Miracle-dz/UniFAR-FTUs huggingface dataset:
UvA-DARE (Digital Academic Repository) Designing circular waste management strategies: The case of organic waste in

circular approach. We provide a toolbox for transforming current linear and fragmented waste management processes into integrative and circular strategies. This objective is attained in three stages. We first recognize urban waste management as a complex mul-tidimensional system and we outline its boundaries and its core components. We then map these components individually through a multipillar approach, identifying leverage points, relationships, and connection nodes within and between them. Finally, we reintegrate the results from the mapping process through the connection nodes, obtaining a systemic overview of relationships and processes, which serves as a frame for a circular strategy. To this end, we propose a set of guidelines for converting the results of the mapping process into circular Urban waste management is one of the most complex and urgent challenges that the society faces. In this paper, an innovative research methodology is proposed, introducing a systemic approach to circular waste management strategy-making. Urban waste management is a complex system that needs to be tackled in a holistic, yet context-specific manner. To produce truly integrative outcomes, this paper provides insight into the system as a whole, its components and the relationships between them, using specific tools, to form the basis for a circular strategy. A toolbox is presented for transforming the current linear and fragmented waste management processes into integrative and circular strategies. The proposed integrative methodology encompassing outline, multipillar mapping, and synthesis can be applied to different locations and waste streams. The concept is subsequently demon-strated through a case study focusing on the municipality of Amsterdam (the Netherlands) and the management of organic waste streams.
A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography

In recent years, multimodal thermoacoustic imaging has demonstrated superior imaging quality compared to other emerging modalities. It provides functional and molecular information, arising due to electromagnetic absorption contrast, at ultrasonic resolution using inexpensive and non-ionizing imaging methods. The development of optical- as well as radio frequency (RF)-induced thermoacoustic imaging systems would benefit from reliable numerical simulations. To date, most numerical models use a combination of different software in order to model the hybrid thermoacoustic phenomenon. Here, we demonstrate the use of a single open source finite element software platform (ONELAB) for photo- and RF-acoustic computed tomography. The solutions of the optical diffusion equation, frequency domain Maxwell’s equations, and time-domain wave equation are used to solve the optical, electromagnetic, and acoustic propagation problems, respectively, in ONELAB. The results on a test homogeneous phantom and an approximate breast phantom confirm that ONELAB is a very effective software for both photo- and RF-acoustic simulations, and invaluable for developing new reconstruction algorithms and hardware systems.
NH 3 spatio-temporal variability over Paris, Mexico and Toronto and its link to 1 PM 2.5 during pollution events 2

Megacities can experience high levels of fine particulate matter (PM 2.5 ) pollution linked to ammonia 16 (NH 3 ) mainly emitted from agricultural activities. Here, we investigate such pollution in the cities of 17 Paris, Mexico and Toronto, each of which have distinct emission sources, agricultural regulations, and 18 topography. Ten years of measurements from the Infrared Atmospheric Sounding Interferometer 19 (IASI) are used to assess the spatio-temporal NH 3 variability over and around the three cities. 20 In Europe and North America, we determine that temperature is associated with the increase in NH 3 21 atmospheric concentrations with coefficient of determination (r²) of 0.8 over agricultural areas. The 22 variety of the NH 3 sources (industry and agricultural) and the weaker temperature seasonal cycle in 23 southern North America induce a lower correlation factor (r² = 0.5). The three regions are subject to 24 long range transport of NH 3 , as shown using HYSPLIT cluster back-trajectories. The highest NH 3 25 concentrations measured at the city scales are associated with air masses coming from the surrounding 26 and north-northeast regions of Paris, the south-southwest areas of Toronto, and the 27 southeast/southwest zones of Mexico City. 28 Using NH 3 and PM 2.5 measurements derived from IASI and surface observations from 2008 to 2017, 29 annually frequent pollution events are identified in the three cities. Wind roses reveal statistical 30 patterns during these pollution events with dominant northeast-southwest directions in Paris and 31 Mexico City, and the transboundary transport of pollutants from the United-States in Toronto. To 32 check how well chemistry transport models perform during pollution events, we evaluate simulations 33 made using the GEOS-Chem model for March 2011. In these simulations we find that NH 3 34 concentrations are overall underestimated, though day-to-day variability is well represented. PM 2.5 is 35 generally underestimated over Paris and Mexico, but overestimated over Toronto.
Overlapping semantic representations of sign and speech in novice sign language learners

