ethicalabs/Research-Intent-Collab huggingface dataset:
Projected impacts of climate change on viticulture over French wine regions using downscaled CMIP6 multi-model data

Climate change is a major challenge for the French wine industry. Climatic conditions in French vineyards have already changed and will continue to evolve impacting viticulture.This study aims to analyse the evolution of agro- and eco-climatic indices based on phenology simulation of French wine-growing regions. This evolution was analysed on a recent-past period (1962–1991 to 1992–2021) using SAFRAN climate data and on a future projected period (1985–2014 to 2041–2070) with two SSP trajectories (SSP2-4.5 and SSP5-8.5). A set of 19 CMIP6 climate models downscaled at 8 km grid resolution over France coupled with three phenological and a water balance model were used. Phenological model parameters and training system characteristics were adapted to each region to match as much as possible current practices.Temperatures during the growing season have increased by +1 °C to +2.1 °C since the second half of the 20th century and could rise to +3.7 °C in regions around the Mediterranean by 2070. The inter-model variance concerning the precipitation is high, a significant change (decrease) in precipitation during the grapevine growing season is observed only for the regions of western France (Oceanic climate) over the period 2040–2071 with the SSP5 trajectory. All simulated phenological stages have shifted toward earlier dates. Their occurrence should be even earlier by 2070 with an average advance of up to 22 days for the mid-veraison of Pinot noir in eastern France. The theoretical maturity date (sugar content) should also be advanced from 19 to 30 days depending on the considered region and SSP. Thermal conditions closer to the photosynthetic optimum should promote onset by the early second half of the 21st century. The increase in both the number of hot days and grapevine water deficit during the period of fruit development should impact grape production in quality and quantity in all wine-growing regions. Spring frost projections show no significant change in risk for the second half of the 21st century, compared to current conditions.
Hacking the centromere chromatin code: dissecting the epigenetic regulation of centromere identity

The centromere is a specialized chromatin domain that serves as the assembly site for the mitotic kinetochore structure, thereby playing a fundamental role in facilitating the maintenance of the genetic information. A histone H3 variant termed CENP-A is specifically found at all active centromeres. Beyond this, however, little is known about how and to which extent the chromatin environment of centromeres modulates and contributes towards centromere identity and function. Here, I have employed a novel Human Artificial Chromosome (HAC), the centromere of which can be targeted by fusions to the tet repressor, to characterize the chromatin environment underlying active kinetochores, as well as to specifically probe the role of this environment in the maintenance of kinetochore structure and function. My data demonstrate that centromeric chromatin resembles the downstream regions of actively transcribed genes. This includes the previously unrecognized presence of histone H3 nucleosomes methylated at lysine 36 within the chromatin underlying functional kinetochores. Targeted manipulation of this chromatin through tethering of a heterochromatin-seeding transcriptional repressor results in the inactivation of HAC kinetochore function concomitant with a hierarchical disassembly of the structure. Through an even more selective engineering of the HAC centromere chromatin, I have provided evidence supporting a critical role for nucleosomes dimethylated at lysine 4 on histone H3 in facilitating local transcription of the underlying DNA. Tethering of different chromatin-modifying activities into the HAC kinetochore collectively reveals a critical role for both, histone H3 dimethylated on lysine 4 and low-level, non-coding transcription in the maintenance of the CENP-A chromatin domain. On one hand, repression of centromeric transcription negatively correlates with the maintenance of CENP-A and ultimately results in the loss of kinetochore function. On the other hand, increasing kinetochore-associated RNA polymerase activity to within physiological levels for euchromatin is associated with rapid loss of CENP-A from the HAC centromere. Together, my data point towards the requirement for a delicate balance of transcriptional activity that is required to shape and maintain the chromatin environment of active centromeres.
Experimental design modeling of carryover to optimize air-segmented continuous flow analysis

Abstract   Multiple blood chemistry assays were performed in an air-segmented continuous flow analysis system with plasma/reagent pairs separated from each other by air bubbles and water segments. Carryover of one reaction pair to subsequent ones was investigated in this study where red dye simulates the original reaction pair and water segments simulate the target reaction pair. Several factors were tested for their effects on the carryover coefficient by using two three-factorial experimental designs. The carryover coefficient in each design was regressed as a function of three parameters by fitting to a second-order model. The significance probabilities for individual parameters revealed that the size of air bubbles and of the initial slug do not have significant effects, so these were excluded from the two designs and two-parameter models re-fit to the data. The statistical analysis for each case confirms that the two-parameter model for the second design gives the best fit with an F-value of 147.44 and R2 of 0.955. The average absolute difference between the model and data is 2.3%. An objective function, that balances carryover with total processing time, shows optimal loading when six bubbles and five 5.3 μl (2 s) water segments are inserted. Under this condition, carryover is essentially zero and total loading time is 22 s. This scheme will allow processing of three chemistries in 3 min. Larger numbers of chemistries can be processed in the same time by re-configuring the system to allow smaller sub-second samples.
Design and Implementation of a Novel Parallel Algorithm for Efficient Image Compression in High-Performance Computing

