ethicalabs/Research-Intent-Collab huggingface dataset:
DEVELOPING A CROP SIMULATION MODEL TO PREDICT RICE YIELD UNDER DIFFERENT IRRIGATION AND FERTILIZATION REGIMES

A dynamic crop simulation model was developed to predict rice (Oryza sativa L.) yield under varied irrigation and fertilization regimes, with the objective of informing sustainable management decisions. Field experiments across three distinct sites provided data on soil properties, weather variables, irrigation schedules, and nitrogen inputs. The model integrated physiological growth processes, water and nutrient uptake dynamics, and stress response functions, and was implemented in Python and R. Calibration against a comprehensive dataset achieved root mean square error (RMSE) values of 230–260 kg ha⁻¹ and coefficients of determination (R²) ≥ 0.88. Independent validation yielded RMSE ≤ 290 kg ha⁻¹, mean absolute error (MAE) ≤ 225 kg ha⁻¹, and Nash–Sutcliffe efficiency (NSE) ≥ 0.82, demonstrating robustness across spatial and temporal scales. Scenario analysis indicated that alternate wetting and drying (AWD) produced the highest mean yield (7 200 kg ha⁻¹) and superior water use efficiency (1.35 kg m⁻³) compared to continuous flooding (6 800 kg ha⁻¹, 1.20 kg m⁻³) and rainfed conditions (6 100 kg ha⁻¹, 0.90 kg m⁻³). High nitrogen application maximized yield (7 500 kg ha⁻¹), while site-specific nitrogen management (7 300 kg ha⁻¹) achieved comparable performance with reduced input. Combined strategies revealed that flooding with high nitrogen yielded 7 700 kg ha⁻¹, whereas alternate drying with low nitrogen produced 6 900 kg ha⁻¹. Sensitivity analysis showed that a 10 % increase in irrigation and nitrogen input resulted in yield gains of 5.2 % and 4.8 %, respectively. Error decomposition attributed 30 % of overall uncertainty to calibration, with input uncertainty, model structure, and validation each contributing 20–25 %. These results underscore the model’s potential as a decision-support tool for optimizing rice production under resource constraints. Future enhancements will incorporate remote sensing inputs and expand stress-response modules to address climate variability.
Interoperable Electronic Health Records and Health Information Exchanges: Systematic Review

As the availability of interoperable electronic health records (iEHRs) or health information exchanges (HIEs) continues to increase, there is greater need and opportunity to assess the current evidence base on what works and what does not regarding the adoption, use, and impact of iEHRs.The purpose of this project is to assess the international evidence base on the adoption, use, and impact of iEHRs.We conducted a systematic review, searching multiple databases-MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL)-with supplemental searches conducted in Google Scholar and grey literature sources (ie, Google, Grey Literature Report, and OpenGrey). All searches were conducted in January and February 2017. Articles were eligible for inclusion if they were published in English, were published from 2006 to 2017, and were either an original research study or a literature review. In order to be included, articles needed to focus on iEHRs and HIEs across multiple health care settings, as well as on the impact and effectiveness of iEHR adoption and use.We included 130 articles in the synthesis (113 primary studies, 86.9%; 17 reviews, 13.1%), with the majority focused on the United States (88/130, 67.7%). The primary studies focused on a wide range of health care settings; the three most prevalent settings studied included acute care (59/113, 52.2%), primary care (44/113, 38.9%), and emergency departments (34/113, 30.1%). We identified 29 distinct measurement items in the 113 primary studies that were linked to 522 specific measurement outcomes. Productivity and quality were the two evaluation dimensions that received the most attention, accounting for 14 of 29 (48%) measurement items and 306 of 522 (58.6%) measurement outcomes identified. Overall, the majority of the 522 measurement outcomes were positive (298/522, 57.1%). We also identified 17 reviews on iEHR use and impact, 6 (35%) that focused on barriers and facilitators to adoption and implementation and 11 (65%) that focused on benefits and impacts, with the more recent reviews finding little generalizable evidence of benefit and impact.This review captures the status of an evolving and active field focused on the use and impact of iEHRs. While the overall findings suggest many positive impacts, the quality of the primary studies were not evaluated systematically. When broken down by specific measurement item, the results directed attention both to measurement outcomes that were consistently positive and others that were mostly negative or equivocal.
The statistical mechanics of phase transitions

