ethicalabs/Research-Intent-Collab huggingface dataset:
Surgical robot simulation with BBZ console

This paper presents a lean approach to training in robot assisted surgery. Minimally Invasive Surgical procedures can be decomposed in a sequence of tasks, each surgical task can be further decomposed in basic gestures. Each surgical gesture seems similar to perform rather in laparoscopic than in robot assisted technique, but surgeon posture, tools dexterity, force and vision feedback are different. As a consequence, performing a robot-assisted procedure needs specific training. Currently, the most used robot in in abdominal and pelvic surgery is the da Vinci Surgical System and a different set of skills is needed to master the human-machine interface of this device. The training with the real robot is very expensive due to the high initial cost of purchasing and maintaining the robotic surgical system, and the ethic involved in vivo practice. For these reasons, different training systems based on virtual reality were developed. The simulation physics realism and the objective metrics collected during the task execution are the main features for the effectiveness of a virtual reality based training device. Availability of training systems is another issue. To help surgeons to train in virtual reality, BBZ presents a compact, lightweight and portable console, suitable also for home training.
Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients

This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented.
Non-refoulement in International Refugee Law, Human Rights Law and Asylum Laws

The twenty-first century has born witness to several armed conflicts leading to displacement within countries, regions or across the globe that underline the need for a solid legal framework. The principle of non-refoulement is a cornerstone of refugee protection and is codified in refugee law, human rights laws and asylum laws. In light of current State practices of migration control on one hand and the net of diverse non-refoulement obligations in international law on the other hand, it is essential to clarify and compare the scope of its protection for people fleeing armed conflicts.
Will the Occupational Safety and Health Administration's Proposed Standards for Occupational Exposure to Respirable Crystalline Silica Reduce Workplace Risk?

The Occupational Safety and Health Administration (OSHA) is developing regulations to amend existing standards for occupational exposure to respirable crystalline silica by establishing a new permissible exposure limit as well as a series of ancillary provisions for controlling exposure. This article briefly reviews OSHA's proposed regulatory approach and the statutory authority on which it is based. It then evaluates OSHA's preliminary determination of significant risk and its analysis of the risk reduction achievable by its proposed controls. It recognizes that OSHA faces multiple challenges in devising a regulatory approach that reduces exposures and health risks and meets its statutory goal. However, the greatest challenge to reducing risks associated with silica exposure is not the lack of incentives (for either employers or employees) but rather lack of information, particularly information on the relative toxicity of different forms of silica. The article finds that OSHA's proposed rule would contribute little in the way of new information, particularly since it is largely based on information that is at least a decade old—a significant deficiency, given the rapidly changing conditions observed over the last 45 years. The article concludes with recommendations for alternative approaches that would be more likely to generate information needed to improve worker health outcomes.
Conserving Biodiversity: Practical Guidance about Climate Change Adaptation Approaches in Support of Land-use Planning

ABSTRACT: As species' geographic ranges and ecosystem functions are altered in response to climate change, there is a need to integrate biodiversity conservation approaches that promote natural adaptation into land use planning. Successful conservation will need to embrace multiple climate adaptation approaches, but to date they have not been conveyed in an integrated way to help support immediate conservation planning and action in the face of inherent spatial uncertainty about future conditions. Instead, these multiple approaches are often conveyed as competing or contradictory alternatives, when in fact, they are complementary. We present a framework that synthesizes six promising spatially explicit adaptation approaches for conserving biodiversity. We provide guidance on implementing these adaptation approaches and include case studies that highlight how biodiversity conservation can be used in planning. We conclude with general guidance on choosing appropriate climate adaptation approaches to amend f...
Analysing Waste Management Narratives in Corporate Social Responsibility Reports

