SIRIS-Lab/unlabelled-sti-corpus huggingface dataset:
GASIFICATION REACTOR FOR PRODUCING CRUDE GAS CONTAINING CO OR H2

The invention relates to a gasification reactor for producing crude gas containing CO or H2 by gasification of ash-containing fuel with oxygen-containing gas at temperatures above the fusion temperature of the ash, wherein a reaction chamber, formed by a membrane wall that is flown through by a cooling medium, is provided inside a pressure vessel, comprising a transition channel that is tapering into a gas cooling chamber. According to the invention, spin-reducing, cooled bulkheads are provided in the transition channel. The aim of the invention is, on the one hand, to achieve a strain formation of the draining ash and, on the other hand, to provide another slag drip edge that ensures an optimal slag drain. Said aim is achieved by the provision that the wall (14) supporting the bulkheads (9) changes below the bulkheads into a cylinder wall (17) having a reduced diameter via an step (21) with a corrugated surface, that the cylinder wall (17) having a reduced diameter is surrounded by another cylindrical wall (19) having a larger diameter, which forms on its end and in the direction of gravity a second slag drip edge (10) and that the additional cylindrical wall (19) is adjustably (arrow 22) arranged in its vertical position with respect to the first drip edge (18).
Neurocomputational mechanisms underlying age-related performance changes in goal-directed decisions from experience

The ability to make goal-directed decisions concerning one’s general well-being declines in later life. The primary—and potentially improvable—factor impacting this decline in decision-making performance is the learning process that precedes a decision. However, little is known about age-related changes in the neurocomputational mechanisms that underlie these learning processes. The goal of the proposed project is to identify these changes. The project will first identify age-related variability in decision-making performance across individuals and task environments, and then test what computational models from artificial intelligence best explain this variability. Because previous research has shown that decision-making performance declines as the complexity of the choice environment increases, the focus will be on performance in cognitively demanding environments that require goal-directed decisions from experience. The project will combine data from lab-based and home-based neurocognitive experiments, capitalizing on new developments in online experiments and webcam based eye tracking. The applicant’s unique research background brings the interdisciplinary range of skills together that is required for the project, combining experience in computational modelling techniques from artificial intelligence, large-scale data analysis from computational linguistics, and neurocognitive experiments. The proposed project promises to further the scientific understanding of the functional link between aging and decision-making performance. Its findings will ultimately help to empower aging decision makers to navigate cognitively demanding choice environments.
Wozu brauchen wir Österreich?

Die Frage nach einer nationalen österreichischen Identität ist historisch an den dynastischen Konflikt zwischen Habsburgern und Hohenzollern gebunden und gekennzeichnet durch das Auseinanderklaffen von Staats- und Nationsbewusstsein.
Modelling of Demand Managing Based MIMO System for Price/Load Forecasting in Smart Grid

Since in smart grid the information flow has two ways between customers and manufacturers, the individual forecasting of electricity price and load won't be reasonable. Basically, short term forecasting is more difficult compare to medium and long term forecasting since the short term forecasting needs more accuracy in pick and valleys of a signal. Hereby, this dissertation addresses the multi-input multi-output system to forecast the electricity price and load in a simultaneous form for short term periods in a day and a week while consider their correlation. Therefore, a forecasting model is divided into three main parts, namely, preprocessing, learning engine and adjusted algorithm and each one of them carried out some new suggestions in order to improve forecasting accuracy. In first part consists of wavelet transform and mutual information to remove noisy term and select valuable input data, the second part used multi-input multi-output support vector machine to consider correlation between price and load signals and the last part employs an optimization algorithm (all parts will be with their derivation) in order to optimally set the learning engine control variables e.g. weight, Bayes, penalty factor and etc. As an earlier description, the principal aim of the smart grid is to improve the social benefits to the customers and the manufacturers so that the customer can change their usage profile that means they can shift their load peak on hour-to-hour in an economical way. This means a practical definition of demand-side management in a smart grid. Responsiveness of the market participants can improve the reliability of system operation as well as capital cost investments. As a result, in this dissertation the demand-side management is converted to an optimization problem in smart grid and its effect on load profile is evaluated. The proposed forecasting algorithms are evaluated on several real and well-known markets illustrating its high accuracy in a simultaneous forecast of electricity price and load. Moreover, the interactive effects of demand-side management programs on load factor (load curve) and electricity price signal are investigated by numerical indices. They are shown that the proposed multi input multi output forecasting algorithms have a good potential for simultaneous forecasting of electricity price and load in a smart grid compare to available methods. Moreover, demand-side management makes better load factor and forecasting accuracy in the lowest error value.
Keywords: Simultaneous forecast of electricity price and load, multi-input multi-output, demand-side management, wavelet transform, feature selection, optimization, smart grid.
Coverage of communication-based sensor nodes deployed location and energy efficient clustering algorithm in WSN