Abstract This article traces the development and study of phase transitions from late last century to the present day. We begin with a brief historical sketch and a description of the statistical mechanics of phase transitions. Particular attention is given to the modern era which began in 1944 with Onsager's celebrated solution of the two-dimensional Ising model. Points of development since Onsager, which are highlighted in this article, include the study of critical exponents, the scaling hypothesis, realisation of the universality of critical exponents and the recent renormalization group approach to critical phenomena. The basic idea of the renormalization group method, rather than the detailed application of the recipe to particular cases, is stressed and is discussed critically in some detail.
Cosmic rays modulation in heliosphere models on GPU

Parker’s transport equation stochastic solution for simulation cosmic rays distribution in the heliosphere is demanding on computing resources. Simulations can last days, weeks, or even months with certain input parameters. We implemented 1D Forward-in-time and Backward-in-time models for GPU with successful acceleration ranged from 7x to 86x. This acceleration was gained with not a negligible reduction of accuracy, especially with changing the entire simulation from double-precision float-point format to floating-point format. This led to a certain deviation that we called pulsations that showed in results with input time step less than 2.0 s. In this paper, we discuss the parallelization process on GPU. We also discuss the comparison of our solution with Dunzlaff et al. and the overall accuracy of results gained from GPU implementation of 1D Forward-in-time and Backward-in-time models.
Multiple Peaks in the Angular Power Spectrum of the Cosmic Microwave Background: Significance and Consequences for Cosmology

Three peaks and two dips have been detected in the power spectrum of the cosmic microwave background from the BOOMERANG experiment, at l=(213+10/- 13),(541+20/-32),(845+12/-25) and l=(416+22/-12), (750+20/750) respectively. Using model-independent analyses, we find that all five features are statistically significant and we measure their location and amplitude. These are consistent with the adiabatic inflationary model. We also calculate the mean and variance of the peak and dip locations and amplitudes in a large 7-dimensional parameter space of such models, which gives good agreement with the model-independent estimates, and forecast where the next few peaks and dips should be found if the basic paradigm is correct. We test the robustness of our results by comparing Bayesian marginalization techniques on this space with likelihood maximization techniques applied to a second 7-dimensional cosmological parameter space, using an independent computational pipeline, and find excellent agreement: Omega 1.02 +0.06/-0.05 versus 1.04 ++0.05/-0.05, Omega_b.h^2=0.022+0.004/- 0.003 versus 0.019(+0.005/-0.004) and n_s =0.96{+0.09/-0.08)versus 0.90 (+0.08/-0.08). The determination of thr best fit by the maximization procedure effectively igrores nonzero optical depth of reionization Tau_c >0 and the difference in primordial spectral index n_s between the two methods is thus is a consequence of the strong correlation of n_s with the Tau_c
Inclusive and Accessible Workplaces: AI- and Robotics-Based Assistive Technologies for Professionals with High-Functioning Autism

Although every society member should be offered an equal chance to participate in working life, individuals with high-functioning autism encounter many barriers. Here, assistive technologies using AI and robotics can provide new possibilities to create accessibility and inclusion. This thesis followed the methodology of user-centred design by performing an online survey with 48 autistic professionals to understand their current work experiences and attitudes towards AI- and robotics-based assistive technologies. Based on the survey findings, neurotypical participants from different educational backgrounds ideated in workshops novel technological solutions. The survey results indicate that autistic professionals are open to AI and robotics to overcome, among others, barriers of sensory overload resulting from sounds and many simultaneous social interactions. Thus, the ideas of headphones using personalisation to cancel specific noises and a device summarising conversation content from team meetings for the remotely located professional can be supportive. Finally, the research results can be a starting point for future design ambitions addressing autistic individuals in their work environments.
Engineering Cyber Resilience Under Resource Constraints - An Adapted CCE Methodology for Critical Infrastructure Protection

