ethicalabs/Research-Intent-Collab huggingface dataset:
AI apology: interactive multi-objective reinforcement learning for human-aligned AI

AbstractFor an Artificially Intelligent (AI) system to maintain alignment between human desires and its behaviour, it is important that the AI account for human preferences. This paper proposes and empirically evaluates the first approach to aligning agent behaviour to human preference via an apologetic framework. In practice, an apology may consist of an acknowledgement, an explanation and an intention for the improvement of future behaviour. We propose that such an apology, provided in response to recognition of undesirable behaviour, is one way in which an AI agent may both be transparent and trustworthy to a human user. Furthermore, that behavioural adaptation as part of apology is a viable approach to correct against undesirable behaviours. The Act-Assess-Apologise framework potentially could address both the practical and social needs of a human user, to recognise and make reparations against prior undesirable behaviour and adjust for the future. Applied to a dual-auxiliary impact minimisation problem, the apologetic agent had a near perfect determination and apology provision accuracy in several non-trivial configurations. The agent subsequently demonstrated behaviour alignment with success that included up to complete avoidance of the impacts described by these objectives in some scenarios.
Metagenomic Analysis of the Saliva Microbiome with Merlin

In recent years, metagenomics has demonstrated to play an essential role on the study of the microorganisms that live in microbial communities, particularly those who inhabit the human body. Several bioinformatics tools and pipelines have been developed for the analysis of these data, but they usually only address one topic: to identify the taxonomic composition or to address the metabolic functional profile. This work aimed to implement a computational framework able to answer the two questions simultaneously. Merlin, a previously released software aiming at the reconstruction of genome-scale metabolic models for single organisms, was extended to deal with metagenomics data. It has an user-friendly and intuitive interface, being suitable for those with limited bioinformatics skills. The performance of the tool was evaluated with samples from the Human Microbiome Project, particularly from saliva. Overall, the results show the same patterns reported before: while the pathways needed for microbial life remain relatively stable, the community composition varies extensively among individuals.
The Lived Experiences of Female Relatives of Child Sexual Abuse Material (CSAM) Offenders in Ireland and the United Kingdom

There is a limited understanding about how an association with those that download Child Sexual Abuse Material (CSAM), a highly stigmatized crime, impacts the lives of their innocent family members. Non-offending family members are often considered a valuable protective resource for offender desistance and in safeguarding children from abuse. Therefore, the present study aimed to explore the lived experiences of female family members of CSAM offenders in Ireland and the United Kingdom to both identify and target areas for intervention thus ameliorating their ability to protect. A qualitative research design was adopted, and data analyzed via reflexive thematic analysis. Fifteen individuals self-selected for participation and interviews resulted in the identification of three key themes: Shattered Worldview, The Injured Self; Contamination by Association. The analysis highlighted how non-offending family members experienced considerable shame, trauma, and stigma with consequences that reached into every aspect of their lives. The findings are discussed in the context of the limited available literature along with research implications and recommendations for both policy and practice.
Developing Frameworks to Understand Disaster Causation: From Forensic Disaster Investigation to Risk Root Cause Analysis

The Sendai Framework for Disaster Risk Reduction 2015–2030 calls for science to support policy move toward more holistic solutions to disaster risk. This paper outlines an original framework to promote inter-disciplinary research into disaster causation, identifying the basis for holistic solutions. The PEARL Risk Root Cause Analysis framework responds to limits identified in the established FORensic INvestigations of disasters (FORIN) approach to root cause analysis. The paper documents a systematic review of the FORIN approach as a starting point for the development of the PEARL framework. The proposed PEARL framework offers a broad and adaptable conceptual, methodological and practical approach. In particular, we demonstrate the centrality of governance, including the role of disaster risk management in risk creation, of bringing historical insights into contemporary and future scenarios planning and of integrating research methods. These core elements can assist in repositioning science to better support the goals of the Sendai Framework.
Annotated Dataset for Training Cloud Segmentation Neural Networks Using High-Resolution Satellite Remote Sensing Imagery

