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This work addresses the limitations of traditional probability geometry by incorporating a dynamic structural adjustment component, offering enhanced computational and optimization capabilities."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.22205138","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-08-31T09:56:16Z","registered":"2026-08-31T09:56:16Z","published":null,"updated":"2026-09-07T13:07:21Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19856650","type":"dois","attributes":{"doi":"10.5281/zenodo.19856650","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Neuchâtel"],"givenName":"Arnaud","familyName":"Chanex","name":"Chanex, Arnaud","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0001-6948-8222"}]}],"titles":[{"title":"The ecological potential of snow-making reservoirs for amphibians in the Swiss Alps"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Snow-making reservoirs"},{"subject":"Amphibian"},{"subject":"Artificial habitats"},{"subject":"Alps"}],"contributors":[{"nameType":"Personal","affiliation":["University of Zurich","info fauna karch"],"givenName":"Benedikt R.","familyName":"Schmidt","name":"Schmidt, Benedikt R.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-4023-1001"}],"contributorType":"Supervisor"},{"nameType":"Personal","affiliation":["University of Neuchâtel"],"givenName":"Delphine Clara","familyName":"Zemp","name":"Zemp, Delphine Clara","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-2239-2995"}],"contributorType":"Supervisor"}],"dates":[{"date":"2026-05","dateType":"Issued"},{"date":"2025-06/2025-08","dateType":"Collected","dateInformation":"Data collection"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19856651","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"V1.0.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset and associated R code supplement the Master's thesis \"The Ecological Potential of Snow-Making Reservoirs for Amphibians in the Swiss Alps\" (University of Neuchâtel, 2026). 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Traditional software development methodologies often struggle with complexity, adaptability, and optimization. We propose a novel approach leveraging MAL, where individual software components are treated as intelligent agents, capable of interacting and learning collaboratively. This system dynamically evolves and optimizes software through the strategic interactions and knowledge sharing among these agents. The core mechanism involves utilizing multi-agent learning algorithms to facilitate intelligent coordination and adaptation within the software ecosystem. We present a framework for designing and training such systems, focusing on the key aspects of agent representation, communication protocols, and learning strategies. The potential of this approach to automate software development, enhance adaptability, and improve overall system performance is explored. Experimental results (simulated) demonstrate the effectiveness of the proposed framework in achieving desired software evolution goals. This work contributes a new paradigm for software development, shifting from centralized control to a decentralized, adaptive, and self-organizing system."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.22334198","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-09-05T07:56:49Z","registered":"2026-09-05T07:56:49Z","published":null,"updated":"2026-09-07T13:07:10Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.10291994","type":"dois","attributes":{"doi":"10.5281/zenodo.10291994","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["none"],"familyName":"mohamedkhalifa","name":"mohamedkhalifa","nameIdentifiers":[]}],"titles":[{"title":"Tombstone in Egypt Cairo"}],"publisher":"Zenodo","container":{},"publicationYear":2022,"subjects":[],"contributors":[],"dates":[{"date":"2022-03-17","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.10291995","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Tombstone in Egypt CairoCAIR\n\nSource: Objaverse 1.0 / Sketchfab"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.10291994","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":1,"created":"2023-12-28T12:42:01Z","registered":"2023-12-28T12:42:02Z","published":null,"updated":"2026-09-07T13:07:10Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.22220898","type":"dois","attributes":{"doi":"10.5281/zenodo.22220898","identifiers":[],"creators":[{"nameType":"Personal","givenName":"Jincheng","familyName":"Zhang","name":"Zhang, Jincheng","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Brazilian Big-Eyed Bat Inspired Metaheuristic Algorithm for Complex Optimization Problems"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-09-01","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.22220899","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This paper proposes a novel metaheuristic algorithm, termed \"Bat-Inspired Adaptive Search (BIAS),\" inspired by the foraging behavior of the Brazilian big-eyed bat ( *Pteropedes brasilianus*). These bats exhibit a unique aerial hunting strategy characterized by rapid dives, precise adjustments based on echo location, and a collective search pattern. BIAS leverages these characteristics to develop an optimization algorithm suitable for tackling complex, non-linear optimization problems. The algorithm incorporates key elements of bat behavior, including an intrinsic motivation parameter (representing the bat's desire to search), an extrinsic motivation parameter (reflecting the attractiveness of potential solutions), and an adaptive adjustment mechanism mimicking the bats' directional adjustments during flight. Mathematical formulations describing the core components of BIAS, including the intrinsic and extrinsic motivation functions, and the adaptive adjustment mechanism, are presented. The algorithm's convergence properties are discussed, and preliminary simulation results demonstrate its effectiveness in solving benchmark optimization problems, showcasing its potential as a robust and efficient metaheuristic approach. The algorithm's performance is evaluated based on metrics such as solution accuracy, convergence speed, and computational complexity."