Miracle-dz/UniFAR-FTUs huggingface dataset:
An investigation of belief-free DRL and MCTS for inspection and maintenance planning

We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I&M) planning. Unlike other DRL algorithms for (I&M) planning, the proposed +RQN architecture dispenses with computing the belief state and directly handles erroneous observations instead. We apply the algorithm to a basic I&M planning problem for a one-component system subject to deterioration. In addition, we investigate the performance of Monte Carlo tree search for the I&M problem and compare it to the +RQN. The comparison includes a statistical analysis of the two methods' resulting policies, as well as their visualization in the belief space.
Quantum Entanglement on Black Hole Horizons in String Theory and Holography

We compute the exact one-loop partition function of $\mathbb{Z}_N$ orbifolds of Euclidean BTZ black hole with the aim to compute the entanglement entropy of the black hole horizon in string theory as a function of the mass and spin of the black hole and the $\mathrm{AdS}_3$ radius. We analyze the tachyonic contribution to the modular integrand for the partition function known for odd integers $N>1$ and show that it admits an analytic continuation resulting in a finite answer for the modular integral in the physical region $0<N \leq 1$. We discuss the flat space limit and the relevance of this computation for quantum gravity near black hole horizons and holography in relation to the thermal entropy.
Proposal for a Global Adherence Scale for Acute Conditions (GASAC): A prospective cohort study in two emergency departments

Background Adherence in the context of patients with acute conditions is a major public health issue. It is neglected by the research community and no clinically validated generic scale exists to measure it. Objective To construct and validate a Global Adherence Scale usable in the context of Acute Conditions (GASAC) that takes into account adherence both to advice and to all types of prescriptions that the doctor may give. To measure adherence and to study its determinants. Materials and method We based the construction of the GASAC questionnaire on a theoretical model and a literature search. Then, between 2013 and 2014, we validated it in a prospective observational study in two hospital emergency departments. Patients were contacted by phone about one week after their consultation to answer several questionnaires, including GASAC and the Girerd self-administered questionnaire about medication adherence as a control. Results GASAC consists of four adherence subscales: drug prescriptions; blood test/ radiography prescriptions; lifestyle advice and follow-up instructions. An analysis of the 154 sets of answers from patients showed that the GASAC drug subscale had satisfactory internal coherence (Cronbach’s alpha = 0.78) and was correlated with the Girerd score, as was GASAC as a whole (p<0.01)). The median score was 0.93 IQR [0.78-1] for a maximum value of 1 (n = 154). In multivariate analysis, infection was more conducive of good adherence (cut off at ≥ 0.8; n=115/154; 74.7% [67.0-81.3]) than trauma (OR 3.69; CI [1.60-8.52]). The Doctor-Patient Communication score (OR 1.06 by score point, CI [1.02-1.10]) also influenced adherence. Conclusions GASAC is a generic score to measure all dimensions of adherence in emergency departments for clinical research and the evaluation of clinical practice. The level of adherence was high for acute conditions and could be further improved by good Doctor-Patient Communication.
Evaluating the Security and Privacy Risk Postures of Virtual Assistants

Virtual assistants (VAs) have seen increased use in recent years due to their ease of use for daily tasks. Despite their growing prevalence, their security and privacy implications are still not well understood. To address this gap, we conducted a study to evaluate the security and privacy postures of eight widely used voice assistants: Alexa, Braina, Cortana, Google Assistant, Kalliope, Mycroft, Hound, and Extreme. We used three vulnerability testing tools, AndroBugs, RiskInDroid, and MobSF, to assess the security and privacy of these VAs. Our analysis focused on five areas: code, access control, tracking, binary analysis, and sensitive data confidentiality. The results revealed that these VAs are vulnerable to a range of security threats, including not validating SSL certificates, executing raw SQL queries, and using a weak mode of the AES algorithm. These vulnerabilities could allow malicious actors to gain unauthorized access to users' personal information. This study is a first step toward understanding the risks associated with these technologies and provides a foundation for future research to develop more secure and privacy-respecting VAs.
Designing a Skilled Soccer Team for RoboCup: Exploring Skill-Set-Primitives through Reinforcement Learning

