Long-Wavelength Quantum Well Infrared Photodetectors

A majority of IR sensors used for imaging arrays operating in the long-wavelength IR region between 8 µm-12 µm are based on mercury cadmium telluride (HgCdTe). This material system is unable to satisfy all the requirements imposed by modem applications. Structural difficulties due to poor uniformity, high defect densities, and weak bond strengths cause difficulties in manufacturing large IR focal plane array cameras. As an alternative, quantum well infrared photodetectors (QWIPs) utilising intersubband absorption between gallium arsenide (GaAs) wells and aluminium gallium arsenide (AIGaAs) barriers were perfected. These QWIPs possess better uniformity in comparison to HgCdTe detectors, and QWIP imaging arrays have recently become commercially available. However, the responsivity of GaAs/AlGaAs QWIPs is still lower than HgCdTe detectors. To further improve the responsivity of QWIP detectors, QWIPs with wells or barriers of GaInAsP instead of AlGaAs have been developed. Results of QWIPs made from the material systems GaAs/GaInP, GaInAs(P)/InP, (Al)GaInAs/InP, and GaInAs/AllnAs have been discussed

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenanceprogram. Partialdischarges(PDs)phenomenaaffecttheinsulationsystemofanelectrical machine and\u2014in the long term\u2014can lead to a breakdown, with a consequent, signi\ufb01cant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is speci\ufb01cally designed to run on low-cost embedded devices

Prediction of severe thunderstorm events with ensemble deep learning and radar data

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the most used techniques rely on video prediction deep learning methods which take in input time series of radar reflectivity images to predict the next future sequence of reflectivity images, from which the predicted rainfall quantities are extrapolated. Differently from the previous works, the present paper proposes a deep learning method, exploiting videos of radar reflectivity frames as input and lightning data to realize a warning machine able to sound timely alarms of possible severe thunderstorm events. The problem is recast in a classification one in which the extreme events to be predicted are characterized by a an high level of precipitation and lightning density. From a technical viewpoint, the computational core of this approach is an ensemble learning method based on the recently introduced value-weighted skill scores for both transforming the probabilistic outcomes of the neural network into binary predictions and assessing the forecasting performance. Such value-weighted skill scores are particularly suitable for binary predictions performed over time since they take into account the time evolution of events and predictions paying attention to the value of the prediction for the forecaster. The result of this study is a warning machine validated against weather radar data recorded in the Liguria region, in Italy

Pd-Catalyzed α-Arylation of Sulfones in a Three-Component Synthesis of 3-[2-(Phenyl/methylsulfonyl)ethyl]indoles

A novel four-step domino process for the synthesis of 3-[2-(aryl/alkylsulfonyl)ethyl]indoles starting from readily available 2-iodoanilines is reported. The domino reaction is based on the intramolecular palladium-catalyzed α-arylation of sulfones, which was combined with both intermolecular aza-Michael and Michael addition reactions using vinyl sulfones as the electrophile. The domino process produced good yields and tolerated the presence of substituents with different electronic properties on the aniline ring. In addition, density functional theory (DFT) calculations were carried out to gain more insight into the formation of the observed indole derivatives. Keywords: arylation; density functional calculations; domino reactions; indoles; palladium-catalyze

Abstracts of the Second Urban Sound Symposium

Following the successful first Urban Sound Symposium held at Ghent University in 2019, the second edition in 2021 had to face the challenges of the pandemic. The symposium turned this challenge into an opportunity for giving easier access to practitioners and experts from around the globe who are confronted with urban sound in their professional activities. It was organized simultaneously in Ghent, Montreal, Nantes, Zurich, London and Berlin by researchers at Ghent University, Mc Gill University, Université Gustave Eiffel, EMPA, University College London and TU Berlin. The online event created opportunities for interaction between participants at poster-booths, virtual coffee tables, and included social activities

Production and Sensory Evaluation of Novel Cheeses Made with Prebiotic Substances: Inulin and Oligofructose

