Background Recommender systems have become inherent in personalizing experiences, especially digital experiences, across domains such as e-commerce, media, and entertainment. These systems use the user to item interactions data (how an user reacts to an item) to identify patterns that predict preference and rank content. Collaborative filtering is one of the most widely used approaches, relying on similarity between users or items to generate recommendations. Methods This study examines collaborative filtering using similarity metrics applied to a curated IMDB movie dataset. Data was preprocessed using merging metadata and ratings, encoding categorical fields, and constructing feature vectors for each movie. The primary metric to compute pairwise distances between items was Cosine similarity. An item-item recommendation engine was then created and implemented, and the output was evaluated using a movie example (the Saw 2004). Results The system produced coherent recommendations aligned with the genre and thematic characteristics of the input movie used, Saw (2004). The top-ranked films exhibited high cosine similarity scores, indicating strong vector space proximity and consistent user engagement patterns. Visual exploration of the data confirmed that the similarity-based approach captured meaningful behavioral relationships. Conclusions The findings show that a simple similarity-based collaborative filtering model can effectively identify related movies without complex model architectures. Even with lightweight feature engineering, the system generated relevant recommendations that mirror typical user preferences. This demonstrates the practicality of similarity-based methods for scalable and interpretable recommendation tasks, and highlights opportunities for future extensions using hybrid or embedding based models.
In the implementation of effective and efficient exploration of mineral and coal resources, it is required to carry out geostatistical analysis to determine the relative error value of the optimum spacing of the drilling and its thickness and quality distribution. This study uses the application of geostatistics with the sill variogram method and global estimation variance (GEV), based on the relative value of the error of the thickness of the coal seam. This research was conducted in the concession area of PT. Kaltim Prima Coal. Based on the case study, the coalfield consisted of two seams, namely the North BE and South BE seams, with moderate geological conditions. From the variogram analysis of the thickness of the northern BE layer, the range is 151, the sill is 0.65, and the Nugget Effect is 0.04, while the BE South has a range value of 209, the sill is 0.16, and the nugget effect is 0.26. Based on field data, the average drill hole distance in the North BE seam is 131 meters. The distance of 1/3 of the sill gets a value of 50 meters (measured resource), while for the South BE seam it is 126 meters. The distance of 1/3 sill gets a value of 60 meters (measured resource), the distance of 2/3 sill gets a value of 122 meters (resource indicated), and the distance of 3/3 sill gets a value of 210 meters (inferred resource). Based on global estimation variance analysis. For the North BE Seam, the drill hole spacing for the measured resource category was 350 meters with a total of 29 drills, while for the South BE Seam, the drill hole spacing for the measured resource category was 350 meters with a total of 104 drills. The results of this classification produce an area of influence that is smaller than the SNI standard of moderate geological complexity. By using the sill variogram analysis, the results of the drill hole spacing are dense when compared to the results of the GEV analysis. The results of this GEV classification produce an area of influence that is relatively like to the SNI standard of moderate geological complexity.
Introduction. Maloben is a new drug for the treatment of liver diseases with previously unstudied pharmacokinetics in humans. Aim. To determine the pharmacokinetic (PK) parameters of Maloben tablets (SPCPU, Russia) after single and multiple administrations in healthy volunteers as part of a phase I clinical trial. Materials and methods. The phase I clinical trial was conducted in two stages. Stage I involved 24 volunteers divided into 3 cohorts of 8 volunteers each: cohort 1 received a single 60 mg dose of maloben, cohort 2 a single 120 mg dose, and cohort 3 a single 180 mg dose. Stage II included healthy volunteers who completed the previous stage: cohort 4 received a daily dose of 60 mg, cohort 5 a daily dose of 120 mg, and cohort 6 a daily dose of 180 mg. To determine maloben concentrations in plasma, high-performance liquid chromatography with tandem mass spectrometric detection (HPLC-MS/MS) was used. Based on plasma concentrations obtained during the analytical phase after single and multiple oral administrations, pharmacokinetic parameters of maloben were evaluated. Results and discussion. Pharmacokinetic parameters of maloben were evaluated in 6 cohorts of 8 volunteers each. Mean C max values were 18.09 ± 8.06, 41.36 ± 5.63, and 51.81 ± 11.05 ng/mL after single dosing in cohorts 1–3, and 37.93 ± 20.98, 70.83 ± 37.37, and 78.98 ± 37.03 ng/mL after multiple dosing in cohorts 4–6. Mean AUC (0–t) values were 348.59 ± 200.65, 938.32 ± 344.95, and 1177.13 ± 221.81 ng · h/L after single dosing in cohorts 1–3, and 3142.22 ± 2091.08, 5714.73 ± 2482.56, and 7799.02 ± 3829.67 ng · h/L after multiple dosing in cohorts 4–6. Mean AUC (0–∞) values were 623.05 ± 390.08, 1171.68 ± 471.89, and 1666.93 ± 596.25 ng · h/L after single dosing in cohorts 1–3, and 3228.41 ± 2141.08, 5789.32 ± 2539.34, and 8970.72 ± 5143.42 ng · h/L after multiple dosing in cohorts 4–6. Dose proportionality (linear PK) was established for PK parameters C max , AUC (0–t) , and AUC (0–∞) after single dosing, and for AUC (0–t) and AUC (0–∞) after multiple dosing. Conclusion. For the first time, the pharmacokinetics of maloben under various dosing regimens in volunteers were investigated, as well as the assessment of changes in PK parameters.
This study addresses the problem that enterprise IT service desks increasingly embed AI assistants in support portals and ticket workflows, yet many organizations lack quantitative evidence on whether human oversight and automation quality jointly improve user experience and service performance. The purpose was to test a quantitative, cross-sectional, case study-based model linking Human-AI Collaboration (HAC) and Workflow Automation Effectiveness (WAE) to User Experience (UX) and perceived IT Support Service Performance (SP). Survey data were collected from enterprise support cases; 320 questionnaires were distributed, 259 were returned, and 247 valid responses were analyzed (77.2% usable response rate; 71.7% end users and 28.3% IT support personnel; 54.3% used AI support weekly or more). Constructs were measured with multi-item five-point Likert scales and showed favorable perceptions: HAC M = 3.91 (SD = 0.64), WAE M = 3.84 (SD = 0.69), UX M = 3.88 (SD = 0.62), and SP M = 3.79 (SD = 0.66), with good to excellent reliability (Cronbach alpha 0.86 to 0.91). The analysis plan applied descriptive statistics, reliability testing, Pearson correlations, multiple regression, and bootstrapped mediation (5,000 samples). Associations were positive and significant (HAC with UX r = 0.62 and UX with SP r = 0.63, both p < .001). Regression indicated that HAC (beta = 0.41) and WAE (beta = 0.33) explained 49% of UX variance (R2 = 0.49, p < .001); WAE (beta = 0.38), HAC (beta = 0.21), and UX (beta = 0.29) explained 56% of SP variance (R2 = 0.56, p < .001). UX partially mediated the HAC to SP relationship (indirect beta = 0.29, 95% CI [0.19, 0.40]). Implications suggest that AI enabled IT support should be governed as a hybrid workflow with clear escalation rules and reliable automation, and continuously evaluated using joint metrics that track experience alongside efficiency outcomes.
