ethicalabs/Research-Intent-Collab huggingface dataset:
Artificial Intelligence Perspective on MRI-Driven Computer-Aided Diagnosis and Prognosis of the Brain: A Systematic Review

Recently, the involvement of Artificial Intelligence (AI) has empowered CAD in the diagnosis of brain illnesses. Due to the escalating incidence of brain diseases, there has been a low awareness among the population, and thus, the need for AI emerges to fulfill the requirements in this domain. Artificial intelligence (AI), encompassing various subfields of computer science, is essential for analyzing medical data and extracting datasets, which augment human intelligence. Specifically, in the brain investigation domain, several productive measures have been taken and executed remarkably in the disciplines of diagnosis, framing, and outcomes. In this study, we outline different artificial intelligence techniques used in diagnosing the brain sphere. Eventually, we will notice that AI has made it possible to revamp medical images in neuroscience applications. This study directs the current revolutionary trends as well as considers future diagnostic research that is based on Computer-Aided Diagnose and Computer-Aided Prognosis for the explicit detection of patients with brain disorders.
Utilization of deformations in molecular quantum chemistry and application to density functional theory

The aim of this paper is to present in a way accessible to most quantum chemists a general mathematical method which consists in deforming wave-functions and density functions (in the spirit of the Local Scaling Transformation). This deformation method allows us to obtain several new results including a characterization of the set of wave-functions that have the same given density function (which gives a new insight on a result of G. Zumbach and K. Maschke) and an N -representability result where symmetry is taken into account. We also propose new theoretical ways to generate approximations of the exact density functional, and give a numerical example.
Pengaruh Teknologi Human Resource Information  System (HRIS) terhadap kinerja karyawan pada  Kantor Cabang Utama Pos Bandung

This research aims to determine the effect of Human Resource Information System (HRIS) technology on employee performance. This research uses a descriptive approach with a quantitative approach. Analyze causal relationships using hypothesis testing. Data was collected through distributing questionnaires. All 54 employees of KCU Pos Bandung were the population in this study. Data analysis uses validity, reliability, normality and simple linear regression test procedures, correlation coefficients, determining factors and hypothesis testing. The research results show that HRIS Technology influences employee performance at KCU Pos Bandung
Gli orizzonti del diritto dell’ambiente a partire dal contenzioso climatico = The horizons of environmental law from climate litigation

The contribution reflects on the numerous complexities accompanying climate policies, even when their ineffectiveness becomes the subject of judicial actions brought against the State. The breadth of interests underlying the logical and legal framework of environmental law leads to a valorisation of the concepts inherent to administrative law and to the administrative judge, making it particularly challenging to establish subjective, individual, and personal rights, even within the context of the so-called “reserve of scientific knowledge”.
Possible Couplings of Dark Matter

Dark matter interacts gravitationally, but it presumably interacts weakly through other channels, especially with respect to regular luminous matter. We look at different ways in which dark matter may couple to other fields. We briefly review some example approaches in the literature for modeling the coupling between dark energy and dark matter and examine the possibility of an arguably better-motivated approach via non-minimal coupling between a scalar field and the Ricci scalar, which is necessary for renormalization of the scalar field in curved space-time. We also show an example of a theory beyond the Standard Model in which dark matter is uniquely connected to the inflaton, and we use observational astrophysical constraints to specify an upper bound on the dark matter mass. In turn, this mass constraint implies a limit on the unification scale of the theory, a decoupling scale of the theory, and the number of $e$-folds of inflation allowed.
Do Your Genes Fit: How Life Insurance Companies Discriminate Based on Genetic Test Results

Abstract                The Genetic Information Nondiscrimination Act of 2008 (GINA) represents a significant legislative step in protecting U.S. citizens from genetic discrimination in health insurance and employment. However, despite its broad preventive measures, GINA has limitations in scope and enforcement. Notably, it does not extend protections to life insurance, disability, and long-term care insurance, leaving U.S. citizens susceptible to genetic discrimination. State-level legislative efforts have tried to reconcile these vulnerabilities. Many state-level legislative efforts vary in effectiveness, with some states like Florida and Illinois enacting more robust laws. The future of genetic information protection requires an amendment to GINA or new federal legislation, drawing from successful state models to ensure adequate protections. This paper advocates for such national legislation, highlighting the minimal impact on the life insurance market and the importance of safeguarding genetic privacy to foster trust in genetic testing and scientific progress.
A new empirical distribution for the uncertainty analysis of the top event of a fault tree

