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Macrophytes, visible aquatic and semi-aquatic plants, are integral components of wetland ecosystems, playing crucial roles in maintaining biodiversity, providing ecosystem services, and ensuring ecological stability. This research examines the distribution, abundance, frequency, diversity, and ecosystem services of macrophytes in several wetlands (W1-W7) located in the eastern part of Ranchi district, Jharkhand, India. The study sites include Bundu Lake, Hindalco Pond, Choga Bada Talab, Raja Bandh, Kita Uparbandh, Tamar Bada Talab, and Rukka Dam. A quantitative assessment using quadrat sampling and phytosociological methods was conducted from September 2022 to March 2023 to ensure accurate data collection and analysis. The study documented 78 macrophyte species belonging to 33 families and 58 genera. Emergent macrophytes were found to be the dominant life form, constituting 69% of the recorded species. Several species demonstrated high frequency and abundance across multiple study sites, including Pontederia crassipes Mart., Alternanthera philoxeroides (Mart) Griseb., Hydrilla verticillata (L.f.) Royle, Ipomoea aquaticaForssk., and Nymphoides hydrophyllum (Lour.) kuntze. To assess the diversity and ecological characteristics of the macrophyte communities, various diversity indices were calculated. These included the Shannon-Wiener index, Simpson's index, Margalef index, and Pielou's Evenness index. The results of these analyses revealed high levels of macrophyte diversity and evenness within the studied wetlands. Twenty-one macrophytes were identified as key species and nine species as rare species based on their IVI (Importance Value Index) scores. The key species were found to provide essential ecosystem services, including erosion control, water quality improvement, nutrient cycling, carbon sequestration, and habitat provision for other organisms. This research contributes significantly to our understanding of macrophyte diversity patterns and traits-based ecosystem services in wetland ecosystems. The findings of this study have important implications for the development and implementation of effective conservation strategies for these vital wetland ecosystems in the eastern Ranchi (W1- W7).
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As part of an ongoing MARAD alternative fuels test initiative, shipboard tests were conducted using an algal fuel that the U.S. Navy is currently evaluating. Details about the test planning and preparation, performance and emissions testing, and test results are provided. Testing was performed on the T/S STATE OF MICHIGAN which is a training ship operated by the Great Lakes Maritime Academy and owned by MARAD.\n
As part of the U.S. Maritime Administration (MARAD) marine application of alternative fuel initiative, the U.S. Navy provided neat hydrotreated renewable diesel (HRD), derived from the hydroprocessing of algal oils, for operational and exhaust emission testing onboard the T/S STATE OF MICHIGAN. This vessel has diesel-electric propulsion with four caterpillar D-398 compression ignition engines; one of these ship service diesel engines was selected as the test engine. The diesel generator sets power both the propulsion motors propelling the ship and provide the electrical power for the hotel loads of the ship. Ultra-low sulfur diesel (ULSD) was blended with the neat HRD fuel in a 50/50-by-volume blend and tested for over 440 hours on the vessel. Exhaust emissions testing was performed while underway on Lake Michigan using the baseline ULSD assessed earlier. A similar profile was run using the blended test fuel. Emission testing was conducted using the ISO 8178 (D2) test cycle. When emissions testing was completed a series of underway and pierside test runs were conducted to accumulate the remaining engine hours, After all testing, the engine conditions were assessed again using a combination of visual inspection and oil analysis. The remainder of the test fuel will be used to conduct a long-term stability test. The setup, test, and results of this testing, currently underway, are reported here with a discussion of MARAD’s alternative fuels test initiative.
This research develops a mixture regression model that is shown to have advantages over the classical Tobit model in model fit and predictive tests when data are generated from a two step process. Additionally, the model is shown to allow for flexibility in distributional assumptions while nesting the classic Tobit model. A simulated data set is utilized to assess the potential loss in efficiency from model misspecification, assuming the Tobit and a zero-inflated log-normal distribution, which is derived from the generalized mixture model. Results from simulations key on the finding that the proposed zero-inflated log-normal model clearly outperforms the Tobit model when data are generated from a two step process. When data are generated from a Tobit model, forecasts are more accurate when utilizing the Tobit model. However, the Tobit model will be shown to be a special case of the generalized mixture model. The empirical model is then applied to evaluating mortality rates in commercial cattle feedlots, both independently and as part of a system including other performance and health factors. This particular application is hypothesized to be more appropriate for the proposed model due to the high degree of censoring and skewed nature of mortality rates. The zero-inflated log-normal model clearly models and predicts with more accuracy that the tobit model.
This dataset contains the digitized treatments in Plazi based on the original journal article Ghosh, Joyjit, Saini, Jagdish, Gupta, Devanshu, Ghosh, Sujit Kumar, Chandra, Kailash (2022): A Catalogue of Indian Hydraenidae (Insecta: Coleoptera). Zootaxa 5087 (4): 558-570, DOI: https://doi.org/10.11646/zootaxa.5087.4.4
Hyphydrus biswasi Ghosh, sp. nov. (Coleoptera: Dytiscidae: Hyphydrini), is described in H. signatus species group from Namdapha National Park, Arunachal Pradesh, India. An identification key to the Indian species of genus Hyphydrus Illiger, is also provided.
Lacconectus ishae Ghosh, sp. nov. (Coleoptera: Dytiscidae: Copelatinae), is described from Sairep, district Lunglei, Mizoram, India. The newly described species is compared with the allied species, L. scholzi Gschwendtner, 1922, L. regimbarti Brancucci, 1986, and L. satoi Brancucci, 2003.
The diving beetle genus Hydrovatus (Dytiscidae: Hydroporinae: Hydrovatini) comprises 214 global species, with 18 in India. These beetles inhabit aquatic environments such as ponds, lakes, and slow-moving streams. They are characterized by streamlined, oval bodies and robust, well-developed hind legs adapted for swimming. The Hydrovatus collection housed at the Zoological Survey of India, Kolkata, was studied, and a total of eight species were identified: Hydrovatus obtusus Motschulsky, 1855; H. acuminatus Motschulsky, 1859; H. bonvouloiri Sharp, 1882; H. cardoni Severin, 1890; H. castaneus Motschulsky, 1855; H. confertus Sharp, 1882; H. fractus Sharp, 1882; and H. pinguis Régimbart, 1892. The faunistic records of these species are presented in this paper, which reports the first confirmed record of Hydrovatus obtusus Motschulsky, 1855, from India after over 125 years. This paper also includes habitus photographs and distribution maps of the studied species.
Forecast combination methods have traditionally emphasized symmetric loss functions, particularly squared error loss, with equally weighted combinations often justified as a robust approach under such criteria. However, these justifications do not extend to asymmetric loss functions, where optimally weighted combinations may provide superior predictive performance. This study introduces a novel contribution by incorporating modal regression into forecast combinations, offering a Bayesian hierarchical framework that models the conditional mode of the response through combinations of time-varying parameters and exponential discounting. The proposed approach utilizes error distributions characterized by asymmetry and heavy tails, specifically the asymmetric Laplace, asymmetric normal, and reverse Gumbel distributions. Simulated data validate the parameter estimation for the modal regression models, confirming the robustness of the proposed methodology. Application of these methodologies to a real-world analyst forecast dataset shows that modal regression with asymmetric Laplace errors outperforms mean regression based on two key performance metrics: the hit rate, which measures the accuracy of classifying the sign of revenue surprises, and the win rate, which assesses the proportion of forecasts surpassing the equally weighted consensus. These results underscore the presence of skewness and fat-tailed behavior in forecast combination errors for revenue forecasting, highlighting the advantages of modal regression in financial applications.