Consequence-driven Cyber-informed Engineering (CCE) offers a powerful methodology for protecting critical infrastructure, but its significant resource requirements often prevent adoption by organizations with limited financial capacity, personnel availability, or technical expertise. This research addresses this implementation gap, particularly relevant for sectors like wastewater treatment. The primary aim was to adapt the CCE method to make it more resource-efficient while maintaining its core protective principles. Employing a Design Science Research (DSR) approach, this study involved analyzing the original CCE method using literature review, theoretical frameworks (including Resource-Based View and Systems Thinking), and expert consultations. Based on this analysis, specific adaptations were developed for each CCE phase, focusing on streamlining activities, utilizing templates and existing resources, prioritizing critical elements, and incorporating flexibility in the form of an iterative approach to the method itself. These adaptations were evaluated theoretically through comparative analysis against the original method and validated via structured expert feedback sessions using feasibility, effectiveness, and resource efficiency criteria. The research resulted in a systematically developed adapted CCE framework, presented as a guiding artifact (Appendix E). The evaluation indicates that the adapted method significantly reduces estimated resource needs (particularly regarding time and required expertise) compared to the original CCE. Expert validation generally supported the feasibility and potential effectiveness of the adaptations, confirming that the method's core principles—a focus on consequences and protective engineering—were preserved. This adapted CCE process provides a more attainable and pragmatic pathway for resource-constrained organizations to improve their cyber resilience against HCEs.
Thermodynamics of rotating fermions

We consider the thermodynamic properties of a rotating gas of fermions. We begin by constructing the thermodynamic potential $Φ$ and its associated current $φ^μ$ within the grand canonical ensemble of a macroscopic rigidly rotating body, where the ensemble parameters are the temperature $T_0$ and chemical potential $μ_0$ on the rotation axis, as well as the rotation angular velocity $Ω_0$. We then consider the problem of local thermodynamics, where the thermodynamic state is defined by the local temperature $T$ and chemical potential $μ$, as well as the local spin potential tensor, $Ω_{μν}$. We find the thermodynamic pressure $P$, given as the sum of the usual classical (non-quantum) pressure and other corrections due to the spin potential and the kinematic state of the fluid. We compute the associated entropy, charge and spin densities, and show they are consistent with the Euler relation.
Plasma Physics in Fusion Energy Research: Challenges and Opportunities

Nuclear fusion holds the promise of providing a virtually limitless and environmentally friendly source of energy. However, achieving practical fusion energy remains a formidable challenge, primarily due to the complexities of plasma physics. This paper explores the critical challenges in plasma physics that must be overcome to realize fusion energy, including the need to achieve and sustain extremely high temperatures, effectively confine the plasma, manage plasma-material interactions, and control plasma instabilities. Despite these challenges, significant advancements are being made in magnetic confinement techniques, plasma heating methods, and the development of advanced diagnostic and control systems. Innovations in materials science are also paving the way for the construction of fusion reactors capable of withstanding the harsh conditions inside the plasma. As the global pursuit of fusion energy continues, the opportunities for breakthrough advancements in plasma physics are growing, bringing us closer to the goal of harnessing fusion power. This paper highlights the importance of continued research, investment, and international collaboration in overcoming the remaining obstacles and realizing the potential of fusion energy for a sustainable future.
Studying the Structure of Parallel Algorithms as a Key Element of High-Performance Computing Education

Since the computing world has become fully parallel, every software developer today should be familiar with the notion of “parallel algorithm structure.” If in recent years, students have studied a basic introduction to algorithms; today, parallel algorithm structure must become a vital part of computer science education. In this work we present two years of experience teaching a “Supercomputer Modeling and Technologies” course, and running practical assignments at the Computational Mathematics and Cybernetics faculty of Lomonosov Moscow State University, aimed at teaching students a methodology for analyzing parallel algorithm properties.
The Importance of Business Ethics to Enhance Corporate  Social Responsibility