Abstract   A new empirical statistical distribution is reported, which describes the probability distribution of the unavailability  Q  TOP  for the top event of a fault tree. The distribution can be fitted using linear regression analysis. Investigations into the fit of the new distribution are reported, and as specific examples, two fault trees described in  Reactor Safety Study  are studied. The proposed distribution fits very well in both the cases, as shown by the  χ  2  test. For comparison, Johnson's  S  L  and  S  B  distributions, the log-normal distribution and the hybrid log-normal distribution are fitted to the same fault trees using the exact regression analysis method. Johnson's  S  B  distribution cannot be fitted in the case of one of the fault trees as the necessary iterative process does not converge. For the other fault tree, the  S  B  distribution fits very well. Johnson's  S  L  distribution, the log-normal distribution and the hybrid log-normal distribution do not fit as well as the distribution proposed here and the  χ  2  test failed. Also, Johnson's distributions require the use of either an approximate method, involving a number of trials and errors, or non-linear regression analysis for fitting. The proposed distribution can, on the other hand, be fitted exactly using linear regression analysis and is, therefore, no more difficult to fit than the log-normal distribution. It is therefore concluded that the proposed distribution is most suited to describe the probability distribution of the unavailability for the top event of fault tree. Its suitability as a substitute for Johnson's distributions is also investigated. As specific examples, two statistical distributions in a text-book, for which fitting of Johnson's distributions has been suggested, are studied. The proposed distribution fits very well, as shown by the  χ  2  test. The proposed distribution is thus a good substitute for Johnson's  S  B  distribution.
From AI-Assisted In Silico Computational Design to Preclinical In Vivo Models: A Multi-Platform Approach to Small Molecule Anti-IBD Drug Discovery

Background: Inflammatory Bowel Disease (IBD), including Ulcerative Colitis and Crohn’s Disease, is a multifactorial inflammatory condition of the intestinal tract driven by a complex interplay of genetic factors, immune system dysfunction, and gut microbiota alterations. This review aims to synthesize current advancements in modern drug development strategies for IBD. It emphasizes the integration of computational modelling, cell-based experiments, and animal model studies to enhance translational outcomes. Methods: To compile this review, an extensive literature search was performed utilizing PubMed, Scopus, and Google Scholar databases for English-language research and review articles published between 2000 and 2025 using keywords such as “IBD,” “molecular docking,” “bioinformatics,” “organoids,” “animal models,” and “network pharmacology,” among others. A total of 199 peer-reviewed studies were identified for inclusion based on relevance, transparency, and methodological robustness. Results: The review outlines a range of cutting-edge approaches to IBD drug discovery. These include computer modelling, molecular docking, and network analysis to accelerate early-stage target prediction and drug screening. The review further highlights the critical importance of utilizing 2D and 3D cell culture systems in parallel with advanced animal models. It emphasizes the critical integration of computational predictions with biologically relevant in vitro and in vivo validations to improve the reliability and efficiency of drug development. Conclusions: The integration of computer modelling, cell culture systems, and animal studies provides a revolutionary paradigm for accelerating drug discovery to IBD and other diseases enabling personalized and more effective treatment approaches.
Genetic discrimination in insurance and employment based on personalized risk stratification for breast cancer screening

BackgroundThe Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) incorporates the effects of common genetic variants, from polygenic risk scores, pathogenic variants in major breast cancer (BC) susceptibility genes, lifestyle/hormonal risk factors, mammographic density, and cancer family history to predict risk levels of developing breast and ovarian cancer. While offering multifactorial risk assessment to the population could be a promising avenue for early detection of BC, obstacles to its implementation including fear of genetic discrimination (GD), could prevent individuals from undergoing screening.MethodsThe aim of our study was two-fold: determine the extent of legal protection in Canada available to protect information generated by risk prediction models such as the BOADICEA algorithm through a literature review, and then, assess individuals’ knowledge of and concerns about GD in this context by collecting data through surveys.ResultsOur legal analysis highlighted that while Canadian employment and privacy laws provide a good level of protection against GD, it remains uncertain whether the Genetic Non-Discrimination Act (GNDA) would provide protection for BC risk levels generated by a risk prediction model. The survey results of 3,055 participants who consented to risk assessment in the PERSPECTIVE I&amp;amp;I project showed divergent perspectives of how the law would protect BC risk level in the context of employment and that a high number of participants did not feel that their risk level was protected from access and use by life insurers. Indeed, 49,1% of participants reckon that the level of breast cancer risk could have an impact on a woman’s ability to buy insurance and 58,9% of participants reckon that a woman’s insurance might be cancelled if important health information (including level of breast cancer risk) is not given when buying or renewing life or health insurance.ConclusionThe results indicate that much work needs to be done to improve and clarify the extent of protection against GD in Canada and to inform the population of how the legal framework applies to risk levels generated by risk prediction models.
2006 SQ<sub>372</sub>: A LIKELY LONG-PERIOD COMET FROM THE INNER OORT CLOUD

