Miracle-dz/UniFAR-FTUs huggingface dataset:
Qualité microbiologique du lait cru produit dans la région de Tadla au Maroc

Summary In the present study, we aimed to analyse the microbiological quality of raw cow milk produced in four cooperatives in Tadla area in Morocco. For this, the cow milk samples were collected during three periods (mid-June to mid-August, mid-November to mid-January and mid March to June). The analysis samples showed that from ten miccrobiological groups investigated, many differences exist among eight ones only, i.e. standard plate count (SPC), total coliform (TC), feacal coliform (FC), lactic acid bacteria, spoilage microorganisms (proteolytic, lipolytic) and staphylococci. Concerning the two remaining groups, salmonella and clostridium, we have not been able to detect their presence. Taken together, the whole results obtained show that cooperatives belonging to irrigated zone are the heavily contaminated ones, whereas the less contaminated belong to the bour and mountain zones. This could be explained by breeding and milking procedures in that the non respect of good practices leads to the high contamination levels observed in some cooperatives. Finally our result evidenced the presence of a period effect concerning the bacterial levels in milk.
Mobile health interventions in multiple sclerosis: A systematic review

Background: Persons with multiple sclerosis (pwMS) might be particularly well suited to benefit from digital health applications because they are, on average, younger and less severely disabled than patients with many other chronic diseases. Many digital health applications for pwMS have been developed. Objectives: Analysis of the evidence of digital health applications to improve health outcomes from a patient perspective. Methods: A systematic review was performed on all randomized controlled trials (RCTs) that have studied mobile health interventions for pwMS, that is, which can be applied with a smartphone, tablet, or laptop to improve patient-reported outcomes. Results: Of the 1127 articles identified in the literature search, 13 RCTs fit the inclusion criteria. Two trials studied messaging systems, two depression interventions, one addressed MS fatigue, five cognition, and three mobility issues, of which two focused on spasticity management. One trial aimed to enhance physical activity. Most were pilot studies that cannot yield definitive conclusions regarding efficacy. One depression intervention and one fatigue intervention showed significant results across several outcomes. Conclusion: Several mobile self-guided digital health applications for pwMS have been tested in RCTs, and two interventions targeting depression and fatigue have demonstrated significant effects. Challenges remain regarding implementation into routine care.
Evaluation of Precise, Kinematic GPS Point Positioning

We present the results of precise static and kinematic point-positioning solutions obtained in post-processing, with a recursive Kalman filter and smoother estimator (i.e., a state space approach). The observations are dualfrequency GPS carrier phase and pseudo-range, treated as two distinct data types, each with its own measurement equations. The receiver data is single-differenced between satellites, to eliminate the receiver clock, and processed using precise satellite ephemerides and clock corrections available from the Analysis Centers of the International GPS Service (IGS), while estimating: receiver coordinates, ionosphere-free carrier phase Lc biases, and tropospheric refraction model errors. Phase windup, relativity, satellite and receiver antenna corrections are all applied, as well as receiver position corrections for earth tides, ocean loading, and pole tide, in accordance with the IERS Conventions 2003. These features are part of a general upgrade to longbaseline differential software created by the first author, which now can be used also in point-positioning, and in mixed modes. Part of the motivation has been to gain experience that could be useful in developing a real-time system. The point-positioning accuracy has been evaluated using data from different test data sets, including fixed receivers at known locations and a moving receiver mounted on a van. The receiver positions so obtained have been verified using as “truth”, in some tests, the known coordinates of the fixed sites, and, in the case of the van, a precise trajectory, differentially determined relative to a nearby receiver, with correctly fixed carrier phase ambiguities. The results indicate a precision at the level of a few centimeters, for static solutions, and below one decimeter, in kinematic mode.
Assessing the Policy of Non-Smoking Areas in Schools in Indonesia: A Mixed Methods Study

