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This paper presents the e-Government capabilities and thereby identifies barriers within eGovernment implementation in

Objectives:The objective of this study was to report causes, management options, and complications of facial fractures among children.Materials and Methods:The groups were defined on the basis of age, gender, cause of injuries, location, and type of injuries. The treatment modalities ranged from no intervention, closed reduction alone or with open reduction internal fixation (ORIF).Statistical Analysis:Descriptive statistics were generated by using SPSS software for the entire range of the variables under study.Results:Records of 240 pediatric patients were obtained and a total of 322 fractures were found among a study sample. Among these, one-thirds were due to road traffic accidents (RTAs) (37.26%) and fall injuries (36.64%), making them the leading causes of facial fractures. Mandibular fractures were the most common and they accounted for 46% (n = 148) of all fractures. The highest number of RTA (n = 27) was found in adolescents and fall injuries were more prevalent in preschool children (n = 34). Forty-two percent of the fractures (n = 101) were treated with close treatment using arch bars and splints, followed by ORIF (n = 68). The rest, 29.6% (n = 71), received conservative treatments. Postoperative complications were observed in 18.33% (n = 44) of cases, of which jaw deviation, growth disturbance, and trismus were more frequently encountered.Conclusion:Pediatric facial fractures if not managed properly can cause severe issues; therefore, injury prevention strategies should be strictly followed to reduce pediatric injuries in low socioeconomic countries.

Person re-identification (re-ID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearance-based features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise re-identification of an individual. To overcome such strong dependence on visual features, many researchers have tackled the re-identification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for re-ID in uncontrolled environments. However, image-based gait analysis often fails to extract quality measurements of an individual’s motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on image-based motion measurement, this paper demonstrates the potential to re-identify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary long-term temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of re-identification. The obtained experimental results demonstrate the potential to reliably and accurately re-identify an individual based on one’s inertial sensor data.