Li, Tong · Bresser, Dominic

1 files · 100 MB · zip

Mendes, Ana · Agonia Pereira, Luís · Vairinhos, Valter

1 files · 83 KB · zipdeclared

Dataset and commented Python program that reproduce, end to end, all the quantitative and lexical analyses of a study on study habits, self-regulation and well-being in hybrid higher education (n = 69). The associated article is currently under review; its full reference will be added upon acceptance. The package includes the anonymised questionnaire data, the reproduction script (fixed random seed), the exact library versions (requirements.txt) and full documentation of the composite indices.

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

70 files · 34 GB · netcdfdeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil water content layers 3 and 4, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables swc_l03: Soil water content layer 3 (150-300 mm depth) [mm] swc_l04: Soil water content layer 4 (300-500 mm depth) [mm] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

70 files · 34 GB · netcdfdeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil moisture layers 2 and 3, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables sm_l02: Volumetric soil moisture layer 2 (50-150 mm depth) [mm mm-1, fraction between 0 and 1] sm_l03: Volumetric soil moisture layer 3 (150-300 mm depth) [mm mm-1, fraction between 0 and 1] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

70 files · 33 GB · netcdfdeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil water content layers 5 and 6, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables swc_l05: Soil water content layer 5 (500-1000 mm depth) [mm] swc_l06: Soil water content layer 6 (1000-2000 mm depth) [mm] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

70 files · 34 GB · netcdfdeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil water content layers 1 and 2, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables swc_l01: Soil water content layer 1 (0-50 mm depth) [mm] swc_l02: Soil water content layer 2 (50-150 mm depth) [mm] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems

Vasic, Iva · Reitemeyer, Benedikt · Fill, Hans-Georg

1 files · 849 KB · zipdeclared

These are code and supplementary material for the BIR 2026 conference paper on formal evaluation of LLM-generated conceptual models. Title: Evaluating LLM-Generated Conceptual Models: A Theoretical Approach for Formalizing the Calculation of Metrics on the Meta Level The work was financially supported by the Smart Living Lab , a joint project funded by the University of Fribourg, EPFL, and HEIA-FR.

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

72 files · 36 GB · netcdfdeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625° . The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components (streamflow 'q', soil moisture 'sm_l01', domain mask, and uparea assets) , consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ) . The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch ( https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads ) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Model version: mHMv5.11.3 (Release mRMv1.0) Setup Scope: Model run for domain 1020011530, post-processed and clipped. Simulation Version: v1.0 Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline ( https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads ). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables q: Routed streamflow (discharge) [m3 s-1] sm_l01: Volumetric soil moisture of soil layer 1 (top 50 mm) [mm mm-1, fraction between 0 and 1] mask: Domain clipping structure [binary flag / dimensionless] uparea: Upstream catchment area matrix [m2] 📫 Contact For questions or collaboration inquiries, please contact: Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de 📚 References Boeing, F. et al., 2022. Hydrol. Earth Syst. Sci. , 26, 5137-5161. Hargreaves, G.H. & Samani, Z.A., 1985. Applied Engineering in Agriculture , 1(2), pp.96-99. Hartmann, J. & Moosdorf, N., 2012. Geochem. Geophys. Geosyst. , 13(12). Hengl, T. et al., 2017. PLoS One , 12(2), e0169748. Hersbach, H. et al., 2020. QJRMS , 146(730), pp.1999-2049. Kumar, R. et al., 2013. Water Resources Research , 49(1), pp.360-379. Lehner, B. et al., 2011. Front. Ecol. Environ. , 9(9), pp.494-502. Rakovec, O. et al., 2016. J. Hydrometeorology , 17(1), pp.287-307. Rakovec, O. et al., 2022. Earth's Future , 10(3), e2021EF002394. Samaniego, L. et al., 2010. Water Resources Research , 46(5). Samaniego, L. et al., 2023. mhm-ufz/mHM: v5.13.1, Zenodo. DOI: 10.5281/zenodo.8279545 Thober, S. et al., 2019. Geosci. Model Dev. , 12(6), pp.2501-2521.

Bunn, Andrew G.

1 files · 27 MB · zipdeclared

An applied, R-based introduction to time series analysis for environmental scientists and ecologists. It covers autocorrelation and stationarity, ARMA models, cross-correlation, regression with autocorrelated errors, trend detection, forecasting and reconstruction, and frequency-domain methods including wavelets, with an emphasis on building intuition and getting things done in R rather than mathematical derivation. The book originated as graduate course notes at Western Washington University.

