Miracle-dz/UniFAR-FTUs huggingface dataset:
Lichens and lichenicolous fungi on Juniperus oxycedrus L. in Campu Su Disterru (Sardinia, Italy)

The \iehen flora on Juniperus oxycedrus L. in the aneient, montane holm-oak forest at Campu Su Disterru (Supramonte di Orgosolo, eentral-east Sardina, Italy) has been investigated and 68 taxa of \iehens plus 3 liehenieolous fungi were identifi ed. The most remarkable findings are: Ophioparma rubricosa (Miill. Arg.) Ekman. , new to Europe, and Chaenotecopsis nana Tibell , new to Ttaly. 8 species are reported for the first time from Sardinia: Agonimia octospora Coppins & P. James, Calicium adspersum Pers. , Ca/op/aca hungarica Magnusson, Chaenotheca phaeocepha/a (Turn .) Th. Fr. , Hypocenomyce sea/aris (Ach. ex Lilj.) M. Choisy, Lecanora argentata (Aeh.) Malme, Lepraria umbrico/a Tensb. and Rinodina archaea (Ach. ) Arnold. Many further speeies are rare in Ttaly and in Sardinia. The number of speeies exeeeds that on Quereus ilex L. on the same site. This demonstrates the importanee of a non-dominant tree species for the epiphytie liehen flora of a forest. A phytogeographieal analysis shows that speeies with a wide distribution in temperate areas of Europe are dominant (Temperate element, 72 %). The Atlantic/Oeeanie element eomprises 13%, while few speeies belong to other elements: Aretie-alpine / Boreal-montane 3%, Mediterranean-montane 5% and Mediterranean 7%.
Jejunovesical Fistula Diagnosis After Normal Vaginal Delivery: A Case Report

ABSTRACT Enterovesical fistula represents an abnormal communication between the intestine and bladder. The causes are diverticulitis (56.3%), malignant tumours, which are located mainly in the intestine (20.1 %), and Crohn's disease (9.1%). Other causes include iatrogenic injury (3.2%); trauma; foreign bodies in the intestinal tract; radiotherapy; chronic appendicitis; tuberculosis; and syphilis. Normal vaginal delivery as a cause for enterovesical fistula has not been reported in many publications yet. We report a case of a 30-year-old female, who developed an jejunovesical fistula after normal vaginal delivery. It was diagnosed after diagnostic cystoscopy and computed tomography of the abdomen and pelvis. There was jejuno-vesical fistula. Resection of the segment of the jejunum with side-to- side anastomosis with bladder repair was done. A follow-up cystogram was done which showed no contrast extravasation into the peritoneum. The patient was followed up for 9 months after surgery.
Effect of Temperature to Fabrication Cigs Solar Cell Using the Sputtering Method

Copper-indium-gallium diselenide (CuInGaSe2) or CIGS is one of the most promising materials for thin film solar cell applications. CIGS solar cells were deposited by sputtering method on ZnO/ZnS/CIGS/Mo arrays. Various parameters in sputtering greatly influence the efficiency of CIGS solar cells such as temperature. Thermal parameters are used to compare the effect of the CIGS layer on optimizing the efficiency of CIGS solar cells. The results show that the CIGS layer deposited using temperature has a crystalline structure, besides that the resulting efficiency is also higher than CIGS solar cells deposited without temperature, namely 0.177%.
A novel rain removal technology based on video image

Due to the effect of bad weather conditions, it often conducts visual distortions on images for outdoor vision systems. Rain is one specific example of bad weather. Generally, rain streak is small and falls at high velocity. Traditional rain removal methods often cause blued visual effect. In addition, there is high time complexity. Moreover, some rain streaks are still in the de-rained image. Based on the characteristics of rain streak, a novel rain removal technology is proposed. The proposed method is not only removing the rain streak effectively, but also retaining much detail information. The experiments show that the proposed method outperform traditional rain removal methods. It can be widely used in intelligent traffic, civilian surveillance and national security so on.
Smith ScholarWorks Smith ScholarWorks

