Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal Energy Storage Technology Developments

This paper describes recent technology developments in thermal energy storage devices for use on spacecraft for thermal control of electronic components requiring thermal stability. A thermal energy storage module has been designed, built and tested using lithium nitrate as the melt material and carbon foam as the conductive filler. For this unit the observed melt temperature was around 30 °C. The supercooling property of lithium nitrate was reduced by adding zinc nitrate as a catalyst. The hydrophobic nature of the carbon foam was overcome by adding a surfactant to the lithium nitrate. The resulting energy storage capacity of the prototype module during the melt was 40 W‐hr/kg. There was very good thermal conductivity through the module resulting in very low temperature gradients even at high power levels.
Inlining java native calls at runtime

We introduce a strategy for inlining native functions into Java™ applications using a JIT compiler. We perform further optimizations to transform inlined callbacks into semantically equivalent lightweight operations. We show that this strategy can substantially reduce the overhead of performing JNI calls, while preserving the key safety and portability properties of the JNI. Our work leverages the ability to store statically-generated IL alongside native binaries, to facilitate native inlining at Java callsites at JIT compilation time. Preliminary results with our prototype implementation show speedups of up to 93X when inlining and callback transformation are combined.
Erlotinib or Gefitinib for the Treatment of Relapsed Platinum Pretreated NSCLC and Ovarian Cancer: a Systematic Review

Background: Platinum-based chemotherapy is the standard of care for ovarian cancer and non-small cell lung cancer (NSCLC). However, resistance to platinum agents invariably develops. Targeted therapies, such as tyrosine kinase inhibitors (TKIs), have great potential here as they exert their anti-tumour effect via alternative mechanisms to platinum-based drugs and as such may remain unaffected by emergent resistance to platinum.
RNA-seq identifies a role for the PPARβ/δ inverse agonist GSK0660 in the regulation of TNFα-induced cytokine signaling in retinal endothelial cells

Purpose The peroxisome proliferator-activated receptor beta/delta (PPARβ/δ) is a transcription factor with roles in metabolism, angiogenesis, and inflammation. It has yet undefined roles in retinal inflammation and diabetic retinopathy (DR). We used RNA-seq to better understand the role of the antagonist and inverse agonist of PPARβ/δ, GSK0660, in TNFα-induced inflammation. Understanding the underlying mechanisms of vascular inflammation could lead to new treatments for DR. Methods RNA was isolated from human retinal microvascular endothelial cells treated with a vehicle, TNFα, or TNFα plus GSK0660. RNA-seq was performed with a 50 bp single read protocol. The differential expression was determined using edgeR and gene ontology, and a pathway analysis was performed using DAVID. RNA-seq validation was performed using qRT-PCR using the primers for ANGPTL4, CCL8, NOV, CXCL10, and PDPK1. Results TNFα differentially regulated 1,830 transcripts, many of which are involved in the cytokine–cytokine receptor interaction, chemokine signaling, and inflammatory response. Additionally, TNFα highly upregulated genes involved in leukocyte recruitment, including CCL5, CX3CL1, and CXCL10. GSK0660 differentially regulated 273 transcripts in TNFα-treated cells compared to TNFα alone. A pathway analysis revealed the enrichment of cytokine–cytokine receptor signaling. In particular, GSK0660 blocks the TNFα-induced upregulation of CCL8, a chemokine involved in leukocyte recruitment. Conclusions TNFα regulates several genes related to retinal leukostasis in retinal endothelial cells. GSK0660 blocks the effect of TNFα on the expressions of cytokines involved in leukocyte recruitment, including CCL8, CCL17, and CXCL10 and it may therefore block TNFα-induced retinal leukostasis.
City tourism: pedestrian orientation behaviour

This paper focuses on the orientation behaviour of pedestrian city tourists. It explores the importance of landmarks for pedestrian orientation and shows the results of a study conducted in 2005/06 in Vienna. The hypothesis of the study is that better orientation and guiding systems will lead to more and longer trips on foot. The study explores the navigation behaviour of tourists and points out which elements are to be used in wayfinding instructions to enhance the vacation experience for individual tourists and increase the distance and number of walks. Some of the major questions of the study regard the tourist’s behaviour concerning the proceeding of planning day-trips, the landmarks being used for orientation and the route choice behaviour of tourists. This contribution focuses on the extent of landmark use and the categories of landmarks being used by tourists. (a) For the covering entry of this conference, please see ITRD abstract no. E215407.
From Sequence to Spike to Spark: Evo-devo-neuroethology of Electric Communication in Mormyrid Fishes