We report the discovery of a minor planet (2006 SQ372) on an orbit with a perihelion of 24 AU and a semimajor axis of 796 AU. Dynamical simulations show that this is a transient orbit and is unstable on a timescale of 200 Myrs. Falling near the upper semimajor axis range of the scattered disk and the lower semimajor axis range of the Oort Cloud, previous membership in either class is possible. By modeling the production of similar orbits from the Oort Cloud as well as from the scattered disk, we find that the Oort Cloud produces 16 times as many objects on SQ372-like orbits as the scattered disk. Given this result, we believe this to be the most distant long-period comet ever discovered. Furthermore, our simulation results also indicate that 2000 OO67 has had a similar dynamical history. Unaffected by the "Jupiter-Saturn Barrier," these two objects are most likely long-period comets from the inner Oort Cloud.
The ‘Responsibility to Protect’ and the Structural Problems of Preventive Humanitarian Intervention

While the normative and legal aspects of the Responsibility to Protect (R2P) doctrine have been explored in great detail, scholars have largely overlooked the more practical question of whether and how international military action can avert mass atrocities. To shed light on this question, this article investigates the ‘strategic logic’ of preventive humanitarian intervention, or the assumed link between external military action and the desired outcome of preventing or stopping mass killing. It contends that there are five fundamental and seemingly irremediable tensions in this logic, all of which cast doubt on the feasibility of preventive humanitarian intervention and on the long-term prospects of R2P.
Dynamical cluster properties in the quantum statistical mechanics of phase transitions

Investigates the influence of spontaneous symmetry breaking on the dynamical cluster properties of correlation functions of a quantum statistical ensemble, that is clustering with respect to time and space-time. While the impact of phase transitions on pure space-like clustering, i.e. the static results, is well known, less seems to be known about the dynamical behaviour of response functions and susceptibilities. Precise statements are made which show that, contrary to the static case, the decay properties depend sensitively on the type of Goldstone excitation.
Improving Patient Communication by Simplifying AI-Generated Dental Radiology Reports With ChatGPT: Comparative Study

Background                     Medical reports, particularly radiology findings, are often written for professional communication, making them difficult for patients to understand. This communication barrier can reduce patient engagement and lead to misinterpretation. Artificial intelligence (AI), especially large language models such as ChatGPT, offers new opportunities for simplifying medical documentation to improve patient comprehension.                                                           Objective                     We aimed to evaluate whether AI-generated radiology reports simplified by ChatGPT improve patient understanding, readability, and communication quality compared to original AI-generated reports.                                                           Methods                     In total, 3 versions of radiology reports were created using ChatGPT: an original AI-generated version (text 1), a patient-friendly, simplified version (text 2), and a further simplified and accessibility-optimized version (text 3). A total of 300 patients (n=100, 33.3% per group), excluding patients with medical education, were randomly assigned to review one text version and complete a standardized questionnaire. Readability was assessed using the Flesch Reading Ease (FRE) score and LIX indices.                                                           Results                     Both simplified texts showed significantly higher readability scores (text 1: FRE score=51.1; text 2: FRE score=55.0; and text 3: FRE score=56.4; P&lt;.001) and lower LIX scores, indicating enhanced clarity. Text 3 had the shortest sentences, had the fewest long words, and scored best on all patient-rated dimensions. Questionnaire results revealed significantly higher ratings for texts 2 and 3 across clarity (P&lt;.001), tone (P&lt;.001), structure, and patient engagement. For example, patients rated the ability to understand findings without help highest for text 3 (mean 1.5, SD 0.7) and lowest for text 1 (mean 3.1, SD 1.4). Both simplified texts significantly improved patients’ ability to prepare for clinical conversations and promoted shared decision-making.                                                           Conclusions                     AI-generated simplification of radiology reports significantly enhances patient comprehension and engagement. These findings highlight the potential of ChatGPT as a tool to improve patient-centered communication. While promising, future research should focus on ensuring clinical accuracy and exploring applications across diverse patient populations to support equitable and effective integration of AI in health care communication.
Statistical Mechanics of Phase Transition

