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This paper presents a survey of cybercafes in a developing country to reveal their design pitfalls and propose appropriate solutions to the observed problems based on ergonomic principles. These cybercafes provide Internet services to communities but the concern is to make the work convenient at the computer workplace and comfortable at work thus reducing the risk of musculoskeletal injury and minimising energy expended at work. A survey was conducted in fourteen cybercafes using questionnaires, personal interviews and actual field measurements. Ergonomic analysis revealed that cybercafe chairs are the major pitfalls of the café design: they are often too short with no arm rests and with insufficient backrests. Chairs are also stationary as opposed to being mobile with rollers. Customers' leg positioning under the table is restricted and the workspace is too small. Also, the top of computer monitors often is above eye level for an average height customer. Since very little documentation exists on the subject in developing countries, the work opens a gate of opportunities for those in the cybercafé business with financial constraints to implement standard designs. The work also complements the dearth of expertise in the area since many towns in Nigeria do not have the required expertise for cybercafé design and construction. [ABSTRACT FROM AUTHOR]

Quality detection has been a major problem in the agriculture and food industries. This operation is mostly done by a subjective sensory method which is prone to high error and food destruction. Therefore, there is a need to apply artificial intelligence using a machine learning approach. This study developed two intelligent acoustic yam quality detection and classification devices using two sound-generating techniques. The software (multi-wave frequency generator) sound-generating technique generated sound from a laptop to a speaker inside a detecting chamber. This sound passes through the yam and was received on the opposite side by a microphone, into another laptop for analysis using visual analyzer software. The impact sound-generating technique used sound generated from a gentle impact of the yam on a flat surface placed inside the detection chamber. The sound produced was picked up by a microphone into a laptop for analysis. Acoustic properties considered were amplitude, frequency, sound velocity, wavelength, period and sound intensity. Discriminant analysis algorithm only was used in this first stage of the study to prove the applicability of machine learning. Three qualities (good, diseased damaged and insect-damaged) of two yam varieties (white and yellow yam) were tested. The device's performance of white yam was 79% and 68.7%, yellow yam was 82.3% and 68.7% for the software sound generation-technique and surface impact sound-generating technique, respectively. The study shows that the software sound-generating technique performed better in terms of overall yam quality detection and also proves the applicability of machine learning., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors.)