ethicalabs/Research-Intent-Collab huggingface dataset:
Climate Change Litigation and Enforcement in South African Environmental Law Context,

Climate change litigation has emerged as a significant legal avenue for addressing environmental challenges in South Africa, particularly within the ambit of environmental law enforcement. Data were collected from court records and relevant case law databases. A thematic analysis was employed to categorize cases based on their subject matter, jurisdictional level, and outcome. Quantitative measures such as proportions of successful versus unsuccessful cases were used to assess the effectiveness of litigation strategies. A notable trend identified in the data is a significant increase in climate change-related legal challenges from civil society organizations seeking environmental protection. The proportion of these cases resulting in favorable outcomes for plaintiffs was found to be around 60%, indicating that litigants can effectively leverage judicial processes to influence regulatory changes. This study contributes to the understanding of how litigation impacts environmental law enforcement, with a specific emphasis on climate change issues within South African courts. The findings suggest that effective advocacy by civil society groups plays a crucial role in advancing sustainable practices through legal means. Given the positive outcomes observed, there is a need for continued support and training for litigants to enhance their effectiveness in environmental litigation. Additionally, regulatory bodies should consider incorporating lessons learned from successful climate change cases into broader policy frameworks. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
On the Finite-Size Behavior of one Basic Model of Statistical Mechanics Describing Second Order Phase Transition

ABSTRACT: We present a short review and some new results on the finite-size behavior of finite systems exhibiting second order phase transition. We will be mainly focused on one basic model of statistical mechanics – the GinzburgLandau φ 4 model under the influence of temperature bath and an external ordering field.
Design Model of Digital Dashboard Human Resources Information System based on Competence (Case Study at PT. Daya Adicipta Mustika)

CBHRM is one of the patterns of human resource management is being grown in Indonesia. In this approach, the vocabulary of competence to be a key element. Each employee will be assessed on its competence. In order results can be more optimally achieved, it would require a model of presentation of the good report/well output. One of the solutions the authors propose is to use a digital model of the dashboard. The model was built using the dashboard frame of mind which has been adapted to the life cycle model. Case studies conducted at PT. Daya Adicipta Mustika, and digital dashboards built CBHRM consist of Talent Dashboard, Dashboard Gap Competencies, and Comparison Dashboard interrelated with each other.
A Comprehensive Review of Vaccine Development: From Traditional Platforms to Messenger RNA (mRNA) Technologies

The trend of vaccine development over the last few years is that the traditional platform has been abandoned and moved towards newer modalities, and the current COVID-19 pandemic has accelerated this shift. Classical approaches (such as inactivated, live attenuated, and recombinant) can be shown to have reduced the burden of infectious diseases; however, they are also limited by their inability to scale production and prolonged development, as well as cold-chain limitations. The lacks became apparent through the pandemic and drove the demand for more agile, adaptive technology that COVID-19 has highlighted. The current review questions the goal of streamlining the immunization approaches in the face of emerging pathogens through a structured narrative comparison of immunogenicity, safety, efficacy, and platform stability of modern vaccination platforms within a narrative review framework. This analysis is anchored by a narrative synthesis of immunological, regulatory, and clinical literature between 2018 and 2025. Messenger RNA (mRNA)-based vaccines have demonstrated strong immunogenicity and practical efficacy in the real world, as well as varying safety profiles across platforms and the potential of new modalities that will be developed both to treat infectious diseases and to be applied in other contexts. The discussion also dwells upon the flexibility of regulation, unequal distribution, and surveillance-based pharmacovigilance. This review can be used to compile a thoroughly comparative view of modern vaccines, based on bringing together mechanistic insights and implementation barriers, which can guide future innovation and policy reconciliation and global resilience. It makes inferences in support of the more progressive, equity-based paradigm of pandemic preparedness and immunization strategy in the 21st century. This review highlights how mRNA technology has transformed the landscape of vaccinology, offering a foundation for faster, safer, and more equitable global immunization strategies in the post-pandemic era.
Predicting the Temporal Variability of Error Rates in Superconducting Quantum Processors: Mitigating Error Accuracy Loss in Queue-Based Quantum Computing Platforms [Focus: Quantum Software and its Engineering]

