ethicalabs/Research-Intent-Collab huggingface dataset:
Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions

The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its successful execution. This resource allocation influences both the operational expense and the performance quality of these functions. However, a noticeable lack of platform transparency forces developers to rely on expert knowledge or experience-based ad-hoc decisions to request desired function resources. This makes optimal resource configuration a non-trivial task while adhering to performance constraints. Furthermore, while commercial platforms often scale resources like CPU and network bandwidth proportional to memory, open-source frameworks permit independent configuration of function resources, introducing additional complexity for developers aiming to optimise their functions. These complexities have directed researchers to resolve developer challenges and advance towards an efficient server-less execution model. In this article, we identify different aspects of resource configuration techniques in FaaS settings and propose a taxonomy of factors that influence function design, configuration, run-time cost, and performance guarantees. We conduct an analysis of existing literature on resource configuration to present a comprehensive review of current studies on function configuration. We also identify existing research gaps and suggest future research directions to enhance function configuration and strengthen the capabilities of serverless computing environments to drive its broader adoption.
AI-FMECA: A Reliability Analysis Method Integrating Large Language Models, Knowledge Graphs, and Graph Neural Networks

Failure Mode, Effects, and Criticality Analysis (FMECA) is indispensable for ensuring reliability and safety in Computer Numerical Control (CNC) machine tools. Although multiple failure analysis and evaluation methodologies exist within reliability engineering, FMECA—as a core analytical tool—still confronts two major challenges in practical implementation: (1) conventional FMECA approaches are inadequate for fully characterizing complex failure mechanisms and couplings among subsystems, and (2) AI-driven intelligent FMECA systems are susceptible to hallucination, compromising the accuracy and robustness of the results. To overcome these limitations, this study proposes a novel Artificial Intelligence-driven FMECA (AI-FMECA) framework that integrates knowledge graphs (KGs), graph neural networks (GNNs), and large language models (LLMs). The proposed methodology utilizes an LLM to extract subsystem component information from unstructured textual data, establishing a hierarchical system architecture, and then constructs a KG from structured data. Leveraging the structurally validated KG refined by GNN-based node classification, this method quantifies fault propagation paths and subsystem coupling effects to generate risk factors γ that model multi-step risk propagation. Constrained by the KG enriched with γ-information, the LLM quantifies the severity (S), occurrence (O), and detection (D) parameters of failure effects, and computes a risk priority number (RPN) that incorporates γ to identify high-risk failure modes. The efficacy of the proposed approach is validated using real-world failure data. Experimental results demonstrate that the method achieves superior analytical accuracy and operational efficiency compared to both traditional FMECA and existing AI-enhanced FMECA techniques.
Case Studies on using Natural Language Processing Techniques in Customer Relationship Management Software

How can a text corpus stored in a customer relationship management (CRM) database be used for data mining and segmentation? In order to answer this question we inherited the state of the art methods commonly used in natural language processing (NLP) literature, such as word embeddings, and deep learning literature, such as recurrent neural networks (RNN). We used the text notes from a CRM system which are taken by customer representatives of an internet ads consultancy agency between years 2009 and 2020. We trained word embeddings by using the corresponding text corpus and showed that these word embeddings can not only be used directly for data mining but also be used in RNN architectures, which are deep learning frameworks built with long short term memory (LSTM) units, for more comprehensive segmentation objectives. The results prove that structured text data in a CRM can be used to mine out very valuable information and any CRM can be equipped with useful NLP features once the problem definitions are properly built and the solution methods are conveniently implemented.
An integrated system for tourism destination management: reflections and proposal of a conceptual model from the perspective of hospitality

This report presents the experience of the implementation of the “Tourism Observatory | SIGESTur” project, an action developed at the Federal Institute of São Paulo (IFSP Campus Cubatão). The project began in 2016, as result of academic research and university extension activity. This work reports and analyzes the results of the project, and proposes a model for innovative management of tourism destinations in the Brazilian context. In its first year, the project was considered one of the ten most innovative ideas in Brazilian tourism, at the I Tourism Innovation Camp, a contest promoted by the Panrotas portal with technical support from Sebrae (national development agency). In 2017, the case entitled “Hospitality and hostility relations among stakeholders: Influences in the competitiveness of tourist destinations” received an award during the IV World Research Summit for Tourism and Hospitality, a congress held in Orlando (USA). The project consists of two work fronts. For the elaboration of this report of experience, bibliographical and documentary research were carried out. The data analysis was done in 2019 to ensure that all previous actions were recorded. In the results, the establishment of a model is highlighted, for collecting, processing and analyzing data and distributing information to professionals in the travel and tourism industry.
SEX OFFENDER REGISTRY IN VIEW OF THE RIGHT TO PRIVACY AND THE RIGHT TO BE FORGOTTEN IN THE LIGHT OF THE GENERAL DATA PROTECTION REGULATION (GDPR) DIRECTIVE OF THE EUROPEAN PARLIAMENT AND COUNCIL (EU) 2016/680, DATED 04.27.2016, AND OTHER ACTS

