ethicalabs/Research-Intent-Collab huggingface dataset:
Toward Modeling Protein Multimers by Combining AlphaFold 3 Predictions with Secondary Structures from Medium-Resolution Cryo-EM Maps

AlphaFold 3 (AF3) has recently been shown to offer improved accuracy in predicting the structures of protein multimers. Improved models may lead to new opportunities for fitting them to cryo-electron microscopy (cryo-EM) maps with medium resolution (5-10 Å). Deriving atomic models from such cryo-EM maps is still challenging due to the lack of high-resolution features. Our case study involving four AF3 multimer models and corresponding cryo-EM maps with 7-8 Å resolution showed that the predicted multimer models were partially correct. The predicted models contained fairly accurate domains, secondary structures, and individual chains, since 9 of the 17 chains exhibit TM-scores higher than 0.8 and 16 chains had TM-scores above 0.5 compared with the official atomic structures that were deposited with the cryo-EM maps. However, some cases exhibited incorrect relative positions of individual chains or domains. We observed that the order of cross-correlation (CC) scores between the multimers and their corresponding cryo-EM maps aligned with the order of the TM-scores. This shows that if regions are masked correctly, CC scores are sensitive enough to distinguish among the multimer models. A masking of monomeric chains may not always be attainable, so we also explored the level of accuracy in secondary structure segmentation for one of the cases in greater detail. Although molecular details are not fully visible in cryo-EM maps at medium resolution, the location of major secondary structures, such as α-helices and β-sheets, were detectable using our DeepSSETracer tool. Our analysis illustrates the potential for improvements in the accuracy of AF3-predicted multimer models by combining the density map-model similarity (CC scores) and the secondary structure map-model similarity in a future approach.
Optical bistability in topological layered structure and its application in photonic neural network

Optical bistable state exhibits its great potential applications in photonic integrated circuit and photonic neural network. However, the traditional optical bistable state will be influenced by the system disorders, which are not suitable for application. In this work, we investigate the topological bistable states in a layered structure with center inversion symmetry consisting of alternating layers of high index material TiO&lt;sub&gt;2&lt;/sub&gt; and low index material SiO&lt;sub&gt;2&lt;/sub&gt;. In the topological mode, the electric field is highly localized in the inversion center of the layered structure (also known as the interface) and exponentially decays into the bulk. Thus, when the nonlinear permittivity is strategically introduced into those layers, nonlinear phenomena such as the bistable state appears. The finite element numerical simulations reveal that the optimal bistable state appears when the layer period is 5 with a threshold power around 1.2 W/m. Benefiting from the topological characteristics, such a bistable state persists when random perturbations are introduced into the layer thickness and refractive index. Finally, we apply the bistable states to a photonic neural network. The bistable function shows prediction accuracy similar to the classic activation function ReLu and sigmoid in various learning tasks. These results provide a novel method for inserting highly robust optical bistable states from topological layered structure into photonic neural network.
Use of Indicators to Improve Communication on Energy Systems Vulnerability, Resilience and Adaptation to Climate Change

This paper outlines anticipated climate change impacts on energy systems. It presents a set of indicators to determine: the level of vulnerability of a particular energy system; the capacity to implement energy adaptation projects; and how successful proposed implementation measures will be in increasing energy system resilience. Wind, solar, hydro, biomass, nuclear and fossil fuel energy systems are addressed.
Best practice in communications training for public engagement with science, technology, engineering and mathematics

Effective training in key communications skills is critical for successful public engagement. However, what are the secrets to designing and delivering an effectual training course? This paper outlines key findings from a research study into communication training programmes for public engagement with STEM (science, technology, engineering and mathematics). The research focused on training in direct communication methods, (as separate from media training) and encompassed both trainers and trainees, the latter group spanning across both scientists and explainers. The findings indicated that training courses are effective at increasing involvement in science communication events and trainees feel more confident and able to engage due to training. An interactive style was found to be a key element of training courses. Demonstrations of good practice followed by own performance with feedback were also important, preferably involving a ‘real’ audience. A list of guidelines on best practice has been developed which offers practical advice.
Computed Tomography‐Based Radiomics and Genomics Analyses for Survival Prediction of Stage III Unresectable Non‐Small Cell Lung Cancer Treated With Definitive Chemoradiotherapy and Immunotherapy