This work is supported by the QuantERA grant EQUIP with Grants PCI2022-133004 and PCI2022-132922, funded by Agencia Estatal de Investigacion, Ministerio de Ciencia e Innovacion, Gobierno de Espana, MCIN/AEI /10.13039/501100011033, and by the European Union "NextGenerationEU/PRTR." CGA acknowledges funding from the Spanish Ministry of Science, In-novation, and Universities through the Beatriz Galindo program 2020 (BG20-00023), from the European ERDF under Grant PID2021-123627OB-C51, and from the Ministry for Digital Transformation and of Civil Service of the Spanish Government through the QUANTUM ENIA project call-Quantum Spain project, and by the European Union through the Recovery, Transformation and Resilience Plan - NextGenerationEU within the framework of the Digital Spain 2026 Agenda. We acknowledge the use of IBM Quantum services for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Quantum team.
AI for Climate Resilience: Hazard Forecasting, Vulnerability Mapping, and Adaptation Planning

Abstract: This manuscript develops an academically integrated account of how artificial intelligence can strengthen climate resilience through hazard forecasting, vulnerability mapping, and adaptation planning. Rather than presenting climate analytics as a loose collection of models, it frames the field as a research and governance domain in which uncertainty, data generation, causal reasoning, machine learning, engineering infrastructure, and institutional accountability must be aligned. The volume begins with conceptual foundations and research design for climate-risk inquiry, then advances to statistical modelling for explanation and causal inference, examines machine learning for prediction and trustworthy generalization, and situates these analytical capabilities within scalable data engineering and reproducible MLOps. The closing chapter interprets climate intelligence through applied sectors such as agriculture, water systems, cities, public health, infrastructure, finance, and public administration. Throughout the manuscript, emphasis is placed on realistic operational contexts across South Asia, Europe, Africa, and the Americas in order to show that climate risk is always regionally conditioned even when analytical methods travel across settings. The principal scholarly contribution is a coherent framework linking models to decision protocols, metrics to governance artifacts, and technical performance to resilient public value for researchers, practitioners, and policymakers. Keywords climate resilience, hazard forecasting, vulnerability mapping, adaptation planning, causal inference, machine learning, trustworthy AI, geospatial analytics, MLOps, reproducibility, disaster governance, early warning systems, spatial statistics, policy analytics, risk communication, public value
A comparison of the performance of the finite difference time‐domain, finite element time‐domain, and planar generalized Yee algorithms of high‐performance parallel computers

AbstractParallel algorithms for the finite difference time‐domain (FDTD), the planar generalized Yee (PGY), and the finite element time‐domain (FETD) methods are presented. The FDTD and the PGY algorithms are both explicit time‐domain solutions of Maxwell's equations, while the PGY algorithm is based on an unstructured grid. The FETD algorithm is a semi‐implicit solution of Maxwell's equations using variational principles, and thus requires a matrix inversion for every time iteration. The three parallel algorithms are based on spatial decompositions of the discrete three‐dimensional problem spaces. A comparative analysis of the parallel algorithms is presented based on their memory and computational efficiency as well as their parallel efficiency.
Ownership of mainstream media devices and digital access tools predict health insurance coverage status among Kenyans: A cross-sectional study of the Kenya Demographic and Health Survey (KDHS) 2022 dataset

Abstract                                    Background                   Low health insurance coverage, poverty, and reduced donor funding impede access to healthcare in Kenya. The Social Health Authority (SHA) unveiled in Kenya in October 2024 can provide the healthcare financing solutions needed through targeted interventions to improve health insurance coverage. This study sought to identify modifiable factors that influence health insurance coverage status in Kenya to provide the basis for targeted interventions by SHA.                                                     Methods                   The Household Recode, Individual Recode, and Men’s Recode datasets from the Kenya Demographic and Health Survey conducted between February and July 2022 were combined to form the dataset. Proportions of individuals with and without health insurance as well as those with the various potential determinants were calculated. The associations between the potential determinants and health insurance coverage status were estimated using bivariate analysis through Pearson’s Chi-square test. Multivariable logistic regression facilitated the identification of determinants of health insurance coverage in Kenya.                                                     Results                   Data of 14232 participants aged 15-54 years whose health insurance coverage status were indicated were analyzed. The participants were mainly female (66%), in good health (79%), literate (75%), relatively poor (56%), connected to electricity (55%), and radio listeners (61%). The rate of health insurance coverage was 34%, with 93% of them covered by NHIF. Out of the 16 potential predictors (chi-square range = 21-2694, p &lt; 0.0001) included in the logistic regression model, only six were significant predictors of health insurance coverage: education level, wealth index, ownership of a solar panel, and television, mobile phone, and computer ownership (14-47% difference in odds).                                                     Conclusion                   Health insurance coverage remains low in Kenya. Education levels, economic status, and media factors are significant determinants of health insurance coverage. SHA can leverage the identified determinants to strengthen interventions that worked for NHIF and address the challenges that impede health insurance uptake among Kenyans in the informal sector.
Development cooperation in water and sanitation: is it based on the human rights framework?

