Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Reliability of the Complex System Having Two Standby Units: A Transdisciplinary Approach

The main objective of this study is to thoroughly investigate the reliability of a complex system that consists of four components and two standby units. The complicated system is modeled using the Markov technique. The study formulates differential equations and solves them using a matrix method with the assistance of a computer program. Statistical methods, such as correlation and regression, are applied using SPSS software to understand the relationship between time and the reliability of this complex system. A statistical model is utilized to determine the system's reliability, identify potentially faulty components, and prioritize the protection of the most critical elements. The presentation of significant findings involves conducting a comprehensive analysis using tables and graphs created with Python programming language. The outcomes of this study aim to assist in identifying crucial units in various process industries, thereby facilitating the establishment of effective management policies and strategies for the optimal operation of complex and large-scale systems in the industry. This paper incorporates numerous disciplines, viewpoints, and methodologies to examine and enhance reliability in process industries. Reliability can indeed benefit from a matrix approach, which can be considered transdisciplinary. This study serves as both an introductory guide to reliability analysis for novice researchers and a survey highlighting the latest advancements in the field for experienced researchers.
Recent Developments and Trends in Volcano Gravimetry

The aim of this chapter is to take a look at some developments and new trends in volcano gravimetry. First, we will review the objectives of the research work within this subfield of geophysics, discuss the data and methods it uses, and outline the outputs it strives for. Then, we will turn our attention to three areas where innovative approaches possibly can forward this field of study. The first has to do with the coupling between vertical deformations of the topographic surface (elevation changes) and the observed gravity changes or, in other words, with the removal of the deformation-induced gravimetric signal from the observed gravity changes to obtain the net gravity changes caused by volcanic signals. The second and third areas regard the inversion of the observed gravity changes and deal with two recently or newly developed inversion approaches that both are characterized by the ability to produce a suite of diverse solutions that can be analyzed and discriminated based on additional independent constraints stemming from other earth science disciplines or from the cognition of the interpreter. With this in mind, the final goal is a better understanding of the mechanisms and processes of volcanic unrest or reawakening of a volcano and forecasting the threat of consequent activity and impacts.
Deep Knowledge Tracing Incorporating a Hypernetwork With Independent Student and Item Networks

Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student and the difficulty of each item such as IRT. Nevertheless, its interpretability is inadequate compared to that of IRT because the ability parameter depends on each item. Deep-IRT implicitly assumes that items with the same skills are equivalent, which does not hold when item difficulties for the same skills differ greatly. For identical skills, items that are not equivalent hinder the interpretation of a student's ability estimate. To overcome those difficulties, this study proposes a novel Deep-IRT that models a student response to an item using two independent networks: 1) a student network and 2) an item network. The proposed Deep-IRT method learns student parameters and item parameters independently to avoid impairing the predictive accuracy. Moreover, we propose a novel hypernetwork architecture for the proposed Deep-IRT to balance both the current and the past data in the latent variable storing student's knowledge states. Results of experiments with six benchmark datasets demonstrate that the proposed method improves the prediction accuracy by about 2.0%, on average. In addition, experiments for the simulation dataset demonstrated that the proposed method provides a stronger correlation with true parameters than the earlier Deep-IRT method does at the $p< 0.5$ significance level.
New Prospects for Organizational Democracy?: How the Joint Pursuit of Social and Financial Goals Challenges Traditional Organizational Designs

Some interesting exceptions notwithstanding, the traditional logic of economic efficiency has long favored hierarchical forms of organization and disfavored democracy in business. What does the balance of arguments look like, however, when values besides efficient revenue production are brought into the picture? The question is not hypothetical: In recent years, an ever increasing number of corporations have developed and adopted socially responsible behaviors, thereby hybridizing aspects of corporate businesses and social organizations. We argue that the joint pursuit of financial and social objectives warrants significant rethinking of organizational democracy’s merits compared both to hierarchy and to non-democratic alternatives to hierarchy. In making this argument, we draw on an extensive literature review to document the relative lack of substantive discussion of organizational democracy since 1960. And we draw lessons from political theory, suggesting that the success of political democracy in integrating diverse values offers some grounds for asserting parallel virtues in the business case. Battilana, Fuerstein, Lee, “New Prospects for Organizational Democracy? ”
Structural Evolution of Primate Glutamate Dehydrogenase 2 as Revealed by In Silico Predictions and Experimentally Determined Structures