The presence of semantic information in multivariate patterns of neural activity has been explored as a method of measuring knowledge and learning. Using fMRI, we investigated whether novice learners of American Sign Language (ASL) showed overlapping representations of semantic categories for words presented in a well-known (English) or newly learned (ASL) language. We find evidence of neural patterns that were partially shared between sign and speech in novice participants. This result provides evidence for the influence of even brief learning on neural representations in cross-modality language processing.
Molecular Adsorption of H 2 O on TiO 2 and TiO 2 :Y Surfaces

In this work, using theoretical calculations within the framework of the density functional theory, taking into account the dispersive VDW interaction, the processes of adsorption and interaction of a water molecule with a TiO 2 surface in various configurations are investigated. At the atomic / molecular level, the interactions of a water molecule with a TiO 2 surface have been studied for various orientations. The results of calculations within the framework of DFT + VDW show that the adsorption energies of single water molecules in different initial positions on the substrate surface vary from -0.72 to -0.84 eV, and the most stable adsorbate structure is the TiO 2 + H 2 O system upon adsorption of a molecule of water, parallel to the Y axis, because during the adsorption of H 2 O parallel to the Y axis, some favorable effects are observed in the band structure of titanium dioxide. On the one hand, the band gap decreases to 2.59 eV, and on the other hand, a new energy state appears in the band gap with an energy contribution of 0.17 eV, when water is physisorbed and interacts with a titanium atom at a distance of 2.12 Å and occupies a perpendicular position relative to the surface.
Intrinsic Storage Valuation by Variational Analysis

The mathematical problem concerning intrinsic storage optimisation is formulated and solved by means of variational analysis. The solution, though obtained in implicit form, still sheds light on many important features of the optimal exercise strategy. It is shown how the solution depends on different constraint types including carry cost and cycle constraint. Additionally, the relationship between intrinsic and stochastic solutions is investigated. In particular, we show that the optimal stochastic exercise decision is always close to the intrinsic one.
Design and Implementation of a Tree-Based Blind Modulation Classification Algorithm for Multiple-Antenna Systems

This paper presents the design and testbed implementation of a tree-based algorithm, which classifies many of the linearly modulated signals, such as binary phase-shift keying (PSK), quadrature PSK (QPSK), offset QPSK, $\pi $ /4-QPSK, 8-PSK, minimum shift keying, and 16-quadrature amplitude modulation. The proposed modulation classification algorithm is applicable to spatially multiplexed multiple antenna systems over frequency-selective fading channels. It works in the presence of timing, phase, and frequency offsets, without having prior knowledge of the channel state information. Classification is performed by utilizing the combined properties of the correlation functions, cyclic cumulant (CC), and cumulant. The correlation function of the received baseband signal exhibits peaks at a particular set of time lag, which represents distinctive features for the modulation formats. The CC uses the position of nonzero cycle frequency of the received baseband signals to classify modulation formats, while the cumulant employs threshold values. To authenticate the proposed algorithm, implementation and measurement are performed in an indoor propagation environment by using a National Instrument testbed setup. The performance of the proposed algorithm is compared with existing methods via Monte Carlo simulations.
SPAD-based flash LiDAR sensor with high ambient light rejection for automotive applications

