Miracle-dz/UniFAR-FTUs huggingface dataset:
The association between hypertensive angiopathy and cerebral amyloid angiopathy in primary intracerebral hemorrhage

Objective To determine the association between the burden of cerebral small vessel disease (CSVD) due to hypertensive angiopathy (HA) and cerebral amyloid angiopathy (CAA) on MRI in patients with primary intracerebral hemorrhage (ICH). Methods Patients with primary ICH admitted to our center from March 2012 to November 2021 were consecutively enrolled. We used multivariate binary and ordinal regression analyses to assess the association between HA-CSVD burden and CAA-CSVD burden. Lobar cerebral microbleeds (CMBs) were categorized into three level of severity: 0–1, 2–4, and ≥ 5 lobar CMBs. A high CAA-CSVD score was defined as a CAA-CSVD score of ≥3. Results Overall, 222 participants (mean age 59.88 ± 13.56) were included into analysis. Age and ICH etiology differed among different lobar CMB severity and between the presence and absence of high CAA-CSVD score (all p < 0.05). Positive associations between HA-related markers and both lobar CMB severity and high CAA-CSVD score (p < 0.05 for the presence of lacune, deep CMBs ≥5, the presence of WMH, and HA-CSVD score) were observed in univariate analysis. These associations remained significant after adjusting for age, sex, ICH etiology, and potential vascular risk factors. The distribution of CAA-CSVD score was significantly different between patients with and without CMBs ≥5 (adjusted OR 2.351, 95% CI 1.242–4.455, p = 0.009) after correcting for age, sex, ICH etiology, and vascular risk factors. Conclusion Our study provides evidence of an association between HA-CSVD and CAA-CSVD in patients with primary ICH, which needs to be verified in future studies.
Genomic Markers and Machine Learning for Improving Ovarian Cancer Prognosis

Ovarian cancer is a significant health concern, often diagnosed at advanced stages, leading to limited treatment options and poor prognosis. Genomic biomarkers have the potential to revolutionize ovarian cancer prognosis by providing insights into tumor biology and personalized treatment strategies. This scientific research paper explores the discovery of genomic biomarkers for ovarian cancer prognosis using advanced machine learning algorithms. Leveraging high-throughput sequencing data and computational techniques, this study aims to identify robust biomarkers that can enhance prognostic accuracy, ultimately contributing to improved patient outcomes.
One-Shot Learning for Task-Oriented Grasping

Task-oriented grasping models aim to predict a suitable grasp pose on an object to fulfill a task. These systems have limited generalization capabilities to new tasks, but have shown the ability to generalize to novel objects by recognizing affordances. This object generalization comes at the cost of being unable to recognize the object category being grasped, which could lead to unpredictable or risky behaviors. To overcome these generalization limitations, we contribute a novel system for task-oriented grasping called the One-Shot Task-Oriented Grasping (OS-TOG) framework. OS-TOG comprises four interchangeable neural networks that interact through dependable reasoning components, resulting in a single system that predicts multiple grasp candidates for a specific object and task from multi-object scenes. Embedded one-shot learning models leverage references within a database for OS-TOG to generalize to novel objects and tasks more efficiently than existing alternatives. Additionally, the paper presents suitable candidates for the framework's neural components, covering essential adjustments for their integration and evaluative comparisons to state-of-the-art. In physical experiments with novel objects, OS-TOG recognizes 69.4% of detected objects correctly and predicts suitable task-oriented grasps with 82.3% accuracy, having a physical grasp success rate of 82.3%.
Design of a Single-Input Triple-Output Hybrid Converter With Decoupled Control for Reverse Electrodialysis Systems

