ethicalabs/Research-Intent-Collab huggingface dataset:
Marking Systemic Portfolio Risk with Application to the Correlation Skew of Equity Baskets

The downside risk of a portfolio of (equity)assets is generally substantially higher than the downside risk of its components. In particular in times of crises when assets tend to have high correlation, the understanding of this difference can be crucial in managing systemic risk of a portfolio. In this paper we generalize Merton's option formula in the presence jumps to the multi-asset case. It is shown how common jumps across assets provide an intuitive and powerful tool to describe systemic risk that is consistent with data. The methodology provides a new way to mark and risk-manage systemic risk of portfolios in a systematic way.
Integrating Crop and Landscape Management into New Crop Protection Strategies to Enhance Biological Control of Oilseed Rape Insect Pests

The development of sustainable cropping systems is a major challenge for agronomists and crop scientists in many regions of the world. The prophylactic uses of broad spectrum insecticides are actually the main solution for farmers to control insect pests. Therefore, there is a growing need to develop innovative crop protection strategies through an integrated approach which aims at favouring natural enemies and enhancing biological control in agroecosystems. The development of such strategies requires a thorough understanding of agroecosystem functioning. In this chapter, we present the effects of different elements, from the field scale to the landscape scale that are known to enhance biological control in agroecosystems and limit pest damage with particular reference to oilseed rape. Linking integrated pest management and landscape ecology brings a regional perspective to the management of pest populations. Available techniques are often added together rather than combined in an integrated way and are rarely evaluated through environmental and economical criteria. Therefore, we present a methodological framework to design and assess sustainable cropping systems, with a particular emphasis on complementariness between models, systemic trials and more analytical approaches. Implementation of sustainable cropping systems implies the development of new integrated pest management strategies and thereby an increased participation of the different stakeholders from farmers to policy makers.
For what it's worth. Unearthing the values embedded in digital phenotyping for mental health

Digital phenotyping for mental health is an emerging trend which uses digital data, derived from mobile applications, wearable technologies and digital sensors, to measure, track and predict the mental health of an individual. Digital phenotyping for mental health is a growing, but as yet underexamined, field. As we will show, the rapid growth of digital phenotyping for mental health raises crucial questions about the values that underpin and are reinforced by this technology, as well as regarding to whom it may become valuable. In this commentary, we explore these questions by focusing on the construction of value across two interrelated domains: user experience and epistemologies on the one hand, and issues of data and ownership on the other. In doing so, we demonstrate the need for a deeper ethical and epistemological engagement with the value assumptions that underpin the promise of digital phenotyping for mental health.
APPLYING LATENT DIRICHLET ALLOCATION (LDA) FOR TOPIC DISCOVERY IN INDIAN TRADITIONAL KNOWLEDGE RESEARCH: A STUDY WITH WEB OF SCIENCE-INDEXED INDIAN JOURNAL

In many facets of human existence, Indian traditional knowledge is essential and constitutes a substantial field of study. Unlike earlier studies of the Indian Knowledge System, this research analysed research publications on the Indian knowledge system using the Latent Dirichlet Allocation (LDA) topic model system, highlighting the latent topics and research trends in traditional Indian knowledge. From the Web of Science database, 1994 articles were retrieved for this study, which were published in the Indian Journal of Traditional Knowledge from 2007 to 2024. For topic modeling, LDA with a 100% text corpus, 6 hidden topics are identified, and each topic has ten related keywords. The analysis result of this research shows that the maximum number of documents 564, falls under the Herbal Medicine and Ethnobotany topic, followed by 450 and 356 documents under the Indigenous knowledge system and Clinical and Pharmacological Studies topics, respectively. This study found that among the coherence scores calculated by 30 different training LDA modules, one module, namely alpha 0.01 and beta 0.01, had the highest coherence score - 0.613233, indicating that one or two topics dominate each document, and the keywords within each topic are distinct. The study also highlights the latent trends in the IKS and visualises the inter-topic distance map, indicating semantic relationships between topics. In addition, using the Pearson correlation matrix to measure inter-topic correlations, the output heatmap indicates that the six topics are fairly independent, distinct, and non-overlapping.
Epigenetic regulation of REX1 expression and chromatin binding specificity by HMGNs

