ethicalabs/Research-Intent-Collab huggingface dataset:
Development of a SiPM camera for detection and measurement of fluorescence emission from extensive air-showers generated by ultra high energy cosmic rays

Diese Arbeit behandelt die Entwicklung, Charakterisierung und Kalibrierung von SiECA, der “SIlicon photomultiplier Elementary Cell Add-on“ Fluoreszenzkamera. Diese Kamera wurde entwickelt für einen Einsatz zur Erkennung der Fluoreszenzsignatur von ausgedehn- ten Luftschauern, welche bei Kollisionen von ultrahochenergetischer kosmischer Strahlung mit der Erdatmosphäre entstehen. Insbesondere die Korrekturmechanismen um eine uniforme Photondetektorfläche zu erreichen und die Bestimmung der absoluten Photosensitivität wurden detailliert analysiert. Diese Arbeit ist überwiegend experimentell-technisch orientiert mit der astrophysikalischen Motivation, den Betrieb von SiPM (Silicon Photomultiplier) als Teil von geplanten Weltraum- teleskopen für kosmische Strahlung, zum Beispiel das Extreme Universe Space Observatory (EUSO) oder das Probe Of Extreme Multi-Messenger Astrophysics (POEMMA), zu untersu- chen. SiECA ist ein Machbarkeitsnachweis für aktuell verfügbare Komponenten, wie Sensoren, ASICs und Unterstützungselektroniken und damit ein Prototyp für zukünftige Fluoreszenz- kameras. Die Bewertung der derzeit verfügbaren Komponenten gibt einen Einblick in die notwendigen technologischen Fortschritte, die für die erfolgreiche Entwicklung größerer, auf kleinste Lichtmengen sensitiver Kamerasysteme mit schneller Auslese in der Zukunft erforderlich sind. In dieser Arbeit werden der Entwicklungsprozess, die Sensorcharakteri- sierung, der Kamerabau, die Laborprüfung sowie die Möglichkeiten der Kalibrierung und die Einsätze der Kamera detailliert beschrieben. Entwicklung einer SiPM-Kamera zur Erken- nung und Messung der Fluoreszenz-Emission von durch ultra-hochenergetische kosmische Strahlung ausgelöste ausgedehnte Luftschauer. Die Entwicklung von SiECA vom Konzept bis zum ersten Einsatz an Bord des Ballonteleskops EUSO-SPB1 dauerte nur 18 Monate. Viele Verbesserungen sind noch erforderlich, dennoch wurden in dieser Arbeit wesentliche Erkenntnisse über die Eigenschaften aktueller SiPMs und ASICs gewonnen, die in folgender Arbeit beschrieben werden.
Towards sustainable land use: identifying and managing the conflicts between human activities and biodiversity conservation in Europe

Conflicts between biodiversity conservation and human activities are becoming increasingly apparent in all European landscapes. The intensification of agricultural and silvicultural practices, land abandonment and other land uses such as recreation and hunting are all potential threats to biodiversity that can lead to conflicts between stakeholder livelihoods and biodiversity conservation. To address the global decline in biodiversity there is, therefore, a need to identify the drivers responsible for conflicts between human activities and the conservation of European biodiversity and to promote the management of these conflicts. Here, the drivers of biodiversity conflicts are analysed in a European context for five habitat types: agricultural landscapes, forests, grasslands, uplands and freshwater habitats. A multi- disciplinary approach to conflict management is described, with active stakeholder involvement at every stage of conflict identification and management as well as a range of other approaches including stakeholder dialogue and education, consumer education, improvement of political and legislative frameworks, financial incentives, and planning infrastructure.
Cost-effectiveness analysis of an mHealth application (SMART4MD) and analysis of the effect of dialysis treatments on labor market outcomes : Health technology assessment of two treatment methods

Health Technology Assessment is an important factor for decision making in the healthcare sector in Sweden. It helps to curtail the rising costs associated with the healthcare sector and aids in the efficient allocation of scarce public health resources. This thesis investigates the cost-effectiveness and the effectiveness in general of two health technologies, addressing the following research objectives: i) assessing the cost-effectiveness of mobile health (mHealth) interventions designed for older adults diagnosed with mild cognitive impairment, and ii) assessing the effectiveness of peritoneal dialysis (PD) treatment on labor market outcomes in comparison with institutional hemodialysis (IHD) treatment in Swedish settings. Study I and Study II are related to the first research objective. In Study I, we summarized and critically assessed the current evidence on the cost-effectiveness of mHealth interventions focusing on older adults; we found some evidence supporting the cost-effectiveness of these interventions. In Study II, we conducted a within-trial cost-effectiveness analysis of the software application Support, Monitoring and Reminder Technology for Mild Dementia (SMART4MD) from a healthcare perspective for a period of six months. A total of 345 Swedish dyads (MCI patient and informal caregiver) participated in this study. For a short time period of six months, we found that SMART4MD is not cost-effective for MCI patients (statistically insignificant); however, a trend was observed that indicated that it might be cost-effective for informal caregivers, although results remained statistically insignificant (p &gt; 0.05). Study III is related to the second research objective. In Study III, we investigated the effect of PD on labor market outcomes (employment rate, work income, and disability pension) in comparison to IHD. We found that PD is associated with a treatment advantage over IHD in terms of increased employment, work income, and reduced disability pension in the Swedish population after controlling for non-random selection for the treatment.
Remote Sensing Retrieval-Augmented Generation: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model