The RoboCup 3D Soccer Simulation League serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team's performance is based on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the Proximal Policy Optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast sprint-kick developed in 2021 and progress to the most recent skill set, which includes a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push tackles both low-level collision-prone scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the codebase of our team to the RoboCup community, enabling other teams to transition to Python more easily and providing new teams with a robust and modern foundation upon which they can build new features.
Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant

Traditional clustering algorithms often struggle to capture the complex relationships within graphs and generalise to arbitrary clustering criteria. The emergence of graph neural networks (GNNs) as a powerful framework for learning representations of graph data provides new approaches to solving the problem. Previous work has shown GNNs to be capable of proposing partitionings using a variety of criteria, however, these approaches have not yet been extended to work on Markov chains or kinetic networks. These arise frequently in the study of molecular systems and are of particular interest to the biochemical modelling community. In this work, we propose several GNN-based architectures to tackle the graph partitioning problem for Markov Chains described as kinetic networks. This approach aims to minimize how much a proposed partitioning changes the Kemeny constant. We propose using an encoder-decoder architecture and show how simple GraphSAGE-based GNNs with linear layers can outperform much larger and more expressive attention-based models in this context. As a proof of concept, we first demonstrate the method's ability to cluster randomly connected graphs. We also use a linear chain architecture corresponding to a 1D free energy profile as our kinetic network. Subsequently, we demonstrate the effectiveness of our method through experiments on a data set derived from molecular dynamics. We compare the performance of our method to other partitioning techniques such as PCCA+. We explore the importance of feature and hyperparameter selection and propose a general strategy for large-scale parallel training of GNNs for discovering optimal graph partitionings.
ROS package search for robot software development: a knowledge graph-based approach

ROS (Robot Operating System) packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development. Indeed, finding suitable ROS packages that closely match the software's functional requirements from the vast number of available packages is a nontrivial task using current search methods. The traditional search methods for ROS packages often involve inputting keywords related to robotic tasks into general-purpose search engines or code hosting platforms to obtain approximate results of all potentially suitable ROS packages. However, the accuracy of these search methods remains relatively low because the task-related keywords may not precisely match the functionalities offered by the ROS packages. To improve the search accuracy of ROS packages, this paper presents a novel semantic-based search approach that relies on the semantic-level ROS Package Knowledge Graph (RPKG) to automatically retrieve the most suitable ROS packages. Firstly, to construct the RPKG, we employ multi-dimensional feature extraction techniques to extract semantic concepts from the dataset of ROS package text descriptions. The semantic features extracted from this process result in a substantial number of entities and relationships. Subsequently, we create a robot domain-specific small corpus and further fine-tune a pre-trained language model, BERT-ROS, to generate embeddings that effectively represent the semantics of the extracted features. These embeddings play a crucial role in facilitating semantic-level understanding and comparisons during the ROS package search process within the RPKG. Secondly, we introduce a novel semantic matching-based search algorithm that incorporates the weighted similarities of multiple features from user search queries, which searches out more accurate ROS packages than the traditional keyword search method.
High-throughput computational discovery of inhibitory protein fragments with AlphaFold

Peptides can bind to specific sites on larger proteins and thereby function as inhibitors and regulatory elements. Peptide fragments of larger proteins are particularly attractive for achieving these functions due to their inherent potential to form native-like binding interactions. Recently developed experimental approaches allow for high-throughput measurement of protein fragment inhibitory activity in living cells. However, it has thus far not been possible to predict de novo which of the many possible protein fragments bind their protein targets, let alone act as inhibitors. We have developed a computational method, FragFold, that employs AlphaFold to predict protein fragment binding to full-length protein targets in a high-throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. These predictions were compared with experimentally measured peaks of inhibitory activity in E. coli. We establish that our approach is a sensitive predictor of protein fragment function: Evaluating inhibitory fragments derived from known protein-protein interaction interfaces, we found 87% were predicted by FragFold to bind in a native-like mode. Across full protein sequences, 68% of FragFold-predicted binding peaks match experimentally measured inhibitory peaks. This is true even when the underlying inhibitory mechanism is unclear from existing structural data, and we find FragFold is able to predict novel binding modes for inhibitory fragments of unknown structure, explaining previous genetic and biochemical data for these fragments. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.
The first record of the plesiosaurian genus Polyptychodon (Pliosauridae) from the New World