Mehmetaj, Ilir

9 files · 12 MB · jpeg, pdf, pngdeclared

ABSTRACT - Seraphim Skin v1.0 Can the Seraphim UV-Protection Layer Reshape the USD 2.26 Trillion Clothing Industry? The global apparel market - worth roughly USD 1.8 trillion in 2024/25 and projected to reach USD 2.26 trillion by 2030 - is not the target of a niche within it, but of a material layer that integrates into any garment across the whole of it. Offered two identical garments at one price, one plain and one with permanent UV protection and passive cooling woven into the material itself, the buyer needs no persuasion. That is the lever, and the trend. The stakes are health, not fashion alone: ultraviolet radiation drives 83 percent of melanoma, the World Health Organization projects a 50 percent rise in incidence by 2040, two billion people work outdoors under rising heat stress, and fifty million live with UV-sensitive medical conditions. For millennia the answer has been oils, fabrics, and chemicals - each partial, each reapplied. This concept proposes that the protection be the fabric itself: permanent, physical, effective from first wear to hundredth wash. Seraphim Skin is a 35-micrometre, 45-gram-per-square-metre multilayer laminate - the same material family documented across this series, tuned here to the everyday-climate design point rather than the fire or energy-harvest ones. A diamond-like-carbon outer layer reflects ultraviolet across UV-A and UV-B through bandgap engineering and carries a self-cleaning lotus surface; a roughly 20-micrometre gradient graphene layer manages the infrared; a passivation layer isolates the active layer; a bio-compatible inner layer carries comfort and sensing. The concept states its layer hierarchy explicitly to prevent a contradiction: the 20-micrometre graphene layer carries the thermal work through thickness-tolerant mechanisms - isotope (¹²C/¹³C) phonon scattering and emission in the 8-to-13-micrometre atmospheric window - while the electronic decoupling of the Wiedemann-Franz violation at the Dirac point (Nature Physics, 2025) belongs only to the tens-of-nanometre sensor zone that powers biosensors from body heat. The fabric manages the full electromagnetic spectrum in one architecture: permanent UV-B and UV-A reflection at the material level, and emission of the body's 9-micrometre heat through the atmospheric window to the cold sky - the passive daytime radiative cooling demonstrated in peer-reviewed metafabrics. The thermal behaviour is described as a bounded rectifier (an outward bias in the class of 1.3-to-2-to-1, not an absolute valve), and the cooler-than-skin result is stated with its mechanism so it cannot be read as a violation of thermodynamics. Layer count is a design variable - the N-Factor: stacking N laminates compounds protection and answers what happens if a layer is damaged, since the remaining skins keep functioning and performance degrades gradually. Twelve novel contributions (NC-SKN-1 through NC-SKN-12) span five garment markets - outdoor labour, children, medical photoprotection, defence signature management, and luxury performance - and every surface between people and the sun: building and glazing envelopes, closed UV-filtering radiatively cooled desert greenhouses, and vehicle surfaces that lower cabin heat load to extend electric-vehicle range. Those who bear the downstream cost of UV damage and heat stress - insurers, employers with outdoor workforces, public health systems - are the structural payers: prevention shifts value from treatment toward avoidance. Because sub-ambient radiative-cooling textiles exist as prior art, each contribution is framed by its integration rather than any single function. Every load-bearing element exists at high maturity in another industry; the integrated laminate stands at TRL 2 to 3. The decisive experiment is a fabric coupon under a solar simulator - measured UV transmission, 8-to-13-micrometre emissivity, and sub-ambient temperature difference against exposed skin - turning the central claims into data for a few thousand euros. All parameters are theoretical design estimates requiring independent validation. This concept consolidates and refines a disclosure of March 2026 and operates at the everyday-climate design point (around 50°C), standing independent of the fire branch (600 to 1,200°C) and the energy-harvest branch of the same laminate family. The twelve novel contributions are placed on the public record of prior art as of the Zenodo publication date under CC BY-NC-ND 4.0, preventing future patent claims on these specific architectures by any party. Ilir Mehmetaj | Independent Concept Developer | CC BY-NC-ND 4.0 | 2026

Newman-Norlund, Roger

1 files · 51 MB · zipdeclared

brainWhiz is a static, single-page Three.js viewer for multi-atlas neuroimaging figures. It renders brain parcellations in 3D, colors regions by per-region CSV values or voxelwise .nii statistical maps (auto-matching a CSV to its atlas), draws DTI / resting-state connectivity, shows native-resolution slices and mosaics, and composes publication-ready figure panels — entirely in the browser. Bundled atlases, NeuroQuery task maps, and connectivity are third-party data with their own terms, for non-commercial research; cite the original sources.

