Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulation of the southern Indian Ocean dipole on the impact of El Niño–Southern Oscillation on Australian summer rainfall

This study investigates the modulation of the southern Indian Ocean dipole (SIOD) upon the impacts of the El Niño–Southern Oscillation (ENSO) on Australian summer rainfall (ASR) on an inter‐annual timescale. First the SIOD is identified as an important factor influencing rainfall anomalies over northeastern Australia via a zonal vertical circulation spanning from the southern Indian Ocean (SIO) to northern Australia. When ENSO and the SIOD are out of phase, the El Niño‐induced anticyclonic and convection anomalies shift eastwards to Australia and induce pronounced negative rainfall anomalies in northern Australia. When ENSO and the SIOD are in phase, the anticyclonic and convection anomalies extend westwards and generate an anomalous zonal vertical circulation, inducing positive rainfall anomalies in northeastern Australia. These different shifts in the circulation and rainfall anomalies with the two types of ENSO–SIOD occurrences are essentially attributed to the opposite effects of the different phases of SIOD events. The out‐of‐phase and in‐phase combinations of ENSO and the SIOD also largely form the dominant patterns of ASR variation. The significant influence of the SIOD on ASR indicates that the phase of the SIOD should be taken into account in the prediction of austral summer climate over Australia.
Enhanced discriminative models with tree kernels and unsupervised training for entity detection

This work explores two approaches to improve the discriminative models that are commonly used nowadays for entity detection: tree-kernels and unsupervised training. Feature-rich classifiers have been widely adopted by the Natural Language processing (NLP) community because of their powerful modeling capacity and their support for correlated features, which allow separating the expert task of designing features from the core learning method. The first proposed approach consists in leveraging the fast and efficient linear models with unsupervised training, thanks to a recently proposed approximation of the classifier risk, an appealing method that provably converges towards the minimum risk without any labeled corpus. In the second proposed approach, tree kernels are used with support vector machines to exploit dependency structures for entity detection, which relieve designers from the burden of carefully design rich syntactic features manually. We study both approaches on the same task and corpus and show that they offer interesting alternatives to supervised learning for entity recognition.
Hybrid generation systems planning expansion forecast: A critical state of the art review

In recent years the electric power generation has entered into a new development era, which can be described mainly by increasing concerns about climate change, through the energy transition from hydrocarbon to clean energy resources. In order to power system enhance reliability, efficiency and safety, renewable and nonrenewable resources are integrated together to configure so-called hybrid systems. Despite the experience accumulated in the power networks, designing hybrid system is a complex task. It has become more challenging as far as most renewable energy resources are random and weather/climatic conditions-dependant. In this challenging context, this paper proposes a critical state-of-the-art review of hybrid generation systems planning expansion and indexes multi-objective methods as strategies for hybrid energy systems optimal design to satisfy technical and economical constraints.
[The effects of a pulmonary rehabilitation program for chronic obstructive pulmonary disease patients].

PURPOSE This study was designed to investigate the effects of a pulmonary rehabilitation program for patients with COPD. METHOD 37 subjects, who had a FEV1/FVC below 70%, participated. 18 were assigned to the experimental group and 19 to the control group. The program consisted of individualized education program and exercise program for 6 weeks, 3 times a week. Data was collected through questionnaire surveys of general characteristics, anxiety and depression, blood tests for lactic acid and cardiopulmonary exercise tests, and also using bicycle ergometer, for exercise capacity. As for data analyses, paired and unpaired t-test and chi2-test were adopted using an SPSS program. RESULT The result revealed that the increase in VT, peak O2, Emax, HRmax and Wmax, at the maximal exercise, were significantly high in the experimental group. However, the anxiety and depression scores were not significantly high in the experimental group. CONCLUSION The pulmonary rehabilitation program was effective in increasing cardiopulmonary endurance in patients with COPD. Accordingly, we should seriously consider an individualized pulmonary rehabilitation program as a nursing intervention.
A text-mining approach to explain unwanted behaviours

