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Quantitative filter forensics (QFF) is a method for estimating the time-averaged concentration of particle-bound containments using filter metadata and its extracted dust. In this study, QFF was applied to dust samples extracted from four types of HVAC filters deployed in 20 homes from December 2016 to December 2017 in Toronto, Canada. The analysis covered six allergens, eight phthalates, and 12 polycyclic aromatic hydrocarbons (PAHs). The allergen results from four homes showed elevated concentrations of cat and dog allergens (Fel d 1 and Can f 1) in homes with cats or dogs. All eight phthalates were detected, dominated by diisononyl phthalate (DiNP) and bis(2-ethylhexyl) phthalate (DEHP), with median concentrations of 4.27 ng/m³ and 4.20 ng/m³, respectively. All PAHs, except anthracene, were detected in over 50 % of the homes, with benzo[a]pyrene being the most abundant (median concentration of 0.067 ng/m³). Median concentrations of all semivolatile organic compounds (SVOCs) were lower in winter 2016 compared to spring 2017, potentially due to losses of more volatile SVOCs from filters because of higher filtration volumes in winter, in addition to differences in sources and ventilation rates. All phthalate and most PAH concentrations were one to two orders of magnitude lower compared to those found from QFF in apartments in Toronto social housing multi-unit residential buildings (MURB), indicating those with lower socio-economic status (SES) were exposed to higher SVOC concentrations. Occupant density was positively related to most phthalate and specific PAH concentrations, while environmental factors including room temperature, relative humidity, and supply air temperature were not. • Quantitative filter forensics enables long-term mean SVOC concentration estimation. • Large variations in phthalate and PAH concentrations across homes. • Seasonal variations likely caused by sources, ventilation, and sampling artefacts. • SVOC concentrations are not strongly correlated with filter efficiency. • SVOC concentrations were lower in single-family homes than in social housing units. [ABSTRACT FROM AUTHOR]

Building electrification is the movement to shift building operational energy use from fossil fuels toward electricity. It has been pursued mainly to reduce greenhouse gas (GHG) emissions from the building sector. We present here ten questions concerning building electrification and attempt to answer them in the context of the existing literature. Our questions span dimensions of policy, life cycle impacts on energy and environment, technological advances and challenges, indoor and outdoor air quality, health, economics, and social-behavioral factors. We find that while much of the extant research predicts that building electrification will provide benefits in terms of GHG emissions, pollutant exposures, and economic impacts, it remains limited to a narrow set of geographic regions and typically fails to capture the full extent of life cycle environmental impacts. Additionally, despite logical inferences for likely health benefits, we were unable to identify explicit studies of the health impacts of building electrification. We also find a common theme that, for building electrification to be successful in reducing GHG emissions, costs, and adverse grid impacts, it should be approached in parallel with increased building energy efficiency, grid renewable power, and smart grid infrastructure. Finally, we find that people hold strong opinions about fuel options in their homes, and the relationship between preferences and energy use is complicated. To shift people's beliefs around electrification, government-originating communications can highlight the benefits, but one must still account for heterogeneous household conditions. We conclude by suggesting key research areas needed to approach building electrification effectively and equitably. • Comprehensive LCAs are needed to understand building electrification impacts. • Electric grid resilience is a key research area for a successful transition. • People hold strong opinions about fuel options, especially for cooking in homes. • Social-behavioral research is needed to promote building electrification across communities. • Studies should consider broad geographic locations and diverse populations. [ABSTRACT FROM AUTHOR]

• The Black-Litterman model is extended to build an efficient portfolio. • The newly constructed portfolio enjoys a higher Sharpe ratio. • The result is robust under different confidence levels and sample intervals. Macroeconomic environment is an essential factor affecting asset return, but it is difficult to construct portfolios using macro information quantitatively in traditional models. In this paper, we extend the Black-Litterman framework to build an efficient portfolio by using views containing macro information and prior market distribution reflecting micro information. In order to enhance the model's flexibility, entropy pool method is used. Empirical evidence shows that with reasonable allocation across economic stages, the newly constructed portfolio enjoys a higher level of Sharpe ratio compared to classic allocation models, and the result is robust under different circumstance. [ABSTRACT FROM AUTHOR]

