ethicalabs/Research-Intent-Collab huggingface dataset:
Protein structure prediction by                     <i>AlphaFold</i>                     2: are attention and symmetries all you need?

The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a protein from its primary sequence has long been a grand challenge in bioinformatics, intimately connected with understanding protein chemistry and dynamics. Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is complementary to longstanding physics-based approaches. The outstanding performance of                     AlphaFold                     2 in the recent Critical Assessment of protein Structure Prediction (CASP14) experiment demonstrates the remarkable power of deep learning in structure prediction. In this perspective, we focus on the key features of                     AlphaFold                     2, including its use of (i) attention mechanisms and Transformers to capture long-range dependencies, (ii) symmetry principles to facilitate reasoning over protein structures in three dimensions and (iii) end-to-end differentiability as a unifying framework for learning from protein data. The rules of protein folding are ultimately encoded in the physical principles that underpin it; to conclude, the implications of having a powerful computational model for structure prediction that does not explicitly rely on those principles are discussed.
Balancing privacy rights and surveillance analytics: a decision process guide

The right to privacy has been discussed by scholars in multiple disciplines, yet privacy issues are increasing due to technological advances and lower costs for organisations to adopt smart surveil...
TUDataset: A collection of benchmark datasets for learning with graphs

Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDataset for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at www.graphlearning.io. The experiments are fully reproducible from the code available at www.github.com/chrsmrrs/tudataset.
Public Security Policy: : The Right to Privacy in the Age of Video Surveillance

Dada la falibilidad del modelo represivo de seguridad pública en Brasil, cada vez es más común la elaboración de políticas públicas de carácter controlador como forma de prevenir la criminalidad. En Belo Horizonte, el programa Olho Vivo, basado en la vigilancia de la zona comercial de la ciudad a través de cámaras de seguridad, es un buen ejemplo de esta práctica. En este contexto, este artículo pretende analizar la legitimidad de esta medida frente al derecho a la privacidad en el Estado democrático de derecho.
Reconciling Biodiversity Conservation and Timber Production in Mixed Uneven-Aged Mountain Forests: Identification of Ecological Intensification Pathways

Mixed uneven-aged forests are considered favorable to the provision of multiple ecosystem services and to the conciliation of timber production and biodiversity conservation. However, some forest managers now plan to increase the intensity of thinning and harvesting operations in these forests. Retention measures or gap creation are considered to compensate potential negative impacts on biodiversity. Our objectives were to assess the effect of these management practices on timber production and biodiversity conservation and identify potential compensating effects between these practices, using the concept of ecological intensification as a framework. We performed a simulation study coupling Samsara2, a simulation model designed for spruce-fir uneven-aged mountain forests, an uneven-aged silviculture algorithm, and biodiversity models. We analyzed the effect of parameters related to uneven-aged management practices on timber production, biodiversity, and sustainability indicators. Our study confirmed that the indicators responded differently to management practices, leading to trade-offs situations. Increasing management intensity had negative impacts on several biodiversity indicators, which could be partly compensated by the positive effect of retention measures targeting large trees, non-dominant species, and deadwood. The impact of gap creation was more mitigated, with a positive effect on the diversity of tree sizes and deadwood but a negative impact on the spruce-fir mixing balance and on the diversity of the understory layer. Through the analysis of compensating effects, we finally revealed the existence of possible ecological intensification pathways, i.e., the possibility to increase management intensity while maintaining biodiversity through the promotion of nature-based management principles (gap creation and retention measures).
CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI

AbstractCardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The ‘CMRxRecon’ dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.
Additive Manufacturing (AM) / 3D Printing – Giving you the freedom to design (Opening keynote)

