Miracle-dz/UniFAR-FTUs huggingface dataset:
Progress in the biological control of Argentine stem weevil and comment on its potential

Between 1988 and 1990 three visits were made to South America to search for and collect the Argentine stem weevil parasitoid Microctonus hyperodue. ‘Ibis resulted in the importation of 240 parthenogenetic parasitoid lines collected from seven very different ecoclimatic zones. Each zone was assumed to have resulted in a corresponding ‘ecotype’. Throughout the winter of 1991,100 000 parasitised weevils were liberated at selected sites in the northern North Island, northern South Island and Otago/Southland. During this work, care was taken to rear and release the parasitoid ecotypes in equal numbers. In this way establishment patterns of the different ecotypes in different climate zones should eventually be able to be established thereby giving new insights into the importance of parasitoid preadaptation in achieving biological control. Since its release, detailed research has been conducted into the spread and build up of Microctonus hyperodue. In most places, especially ‘AgResearch Lincoln, PO Box 60, Lincoln 2AgResearch Ruakura, Private Bag 3123, Hamilton 3AgResearch Invermay, Private Bag 50034, Mosgiel after summer drought (e.g. Whatman 1959) and/or change in composition to clover or weed dominance (e.g. May 1961). L. bonariensis damages many pasture species and cereal crops and is particularly severe in short rotation stands (Lohum multQ7orum) (e.g. Kelsey 1958) which have useful cool-season characteristics. There have been ongoing efforts to control L. bonariensis in established pasture using insecticides (e.g. Pottinger et al. 1984). However, the development of practical solutions has been thwarted by the species’ high reproductive potential, the sheltered stem-mining habit of its larvae and the weevils’ unpredictable and sometimes large dispersal flights (particularly in dry east coast areas) (e.g. Pottinger 1966; Goldson 1981). Additionally there has been growing awareness of the potentially adverse effects of insecticidal treatment necessary to give season-long control. Conversely, however, systemic insecticide treatment at sowing (as granular formulations or through insecticidally treated seed) has been shown to usefully increase establishment by protecting the emerging cotyledons (Trought 1976; Barker er al. 1990; Addison ef al. 1993); such use is ? n the north,the-parasitoid-has-estahlished-and-no.w-routine among some farmers to enhance seedling establishment. built up to high levels of parasitism (80%) with unexpected rapidity, although its rate of spread has been modest at around l-3 km p.a. Preliminary results from both Canterbury and the northern North Island indicate that the pest potential of Argentine stem weevil population has already been substantially reduced in its release areas.
A Process for the Separation of Noble Metals from HCl Liquor Containing Gold(III), Palladium(II), Platinum(IV), Rhodium(III), and Iridium(IV) by Solvent Extraction

The demand for noble metals is increasing, owing to their excellent chemical and physical properties. In order to meet the demand, the recovery of noble metals with high purity from diverse secondary resources, which contain small amounts of noble metals, is of immense value. In this work, the possibility of the separation of Au(III), Pd(II), Pt(IV), Rh(III), and Ir(IV) by solvent extraction from a synthetic HCl solution is investigated. Only Au(III) was selectively extracted by Cyanex 272 in the HCl concentration range from 0.5 M to 9 M, leaving the other metal ions in the raffinate. The loaded Au(III) in Cyanex 272 was efficiently stripped by (NH2)2CS. The other four noble metals were sequentially separated on the basis of the procedures reported in the previous work. The mass balance showed that about 98% of each metal, except Pt(IV), was recovered by the proposed process. An efficient process for the recovery of the five noble metal ions from the HCl leaching solution of secondary resources containing these metals can be developed.
Maximum margin clustering on evolutionary data

Evolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clustering. To incorporate the time factor, existing methods mainly regard the evolutionary clustering problem as a linear combination of snapshot cost and temporal cost, and reflect the time factor through the temporal cost. It still faces accuracy and scalability challenge though promising results gotten. This paper proposes a novel evolutionary clustering approach, evolutionary maximum margin clustering (e-MMC), to cluster large-scale evolutionary data from the maximum margin perspective. e-MMC incorporates two frameworks: Data Integration from the data changing perspective and Model Integration corresponding to model adjustment to tackle the time factor and change, with an adaptive label allocation mechanism. Three e-MMC clustering algorithms are proposed based on the two frameworks. Extensive experiments are performed on synthetic data, UCI data and real-world blog data, which confirm that e-MMC outperforms the state-of-the-art clustering algorithms in terms of accuracy, computational cost and scalability. It shows that e-MMC is particularly suitable for clustering large-scale evolving data.
An Automated Vision-Based Inspection System for Bearing Gland Covers

This paper presents an automatic vision-based system for bearing gland cover quality control. The system employs the method of gradually refined scheme to locate regions of interests. Several types of defects are detected from their corresponding regions by utilizing image segmentation, curve fitting, feature validation, and other image processing methods. Although each technology is not strange to us, how to integrate them into an entire inspection system efficiently and effectively is a huge challenge. In addition, some useful visual features such as maximum of orientation difference and maximum rectangular feature are proposed to validate candidate defects. Field tests demonstrat that the proposed system gains an excellent performance.
B-Type Natriuretic Peptide and Clinical Judgment in Emergency Diagnosis of Heart Failure: Analysis From Breathing Not Properly (BNP) Multinational Study

Background—We sought to determine the degree to which B-type natriuretic peptide (BNP) adds to clinical judgment in the diagnosis of congestive heart failure (CHF). Methods and Results—The Breathing Not Properly Multinational Study was a prospective diagnostic test evaluation study conducted in 7 centers. Of 1586 participants who presented with acute dyspnea, 1538 (97%) had clinical certainty of CHF determined by the attending physician in the emergency department. Participants underwent routine care and had BNP measured in a blinded fashion. The reference standard for CHF was adjudicated by 2 independent cardiologists, also blinded to BNP results. The final diagnosis was CHF in 722 (47%) participants. At an 80% cutoff level of certainty of CHF, clinical judgment had a sensitivity of 49% and specificity of 96%. At 100 pg/mL, BNP had a sensitivity of 90% and specificity of 73%. In determining the correct diagnosis (CHF versus no CHF), adding BNP to clinical judgment would have enhanced diagnostic accuracy from 74% to 81%. In those participants with an intermediate (21% to 79%) probability of CHF, BNP at a cutoff of 100 pg/mL correctly classified 74% of the cases. The areas under the receiver operating characteristic curve were 0.86 (95% CI 0.84 to 0.88), 0.90 (95% CI 0.88 to 0.91), and 0.93 (95% CI 0.92 to 0.94) for clinical judgment, for BNP at a cutoff of 100 pg/mL, and for the 2 in combination, respectively (P <0.0001 for all pairwise comparisons). Conclusions—The evaluation of acute dyspnea would be improved with the addition of BNP testing to clinical judgment in the emergency department.
The study of edible film production from unriped banana flour and riped banana puree

The edible film made from banana flour and banana puree (Musa AAA) were estimated the mechanical properties, water vapor permeability (WVP), color and solubility. The potassium metabisulfite (KMS) and ascorbic acid were used as the browning prevention chemicals. The banana flour film had 3x2 factorials experiment design (Flour 3% and 5%, glycerol 1% and 2% and 2 chemicals KMS and ascorbic acid). The results showed that the film made from banana flour was higher tensile strength (TS), elongation (E), water vapor permeability (WVP) and L value than the film made from banana puree. The KMS affected high L value and low b value in the both of banana flour film and banana puree film. The suitable treatment of banana film was the film made from banana flour which treated with KMS and composed of 5% banana flour and 2% glycerol. The obtained film would be applicable for food packaging to protect foods.
Navigation- vs. index-based XML multi-query processing

