ethicalabs/Research-Intent-Collab huggingface dataset:
A Comprehensive Study on Algorithms and Applications of Artificial Intelligence in Diagnosis and Prognosis

Machine learning and deep learning are branches of artificial intelligence consisting of statistical, probabilistic, and optimisation techniques that allow machines to learn from previous observations recorded by humans. These machine learning algorithms, when combined with other technologies, can be used to perform very intuitive yet awkward human-like tasks. Using these algorithms, humans can enable computers to learn about certain things like recognising an object in an image. Prognosis is an important clinical skill, particularly for cancer patients' clinicians, neurooncologists. One of the biggest challenge for AI in prognosis is to verify and validate its models. Unlike diagnosis, the prognosis models are centered on predictive data that usually addresses the patient and not the disease. Prognosis models were developed to aid in the decision-making of patients' treatment.AI can have bigger impact in the health care domain with more healthcare providers using AI to make the first diagnosis and prognosis more accurate and interpretable with the available patient data for better therapy.
Inverse Reinforcement Learning through Policy Gradient Minimization

Inverse Reinforcement Learning (IRL) deals with the problem of recovering the reward function optimized by an expert given a set of demonstrations of the expert's policy.Most IRL algorithms need to repeatedly compute the optimal policy for different reward functions.This paper proposes a new IRL approach that allows to recover the reward function without the need of solving any direct RL problem.The idea is to find the reward function that minimizes the gradient of a parameterized representation of the expert's policy.In particular, when the reward function can be represented as a linear combination of some basis functions, we will show that the aforementioned optimization problem can be efficiently solved.We present an empirical evaluation of the proposed approach on a multidimensional version of the Linear-Quadratic Regulator (LQR) both in the case where the parameters of the expert's policy are known and in the (more realistic) case where the parameters of the expert's policy need to be inferred from the expert's demonstrations.Finally, the algorithm is compared against the state-of-the-art on the mountain car domain, where the expert's policy is unknown.
Advances in Personalized Medicines along with Functional Genomics and Pharmacogenomics

State-of-the-art research on the human genome has produced remarkable research achievements in pharmacogenomics and functional genomics, and these research results are making an invaluable contribution to the advancement of personalized medicine [...]
Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities

For many neurological disorders, prediction of disease state is an important clinical aim. Neuroimaging provides detailed information about brain structure and function from which such predictions may be statistically derived. A multinomial logit model with Gaussian process priors is proposed to: (i) predict disease state based on whole-brain neuroimaging data and (ii) analyze the relative informativeness of different image modalities and brain regions. Advanced Markov chain Monte Carlo methods are employed to perform posterior inference over the model. This paper reports a statistical assessment of multiple neuroimaging modalities applied to the discrimination of three Parkinsonian neurological disorders from one another and healthy controls, showing promising predictive performance of disease states when compared to nonprobabilistic classifiers based on multiple modalities. The statistical analysis also quantifies the relative importance of different neuroimaging measures and brain regions in discriminating between these diseases and suggests that for prediction there is little benefit in acquiring multiple neuroimaging sequences. Finally, the predictive capability of different brain regions is found to be in accordance with the regional pathology of the diseases as reported in the clinical literature.
Allpix Squared - Silicon Detector Monte Carlo Simulations for Particle Physics and Beyond

Allpix Squared is a versatile, open-source simulation framework for silicon pixel detectors. Its goal is to ease the implementation of detailed simulations for both single sensors and more complex setups with multiple detectors. While originally created for silicon detectors in high-energy physics, it is capable of simulating a wide range of detector types for various application scenarios, e.g. through its interface to Geant4 to describe the interaction of particles with matter, and the different algorithms for charge transport and digitization. The simulation chain is arranged with the help of intuitive configuration files and an extensible system of modules, which implement the individual simulation steps. Detailed electric field maps imported from TCAD simulations can be used to precisely model the drift behavior of the charge carriers, bringing a new level of realsim to the Monte Carlo simulation of particle detectors.Recently, Allpix Squared has seen major improvements to its core framework to take full advantage of multi- and many-core processor architectures for simulating events fully parallel. Furthermore, new physics models such as charge carrier recombination have been introduced, further extending the application range. This contribution provides an overview of the framework and its components, highlighting the versatility and recent developments.
Designing ecological corridors in a fragmented landscape: A fuzzy approach to circuit connectivity analysis