Current machine-learning-based malware detection seldom provides information about why an app is considered bad. We study the automatic explanation of unwanted behaviours in mobile malware, e.g., sending premium SMS messages. Our approach combines machine learning and text mining techniques to produce explanations in natural language. It selects keywords from features used in malware classifiers, and presents the sentences chosen from human-authored malware analysis reports by using these keywords. The explanation elaborates how a system decision was made. As far as we know, this is the first attempt to generate explanations in natural language by mining the reports written by human malware analysts, resulting in a scalable and entirely data-driven method.
Converged infrared light as heat source for thermal to electrical energy generation

Sunlight is a prefer source to generate energy by using PV cell especially in equatorial country. The heat released from the sun is one kind of energy, which is heat energy. Usually, the heat energy released from sun is not fully utilized. However, this energy can be reused by using thermoelectric generator (TEG) also. TEG transfer heat energy into electricity, prevent heat energy from being wasted. Generally, part of the Sunlight's heat is in the form of infrared light. Thus, infrared (IR) light bulb was used to simulate the sunlight when carry out the experiment in the lab. In addition, IR light was converged by using convex lens so it can focus IR light on one point and it can reach to higher temperature. There are two experiments carried out by using converged IR light as heat source, which are focus light direct irradiate on TEG and using heat sink to gather the heat before it apply on TEG. Both of the result were analyzed and compared with using heater as heat source. Lastly, voltage output of TEG by using converged IR light as heat source is compare with previous work related.
Pure climb creep mechanism drives flow in Earth’s lower mantle

Climb creep provides an efficient deformation mechanism for bridgmanite under lower mantle conditions. At high pressure prevailing in the lower mantle, lattice friction opposed to dislocation glide becomes very high, as reported in recent experimental and theoretical studies. We examine the consequences of this high resistance to plastic shear exhibited by ringwoodite and bridgmanite on creep mechanisms under mantle conditions. To evaluate the consequences of this effect, we model dislocation creep by dislocation dynamics. The calculation yields to an original dominant creep behavior for lower mantle silicates where strain is produced by dislocation climb, which is very different from what can be activated under high stresses under laboratory conditions. This mechanism, named pure climb creep, is grain-size–insensitive and produces no crystal preferred orientation. In comparison to the previous considered diffusion creep mechanism, it is also a more efficient strain-producing mechanism for grain sizes larger than ca. 0.1 mm. The specificities of pure climb creep well match the seismic anisotropy observed of Earth’s lower mantle.
Automatic failure-path inference: a generic introspection technique for Internet applications

Automatic failure-path inference (AFPI) is an application-generic, automatic technique for dynamically discovering the failure dependency graphs of componentized Internet applications. AFPI's first phase is invasive, and relies on controlled fault injection to determine failure propagation; this phase requires no a priori knowledge of the application and takes on the order of hours to run. Once the system is deployed in production, the second, noninvasive phase of AFPI passively monitors the system, and updates the dependency graph as new failures are observed. This process is a good match for the perpetually-evolving software found in Internet systems; since no performance overhead is introduced, AFPI is feasible for live systems. We applied AFPI to J2EE and tested it by injecting Java exceptions into an e-commerce application and an online auction service. The resulting graphs of exception propagation are more detailed and accurate than what could be derived by time-consuming manual inspection or analysis of readily-available static application descriptions.
Camera auto-calibration using zooming and zebra-crossing for traffic monitoring applications

This paper describes a camera auto-calibration system, based on monocular vision, for applications in the framework of Intelligent Transportation Systems (ITS). Using camera zoom and a very common element of urban traffic infrastructures as it is a zebra crossing, a principal point and vanishing point extraction is proposed to obtain an automatic calibration of the camera, without any prior knowledge of the scene. This calibration is very useful to recover metrics from images or apply information of 3D models to estimate 2D pose of targets, making a posterior object detection and tracking more robust to noise and occlusions. Moreover, the algorithm is independent of the position of the camera, and it is able to work with variable pan-tilt-zoom cameras in fully self-adaptive mode. In the paper, the results achieved up to date in real traffic conditions are presented and discussed.
The Strica Homolog AaCASPS16 Is Involved in Apoptosis in the Yellow Fever Vector, Aedes albopictus