ABSTRACTThe standard therapy for locally unresectable advanced non‐small cell lung cancer (NSCLC) is comprised of chemoradiotherapy (CRT) before immunotherapy (IO) consolidation. However, how to predict treatment outcomes and recognize patients that will benefit from IO remain unclear. This study aimed to identify prognostic biomarkers by integrating computed tomography (CT)‐based radiomics and genomics. Specifically, our research involved 165 patients suffering from unresectable Stage III NSCLC. Cohort 1 (IO following CRT) was divided into D1 (n = 74), D2 (n = 32), and D3 (n = 26) sets, and the remaining 33 patients treated with CRT alone were grouped in D4. According to the CT images of primary tumor regions, radiomic features were analyzed through the least absolute shrinkage and selection operator (LASSO) regression. The Rad‐score was figured out to forecast the progression‐free survival (PFS). According to the Rad‐score, patients were divided into high and low risk groups. Next‐generation sequencing was implemented on peripheral blood and tumor tissue samples in the D3 and D4 cohorts. The maximum somatic allele frequency (MSAF) about circulating tumor DNA levels was assessed. Mismatch repair and switching/sucrose non‐fermenting signaling pathways were significantly enriched in the low‐risk group compared to the high‐risk group (p &lt; 0.05). Moreover, patients with MSAF ≥ 1% and those showing a decrease in MSAF after treatment significantly benefited from IO. This study developed a radiomics model predicting PFS after CRT and IO in Stage III NSCLC and constructed a radio‐genomic map to identify underlying biomarkers, supplying valuable insights for cancer biology.
Adaptive Collaborative Filtering for Recommender System

On online websites or e-commerce services, the explosive growth of resource makes the problem of content exploring increasingly challenging. The recommender system is a powerful information filtering tool to support user interaction and promote products. Dealing with determining customer interests, graph-based collaborative filtering is recently the most popular technique. Its only drawback is high computing cost, leads to bad scalability and infeasibility for large size network. Moreover, most previous studies concentrate solely on the accuracy of user preference prediction, while the efficiency of recommendation methods should be considered in many characteristics with complicated relationships, depending on particular systems: popularity, diversity, coverage, congestion. Attempt to conquer these challenges, we propose Adaptive Weighted Conduction formula handling multiple metrics, then construct a scalable model with small complexity, named Adaptive Collaborative Filtering. Experiments are conducted on Movielens, a public dataset, and FPT PLAY, a dataset of our media service. We have an increase of \(6\%\) on precision and get close to the best of previous methods on diversity, coverage and congestion. This result shows that the proposed model automatically reveals and adapts to varied requirements of recommender systems, reaches high accuracy and still balances other evaluation aspects.
Protein structure prediction with AlphaFold-Multimer v1

This protocol describes the protein structure prediction with AlphaFold-Multimer.
Structural Analysis of Construction Risk Management Assessment Model Based on Digital Twins

Construction risk categories are complex and the most current assessment platforms tend to place focus on theoretical assessments. There is an urgent need to simulate construction risks to manage projects effectively. As such, this article proposes a digital twin construction risk assessment model (DTCRAM) based on Delphi and a case study for predicting and controlling construction risk to reduce project loss. The model has been proven to detect risks quickly and match strategies efficiently. The outcomes revealed that DTCRAM displayed better results (key risks) in predicting and controlling construction management (more than 1% probability reduction/project) while simultaneously improving management efficiency (screen out risk factors). As a result, scholars and companies can gain risk management experience by using this tool in advance.
High-speed electro-optic engine for a quantized photonic neural network

Integrated photonic computing possesses remarkable advantages in computational power, becoming a promising candidate for high-performance computing in the post-Moore era. However, integrated photonic computing systems face trade-offs between scale, speed, and precision, hindering their development into high-performance processors. In this work, we propose and experimentally demonstrate a photonic quantized neural network using a large-scale lithium niobate on insulator high-speed electro-optic engine. With the electro-optic engine providing 1.6 TeraOPS computational power, our photonic quantized neural network experiments achieve a high accuracy of 97% in CNN-based MNIST handwritten digit recognition and 80% in GCN-based Cora data node classification, while also realizing over 95% model compression. Based on the quantized input-weight parallel encoding scheme, our high-speed electro-optic engine achieves a 100-fold improvement in system frequency over digital computing architectures and a 10-fold improvement in both modulation speed and compute density over optoelectronic computing architectures, while also achieving competitive energy efficiency. Our experimental findings indicate that our photonic quantized neural network technology paves the way for large-scale and high-speed integrated photonic computing, further promoting its practical application.
How Technical Variations in 5G Network Planning for Urban Areas Impact Carbon Footprint: A Life Cycle Assessment (LCA) Approach

