PurposeThe potential effect of gonadotropin-releasing hormone agonist (GnRHa) treatment on the weight of girls with central precocious puberty (CPP) remains a controversy. We investigated anthropometric changes during and after GnRHa treatment among girls with CPP.MethodsThis retrospective study evaluated data from 127 girls with CPP who received GnRHa treatment for ≥2 years. Height, weight, and body mass index (BMI) values were compared at the baseline (visit 1), after 1 year of GnRHa treatment (visit 2), the end of GnRHa treatment (visit 3), and 6–12 months after GnRHa discontinuation (visit 4).ResultsThe height z score for chronological age (CA) increased continuously between visit 1 and visit 4. No significant differences were observed in BMI z score for CA between visits 1 and 4. However, an increasing trend in the BMI z score for bone age (BA) was observed between visits 1 and 4. The numbers of participants who were of normal weight, overweight, and obese were 97, 22, and 8, respectively, at visit 1, compared to 100, 16, and 11, respectively, at visit 4 (P=0.48).ConclusionAmong girls with CPP, the overall BMI z score for CA did not change significantly during or after GnRHa treatment discontinuation, regardless of their BMI status at visit 1. However, the BMI z score for BA showed an increasing trend during GnRHa treatment and a decreasing trend after discontinuation. Therefore, long-term follow-up of BMI changes among girls with CPP is required until they attain adult height.

In smart environments, target tracking is an essential service used by numerous applications from activity recognition to personalized infotaintment. The target tracking relies on sensors with known locations to estimate and keep track of the path taken by the target, and hence, it is crucial to have an accurate map of such sensors. However, the need for manually entering their locations after deployment and expecting them to remain fixed, significantly limits the usability of target tracking. To remedy this drawback, we present a self-configuring and device-free localization protocol based on genetic algorithms that autonomously identifies the geographic topology of a network of ultrasonic range sensors as well as automatically detects any change in the established network structure in less than a minute and generates a new map within seconds. The proposed protocol significantly reduces hardware and deployment costs thanks to the use of low-cost off-the-shelf sensors with no manual configuration. Experiments on two real testbeds of different sizes show that the proposed protocol achieves an error of 7.16∼17.53 cm in topology mapping, while also tracking a mobile target with an average error of 11.71∼18.43 cm and detecting displacements of 1.41∼3.16 m in approximately 30 s.

Precise estimation of inter-sensor distances is essential for reliable localisation in Internet of Things sensor networks. A cost-effective, scalable, asynchronous solution to estimate inter-sensor distances based solely on measurements of distances to a moving object is proposed. More specifically, the proposed solution estimates uncharted distances using trigonometry and processes these estimated distances with a distributed weighted multi-dimensional scaling algorithm for more precise localisation of sensors. It is demonstrated that the proposed solution achieves the localisation error of 4.8-33.9 cm when the measurement errors of sensor devices are in the range of 5-40%.

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