Caspases are a family of cysteine proteases playing essential roles during apoptosis. Seven caspases identified in Drosophila were Dronc, Dredd, Strica, Dcp-1, Decay, Drice and Damm. Among them, Strica is an insect-specific caspase containing a long serine- and threonine- rich prodomain, of which function is not yet well studied. Here we identified a homolog of strica from Aedes albopictus, named as Aacasps16. Aacasps16 encoded a protein containing a putative serine- and threonine-rich prodomain and a well conserved caspase catalytic domain. AaCASPS16 shared high identity with dipteran insects Strica homologs. Alignment showed that the closest relative of AaCASPS16 was Aedes aegypti AeCASPS16. The expression profiles of Aacasps16 during developmental and adult stages were analyzed. Purified recombinant AaCASPS16 exhibited the highest caspase activity to WEHD, which is the substrate preferred by human caspase-9. AaCASPS16 induced apoptosis when over-expressed in C6/36 cells. AaCASPS16 was processed during apoptosis induced by actinomycin D and ultraviolet irradiation treatment, whereas partial silencing of Aacasps16 reduced actinomycin D- and ultraviolet irradiation-triggered apoptosis in C6/36 cells. Taken together, our study identified AaCASPS16 as a novel apoptotic caspase in Aedes albopictus.
NVMcached: An NVM-based Key-Value Cache

As byte-addressable, high-density, and non-volatile memory (NVM) is around the corner to be equipped alongside the DRAM memory, issues on enabling the important key-value cache services, such as memcached, on the new storage medium must be addressed. While NVM allows data in a KV cache to survive power outage and system crash, in practice their integrity and accessibility depend on data consistency enforced during writes to NVM. Though techniques for enforcing the consistency, such as journaling, COW, or checkpointing, are available, they are often too expensive by frequently using CPU cache flushes to ensure crash consistency, leading to (much) reduced performance and excessively compromised NVM's lifetime. In this paper we design and evaluate NVMcached, a KV cache for non-volatile byte-addressable memory that can significantly reduce use of flushes and minimize data loss by leveraging consistency-friendly data structures and batched space allocation and reclamation. Experiments show that NVMcached can improve its system throughput by up to 2.8x for write-intensive real-world workloads, compared to a non-volatile memcached.
Correlation of the Abundance of Betaproteobacteria on Mineral Surfaces with Mineral Weathering in Forest Soils

ABSTRACT Pyrosequencing-based analysis of 16S rRNA gene sequences revealed a significant correlation between apatite dissolution and the abundance of betaproteobacteria on apatite surfaces, suggesting a role for the bacteria belonging to this phylum in mineral weathering. Notably, the cultivation-dependent approach demonstrated that the most efficient mineral-weathering bacteria belonged to the betaproteobacterial genus Burhkolderia.
Electro-optofluidics: Achieving Dynamic Control On-chip References and Links