LiDAR is a key sensor technology for future driving. For autonomous vehicles a fast and reliable three dimensional monitoring of the environment is essential for managing a wide variety of common traffic situations. Since these kinds of systems use typically light in the near infrared range, ambient light of the sun is a serious problem due to its high intensity compared to the laser source. Therefore, reducing the influence of ambient light on the distance measurement is very important. In this paper we present a 2 × 192 pixel SPAD-based direct time-of-flight line sensor for flash LiDAR applications with high ambient light rejection integrated in standard CMOS technology. Two commercially available 905 nm laser diodes emitting short pulses are employed for scene illumination. For time measurement an in-pixel timeto- digital-converter with a resolution of 312.5 ps and full range of 1.28 μs has been implemented. Each pixel uses four vertically arranged single SPADs for background light rejection based on the detection of temporal correlated photons. This technique allows the discrimination of the received laser pulse buried in the superimposed background light and, hence, to improve the measurement quality. Additionally, different parameters of the coincidence detection circuit, such as coincidence depth and time, can be varied during operation to enable a real time adjustment to the present ambient light condition, which is measured between each laser shot by operating the sensor in photon counting mode. By using this technique the sensor allows a reliable distance measurement at various ambient and target conditions.
Indonesian Journal of Electrical Engineering and Computer Science

Received Mar 26, 2022 Revised Jun 24, 2022 Accepted Jul 8, 2022 The retinal vascular morphological caliber changes reveal the signs of systemic health disorder and life threat diseases such as cardio and cerebral diseases. The quantitative vascular parameters like narrowed arteries, widened venules, reduced artery-vein ratio (AVR) have been associated with aforesaid disorders and diseases. Hence the quantitative biomarker AVR is important parameter in diagnosing variety of diseases. The accurate quantification of AVR be possible if and only if accurate classification of arteries and vein is done. In this paper, we proposed a deep learning based robust vessel segmentation and classification algorithm based on spatial U-Net and the accuracy of the algorithm is 97.8%. In the quantification process, this algorithm is applied on region of interest (RoI) of a fundus image and measured the AVR values using central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE). The experimentation is carried on the digital retinal images for vessel extraction (DRIVE) dataset. The outcome of this work is the AVR observed to be >0.4 in normal retinal case and AVR value <0.4 for unhealthy case.
Can We Automatically Generate Class Comments in Pharo?

Code comments support developers in understanding and maintaining codebases. Specifically in the Pharo environment, code comments serve as the main form of code documentation and usually convey information ranging from high-level design descriptions to low-level implementation details. Neverthe-less, numerous important classes in Pharo still lack comments as developers find writing comments to be a tedious and effort-intensive task. Previous works in Java have recommended generating comments automatically to reduce commenting effort and save developers time. There exist several approaches to achieve this goal. One such popular approach is based on identifying stereotypes, i.e. , a generalized set of characteristics supposed to represent an entity (object, class). However, this approach has not been tested for other programming languages. In this paper, we adopt the stereotype-based approach to automatically generate class comments in the Pharo programming environment. Specifically, we generated information about the class type, collaborators and key methods. We surveyed seven developers to evaluate the generated comments for 24 classes. The responses suggest that, although more information could be added to the comments, the generated class comments are readable and understandable, and the majority of comments do not contain unnecessary information.
Very Large Telescope MUSE Observations of the Bubble Nebula around NGC 1313 X-2 and Evidence for Additional Photoionization

The bubble nebula surrounding NGC 1313 X-2 is believed to be powered by high velocity winds from the central ultraluminous X-ray source (ULX) as a result of supercritical accretion. With the Multi-Unit Spectroscopic Explorer (MUSE) observation of the nebula, we find enhanced [O iii] emission at locations spatially coincident with clusters of stars and the central X-ray source, suggesting that photoionization in addition to shock ionization plays an important role in powering the nebula. The X-ray luminosity of the ULX and the number of massive stars in the nebula region can account for the required ionizing luminosity derived with MAPPINGS V, which also confirms that pure shocks cannot explain the observed emission line ratios.
The Mixed-Electrode Concept for Understanding Growth and Aggregation Behavior of Metal Nanoparticles in Colloidal Solution

