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This archive accompanies “Reward History and Voluntary Re-engagement: A Dynamical Model of Contact, Pressure, and Persistence.” It contains executable simulation and plotting code, fixed p…

This archive accompanies “Reward History and Voluntary Re-engagement: A Dynamical Model of Contact, Pressure, and Persistence.” It contains executable simulation and plotting code, fixed parameter and analysis specifications, the disclosed supplemental initial-state diagnostic, synthetic observations and numerical result files, table-generation code, exact-arithmetic checks, original figure assets, and a SHA-256 manifest. All observations are theoretical model outputs or synthetic samples. No human participant data were collected or fitted. The archive supports a conditional account of post-withdrawal persistence and voluntary re-engagement, comparisons over a fixed 1,116-point pressure grid, mechanism ablations, equal-budget schedule comparisons, observation-window sensitivity, and conditional recovery of two pressure parameters. It does not establish that voluntary return measures attention, that tapering is universally preferable, or that the full model is identified from behavior. The numerical payload and scientific simulation code are those of the completed fixed analysis; this release packages them for the CSR manuscript preparation version 2.2. The README supplies execution instructions and required libraries. This is a substantial update from the earlier-model reproduction code associated with the prior manuscript “The Dynamics of Entry into Attention.” It is a research-software release, not an accepted journal publication. AI assistance is disclosed in the README and manuscript: ChatGPT (OpenAI) supported conceptual, mathematical and code development and verification; Claude (Anthropic) supported production of the earlier manuscript and associated computations. The author takes responsibility for the research and its interpretation.

The 2027 Year 7 neighbourhood zone of each of about 320 Victorian government secondary schools, one shape per zone part, from the Department of Education.This is the 2026-03-30 version of this dataset…

The 2027 Year 7 neighbourhood zone of each of about 320 Victorian government secondary schools, one shape per zone part, from the Department of Education.This is the 2026-03-30 version of this dataset on publicdata.au, with 326 rows and 5 fields. The same version is kept at https://publicdata.au/d/vic-school-zones-year7-2027/v/2026-03-30/ in 12 formats, with every earlier version and a query API. Each Zenodo version of this record is one publicdata.au version.The data is published by Department of Education under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. The licence requires this attribution:Department of Education, Victorian Government, Victorian Government School Zones 2027, sourced 3 October 2026, https://discover.data.vic.gov.au/dataset/victorian-government-school-zones-2027, licensed under CC BY 4.0.This is an independent republication. The publisher has not endorsed this site.The rows are in data.parquet, data.csv, data.json and data.xlsx. schema.json describes the fields, and publicdata.json names the version, licence, attribution and the SHA-256 of the publisher's file.

The 2027 neighbourhood zone of each of about 1,270 Victorian government primary schools, one shape per school, from the Department of Education.This is the 2026-03-30 version of this dataset on public…

The 2027 neighbourhood zone of each of about 1,270 Victorian government primary schools, one shape per school, from the Department of Education.This is the 2026-03-30 version of this dataset on publicdata.au, with 1,269 rows and 5 fields. The same version is kept at https://publicdata.au/d/vic-school-zones-primary-2027/v/2026-03-30/ in 12 formats, with every earlier version and a query API. Each Zenodo version of this record is one publicdata.au version.The data is published by Department of Education under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. The licence requires this attribution:Department of Education, Victorian Government, Victorian Government School Zones 2027, sourced 3 October 2026, https://discover.data.vic.gov.au/dataset/victorian-government-school-zones-2027, licensed under CC BY 4.0.This is an independent republication. The publisher has not endorsed this site.The rows are in data.parquet, data.csv, data.json and data.xlsx. schema.json describes the fields, and publicdata.json names the version, licence, attribution and the SHA-256 of the publisher's file.

Every registered school in Victoria in 2025, about 2,300 government, Catholic and independent schools, with type, address, phone number, region, council area and coordinates.This is the 2025-08-29 ver…

Every registered school in Victoria in 2025, about 2,300 government, Catholic and independent schools, with type, address, phone number, region, council area and coordinates.This is the 2025-08-29 version of this dataset on publicdata.au, with 2,301 rows and 24 fields. The same version is kept at https://publicdata.au/d/vic-school-locations/v/2025-08-29/ in 12 formats, with every earlier version and a query API. Each Zenodo version of this record is one publicdata.au version.The data is published by Department of Education under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. The licence requires this attribution:Department of Education, Victorian Government, School Locations 2025, sourced 3 October 2026, https://discover.data.vic.gov.au/dataset/school-locations-2025, licensed under CC BY 4.0.This is an independent republication. The publisher has not endorsed this site.The rows are in data.parquet, data.csv, data.json and data.xlsx. schema.json describes the fields, and publicdata.json names the version, licence, attribution and the SHA-256 of the publisher's file.