Sustainability reporting, particularly through Corporate Social Responsibility (CSR) disclosures, plays a critical role in assessing corporate contributions to environmental and social goals. While much attention has been given to other environmental dimensions in CSR reports, waste management remains an underexplored yet significant area. This research addresses this gap by developing methodologies to analyze and classify waste management disclosures, distinguishing between substantive and symbolic CSR reporting. A key challenge in CSR reporting is the presence of boilerplate language and greenwashing, where companies present sustainability commitments without substantive action. This study differentiates symbolic CSR, which prioritizes corporate image over tangible sustainability efforts, from substantive CSR, which reflects genuine, measurable contributions to waste management. Using a pre-processed dataset of 9,500 CSR reports across multiple industries, the research employs natural language processing (NLP) techniques, including Stanford's Stanza NLP model, to automate the classification of CSR disclosures. The methodology involves sentence segmentation, dependency parsing, and keyword-based classification, ensuring a robust distinction between symbolic and substantive statements. Furthermore, this research examines how waste management disclosures vary across different industry sectors and evaluates their alignment with the Sustainable Development Goals (SDGs). By integrating computational text analysis with manual validation, the study enhances the methodological rigor in assessing CSR transparency. The findings contribute to both theoretical and practical discussions on CSR reporting, offering insights for policymakers, investors, and sustainability advocates seeking to hold corporations accountable for their environmental commitments.
CONFLICT RESOLUTION AND PEACE BUILDING: THE GENDER QUESTION IN OLAMMA BY TRACIE EZEAJUGH

The paper examines the strategic approaches adopted by women and the socio-cultural obstacles that impede women‟s active participation in conflict resolution and peace building process as seen in the play understudy. African nations have been ravaged by conflict, resulting in destabilization, displacement, and infrastructural destruction, all of which have gender-specific consequences. Women are often portrayed as victims of conflict; and in large part, they are. Yet they are largely neglected once peace occurs. Despite active role in war, women are often neglected in the post-conflict situation and in peace negotiations. Studies have shown that despite the fact that women play active roles in developing coalitions across fighting groups within and outside Nigeria, women are neither represented nor consulted in peace negotiations. Often discrimination against women in post-conflict land settlement buttresses this fact. This is essential for sustaining peace.
What Aspects of Quality of Life are Important from Palliative Care Patients’ Perspectives? A Framework Analysis to Inform Preference-Based Measures for Palliative and End-of-Life Settings

Preference-based outcome measures are commonly applied in economic analyses to inform healthcare resource allocation decisions. Few preference-based outcome measures have been specifically developed for palliative and end-of-life settings. This study aimed to identify which quality-of-life domains are most important to Australians receiving specialised palliative care services to help determine if the development of a new condition-specific preference-based outcome measure is warranted.In-depth face-to-face interviews were conducted with 18 participants recruited from palliative care services in South Australia. Data were analysed using a framework analysis drawing on findings from a systematic review of international qualitative studies investigating the quality-of-life preferences of patients receiving palliation (domains identified included cognitive, emotional, healthcare, personal autonomy, physical, preparatory, social, spiritual). Participants identified missing or irrelevant domains in the EQ-5D and QLU-C10D questionnaires and ranked the importance of domains.A priori domains were refined into cognitive, environmental, financial, independence, physical, psychological, social and spiritual. The confirmation of the eight important quality-of-life domains across multiple international studies suggests there is a relatively high degree of convergence on the perspectives of patients in different countries. Four domains derived from the interviews are not covered by the EQ-5D and QLU-C10D (cognitive, environmental, financial, spiritual), including one of the most important (spiritual).Existing, popular, preference-based outcome measures such as the EQ-5D do not incorporate the most important, patient-valued, quality-of-life domains in the palliative and end-of-life settings. Development of a new, more relevant and comprehensive preference-based outcome measure could improve the allocation of resources to patient-valued services and have wide applicability internationally.
Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine

Surgical resection only remains the standard choice for the treatment of early-stage non-small cell lung cancer (NSCLC) patients. Preliminary studies suggest that the application of adjuvant chemotherapy with surgery (ACT) is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. However, at an individual level, not all patients may benefit from ACT. Given the well-known adverse effects and toxicity of ACT, finding the patients that are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to develop a statistical decision support algorithm to find predictive genomic biomarkers and identify subgroups of patients who benefit from ACT. Cox regression models are trained using a randomized controlled trial gene expression data from the National Center for Biotechnology Information (NCBI) utilizing explicit treatment interaction terms. To handle high dimensions inherent in gene expression data, a regularized Cox regression model with lasso penalty is applied to find the most significant interacting markers. Risk scores are estimated from the proposed model and are used to stratify patients into a high risk or low risk group respective to ACT treatment. After applying the model to an independent validation genomic data set, we show that patients who underwent the recommended treatment according to their risk group estimated by our proposed algorithm exhibit a slightly higher survival rate than those who do not.
A Proposal of Automatic Error Correction in Text