HMGN proteins localize to chromatin regulatory sites and modulate the cell-type specific transcription profile; however, the molecular mechanism whereby these ubiquitous nucleosome binding proteins affect gene expression is not fully understood. Here, we show that HMGNs regulate the expression of Rex1, one of the most highly transcribed genes in mouse embryonic stem cells (ESCs), by recruiting the transcription factors NANOG, OCT4 and SOX2 to an ESC-specific super enhancer located in the 5' region of Rex1. HMGNs facilitate the establishment of an epigenetic landscape characteristic of active chromatin and enhancer promoter interactions, as seen by chromatin conformation capture. Loss of HMGNs alters the local epigenetic profile, increases histone H1 occupancy, decreases transcription factors binding and reduces enhancer promoter interactions, thereby downregulating, but not abolishing Rex1 expression. ChIP-seq analyses show high colocalization of HMGNs and of REX1, a zinc finger protein, at promoters and enhancers. Loss of HMGNs preferentially reduces the specific binding of REX1 to these chromatin regulatory sites. Thus, HMGNs affects both the expression and the chromatin binding specificity of REX1. We suggest that HMGNs affect cell-type specific gene expression by modulating the binding specificity of transcription factors to chromatin.
Gölbaşı (Isparta) damar tipi as cevherleşmesinin iz element ve kararlı izotop jeokimyası özelliklerinin incelenmesi = Investigation of trace element and stable isotope geochemistry features of Golbasi (Isparta) vein type arsenice ore /

The mineralization occurs as a main vein that is 38 m in length, 1-2 m in thickness with 1-50 cm thick stockwork veinlets which developed arround the main vein. It is found along a normal fault zone trending approximately N-S. In this study, the veins have mainly mineral paragenesis consisted of coarse grained calcite, baryte, realgar and orpiment. The arsenic ore is an epithermal mineralization that was formed from magmatic activity. The arsenic mineralization is related to Pliocene aged trachyandesitic-latitic Gölcük volcanism. This origin for the As ore is known from previous publications. The (34S) isotope values of realgar samples are shows changes between %0 - 1.28 and %0 2.31 and the average is %0 -0.019. The values of 34S for barite samples of the ore vary from %0 29,79 to 30,72. The arithmetic average of 8 samples of the barite 34S is %0 30,32. According to the results from the (18O) and (13C) isotope analysis which was performed on the samples of calcite collected from the researching site; the values of (18O) isotope are between from %0 28.06 to %0 30.69 and the average is %0 29.37. The values for (13C) are between from %0 - 1,31 to %0 1.05 and the average value is %0 0.019. While the quantity of trace elements shows the low value in the As mineralization of Gölbaşı, the some elements namely Sr, V, Sb and Se elements showed the higher values than other trace elements in ore. Due to The values of the 34S isotope showing the value close to zero, the mineralization is thought to be formed from the magmatic activity. At the same time,according to the values of 18O and 13C, the hydrothermal solution forming arsenic ore may be thought rich in meteoric water as well as the magmatic water. Keywords: Arsenic mineralization, trace element geochemistry, stable isotope ration, origin.
Bayesian Evidential Learning Approach to Uncertainty Quantification in THM Model of Geothermal Energy Extraction in Deep Mines

Utilizing existing deep mining systems for geothermal extraction not only facilitates the development of geothermal systems but also helps meeting the cooling requirements for deep mining operations. In this study, a thermo-hydro-mechanical model of geothermal extraction in deep mines is developed to investigate the evolution of mine galleries stability and temperature, and the temperature changes in geothermal production wells. The uncertainty in system responses is predicted through the Bayesian Evidential Learning framework.Due to our limited understanding of the material properties and the scarcity of measurement data, uncertainties emerge in the forward simulations. Ideally, a comprehensive uncertainty analysis would be conducted to predict all possible outcomes and assess any risks. However, In light of the intractability of performing comprehensive uncertainty analyses in scenarios with vast unknown data, particularly due to the computational overhead of multiple inverse problem-solving, we employ the Bayesian Evidential Learning framework, which provides a feasible and rapid alternative for approximating prediction post-distributions and choosing the most informative data sets. Before implementing BEL, we employed Latin Hypercube Sampling to create 500 sets of realizations for forward simulations, and subsequently utilized global sensitivity analysis to evaluate the data's informational value, aiming to diminish the uncertainty in predictions. In this paper, the BEL framework is utilized to achieve two: firstly, to stochastically predict the responses of the system (stability and temperature) within the BEL framework, using machine learning to discover direct correlations between predictors (sensitive parameters) and targets (system responses). Subsequently, newly collected data can be utilized to predict the approximate posterior distributions of the corresponding gallery stability, temperature, and production well temperature, thus circumventing traditional data inversion steps. This framework can be adjusted to accommodate any predictions related to subsurface conditions; hence, our second goal involves predicting the system's long-term responses within the BEL based on short-term data collection, forecasting posterior distributions from the acquired short-term data, and validating the efficacy of this approach.Our study indicates that in practical engineering, by (1) obtaining data of material properties and (2) key responses of short-term simulation, it is possible to predict the critical responses of the system in long-term geothermal extraction, thereby maximizing the information content of any measurement data while minimizing budget constraints and computational costs.
Modelling and simulating retail management practices: a first approach

Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail performance. Despite the fact we are working within a relatively novel and complex domain, it is clear that using an agent-based approach offers great potential for improving organizational capabilities in the future. Our multi-disciplinary research team has worked closely with one of the UK's top ten retailers to collect data and build an understanding of shop-floor operations and the key actors in a department (customers, staff, and managers). Based on this case study we have built and tested our first version of a retail branch agent-based simulation model where we have focused on how we can simulate the effects of people management practices on customer satisfaction and sales. In our experiments we have looked at employee development and cashier empowerment as two examples of shop floor management practices. In this paper we describe the underlying conceptual ideas and the features of our simulation model. We present a selection of experiments we have conducted in order to validate our simulation model and to show its potential for answering "what-if" questions in a retail context. We also introduce a novel performance measure which we have created to quantify customers' satisfaction with service, based on their individual shopping experiences.
Early Warning Systems and Their Role in Disaster Risk Reduction

AbstractIn this chapter, we introduce early warning systems (EWS) in the context of disaster risk reduction, including the main components of an EWS, the roles of the main actors and the need for robust evaluation. Management of disaster risks requires that the nature and distribution of risk are understood, including the hazards, and the exposure, vulnerability and capacity of communities at risk. A variety of policy options can be used to reduce and manage risks, and we emphasise the contribution of early warnings, presenting an eight-component framework of people-centred early warning systems which highlights the importance of an integrated and all-society approach. We identify the need for decisions to be evidence-based, for performance monitoring and for dealing with errors and false information. We conclude by identifying gaps in current early warning systems, including in the social components of warning systems and in dealing with multi-hazards, and obstacles to progress, including issues in funding, data availability, and stakeholder engagement.
Requirements Engineering for Older Adult Digital Health Software: A Systematic Literature Review

Growth of the older adult population has led to an increasing interest in technology-supported aged care. However, the area has some challenges such as a lack of caregivers and limitations in understanding the emotional, social, physical, and mental well-being needs of seniors. Furthermore, there is a gap in the understanding between developers and ageing people of their requirements. Digital health can be important in supporting older adults wellbeing, emotional requirements, and social needs. Requirements Engineering (RE) is a major software engineering field, which can help to identify, elicit and prioritize the requirements of stakeholders and ensure that the systems meet standards for performance, reliability, and usability. We carried out a systematic review of the literature on RE for older adult digital health software. This was necessary to show the representatives of the current stage of understanding the needs of older adults in aged care digital health. Using established guidelines outlined by the Kitchenham method, the PRISMA and the PICO guideline, we developed a protocol, followed by the systematic exploration of eight databases. This resulted in 69 primary studies of high relevance, which were subsequently subjected to data extraction, synthesis, and reporting. We highlight key RE processes in digital health software for ageing people. It explored the utilization of technology for older user well-being and care, and the evaluations of such solutions. The review also identified key limitations found in existing primary studies that inspire future research opportunities. The results indicate that requirement gathering and understanding have a significant variation between different studies. The differences are in the quality, depth, and techniques adopted for requirement gathering and these differences are largely due to uneven adoption of RE methods.
The Afterglow, Energetics, and Host Galaxy of the Short‐Hard Gamma‐Ray Burst 051221a

Final version (to appear in ApJ on 20 September 2006)
High-resolution x-ray analysis with multilayer gratings

Periodic multilayers are nowadays widely used to perform x-ray analysis in the soft x-ray range (photon energy lower than 1 keV). However, they do not permit to obtain high-resolution spectra like natural or synthetic crystals. Thus, multilayers cannot resolve interferences between close x-ray lines. It has been shown and demonstrated experimentally that patterning a grating profile within a multilayer structure leads to a diffractive optics with improved resolving power. We illustrate the use of a Mo/B4C multilayer grating in the Fe L and C K spectral ranges, around 700 eV and 280 eV respectively. First, in the Fe L range, the improved spectral resolution enables us to distinguish the Fe Lαand Lβemissions (separated by 13 eV). In addition, using a sample made of a mix of LiF and an iron ore, we show that it is possible to easily resolve the F K and Fe L emissions. These examples demonstrate that an improved x-ray analysis can be obtained with multilayer gratings when there is the need to study samples having elements giving rise to close emission lines. Second, in the C K range, by comparing C Kαspectra from B4C and cellulose, we show that the shape of the emission band is sensitive to the chemical state of the carbon atom.
Digital phenotyping data to predict symptom improvement and mental health app personalization in college students (Preprint)