This paper aims to reflect upon the possibility of applying the GDPR and so-called police directive to a particular category of natural persons, i.e. persons convicted of sex offences. By comparing the provisions in force under the now-repealed Personal Data Protection Act of 29/08/1997 with the provisions introduced by the GDPR and regulations resulting, among others, from the European Convention on Human Rights, the authors attempt to indicate the directions being taken by current solutions and considerations concerning the guarantee of protection for the weaker party from a sex offender who – due to the offence that they have committed – can benefit to a much lesser degree from the legal protection of their image, data or the right to privacy. Modern regulations relating to personal data protection, including the possibility of exercising the right to be forgotten, are currently at the stage of numerous interpretations, thereby enabling the development of optimal solutions. The changes brought about on the one hand by GDPR, and on the other by the creation of the Sex Offender Registry, have opened up a broad range of possibilities apart from imprisonment enabling an impact on sex offenders.
Neural Natural Language Processing for unstructured data in electronic health records: A review

33 pages, 11 figures
Physics Design and Beam Dynamics Optimization of the SHINE Accelerator

Shanghai HIgh Repetition Rate X-ray Free Electron Laser and Extreme Light Facility (SHINE) is a hard X-ray FEL facility which is driven by a 1.3 km supercon-ducting Linac, aims to provide high repetition rate pulses up to 1 MHz . In this study, we present the physics design of the SHINE accelerator and considerations of beam dynamics optimizations. Start-to-end simulation results show that, a high brightness electron beam with over 1500 A quasi-flat-top current can be attained which fully meet the requirements of FEL lines. Furthermore, design of the bypass line is also discussed.
Chirality Reversal for Enhanced Quantum Error Correction in Bosonic Quantum Computing

This work presents a novel, provisionally patented error-mitigation protocol for bosonic quantum computing based on dynamic chirality reversal. By inverting the parametric drive term (ε → −ε) and redistributing entanglement via beamsplitter (logical iSWAP) gates, the scheme enables robust partial state recovery without full syndrome decoding. QuTiP simulations (N=6) with realistic noise models (photon loss, dephasing, 1/f processes) demonstrate fidelity improvements of 12–25% over cat codes, with near-perfect recovery (~99.999%) in low-noise regimes and 70–85% ancilla overhead reduction compared to surface codes. Hardware pathways are outlined for IBM Quantum and Alice & Bob platforms. This thesis establishes a scalable, patent-covered route to fault tolerance in NISQ devices, bridging theoretical insights from multi-time frameworks with practical superconducting controls.
Molecular ordering of Fluoro-Nematogen in a dielectric medium at phase-transition temperature: a statistical–mechanical approach based on computer simulation

A molecular-ordering study of p-phenylene-4-methoxy benzoyl 4-trifluoromethylbenzoate (FLUORO) in a dielectric medium (benzene) has been carried out on the basis of statistical mechanics and computer simulation. The atomic net charge and atomic dipole moment at each atomic centre has been evaluated using the CNDO/2 method. The modified Rayleigh–Schrodinger perturbation theory with multicentered–multipole expansion method has been employed to evaluate the long-range intermolecular interactions, while ‘6-exp’ potential function has been assumed for the short-range interactions. The total interaction energy values obtained through these computations were used to calculate the probability of each configuration in a dielectric medium at the phase-transition temperature (506 K), using the MB-formula. The flexibility of various interacting configurations was studied in terms of variation of probability due to departure from the most probable configuration. It has been observed that in dielectric medium the energ...
Before shots are fired: The occupational health and safety professional’s role in addressing active shooter and violent threats in the workplace

This article examines active shooters and mass shootings and their implications for occupational health and safety professionals as they relate to industrial hygiene and public health. The article explores the overlapping workplace threat landscape and the roles of homeland security and industrial hygiene enterprises in preventing, mitigating, responding to, and helping people recover from workplace violence, including active shooters, as a natural extension of the purview of those enterprises in addressing all hazards. The article concludes by setting forth a means for improving workplace violence prevention and response through industrial hygiene concepts and through the contributions of industrial hygiene professionals.
TRADITIONAL ECOLOGICAL PRACTICES OF FOREST GOVERNANCE,  REPERCUSSION TO BIODIVERSITY CONSERVATION AND CLIMATE CHANGE  MITIGATION OF SODHE FOREST, DERAMALO WOREDA, SOUTH ETHIOPIA

