Miracle-dz/UniFAR-FTUs huggingface dataset:
A neural network gravitational arc finder based on the Mediatrix filamentation method

Context. Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. Aims. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements that are derived from the Mediatrix filamentation method as entries to an artificial neural network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. Methods. The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. Results. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input into the ANN after a preselection. However, this completeness drops to ~ 70% on the HST images. The false detections are on the order of 3% of the objects detected in these images. Conclusions. The combination of Mediatrix measurements with an ANN is a promising tool for the pattern-recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.
Spatial distribution of the emission intensity in a photonic crystal: Self-interference of Bloch eigenwaves

The analysis of an angular distribution of the emission intensity of a two-level atom (dipole) in a photonic crystal reveals an enhancement of the emission rate in some observation directions. Such an enhancement is the result of the bunching of many Bloch eigenwaves with different wave vectors in the same direction due to the crystal anisotropy. If a spatial distribution of the emission intensity is considered, the interference of these eigenwaves should be taken into account. In this paper, the far-field-emission pattern of a two-level atom is discussed in the framework of the asymptotic analysis of the classical macroscopic Green's function. Numerical example is given for a two-dimensional square lattice of air holes in polymer. The relevance of results for experimental observation is discussed.
Etude experimentale et numerique du comportement de materiaux de type verre soumis a des ondes de choc

Cette etude concerne la caracterisation du comportement de materiaux de type verre sous chocs, et plus precisement l'endommagement dynamique sous chocs generes par irradiation laser. Ce phenomene etant conditionne par l'histoire des contraintes induites dans les echantillons, une premiere partie du travail realise a consiste a caracteriser la propagation des ondes de choc dans les verres etudies. Des mesures ont ete effectuees sous chocs generes par explosifs. Les resultats obtenus ont permis d'elaborer un modele de comportement original, fonde sur la densification permanente du verre sous choc. Ce modele a ete implemente dans un code de calcul, puis valide par des comparaisons entre experiences et simulations. L'etude de l'endommagement a ensuite ete abordee. Des experiences de chocs induits par irradiation laser de forte puissance ont permis d'etudier l'ecaillage du verre dans des conditions originales, caracterisees par des temps de maintien de la pression de choc tres courts, de l'ordre de la ns. L'endommagement a ete quantifie en mesurant la zone fracturee a l'interieur des cibles. Les influences de divers parametres experimentaux sur l'epaisseur de cette zone ont ete degagees. Un modele de fragmentation du verre en traction a ete elabore et couple au modele de comportement. Il permet de predire par simulation l'etendue approximative de la zone fracturee dans une cible donnee soumise a un choc de tres courte duree
A SURVEY: HEART DISEASE PREDICTION USING
MACHINE LEARNING TECHNIQUES

This Heart attacks and the deaths caused by heart diseases are increasing at every part of the world. Heart troubles are caused by people's lives being under more stress. One of the most important organs in our body is probably the heart. It assists in controlling blood circulation and flow to all of the body's organs. Today, this disease is responsible for the greatest number of fatalities. Generally a person suffering from any kind of heart problem shows few signs and symptoms from which we can easily judge it and thus can help the individual on time. The symptoms are like high intensity chest pain, fast rate of heartbeat, and pain with some uneasiness in breathing are recorded. This information is dissected on customary premise. In this survey, an outline of the coronary illness furthermore, its present techniques is right off the bat presented. Besides, a top to bottom examination of the most important AI methods accessible on the writing for coronary illness forecast is momentarily expounded. Various machine learning algorithms that performs well in every detection mechanism are Naive Bayes, Decision Tree, Support Vector Machine, Artificial Neural Network, Random Forest, K-Nearest Neighbours and many more. The performance of every machine learning model depends on how accurately it makes predictions. Therefore, one of the key components of evaluating the model's efficiency is accuracy. In our research, Random Forest provides the best accuracy of 91% in predicting heart disease.
The Impact of Time Spent in Natural Outdoor Spaces on Children’s Language, Communication and Social Skills: A Systematic Review Protocol

