ethicalabs/Research-Intent-Collab huggingface dataset:
Improving Science Communication & Public Engagement with Science in Switzerland. Recommendations of the Expert Group "Communicating Sciences and Arts in Times of Digital Media"

Swiss Academies of Arts and Sciences (2021) Improving Science Communication & Public Engagement with Science in Switzerland. Recommendations of the Expert Group "Communicating Sciences and Arts in Times of Digital Media" In Science in the Swiss Public. The State of Science Communication and Public Engagement with Science in Switzerland. Swiss Academies Reports 16 (8).
Primer on the Gene Ontology

The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essential resource for data analysis. In this chapter, we provide a concise primer for all users of the GO. We briefly introduce the structure of the ontology and explain how to interpret annotations associated with the GO.
Whole metagenome shotgun sequencing analysis of microbiome of cystic fibrosis- and COPD patients

Metagenomic analysis allows studying microbial communities in their natural habitats. A common technique for metagenomic analysis is targeted sequencing of 16S rDNA. The limitation of this approach is that the metabolic capacity and physiological features of the community can only be indirectly inferred for those members whose genotypes and phenotypes are already known. In addition, the “universal” 16S PCR primers lack sequence homology with some targets. Copy number variation of 16S genes can further confound estimation of taxonomic abundance in metagenomic samples. To overcome these problems we perform whole genome shotgun sequencing to fully characterize microorganisms contained in a metagenomic sample. Individual reads are aligned to known microbial references for estimation of species abundances and functional analysis. To improve accuracy of the species abundance estimation we introduce a method to account for biases that are common in next-generation sequencing technologies. Namely, we normalize the counts of reads aligned to each reference genome by the genome length and GC-content affecting the coverage of these genomes. The remaining unaligned reads are used for oligomer compositional analysis with further quantification up to the genus level. Unaligned reads are assembled into contigs for functional analysis using blastx. In addition, we perform clone identification of the major cystic fibrosis pathogens                     Staphylococcus aureus                     and                     Pseudomonas aeruginosa                     and analyze their spectrum of mutations.
Collaborative filtering for recommender systems: a scalability perspective

Recommender systems, now used in many websites, have a critical role in e-commerce systems. Among many techniques for recommender systems so far, collaborative filtering has proved to be one of the best solutions. However, there still remain some problems for collaborative filtering recommender systems, i.e. the scalability problem, which is the most critical one. In this paper, we focus on the scalability problem and present some algorithmic elements to improve the scalability of recommender systems. Then we show the trade-offs which the newly introduced elements bring about.
Chekenkov radiation of broad atmospheric showers (BAS) and analysis of ultra-high energy cosmic rays

A new method for analyzing BAS of cosmic rays is presented, along with calculation results.
Noncoding RNAs in Chromatin Organization and Transcription Regulation: An Epigenetic View

The Genome of a eukaryotic cell harbors genetic material in the form of DNA which carries the hereditary information encoded in their bases. Nucleotide bases of DNA are transcribed into complimentary RNA bases which are further translated into protein, performing defined set of functions. The central dogma of life ensures sequential flow of genetic information among these biopolymers. Noncoding RNAs (ncRNAs) serve as exceptions for this principle as they do not code for any protein. Nevertheless, a major portion of the human transcriptome comprises noncoding RNAs. These RNAs vary in size, as well as they vary in the spatio-temporal distribution. These ncRnAs are functional and are shown to be involved in diverse cellular activities. Precise location and expression of ncRNA is essential for the cellular homeostasis. Failures of these events ultimately results in numerous disease conditions including cancer. The present review lists out the various classes of ncRNAs with a special emphasis on their role in chromatin organization and transcription regulation.
Nuclear Reactor Physics and Operation

This book serves as a thorough reference for students, researchers, and professionals in nuclear engineering and reactor physics, offering a detailed exploration of the core principles behind nuclear reactor theory, neutron transport, neutronic analysis, and reactor core design and calculations. Each chapter includes at least one example to illustrate the topics covered, and the latter half focuses on key areas relevant to operating reactors – reactor kinetics/dynamics and in-core fuel management. Building on the foundational physics presented in the first half, it develops reactivity models using realistic reactor cross-section data and advanced analytic tools. This book is a valuable resource for engineers and scientists in the nuclear industry, as well as senior and graduate students in Nuclear Engineering, Mechanical Engineering, and Physics.
Pygmalion Training Made Effective: Greater Mastery Through Augmentation of Self-Efficacy and Means Efficacy*This article is based on the thesis submitted by Roni Sulimani as part of the requirements for his master’s degree in organizational behavior at Tel Aviv University’s Faculty of Management. Dov Eden served as thesis advisor.The Lilly and Alejandro Saltiel Chair in Corporate Leadership and Social Responsibility and the Israel Institute of Business Research supported part of Dov Eden’s work on this experiment.