Hoicka, David · 大卫·霍伊卡

2 files · 1.1 MB · jpegdeclared

Chinese translation of "Singapore as Model for Ukraine Russia Peace: Proactive Leadership, Mediation and Hard Work" (Mediation for Life and Peace, volume 14) by David Hoicka, published by Singapore Mediation Solutions. 书籍序言:战争的伤痕,和平的承诺 1942年初,当世界陷入二战烈火之中时,新加坡人民发现自己站在一场残酷恐怖冲突的前线。多年来,这座岛屿一直是大英帝国的重要战略前哨,是繁忙的港口城市,也是来自亚洲各地移民和商人的避风港。但随着马来亚沦陷于日军手中,英军撤退,新加坡变得脆弱且暴露,成为敌军推进的主要目标。 接下来是新加坡历史上最黑暗的篇章之一,一段充满暴力、压迫和难以言说暴行的时期,在岛上集体心灵中留下了深刻而持久的创伤。在英国投降后的几天和数周内,日本士兵逮捕了数千名新加坡华裔,其中许多是年轻男子和男孩,并对他们实施了一场被称为"肃清"(Sook Ching,意为"通过清洗")的残酷酷刑、审讯和大规模处决。 对于亲眼目睹这些事件的年轻律师、未来新加坡总理李光耀来说,淑清的记忆将成为他个人和政治身份中挥之不去且难以磨灭的一部分。正如他后来在回忆录中回忆的那样,看到中国尸体漂浮在新加坡河中,尸体被肢解肿胀,令人痛心地提醒着人们人类的残酷,以及在战争和压迫面前生命的脆弱。 然而,即使在这恐怖与毁灭之中,也有希望和坚韧的曙光,勇气与同情的时刻,这些后来成为新加坡及其人民精神的标志。有那些冒着生命危险,躲避日军巡逻队追捕的勇敢男女,有在临时诊所和医院照顾伤员和垂死者的医生和护士,还有即使在最黑暗时期也保持学问和文化之火的教师和学生。 这些善意与团结的行为,无论多么微小或短暂,都见证了人类即使在难以想象的痛苦和创伤面前,依然保持着持久的同理心与连接能力。它们提醒我们,即使在最黑暗的时刻,也总有光明、疗愈和更美好未来的可能。 随着新加坡从战争废墟中走出,踏上漫长而艰难的独立与繁荣之路,这些教训成为其领导人和人民的指引和灵感。从国家建设的早期到当今的挑战,新加坡借鉴了自身的创伤与韧性经验,开辟了一条基于包容、和谐与共同目标原则的新道路。 如今,当我们展望一个仍被冲突和分裂困扰的世界时,新加坡转型的故事为那些寻求克服自身暴力和创伤历史的国家和社区传递了强烈的希望与可能性。在乌克兰和俄罗斯这两个国家的背景下,这一信息尤为相关和紧迫,这两个国家的过去与现在因战争的创伤和代际创伤的持续影响而深深交织在一起。 几个世纪以来,乌克兰和俄罗斯的人民一直被帝国和意识形态的潮起潮落、入侵和占领的创伤、争取自决和民族认同的斗争所塑造。