Abstract Mormyrid fishes communicate using pulses of electricity, conveying information about their identity, behavioral state, and location. They have long been used as neuroethological model systems because they are uniquely suited to identifying cellular mechanisms for behavior. They are also remarkably diverse, and they have recently emerged as a model system for studying how communication systems may influence the process of speciation. These two lines of inquiry have now converged, generating insights into the neural basis of evolutionary change in behavior, as well as the influence of sensory and motor systems on behavioral diversification and speciation. Here, we review the mechanisms of electric signal generation, reception, and analysis and relate these to our current understanding of the evolution and development of electromotor and electrosensory systems. We highlight the enormous potential of mormyrids for studying evolutionary developmental mechanisms of behavioral diversification, and make the case for developing genomic and transcriptomic resources. A complete mormyrid genome sequence would enable studies that extend our understanding of mormyrid behavior to the molecular level by linking morphological and physiological mechanisms to their genetic basis. Applied in a comparative framework, genomic resources would facilitate analysis of evolutionary processes underlying mormyrid diversification, reveal the genetic basis of species differences in behavior, and illuminate the origins of a novel vertebrate sensory and motor system. Genomic approaches to studying the evo-devo-neuroethology of mormyrid communication represent a deeply integrative approach to understanding the evolution, function, development, and mechanisms of behavior.
Reducing Access Latency in Virtual Machine Assignments

In cloud systems, big data must be partitioned and then stored over several data nodes (DNs) for being processed by virtual machines(VMs). Thus, there exists access latency among DNs and their assigned VMs. Meanwhile, the access latency also exists among the pairs of assigned VMs for the computing result collection. Different VM assignment strategies for DNs result in different access latency. It has been proved that how to assign VMs for DNs to minimize the maximum access latency is the problem of NP-hard. This paper proposes a new algorithm for virtual machine assignment. The proposed algorithm initially selects a group of VMs which communicate with each other under a certain threshold limit in access latency. Then, it utilizes an efficient heuristic algorithm, that is also proposed in this paper, to find a clique containing a certain number of VMs from the obtained group of VMs. After that, the proposed algorithm employs the Hopcroft-Karp algorithm to assign the VMs in the clique for DNs. Extensively experimental results show that the maximum access latency is reduced by 10.39%,5.68%,9.09%,5.45% on average, on the popular four network architectures, Tree, VL2, Fat-Tree and BCube, respectively, in comparison to the state-of-the-art of approximation algorithms. In addition, the proposed algorithm is faster by 7.42% than the existing algorithm in clique finding.
Dry eye disease

Dry eye disease refers to a group of disorders that involve reduced tear production or excessive tear evaporation. [1][1] Dry eye affects up to 30% of people older than 50 years of age. [2][2] Studies evaluating the effect of moderate and severe dry eye on quality of life have shown utility scores
Transparency, consistency and modularity of strategic reasoning: An agent architecture for interactive business simulations

Interactive business simulations are widely used to explore and compare business strategies from both practice and theory. In many business simulations however, educators and researchers lack support in observing how the simulated actors operationalize their strategies, in validating whether operations have been aligned with the strategy, and also in (re)configuring available player and opponent strategies based on new theoretical or practical insights. This paper specifies requirements for a novel business simulation architecture that facilitates transparency, consistency and modularity of strategic decision making by simulated actors in interactive business simulations. A system architecture is proposed that integrates three components: an extensible agent middleware, a distributed simulation engine and a modular reasoning framework. How the architecture fulfills the three requirements of strategic reasoning transparency, consistency and modularity is illustrated in a use case of a business simulation game for supply chain management education.
Training the Motor Cortex by Observing the Actions of Others During Immobilization

Limb immobilization and nonuse are well-known causes of corticomotor depression. While physical training can drive the recovery from nonuse-dependent corticomotor effects, it remains unclear if it is possible to gain access to motor cortex in alternative ways, such as through motor imagery (MI) or action observation (AO). Transcranial magnetic stimulation was used to study the excitability of the hand left motor cortex in normal subjects immediately before and after 10 h of right arm immobilization. During immobilization, subjects were requested either to imagine to act with their constrained limb or to observe hand actions performed by other individuals. A third group of control subjects watched a nature documentary presented on a computer screen. Hand corticomotor maps and recruitment curves reliably showed that AO, but not MI, prevented the corticomotor depression induced by immobilization. Our results demonstrate the existence of a visuomotor mechanism in humans that links AO and execution which is able to effect cortical plasticity in a beneficial way. This facilitation was not related to the action simulation, because it was not induced by explicit MI.
Renal dysfunction and anemia in patients with heart failure — the cardio-renal anemia syndrome