XML path queries form the basis of complex filtering of XML data. Most current XML path query processing techniques can be divided in two groups. Navigation-based algorithms compute results by analyzing an input document one tag at a time. In contrast, index-based algorithms take advantage of precomputed numbering schemes over the input XML document. We introduce a new index-based technique, index-filter, to answer multiple XML path queries. Index-filter uses indexes built over the document tags to avoid processing large portions of the input document that are guaranteed not to be part of any match. We analyze index-filter and compare it against Y-filter, a state-of-the-art navigation-based technique. We show that both techniques have their advantages, and we discuss the scenarios under which each technique is superior to the other one. In particular, we show that while most XML path query processing techniques work off SAX events, in some cases it pays off to preprocess the input document, augmenting it with auxiliary information that can be used to evaluate the queries faster. We present experimental results over real and synthetic XML documents that validate our claims.
Aging effects on consumption risk-sharing channels in European countries

This study tests the hypothesis that aging affects risk sharing via capital markets, federal governments, and credit markets. We apply the decomposition method to both simulated and European country data. For the simulation, we consider a simple overlapping-generations model and generate data that theoretically fit the model. Empirically, we apply the variance decomposition method using data from Europe. We find some evidence that aging has a significant effect on consumption risk-sharing via the credit market channel, whereas the results for other channels are mixed. With moderate reservations, our empirical results for Europe are consistent with the results derived from the simulated data.
Collateral value and forbearance lending

We investigate the foreclosure policy of collateral-based loans in which the endogenous collateral value plays a crucial role. If creditors are able to commit, then the equilibrium arrangement is more likely to feature forebearance lending by specifying a lower level of liquidation (or roll over all of the loans) relative to the expost efficiency criterion for each realization of the interim signal. The key is that collateral value may drop too low when banks call in loans by auctioning off borrowers? collateral and this makes clearing up non-performing loans less attractive. We attribute the banks? leniency as we have observed in Japan during the 1990s to an equilibrium arrangement where banks can commit due to either relationship banking or an implicit lenderborrower contract, such as the arrangement under Japan? s main-bank system.
Neural signal multiplexing via compressed sensing

Transport of neural signals in the brain is challenging, owing to neural interference and neural noise. There is experimental evidence of multiplexing of sensory information across population of neurons, particularly in the vertebrate visual and olfactory systems. Recently, it has been discovered that in lateral intraparietal cortex of the brain, decision signals are multiplexed with decision-irrelevant visual signals. Furthermore, it is well known that several cortical neurons exhibit chaotic spiking patterns. Multiplexing of chaotic neural signals and their successful demultiplexing in the neurons amidst interference and noise, is difficult to explain. In this work, a novel compressed sensing model for efficient multiplexing of chaotic neural signals constructed using the Hindmarsh-Rose spiking model is proposed. The signals are multiplexed from a pre-synaptic neuron to its neighbouring post-synaptic neuron, in the presence of 104 interfering noisy neural signals and demultiplexed using compressed sensing techniques.
Beyond Collective Beliefs

This study used data from 818 master’s students, organized into 199 teams, to examine the influence of collective emotional intelligence (EI) on team academic performance (measured by a common academic grade based on two project reports at the team level) above the effects of collective general self-efficacy (GSE) and team-level GSE, termed team potency. All three variables predicted team academic performance positively, beyond the effect of each other. The research model explained 20% of the variance in team academic performance. A negative interaction effect between collective EI and collective GSE was detected, indicating that the two variables may replace each other in teamwork. Exploratory analyses of the four EI dimensions showed that particularly other emotion appraisal and regulation of emotions predicted team academic performance. Finally, maximum EI within each team predicted team academic performance at about the same level as collective EI.
Object Proposals for Pedestrian Detection in Stereo Images

Pedestrian detection is an active research field in computer vision and photogrammetry today due to its importance for applications related to autonomous driving, machine human interaction, and surveillance. Object proposals play a significant role in guiding a classifier where to examine image regions that may contain pedestrians. In this paper, we present a proposal framework employing both 3D information derived from stereo images and RGB cues to generate pedestrian bounding box proposals with high recall and relatively small number of candidates. Our proposal generator has two stages: (1) generating a large number of proposals to achieve a good recall value; and (2) eliminating unreasonable initial candidates based on 3D cues and pedestrian geometric constraints. Preliminary experiments on the Kitti benchmark dataset show that our proposal framework is comparable to state-of-the-art methods.
THE EFFECT OF GROwING SYSTEMS ON THE QUALITY OF CARROTS

