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Increasing the penetration of photovoltaics (PV) reduces the marginal grid value of PV electricity. The declining grid value of PV with higher penetration could limit the technology's economic attractiveness and future demand. Various strategies have been proposed for preserving this value. Using a consistent framework, we analyze the net value (accounting for both cost and grid value) of more than ten strategies in the United States. Here, grid value is estimated from coincident wholesale power market prices and PV generation using observed historical prices or modeled future prices with up to 30% PV penetration. We find that established and emerging strategies designed to shift the timing of standalone PV generation at the expense of total generation—including orienting monofacial PV modules west or bifacial modules vertically—result in minor net-value benefits or penalties. Adding energy storage to such systems magnifies the net-value loss, because configurations that change the timing of PV production become redundant when the energy-shifting capabilities of storage are added. The largest net-value gains come from strategies that maximize generation (solar tracking plus oversized PV arrays) in conjunction with storage, especially at high PV penetrations. PV systems are long-lived assets. Our results suggest that efforts to promote generation-maximizing strategies today may yield increasing net-value benefits as PV and storage deployments continue to accelerate in the United States over the coming decades.

Prices of solar PV have dropped dramatically, by half in just the past 6 years. But looking simply at prices paid today, there is considerable heterogeneity. For systems installed in 2014, the 10-90 percentile range for the observed $/W spans nearly a factor of 2. This apparent price dispersion raises policy-relevant questions, such as: why are consumers paying more than they need to? And would better-informed consumers increase the social benefits of solar PV? This paper analyzes price dispersion in U.S. residential PV installations between 2008 and 2014. Focusing on the most commonly used metric in previous studies of price dispersion, we use the quarterly coefficient of variation (CV) as our measure of price dispersion. We find higher levels of price dispersion in our data (0.22) than the average of 55 previous studies we reviewed (0.16). We also find that price dispersion has been persistent; it has remained above 0.15 since 2000 with no trend over that period. If anything, price dispersion has been increasing recently during the period for which we have complete data, 2008-14. Econometric analysis of the factors affecting price dispersion supports theories from the economic literature focusing on access to information and the costs and benefits of consumer search. Factors that increase the consumer payoffs of investing time in searching for information—system size and the value of solar—are associated with lower levels of price dispersion. Factors that reduce the costs of search—neighbors who have recently installed solar and having third-party quotes available—are also associated with less price dispersion. These results provide support for the importance of public efforts to enhance access to price information, e.g. by supporting price quote providers. The results also point to the particular need for information in nascent markets for PV in which access to the experience of neighbors is not available.

Low- and moderate-income (LMI) households are less likely to adopt rooftop solar photovoltaics (PVs) than higher-income households in the United States. As the existing literature has shown, this dynamic can decelerate rooftop PV deployment and has potential energy justice implications, in light of the cost-shifting between PV and non-PV households that can occur under typical rate structures and incentive programmes. Here we show that some state policy interventions and business models have expanded PV adoption among LMI households. We find evidence that LMI-specific financial incentives, PV leasing and property-assessed financing have increased the diffusion of PV adoption among LMI households in existing markets and have driven more installations into previously underserved low-income communities. By shifting deployment patterns, we posit that these interventions could catalyse peer effects to increase PV adoption in low-income communities even among households that do not directly benefit from the interventions.

Local permitting can ensure the safe installation and operation of rooftop solar photovoltaic (PV) systems. At the same time, burdensome local permitting processes and local variation in requirements may pose challenges to PV deployment. In this article, we explore new data on the durations between key steps in the PV permitting process in the United States. The data suggest that a typical customer can expect to wait around 25–100 days from permit application until an installed system passes inspection. Permit durations vary significantly across jurisdictions, due in part to differences in local permitting policies. However, permit durations vary as significantly within jurisdictions as across them, in part due to significant variation across installers, suggesting that installer strategies and practices play an important role in permitting timelines. Permit durations have declined over time, reflecting progress from permit streamlining policies and jurisdiction learning-by-doing, though durations have stabilized in recent years. The data suggest that typical PV customers still face long and uncertain permitting timelines in the United States.