The focus of this paper is to describe the development and the architecture of a new parallel algorithm targeted for image compression within High Performance Computing context. The suggested algorithm apply parallel processing strategies for the image data set in order to minimize the amount of computation while at the same time optimize the compression ratio and speed. When combining new parallelism strategies with newly developed and existing methods of data compression, the result is visibly better in both, compression ratio and time, as opposed to comparable existing algorithms. Experimental results performed on different HPC environment prove that the solution put forward is quite scalable and efficient; therefore, it should be considered in applications where real time image processing is crucial. It provides a major contribution to the literature in image compression with a special focus on parallel computations.
Continuous audio-visual speech recognition

We address the problem of robust lip tracking, visual speech feature extraction, and sensor integration for audio-visual speech recognition applications. An appearance based model of the articulators, which represents linguistically important features, is learned from example images and is used to locate, track, and recover visual speech information. We tackle the problem of joint temporal modelling of the acoustic and visual speech signals by applying Multi-Stream hidden Markov models. This approach allows the use of different temporal topologies and levels of stream integration and hence enables to model temporal dependencies more accurately. The system has been evaluated for a continuously spoken digit recognition task of 37 subjects.
<scp>SDHO</scp>‐<scp>KGNN</scp>: An Effective Knowledge‐Enhanced Optimal Graph Neural Network Approach for Fraudulent Call Detection

ABSTRACTRapid advancements in mobile communication technologies have led to the progression of telecom scams that not only deplete individual fortunes but also affect social income. Hence, fraudulent call detection gains significance, which not only aims to proactively recognize the frauds, but also alleviate the fraudulent activities to manage external losses. Though the traditional methods, such as rule‐based systems and supervised machine learning techniques, actively engage in detecting such fraudulent activities, they fail to adapt to the evolving fraud patterns. Therefore, this research introduces a sheepdog hunt optimization‐enabled knowledge‐enhanced optimal graph neural network classifier (SDHO‐KGNN) approach for detecting fraudulent calls accurately. The effectiveness of the proposed SDHO‐KGNN approach is achieved through the combination of the power of graph representation learning with expert insights, which allows the proposed SDHO‐KGNN approach to capture complex relationships and patterns within telecom data. Additionally, the integration of the SDHO algorithm enhances model performance by optimizing the discrimination between legitimate and fraudulent calls. Moreover, the SDHO‐KGNN classifier captures the intricate call patterns and relationships within dynamic call networks, thereby attaining a better accuracy, precision, and recall of 93.8%, 95.91%, and 95.53% for 90% of the training.
GREEN LEAN SIX SIGMA ON BRAND QUALITY MANAGEMENT IN A MANUFACTURING FIRM IN SOUTH EAST NIGERIA

The study examined the effect of green lean six sigma on brand quality management in a manufacturing firm south east Nigeria. The specific objectives are to; examine the effect of customer focus on product quality of manufacturing firms and determine the effect of control processes on the quality service of manufacturing firms in South-East, Nigeria. A survey research design was chosen. Data for the study was gathered using a standardized questionnaire that employed a point Likert scale. The mean, standard deviation, and basic percentages were used to examined the data. While SPSS version 20, a descriptive statistics application was used to analyzed the hypotheses. The result revealed that Customer Focus have a significant positive effect on the product quality of manufacturing firms with a P-value of (0.000 <0.05). Control processes have a significant positive effect on the quality service of manufacturing firms with a P-value of (0.000 <0.05) in South-East, Nigeria. The study concluded that Green Lean Six Sigma not only strengthens the competitive positioning of manufacturing firms through improved product and service quality but also promotes sustainable operational practices. The study recommended that manufacturing firms in South-East Nigeria strengthen the adoption of Green Lean Six Sigma practices as a means of improving brand quality management.
Physics of self-sustained oscillations in the positive glow corona

The physics of self-sustained oscillations in the phenomenon of positive glow corona is presented. The dynamics of charged-particle oscillation under static electric field has been briefly outlined; and, the resulting self-sustained current oscillations in the electrodes have been compared with the measurements from the positive glow corona experiments. The profile of self-sustained electrode current oscillations predicted by the presented theory qualitatively agrees with the experimental measurements. For instance, the experimentally observed saw-tooth shaped electrode current pulses are reproduced by the presented theory. Further, the theory correctly predicts the pulses of radiation accompanying the abrupt rises in the saw-tooth shaped current oscillations, as verified from the various glow corona experiments.
Error-Mitigated Hamiltonian Simulation: Complexity Analysis and Optimization for Near-Term and Early-Fault-Tolerant Quantum Computers