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this task has been hindered by the scarcity of specialized datasets and annotation tools. This study addresses this challenge by introducing CloudLabel, a semi-automatic annotation technique leveraging region growing and morphological algorithms including flood fill, connected components, and guided filter. CloudLabel v1.0 streamlines the annotation process for high-resolution satellite images, thereby addressing the limitations of existing annotation platforms which are not specifically adapted to cloud segmentation, and enabling the efficient creation of high-quality cloud segmentation datasets. Notably, we have curated the Annotated Dataset for Training Cloud Segmentation (ADTCS) comprising 32,065 images (512 × 512) for cloud segmentation based on CloudLabel. The ADTCS dataset facilitates algorithmic advancement in cloud segmentation, characterized by uniform cloud coverage distribution and high image entropy (mainly 5–7). These features enable deep learning models to capture comprehensive cloud characteristics, enhancing recognition accuracy and reducing ground object misclassification. This contribution significantly advances remote sensing applications and cloud segmentation algorithms.
Reverse Logistics and Inventory Management as critical issues for Supply Chain Sustainability

The authors deal with the problem of sustainability within supply chains considering as critical aspects, both in terms of preservation of limited available resources and ensuring higher profitability, the inventory management in the case of reverse flow of products (reverse logistics). The most important deterministic and stochastic inventory models in the reverse logistics field are briefly presented. A supply chain conceptual model with products return (double options remanufacturing and disposal) including twodifferent inventory control policies is considered. The inventory control policies sustainability in terms of total cost (including inventory costs, remanufacturing costs and disposal costs) is finally achieved by adapting and using the GreenLogistics simulator (Bruzzone et al. 2009).
Simulating a Quantum Computer : Grover's Search Algorithm with Error Correction

Classical simulations of quantum computers give us an insight into various things such as what quantum algorithms can achieve, whether it is possible to verify that they function as postulated, the difficulties that have to be overcome before quantum computers can be realized, as well as how we can handle these difficulties. We build a Python library to simulate a quantum computer that can perform all quantum algorithms by defining an universal gate set, from which all quantum gates – and thereby all circuits – can be constructed. We implement two algorithms that both highlight two important aspects of quantum computing: Grover’s quantum search algorithm, which demonstrates the efficiency of quantum algorithms and their superiority over their classical counterparts by searching an unsorted list quadratically faster; and an error correcting code, the Shor code, which highlights the cost of correcting the possible errors in a quantum computer.We test the rigidity of Grover’s algorithm by introducing errors without correction, and find that the algorithm shows resilience to smaller 1-qubit Pauli errors, but looses its efficiency under larger errors and thus the need for Pauli error correction arise.
A deep search for the host galaxies of gamma-ray bursts with no detected optical afterglow

Methods. Our study is based on deep RC and K s-band observations performed with FORS1, FORS2, VIMOS, ISAAC, and HAWK-I at the ESO/VLT, partly supported by observations with the seven-channel imager GROND at the 2.2-m telescope on La Silla, and supplemented by observations with NEWFIRM at the 4-m telescope on Kitt Peak. To be conservative, we searched for host galaxy candidates within an area of twice the radius of each associated 90% c.l. Swift/XRT error circle.
Sustainable Agricultural Water Managment to Address Food Security and Social Instability in Pakistan

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Reliability improvement of modern aircraft engine through failure modes and effects analysis of rotor support system

PurposeThe purpose of this analysis was to attempt to improve the reliability of a rotor support system of a modern aircraft engine.Design/methodology/approachThe process used for carrying out FMEA is specified by MIL‐STD‐1629A procedure for carrying out failure mode, effects and criticality analysis.FindingsIn increasing demand in the avionics sector, particularly in modern defence and civil aircraft, safety and reliability are the prime concerns to complete the mission successfully. Technocrats are made to rethink the safety of complete systems by adding redundancy to the critical activities. A rotor support system (RSS) is an integral part of a gas turbine engine used in any aircraft. As its name implies, the rotor support system shares the load of the rotating component of an engine, hence the rotor support system plays a vital role in any aircraft engine. It shares the load of compressor rotor and stator, turbine rotor and stator, inter‐casing, and exhaust system of a gas turbine engine. Any failure in such a system may make the entire aircraft fail. Therefore it is worth carrying out Failure Modes and Effects Analysis (FMEA) on such a critical system. FMEA is one of the effective reliability assessment tools, which evaluate systematically and document the potential failure modes of a system or equipment and their causes. It helps in grading the severity of all potential failure modes and is useful in carrying out the changes in the early phase of design. The analysis starts with the potential failure of the smallest component at the final indenture and goes up to the initial indenture level.Originality/valueThe paper adds insight into the reliability improvement of the rotor support system of modern aircraft.
Research and innovation as tools for sustainable agriculture, food and nutrition security : extended abstracts and papers