The aim of the research was to evaluate how the quality of carrots is affected by organic and conventional production systems. The experiment was carried out at the Estonian Crop Research Institute in 2009. Conventional treatment received the following amount of nutrients via mineral fertilizers: N 115, P 40 and K 152 kg ha-1. For plant protection, the following pesticides were used: Fenix, Fastac 50, Agil and Signum. In organic cultivation system compost and humic acid solution Humistar were used for fertilization and polypropylene non-woven fabric Agryl P-17 for plant protection. Marketable yield of organic carrots was 8% lower compared to the conventional carrots. Conventional carrots contained pesticide residues and had significantly higher nitrate concentration than organic carrots. The contents of total sugars, phosphorus, potassium, calcium and magnesium did not differ significantly between carrots from different cultivation systems. At harvest, dry matter (DM) content of organically grown carrots was significantly higher, whereas vitamin C and ß-carotene content was significantly lower in organically grown carrots. However, after 5-months of storage, the organic carrots had significantly higher total soluble solids (TSS) and β-carotene content compared to the conventional ones indicating that organically grown carrots were less susceptible to storage conditions.
Distributed Core Migration in Multicast Peer-to-Peer Streaming

Recently, a number of application-level multicast protocols have been proposed using a center based approach. These multicast trees critically depend on the position of the core for balanced tree organization. Motivated by peer-to-peer streaming media applications, this paper presents a novel approach for core migration in application-level multicast trees which aims to reduce the maximum overall delay. Our architecture is inspired by previous research that proposed migration as an approach to reduce the overall delay in the tree. Our core migration protocol uses a heuristic approach based on the tree diameter to determine the node to be elected as the core. We also present an alternative tree reorganization approach which we compare with core migration. We present simulation results with our overlay protocol YimaCast and show the feasibility of the approach for trees of different sizes. We demonstrate that core migration works well in a very dynamic environment while tree reconstruction is beneficial in more stable scenarios.
NLP to identify and rank clinically relevant information from EHRs in the Emergency Department - Final Report

Purpose: Timely identification and extraction of relevant or “need to know” clinical information about a patient’s history in the emergency department (ED) setting is critical for patient safety and medical decision-making. Scope: Develop a corpus of relevant information elements for the complaints of chest pain and back pain and then develop a search tool to automatically identify these items within the EHR. Methods: The relevant information elements for each of these complaints were developed using subject matter experts(SME) in Emergency Medicine, Cardiology and Orthopedics. We used the Medical Term Extraction and Reasoning System (MTERMS) and SOLR/Lucene to extract and pre-process these information elements from clinical notes. We developed and evaluated both manual (SME) and unsupervised machine learning ranking methods and compared their accuracy head-to-head on 1,010 medications and 2,913 problems from 99 patients with a chief complaint of chest or back pain, where each item was manually labeled as relevant or not to the chief complaint. A graphical user interface was developed. Results: For chest pain, the following relevant items were identified: 12 risk factors, 40 diagnoses, 20 diagnostic tests, 14 procedures, and 10 therapeutic drug classes. For back pain, the following relevant items were identified:13 risk factors, 25 diagnoses, 16 diagnostic tests, 4 procedures, and 5 therapeutic drug classes. Using mean average precision (MAP), the manual ranking out-performed the unsupervised methods on medications (92.2%-98.2% vs 84.8%-90.8%) but underperformed on problems (57.6%-84.9% vs 71.7%88.7%). The results demonstrate how information retrieval methods using NLP and unsupervised machine learning can provide a reasonably accurate, low-effort, and scalable method for situation-specific clinical relevancy ranking.
Poster Abstract : MSPsim – an Extensible Simulator for MSP 430-equipped Sensor Boards