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.22220898","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-09-01T03:50:02Z","registered":"2026-09-01T03:50:02Z","published":null,"updated":"2026-09-07T13:07:09Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.22220640","type":"dois","attributes":{"doi":"10.5281/zenodo.22220640","identifiers":[],"creators":[{"nameType":"Personal","givenName":"Jincheng","familyName":"Zhang","name":"Zhang, Jincheng","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Brandt's Bat: A Metaheuristic Algorithm Inspired by Echolocation"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-09-01","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.22220641","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This paper presents Brandt's Bat, a novel metaheuristic algorithm inspired by the echolocation behavior of bats. The algorithm leverages the principles of bat sonar, utilizing a population of bats and a dynamic landscape to explore and optimize solutions. The core of Brandt's Bat lies in its \"frequency-modulated\" and \"phase-modulated\" parameters, which govern bat movement and decision-making. These parameters are dynamically adjusted based on the fitness of the solutions in the population. The algorithm employs a circular search space and a stochastic update mechanism to navigate the solution space effectively. The performance of Brandt's Bat is evaluated on several benchmark optimization problems, demonstrating its effectiveness in finding near-optimal solutions within reasonable computational time. The algorithm's parameters are carefully tuned to balance exploration and exploitation, leading to robust performance across a range of problem types. The key innovations of Brandt's Bat include the dynamic adjustment of modulation frequencies and phases, and the utilization of a circular search space to mimic the bat's continuous monitoring of its environment. The results indicate that Brandt's Bat presents a promising alternative to established metaheuristic algorithms, particularly in scenarios where rapid exploration and adaptation are crucial."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.22220640","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-09-01T03:43:26Z","registered":"2026-09-01T03:43:26Z","published":null,"updated":"2026-09-07T13:07:09Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19697924","type":"dois","attributes":{"doi":"10.5281/zenodo.19697924","identifiers":[{"identifier":"oai:zenodo.org:19697924","identifierType":"oai"}],"creators":[{"nameType":"Personal","givenName":"Uranía","familyName":"Lavín","name":"Lavín, 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Begoña","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0001-6172-0528"}],"affiliation":[]},{"nameType":"Personal","givenName":"Bárbara","familyName":"Larraín","name":"Larraín, Bárbara","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-0494-7707"}],"affiliation":[]}],"titles":[{"title":"Hacia la restauración de bancos de choritos en el Fiordo de Reloncaví, resultados de un taller participativo en Río Puelo"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-06-25","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"Report","resourceTypeGeneral":"Report","citeproc":"report","bibtex":"misc","ris":"RRPT","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19697923","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Este documento reúne las principales reflexiones, aprendizajes y resultados del taller “Co-produciendo conocimiento para restaurar bancos de choritos y reimaginar el bienestar costero en el Fiordo de Reloncaví”, realizado en la localidad de Río Puelo, comuna de Cochamó, Región de Los Lagos.\n\nEl encuentro convocó a representantes del sector público, la academia y la mitilicultura, en un espacio de diálogo e intercambio multiactor orientado a abordar los desafíos y oportunidades para la restauración de bancos naturales de mitílidos y la sustentabilidad del Fiordo de Reloncaví.\n\nMediante herramientas de mapeo participativo, trabajo grupal y sesiones plenarias, se identificaron percepciones, problemáticas, oportunidades y prioridades territoriales vinculadas al uso del estuario, la disminución de bancos naturales de choritos y las posibilidades de avanzar hacia iniciativas de restauración socioecológica basadas en la colaboración entre actores locales, sector productivo, instituciones públicas e investigadores.\n\nLos resultados presentados en este documento contribuyen a la construcción de una agenda colaborativa para la conservación y restauración del fiordo, fortaleciendo las bases para futuras acciones de monitoreo, investigación aplicada y restauración ecológica en el territorio."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"Crossref Funder ID","funderName":"Agencia Nacional de Investigación y Desarrollo","funderIdentifier":"10.13039/501100002848","awardTitle":"Anillos","awardNumber":"ACT240004"},{"funderIdentifierType":"Crossref Funder ID","funderName":"Agencia Nacional de Investigación y Desarrollo","funderIdentifier":"10.13039/501100002848","awardTitle":"Fondef","awardNumber":"ID24i10031"},{"funderIdentifierType":"Crossref Funder ID","funderName":"Agencia Nacional de Investigación y Desarrollo","funderIdentifier":"10.13039/501100002848","awardTitle":"Fondecyt","awardNumber":"1221322"},{"funderIdentifierType":"Crossref Funder ID","funderName":"Agencia Nacional de Investigación y 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The core idea centers around creating adaptive neural network architectures that dynamically adjust their structure based on internal state feedback and environmental information. We introduce a reinforcement learning-based algorithm to train a neural network to optimize its architecture. This algorithm adjusts neural connection weights, neural number, and the allocation of computational modules based on task demands and resource constraints. The key innovation lies in decoupling architecture design from fixed hardware, enabling truly adaptive optimization within a non-Von Neumann framework. This approach offers potential improvements in efficiency, flexibility, and performance for neural networks across a wide range of applications. The presented system demonstrates a significant departure from static architecture designs, paving the way for more intelligent and responsive neural network systems."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.22223006","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-09-01T04:39:15Z","registered":"2026-09-01T04:39:15Z","published":null,"updated":"2026-09-07T13:07:08Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}}],"meta":{"total":134277764,"totalPages":400,"page":1},"links":{"self":"https://api.datacite.org/dois/","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25"}}