Growth in U.S. utility-scale hybrid battery projects suggests potential advantages currently outweigh disadvantages. Today's 4.6 GW of hybrid capacity is accompanied by 14.7 GW in the immediate development pipeline, and 69 GW in select interconnection queues. Analysis using wholesale market prices finds that additional revenues from adding a 4-hour battery to solar can exceed additional costs. However, realizing hybrid projects’ full value depends on nascent strategies for integrating them in current/future wholesale market design paradigms.

The solar photovoltaic (PV) installation industry comprises thousands of firms around the world who collectively installed nearly 200 million panels in 2015. Spatial analysis of the emerging industry has received considerable attention from the literature, especially on the demand side concerning peer effects and adopter clustering. However this research area does not include similarly sophisticated spatial analysis on the supply side of the installation industry. The lack of understanding of the spatial structure of the PV installation industry leaves PV market research to rely on jurisdictional lines, such as counties, to define geographic PV markets. We develop an approach that uses the spatial distribution of installers’ activity to define geographic boundaries for PV markets. Our method is useful for PV market research and applicable in the contexts of other industries. We use our approach to demonstrate that the PV industry in the United States is spatially heterogeneous. Despite the emergence of some national-scale PV installers, installers are largely local and installer communities are unique from one region to the next. The social implications of the spatial heterogeneity of the emerging PV industry involve improving understanding of issues such as market power, industry consolidation, and how much choice potential adopters have.

The price of solar PV systems has declined rapidly, yet there are some much lower-priced systems than others. This study explores the factors that determine prices in these low-priced (LP) systems. Using a data set of 42,611 residential-scale PV systems installed in the U.S. in 2013, we use quantile regressions to estimate the importance of factors affecting the installed prices for LP systems (those at the 10th percentile) in comparison to median-priced systems. We find that the value of solar to consumers–a variable that accounts for subsidies, electric rates, and PV generation levels–is associated with lower prices for LP systems but higher prices for median priced systems. Conversely, systems installed in new home construction are associated with lower prices at the median but higher prices for LP. Other variables have larger price-reducing effects on LP than on median priced systems: systems installed in Arizona and Florida, as well as commercial and thin film systems. In contrast, the following have a smaller effect on prices for LP systems than median priced systems: tracking systems, self-installations, systems installed in Massachusetts, the system size, and installer experience. These results highlight the complex factors at play that lead to LP systems and shed light into how such LP systems can come about.

Prices of solar PV have dropped dramatically, by half in just the past 6 years. But looking simply at prices paid today, there is considerable heterogeneity. For systems installed in 2014, the 10-90 percentile range for the observed $/W spans nearly a factor of 2. This apparent price dispersion raises policy-relevant questions, such as: why are consumers paying more than they need to? And would better-informed consumers increase the social benefits of solar PV? This paper analyzes price dispersion in U.S. residential PV installations between 2008 and 2014. Focusing on the most commonly used metric in previous studies of price dispersion, we use the quarterly coefficient of variation (CV) as our measure of price dispersion. We find higher levels of price dispersion in our data (0.22) than the average of 55 previous studies we reviewed (0.16). We also find that price dispersion has been persistent; it has remained above 0.15 since 2000 with no trend over that period. If anything, price dispersion has been increasing recently during the period for which we have complete data, 2008-14. Econometric analysis of the factors affecting price dispersion supports theories from the economic literature focusing on access to information and the costs and benefits of consumer search. Factors that increase the consumer payoffs of investing time in searching for information—system size and the value of solar—are associated with lower levels of price dispersion. Factors that reduce the costs of search—neighbors who have recently installed solar and having third-party quotes available—are also associated with less price dispersion. These results provide support for the importance of public efforts to enhance access to price information, e.g. by supporting price quote providers. The results also point to the particular need for information in nascent markets for PV in which access to the experience of neighbors is not available.