Abstract Recirculating central forced-air heating, ventilation, and air conditioning (HVAC) systems are common in residential buildings in North America. Runtime, the fraction of time any part of the HVAC system operates, is an important parameter to evaluate system performance and its impact on building energy use and indoor air quality. Different methods have been developed to assess runtime. In this paper, we evaluate the strengths and limitations of existing direct and indirect approaches to access runtime. Using data collected from one study in Toronto, Ontario and one in Austin, Texas, we improved the algorithm of an indirect method by Thornburg et al. (2004) which calculates conditioning runtime based on the air temperature in the supply duct. We applied the improved algorithm to two additional studies in Toronto and compared runtime results from this temperature method with other direct methods. The results show that the algorithm is reliable with a small annual underestimation (1.5–2.5% absolute difference). We also found that runtime varies considerably among similar homes with the same ambient conditions, thus it is not reliable to use outdoor temperatures to predict runtime. A further analysis on the real-time temperature and motor signals shows that the accuracy of the algorithm is influenced by system sequencing (i.e. fan and conditioning unit starting and/or stopping not being coincident in time) and fan-only operation. Overall, this work illustrates the importance to measure runtime due to its large variation and that the temperature method is a reliable approach to access runtime in appropriate homes. Highlights • Residential HVAC runtime varies considerably between homes and over time in the same home. • Existing approaches often require access to HVAC system components and specialized knowledge. • We present a method to calculate runtime using duct air temperatures and an algorithm. • Heating and cooling runtime results are predicted with high accuracy, but fan-only periods cannot be assessed. • The results show the algorithm is reliable with 1.5–2.5% (abs. diff.) annual underestimation of runtime. [ABSTRACT FROM AUTHOR]

• This paper builds a unilateral and bilaterally internal leakage detection experimental platform based on the pressure detection method. • The proposed CSA-SVR-Adaboost is used to achieve the prediction of the internal leakage rate of ball valves. • The proposed DBO-KELM is used to establish the identification model of unilateral and bilateral internal leakage of ball valves. In the petrochemical industry, the ball valve internal leakage has been identified as a significant cause of production safety accidents, leading to mass casualties and economic losses. Depending on the flowing direction of the fluid, the form of ball valve internal leakage can be separated into unilateral and bilateral internal leakage. If a ball valve has a unilateral internal leakage, it is capable to play a sealing role, which can reduce gas venting and economic losses during natural gas fire operations. Therefore, this study aimed to develop a method for predicting the internal leakage rate and recognizing the unilateral and bilateral internal leakage used in gas transmission pipelines. For predicting the ball valve internal leakage rate, an Adaboost Integrated Circular Search Algorithm Optimised Support Vector Regression (CSA-SVR-Adaboost) model was employed and compared with other models. The CSA-SVR-Adaboost model demonstrated superior performance, displaying the lowest errors and highest correlation coefficients compared to the other models considered. Furthermore, this study aimed to identify unilateral and bilateral internal leakage of ball valves. To achieve this, a Dung Beetle Optimization Algorithm for Optimizing Kernel Extreme Learning Machine (DBO-KELM) model was adopted and evaluated based on its recognition performance. The recognition accuracy of DBO-KELM was 85.19%, surpassing the recognition accuracy of the Extreme Learning Machine (ELM) and Kernel Extreme Learning Machine (KELM) algorithms by 9.5% and 14.9%, respectively. [ABSTRACT FROM AUTHOR]

