ethicalabs/Research-Intent-Collab huggingface dataset:
Digital finance and poverty in fragile states: Evidence from Somalia’s mobile money diffusion

Financial inclusion is increasingly recognized as a catalyst for achieving the Sustainable Development Goals (SDGs), particularly in reducing poverty (SDG 1), ensuring food security (SDG 2), and promoting reduced inequalities (SDG 10). In fragile states such as Somalia, where decades of conflict and institutional weakness constrain formal financial systems, digital financial services have emerged as a critical alternative. This study investigates the determinants of household wealth in Somalia using nationally representative data from the Somali Demographic and Health Survey (SDHS), comprising 26,180 households after cleaning. Wealth is measured via the standardized Wealth Index, with explanatory variables capturing demographic, socioeconomic, and infrastructural conditions-including household size, education, property ownership, electricity access, and mobile money use. Ordered logit and probit models are employed to assess these relationships. The results demonstrate that access to mobile money significantly enhances household wealth, alongside educational attainment, electricity supply, and secure home ownership. Spatial disparities are observed, with northern and eastern regions outperforming the conflict-affected south, while rural and nomadic households remain structurally disadvantaged. These findings highlight the transformative role of mobile money in advancing financial inclusion, while also emphasizing the need for complementary investments in infrastructure and education to ensure equitable development. Policy efforts should prioritize expanding digital financial services, bridging urban-rural divides, and addressing geographic inequalities to accelerate progress toward SDG 1, SDG 2, and SDG 10 in Somalia.
Dual-Double Stem Cell Ovarian Therapy: A Comprehensive Approach in Regenerative Medicine

Dual-double stem cell therapy, which integrates mesenchymal stem cells (MSCs) and hematopoietic stem cells (HSCs), represents a cutting-edge approach in regenerative medicine, particularly for conditions such as ovarian decline, premature ovarian insufficiency (POI), and induced ovarian failure. This therapy leverages the unique properties of MSCs and HSCs, enhancing tissue repair, immune modulation, and overall regenerative outcomes. MSCs, known for their ability to differentiate into various cell types, provide a supportive microenvironment and secrete bioactive molecules that promote angiogenesis and reduce inflammation. HSCs, crucial for hematopoiesis and immune function, further enhance this environment by supporting hematopoietic processes and immune regulation. Clinical evidence increasingly supports the effectiveness of stem cell therapy in ovarian regeneration. Studies have demonstrated improved folliculogenesis, normalization of hormone profiles, and successful pregnancies in patients with POI. Furthermore, recent clinical trials in various medical fields underline the superior potential of dual-double therapy compared to monotherapies involving MSCs or HSCs alone, enhancing tissue repair and functional outcomes. However, despite these benefits, the therapy presents risks that require careful consideration. For autologous MSC therapy involving expanded cell populations, risks include tumorigenic potential, with evidence of sarcoma formation in certain cases of cultured MSCs. In contrast, autologous non-expanded MSC and HSC therapies may be limited by low cell yields, potentially compromising therapeutic efficacy. Additionally, non-expanded HSC therapy poses risks of insufficient cell numbers for successful engraftment and delayed immune reconstitution. These considerations underscore the importance of quality control and rigorous screening to optimize safety and efficacy. This article explores the mechanisms of action, clinical applications, and potential complications of dual-double stem cell therapy, underscoring the need for continued research and optimized protocols to enhance safety and outcomes in ovarian insufficiency and related conditions, offering new hope for affected women.
Strong Labeling of Sound Events Using Crowdsourced Weak Labels and Annotator Competence Estimation

Peer reviewed
The Role of Impression Management in the Impact of Ethical Leadership on Green Organizational Behavior

Green behaviors of employees create value for organizations by improving their environmental performance. Ensuring sustainability is an important issue in healthcare organizations, which have a devastating impact on the environment with their remarkable consumption of resources and generation of waste. The purpose of this study is to examine the mediating role of impression management in the relationship between ethical leadership and green organizational behavior. The sample of the study is 418 nurses working in a public hospital in Afyonkarahisar, Turkey. The results were obtained as a result of descriptive statistics, explanatory factor analysis, reliability analysis, correlation analysis, and mediation analysis. The mediation analysis revealed that ethical leadership negatively affects impression management (β = -0.11, p &lt; 0.05), ethical leadership positively affects green organizational behavior (β = 0.18, p &lt; 0.05), and impression management negatively affects green organizational behavior (β = -0.20, p &lt; 0.05). It was concluded that the mediating effect of impression management on the effect of ethical leadership on green organizational behavior was statistically significant (β = 0.022). Social learning theory was used to support the findings and discuss implications for research and practice.
Additive Manufacturing (3D Printing) in Vocational Training: Preparing the Workforce for Industry 4.0 and Future Industry 5.0