Reverse electrodialysis (RED) is a promising membrane-based technique on extracting salinity gradient power (SGP) from river water and seawater. Despite the prominent advantages of direct electricity conversion and abundant storage, RED stacks suffer from a low energy conversion efficiency, which renders optimized utilization of the attained power quite essential. Consequently, this article presents the design of a single-input triple-output hybrid converter with three independent control variables to address this issue. The hybrid converter integrates a buck converter and two switched-capacitor-based resonant converters (SCRCs) to deliver power from the RED stack to three output ports, including a water pump, a 48-V dc microgrid, and batteries. To avoid cross-coupling of power flow control among these ports, a decoupled control scheme is developed to achieve voltage regulation for the water pump, maximum-power-point-tracking (MPPT) of the RED stack, and battery charging current regulation independently. Moreover, the selection principles of resonant inductors and battery charging current are elaborated to derive the zero-voltage-switching (ZVS) operation regions of six switches under different operating conditions of RED stacks. Simulation and experimental results of both steady-state tests and dynamic tests validate the effectiveness of the proposed decoupled control scheme and ZVS regions.
COMPARISON OF RARE EARTH ELEMENT COMPOSITIONS IN DIFFERENT GRAIN-SIZE FRACTIONS OF SEDIMENTS FROM THE YANGTZE AND YELLOW RIVERS AND THE SEA

To seek effective proxies for discriminating the Yangtze River sediments from the Yellow River sediments in the Yellow and East China Seas,six sediment samples were taken from the Yangtze and Yellow River mouth areas as end members. ≤63 μm fractions of the bulk samples were separated at intervals of 1Φ,and then concentrations of rare earth elements(REE) in these sediments were measured by Inductively Coupled Plasma-Mass-Spectrometry(ICP-MS). The results show that:(1)The ranges of REEs in sediments from the Yangtze and the Yellow Rivers are 145.49~256.77 and 121.22~135.56 μg/g respectively,and the REE distributions with increasing grain sizes show bi-modal type for the Yangtze River sediments,and inverse "S" for the Yellow River sediments. (2) REE concentrations in the Yangtze River sediments are relatively higher.REE values are 1.5 times of those in the Yellow River sediments,and the lowest REE in the Yangtze River sediments is 20% higher than the highest value in the Yellow River sediments. (3) 2~4 and 8~16 μm are the sensitive grain-size fractions to differentiate the Yangtze and Yellow River sediments,and diagnostic parameters of REEs such as REE,LREE/HREE and(La/Yb)n are clearly different in these two fractions of the Yangtze and Yellow River sediments.The result largely enhanced the difference of REE in sensitivity and reliability in the two rivers' sediments,which makes the usage of REE as an effective provenance proxy become possible.
Simultaneous retrieval from the full IASI spectrum of cloud and atmospheric parameters using the new all-sky forward model σ-IASI/F2N: the first day-night infrared retrieval of the Antarctica ozone hole

We have developed a new forward model for all sky radiative transfer calculations in the spectral range 10 to 2760 cm−1 . This new code, which we call σ-IASI/F2N, allows us to calculate in all-sky based on an original parametrization of the optical depth of atmospheric gases and clouds. Clouds are represented through the atmospheric profiles of Liquid Water Content (LWC), Ice Water Content (IWC), and effective radii of both water droplets (re) and ice crystals (De). The cloud parametrization relies on suitable scaling laws, which make the radiative transfer equations for a cloudy atmosphere identical to those for a clear atmosphere. Therefore, the difficulties in applying a multiple-scattering algorithm to a partly cloudy atmosphere are avoided, and the computational efficiency is practically the same as that for a clear atmosphere. The new radiative transfer code has been coupled with an inverse scheme based on the Optimal Estimation methodology. The problem of dimensionality of the data and parameter space is handled by considering suitable transforms, which allow the representation of the radiances (data space) and the atmospheric state vector (parameter space) through a set of reduced components. The dimensionality is diminished through the random Projections transform for the radiance space, whereas we use the usual Principal Component Analysis for the parameter space. The scheme’s performance has been evaluated using the Infrared Atmospheric Sounder Interferometer (IASI) spectral radiances. The soundings are collocated with analyses from the European Centre for Medium-Range Forecasts (ECMWF) model. The ECMWF analyses provide the basic information, i.e., the first guess state vector to initialize the inverse scheme. The forward/inverse technique uses the whole IASI spectral coverage (645 to 2760 cm−1 ). As such, it is the first scheme for all sky using the full IASI spectrum to retrieve clouds and atmospheric parameters simultaneously. This new forward/inverse model is exemplified through the analysis of a set of IASI soundings over the Antarctica continent on 9 September 2021 at the onset of the ozone hole. We will show that infrared retrievals add information to assess ozone’s spatial extent and depletion.
Decoupling of rainfall and vegetation greening in the arid Asian endorheic basins due to the irrigation intensiﬁcation