Abstract   Landscape connectivity analysis is a major tool in supporting biodiversity conservation. Several methodologies have been developed to tackle it by following two main paths. The first path exploits graph approaches and models focal nodes’ connections on a resistance/conductance matrix depending on focal species’ movement potential. The second path considers geometrical pattern analyses based on the calculation of structural landscape metrics. These approaches separately investigate functional and structural features of the landscape, and may come short of a total definition if used separately. Here we propose a new scalable, modular, participative and open-source procedure based on Fuzzy logic to combine the functional and structural aspects of connectivity. We applied this method on the highly fragmented landscape of the Po Plain, focusing on its rare and endangered plain springs named fontanili. We identified an expert panel and involved it in the assignation of permeability values of land use classes with respect to the capacity of movement of animal species typical of fontanili. We concurrently performed a quantitative evaluation of the landscape fragmentation with a moving window. We found that the functional and structural evaluations were poorly correlated in the area under study (Pearson's r = −0.35, p
The Case Law of International Criminal Tribunals in the Two-Year Period 2005-2006

A notable feature of International and ‘‘Hybrid’’ Criminal Tribunals’ case-law in the 2005-2006 period is their sustained effort to be in keeping with one another, as well as with other international Courts’ jurisprudence. Each ad hoc Criminal Tribunal expressly followed the solutions given in the other‘s previous case-law dealing with identical or similar issues and in the case-law of other international human rights judicial organs as well. Where occasionally a different conclusion was reached, ad hoc Criminal Tribunals proved to be greatly concerned with providing a justification based on objective differences in their Statutes and Rules of Procedure and Evidence (3). The same holds true for the International Criminal Court and for Hybrid Criminal Tribunals, which drew heavily on the ad hocTribunals’ case-law. The International Court of Justice, in turn, has recently shown to be prepared to rely strongly on the findings of the International Criminal Tribunal for the former Jugoslavia. All this clearly proves that, although some differences and specific problems occasionally arise and justify dissimilar conclusions, the problem of ‘‘fragmentation’’ of internationallaw lately discussed in the ILC Study Group’s Report submitted to the UN General Assembly in 2006 is far more speculative than practical, at least as far as this important part of international practice is concerned. This essay focuses on the major issues in the Criminal Tribunals’ judicial activities in the two-year period 2005-2006 and is articulated into three sections, dedicated to ad hoc Criminal Tribunals, i.e. the International Criminal Tribunal for the former Yugoslavia (ICTY) and the International Criminal Tribunal for Rwanda (ICTR), to the International Criminal Court (ICC) and to ‘‘Hybrid’’ Criminal Tribunals respectively.
Conjugative Plasmids Disseminating CTX-M-15 among Human, Animals and the Environment in Mwanza Tanzania: A Need to Intensify One Health Approach

Background: Globally, blaCTX-M-15 beta-lactamases are the most popular extended spectrum beta-lactamase alleles that are widely distributed due its mobilisation by mobile genetic elements in several compartments. We aimed to determine the conjugation frequencies and replicon types associated with plasmids carrying blaCTX-M-15 gene from Extended Spectrum Beta-lactamase producing isolates in order to understand the dissemination of resistance genes in different compartments. Material and methods: A total of 51 archived isolates carrying blaCTX-M-15 beta-lactamases were used as donors in this study. Antibiotic susceptibility tests were performed as previously described for both donors and transconjugants. Conjugation experiment was performed by a modified protocol of the plate mating experiment, and plasmid replicon types were screened among donor and transconjugant isolates by multiplex Polymerase Chain Reaction in a set of three primer panels. Results: The conjugation efficiency of plasmids carrying blaCTX-M-15 was 88.2% (45/51) with conjugation frequencies in the order of 10−1 to 10−9 and a 100% transfer efficiency observed among E. coli of animal origin. Majority of donors (n = 21) and transconjugants (n = 14) plasmids were typed as either Inc FIA or Inc FIB. Resistance to non-beta-lactam antibiotics was transferrable in 34/45 (75.6%) of events. Ciprofloxacin, tetracycline and sulphamethoxazole-trimethoprim resistance was co-transferred in 29/34 (85.3%) such events. Gentamicin resistance was transferred in 17/34 (50%) of events. Conclusions: Majority of plasmids carrying blaCTX-M-15 were conjugatively transferred by IncF plasmids along with non-beta lactam resistance. There is a need for more research on plasmids to understand how plasmids especially multi replicon plasmids interact and the effect of such interaction on conjugation. One Health approach is to be intensified to address antimicrobial resistance which is a public health threat.
State-selective EIT for quantum error correction in neutral atom quantum computers

14 pages, 16 figures
Systems Biology Approaches to Enzyme Kinetics: Analyzing Network Models of Drug Metabolism