An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor networks. This algorithm consists of two sub-algorithms. One is the multi-hop partition subspaces clustering algorithm for ensuring local energy- balanced consumption ascribed to the deployment from another algorithm of distributed locating deployment based on efficient communication coverage probability (DLD-ECCP). DLD-ECCP makes use of the characteristics of Markov chain and probabilistic optimization to obtain the optimum topology and number of sensor nodes. Through simulation, the relative data demonstrate the advantages of the proposed approaches on saving hardware resources and energy consumption of networks.
Spin-glass models and interdisciplinary applications

The main subject of this thesis is the physics of spin glasses. After their introduction in the 70s in order to describe dilute magnetic alloys, spin-glass models have been considered prototype models to understand the behavior of supercooled liquids. Among the systems that can be described and analyzed using the language of disordered systems, there are problems of combinatorial optimization. In the first part of the thesis, we consider spin-glass models with Kac interactions in order to investigate the supercooled phase of glass-forming liquids. Afterwards, we show how some features of spin-glass models can be described by ubiquitous results of Random Matrix Theory in connection with Extreme Value Statistics. Finally, from the interaction of spin-glass theory and computer science, we put forward a new algorithm of immediate application in Financial problems.
Timelapsereader for quality control of immune cells used for cell-based therapies

Cell-based therapy (CBT) for cure of difficult-to-treat cancer types is the most disruptive innovation in personalized medicine. However, patient-specific production of therapeutic immune cells is expensive and associated with remaining risks. In particular, it is known that only actively migrating immune cells are efficient against tumours cells, while inactive therapeutic cells can elicit adverse effects. Yet, there is no quality control (QC) regarding motility of therapeutic cells before patient treatment. The goal of the proposal is to promote an integrated bench-top instrument for reliable assessment of motility parameters in standardized cell culture carriers, i.e. the TimeLapseReader.

As leading provider of live cell migration assays ibidi strives to market a complete QC solution based on an automated instrument in combination with ready-to-use QC kits, slides and reagents. The TimeLapseReader will allow robust and reproducible migration tests in manufacturing facilities and set a new standard for QC of immune cell in CBT.

In Phase 1 the feasibility study will explore key parameters for QC in the most relevant therapeutic cell types and the best market entry strategy. Objectives include:
• Identification of relevant QC parameters for therapeutic cells.
• Setting up the business plan including competitor and freedom-to-operate analysis.
• Strategy to obtain regulatory approval / CE-Marking for the final device version.

Expected impact is eminent as CBT is going to enter the medical market with serval hundred to thousands of patients treated worldwide per year. It is expected, that up to 20% of all cancer patients can be cured with CTB within 10-15 years assuming serval millions QC per year. ibidi will apply for funding to validate QC tests in a Phase 2 proposal. Phase 2 will consolidate the technology and transform ibidi into a global player of medical QC  in the CBT field.
Enzyme-Powered dna nanorobotic devices

WHY: Enzyme-powered nanorobotic devices are able to self-propel thanks to the conversion of a substrate into products, holding a great potential for biomedical applications. A step forward in the field would be the precise control and design over the structure and enzyme spatial configuration, as well as the integration of complex functions. WHAT: DNA-bots is an interdisciplinary approach in which enzyme-propulsion and DNA nanotechnology will be merged to develop a new class of biocompatible and biodegradable nanorobotic devices able to self-propel, sense and actuate in front of specific stimuli. To achieve this goal, I will undertake a high risk/high gain research approach at the forefront of nanotechnology. The leading idea is to design and fabricate DNA nanostructures which will be site-specifically functionalized with enzymes to generate self-propulsion. The enzyme-powered DNA nanorobots will be further engineered by integrating DNA nanoswitches able to sense and perform loading and release of cargoes upon specific stimuli. HOW: The challenge met by this Project is to provide a groundbreaking contribution to DNA nanotechnology and nanorobotics. To achieve these objectives, I will be trained at the Host group led by Prof. Francesco Ricci (University of Rome Tor Vergata, Italy), which is a young and dynamic team at the vanguard of the use of DNA nanotechnology for sensing and drug-delivery applications. The Fellowship will proceed through a well-defined set of research and training tasks, organized into four work packages. Thanks to this MSCA Fellowship, I will continue expanding my scientific knowledge and acquiring cutting-edge skills (in synthetic biology, DNA nanotechnology, and nanorobotics), which will significantly impact my career as a researcher and help me to strenghten my position as an independent and mature researcher.
Directing the immune response through designed nanomaterials

DIRNANO provides a highly integrated and interdisciplinary training of next-generation Early Stage Researchers (ESRs) at the interface of nanopharmaceutical bioengineering and its translation on preclinical and human immunology. 

