∼800 high school students and used them to elucidate the genetic makeup of the Danish population, as well as to assess polygenic predictions of phenotypic traits in adolescents. We observed remarkable homogeneity across different geographic regions, although we could still detect weak signals of genetic structure reflecting the history of the country. Denmark presented genomic affinity with primarily neighboring countries with overall resemblance of decreasing weight from Britain, Sweden, Norway, Germany, and France. A Polish admixture signal was detected in Zealand and Funen, and our date estimates coincided with historical evidence of Wend settlements in the south of Denmark. We also observed considerably diverse demographic histories among Scandinavian countries, with Denmark having the smallest current effective population size compared to Norway and Sweden. Finally, we found that polygenic prediction of self-reported adolescent height in the population was remarkably accurate (R2 = 0.639 ± 0.015). The high homogeneity of the Danish population could render population structure a lesser concern for the upcoming large-scale gene-mapping studies in the country

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting