ethicalabs/Research-Intent-Collab huggingface dataset:
Design of the incentive mechanism in electricity auction market based on the signaling game theory

Abstract   At present, designing a proper bidding mechanism to decrease the generators' market power is considered to be one of the key approaches to deepen the reform of the electricity market. Based on the signaling game theory, the paper analyzes the main electricity bidding mechanisms in the electricity auction markets and considers the degree of information disturbance as an important factor for evaluating bidding mechanisms. Under the above studies, an incentive electricity bidding mechanism defined as the Generator Semi-randomized Matching (GSM) mechanism is proposed. In order to verify the new bidding mechanism, this paper uses the Swarm platform to develop a simulation model based on the multi-agents. In the simulation model, the generators and purchasers use the partly superior study strategy to adjust their price and their electricity quantity. Then, the paper examines a simulation experiment of the GSM bidding mechanism and compares it to a simulation of the High-Low Matching (HLM) bidding mechanism. According to the simulation results, several conclusions can be drawn when comparing the proposed GSM bidding mechanism to the equilibrium state of HLM: the clearing price decreases, the total transaction volume increases, the profits of electricity generators decreases, and the overall benefits of purchasers increases.  Index Terms—signaling game; semi-randomized matching; high-low match.
The Servers of Serverless Computing: A Formal Revisitation of Functions as a Service.

Serverless computing is a paradigm for programming cloud applications in terms of stateless functions, executed and scaled in proportion to inbound requests. Here, we revisit SKC, a calculus capturing the essential features of serverless programming. By exploring the design space of the language, we refined the integration between the fundamental features of the two calculi that inspire SKC: the λ-and the π-calculus. That investigation led us to a revised syntax and semantics, which support an increase in the expressiveness of the language. In particular, now function names are first-class citizens and can be passed around. To illustrate the new features, we present step-by-step examples and two non-trivial use cases from artificial intelligence, which model, respectively, a perceptron and an image tagging system into compositions of serverless functions. We also illustrate how SKC supports reasoning on serverless implementations, i.e., the underlying network of communicating, concurrent, and mobile processes which execute serverless functions in the cloud. To that aim, we show an encoding from SKC to the asynchronous π-calculus and prove it correct in terms of an operational correspondence.
Physical Conditions and Spatial Structure in the Molecular Interstellar Medium of M33

In this review, I discuss recent detailed studies of individual molecular clouds in M33 in higher rotational transitions of CO, 13CO, and the fine structure line of atomic carbon. These data reveal that the temperature and density of the molecular gas is correlated with the presence of nearby massive star formation, with clouds with more intense star formation having higher temperatures and lower densities. This effect is most likely due to post-star formation processing of the molecular gas. A comparison of 13CO observations for M33 and individual molecular clouds in the Milky Way suggests that a significant fraction of the molecular interstellar medium resides in low column density regions and accounts for 30-60% of the CO luminosity. Finally, a detailed map of a single molecular cloud in the giant HII region NGC 604 reveals an offset between the atomic carbon and CO emission, which indicates that the dominant source of atomic carbon in this cloud is likely photodissociation of CO by ultraviolet photons from the massive stars. A complete map of the CO emission in M33 would be extremely helpful to guide further detailed studies.
The impact of home care nurses’ numeracy and graph literacy on comprehension of visual display information: implications for dashboard design

Abstract                                   Objective                   To explore home care nurses’ numeracy and graph literacy and their relationship to comprehension of visualized data.                                                   Materials and Methods                   A multifactorial experimental design using online survey software. Nurses were recruited from 2 Medicare-certified home health agencies. Numeracy and graph literacy were measured using validated scales. Nurses were randomized to 1 of 4 experimental conditions. Each condition displayed data for 1 of 4 quality indicators, in 1 of 4 different visualized formats (bar graph, line graph, spider graph, table). A mixed linear model measured the impact of numeracy, graph literacy, and display format on data understanding.                                                   Results                   In all, 195 nurses took part in the study. They were slightly more numerate and graph literate than the general population. Overall, nurses understood information presented in bar graphs most easily (88% correct), followed by tables (81% correct), line graphs (77% correct), and spider graphs (41% correct). Individuals with low numeracy and low graph literacy had poorer comprehension of information displayed across all formats. High graph literacy appeared to enhance comprehension of data regardless of numeracy capabilities.                                                   Discussion and Conclusion                   Clinical dashboards are increasingly used to provide information to clinicians in visualized format, under the assumption that visual display reduces cognitive workload. Results of this study suggest that nurses’ comprehension of visualized information is influenced by their numeracy, graph literacy, and the display format of the data. Individual differences in numeracy and graph literacy skills need to be taken into account when designing dashboard technology.
Tanzania’s Withdrawal from the African Court and its Effects in Safeguarding of Human and Peoples’ Rights

