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Realization of 2D metals at the ångström thickness limit
Inorganic–organic hybrid cobalt spinel oxides for catalyzing the oxygen evolution reaction
Impact of climatic and water quality parameters on Tilapia (Oreochromis niloticus) broodfish growth: Integrating ARIMA and ARIMAX for precise modeling and forecasting
This study aims to assess the impact of climatic factors and water quality parameters on the growth of tilapia broodfish and develop time series growth models using ARIMA and ARIMAX. Three years longitudinal data on tilapia growth, including length and weight were collected monthly from February 2021 to January 2024. Climatic data were obtained from the Bangladesh Meteorological Department, while water quality parameters in the broodfish pond were measured daily on-site. Key variables such as air temperature, humidity, rainfall, solar intensity, water temperature, dissolved oxygen (DO), pH, and ammonia, showed fluctuation in the ponds. The highest growth rate (5.93%) occurred in April, and the lowest (0.023%) in December. Overall, tilapia growth in weight followed an exponential trend, while the percent growth rate exhibited a seasonal pattern. Pearson correlation analysis indicated a significant association between growth increments and water quality parameters. The ARIMA (3,0,3) model predicted a consistent upward trend in tilapia weight from February 2024 to January 2027. If the pattern continues, the estimated weight of tilapia will reach 803.58 g by the end of January 2027, a 17.05% increase from January 2024, indicating a positive outlook for broodfish health and production. However, the ARIMAX (1,1,1) model for percent weight gain revealed seasonal fluctuations that were strongly influenced by water temperature and solar intensity. Over the three-year period, forecasts indicated a downward trend in percent weight gain during the first year, followed by an upward trend in the second and third years. This indicates the influence of seasonal changes on percent weight gain. The simulation behaviors were consistent with the forecasted trend. These findings have important implications for planning and managing tilapia broodfish production, highlighting the need to consider environmental factors in future aquaculture management.
Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction
Racial bias eliminated when ratings switch from five stars to thumbs up or down
Epidemiology and transmission dynamics of multidrug-resistant organisms in nursing homes within the United States
Abstract Nursing home (NH) residents in the United States routinely attend interactive visits for services such as therapy or dialysis, creating opportunities for pathogen transmission. A paucity of studies exist which delineate spread of pathogens beyond residents’ in-room environment. In this prospective cohort study, we recruited 197 newly-admitted residents across three Veterans Affairs NHs to characterize multidrug-resistant organism (MDRO) prevalence, acquisition, and transmission. Participant hands, nares, groin, and seven environmental surfaces were swabbed during 758 regularly scheduled in-room visits; participant hands, healthcare personnel hands, and equipment were swabbed during 345 unscheduled interactive visits. We demonstrate that baseline MDRO colonization and new acquisition is common, and one in six interactive visits result in MDRO transmission. Whole genome sequencing on a subset of participants enabled us to identify sources of transmission where it was unknown using microbiologic methods alone. Our results illustrate MDRO transmission pathways and highlight the need for innovative, multidisciplinary interventions.
Energy assessment of BIPV system in code-compliant residential building in cooling-dominated climates
This study investigates the effects of climate and geographical location on the building integrated photovoltaics (BIPV). An existing residential building was simulated by using IES-VE software for five different climatic zones of Saudi Arabia, which was in accordance with ASHRAE Standard 169–2021 and Saudi Building Codes (SBC) 601/ 602. The results showed that the annual energy production of rooftop PV systems ranged from 49,810.29 kWh to 60,204.29 kWh, with cities such as Najran and Tabuk having higher energy production due to higher solar radiation and better performance of PV systems. The average annual global radiation ranged from 188.15 kWh/m2 to 212.52 kWh/m2, with cities such as Najran and Tabuk having the highest radiation levels. The study found that solar radiation, temperature, cloud cover and regional climate patterns significantly influence monthly energy generation, with cities closer to the equator experiencing higher solar radiation and longer daylight hours. The study also highlighted the importance of considering angular, spectral, temperature and low radiation losses, which range from 2.47% to 2.71%, 0.84% to 1.36% and 8% to 15.4%, respectively for the studies locations. This study would shed light on the impact of climate and location on the performance of PV systems and would therefore be of great interest to policy makers, energy planners and solar industry professionals to make informed decisions about the deployment of rooftop PV systems in different climate regions meet. Enabling a more sustainable energy strategy and a successful transition to a low-carbon future.
Wild grown Portulaca oleracea as a novel magnetite based carrier with in vitro antioxidant and cytotoxicity potential
Structure-guided design of partial agonists at an opioid receptor
Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States
The naive approach to preventing discrimination in algorithmic decision-making is to exclude protected attributes from the model’s inputs. This approach, known as “equal treatment,” aims to treat all individuals equally regardless of their demographic characteristics. However, this practice can still result in unequal impacts across different groups. Recently, alternative notions of fairness have been proposed to reduce unequal impact. However, these alternative approaches may require sacrificing predictive accuracy. The present research investigates public attitudes toward these trade-offs in the United States. When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. Additionally, preferences polarized along partisan identities, with Democrats favoring impact parity over accuracy maximization while Republicans displayed the reverse preference. Gender and social justice orientations also significantly predicted EIA support. Overall, findings demonstrate multidimensional drivers of algorithmic fairness attitudes, underscoring divisions around equality versus equity principles. Achieving standards around fair AI requires addressing conflicting human values through good governance.