In an increasingly complex era of globalization, companies are expected not only to be profit-oriented, but also to pay attention to their social responsibility. This study aims to analyze the effect of business ethics on the effectiveness of Corporate Social Responsibility (CSR) programs in companies listed on the Indonesia Stock Exchange (IDX). The method used is quantitative analysis with a Structural Equation Modeling (SEM) approach using data from questionnaires distributed to 100-150 companies during the 2019-2023 period. The results of the analysis show that there is a significant relationship between business ethics and CSR performance, where companies that apply ethical principles tend to have more effective CSR programs. The implications of this study emphasize the importance of integrating ethics in business strategy to enhance corporate reputation and meet stakeholder expectations. This study also provides recommendations for companies and policy makers to formulate policies that support ethics-based CSR practices.
UN PEACEKEEPING, RESPONSIBILITY TO PROTECT AND HUMANITARIAN INTERVENTION

The question of armed intervention on behalf of the international community, in the internal affairs of a state against the wishes of the government of that state, in order to prevent widespread death or suffering amongst the population, is not a new one. Indeed, Imperial Rome grappled with the same problems in Dalmatia and Judaea two thousand years ago, as the international community does in those same regions today. How effective are peacekeeping operations in preventing and stopping violence? Is there an alternative to UN and regional peacekeeping operations? The practice of UN peacekeeping is evolving in many instances into robust peacemaking actions with a positive responsibility to protect civilians within the field of operations. The “responsibility to protect” (R2P) (and “responsibility while protecting” - RwP) concept sets out a key principle to enable the international community to prevent atrocity crimes. Since its emergence, however, there have been intense discussions over how to put the principle into practice. Some aspects of the concept remain unclear, including how to undertake, as the last resort, the use of military force. These issues must be considered within the boundaries set by R2P which seek at all costs to avoid the use of force for other reasons than ceasing mass atrocity crimes. The use of force, therefore - including possible military action by the international community, given growing international reluctance to accept grave threats to peace and security, including mass crimes against defenseless populations - have to be thoroughly analyzed and comprehended. This article presents an analysis of the development of civilian peacekeeping, its relevance in the field of conflict resolution and its autonomy from multidimensional peacekeeping, championed by the UN.
Emerging Regulations on Content Moderation and Misinformation Policies of Online Media Platforms: Accommodating the Duty of Care into Intermediary Liability Models

AbstractDisinformation, hate speech and political polarization are evident problems of the growing relevance of information and communication technologies (ICTs) in current societies. To address these issues, decision-makers and regulators worldwide discuss the role of digital platforms in content moderation and in curtailing harmful content produced by third parties. However, intermediary liability rules require a balance that avoids the risks arising from the circulation at scale of harmful content and the risks of censorship if excessive burdens force content providers to adopt a risk-averse posture in content moderation. This piece examines the trend of altering intermediary liability models to include ‘duty of care’ provisions, describing three models in Europe, North America and South America. We discuss how these models are being modified to include greater monitoring and takedown burdens on internet content providers. We conclude with a word of caution regarding this balance between censorship and freedom of expression.
Helix–random coil phase transitions in polypeptide systems with random defects: A statistical mechanical model

AbstractThe statistical mechanics of the effect of a small number of randomly occurring defects on the melting of a polypeptide chain is developed by using a perturbation of Boltzmann factors technique. The method is illustrated by an example where a second‐component impurity contributes more to the energy of the hydrogen‐bonded superstructure of the polypeptide chain than does the dominant amino acid residue, resulting in the overall destabilization of the helical conformation.
Previously defined 53 immune gene panel predicts melanoma survival using The Cancer Genome Atlas (TCGA)

