Our Work

Circuits, simulation results and scripts for the numerical results of E. Weilandt, T. Peham, R. Wille, "Synthesis of Fault-tolerant State Preparation Circuits using Steane-type Error Detection", Phys.…

Circuits, simulation results and scripts for the numerical results of E. Weilandt, T. Peham, R. Wille, "Synthesis of Fault-tolerant State Preparation Circuits using Steane-type Error Detection", Phys. Rev. A (2026), doi:10.1103/y9ln-b4t9.The record contains the following:- the four state-preparation circuits per code synthesized with fault-set-guided synthesis, for the [[17,1,5]], [[19,1,5]], [[25,1,5]], [[20,2,6]], [[31,1,7]] and [[37,1,7]] codes (OpenQASM 2);- the flag-at-origin baseline circuits used for comparison;- the logical error and acceptance rates from circuit-level Stim simulations;- the lookup-table decoder used for the [[31,1,7]] code;- the numbers of the results table;- scripts to check the table against the files, to regenerate the results figure, and to run the simulations.See README.md for the mapping between files and the paper.The implementation is part of MQT QECC (https://github.com/munich-quantum-toolkit/qecc). Version 2 of this record will add a frozen snapshot of the source code and the circuit-synthesis notebooks.

The endothelial protein C receptor (EPCR) has emerged as a clinically significant biomarker for a distinct subgroup of triple-negative breast cancers (TNBC), where its overexpression is associated wit…

The endothelial protein C receptor (EPCR) has emerged as a clinically significant biomarker for a distinct subgroup of triple-negative breast cancers (TNBC), where its overexpression is associated with stem-like tumour phenotypes and poor prognosis. Rapid and accessible detection of EPCR is therefore important for future diagnostic stratification. In this study, we present a new electrochemical immunosensor for EPCR detection based on a graphene–arginine (G-Arg) nanointerface synthesised via fluorographene chemistry. The G-Arg layer provides abundant carboxyl groups, enabling efficient EDC/Sulfo-NHS-mediated covalent immobilisation of anti-EPCR antibodies and formation of a stable electrochemically active biointerface. The resulting immunosensor exhibited a linear impedimetric response to EPCR over the 49.6–496 ng mL−1 range, with a calculated detection limit of 10.48 ng mL−1, together with good selectivity against non-specific proteins. In addition, the analytical signal remained stable after 30 days of ambient storage, indicating good operational stability. Preliminary experiments further confirmed sensor functionality in human plasma, where a distinguishable response was observed between non-spiked and EPCR-spiked samples. Overall, this work introduces a new G-Arg-based electrochemical platform for EPCR detection and provides a basis for further development of biosensing strategies targeting aggressive breast cancer biomarkers.

Precomputed promoter-background score distributions for the PscanR transcription factor binding motif enrichment package, for the JASPAR 2024 CORE motif collection. The record holds 35 backgrounds: se…

Precomputed promoter-background score distributions for the PscanR transcription factor binding motif enrichment package, for the JASPAR 2024 CORE motif collection. The record holds 35 backgrounds: seven genome assemblies (hg38, hs1, mm10, mm39, dm6, sacCer3, TAIR9) times five promoter windows (200u_50d, 450u_50d, 500u_0d, 950u_50d, 1000u_0d, i.e. bp upstream/downstream of the TSS). Files are named <collection>_<assembly>_<window>_<annotation>.psbg2.txt and are tab-separated tables of per-motif background statistics. SHA256SUMS_J2024.txt lists the SHA-256 checksums.The files are served individually to R users through Bioconductor's ExperimentHub by the PscanRBackgrounds package (https://github.com/Federico77z/PscanRBackgrounds) and read by PscanR (https://github.com/Federico77z/PscanR). The same files, together with the other JASPAR releases, are archived as a single ZIP in record 10.5281/zenodo.21821764.

Precomputed promoter-background score distributions for the PscanR transcription factor binding motif enrichment package, for the JASPAR 2022 CORE motif collection. The record holds 35 backgrounds: se…

Precomputed promoter-background score distributions for the PscanR transcription factor binding motif enrichment package, for the JASPAR 2022 CORE motif collection. The record holds 35 backgrounds: seven genome assemblies (hg38, hs1, mm10, mm39, dm6, sacCer3, TAIR9) times five promoter windows (200u_50d, 450u_50d, 500u_0d, 950u_50d, 1000u_0d, i.e. bp upstream/downstream of the TSS). Files are named <collection>_<assembly>_<window>_<annotation>.psbg2.txt and are tab-separated tables of per-motif background statistics. SHA256SUMS_J2022.txt lists the SHA-256 checksums.The files are served individually to R users through Bioconductor's ExperimentHub by the PscanRBackgrounds package (https://github.com/Federico77z/PscanRBackgrounds) and read by PscanR (https://github.com/Federico77z/PscanR). The same files, together with the other JASPAR releases, are archived as a single ZIP in record 10.5281/zenodo.21821764.