There has been increasing interest over the past decade with regard to the health and wellbeing implications of time spent outdoors in nature for children. Universal systematic reviews of evidence report benefits to physical health, social-emotional mental health and wellbeing, cognition and academic learning. Internationally, there is indicative evidence to suggest outdoor engagement with nature may also impact children’s language and communication skills, skills that are critical to development, education, social relationships and life opportunities. Yet, at present such evidence has not been synthesised. Despite evidence for the benefits of the outdoors, the amount of time children are spending outdoors is in rapid decline, and has been further exacerbated by the COVID-19 pandemic. Alongside this are increasing numbers of children starting primary education with significant speech, language and communication needs (SLCN) which remain persistent over time. With established wide-reaching benefits of nature to children’s physical and mental health and psychological development, there is a need to further explore the more specific impacts of the natural environment on children’s language, communication and social skills, which could provide a unique opportunity to consider nature as a universal public health intervention for SLCN. The current review will aim to synthesise existing qualitative and quantitative evidence of the impact of time spent in natural outdoor spaces on the language, communication and social skills of 2–11-year-old children. Literature will be searched across seven databases and considered for inclusion against inclusion and exclusion criteria. Potential implications of the review include informing public health practice and policy for child development and education, informing priorities for speech, language, and communication interventions, and providing directions for future international research.
Interactive Planning for Autonomous Driving in Intersection Scenarios Without Traffic Signs

Efficient intersection planning is one of the most challenging tasks for an autonomous vehicle at present. Politeness to other traffic participants and reaction to surrounding information are the key aspects that determine the performance of planning algorithm for an autonomous vehicle. In order to capture these aspects, we propose a planning framework, based on the partially observable Markov decision process (POMDP), to ensure social compliance and optimize the motion response of autonomous vehicles. The framework designs a novel POMDP model to ensure effective prediction of behavioral intentions and decision-making at intersections with no traffic signs and low traffic volume. Then, a deterministic planner is employed as the baseline to realize planning. Specifically, we consider both the driving position and the facing angle of the vehicle in real-world situations. At the same time, we utilize scattering methods for probability updating and intent determination to improve the algorithm’s adaptability to real-world scenarios. The proposed framework is evaluated in different scenarios to demonstrate its capabilities in terms of the interactive planning for an autonomous vehicle.
Monte Carlo approach to turbulence

The behavior of the one-dimensional random-force-driven Burgers equation is investigated in the path integral formalism on a discrete space-time lattice. We show that by means of Monte Carlo methods one may evaluate observables, such as structure functions, as ensemble averages over different field realizations. The regularization of shock solutions to the zero-viscosity limit (Hopf-equation) eventually leads to constraints on lattice parameters required for the stability of the simulations. Insight into the formation of localized structures (shocks) and their dynamics is obtained.
Comparison of Regionalisation Techniques for Peak Streamflow Estimation in Small Catchments in the Pilbara, Australia

Arid and semi-arid regions typically lack high-resolution river gauging data causing difficulties in understanding rainfall-runoff patterns. A common predictive method for discharge estimation within ungauged catchments is regional flood frequency estimation (RFFE), deriving peak discharge estimates from similar, gauged catchments and applying them to the catchment of interest. The majority of RFFE equations are developed for larger catchments where flow events may be larger and of greater interest. We test a series of RFFE methods derived for the Pilbara region, applying them to new ungauged small catchments under 10km2. Rainfall values are derived from a guideline Australian design rainfall database, Australian Rainfall and Runoff 2019 (ARR2019) which was recently updated with an additional 30 years of rainfall data. RFFE equations are compared to a direct rainfall model to evaluate their performance within small catchments, identifying key limitations and considerations when modelling small headwater catchments.
Effects of Straw Return and Moisture Condition on Temporal Changes of DOM Composition and Cd Speciation in Polluted Farmland Soil

Straw return can improve soil quality and change the mobility and bioavailability of pollutants in soil. Elevated cadmium (Cd) contents in farmland soils were often reported. However, the impacts of straw-derived dissolved organic matter (DOM) on Cd speciation in soil remain poorly understood. In this study, the effects of straw return and moisture condition on temporal changes of DOM composition and Cd speciation in farmland soils were explored through a laboratory incubation experiment. The humified components of DOM were negatively correlated with exchangeable, carbonate-bound, and Fe-Mn oxide-bound Cd (p < 0.01), while its protein-like component was negatively correlated with residual Cd (p < 0.01). It was found that selected fluorescence parameters could be used to predict temporal changes of Cd geochemical fractions. Straw addition led to increases in soil DOM content during the first three days of the incubation. Flooding should be avoided in the first three days following the straw application to reduce the risk of DOM-facilitated Cd mobilization.
Landscape of Cellular Bioeffects Triggered by Ultrasound-Induced Sonoporation

