Design Optimization Module For Hierarchical Research and Learning Environment

The present paper describes a learning module on design optimization courses within a hierarchical research and learning network (HRLN). In this environment a knowledge organization can be created as a hierarchical learning network to link diverse inter- and trans- disciplinary teams from a consortium of universities, industry, government agencies and the providers of learning technologies. It is an approach that builds on computer-based training, intelligent tutoring systems, interactive learning, collaborative-distributed learning, and learning networks. The present design optimization module has been developed and described herein, as a demonstrator of a learning module in this environment. This module allows for the learners of design optimization to get the course material at their own convenience and time either via the internet or packaged files. Consequently, it is expected that the learner’s ability to understand design optimization and review its pertinent details will be enhanced significantly

River water quality assessment using environmentric techniques : case study of Jakara River Basin.

akara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p = 0.930, p = 0.001) and BOD5 and COD (r p = 0.839, p = 0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin

A nanobody-functionalized organic electrochemical transistor for the rapid detection of SARS-CoV-2 and MERS antigens at the physical limit

The COVID-19 pandemic highlights the need for rapid protein detection and quantification at the single-molecule level in a format that is simple and robust enough for widespread point-of-care applications. We here introduce a modular nanobody-organic electrochemical transistor architecture that enables the fast and specific detection and quantification of single-molecule to nanomolar protein antigen concentrations in complex bodily fluids. The sensor combines a new solution-processable organic semiconductor material in the transistor channel with the high-density and orientation-controlled bioconjugation of nanobody fusion proteins on disposable gate electrodes. It provides results after a 10 minutes exposure to 5 µL of unprocessed samples, maintains high specificity and single-molecule sensitivity in human saliva or serum, and is rapidly reprogrammed towards any protein target for which nanobodies exist. We demonstrate the use of this highly modular platform for the detection of green fluorescent protein, SARS-CoV-1/2, and MERS-CoV spike proteins and validate the sensor for COVID-19 screening in unprocessed clinical nasopharyngeal swab and saliva samples