The advent of Additive Manufacturing (3D Printing) in vocational training and education (VET) is increasing in importance in the harmonization of employees' skills with the changing needs of Industry 4.0 and the innovative models of Industry 5.0. This paper examines the transformational potential of Additive Manufacturing (AM) as a technology tool and pedagogical medium in vocational environments. Drawing on socio-technical systems theory and constructivist learning models, the research investigates how Additive Manufacturing promotes experiential, iterative learning as well as the building of key competencies like digital literacy, design thinking, and problem-solving. It also investigates how Additive Manufacturing responds to the skill gaps revealed by global industrial transformation, with a focus on its intersection with smart manufacturing, digital twins, and cyber-physical systems. Furthermore, the research addresses the Additive Manufacturing role in preparing for Industry 5.0, which emphasizes human-centered innovation, sustainability, and the meeting point of modern technology and human imagination. Recent empirical evidence and policy recommendations from the European Commission, OECD, and UNESCO-UNEVOC, all of which emphasize the need for a responsive and forward-looking VET system, support the research. In conclusion, this study views Additive Manufacturing as an industrial transformation enabler and available workforce development, rather than as a technical innovation of vocational training and education.
Object Contour and Edge Detection with RefineContourNet

A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse the high, mid and low-level features in that specific order, which differs from many other approaches. It uses the tensor with the highest-levelled features as the starting point to combine it layer-by-layer with features of a lower abstraction level until it reaches the lowest level. We train this network on a modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a refined PASCAL-val dataset reaching an excellent performance and an Optimal Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500 dataset we reach state-of-the-art results for edge-detection with an ODS of 0.824.
Attrition in Randomized Controlled Clinical Trials: Methodological Issues in Psychopharmacology

Attrition is a ubiquitous problem in randomized controlled clinical trials (RCT) of psychotropic agents that can cause biased estimates of the treatment effect, reduce statistical power, and restrict the generalizability of results. The extent of the problem of attrition in central nervous system (CNS) trials is considered here and its consequences are examined. The taxonomy of missingness mechanisms is then briefly reviewed in order to introduce issues underlying the choice of data analytic strategies appropriate for RCTs with various forms of incomplete data. The convention of using last observation carried forward to accommodate attrition is discouraged because its assumptions are typically inappropriate for CNS RCTs, whereas multiple imputation strategies are more appropriate. Mixed-effects models often provide a useful data analytic strategy for attrition as do the pattern-mixture and propensity adjustments. Finally, investigators are encouraged to consider asking participants, at each assessment session, the likelihood of attendance at the subsequent assessment session. This information can be used to eliminate some of the very obstacles that lead to attrition, and can be incorporated in data analyses to reduce bias, but it will not eliminate all attrition bias.
Carbon Footprint and Sensitive Design Parameters of Residential and Industrial (Whey) Wastewater Treatment Plants: A Comparative Life Cycle Assessment (LCA)

This study calculates the carbon footprint of chemical coagulants and operational energy for residential and industrial (whey digestion) wastewater treatment using ReCiPe 2016 methodology within a clearly defined system boundary from cradle to gate. Data from water treatment facilities have been analyzed to quantify environmental impacts and identify sensitive design parameters. The estimated emission of treating 1 m3 of wastewater from whey digestion (7.1195 kg CO2 eq) is over 50 times higher than that of a residential one (0.1349 kg CO2 eq). Life cycle impact assessment (LCIA) reveals that iron (III) chloride (40% in H2O) and operational electricity consumption have higher impact categories compared to other design components. The uncertainty analysis indicates that electricity consumption (r = 0.4) is the dominant contributor to emissions, with a mean value of 4.22 kg CO2-eq per m3 of wastewater treated. In contrast, iron (III) chloride emerges as the most sensitive parameter (r = 0.88) with small variations in dosing causing a disproportionately large impact on overall emissions. Therefore, the optimized use of an iron-based coagulant, the adoption of membrane electrolysis, and the integration of renewable electricity into the process supply chains have been identified as effective strategies for reducing emissions.
Rethinking Crowdsourcing Annotation: Partial Annotation With Salient Labels for Multilabel Aerial Image Classification

