ethicalabs/Research-Intent-Collab huggingface dataset:
Climate adaptation to change in high-flows: Comparison of high-resolution climate model projections

Climate change is altering the water cycle from the global to the local scale. The increase in temperatures and changing precipitation patterns intensify not only mean values, but also the frequency and severity of extreme weather events, leading to alterations in water availability and distribution.This study assesses the impact of climate change on flood patterns (maximum annual river discharge) in Germany. Climate models ranging from different spatial scales will be compared for the five largest German catchments outlets (including headwaters). The climate model ensembles from the EURO-CORDEX initiative, and the ICON climate model from the Destination Earth Initiative / NextGEMS project will be used along with the mHM (mhm-ufz.org) model. The river discharge values produced from the mHM model will be used to calculate the Q90 (90th percentile of daily discharge) and the Qmax (maximum annual discharge) parameters.Initial results from the EURO-CORDEX initiative predict a 5-15% reduction in Q90 and Qmax in the summer half year, and a 5-30% increase in Q90 and Qmax in the winter half year, in the alpine regions in Germany. In the Elbe and Oder catchments (north-eastern part of Germany) there in a greater increase in Q90 and Qmax in the summer half year than the winter half year. This increase becomes more prominent with increasing warming. However, there is a large spread in the ensemble predictions, with uncertainty reducing with increasing warming. These parameters and results will be compared with the results from the ICON climate model to understand the contribution of spatial and/or temporal resoltion towards flood prediction.
Submission to the United Nations Special Rapporteur on the human rights to safe drinking water and sanitation during his Official Visit to Canada

&lt;p&gt;Canada’s record on climate action has been the object of a long line of criticism. Your visit and ongoing engagement with Canada provide a momentous opportunity to 1) highlight how this inadequate climate action threatens the human rights to safe drinking water and sanitation in Canada, in particular insofar as disadvantaged groups are concerned, 2) highlight how this inadequate climate action also threatens the human rights to safe drinking water and sanitation outside of Canada, also with a differential impact on disadvantaged groups, and 3) call for effective mitigation and adaptation action aligned with Canada’s international obligations. After providing an overview of Canada’s climate record, I discuss all three issues. &lt;/p&gt; &lt;p&gt;&lt;em&gt;&lt;strong&gt;April 11, 2024&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;
Beyond Exchangeability: Distribution-Shift-Aware Integration of External Control Data in Randomized Trials

Randomized controlled trials (RCTs) are the gold standard for evaluating causal effects but are often costly and difficult to scale; consequently, they are frequently augmented with auxiliary external controls in many applications. Prior approaches for borrowing such data typically rely on exchangeability, under which the external controls are readily usable for inference in the trial population. In practice, however, differences in eligibility criteria, standard of care, and data collection procedures may induce distribution shifts between the RCT and the external controls, rendering exchangeability implausible. In this paper, we propose a novel framework for integrating external controls by explicitly modeling these distribution shifts. We construct augmented estimators by adapting trial-only efficient influence functions through calibration equations that balance the trial and external populations, thereby fully exploiting the external control data even when exchangeability fails. We further develop an adaptive shrinkage estimator that preserves consistency while guaranteeing efficiency dominance over the trial-only benchmark. Synthetic experiments and a real data application demonstrate the practical advantages of the proposed approaches.
The long-term modulation of galactic cosmic rays in the heliosphere

Abstract   The present declining phase of the solar modulation cycle is important to cosmic ray modulation because it should show to what extent gradient and curvature drifts of charged particles through the polar regions of the heliosphere determine the shape of the intensity-time (IT) profiles of long-term modulation. It is well known that standard drift models predict a clear 22-year cycle with alternating peaked-flattened 11-year profiles at all relevant energies. It has also become evident that the IT profile for the 1954 minimum modulation period was not nearly as flat as the 1976 profile. The 1965 and 1987 profiles, however, were perfectly peaked in direct correspondence to what drift models predict. The forthcoming solar minimum modulation period should therefore be most interesting: if not as flat as in 1976, it may indicate that drifts do not occur to the same extent as in 1972–1978 and that other modulation features e.g., merged interaction regions are important in determining the shape of the 22-year cycle. On the other hand, it may imply that predictions by current models are too idealistic because they are based on oversimplified assumptions e.g., a Parker magnetic field in the polar regions of the heliosphere. In this paper it will be discussed what modulation effects are important for the declining phase of the solar cycle. In this review some recent general observations of the long-term modulation of galactic cosmic rays in the heliosphere and the progress made in the modeling thereof will be summarized.
Social sciences via network analysis and computation