Emerging application summit: Additive Manufacturing (AM) / 3D Printing – Giving you the freedom to design - Opening Keynote Understanding the capabilities and commercial progress of Additive Manufacturing (AM) and how it is opening new applications and markets The flexibility offered by digital design and manufacturing technologies allows us to reassess the relationship between consumer, manufacture and designer. Direct digital manufacturing can deliver bespoke solutions, industrially produced or simply printed at home. Using Additive Manufacture (AM) the economies of scale associated serial production can still be enjoyed whilst employing a more personal approach to design and manufacture. Consumer products can be tailored to an individual’s need or desire; so called mass customisation. For well over a decade Lionel T Dean has been exploring AM business models that seek to engage the consumer in the creative process. These ideas range from an automated, computer generated approach termed mass individualisation, to co-creation, where the consumer takes a more active role in the creative process. This presentation explores the issues that surround a paradigm shift in consumerism. To what extent do consumers wish to shape the world them around them and what tools might they need to facilitate this? Can consumers become ‘prosumers’ without undue effort or the compromise of amateur results? Can the designer retain control as creative ground is ceded to the user and what are the implications for creative ownership, identity and brand.“
Modelling Malaysia’s energy transition pathways: the role of renewable energy policy measures

Abstract. Coal’s rising prominence in the power industry has raised concerns about future CO2 emissions and energy reliability. As of 2017, it is estimated that Malaysia’s existing natural gas production can only be maintained for another 40 years. Consequently, the carbon intensity of electricity production has increased due to the increasing share of coal-fired plants and electricity infrastructure inefficiencies. To sum it up, energy industries were the highest emitters of CO2 emissions, with a 54 percent share. In response to these challenges, the government implemented series of Renewable Energy (RE) policy measures. Whether these policies are sufficient in driving Malaysian energy decarbonization is yet to be answered. In the study, we simulate different scenarios from 2015 to 2050 with an agent-based model to explore the roles of renewable energy policies toward emission reduction in the energy sector. The simulation results reveal that when all renewables initiatives were implemented, the share of RE increased to 16 percent, and emissions intensity fell by 26 percent relative to its level in 2005, albeit with increasing absolute carbon emissions. This milestone is still far below the government’s 45 percent reduction target. The simulation results demonstrated that renewable energy policies alone are less effective in driving Malaysian electricity toward desired low-carbon pathways. Furthermore, it is evidenced that no single policy can achieve the emission reduction target. Therefore, a combination of energy efficiency and renewable energy policy measures is unavoidable to decarbonize the electricity sector in Malaysia.
Investigation on design and control aspects of a new autonomous mobile agricultural fruit harvesting robot

This paper focuses its attention on investigating the mechanical design and control aspects of a new autonomous mobile agricultural robot being developed for the purpose of automated harvesting of fruits in plantations. The paper goes from discussing the need of automated harvesting of fruits and the problems involved to conceptualizing the design of such a robot and ends with a brief discussion on the control aspects of the robot.
Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models

Thin films are ubiquitous in modern technology and highly useful in materials discovery and design. For achieving optimal extrinsic properties their microstructure needs to be controlled in a multi-parameter space, which usually requires a too-high number of experiments to map. We propose to master thin film processing microstructure complexity and to reduce the cost of microstructure design by joining combinatorial experimentation with generative deep learning models to extract synthesis-composition-microstructure relations. A generative machine learning approach comprising a variational autoencoder and a conditional generative adversarial network predicts structure zone diagrams. We demonstrate that generative models provide a so far unseen level of quality of generated structure zone diagrams comprising chemical and processing complexity for the optimization of chemical composition and processing parameters to achieve a desired microstructure.
Central Bank Digital Currencies: Towards a Chinese Approach : Design Choices of Digital Currency Electronic Payment