从大饥荒的毁灭、二战的恐怖,到近年克里米亚和顿巴斯的冲突,这两个国家的历史充满了深刻的痛苦与失落,家庭支离破碎,社区被暴力和压迫摧毁。 但这也是一段令人难以置信的韧性和勇气的历史,是人们在逆境中团结起来重建家园和生活,保护语言和文化,为权利和自由而战的历史。正如新加坡人民从最黑暗的时刻汲取力量和灵感,乌克兰和俄罗斯人民也一次又一次展现出疗愈与更新的能力,寻找共同点,建立新的团结与理解纽带。 这种同理心和连接的能力,或许是我们对抗长期困扰这两个国家乃至更广泛世界的暴力与创伤循环中最强大的武器。通过认识到冲突各方人民共同的人性和经历,跨越政治与历史的鸿沟,搭建对话与合作的桥梁,我们可以开始打破长期以来将我们分隔开的不信任与仇恨的墙壁。 当然,这并不是一件容易或直接的任务。过去的伤痕深重,当前的挑战复杂且令人望而生畏。但正如新加坡的例子所示,即使是最棘手的冲突和创伤,也能通过领导力、决心和致力于为所有人建设更美好未来的努力克服。 在本书中,我们共同寻求借鉴新加坡的经验和经验,为乌克兰和俄罗斯的领导者和公民提供新的视角和一套实用工具,帮助他们开辟通往持久和平与繁荣的道路。通过对新加坡从冲突到合作、从创伤到坚韧的历程进行深入审视,我们希望激发一场新的对话和新的应对两国及更广泛世界挑战的思考。 这种方法的核心是认识到同理心、对话和集体行动的力量,能够转化即使是最根深蒂固、最激烈的冲突。通过聚焦调解原则与实践、包容性治理与经济发展、社会凝聚力与共同身份认同,我们相信乌克兰和俄罗斯可以开始超越暴力和相互指责的循环,迈向合作与相互理解的新时代。 当然,这不会是一个轻松或快速的过程。战争和创伤的伤痕深重,和平与和解的障碍众多且复杂。但正如新加坡的故事提醒我们的,人类精神极其坚韧和适应力强,能够克服最黑暗的时刻和最艰巨的挑战。 对于历代经历无数痛苦和煎熬的乌克兰和俄罗斯人民来说,这一充满希望与可能性的信息比以往任何时候都更为重要。通过汲取共同的历史和文化,挖掘他们深厚的同理心与同情心,并共同努力追求更美好未来的愿景,他们可以开始疗愈过去的创伤,为和平与繁荣建立新的基础。 本书怀着希望与团结的精神,作为对争取更公正和平世界的持续斗争的谦逊贡献。这既是对乌克兰和俄罗斯面临巨大挑战的认可,也是对他们人民和社区中巨大变革与更新潜力的认可。 在我们探索新加坡非凡转型的故事及其对当代的启示时,我们怀着深切的尊重和钦佩,敬仰所有在冲突和压迫面前受苦挣扎的人们,并深切支持他们建设更光明、更有希望的未来。 最终,通往持久和平与和解的道路必须由乌克兰和俄罗斯人民亲自一步、日复一日地走过。但这条道路被他人的榜样和经历照亮,被同理心、同情心和理解这一普世人类价值照亮。 愿这本书成为那条道路上的一盏虽小却意义深远的光,提醒人们人类精神的非凡韧性和潜力,也是所有相信对话、合作力量和美好明天承诺者的行动号召。 大卫·霍伊卡 新加坡