This study aims to address the question of how technical variations in 5G network planning in urban areas impact carbon footprint emissions. In order to answer the question, this study simulated the carbon footprint impact of 5G network design in the city of Depok, Indonesia, through scenarios of variation in three technical parameters: bandwidth, modulation, and MIMO antenna configuration (V-layer). The analysis focuses on the specific global warming potential (GWP) over a 20-year horizon, serving as the key parameter in assessing environmental impact. The findings reveal that increasing throughput per site significantly reduces the carbon footprint of passive tower components. In Scenario #1, wider bandwidths yield substantial reductions in carbon emissions, as increasing bandwidth from 20 MHz to 100 MHz reduces the carbon footprint by 81.40%. Scenario #2 demonstrates the effectiveness of higher-order modulations, such as 256QAM, in reducing carbon emissions. Transitioning from QPSK to 256QAM achieves a 75.00% reduction in carbon footprint. The results reveal a clear trend: as throughput per site increases, the carbon footprint of the passive 5G tower component decreases. However, Scenario #3 highlights a trade-off between energy consumption and antenna configuration, as higher MIMO configurations require more energy. These results signified the importance of optimizing bandwidth and modulation in 5G network design to minimize environmental impact, providing actionable insights for network operators aiming to integrate sustainability into their strategies.
Impact of long COVID on health-related quality-of-life: an OpenSAFELY population cohort study using patient-reported outcome measures (OpenPROMPT)Research in context

Summary: Background: Long COVID is a major problem affecting patient health, the health service, and the workforce. To optimise the design of future interventions against COVID-19, and to better plan and allocate health resources, it is critical to quantify the health and economic burden of this novel condition. We aimed to evaluate and estimate the differences in health impacts of long COVID across sociodemographic categories and quantify this in Quality-Adjusted Life-Years (QALYs), widely used measures across health systems. Methods: With the approval of NHS England, we utilised OpenPROMPT, a UK cohort study measuring the impact of long COVID on health-related quality-of-life (HRQoL). OpenPROMPT invited responses to Patient Reported Outcome Measures (PROMs) using a smartphone application and recruited between November 2022 and October 2023. We used the validated EuroQol EQ-5D questionnaire with the UK Value Set to develop disutility scores (1-utility) for respondents with and without Long COVID using linear mixed models, and we calculated subsequent Quality-Adjusted Life-Months (QALMs) for long COVID. Findings: The total OpenPROMPT cohort consisted of 7575 individuals who consented to data collection, with which we used data from 6070 participants who completed a baseline research questionnaire where 24.6% self-reported long COVID. In multivariable regressions, long COVID had a consistent impact on HRQoL, showing a higher likelihood or odds of reporting loss in quality-of-life (Odds Ratio (OR): 4.7, 95% CI: 3.72–5.93) compared with people who did not report long COVID. Reporting a disability was the largest predictor of losses of HRQoL (OR: 17.7, 95% CI: 10.37–30.33) across survey responses. Self-reported long COVID was associated with an 0.37 QALM loss. Interpretation: We found substantial impacts on quality-of-life due to long COVID, representing a major burden on patients and the health service. We highlight the need for continued support and research for long COVID, as HRQoL scores compared unfavourably to patients with conditions such as multiple sclerosis, heart failure, and renal disease. Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).
Open statistical issues in particle physics

Many statistical issues arise in the analysis of Particle Physics experiments. We give a brief introduction to Particle Physics, before describing the techniques used by Particle Physicists for dealing with statistical problems, and also some of the open statistical questions.
BMC Evolutionary Biology / A DNA and morphology based phylogenetic framework of the ant genus Lasius with hypotheses for the evolution of social parasitism and fungiculture

Background: Ants of the genus Lasius are ecologically important and an important system for evolutionary research. Progress in evolutionary research has been hindered by the lack of a well-founded phylogeny of the subgenera, with three previous attempts disagreeing. Here we employed two mitochondrial genes (cytochrome c oxidase subunit I, 16S ribosomal RNA), comprising 1,265 bp, together with 64 morphological characters, to recover the phylogeny of Lasius by Bayesian and Maximum Parsimony inference after exploration of potential causes of phylogenetic distortion. We use the resulting framework to infer evolutionary pathways for social parasitism and fungiculture. Results: We recovered two well supported major lineages. One includes Acanthomyops, Austrolasius, Chthonolasius, and Lasius pallitarsis, which we confirm to represent a seventh subgenus, the other clade contains Dendrolasius, and Lasius sensu stricto. The subgenus Cautolasius, displaying neither social parasitism nor fungiculture, probably belongs to the second clade, but its phylogenetic position is not resolved at the cutoff values of node support we apply. Possible causes for previous problems with reconstructing the Lasius phylogeny include use of other reconstruction techniques, possibly more prone to instabilities in some instances, and the inclusion of phylogenetically distorting characters. Conclusion: By establishing an updated phylogenetic framework, our study provides the basis for a later formal taxonomic revision of subgenera and for studying the evolution of various ecologically and sociobiologically relevant traits of Lasius, although there is need for future studies to include nuclear genes and additional samples from the Nearctic. Both social parasitism and fungiculture evolved twice in Lasius, once in each major lineage, which opens up new opportunities for comparative analyses. The repeated evolution of social parasitism has been established for other groups of ants, though not for temporary social parasitism as found in Lasius. For fungiculture, the independent emergence twice in a monophyletic group marks a novel scenario in ants. We present alternative hypotheses for the evolution of both traits, with one of each involving loss of the trait. Though less likely for both traits than later evolution without reversal, we consider reversal as sufficiently plausible to merit independent testing.
Detection and Prevention of Attacks on the Internet of Things (IoT) and Wireless Sensor Networks