Despite impressive declines in average prices, there is wide dispersion in the prices of U.S. solar photovoltaic (PV) systems; prices span more than a factor of four. What are the characteristics of the systems with low-prices? Using detailed characteristics of 42,611 small-scale (

Radiology generates both qualitative and quantitative data. As a consequence, statistical analysis is essential to validate data interpretation, and support reliable conclusions. Statistics serves as a cornerstone of radiology research, objectively verifying observations and establishing relationships between variables. This article provides a practical guide to basic statistical methods for radiology researchers, enabling them to structure their analyses more effectively and highlight their findings in a meaningful way. Although not exhaustive, this article covers basic statistical principles., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to disclose., (Copyright © 2024. Published by Elsevier Masson SAS.)

Background: Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes., Purpose: This study aimed to evaluate the performance of machine learning models in accurately distinguishing malignant orbital tumors from benign ones using multiparametric 3 T magnetic resonance imaging (MRI) data., Materials and Methods: In this single-center prospective study, patients with orbital masses underwent presurgery 3 T MRI scans between December 2015 and May 2021. The MRI protocol comprised multiparametric imaging including dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), as well as morphological imaging acquisitions. A repeated nested cross-validation strategy using random forest classifiers was used for model training and evaluation, considering 8 combinations of explanatory features. Shapley additive explanations (SHAP) values were used to assess feature contributions, and the model performance was evaluated using multiple metrics., Results: One hundred thirteen patients were analyzed (57/113 [50.4%] were women; average age was 51.5 ± 17.5 years, range: 19-88 years). Among the 8 combinations of explanatory features assessed, the performance on predicting malignancy when using the most comprehensive model, which is the most exhaustive one incorporating all 46 explanatory features-including morphology, DWI, DCE, and IVIM, achieved an area under the curve of 0.9 [0.73-0.99]. When using the streamlined "10-feature signature" model, performance reached an area under the curve of 0.88 [0.71-0.99]. Random forest feature importance graphs measured by the mean of SHAP values pinpointed the 10 most impactful features, which comprised 3 quantitative IVIM features, 4 quantitative DCE features, 1 quantitative DWI feature, 1 qualitative DWI feature, and age., Conclusions: Our findings demonstrate that a machine learning approach, integrating multiparametric MRI data such as DCE, DWI, IVIM, and morphological imaging, offers high-performing models for differentiating malignant from benign orbital tumors. The streamlined 10-feature signature, with a performance close to the comprehensive model, may be more suitable for clinical application., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: In England, nearly a quarter of people with intellectual disability (PwID) have epilepsy. Though 70 % of PwID have pharmaco-resistant seizures only 10 % are prescribed anti-seizure medication (ASMs) licenced for pharmaco-resistance. Brivaracetam (BRV) licenced in 2016 has had nine post-marketing studies involving PwID. These studies are limited either by lack of controls or not looking at outcomes based on differing levels of ID severity. This study looks at evidence comparing effectiveness and side-effects in PwID to those without ID prescribed Brivaracetam (BRV)., Methods: Pooled case note data for patients prescribed BRV (2016-2022) at 12 UK NHS Trusts were analysed. Demographics, starting and maximum dose, side-effects, dropouts and seizure frequency between ID (mild vs. moderate-profound (M/P)) and general population for a 12-month period were compared. Descriptive analysis, Mann-Whitney, Fisher's exact and logistic regression methods were employed., Results: 37 PwID (mild 17 M/P 20) were compared to 102 without ID. Mean start and maximum dose was lower for PwID than non-ID. Mean maximum dose reduced slightly with ID severity. No difference was found between ID and non-ID or between ID groups (Mild vs M/P) in BRV's efficacy i.e. >50 % seizure reduction or tolerability. Mental and behavioural side-effects were more prevalent for PwID (27.0 % ID, 17.6 % no ID) but not significantly higher (P = 0.441) or associated with ID severity (p = 0.255)., Conclusion: This is the first study on BRV, which compares ID cohorts with differing severity and non-ID. Efficacy, tolerability and side-effects reported are similar across differing ID severity to those with no ID., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: UCB pharma provided an investigator initiated support grant which part paid the research co-ordinator JA’s time. PT has received honoraria and support for educational projects from UCB Pharma. AS hold a current research grant with UCB Pharma looking at the possible immune basis for drug resistance in epilepsy. It is not a direct conflict with the manuscript. AS and the Oxford Research Group have received institutional and research support from Bial, Eisai, Livanova, UCB Pharma. RS has received Honoria, institutional and research support from LivaNova, UCB, Eisai, Veriton Pharma, Bial, Angelini, UnEEG and Jazz/GW pharma outside the submitted work. He holds grants from NIHR AI, SBRI and other funding bodies all outside this work. No other author has any declared conflict of interest related to this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Policymakers at the federal and state level have begun to incorporate energy burden into equity goals and program evaluations, aiming to reduce energy burden below a high level of 6% for lower income households in the United States. Pairing an empirical household-level dataset spanning United States geographies together with modeled hourly energy demand curves, we show that rooftop solar reduces energy burden across a majority of adopters during our study period from a median of 3.3% to 2.6%. For low- and moderate-income adopters (at or below 80% and 120% of area median income, respectively), solar reduces median 2021 energy burden from 7.7% to 6.2%, and 4.1% to 3.3%, respectively. Importantly, solar reduces the rate of high or severe energy burden from 67% of all low-income households before adoption to 52% of households following adoption, and correspondingly from 21% to 13% for moderate-income households. Here, we show rooftop solar can support policy goals to reduce energy burden along with strategies such as weatherization and bill assistance., (© 2024. The Author(s).)

