Future Internet

Single observations can mischaracterize dynamic web tracking and browser fingerprinting. We measured privacy- and security-relevant indicators across a fixed, purposively curated frame of 947 Ecuador-associated organizational domains using Blacklight on 25 June, 25 July, and 25 August 2026. A completion-aware Playwright pipeline preserved the original JSON artifacts, acquisition provenance, SHA-256 hashes, quality-control results, and contextual taxonomy. Successful JSON export rates were 58.0%, 55.8%, and 59.0%; 500 domains succeeded in all three rounds, 497 satisfied measurement-level quality control in all three rounds, and the strict repeated-measures panel contained 381 domains (40.2% of the target frame) after the contextual-classification-confidence criterion was applied. Within the strict panel, six primary binary indicators varied by less than one percentage point, and none changed significantly after Holm correction (all adjusted $p\ge 0.812$); three-round exact agreement was 96.6–99.5%. The 497-domain sensitivity analysis produced closely similar results. Attrition was sector-dependent, so the strict panel is a complete-case analytical population rather than a compositionally representative subset of the original frame. A prospective direct-browser runtime-corroboration campaign on 120 stratified domains yielded 118 Firefox artifacts and 109 quality-valid observations, independently recording third-party domains, cookies, CSP headers, tracker-list matches, and fingerprinting-relevant API activity. Because contemporaneous Blacklight artifacts were not collected, this branch provides runtime corroboration rather than paired accuracy validation. The findings support traceable short-window audit prioritization and repeated-measures inference, but they do not establish national prevalence, long-term website stability, or domain-specific LOPDP violations.

Freight logistics on the Trans-Caspian International Transport Route (Middle Corridor) suffer from long dwell times, limited cargo visibility and frequent incidents. This study quantifies how a digital escort of freight, built on a four-layer 5G-IoT-AI architecture, could change the key performance indicators of the Kazakhstan section of the corridor. Each architectural layer was mapped to a measurable mechanism and coefficient, and three segments of a Kazakhstan freight forwarder’s chain were simulated, including the railway corridor (Monte Carlo simulation, N = 10,000), the port of Aktau (discrete-event simulation) and China–Europe transit, compared with a route attractiveness index. In the baseline digital scenario, railway lead time fell by 6.3% (Cohen’s d = 1.26) and expected incidents per container by 35.0%. At the port, operational errors decreased by 34.8%, whereas dwell time decreased by only 3.2% because ferry waiting time remained the dominant contributor to overall port dwell time.r. A factorial analysis showed that AI-supported processing drives lead time, IoT monitoring drives incidents and variability, and 5G affects only response time. With the current monitored share of about 35%, the lead-time gain fell below the practical-relevance threshold. The Kazakhstan route ranked first for 95.8% of 100,000 random weight vectors and remained first when the visibility criterion was removed.

Fifth-generation (5G) wireless networks have been key enablers for information societies over the last few years [...]