In recent years, the processing and consumption of functional foods worldwide have greatly increased. These foods benefit the body functions which improve consumers’ health and also reduce the risk factors that cause the onset of disease. Furthermore, prebiotic substances favor the multiplication of beneficial intestinal bacteria rather than harmful ones. The purpose of this study was to conduct the sensory evaluation of two functional cheeses containing inulin and oligofructose as a distinctive ingredient, including testing a cheese made with conventional ingredients, called control cheese. Affective type tests, which measured the degree of liking or disliking, were conducted using a verbal 7-point hedonic scale. According to the inclusion and exclusion criteria, 57 untrained judges were selected. This study is a quantitative, analytic and experimental-cross design. Statistical analysis of the data was performed by ANOVA with repeated measures. The results show a similar average degree of liking for the three cheeses, above 5 on the scale or “like”. By analyzing the critical level and the result of the Mauchly’s sphericity test, it is concluded that there is no statistically significant difference in the degree of liking for the three cheeses. Therefore, the addition of prebiotics to artisanal cheeses achieves to satisfy consumers and provide them benefits superior to those provided by traditional foods.Fil: Machuca, Laura Marcela. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Rodriguez, Yamila E.. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Guastavino Meneguini, Daniela E.. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Bruzzo, Maria E.. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Acuña Ojeda, María F.. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Murguia, Marcelo Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentin

FABRIKASI DAN KARAKTERISASI PADUAN Cu2Se.3In2Se3 SUATU KOMPONEN SEMIKONDUKTOR

Telah diketahui bahwa paduan Cu2Se.31n2Sed, ari sistem pseudobinari Cu2Se-lnSe3k elihatannya pegangr eran utama pada mekanisme photovoltaik sel-surya ZnO/CdS/CuInSe2/Mo. Akan tetapi belum banyak infonnasi yang didapat tentang sifat paduan ini. Untuk alasan inilah kami telah memulai mempelajari secara sitematik paduan Cu2Se.31n2Se3te rsebutdalam bentuk kristal batangan dan lapisan film tipis yang masingmasing dibuat dengan metoda Bridgman horisontal daD deposisi daTi evaporasi-cepat. Pada makalah ini disajikan hasil karakterisasi difraktografi sinar-x dari paduan yang dibuat dengan metoda pemanasan bertahap berdasarkan energi bebas Gibbs untuk pembuatan paduano Sifat optik paduan diambil dari hasil photoluminesen pada suhu 4.2 oK dan 77 oK, daD transmisi daD refleksi pada 300 oK untuk lapisan tipis. Komposisi paduan ditentukan dengan mikroanalisa probe elektron

Flare forecasting and feature ranking using SDO/HMI data

We describe here the application of a machine learning method for flare forecasting using vectors of properties extracted from images provided by the Helioseismic and Magnetic Imager in the Solar Dynamics Observatory (SDO/HMI). We also discuss how the method can be used to quantitatively assess the impact of such properties on the prediction process

Active Learning for Auditory Hierarchy

Much audio content today is rendered as a static stereo mix: fundamentally a fixed single entity. Object-based audio envisages the delivery of sound content using a collection of individual sound ‘objects’ controlled by accompanying metadata. This offers potential for audio to be delivered in a dynamic manner providing enhanced audio for consumers. One example of such treatment is the concept of applying varying levels of data compression to sound objects thereby reducing the volume of data to be transmitted in limited bandwidth situations. This application motivates the ability to accurately classify objects in terms of their ‘hierarchy’. That is, whether or not an object is a foreground sound, which should be reproduced at full quality if possible, or a background sound, which can be heavily compressed without causing a deterioration in the listening experience. Lack of suitably labelled data is an acknowledged problem in the domain. Active Learning is a method that can greatly reduce the manual effort required to label a large corpus by identifying the most effective instances to train a model to high accuracy levels. This paper compares a number of Active Learning methods to investigate which is most effective in the context of a hierarchical labelling task on an audio dataset. Results show that the number of manual labels required can be reduced to 1.7% of the total dataset while still retaining high prediction accuracy