Tanzania is one of the African Union (AU) Member States committed to protecting human rights at the regional level. This was revealed by the deposition of declaration to allow individuals and Non Governmental Organisations (NGOs) direct access to the African Court of Human and Peoples’ Rights, (the African Court). The deposited declaration was currently withdrawn by the State, hence curtailing individual Tanzanians and NGOs freedom to have direct access to the African Court. Although, the doors are not closed, individual and NGOs can still challenge the State on violation of human rights via the African Commission on Human and Peoples’ Rights, (the African Commission), which do not have legal force. Tanzania’s decision has been considered to have effects not only on individuals and NGOs but also on AU as it impairs the AU agenda of 2063 focusing, among others, on improving human rights, democracy and good governance throughout the country.
The optimisation approach of ALARA in nuclear practice: an early application of the Precautionary Principle. Scientific uncertainty versus legal uncertainty

The late health effects of exposure to low doses of ionising radiation are subject to scientific controversy: one view finds threats of high cancer incidence exaggerated, while the other view thinks the effects are underestimated. Both views have good scientific arguments in favour of them. Since the nuclear field, both industry and medicine have had to deal with this controversy for many decades. One can argue that the optimisation approach to keep the effective doses as low as reasonably achievable, taking economic and social factors into account (ALARA), is a precautionary approach. However, because of these stochastic effects, no scientific proof can be provided. This paper explores how ALARA and the Precautionary Principle are influential in the legal field and in particular in tort law, because liability should be a strong incentive for safer behaviour. This so-called “deterrence effect” of liability seems to evaporate in today's technical and highly complex society, in particular when dealing with the late health effects of low doses of ionising radiation. Two main issues will be dealt with in the paper:How are the health risks attributable to “low doses” of radiation regulated in nuclear law and what lessons can be learned from the field of radiation protection?What does ALARA have to inform the discussion of the Precautionary Principle and vice-versa, in particular, as far as legal sanctions and liability are concerned?It will be shown that the Precautionary Principle has not yet been sufficiently implemented into nuclear law.
OPTIMIZATION OF THE LOGISTICS PROCESS IN TRANSPORT IN THE CONTEXT OF DIGITAL TRANSFORMATIONS

It is proved that a significant potential for increasing the competitiveness of the transport industry in the logistics services market lies in optimizing the logistics process, which is aimed at reducing costs, improving the quality of customer service and overall business efficiency. It is emphasized that increasing the complexity and volume of logistics operations create new challenges in the transport industry, requiring changes in traditional approaches to supply chain management. It is established that the introduction of digital innovative solutions creates additional opportunities for optimizing logistics processes in the transport sector, affecting the entire system, acting as a trigger for the growth of competitiveness of both the industry and the national economy as a whole. Attention is focused on the fact that digital transformations are characterized by complexity, which implies the integration of relevant technologies into various elements of the transport process, including technological, infrastructure, organizational and managerial aspects. Areas of optimization of logistics processes in the transport industry through the introduction of digital innovations are identified. It is established that digital tools and technologies are of practical interest, including: Big Data, Internet of Things (, Blockchain, artificial intelligence, robotization. This is due to their ability to track relevant operations in real time and manage logistics activities in supply chains, ensuring the security of data exchange between parties, changing traditional approaches to the organization of logistics processes and document management. The potential of digital solutions and technologies for optimizing the logistics process in transport is studied. Their technological features, advantages and disadvantages, and vectors of logistics process optimization are determined. It is emphasized that the use of such tools makes it possible to optimize logistics processes in transport by increasing the efficiency of technical, technological, economic and marketing components. The main difficulties that hinder the optimization of logistics processes in transport in the context of digitalization are identified. Priority areas aimed at supporting the optimization of logistics processes in this area at various management levels in the context of digital changes are proposed.
Monte Carlo EM for Deep Time Series Anomaly Detection