Abstract Ventilation using outdoor air can have both favorable and unfavorable impacts on indoor air pollution. It also can be an important contributor to energy use in buildings. Outdoor ventilation air change rate (ACR), the rate at which outdoor air enters a building divided by its volume, is a temporally dynamic metric that can be used to characterize ventilation performance of buildings. Conventional measurement techniques for ACR have either complex or invasive experimental procedures, or present a temporal snapshot of ACR. In this study, we further developed and explored a novel signal processing approach to measure yearlong time-resolved ACR in a residence using the variations in indoor and outdoor CO 2 concentrations. Results showed that ACR varied considerably over the year [geometric mean (GM) = 0.47/h, geometric standard deviation (GSD) = 3.44] and that the air change rates calculated from the signal processing approach were in good agreement (on average, within 13%) with those measured simultaneously from 15 hour-long decay periods. In addition, estimates of ACR were largely insensitive to the occupancy status of the building. This behavior may be because the indoor CO 2 concentration variations introduced by changes in occupancy status were not large enough to impact long-term ACR values, but they may be sufficient to impact short-term ACR values. Moreover, we anticipate that cut-off frequency and filter order, two parameters needed for the signal processing approach, may be building-specific and can influence calculations of air change rates in a given building. The results suggest that this approach has promise for assessing time-varying ACR in buildings with time-resolved indoor and outdoor concentration measurements. Highlights • Ventilation measured with a signal processing approaches reveals temporal dynamics in air exchange rate (AER). • The approach requires measurement of indoor and outdoor CO 2 concentrations but no release of a tracer gas. • Over a year the geometric mean (geometric standard deviation) AER in a residence was 0.47/h (3.44). • The application of the signal processing approach is sensitive to the selection of the concentration filtering parameters. • The results suggest a valuable way to assess time-resolved ventilation rates in buildings [ABSTRACT FROM AUTHOR]

Trigeminal neuralgia is a common chronic maxillofacial neuropathic pain disorder, and voltage-gated sodium channels (VSGCs) are likely involved in its pathology. Prior studies report that pituitary adenylate cyclase-activating polypeptide (PACAP), a neuropeptide highly expressed in the trigeminal ganglion, may contribute to dorsal root ganglion neuron excitability by modulating the Nav1.7. We investigated whether PACAP can regulate Nav1.7 through the mitogen-activated protein kinase/ERK kinase/extracellular-signal-regulated kinase (MEK/ERK) pathway in the trigeminal ganglion after chronic constriction injury of the infraorbital nerve (ION-CCI) in rats. Sprague-Dawley rats underwent ION-CCI, followed by intrathecal injection of PACAP 6–38 (PAC1 receptor antagonist) and PD98059 (MEK/ERK antagonist). Quantitative real-time PCR and western blot were used to quantify ATF3, PACAP, ERK, p-ERK, and Nav1.7 expression. The mechanical pain threshold decreased from day 3 to day 21 after ION-CCI and reached the lowest testing value by day 14; however, it increased after PACAP 6–38 and PD98059 injections. Additionally, ION-CCI surgery increased ATF3, PACAP, and p-ERK expression in the rat trigeminal ganglion and decreased Nav1.7 and PAC1 receptor expression; however, there was no difference in ERK expression. PACAP 6–38 injection significantly decreased PACAP, p-ERK, and Nav1.7 expression and increased the PAC1 receptor expression, with no change in ERK expression. Moreover, PD98059 injection decreased PACAP, p-ERK, and Nav1.7 expression and increased the expression of PAC1 receptor. After ION-CCI, PACAP in the rat trigeminal ganglion can modulate Nav1.7 through the MEK/ERK pathway via the PAC1 receptor. Further, PACAP inhibition alleviates allodynia in ION-CCI rats. [ABSTRACT FROM AUTHOR]

The transport behavior of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) in porous media controls their final environmental fate and risk. Current work used column experiments and a two-site kinetic retention model to reveal the effect of dissolved organic matter (DOM) on their migration under the interference of solution ionic strength and cation type. Results showed the migration of PFOA and PFOS was inhibited by 1 mg/L DOM but promoted by 10 mg/L DOM. Compared with the presence of 1 mg/L DOM, increasing ionic strength (1–10 mM) and changing cation type (Na+ to Ca2+) had a stronger inhibitory effect on PFOS transport. However, the opposite is true for PFOA due to its different molecular structure and adsorption behavior from PFOS. Ca2+ was more tended to interact with PFOS (owing to its sulfonate group) than with PFOA, and PFOS had a higher affinity with Ca2+ than with DOM. Cation bridging and increasing ionic strength could promote the inhibition of PFOA and PFOS transport by 1 mg/L DOM. The influence of DOM on PFASs transport could be very complex in the natural environment due to the variable solution chemistry conditions of pore water and physico-chemical properties of PFASs. The results of this work have scientific significance for better evaluating and managing the environmental risk of soil PFASs. [Display omitted] • DOM-mediated transport of PFOA and PFOS is solution chemistry dependent. • PFOA and PFOS transport was inhibited by deposited DOM but facilitated by free DOM. • The DOM-affected transport of PFOA was less disturbed by ionic strength and Ca2+. • PFOS preferred to interact with Ca2+ rather than DOM. • Ca2+ and higher ionic strength promoted the effect of deposited DOM. [ABSTRACT FROM AUTHOR]

