ethicalabs/Research-Intent-Collab huggingface dataset:
Embracing AI Era: Challenges and Strategies in Translation and Interpreting Teaching in Universities

Against the backdrop of rapid development in artificial intelligence technology, the translation and interpreting industry is undergoing profound transformation. Large language models represented by ChatGPT have quickly entered public attention, promoting the development of translation technology and triggering widespread discussion on the future of the translation profession. Faced with both external skepticism and internal transformation, translation and interpreting teaching in universities faces challenges while also welcoming opportunities for transformation. This paper, starting from the developmental background of the translation discipline, combines an analysis of the current status of the translation and interpreting industry and the development trend of AI technology to reflect on the main problems in current university translation and interpreting teaching. It also proposes several pathways based on front-line teaching practice, aiming to provide reference for cultivating translation talents with core competitiveness in the AI era.
Projecting Current and Future Habitat for the European Ground Squirrel Using MaxEnt Modeling under Climate Change

Climate change and human activities are changing the places where different species live more and more quickly. The European ground squirrel (Spermophilus citellus) is a species at risk, and it plays a very important role in keeping grassland environments healthy. But right now, we still do not know clearly where this threatened species lives now and where it will live in the future as the climate changes. This study combined data from many different sources and used an improved Maximum Entropy (MaxEnt) model (the test AUC value is more than 0.87) to carefully study the current situation and future changes of the good living areas for this species. The results found that the number of people living in an area and isothermality are the main environmental factors that affect where this species lives. Future guesses show that the good living areas for it will keep getting smaller. This is especially true under the high-pollution SSP5-8.5 scenario. By the end of the century, this might lead to a total loss of more than 20% of its suitable living areas. At the same time, there will be a big drop in the number of high-quality living areas for the species. It is projected that the area where this species lives will have a big move to the north. This study points out that we badly need active and flexible management methods to protect this species. The species can survive only if the world works to cut greenhouse gases. The results give important scientific support for creating specific ways to protect the species.
Natural capital accounts for the North Sea : Suggestions for additions and valuation of ecosystem services

Natural capital accounts (NCA), also known as ecosystem accounts, is an approach for systematically measuring and monitoring the condition of ecosystems and corresponding ecosystem services over time, with the aim to support research, decision-making and planning. It is argued that natural resources will be more sustainably used and managed when the relationship between ecosystems and economic and other human activities is considered. In a reflection of the Statistics Netherlands (CBS) report Natural capital accounts for the North Sea: The physical SEEA EEA accounts (CBS, 2019), the objective of this report is to identify and improve ecosystems, conditions and physical accounts for the North Sea as well as to identify methods and sources to calculate values (prices) of ecosystem services provided, in preparation for the North Sea natural capital accounts.
Evaluasi Peran Corporate Social Responsibility (CSR), Good Corporate Governance (GCG) dan Kinerja Keuangan terhadap Pengungkapan Sustainability Report pada Perusahaan Pertambangan di BEI

This study investigates the influence of financial performance and corporate social responsibility (CSR) on sustainability report disclosure among mining sub-sector companies listed on the Indonesia Stock Exchange. The research uses a quantitative approach with secondary data collected from annual reports, financial statements, and sustainability reports covering the 2022–2024 period. A total of 14 companies were selected as research samples through purposive sampling based on specific criteria related to the availability and relevance of the required data. The analysis aims to determine whether companies with stronger financial conditions and CSR activities tend to disclose sustainability reports more comprehensively. The findings reveal that CSR does not significantly influence sustainability report disclosure, even when companies demonstrate strong financial performance. This suggests that financial capability alone does not necessarily encourage broader transparency in sustainability reporting. The results imply that the implementation of social and environmental initiatives has not been fully integrated into sustainability reporting practices within the mining sector. Therefore, this study recommends improving the documentation and reporting of sustainability-related programs and encourages future research to include additional variables to broaden the understanding of factors affecting sustainability report disclosure.
Zooming in on the place of rocky planet formation: infrared interferometric observations of protoplanetary disks

Spatially resolved observations from ALMA or direct imaging instruments revealed an extreme diversity and complexity of structures and substructures in the outer parts of protoplanetary disks.However, these techniques do not resolve the inner regions of protoplanetary disks, typically at less than 5 astronomical units from the star.These inner regions are crucial to understand the formation of telluric planets.They are also the theatre of strong interactions between the star and the disk that can influence planet formation.Thanks to infrared interferometry we can reach an angular resolution of ~1mas reaching sub-astronomical unit physical scales.We can, therefore use infrared interferometry to reveal and study the structure, composition, and dynamics of the inner parts of protoplanetary disks.In the past few years, the advent of infrared interferometers combining four telescopes such as PIONIER, MATISSE or GRAVITY enabled us to study these disks with an unprecedented detail.In this talk, I will review the recent results of near and mid-infrared interferometric observations of protoplanetary disks.
The "Environmental Lawsuit" as an Instrument of Preventive Protection of the Constitutional Right to Healthy Environment in the Law of the Republic of Serbia