Background and Purpose: Orbital lesions are rare but serious. Their characterization remains challenging. Diagnosis is based on biopsy or surgery, which implies functional risks. It is necessary to develop noninvasive diagnostic tools. The goal of this study was to evaluate the diagnostic performance of dynamic contrast-enhanced MR imaging at 3T when distinguishing malignant from benign orbital tumors on a large prospective cohort., Materials and Methods: This institutional review board-approved prospective single-center study enrolled participants presenting with an orbital lesion undergoing a 3T MR imaging before surgery from December 2015 to May 2021. Morphologic, diffusion-weighted, and dynamic contrast-enhanced MR images were assessed by 2 readers blinded to all data. Univariable and multivariable analyses were performed. To assess diagnostic performance, we used the following metrics: area under the curve, sensitivity, and specificity. Histologic analysis, obtained through biopsy or surgery, served as the criterion standard for determining the benign or malignant status of the tumor., Results: One hundred thirty-one subjects (66/131 [50%] women and 65/131 [50%] men; mean age, 52 [SD, 17.1] years; range, 19-88 years) were enrolled. Ninety of 131 (69%) had a benign lesion, and 41/131 (31%) had a malignant lesion. Univariable analysis showed a higher median of transfer constant from blood plasma to the interstitial environment ( K trans ) and of transfer constant from the interstitial environment to the blood plasma (minute -1 ) (Kep) and a higher interquartile range of K trans in malignant-versus-benign lesions (1.1 minute -1 versus 0.65 minute -1 , P  = .03; 2.1 minute -1 versus 1.1 minute -1 , P  = .01; 0.81 minute -1 versus 0.65 minute -1 , P  = .009, respectively). The best-performing multivariable model in distinguishing malignant-versus-benign lesions included parameters from dynamic contrast-enhanced imaging, ADC, and morphology and reached an area under the curve of 0.81 (95% CI, 0.67-0.96), a sensitivity of 0.82 (95% CI, 0.55-1), and a specificity of 0.81 (95% CI, 0.65-0.96)., Conclusions: Dynamic contrast-enhanced MR imaging at 3T appears valuable when characterizing orbital lesions and provides complementary information to morphologic imaging and DWI., (© 2024 by American Journal of Neuroradiology.)