[Display omitted] • Continuous filtration systems operation maximizes exposure reduction and energy use. • Operation strategies based on monitoring and cycling can optimize filtration. • Concentration threshold can reduce runtime while causing little increase in exposure. • The benefits come from synchronizing operation with high concentration periods. • Benefits depends on outdoor concentration, infiltration, HVAC sizing, and emissions. Particle filtration systems are widely used indoors to reduce particle concentration. Increasing the runtime, the fraction of time a system operates, can improve the removal performance but also comes at a greater energy cost. Thus, operation strategies are needed to improve performance while conserving energy. In this work, we used exposure reduction, a metric for filtration effectiveness, and removal efficacy, the ratio of exposure reduction to runtime to provide a quantitative assessment on four operation strategies including strategies sensitive to concentration and/or presence of emission events, pulsing the system regardless of concentration, and pulsing during elevated concentration periods. A time-varying mass balance model is used to estimate indoor concentration with stochastic emission events. The results show that the concentration strategy, which synchronizes the system operation with high indoor concentrations has the highest removal efficacy. It could achieve a comparable level of concentration reduction as continuous operation at runtimes as low as 80%. The runtime of this strategy can be further reduced by increasing its concentration threshold. This strategy provides opportunities to regulate indoor concentration while conserving fan energy use, especially in regions with relatively low ambient concentrations and strong episodic sources. [ABSTRACT FROM AUTHOR]

Effectiveness is commonly used to evaluate the particle removal performance of residential filtration systems. Previous investigations have used various effectiveness metrics to assess system performance through modeling efforts. However, the model assumptions often lead to an overestimation of effectiveness. Further, none of the studies considered the impact of realistic residential heating, ventilation, and air conditioning (HVAC) system operation patterns. In this investigation, we evaluated the strength and limitations of various effectiveness metrics. We identified exposure reduction as the most appropriate metric for modeling analyses because it does not rely on any steady-state assumptions. A time-varying mass balance model that accounts for system operation parameters including runtimes and cycle on-times, and the impact of indoor emission sources is used to simulate the changes in indoor particulate matter smaller than 2.5 μm (PM 2.5) concentration with and without system operation. The effectiveness results show that runtime has the biggest impact on exposure reduction. At a fixed runtime, short cycle on-times and the coincidence of system operation with emission events can further improve the removal performance of the system. Thus, the particle removal performance of a filtration system can be optimized by changing its operation pattern while still maintaining similar fan energy use. • Existing effectiveness metrics could overestimate HVAC filtration performance. • We present a framework to directly assess filtration performance. • The simulation results suggest runtime has the biggest impact on effectiveness. • Shorter cycle on-times bring more filtration benefit. • Filtration performance can be optimized by synchronizing operation with emissions. [ABSTRACT FROM AUTHOR]

This study explores the impact of filtration on energy use in residences based on yearlong measurements in 19 homes in Toronto, Canada. Results show that permanent split capacitor (PSC) motor fans drew approximately 40% more power than electronically commutated motor (ECM) fans and the median efficacy (flow divided by power) of ECM fans was 43% higher than that of PSC motor fans. In response to a pressure drop of 150 Pa, ECM fans drew more power (46% − 58% increase) and generally tended to maintain airflow rate, while PSC motor fans drew less power (10% − 23% decrease), but generally tended to reduce airflow rate. Filter pressure drop was not strongly correlated with filter nominal efficiency; thus, the use of higher-efficiency filters may not impact fan power draw. Fan energy consequences of high-efficiency filters are negligible in our sample, and because HVAC runtimes were generally low (median = 11%), the difference between the energy consumption of ECM and PSC motor fans at typical runtimes was small (less than 10 kWh per month). Filters in systems with ECM fans have higher effectiveness per unit of power draw than those in systems with PSC motor fans. [ABSTRACT FROM AUTHOR]

