ethicalabs/Research-Intent-Collab huggingface dataset:
Message Passing-Based Prediction of Unlabelled Node Embedding Using Graph Neural Network

- Graph neural network are a part of deep learning methods created to perform presumption on data described by graphs. Graph neural network is a neutral network that can straight away be applied to graphs. It provides a agreeable way for node level, edge level and graph level prediction tasks. Moreover, most GNN models do not account for long distance relationships in graphs and instead simply aggregate data from short distances (e.g., 1-hop neighbours) in each round. In this paper work, we carry out node classification using graphs which can be put into large graphs comprise of labelled and unlabelled nodes. Here we can predict the node embeddings of the unlabelled node by using an approach called message passing. For executingthis, we took Cora dataset, provided a overview of it, builded ourgraph neural network, splitted the data to test and train data, trained it and finally visualised the output.
The unknown known – A review of local ecological knowledge in relation to forest biodiversity conservation

Abstract   Local ecological knowledge and the land use practices of forest resource users who rely on this form of knowledge play a crucial role for biodiversity conservation in managed forests. The understandings of, and approaches taken to analyze, such knowledge are diverse. To systematize the available knowledge, we conduct a review of 51 studies addressing local ecological knowledge (LEK) and forest biodiversity conservation practice. We analyze what specific kind of knowledge is considered, who holds the knowledge, how this knowledge is actively applied in practice and how it relates to biodiversity conservation.  The review shows that local ecological knowledge and forest biodiversity conservation are linked through various socially shared aspects, such as values and norms, spiritual beliefs and perceptions of ecosystem functions and benefits as well as operational conditions, including livelihood strategies and economic constraints. While many of the reviewed studies evaluate local knowledge as holding great promise for biodiversity conservation, the conclusions regarding practical implications of including this knowledge into forest and conservation management are mixed. In particular, the interaction of “traditional” conservation paradigms rooted in local ecological knowledge and science-based “modern” paradigms is not thoroughly addressed. This applies especially to European countries, where research on local ecological knowledge is scattered. Drawing on these observations, we conclude that a greater focus on the ways in which societies in these countries can (re)generate, transform and apply local ecological knowledge can play a crucial role in integrating conservation objectives into forest management under changing environmental conditions.
Failure analysis and reliability assessment of a type of aero-engine combustion chamber thermal barrier coatings

During a routine hole probe inspection, the aviation division found that the combustion chamber flame tube of a V2500 aero engine had failed early and that the TBCs around the vent holes on the flame tube liner panel 4 had peeled off over a large area, causing the flame tube liner to burn through. This work studies the premature failure of the V2500 aero-engine flame tube and, using modeling and simulation tests, finds the reason for premature failure as a substantial area of thermal barrier coating peeling owing to sintering. The sintering procedure significantly increases the thermal barrier coating's Young's modulus, which reduces the coating's resistance to thermal mismatch strain. The reliability of the TBCs was then evaluated using a probabilistic approach. Owing to the non-uniformity of temperature distribution induced by the hot airflow, the lifetime of the TBCs in the high temperature region is only around 3000 h, but the lifetime in the low temperature region is at least 25,000 h. Using the method of hole-probing, the values of the calculated and real outcomes are compared. The relative error is less than 5 %, demonstrating that the suggested approach for evaluating dependability has high credibility and applicability.
Judicial Independence and the Constitution Hill Global Guidelines on Apex Court Appointments

A key goal for any polity in transition should be the establishment of an independent judiciary, alongside the incorporation of new constitutional rules, guidelines – such as those articulated by the Constitution Hill document – to secure judicial independence in the future. Only independent judges can ensure that the aims of transitional justice are achieved.
The Human Right to the Healthy Environment: perspectives on the indivisibility between environmental rights and human rights

A discussão sobre como melhor proteger o ser humano e sua dignidade das injustiças perpetradas pelo de- senvolvimento assimétrico das diferentes nações e sociedades se tornou um dos destaques da agenda internacional a partir do fim da Guerra Fria. Nesse contexto, começam a emergir, no âmbito das Nações Unidas, pautas de Direitos Humanos que anteriormente se encontravam limitadas ao campo do Direito Ambiental, como o direito humano ao ambiente sadio, ainda não reconhecido formalmente. Este artigo busca compreender quais são as conexões existentes entre os dois campos e se os direitos referentes ao Meio Ambiente devem ou não ser considerados indivisíveis em relação aos demais Direitos Humanos, sendo está uma das condições sugeridas pelo Conselho de Direitos Humanos da Organização das Nações Unidas (ONU). A discussão proposta por este artigo é de caráter teórico-conceitual, no intuito de fornecer maior clareza sobre as interseções existentes entre os dois campos mencionados previamente, e é realizada através de análise documental e bibliográfica.
Assuring Quality and Patient Safety - A Nursing Quality Care-Metrics Implementation and Governance Plan for Older Person Services