Background: Regulations in the form of Regional Regulations on Non-Smoking Areas are efforts to prevent smoking in schools. We will show qualitatively and quantitatively whether the policy can control smoke-free schools since 2015 in Muaro Jambi Regency. However, the implementation is still not optimal, even almost not implemented following the mandate of government regulation Number 109 of 2012 and regional regulation Number 5 of 2018. This study aims to evaluate the implementation of the smoke-free area policy in schools in Muaro Jambi Regency. Method: Research using mixed method study employing quantitative through distribution survey of smoke-free zones and qualitative by interviewing 31 schools of informants and observing 499 points of educational institutions. Selection of informants by considering the criteria of adequacy and suitability of data analysis with content analysis. Results: The cause of the non-implementation of the smoke-free area policy is the lack of socialization of local regulations. The regulation of non-smoking area bylaws has not been made, implementing rules with the decree of the law. Schools do not run areas without cigarettes because they do not fully understand the rules, namely that they are not allowed to smoke in all school environments and health facilities. Oversight of the no-smoking area policy has not been carried out due to the lack of policy support from policymakers and sources of funds for monitoring the no-smoking area. Conclusion: No smoking area policy is meaningless if there is neglect in its implementation so that it does not impact the compliance of smoking behavior following the No Smoking Area policy. The need for the commitment of school leaders in implementing and operational rules from the regional regulations in the implementation of smoke-free areas.
The IUPHAR/BPS Guide to PHARMACOLOGY in 2024

Abstract The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; https://www.guidetopharmacology.org) is an open-access, expert-curated, online database that provides succinct overviews and key references for pharmacological targets and their recommended experimental ligands. It includes over 3039 protein targets and 12 163 ligand molecules, including approved drugs, small molecules, peptides and antibodies. Here, we report recent developments to the resource and describe expansion in content over the six database releases made during the last two years. The database update section of this paper focuses on two areas relating to important global health challenges. The first, SARS-CoV-2 COVID-19, remains a major concern and we describe our efforts to expand the database to include a new family of coronavirus proteins. The second area is antimicrobial resistance, for which we have extended our coverage of antibacterials in partnership with AntibioticDB, a collaboration that has continued through support from GARDP. We discuss other areas of curation and also focus on our external links to resources such as PubChem that bring important synergies to the resources.
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy

In an era where scientific experiments can be very costly, multi-fidelity emulators provide a useful tool for cost-efficient predictive scientific computing. For scientific applications, the experimenter is often limited by a tight computational budget, and thus wishes to (i) maximize predictive power of the multi-fidelity emulator via a careful design of experiments, and (ii) ensure this model achieves a desired error tolerance with some notion of confidence. Existing design methods, however, do not jointly tackle objectives (i) and (ii). We propose a novel stacking design approach that addresses both goals. A multi-level reproducing kernel Hilbert space (RKHS) interpolator is first introduced to build the emulator, under which our stacking design provides a sequential approach for designing multi-fidelity runs such that a desired prediction error of $\epsilon>0$ is met under regularity assumptions. We then prove a novel cost complexity theorem that, under this multi-level interpolator, establishes a bound on the computation cost (for training data simulation) needed to achieve a prediction bound of $\epsilon$. This result provides novel insights on conditions under which the proposed multi-fidelity approach improves upon a conventional RKHS interpolator which relies on a single fidelity level. Finally, we demonstrate the effectiveness of stacking designs in a suite of simulation experiments and an application to finite element analysis.
Deep Transformed Gaussian Processes

Transformed Gaussian Processes (TGPs) are stochastic processes specified by transforming samples from the joint distribution from a prior process (typically a GP) using an invertible transformation; increasing the flexibility of the base process. Furthermore, they achieve competitive results compared with Deep Gaussian Processes (DGPs), which are another generalization constructed by a hierarchical concatenation of GPs. In this work, we propose a generalization of TGPs named Deep Transformed Gaussian Processes (DTGPs), which follows the trend of concatenating layers of stochastic processes. More precisely, we obtain a multi-layer model in which each layer is a TGP. This generalization implies an increment of flexibility with respect to both TGPs and DGPs. Exact inference in such a model is intractable. However, we show that one can use variational inference to approximate the required computations yielding a straightforward extension of the popular DSVI inference algorithm Salimbeni et al (2017). The experiments conducted evaluate the proposed novel DTGPs in multiple regression datasets, achieving good scalability and performance.
A Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties

The discovery and design of new materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies. Numerous studies have produced correlations between catalytic properties, such as oxygen surface exchange or electrode area specific resistance (ASR), and properties of the catalyst material. However, correlations have historically been limited in scope (e.g., using only a few materials or at a single temperature) and it has been difficult to provide detailed assessments of their robustness. Here, we assess the ability of the O p-band center electronic structure descriptor, obtained from density functional theory (DFT) calculations, to correlate with oxygen surface exchange rates, diffusivities, and area specific resistances for a large database of perovskite oxide catalytic properties. By data mining the literature, we obtain 747 catalytic property value data points spanning 299 unique perovskite compositions from 313 studies. We assess linear correlations of each property with the O p-band center and find generally modest correlations that are qualitatively useful (prediction mean absolute errors of about 0.5 log units are typical), where the correlations are improved at higher temperatures (e.g., 800 {\deg}C vs. 500 {\deg}C) and significantly improve when considering fits to the subset of materials which have multiple independent measurements. These findings suggest that the spread of property data is significantly influenced by experimental uncertainty, and subsequent measurements of additional materials will likely improve the O p-band center correlations.
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese

In recent years, Visual Question Answering (VQA) has gained significant attention for its diverse applications, including intelligent car assistance, aiding visually impaired individuals, and document image information retrieval using natural language queries. VQA requires effective integration of information from questions and images to generate accurate answers. Neural models for VQA have made remarkable progress on large-scale datasets, with a primary focus on resource-rich languages like English. To address this, we introduce the ViCLEVR dataset, a pioneering collection for evaluating various visual reasoning capabilities in Vietnamese while mitigating biases. The dataset comprises over 26,000 images and 30,000 question-answer pairs (QAs), each question annotated to specify the type of reasoning involved. Leveraging this dataset, we conduct a comprehensive analysis of contemporary visual reasoning systems, offering valuable insights into their strengths and limitations. Furthermore, we present PhoVIT, a comprehensive multimodal fusion that identifies objects in images based on questions. The architecture effectively employs transformers to enable simultaneous reasoning over textual and visual data, merging both modalities at an early model stage. The experimental findings demonstrate that our proposed model achieves state-of-the-art performance across four evaluation metrics. The accompanying code and dataset have been made publicly accessible at \url{https://github.com/kvt0012/ViCLEVR}. This provision seeks to stimulate advancements within the research community, fostering the development of more multimodal fusion algorithms, specifically tailored to address the nuances of low-resource languages, exemplified by Vietnamese.
Downscaling climate and vegetation variability associated with global climate signals: A new statistical approach applied to the Colorado River Basin

The spatial and temporal distribution of point precipitation quantiles representing abnormal moisture conditions over the Colorado River Basin (CRB) is analyzed by means of the Standardized Precipitation Index (SPI), calculated in annual and seasonal aggregations. From a cell-by-cell analysis, the area covered by abnormally wet and dry conditions during the last century shows an inverse relationship with their frequency of occurrence, with frequent events (occurring 80% of the time) in which abnormal conditions cover less than 10% of the basin and infrequent events (occurring 5% of the time) in which abnormal conditions cover around 50% of the basin. During El Nino years, both extremely wet and dry conditions are likely to occur, whereas only extremely dry conditions are probable during La Nina years. Regions of homogeneous SPI realizations were delimited using principal components analysis (PCA) to highlight major variation modes distinguishable in the basin, and a frequency analysis was performed over the reconstructed values of SPI to identify their multiannual oscillation modes. Common multiannual oscillations between the SPI index, the Pacific Decadal Oscillation (PDO) and the Bivariate El Nino-Southern Oscillation (BEST) time series were explored using multichannel singular spectrum analysis (M-SSA). The coupled impact of PDO63 ENSO indicates the presence of a trend and two significant oscillations around five and 15 years on the SPI time series. The occurrence of extreme SPI values associated with ENSO and PDO was also evaluated as a common product of these indices that highlights moisture conditions affected by common enhancement phases of ENSO and PDO.
Enhancing the Performance of a Biomimetic Robotic Elbow-and-Forearm System Through Bionics-Inspired Optimization

This paper delineates the formulation and verification of an innovative robotic forearm and elbow design, mirroring the intricate biomechanics of human skeletal and ligament systems. Conventional robotic models often undervalue the substantial function of soft tissues, leading to a compromise between compactness, safety, stability, and range of motion. In contrast, this study proposes a holistic replication of biological joints, encompassing bones, cartilage, ligaments, and tendons, culminating in a biomimetic robot. The research underscores the compact and stable structure of the human forearm, attributable to a tri-bone framework and diverse soft tissues. The methodology involves exhaustive examinations of human anatomy, succeeded by a theoretical exploration of the contribution of soft tissues to the stability of the prototype. The evaluation results unveil remarkable parallels between the range of motion of the robotic joints and their human counterparts. The robotic elbow emulates 98.8% of the biological elbow's range of motion, with high torque capacities of 11.25 Nm (extension) and 24 Nm (flexion). Similarly, the robotic forearm achieves 58.6% of the human forearm's rotational range, generating substantial output torques of 14 Nm (pronation) and 7.8 Nm (supination). Moreover, the prototype exhibits significant load-bearing abilities, resisting a 5kg dumbbell load without substantial displacement. It demonstrates a payload capacity exceeding 4kg and rapid action capabilities, such as lifting a 2kg dumbbell at a speed of 0.74Hz and striking a ping-pong ball at an end-effector speed of 3.2 m/s. This research underscores that a detailed anatomical study can address existing robotic design obstacles, optimize performance and anthropomorphic resemblance, and reaffirm traditional anatomical principles.
Alternative fast quantum logic gates using nonadiabatic Landau-Zener-St\"{u}ckelberg-Majorana transitions