Abstract The water and sanitation sector is verifiably receiving increased attention and funding through international development cooperation. Not least because of the way that it affects incentives and institutions in partner countries, development cooperation can have either positive or negative effects on human rights though. The consolidated frameworks for the human rights to water and sanitation is becoming linked to the international community’s coordinated development efforts, as evidenced notably in the 2030 Agenda for Sustainable Development. However, a review of major funders’ official policies for development cooperation in the sector suggests that many only partially endorse the frameworks for the human rights to water and sanitation. An observation of development cooperation flows to the sector allows the hypothesis to be advanced that worldwide inequalities in access to these services may be reduced through a full and clear application of the human rights framework in development cooperation activities. The article presents findings of this research and explores key stakes for development cooperation in the water and sanitation sector that are relevant for their ability to either negatively or positively contribute to the realization of human rights.
The Impact of post-retirement careers and executive appointments on judicial independence of current judges in Kenya

Judges constantly face actions from the Executive that undermine their judicial independence. In the recent years Kenya and the rest of the world have placed measures meant to prevent the Executive from controlling the Judiciary. Such measures include inclusion judicial independence in constitutions and international conventions, secure tenure and creation of institutions that regulate the nomination and confirmation process of Judges. These measures have succeeded in protecting the independence of the Judiciary. The means used by the Executive in attempted to control the Judiciary have however evolved requiring that the protection given to the Judiciary be strengthened as well. This paper focuses on the influence of Executive employment decisions, particularly the appointment of retired judges, on current judges within the Judiciary, as one of the methods employed by the Executive to gain control of the Judiciary. To achieve this, this paper discusses the judge as person as the conceptual underpinning of the study. The paper discusses judicial independence to understand its meaning, its application in Kenya and both recent and past attempts and response by the Judiciary to undermine judicial independence. It also discusses the effect the appointment of retired judges has on decision making of judges within the Judiciary. Lastly it recommends adoption of laws aimed at controlling the employment opportunities of judges. The recommendations are drawn from nations with laws that control the employment options of judges while balancing the right to employment held by retired judges.
Coordinated epigenetic regulation of Engrailed-1 by the chromatin remodelers Smarca1 and Smarca5 mediates cerebellar morphogenesis

Background Morphological patterning of the cerebellum requires precise changes in Engrailed homeotic gene expression yet the mechanisms controlling this process remain elusive. Here, we show that the Iswi chromatin remodeling proteins, Smarca5 and Smarca1, are required for the dynamic regulation of Engrailed-1 (En1). Conditional Smarca5-null mice display abnormal cerebellar foliation, ataxia-like symptoms and young mortality. Postnatal granule neuron progenitor expansion and Purkinje cell (PC) development are compromised and attributed to loss of En1 expression. Mutants survive to early adulthood via upregulation of Smarca1 and restoration of En1 expression in PCs, while ablation of both Iswi genes results in lethality at birth. During late cerebellar development, we observe co-binding of the Iswi proteins at the En1 locus and an altered H2AZ/ H3.3 chromatin profile that accompanies changes in En1 expression.
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior

In sensing applications, sensors cannot always measure the latent quantity of interest at the required resolution, sometimes they can only acquire a blurred version of it due the sensor's transfer function. To recover latent signals when only noisy mixed measurements of the signal are available, we propose the Gaussian process mixture of measurements (GPMM), which models the latent signal as a Gaussian process (GP) and allows us to perform Bayesian inference on such signal conditional to a set of noisy mixture of measurements. We describe how to train GPMM, that is, to find the hyperparameters of the GP and the mixing weights, and how to perform inference on the latent signal under GPMM; additionally, we identify the solution to the underdetermined linear system resulting from a sensing application as a particular case of GPMM. The proposed model is validated in the recovery of three signals: a smooth synthetic signal, a real-world heart-rate time series and a step function, where GPMM outperformed the standard GP in terms of estimation error, uncertainty representation and recovery of the spectral content of the latent signal.
Value of robotic surgery simulation for training surgical residents and attendings: a systematic review protocol

