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We believe it's TRUCURC are absolutely committed to being the best turmeric curcumin supplement on the market. We've taken steps to ensure this by scrutinizing and optimizing our process for maximum potency and safety.

Shipping emissions are likely to increase significantly in the coming decades, alongside increasing emphasis on the sustainability and environmental impacts of the maritime transport sector. Exhaust gas cleaning systems ("scrubbers"), using seawater or fresh water as cleaning media for sulfur dioxide, are progressively used by shipping companies to comply with emissions regulations. Little is known about the chemical composition of the scrubber effluent and its ecological consequences for marine life and biogeochemical processes. If scrubbers become a central tool for atmospheric pollution reduction from shipping, modeling, and experimental studies will be necessary to determine the ecological and biogeochemical effects of scrubber wash water discharge on the marine environment. Furthermore, attention must be paid to the regulation and enforcement of environmental protection standards concerning scrubber use. Close collaboration between natural scientists and social scientists is crucial for progress toward sustainable shipping and protection of the marine environment.

The global COVID-19 pandemic caused a sharp decline in vessel traffic in many areas around the world, including vessel-based tourism throughout Alaska, USA in 2020. Marine vessel traffic has long been known to affect the underwater acoustic environment with direct and indirect effects on marine ecological processes. Glacier Bay National Park in southeastern Alaska has monitored underwater sound since 2000. We used continuous, calibrated hydrophone recordings to examine 2020 ambient sound levels compared with previous years: 2018, the most recent year with data available, and 2016 for historical perspective. Park tourism occurs mainly in May-September. Overall, the number of vessel entries in Glacier Bay was 44-49% lower in 2020 (2020: n = 1,831; 2018: n = 3,599; 2016: n = 3,212) affecting all vessel classes, including the complete absence of cruise ships and only three tour vessel trips. In all years, we found clear seasonal and diurnal patterns in vessel generated noise, focused from 06:00 to 20:00 local time (LT) in the summer months. Broadband (17.8-8,910 Hz) sound levels in the 2020 Visitor Season were 2.7 dB lower than 2018 and 2.5 dB lower than 2016. Focusing on morning (06:00-09:00 LT) and afternoon (15:00-18:00 LT) time-blocks when tour vessels and cruise ships enter and exit Glacier Bay, median broadband sound levels were 3.3-5.1 dB lower in 2020 than prior years. At the 95th percentile levels, morning and afternoon peak times in 2020 were 6.3-9.0 dB quieter than previous years. A 3 dB decline in median sound level in the 125 Hz one-third octave band in 2020 reflects a change in medium and large vessel noise energy and/or harbor seal vocalizations. Our results suggest that all types of vessels had a role in the quieter underwater sound environment in 2020, with the combined acoustic footprint of tour vessels and cruise ships most evident in the decrease in the 95th percentile loudest sounds. This and other descriptions of the pandemic-induced

The ESAIL microsatellite for tracking ships at sea has captured 57 000 messages in its first 24 hours of operation, using advanced on-board processing algorithms.

Globally, movements of commercial vessels can facilitate the spread of marine non-indigenous species (NIS) beyond their current biogeographic ranges. Authorities at potential destination locations employ a number of biosecurity risk assessment strategies to estimate threat levels from potential origin locations, vulnerability levels of specific destination regions, or the consequences of successful establishment of particular NIS species. Among the many factors and processes that have an influence on the probability that NIS will survive transport and establish successfully at new locations, vessel type has been identified as an important risk factor. Different vessel types have different structural and operational characteristics that affect their overall level of marine biosecurity risk. Several recent studies have examined subsets of vessel types or vessel characteristics for their ability to spread NIS. While high-quality information is available via these endeavors, it is fragmented and not readily available as an integrated resource to support biosecurity regulators or other end-users. In this study, we synthesize available empirical data on a wide range of vessel types and characteristics to develop a framework that allows systematic quantification of the relative risk of NIS transfer by common commercial vessel types. We explain our approach for constructing the framework, from selection of key risk factors for inclusion, to selection of which datasets to use for those risk factors. The framework output is a set of risk scores which denote the relative biosecurity risk of common commercial vessel types. To demonstrate a potential application of our framework, we applied the risk scores to vessel visit data for commercial ports around New Zealand and assigned a relative risk level per port based on the arrival frequencies of different vessel types. The resulting per-port risk levels matched closely with the results of a prior benchmark study that employed sta

Sound speed distribution, represented by a sound speed profile (SSP), is of great significance because the nonuniform distribution of sound speed will cause signal propagation path bending with Snell effect, which brings difficulties in precise underwater localization such as emergency rescue. Compared with conventional SSP measurement methods via the conductivity-temperature-depth (CTD) or sound-velocity profiler (SVP), SSP inversion methods leveraging measured sound field information have better real-time performance, such as matched field process (MFP), compressed sensing (CS) and artificial neural networks (ANN). Due to the difficulty in measuring empirical SSP data, these methods face with over-fitting problem in few-shot learning that decreases the inversion accuracy. To rapidly obtain accurate SSP, we propose a task-driven meta-deep-learning (TDML) framework for spatio-temporal SSP inversion. The common features of SSPs are learned through multiple base learners to accelerate the convergence of the model on new tasks, and the model's sensitivity to the change of sound field data is enhanced via meta training, so as to weaken the over-fitting effect and improve the inversion accuracy. Experiment results show that fast and accurate SSP inversion can be achieved by the proposed TDML method.