Abstract                   Seven past field-experimental attempts to produce Pygmalion effects by training managers yielded meager results (Eden et al, 2000). The present effort bolstered the Pygmalion approach with special emphasis on means efficacy, defined as belief in the utility of the tools available for performing a job. Six randomly assigned anti-aircraft gunnery instructors received a one-day Pygmalion workshop with special emphasis on self-efficacy and means efficacy before beginning instruction in a new round of a course; eight control instructors received an interpersonal communication workshop. The trainees of the experimental instructors reported higher self-efficacy, means efficacy, and motivation, and obtained higher scores on written examinations and on performance tests than did the trainees of the control instructors. This is the first true-experimental confirmation of the effectiveness of Pygmalion training among instructors of adults and the first replication of the means-efficacy findings.
Privacy and the secondary use of data for health research: experience in Canada and suggested directions forward

This paper has three parts. The first part describes developments in the use of personal information for health research in Canada, challenges in responding to new data protection legislation and the work of the Canadian Institutes of Health Research in assisting the research community to adapt to these developments.                   The second part critiques two aspects of the stakeholder analysis posited in William Lowrance's report: the lack of an analysis of researchers'interests as distinct from those of society at large, and the framing of privacy as an individual interest. I argue for recasting the privacy and research tension as a values conflict between two societal goods and not an individual versus a societal interest, and for explicitly acknowledging researchers’ interests as data users. In so doing, there is no clear trump of privacy or research, and greater importance is placed upon the weighing of benefits and risks of individual studies to determine whether the waiving of consent for a particular study or programme of research is justified.                   The third part makes specific suggestions as to future ways in which data use practices may evolve. I argue the need to engage the public in deliberating the broad parameters under which personal information will be used, as the public collectively has a major stake in this issue and their input provides legitimacy for these uses of information. In addition, given the proliferation of prospectively-developed databases for research purposes, a “systems” approach for obtaining consent for participation to replace the current study-by-study recruitment of individuals is appropriate.
The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps

Abstract                We present the XFaster analysis package, a fast, iterative angular power spectrum estimator based on a diagonal approximation to the quadratic Fisher matrix estimator. It uses Monte Carlo simulations to compute noise biases and filter transfer functions and is thus a hybrid of both Monte Carlo and quadratic estimator methods. In contrast to conventional pseudo-C                                         ℓ                   –based methods, the algorithm described here requires a minimal number of simulations and does not require them to be precisely representative of the data to estimate accurate covariance matrices for the bandpowers. The formalism works with polarization-sensitive observations and also data sets with identical, partially overlapping, or independent survey regions. The method was first implemented for the analysis of BOOMERanG data and also used as part of the Planck analysis. Here we describe the full, publicly available analysis package, written in Python, as developed for the analysis of data from the 2015 flight of the Spider instrument. The package includes extensions for self-consistently estimating null spectra and estimating fits for Galactic foreground contributions. We show results from the extensive validation of XFaster using simulations and its application to the Spider data set.
Intelligent photonic crystal-based optical sensor for accurate glycerol-water mixture measurement using artificial neural networks