Describing the content of a visual image is a fundamental ability of human vision and language systems. Over the past several years, researchers have published on major improvements on image captioning, largely due to the development of deep learning systems trained on large data sets of images and human-written captions. However, these systems have major limitations, and their development has been narrowly focused on improving scores on relatively simple “bag-of-words” metrics. Very little work has examined the overall complex patterns of the language produced by image-captioning systems and how it compares to captions written by humans. In this paper, we closely examine patterns in machine-generated captions and characterize how conventional metrics are inconsistent at penalizing them for nonhuman-like erroneous output. We also hypothesize that the complexity of a visual scene should be reﬂected in the linguistic variety of the captions and, in testing this hypothesis, we ﬁnd that human-generated captions have a dramatically greater degree of lexical, syntactic, and semantic variation. These results have important implications for the design of performance metrics, gauging what deep learning captioning systems really understand in images, and the importance of the task of image captioning for cognitive systems research.
Abstracting road traf ﬁ c via topological braids: Applications to traf ﬁ c ﬂ ow analysis and distributed control

Despite the structure of road environments, imposed via geometry and rules, traf ﬁ c ﬂ ows exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to the high dimensionality of possible behavior, the heterogeneity of agents, and the stochasticity of their decision-making. Modeling approaches learning associations in Euclidean spaces are often limited by their high sample complexity and the sparseness of available datasets. Our key insight is that the structure of traf ﬁ c behavior could be effectively captured by lower-dimensional abstractions that emphasize critical interaction relationships. In this article, we abstract the space of behavior in traf ﬁ c scenes into a discrete set of interaction modes, described in interpretable, symbolic form using topological braids. First, through a case study across real-world datasets, we show that braids can describe a wide range of complex behavior and uncover insights about the interactivity of vehicles. For instance, we ﬁ nd that high vehicle density does not always map to rich mixing patterns among them. Further, we show that our representation can effectively guide decision-making in traf ﬁ c scenes. We describe a mechanism that probabilistically maps vehicles ’ past behavior to modes of future interaction. We integrate this mechanism into a control algorithm that treats navigation as minimization of uncertainty over interaction modes, and investigate its performance on the task of traversing uncontrolled intersections in simulation. We show that our algorithm enables agents to coordinate signi ﬁ cantly safer traversals for similar ef ﬁ ciency compared to baselines explicitly reasoning in the space of trajectories across a series of challenging scenarios.
HD 214220 (BD+56 2813): An eclipsing binary with its primary component at the end of the main sequence

Context. Analysis of the light curves of an eclipsing binary allows one to derive the absolute dimensions of the system. This in turn yields information on the radii of the components, which allows the stars to be accurately placed on the Hertzsprung-Russell diagram and their evolutionary phase to be interpreted via comparisons to tracks of stellar evolution models. Aims. I aim to derive the stellar and system parameters of HD 214220. Methods. I measured the epochs of three primary and three secondary minima of the eclipsing binary HD 214220 from 2019 to 2022 from photometric fluxes obtained by the TESS satellite. I modeled the light curve and the velocity amplitudes, which were obtained by the Gaia satellite, with the software PHOEBE. Results. HD 214220 is an eclipsing binary system with an orbital period of P = 43.14 d, eclipse depths of 17% and 13%, and masses of 2.49 M⊙ and 2.42 M⊙. The sum of the radii is R1 + R2 ≈ 8.5 R⊙, and the temperatures of the components are similar, with a ratio of T2/T1 ≈ 1.03. Conclusions. By consulting stellar evolution models, I find that the primary component has ended core hydrogen burning and is potentially in the contraction phase, prior to shell burning.
Modeling Influenza-Like Illness Activity in the United States

Influenza causes yearly costs for hospitalization and outpatient visits of more than $10 billion in the United States. The prediction of influenza epidemics is thus relevant for health insurance providers and public health facilities, among others. A useful piece of information is the probability distribution of influenza epidemics occurring within a given time horizon of one or two years. We present a model that delivers confidence intervals for future influenza activity in different regions in the United States. The model takes into account the specific statistical characteristics of influenza activity such as volatility clusters, seasonal effects, and dependencies between different regions. Confidence intervals for the regions are obtained using ARMA-GARCH models, and regional dependencies are captured by a pair-copula construction, describing jointly the residuals of the ARMA-GARCH models. Our model allows us to simulate influenza activity over a future time horizon.
A Novel Transformer Model With Multiple Instance Learning for Diabetic Retinopathy Classification