Abstract: The increasing use of online shopping and social media has contributed to a rise in impulsive buying behaviour among teenagers, with peer influence playing a significant role in shaping pur…

Abstract: The increasing use of online shopping and social media has contributed to a rise in impulsive buying behaviour among teenagers, with peer influence playing a significant role in shaping purchasing decisions. This study examined the relationship between peer influence and impulsive buying behaviour among Maldivian teenagers and determined whether peer influence significantly predicts impulsive buying behaviour. A quantitative cross-sectional research design was employed, and data were collected from 255 teenagers using a structured questionnaire. Data were analysed using descriptive statistics, Pearson's correlation, and simple linear regression. The findings revealed a significant moderate positive relationship between peer influence and impulsive buying behaviour (r = 0.523, p < 0.001). Regression analysis showed that peer influence significantly predicted impulsive buying behaviour (β = 0.523, p < 0.001), explaining 27.4% of the variance (R² = 0.274). These results indicate that teenagers who experience greater peer influence are more likely to make spontaneous and unplanned purchasing decisions. The findings support the Stimulus–Organism–Response (S-O-R) Theory and contribute to the limited literature on adolescent consumer behaviour in the Maldives. The study highlights the importance of promoting financial literacy and awareness of peer influence to encourage responsible purchasing behaviour among teenagers.

Precomputed promoter-background score distributions for the PscanR transcription factor binding motif enrichment package, for the JASPAR 2020 CORE motif collection. The record holds 35 backgrounds: se…

Precomputed promoter-background score distributions for the PscanR transcription factor binding motif enrichment package, for the JASPAR 2020 CORE motif collection. The record holds 35 backgrounds: seven genome assemblies (hg38, hs1, mm10, mm39, dm6, sacCer3, TAIR9) times five promoter windows (200u_50d, 450u_50d, 500u_0d, 950u_50d, 1000u_0d, i.e. bp upstream/downstream of the TSS). Files are named <collection>_<assembly>_<window>_<annotation>.psbg2.txt and are tab-separated tables of per-motif background statistics. SHA256SUMS_J2020.txt lists the SHA-256 checksums.The files are served individually to R users through Bioconductor's ExperimentHub by the PscanRBackgrounds package (https://github.com/Federico77z/PscanRBackgrounds) and read by PscanR (https://github.com/Federico77z/PscanR). The same files, together with the other JASPAR releases, are archived as a single ZIP in record 10.5281/zenodo.21821764.

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23183692. Thank you for this paper. You ran five models thro…

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23183692. Thank you for this paper. You ran five models through three production harnesses (Claude Code, mini-SWE-agent and OpenCode) on SWE-bench Verified, and reran the same configurations to see how much a score moves when nothing changes but the run (Section 3). On the 45 hardest tasks, swapping the harness flips 13% of tasks - the same as rerunning the same harness (Section 4.2). The one effect that clears the noise is a loss, with OpenCode trailing by up to 9 points (Section 4.3). And cost per task differs by up to 3x, set mostly by the preamble each harness pays again on every step (16,581 tokens in Claude Code, 829 in mini-SWE-agent) times the number of steps (Section 4.4, Table A4). I liked the recommendation in Section 5 to report a second run of the same cell next to any harness gap. I read it as a practitioner who runs coding agents every day. In my company the software development is done by AI agents, so maybe my practical side is useful here. Four comments. 1. On the bill - Section 4.4 says 96 to 99% of input tokens are cache hits, and the cache price scales the bill but does not reorder it. In my own 722 agent sessions (DOI 10.5281/zenodo.22759216) more than 85% of the modeled cost was work with context, meaning cache reads plus cache writes. These ran on a subscription, so the cost comes from a price list (the same caveat as your Opus column, Table A5). In a separate set of 168 sessions, 94.4% of billed tokens were re-reading context already sent (DOI 10.5281/zenodo.22712985) - a share of tokens, not of money. Where a provider prices cache writes separately, could you report cache-write tokens as a fourth line, and mark which steps followed a compaction (a compaction rewrites the cached prefix)? My IETF Internet-Draft (draft-arsentev-agent-run-metrics-00, an individual draft, not a standard) proposes a common format for agent run cost records, and your three numbers - first call, growth per step, step count - are what such a record should carry. 2. On compaction - Claude Code compacts in a fifth to a third of its trials (Section 4.4). On the Qwen arms its window was set to 110k, but on the vendor arms it kept the default and compacted much later, at a median of 168k prompt tokens on GLM-5.3-Flash (Table A1, Table A4 note d). In my own experiment (36 coding-agent runs, six context-clearing policies with six repeats each, DOI 10.5281/zenodo.22759217), clearing the context after every task cost about a third more (+33.5%) than clearing every three tasks. So for me, when to compact is a cost setting of its own. Could you show the cost per trial split by trials with and without a compaction? 3. On the runs where the model stopped on its own - under OpenCode the model stops sooner: on Qwen3.6-35B-A3B over the pool, a median of 28 model calls against 71 under mini-SWE-agent and 55 under Claude Code, and the data don't say why (Section 4.3). In my guides I write that every task needs its own check the agent can run. Without it, "looks done" is the only signal the agent has, and a model sounds equally sure when it is right and when it is wrong. So I wonder about the failures where the model stopped on its own (Figure 3). From the tool-call sequences, did the agent run the repository's tests before it stopped? Splitting these failures by whether any test was run might show part of what makes the model stop sooner under OpenCode. 4. On reruns and the data - I agree with the second-run recommendation in Section 5. In my 36-run experiment every policy had six repeats, and the run data is public on Hugging Face (DOI 10.57967/hf/10366), so anyone can recompute it. The trajectories will be released with the paper (Reproducibility statement). Could you deposit them with a DOI and include per-step token counts split by cache hit, miss and output? Then readers could reprice the bill under other price lists, as you did yourselves in Appendix G. Thank you for a clear and useful paper. Competing interests Yes: the text cites the author's own technical reports (DOI 10.5281/zenodo.22759216, 10.5281/zenodo.22759217, 10.5281/zenodo.22712985), dataset (DOI 10.57967/hf/10366) and IETF Internet-Draft draft-arsentev-agent-run-metrics-00; no connection to the article's authors. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.

