Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Confining Effect of Geogrid Reinforced Soil with Plane Strain Model Wall Tests

Large scale laboratory testing of unreinforced and geogrid reinforced soil has been carried out at RWTH Aachen University to investigate the confining effect of geogrids in reinforced soil in principle. With a series of triaxial tests the development of the additional confining effect due to the reinforcement has been confirmed. This confining effect is necessary for the increase of the load bearing capacity of the soil. Furthermore, to determine the development of shear zones within the geogrid reinforced soil a test apparatus with a transparent sidewall has been constructed to monitor soil particle displacements and rotations during a controlled facing movement. The evaluation of the soil particle movements confirmed the development of the confining effect and explains the development of an earth pressure from the composite material which is far less than the active earth pressure from the unreinforced soil.
Extension of the geomagnetic polarity time scale to 6 . 5 m . y . : KAr dating , geological and paleomagnetic study of a 3 , 500-m lava succession IN western Iceland

More than 400 successive lavas in Bo.saridrdur, western Iceland, have b@n subjected ro paleomagneric and K-Ar ase analysir. Volcanisn in the region wa virtually continuous berween abour 7.0 and 2 m.y. ago, du.ingwhich time more than 3,500 m ofvol, cani.s and int€. bedded fdimenrs aeunulat€d. ReEession analvsis ol rhe l-Ar rse rnd eg8ree r rhickne* dara deho;srrJre. ihr, rhe . ate of growlh of the lava pile vas rema.kably uniforn at 730 mh.y. rhroughour the uhole penod. lhr nJgnero.rrarsraphic rrd K-Ar dJta indi. ,re r re:rly omplere reord of rhe geomag netic polarity hislory and exrend the polarity time scale based upon data f.om subaerial volcanic rocks to abour 5.5 m.v. aeo. The bourdarerol polariryepoch5 a'e,hosn robe r.14 a;d rlr m.y. Fpoch b lie\ beM(n Jbou b.sa and J.81 m.). aso. durin8 shrh rimr rhree normrl polanq e\enG rre re'ognrrd. TFe ager delerm,ned ior lhe pol,nq -rnrenal boundarie' in thn study connrm re. cent estimates derived from anal)'ses of marin€ magneric anomalies using sea-floor-spreading arsumptiors. lhe resuits show that it is possible to obtain reliable K-Ar ages on lavd that have undersone zeolire facies detamorphism ar temperailres up ro about 150'C by careful selection of sanples that are well crystallized od have tbeir original high+emperature mrnerabSy p.€seneo. A revised estimare of5.2 i 0.1 m.y. is proposed for the ase of the Miocene'Pliocene boundary.
EFFECT OF POTASSIUM FERTILIZER, BIOSTIMULANTS AND EFFECTIVE MICROORGANISMS AS WELL AS THEIR INTERACTIONS ON POTATO GROWTH, PHOTOSYNTHETIC PIGMENTS AND STEM ANATOMY

Increasing potassium fertilizer rate up to 80 kg K2O/ fed significantly increased shoot fresh and dry weights, photosynthetic pigments concentration as well as stem anatomical characters. The highest weight was obtained due to application of 40 kg K2 As for the interactions, application of bostimulants, in particular, seaweed extract, significantly increased fresh and dry weights of potato shoot, photosynthetic pigments concentration as well as improved anatomical characteristics of potato stem grown under all potassium fertilizer rates with or without anaddition of effective microorganisms. The highest value was obtained under the treatment of 40 kg K O/ fed in the first and seconds season as compared with untreated plants. Exogenous application of biostimulants, in particular, seaweed extract significantly increased shoot fresh and dry weights, photosynthetic pigment concentration as well as increased the diameter of stem, pith and thickness of collenchyma layers, and xylem tissue as well as the length and width of large vascular bundle beside the thickness of external phloem as compared to control in both seasons. Addition of effective microorganisms to the soil significantly increased shoot fresh and dry weight, photosynthetic pigments concentration as well as the diameter of stem, length and width of vascular bundle, and the thickness of xylem. 2 O/fed with addition of effective microorganisms and spraying plants with 500 mg/l seaweed extract as compared with control plant in the first and second growing seasons.
Single-Agent LSTM Encoders Single-Agent LSTM Decoders Output : Multi-Agent Future Trajectories Input 1 : Multi-Agent Past Trajectories Input 2 : Scene Context Image Scene Context Channels Multi-Agent Channels Convolutional Operator : Multi-Agent Tensor Fusion