Recent progress in VLMs has demonstrated impressive capabilities across a variety of tasks in the natural image domain. Motivated by these advancements, the remote sensing community has begun to adopt VLMs for remote sensing vision-language tasks, including scene understanding, image captioning, and visual question answering. However, existing remote sensing VLMs typically rely on closed-set scene understanding and focus on generic scene descriptions, yet lack the ability to incorporate external knowledge. This limitation hinders their capacity for semantic reasoning over complex or context-dependent queries that involve domain-specific or world knowledge. To address these challenges, we first introduced a multimodal Remote Sensing World Knowledge (RSWK) dataset, which comprises high-resolution satellite imagery and detailed textual descriptions for 14,141 well-known landmarks from 175 countries, integrating both remote sensing domain knowledge and broader world knowledge. Building upon this dataset, we proposed a novel Remote Sensing Retrieval-Augmented Generation (RS-RAG) framework, which consists of two key components. The Multi-Modal Knowledge Vector Database Construction module encodes remote sensing imagery and associated textual knowledge into a unified vector space. The Knowledge Retrieval and Response Generation module retrieves and re-ranks relevant knowledge based on image and/or text queries, and incorporates the retrieved content into a knowledge-augmented prompt to guide the VLM in producing contextually grounded responses. We validated the effectiveness of our approach on three representative vision-language tasks, including image captioning, image classification, and visual question answering, where RS-RAG significantly outperformed state-of-the-art baselines.
OPC UA for IO-Link Wireless in a Cyber Physical Finite Element Sensor Network for Shape Measurement

This paper presents the integration of OPC UA as a communication protocol in a wireless sensor network and the associated companion specifications as a semantic template for an information model. The Cyber Physical Finite Element Sensor Network (CPFEN ) for Shape Measurements, a distributed wireless system, uses IO-Link Wireless for data transmission at the sensor level, OPC UA provides a unified interface for data access, configuration, monitoring, and calibration tailored to the needs of the CPFEN for all level above. This opens up additional possibilities, such as integrated quality assurance or creating a digital twin, while improving scalability.
International standards for the analysis of quality-of-life and patient-reported outcome endpoints in cancer randomised controlled trials:recommendations of the SISAQOL Consortium

Patient-reported outcomes (PROs), such as symptoms, function, and other health-related quality-of-life aspects, are increasingly evaluated in cancer randomised controlled trials (RCTs) to provide information about treatment risks, benefits, and tolerability. However, expert opinion and critical review of the literature showed no consensus on optimal methods of PRO analysis in cancer RCTs, hindering interpretation of results. The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data Consortium was formed to establish PRO analysis recommendations. Four issues were prioritised: developing a taxonomy of research objectives that can be matched with appropriate statistical methods, identifying appropriate statistical methods for PRO analysis, standardising statistical terminology related to missing data, and determining appropriate ways to manage missing data. This Policy Review presents recommendations for PRO analysis developed through critical literature reviews and a structured collaborative process with diverse international stakeholders, which provides a foundation for endorsement; ongoing developments of these recommendations are also discussed.
Citizen expectations of ‘academic entrepreneurship’ in health research: public science, practical benefit

AbstractBackgroundResponsiveness to citizens as users of technological innovation helps motivate translational research and commercial engagement among academics. Yet, retaining citizen trust and support for research encourages caution in pursuit of commercial science.ObjectivesWe explore citizen expectations of the specifically academic nature of commercial science [i.e. academic entrepreneurship (AE)] and the influence of conflict of interest concerns, hopes about practical benefits and general beliefs.Design, setting and participantsWe conducted a cross‐sectional national opinion survey of 1002 Canadians online in 2010.ResultsApproval of AE was moderate (mean 3.2/5, SD 0.84), but varied by entrepreneurial activity. Concern about conflict of interests (COI) was moderate (mean 2.9/5, SD 0.86) and varied by type of concern. An ordinary least‐squares regression showed that expectations of practical benefits informed support for AE, specifically that academic–industry collaboration can better address real‐world problems; conflict of interest concerns were insignificant.ConclusionsThese findings suggest that citizens support AE for its potential to produce practical benefits, but enthusiasm varies and is reduced for activities that may prioritize private over public interests. Further, support exists despite concern about COI, perhaps due to trust in the academic research context. For user engagement in research priority setting, these findings suggest the need to attend to the commercial nature of translational science. For research policy, they suggest the need for governance arrangements for responsible innovation, which can sustain public trust in academic research, and realize the practical benefits that inform public support for AE.
Hierarchical protein backbone generation with latent and structure diffusion

