OBSERVATION ON THE INDUCED SPAWNING PRACTICES IN THE HATCHERIES OF RAJSHAHI DISTRICT OF BANGLADESH
An investigation was conducted during the period from March to December 2009 to investigate the induced spawning practices in 22 hatcheries of 6 Upazilas of Rajshahi District of Bangladesh. It was observed that, 6 native fish species viz. rui ( Labeo rohita ), catla ( Catla catla ), mrigal ( Cirrhina mrigala ), calibaush ( Labeo calbasu ), bata ( Labeo bata ), gonia ( Labeo gonius ) and six exotic fish species such as silver carp ( Hypophthalmichthys moltrix ), bighead carp ( Aristichthys nobilis ), grass carp ( Ctenopharyngodon idella ), common carp ( Cyprinus carpio ), Thai pangus ( Pangasius sutchi ), rajputi ( Puntius gonionotus ) were used in the surveyed hatcheries for induced spawning purpose. The average length limit was 41.70±13.02 to 72.81±25.98 cm, average age limit was 1.96±0.75 to 3.08±0.90 years and average weight limit was 1.83±0.79 to 7.54±3.92 kg of the brood fish. Two types of hormone injections like PG (pituitary gland) and HCG (human chorionic gonadotropin) were used for induced spawning. The rate of 1st doses of injections of PG were from 1 to 4 mg/kg and HCG from 150 to 500 IU/kg and the rate of 2nd doses of injections of PG for native and exotic species were from 4 to 8 mg/kg and 4 to 10 mg/kg respectively. The incubation period and hatching rate in different fish species varied from 10 to 72 hours and 55% to 80%, respectively. DOI: http://dx.doi.org/10.3329/jles.v6i0.9723 JLES 2011 6: 65-68
Induced Spawning of Puntius gonionotus (Bleeker)
An experiment was conducted to study the induced spawning of Rajputi Puntius gonionotus (Bleeker) in the months of April, May, June and July 2005. Five different doses of PG were used in the experiment. In Puntius gonionotus all the different doses (doses D 1 -3.00 mg/kg, D 2 -6.00 mg/kg, D 3 -9.00 mg/kg, D 4 - 12.00 mg/kg and D 5 -15.00 mg/kg) were affected equally (100%) in egg release. In the experiment both the males and females were given the 1st dose but the 2nd injection was given only to the females after 6 hours. The highest fertilization rates were 70.10±2.25% in April (dose D 4 ), 75.00±2.10% in May (dose D 3 ), 80.00±2.14% in June and 79.20±2.65% in July (dose D 3 ). The highest hatching and survivability rates were 61.50±3.10 and 55.00±2.13 in April (dose D 4 ), 68.00±3.5 and 63.00±2.20 in May (dose D 3 ), 71.80±2.00 and 68.50±2.60 in June (dose D 2 ), and 70.25±3.50 and 65.70±2.16 in July (dose D 3 ) respectively. The month of June and the dose D 2 (2mg in first injection and 4 mg in second injection) were found to be most effective for induced spawning of P. gonionotus . Key words: Induced spawning, hatchling, PG dose. 
 
J. bio-sci. 14: 121-125, 2006
Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.