This paper sets out an implementation and governance plan for the introduction of a Quality Care Metrics system into an Older Person Service. Increasing focus on healthcare failures to deliver safe quality care to service users requires healthcare leaders to assure the public, the regulators and the commissioning agents is driving organisational development programmes. Nursing Quality Care Metrics allow nurses to systematically measure the quality of the care they deliver and can be used by senior nurse managers to evaluate the nursing structure, processes and outcomes of care thereby strengthening their accountability and their negotiating power at the board table. The HSE Organisational Development model is used to assess the current situation and create a plan to oversee the successful implementation of the project in a public residential facility for Older Person Services and Stufflebeam’s Context, Input, Process, Product (CIPP) will formatively and summatively evaluate the project from initiation through to mainstreaming stages. The importance of setting up of governance systems to oversee performance as well as the requirement to examine metrics data in the context of leadership ability, workplace culture and other quality indicators is acknowledged. Choice of nursing metrics used to measure quality of care should reflect interests of nursing team and recipients of care and should be evidence based and embedded in quality standards to assure professional and service accountability.
Cross-relational attention mechanism-driven graph neural network and Apriori algorithm are used for knowledge reasoning in knowledge graphs

Knowledge reasoning for knowledge graphs refers to predicting unknown re-lationships in knowledge graphs to achieve automatic completion and expan-sion of knowledge. For the task of link prediction in knowledge graphs, this paper proposes an improved algorithm for link prediction in knowledge graphs based on relational graph neural networks and cross-relation attention mechanism, and integrates the Apriori association rule mining algorithm. The cross-relation attention mechanism enables information transfer across mul-tiple relationships between nodes, improving the performance of graph neu-ral networks. Using the Apriori association rule mining algorithm for data preprocessing can greatly filter out useless information in the inference in-put, improving the quality of the inference results. Finally, this model was compared with GCN, GAT, and R-GCN on two datasets, FB15K-237 and WN18, and the effectiveness of the proposed method was demonstrated. When training with a subgraph size of 80,000, the model achieves an MRR of 0.2753 on the FB15K-237 dataset and 0.9054 on the WN18 dataset.
Technology-Enhanced Team-Based Learning in Computing Education: Learning Analytics from Digital iRAT and tRAT Assessments

Team-Based Learning (TBL), implemented through digital platforms such as Google Classroom and Google Forms, enables technology-enhanced collaborative learning in computing education. This study evaluates the impact of digitally mediated TBL using a quasi-experimental multi-cohort design, demonstrating that cloud-based digital assessment ecosystems can support scalable collaborative learning analytics.The intervention utilized Google Classroom for content delivery and Google Forms/Quizzes for individual readiness assurance tests (iRAT) and team readiness assurance tests (tRAT), enabling automated score collection and learning analytics extraction. Results indicate that team scores consistently exceeded individual scores, with significantly higher performance in TBL groups compared to the control group (p &amp;lt; 0.001). Large effect sizes across cohorts (d &amp;gt; 1.3) suggest substantial gains from collaborative learning in technology-enhanced environments.These findings demonstrate that digitally mediated TBL enhances learning outcomes while generating valuable learning analytics, positioning cloud-based assessment ecosystems as effective infrastructure for supporting active learning strategies in computing education.
Conception and evaluation of a clinical decision support system implementing the medical reasoning of the experts writing clinical practice guidelines for the empiric presciption of antibiotics