Annotated images are required for both supervised model training and evaluation in image classification. Manually annotating images is arduous and expensive, especially for multi-labeled images. A recent trend for conducting such laboursome annotation tasks is through crowdsourcing, where images are annotated by volunteers or paid workers online (e.g., workers of Amazon Mechanical Turk) from scratch. However, the quality of crowdsourcing image annotations cannot be guaranteed, and incompleteness and incorrectness are two major concerns for crowdsourcing annotations. To address such concerns, we have a rethinking of crowdsourcing annotations: Our simple hypothesis is that if the annotators only partially annotate multi-label images with salient labels they are confident in, there will be fewer annotation errors and annotators will spend less time on uncertain labels. As a pleasant surprise, with the same annotation budget, we show a multi-label image classifier supervised by images with salient annotations can outperform models supervised by fully annotated images. Our method contributions are 2-fold: An active learning way is proposed to acquire salient labels for multi-label images; and a novel Adaptive Temperature Associated Model (ATAM) specifically using partial annotations is proposed for multi-label image classification. We conduct experiments on practical crowdsourcing data, the Open Street Map (OSM) dataset and benchmark dataset COCO 2014. When compared with state-of-the-art classification methods trained on fully annotated images, the proposed ATAM can achieve higher accuracy. The proposed idea is promising for crowdsourcing data annotation. Our code will be publicly available.
Virtual Reality Surgical Simulators- A Prerequisite for Robotic Surgery

The field of computer assisted minimally invasive surgery is rapidly expanding worldwide, including in India. With more hospitals in India contemplating the acquisition of a robotic platform, training of robotic surgeons is becoming essential. Virtual reality simulators can be used for surgeons to become acquainted with the robotic console prior to live surgery. Our aim was to evaluate the amount of simulator training required before a surgeon first operates on the da Vinci® Surgical System. Simulations were conducted on the Intuitive Surgical's da Vinci® Robot Skill Simulator using the software obtained from Mimic Technologies. Participants included attending staff surgeons experienced in robotic surgery and novices. A set of seven activities were chosen for each participant. Based on the mean exercise score from the first attempt, staff surgeons outperformed the novices in all exercises. However, the difference in score between the staff and the novices decreased after the participants repeated the exercises and by the sixth attempt most of the novices obtained similar scores to the staff, suggesting that this might be at present the minimum set of repetitions indicated (or required) prior to performing life robotic surgery.
Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer

Cervical cancer (CC) is the fourth most common type of cancer among women. Majority of CC cases (84%–90%) have been reported in low- and middle-income countries (LMICs). Persistent infection with high-risk human papilloma virus (HR-HPV) subtypes is responsible for &amp;gt;90% of the cervical cancers. CC is preventable by timely vaccination with HPV vaccine. Early diagnosis, treatment and disease recurrence prediction markers play a significant role in improving the patient outcome, yet LMICs are seeing a continuous increase in CC cases. Lack of sensitive self-screening technologies, effective diagnostic methods and accessible treatment options are predominantly contributing to this increase. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) methods have revolutionized CC diagnosis, prognosis, and treatment agent selection by (a) enhancing the accuracy of screening and diagnostic methods; (b) assisting the clinicians in predicting various prognostic factors such as lymph node metastasis, treatment response, survival outcome, postoperative risk factors; and (c) providing dose prediction, treatment planning, segmentation of target volume and organ at risk. AI algorithms can analyze complex datasets, including medical images, patient data, and genetic information, to identify patterns and predict outcomes that might have not been considered by traditional methods. AI-based analysis provides more accurate diagnosis and improved risk stratification in a very short duration, while helping in the design of tailored treatment strategies. In this article, we aim to provide an overview of emerging ML and DL methods and comprehensively evaluate the role of AI-based approaches in the screening, diagnosis, prognosis and treatment of CC. Finally, we discuss challenges and limitations associated with the use of AI models in CC screening, diagnosis, prognosis and treatment. We have also focused on emerging AI models that can be applied in CC research and treatment to overcome the current challenges and limitations.
A Multi-Agent System for Semantic Mapping of Relational Data to Knowledge Graphs

Enterprises often maintain multiple databases for storing critical business data in siloed systems, resulting in inefficiencies and challenges with data interoperability. A key to overcoming these challenges lies in integrating disparate data sources, enabling businesses to unlock the full potential of their data. Our work presents a novel approach for integrating multiple databases using knowledge graphs, focusing on the application of large language models as semantic agents for mapping and connecting structured data across systems by leveraging existing vocabularies. The proposed methodology introduces a semantic layer above tables in relational databases, utilizing a system comprising multiple LLM agents that map tables and columns to Schema.org terms. Our approach achieves a mapping accuracy of over 90% in multiple domains.
Joint Sparse Recovery Method for Compressed Sensing with Structured Dictionary Mismatches