Zatajivanje srca (ZS) predstavlja patofizioloπko stanje u kojem srce kao crpka nije sposobno zadovoljiti metaboliËke potrebe organizma. Radi se o kliniËkom sindromu kojega Ëine simptomi i znaci poremeÊene srËane funkcije te povoljan odgovor na odgovarajuÊu terapiju. VodeÊi uzroci kroniËnog ZS su koronarna bolest srca i arterijska hipertenzija, posebno u starijih osoba. U europskim zemljama i Hrvatskoj ZS jedan je od najËeπÊih razloga hospitalizacije osoba starijih od 65 godina te stoga predstavlja znaËajan javno-zdravstveni problem. Prevalencija kroniËnog ZS u opÊoj populaciji iznosi oko 2%, raste s dobi pa je u osoba starijih od 65 godina veÊa od 5%, a u dobi izmeu 70 i 80 godina 10-20%. UnatoË znatnom napretku u dijagnostici i lijeËenju, sindrom kroniËnog ZS ima ozbiljnu prognozu, broj smrti i dalje raste, a godiπnja smrtnost najteæih bolesnika refraktornih na standardno lijeËenje veÊa je od 50%. Iako se odgovarajuÊim lijeËenjem Heart failure (HF) is a pathological condition where the heart as a pump is not capable of meeting the metabolic requirements of the body. This is a clinical syndrome including the symptoms and signs of impaired cardiac function and a positive response to appropriate therapy. The leading causes of HF are coronary heart disease and hypertension, especially in elderly persons.2 In the European countries and Croatia, HF is one of the most frequent causes of hospitalization of persons over 65 years of age and therefore it represents a significant public and health problem. The prevalence of chronic HF in general population is around 2% and it rises with age, so it is greater than 5% in persons over 65 years of age and in persons between 70 and 80 years of age it is 10-20%. Despite a significant progress in diagnostics and treatment, the syndrome of chronic HF has a serious prognosis, the number of deaths is constantly rising, while an annual mortality of the most serious patients resistant to standard treatment is Renal dysfunction and anemia in patients with heart failure — the cardio-renal anemia syndrome
Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality around the world. Identifying potential DDIs during the drug design process is critical in guiding targeted clinical drug safety testing. Although detection of adverse DDIs is conducted during Phase IV clinical trials, there are still a large number of new DDIs founded by accidents after the drugs were put on market. With the arrival of big data era, more and more pharmaceutical research and development data are becoming available, which provides an invaluable resource for digging insights that can potentially be leveraged in early prediction of DDIs. Many computational approaches have been proposed in recent years for DDI prediction. However, most of them focused on binary prediction (with or without DDI), despite the fact that each DDI is associated with a different type. Predicting the actual DDI type will help us better understand the DDI mechanism and identify proper ways to prevent it. In this paper, we formulate the DDI type prediction problem as a multitask dyadic regression problem, where the prediction of each specific DDI type is treated as a task. Compared with conventional matrix completion approaches which can only impute the missing entries in the DDI matrix, our approach can directly regress those dyadic relationships (DDIs) and thus can be extend to new drugs more easily. We developed an effective proximal gradient method to solve the problem. Evaluation on real world datasets is presented to demonstrate the effectiveness of the proposed approach.
Building empirical support for automated code smell detection

Identifying refactoring opportunities in software systems is an important activity in today's agile development environments. The concept of code smells has been proposed to characterize different types of design shortcomings in code. Additionally, metric-based detection algorithms claim to identify the "smelly" components automatically. This paper presents results for an empirical study performed in a commercial environment. The study investigates the way professional software developers detect god class code smells, then compares these results to automatic classification. The results show that, even though the subjects perceive detecting god classes as an easy task, the agreement for the classification is low. Misplaced methods are a strong driver for letting subjects identify god classes as such. Earlier proposed metric-based detection approaches performed well compared to the human classification. These results lead to the conclusion that an automated metric-based pre-selection decreases the effort spent on manual code inspections. Automatic detection accompanied by a manual review increases the overall confidence in the results of metric-based classifiers.
Dependency-Aware Data Locality for MapReduce