HUDA, SABIN

1 files · 729 MB · csvdeclared

AMLNet - Synthetic Anti-Money Laundering Benchmark Dataset, Version 2.0 Relation: Is new version of identifier: 10.5281/zenodo.16736515 AMLNet is a synthetic anti-money laundering benchmark dataset created for machine learning evaluation. This Version 2.0 release is associated with the paper: "AMLNet: A Knowledge-Guided Synthetic Benchmark for Machine Learning Evaluation in Anti-Money Laundering" The dataset contains a fixed synthetic benchmark instance generated using the AMLNet framework. It includes approximately 1.09 million transactions over a 195-day simulation period, from 13 October 2025 to 27 April 2026. The benchmark contains 1,411 suspicious transactions, corresponding to a suspicious rate of approximately 0.13%. The dataset is fully synthetic. The accounts, transactions, timestamps, locations, balances, metadata, labels, and customer activity patterns do not correspond to real customers, real institutions, or real banking activity. AMLNet was designed to support machine learning experiments under rare-event anti-money laundering conditions. The dataset includes ordinary transactions and suspicious transaction sequences representing structuring, layering, and integration patterns. Suspicious activity is embedded within ordinary account activity to make the detection task more realistic and non-trivial. CONTENTS This release includes: - the fixed AMLNet Version 2.0 synthetic transaction dataset; - transaction labels; - laundering typology labels; - transaction metadata; - dataset documentation and column descriptions. Evaluation scripts will be added to this Zenodo record after publication of the associated paper. The AMLNet generator source code is not included in this release due to security concerns. DATA FORMAT The main dataset is provided as a CSV file with the following columns: - step: Sequential simulation step or transaction index. - type: Transaction type, such as BPAY, CASH_OUT, DEBIT, EFTPOS, NPP, OSKO, PAYMENT, or TRANSFER. - amount: Transaction amount in Australian dollars. - category: Transaction category, such as housing, food, transport, recreation, healthcare, education, utilities, shell company, property investment, cryptocurrency, or other. - nameOrig: Originating account or customer identifier. - nameDest: Destination account, customer, or merchant identifier. - oldbalanceOrg: Originating account balance before the transaction. - newbalanceOrig: Originating account balance after the transaction. - isFraud: Binary label used for suspicious/fraudulent transaction detection. - isMoneyLaundering: Binary AML label, where 1 indicates suspicious money laundering activity and 0 indicates ordinary activity. - laundering_typology: Laundering typology label. Values include normal, structuring, layering, and integration. - metadata: JSON-style metadata containing timestamp, location, device information, payment method, risk indicators, and typology-specific details where applicable. - fraud_probability: Model-generated or risk-score field used in selected experiments. This field may be empty for some records. - hour: Hour of transaction. - day_of_week: Day of week. - day_of_month: Day of month. - month: Month number. LABELS The dataset includes two binary label fields: - isFraud: Binary suspicious/fraudulent transaction label. - isMoneyLaundering: Binary anti-money laundering label. The laundering_typology column provides the suspicious activity type for labeled suspicious transactions. Values include: - normal - structuring - layering - integration DATASET STATISTICS - Total transactions: approximately 1.09 million - Suspicious transactions: 1,411 - Suspicious transaction rate: approximately 0.13% - Simulation period: 195 days - Simulation dates: 13 October 2025 to 27 April 2026 - Generated customer accounts: 10,000 - Observed graph nodes: 11,000, including customer and merchant nodes - Transaction types: BPAY, CASH_OUT, DEBIT, EFTPOS, NPP, OSKO, PAYMENT, and TRANSFER - Payment identifiers: BSB_Account, CardNumber, and PayID - Laundering typologies: structuring, layering, and integration - Geographic setting: Australian synthetic banking context SPLIT AND EVALUATION PROTOCOL The associated manuscript describes the split protocol and evaluation protocol used in the reported experiments. For transaction-level experiments, transactions are ordered chronologically to reduce temporal leakage. For node-level graph experiments, the dataset is converted into an account/entity graph, and node features are computed from aggregated transaction statistics. The manuscript describes the chronological train, validation, and test protocol used for model evaluation. Evaluation scripts will be added to this Zenodo record after publication of the associated paper. SYNTHETIC DATA NOTICE This dataset is fully synthetic. It does not contain real customer records, real account information, real banking transactions, real IP addresses, or real financial institution data. All customer identifiers, merchant identifiers, balances, timestamps, locations, transaction patterns, labels, and metadata values were generated for research purposes. The dataset should not be interpreted as a sample of actual banking activity. GENERATOR SOURCE CODE The AMLNet generator source code is not included in this release due to security concerns. Requests for access to the generator source code may be considered for legitimate research purposes, subject to identity verification, institutional affiliation, and appropriate use conditions. The associated manuscript provides the generator pseudocode, main configuration details, demographic assumptions, regulatory constraints, laundering typologies, timing rules, routing rules, split protocol, and evaluation protocol. This public release provides the fixed generated dataset instance used in the associated paper. Evaluation scripts will be added to this Zenodo record after publication. USAGE This dataset can be used for: - anti-money laundering machine learning research; - rare-event classification experiments; - transaction-level suspicious activity detection; - account-level graph classification; - feature ablation studies; - explainability experiments; - synthetic-to-real transfer learning research; - educational and academic research on financial crime detection. LICENSE This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License. You may share and adapt the dataset for non-commercial purposes, provided that appropriate credit is given. Commercial use is not permitted without prior permission. For commercial licensing enquiries, please contact: s.huda@griffith.edu.au, hudasabin@gmail.com CITATION If you use this dataset, please cite the Zenodo dataset record and the associated paper: Huda, S., Foo, E., Newton, M. A. H., Jadidi, Z., Huda, S., Morgan, G., and Sattar, A. AMLNet: A Knowledge-Guided Synthetic Benchmark for Machine Learning Evaluation in Anti-Money Laundering. Under review. Dataset: Huda, S. et al. AMLNet Synthetic Anti-Money Laundering Benchmark Dataset, Version 2.0. Zenodo. 10.5281/zenodo.21237971 CONTACT For questions about the dataset, please contact: Sabin Huda School of Information and Communication Technology Faculty Member, Academy of Excellence in Financial Crime Investigation & Compliance Griffith University Email: s.huda@griffith.edu.au, hudasabin@gmail.com