Forest management problems are currently serious issues in Ethiopia  and other  countries. The  degradation of  these forest resources has resulted in high concentration of GHG in the atmosphere  leading to global warming. The objective of this study was to assess traditional ecological  management practices and its contribution towards biodiversity conservation and  climate change  mitigation. The study was conducted in Sodhe forest,  Daramallo  Woreda, South Ethiopia. To meet  the objectives field observation and survey methods were employed. The forest site altitude ranges  from 2490 to 2805m a.s.l. In this study, carbon  stock estimation study Plots were designed as  10m*10m, 5m*5m, 2m*2m, and 1m*1m to measure trees with DBH &gt; 10cm,DBH  &gt;5-10cm ,DBH2.5-5,  shrub  and undergrowth LHGs and SOC    sampling respectively. Soil organic carbon  and bulk density sampling was done at  four corners of 10m*10m main plots and at the center from  the 1m*1m depth 10cm, 20cm and 30cm by using auger. For socioeconomic and traditional  ecological knowledge assessment a systematic sampling method was used by sampling 32 HHs and  32 nested plots using global positioning system (GPS). Field data analysis has resulted in estimated  total mean carbon stock of Sodhe  traditional forest to be 393.63t.h -1 which is equivalent to 1444.6  t.h -1 of CO 2  equivalent. The total amount of CO 2 sequestered by the traditionally managed Sodhe  forest is estimated at present  was 98234.16 tones.  Out of these the soil carbon accumulation was  found to be the highest (264.29t.h -1 ) . Socio-cultural assessment revealed the importance of  cultural,  spiritual, and economic value of  traditional  forest governance  acceptability by local people.  The  strong support by government administration has significantly supported the existing traditional  effort of conservation).  However, more than half of the respondents (65.6%)  revealed  the status of  the forest as stable. This could be attributed to the presence of strong  relationship of the forest with community and perception of strong traditional governance system.  Therefore,  the    empowerment,  valorization and recognition of the traditional ecological conservation efforts by local government  institutions has to be strengthened so that the sustainable use of the forest resource  could    be  insured.
North Korea's Peace Building in the Light of Conflict Resolution Curve

Conflict resolution curve derived from an analytical model, is a path to peace building. Previously this model was applied to a problem of micro level conflict giving satisfactory results. In the present study, this model has been empirically tested to resolve inter-state conflict. Application shows that the interest of the ruler rather than the interest of the nation plays the decisive role to predict styles of conflict in Korean peninsula. Results of the model also indicate how the present crisis might be solved towards peace building. The ongoing negotiation process in North Korea might verify how far this model is applicable in reality.
Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

13 pages, 7 figures, 5 tables. Data from the experiments and source code can be found here: https://doi.org/10.5281/zenodo.15622123
INFLUENCE OF SOCIAL MEDIA ON MENTAL HEALTH OF CHILDREN AND ADOLESCENTS: MODERN CHALLENGES AND PROSPECTS (THE IMPACT OF SOCIAL MEDIA ON THE DEVELOPMENT OF ANXIET

In today’s digital world, social media has become an integral part of the lives of children and adolescents. However, excessive engagement with these platforms leads to an increase in anxiety disorders, depressive states, decreased self-esteem, and emotional exhaustion. The purpose of this paper is to analyze the impact of social media on adolescents’ mental health, to identify the main mechanisms underlying the formation of psychological disorders, and to outline prospects for their prevention. Based on contemporary international research, it has been demonstrated that social platforms such as Instagram, TikTok, and Facebook contribute to the development of idealized life representations, social comparison, fear of missing out (FOMO), and digital addiction. The article examines the characteristics of adolescents’ psycho-emotional development in the context of digital culture, highlights both positive and negative aspects of social media use, and offers recommendations to foster digital literacy and psychological resilience among young people.
Anti-corruption in global health systems: using key informant interviews to explore anti-corruption, accountability and transparency in international health organisations