Recent years have witnessed the prevalence of MapReduce-based systems, e.g., the Apache Hadoop, in large-scale distributed data processing. Fetching data from remote servers across multiple network switches is known to be costly. Hence, it is highly desirable to co-locate computation with data. State-of-the-art popularity-based replication achieves data locality through replicating popular files and spreading the replicas over multiple servers. While working well for independent files, they can store highly dependent files in different servers, resulting in excessive remote data accesses exchanges and consequently prolonging the job completion time. In this paper, we develop DALM (Dependency-Aware Locality for MapReduce), a novel replication strategy for general real-world input data that can be highly skewed and dependent. DALM accommodates data-dependency in a data-locality framework that comprehensively weights such key factors as popularity and storage budget. We extensively evaluate DALM through both simulations and real-world implementations, and have compared with state-of-the-art solutions, including the Hadoop system and the popularity-based Scarlett. The results show that DALM can significantly improve data locality for different inputs. For a popular iterative graph processing application on Hadoop, our prototype implementation of DALM reduces the remote data access and job completion time by 34.3% and 9.4%, respectively.
Predictability of real-time systems: a process-algebraic approach

This paper presents a testing-based semantic preorder that relates real-time systems given in the process description language TPL on the basis of the predictability of their timing behavior. This predictability is measured in terms of the amount of variability present in processes' "activity-completion times". The semantic preorder is shown to coincide with an already existing, well-investigated implementation relation for TPL-the must-preorder. The optimality of our relation is also established by means of a full abstraction result. An example is provided to illustrate the utility of this work.
Low-Temperature Combustion Synthesis of Cordierite Powder and its Characteristic

The MgO-Al2O3-SiO2 (MAS) glass powders have been synthesized by low-temperature combustion technique using magnesium nitrate, aluminium nitrate, silicic acid as material, urea as fuel. The crystallization process, sintering behavior and dielectric properties were investigated by means of DTA, XRD, TMA and SEM techniques. The results showed that the glass powders could be sintered at lower than 1000°C. The μ-cordierite phase was first crystallized from glass and then transformed into α-cordierite phase during sintering process. The obtained cordierite-based glass-ceramics at 950°C and 1000°C have low dielectric constant (4.00 ~ 4.96 at 1 MHz), low dielectric dissipation factor (≈ 0.003) and high sintering density (which is above 98% of the theoretical density), which are used for electronic packaging.
TanDEM-X: a TerraSAR-X add-on satellite for single-pass SAR interferometry

TanDEM-X is a mission proposal for a TerraSAR-X add-on satellite for high-resolution single-pass SAR interferometry. This mission proposal has been selected for a Phase A study within the scope of a Call for Proposals for a next German Earth Observation Mission to be launched in 2008/2009. The mission has the goal of generating a global Digital Elevation Model (DEM) with an accuracy corresponding to the DTED-3 specifications (12 m posting, 2 m relative height accuracy for flat terrain). This goal will be achieved by means of a second, TerraSAR-X like satellite (TanDEM-X) flying in a close orbit configuration with TerraSAR-X. This paper describes the mission concept and requirements, including several innovative aspects like operation modes, orbit selection and maintenance as well as PRF and phase synchronization. Results from a detailed performance estimation show the achievable DEM accuracy. Finally, an overview of the potential of the TanDEM-X mission for several scientific applications is presented.
Millisecond encoding precision of auditory cortex neurons

Neurons in auditory cortex are central to our perception of sounds. However, the underlying neural codes, and the relevance of millisecond-precise spike timing in particular, remain debated. Here, we addressed this issue in the auditory cortex of alert nonhuman primates by quantifying the amount of information carried by precise spike timing about complex sounds presented for extended periods of time (random tone sequences and natural sounds). We investigated the dependence of stimulus information on the temporal precision at which spike times were registered and found that registering spikes at a precision coarser than a few milliseconds significantly reduced the encoded information. This dependence demonstrates that auditory cortex neurons can carry stimulus information at high temporal precision. In addition, we found that the main determinant of finely timed information was rapid modulation of the firing rate, whereas higher-order correlations between spike times contributed negligibly. Although the neural coding precision was high for random tone sequences and natural sounds, the information lost at a precision coarser than a few milliseconds was higher for the stimulus sequence that varied on a faster time scale (random tones), suggesting that the precision of cortical firing depends on the stimulus dynamics. Together, these results provide a neural substrate for recently reported behavioral relevance of precisely timed activity patterns with auditory cortex. In addition, they highlight the importance of millisecond-precise neural coding as general functional principle of auditory processing—from the periphery to cortex.