DIRNANO will develop biocompatible nanopharmaceuticals with either “super”-stealth or immune-specific behavior for cancer immunotherapy and vaccination by mapping nanoparticle-immune interactions through two core approaches: 1) inception of novel surface engineering approaches, based on new organic polymers, zwitterionic lipids and conjugation chemistry strategies, 2) engineering of host or microbial-derived modulators of innate immunity (e.g. complement system).

DIRNANO team comprises internationally renowned scientists and industrialists at the forefront of nanoengineering, pharmaceutical sciences, molecular biosciences, commerce and business, thereby generating a unique pan-European macro-environment for interdisciplinary training of ESRs at the highest international level. Through participation of industrial partners, we will furnish ESRs with in-demand industrial and business skills, including process manufacturing, reproducibility and regulatory challenges, intellectual property and commercialization strategies. 

DIRNANO will lead to rational engineering of broader libraries of NPs with tunable immune-modulating functions. The combinatorial analysis of new nanomaterial core-coat scaffolds will improve temporal and spatial understanding of biomaterial-innate immune interactions at the molecular level, thereby filling the void in overcoming adverse reactions to nanopharmaceuticals injection. DIRNANO will drive future development of small molecules and biologics-based nanopharmaceuticals through a “low-risk-high gain” perspective and within the context of personalized therapies and precision medicine. As such, DIRNANO, will extensively contribute to European science, education and socioeconomics value, skill retention and brain-gain.
THERMAL VIAS IN AN INTEGRATED CIRCUIT PACKAGE WITH AN EMBEDDED DIE

In a multi-module integrated circuit package having a package substrate and package contacts, a die is embedded in the package substrate with thermal vias that couple hotspots on the embedded die to some of the package contacts.
Reconciling tectonic shortening, sedimentation and spatial patterns of erosion from 10Be paleo-erosion rates in the Argentine Precordillera

Abstract The temporal evolution of erosion over million-year timescales is key to understand the development of mountain ranges and adjacent fold-and-thrust belts. While models of orogenic wedge dynamics predict an instantaneous response of erosion to pulses of rock uplift, stream-power based models predict that catchment-wide erosion maxima significantly lag behind a pulse of rock uplift. Here, we explore the relationships between rock uplift, erosion, and sediment deposition in the Argentine Precordillera fold-and-thrust belt at 30°S. Using a combination of 10Be-derived paleo-erosion rates, constraints on re-exposure using 26Al/10Be ratios, geomorphic observations and detrital zircon provenance, we demonstrate that the attainment of maximum upland erosion rates lags the maximum rate of deformation over million-year timescales. The magnitudes and causes of the erosional delays shed new light on the catchment erosional response to tectonic deformation and rock uplift in orogenic wedges.
Probing the geochemistry of the first oceans with novel isotope proxies in banded iron formations (bifs): new perspectives into early continental weathering, ocean oxygenation and mantle geodynamics

Since the beginning of Earth’s history, the oceans acted as an interface between the atmosphere, the continents, and the mantle via atmospheric exchange, weathering, sedimentation, and submarine volcanism. Such interactions make the composition of early seawater a unique recorder of the processes that have affected the Earth’s major reservoirs. Yet, this record has not been explored in details. Here, I propose to investigate the geochemistry of the first oceans that is recorded in Precambrian banded iron formations (BIFs) to address first-order questions related to the early evolution of our planet.
The proposed project combines well-established radiogenic isotopic systems with novel and highly promising stable isotope tracers that will be developed as part of the project. These geochemical proxies will be measured on a unique collection of BIF samples deposited from ~3.8 to 0.7 Ga ago with the following objectives: (1) determine the origin, composition, and size of the first continents, (2) detect whiffs of oxygen in the early oceans in various depositional settings before and after the Great Oxidation Event (GOE), (3) identify the periods of intense hydrothermal activity through Earth’s history and link them to global mantle geodynamics. 
The proposed project will promote the application of state-of-the-art isotopic measurements, with an emphasis on further methodological development to set-up new isotope proxies. This innovative approach is needed to test previously suggested hypotheses, develop new ideas, and to tackle the challenging questions of the early Earth evolution from a different angle.
PENGGUNAAN GRINDR SEBAGAI MEDIA KOMUNIKASI DI KALANGAN GAY (Studi pada Kalangan Gay di Komunitas Dancer Kota Malang)