A vital element in integrated optofluidics is dynamic tuning and precise control of photonic devices, especially when employing electronic techniques which are challenging to utilize in an aqueous environment. We overcome this challenge by introducing a new platform in which the photonic device is controlled using electro-optical phase tuning. The phase tuning is generated by the thermo-optic effect using an on-chip electric microheater located outside the fluidic channel, and is transmitted to the optofluidic device through optical waveguides. The microheater is compact, high-speed (> 18 kHz), and consumes low power (~mW). We demonstrate dynamic optical trapping control of nanoparticles by an optofluidic resonator. This novel electro-optofluidic platform allows the realization of high throughput optofluidic devices with switching, tuning, and reconfiguration capability, and promises new directions in optofluidics. Optical waveguiding using thermal gradients across homogenous liquids in microfluidic channel, " Appl. Microfluidic tuning of distributed feedback quantum cascade lasers, " Opt. Electrically reconfigurable silicon microring resonator-based filter with waveguide-coupled feedback, " Opt. Coupling of modes analysis of resonant channel add-drop filters, " IEEE J. Optofluidic trapping and transport on solid core waveguides within a microfluidic device, " Opt. Optimization of metallic microheaters for high-speed reconfigurable silicon photonics, " Opt. Optical 4x4 hitless slicon router for optical networks-on-chip (NoC), " Opt. Thermally tunable silicon racetrack resonators with ultralow tuning power, " Opt. Interlaced optical force-fluorescence measurements for single molecule biophysics, " Biophys. Improvement of thermal properties of ultra-high Q silicon microdisk resonators, " Opt.
Suppression of random coincidences during in-beam PET measurements

In-beam positron emission tomography (PET) is currently the only method for an in-situ monitoring of charged hadron therapy. However, in-beam PET data, measured at beams with a sub-/spl mu/s-microstructure due to the accelerator radiofrequency (RF), are highly corrupted by random coincidences arising from prompt /spl gamma/-rays following nuclear reactions as the projectiles penetrate the tissue. Therefore, since random-correction techniques from conventional PET cannot be applied, the clinical in-beam PET at the therapy facility at the Gesellschaft fur Schwerionenforschung (GSI) Darmstadt merely reconstructs events registered in the pauses (/spl sim/2-4 s) between the beam macropulses (/spl les/2 s). We have successfully tested at GSI two methods for suppressing, during beam extraction, the micropulse-induced random coincidences. Image statistics increased /spl sim/90%. Both methods rely on the synchronization of the /spl gamma//spl gamma/-coincidences measured by the positron camera with the time microstructure of the beam, either by using the RF-signal from the accelerator or the signal of a thin diamond detector placed in the beam path in front of the target. Energy and triple-coincidence time correlated spectra first-measured during beam extraction, combined with the corresponding tomographic images of the /spl beta//sup +/-activity induced by the beam in a plastic phantom, clearly confirm the feasibility of the proposed random suppression methods. These methods provide the solution for applying in-beam PET at future synchrotron and even cyclotron facilities with reduced beam acceleration (delivery pause) times.
Disaster Dynamics: Understanding the Role of Quantity in Organizational Collapse

This article examines the role that the quantity of non-novel events plays in precipitating disaster through the development of a formal (mathematical) system-dynamics model. Building on existing case studies of disaster, we develop a general theory of how an organizational system responds to an on-going stream of non-novel interruptions to existing plans and procedures. We show how an overaccumulation of interruptions can shift an organizational system from a resilient, self-regulating regime, which offsets the effects of this accumulation, to a fragile, self-escalating regime that amplifies them. We offer a new characterization of the conditions under which organizations may be prone to major disasters caused by an accumulation of minor interruptions. Our analysis provides both theoretical insights into the causes of organizational crises and practical suggestions for those charged with preventing them.
On Computing the Critical Coupling Coefficient for the Kuramoto Model on a Complete Bipartite Graph

We extend recent results on the existence of global phase-locked states (GPLS) in the Kuramoto model on a complete graph to the case of a complete bipartite graph. In particular, we prove that, for the Kuramoto model on a complete bipartite graph, the value of the critical coupling coefficient, i.e., the smallest coupling coefficient for which a GPLS can exist, can be determined by solving a system of two nonlinear equations that do not depend on the coupling coefficient itself. We show that said system of equations can be solved using an efficient algorithm described in the paper.
Deer Improvement – A breeding program for farmed red deer in New Zealand.