Intracellular drug metabolism involves transport, bioactivation, conjugation, and other biochemical steps. The dynamics of these steps are each dependent on a number of other cellular factors that can ultimately lead to unexpected behavior. In this review, we discuss the confounding processes and coupled reactions within bioactivation networks that require a systems-level perspective in order to fully understand the time-varying behavior. When converting known in vitro characteristics of drug-enzyme interactions into descriptions of cellular systems, features such as substrate availability, cell-to-cell variability, and intracellular redox state deserve special focus. An example of doxorubicin bioactivation is used for discussing points of consideration when constructing and analyzing network models of drug metabolism.
The seventh blind test of crystal structure prediction: structure generation methods

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.
ENERGY ESTIMATION OF ULTRA-HIGH ENERGY COSMIC RAY PHOTONS BY MONTE CARLO SIMULATIONS OF EXTENSIVE AIR SHOWERS

Ultra-High Energy Cosmic Rays (UHECR) (E ≥ 5 × 1019 eV ) are detected through Extensive Air Showers that are created when a primary cosmic ray particle interacts with the atmosphere of the Earth. The energy of the primary particle can be estimated experimentally based on simulations. In this paper, we attempt to estimate the energy of UHECR gamma ray photons by applying a Monte Carlo simulation code and we compare the results with the ones derived in our previous papers for hadron initiated showers. The scenario of simulations is adapted to the P. Auger Observatory site.
Differentiable simulation to develop molecular dynamics force fields for disordered proteins

The a99SB-                     disp                     force field and GBNeck2 implicit solvent model are improved to better describe disordered proteins. 5 ns differentiable molecular simulations are used to jointly optimise 108 parameters to better match explicit solvent trajectories.
Constraints on the reheating parameters after Gauss-Bonnet inflation from primordial gravitational waves

We study the effects of the Gauss-Bonnet term on the energy spectrum of inflationary gravitational waves. The models of inflation are classified into two types based on their predictions for the tensor power spectrum: red-tilted ($n_T&lt;0$) and blue-tilted spectra ($n_T&gt;0$), respectively, and then the energy spectra of the gravitational waves are calculated for each type of model. We find that the gravitational wave spectra are enhanced depending on the model parameter if the predicted inflationary tensor spectra have a blue tilt, whereas they are suppressed for the spectra that have a red tilt. Moreover, we perform the analyses on the reheating parameters involving the temperature, the equation-of-state parameter, and the number of $e$-folds using the gravitational wave spectrum. Our results imply that the Gauss-Bonnet term plays an important role not only during inflation but also during reheating whether the process is instantaneous or lasts for a certain number of $e$-folds until it thermalizes and eventually completes.
The impacts and resilience of upland farming communities in  Davao Oriental, Philippines through vulnerabilities and  adaptation to climate change

The impacts of climate change are inevitable. Farming communities will be vulnerable to these climate impacts, but the proper assessment of site-specific community resilience is the key to improving and strengthening necessary resilience indicators needed to address the ever-increasing effects of climate change, such as drought and flood/landslide. This study highlights the importance of assessing the right resilience indicators to achieve long-lasting sustainability in upland farming communities, particularly in Davao Oriental, Philippines. The study aims to assess climate change effects, determine site-specific resilience indicators, and provide a framework to enhance resilience among upland farmers. The study was conducted in six upland farming communities in Mati City. The paper was based on qualitative interviews using focus groups and participatory approaches to understand and identify climate resilience among participants from various rural communities. Seventy-three farmers (33 male/40 female) participated in the half-day FGD workshops. Results of the workshop showed that each upland community was able to experience two to three climate-related hazards (i.e., floods, drought, and rain-induced landslide). Moreover, communities with more diverse sources of crops, livestock, and income showed higher resilience to climate change impacts than upland farming communities with lesser crop and livestock diversity. The results of this paper could be used as baseline data in drafting site-specific policies to address and mitigate the adverse effects of climate change at the local and national levels.
Detection of Retinopathy Diabetic Using Explainable AI: Interpretable Deep Learning Models in Clinical Practice

