/var/www/htdocs/pustaka-digital/lib/SearchEngine/SearchBiblioEngine.php:688 "Search Engine Debug 🔎 🪲"
Engine Type ⚙️: "SLiMS\SearchEngine\SearchBiblioEngine"
SQL ⚙️: array:2 [ "count" => "select count(sb.biblio_id) from search_biblio as sb where sb.opac_hide=0 and ((match (sb.topic) against (:subject in boolean mode)))" "query" => "select sb.biblio_id, sb.title, sb.author, sb.topic, sb.image, sb.isbn_issn, sb.publisher, sb.publish_place, sb.publish_year, sb.labels, sb.input_date, sb.edition, sb.collation, sb.series_title, sb.call_number from search_biblio as sb where sb.opac_hide=0 and ((match (sb.topic) against (:subject in boolean mode))) order by sb.last_update desc limit 10 offset 0" ]
Bind Value ⚒️: array:1 [ ":subject" => "'+\"learning systems\"'" ]
Missing data is a common occurrence in the time series domain, for instance due to faulty sensors, server downtime or patients not attending their scheduled appointments. One of the best methods to impute these missing values is Multiple Imputations by Chained Equations (MICE) which has the drawback that it can only model linear relationships among the variables in a multivariate time series. T…