A conventional realization of quantum logic gates and control is based on resonant Rabi oscillations of the occupation probability of the system. This approach has certain limitations and complications, like counter-rotating terms. We study an alternative paradigm for implementing quantum logic gates based on Landau-Zener-St\"{u}ckelberg-Majorana (LZSM) interferometry with non-resonant driving and the alternation of adiabatic evolution and non-adiabatic transitions. Compared to Rabi oscillations, the main differences are a non-resonant driving frequency and a small number of periods in the external driving. We explore the dynamics of a multilevel quantum system under LZSM drives and optimize the parameters for increasing single- and two-qubit gates speed. We define the parameters of the external driving required for implementing some specific gates using the adiabatic-impulse model. The LZSM approach can be applied to a large variety of multi-level quantum systems and external driving, providing a method for implementing quantum logic gates on them.
Neutrino Oscillations and Mass Models

These notes provide a review of various neutrino mass model and their implications for particle physics and the Standard Model. We discuss how mass terms are incorporated into the Standard Model, including the Dirac mass term and the Majorana mechanism. We explore experimental evidence supporting the existence of non-zero neutrino mass and develop the formalism of neutrino oscillations. Additionally, we provide a brief overview of some famous radiative neutrino mass models. This notes draws heavily from the references given below and can serve as a valuable resource for students and new researchers interested in the field of neutrino physics.
A Control-Bounded Quadrature Leapfrog ADC

In this paper, the design flexibility of the control-bounded analog-to-digital converter principle is demonstrated. A band-pass analog-to-digital converter is considered as an application and case study. We show how a low-pass control-bounded analog-to-digital converter can be translated into a band-pass version where the guaranteed stability, converter bandwidth, and signal-to-noise ratio are preserved while the center frequency for conversion can be positioned freely. The proposed converter is validated with behavioral simulations on several filter orders, center frequencies, and oversampling ratios. Additionally, we consider an op-amp circuit realization where the effects of first-order op-amp non-idealities are shown. Finally, robustness against component variations is demonstrated by Monte Carlo simulations.
AutoCT: Automated CT registration, segmentation, and quantification

The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, registration, segmentation, and quantitative analysis of 3D CT scans. The engineered pipeline enables atlas-based CT segmentation and quantification leveraging diffeomorphic transformations through efficient forward and inverse mappings. The extracted localized features from the deformation field allow for downstream statistical learning that may facilitate medical diagnostics. On a lightweight and portable software platform, AutoCT provides a new toolkit for the CT imaging community to underpin the deployment of artificial intelligence-driven applications.
Unified Segment-to-Segment Framework for Simultaneous Sequence Generation

Simultaneous sequence generation is a pivotal task for real-time scenarios, such as streaming speech recognition, simultaneous machine translation and simultaneous speech translation, where the target sequence is generated while receiving the source sequence. The crux of achieving high-quality generation with low latency lies in identifying the optimal moments for generating, accomplished by learning a mapping between the source and target sequences. However, existing methods often rely on task-specific heuristics for different sequence types, limiting the model's capacity to adaptively learn the source-target mapping and hindering the exploration of multi-task learning for various simultaneous tasks. In this paper, we propose a unified segment-to-segment framework (Seg2Seg) for simultaneous sequence generation, which learns the mapping in an adaptive and unified manner. During the process of simultaneous generation, the model alternates between waiting for a source segment and generating a target segment, making the segment serve as the natural bridge between the source and target. To accomplish this, Seg2Seg introduces a latent segment as the pivot between source to target and explores all potential source-target mappings via the proposed expectation training, thereby learning the optimal moments for generating. Experiments on multiple simultaneous generation tasks demonstrate that Seg2Seg achieves state-of-the-art performance and exhibits better generality across various tasks.