Associations between sleep duration and quality and physical frailty in community-dwelling older adults: a cross-sectional study
Controlled chain-growth polymerization via propargyl/allenyl palladium intermediates
Study on the impact of China’s urban agglomerations’ tiered spatial structure on regional economic resilience
Urban agglomerations serve as crucial spatial carriers of economic development, and their spatial structure profoundly influences regional economic resilience. This study draws on Martin’s conceptualization of economic resilience and, considering the administrative hierarchy and development stage of China’s urban system, examines the impact of the layered spatial structure of urban agglomerations on regional economic resilience. Based on data from 17 Chinese urban agglomerations from 2005 to 2019, this research employs a one-step system Generalized Method of Moments (GMM) model to empirically analyze the effects of three mechanisms – polycentricity within urban agglomerations, inter-city development disparities, and inter-city industrial gradients – on regional economic resilience. The findings reveal that both the polycentric distribution of population and economy and the coupling of dual centers significantly positively affect the economic resilience of urban agglomerations. The potential development energy difference between core and peripheral cities can be transformed into developmental momentum for peripheral cities, thereby generating positive spatial externalities that play a significant and positive role in enhancing economic resilience. As the ratio of secondary to tertiary industry employees in peripheral and core cities nears 1, urban agglomerations’ economic resilience strengthens, underscoring the importance of a balanced industrial gradient and regional collaboration in mitigating economic shocks. This study also considers the heterogeneity of whether the urban agglomerations are coastal and whether they contain a national-level central city. The research finds that for inland urban agglomerations and those with existing national-level central cities, developing a polycentric spatial structure can effectively enhance the region’s economic resilience in responding to various shocks. Furthermore, aside from a few more developed coastal urban agglomerations, other inland urban agglomerations should continue to focus on spatial agglomeration, fostering core cities to strengthen economic competitiveness. Finally, given the varying industrialization stages and structures within urban agglomerations, appropriately adjusting the industrial gradient is essential.
Nine complete chloroplast genomes of the Camellia genus provide insights into evolutionary relationships and species differentiation
Abstract The genus Camellia, known for species such as Camellia japonica, is of significant agricultural and ecological importance. However, the genetic diversity and evolutionary relationships among Camellia species remain insufficiently explored. In this study, we successfully sequenced and assembled the complete chloroplast (cp) genomes of nine Camellia accessions, including the species Camellia petelotii, and eight varieties of C. Japonica (C. Japonica ‘Massee Lane’, C. Japonica ‘L.T.Dees’, C. Japonica ‘Songzi’, C. Japonica ‘Kagirohi’, C. Japonica ‘Sanyuecha’, C. Japonica ‘Xiameng Hualin’, C. Japonica ‘Xiameng Wenqing’, and C. Japonica ‘Xiameng Xiaoxuan’). These genomes exhibited conserved lengths (~ 156,580–157,002 bp), indicating minimal variation in genome size. They consistently predicted 87 protein-coding genes, although variations were observed in the rRNA and tRNA genes. Structural and evolutionary analyses revealed the highly conserved nature of these cp genomes, with no significant inversions or gene rearrangements detected. Consistent codon usage patterns were observed across these accessions. Five hypervariable regions (rpsbK, psbM, ndhJ, ndhF, and ndhD) were identified as potential molecular markers for species differentiation. Phylogenetic analysis of 82 accessions from the Camellia genus, along with outgroup accessions revealed close genetic relationships among certain C. japonica varieties, including Songzi, Sanyuecha, L.T.Dees, and Kagirohi, which formed sister groups. Massee Lane was located within Sect. Camellia. Moreover, Xiameng Hualin, Xiameng Wenqing, Xiameng Xiaoxuan, and C. petelotii demonstrated a strong genetic affinity. These findings provide valuable insights into the structural and evolutionary dynamics of Camellia cp genomes, contributing to species identification and conservation.
Similar chiral phenomena occur in cell cultures and human crowds
Pickering emulsions with low interface coverage but enhanced stability for emulsion interface catalysis and SERS-based detection
Sine-G family of distributions in Bayesian survival modeling: A baseline hazard approach for proportional hazard regression with application to right-censored oncology datasets using R and STAN
In medical research and clinical practice, Bayesian survival modeling is a powerful technique for assessing time-to-event data. It allows for the incorporation of prior knowledge about the model’s parameters and provides a more comprehensive understanding of the underlying hazard rate function. In this paper, we propose a Bayesian survival modeling strategy for proportional hazards regression models that employs the Sine-G family of distributions as baseline hazards. The Sine-G family contains flexible distributions that can capture a wide range of hazard forms, including increasing, decreasing, and bathtub-shaped hazards. In order to capture the underlying hazard rate function, we examine the flexibility and effectiveness of several distributions within the Sine-G family, such as the Gompertz, Lomax, Weibull, and exponentiated exponential distributions. The proposed approach is implemented using the R programming language and the STAN probabilistic programming framework. To evaluate the proposed approach, we use a right-censored survival dataset of gastric cancer patients, which allows for precise determination of the hazard rate function while accounting for censoring. The Watanabe Akaike information criterion and the leave-one-out information criterion are employed to evaluate the performance of various baseline hazards.