Inspired by the digital revolution to the financial industry, the discussion around central bank digital currency also attract attention from academics and central banks. The People’s Bank of China (PBOC) is also researching on China’s CBDC: digital currency electronic payment (DCEP) and announced that DCEP would be issued as soon as possible.  However, the PBOC does not systematically disclose the information of DCEP. The characteristics and mechanism design are still obscured and need to be explored deeply. This thesis analysed the classification and mechanism design choices of DCEP from the perspective of two different demands: general demand and central bank demand. Based on pragmatism philosophy, we use a mixed-methods approach that is a combination of qualitative and quantitative research. Through the interview and surveys, we identified the demands from the PBOC and the general public in China and the characteristics of DCEP from official claims. Then generate the design choices via the money flower and the pyramid of CBDC models and compare the result with the demand. The analysis shows that the DCEP belongs to type B general-purpose CB digital tokens, and it would adopt a complex multi-layer hybrid architecture design, with the support from both DLT and conventional way. In conclusion, the current mechanism design choices can meet the demands from each side to a certain extent and reached a delicate balance under the trade-off between privacy and security issues. This thesis provides an insightful view on the classification and design choices of DCEP, fulfils the lack of systematic research relating to the demand and design choices of DCEP, and reveals the public’s insufficient knowledge in DCEP.
Optimization of Logistics Inventory Management in Enterprises from the Supply Chain Perspective

For enterprise logistics inventory management, it also brings greater challenges and opportunities. The traditional enterprise logistics inventory management is relatively decentralized and independent, so from this point of view, it is difficult to achieve overall economic benefits in the process of operation. So, from this point of view, under the perspective of the supply chain, through the continuous optimization and integration of the enterprise's logistics inventory, so as to improve the quality and efficiency of its warehousing, to meet the actual needs of the enterprise, which is also a prerequisite and basis for improving the core competitiveness of modern enterprises in the fierce market competition. This paper describes the current situation of inventory management in logistics enterprises, analyses the inventory management problems of logistics enterprises, and puts forward the management path under the supply chain perspective, with a view to promoting and improving the development of enterprise inventory management under the supply chain management environment.
Representation and its importance for women's sexual and reproductive health and rights : Does the proportion of women in national parliaments matter for the extent to which legislation and policy guarantee women's sexual and reproductive health and rights?

This thesis sets out to investigate if the proportion of women in the national parliament correlates with the extent to which national laws and policies guarantee women's sexual and reproductive health and rights. By conducting a cross-national comparison, this thesis contributes to the existing literature by offering an analysis of the relationship, utilizing a comprehensive measurement of states national legal and regulatory framework relating to women's sexual and reproductive health and rights. Further, it aims to offer an analysis of how the relationship appears in different regime-types, including both democratic and non- and partial democratic states. The results did not provide any support for a correlation between the proportion of women in the national parliament and the extent to which national laws and policies guarantee women's sexual and reproductive health and rights, regardless of the regime-type. While the results are inconsistent with the predictions of a correlation, this thesis is able to conclude that the concept of women's representation and how it may be connected to substantial changes in national legislation and policy is a complex relationship, worthy of further research.
Climate Change Projections: Characterizing Uncertainty Using Climate Models

The atmosphere, ocean, land surfaces, and ice sheets of the Earth are highly complex and coupled systems, with physical laws which describe behavior from the microscopic to the planetary scale.
Exploring symptom burden, comfort, quality of life-care and satisfaction : a systematic review of palliative care interventions at the end of life

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2024
Root cause analysis investigation of visible particulates in therapeutic protein drug products using morphologically directed Raman spectroscopy

Abstract                   Visible particulate (VP) matter in therapeutic protein drug products (DPs) is a quality attribute that can impact product efficacy and patient safety across the product lifecycle. This study evaluates Morphologically Directed Raman Spectroscopy (MDRS) for detecting and characterizing VPs in therapeutic protein DPs. MDRS combines morphological and chemical analyses, which are essential for root cause analysis (RCA) during incident investigations of VP formation. We evaluated MDRS using polystyrene (PS) microsphere standards, candidate reference materials from the US National Institute of Standards and Technology (NIST), and stressed therapeutic protein DPs, insulin lispro and rituximab. Using MDRS, VPs were imaged and chemically characterized in insulin or rituximab drug products with varying morphologies and ranged from 100 to 400 μm in diameter. The intensity of Raman spectral peaks varied with VP size, transparency, morphological complexity, and between therapeutic protein drug classes. Principal component analysis (PCA) of morphological features (e.g., size, shape, and transparency) revealed that NIST ethylene tetrafluoroethylene (ETFE) particles displayed a range of morphologies, akin to stressed therapeutic protein VPs. These results highlight the potential of combining morphological analysis with Raman spectroscopy to support RCA in therapeutic protein DPs for both pre- and post-market quality assessments.
Analysis of the Effect of Control Environment, Risk Assessment, Control Activities, Information and Communication, and Monitoring on the Performance of Local Government Financial Management in Jambi Province with Human Resource Capacity as a Moderating Variable

