LOF calculation time (seconds) comparison.

This study underscores the significance of assessing the capabilities of rehabilitation officers in navigating challenges, devising innovative work methods, and successfully executing the rehabilitation process. This is particularly crucial amid the dual challenges of overcapacity and the repercussions of the Covid-19 pandemic, making it an essential area for research. To be specific, it aims to obtain empirical evidence about the influence of proactive personality and supportive supervision on proactive work behavior, as well as the mediating role of Role Breadth Self-efficacy and Change Orientation. This research was conducted on all rehabilitation officers at the Narcotics Penitentiary in Sumatra, totaling 272 respondents. This study employs a quantitative method via a questionnaire using a purposive sampling technique. The data was subsequently examined using the Lisrel 8.70 software and Structural Equation Modeling (SEM). It can be concluded from the results that the rehabilitation officers for narcotics addicts at the Narcotics Penitentiary can create and improve proactive work behavior properly through the influence of proactive personality, supportive supervision, role breadth self-efficacy, and change orientation. The study may suggest new ways of working and generate new ideas to increase initiative, encourage feedback, and voice employee concerns. Furthermore, this research has the potential to pinpoint deficiencies in proactive work behavior, serving as a foundation for designing interventions or training programs. These initiatives aim to enhance the innovative and creative contributions of rehabilitation officers in the rehabilitation process.

Total DVF statistics: statistics by geographical scale, over the 10 semesters available. Monthly DVF statistics: statistics by geographical scale and by month. Description of treatment The code allows statistics to be generated from the data of land value requests, aggregated at different scales, and their evolution over time (monthly). The following indicators have been calculated on a monthly basis and over the entire period available (10 semesters): * number of mutations * average prices per m2 * median of prices per m2 * Breakdown of sales prices by tranches for each type of property from: * houses * apartments * houses + apartments * commercial premises and for each scale from: * nation * Department * EPCI * municipality * Cadastral section The source data contain the following types of mutations: sale, sale in the future state of completion, sale of building land, tendering, expropriation and exchange. We have chosen to keep only sales, sales in the future state of completion and auctions for statistics*. In addition, for the sake of simplicity, we have chosen to keep only mutations that concern a single asset (excluding dependency)*. Our path is as follows: 1. for a transfer that would include assets of several types (e.g. a house + a commercial premises), it is not possible to reconstitute the share of the land value allocated to each of the assets included. 2. for a transfer that would include several assets of the same type (e.g. X apartments), the total value of the transfer is not necessarily equal to X times the value of an apartment, especially in the case where the assets are very different (area, work to be carried out, floor, etc.). We had initially kept these goods by calculating the price per m2 of the mutation by considering the goods of the mutation as a single good of an area to the sum of the surfaces of the goods, but this method, which ultimately concerned only a marginal quantity of goods, did not convince us for the final version. The price per m2 is then calculated by dividing the land value of the change by the surface area of the building of the property concerned. We finally exclude mutations for which we could not calculate the price per m2, as well as those whose price per m2 is more than € 100k (arbitrary choice)*. We have not incorporated any other outlier restrictions in order to maintain fidelity to the original data and to report potential anomalies. Displaying the median on the site reduces the impact of outliers on color scales. _*: The mentioned filters are applied for the calculation of statistics, but all mutations of the source files are well displayed on the application at the plot level.

Differences in the Dietary Inflammatory Index (DII) calculated according to Shivappa et al. [17] or the Scaling-Formula With Outlier Detection (SFOD) method based on similar food consumption data between subject pairs.

Note:* balancing selection; Ho, observed heterozygosity.Outlier SNPs detected using the finite island model and hierarchical island model for Fst calculation.

The Pearson’s R measures the linear correlations between the models, and the NRMS measures the misfit between the true model and the reconstructed models.

Anomaly detection is widely used in cold chain logistics (CCL). But, because of the high cost and technical problem, the anomaly detection performance is poor, and the anomaly can not be detected in time, which affects the quality of goods. To solve these problems, the paper presents a new anomaly detection scheme for CCL. At first, the characteristics of the collected data of CCL are analyzed, the mathematical model of data flow is established, and the sliding window and correlation coefficient are defined. Then the abnormal events in CCL are summarized, and three types of abnormal judgment conditions based on cor-relation coefficient ρjk are deduced. A measurement anomaly detection algorithm based on the improved isolated forest algorithm is proposed. Subsampling and cross factor are designed and used to overcome the shortcomings of the isolated forest algorithm (iForest). Experiments have shown that as the dimensionality of the data increases, the performance indicators of the new scheme, such as P (precision), R (recall), F1 score, and AUC (area under the curve), become increasingly superior to commonly used support vector machines (SVM), local outlier factors (LOF), and iForests. Its average P is 0.8784, average R is 0.8731, average F1 score is 0.8639, and average AUC is 0.9064. However, the execution time of the improved algorithm is slightly longer than that of the iForest.