The growth and aggregation behavior of metal nanoparticles can be modulated by surfactants and different other additives. Here the concept of how open-circuit mixed electrodes helps to understand the electrical aspects of nanoparticle growth and the consequences for the particle geometries is discussed. A key issue is the self-polarization effect of non-spherical metal nanoparticles, which causes a local decoupling of anodic and partial processes and asymmetry in the local rates of metal deposition. These asymmetries can contribute to deciding to the growth of particles with high aspect ratios. The interpretation of electrochemical reasons for particle growth and behavior is supported by experimental results of nanoparticle syntheses supported by microfluidics which can supply high yields of non-spherical nanoparticles and colloidal product solutions of high homogeneity.
Supplement to “Minimizing sensitivity to model misspeciﬁcation”

Lemma S1. Let Assumption A1 hold. Let q (y ) and h (y, β0, γ∗ ) be sequences of functions with supπ0∈ (γ∗ ) Eβ0,π0 |q(Y )|ζ = O(1), for some ζ > 1, and supπ0∈ (γ∗ ) Eβ0,π0 |h (Y , β0, γ∗ )|2 = O(1). Then we have: (i) supπ0∈ (γ∗ ) |δβ0,π(γ∗ ) − δβ0,π0 | =O( 1/2 ), (ii) supπ0∈ (γ∗ ) |Eβ0,π0q (Y ) −Eβ0,π(γ∗ )q (Y )| = O( 1/2 ), (iii) supπ0∈ (γ∗ ) |Eβ0,π0h (Y , β0, γ∗ ) − Eβ0,π(γ∗ )h (Y , β0, γ∗ ) − 〈π0 − π(γ∗ ), Eβ0,π(γ∗ )h (Y , β0, γ∗ )∇π log fβ0,π(γ∗ )(Y )〉| = o( 1/2 ). Proof of Lemma S1. # Part (i): By a mean-value expansion around π(γ∗ ), we find |δβ0,π0 − δβ0,π(γ∗ )| = ∣∣〈π0 −π(γ∗ ), ∇πδβ0,π̃ 〉∣∣≤ ∥∥π0 −π(γ∗ )∥∥ind,γ∗‖∇πδβ0,π̃‖γ∗ , where π̃ is between π(γ∗ ) and π0. Therefore, sup π0∈ (γ∗ ) |δβ0,π0 − δβ0,π(γ∗ )| ≤ sup π0∈ (γ∗ ) ∥∥π0 −π(γ∗ )∥∥ind,γ∗ sup π0∈ (γ∗ ) ‖∇πδβ0,π0‖γ∗ =O( 1/2)O(1) =O( 1/2).
The Impact of the COVID-19 Pandemic on Foreign Body Ingestion Trends in Children: A Comparison of the Pre-Pandemic Period to 2020

Foreign body ingestion (FBI) among children is associated with morbidity and mortality. We used the National Electronic Injury Surveillance System to compare FBI trends from 2017–2019 to 2020 during the spread of SARS-CoV-2. The pandemic and associated stay-at-home orders were associated with uptrends in button battery and magnet ingestions but unchanged total FBI trends.
Prediction the survival of patients with breast cancer using random survival forests for competing risks

Summary Objectives Breast cancer (BC) is the most common cause of cancer death in Iranian women. Sometimes death from other causes precludes the event of interest and makes the analysis complicated. The purpose of this study was to identify important prognostic factors associated with survival duration among patients with BC using random survival forests (RSF) model in presence of competing risks. Also, its performance was compared with cause-specific hazard model. Methods This retrospective cohort study assessed 222 patients with BC who were admitted to Ayatollah Khansari hospital in Arak, a major industrial city and the capital of Markazi province in Iran. The cause-specific Cox proportional hazards and RSF models were employed to determine the important risk factors for survival of the patients. Results The mean and median survival duration of the patients were 90.71 (95%CI: 83.8-97.6) and 100.73 (95%CI: 89.2-121.5) months, respectively. The cause-specific model indicated that type of surgery and HER2 had statistically significant effects on the risk of death of BC. Moreover, the RSF model identified that HER2 was the most important variable for the event of interest. Conclusion According to the results of this study, the performance of the RSF model was better than the cause-specific hazard model. Moreover, HER2 was the most important variable for death of BC in both of the models.
COVID-19 vaccination intention and hesitancy: Mistrust on COVID-19 vaccine benefit a major driver for vaccine hesitancy among healthcare workers; a cross-sectional study in North India