Pressing challenges in the treatment of invasive fungal infections (IFIs) include emerging and rare pathogens, resistant/refractory infections, and antifungal armamentarium limited by toxicity, drug-drug interactions, and lack of oral formulations. Development of new antifungal drugs is hampered by the limitations of the available diagnostics, clinical trial endpoints, prolonged trial duration, difficulties in patient recruitment, including subpopulations (eg, pediatrics), and heterogeneity of the IFIs. On 4 August 2020, the US Food and Drug Administration convened a workshop that included IFI experts from academia, industry, and other government agencies to discuss the IFI landscape, unmet need, and potential strategies to facilitate the development of antifungal drugs for treatment and prophylaxis. This article summarizes the key topics presented and discussed during the workshop, such as incentives and research support for drug developers, nonclinical development, clinical trial design challenges, lessons learned from industry, and potential collaborations to facilitate antifungal drug development., Competing Interests: Potential conflicts of interest. P. C. was employed at the Food and Drug Administration until 31 December 2020. S. N.'s contributions occurred entirely while she worked at the Food and Drug Administration. She is currently employed by Johnson & Johnson, and as an employee her travel to conferences/meetings is covered; she also owns Johnson & Johnson stock or stock options. D. A. is an employee and own shares of Scynexis. A. D. of DaneStat Consulting is a statistical consultant for the Pharmaceutical and Biotechnology Industry; he has acted as a consultant for Adaigo, AN2, Achaogen, Allecra, Amicrobe, Amplyx, Artizan, Bioscript, Bugworks, CARBX, Cidara, Closed Loop Medicine, Correvio, ContraFect, DaVolterra, Destiny, F2G, Entasis, Gates, Geom, GSK, Gyroscope, Humanigen, Kymab, Liverpool University, Melinta, Mironid, Modis, Nabriva, Orca, Pfizer, Phico, Pled, Quince, Roche, SFunga, Scynexis, Spero, Sinovent, SNIPR Biome, TenNor, Transcrip, VenatoRx, and Zavante (payments to DaneStat Consulting). He also reports as the independent statistician on data safety monitoring boards for a number of companies, including Aridis, ContraFect Midatech, Pfizer, Pled, Rare Thyroid, Sanofi, and Transcrip. M. R. H. reports support as a full-time employee of Amplyx Pharmaceuticals, a biotech company that was developing fosmanogepix, and received stock potions and a salary. T. S. is a full-time employee of and owns stock or stock options in Cidara Therapeutics. W. H. reports grants or contracts from F2G and Pfizer; personal consulting fees from F2G, Amplyx, Pfizer, Mundipharma, and Gilead (payments to DaneStat Consulting); participation on F2G's advisory board; a role as National Institutes of Health Research Specialty Co-lead for Infection; and a data sharing agreement with Astellas without any financial commitment. T. J. W. has received grants for experimental and clinical antimicrobial pharmacology and therapeutics to his institution from Allergan, Amplyx, Astellas, Lediant, Medicines Company, Merck, Scynexis, and Tetraphase; has served as consultant to Amplyx, Astellas, Allergan, ContraFect, Gilead, Leadiant, Medicines Company, Merck, Methylgene, Pfizer, and Scynexis; reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Astellas, Leadiant, Scynexis, Shinogi, Statera, and T2 Biosystems; and reports a leadership or fiduciary role in the Medical Mycological Society of the Americas. P. P. reports grants from Merck, Astellas, Scynexis, Cidara, and F2G and consulting fees from F2G and Cidara. L. K. reports support as a full-time employee of Astellas Pharma Global Development. J. H. R. is chief medical officer and director of F2G, operating partner and consultant for Advent Life Sciences, and expert in residence for Wellcome Trust. He sits on the scientific advisory boards of Bugworks Research, Basilea Pharmaceutica, Forge Therapeutics, Novo Holdings, and Roche Pharma Research & Early Development and is a shareholder in AstraZeneca Pharmaceuticals, F2G, Advent Life Sciences, Macrolide Pharmaceuticals, and Bugworks Research. He has also received consulting fees from Phico Therapeutics, ABAC Therapeutics, Polyphor, Heptares Therapeutics, Gangagen, Meiji Seika Pharma, Basilea Pharmaceutica International, Allecra Therapeutics, Forge Therapeutics, SinSa Labs, AtoxBio, Peptilogics, F. Hoffmann-LaRoche, Novo Holdings, Innocoll, Vedanta, Progenity, Nosopharm, Roivant Sciences, and Shionogi. K. A. M. is founder and chief executive officer of MycoMed Technologies and a consultant and advisory board member for Cidara Therapeutics and Sfunga Therapeutics. She has consulted for Amplyx Pharmaceuticals, Astellas Pharma, Advaxis, Chimerix, F2G, Genentech, Incyte, Merck & Co, OncoSec, RTI Surgical, Scynexis. and Vical. She receives authorship and editorial royalties from UpToDate and a grant from Merck, owns shares in Pearl Diagnostics and Sfunga Therapeutics, and is an employee of Sfunga Therapeutics. L. O. Z. has received research funding and/or personal honoraria for consulting or speaking from Pulmocide, Pfizer, Merck, Astellas, Cidara, Scynexis, F2G, Amplyx, Gilead, Therapeutics, Viracor, Octapharma, Biotoscana, Stendhal, Mayne, Takeda, RealTime Labs, and Viracor. L. O. Z. also reports honoraria from F2G, Pfizer, and Gilead; participation on a data safety monitoring board or advisory board for Octapharma, Cidara, F2G, and Pfizer; and leadership roles as president for the Mycoses Study Group and Education Consortium, president-elect for the Immunocompromised Host Society, senior editor for Journal of Antimicrobial Chemotherapy, and an associate editor for Clinical Infectious Diseases. J. F. reports being a US government employee. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2023.)