Rapidly increasing urbanization in recent decades has elevated the subway as the primary public transportation mode in metropolitan areas. Indoor air quality (IAQ) inside subways is an important factor that influences the health of commuters and subway workers. This review discusses the subway IAQ in different cities worldwide by comparing the sources and abundance of particulate matter (PM 2.5 and PM 10) in these environments. Factors that affect PM concentration and chemical composition were found to be associated with the subway internal structure, train frequency, passenger volume, and geographical location. Special attention was paid to air pollutants, such as transition metals, volatile/semi-volatile organic compounds (VOCs and SVOCs), and bioaerosols, due to their potential roles in indoor chemistry and causing adverse health impacts. In addition, given that the IAQ of subway systems is a public health issue worldwide, we calculated the Gini coefficient of urban subway exposure via meta-analysis. A value of 0.56 showed a significant inequity among different cities. Developed regions with higher per capita income tend to have higher exposure. By reviewing the current advances and challenges in subway IAQ with a focus on indoor chemistry and health impacts, future research is proposed toward a sustainable urban transportation systems. [ABSTRACT FROM AUTHOR]

Developing new materials through traditional trial-and-error is time-consuming and costly due to the vast potential compositions to explore. Moreover, some desirable properties are mutually exclusive, presenting challenges for materials design. Herein, we employed an active learning strategy to address the strength-ductility trade-off in SAC105 lead-free solders. We developed two Gaussian regression models, one for strength and one for elongation representing ductility. The Gaussian Upper Confidence Boundary algorithm, a Bayesian optimization method, was used to balance exploitation and exploration by considering prediction uncertainty. The variance weight was designed to decay with adaptive iterations. The two models were linearly combined and the maximal combined model recommended alloys on the Pareto front for experiments. After just three iterations, a new 91.4Sn-1.0Ag-0.5Cu-1.5Bi-4.4In-0.2Ti low-silver SAC solder was discovered, exhibiting 73.94±5.05 MPa strength and 24.37±5.92% elongation, representing the best comprehensive mechanical properties. Weldability experiments also showed excellent performance. Based on Ab initio molecular dynamics, a precise machine learning potential revealed the alloying mechanism in Sn-In solid solutions with quantum accuracy in systems of over 103 atoms, closely matching experimental X-ray diffraction pattern. To facilitate materials informatics development, all active learning algorithms was made open-source in our designed framework, Bgolearn. • The active learning approach dynamically balances exploration and exploitation. • A new low-silver SAC solder is discovered shows exceptional mechanical properties. • All active learning algorithms in Bgolearn framework are open-sourced, supporting materials informatics development. [ABSTRACT FROM AUTHOR]

Melanoma is an extremely aggressive disease with rapid progression, high metastatic potential and recurrence. Simultaneous correction of multiple tumor-specific gene abnormalities has become an attractive approach for developing therapeutics to treat melanoma. To potentiate anti-melanoma activity, we tested a "domino effect-like" therapeutic approach by uniquely targeting one defect and automatically triggering the endogenous corrections of other defects. Using this strategy, in a suspicious INK4b-ARF-INK4a gene cluster at chromosome 9p21, aberrant INK4a and INK4b defects were simultaneously endogenously auto-corrected after targeting the suppression of abnormal ANRIL lncRNA. In cell culture, this treatment significantly reduced the tumor metastatic capacity and tumor formation compared with absence of treatment. In animals harboring tumor xenografts, this therapeutic approach significantly inhibited tumor growth and reduced the tumor weight. Our results reveal a novel therapeutic strategy that significantly potentiates anti-melanoma efficiency by reprogramming the aberrant INK4-hub. [ABSTRACT FROM AUTHOR]

In this paper, we report a new multi-scale NPR composite based on auxetic fabric/elastomer matrix (AF/EM) composite structure. The multi-scale AF was obtained via using helical auxetic yarn (HAY) as weft yarns and constructing re-entrant hexagonal (REH) geometry structure on the macro scale, then the AF was combined with EM to fabricate the auxetic composite (AC). The AF and AC exhibit the NPR of −1.492 and −0.379. Simultaneously, high tensile strain over 108% (AF) and 82% (AC) can be subjected, indicating good mechanical performance. The superior performance of the AC is its ability to maintain NPR and resilience, with elastic recovery ratio over 94% and work recovery coefficient with a stable value over 80% after 10 cycles. Also, the damping properties results further evidence the good elasticity of our AC. The paper shows that our AF and AC are promising candidates in protective composites. [ABSTRACT FROM AUTHOR]