Miracle-dz/UniFAR-FTUs huggingface dataset:
Discussion of the paper ”Rank-Normalization, Folding, and Localization: An Improved c R for Assessing Convergence of MCMC”

: In this note, we discuss an adaptation of the quantile transformation introduced in Vehtari et al. (2021) to the computation of some new ˆ R and analyse associated theoretical properties. We have highly appreciated this contribution to improve MCMC convergence diagnostics and would like to thank the authors for their insightful paper. In this note we discuss an adaptation of the quantile transformation introduced in Vehtari et al. (2021) to the computation of some new ˆ R and analyse associated theoretical properties. More speciﬁcally, we propose to compute a continuous version ˆ R ( x ) for any level x based on indicator variables I( θ ( n,m ) ≤ x ), rather that on the parameter values θ ( n,m ) themselves or their Gaussian version, see Equation (4.1) in Vehtari et al. (2021). This provides us with a function ˆ R ( x ) deﬁning a local convergence diagnostic for any x . The rank-normalization step is circumvented since working on Bernoulli random variables I( θ ( n,m ) ≤ x ) ensures the existence of all moments whatever the θ ( n,m ) distribution is. Assume that all elements θ ( m, 1) , . , θ ( m,N ) of a given chain m follow the same distribution F m (without independence assumption) which may vary with m . Under this stationarity assumption, mean and variance of Bernoulli random variables I( θ ( n,m ) ≤ x ) can be easily written as functions of F m , leading to an explicit expression for R 2 ( x ), the population version of ˆ R 2 ( x ):
5G Radio Link Failure Prediction using Quantum Machine Learning

The next generation of mobile communication networks, 5G, is expected to provide ultra-high-speed, low-latency, and high-reliability wireless communication services. However, radio link failures (RLFs) in 5G networks can degrade network performance and user experience. We present a novel approach for forecasting RLFs in 5G networks using quantum machine learning (QML) in this paper. We employ quantum long short-term memory (QLSTM) with different embedding techniques like Angle embedding, amplitude Embedding, and QAOA embedding in quantum circuits to predict RLFs. We have also merged Quantum circuits to the output of classical Long Short-Term Memory (LSTM) to study any improvement over the existing LSTM model. Our approach involves training a QLSTM model using quantum circuits with different embedding techniques to capture the underlying patterns of RLFs. We evaluate our proposed approach on a real-world dataset and the experimental results indicate that our QML approach outperforms classical machine learning techniques in terms of accuracy and loss.
The Power of Characterizing Pore-Fluid Distribution for Microscopic CO2 Injection Studies in Tight Sandstones

The microscopic structure of low-permeability tight reservoirs is complicated due to diagenetic processes that impact the pore-fluid distribution and hydraulic properties of tight rocks. As part of an ongoing study of carbon dioxide-enhanced oil and gas recovery (CO2-EOR/EGR) and CO2 sequestration, this research article adopts an integrated approach to investigate the contribution of the micropore system in pore-fluid distribution in tight sandstones. A new dimensionless number, termed the microscopic confinement index (MCI), was established to select the right candidate for microscopic CO2 injection in tight formations. Storativity and containment indices were essential for MCI estimation. A set of experiments, including routine core analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM), mercury injection capillary pressure (MICP), and nuclear magnetic resonance (NMR), was performed on three tight sandstone rock samples, namely Bandera, Kentucky, and Scioto. Results indicate that the presence of fibrous illite acting as pore bridging in Bandera and Kentucky sandstone samples reduced the micropore-throat proportion (MTMR), leading to a significant drop in the micropore system confinement in Kentucky and Bandera sandstone samples of 1.03 and 0.56, respectively. Pore-filling kaolinite booklets reduced the micropore storativity index (MSI) to 0.48 in Kentucky and 0.38 in Bandera. On the other hand, the absence of fibrous illite and kaolinite booklets in Scioto sandstone led to the highest micropore system capability of 1.44 MTMR and 0.5 MSI to store and confine fluids. Therefore, Scioto sandstone is the best candidate for CO2 injection and storage among the tested samples of 0.72 MCI.
Long-term performance assessment of biomedical image segmentation at the point of care

Deep Learning has a large impact on medical image analysis and lately has been adopted for clinical use at the point of care. However, there is only a small number of reports of long-term studies that show the performance of deep neural networks (DNNs) in such a clinical environment. In this study, we measured the long-term performance of a clinically optimized DNN for laryngeal glottis segmentation. We have collected the video footage for two years from an AI-powered laryngeal high-speed videoendoscopy imaging system and found that the footage image quality is stable across time. Next, we determined the DNN segmentation performance on lossy and lossless compressed data revealing that 9% of recordings contain segmentation artefacts. We found that lossy and lossless compression are on par for glottis segmentation, however, lossless compression provides significantly superior image quality. Lastly, we employed continual learning strategies to continuously incorporate new data to the DNN to remove aforementioned segmentation artefacts. With modest manual intervention, we were able to largely alleviate these segmentation artefacts by up to 81%. We believe that our suggested deep learning-enhanced laryngeal imaging platform consistently provides clinically sound results, and together with our proposed continual learning scheme will have a long-lasting impact in the future of laryngeal imaging.
Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression

The paper proposes a deep hybrid forest regression-based modeling method for measuring the compressive strength (CS) of concrete. Then, the reduced feature vector is used as input to train multiple subforest models (SFM), the predicted values are selected from multiple subforests via the KNN (K-nearest neighbor) method to combine them to obtain the layer regression vector (LRV), and it is combined with the reduced feature vector to obtain the improved LRV, then the output of this layer is taken; second, the regression vector (RV) of the input layer enhancement layer is used as input to obtain the output of the second layer FM, and the steps are repeated until the output of the input layer FM is complete. Finally, the output of the FM of the first layer is obtained. Several SFMs are trained and the result is obtained. The final prognosis is obtained by arithmetically averaging the forecast results of the SFMs of this layer.
Environmental and Health Consequences of E-Waste Dumping and Recycling Carried out by Selected Countries in Asia and Latin America

The volume of e-waste generated worldwide is surging, and it is set to escalate further due to continuing technological innovation and the early obsolescence of most electrical and electronic equipment (EEE). Even though there are many studies on e-waste management, the environmental and health consequences of e-waste regarding direct exposure during informal recycling and indirect exposure through environmental contamination are poorly studied. This study analyses the environmental and health consequences of e-waste dumping and informal recycling practices in selected countries such as Brazil, China, India, Mexico, and Pakistan. Several databases, such as Science Direct, ProQuest, Web of Science, and Emerald, were used to analyse studies from 2005 to 2022. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 179 journal articles were evaluated. This study found that the majority of e-waste is poorly managed in terms of ecological safety and soundness. This study also found that illegal dumping, acid leaching, and open burning, which are practices that harm the environment and the well-being of e-waste workers, are still being performed by the selected countries. This study provides several practical suggestions for addressing the environmental and health consequences of e-waste dumping and recycling.
Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L2‐Gain Approach

In this study, a new backstepping control scheme is proposed to deal with the high accuracy flexible joint servo system's position control. Based on the introduction of non‐consecutive friction, the cascade dynamics equations of flexible joint are established. The macroscopic controller is designed using a backstepping design technique to suppress the flexibility and external disturbance based on the L2 property. To identify the non‐consecutive function, the wavelet neural networks (WNN) are utilized in the microscopic controller to compensate for nonlinear friction and uncertainties. The combined strategy of macro and micro controller can overcome the derivation explosion problem and avoid the joint acceleration measurement and upper bound forecast. Finally, stability analysis and mathematical simulations are presented to verify the effectiveness of this new controller.
NLPashto: NLP Toolkit for Low-resource Pashto Language

—In recent years, natural language processing (NLP) has transformed numerous domains, becoming a vital area of research. However, the focus of NLP studies has predominantly centered on major languages like English, inadvertently neglecting low-resource languages like Pashto. Pashto, spoken by a population of over 50 million worldwide, remains largely unexplored in NLP research, lacking off-the-shelf resources and tools even for fundamental text-processing tasks. To bridge this gap, this study presents NLPashto, an open-source and publicly accessible NLP toolkit specifically designed for Pashto. The initial version of NLPashto introduces four state-of-the-art models for Spelling Correction, Word Segmentation, Part-of-Speech (POS) Tagging, and Offensive Language Detection. The toolkit also includes essential NLP resources like pre-trained static word embeddings, Word2Vec, fastText, and GloVe. Furthermore, we have pre-trained a monolingual language model for Pashto from scratch, using the Bidirectional Encoder Representations from Transformers (BERT) architecture. For the training and evaluation of all the models, we have developed several benchmark datasets and also included them in the toolkit. Experimental results demonstrate that the models exhibit satisfactory performance in their respective tasks. This study can be a significant milestone and will hopefully support and speed-up future research in the field of Pashto NLP.
Harmful Algae Blooms: A Proliﬁc Issue in Urban Stormwater Ponds