This paper proposes an intelligent photonic crystal-based optical sensor designed for the first time to accurately measure glycerol-water concentration and temperature. The proposed sensor features a novel two-dimensional (2D) photonic crystal structure with an optimized waveguide configuration to enhance refractive index sensitivity. The sensor structure does not include defect rods, which simplifies fabrication and enhances stability. By using the unique optical properties of photonic crystals and the artificial neural network (ANN), the proposed design ensures high precision and stability in detecting changes in the glycerol concentration. The performance of the sensor was evaluated based on sensitivity, detection limit (DL), figure of merit (FOM), and quality factor (Q-F) across different temperatures and glycerol concentrations. The optical response of the sensor was numerically analyzed and simulated using the finite-difference time-domain (FDTD) method. Then, a feedforward ANN model was developed and trained to predict glycerol concentration and temperature from the output spectral data, enabling intelligent and real-time analysis. The results demonstrate that the proposed sensor achieves high sensitivity (up to 89.9 nm/RIU), a low detection limit (0.0003–0.0010 RIU−1), and an excellent Q-factor (5233), making it a highly effective solution for refractive index sensing. Overall, the findings confirm that the proposed photonic crystal sensor, enhanced with ANN-based intelligent analysis, offers high accuracy, stability, and fast response, making it suitable for biomedical, pharmaceutical, and industrial applications where precise glycerol concentration measurements are required.
Researching Health Care Management Using Secondary Data

Aimsa) To highlight gaps in the extant research on the characteristics and impact of managers in healthcare.b) To define and provide a taxonomy of administrative data sources relevant to researching healthcare management.c) To review the range of statistical techniques used to analyse administrative data.d) To illustrate how the analysis of administrative data sources can advance knowledge both with regard to the characteristics of managers in healthcare and their impact.e) To discuss the limitations of administrative data and avenues for future research.
The withdrawal of state consent to the optional jurisdiction of the African Court on Human and Peoples’ Rights: a denial of citizens’ right of access to regional justice? A review of the case Glory Cyriaque Hossou and Another v Benin

This commentary reviews the judgment of the African Court on Human and Peoples’ Rights in Glory Cyriaque Hossou and Another v Benin, concerning the withdrawal of Benin’s declaration made under article 34(6) of the African Court’s Protocol. The commentary discusses the judgment in the light of the principle of acquired right, a fundamental right to approach the African regional court on human and peoples’ rights. In practical terms, it discusses the legal relevance of the state consent rule and its withdrawal under the Protocol. The exercise of the African Court’s power to enforce fundamental rights enshrined in the African Charter on Human and Peoples’ Rights and relevant legal instruments or international human rights instruments to which states are parties is subjected to their prior consent pursuant to article 34(6) of the African Court’s Protocol. While it is legally permissible for states to accept or not the Court’s jurisdiction, the withdrawal of state consent can be questioned due to its adverse effects on the acquired fundamental right to legal remedy recognised by the African Charter. It seems relevant to question the meaning and legal scope of the rule enshrined under article 34(6) of the Protocol. Can an alternative interpretation of the right of the state to withdraw its declaration 34(6) be envisaged, in the name of the effectiveness of continental protection of human rights in Africa?
Emergent nonreciprocity in open thermodynamically-consistent chemical reaction networks

Nonreciprocity, a hallmark of nonequilibrium systems, can generate dynamics not possible near thermodynamic equilibrium, including oscillatory and rotating patterns. The onset of temporal oscillations is often evident in linearized dynamics, where nonreciprocity appears as complex eigenvalues of an asymmetric Jacobian. Here, we show that the topology of open, thermodynamically-consistent chemical reaction networks can result in oscillatory instabilities near nonequilibrium steady states. These instabilities arise from chemostat-induced breaking of Onsager reciprocity, while the local equilibrium hypothesis preserves the variational structure of the dissipative part of the dynamics. Numerical results confirm that such nonreciprocity in reaction-diffusion systems produces oscillatory dynamics that nevertheless minimize a free energy.
Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students

Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.
Climate-Conscious Management: Exploring the Role of Green Leadership and Green Organizational Climate in Driving Pro-Environmental Employee Behavior