Time series data are often corrupted by outliers or other kinds of anomalies. Identifying the anomalous points can be a goal on its own (anomaly detection), or a means to improving performance of other time series tasks (e.g. forecasting). Recent deep-learning-based approaches to anomaly detection and forecasting commonly assume that the proportion of anomalies in the training data is small enough to ignore, and treat the unlabeled data as coming from the nominal data distribution. We present a simple yet effective technique for augmenting existing time series models so that they explicitly account for anomalies in the training data. By augmenting the training data with a latent anomaly indicator variable whose distribution is inferred while training the underlying model using Monte Carlo EM, our method simultaneously infers anomalous points while improving model performance on nominal data. We demonstrate the effectiveness of the approach by combining it with a simple feed-forward forecasting model. We investigate how anomalies in the train set affect the training of forecasting models, which are commonly used for time series anomaly detection, and show that our method improves the training of the model.
Holistic Pastoral Transformation: An Input for the New Initiative to Realize Economic Revolution in the Pastoral Areas of Oromia Region, Ethiopia Conflict Resolution and Peace Building in the Pastoralist Areas of Oromia Region

research done , 2021
Association between Insight, Appointment Attendance and Secondary Mental Health Service Use a Naturalistic Cohort Study using Electronic Health Records Research Assistant Data Scientist

Background We aimed to determine whether insight at discharge from Crisis treatment and home Resolution Teams (CRTs) was associated with appointment attendance and further use of CRTs or inpatient admission.  Methods We extracted routine electronic health data from Camden &amp; Islington(C&amp;I) NHS Foundation Trust including individuals with a primary diagnosis of non-affective psychosis (ICD-10, F20-29) who presented at least once to C&amp;I CRTs between January 1, 2008 and December 31, 2017. We defined insight as a composite construct, including Illness Recognition, Treatment Compliance and Symptom Relabelling, and tested whether insight could be coded using data extracted from progress notes. A total of 317 records were analysed. Multivariable Poisson, Negative Binomial regressions were used to estimate differences in attendance rates; Cox modelling was used to estimate differences in risk of service use. We analysed the models at 12-months from first CRTs discharge, and at the end of cohort (all-time).  Results Most of the descriptions of insight did not map onto our conceptualisation of insight, only 87(27.4%) had no missing data in all 3 components. People with partial insight had a lower attendance rate than people with lack of insight at 12 months (incident-rate-ratio = 0.945,95% CI 0.896-0.997). People with full insight had a decreased rate of service use compared to people with lack of insight at all-time (hazard-ratio = 0.638, 95% CI 0.424-0.960).   Conclusions Overall, better insight was not associated with higher face-to-face appointment attendance, but was associated with lower rates of service use after first discharge from CRTs.
Understanding urban vulnerability, adaptation and resilience in the context of climate change

This paper introduces a special issue of Local Environment that has arisen through collaboration between academic researchers and the Urban Planning and Design Branch of UN-Habitat, focused on how we can understand and respond to the challenges of urban vulnerability, adaptation and resilience in the context of climate change. The paper establishes the existing state of the art in the field, and considers critical challenges that are emerging in the research-based literature. In this context, it introduces UN-Habitat's Cities and Climate Change Initiative and reflects on the lessons learnt and challenges ahead, drawing on insights from across the papers in the special issue. In conclusion, it identifies the role of international/transnational co-operation, the relation between adaptation and mitigation, issues of multi-level governance and the ways in which change in urban socio-technical systems might be achieved as critical issues across the science/policy interface where increased dialogue and the co-p...
Admissibility of Requests for a Preliminary Ruling in the Case Law of the CJEU on Judicial Independence

In recent years, the CJEU has experienced a significant increase in requests for preliminary rulings on the interpretation of Article 19(1) TEU in the area of judicial independence. However, not all requests concerning this provision have been deemed admissible by the CJEU. This article examines the underlying principles of the preliminary ruling procedure under Article 267 TFEU that guide the CJEU in accepting or rejecting requests from national courts in this critically important area of EU law. It focuses on the concept of ‘court or tribunal’ within the meaning of Article 267 TFEU and the ‘necessity’ of the Court’s ruling.
Molecular phylogenetics in Hydra, a classical model in evolutionary developmental biology

Among the earliest diverging animal phyla are the Cnidaria. Freshwater polyps of the genus Hydra (Cnidaria, Hydrozoa) have long been of general interest because different species of Hydra reveal fundamental principles that underlie development, differentiation, regeneration and also symbiosis. The phylogenetic relationships among the Hydra species most commonly used in current research are not resolved yet. Here we estimate the phylogenetic relations among eight scientifically important members of the genus Hydra with molecular data from two nuclear (18S rDNA, 28S rDNA) and two mitochondrial (16S rRNA, cytochrome oxidase subunit I (COI)) genes. The phylogenetic trees obtained by maximum parsimony (MP), maximum likelihood (ML) and Bayesian inference (BI) methods were generally compatible with present morphological classification patterns. However, the present analysis also bears on several long-standing questions about Hydra systematics and reveals some characteristics of the phylogenetic relationships of this genus that were unknown so far. It indicates that Hydra viridissima, the only species in Hydra, which contains symbiotic algae, might be considered as the sister group to all other species within this genus. Analyses of both nuclear and mitochondrial sequences support the view that Hydra oligactis and Hydra circumcincta are sisters to all other Hydra species. Unexpectedly, we also find that in contrast to its initial description, the strain used for making transgenic Hydra, Hydra vulgaris (strain AEP) is more closely related to Hydra carnea than to other species of Hydra.
CENTRAL BANK DIGITAL CURRENCY: AN INTRODUCTION