Mayer's theory about the, relation between the phase transitions and the singular points has been criticized. The matrix method is connected WIth Mayer's duster theory applied to regular solutions. It is shown that the following two cases ate possible. They are i) that the density ,of atoms is a single analytic function of the active number density, at least" on the positive real axis, and the first singularity of this function is an essential singular point and the phase transition occurs at this point, ii) that the density of atoms may consist of different analytic functions of the active number density, and the phase transition may occur at the point which is not analytic singular point of the function. The same is true of continuous aggregate systems such as imperfect gases. In the continuous case the p:trtition function may be obtained as the smallest eigenvalue of Fredholm's integral equation. § 1. 1n1l'oduction 1 The eXlstmg theories, which deal with the systems composed of strongly interacting particles; suCh as imperfect gases, liquids and crystalline solids, are classified into three types, except very simple ones. The first of them is the integral equation method developed by Kirkwoodl ), Yvon~) and Born and Green3). This method is only approximative, and we can place trust in not all of quantitative results of it, since the Kirkwood approximation is utilized in this method. The same is true of the customary approximate theories applied to crystal statistics, such as Bethe's theory. The next is the matrix method elaborated by Krammers and Wannier4), Montroll"), Lassettre and Howe6l , Onsager7) and others. This method is strict, but applicable only to the case of crystal statistics. The last one is the method of cluster intagrals of M ayer8)9) , which is considered as the most general one. This method is, however, not so useful for practical calculations and some of its important conclusions have been drawn without complete proof, as pointed out by Katsura and Fujita10). The purposes of the present paper are to criticize the results of Mayer's theory about the relation between the phase transitions and the singular points, to investigate properties of the singularities in detail, and to give the proper statistical interpretation of the phase transltlOn. Since the matrix method seems to give more definitive conclusion, we may make clear the ambiguous points of Mayer's theory if we connect the matrix method with Mayer's method of series developments. Since the matrix method is established only for the case of crystal statistics, we shall, at first, study the connection between these two methods for' this case. And an attempt to extend the eigenvalue method to continuo\.\~
Representations of hom-Lie algebras

In this paper, we study representations of hom-Lie algebras. In particular, the adjoint representation and the trivial representation of hom-Lie algebras are studied in detail. Derivations, deformations, central extensions and derivation extensions of hom-Lie algebras are also studied as an application.
Engineering of AI-Powered Cyber Defense Tools to Protect Immigration Databases, Biometric Identity Systems, and Border-Control Infrastructure from Nation-State Attacks

The growing use of digital systems to check immigration, biometric identity checks and border control, has reduced these infrastructures to the main targets of cyber-attacks, especially by nation-state actors. The conventional cybersecurity tools are not always sufficient to handle the sophistication and magnitude of current, focused threats. In this article, the author discusses the implementation of machine learning (ML) systems based on AI to defend against sophisticated cyber attacks on the critical systems of the immigration database and biometric identification system. The proposed AI-based tools of cyber defense are based on deep learning, reinforcement learning, and anomaly detection to monitor all network traffic, user behavior, and system activity and prevent possible attacks immediately. The tools are more proactive and automated in preventing threats as they incorporate AI systems and biometric security infrastructure, which can bring down the reaction time of the perceived threats. The article addresses the possible opportunities of machine learning in forecasting and reacting on attacks like data breaches, identity theft, and denial-of-service attacks. The results indicate that AI-based defense tools play an important role to enhance the security stance of immigration and border control facilities against complex and enduring nation-state risks.
Systematic Review of Hepatitis E Virus in Brazil: A One-Health Approach of the Human-Animal-Environment Triad

Brazil is the fifth largest country in the world with diverse socioeconomic and sanitary conditions, also being the fourth largest pig producer in the world. The aim of the present systematic review was to collect and summarize all HEV published data from Brazil (from 1995 to October 2020) performed in humans, animals, and the environment, in a One Health perspective. A total of 2173 papers were retrieved from five search databases (LILACs, Mendeley, PubMed, Scopus, and Web of Science) resulting in 71 eligible papers after application of exclusion/inclusion criteria. Data shows that HEV genotype 3 (HEV-3) was the only retrieved genotype in humans, animals, and environment in Brazil. The South region showed the highest human seroprevalence and also the highest pig density and industry, suggesting a zoonotic link. HEV-1 and 2 were not detected in Brazil, despite the low sanitary conditions of some regions. From the present review we infer that HEV epidemiology in Brazil is similar to that of industrialized countries (only HEV-3, swine reservoirs, no waterborne transmission, no association with low sanitary conditions). Hence, we alert for the implementation of HEV surveillance systems in swine and for the consideration of HEV in the diagnostic routine of acute and chronic hepatitis in humans.
Generic virtual memory management for operating system kernels

We discuss the rationale and design of a Generic Memory management Interface, for a family of scalable operating systems. It consists of a general interface for managing virtual memory, independently of the underlying hardware architecture (e.g. paged versus segmented memory), and independently of the operating system kernel in which it is to be integrated. In particular, this interface provides abstractions for support of a single, consistent cache for both mapped objects and explicit I/O, and control of data caching in real memory. Data management policies are delegated to external managers.           A portable implementation of the Generic Memory management Interface for paged architectures, the Paged Virtual Memory manager, is detailed. The PVM uses the novel history object technique for efficient deferred copying. The GMI is used by the Chorus Nucleus, in particular to support a distributed version of Unix. Performance measurements compare favorably with other systems.