Accurate prediction of others’ trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents’ past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents’ past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents’ future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-ofthe-art prediction accuracy.
Experimental and theoretical study of iron concentration on clogging phenomenon in secondary circuit of pressurized nuclear power plant

The clogging phenomenon can be observed in both primary and secondary circuits of a Pressurized Nuclear Power Plant (PWR). This effect is associated to hydrodynamic singularity produced by an important change in flow velocity (e.g. SG tube' support plates) and physico-chemical conditions. In Steam Generator (SG) secondary side the thickness of the clogging can become so important that the performance could be reduced. Two experimental tests were carried out to study the evolution of the clogging on a specimen (important section flow reduction) in single phase conditions of secondary circuit (245 – 275°C, 10 MPa) with low (5 to 9.5 ppb) and high (5 to 27 ppb) iron concentration for 760 and 470 hours respectively. To increase the deposits kinetics, the velocities were set between 5 to 30 m/s. The deposit mean thickness change was calculated by the evolution of the pressure drop coefficient assuming deposit profile and geometry measured at the end of the tests. Moreover, a model based on Graetz mass transfers has been used to estimate the kinetics of deposit formation. The results showed that experimental and model results are in good agreement. It suggests that the clogging effect in our experimental conditions is controlled by mass transfer and not by the electro-kinetic effect. However, the electro-kinetic effect would provide the conditions to initiate the deposit formation. The model was implemented in the CLOSIS program (developed by AREVA) to simulate the clogging effect in a steam generator. Theoretical estimations are in good agreement with SG on-site measurement and this for 20 years experience feedback.
Multi-task Osteoporosis Pre-screening Using Dental Panoramic Radiographs with Feature Learning

Known as the most common bone disease, osteoporosis is affecting millions of people every year. Clinical diagnostic methods for osteoporosis are expensive and therefore have limited availability in population. Recent studies have shown that Dental Panoramic Radiography (DPR) images can provide the bone density change clues in trabecular bone structure analysis. We collected 108 DPR images from 108 different subjects with 52 osteoporosis and 56 normal subjects, and aim to use the powerful deep neural network (DNN) on these DPR images for osteoporosis classification. However, DNN, such as AlexNet, requires millions of images for model learning. For many clinical related tasks, the available amount of data is far from adequate to train a DNN from scratch. One common solution is to use a pre-trained DNN and fine-tune it on the target dataset. Due to limited number of images and the big difference in image styles between ImageNet and our DPR dataset, direct fine-tuning of DNN can hardly achieve promising performance. In this paper, we propose a multi-task scheme to transfer the network pre-trained on ImageNet to our DPR dataset and improve the accuracy in osteoporosis pre-screening. Specifically, the AlexNet pre-trained on ImageNet is first fine-tuned on the task of (patient) identity classification using the abundant patches extracted from 108 DPR images. Features learned from such task are more dental-data friendly and are better for our main task, i.e., osteoporosis pre-screening. For this purpose, the Octuplet Siamese Network (OSNet) is built on top of such features and trained on eight regions of interest (ROIs) in each DPR image for the osteoporosis classification task. Finally, the leave-one-out cross-validation shows that the highest overall accuracy of osteoporosis prescreening is 92.6%, showing significant advantage over previous methods.
Culinary experiences as a key tourism attraction . Case Study : Bra ş ov County Codru ţ a

Providing authentic travel services is a prerequisite of increasing the attractiveness of a holiday destination. The development of the culinary tourism by integrating the traditional menus in the offer of food services or the organization of festivals based on culinary traditions of the visited area are components that highlight local specificities and which ensure the differentiation of the tourism offer on the national and international market. This article analyses the potential which favours the development of culinary tourism in Brasov county and which can represent a key element for improving the image of this tourism destination. Key-words: culinary experiences, food tourism, food festivals, destination image
Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features

Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas.
Hydraulic Traits Performances of Three Pine Species in Tunisia

Mediterranean forests including Tunisian pine species are threatened by the rising of temperature and decreasing of precipitation. The impact of the increase of aridity differs across species depending on their stomatal and hydraulic responses. In this paper, three pine species: P. halepensis, P. brutia and P. canariensis growing in three different climatic zones: humid, sub-humid and semi-arid, were studied to detect their different responses to drought and guide their selection for reforestation program. Measurements carried out are hydraulic conductivity at point P50, specific conductivity (Ks), midday stem water potential and hydraulic safety margins. Results showed that during summer, vulnerability to embolism, estimated by water potential inducing 50% loss of xylem hydraulic conductivity (P50), is strongly associated with the capacity for drought resistance. Pinus halepensis (P50 = -4.19 MPa) was found to be more resistant to drought than P. brutia and P. canariensis in the semi-arid climate, whereas P. brutia tolerated the humid climate (P50 = -3.7 MPa) and P. canariensis seems more adapted to the sub-humid climate (P50 = -4.08 MPa). Hydraulic safety margins confirmed the conservative behavior of pine species to avoid drought and for maintaining relatively high water potential in dry conditions. These findings help to assess the impact of mid-summer water deficit on pine species in the context of climate change and to select among these species the most resistant for future reforestation programs.
Prognostic value of MRP , LRP and Pgp in patients with non-small cell lung cancer

The aim of this study was to investigate the prognostic value of glutathione-S-transferase p1 (GST-π), multidrug resistance proteins (MRP), lung resistance-related protein (LRP), P-glycoprotein (P-gp) and topoisomerase 2 expression (Topo IIα) in patients with non-small cell lung cancer (NSCLC) treated with platinum-based chemotherapy. Expression of GST-π, MRP, LRP, P-gp and Topo IIα were conducted using immunohistochemistry. Chi-Square, Kaplan-Meier, Log-rank test and multivariate Cox’s regression analysis were used to detect the correlation of proteins, clinicopathological characteristics and survivals. MRP and P-gp were associated with tumor differentiation, respectively (MRP: P=0.020; P-gp: P=0.030), LRP was associated with tumor type (P<0.001), regional lymph node metastasis (P=0.014) and tumor stage (P=0.032). No significant association was observed between clinical features and GST-π and Topo IIα (P>0.05). Poor survivals were markedly correlated with higher expression of MRP, LRP or P-gp, respectively. Besides, combination of MRP, LRP and P-gp had a better predictive value compare with single marker. No significant difference was observed between GST-π or Topo IIα and survival time. MRP and P-gp were observed as independent prognostic factors. This is the first report P-gp overexpression is associated with poor survival in NSCLC, furthermore, this is the first study to evaluate the combination predictive value of MRP, LRP and P-gp in NSCLC. Our findings suggest combination of MRP, LRP and P-gp are more useful predictors in NSCLC patients treated with platinum-based chemotherapy.
Allelopathic Effect of Intercropping with Marigold and Common Rosemary on Tomato Early Blight Disease Development

In order to evaluate the allelopathic effects of intercropping with marigold and common rosemary on tomato early blight disease development Intercropped systems promote biological diversity, improve the use of natural resources, diminish the risk of crop losses and can provide barriers to the spread of plant diseases. Medicinal plants can grow in intercropping. Common Rosemary (Rosmarinus officinalis) and Marigold (Tagetes erecta L.) are multipurpose crops with remedial, ornamental, medical and pharmaceutical uses, and reported antimicrobial properties. Tomato early blight, caused by Alternaria solani, is perhaps the most common leaf disease in this crop and is considered one of the main diseases affecting tomato production in Iran. In this experiment, we evaluated the effects of marigold intercropped with tomato (Lycopersicon esculentum Mill.) and Common Rosemary (Rosmarinus officinalis) on A. solani on tomato leaf damage in farm condition. Results showed that intercropping with marigold and common rosemary induced a significant (p < 0.05) reduction in tomato early blight caused by A. solani, by means of two different mechanisms. The first mechanism was the allelopathic effects of marigold and common rosemary on A. solani conidia germination. The second way was by altering the microclimatic conditions around the canopy, particularly by reducing the number of hours per day with relative humidity ≥92 %, thus diminishing conidial development. The third mechanism was to provide a physical barrier against conidia spreading. When intercropped with tomato, Common Rosemary plants worked also as a physical barrier and promoted reductions in the maximum relative humidity surrounding the canopy, but to a lesser extent than marigold.
Antibacterial activity of Nicolaia speciosa fruit extract