A lot of attention has been paid to the environment and its protection in Serbian legislation. The right to healthy environment is guaranteed by the Constitution, and in the last two decades numerous laws have been passed regulating various aspects of the environment in order to ensure its protection. The subject of the paper is the claim to eliminate the danger of damage, stipulated by the Law on Obligations from 1978. From the enactment of the law, this legal institution has been considered as a means suitable for providing preventive environmental protection, which is why it is often called an “environmental lawsuit” in Serbian legal theory.
Numerical Analysis of Volatility Change Point Estimators for Discretely Sampled Stochastic Differential Equations

In this paper, we review recent advances on change point estimation for the volatility component of stochastic differential equations under different discrete sampling schemes. We consider both ergodic and non‐ergodic cases, and present a Monte Carlo study on the change point estimator to compare the three methods under different setups.
AR-SFQ: Asynchronous Reset Library Using α-Cell Design

Rapid Single Flux Quantum (RSFQ) circuits are the most evolved superconductor logic family. However, the need to clock each cell and the deep pipeline causes a complex clock network with a large skew. This results in lower throughput and high latency in RSFQ. This work introduces an asynchronous RSFQ cell library that incorporates the α-cell, enabling bidirectional signal paths in RSFQ circuits. The α-cell mitigates the need for a large clock network by allowing reverse signal flow, minimizing routing, and enabling compact circuit designs. We demonstrate the library's reliability and efficiency by analog simulations and using in-house optimization tools. The asynchronous reset RSFQ (AR-SFQ) will enable efficient implementation of scalable, high-performance computing frameworks, such as state machines, neuromorphic computing, and higher fan-in circuits.
The Role of Expertise in Discovery. Comment on Sutton and Griffiths (2018). Using Date Specific Searches on Google Books to Disconfirm Prior Origination Knowledge Claims for Particular Terms, Words, and Names. Social Sciences 7: 66

In claiming that expertise are unnecessary for making discoveries that contribute to knowledge, Sutton and Griffiths in their 2018 paper made analogous comparisons with metal detection, then proceeded to provide six examples in support of their argument. This response demonstrates the fallacy of that analogy, and reveals how each of those six examples were undermined by a lack of expertise in the relevant disciplines. The mistakes contained in that paper by Sutton and Griffiths make it evident that expertise are required to identify the validity of a discovery, and ensure that a claim is not false. This assurance is particularly needed for the bold claims made by the Sutton and Griffiths paper.
Corruption and Transactional Crime: Building up Effective Accountable Inclusive and Transparent Institutions as Ground for Sustainable Finance

Corruption, defined by the misuse of public power for private gains, is endemic, pervasive, and a significant contributor to low economic growth. There is a vast volume of research on the relationship between corruption and economic growth that empirically show the negative effects of corruption on growth, development, and investment. Corruption distorts investment and provision of public services and increases inequality to such an extent that international organizations like the World Bank, the IMF, and the UN have identified corruption as “the single greatest obstacle to economic and social development” and gave the fight against corruption high priority. To fight the multidimensional issue of corruption, its causes should be studies. Based on the literature, there are three conditions necessary for corruption to arise and persist: (1) discretionary power of public officials, (2) the possibility of economic rent extraction, and (3) weak institutions. Many scholars have emphasized on the role of institution
Heterogeneous Bayesian compressive sensing for sparse signal recovery

This study focuses on the issue of sparse signal recovery with sparse Bayesian learning in the context of a heterogeneous noise model, called by the heterogeneous Bayesian compressive sensing. The main contribution is to exploit the capability of noise variance learning in performance improvement and applicability enhancement. Experimental results on synthetic and real‐world data demonstrate that heterogeneous Bayesian compressive sensing has superior performance in terms of accuracy and sparsity for both homogeneous and heterogeneous noise scenarios.
The pulsar magnetosphere with machine learning: methodology

ABSTRACT                In this study, we introduce a novel approach for deriving the solution of the ideal force-free steady-state pulsar magnetosphere in three dimensions. Our method involves partitioning the magnetosphere into the regions of closed and open field lines, and subsequently training two custom Physics Informed Neural Networks to generate the solution within each region. We periodically modify the shape of the boundary separating the two regions (the separatrix) to ensure pressure balance throughout. Our approach provides an effective way to handle mathematical contact discontinuities in force-free electrodynamics. We present preliminary results in axisymmetry, which underscore the significant potential of our method. Finally, we discuss the challenges and limitations encountered while working with Neural Networks, thus providing valuable insights from our experience.
RNP-Based Control Systems for Genetic Circuits in Synthetic Biology Beyond CRISPR