Diabetic Retinopathy (DR) is a significant threat to eyesight and blindness globally, particularly for those with a more extended diabetes history. Deep learning has achieved high accuracy for DR detection using fundus images; however, the "black-box" nature hinders its application in clinical practice, where interpretability is important. In this work, we propose a solution to the model transparency problem by introducing an XAI-enhanced diagnostic framework utilizing CNNs. We present an explainable deep learning framework based on a convolutional neural network (CNN), specifically ResNet-50, which has been fine-tuned on the APTOS 2019 Blindness Detection dataset. To narrow the interpretability gap, we utilize the Grad-CAM and SHAP visualization methods, which generate class-discriminative heatmaps and feature-attribution plots, respectively. The multi-class diabetes retinopathy (DR) classification result yielded an overall accuracy of 83% for the model. Importantly, the explanation agreement score with ophthalmologists is over 78%, indicating a high correlation between the AI-based saliency maps and expert-annotated lesion regions. Our findings show that XAI can not only maintain diagnostic accuracy but also enhance model interpretability, rendering AI-based DR screening systems more acceptable and usable in clinical practice. This study reinforces the importance of explainability as an integral part of implementing medical AI.
Preparing Teacher Educators for Sustainable Agriculture and Food Security in Nigeria

Agriculture is a cornerstone of Nigeria’s economy, yet it faces challenges such as climate change, soil degradation, and population growth that threaten food security. Addressing these challenges requires the integration of sustainable agricultural practices, facilitated through education and training. Teacher educators play a critical role in this effort, as they prepare future educators to impart knowledge and skills essential for sustainability in agriculture. This paper examines the relationship between sustainable agriculture and food security, highlighting the gaps in Nigeria's agricultural education system, including outdated curricula, inadequate resources, and limited teacher preparedness. The paper calls for curriculum reform, capacity-building programs, and the integration of ICT to address these challenges. Furthermore, fostering collaboration between educational institutions, research organizations, and policymakers is essential for creating innovative solutions. By reforming teacher education, Nigeria can equip educators with the skills to promote sustainable farming practices, thereby enhancing food security and environmental resilience. The paper concludes with policy recommendations for strengthening agricultural education and achieving sustainability goals in Nigeria
Modulation of low-energy cosmic ray electrons in the outer heliosphere

Abstract   A drift-modulation model with a solar wind termination shock (TS), including a jovian electron source, was used to study the effects of this heliospheric shock on electron radial intensity profiles, with emphasis on the outer heliosphere. Electron measurements between 1977 and late 2001 at 4–8 MeV and up to ∼82 AU from the Voyager spacecraft make it possible to compare computations with observations using new diffusion coefficients and a revised local interstellar electron spectrum (LIS). Compatibility between the model and the measurements are produced with the shock radius at ∼90 AU, the shock compression ratio at ∼3, and the outer boundary radius between 120 and 140 AU. The small radial gradients observed for these electrons between 15 and 82 AU can easily be reproduced with the consequence that a considerable amount of modulation must take place in the region between the TS and the modulation boundary, depending on the energies considered and the value of the LIS.
Evolution and Perspective of Sea/Ocean Facilities Critical Infrastructure Protection and Resilience in Europe

The issue of security currently has a number of challenges. One of the most important is global warming, another is the significant increase in the global population, cyber threats and the accelerating development of SMART technologies in connection with artificial intelligence. More than 71 per cent of the Earth’s surface is covered by seas and oceans. Much of it is very remote from human settlements. Changes in the global environment have led to a shift in the approach in protecting critical infrastructure and their transition to enhancing the resilience of critical entities. Research on the new approach has been successfully conducted at the University of Žilina for a long period of time. Past and current research projects are directed to the area of linking high technologies with social sciences. Security research is always multi-level and multi-disciplinary.
A new perspective on interiors of ice-rich planets: Ice-rock mixture instead of ice on top of rock

Ice-rich planets are formed exterior to the water ice-line and thus are expected to contain a substantial amount of ices. The high ice content leads to unique conditions in the interior, under which the structure of a planet is affected by ice interaction with other metals. We apply experimental data of ice-rock interaction at high pressure, and calculate detailed thermal evolution for possible interior configurations of ice-rich planets, in the mass range of super-Earth to Neptunes (5-15 Earth masses). We model the effect of migration inward on the ice-rich interior by including the influences of stellar flux and envelope mass loss. We find that ice and rock are expected to remain mixed, due to miscibility at high pressure, in substantial parts of the planetary interior for billions of years. We also find that the deep interior of planetary twins that have migrated to different distances from the star are usually similar, if no mass loss occurs. Significant mass loss results in separation of the water from the rock on the surface and emergence of a volatile atmosphere of less than 1 percent of the planet's mass. The mass of the atmosphere of water/steam is limited by the ice-rock interaction. We conclude that when ice is abundant in planetary interiors the planet structure may differ significantly from the standard layered structure of a water shell on top of a rocky core. Similar structure is expected in both close-in and further-out planets.