Sonoporation is the process of transient pore formation in the cell membrane triggered by ultrasound (US). Numerous studies have provided us with firm evidence that sonoporation may assist cancer treatment through effective drug and gene delivery. However, there is a massive gap in the body of literature on the issue of understanding the complexity of biophysical and biochemical sonoporation-induced cellular effects. This study provides a detailed explanation of the US-triggered bioeffects, in particular, cell compartments and the internal environment of the cell, as well as the further consequences on cell reproduction and growth. Moreover, a detailed biophysical insight into US-provoked pore formation is presented. This study is expected to review the knowledge of cellular effects initiated by US-induced sonoporation and summarize the attempts at clinical implementation.
Potential of Alkali–Silica Reactivity of Unexplored Local Aggregates as per ASTM C1260

Alkali–silica reaction (ASR) is one of the major durability issues that affect the material degradation and structural performance, compromising the service life of concrete structures. Therefore, this study was planned to investigate the potential of ASR for locally available unexplored and vastly used aggregates, as per ASTM C1260. Aggregates from five different sources (Shalozan, Abbotabad, Orakzai, Swabi and Sada) were procured from their respective crusher sites. Mineralogical components of these aggregates were studied using the petrographic analysis. Cube, prism and mortar bar specimens were cast using mixture design in accordance with ASTM C1260 and placed in sodium hydroxide solution at 80 °C for 90 days. Identical specimens were also cured in water for the purpose of comparison. It was observed that mortar bar expansion of Orakzai aggregate was higher among the other tested aggregates and greater than 0.20% at 28 days, indicating the reactive nature according to ASTM C1260. Petrographic analysis also revealed the presence of reactive silica (quartzite) in the tested Orakzai source. It was observed that the compressive and flexural strengths of specimens exposed to ASR conducive environment was lower than the identical specimens placed in water. For instance, an approximately 9% decrease in compressive strength was observed for Orakzai aggregates exposed to ASR environment at 90 days compared to similar specimens placed in water curing. Moreover, microstructural analysis showed the development of micro-cracks for specimens incorporating Orakzai source aggregates. This study assists the construction stakeholders for the potential of unexplored local aggregates with regard to ASR before its utilization in mega construction projects.
Raw Milk for Provolone Valpadana PDO Cheese: Impact of Modified Cold Storage Conditions on the Composition of the Bacterial Biota

The raw milk for production of long-ripened, spicy type, Provolone Valpadana (PV) PDO cheese must be stored, refrigerated, and processed within 60 h from the first milking, according to European and Consortium regulations. Low-temperature storage conditions preserve the hygienic quality, but also reduce the diversity and content of dairy microbiota, which is important to define the characteristics and quality of raw milk cheeses. Eleven bulk, raw milk samples were stored, at laboratory level, under two different time/temperature conditions (i.e., 10 °C or 12 °C for 15 h, then cooled to 4 °C for 45 h). The count of different bacterial groups and the diversity of bacterial communities were determined before and after storage by culture-dependent and DNA metabarcoding methods, respectively. The two-step cold storage conditions increased the mesophilic, psychrotrophic, lipolytic, and proteolytic bacterial load, without affecting the hygienic quality of milk. Among the 66 dominant and 161 subdominant taxa retrieved by DNA metabarcoding, Acinetobacter, Pseudomonas, and the lactic acid bacteria belonging to the genera Lactococcus and Streptococcus were present in almost all the raw milk samples, and their relative abundance was positively related with the total bacterial count. The storage conditions tested could be considered for eventual application in long-ripened PV cheese production to rationalize storage, transfer, and processing of raw milk.
Optical properties and Poincaré mapping in spherical multilayered systems: periodic, quasiperiodic, and disordered

We systematically studied the optical properties (narrows peaks position) of the transmission spectra for microspheres coated by a multilayered stack. Three different sequences of spherical stack—periodic, quasiperiodic, and disordered—are studied by the transfer matrix approach. Dependence of the number of resonances in the transmission spectrum as function of number of layers in the stack is numerically investigated with details. It is shown that characteristic shape of the recursive return map forms well‐defined ordered spectrum in the state space. The shape of such structures is different for different frequency ranges and various spherical quantum numbers. For quasiperiodic case, the latter leads to a specific signature of studied sequences and generates the self‐similarity in the transmittance spectra. Copyright © 2014 John Wiley & Sons, Ltd.
Interoperability of -Band Sentinel-1 SAR and GRACE Satellite Sensors on PSInSAR-Based Urban Surface Subsidence Mapping of Varanasi, India