A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEB). As these retention basins are only being replenished by the intermittent precipitation, increasing droughts in the region and a growing demand for water have been presumed to jointly contributed to the land degradation. To understand the impact of climate change and human activities on dryland vegetation over the AEB, we conducted trend and partial correlation analysis of vegetation and hydroclimatic change from 2001 to 2021 using multi-satellite observations, including vegetation greenness, total water storage anomalies (TWSA) and meteorological data. Here we show that much of the AEB (65.53%) exhibited a greening trend over the past two decades. Partial correlation analyses indicated that climatic factors had varying eﬀects on vegetation productivity as a function of vegetation types and aridity. In arid AEB, precipitation dominated the vegetation productivity trend. Such a rainfall dominance gave way to TWSA dominance in the hyper-arid AEB. We further showed that the decoupling of rainfall and hyper-arid vegetation greening was largely due to a signiﬁcant expansion (17.3%) in irrigated cropland across the hyper-arid AEB. Given the extremely harsh environment in the hyper-arid AEB, our results therefore raised the concerns on the ecological and societal sustainability in this region, where a mild increase in precipitation might not be able to catch up the rising evaporative demand and water consumption resulted from global warming and irrigation intensiﬁcation.
Two-Stage Iterative Finite-Memory Neural Network Identification for Unmanned Aerial Vehicles

Recently, a system identification technique called FiMos-TA (Kang et al., 2022) has been introduced for the accurate identification of unmanned aerial vehicles (UAVs). This approach offers impressive performances, such as robustness and accuracy against disturbances and error accumulation, by utilizing a finite-memory-based training scheme. However, a major limitation of FiMos-TA (Kang et al., 2022) is that it requires inverse matrix operations on large matrices to obtain training gains, which severely affects its real-time implementation performance. Moreover, it relies on the amount of variations in the initial weights and the measurement model in finite-memory is insufficiently adaptable to changing conditions. Therefore, we propose a new approach called the two-stage iterative finite-memory neural network (TSIFNN) identification strategy for UAVs that overcomes all the limitations of FiMos-TA (Kang et al., 2022), ensuring not only robustness and accuracy but also real-time performance. We demonstrate the real-time performance, robustness, and accuracy of the proposed TSIFNN identification through a UAV experiment.
DOCUMENT CLASSIFICATION SYSTEM FOR THE SPANISH LANGUAGE

The classification of documents is a relevant task in companies to save time in managing information present in specific documents; therefore, the health sector seeks to prioritize documents performing the traceability of any process within its network. This article presents a document classification system to provide a tool divided in software components that faces the challenges of binding to the Spanish language using public sources such as Google and Wikipedia applying long documents related to the health sector in Colombia. For this purpose, a set of Machine Learning classifiers is performed to compare F1-score, Precision, and Recall metrics obtaining the best performance in the Logistic Regression classifier. In addition, the article makes a theoretical survey on the relationships that text mining, Information Retrieval, and Text Summarization have with document classification.
Property Crimes and Violence in United States: An Analysis of the influence of Population density