Meeting abstracts  Our laboratory has discovered a 53-immune gene panel predictive of survival in patients with stage II-III melanoma[[1][1]]. Validation is planned using tissues from a multi-institutional cooperative group study. We tested the 53-immune gene panel using publicly available data for
Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience

The energy industry is undergoing significant transformations as it strives to achieve net-zero emissions and future-proof its infrastructure, where every participant in the power grid has the potential to both consume and produce energy resources. Federated learning - which enables multiple participants to collaboratively train a model without aggregating the training data - becomes a viable technology. However, the global model parameters that have to be shared for optimization are still susceptible to training data leakage. In this work, we propose confined gradient descent (CGD) that enhances the privacy of federated learning by eliminating the sharing of global model parameters. CGD exploits the fact that a gradient descent optimization can start with a set of discrete points and converges to another set in the neighborhood of the global minimum of the objective function. As such, each participant can independently initiate its own private global model∼(referred to as the confined model ), and collaboratively learn it towards the optimum. The updates to their own models are worked out in a secure collaborative way during the training process.In such a manner, CGD retains the ability of learning from distributed data but greatly diminishes information sharing. Such a strategy also allows the proprietary confined models to adapt to the heterogeneity in federated learning, providing inherent benefits of fairness. We theoretically and empirically demonstrate that decentralized CGD øne provides a stronger differential privacy (DP) protection; \two is robust against the state-of-the-art poisoning privacy attacks; þree results in bounded fairness guarantee among participants; and \four provides high test accuracy (comparable with centralized learning) with a bounded convergence rate over four real-world datasets.
Ukraine-EU cooperation in crisis management and disaster risk reduction: the perspectives of implementation of the complex early-warning systems

This article analyzes experience of Ukraine-EU cooperation in crisis management and disaster risk reduction, in particular, experience of implementation of the early-warning and conflict prevention systems. Basing on above developments, the article discusses the necessity to implement a complex early warning and conflict prevention system with usage of modern Internet-based technologies of data collection.
Covariant quantum error correction in a three-layer quantum brain model: computational analysis of layer-specific coherence dynamics

Quantum brain proposals require coherence on behaviorally relevant timescales, yet the gap between spin coherence times and neural decision windows has remained a quantitative obstacle. We evaluate approximate covariant quantum error correction (CQEC) -- a purification protocol constrained by the Eastin-Knill theorem -- across two radical-pair proteins parameterized by \textit{ab initio} spin Hamiltonians: monoamine oxidase~A (MAO-A) and cryptochrome (CRY, PDB~4I6G). Both share a three-layer architecture (${}^{31}$P nuclear spin memory, electron spin interface, classical electrochemistry) and identical hyperfine coupling ($A = 200$~MHz), but differ 16-fold in nuclear $T_2$: 3.2~ms (MAO-A) versus 52~ms (CRY). We test whether CQEC preserves coherence over the 200~ms Schultze-Kraft veto window by mapping each protein's $T_2$ gap onto a simulation decoherence rate ($γ_\mathrm{veto} = T_2~\text{gap}/2T_\mathrm{sim}$): 3.08 for MAO-A, 0.19 for CRY. At $γ_\mathrm{veto} = 0.19$, CQEC maintains tunneling coherence of 0.83 (95\% CI [0.76, 0.79]; versus 0.12 without correction, $\times$6.9 improvement). At $γ_\mathrm{veto} = 3.08$, coherence collapses to 0.012 even with CQEC. A $T_2$ sensitivity analysis confirms robustness: at $T_2 = 26$~ms (half the CRY estimate), CQEC-protected coherence remains 0.69. A classical Markov baseline produces only monotonic relaxation, confirming that CQEC-maintained oscillatory dynamics are genuinely quantum. However, no single protein optimizes both layers: CRY's shorter $T_2^e$ (0.53~ns versus 1.1~ns) worsens Layer~2 fidelity. This layer-protein tradeoff, together with unresolved challenges in state preparation and entanglement distribution, defines the next targets for quantum brain research.