This research based on the emergence of LGBT phenomena (Lesbian, Gay, Bisexual, and Transgender) which is currently under the spotlight. Gay and bisexual searching some way to stay connected with friends who have same sex orientation. Grindr is a form of communication media which addressed to gay and bisexual people, which in its operation using geo-location system to help finding fellow users. The purpose is to know the using of Grindr as a communication media in gay circle in dancer community in Malang City.
This research is using case study method, which the oncoming concept using social penetration theory, FIRO theory, and social exchange theory. This research is a qualitative research with the Grindr users as the subject. The researcher using purposing sampling technique and the samples will be considered in data collections which according to the researcher are match with the target of this research, that is the members of dancer community in Malang City, the total of the informants are five. While the data analysis techniques that have been used are: data reduction, data display, and drawing and verifying conclusions.
Grindr is social media which exist in smartphone with the ability to finding fellow users based on location. Grindr is useful to find user with sex orientation gay or bisexual, because Grindr created for men. Grindr has function to do chat with fellow users. Not only have that, the users also able to send document. The advantage of Grindr lies on the user view that shows the user by the certain location. For the free version user, the view will be limited only a hundred users. Different with the paid version, it able to shows more users around. The problem that often found lately in its use is the using of Grindr is limited by the government, so the users cannot freely open Grindr. The solution of it to resolve this problem is by using VPN or the third party application that using provider to giving free access to open Grindr.
The conclusion of this research’s result is to fully support the needs of informant to search and to have a friends with same sex orientation that also convenient with the user’s location. The simple view that the application has also make it easier for the user to operate it.
Biomarkers of disordered language in autism

We know talk of ‘the autisms.’ Evidence shows us that autism refers to a heterogeneous group of neurodevelopmental disorders. This heterogeneity has greatly hindered our understanding of autism. This proposal will attempt to disentangle this heterogeneity by focusing on one piece of the autism phenotype: disordered language. More specifically, we will focus on impairments involving phonology, the sounds of language. Infants acquire phonological skills in the first year of life. A disruption in the development of these skills can compromise or even preclude language acquisition. Behavioral studies have provided evidence for an autism subgroup defined by phonological deficits. This subgroup is hypothesized to overlap with specific language impairment (SLI), a developmental language disorder clinically marked by phonological deficits. Given that the presence of speech before age five is the strongest predictor of a better outcome for a child with autism, understanding language deficits that can be present at a very early age is critical. This study will be the first to utilize EEG to examine neural activity relevant to phonological processing in children with autism with and without SLI as well as children with SLI. Our primary goal is to identify biomarkers indexing impaired phonology that can inform earlier diagnoses and treatments for children with disordered language. In addition to this goal, undertaking this research at the BCBL and the University of Seville, which will provide access to both mono- and bilingual children, will enable us to address a critical gap in both autism and SLI research. Developing bilinguals are the rule in the world. Despite this, little is known about bilingualism in autism or SLI. Scientific evidence suggests that there is a ‘bilingual advantage’ that sharpens the mind of bilingual speakers. Could there also be a ‘bilingual advantage’ for children with disordered language? Or is bilingualism simply an extra burden for these children?
Audio-Based mobile health diagnostics

Mobile health is becoming the holy grail for affordable medical diagnostics. It has the potential of associating human behaviour with medical symptoms automatically and at early disease stage; it also offers cheap deployment, reaching populations generally not able to afford diagnosis and delivering a level of monitoring so fine which will likely improve diagnostic theory itself. The advancements of technology offer new ranges of sensing and computation capability with the potential of further improving the reach of mobile health.  Audio sensing through microphones of mobile devices has recently being recognized as a powerful and yet underutilized source of medical information: sounds from the human body (e.g., sighs, breathing sounds and voice) are indicators of disease or disease onsets.  The current pilots, while generally medically grounded, are potentially ad-hoc from the perspective of key areas of computer science; specifically, in their approaches to computational models and how the system resource demands are optimized to fit within the limits of the mobile devices, as well as in terms of robustness needed for tracking people in their daily lives. Audio sensing also comes with challenges which threaten its use in clinical context: its power hungry nature and the fact that audio data is very sensitive and the collection of this sort of data for analytics violates obvious ethical rules.   This work proposes models to link sounds to disease diagnosis and to deal with the inherent issues raised by in-the-wild sensing: noise and privacy concerns. We exploit these audio models in wearable systems maximizing the use of local hardware resources with power optimization and accuracy in both near real time and sparse audio sampling. Privacy will arise as a by-product taking away the need of cloud analytics. Moreover, the framework will embed the ability to quantify the diagnostic uncertainty and consider patient context as confounding factors via additional sensors.