‘Deer Improvement’ is a commercial red deer breeding program based in the South Island of New Zealand. The breeding program utilises progeny testing, AI, MOET, foetal aging, DNA parentage testing, computer tomography and ultrasound carcass scanning, to maximise genetic progress towards the objective of improving the profitability of venison production. Annual genetic trends (based on the industry DEERSelect breeding values) of +1.3kg/yr in weaning weight, +2.02kg/yr in yearling weight, +1.0kg/yr in carcass weight and -0.18 days/yr in conception date have been realised and the Deer Improvement herd has bred 18 of the top 20 stags on the DEERSelect replacement index (August 2017 across herd evaluation). This paper discusses how ‘Deer Improvement’ was founded, and the current structure of the breeding program.
Microcontroller Based LPG Gas Leakage Alert System

The main focus of this paper is to discuss how gas sensors are made using Arduino microcontroller. Arduino is a microcontroller by which many instruments can be made; from these we have made “Gas sensor”. In this authentic model a gas sensor model i.e. mq-6 gas sensor is used for sensing LPG gas from environment and allows user to be aware of the leakage of that. For being aware a buzzer is used to be aware of and interrupt the user to the hazards of the consumption of the destruction of gas leaking. This module will effective to detect LPG leakage in domestic purpose, factory, petrol pump etc.
Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain

Sampling-based planners have solved difficult problems in many applications of motion planning in recent years. In particular, techniques based on the Rapidly-exploring Random Trees (RRTs) have generated highly successful single-query planners. Even though RRTs work well on many problems, they have weaknesses which cause them to explore slowly when the sampling domain is not well adapted to the problem. In this paper we characterize these issues and propose a general framework for minimizing their effect. We develop and implement a simple new planner which shows significant improvement over existing RRT-based planners. In the worst cases, the performance appears to be only slightly worse in comparison to the original RRT, and for many problems it performs orders of magnitude better.
Using Off-the-Shelf Exception Support Components in C++ Verification

An important step toward adoption of formal methods in software development is support for mainstream programming languages. Unfortunately, these languages are often rather complex and come with substantial standard libraries. However, by choosing a suitable intermediate language, most of the complexity can be delegated to existing execution-oriented (as opposed to verification-oriented) compiler frontends and standard library implementations. In this paper, we describe how support for C++ exceptions can take advantage of the same principle. Our work is based on DiVM, an LLVM-derived, verification-friendly intermediate language.Our implementation consists of 2 parts: an implementation of the 'libunwind' platform API which is linked to the program under test and consists of 9 C functions. The other part is a preprocessor for LLVM bitcode which prepares exception-related metadata and replaces associated special-purpose LLVM instructions.
A Study of Probabilistic Password Models

A probabilistic password model assigns a probability value to each string. Such models are useful for research into understanding what makes users choose more (or less) secure passwords, and for constructing password strength meters and password cracking utilities. Guess number graphs generated from password models are a widely used method in password research. In this paper, we show that probability-threshold graphs have important advantages over guess-number graphs. They are much faster to compute, and at the same time provide information beyond what is feasible in guess-number graphs. We also observe that research in password modeling can benefit from the extensive literature in statistical language modeling. We conduct a systematic evaluation of a large number of probabilistic password models, including Markov models using different normalization and smoothing methods, and found that, among other things, Markov models, when done correctly, perform significantly better than the Probabilistic Context-Free Grammar model proposed in Weir et al., which has been used as the state-of-the-art password model in recent research.
Bioinformatics Applications Note Systems Biology Astream: an R Package for Annotating Lc/ms Metabolomic Data

AStream, an R-statistical software package for the curation and identification of feature peaks extracted from liquid chromatography mass spectrometry (LC/MS) metabolomics data, is described. AStream detects isotopic, fragment and adduct patterns by identifying feature pairs that fulfill expected relational patterns. Data reduction by AStream allows compounds to be identified reliably and subsequently linked to metabolite databases. AStream provides researchers with a fast, reliable tool for summarizing metabolomic data, notably reducing curation time and increasing consistency of results. Availability: The AStream R package and a study example can be freely accessed at