Contents: Joze Bucar/Janez Povh/Andrej Dobrovoljc: Web Clipping and Sentiment Analysis of Slovenian news articles - Renato Barisic/Joze Bucar: Machine Learning in Classification of News Portal Articles - Hana Rizvic/Sanda Martincic-Ipsic/Ana Mestrovic: Network Motifs Analysis of Croatian Literature - Sabina Sisovic/Sanda Martincic-Ipsic/Ana Mestrovic: Toward Network-based Keyword Extraction from Multitopic Web Documents - Darko Zelenika/Andrej Dobrovoljc/Robert Pezdirc/Helena Novosel/Simon Kegljevic/Janez Povh/Bernard Zenko: Automatic invoice capture in small and medium-sized Slovenian enterprises - project report - Bostjan Delak: Information System Mirror - Approach How to Analyze Information System Strengths and Weaknesses within Organization - Stefana Janicijevic/Dragan Urosevic/Nenad Mladenovic: Comparison of SAS/STAT Procedures and Variable Neighborhood Search Based Clustering Applied on Telekom Serbia Data - Rok Piltaver/Mitja Lustrek/Matjaz Gams/Sanda Martincic-Ipsic: Measuring Classification-Tree Comprehensibility - Borut Luzar/Mirko Petrusevski/Riste Skrekovski: On vertex-parity and weak vertex-parity edge-colorings - Vesna Andova/Frantisek Kardos/Riste Skrekovski: Diameter on some classes of fullerene graphs.
Human trafficking and the Rome statute of the International Criminal Court.

The case for extending the reach of the Rome Statute to the crime of human trafficking has not yet been made in detail. The brutality which occurs when human beings are trafficked by criminal gangs is of an equally egregious nature as the other crimes covered by the Rome Statute and yet it does not fall within the remit of the International Criminal Court. Such trafficking may also fall outwith the definition of slavery as a crime against humanity, particularly given the State policy threshold set by the Statute. This paper seeks to explore the viability of the inclusion of human trafficking as a discrete international crime within the Rome Statute as a response to this loophole.
The Reporting of Corporate Social Responsibility (CRS): Information Sources and Documentation

The compromising towards the CSR includes not only the good global government, discipline, sense of duty, transparency and accountability, but also ethical compromising, participation and dialogue with stakeholders. This article aims to contribute to the establishment of a more structurally of Reporting Corporate -information and documentation of CSRoriented framework for analyzing the Social Responsibility (SR) of the enterprise. To this end, the article resumes the main actions and instruments of reporting integrating the Corporate Social Responsibility (CSR) and the principles of the Sustainability Developed by the systems management of organizations private sector/public sector.
An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery

Semantic segmentation is an essential part of deep learning. In recent years, with the development of remote sensing big data, semantic segmentation has been increasingly used in remote sensing. Deep convolutional neural networks (DCNNs) face the challenge of feature fusion: very-high-resolution remote sensing image multisource data fusion can increase the network's learnable information, which is conducive to correctly classifying target objects by DCNNs; simultaneously, the fusion of high-level abstract features and low-level spatial features can improve the classification accuracy at the border between target objects. In this paper, we propose a multipath encoder structure to extract features of multipath inputs, a multipath attention-fused block module to fuse multipath features, and a refinement attention-fused block module to fuse high-level abstract features and low-level spatial features. Furthermore, we propose a novel convolutional neural network architecture, named attention-fused network (AFNet). Based on our AFNet, we achieve state-of-the-art performance with an overall accuracy of 91.7% and a mean F1 score of 90.96% on the ISPRS Vaihingen 2D dataset and an overall accuracy of 92.1% and a mean F1 score of 93.44% on the ISPRS Potsdam 2D dataset.
Deep Learning-Based Survival Analysis and Recurrence Prediction in Breast Cancer Patients Using Clinical and Genomic Data