Mehmetaj, Ilir

6 files · 25 MB · jpeg, pdf, pngdeclared

ABSTRACT - Seraphim Fire Response Ecosystem v1.0 Fire rescue runs on two clocks that have never agreed: an average emergency response of 8 to 14 minutes, and a survivable window of 2 to 4 minutes once a person stands inside an active fire zone. The gap is a material gap - Nomex, the aramid standard of firefighting since the 1970s, chars beyond 260°C, while a wildfire front burns at 600 to 900°C and a firestorm exceeds 1,100°C. For fifty years that difference has been managed with tactics: retreat lines, safety zones, the discipline of staying away. This concept closes it with material, and documents the ground branch of the HAAP platform family - the systems that operate inside the zone every current doctrine writes off. The foundation is one laminate. Seraphim S-100 is a four-layer stack of 35 micrometres and 45 grams per square metre: a diamond-like-carbon outer armour (tetrahedral amorphous carbon class with an oxidation-barrier overcoat, rated for transient direct-flame contact beyond 1,200°C, with sustained-air oxidation above 600°C carried as a named validation parameter), a Gradient Graphene layer whose active thickness of roughly 35 nanometres is a functional requirement of its physics, a Parylene HT dielectric, and a refractory nano-felt backplane. Two mechanisms carry the thermal claim: phonon scattering by engineered ¹²C/¹³C isotope disorder as the load-bearing channel at fire temperature, and the experimentally confirmed violation of the Wiedemann-Franz law in ultraclean graphene at the Dirac point (Nature Physics, 2025) as the laboratory anchor of the electronic channel. A division of labour governs the family: v1 skin rejects, v2 patches harvest - 1.3 to 2 kW of modelled power from the fire's own gradient feeding the coolant pump, the ember-deflection field, and the beacon, stated as a separate and smaller quantity than the 22 kW/m² resistance rating of the stack. Fourteen novel contributions (NC-SFR-1 through NC-SFR-14) document five systems built on that laminate. The AEGIS SUIT states its envelope in the grammar of protective-equipment standards: 600 to 1,000°C operating environment, 300 to 600°C sustained outer surface, 1,200°C peak contact - with an electrostatic Micro-PAML field of 5 to 10 kV/m deflecting embers before contact and a self-powered microfluidic-PCM cooling chain behind the armour. The Seraphim Survival Tent shelters six to eight standing people behind an atmosphere-capable multilayer-insulation wall - ten laminate skins with aerogel interlayers suppressing the gas conduction that defeats spacecraft MLI at ground level - with onboard oxygen, HCN/CO filtration, and an automatic beacon, differentiated point by point from the single-person USFS fire shelter, and deployable four ways, including stationary mounting in fire-extinguisher status for homes and businesses. The Seraphim House Plane is a direct-contact-rated, reusable building envelope in the USD 25,000 class protecting structures of two hundred times that value. The MGU is a single-mission tracked robot delivering one shelter through conditions that exclude aircraft and humans. HAAP 10 is the aircraft that lands inside the burn perimeter on a steam-pre-cooled corridor under automatic limits, with a fixed 60/40 water split reserved for its own protection and the turbine intake-temperature constraint placed openly on the record. A WASP Zone Guardian doctrine and the Prometheus Link - laser power down, biometrics up - bind every suit, tent, robot, and aircraft into one AI-coordinated network. Every load-bearing component exists at TRL 7 to 9 in other industries; the integrated systems stand at TRL 1 to 3. The fastest way forward is a coupon flame test: a laminate sample under metered radiant and direct-flame load, rear-face temperature and harvested watts measured for a few thousand euros, setting the tent wall, the full-scale burn test, and everything above them. A dedicated insurance chapter carries the adoption logic from expected-loss arithmetic to the market re-entry argument, with an instrumented pilot fire season as the underwriting-data instrument. All parameters are theoretical design estimates requiring independent validation. These contributions consolidate and refine three disclosures of March 2026 (two LinkedIn articles, 22 and 24 March; one X publication, 29 March). The fourteen novel contributions are placed on the public record of prior art as of the Zenodo publication date under CC BY-NC-ND 4.0, preventing future patent claims on these specific architectures by any party. Ilir Mehmetaj | Independent Concept Developer | CC BY-NC-ND 4.0 | 2026

Jin, Yinuo · Myers, Joshua · Rajbhandari, Presha · et al.

1 files · 20 GB · zipdeclared

Dataset overview Pre-aligned multi-modal liver dataset for sample NIH_F5 with 2D (single tissue section) and a 3D (serial sections section_01...08 ) variants. Each variant pairs two spatially co-registered modalities - Xenium (spatial transcriptomics / RNA) and DESI (mass-spectrometry imaging / metabolomics), with cross-modal aligned spatial coordinates stored in the SpatialData .zarr stores ( obsm/xenium_map , obsm/desi_map ). data/LYNX_liver_dataset/ ├── NIH_F5_2D/ # single 2D section │ ├── xenium/ # Xenium (RNA) │ │ └── cell_feature_matrix.h5 # cell × gene counts (.h5ad) │ └── DESI/ # DESI (metabolomics) │ ├── NIH_F5.h5 # procesed pixel × ion intensity matrix │ └── NIH_F5.ome.tif # raw ion-image file │ └── NIH_F5_3D/ # serial 3D stack ├── xenium/ │ └── section_{}/ # one Xenium bundle per section │ └── sdata.zarr └── DESI/ └── section_{}_sdata.zarr # aligned DESI SpatialData store per section Formats: .zarr - SpatialData/OME-Zarr stores (images + AnnData tables with aligned spatial / xenium_map / desi_map coordinates); .h5 - cell×gene (Xenium) or pixel×ion (DESI) matrices; .ome.tif - morphology / DESI ion images.

Souza, Caio · Correia, João

54 files · 16 MB · csvdeclared

This repository contains the second version of the metamodel-based rescoring framework for protein-ligand binding affinity prediction. The first version introduced a linear metamodel combined with random train/test splits, but did not explicitly account for structural similarity or information leakage between training, validation, and test partitions. In this updated version, we included: Python scripts for data preprocessing, model training, and evaluation Pairwise ΔΔG evaluation code and associated data Visualization scripts for generating publication figures This dataset and codebase are intended to support reproducibility and further development of metamodel-based rescoring strategies in structural bioinformatics and computational drug design.