: Nutrient-driven cyanobacteria blooms are an increasingly common issue in freshwater environments, particularly in anthropogenically altered landscapes. As stormwater runoff is one of the largest sources of nutrients for freshwater bodies, stormwater retention ponds in urban and sub-urban areas are likely environments for harmful cyanobacteria blooms and were thus targeted for an in-depth investigation assessing taxonomic composition, bloom morphological composition, toxicity, and impact of nutrients and other environmental drivers. Eighty-seven algal blooms were sampled from 2019 to 2022 in the greater Wilmington, North Carolina, area. Physicochemical parameters were recorded, and blooms were classiﬁed by type (deﬁned as surface mat, surface scum, water column distribution, or benthic mat) and dominant taxa. Blooms of potentially toxic cyanobacteria genera in the water column of stormwater retention ponds were most prevalent. Dissolved inorganic phosphorus was signiﬁcantly related to chlorophyll-α , Microcystis bloom formation, and the production of microcystin. Seventeen potentially toxic cyanobacteria genera were identiﬁed in retention ponds, some of whose blooms demonstrated detectable microcystin. Monoclonal cultures isolated from some blooms were found to produce anabaenopeptin and saxitoxin. The results demonstrate a higher incidence of potentially toxic cyanobacteria over other bloom-forming taxa (chlorophytes, euglenoids, chrysophytes, dinoﬂagellates, and diatoms) in the 39 water bodies sampled. The frequency of blooms occurring in stormwater ponds and the diversity of potentially toxic cyanobacteria identiﬁed suggest such harmful blooms are likely widespread in similar freshwater environments across multiple urbanizing areas. The blooms sampled in this study were all within residential, commercial, or recreational areas easily accessible to people, presenting serious hazards to both environmental and public health.
Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

—In image classification, multi-granularity refers to the ability to classify images with different levels of detail or resolution. This is a challenging task because the distinction between subcategories is often minimal, needing a high level of visual detail and precise representation of the features specific to each class. In dental informatics, and more specifically tooth classification poses many challenges due to overlapping teeth, varying sizes, shapes, and illumination levels. To address these issues, this paper considers various data granularity levels since a deeper level of details can be acquired with increased granularity. Three tooth granularity levels are considered in this study named Two Classes Granularity Level (2CGL), Four Classes Granularity Level (4CGL), and Seven Classes Granularity Level (7CGL) to analyze the performance of teeth detection and classification at multi-granularity levels in Granular Intra-Oral Image (GIOI) dataset. Subsequently, a Faster Region-Convolutional Neural Network (FR-CNN) based on three ResNet models is proposed for teeth detection and classification at multi-granularity levels from the GIOI dataset. The FR-CNN-ResNet models exploit the effect of the tooth classification granularity technique to empower the models with accurate features that lead to improved model performance. The results indicate a remarkable detection effect in investigating the granularity effect on the FR-CNN-ResNet model's performance. The FR-CNN-ResNet-50 model achieved 0.94 mAP for 2CGL, 0.74 mAP for 4CGL, and 0.69 mAP for 7CGL, respectively. The findings demonstrated that multi-granularity enables flexible and nuanced analysis of visual data, which can be useful in a wide range of applications.
Shape Control of a Dual-Segment Soft Robot using Depth Vision

— Pneumatic soft robots outperform rigid robots in complex environments due to the high flexibility of their redundant configurations, and their shape control is considered a prerequisite for applications in unstructured environments. In this paper, we propose a depth vision-based shape control method for a two-segment soft robot, which uses a binocular camera to achieve 3D shape control of the soft robot. A closed-loop control algorithm based on depth vision is designed for shape compensation when subject to its own non-linear responsiveness and coupling by solving for the shape feature parameters used to describe the robot and analytically modeling the motion of curved feature points. Experimental results show that the position and angle errors are less than 2 mm and 1° respectively, the curvature error is less than 0.0001mm-1, and the algorithm has convergence performance for L-type and S-type shape reference 3D shapes. This work provides a general method for being able to adjust the shape of a soft robot without on-board sensors.
Eight scenarios for anticipating sea-level rise through 2100

An important physical response to anthropogenic climate change is the long term rise in global mean sea-level (GMSL). Given this context the French National Alliance for Environmental Research, which brings together the main French research institutions concerned with environment, tasked its foresight group with shedding light on sea-level rise by 2100, estimating the possible impacts and consequences for the environment and societies, and recommending future research priorities. We employ a foresight approach based on the scenario method with 23 driving variables for the coastal system and four physical contexts for the sea-level rise (moderate, serious, severe and extreme) leading to the development of eight contrasted and unconnected scenarios starting in 2020 and extending to 2100. Each scenario is defined by a storyline. Storylines are determined mainly by coastal adaptation efforts and the political will to mitigate climate change. These are structured in three families: (1) Priority given to adaptation (four scenarios: Climate control, Frugality and anticipation, Resilient cities, Early adaptation and late mitigation), (2) Denial of the phenomenon (three scenarios: Passivity, Gradual coastal abandonment, From denial to reaction) and (3) Fragmented world (one scenario: Persistent fragmentation). Scenario trajectories without vigorous and widespread mitigation policies before 2040 show that coastal States will have to deal with ‘severe’ or ‘extreme’ sea level rise by the end of the century. ’Severe’ or ‘Extreme’ sea level rise outcomes are envisaged in five of the eight scenarios in the range of possible futures. Two scenarios lead to ‘serious’ sea level rise and one ideal scenario leads to a ‘moderate’ state. Adaptation efforts will be less cumbersome and costly when mitigation efforts have been early, widespread and sustained. The optimal scenario considers raising awareness of all actors in societies, leading to the introduction of fast and radical mitigation and adaptation efforts. All other scenarios mean that future generations may have to face major or even irreversible upheavals, at increasing costs.
Improved one‐month lead‐time forecasting of the SPI over Russia with pressure covariates based on the SL–AV model