BACKGROUND                         Mental health apps offer a transformative means to increase access to scalable evidence-based care for college students. Yet low rates of engagement currently preclude the effectiveness of these apps. One promising solution is to make these apps more responsive and personalized through digital phenotyping methods able to predict symptoms and offer tailored interventions.                                                                       OBJECTIVE                         Following our protocol and utilizing the exact model shared in that paper, in this work we assess the prospective validity of mental health symptom prediction using the mindLAMP app. We also explore secondary aims around app intervention personalization and correlations of engagement with the Technology Acceptance Model (TAM) and Digital Working Alliance Inventory (D-WAI) scale.                                                                       METHODS                         The study was 28 days in duration and followed the published protocol with participants collecting digital phenotyping data and being offered optional scheduled as well as algorithm recommend app interventions. Study compensation was tied to the completion of Weekly Surveys and was not otherwise tied to engagement or use of the app.                                                                       RESULTS                         170 college student participants completed informed consent, of which 108 passed the study trial period, and 74 completed the study. The area under the curve values for the symptom prediction model ranged from 0.58 for the UCLA Loneliness Scale to 0.71 for the Patient Health Questionaire-9. Engagement with the app interventions was high with a study mean of 73% but few participants engaged with the optional recommended interventions. The perceived utility of the app in the TAM was higher among those completing at least one recommended intervention                                                                       CONCLUSIONS                         Our results suggest how digital phenotyping methods can be used to create generalizable models that may help create more personalized and engaging mental health apps.
APPLICATION OF ROOT CAUSE ANALYSIS TECHNIQUES FOR THE INVESTIGATION OF RAILWAY TRANSPORT INCIDENTS

Purpose. Enhancing railway traffic safety in Ukraine by implementing root cause and intermediate cause identification techniques in the processes of internal and technical investigations, as well as in forensic expert practice for railway transport incidents (RTIs), as stipulated by EN standards adopted in Ukraine. Methods. The method of RTI mechanism construction, root cause analysis (RCA) techniques reflected in DSTU EN 62740:2022, as well as methods of analysis, comparison, and synthesis. Results. A comparison between the RTI mechanism construction method currently used in Ukraine for determining RTI causes and the RCA techniques provided in DSTU EN 62740:2022 has demonstrated that RCA allows for a flexible approach to the investigation process depending on the RTI type. It enables the study of RTIs at different levels while considering not only technical but also organizational causes and their complex interrelations. Practical significance. The application of RCA techniques in accordance with DSTU EN 62740:2022 in internal and technical investigations of RTIs, as well as in forensic railway transport expertise, will ensure that the investigation process complies with European regulatory documents adopted in Ukraine as national standards. The techniques proposed in DSTU EN 62740:2022 allow for an in-depth examination of various causes leading to RTIs and provide a substantiated basis for preventive measures, thereby contributing to increased railway traffic safety.
Optimal Serverless Networks Attacks, Complexity and some Approximate Algorithms

JUCS - Journal of Universal Computer Science Volume Nr. 15 Issue Nr. 14
Depth of Ultra High Energy Cosmic Ray Induced Air Shower Maxima Measured by the Telescope Array Black Rock and Long Ridge FADC Fluorescence Detectors and Surface Array in Hybrid Mode

info:eu-repo/semantics/published
Community concerns about genetic discrimination in life insurance persist in Australia: A survey of consumers offered genetic testing

AbstractFears of genetic discrimination in life insurance continue to deter some Australians from genetic testing. In July 2019, the life insurance industry introduced a partial, self-regulated moratorium restricting the use of genetic results in underwriting, applicable to policies up to certain limits (eg AUD$500,000 for death cover).We administered an online survey to consumers who had taken, or been offered, clinical genetic testing for adult-onset conditions, to gather views and experiences about the moratorium and the use of genetic results in life insurance, including its regulation.Most respondents (n = 367) had undertaken a genetic test (89%), and had a positive test result (76%; n = 243/321). Almost 30% (n = 94/326) reported testing after 1 July 2019. Relatively few respondents reported knowing about the moratorium (16%; n = 54/340) or that use of genetic results in life insurance underwriting is legal (17%; n = 60/348). Only 4% (n = 14/350) consider this practice should be allowed. Some respondents reported ongoing difficulties accessing life insurance products, even after the moratorium. Further, discrimination concerns continue to affect some consumers’ decision-making about having clinical testing and applying for life insurance products, despite the Moratorium being in place. Most respondents (88%; n = 298/340) support the introduction of legislation by the Australian government to regulate this issue.Despite the introduction of a partial moratorium in Australia, fears of genetic discrimination persist, and continue to deter people from genetic testing. Consumers overwhelmingly consider life insurers should not be allowed to use genetic results in underwriting, and that federal legislation is required to regulate this area.