We propose a hierarchical protein backbone generative model that separates coarse and fine-grained details. Our approach called LSD consists of two stages: sampling latents which are decoded into a contact map then sampling atomic coordinates conditioned on the contact map. LSD allows new ways to control protein generation towards desirable properties while scaling to large datasets. In particular, the AlphaFold DataBase (AFDB) is appealing due as its diverse structure topologies but suffers from poor designability. We train LSD on AFDB and show latent diffusion guidance towards AlphaFold2 Predicted Alignment Error and long range contacts can explicitly balance designability, diversity, and noveltys in the generated samples. Our results are competitive with structure diffusion models and outperforms prior latent diffusion models.
NLP-based classification of software tools for metagenomics sequencing data analysis into EDAM semantic annotation

Motivation: The rapid growth of metagenomics sequencing data makes metagenomics increasingly dependent on computational and statistical methods for fast and efficient analysis. Consequently, novel analysis tools for big-data metagenomics are constantly emerging. One of the biggest challenges for researchers occurs in the analysis planning stage: selecting the most suitable metagenomics software tool to gain valuable insights from sequencing data. The building process of data analysis pipelines is often laborious and time-consuming since it requires a deep and critical understanding of how to apply a particular tool to complete a specified metagenomics task.   Results: We have addressed this challenge by using machine learning methods to develop a classification system of metagenomics software tools into 13 classes (11 semantic annotations of EDAM and two virus-specific classes) based on the descriptions of the tools. We trained three classifiers (Naive Bayes, Logistic Regression, and Random Forest) using 15 text feature extraction techniques (TF-IDF, GloVe, BERT-based models, and others). The manually curated dataset includes 224 software tools and contains text from the abstract and the methods section of the tools' publications. The best classification performance, with an Area Under the Precision-Recall Curve score of 0.85, is achieved using Logistic regression, BioBERT for text embedding, and text from abstracts only. The proposed system provides accurate and unified identification of metagenomics data analysis tools and tasks, which is a crucial step in the construction of metagenomics data analysis pipelines.
Federated Learning for Thoracic Disease Classification Using Convolutional Neural Networks and Differential Privacy

ABSTRACT                                        Early diagnosis of thoracic diseases using chest x‐ray imaging remains a critical challenge, particularly in resource‐constrained healthcare environments where data sharing is restricted due to privacy concerns. Federated learning (FL) offers a decentralized solution by enabling collaborative model training without sharing sensitive patient data. However, integrating privacy‐preserving mechanisms such as differential privacy (DP) introduces additional challenges related to performance degradation and computational overhead. In this study, we present a unified FL framework for multi‐label thoracic disease classification using multiple convolutional neural network (CNN) architectures, including ResNet50, DenseNet169, EfficientNet variants and MobileNetV3. Unlike prior studies focusing on single‐model evaluation, this work provides a controlled comparative analysis under identical FL settings and investigates the impact of client scalability (5–10 clients) on model performance. Furthermore, we conduct a comprehensive empirical analysis of the privacy utility trade‐off by integrating DP with varying privacy budgets (                     ε                      = 1, 15 and 30). Experimental results on the CheXpert and NIH Chest x‐ray14 datasets demonstrate that the proposed EfficientNet‐B3‐based federated model achieves a mean AUC of 0.8027, while maintaining robustness across decentralized settings. The integration of DP leads to a predictable reduction in performance, with mean AUC ranging from 0.60 to 0.64, highlighting the inherent trade‐off between privacy and diagnostic accuracy. The findings emphasize the practical viability of FL for privacy‐sensitive medical imaging applications and provide insights into model selection, scalability and privacy configuration for real‐world deployment. The source code for this study is publicly accessible at                     https://github.com/Zulqarnain8‐8/FEDERATED_LEARNING_FOR_THORACIC_DISEASE_CLASSIFICATION                     .
Source and evolution of the ore-forming fluid of the Cuonadong Sn-W-Be polymetallic deposit (southern Tibet, China): Constraints from scheelite trace element and Sr isotope geochemistry