The standardized precipitation index (SPI) is an important yet easy‐to‐calculate means to describing wet or dry conditions in very different climates. In this work, we develop a new scheme for improved one‐month lead‐time forecasts of this index over Russia. As a basic seasonal forecasting model, we utilize the semi‐implicit semi‐Lagrangian vorticity‐divergence (SL–AV) model of the Hydrometeorological Centre of Russia and the Institute of Numerical Mathematics of the Russian Academy of Sciences. Based on hindcast simulations of this model, we demonstrate its relatively poor skills in obtaining direct one‐month lead‐time SPI forecasts in the region of interest. In order to improve the accuracy of these forecasts, we use mean sea‐level pressure and 500 hPa geopotential height fields from model output of the same SL–AV hindcasts to identify informative predictors for the local SPI values, based on the observation that the cross‐correlation structure between the three different fields reveals relevant interdependencies between precipitation, mean sea‐level pressure and 500 hPa geopotential height in different regions. Using this information in terms of regression models for obtaining both, deterministic and probabilistic forecasts provides a significant improvement of the SPI forecast skills, pointing to the potential for implementing the proposed scheme in operational one‐month lead‐time precipitation forecasts.
A Rare Case of Massive Left Atrial Myxoma Presenting as Syncope

We report a rare case of a large left atrial myxoma that manifested as syncope in a patient who presented to the hospital following a syncopal episode. Our patient had a history of hypertension and anemia with reported two months of dyspnea on exertion. He was found to have a large left atrial myxoma. Atrial myxomas are the most common benign primary cardiac tumors. Patients may be asymptomatic or experience shortness of breath, palpitations, syncope, or sudden death. Cases of syncope caused by left atrial myxoma have been rarely documented. Our case report adds to the growing literature documenting this phenomenon. Larger observational studies are needed to properly define the incidence of left atrial myxoma causing syncope.
VAMP2 Gene-Related Neurodevelopmental Disorder: A Differential Diagnosis for Rett/Angelman-Type Spectrum of Disorders

Introduction: VAMP2 is an instrumental protein in neuronal synaptic transmission in the brain, facilitating neurotransmitter release. It is encoded by the VAMP2 gene, and pathogenic variants in this gene cause neurodevelopmental features including early onset axial hypotonia, intellectual disability, and features of autism spectrum disorder. To date, only three types of allelic variants (loss of function, in-frame deletions, and missense variants) in the VAMP2 gene have been previously reported in 11 patients with learning difficulties. Here, we describe a patient in whom a novel de novo pathogenic variant in the VAMP2 gene was identified. Case Presentation: A 15-month-old girl presented with early onset hypotonia, global developmental delay, learning difficulties, microcephaly, nystagmus, strabismus, and stereotypies. Later, she developed a sleep disorder, challenging behaviour with self-injury, and scoliosis. Gene agnostic analysis of whole genome sequencing data identified a novel de novo heterozygous missense variant c.197G>C (p.Arg66Pro) in the VAMP2 gene SNARE motif region. Discussion: This is the fourth report describing VAMP2 gene-related neurodevelopmental disorder. This report adds to the genotype-phenotype correlation and highlights this condition as an important differential diagnosis of Rett/Angelman-type spectrum of disorders. Patients presenting with features of either Rett syndrome or Angelman syndrome, in whom genetic testing is not suggestive, should be evaluated for variants in the VAMP2 gene, given the significant overlap in clinical presentation of these disorders.
Ranqueamento de Licitações Públicas a partir de Alertas de Fraude

A detecção de fraudes é uma tarefa complexa nos diversos cenários. Este trabalho representa uma contribuição para essa tarefa ao propor uma abordagem para ranquear licitações públicas suspeitas de possuírem algum tipo de fraude. Para isso, foram elaboradas 19 trilhas de auditagem, que foram modeladas como uma rede social, e uma estratégia para considerar os alertas levantados por elas no ranqueamento. Os resultados revelam que a abordagem de ranqueamento foi capaz de identificar licitações suspeitas de fraude.