Diabetic retinopathy (DR) is an irreversible fundus retinopathy. A deep learning-based automated DR diagnosis system can save diagnostic time. While Transformer has shown superior performance compared to Convolutional Neural Network (CNN), it typically requires pre-training with large amounts of data. Although Transformer-based DR diagnosis method may alleviate the problem of limited performance on small-scale retinal datasets by loading pre-trained weights, the size of input images is restricted to <inline-formula> <tex-math notation="LaTeX">$224\times 224$ </tex-math></inline-formula>. The resolution of retinal images captured by fundus cameras is much higher than <inline-formula> <tex-math notation="LaTeX">$224\times 224$ </tex-math></inline-formula>, reducing resolution in training will result in the loss of valuable information. In order to efficiently utilize high-resolution retinal images, a new Transformer model with multiple instance learning (TMIL) is proposed for DR classification. A multiple instance learning approach is firstly applied on the retinal images to segment these high-resolution images into <inline-formula> <tex-math notation="LaTeX">$224\times 224$ </tex-math></inline-formula> image patches. Subsequently, Vision Transformer (ViT) is used to extract features from each patch. Then, Global Instance Computing Block (GICB) is designed to calculate the inter-instance features. After introducing global information from GICB, the features are used to output the classification results. When using high-resolution retinal images, TMIL can load pre-trained weights of Transformer without being affected by weight interpolation on model performance. Experimental results using the APTOS dataset and the Messidor-1 dataset demonstrate that TMIL achieves better classification performance and reduces inference time by 62% compared with that directly inputting high-resolution images into ViT. And TMIL shows highest classification accuracy compared with the current state-of-the-art results. The code will publicly available at <uri>https://github.com/CNMaxYang/TMIL</uri>.
INTERPRETATION OF GEOCHEMICAL WELL TEST DATA FOR WELLS OW-903B, OW-904B AND OW-909 OLKARIA DOMES, KENYA

Three wells from the Domes field were allowed to discharge for 3 months in early 2009. This report describes the chemical composition of the fluids from these wells, OW-903B, OW-904B and OW-909. Downhole temperatures range from 250 to 350 o C which agree with the solute and gas geothermometers. The wells produce fluids with high pH; deep fluid pH ranges from 6.9 to 8.3. OW-909 has higher values for deep fluid TDS, pH and concentrations of anions (B, Cl, F), Na and K than OW-903B and OW-904B. Olkaria Domes wells have a sodium bicarbonate water type similar to those in Olkaria West and Olkaria Central fields. Concentrations of most dissolved constituents were initially low but increased with increasing enthalpy, a considerable range is observed in concentrations. Deep fluids appear to be close to equilibrium with quartz and calcite. Very low Ca concentrations suggest that calcite scaling will not be a problem in the utilization of the Olkaria Domes geothermal fluids.
A more-Comers populAtion trEated with an ultrathin struts polimer-free Sirolimus stent: an Italian post-maRketing study (the CAESAR registry)

Introduction The use of contemporary drug-eluting stents (DES) has significantly improved outcomes of patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI). However, concerns exist regarding the long-term proinflammatory effects of durable polymer coatings used in most DES, potentially leading to long-term adverse events. First-generation polymer-free stent technologies, such as sirolimus- and probucol-eluting stents (PF-SES), have shown an excellent safety and efficacy profile. The aim of this study was to evaluate the safety and efficacy of the new ultrathin Coroflex ISAR NEO PF-SES, in a more-comers PCI population. Methods The CAESAR (a more-Comers populAtion trEated with an ultrathin struts polimer-free Sirolimus stent: An Italian post-maRketing study) registry is a multicenter, prospective study conducted in Italy, enrolling more-comers CAD patients undergoing PCI with the Coroflex ISAR NEO stent. Patients with left main (LM) disease, cardiogenic shock (CS), or severely reduced left-ventricular ejection fraction (LVEF) were excluded. The primary endpoint was target-lesion revascularization (TLR) at 1 year. Results A total of 425 patients were enrolled at 13 centers (mean age 66.9 ± 11.6 years, Diabetes mellitus 29%, acute coronary syndrome 67%, chronic total occlusion 9%). Of these, 40.9% had multivessel disease (MVD) and in 3.3% cases, the target lesion was in-stent restenosis (ISR). Clinical device success was reached in 422 (99.6%) cases. At 1 year, only two (0.5%) subjects presented ischemia-driven TLR. The 1-year rates of target vessel revascularization and MACE were 0.5% and 5.1%, respectively. Major bleeding was observed in four (1.0%) patients. Conclusion In this multicenter, prospective registry, the use of a new ultrathin Coroflex ISAR NEO PF-SES in a more-comers PCI population showed good safety and efficacy at 1 year.
Effect of the shape of fundamental particles on XRD characteristics of illitic minerals