Nicolaia speciosa fruit contains bioactive compounds which have potentials as natural antibacterial agents. Two extraction methods of bioactive compounds of N. speciosa fruit, i.e. single and multistage extractions, were evaluated. The multistage extraction utilized series of solvents in sequence involving hexane (nonpolar solvent), ethyl acetate (semipolar) and ethanol (polar). The antibacterial activities of the extracts were evaluated against Escherichia coli, Bacillus cereus and Pseudomonas aeruginosa, using agar Well diffusion method in combination with Thin Layer Chromatography (TLC) method and by observing the cell membrane leakage. The multistage extraction provided higher yields and reduced extraction time compared to the single step method. Ethanolic extract of N. speciosa showed the strongest inhibitory effect against bacteria. The minimum inhibitory concentration (MIC) of the ethanolic extract ranged from 20 mg/mL to 32 mg/mL. The TLC method showed that the fractions of ethanolic extract of N. speciosa with Rf 4= 0.33; Rf 8 = 0.82 and Rf 9 = 0.83 showed better antibacterial activity. The release of cells materials, measured at 260 and 280 nm using UV Vis spectrophotometer was observed, indicative of leakage of the cytoplasmic membrane.
How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?

Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun’s gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While “embedding debiasing” methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words’ context when training word embeddings is effective in removing it. Fixing the grammatical gender bias results in a positive effect on the quality of the resulting word embeddings, both in monolingual and cross lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.
Modality prediction of biomedical literature images using multimodal feature representation

This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Obioma Pelka Christoph M. Friedrich Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text represen1 Department of Computer Science, University of Applied tation was obtained by vector quantisation on a Bag-of-Words dictionary Sciences and Arts Dortmund, Germany generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.
Risk factors for respiratory distress syndrome among Chinese infants of 34-42 weeks gestational age : a multi-center observational study

To compare the risk factors for respiratory distress syndrome (RDS) at different gestational ages among Chinese infants 34-42 weeks of gestational age (GA), we reviewed the clinical records of 4,032 newborns. The case (RDS) and control groups were subdivided into three groups according to GA: 34-36 weeks, 37-38 weeks, and 3942 weeks. An Apgar score of < 7 at 5 min post-birth, male gender and gestational diabetes were independent risk factors for RDS in late preterm and term infants. For infants of 34-36 weeks GA, additional independent risk factors included placental abnormalities and cesarean section, whereas the administration of antenatal dexamethasone and increased gestational age were protective. For infants of 37-38 weeks GA, additional risk factors included meconium stained amniotic fluid and cesarean section without labor, and increased gestational age was also protective (OR = 0.59). Fetal distress and umbilical cord abnormalities were the additional risk factors for infants of 39-42 weeks gestational age, but not for infants of 37-38 weeks gestational age. The results of the current study showed that an Apgar score of < 7 at 5 min post-birth, male gender and gestational diabetes were independent risk factors for both late preterm and full-term infants with RDS. For newborns of 34-38 weeks gestational age, the incidence of RDS decreased with advancing gestational age, and cesarean section was found to increase this risk, more so in cesarean delivery without labor. The administration of antenatal dexamethasone was found to reduce the incidence of RDS in late preterm infants.
Inhibiting the Evolution of Antibiotic Resistance Graphical Abstract Highlights

In Brief Bacterial evolution drives antimicrobial resistance (AMR) development. We identify the protein Mfd as a highly conserved ‘‘evolvability factor’’ that increases mutagenesis and the capacity of bacteria to evolve antibiotic resistance. We propose inhibiting the activity of evolvability factors through ‘‘antievolution’’ drugs during antibiotic treatment to ameliorate the global AMR crisis.