The growing urgency of environmental challenges necessitates a deeper understanding of how organizations can foster pro-environmental employee behavior (PEB). Drawing on transformational leadership theory, organizational climate theory, and person–organization fit perspectives, this study examines a moderated mediation model linking green transformational leadership (GTL) to PEB via green organizational climate (GOC), with green value congruence (GVC) as a boundary condition. Data were collected from 392 employees across six firms in Pakistan’s production sector, a context where sustainability imperatives are mounting amid industrial growth and regulatory pressures. Using the PROCESS Macro in SPSS, results reveal that GTL significantly enhances PEB both directly and indirectly through GOC. The mediation is partial, suggesting that leaders not only model environmental responsibility but also shape collective perceptions that guide behavior. Furthermore, GVC strengthens the effect of GOC on PEB, indicating that climate perceptions are most influential when employees’ personal environmental values align with organizational priorities. The moderated mediation analysis confirms that the indirect effect of GTL on PEB via GOC is amplified under conditions of high value congruence. These findings contribute to theory by integrating leadership, climate, and fit literatures into a unified framework and extend knowledge on the mechanisms and contingencies driving sustainability-oriented behaviors. Practically, the study underscores the need for production-sector organizations to develop green leaders, institutionalize sustainability within organizational climates, and align values through recruitment, training, and communication strategies. Together, the results provide actionable pathways for embedding environmental responsibility into everyday organizational life.
Native Voices of the Americas: Of Literature, Decolonization and Self-Determination

O trabalho analisa como escritores ameríndios desenham um mapa de uma geografia crítica ao delinear a inter-relacionada brutalização dos seres humanos e do (meio) ambiente na interface colonização/ descolonização. Baseado numa abordagem teórica comparativa e interdisciplinar, embutida nos Estudos Culturais/ Pós-Coloniais e na Ecocrítica, o ensaio problematiza a questão da identidade ameríndia atrelada à terra e à prática da cultura e língua dentro de contextos de (neo)colonialidade e revela a relação quebrada entre sujeito, língua, lugar e mundo que constitui uma das fontes principais do entre lugar em textos de autores ameríndios do Brasil, do Peru e dos Estados Unidos.
Optical model compression learning for galaxy morphology classification with diffractive deep neural networks

Galaxy morphology is key to understanding cosmic evolution. While large astronomical surveys such as the Sloan Digital Sky Survey produce vast amounts of data, energy–efficient processing remains a challenge. Diffractive deep neural networks (D                     2                     NNs) offer a promising optical computing solution with high throughput and low power consumption. However, deep D                     2                     NNs suffer from optical attenuation, while shallow networks have limited representation capability. To address this trade–off, we extend the concept of knowledge distillation with optical–domain considerations and develop an optical model compression learning (OMCL) framework for D                     2                     NNs. Numerical simulations on SDSS galaxy images show that the compressed model achieves 69.25% classification accuracy, representing a 27.5% improvement over conventional training, and exhibits partial recognition ability for unseen categories. Further analysis reveals that the compressed model develops smoother and sparser phase patterns, leading to improved robustness to noise and stronger tolerance to low–precision phase quantization (e.g., 3–bit). These results indicate that transferring optical–domain features from deeper networks can effectively mitigate the limitations of shallow D                     2                     NNs, providing a practical pathway toward fabrication–friendly and high–performance optical neural networks for large–scale astronomical data analysis.
Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomic, and Demographic Data

Integrating cross-department multi-modal data (e.g., radiological, pathological, genomic, and clinical data) is ubiquitous in brain cancer diagnosis and survival prediction. To date, such an integration is typically conducted by human physicians (and panels of experts), which can be subjective and semi-quantitative. Recent advances in multi-modal deep learning, however, have opened a door to leverage such a process to a more objective and quantitative manner. Unfortunately, the prior arts of using four modalities on brain cancer survival prediction are limited by a "complete modalities" setting (i.e., with all modalities available). Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e.g., one or more modalities might not be collected for a patient). For instance, should we use both complete and incomplete data, and more importantly, how to use those data? To answer the preceding questions, we generalize the multi-modal learning on cross-department multi-modal data to a missing data setting. Our contribution is three-fold: 1) We introduce optimal multi-modal learning with missing data (MMD) pipeline with optimized hardware consumption and computational efficiency; 2) We extend multi-modal learning on radiological, pathological, genomic, and demographic data into missing data scenarios; 3) a large-scale public dataset (with 962 patients) is collected to systematically evaluate glioma tumor survival prediction using four modalities. The proposed method improved the C-index of survival prediction from 0.7624 to 0.8053.
AI Ethics Auditing. Risk Impact Assessment of Artificial Intelligence in the Public Administration of Justice

Thesis written in the contest of the MA in Politics, Philosophy and Public Affairs at University of Milan - Vita-Salute University San Raffaele