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Hybrid-BPR (Bayesian Personalized Ranking with Feature Embeddings and Explicit Negative Sampling) [SWR-26-039]

Hybrid-BPR is a Python library for Bayesian Personalized Ranking (BPR) with two key capabilities that go beyond standard BPR implementations: 1. User and item feature embeddings - incorporate content-based signals (genres, tags, metadata) alongside collaborative filtering. 2. Implicit negative interactions - use observed non-interactions (e.g. viewed-but-not-clicked) as negative training signal instead of random sampling from the full item space. The software is built for recommender systems research with MLflow experiment tracking, parallel hyperparameter sweeps, and standard ranking metrics.

Sandhu, Rimple [National Laboratory of the Rockies↗

Introducing the Baseline Performance Reference (BPR) for Irradiance in PV System Applications

Photovoltaic (PV) reference cells, modules, and arrays respond in a complex manner to the many variables that define their operating environment. PV reference cells for irradiance measurement have similarities but also differences to operational PV modules. For this reason, a more neutral and a well-defined generic PV reference cell is needed, whose characteristics are close enough to most operational PV devices to make stable performance indicators possible but whose characteristics are not necessarily identical to any of them. This poster describes a new well-defined reference quantity for outdoor PV measurements, called the baseline performance reference (BPR).

baseline performance reference↗

The Baseline Performance Reference for Irradiance in PV System Applications

This report proposes the definition of a new baseline performance reference (BPR). The definition goes beyond existing standards pertaining to photovoltaic (PV) reference cells and devices to define the response under all possible operating conditions in the field. Field evaluations using BPR devices will be more sensitive to performance anomalies than pyranometers because they track PV system power output more closely. At the same time, they will be able to detect a broader range of performance anomalies than traditional matched reference devices, which might have matching defects. The BPR definition also opens the door to new practices in resource assessment and yield prediction. Solar resource data can be collected or modeled and validated directly as BPR irradiance, and PV system simulations based on BPR irradiance need fewer assumptions and less processing to obtain the effective irradiance on modules. As a result, lower uncertainty in yield assessments can be expected.

14 SOLAR ENERGY↗

Binary pseudorandom array test standard optimized for characterization of large field-of-view optical interferometers

Recently, a technique for calibrating the modulation transfer function (MTF) of a broad variety of metrology instrumentation has been demonstrated. This technique is based on test samples structured as one-dimensional binary pseudo-random (BPR) sequences and two-dimensional BPR arrays (BPRAs). The inherent power spectral density of BPR gratings (sequences) and arrays has a deterministic white-noise-like character that allows direct determination of the MTF with uniform sensitivity over the entire spatial frequency range and field-of-view of an instrument. As such, the BPR samples satisfy the characteristics of a test standard: functionality, ease of specification and fabrication, reproducibility, and low sensitivity to manufacturing error. Here we discuss our recent developments directed to the optimization of the sample design, fabrication, application, and data processing procedures, suitable for thorough characterization of large aperture optical interferometers. Compared with the previous coded-aperture based design, the improved, 'highly randomized' BPRA pattern of the new test standard provides better accuracy and reliability of instrument MTF and aberration characterization, and enables operation optimization of large aperture optical interferometers. We describe the pattern generation algorithm and tests to verify the compliance to desired BPRA topography. The data acquisition and analysis procedures for different applications of the technique are also discussed.

Yashchuk, Valeriy V.↗

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V D,ss ) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V D,ss , accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V D,ss directly and to predict input parameters required for mechanistic and empirical V D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V D,ss predictions was also investigated. In predicting V D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V D,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V D,ss but was limited in estimating the compounds with low V D,ss .

59 BASIC BIOLOGICAL SCIENCES↗

Evaluation of cooling setpoint setback savings in commercial buildings using electricity and exterior temperature time series data

Commercial buildings account for a significant amount of total energy produced in the US, and the Heating Ventilation and Cooling (HVAC) systems are one of the most significant components of their overall consumption. In this study, we proposed a new data-driven approach to evaluate HVAC cooling systems in commercial buildings and identify savings opportunities. The focus is an investigation of the impact of thermostat setpoint setback but using only whole building, electricity data taken at 15-min intervals for the analysis. We conducted a comparative study of setpoint setback characteristics on 432 commercial buildings with 5 building usage types across the United States. To accomplish this, both piecewise and Random Forest regression algorithms were employed using electricity and exterior temperature datasets to identify operational characteristics and the effective setpoints in the building to determine the corresponding savings opportunities. Both occupied and unoccupied time periods were studied across cooling degree days (CDD), when air conditioning is typically operational. Here the results show that in commercial buildings, on average, cooling systems account for 9.5% of total consumption. When a one degree setback during the cooling season is applied, an average of approximately 1.1% of annual consumption is achieved; retail and office buildings demonstrate the highest potential for savings. Additionally, we identified that the number of cooling degree days and base to peak ratio (BPR) are the most important variables for predicting the magnitude of the consumption of cooling systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Mechanochemical synthesis and structural analysis of trivalent lanthanide and uranium diphenylphosphinodiboranates

Phosphinodiboranates (H 3 BPR 2 BH 3 – ) are a class of borohydrides that have merited a reputation as weakly coordinating anions, which is attributed in part to the dearth of coordination complexes known with transition metals, lanthanides, and actinides. We recently reported how K(H 3 BP t Bu 2 BH 3 ) exhibits sluggish salt elimination reactivity with f-metal halides in organic solvents such as Et 2 O and THF. Here we report how this reactivity appears to be further attenuated in solution when the t Bu groups attached to phosphorus are exchanged for R = Ph or H, and we describe how mechanochemistry was used to overcome limited solution reactivity with K(H 3 BPPh 2 BH 3 ). Grinding three equivalents of K(H 3 BPPh 2 BH 3 ) with UI 3 (THF) 4 or LnI 3 (Ln = Ce, Pr, Nd) allowed homoleptic complexes with the empirical formulas U(H 3 BPPh 2 BH 3 ) 3 (1), Ce(H 3 BPPh 2 BH 3 ) 3 (2), Pr(H 3 BPPh 2 BH 3 ) 3 (3), and Nd(H 3 BPPh 2 BH 3 ) 3 (4) to be prepared and subsequently crystallized in good yields (50–80%). Single-crystal XRD studies revealed that all four complexes exist as dimers or coordination polymers in the solid-state, whereas 1 H and 11 B NMR spectra showed that they exist as a mixture of monomers and dimers in solution. Treating 4 with THF breaks up the dimer to yield the monomeric complex Nd(H 3 BPPh 2 BH 3 ) 3 (THF) 3 (4-THF). XRD studies revealed that 4-THF has one chelating and two dangling H 3 BPPh 2 BH 3 – ligands bound to the metal to accommodate binding of THF. In contrast to the results with K(H 3 BPPh 2 BH 3 ), attempting the same mechanochemical reactions with Na(H 3 BPH 2 BH 3 ) containing the simplest phosphinodiboranate were unsuccessful; only the partial metathesis product U(H 3 BPH 2 BH 3 )I 2 (THF) 3 (5) was isolated in poor yields. Here, despite these limitations, our results offer new examples showing how mechanochemistry can be used to rapidly synthesize molecular coordination complexes that are otherwise difficult to prepare using more traditional solution methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