After the success of the 1st edition of Montpellier in 2016, CIHEAM organized the 2nd edition of the Mediterranean initiative for PhD students and young researchers (MedForum2018) at CIHEAM Bari (Italy) from 18 to 20 September 2018. MedForum2018, entitled “Research and Innovation: Tools for Sustainable Agriculture and Food Security”, focused on the CIHEAM Strategic Agenda 2025 (CAPMED2025) and its Action Plan developed for the implementation of the United-Nations 2030 Agenda for Sustainable Development, in specific fields across the Mediterranean area. Through research work of PhD students and young researchers, MedForum2018 aimed at: - Fostering entrepreneurship, women and youth employment; - Identifying and sharing potential innovation tools geared towards integrated solutions for sustainable agriculture, food and nutrition security; - Enhancing research and innovation impact at national and regional level; - Proposing alternatives to prevent young researchers’ migrations. MedForum2018 was organized by CIHEAM in partnership with MAECI-Italy (Ministry of Foreign Affairs and International Cooperation), CMI (Center for Mediterranean Integration of the World Bank), UfM (Union for the Mediterranean), Agreenium (Institut Agronomique, Vétérinaire et Forestier de France) and with the technical collaboration of the Food and Agriculture Organization of the United Nations (FAO).
Modelling Potential Impacts of Climate Change on Groundwater of the Gaza Coastal Aquifer from Ensemble of Global Climate Model Projections

PUBLISHED
Detection of the Power Spectrum of Cosmic Microwave Background Lensing by the Atacama Cosmology Telescope

WOS
Dark Energy Survey: 2.1% measurement of the Baryon Acoustic Oscillation scale from the final dataset

Here, we present the angular diameter distance measurement obtained from the measurement of the Baryonic Acoustic Oscillation (BAO) feature using the completed Dark Energy Survey (DES) data, summarizing the main results of [Phys. Rev. D 110, 063514] and [Phys. Rev. D 110, 063515]. We use a galaxy sample optimized for BAO science in the redshift range 0.6 < z < 1.2, with an effective redshift of $z_{\rm eff}$ = 0.85. Our consensus measurement constrains the ratio of the angular distance to the sound horizon scale to $D_M(z_{\rm eff})/r_d$ = 19.51 $\pm$ 0.41. This measurement is found to be 2.13$σ$ below the angular BAO scale predicted by Planck. To date, it represents the most precise measurement from purely photometric data, and the most precise from any Stage-III experiment at such high redshift. The analysis was performed blinded to the BAO position and is shown to be robust against analysis choices, data removal, redshift calibrations and observational systematics.
Developing Rail Safety Competencies Based on Accident and Incident Investigations: Using Root Cause Taxonomies to Learn from Accidents

Bilgi çağında; bilginin örgütsel başarının temel belirleyici faktörlerinden biri haline gelmesi ile birlikte, "Örgütsel Emniyet" kavramı da giderek bilgi yönetim sistemlerine daha bağımlı hale gelmektedir. Bir yönetim süreci olarak Bilgi Yönetimi, örgütsel bilginin elde edilmesi, geliştirilmesi, paylaşılması ve etkin biçimde kullanılması ile ilgili faaliyetleri kapsar. Örgütsel etkinliğin sağlanabilmesi için bilgi yönetimi yaklaşımları, örgütsel hafıza ve örgütsel öğrenme sistemleri birbiriyle etkileşim halindedir. Bu çalışmada kaza ve olay araştırma yaklaşımlarını ve "Emniyet Yönetimi" kavramını "Örgütsel Emniyet Stratejileri", "Örgütsel Emniyet Yetkinlikleri" ve "Bilgi Yönetim Sistemleri" açısından yazında mevcut araştırmalar ışığında inceledik.
Informed Dashboard Designs for Microgrid Electricity Market Operators