Glutamate dehydrogenase (GDH) interconverts glutamate to a-ketoglutarate and ammonia, interconnecting amino acid and carbohydrate metabolism. In humans, two functional GDH genes, GLUD1 and GLUD2, encode for hGDH1 and hGDH2, respectively. GLUD2 evolved from retrotransposition of the GLUD1 gene in the common ancestor of modern apes. These two isoenzymes are involved in the pathophysiology of human metabolic, neoplastic, and neurodegenerative disorders. The 3D structures of hGDH1 and hGDH2 have been experimentally determined; however, no information is available about the path of GDH2 structure changes during primate evolution. Here, we compare the structures predicted by the AlphaFold Colab method for the GDH2 enzyme of modern apes and their extinct primate ancestors. Also, we analyze the individual effect of amino acid substitutions emerging during primate evolution. Our most important finding is that the predicted structure of GDH2 in the common ancestor of apes was the steppingstone for the structural evolution of primate GDH2s. Two changes with a strong functional impact occurring at the first evolutionary step, Arg443Ser and Gly456Ala, had a destabilizing and stabilizing effect, respectively, making this step the most important one. Subsequently, GDH2 underwent additional modifications that fine-tuned its enzymatic properties to adapt to the functional needs of modern-day primate tissues.
VAR Analysis and the Great Moderation∗

Most analyses of the U.S. Great Moderation have been based on VAR methods, and have consistently pointed toward good luck as the main explanation for the greater macroeconomic stability of recent years. Using data generated by a New-Keynesian model in which the only source of change is the move from passive to active monetary policy, we show that VARs may misinterpret good policy for good luck. In particular, we detect significant breaks in estimated VAR innovation variances, although in the data generating process the volatilities of the structural shocks are constant across policy regimes. Counterfactual simulations, structural and reduced-form, point toward the incorrect conclusion of good luck. Our results cast doubts on the notion that existing VAR evidence is inconsistent with the good policy explanation of the Great Moderation.
Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed

The ability to monitor blood pressure unobtrusively and continuously, even during sleep, may promote the prevention of cardiovascular diseases, enable the early detection of cardiovascular risk, and facilitate the timely administration of treatment. Publicly available data from forty participants containing synchronously recorded signals from four force sensors (load cells located under each leg of a bed) and continuous blood pressure waveforms were leveraged in this research. The focus of this study was on using a deep neural network with load-cell data as input composed of three recurrent layers to reconstruct blood pressure (BP) waveforms. Systolic (SBP) and diastolic (DBP) blood pressure values were estimated from the reconstructed BP waveform. The dataset was partitioned into training, validation, and testing sets, such that the data from a given participant were only used in a single set. The BP waveform reconstruction performance resulted in an R2 of 0.61 and a mean absolute error < 0.1 mmHg. The estimation of the mean SBP and DBP values was characterized by Bland–Altman-derived limits of agreement in intervals of [−11.99 to 15.52 mmHg] and [−7.95 to +3.46 mmHg], respectively. These results may enable the detection of abnormally large or small variations in blood pressure, which indicate cardiovascular health degradation. The apparent contrast between the small reconstruction error and the limit-of-agreement width owes to the fact that reconstruction errors manifest more prominently at the maxima and minima, which are relevant for SBP and DBP estimation. While the focus here was on SBD and DBP estimation, reconstructing the entire BP waveform enables the calculation of additional hemodynamic parameters.
Panoptic Segmentation With Partial Annotations for Agricultural Robots

A detailed analysis of agricultural fields is key toward reducing the use of agrochemicals to achieve a more sustainable crop production. To this end, agricultural robots equipped with vision-based systems offer the potential to detect individual plants in the field automatically. This capability enables targeted management actions in the field, effectively reducing the amount of agrochemicals. A primary target of such vision systems is to perform a panoptic segmentation, combining the task of semantic and instance segmentation. Recent methods use neural networks for this task, which typically have to be trained on densely annotated images containing the required ground truth information for each pixel. Gathering these dense annotations is generally daunting and requires domain experts' knowledge in the agricultural domain. In this letter, we propose a method to effectively reduce the annotation bottleneck and yet achieve high performance using partial annotations. These partial annotations contain ground truth information only for a subset of pixels per image and are thus much faster to obtain than dense annotations. We propose a novel set of losses that exploit measures from vector fields used in physics, i.e., divergence and curl, to effectively supervise predictions without ground truth annotations. The experimental evaluation shows that our approach outperforms several state-of-the-art methods targeting to reduce the amount of annotations.
Carnosic Acid Shows Higher Neuroprotective Efficiency than Edaravone or Ebselen in In Vitro Models of Neuronal Cell Damage