SOC-MLD (Session-Aware Multi-Layer Attack Detection Dataset) is a cybersecurity dataset designed for machine-learning-based network intrusion detection. The dataset integrates heterogeneous security t…

SOC-MLD (Session-Aware Multi-Layer Attack Detection Dataset) is a cybersecurity dataset designed for machine-learning-based network intrusion detection. The dataset integrates heterogeneous security telemetry collected from a live SOC cyber range built using Containernet and Docker. It covers four telemetry dimensions: L3/L4 network events Flow and session behavior L7 HTTP application requests Suricata Network Intrusion Detection System (NIDS) alerts The dataset introduces an attack-instance-based correlation mechanism that links heterogeneous telemetry through shared identifiers, including attack_instance_id, session_id, flow_id, and request_id. The release includes raw telemetry, curated machine-learning data, feature descriptions, attack scenario documentation, data validation scripts, and benchmark results. Dataset characteristics: 6,006 curated samples (subject to final release validation) 41 predictive features 32 classes, including 31 attack types and NORMAL Attack-instance-disjoint evaluation protocol Machine learning benchmarks and feature ablation experiments Intended use: Network intrusion detection, cybersecurity data analytics, multi-class attack classification, session-aware detection, and machine learning research. Version: 1.0 (2026) License: Creative Commons Attribution 4.0 International (CC BY 4.0).

The number of Victorian driver licences transferred to another jurisdiction in each quarter since the start of 2023.This is the 2026-07-14 version of this dataset on publicdata.au, with 14 rows and 3 …

The number of Victorian driver licences transferred to another jurisdiction in each quarter since the start of 2023.This is the 2026-07-14 version of this dataset on publicdata.au, with 14 rows and 3 fields. The same version is kept at https://publicdata.au/d/vic-driver-licence-transfers-from-victoria/v/2026-07-14/ in 9 formats, with every earlier version and a query API. Each Zenodo version of this record is one publicdata.au version.The data is published by Department of Transport and Planning under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. The licence requires this attribution:Department of Transport and Planning, Victorian Government, Quarterly Driver Licence Transfers from Victoria, sourced 3 October 2026, https://discover.data.vic.gov.au/dataset/quarterly-driver-licence-transfers-from-victoria, licensed under CC BY 4.0.This is an independent republication. The publisher has not endorsed this site.The rows are in data.parquet, data.csv, data.json and data.xlsx. schema.json describes the fields, and publicdata.json names the version, licence, attribution and the SHA-256 of the publisher's file.

Self-Compacting Concrete (SCC) is an advanced concrete that flows and consolidates under its own weight without external vibration. Although SCC improves placement quality, surface finish, labour effi…

Self-Compacting Concrete (SCC) is an advanced concrete that flows and consolidates under its own weight without external vibration. Although SCC improves placement quality, surface finish, labour efficiency and construction speed, its comparatively high cement and powder demand raises cost and environmental concerns. This review examines the potential of Sugarcane Bagasse Ash (SCBA) as a partial cement replacement and Pond Ash (PA) as a partial fine-aggregate replacement in sustainable SCC. Studies on SCC, supplementary cementitious materials, agricultural ashes, recycled materials, SCBA processing, pond ash utilisation, mechanical performance and durability were reviewed. The literature shows that SCBA contains reactive silica and can contribute through pozzolanic reaction and micro-filler action when it is burned, ground and sieved. Moderate SCBA contents, commonly around 5–15%, generally provide satisfactory compressive, tensile and flexural performance, whereas excessive replacement reduces workability and strength because of higher water demand, carbon content and cement dilution. Pond ash can conserve natural sand and improve particle packing at controlled dosages, but high replacement may increase absorption and porosity. The combined use of SCBA and pond ash appears promising; however, limited studies address their interaction in SCC. Future research should standardise processing, optimise admixture dosage, evaluate durability and validate performance under field conditions.

Mazkur tezisda kasbiy ta’lim sifatini oshirishda sun’iy intellekt (SI) texnologiyalaridan foydalanishning istiqbolli yo‘nalishlari tahlil qilinadi. Unda sun’iy intellektning ta…

Mazkur tezisda kasbiy ta’lim sifatini oshirishda sun’iy intellekt (SI) texnologiyalaridan foydalanishning istiqbolli yo‘nalishlari tahlil qilinadi. Unda sun’iy intellektning ta’lim sifatini monitoring qilish, ta’lim oluvchilarning o‘zlashtirish ko‘rsatkichlarini tahlil qilish, mavjud muammolarni aniqlash, ta’lim natijalarini prognozlash hamda boshqaruv qarorlarini qo‘llab-quvvatlashdagi imkoniyatlari yoritilgan. Shuningdek, SI texnologiyalaridan foydalanish orqali ta’lim jarayonini individuallashtirish, pedagogik jarayon samaradorligini oshirish va kasbiy ta’lim mazmunini mehnat bozori talablariga moslashtirish imkoniyatlari ko‘rib chiqiladi. Tadqiqotda sun’iy intellektdan samarali foydalanish uchun pedagoglarning raqamli kompetentligini rivojlantirish, ma’lumotlar xavfsizligini ta’minlash va inson nazoratini saqlash muhim shartlar sifatida asoslanadi.