The Sentinel-1 is an active synthetic aperture radar (SAR) satellite with a <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>-band SAR sensor operating at a center frequency of 5.405 GHz and a wavelength of 5.55 cm. With the availability of freely available SAR datasets from Sentinel-1, the persistent scatterer SAR interferometry (PSInSAR)-based urban surface subsidence mapping has become easy. However, apart from geological causes, the major cause of urban surface subsidence is the over-exploitation of groundwater that results in piezometric pressure loss in the aquifers resulting in net subsidence. With the Gravity Recovery and Climate Experiment (GRACE) satellite sensor, the groundwater level fluctuations can be very easily studied temporally. But the coarse spatial resolution of GRACE data makes the study of groundwater fluctuations difficult to study for smaller watersheds. This study aims to correlate the PSInSAR average surface line of sight (LOS) displacement from Sentinel-1, with the average groundwater fluctuations from the GRACE sensor temporally from May 2017 to February 2022. The study also compared the correlation between PSInSAR displacement in both VV and VH polarizations and observed the <inline-formula> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> values to be 0.63 and 0.65 for VV and VH polarizations, respectively, with GRACE data. After that, using the displacement and groundwater level fluctuation data, an estimation of a gravimetric anomaly due to a decrease in groundwater level was carried out for Varanasi city. The <inline-formula> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula>, mean absolute error (MAE), and root-mean-square error (RMSE) were observed to be 0.84, 1.23, and 2.4, respectively, in gravimetric anomaly estimation, thus giving sufficient acceptance for interoperability of the two sensors.
A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization

This paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunction with k-nearest neighbors (k-NN). In both algorithms, the goal is to determine a small gene subset that can be used to classify samples with a high degree of accuracy. The proposed algorithms are divided into two phases. To obtain an accurate gene set and to deal with the challenge of high-dimensional data, the redundancy analysis and relevance calculation are conducted in the first phase. To solve the gene selection problem, the second phase applies SVM and k-NN with leave-one-out cross-validation. A performance evaluation was performed on six microarray data sets using the two proposed algorithms. A comparison of the two proposed algorithms with several known algorithms indicates that both of them perform quite well in terms of classification accuracy and the number of selected genes.
Search for Fourth Generation Quarks at CMS

We summarise the analyses that search for fourth generation quarks at the Central Muon Solenoid (CMS) experiment. Such particles provide a natural extension to the Standard Model (SM) and are still consistent with precision electroweak measurements. Our searches are not limited to fourth generation chiral quarks and are relevant to many Beyond the Standard Model theories. No excess over the expected SM background is observed in any of these analyses and limits are set on the masses of the $b^\prime$ and $t^\prime$ quarks at 95% confidence level at 361 GeV/$c^2$ and 450 GeV/$c^2$, respectively.
IMPROVING COSMOLOGICAL CONSTRAINTS WITH NEW AND COMBINED SUPERNOVA DATASETS

We present the results of an analysis of the world SNe data, complemented by a new dataset of low-redshift nearby-Hubble-flow SN Ia. This ”Union” compilation of more than 400 SN Ia includes the recent large samples of SNe Ia from the Supernova Legacy Survey and the ESSENCE Survey, the older datasets, as well as the recently extended dataset of distant supernovae observed with HST. A single, consistent and blind analysis procedure is used for all the various SN Ia subsamples. We present the latest results from this Union compilation and its combination with other cosmological measurements (CMB and BAO), and discuss the cosmological constraints on the dark energy density. With the addition of our new nearby Hubble-flow SNe Ia, these resulting cosmological constraints are currently the tightest available. While our results are consistent with a cosmological constant, we obtain only relatively weak constraints on an equation of state, w, that varies with redshift. The Union compilation data along with software for cosmological analysis is provided through the web link “http://supernova.lbl. gov/Union”.
Modeling and Design of a Partially Air-Filled Slow Wave Substrate Integrated Waveguide

In this article, a partially air-filled and slow wave substrate integrated waveguide (PAF-SW-SIW) build in the metallic nanowires membrane (MnM) technology is studied, fabricated, and measured. The impact of the nanowire conductivity on the slow wave factor (SWF) is quantified, and expressions of the effective permittivity and the effective loss tangent are found for the considered propagation medium. These parameters are expressed as a function of the air layer thickness and the equivalent conductivity of copper nanowires embedded in the nanoporous membrane. Electromagnetic (EM) simulations are used to validate these expressions, and a good agreement is obtained on both of these parameters for a conductivity between 1 and 1M S/m and for various air layer thicknesses, from 5 to 20 $\mu \text{m}$ . In addition, the attenuation constant of nanowires is studied, and the results show a rapid increase of losses in the side walls of the waveguide for a conductivity lower than 100k S/m. Two sets of PAF-SW-SIW with the cutoff frequencies at 50 and 75 GHz, respectively, were fabricated and measured, resulting in attenuation constants between 0.25 and 0.38 dB/mm and between 0.28 and 0.75 dB/mm in the respective single-mode frequency band of these integrated waveguides.