Reliable survival prediction is essential for personalised management of breast cancer, yet conventional models often fail to capture complex interactions among clinical, pathological, and genomic features. This study applied a deep learning framework (DeepSurv) to a cohort of 2,509 breast cancer patients, integrating clinical, histopathological, and genomic data to predict overall survival (OS) and relapse-free survival (RFS). Exploratory analysis revealed a median age at diagnosis of 60.4 years, a median tumour size of 26 mm, and a median of 0 positive lymph nodes, with a relapse rate of ~40%. DeepSurv demonstrated superior predictive performance compared to classical Cox regression, achieving C-index values of 0.7567 (OS) and 0.6495 (RFS) versus 0.7038 (OS) and 0.6403 (RFS) for Cox models. SHAP analysis identified positive lymph nodes, tumour grade, tumour size, age, and Nottingham Prognostic Index (NPI) as the most influential predictors, while mutation count and treatment variables contributed moderately. Survival curves indicated higher individualised survival probabilities with DeepSurv, reflecting improved sensitivity to patient-specific risk patterns. Classical Cox regression performed adequately but exhibited reduced discriminatory power, particularly for RFS. These findings demonstrate that deep learning models can integrate multi-modal data, enhance predictive accuracy, and maintain interpretability, supporting patient stratification and informed clinical decision-making. Future work should incorporate additional molecular and treatment-response data to improve relapse prediction further.
Marketability and sustainability of food security programmes: products and productivity of agricultural projects

This paper addresses the marketability and sustainability of food security programmes in Limpopo Province. Food security features prominently because poverty and inequality remains a huge challenge in South Africa’s rural sector. Thus the Government has initiated the establishment of agricultural community projects as part of interventions for creating jobs and improving income levels. However, lack of monitoring mechanisms in established projects create a challenge of nonsustainability of these projects. The study has used formative evaluation approach to determine the effectiveness of the established food security programme. A mixed model approach was used to collect data from key informants and project members. Descriptive statistics were computed using SPSS. Most projects were on vegetable, poultry and piggery production. The study has found that 64.1% of the respondents reported that access to inputs was not a challenge. Project products are sold to community members who accounted to 79%, and few (1%) to individuals owning business, clinics and outside the community. Project members advertised their produce mainly verbally (47.2%). Marketing strategies for project products were lacking and this creates a negative impact on income generated and sustainability of projects. The paper concludes by suggesting that project members should be advised on appropriate marketing strategies.S.Afr. Tydskr. Landbouvoorl./S. Afr. J. Agric. Ext., Vol. 40 2012: 1 – 15
POPULATION GROWTH, FOOD SECURITY, AND SUSTAINABLE AGRICULTURAL DEVELOPMENT UNDER CLIMATE CHANGE

In the context of rapid global population growth and increasing climate instability, ensuring food security is becoming a critical challenge at both the global and regional levels. This article provides a comprehensive analysis of the current global food crisis and examines statistical data on global food production and distribution. Particular attention is paid to the state and specific features of agricultural development in the Republic of Kazakhstan, with a focus on the Kostanay region as one of the country’s key grain-producing areas. Based on long-term climatic observations and grain yield data, a stochastic forecasting model of crop yield is developed, taking into account the influence of climatic factors. The research findings make it possible to identify the main threats and constraints to the sustainable development of agriculture under climate change conditions, as well as to substantiate directions for adaptation measures aimed at enhancing the economic sustainability of agricultural production and strengthening regional food security. The practical significance of the study lies in the potential application of the obtained results in the development of regional programs for agro-industrial development and the adaptation of the agricultural sector of the regional economy to climate change.
Cosmic Microwave Background and  Large Scale Structure Power Spectrum  Estimation with Guided Hamiltonian  Sampling

Master's thesis submitted in partial completion of MSc degree at University College London
Governance and Ethical Oversight of Agentic Artificial Intelligence in Business Process Management: Balancing Human-autonomy, Organizational Accountability and Algorithmic Transparency

This work dwells on Governance and ethical oversight of agentic artificial intelligence in business process management: balancing human-autonomy, organizational accountability and algorithmic transparency with particular reference to four specific objectives: To determine the relationship between human autonomy and ethical decision-making effectiveness in AI-supported BPM, to examine the role of organizational accountability in mitigating governance risks associated with agentic AI, to assess how algorithmic transparency influences trust, ethical compliance, and stakeholder confidence in business operations, to determine an empirically validated governance framework that balances autonomy, accountability, and transparency in AI-driven BPM. The population of the study comprised 325 managers of medium to large organizations employing agentic or semi-autonomous AI systems within their BPM operations. A mixed method approach was utilized, that is, both quantitative and qualitative data were used. Quantitative data were analyzed using SPSS version 29 and Smart PLS 4.0. Descriptive statistics were computed to profile respondents, while Partial Least Squares Structural Equation Modeling (PLS-SEM) tested the hypothesized relationships among governance, autonomy, accountability, and transparency variables while the qualitative data were analysed through thematic analysis using NVivo 14. Results revealed human autonomy positively influences responsible AI governance, organizational accountability exerts the strongest predictive influence on governance effectiveness, and algorithmic transparency significantly enhances trust and ethical compliance. These findings advance the argument that agentic AI governance is not purely a technological problem but an organizational capability, dependent on leadership vision, institutional ethics, and transparent process design. The integration of these dimensions strengthens both ethical resilience and competitive advantage in digital transformation contexts. We recommend that organizations need to establish permanent AI Governance Boards comprising legal, ethical, and technical experts. These boards must oversee AI deployment, set ethical guidelines, and monitor compliance. This institutionalization ensures that decision-making autonomy is balanced by structured accountability mechanisms.
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images

Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge, mainly due to the manual assessment of hematoxylin and eosin-stained whole slide images, which is a time-consuming process and may lead to variability in decision-making. For accurate detection of HCC, we propose a hybrid deep learning-based architecture that uses transfer learning to extract the features from pre-trained convolutional neural network (CNN) models and a classifier made up of a sequence of fully connected layers. This study uses a publicly available The Cancer Genome Atlas Hepatocellular Carcinoma (TCGA-LIHC)database (n=491) for model development and database of Kasturba Gandhi Medical College (KMC), India for validation. The pre-processing step involves patch extraction, colour normalization, and augmentation that results in 3920 patches for the TCGA dataset. The developed hybrid deep neural network consisting of a CNN-based pre-trained feature extractor and a customized artificial neural network-based classifier is trained using five-fold cross-validation. For this study, eight different state-of-the-art models are trained and tested as feature extractors for the proposed hybrid model. The proposed hybrid model with ResNet50-based feature extractor provided the sensitivity, specificity, F1-score, accuracy, and AUC of 100.00%, 100.00%, 100.00%, 100.00%, and 1.00, respectively on the TCGA database. On the KMC database, EfficientNetb3 resulted in the optimal choice of the feature extractor giving sensitivity, specificity, F1-score, accuracy, and AUC of 96.97, 98.85, 96.71, 96.71, and 0.99, respectively. The proposed hybrid models showed improvement in accuracy of 2% and 4% over the pre-trained models in TCGA-LIHC and KMC databases.
Utilizing the assassin bug, <i>Pristhesancus plagipennis</i> (Hemiptera: Reduviidae), as a biological control agent within an integrated pest management programme for <i>Helicoverpa</i> spp. (Lepidoptera: Noctuidae) and <i>Creontiades</i> spp. (Hemiptera: Miridae) in cotton

AbstractHelicoverpa spp. and mirids, Creontiades spp., have been difficult to control biologically in cotton due to their unpredictable temporal abundance combined with a cropping environment often made hostile by frequent usage of broad spectrum insecticides. To address this problem, a range of new generation insecticides registered for use in cotton were tested for compatibility with the assassin bug, Pristhesancus plagipennis (Walker), a potential biological control agent for Helicoverpa spp. and Creontiades spp. Indoxacarb, pyriproxifen, buprofezin, spinosad and fipronil were found to be of low to moderate toxicity on P. plagipennis whilst emamectin benzoate, abamectin, diafenthiuron, imidacloprid and omethaote were moderate to highly toxic. Inundative releases of P. plagipennis integrated with insecticides identified as being of low toxicity were then tested and compared with treatments of P. plagipennis and the compatible insecticides used alone, conventionally sprayed usage practice and an untreated control during two field experiments in cotton. The biological control provided by P. plagipennis nymphs when combined with compatible insecticides provided significant (P&lt;0.001) reductions in Helicoverpa and Creontiades spp. on cotton and provided equivalent yields to conventionally sprayed cotton with half of the synthetic insecticide input. Despite this, the utilization of P. plagipennis in cotton as part of an integrated pest management programme remains unlikely due to high inundative release costs relative to other control technologies such as insecticides and transgenic (Bt) cotton varieties.
Analysis of Data Warehouse for Transactional Analysis Methods Online Analytical Processing (OLAP) at Company XYZ Silver

Utilization of information technology is a necessity in looking at opportunities available for decision making by the management. With the ability of information technology to analyze existing used the data into useful information for a company. OLAP is Able to Overcome problems in the data processing mechanisms guided to know various information from different angles. By utilizing transactional analysis, then we know the reaction of customers in choosing the products that we will market. The result of Web-based sales has Decreased for the Gianyar and Denpasar regions but has Increased for the Ubud and online areas so as to give a benefit in terms of production costs. With the training for a sales increase of sales can evenly across locations and need input from customers related to minimize returns.