Ribonucleoproteins (RNPs) are RNA-protein complexes utilized natively in both prokaryotes and eukaryotes to regulate essential processes within the cell. Over the past few years, many of these native systems have been adapted to provide control over custom genetic targets. Engineered RNP-based control systems allow for fine-tune regulation of desired targets, by providing customizable nucleotide-nucleotide interactions. However, as there have been several engineered RNP systems developed recently, identifying an optimal system for various bioprocesses is challenging. Here, we review the most successful engineered RNP systems and their applications to survey the current state of the field. Additionally, we provide selection criteria to provide users a streamlined method for identifying an RNP control system most useful to their own work. Lastly, we discuss future applications of RNP control systems and how they can be utilized to address the current grand challenges of the synthetic biology community.
The Role of Digital Media in Facilitating Sexual and Reproductive Health and Rights Advocacy and Communication Among Youth in Kenya: A Practitioner’s Perspective

Abstract                   Harnessing the transformative power of digital platforms, this chapter examines the evolving landscape of Sexual and Reproductive Health and Rights (SRHR) communication and advocacy among Kenyan youth. Recognising digital media’s potential to deliver timely and accurate information crucial for informed choices, the chapter analyses its impact on reaching this key demographic. However, it also critically examines how these same digital spaces have become a significant avenue for a growing and emboldened anti-rights movement. This movement, often cloaked in the rhetoric of protecting traditional African values, actively undermines SRHR by disseminating misinformation, fostering stigma and inciting hate. Despite Kenya’s progressive human rights policies and its commitment to international SRHR obligations; these well-organised anti-rights efforts pose a considerable hindrance to progress. This chapter appraises the successes achieved by SRHR advocates utilising digital tools, dissects the strategies and detrimental impact of anti-rights actors online, and ultimately proposes practical recommendations for SRHR activists to strategically navigate this challenging digital environment and continue effectively reaching young Kenyans with essential information about their bodies and rights. The chapter, from a practitioner’s perspective, reiterates the enduring and crucial importance of digital media as a vital tool for advancing SRHR activism and communication among young people in Kenya.
Reasons for the Prevalence of Caste Discrimination and Practice of Untouchability in Itihāsko Ek Piakā and Likhe

This paper examines reasons for the prevalence of caste discrimination and practice of untouchability in Itihāsko Ek Piakā and Likhe. To fulfill the objective, the researcher has implied Stuart Hall's theory of representation under cultural studies as main and other theorists' insights are cited as supporting tools to analyze and interpret the primary texts. This study is primarily a library based qualitative research. The area of this research is confined to novels by both Dalit and non-Dalit Nepali writers. The data were collected from text information, description and record keeping. The researcher found that all the Dalit characters have been facing socio-economic, educational, political and cultural domination, discrimination, humiliation and biasness from the upper caste people in the words and actions. The representation of Dalit characters displays that due to the policy of the nation results the caste discrimination and practice of untouchability. These evil socio-cultural practices result: illiteracy, poverty, unemployment, lack of political access, lack of unity among Dalits and lack of self-esteems. Beside this, characters in both novels: Bhuwansingh and Likhe suffer by same childhood experiences in terms of discrimination and untouchability. These evil socio-cultural practices also affect the learning environment. Thus, Dalits have been suffering lifelong and searching their self-respect and humanly treatment to emancipate them. This study adds new avenue for academic discussion in the field of academia so as to address the age-long discriminatory system in order to create a just and healthy society without any forms of discrimination in the name of race, class caste, color and gender. Therefore, it is significant to the academia because it is a great asset.
Refinement of AlphaFold-Predicted Models Using Cryo-EM Density Maps and Enhancement of Protein Secondary Structure Topologies With Residue Contacts