Summary Background The advent of an effective novel COVID-19 vaccine could extinguish the current devastating pandemic but the vaccine hesitancy is a hurdle for the public health system, so this study estimated the COVID-19 vaccination intention and hesitancy among the healthcare workers, the priority target group for the COVID-19 vaccination in India. Methods A web-based cross-sectional survey was conducted among the healthcare workers in Chandigarh, a union territory in North India, using a Snowball sampling technique. A total of 403 healthcare workers participated in the study between 2nd and 25th January 2021. The primary data collected were the intention to get vaccinated against the available COVID-19 vaccine and the concerns regarding the new vaccines. The attitude towards novel COVID-19 vaccine was assessed using developed Vaccine attitude examination scale. These questionnaire, which were delivered via WhatsApp, was filled by the participants over Google forms. Results Among the 403 respondents surveyed, the majority (54.6%) reported they were definitely intended to get vaccinated against COVID-19, however, 7% expressed a resistance for inoculation with COVID-19 vaccination. The perceived susceptibility (aOR = 0.511, CI 0.265-0.987) and severity of COVID-19 infection (aOR = 0.551 CI 0.196-0.704) and not being concerned about the efficacy of new COVID-19 vaccines (aOR = 0.702 CI 1.109-26.55) were found to have the highest significant odds of intention to take the COVID-19 vaccine. The majority (62%) were concerned about the safety of the vaccine, in terms of side-effects, quality control, and doubted efficacy of the vaccine. The mistrust of the benefits of the vaccine is a significant predictor for vaccine hesitancy among the healthcare workers (aOR = 5.205 CI 3.106-8.723). Conclusion Therefore, strategic communication and vaccine-acceptance programs should be formulated in order to combat the prevailing mistrust on the vaccine safety and efficacy and attain effective coverage to gain herd immunity.
Characterization of an analog-to-digital converter frequency response by a Josephson arbitrary waveform synthesizer

The capability to generate up to 1 V pure AC signals based on quantum standards marked a milestone on electrical metrology opening new applications that were not possible without this standard. Frequency response characterization of analog-to-digital converters (ADC) is fundamental for precision digital metrology. Several methods have been investigated for this characterization based on thermal converters, programmable Josephson voltage standard or input impedance measurements. This paper describes the method, the results obtained and the uncertainty estimation for the characterization of the amplitude frequency response at different aperture times of the DCV sampling function of the Keysight 3458-A using, for the first time, a Josephson arbitrary waveform synthesizer. This new standard allows one to extend the characterization to a higher frequency range and lower aperture times. The results show that the frequency response does not depend on aperture time and the same frequency correction can be applied in an extended frequency range. The knowledge of this correction will facilitate the application of the ADCs to higher frequencies, where low aperture times are required with accuracy in the order of V/V.
Going Deeper Underground: Using accelerometers on mobile devices to enable positioning on underground public transportation systems

Thanks to rapid advances in technologies like GPS and WiFi positioning, smartphone users are now able to determine their location almost anywhere they go. This is not true, however, of people who are travelling in underground public transportation networks, one of the few types of high-traffic areas where smartphones do not have access to accurate position information. In this thesis, we introduce the problem of underground transport positioning on smartphones and present SubwayPS, an accelerometer-based positioning technique that allows smartphones to determine their location substantially better than baseline approaches, even deep beneath city streets. We highlight several immediate applications of positioning in subway networks in domains ranging from mobile advertising to mobile maps and present MetroNavigator, a proof-of-concept smartphone and smartwatch application that notifies users of upcoming points-of-interest and alerts them when it is time to get ready to exit the train.