Costa, Carlos Fransley Scatambulo

3 files · 614 KB · csv, pdfdeclared

Dados anonimizados referentes ao estudo na tese: Formação Docente em Inteligência Artificial para o Ensino Superior: dos Fundamentos Computacionais ao Uso Crítico e Pedagógico da IA Generativa .

Fernández-Martínez, Carolina · Siddiqui, Muhammad Shuaib · Daza, Vanesa

1 files · 5.9 MB · zipdeclared

Dataset for "A Knowledge-Based Multi-Agent Framework for Security Control Recommendation" Authors : Carolina Fernández-Martínez (i2CAT / UPF), Shuaib Siddiqui (i2CAT), Vanesa Daza (UPF) This contains the dataset and its generating code, as used in Section 3 of the article "A Knowledge-Based Multi-Agent Framework for Security Control Recommendation", published in Elsevier's Knowledge-Based Systems in 2026. It provides a security control selection based on NIST SP 800-53 rev5 extended catalogue, Multi-Agent Influence Diagram, Game Theory and No-Regret-based utilities. Besides the dataset itself, the scripts used to correlate and curate this data from InfoSec and academic sources are provided, along with such sources and the links to their original sources. The main repository for the dataset and its code used in Section 3 as well as the code used in Section 4 can be found in GitHub . Section overview This work is structured as follows: . ├── dataset_analysis.py ├── dataset_contribution.py ├── dataset_curation.py ├── dataset_helpers.py ├── ground_truth │ ├── manual │ │ ├── csftools_stridelm.csv │ │ └── gemini3pro_secdims_impl.csv │ ├── papers │ │ ├── doi_10_1007_impl_control.csv │ │ ├── doi_10_1016_jisa_2025_104056 │ │ │ ├── doi_10_1016_jisa_2025_104056_raw.csv │ │ │ └── generation_scripts │ │ │ ├── controls_summary.xlsx │ │ │ ├── controls.xlsx │ │ │ ├── domain.py │ │ │ ├── groups.xlsx │ │ │ ├── main.py │ │ │ ├── patterns.gml │ │ │ ├── README.md │ │ │ ├── requirements.txt │ │ │ ├── software.xlsx │ │ │ ├── technique.xlsx │ │ │ ├── ttp-control.xlsx │ │ │ ├── ttps.xlsx │ │ │ └── utils.py │ │ ├── doi_10_1093_cybsec_tyaf020_mapping.csv │ │ └── doi_10_1093_cybsec_tyaf020_scores.csv │ └── standards │ ├── Cybersecurity_Framework_v2-0_Concept_Crosswalk_800-53_5_2_0_draft.csv │ └── NIST_SP-800-53_rev5_catalog.json ├── output │ └── dataset_curated.csv └── README.md The ground truth contains both manual mappings, academic papers and InfoSec standardised data: ground_truth : hosts sources used for the data curation. manual : data extracted manually, whether directly checking sources or iteratively requested to an LLM. csftools_stridelm.csv : manually extracted data from CSF tools indicating the contribution of each control subfamily to mitigate a given STRIDE-LM threat. Each value follows a comma-separated format (e.g. "0,2,3,4,8,12") or use -1 if there is no contribution. gemini3pro_secdims_impl.csv : LLM-parsed data from CSF tools , requesting Gemini 3 Pro to extract data on the security control subfamilies: (1) whether these can be SW-implementable, (2) their coverage to the different Security Dimensions and (3) a text-based justification regarding such coverage. papers : doi_10_1007_impl_control.csv : dataset post-processed from that provided by paper with DOI:10.1007/s10664-025-10649-7 . Basically, this CSV assigns numeric codes to the column "Related to implementation-level feature? (yes/no)" from the tab "SP800 53 rev. 3 (technical cont" of the "2) Systematic Review - Security Standards.xlsx" file in that dataset. This can tabke the following values: 0 (if not SW-implementable), 1 (if SW-implementable), -1 (if undefined in the original dataset) or -2 (if the security control subfamily is not even present in the original dataset). doi_10_1016_jisa_2025_104056 : dataset provided by paper with DOI:10.1016/j.jisa.2025.104056 . generation_scripts : minor modifications to the original scripts to generate their dataset. See README.md inside. doi_10_1093_cybsec_tyaf020_mapping.csv : dataset post-processed from that provided by paper with DOI:10.1093/cybsec/tyaf020 . This CSV contains the table from "Appendix A" of the "Appendix A - D.docx". doi_10_1093_cybsec_tyaf020_scores.csv : dataset post-processed from that provided by paper with DOI:10.1093/cybsec/tyaf020 . This CSV contains the table from "Appendix C" of the "Appendix A - D.docx". standards : Cybersecurity_Framework_v2-0_Concept_Crosswalk_800-53_5_2_0_draft.csv : NIST resource that maps CSF 2.0 subcategories to security control subfamilies from SP 800-53 rev5. NIST_SP-800-53_rev5_catalog.json : NIST SP 800-53 rev5 catalogue of security control subfamilies as obtained from the full catalogue in JSON format. The output folder contains the generated, curated dataset by default. Upon running the scripts below, more files will follow. Generating the dataset and ancillary files 1. Curated dataset The curated dataset is generated under "output/dataset_curated.csv" after running the following script. This file is required for the other scripts. python3 dataset_curation.py 2. Summaries, statistics and figures The analysis on the dataset extracts statistics (in .csv and .tex files) and generates figures (in .pdf and .png) from the dataset: output dataset_curated_ciatunp.