Chemical and electron-microscope data (Pt-shadowing and HRTEM) on monomineral illitic samples are used to show that XRD peak positions and peak intensities related to the swelling characteristics of the samples strongly depend on the size of the mixed-layer crystals, while the Kubler index (the half-height width of the 001 reflection) of the samples recorded in Na-form is related to the thickness of fundamental particles. The ability of fundamental particles to associate face-to-face into mixed-layer crystals depends on geometric factors: particles of a given thickness are more prone to form mixed-layer crystals inducing more smectitic XRD characteristics if they have flat surfaces, a large diameter and are not elongated. The variability of particle morphologies detected in natural samples explains the lack of a good correlation between the Kubler index and the parameters reflecting the swelling ability of illites such as the percentage of smectite layers or the Ir index of Srodon (1984). In swelling clays, the thickness of coherent scattering domains, calculated from the Kubler index by means of the Scherrer equation, is larger than the mean thickness of the fundamental particles
PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds

Gait recognition, crucial in biometrics and behavioral analytics, has applications in human–computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.
Identification of topographic elements composition based on landform boundaries from radar interferometry segmentation (preliminary study on digital landform mapping)

Dense vegetation that covers most landscapes in Indonesia becomes a common limitation in mapping the landforms in tropical region. This paper aims to examine the use of radar interferometry for landform mapping in tropical region; to examine the application of segmentation method to develop landform type boundaries; and to identify the topographic elements composition for each type of landform. Using Idrisi® and “eCognition ®” softwares, toposhape analysis, segmentation and multi-spectral classification were applied to identify the composition of topographic elements i.e. the types of land-cover from Landsat 8, elevation, slope, relief intensity and curvatures from SRTM (DEM). Visual interpretation on DEM and land-cover fusion imagery was conducted to derive basic control maps of landform and land-cover. The result shows that in segmentation method, shape and compactness levels are essential in obtaining land-cover, elevation, and slope class units to determine the most accurate class borders of each element. Despite a complex procedure applied in determining landform classification, the combination of topographic elements segmentation result presents a distinct border of each landform class. The comparison between landform maps derived from segmentation process and visual interpretation method demonstrates slight dissimilarities, meaning that multi-stage segmentation approach can improve and provide more effective digital landform mapping method in tropical region. Topographic elements on each type of landforms show distinctive composition key containing the percentage of each curvature elements per area unit. Supported by GIS programming and modeling in the future, this finding is significant in reducing effort in landform mapping using visual interpretation method for a very large coverage but in detail scale level.
A deep learning model for brain age prediction using minimally preprocessed T1w images as input

Introduction In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods We used a multicohort dataset of cognitively healthy individuals (age range = 32.0–95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2–4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset’s images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1–4, respectively. The model’s performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63–2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.
Internal Iliac Artery Ligation in Obstetrics and Gynecology: Surgical Anatomy and Surgical Considerations

The internal iliac artery (IIA) is the main arterial vessel of the pelvis. It supplies the pelvic viscera, pelvic walls, perineum, and gluteal region. In cases of severe obstetrical or gynecologic hemorrhage, IIA ligation can be a lifesaving procedure. Regrettably, IIA ligation has not gained widespread popularity, primarily due to limited surgical training and concerns regarding possible complications, including buttock claudication, impotence, and urinary bladder and rectum necroses. Nowadays, selective arterial embolization or temporary balloon occlusion are increasingly utilized alternatives, which can be applied preoperatively or intraoperatively for threatening severe genital or pelvic bleeding. However, IIA ligation retains its relevance, as the previously described procedures are not always available and have limitations. This article provides a step-by-step guide to the IIA ligation procedure and its possible complications. It also includes a detailed description of the anatomy of the IIA and pelvic arterial anastomoses. This review highlights the importance of a thorough understanding of pelvic anatomy as a prerequisite for safe IIA ligation and posits that training in this procedure should be an integral part of obstetrics and gynecology curricula.