Simulating real-time dynamics under a Hamiltonian is a central goal of quantum information science. While numerous Hamiltonian-simulation quantum algorithms have been proposed, the effects of physical noise have rarely been incorporated into performance analysis, despite the non-negligible noise levels in quantum devices. In this work, we analyze noisy Hamiltonian simulation with quantum error mitigation for Trotterized and randomized LCU-based Hamiltonian simulation algorithms. We give an end-to-end comprehensive complexity analysis of error-mitigated Hamiltonian simulation algorithms using the mean-squared error. Because quantum error mitigation incurs an exponential cost with the number of layers in quantum algorithms, there is a trade-off between the sampling cost and the bias in simulation accuracy or the algorithmic sampling overhead. Optimizing this trade-off, we derive an analytic depth-selection rule and characterize the optimal end-to-end scaling as a function of target accuracy and noise parameters. We further quantify the noise-characterization cost required for error mitigation via gate set tomography and the recently proposed space-time noise inversion method, showing that the latter can significantly reduce the characterization overhead.
Citizen Science for Health: an international survey on its characteristics and enabling factors Senior Researcher

Even though citizen and patient engagement in health research has a long tradition, citizen science in health has only recently gained attention and recognition. However, at present, there is no clear overview of what major challenges citizen science initiatives in the health domain are facing, nor what makes citizen science in the health domain distinctive from citizen science in other domains, if at all. Such an overview could contribute to highlighting and articulating the different voices and needs of stakeholders engaged in any form of citizen science in the health domain. Besides that, it may encourage the input of citizens and patients alike in health research and innovation, policy and practice. This paper reports on a survey developed by the European Citizen Science Association (ECSA) Working Group “Citizen Science for Health”, with the overall intention to lay bare the characteristics and enabling factors of citizen science in the health domain, and to formulate a direction for future work and research. The survey was available in six languages  and was open between January and August 2022. The majority of the 254 respondents were from European countries. Ethics, the complexity of the health domain, and the overlap of the role of researcher and the subject of research were the main issues specific for citizen science in health as compared to citizen science in other domains. The top two conditions that need development are Balanced return on investment and Ethics. The paper discusses these and other conditions with references to current literature.
Public Engagement with Science via Social Media: A Case of Communicating the Pandemic on Twitter

AbstractThe poster describes a project which analyzes interactions between laypeople and experts via social media. Our aim is to understand how experts and the general public interact with each other on social media, and how we can use current data to improve these interactions in the future. We created a Twitter bot to obtain data from 15 COVID‐19 experts and 7 federal government‐sponsored public health organizations from English‐speaking countries. The data were analyzed in R to investigate the relationships among Followers, Favorites, Retweets, and Hashtag Count per tweet. The preliminary analysis indicated statistically significant differences between various variables including: Number of Favorites, Number of Retweets, Number of Hashtags, and Number of Followers; the results shed light on the current relationship between the public and experts on social media.
Genetic Discrimination &amp; Health Insurance: An Analysis

A contract of insurance like other contracts is concluded by offer and acceptance and if there is a stipulation that the liability will attach itself under the contract only if premium is paid, then, that will be a condition precedent to the policy taking effect.   ‘Health’ is a state of complete physical, mental and social well being and not merely an absence of sickness, disease or infirmity. Genes are responsible for the various traits that human beings possess, and it is the difference in these traits that make one homosapien different from the other. While the genes pass on several positive traits (characteristics), but they could at times be responsible for some abnormal medical conditions which are passed on from one generation to another; these abnormal medical conditions which are passed on from one generation to another are called genetic disorders. Genetic disorders can be dominant or recessive.   Healthcare without health insurance, with the medical costs escalating each year, is a serious challenge. Health insurance cover with the exclusion of ‘genetic disorders’ batters the most basic right of an individual to avail insurance for the prevention, diagnosis, management and cure of diseases.   Insurance companies cannot deny health insurance to individuals suffering from genetic disorders. Insurance companies are to determine whether or not an individual suffers from genetic disorder, based on genetic testing and not on the basis of past family medical history of an individual. Insurance companies are under a legal duty to protect and safeguard the information received by them qua an individual derived from genetic testing; high degree of confidentiality is to be maintained. Blanket exclusion of genetic disorders from health covers is not purely a contractual issue between the insurance company and the insured but rather the same spills into the canvas of right to health. Primacy is to be given to a contract, but no contract can override the fundamental rights enshrined in the Constitution of India, 1950.
The Lateral Trigger Probability function for the Ultra-High Energy Cosmic Ray Showers detected by the Pierre Auger Observatory (vol 35, pg 266, 2011)