Whilst many energy companies provide dashboard interfaces for consumer data analysis; there are very few examples of microgrid, grid-connected or fringe-of-grid monitoring systems. In this work, we report on a thorough investigation of existing data visualisation and dashboard interfaces, their target users and typical content. These findings, together with the context and needs of a large university campus microgrid help to inform our prototype dashboard designs for microgrid market operators.
Single-cell protein analysis by mass-spectrometry

Human physiology and pathology arise from the coordinated interactions of diverse single cells. However, analyzing single cells has been limited by the low sensitivity and throughput of analytical methods. DNA sequencing has recently made such analysis feasible for nucleic acids, but single-cell protein analysis remains limited. Mass-spectrometry is the most powerful method for protein analysis, but its application to single cells faces three major challenges: Efficiently delivering proteins/peptides to MS detectors, identifying their sequences, and scaling the analysis to many thousands of single cells. These challenges have motivated corresponding solutions, including SCoPE-design multiplexing and clean, automated, and miniaturized sample preparation. Synergistically applied, these solutions enable quantifying thousands of proteins across many single cells and establish a solid foundation for further advances. Building upon this foundation, the SCoPE concept will enable analyzing subcellular organelles and post-translational modifications while increases in multiplexing capabilities will increase the throughput and decrease cost.
Digital Surveillance and Privacy in Smart Campuses: A Policy Framework for Balancing Security and Fundamental Rights in Indian Higher Education.

In this contemporary era rapidly increasing digital transformation of higher education institutions in India has accelerated the development of “smart campuses,” where technologies such as biometric attendance systems, CCTV surveillance, facial recognition, data analytics, and artificial intelligence are increasingly integrated into administrative and academic processes. While these innovations promise enhanced security, efficient governance, and improved campus management, they simultaneously raise significant concerns regarding the protection of individual privacy and fundamental rights of students and faculty. The expansion of digital surveillance within educational spaces has created a complex tension between institutional security measures and the constitutional guarantee of privacy recognized under Article 21 of the Constitution of India following the landmark judgment in Justice K. S. Puttaswamy (Retd.) v. Union of India. This article critically examines the emerging landscape of digital surveillance in Indian higher education institutions and evaluates its implications for the right to privacy, informational autonomy, and academic freedom. It indicates the regulatory gaps in existing legal and policy frameworks governing the collection, storage, and use of personal data within educational environments. Particular attention is given to the policy challenges associated with biometric monitoring, AI-driven proctoring systems, and large-scale data management practices in universities. This article further analyzes how the vision of technologically advanced campuses promoted through the National Education Policy 2020 may inadvertently intensify privacy vulnerabilities in the absence of clear accountability and ethical safeguards. This article Adopting a doctrinal and policy-oriented research approach, the paper proposes a comprehensive framework aimed at harmonizing technological innovation with constitutional protections. It advocates the establishment of institutional data governance mechanisms, transparent surveillance policies, informed consent standards, and independent oversight structures to ensure that digital monitoring practices remain proportionate, necessary, and legally justified. By situating the discussion within the broader discourse of digital rights and educational governance, the study seeks to contribute to the formulation of balanced policy guidelines that protect individual liberties while enabling safe and technologically progressive learning environments in Indian higher education.
Who Appoints Judges? Judicial Independence and Democratisation of the Judiciary in India

Ueda analyses political relations between the Indian government and the Supreme Court of India over judicial appointments, and explains the judicial system reforms that have occurred under the Manmohan Singh and Narendra Modi governments from the point of view of judicial democratisation. The Modi government sought to achieve greater democratic control over the courts and make the judiciary less independent by setting up a commission to administer judicial appointments to higher courts that included a member selected from groups experiencing social discrimination. The commission can be viewed as a ‘contact zone’ between institutional democratisation of the judiciary from above and democratic institutionalisation of human rights from below. Whether institutional democratisation of judicial appointment from above eventually crystallises will have a profound impact on how the institutionalisation of human and collective rights from below, examined in other chapters of this book, proceeds.
An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniques

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.