Flexible real-time cost-effective high-throughput inspection system for pharmaceutical capsules

2009 ◽  
Vol 18 (1) ◽  
pp. 013006 ◽  
Author(s):  
Anthony C. Karloff
2022 ◽  
Vol 8 ◽  
Author(s):  
Ephraim Fass ◽  
Gal Zizelski Valenci ◽  
Mor Rubinstein ◽  
Paul J. Freidlin ◽  
Shira Rosencwaig ◽  
...  

The changing nature of the SARS-CoV-2 pandemic poses unprecedented challenges to the world's health systems. Emerging spike gene variants jeopardize global efforts to produce immunity and reduce morbidity and mortality. These challenges require effective real-time genomic surveillance solutions that the medical community can quickly adopt. The SARS-CoV-2 spike protein mediates host receptor recognition and entry into the cell and is susceptible to generation of variants with increased transmissibility and pathogenicity. The spike protein is the primary target of neutralizing antibodies in COVID-19 patients and the most common antigen for induction of effective vaccine immunity. Tight monitoring of spike protein gene variants is key to mitigating COVID-19 spread and generation of vaccine escape mutants. Currently, SARS-CoV-2 sequencing methods are labor intensive and expensive. When sequence demands are high sequencing resources are quickly exhausted. Consequently, most SARS-CoV-2 strains are sequenced in only a few developed countries and rarely in developing regions. This poses the risk that undetected, dangerous variants will emerge. In this work, we present HiSpike, a method for high-throughput cost effective targeted next generation sequencing of the spike gene. This simple three-step method can be completed in < 30 h, can sequence 10-fold more samples compared to conventional methods and at a fraction of their cost. HiSpike has been validated in Israel, and has identified multiple spike variants from real-time field samples including Alpha, Beta, Delta and the emerging Omicron variants. HiSpike provides affordable sequencing options to help laboratories conserve resources for widespread high-throughput, near real-time monitoring of spike gene variants.


Heliyon ◽  
2020 ◽  
Vol 6 (10) ◽  
pp. e05167 ◽  
Author(s):  
Tanmay Das ◽  
Mrittika Mohar

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Temidayo J Ofusori ◽  
Gbenga D Obikoya ◽  
Christian O Udoh ◽  
Olubusola O Nuga ◽  
Adedayo K Babarinde

This paper gives the description of the design and implementation of a Real Time Cost Effective Vehicle Tracking System making use of Telit GM862 Module. The Module was installed in a vehicle as the Vehicle Unit while a mobile hand set was used as the Remote Tracking Device. The Module was configured using hyper-terminal on a computer system where the necessary properties and parameters were set. SMSATRUN service was also activated on the module. The SMS information to query the location was sent from the Remote Tracking Device using GSM/GPRS modem on any chosen GSM network to the Vehicle Unit (Module). The Vehicle Unit responds with an SMS message of the location information to the Tracking Device with an authorized mobile number on the GSM Network. The coordinates of the location received are then displayed on Google Map.


2016 ◽  
Vol 23 (11) ◽  
pp. 5171-5183
Author(s):  
Zheng Yuan Li ◽  
Sungsoo Ryo ◽  
Hyuk Jin Lee ◽  
Young-Nam Kang ◽  
Sangdeok Park ◽  
...  

Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.


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