Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking

Protein chainmail variants in dsDNA viruses.

First discovered in bacteriophage HK97, biological chainmail is a highly stable system formed by concatenated protein rings. Each subunit of the ring contains the HK97-like fold, which is characterized by its submarine-like shape with a 5-stranded β sheet in the axial (A) domain, spine helix in the peripheral (P) domain, and an extended (E) loop. HK97 capsid consists of covalently-linked copies of just one HK97-like fold protein and represents the most effective strategy to form highly stable chainmail needed for dsDNA genome encapsidation. Recently, near-atomic resolution structures enabled by cryo electron microscopy (cryoEM) have revealed a range of other, more complex variants of this strategy for constructing dsDNA viruses. The first strategy, exemplified by P22-like phages, is the attachment of an insertional (I) domain to the core 5-stranded β sheet of the HK97-like fold. The atomic models of the Bordetella phage BPP-1 showcases an alternative topology of the classic HK97 topology of the HK97-like fold, as well as the second strategy for constructing stable capsids, where an auxiliary jellyroll protein dimer serves to cement the non-covalent chainmail formed by capsid protein subunits. The third strategy, found in lambda-like phages, uses auxiliary protein trimers to stabilize the underlying non-covalent chainmail near the 3-fold axis. Herpesviruses represent highly complex viruses that use a combination of these strategies, resulting in four-level hierarchical organization including a non-covalent chainmail formed by the HK97-like fold domain found in the floor region. A thorough understanding of these structures should help unlock the enigma of the emergence and evolution of dsDNA viruses and inform bioengineering efforts based on these viruses

Building thermal load prediction through shallow machine learning and deep learning

Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty

QCD phase diagram and the critical point

The recent progress in understanding the QCD phase diagram and the physics of the QCD critical point is reviewed.Comment: 18 pages, 11 figures, for proceedings of "Finite Density QCD at Nara", July 200

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Practical Certificateless Aggregate Signatures From Bilinear Maps

Aggregate signature is a digital signature with a striking property that anyone can aggregate n individual signatures on n different messages which are signed by n distinct signers, into a single compact signature to reduce computational and storage costs. In this work, two practical certificateless aggregate signature schemes are proposed from bilinear maps. The first scheme CAS-1 reduces the costs of communication and signer-side computation but trades off the storage, while CAS-2 minimizes the storage but sacrifices the communication costs. One can choose either of the schemes by consideration of the application requirement. Compare with ID-based schemes, our schemes do not entail public key certificates as well and achieve the trust level 3, which imply the frauds of the authority are detectable. Both of the schemes are proven secure in the random oracle model by assuming the intractability of the computational Diffie-Hellman problem over the groups with bilinear maps, where the forking lemma technique is avoided

A putative ATPase mediates RNA transcription and capping in a dsRNA virus.

mRNA transcription in dsRNA viruses is a highly regulated process but the mechanism of this regulation is not known. Here, by nucleoside triphosphatase (NTPase) assay and comparisons of six high-resolution (2.9-3.1 Å) cryo-electron microscopy structures of cytoplasmic polyhedrosis virus with bound ligands, we show that the large sub-domain of the guanylyltransferase (GTase) domain of the turret protein (TP) also has an ATP-binding site and is likely an ATPase. S-adenosyl-L-methionine (SAM) acts as a signal and binds the methylase-2 domain of TP to induce conformational change of the viral capsid, which in turn activates the putative ATPase. ATP binding/hydrolysis leads to an enlarged capsid for efficient mRNA synthesis, an open GTase domain for His217-mediated guanylyl transfer, and an open methylase-1 domain for SAM binding and methyl transfer. Taken together, our data support a role of the putative ATPase in mediating the activation of mRNA transcription and capping within the confines of the virus