Background: The burden of vancomycin-associated acute kidney injury (V-AKI) is unclear because it is not systematically monitored. The objective of this study was to develop and validate an electronic algorithm to identify cases of V-AKI and to determine its incidence., Methods: Adults and children admitted to 1 of 5 health system hospitals from January 2018 to December 2019 who received at least 1 dose of intravenous (IV) vancomycin were included. A subset of charts was reviewed using a V-AKI assessment framework to classify cases as unlikely, possible, or probable events. Based on review, an electronic algorithm was developed and then validated using another subset of charts. Percentage agreement and kappa coefficients were calculated. Sensitivity and specificity were determined at various cutoffs, using chart review as the reference standard. For courses ≥48 hours, the incidence of possible or probable V-AKI events was assessed., Results: The algorithm was developed using 494 cases and validated using 200 cases. The percentage agreement between the electronic algorithm and chart review was 92.5% and the weighted kappa was 0.95. The electronic algorithm was 89.7% sensitive and 98.2% specific in detecting possible or probable V-AKI events. For the 11 073 courses of ≥48 hours of vancomycin among 8963 patients, the incidence of possible or probable V-AKI events was 14.0%; the V-AKI incidence rate was 22.8 per 1000 days of IV vancomycin therapy., Conclusions: An electronic algorithm demonstrated substantial agreement with chart review and had excellent sensitivity and specificity in detecting possible or probable V-AKI events. The electronic algorithm may be useful for informing future interventions to reduce V-AKI., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs-now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15% ($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality., (© 2023. The Author(s).)