Contains fulltext : 94129.pdf (Publisher’s version ) (Open Access)
Two Faces of the Polish Supreme Court After 'Reforms' of the Judiciary System in Poland: The Question of Judicial Independence and Appointments

European Papers - A Journal on Law and Integration, 2020 5(1), 347-362
Compliance issues within Europe's General Data Protection Regulation in the context of information security and privacy governance in Swedish corporations : A mixed methods study of compliance practices towards GDPR readiness

The European Union has introduced a new General Data Protection Regulation that regulates all aspects of privacy and data protection for the data of European citizens. To transition to the new rules, companies and public institutions were given two years to adapt their systems and controls. Due to the large area of changes the GDPR requires, many companies are facing severe problems to adapt the rules to be ready for enforcement. This marks the purpose of this study which is to look into compliance practices in the implementation of GDPR requirements. This includes a prospect of compliance mechanisms that may remain insufficiently addressed when the regulation comes into force on May 25, 2018. The study is conducted in Sweden and aims to investigate the situation in corporations and not in public institutions. Mixed methods have been applied by surveying and interviewing Swedish GDPR experts and consultants to gain an understanding of their view by using capability maturity scales to assess a variety of security processes and controls. The analysis shows a low implementation in GDPR requirements while having seen improvements over the past two years of transition. It points out that a holistic strategy towards compliance is mostly missing and many companies face obstacles that are difficult to overcome in a short period. This may result in non-compliance in many Swedish corporations after the regulation comes into force on May 25.
SOME METHODOLOGICAL ASPECTS OF MACHINE LEARNING

There is a lack of theory and methodology for the development and proper evaluation of the machine learning (ML) applications built so far. This paper presents some methodological considerations concerning a particular ML perspective expressed in a methodological model called 2D-L1M, a multilevel paradigm-independent methodology that is intended to help during the continuous specification mechanism implicitly present on developing learning programs. Taking into account the striking differences between the knowledge level description of these systems and the symbolic ones, we have identified new layers for their characterization, from the observer point of view and from a pure computational perspective. Our proposal is supported with symbolic structures and arguments from distinguished machine learning strategies and applications (Winston et al., 1983; Mitchell et al, 1983, 1986; Dejong and Mooney, 1986; Carbonell, 1986; Quinlan, 1986; Hammond, 1986; Minton, 1990; Bergadano and Giordana, 1988; Hirsh 1990) ...
The Responsibility to Protect: possible answer to the dilemma of Humanitarian Intervention

The purpose of the article is to analyze the Responsibility to Protect as a possible response to the dilemma of humanitarian intervention considering the precepts set out in the United Nations Charter. To this end, the concepts of sovereignty and non-use of force delimited in the Charter will be addressed, as these intrinsically imply the possible application of R2P or not. Subsequently, the objective is to present R2P from its genesis to the delimitation of its scope of application based on its legal contour; and, finally, the final considerations about the viability of R2P, as a possible response to the dilemma of humanitarian interventions. From this perspective, this article will use the hypothetical deductive method, in addition to bibliographical research and documentary analysis in order to develop a study with primacy.
Citizen Science Models in Health Research: an Australian Commentary

This commentary explores how established citizen science models can inform and support meaningful engagement of the public in health research in Australia.  In particular, with the growth in participatory health research approaches and increasing consumer participation in contributing to this research through digital technologies, there are gaps in our understanding of best practice in health and biomedical citizen science research to address these paradigm shifts.  Notable gaps are how we might more clearly define the parameters of such research and which citizen science models might best support digitally-enabled participation falling within these.  Further work in this area is expected to lead to how established citizen science methods may help improve the quality of and the translation of public engagement in health research.
Balancing Privacy and Performance in Federated Learning with Adaptive Differential Privacy and Secure Multi-Party Computation

Federated Learning (FL) enables collaborative model training without sharing raw data, but exchanged updates remain vulnerable to inference attacks. While Differential Privacy (DP) adds noise to protect privacy, client-side noise in methods like Adaptive DP-FL introduces high variance and relies on server trust. Secure Multi-Party Computation (SMPC) safeguards updates via secret sharing but lacks integration with differential privacy. We propose a hybrid FL framework combining adaptive DP-FL with SMPC. Clients clip gradients adaptively and secret-share updates across non-colluding servers. Servers reconstruct only the global sum, add a single calibrated Gaussian noise term, and account privacy using R'enyi DP. This design reduces sensitivity by 1/K, cuts noise variance, and streamlines privacy accounting. Experiments on MNIST and Fashion-MNIST under varying budgets show improved accuracy over the baseline, demonstrating enhanced privacy–utility trade-offs for sensitive applications.