In traditional compressed sensing theory, the dictionary matrix is given a priori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in direction-of-arrival (DOA) estimation of off-grid targets, encountered in both radar systems and array processing. We propose to use joint sparse signal recovery to solve the compressed sensing problem with structured dictionary mismatches and also give an analytical performance bound on this joint sparse recovery. We show that, under mild conditions, the reconstruction error of the original sparse signal is bounded by both the sparsity and the noise level in the measurement model. Moreover, we implement fast first-order algorithms to speed up the computing process. Numerical examples demonstrate the good performance of the proposed algorithm, and also show that the joint-sparse recovery method yields a better reconstruction result than existing methods. By implementing the joint sparse recovery method, the accuracy and efficiency of DOA estimation are improved in both passive and active sensing cases.
Supplementary Figure S4 from Blood-Based Genomic Alteration Signature for Predicting Progression-Free Survival in &lt;i&gt;De Novo&lt;/i&gt; Metastatic Hormone-Sensitive Prostate Cancer: A Real-World Study

&lt;p&gt;Figure S4. External validation and clinical applicability of the t.TRPC-based prognostic model in the validation cohort.&lt;/p&gt;
PROTECTING THE RIGHTS OF MINORS IN THE NEW UZBEKISTAN IS THE MAIN CONDITION FOR BUILDING A JUST SOCIETY

This article emphasizes the crucial role of safeguarding the rights of minors in the construction of a fair and equitable society in Uzbekistan. As the nation undergoes significant transformations, it is imperative to prioritize the well-being and development of its young citizens. By examining the legal framework  and social initiatives aimed at protecting minors, this study seeks to shed light on the importance of their rights and the impact they have on the overall fabric of  society.  The  article  underscores  the  multifaceted  nature  of  minors'  rights,  encompassing  education,  health, protection against exploitation, and access to justice. It explores the progress made in Uzbekistan's child protection efforts, including legislative reforms, institutional advancements, and community-based interventions. Furthermore, it highlights the challenges and gaps that persist, such as addressing child labor, enhancing access to quality education, and tackling gender disparities. The article argues that by prioritizing therights of minors, Uzbekistan can foster a just society  that  nurtures  the  potential  of  its  future  generations,  ensuring  their  well-rounded  development  and  active participation   in   the   nation's   progress.   The   findings   underscore   the   need   for   continued   commitment   from policymakers,  civil  society  organizations,  and  the  wider  community  to  create  an  environment  where  every  child's rights are protected, enabling them to thrive and contribute meaningfully to Uzbekistan's inclusive and prosperous future.
Functional Synthetic Biology

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility, and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability, and reductions in technical risk and cost.
Multireference error mitigation for quantum computation of chemistry

MREM extends reference-state error mitigation by incorporating multireference wavefunctions, enabling accurate and noise-resilient quantum simulations of strongly correlated molecules.
Bridge Inspection Using A Multi-Modal Vision Language Model

Using an Unmanned Aerial Vehicle (UAV) in bridge inspections can reduce human involvement in complex and hazardous inspection environments and automate the inspection process. Current practices require human operators to define task objectives, oversee safe flight operations, and evaluate bridge conditions. There is a growing demand for improving the seamless collaboration between UAVs and human inspectors to complete the inspection task efficiently and more safely, especially in post-disaster scenarios where critical bridges and other infrastructure facilities need to be inspected within hours or days. A significant gap exists in enabling UAVs to intelligently perceive and understand the bridge inspection scene according to human instructions. An intuitive human-UAV collaboration system using a multi-modal Vision Language Model (VLM) was proposed to partially fill this gap. This system leverages a few-shot Contrastive Language–Image Pretraining (CLIP)-based model to enable UAVs to visually and semantically understand the bridge inspection environment based on human commands. By incorporating text prompt learning with a cache adapter, the proposed model enhances the ability of CLIP to interpret both textual and visual inputs in the context of bridge inspection. The model was trained and evaluated in a bridge inspection image dataset and achieved an accuracy of 83.33%, outperforming other few-shot image classification methods, demonstrating its effectiveness in the bridge inspection domain. This approach is expected to improve collaboration between AI-empowered UAVs, inspectors, and bridge environments, thereby enhancing the overall efficiency of bridge inspections.
Does Paid Crowdsourcing Still Pay Off? Sifting Through Annotator Noise in Crowdsourced Audio Labels

Peer reviewed