The role of population density in the generation or suppression of crime has been the subject of debate for decades. The classic argument is that high density offers opportunities for property crimes, given that it is a surrogate for the distribution of private property, much of which offers attractive targets to thieves. On the other hand, densely populated areas offer natural surveillance that has the effect of inhibiting violent crimes in so far as witnesses are more abundant and events are more likely to be reported to police. In this analysis, property and violent crimes were selected from a database of over 100,000 crimes reported in Baltimore County, Maryland, U.S.A. , in the year 2000. Densities of population and of property and violent crimes were calculated for city blocks. Blocks with population densities above the mean of all blocks were then retained for further consideration. Analysis demonstrated that both property and violent crimes were moderately correlated with population density, and these crimes largely affected the same blocks. It was concluded that at the block level of geography, no evidence of a differential between property and violent crimes based on population density could be detected.
I. Réseaux d'acteurs et innovation située. La construction d'une identité territoriale: l'exemple de Mécanic Vallée (Lot et Aveyron)

A partir de l’observation du nord de la region Midi-Pyrenees, cet article defend l’hypothese que les logiques economiques, les reseaux sociaux et les jeux d’acteurs locaux sont a l’origine de la construction d’une identite territoriale. Fruit d’une histoire collective celle-ci n’empeche pas l’expression d’enjeux et de strategies aux interets divergents mais entraine deux changements majeurs. Elle permet de penser de facon positive le futur de ce territoire et conduit a l’envisager a partir des relations qu’il entretient avec d’autres espaces parfois fort eloignes.
Irreducible Complexity : Obstacle to Darwinian Evolution

Darwinsche Evolution setzt voraus, dass der Zufall die verschiedenen Arten hervorgebracht hat. Allerdings scheint es in der Natur spezifisch komplexe Gebilde zu geben, die irrreduzibel sind, d.h. sie verlieren ihre Funktionalität, wenn auch nur eine Komponente dieses Mechanismus fehlt. Solche Gebilde sind, wie man heute wie, wesentlich komplexer aufgebaut als Darwin dachte. Die Mathematik lehrt uns zudem, dass solche Gebilde sich aber nicht zufällig entwickelt haben können, dazu ist das Universum nicht groß und alt genug. Solche irrreduzible Komplexität in der Biologie ist Gegenstand der nachfolgenden Betrachtungen.
Pyknodysostosis: Anaesthetic considerations – A Case report

Pyknodysostosis is a rare autosomal-recessive disorder of osteoclast dysfunction. We describe anaesthetic management of a 35 year old male having pyknodysostosis with fracture shaft left femur. He had anticipated difficult intubation. Therefore, spinal anaesthesia was provided for his fracture fixation. The intra and post-operative period remain uneventful. IMPLICATION STATEMENT Patients having pyknodysostosis usually have frequent fractures and may present for open reduction and internal fixation. Their characteristic features predispose them to difficult intubation. Regional anaesthesia is the technique of choice whenever applicable.
Machine learning algorithms in microbial classification: a comparative analysis

This research paper presents an overview of contemporary machine learning methodologies and their utilization in the domain of healthcare and the prevention of infectious diseases, specifically focusing on the classification and identification of bacterial species. As deep learning techniques have gained prominence in the healthcare sector, a diverse array of architectural models has emerged. Through a comprehensive review of pertinent literature, multiple studies employing machine learning algorithms in the context of microbial diagnosis and classification are examined. Each investigation entails a tabulated presentation of data, encompassing details about the training and validation datasets, specifications of the machine learning and deep learning techniques employed, as well as the evaluation metrics utilized to gauge algorithmic performance. Notably, Convolutional Neural Networks have been the predominant selection for image classification tasks by machine learning practitioners over the last decade. This preference stems from their ability to autonomously extract pertinent and distinguishing features with minimal human intervention. A range of CNN architectures have been developed and effectively applied in the realm of image classification. However, addressing the considerable data requirements of deep learning, recent advancements encompass the application of pre-trained models using transfer learning for the identification of microbial entities. This method involves repurposing the knowledge gleaned from solving alternate image classification challenges to accurately classify microbial images. Consequently, the necessity for extensive and varied training data is significantly mitigated. This study undertakes a comparative assessment of various popular pre-trained CNN architectures for the classification of bacteria. The dataset employed is composed of approximately 660 images, representing 33 bacterial species. To enhance dataset diversity, data augmentation is implemented, followed by evaluation on multiple models including AlexNet, VGGNet, Inception networks, Residual Networks, and Densely Connected Convolutional Networks. The results indicate that the DenseNet-121 architecture yields the optimal performance, achieving a peak accuracy of 99.08%, precision of 99.06%, recall of 99.00%, and an F1-score of 98.99%. By demonstrating the proficiency of the DenseNet-121 model on a comparatively modest dataset, this study underscores the viability of transfer learning in the healthcare sector for precise and efficient microbial identification. These findings contribute to the ongoing endeavors aimed at harnessing machine learning techniques to enhance healthcare methodologies and bolster infectious disease prevention practices.
Socio-ecological Production Landscapes in Asia