Cryptocurrencies have received a lot of attention by media, international organizations, Central Banks and academics. It has been argued if it would be of interest of Central Banks to issue a cryptocurrency or an electronic money. The diference between these two ways to access the Central Bank balance sheet is the transmission of units from an user to another. While the Central Bank cryptocurrency changes hands through a decentralized system, the Central Bank electronic money is transferred through a centralized system. In this dissertation, we show that the main difference between centralized and decentralized systems is the need to monitor the intermediaries’ actions. We conclude that the permissioned system may be regarded as a centralized system because it requires monitoring the intermediaries. We also study other questions related to the creation of a Central Bank digital currency (CBDC). Specially, we study the benefits that the issuance of a CBDC might have and the effects it may cause to thebanking sector.
Consensus and Gaps in AI Ethics: An Integrated Analysis of 200 Guidelines Reveals Core Principles and Emerging Themes

Abstract                 This study analyzes 200 AI ethics guidelines (2014-2022) from 46 countries using two complementary methods: statistical analysis of 17 predefined principles and BERTopic modeling for emergent themes. We found broad consensus on three 1 core principles: transparency (82.5%), reliability (78.0%), and fairness (75.5%). However, critical gaps exist: children’s rights appear in only 6% of documents, labor rights in 19.5%, and sustainability in 22.0%. Regional variations reveal European emphasis on data protection, North American focus on corporate commitment , and Asian attention to technology governance. Temporal analysis shows human-centeredness (+2.2% annually) and corporate commitment (+2.4% annually) as emerging trends. These findings offer benchmarks for policymakers and organizations developing AI ethics frameworks, while highlighting urgent gaps requiring attention.
The Traditional Mechanisms of Conflict Resolution &amp; Peace Building in Darfur; From an Anthropological Perspective

The study assesses the Volunteering & social interaction between components of Darfur that remain active and peaceful as long as these mechanisms remain active. Historically, Darfur society enjoys accumulative and stratified stage of norms and traditions that processed over experiments and experience of generation throughout centuries and converged to unrecorded law which all subjected to it. Norms and traditions were transformed orally from one generation to another. The research found that many indigenous affected by conflicts resolution in Darfur, i.e. the native administration, ajaweed council (al-judia), mechanisms of farmer, herder conflict the rakuba and so on. And on other side their mechanisms for peace building like: al- Nafeir, Social  relations, Hunting group, al-fazza, al-darra, local markets, life cycle event, folke literature …etc. The study concludes with recommendations to for promoting these mechanisms in order to contribute efforts aimed at identifying ways of achieving sustained peace in Darfur.  DOI:  10.5901/mjss.2013.v4n9p132
M<sup>3</sup>-20M: A large-scale multi-modal molecule dataset for AI-driven drug design and discovery

This paper introduces M3-20M, a large-scale Multi-Modal Molecule dataset that contains over 20 million molecules, with the data mainly being integrated from existing databases and partially generated by large language models. Designed to support AI-driven drug design and discovery, M3-20M is 71 times more in the number of molecules than the largest existing dataset, providing an unprecedented scale that can highly benefit the training or fine-tuning of models, including large language models for drug design and discovery tasks. This dataset integrates one-dimensional SMILES, two-dimensional molecular graphs, three-dimensional molecular structures, physicochemical properties, and textual descriptions collected through web crawling and generated using GPT-3.5, offering a comprehensive view of each molecule. To demonstrate the power of M3-20M in drug design and discovery, we conduct extensive experiments on two key tasks: molecule generation and molecular property prediction, using large language models including GLM4, GPT-3.5, GPT-4, and Llama3-8b. Our experimental results show that M3-20M can significantly boost model performance in both tasks. Specifically, it enables the models to generate more diverse and valid molecular structures and achieve higher property prediction accuracy than existing single-modal datasets, which validates the value and potential of M3-20M in supporting AI-driven drug design and discovery. The dataset is available at https://github.com/bz99bz/M-3.