Teeth, skull fragments, lower jaw, and an isolated vertebra of a short-necked plesiosaur were collected from the Eagle Ford Shale (Turonian) near Dallas, Texas. Close to Polyptychodon interruptus Owen, but smaller and lacking the high parietal crest, this is given a new specific name and is the first representative of this genus from the Western Hemisphere.
Superharmonic functions of Schrödinger operators and Hardy inequalities

Given a Dirichlet form with generator L and a measure μ, we consider superharmonic functions of the Schrödinger operator L + μ. We probabilistically prove that the existence of superharmonic functions gives rise to the Hardy inequality. More precisely, the L2-Hardy inequality is derived from Itô’s formula applied to the superharmonic function.
Urban ventilation design for megacities: Hong Kong and beyond

The 2002 Sars outbreak in China focused global attention on the issue of poor air quality in major cities. The Hong Kong government responded by developing a mandatory air ventilation assessment framework for new master plans. The aim is to ensure that massing of new developments is specifically designed to enhance air ventilation at street level and avoid stagnant pockets of contaminated air. With 60% of China’s population likely to be living in cities by 2020, the framework could have wide application beyond Hong Kong. This paper introduces the framework and looks at outdoor airflow in cities from a building aerodynamics standpoint. A number of measures are provided that can be adopted to improve and enhance air ventilation at pedestrian level.
Pasadena’s Experience using Vehicle Miles Traveled (VMT) to Measure Transportation Impact

The City of Pasadena adopted new metrics for measuring transportation impact in 2015 that promote sustainable transportation and urban form. This paper describes the process by which conventional impact measures were replaced with metrics for sustainability and describes experiences with implementation of the metrics, development of companion elements, and monitoring performance. The integrated approach to planning, development review, and system management in use in Pasadena provides both a model framework for other cities and highlights the issues that can be encountered when using metrics that are less familiar to the public and decision makers.
The GHG-CCI project of ESA's climate change initiative: Data products and application

The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO2) and methane (CH4) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Data Package No. 3 (CRDP3). We focus on the GHG-CCI project core data products, which are near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 (in ppm) and XCH4 (in ppb) retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode radiance observations in the near-infrared/shortwave-infrared spectral region. The GHG-CCI products are primarily individual sensor Level 2 products. However, we also generate merged Level 2 products ("EMMA products"). Here we also present a first GHG-CCI Level 3 product, namely XCO2 and XCH4 in Obs4MIPs format (monthly, 5°×5°).
Analysis of sex-specific spawning biomass per recruit of the sailfish (Istiophorus platypterus) in the waters off eastern Taiwan

Analyses of sex-specific yield per recruit and spawning stock biomass per recruit were conducted to evaluate the current status of the sailfish (Istiophorus platypterus) fishery in the waters off eastern Taiwan. Natural mortality rates estimated from Pauly’s empirical equation were 0.26/yr for females and 0.27/yr for males. The current fishing mortality rates were estimated as 0.24/yr and 0.43/yr for females and males, respectively, which are much lower than the estimated F0 . 1 (0.62/yr and 0.79/yr for females and males, respectively) and FSSB40 (0.46/yr for females) which are commonly used as target reference points in fisheries management. The effects of the fishing mortality, natural mortality, and age at first capture on the estimates of biological reference points were evaluated by using the Monte Carlo simulation. The results indicate that failure to consider the uncertainty in parameters such as natural mortality or age at first capture may lead to the improper estimation of biological reference points. This study indicates the possibility of current fishing mortality exceeding the target biological reference points may be negligible for sailfish in the waters off eastern Taiwan. However, in view of the recent rapid increase in fishing effort, it is evident that the stock status and development of the fishery need to be closely monitore
Automatic Generation of Pronunciation Variants for Korean Continuous Speech Recognition

derived about 700 phonemic contexts that would trigger the multilevel application of the corresponding phonological process, which consists of phonemic and allophonic rules. In generating pronunciation variants, morphological analysis is preceded to handle variations of phonological words. According to the morphological category, a set of tables reflecting phonemic context is looked up to generate pronunciation variants. Our experiments show that the proposed model produces mostly correct pronunciation variants of phonological words. Then we estimated how useful the pronunciation lexicon and training phonetic transcription using this proposed systems.
Biologically Inspired Design