This study compared the neuroprotective efficacy of three antioxidants—the plant-derived carnosic acid (CA), and two synthetic free radical scavengers: edaravone (ED) and ebselen (EB)—in in vitro models of neuronal cell damage. Results showed that CA protected mouse primary neuronal cell cultures against hydrogen peroxide-induced damage more efficiently than ED or EB. The neuroprotective effects of CA were associated with attenuation of reactive oxygen species level and increased mitochondrial membrane potential but not with a reduction in caspase-3 activity. None of the tested substances was protective against glutamate or oxygen-glucose deprivation-evoked neuronal cell damage, and EB even increased the detrimental effects of these insults. Further experiments using the human neuroblastoma SH-SY5Y cells showed that CA but not ED or EB attenuated the cell damage induced by hydrogen peroxide and that the composition of culture medium is the critical factor in evaluating neuroprotective effects in this model. Our data indicate that the neuroprotective potential of CA, ED, and EB may be revealed in vitro only under specific conditions, with their rather narrow micromolar concentrations, relevant cellular model, type of toxic agent, and exposure time. Nevertheless, of the three compounds tested, CA displayed the most consistent neuroprotective effects.
Capuchin Search Algorithm With Deep Learning-Based Data Edge Verification for Blockchain-Assisted IoT Environment

Internet of Things (IoT) devices generate enormous quantities of data, and ensuring the authenticity and integrity of this data is essential. Blockchain (BC) serves as a transparent and secure ledger for recording each data transaction, which prevents unauthorized modification and provides a trust layer for the IoT ecosystem. Data edge verification for BC-enabled IoT platforms is a fundamental aspect of ensuring data trustworthiness, integrity, and security in the system. In such an environment, IoT devices generate vast amounts of data, and BC technology can be used to create a decentralized and immutable ledger that records all the transactions and data exchanges Machine learning (ML) in the context of BC and the IoT offers a powerful toolkit for optimizing and securing these technologies. It enables the analysis of vast and complex data generated by IoT devices, allowing for predictive maintenance, anomaly detection, and pattern recognition. ML also enhances security by developing intrusion detection systems and supporting smart contracts in BC networks. This article introduces a new Capuchin Search Algorithm with a Deep Learning based Data Edge Verification model (CSADL-DEVM) for the Blockchain assisted IoT platform. The purpose of the CSADL-DEVM technique is to integrate the BC with DL and hyperparameter tuning concepts for data edge verification in the IoT environment. In addition, IoT devices comprise a considerable level of decentralized decision-making ability, which accomplishes a consensus on the performance of intra-block transactions. In addition, the CSADL-DEVM technique applies the Elman recurrent neural network (ERNN) model for the identification and classification of faults. Moreover, the hyperparameters related to the ERNN model can be adjusted by the use of CSA which in turn better the detection results. A comprehensive set of simulations has been conducted to exhibit the enhanced results of the CSADL-DEVM method. The obtained outcomes exhibit the superior outcome of the CSADL-DEVM algorithm.
Mass Spectrometric ITEM-ONE and ITEM-TWO Analyses Confirm and Refine an Assembled Epitope of an Anti-Pertuzumab Affimer

Intact Transition Epitope Mapping—One-step Non-covalent force Exploitation (ITEM-ONE) analysis reveals an assembled epitope on the surface of Pertuzumab, which is recognized by the anti-Pertuzumab affimer 00557_709097. It encompasses amino acid residues NSGGSIYNQRFKGR, which are part of CDR2, as well as residues FTLSVDR, which are located on the variable region of Pertuzumab’s heavy chain and together form a surface area of 1381.46 Å2. Despite not being part of Pertuzumab’s CDR2, the partial sequence FTLSVDR marks a unique proteotypic Pertuzumab peptide. Binding between intact Pertuzumab and the anti-Pertuzumab affimer was further investigated using the Intact Transition Epitope Mapping—Thermodynamic Weak-force Order (ITEM-TWO) approach. Quantitative analysis of the complex dissociation reaction in the gas phase afforded a quasi-equilibrium constant (KD m0g#) of 3.07 × 10−12. The experimentally determined apparent enthalpy (ΔHm0g#) and apparent free energy (ΔGm0g#) of the complex dissociation reaction indicate that the opposite reaction—complex formation—is spontaneous at room temperature. Due to strong binding to Pertuzumab and because of recognizing Pertuzumab’s unique partial amino acid sequences, the anti-Pertuzumab affimer 00557_709097 is considered excellently suitable for implementation in Pertuzumab quantitation assays as well as for the accurate therapeutic drug monitoring of Pertuzumab in biological fluids.
The role of impaired de novo Coenzyme A biosynthesis in pantothenate kinase-associated neurodegeneration