Context: CDSS (Clinical Decision Support System) for the empiric prescription of antibiotics present many limits: (i) recommendations are only given for clinical situations that are described in CPGs (Clinical Practice Guidelines); (ii) updating is infrequent; (iii) poor ergonomics is an impediment to clinical adoption. To overcome these limits, we propose an approach leading to a CDSS that is able to generate ecommendations as in CPGs, and to display recommendations in an interface supporting knowledge. Methods: Our approach consists in (i) extracting from CPGs the deep medical reasoning used by experts to establish recommendations, (ii) using the decision process in empiric antibiotherapy and the usability principles to design a pleasant interface. Results: The implementation of the medical reasoning of the experts as an algorithm using 12 properties of antibiotics, allows to retrieve automatically the recommended antibiotics for all clinical situations, without the intervention of the experts. These properties could be updated automatically thanks to others resources like drugs data bases. The design of an interface supporting knowledge according to the decision process in empiric antibiotherapy and the usability principles, improve significantly the perceived usability and the confidence in the system. Conclusion: The extrapolation of our approach to the chronic diseases should be assessed.
DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer

AbstractRecently, lung cancer prediction based on imaging genomics has attracted great attention. However, such studies often have many challenges, such as small sample size, high-dimensional information redundancy, and the inefficiency of multimodal fusion. Therefore, in this paper, a deep convolution cascade attention fusion network (DCCAFN) based on imaging genomics is proposed for the prediction of lung cancer patients’ survival. The network consists of three modules: an image feature extraction module (IFEM), a gene feature extraction module (GFEM), and an attention fusion network (AFN). In the IFEM, a pretrained residual network based on transfer learning is used to extract deep image features to fully capture the computed tomography (CT) image information conducive to prognosis prediction. In the GFEM, the F-test is first used for gene screening to eliminate redundant information, and then, a cascade network with the convolution cascade module (CCM) that contains a convolution operation, a pooling operation, and an ensemble forest classifier is designed to better extract the gene features. In the AFN, a bimodal attention fusion mechanism is proposed to fuse deep image features and gene features to improve the performance of predicting lung cancer survival. The experimental results show that the DCCAFN model achieves good performance, and its accuracy and AUC are 0.831 and 0.816, respectively. It indicates that the model is an effective multimodal data fusion method for predicting the survival prognosis of lung cancer, which can greatly help physicians stratify patients' risks, and achieve personalized treatment for improving the quality of patients' lives.
Equilibrium Statistical Mechanics: Phases of Matter and Phase Transitions

info:eu-repo/semantics/published
Re-Establishing Agency in the Narrative of International Norm Diffusion Theories: Bringing in the Local in the Exploration of Sexual and Reproductive Health and Rights in the Philippines

This research critically unpacks existing international norm diffusion and domestication theories and amends them, focusing on resistance, agency, and the pivotal role of domestic agents. It argues that domestic agency is marginalized by current theories of international norm diffusion and that current models place undue emphasis on the power of ‘critical States’, which are invariably Western. It is implied, though often explicitly stated, that international norms are created in and spread by Western States. This research argues that while this may be the case, the application of the same limited models create such a situation which hinders instead of helps the understanding of norm diffusion. Arguing that domestic agency is marginalized by current theories of international norm diffusion, this study investigated the SRHR norm in the Philippines as a case study to examine the limitations of current models and the benefits of introducing a local agency approach. This research is divided into two sections: the national and the sub-national, to provide a broad-lens perspective on the specific case of Sexual Health and Reproductive Rights (SRHR) in the Philippines using the framework of norm diffusion. Empiric research was conducted on two levels of analysis: first, a Critical Discourse Analysis (CDA) of the national-level Congressional deliberations and debates on SRHR from the period of the 8th Congress to the 15th Congress; and second, a micro-comparative analysis of three selected municipalities, namely Manila, Cebu, and Davao, involving a case-study based process-tracing methodology of the local diffusion of the SRHR norm(s).The theoretical critique and empirical case study proved that there are indeed limitations present within current diffusion conventions and furthermore, that local agency is a powerful and understudied tool in norm diffusion.
Photovoltaics in agricultural landscapes: “Industrial land use” or a “real compromise” between renewable energy and biodiversity? Perspectives of German nature conservation associations

