Background:Iodine deficiency is the world's single greatest cause of preventable mental retardation. In developing countries, only 69% of households are consuming iodized salt.Objective:To assess knowledge and practices with respect to the current use of iodized salt, and to estimate its uptake at the household level.Materials and Methods:This cross-sectional survey was conducted in six villages under Rural Health Training Center. A total number of households surveyed were 253. The data collectors obtained verbal consent from the Family, and Pretested Standardized Questionnaire was administered in every selected household. The respondents were asked questions regarding salt purchasing and consumption habits, salt storage, awareness of iodized salt, and iodine deficiency diseases. Rapid iodized salt test kit (MBI kit) was used in the survey to assess iodine content in salt used in households.Results:In this study, 93.7% households were using packet salt. The most common source of information was a television (31.1%). More than half (53.8%) of the households were unaware of the benefits of iodine. About 62.5% of households were consuming adequately iodized salt. Significant association was found between the practice of storing salt in closed containers and use of packaged iodized salt (Chi-square value −37.6, P < 0.001), awareness about the benefits of iodine and type of salt used (P = 0.02) while no association was observed between the socioeconomic status and type of salt used in the household.Conclusions:Though the use of packet salt was more than 90%, adequately iodized salt was consumed only in 62.5%, and more than half of the subjects lacked the knowledge about iodine deficiency diseases.

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Summary The rise in traffic congestion has become a significant concern in the urban city environment. The conventional traffic control systems with inefficient human resources management fail to control the traffic discipline, leading to increased traffic density and road offenses. However, the intelligent transportation system (ITS) can provide safety, efficiency, and sustainability for large‐scale vehicular traffic dilemmas. ITS unites machine learning with the available traffic control force and performs real‐time police scheduling to ensure the smooth flow of traffic. Many researchers have demonstrated notable work in intelligent traffic police scheduling and deployment using various optimization algorithms. However, the compilation of such praiseworthy work as a whole is still missing. Motivated by these facts, we provide a comprehensive review of the machine learning‐based state‐of‐the‐art technologies that can be used to form a three‐tier solution taxonomy. The first tier describes various tools and technologies that can be utilized to collect traffic data. The second tier highlights the machine learning algorithms and their accuracy, which forms a pattern in the collected data and then yields some crucial information about traffic flow, congestion levels, and so forth. In the third tier, the most vital taxonomy layer, various traffic police scheduling strategies are discussed. The proposed survey also presents the use of cases of traffic police scheduling that elaborates this review's applicability in various domains. Finally, some of the key challenges in the subject being reviewed are discussed, which initiates a further scope of improvement.

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