Proteins play an important role in almost every biological process. Understanding the mechanism of protein function requires knowledge of three-dimensional (3D) structures. Traditionally, the determination of 3D structures has presented significant challenges. However, Cryo-Electron Microscopy (Cryo-EM) has revolutionized the field of structural biology, providing a powerful technique for atomic structure determination. This dissertation delves into the potential of cryoEM in two ways. First, this dissertation presents a new flexible fitting approach, utilizing Normal Mode Analysis (NMA) and Elastic Network Models (ENMs) to refine AlphaFold-predicted models by optimizing the structures to match cryo-EM density maps. This approach identifies the optimal mode elongation vector that minimizes the negative cross-correlation between the simulated map generated from the deformed structures and the target map. The methodological affirmation in this dissertation includes local and global optimization methods, three different sets of basis functions, masked and box-cropped density maps, and two cross-correlation similarity measures. Four AlphaFold models with notable discrepancies from the true structure were tested, showing significant improvements in structure prediction accuracy. Second, an exploratory approach combined cryoEM density maps with residue contact information to enhance the secondary structure topology. This approach integrates three sources of information: sequence segments, amino acid contact pairs, and traces at the secondary structure level. A test involving fourteen cases demonstrates that the accuracy of predicted secondary structures is crucial for determining protein topologies. If the secondary structure prediction is reasonably accurate, utilizing significant long-range contact pairs is particularly effective in improving the rank of the topology for proteins with a large number of secondary structures. Through these focused efforts, this dissertation aims to contribute to a deeper understanding of protein function at the molecular level. This understanding has the potential to open new doors in fields, including drug discovery and novel treatments.
Artificial Intelligence for Multimodal Clinical Inference in Diagnosis, Prognosis and Therapeutic Decision-Making

Abstract: This study investigates how multimodal artificial intelligence can improve clinical inference across diagnosis, prognosis, and therapeutic decision-making by integrating heterogeneous medical data sources rather than relying on single-modality prediction pipelines. The manuscript is structured as a PhD-level secondary analysis built on real-world clinical and biomedical data ecosystems, including MIMIC-IV, eICU, The Cancer Genome Atlas, the Alzheimer’s Disease Neuroimaging Initiative, and UK Biobank, alongside published benchmark studies in cardiovascular risk prediction, kidney oncology, Alzheimer biomarker assessment, precision oncology, and pediatric pharmacotherapy. The central proposition is that multimodal fusion yields higher clinical utility when predictive modelling is linked to interpretability, calibration, causal reasoning, and explicit decision rules for action selection. The proposed framework formalises multimodal clinical inference as a sequential pipeline spanning data curation, representation learning, probabilistic risk estimation, and treatment utility optimisation. Real data collection is explicitly defined as the extraction of structured evidence from official repositories and peer-reviewed reports rather than the fabrication of synthetic cohorts. Quantitative synthesis shows that multimodal systems are associated with stronger discrimination and broader clinical contextualisation than unimodal baselines. In a recent scoping review of 432 studies, multimodal models outperformed unimodal comparators by an average of 6.2 percentage points in AUC, while individual studies reported AUROC values of 0.79 and 0.84 for amyloid beta and tau status estimation in Alzheimer’s disease, and accuracy above 98% in multimodal kidney-tumour classification. The findings indicate that multimodal AI is best understood not as a single algorithmic family but as a clinical decision architecture that combines imaging, longitudinal records, biomarkers, genomics, physiological signals, and clinician-interpretable outputs. The research argues that future deployment must prioritise calibration, external validation, equity, workflow integration, and traceable treatment recommendations. This study therefore contributes a structured conceptual and empirical synthesis suitable for journal submission or adaptation into a thesis chapter at Oxford/Cambridge-style standards. Keywords: multimodal artificial intelligence; clinical inference; diagnosis; prognosis; therapeutic decision-making; multimodal imaging; biomarkers; electronic health records; precision medicine; explainable AI
Why do some predicted protein structures fold poorly? Benchmarking AlphaFold, ESMFold, and Boltz in maize

Abstract                                    Protein structure prediction tools have significantly reduced the time and cost to generate protein structures and accelerated protein discovery and design. However, plant proteins are underrepresented in sequence and structural datasets used to train these programs. To quantify the downstream impact of this deficiency, we benchmarked five structure-prediction programs (AlphaFold 2, AlphaFold 3, ESMFold, Boltz-1, and Boltz-2) across 417 well-characterized                   Zea mays                   genes. These “classical” genes represent a set of well-studied genes with known genetic and phenotypic effects. We generated structures for each gene using these programs and compared how sequence, structural, and evolutionary conservation impacted the structures’ confidence and geometric features. Proteins lacking conserved sequence and/or structural domains had on average 25% to 43% lower confidence scores than proteins having both domains. Proteome-wide phylostratigraphy revealed that species-specific proteins had substantially lower confidence scores than proteins conserved amongst angiosperms and Eukaryotes. Boltz-1 and ESMFold structures had the highest occurrence of structures with severe geometry issues, including overlapping atoms and unlikely bond angles. We also compared computational and experimental alignments of Arabidopsis, maize, rice, wheat, and soybean proteins from the Protein Data Bank and identified structures showing incongruences with experimental data. This study challenges the assertion that protein folding has been completely ‘solved’, and urges more investigation into benchmarking and standardized evaluation frameworks to improve model performance and assessment in agricultural crops.