Pantothenate kinase–associated neurodegeneration (PKAN), a progressive neurodegenerative disorder, is associated with impairment of pantothenate kinase function. Pantothenate kinase is the first enzyme required for de novo synthesis of CoA, an essential metabolic cofactor. The pathophysiology of PKAN is not understood, and there is no cure to halt or reverse the symptoms of this devastating disease. Recently, we and others presented a PKAN Drosophila model, and we demonstrated that impaired function of pantothenate kinase induces a neurodegenerative phenotype and a reduced lifespan. We have explored this Drosophila model further and have demonstrated that impairment of pantothenate kinase is associated with decreased levels of CoA, mitochondrial dysfunction, and increased protein oxidation. Furthermore, we searched for compounds that can rescue pertinent phenotypes of the Drosophila PKAN model and identified pantethine. Pantethine feeding restores CoA levels, improves mitochondrial function, rescues brain degeneration, enhances locomotor abilities, and increases lifespan. We show evidence for the presence of a de novo CoA bio-synthesis pathway in which pantethine is used as a precursor compound. Importantly, this pathway is effective in the presence of disrupted pantothenate kinase function. Our data suggest that pantethine may serve as a starting point to develop a possible treatment for PKAN.
Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living: A 3D Vision Approach

Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and Long Short-Term Memory Autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system’s performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection.
Advanced Integration of Glutathione-Functionalized Optical Fiber SPR Sensor for Ultra-Sensitive Detection of Lead Ions

It is crucial to detect Pb2+ accurately and rapidly. This work proposes an ultra-sensitive optical fiber surface plasmon resonance (SPR) sensor functionalized with glutathione (GSH) for label-free detection of the ultra-low Pb2+ concentration, in which the refractive index (RI) sensitivity of the multimode-singlemode-multimode (MSM) hetero-core fiber is largely enhanced by the gold nanoparticles (AuNPs)/Au film coupling SPR effect. The GSH is modified on the fiber as the sensing probe to capture and identify Pb2+ specifically. Its working principle is that the Pb2+ chemically reacts with deprotonated carboxyl groups in GSH through ligand bonding, resulting in the formation of stable and specific chelates, inducing the variation of the local RI on the sensor surface, which in turn leads to the SPR wavelength shift in the transmission spectrum. Attributing to the AuNPs, both the Au substrates can be fully functionalized with the GSH molecules as the probes, which largely increases the number of active sites for Pb2+ trapping. Combined with the SPR effect, the sensor achieves a sensitivity of 2.32 × 1011 nm/M and a limit of detection (LOD) of 0.43 pM. It also demonstrates exceptional specificity, stability, and reproducibility, making it suitable for various applications in water pollution, biomedicine, and food safety.
Knowledge-based Visual Question Answering about Named Entities

This thesis is positioned at the intersection of several research fields, Natural Language Processing, Information Retrieval (IR) and Computer Vision, which have unified around representation learning and pre-training methods. We have defined and studied a new multimodal task: Knowledge-based Visual Question Answering about Named Entities (KVQAE). We were particularly interested in cross-modal interactions and different ways of representing named entities. We also focused on data used to train and, more importantly, evaluate Question Answering systems through different metrics. We annotated a dataset for this purpose, the first in KVQAE comprising various types of entities. We also defined an experimental framework for dealing with KVQAE in two stages through an unstructured knowledge base and identified IR as the main bottleneck of KVQAE, especially for questions about non-person entities. To improve the IR stage, we studied different multimodal fusion methods, which are pre-trained through an original task: the Multimodal Inverse Cloze Task. We found that these models leveraged a cross-modal interaction that we had not originally considered, and which may address the heterogeneity of visual representations of named entities. These results were strengthened by a study of the CLIP model, which allows this cross-modal interaction to be modeled directly. Awarded by : Université Paris-Saclay, Orsay, France on 8 November 2023. Supervised by : Olivier Ferret and Camille Guinaudeau. Available at : https://www.theses. fr/s247993.