The great amount of information that can be stored in electronic media is growing up daily. Many of them is got mainly by typing, such as the huge of information obtained from web 2.0 sites; or scaned and processing by an Optical Character Recognition software, like the texts of libraries and goverment offices. Both processes introduce error in texts, so it is difficult to use the data for other purposes than just to read it, i.e. the processing of those texts by other applications like e-learning, learning of languages, electronic tutorials, data minning, information retrieval and even more specialized systems such as tiflologic software, specifically blinded people-oriented applications like automatic reading, where the text would be error free as possible in order to make easier the text to speech task, and so on. In this paper it is showed an application of automatic recognition and correction of ortographic errors in electronic texts. This task is composed of three stages: a) error detection; b) candidate corrections generation; and c) correction -selection of the best candidate. The proposal is based in part of speech text categorization, word similarity, word diccionaries, statistical measures, morphologic analisys and n-grams based language model of Spanish.
WH-MAVS: A Novel Dataset and Deep Learning Benchmark for Multiple Land Use and Land Cover Applications

Over the past decade, many excellent data sharing efforts have enriched the remote sensing scene classification (SC) methods. These datasets have achieved great success in complex high-level semantic information interpretation. However, most existing datasets are collected from standard and ungeoreferenced image patches for algorithm training and evaluation. These datasets do not fit for practical applications and cannot be directly applied in further geographical study. Accordingly, we provide a large range high-resolution SC dataset with multiple time phases, called &#x201C;Wuhan Multiapplication VHR Scene classification dataset (WH-MAVS).&#x201D; It facilitates the study of SC and scene change detection (SCD) algorithms. Moreover, it can also be directly employed to perform a variety of real-life land use application tasks. To the best of our knowledge, this is the first free, publicly available, georeferenced, and annotated dataset to cover almost an entire megacity. The WH-MAVS was collected and annotated from Google Earth imagery with the same spatial resolution and uniform nonoverlapping patch size, covering the central area of Wuhan, China. The total number of scene samples is 47 137, which belong to 14 classes with 23&#x2009;567 labeled patches for each time phase in 2014 and 2016, respectively. The geographic coordinates of all samples in both time phases exhibit one-to-one correspondence with 23 202 unchanged image patches of scene categories and 365 changed ones. The distribution of the number of samples in each class is highly imbalanced; moreover, there are large intraclass differences and indistinguishable interclass variances. These characteristics are closer to the real land use&#x002F;land cover application tasks and introduce further challenges to the related algorithm research. In addition, we conducted benchmark experiments on SC and SCD based on the WH-MAVS dataset with widely used deep learning models. DenseNet169 was found to achieve the best performance. The overall accuracies are 91.07&#x0025; and 92.09&#x0025;, respectively, in the 2014 and 2016 validation sets of WH-MAVS. Furthermore, SCD obtained by DenseNet169 has a binary change detection accuracy of 89.56&#x0025; and a multiple (from&#x2013;to) change detection accuracy of 86.70&#x0025;. Over and above the research value of the algorithm, it is also proven to have practical applications in fields such as urban planning, landscape pattern analysis, and urban dynamic monitoring and analysis.
Sexual and reproductive health and rights among newly arrived migrants in Sweden : knowledge gaps and healthcare challenges

Introduction: Migration is one of today’s most pressing issues, especially concerning sexual and reproductive health and rights (SRHR) and human rights. Migrants may face heightened health vulnerabilities due to challenges such as legal status, language, and cultural differences, which can further restrict their access to sexual and reproductive healthcare (SRH) services in a new country. In addition to these challenges, newly arrived migrants in Sweden, especially the youth, face more significant risks of SRHR violations and experience greater inequities in accessing SRH services due to issues such as unfamiliarity with the Swedish health system, long waiting times, language barriers and costs. Another issue is that migrants in Sweden often delay accessing these services until it is critical, contributing to challenges such as late HIV diagnoses among this group. Aim: To identify the knowledge gaps and barriers affecting the utilisation of sexual and reproductive healthcare services and provide a comprehensive understanding of the identified challenges and needs related to SRHR among migrants in Sweden. Methods: Study I. Perspectives of Healthcare Providers - was a qualitative study involving midwives, nurses, and counsellors providing SRH services in Stockholm. This study aimed to understand their experiences and perspectives on barriers and their strategies for improving the provision of sexual and reproductive health to young migrants. Twelve interviews were transcribed and analysed using an inductive content analysis. Study II. Knowledge of SRHR-related laws - was a large anonymous cross-sectional survey conducted primarily at Swedish language schools (SFI) among over 6000 newly arrived migrants in Sweden during 2018-2019. This study aimed to assess factors related to incorrect knowledge of the Swedish abortion law and other key legal issues of SRHR among recently arrived migrants in Sweden. Descriptive statistics were computed, and multivariable logistic regression (including n=3,557 with complete answers ...
Urban cycling-specific active transportation behaviour is sensitive to the fresh start effect: triangulating observational evidence from real world data

