Approaches in fostering quality parameters for medicinal botanicals in the Indian context

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

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Assessment of the Ecological Status of OPA Reservoir, ILE IFE, Nigeria Based on a Comparative Study of its Planktonic Community and Water Quality Parameters

Open Academic Journal of Advanced Science and Technology ◽

10.33094/5.2017.2020.41.1.14 ◽

2020 ◽

Vol 4 (1) ◽

pp. 1-14

Author(s):

Adebukola A Adedeji ◽

Taiwo A Adesakin ◽

Oluseun P Bolawa

Keyword(s):

Water Quality ◽

Comparative Study ◽

Ecological Status ◽

Quality Parameters ◽

Water Quality Parameters ◽

Planktonic Community

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Quality of ram spermatozoa separated with modified swim up method

Bangladesh Veterinarian ◽

10.3329/bvet.v33i2.36459 ◽

2018 ◽

Vol 33 (2) ◽

pp. 62-70 ◽

Cited By ~ 2

Author(s):

A Hossain ◽

MM Islam ◽

F Naznin ◽

RN Ferdousi ◽

FY Bari ◽

...

Keyword(s):

Semen Quality ◽

Membrane Integrity ◽

Quality Parameters ◽

Fluid Medium ◽

Normal Morphology ◽

The Mean ◽

Mass Activity ◽

Fresh Semen ◽

Artificial Vagina

Semen was collected from four rams, using artificial vagina and viability%, motility% and plasma membrane integrity% were measured. Fresh ejaculates (n = 32) were separated by modified swim-up separation using modified human tubal fluid medium. Four fractions of supernatant were collected at 15-minute intervals. The mean volume, mass activity, concentration, motility%, viability%, normal morphology and membrane integrity% (HOST +ve) of fresh semen were 1.0 ± 0.14, 4.1 ± 0.1 × 109 spermatozoa/ml, 85.0 ± 1.3, 89.4 ± 1.0, 85.5 ± 0.7, 84.7 ± 0.5 respectively. There was no significant (P>0.05) difference in fresh semen quality parameters between rams. The motility%, viability% and HOST +ve % of first, second, third and fourth fractions were 53.4 ± 0.5, 68.2 ± 0.3, 74.8 ± 0.3 and 65.5 ± 0.4; 55.5 ± 0.4, 66.2 ± 0.4, 74.5 ± 0.3 and 73.6 ± 0.3 and 66.7 ± 0.5, 66.8 ± 0.5, 65.2 ± 0.4 and 74.7 ± 0.5 respectively. The motility%, viability% and membrane integrity% of separated semen samples differed significantly (P<0.05) between four fractions. The mean motility% and viability% were significantly higher (P<0.05) in third fraction (74.8 ± 0.3%), whereas the mean HOST +ve% was significantly higher (P<0.05) in fourth fraction (74.7 ± 0.5). All quality parameters of separated spermatozoa were significantly (P<0.05) lower than that of fresh semen. The pregnancy rates were higher with fresh semen (71%) in comparison to that of separated sample (57%).Bangl. vet. 2016. Vol. 33, No. 2, 62-70

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