Camellia sinensis (L.) Kuntze, represents a commercially significant crop, primarily recognized for its pharmacologically active secondary metabolites, including catechins and caffeine. However, excessive reliance on agrochemicals threatens both soil fertility and the biochemical quality of tea leaves, underscoring the urgent need for sustainable agronomic alternatives. The present study evaluates the agronomic and biochemical efficacy of Tea Waste Manure (TWM), an organic amendment derived from post-industrial tea processing residues, as a sustainable substitute for synthetic agrochemicals. Field experiments were conducted from July 2023 – October 2024 at two distinct tea-growing areas of Uttarakhand, India. Pre-and post-treatment assessments encompassed quantitative analysis of vegetative biomass, photosynthetic pigment concentrations (chlorophyll a, chlorophyll b, and total carotenoids), as well as targeted metabolite profiling, including total phenolic content, total flavanol content, catechins, and caffeine content. The research additionally emphasizes on evaluating the impact of TWM on soil health via enumerating bacterial load, determining physicochemical and nutritional properties, and enzymatic activity of soil. Application of TWM resulted in statistically significant enhancements in plant growth, pigment biosynthesis, and metabolite accumulation with an increase in catechin and caffeine concentration in leaves. Additionally, soil analyses demonstrated enhanced fertility attributes, including optimised pH, elevated macronutrient levels, and increased microbial proliferation, as well as increased enzymatic activity. In conclusion, the findings reaffirm the viability of converting agro-industrial waste into organic inputs, not only enhancing resource efficiency but also promoting environmentally sustainable tea cultivation practices, paving the way for a greener and more environmentally friendly agricultural future.
Oxytetracycline (OTC) dihydrate, an approved broad-spectrum antibiotic, is widely used in aquaculture to combat bacterial infections like Aeromonas hydrophila in rainbow trout (Oncorhynchus mykiss). However, its optimal dosing, residue depletion kinetics, and withdrawal times remain underexplored in exotic species under Indian conditions, raising food safety concerns due to maximum residue limits (MRL) of 100 µg kg− 1 in muscle. This study evaluates the therapeutic efficacy, tissue residue profiles, and withdrawal times of OTC dihydrate in O. mykiss experimentally infected with A. hydrophila RTS 02, identifying safe doses for aquaculture use. Effective OTC doses (44.1, 88.2, 132.3, and 176.4 mg kg− 1 day− 1) were administered orally for 10 days to infected rainbow trout. Survivability and histopathology (liver, kidney, muscle) were assessed post-treatment. Residues in muscle, liver, kidney, intestine, and plasma were quantified via LC-MS/MS, with withdrawal times calculated using WT 1.4 software for muscle and all tissues. Doses of 88.2–176.4 mg kg− 1 day− 1 boosted survivability to 64–74%, with marked histopathological improvements in liver, kidney, and muscle. Peak residues (Cmax) reached 2120 µg kg− 1 in plasma and 6740 µg kg− 1 in intestine after day one. At the therapeutic dose of 80 mg kg− 1 day− 1, muscle residues fell below 100 µg kg− 1 MRL after 35.89 days; notable levels included 78.0 µg kg− 1 (intestine, day 8), 80.1 µg kg− 1 (plasma, day 12), and 46.6 µg kg− 1 (kidney, day 20). The graded OTC concentrations tested (88.2, 132.3, and 176.4 mg kg− 1 day− 1) demonstrated a balance between therapeutic efficacy and safety. Among these, 88.2 mg kg− 1 day− 1 is suggested as the minimum effective therapeutic dose for rainbow trout under the present experimental conditions. Residue depletion analysis further indicated a muscle withdrawal period of 35.89 days following administration at 80 mg kg− 1 day− 1. To our knowledge, this study provides the first data describing OTC dihydrate residue depletion in rainbow trout under Indian aquaculture conditions.
With the rapid electrification of the automotive industry, the traction battery pack has become the most safety-critical subsystem of an electric vehicle, and any insulation or communication fault must be identified and managed reliably. This work proposes a fuzzy-theory-based diagnosis and control strategy for battery packs that detects and confirms unpredictable faults under diverse operating conditions. The method couples a fuzzy inference system (FIS) with a calibrated expert database: an adaptive fuzzy matching mechanism continuously adjusts the process and observation noise in the state-of-charge and terminal-voltage correction, while triangular membership functions, an IF–THEN rule base, Mamdani inference and centroid defuzzification convert the diagnostic signals into graded fault levels, with rule weights being refined after each confirmed event. Fault confirmation follows the debounce intervals and consecutive-cycle counts defined in the unified threshold tables, and the resulting fault levels activate power-limitation responses. Validation through simulation and vehicle experiments reveals that the judgment time for insulation faults lengthens by 4.6 s (from 3.0 s to 7.6 s), whereas the corresponding accuracy climbs from 81.1% to 92.3%; for communication faults, the accuracy rises from 91.4% to 97.7%, and the interval between fault confirmation and completed power limitation is 16.1 s in simulation, 8.2 s on the test bench, and 24 s under real-world driving. This modest rise in diagnostic time remains within design tolerances, while the achieved accuracy outperforms that of comparable studies and the protective actions taken after confirmation safeguard vehicle occupants and the overall system.
Extreme sea levels (ESLs) threaten coastal zones, and global assessments suggest that future ESLs grow with rising mean sea level (MSL) as the main driver. Conventional practice assumes that present-day return levels are added to a given MSL trajectory into the future to obtain future return levels. Here, we test this additive assumption and show that it may under- or overestimate ESLs with consequences for coastal protection decisions. We fit stationary and non-stationary generalised extreme value (GEV) models to annual maxima of long hourly tide-gauge records. The non-stationary GEV location parameter may depend on MSL, time, or both. Bayesian model comparison and posterior coefficient support classify 200 of the 386 analysed series (52%) as non-stationary and 186 as stationary. At some stations, ESLs increase more than MSL; at others they increase less than MSL or even decrease. Post-fit grouping by seabed depth and slope also suggests stronger morphology-related differences in the fitted MSL coefficient among non-stationary series than among the stationary comparison series.
Serum neurofilament light chain (sNfL) is an increasingly used biomarker of neuroaxonal injury in multiple sclerosis (MS), but differences between immunoassays and sample handling may limit comparability of results. We compared paired fresh and frozen serum samples from 20 patients with MS using four immunoassays: Simoa, Elecsys, Atellica, and ELISA. Matched aliquots were stored at − 70 °C for 10 days and subjected to a single freeze–thaw cycle. Fresh-to-frozen differences varied significantly among platforms (Friedman χ² = 24.06, p < 0.001; Kendall’s W = 0.401). Median concentrations decreased after freezing with Simoa (–12.8%), Elecsys (–5.4%), and ELISA (–9.7%), whereas Atellica showed minimal change (+ 0.9%). Between-assay correlations were moderate to strong in fresh (Spearman ρ = 0.663–0.877) and frozen samples (ρ = 0.677–0.931), despite substantial differences in absolute concentrations. Assay-specific Simoa and Elecsys Z-scores were strongly correlated in fresh (ρ = 0.886) and frozen samples (ρ = 0.923; both p < 0.001), but systematic differences remained. These findings indicate assay-dependent fresh-to-frozen differences and support maintaining consistent analytical immunoassays and sample-processing conditions for longitudinal sNfL monitoring.