Аннотация. Тезисда умумий ўрта таълим мактаблари ва Касбий таълим агентлиги ўртасидаги ҳамкорликнинг электрон портфолио механизми&…

Аннотация. Тезисда умумий ўрта таълим мактаблари ва Касбий таълим агентлиги ўртасидаги ҳамкорликнинг электрон портфолио механизми ишлаб чиқилган. Электрон портфолио ўқувчининг 7-синфдан 9-синфгача бўлган касбий ривожланиши тўғрисидаги барча маълумотларни тизимли тўплаш, сақлаш ва Агентликка узатиш имконини беради. Тадқиқотда ISO-21001 стандартининг “ўзаро манфаатли ҳамкорлик” ва “жараёнли ёндашув” тамойилларига асосланилган. Тажриба-синов ишлари 6 та мактабда, 384 нафар респондент иштирокида ўтказилиб, тажриба гуруҳида электрон портфолио тўлиқ юритилди (100%), назорат гуруҳида эса тизимсиз юритилди (38%). Натижалар электрон портфолио механизмининг амалий самарадорлигини тасдиқлади (p<0,05).

This article examines the theoretical foundations and pedagogical possibilities of using a retrospective approach to develop students’ professional competence. Retrospection is understood as a p…

This article examines the theoretical foundations and pedagogical possibilities of using a retrospective approach to develop students’ professional competence. Retrospection is understood as a purposeful return to prior experience, knowledge, cultural resources, and professional practice in order to interpret them in the light of present tasks and future action. Drawing on the ideas of Dewey, Schön, Kolb, Gibbs, and competence-oriented education, the article argues that retrospective learning can connect theory with practice, strengthen reflective judgment, support professional identity, and make cultural knowledge educationally meaningful. In the context of music education, the evolutionary history of Karakalpak music offers material for listening, analysis, performance, comparison, and pedagogical design. A practical sequence is proposed: encounter a professional or cultural experience, document it, analyze it, connect it with concepts, redesign an action, and evaluate learning. The article is theoretical and does not report experimental results; its proposed mechanisms can inform the design of subsequent empirical research.

This study assessed the influence of the home learning environment on the school readiness of kindergarten learners. Specifically, it described the profile, determined the level of the home learning e…

This study assessed the influence of the home learning environment on the school readiness of kindergarten learners. Specifically, it described the profile, determined the level of the home learning environment, assessed the level of school readiness, and examined the significant relationship between the two variables. The study employed a descriptive correlational research design. From a population of 219 kindergarten learners, 142 parents or guardians were selected through simple random sampling, with the sample size determined using Slovin’s formula at a 0.05 margin of error. Four kindergarten teachers assessed the school readiness of the learners. Data were gathered using an adapted structured questionnaire for parents or guardians and a school readiness checklist completed by teachers. Frequency, percentage, mean, standard deviation, and Spearman rho correlation were used to analyze the data. Findings revealed that the home learning environment was rated Often, indicating that learning support was provided at home. Indoor learning activities obtained the highest rating, while digital learning activities received the lowest. School readiness was rated Always, showing that learners were prepared in social competence, learning dispositions, classroom rules, and cognitive and communication skills. Cognitive and communication skills obtained the highest rating. A significant positive moderate relationship was found between the home learning environment and school readiness, while digital learning activities were not significantly related to school readiness. Based on the findings, an action plan was proposed to strengthen home learning support, guided digital use, warmth and support, outdoor learning experiences, and home-school collaboration.