Internet of Things (IoT) covers every object that connects to the Internet and communicates with other devices. Many IoT devices, such as autonomous vehicles, smart refrigerators, smart washing machines, smart toasters, smart watches that can become part of our lives and they can communicate with each other using different wireless network technologies. Because of using IoT devices in many critical areas, attacks against IoT security have increased. By making these attacks on IoT layers, criteria such as data privacy, data integrity, data freshness, data accessibility and authentication can be violated. Some security solutions have been proposed to prevent these attacks, but it is not possible to apply traditional security methods on low-power IoT devices due to some constraints such as limited energy, limited battery time, limited computational power and limited memory. In this paper, attacks that threaten the security of IoT devices are examined and classified according to network layers and defense techniques are proposed.
RETRACTED: The Impact of Corporate Social Responsibility Reporting Practice on the Financial Performance of Tunisian Banks

This study investigates the link between corporate social responsibility (CSR) disclosure and the financial performance of banks in Tunisia over the period from 2013 to 2023. The analysis was conducted on the financial statements of fourteen banks in Tunisia to gather data on CSR disclosures. The panel data analysis revealed a statistically significant and positive correlation between the level of CSR disclosure and the financial performance of these banks. These results suggest that greater CSR disclosure is associated with improved profitability for banks in Tunisia. This study contributes to the existing literature on the relationship between social responsibility disclosures and financial performance, providing valuable insights for both academics and industry professionals.
The role of democratization in conflict resolution and peace building in Southern Africa : a case study of South Africa

Masters
Artificial Intelligence in Lethal Automated Weapon Systems - What's the Problem? : Analysing the framing of LAWS in the EU ethics guidelines for trustworthy AI, the European Parliament Resolution on autonomous weapon systems and the CCW GGE guiding principles.

Lethal automated weapon systems (LAWS) are developed and deployed by a growing number of state and non-state actors, although no international legally binding framework exists as of yet. As a first attempt to regulate LAWS the UN appointed a group of governmental experts (GGE) to create the guiding principles on the issue of LAWS AI. A few years later the EU appointed an expert group to create the Ethics guideline for trustworthy and the European Parliament passed a resolution on the issue of LAWS.  This thesis attempts to make the underlying norms and discourses that have shaped these guiding principles and guidelines visible. By scrutinizing the documents through the ‘What’s the problem presented to be’-approach, the discursive practices that enables the framing is illuminated. The obscured problems not spoken of in the EU and UN documents are emphasised, suggesting that both documents oversimplifies  and downplays the danger of LAWS, leaving issues such as gender repercussions, human dignity and the dangers of the sophisticated weapons system itself largely unproblematised and hidden behind their suggested dichotomised and anthropocentric solutions, which largely results in a simple “add human and stir”-kind of solution. The underlying cause of this tendency seems to stem from a general unwillingness of states to regulate as LAWS are quickly becoming a matter of have- and have nots and may potentially change warfare as we know it. A case can also be made as to AI’s ‘Hollywood-problem’ as influencing the framing of LAWS, where the dystopian terminator-like depiction in popular culture can be seen reflected in international policy papers and statements.
Slovenian Legislative System for Minority Protection: Different Rights for Old and New Minorities

The three constitutionally recognized autochthonous national minorities in Slovenia (Hungarians, Italians and Roma) enjoy a privileged status in comparison with the other minority ethnic groups that originate from the territories of former Yugoslavia and who greatly outnumbered those traditional national communities. National legislation, case law as well as the practices of national authorities that should provide for a successful accommodation of minority ethnic groups are examined in this paper in order to gain understanding whether Slovenia's accession to the European Union has enhanced the level of a minority protection in the country.
Scientific collaborations between Latin America and Europe: an approach from science diplomacy towards international engagement

Abstract                International collaborations show asymmetries and imbalances that influence how countries take part in international relations. In this context, science diplomacy (SD) could offer possibilities towards building partnerships and meaningful engagement between Europe and Latin America. The purpose of this article is to analyse how scientific collaborations between Latin American and European researchers are carried out, observing trends, behaviours, and perceptions. Qualitative methodologies and the analysis of empirical data collected through a survey allow the extraction of relevant experiences from real cases of international joint projects. Findings indicate that addressing the asymmetries in the collaboration between partnering researchers from Europe and Latin America is essential, and SD approaches may facilitate such endeavour. SD may not represent a panacea; however, it seems to facilitate the internationalisation of research in terms of mobility, international scientific collaborations, and knowledge exchange with under-represented actors in traditional international scientific schemes, such as indigenous communities.