Abstract   The Cuonadong Sn-W-Be polymetallic deposit is the first rare-metal deposit with an extraordinary metallogenic potential that has been discovered in the Tethyan Himalayan metallogenic belt. The deposit shows a wide range of different mineralization types, including greisen-, pegmatite-, skarn-, and hydrothermal vein-type mineralization. Of these mineralization types, the skarn-type has the largest inferred resources. In order to constrain the source and evolution of the ore-forming fluid, we conducted a detailed study of the skarn-hosted scheelite, including cathodoluminescence imaging, in-situ trace elemental analysis, and bulk Sr isotope analysis. In addition, bulk Sr isotope analysis of fluorite, phlogopite 40Ar-39Ar dating, and marble whole-rock geochemical analysis were also conducted. Phlogopite 40Ar-39Ar dating indicates that the skarn was formed at ca. 15 Ma, which is close to the formation age of the Cuonadong muscovite granite reported in previous studies. Cathodoluminescence images indicate that skarn-hosted scheelite can be subdivided into an older type 1 scheelite and a younger type 2 scheelite. Type 1 scheelite is characterized by a negative Eu anomaly, whereas type 2 scheelite shows a positive Eu anomaly. Considering the close temporal and spatial relationship between the Cuonadong Miocene leucogranite and the ore-bearing skarn, and their similar lanthanide tetrad effect as observed in the REE patterns, we propose that the negative Eu anomaly was inherited from the Cuonadong Miocene leucogranite, while the positive Eu anomaly resulted from the addition of Eu2+ to the ore-forming fluid during greisenization. Sr isotope data show that the fluorite (87Sr/86Sr)i value of 0.728885 is within the range of the Cuonadong Miocene leucogranite (87Sr/86Sr)i values, while scheelite (87Sr/86Sr)i values (0.709717-0.713480) are similar to those of the Cuonadong marble (0.709525-0.712146). Since scheelite is intergrown with fluorite, we propose that the hydrothermal fluid that exsolved from the Cuonadong Miocene leucogranite is responsible for formation of both fluorite and scheelite. Water-rock interaction between this magma-derived fluid and the marble is responsible for the loss of the scheelite magmatic Sr isotopic signature.
Future Changes in Monsoon Precipitation and Their Uncertainty Projected by Global Climate Models

2017 【要旨】
Mediating effects of sleep in the association between latent dimensions of smartphone/social media behaviours and mental health in adolescents

There is widespread concern that adolescent mental health is at risk in our “always connected” world. For example, smartphone and social media (S/SM) use around bedtime may displace sleep, putting mental health at risk. However, most research examining these relationships relies on undifferentiated measures of S/SM behaviours, with a need to move beyond oversimplistic models to advance mechanistic understanding. We surveyed 2,333 11-19-year-olds on an array of S/SM behaviours, using exploratory factor analysis to reveal three latent dimensions: (i) S/SM time (i.e., time spent on S/SM), (ii) problematic/negative S/SM use and (iii) SM platform use. Preregistered hypotheses tested whether these dimensions predicted self-reported sleep quality, and whether sleep quality would mediate any associations with depression and anxiety symptoms. Higher S/SM time and problematic/negative S/SM use predicted poorer sleep quality and higher symptoms of anxiety and depression. In contrast, SM platform use was associated with lower anxiety symptoms. Sleep quality partially mediated the associations between S/SM time and problematic/negative S/SM use and depressive and anxiety symptoms, with direct effects also present. These findings highlight sleep as one mechanism underlying the link between S/SM use and mental health, with longitudinal studies incorporating multidimensional S/SM and objective sleep measurement needed to test these causal pathways directly.
Sparse Representation for Wireless Communications: A Compressive Sensing Approach

Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with focus on the most recent compressive sensing (CS) enabled approaches. With the help of the sparsity property, CS is able to enhance the spectrum efficiency and energy efficiency for the fifth generation (5G) networks and Internet of Things (IoT) networks. This article starts from a comprehensive overview of CS principles and different sparse domains potentially used in 5G and IoT networks. Then recent research progress on applying CS to address the major opportunities and challenges in 5G and IoT networks is introduced, including wideband spectrum sensing in cognitive radio networks, data collection in IoT networks, and channel estimation and feedback in massive MIMO systems. Moreover, other potential applications and research challenges on sparse representation for 5G and IoT networks are identified. This article will provide readers a clear picture of how to exploit the sparsity properties to process wireless signals in different applications.
Governmental Accounting, Auditing, and Financial Reporting.

Reviews the book "Governmental Accounting, Auditing, and Financial Reporting."
Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum

We introduce a simple approach to understanding the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. The pipeline involves training deep convolutional neural networks (CNNs) to differentiate between images of plants with reference and alternate versions of various SNPs, and then using visualization approaches to highlight what the classification networks key on. We demonstrate the capacity of deep CNNs at performing this classification task, and show the utility of these visualizations on RGB imagery of biomass sorghum captured by the TERRA-REF gantry. We focus on several different genetic markers with known phenotypic expression, and discuss the possibilities of using this approach to uncover genotype x phenotype relationships.