As an interdisciplinary field lying at the intersection of biology and robotics, bio-inspired robotics has seen considerable interest from a research, education and ultimately application perspective. The scope of this interest has ranged from creating and operating walking, crawling, and flying robots based on biological counterparts; to evaluating biological algorithms for potential engineering applications; deconstructing the functioning of living organisms from the macro to the micro levels; and ultimately to constructing the next generation of bio-inspired robots from the bottom-up. Significant synergies are forthcoming from such an approach but numerous limitations still exist. We begin with the discussion of general principles behind taking inspiration from a biological source and converting it into implementable engineering concepts using case-studies of bird-inspired robots, snake-inspired robots, and mastication robots. These case studies describe how useful features of the biological creatures were selected and simplified so that they can be implemented using the existing technologies.
Motivational effects of digital media on students in physical education: a scoping review.

: Introduction & study purpose Current research indicates that digital media (DM) may positively impact students’ motivation in physical education (PE). However, the theoretical details behind such motivational effects remain to be established. This scoping review provides an overview of the research on the motivational effects of DM in PE and discusses the results in the context of self-determination theory as a suitable basis for PE. Methods This scoping review was conducted according to the PRISMA-ScR guidelines. Six databases (Eric, Psyndex, Pubmed, PsycInfo, SportDISCUS, Web of Science) were searched using the following inclusion criteria: (1) peer-reviewed publication, (2) empirical study, (3) English, (4) published in the last 15 years
J I T A A

The poultry industry plays an important role in the food economics and the food industry in both their layer and broiler chicken. Poultry diseases on the other way impact in the poultry industry especially diseases from virus sources since it does not have a specific treatment except for vaccines with routine prevention and control. This study was designed for the assessment of histopathological chang-es, molecular diagnosis and phylogenetic analyses of the Infectious Bronchitis virus circulating in broilers in Basrah province in the south of Iraq. The tissue samples were (trachea, lung and kidney). One hundred samples were studied with clinical signs and lesions related to IB. The histopathological changes caused by the virus were studied with hematoxylin and eosin stain, and the causative agent Infectious Bronchitis Virus (IBV) was identified using a polymerase chain reaction combined with explaining the phylogenetic analysis. The results of molecular diagnosis and identification showed the twelve Iraqi field strains of IBV were closely related and shared a high identity of 98.44 %- 99.69 % of each other. Variant 2(IS/1494 like) was a predominant genotype of IBV in the present study, and has high similarity with the variant 2(IS/1494) like a strain of IBV circulating in Iran and in the Kurdistan region north of Iraq. The histopathological findings were desquamation of the cilia of the mucosa layer of the trachea, infiltration of inflammatory cells, hyperplasia of goblet cells with thickening of mucosal layer in some areas infiltration of inflammatory exudate within capillaries and Para bronchus, where the microscopic results in kidney were showed interstitial hemorrhage, necrosis of the renal tubular epithelium, infiltration of inflammatory cell. The present results provided useful information on target organs damaged by IBV and the genetic epidemiology of such virus strains circulating in Basrah gov-ernorate Iraq during this year that would be helpful to develop a local vaccine.
Enterprise performance, privatization and the role of ownership in Austria

Abstract In both economically developed and developing countries, privatisation, budget austerity measures and market liberalisations have become key aspects of structural reform programs in the last three decades. These three recommended policies were parts of strong revival of classical and new-classical school of thought since the middle of 70s. Such programs aim to achieve higher microeconomic efficiency and foster economic growth, whilst also aspiring to reduce public sector borrowing requirements through the elimination of unnecessary subsidies. For firms to achieve superior performance a change in ownership from public (state ownership) to private has been recommended as a vital condition. To assess the ownership role, the economic performances of private, public and mixed enterprises in Austria is compared through the use of factor analysis method. The extracted factors, using data of two years, 1998 and 2000, do not pick ownership as a key performance factor.