Software development for wireless sensor networks is a challenging and time consuming task. The resource limited hardware with limited I/O and debugging abilities combined with the often cumbersome hardware debugging tools makes low-level debugging on the target hardware difficult. We present MSPsim, an extensible sensor board platform and MSP430 instruction level simulator that simulates sensor boards with peripherals for the purpose of reducing development and debugging time. The use of a simulator also enables testing without access to the target hardware and makes more advanced debugging and instrumenting possible.
Performance of Evaluation for AES with ECC in Cloud Environment

------------------------------------------------------------------- ABSTRACT --------------------------------------------------------------- During the day; Technology is Cloud, where people can share resources, services and information across the Internet. Due to information on the Internet, security is considered a major problem. The information should be protected by an unauthorized user and should be sent to a person with a private and confidential intent. On this proposed work, to provide a secure method, secure connection, authentication, confidentiality and third-party data protection in cloud computing. In the combination of the Advanced Encryption Standard (AES) and Cryptographic Curve Cryptography (ECC) analyzing of different parameters like storage, encryption time, decryption time and correlation. The results show that the impact of this integrated approach is more important than the other secure algorithms.
Inhibition of the c-Jun N-Terminal Kinase Signaling Pathway by the Mixed Lineage Kinase Inhibitor CEP-1347 (KT7515) Preserves Metabolism and Growth of Trophic Factor-Deprived Neurons

Nerve growth factor (NGF) deprivation triggers metabolic changes in sympathetic neurons that precede cell death. Here, we investigate the role of the c-Jun N-terminal kinase (JNK) pathway in downregulating neuronal metabolism. We show that, in the presence of CEP-1347 (KT7515), a small molecule known to block cell death upstream of JNK, cellular metabolism is preserved in neurons deprived of NGF. Biochemical data that are presented are consistent with the mechanism of action of CEP-1347 being the inhibition of the mixed lineage kinases (MLKs), known activators of JNK signaling. We demonstrate that CEP-1347-saved neurons continue to grow even in the absence of NGF, indicating that inhibition of the JNK pathway is permissive for neuronal growth in the absence of trophic support. These trophic effects are seen despite the fact that CEP-1347 does not stimulate several known survival kinase pathways. In addition to blocking Bax-dependent cytochrome c release, the inhibition of the JNK signaling pathway with CEP-1347 also blocks the development of competence-to-die in response to cytosolic cytochrome c. Therefore, inhibition of the JNK signaling pathway with the MLK inhibitor CEP-1347 inhibits both limbs of the apoptotic pathway. Finally, we demonstrate that neurons that have been NGF-deprived long-term but that have been kept alive by caspase inhibitors can be rescued metabolically by CEP-1347 as assessed by soma size, cytochrome c localization, and protein synthesis rates. Therefore, we conclude that, in addition to converting extracellular signals into decisions of life and death, the JNK pathway can modulate cellular metabolism directly and thereby maintain not only survival but the “quality of life” of neurons.
Experts of probabilistic flow subspaces for robust monocular odometry in urban areas

Visual odometry has been promoted as a fundamental component for intelligent vehicles. Relying solely on monocular image cues would be desirable. Nevertheless, this is a challenge especially in dynamically varying urban areas due to scale ambiguities, independent motions, and measurement noise. We propose to use probabilistic learning with auxiliar depth cues. Specifically, we developed an expert model that specializes monocular egomotion estimation units on typical scene structures, i.e. statistical variations of scene depth layouts. The framework adaptively selects the best fitting expert. For on-line estimation of egomotion, we adopted a probabilistic subspace flow estimation method. Learning in our framework consists of two components: 1) Partitioning of datasets of video and ground truth odometry data based on unsupervised clustering of dense stereo depth profiles and 2) training a cascade of subspace flow expert models. A probabilistic quality measure from the estimates of the experts provides a selection rule overall leading to improvements of egomotion estimation for long test sequences.