Inferring destination from mobility data

Destination prediction in a moving vehicle has several applications such as alternative route recommendations even in cases where the driver has not entered their destination into the system. In this paper a hierarchical approach to destination prediction is presented. A Discrete Time Markov Chain model is used to make an initial prediction of a general region the vehicle might be travelling to. Following that a more complex Bayesian Inference Model is used to make a fine grained prediction within that destination region. The model is tested on a dataset of 442 taxis operating in Porto, Portugal. Experiments are run on two maps. One is a smaller map concentrating specificially on trips within the Porto city centre and surrounding areas. The second map covers a much larger area going as far as Lisbon. We achieve predictions for Porto with average distance error of less than 0.6 km from early on in the trip and less than 1.6 km dropping to less than 1 km for the wider area

Spontaneous recovery from depression in women: a qualitative study of vulnerabilities, strengths and resources

Objective: To gain insight into the perceived vulnerability and restitution factors for anxiety/or depression.Methods: Focus group discussion of seven married women recovered spontaneously from anxiety and/or depression, belonging to a lower middle class semi-urban community of Karachi.Results: Poverty, unemployment, abuse and on going difficulties were perceived as risk factors for depression. A reliable social support system, positive thinking approach, faith, prayers, and experiencing a turning point event were reported as factors that promoted recovery from anxiety and/or depression.CONCLUSION: Individual vulnerabilities, strengths and resources can have an important role in recovery from anxiety and/or depression in women

Breeding performance of sustainable fish Ctenopharyngodon idella through single intramuscular injection of Ovaprim-C at Bahawalpur, Pakistan

Effect of intramuscular injection of Ovaprim–C on the number of eggs/kg,  fertilization rate and hatching percentage were studied at a private Fish Hatchery at Bahawalpur, Pakistan, during April to June 2008, on Ctenopharyngodon idella (Grass carp). Studied fish specimens were spawned successfully following a single dose of injection of Ovaprim-C (LH-RH analogue) with 0.6 ml kg-1 for female and 0.2 ml kg-1 for male brooders. Ova and milt were stripped simultaneously and mixture was stirred for 15 to 30 s during which fertilization occurred. Hatching occurred within 18 to 30 h after fertilization. The experiment was conducted in circular spawning tank with 2 m diameter. It was observed that body weight has positive influence on absolute fecundity (r = 0.967), while relative fecundity remained constant with increasing body weight. If it is impossible to determine the absolute and relative fecundity then these parameters can be determined from the body weight.Key words: Induced spawning, Ovaprim-C, fecundity, Ctenopharyngodon idella

Prevalence of and factors associated with anxiety and depression among women in a lower middle class semi-urban community of Karachi, Pakistan

Objective: To study the prevalence of, and factors associated with anxiety and depression among women.Design: A cross sectional survey.SETTING: A lower middle class semi-urban community of Karachi, Pakistan.PARTICIPANTS: A total of 1218 women between the ages of 18-50 years.METHODOLOGY: Systematically every third household was identified from which a woman was randomly selected. The Aga Khan University Anxiety and Depression Scale and a socio-demographic questionnaire were administered verbally by trained interviewers for assessing the prevalence of, and associated factors for anxiety and depression.Results: A prevalence of 30% was found. Increasing age, lack of education and verbal abuse were the associated factors found to have an independent relationship.CONCLUSION: Providing education and reducing domestic abuse could lead to decrease in the prevalence of anxiety and depression in women

Genomic characterization of antibiotic resistant Escherichia coli isolated from domestic chickens in Pakistan

Poultry husbandry is important for the economic health of Pakistan, but the Pakistani poultry industry is negatively impacted by infections fro

Heuristic edge server placement in Industrial Internet of Things and cellular networks

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current Information and Communications Technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay is a challenging problem. In this paper, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom’s base station the dataset that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multi-objective constraint optimization problem that places edge servers strategically to the balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base-stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hillclimbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost functio

Effect of various nutrient combinations on growth and body composition of rohu (Labeo rohita)

A total of 80 Labeo rohita fingerlings (mean body weight, 14.7 ± 0.08 g and length, 11.0 ± 0.16 cm) were randomly distributed into four treatments with 20 replicates each, for 60 days, to determine the effect of different feed compositions on the growth and body composition of L. rohita. Four isoenergetic (17.05 ± 0.24 kJ g-1) experimental diet viz., control (C), protein rich (PR), fat rich (FR) and carbohydrate rich (CR) were formulated. The proximate composition protein/fat/carbohydrate (P/F/C) of formulated feed were C: P35/F8/C2, PR: P40/F8/C2, FR: P35/F10/C2 and CR: P35/F8/C5. The daily ration size was 5% of fish body weight. The result reveals a highly significant (P≤0.001) difference in specific growth rate (SGR), weight gain (WG) and protein efficiency (PE) among four feeding groups, while differences were significant for feed conversion ratio (FCR). FR showed maximum growth together with high body fat, CR showed low body fat and high proteins. Results indicate that increasing fat up to 9% in diet showed better growth as compared to increasing dietary protein and carbohydratesKey words: Labeo rohita, diet composition, specific growth rate, protein efficiency, body composition