BACKGROUND: This study determined if cycling-specific active transportation (AT) was sensitive to the behavioural economics heuristic “The Fresh Start Effect”, with the beginning of a work week being temporal landmark for cycling to work. METHODS: We triangulated data from five sources to test the study hypothesis. First, publicly available cycling traffic data collected from May to September between 2014 and 2019 using electromagnetic counters (EcoCounter Inc, Montreal Qc.) were used to categorize 5 urban trails as “AT” or “leisure” based on hourly cycling traffic patterns. Linear regression model with repeated measures, compared daily trends in cycling traffic over the course of a work week along the different trail types and then compared with daily trends in occupational bicycle parking (n = 56,307 counts), vehicular traffic (n = 6.2 M counts), and sales from a local coffee shop (n = 166,753 counts) over the same time frame. Effect sizes were compared to daily trends in fitness centre attendance (n = 563,290 counts), a positive control for the Fresh Start Effect. RESULTS: We found a significant ~ 22% decline in daily cycling traffic on both AT (-147 cyclists/day; 95% CI: -199.0 to -95 cyclists/day) and leisure trails (-22 cyclists/day; 95% CI: -59 to + 15 cyclists/day) over the course of a work week. The relative decline over the work week in AT-based cycling traffic was similar to the decline in daily parking (~ 14%; -12 cyclists/day; 95% CI: -17 to -7 cyclists/day). The relative effect size of this trend was nearly identical to the decline in fitness centre attendance over the work week (~ 21%; -592 visits/day; 95% CI: -759 visits/day to -425 visits/day), replicating the original Fresh Start Effect. In contrast to the decline in AT-based cycling traffic, daily vehicular traffic (+ 2248 cars/day; 95% CI: 2022 to + 3674 cars/day) and coffee sales (+ 31 units/day; 95% CI: +22 to + 42 units/day) increased ~ 7% from the beginning to the end of a work week. CONCLUSIONS: The weekly patterns of AT-based cycling are sensitive to the Fresh Start Effect. This observation could be used to inform policies for increasing cycling rates in urban centres. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12966-025-01785-w.
Documenting Disclosure: Limited Reporting of Generative AI Usage in Radiology Research Manuscripts

Large language models (LLMs) show promise in radiology through various clinical applications as well as in assisting with research manuscript development. Recent studies show 52.6% of medical researchers use LLMs in manuscript development, with non-medical researchers showing similar rates. Given concerns about hallucinations, bias, etc., many publishers now require disclosure of LLM use. While most medical imaging journals have LLM policies as of 2025, the actual disclosure rates for LLM usage remain unknown. Our study examines trends in LLM disclosure by analyzing 1998 radiology publications for LLM disclosures.A bibliometric analysis of nine radiology journals with LLM use disclosure requirements was performed. The study included primary investigations and secondary research while excluding short-form publications. The LLM disclosure rate was calculated overall. Logistic regression assessed temporal trends in disclosure rates, while a linear mixed effects model evaluated the relationship between disclosure status and peer review duration. Chi-square analysis examined associations between manuscript type and disclosure rates.Of 1998 manuscripts, 34 (1.7%) declared LLM use. Most disclosures involved ChatGPT (32, 94.1%), primarily for readability/grammar purposes (33, 97.1%). The majority of manuscripts disclosing LLM use originated from institutions in non-English speaking countries (22, 64.7%). No significant increase in disclosure rates over time was observed (OR: 1.06 [95% CI: 0.98, 1.16], p=0.15), and no relationship with peer review duration (coefficient: -4.85, SE=11.25, p=0.67) was found. Secondary research manuscripts disclosed LLM use more frequently (3.9% vs. 1.3%, p<0.001) with a small effect size (Cramer's V: 0.08 [0.04, 1.00]).Our findings demonstrate remarkably low disclosure rates in radiology manuscripts despite surveys indicating significant LLM adoption among researchers. This discrepancy may result from true non-use, fear of stigma, perceived advantages of undisclosed use, disagreement with disclosure requirements for minor editing, or policy unawareness, among other reasons. These findings suggest a need for more accepting research environments that recognize legitimate LLM benefits while developing nuanced disclosure policies addressing risks.