Abstract                                        Background                     One common renewable energy source for substituting fossil sources is photovoltaic (PV) systems. However, installing PV systems in agricultural areas can lead to competition with other land uses. These projects, therefore, often encounter problems with social acceptance in affected communities. Especially from the perspective of nature conservation targets, conflicts can arise. These potential differences are still under-researched but represent important knowledge for the societally broadly accepted design of such facilities and their contribution to energy transformation. In this paper, we investigate the perspectives of nature conservationists on PV in the region of Brandenburg, Germany. We comparatively analyse attitudes towards ground-mounted photovoltaics (GM-PV) and agrophotovoltaics (APV). APV combines energy supply and agricultural production on the same land and could thus be a possible solution for mitigating land-use conflicts.                                                           Results                     We investigated the degree of local acceptability and positive and negative influencing factors through a qualitative text analysis of ten interviews with local representatives and position papers by Nature Conservation Associations. Our findings show a growing consensus around basic assumptions of the need for renewable energies, the prioritised support for PV systems on rooftops over the installation on agricultural land (GM-PV and APV), and the necessity for PV systems to be compatible with nature conservation objectives. Regarding specific site decisions on agricultural land, we find diverging attitudes when comparing the content of position papers and the responses of interviewees. The interviewees advocate taking into account local interests and specific regional conditions, the effects of PV systems on the local environment, and the need for distributional justice. Large-scale plants are locally less accepted than smaller ones and there is a more open attitude towards the expansion of APV than of GM-PV on agricultural land. However, a range of concerns regarding consequences for landscape and biodiversity persists, and further research and clarification is required to address these issues.                                                           Conclusions                     We conclude that basic ecological standards and the demands of local stakeholders and interest groups should be taken into account when planning, implementing and reviewing PV projects in the future.
Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.
Personalized Medicine and Personalized Pricing: Degrees of Price Discrimination

Economics developed a set of three degrees of price discrimination dependent on whether the seller targets individuals or groups, and whether buyers wish to use quantity rebates. The seller’s reason to price discriminate is to capture as much of the buyers utility surplus. Price discrimination is deemed unfair and immoral, and this is especially so in the market for pharmaceutical therapies. However, sometimes it can indeed be socially useful to price discriminate as the practice, under circumstances, enhances efficiency and social welfare. The market for pharmaceuticals is a non-typical market as irreversible costs of research and development form the brunt of the cost structure. As pharmaceutical companies are driven by profit and bounded by patent expiration dates, discriminatory pricing schemes are necessary to recover investment costs of research and development as quickly as possible. The first degree of price discrimination consists of perfect, individually targeted, price/quality combinations that fully extract consumers’ surplus. The second-degree price discrimination consists of quantity rebates. The third degree of price discrimination is based on group targeting according to the group average willingness to pay. We introduce a fourth degree of price discrimination based on qualitative features of pharmaceuticals on a market for antiviral drugs. We use the SVR as an example of quality differentials causing market price differentials. The fourth type of discrimination would be of particular interest to the pharmaceutical industry and health management organisations as it introduces non-linear price- quality combinations.
RESPECTING CHILDREN’S RIGHTS THROUGH THE INSTITUTION OF MINORS PROTECTION BY PARENTS

Every society hopes and expects that the younger generation, meaning children, grow and become capable and responsible citizens that contribute to the welfare of the community. However in the whole world, children are denied the most elementary rights of normal development, active participation and even the survival. Therefore, both at international level and national level have been taken measures to respect children's rights and thus to protect them. Parental care is the care of the juvenile legal means consisting of all rights and duties relating to both person and property belonging of the child by both parents equally. The exercise of parental care is performed under control of the state, represented by the guardianship court. On how to exercise parental care usually is an accomplished both parents jointly and equally. But there are certain situations where it is not possible to apply this rule, given that one of the parents is impossible to exercise their rights and duties incumbent. In these cases exception is exercising parental care by one of the parents.
Benchmarking quantum computers for real-time evolution of a $(1+1)$ field theory with error mitigation

Quantum computers open the possibility of performing real-time calculations for quantum field theory scattering processes. We propose to use an index averaging the absolute value of the difference between the accurately calculated Trotter evolution of site occupations and their actual measurements on NISQ machines. The average is over all the qubits for a certain number of Trotter steps. We use this metric to quantify the progress made in successive state-of-the-art machines and error-mitigation techniques. We illustrate the concept with the transverse Ising model in one spatial dimension with four sites using three of IBM's quantum computers (Almaden, Boeblingen, and Melbourne). We discuss the size of the Trotter steps needed to achieve physics goals. Using the proposed metric, we show that readout mitigation methods and Richardson extrapolations of mitigated measurements are very effective for specific numbers of Trotter steps of a chosen size. This specific choice can be applied to other machines and noise mitigation methods. On the other hand, a reliable algorithmic mitigation would require a significantly larger number of smaller Trotter steps.