Socio-ecological production landscapes (SEPLs) have been developed by interactions between humans and nature, through activities such as agriculture, forestry and fishery. Biodiversity and ecosystem services have been maintained in SEPLs through building on local knowledge, techniques, rules and norms regarding wise use of natural resources as well as the sharing of benefits and burdens within the communities. Diverse human-nature interactions in SEPLs have been observed in the literature reviewed on selected SEPLs in Asia. This literature encompasses aspects of physical and institutional structures, management techniques, contemporary challenges, and some of the ways in which these have been addressed.
Perception of Ticks and Tick-Borne Diseases Worldwide

In this comprehensive review study, we addressed the challenge posed by ticks and tick-borne diseases (TBDs) with growing incidence affecting human and animal health worldwide. Data and perspectives were collected from different countries and regions worldwide, including America, Europe, Africa, Asia, and Oceania. The results updated the current situation with ticks and TBD and how it is perceived by society with information bias and gaps. The study reinforces the importance of multidisciplinary and international collaborations to advance in the surveillance, communication and proposed future directions to address these challenges.
Cost-eﬃcient Selective Network Caching in Large-Area Vehicular Networks using Multi-objective Heuristics

In the last decade the interest around network caching techniques has augmented notably for alleviating the ever-growing demand of resources by end users in mobile networks. This gained momentum stems from the fact that even though the overall volume of traﬃc retrieved from Internet has increased at an exponential pace over the last years, several studies have unveiled that a large fraction of this traﬃc is usually accessed by multiple end users at nearby locations, i.e. content demands are often local and redundant across terminals close to each other, even in mobility. In this context this manuscript explores the application of multi-objective heuristics to optimally allocate cache proﬁles over urban scenarios with mobile receivers (e.g. vehicles). To this end we formulate two conﬂicting objectives: the utility of the cache allocation strategy, which roughly depends on the traﬃc oﬄoaded from the network and the number of users demanding contents; and its cost, given by an cost per unit of stored data and the rate demanded by the cached proﬁle. Simulations are performed and discussed over a realistic vehicular scenario modeled over the city of Cologne (Germany), from which it is concluded that the proposed heuristic solver excels at ﬁnding caching solutions diﬀerently balancing the aforementioned objectives.
Investigations of Single-Subunit tRNA Methyltransferases from Yeast

tRNA methylations, including base modification and 2’-O-methylation of ribose moiety, play critical roles in the structural stabilization of tRNAs and the fidelity and efficiency of protein translation. These modifications are catalyzed by tRNA methyltransferases (TRMs). Some of the TRMs from yeast can fully function only by a single subunit. In this study, after performing the primary bioinformatic analyses, the progress of the studies of yeast single-subunit TRMs, as well as the studies of their homologues from yeast and other types of eukaryotes and the corresponding TRMs from other types of organisms was systematically reviewed, which will facilitate the understanding of the evolutionary origin of functional diversity of eukaryotic single-subunit TRM.