{csv,tex} : table with number of control families contribute to each Security Dimension. dataset_curated_stridelm.{csv,tex} : table with number of control families contribute to each STRIDE-LM threat. dataset_curated_score_summary.{csv,tex} : statistics for minimum, average, mode, maximum, standard deviation and inter-quartile range per control family. figures : dataset_curated_ciatunp_contribution_implementable_cats_ids.{pdf,png} : distribution of the contribution of SW-implementable control families (axis Z) and subfamilies (axis Y) towards security dimensions (axis X). dataset_curated_ciatunp_contribution_total_cats_ids.{pdf,png} : distribution of the contribution of all kinds of control families (axis Z) and sub families (axis Y) towards security dimensions (axis X). dataset_curated_score_contribution_total_cats_ids.{pdf,png} : distribution of the score (axis X, in deciles) for all kinds of control families (axis Z) and subfamilies (axis Y). dataset_curated_stridelm_contribution_implementable_cats_ids.{pdf,png} : distribution of the contribution of SW-implementable control families (axis Z) and subfamilies (axis Y) towards mitigating types of STRIDE-LM threats (axis X). dataset_curated_stridelm_contribution_total_cats_ids.{pdf,png} : distribution of the contribution of all kinds of control families (axis Z) and subfamilies (axis Y) towards mitigating types of STRIDE-LM threats (axis X). python3 dataset_analysis.py Besides this, the following script quantifies the contribution of the academic datasets and sources used during the process, generating these files: output dataset_curated_score_summary_contribution_ds_imp_{all,top}.tex : table comparing the score of each of the top SW-implementable control subfamilies across the curated dataset ("Total" column) and the datasets used from academic papers (other columns). dataset_curated_score_summary_contribution_ds_tot_{all,top}.tex : table comparing the score of each of the top control subfamilies of all kinds across the curated dataset ("Total" column) and the datasets used from academic papers (other columns). dataset_curated_score_stats_imp_{all,top}.tex : table with statistics on the amount and average score for both all and top SW-implementable control subfamilies. dataset_curated_score_stats_mt0_imp_{all,top}.tex : table with statistics on the amount and average score for both all and the top SW-implementable control subfamilies whose score is more than 0. dataset_curated_score_stats_tot_{all,top}.tex : table with statistics on the amount and average score for both all and top control subfamilies of all kinds. dataset_curated_score_stats_mt0_tot_{all,top}.tex : table with statistics on the amount and average score for both all and the top control subfamilies of all kinds whose score is more than 0. dataset_curated_summary_{all,top}.xlsx : sheet files with multiple tabs to determine grouping and statistical data, such as the score of the top security control subfamilies and the score of their counterparts in the used datasets. Tabs "contribution_ds_tot" and "contribution_ds_imp" are the most relevant, performing these calculation for all kinds and SW-implementable security control subfamilies, respectively. In all cases, the first file considers all control subfamilies, whereas the second considers the top 20 ones. Note that this script has a specific pre-requirement that must be installed to generate the excel file. sudo apt install python3-openpyxl python3 dataset_contribution.py Licence This work is dual-licenced according to the type of resource: Datasets: CC BY-NC 4.0 Code: GNU AGPL 3.0 Funding This work was supported by the grants COALESCE-6G PID2024-163028OB-I00, funded by MICIU/AEI/10.13039/501100011033/FEDER, EU; and AEI-PID2021-128521OB-I00, funded by the Spanish Recovery, Transformation and Resilience Plan through the European Union (Next Generation).

Yuan, Qianmu · Zhu, Mingming

1 files · 456 MB · zipdeclared

This ZIP file contains: 1. The DeltaCata-DB dataset preprocessing scripts and the processed dataset. 2. The source code and the trained model checkpoints of DeltaCata.