Introduction                     Robotic surgery is a method of minimally invasive surgery performed through small incisions using a remote robotic console. Surgical residents and attendings participate in simulation training to be able to effectively perform robotic surgery using wet labs, dry labs and virtual reality platforms. Our objective is to identify the effectiveness of robotic simulation on novice robotic surgeons. This review will answer our review question: To what extent are robotic simulations for training novice robotic general surgery residents and attendings associated with improved outcomes in comparison with no simulation training?                                                           Methods and analysis                     A comprehensive search of PubMed, Embase, the Cochrane Library and Web of Science was performed. The studies were then determined to meet initial screening criteria by one individual for abstract and title with full text screening performed by two authors independently and in duplicate. Narrative themes will be collected, analysed and summarised where possible.                                                           Ethics and dissemination                     There is no Institutional Review Board approval required given that the work is carried out on previously published papers. The final manuscript and results will be presented and published at an academic conference and peer -reviewed journal.                                                           PROSPERO registration number                     CRD42021274090.
Advances in Anti-Cancer Immunotherapy: Car-T Cell, Checkpoint Inhibitors, Dendritic Cell Vaccines, and Oncolytic Viruses, and Emerging Cellular and Molecular Targets

Unlike traditional cancer therapies, such as surgery, radiation and chemotherapy that are typically non-specific, cancer immunotherapy harnesses the high specificity of a patient’s own immune system to selectively kill cancer cells. The immune system is the body’s main cancer surveillance system, but cancers may evade destruction thanks to various immune-suppressing mechanisms. We therefore need to deploy various immunotherapy-based strategies to help bolster the anti-tumour immune responses. These include engineering T cells to express chimeric antigen receptors (CARs) to specifically recognise tumour neoantigens, inactivating immune checkpoints, oncolytic viruses and dendritic cell (DC) vaccines, which have all shown clinical benefit in certain cancers. However, treatment efficacy remains poor due to drug-induced adverse events and immunosuppressive tendencies of the tumour microenvironment. Recent preclinical studies have unveiled novel therapies such as anti-cathepsin antibodies, galectin-1 blockade and anti-OX40 agonistic antibodies, which may be utilised as adjuvant therapies to modulate the tumour microenvironment and permit more ferocious anti-tumour immune response.
Review of artificial intelligence methods for video content generation and their application in building digital twins of situation centers

The paper surveys the theoretical foundations and the current state of AI-based video generation. The main model families are discussed, including GAN approaches, diffusion models, and transformer-based (autoregressive and masked) architectures, with an emphasis on mechanisms that improve spatial-temporal consistency, controllability (conditioning on text or images) and scaling to longer clips. Key groups of video quality metrics are summarized (perceptual, distributional, semantic and motion/dynamics), and the main practical constraints are outlined: computational cost, generalization to new domains, data requirements, and safety measures against deepfake misuse (watermarking, copyright and compliance). It is shown how video generation can strengthen digital twins of situation centers by supporting “what – if” visualization and staff training.
Charged Particle and Photon Multiplicity, and Transverse Energy Production in High-Energy Heavy-Ion Collisions

We review the charged particle and photon multiplicity, and transverse energy production in heavy-ion collisions starting from few GeV to TeV energies. The experimental results of pseudorapidity distribution of charged particles and photons at different collision energies and centralities are discussed. We also discuss the hypothesis of limiting fragmentation and expansion dynamics using the Landau hydrodynamics and the underlying physics. Meanwhile, we present the estimation of initial energy density multiplied with formation time as a function of different collision energies and centralities. In the end, the transverse energy per charged particle in connection with the chemical freeze-out criteria is discussed. We invoke various models and phenomenological arguments to interpret and characterize the fireball created in heavy-ion collisions. This review overall provides a scope to understand the heavy-ion collision data and a possible formation of a deconfined phase of partons via the global observables like charged particles, photons and the transverse energy measurement.