Objectives                     Corruption undermines the quality of healthcare and leads to inequitable access to essential health products. WHO, Global Fund, United Nations Development Programme (UNDP) and World Bank are engaged in anti-corruption in health sectors globally. Throughout the COVID-19 pandemic, weakened health systems and overlooked regulatory processes have increased corruption risks. The objective of this study is thus to explore the strengths and weaknesses of these organisations’ anti-corruption mechanisms and their trajectories since the pandemic began.                                                           Design, setting and participants                     25 semistructured key informant interviews with a total of 27 participants were conducted via Zoom between April and July 2021 with informants from WHO, World Bank, Global Fund and UNDP, other non-governmental organisations involved in anti-corruption and academic institutions. Key informant selection was guided by purposive and snowball sampling. Detailed interview notes were qualitatively coded by three researchers. Data analysis followed an inductive-deductive hybrid thematic analysis framework.                                                           Results                     The findings demonstrate that WHO, World Bank, Global Fund and UNDP have shifted from criminalisation/punitive approaches to anti-corruption to preventative ones and that anti-corruption initiatives are strong when they are well funded, explicitly address corruption and are complemented by strong monitoring and evaluation mechanisms. Weaknesses in the organisations’ approaches to anti-corruption include one-size-fits-all approaches, lack of political will to address corruption and zero-tolerance policies for corruption. The COVID-19 pandemic has highlighted the necessity of improving anti-corruption by promoting strong accountability and transparency in health systems.                                                           Conclusions                     Results from this study highlight the strengths, weaknesses and recent trajectories of anti-corruption in the Global Fund, World Bank, UNDP and WHO. This study underscores the importance of implementing strong and robust anti-corruption mechanisms specifically geared towards corruption prevention that remain resilient even in times of emergency.
Dynamic modeling of a parallel robot. Application to a surgical simulator

This paper presents a closed form solution for the dynamic model of a parallel robot. This robot is utilized like a haptic interface for a surgical simulator used in the amnicentesis operation. The dynamic model is obtained by using the Lagrange formulation applied to parallel robot. It is proved that for a large class of parallel robots which has some properties, it is possible to avoid the explicit calculation of Lagrange multipliers. The aim is to simplify the calculation for a real-time application.
The World Trade Organization's Dispute Settlement Understanding: Are All (Un)Equal?

The World Trade Organization (WTO) is one of the most visible and contested institutions within the global economic governance architecture. Both proponents and opponents of trade liberalization regularly voice discontent with the institution’s functioning and its effect on the economies of its members. Of particular interest is the WTO Dispute Settlement Understanding (DSU), often described as the organization’s flagship body. The WTO DSU helps parties to settle disputes related to the interpretation and enforcement of the provisions drafted and agreed upon over several trade negotiation rounds. This paper presents a game-theoretic approach to analyze whether the WTO DSU ensures equal access and outcomes to all parties who are part of a trade dispute. It particularly addresses the costs of litigation. The paper argues that due to these issues, developing countries will not reap equal benefits from the usage of the WTO DSU as their developed counterparts. Developing countries will, due to a relative higher cost of WTO litigation, be worse off, both as a plaintiff and as a defendant. They will file less complaints and settle for less beneficial outcomes. We also find that bilateral settlement is a very important feature in the WTO DSU.
Development and content validity analysis of artificial intelligence-generated Indonesian language insomnia questionnaire based on the International Classification of Sleep Disorders, Third Edition

Objectives:                     Application of artificial intelligence (AI) in the form of a large language model (LLM) in medicine, especially in the field of neurosomnology, is still limited. The generative ability of LLM can be used to develop a specific screening questionnaire in a specific language. In this study, we aimed to measure the ability of LLM to create a prototype, standardized questionnaire for the detection of insomnia in the Indonesian (ID) language based on the International Classification of Sleep Disorders, Third Edition (ICSD-3).                                                           Materials and Methods:                                            Systematic prompting of generative pre-trained transformers 4.0-based LLM was done to create a Likert scale-based insomnia questionnaire set in ID. The Independent Expert Panel was established to assess the questionnaire items using Aiken                       V                       and content validity index (CVI) methods, as well as medical expert opinion for linguistic usage in the questionnaire.                                                                                Results:                                            LLM is able to generate a six-item insomnia questionnaire in ID. Content validity analysis with Aiken showed                       V                       value &gt; 0.75 for all items. Question items number 1 and 2 yield a perfect score (                       V                       = 1), with the alternative form of item number 6 yielding the lowest score (                       V                       = 0.78). CVI analysis showed a value of 1 for scale-level CVI based on the average and a value of 1 for scale-level CVI based on the universal agreement, which indicates a high validity index. Independent feedback from the Expert Panel shows a requirement for linguistic revision despite evidence on content validity.                                                                                Conclusion:                     AI is able to generate ID language and a Likert-scale insomnia questionnaire through a specified prompt based on ICSD-3. The AI-generated ID insomnia questionnaire is able to satisfy content validity criteria based on expert assessment.