This study aims to analyze the influence of Government Internal Control System (SPIP) components control environment, risk assessment, control activities, information and communication, and monitoring on the financial management performance of Jambi Provincial Government, as well as to examine human resource capacity as a moderating variable. The study employs a quantitative approach with descriptive and causal research design. A saturated sampling technique was used, involving all 100 employees of BPKPD Jambi Province. Data were collected through a Likert-scale questionnaire and analyzed using multiple linear regression and Moderated Regression Analysis (MRA) with SPSS, preceded by data quality and classical assumption tests. The findings reveal that all SPIP components have a significant effect on financial management performance. The control environment, risk assessment, and control activities show significant negative effects, while information and communication, as well as monitoring, demonstrate significant positive effects. An R² value of 0.834 indicates that the five SPIP variables explain 83.4% of performance variance. Human resource capacity does not moderate most relationships, but negatively moderates the effect of monitoring. This study highlights the crucial role of effective SPIP implementation and continuous human resource development in ensuring strong financial management performance in regional governments.
Universal access to essential medicines as part of the right to health: a cross-national comparison of national laws, medicines policies, and health system indicators

Access to essential medicines for the world's poor and vulnerable has made little progress since 2000, except for a few specific medicines such as antiretrovirals for HIV/AIDS. Human rights principles written into national law can create a supportive environment for universal access to medicines; however, systematic research and policy guidance on this topic is lacking.To examine how international human rights law and WHO's essential medicines policies are embedded in national law for medicines affordability and financing, and interpreted and implemented in practice to promote universal access to essential medicines.This thesis consists of (1) a cross-national content analysis of 192 national constitutions, 71 national medicines policies, and legislation for universal health coverage (UHC) from 16 mostly low- and middle-income countries; (2) a case study of medicines litigation in Uruguay, and (3) a follow-up report of eight right to health indicators for access to medicines from 195 countries.Some, but not all, of the 12 principles from human rights law and WHO's policy are embedded in national UHC law and medicines policies (part 1). Even the most rights-compliant legislation for access to medicines is subject to the unique and inconsistent interpretation of domestic courts, which may be inconsistent with the right to health in international law (part 2). Many national health systems for which data were available still fail to meet the official targets for eight indicators of access to medicines (part 3).International human rights law and WHO policy are embedded in national law for essential medicines and practically implemented in national health systems. Law makers can use these findings and the example texts in this thesis as a starting point for writing and monitoring governments' rights-based legal commitments for access to medicines. Future research should study the effect of national law on access to medicines and population health.
Parameterization and optimization of the menthol force field for molecular dynamics simulations

Menthol's various biological properties render it a useful component for medical and cosmetological applications, while its three centers of asymmetry mean that it can be used in a range of organic reactions. Menthol-substituted ionic liquids (ILs) have been found to exhibit promising antimicrobial and antielectrostatic properties, as well as being useful in organic catalysis and biochemical studies. However, so far, a force field designed and validated specifically for the menthol molecule has not been constructed. In the present work, the validation and optimization of force field parameters with regard to the ability to reproduce the macroscopic properties of menthol is presented. The set of optimized potentials for liquid simulations all atom (OPLS-AA) compatible parameters was tested and carefully tuned. The refinement of parameters included fitting of partial atomic charges, optimization of Lennard-Jones parameters, and recalculation of the dihedral angle parameters needed to reproduce quantum energy profiles. To validate the force field, a variety of physicochemical properties were calculated for liquid menthol. Both thermodynamic and kinetic properties were taken into account, including density, surface tension, enthalpy of vaporization, and shear viscosity. The obtained force field was proven to accurately reproduce the properties of the investigated compound while being fully compatible with the OPLS-AA force field.