Combining ability analysis in Brassica juncea L. for oil quality traits

This study was conducted in Brassica juncea L. for the determination of good combiners for quality associated traits using 8 x 8 diallel during 2004 - 2005 and 2005 - 2006. Analysis of variance revealed highly significant differences (p 0.01) for all the studied traits. Components of combining ability analysis showed that general combining ability (GCA) was highly significant (p 0.01) for oil percentage (%) and glucosinolates (ìMolg-1) whereas the rest were non-significant. Specific combining ability (SCA)effects were highly significant for all traits except for oleic acids. Reciprocal combining ability (RCA) effects were highly significant (p 0.01) for all traits except for oleic acid which was significant at (p 0.05). The SCA effects were higher than RCA for oil %. The GCA effects were of greater magnitude thanthe SCA effects for glucosinolate, erucic acid and protein content. The parental genotypes NUM009, NUM123, NUM105 and NUM117 and their hybrids NUM009x NUM123, NUM103x NUM105, NUM113x NUM124 and NUM103x NUM120 had high GCA and SCA effects, respectively and therefore these could be exploited for further selection of high yielding progenies. The overall study reveals the importance of both additive and non-additive genetic variability suggesting the use of integrated breeding strategies which can efficiently utilize the additive as well as non-additive genetic variability

Mechanisms of cognitive trust development in artificial intelligence among front line employees: An empirical examination from a developing economy

Drawing upon insights from the trust literature, we conducted two empirical surveys with the front-line employees of firms in Pakistan investigating the factors influencing cognitive trust in artificial intelligence (AI). Study1 consisted of 46 in-depth interviews aimed at exploring factors influencing cognitive trust. Based on the findings of Study 1, we developed a framework to enhance employees’ cognitive trust in AI. We then conducted a quantitative survey (study 2) with 314 employees to validate the proposed model. The findings suggest that AI features positively influence the cognitive trust of employees, while work routine disruptions have negative impact on cognitive trust in AI. The effectiveness of data governance was also found to facilitate employees' trust in data governance and subsequently, employees' cognitive trust in AI. We contribute to the technology trust literature, especial in developing economics. We discuss the implications of our findings for both research and practice

Location‐based social network’s data analysis and spatio‐temporal modeling for the mega city of Shanghai, China

© 2020 by the authors. The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network's Big Data.

The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstrates the uses of location-based social network's data by analyzing the trends in check-ins throughout a three-year term for health purpose. We obtained the geolocation data from Sina Weibo, one of the biggest renowned Chinese microblogs (Weibo). The composed data is converted to geographic information system (GIS) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (KDE) assessment. We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others. The location cataloguing is accomplished via the use of facts about the characteristics of physical places. The findings demonstrate that visitors' spatial operations are more intense than residents' spatial operations, notably in downtown. However, locals also visited outlying regions, and tourists' temporal behaviors vary significantly while citizens' movements exhibit a more steady stable behavior. These findings may be used in destination management, metro planning, and the creation of digital cities

Membrane surface patterning as a fouling mitigation strategy in liquid filtration: A review

© 2019 by the authors. Membrane fouling is seen as the main culprit that hinders the widespread of membrane application in liquid-based filtration. Therefore, fouling management is key for the successful implementation of membrane processes, and it is done across all magnitudes. For optimum operation, membrane developments and surface modifications have largely been reported, including membrane surface patterning. Membrane surface patterning involves structural modification of the membrane surface to induce secondary flow due to eddies, which mitigate foulant agglomeration and increase the effective surface area for improved permeance and antifouling properties. This paper reviews surface patterning approaches used for fouling mitigation in water and wastewater treatments. The focus is given on the pattern formation methods and their effect on overall process performances

A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data

The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.</jats:p

Prevalence of hepatitis B in the blood donors of NW. F.P and FATA regions and the current scenario of HBV infection in Pakistan

Hepatitis B is a fatal liver disease caused by the hepatitis B virus. In this study, blood donors from various districts of the North-western frontier province and the federally administered tribal area (FATA) of Pakistan were tested for HBsAg and HBV DNA by ICT (Immuno-chromatographic test), ELISA and RTPCR. Out of the 7148 blood donors, 244 (3.41%) were positive for HBsAg by ICT, 147 (2.05%) by ELISA while 132 (1.85%) were positive by PCR. Our data indicates that the incidence of hepatitis B has decreased in these regions in recent times.Key words: HBV, HBsAg, Pakistan

3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach

© 2013 IEEE. We consider the recent challenges of 3D shape analysis based on a volumetric CNN that requires a huge computational power. This high-cost approach forces to reduce the volume resolutions when applying 3D CNN on volumetric data. In this context, we propose a multiorientation volumetric deep neural network (MV-DNN) for 3D object classification with octree generating low-cost volumetric features. In comparison to conventional octree representations, we propose to limit the octree partition to a certain depth to reserve all leaf octants with sparsity features. This allows for improved learning of complex 3D features and increased prediction of object labels at both low and high resolutions. Our auxiliary learning approach predicts object classes based on the subvolume parts of a 3D object that improve the classification accuracy compared to other existing 3D volumetric CNN methods. In addition, the influence of views and depths of the 3D model on the classification performance is investigated through extensive experiments applied to the ModelNet40 database. Our deep learning framework runs significantly faster and consumes less memory than full voxel representations and demonstrate the effectiveness of our octree-based auxiliary learning approach for exploring high resolution 3D models. Experimental results reveal the superiority of our MV-DNN that achieves better classification accuracy compared to state-of-art methods on two public databases

Successful removal of a telephone cable, a foreign body through the urethra into the bladder: a case report

The variety of foreign bodies inserted into or externally attached to the genitourinary tract defies imagination and includes all types of objects. The frequency of such cases renders these an important addition to the diseases of the genitourinary organs. The most common motive associated with the insertion of foreign bodies into the genitourinary tract is sexual or erotic in nature. In adults this is commonly caused by the insertion of objects used for masturbation and is frequently associated with mental health disorders. We report a case of insertion of telephone cable wire into the urethra. Our case highlights the importance of good history, clinical examination, relevant radiological investigation and simple measures to solve the problem