Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation of an Immunoaffinity Column with Amino-Silica Gel Microparticles and Its Application in Sample Cleanup for Aflatoxin Detection in Agri-Products

This study established an immunoaffinity column for selective extraction of aflatoxins in agri-products. Specifically, the immunoaffinity column was developed by covalently coupling monoclonal antibody 1C11 against aflatoxins to amino-silica gel microparticles and then packing these into a cartridge. The extraction conditions were thoroughly optimized in terms of loading, washing and eluting solutions. Under the optimal conditions, the immunoaffinity column had a capacity of 200 ng of aflatoxins. The detection limits (S/N = 3) for aflatoxin G1, B1, G2 and B2 were 0.03, 0.07, 0.05 and 0.09 μg·kg−1, and the corresponding quantification limits (S/N = 10) were 0.10, 0.25, 0.18 and 0.30 μg·kg−1, respectively. The recoveries of aflatoxins in samples were 90.1%–104.4% and RSDs were <4.4%. The developed method was further applied to the determination of aflatoxins in peanut, vegetable oil and tea samples, and the results indicated that peanut (26.9%), vegetable oils (28.0%) and tea (5.3%) samples were contaminated with aflatoxins, with levels ranging from 0.49 to 20.79 μg·kg−1.
A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system

In this paper, we propose a novel pilot-aided channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems where the wireless channel is assumed to be both sparse and time-varying. In the proposed method, we firstly model the time-varying sparse channel as an autoregressive (AR) process. Then, utilizing the time-domain convergence property of Kalman filter, we formulate the channel estimation as an iteration problem. During the iteration, the path delays are estimated through a simple reconstruction algorithm. After the path delays are estimated, the Kalman filter is performed on the path delays to obtain the minimum mean squared error (MMSE) estimation of the channel impulse response (CIR). Simulation results demonstrate the effectiveness of the proposed channel estimation method. Compared with the conventional compressed sensing (CS) based channel estimators which perform CS at each time separately, the method proposed here enjoys superior performance in terms of bit error rate (BER) and mean square error (MSE).
WIED: A Web Modelling Language for Modelling Architectural-Level Information Flows

The ability to reliably and consistently develop systems that utilise Internet and Web technologies has become increasingly important. These systems are typically both functionally complex and information-rich, and have a number of unique characteristics that should imply specific changes to the development processes, methods and models that are adopted. One aspect that has received increasing attention is information modelling for these applications, particularly with respect to aspects such as navigation models and their relationships to the underlying content. These models have typically focussed on modelling at a relatively low-level, however, and have failed to address higher-level aspects, such as architectural and even business process modelling. The paper introduces a formal information modelling set which can be considered as a companion to to an existing modelling language - WebML - that facilitates information modelling at this higher level of abstraction. We argue that this modelling approach will provide a clearer connection between an understanding of business models and processes, and the lower-level designs typically represented in existing models.
A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

One of the important tasks for bridge maintenance is bridge deck crack inspection. Traditionally, a human inspector detects cracks using his/her eyes and marks the location of cracks manually. However, the accuracy of the inspection result is low due to the subjective nature of human judgement. We propose a crack inspection system that uses a camera-equipped mobile robot to collect images on the bridge deck. In this method, the Laplacian of Gaussian (LoG) algorithm is used to detect cracks and a global crack map is obtained through camera calibration and robot localization. To ensure that the robot collects all the images on the bridge deck, a path planning algorithm based on the genetic algorithm is developed. The path planning algorithm finds a solution which minimizes the number of turns and the traveling distance. We validate our proposed system through both simulations and experiments.
Multistrategy Learning: A Case Study

Two of the most popular approaches to induction are instance-based learning (IBL) and rule generation. Their strengths and weaknesses are largely complementary. IBL methods are able to identify small details in the instance space, but have trouble with attributes that are relevant in some parts of the space but not others. Conversely, rule induction methods may overlook small exception regions, but are able to select different attributes in different parts of the instance space. The two methods have been unified in the RISE algorithm (Domingos 1995). RISE views instances as maximally specific rules, forms more general rules by gradually clustering instances of the same class, and classifies a test example by letting the nearest rule win. This approach potentially combines the advantages of rule induction and IBL, and has indeed been observed to be more accurate than each on a large number of benchmark datasets. However, it is important to determine if this performance is indeed due to the hypot)hesized advantages, and to define the situations in which RISE’s bias will and will not be preferable to those of the individual approaches. This abstract reports experiments to this end in artificial domains. Compared to rule induction algorithms, RISE should have an advantage when the concept to learn is best described by fairly specific rules, and vice-versa. If concepts are defined by Boolean DNF formulas, their degree of specificity can be measured by L, the average length of the disjuncts. Twenty artificial datasets were randomly generated for each value of L from 1 to 32. C4.5RIJLES was chosen for comparison. The average accuracies obtained are shown in Figure 1. They indicate that RISE’s bias is indeed more appropriate when concepts are fairly to very specific, with the advantage increasing with specificity. ’ More general concepts were learned to a similar degree by both systems. We were unable to determine conditions where C4.5RULES’s bias would be preferable to RISE’s; corrupting the data with 10% and 20% class noise resulted in similar degradation for the two systems. Another surprising observation is that C4.5RULES’s accuracy has an upward trend for length 2 14. This is due to
Reducing Haemorrhagic Transformation after Thrombolysis for Stroke: A Strategy Utilising Minocycline

Haemorrhagic transformation (HT) of recently ischaemic brain is a feared complication of thrombolytic therapy that may be caused or compounded by ischaemia-induced activation of matrix metalloproteinases (MMPs). The tetracycline antibiotic minocycline inhibits matrix MMPs and reduces macroscopic HT in rodents with stroke treated with tissue plasminogen activator (tPA). The West Australian Intravenous Minocycline and TPA Stroke Study (WAIMATSS) aims to determine the safety and efficacy of adding minocycline to tPA in acute ischaemic stroke. The WAIMATSS is a multicentre, prospective, and randomised pilot study of intravenous minocycline, 200 mg 12 hourly for 5 doses, compared with standard care, in patients with ischaemic stroke treated with intravenous tPA. The primary endpoint is HT diagnosed by brain CT and MRI. Secondary endpoints include clinical outcome measures. Some illustrative cases from the early recruitment phase of this study will be presented, and future perspectives will be discussed.
An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing

The determination of flame or fire edges is the process of identifying a boundary between the area where there is thermochemical reaction and those without. It is a precursor to image-based flame monitoring, early fire detection, fire evaluation, and the determination of flame and fire parameters. Several traditional edge-detection methods have been tested to identify flame edges, but the results achieved have been disappointing. Some research works related to flame and fire edge detection were reported for different applications; however, the methods do not emphasize the continuity and clarity of the flame and fire edges. A computing algorithm is thus proposed to define flame and fire edges clearly and continuously. The algorithm detects the coarse and superfluous edges in a flame/fire image first and then identifies the edges of the flame/fire and removes the irrelevant artifacts. The autoadaptive feature of the algorithm ensures that the primary symbolic flame/fire edges are identified for different scenarios. Experimental results for different flame images and video frames proved the effectiveness and robustness of the algorithm.
' s personal copy Continuous Optimization Finding local optima of high-dimensional functions using direct search methods

This paper focuses on a subclass of box-constrained, non-linear optimization problems. We are particularly concerned with settings where gradient information is unreliable, or too costly to calculate, and the function evaluations themselves are very costly. This encourages the use of derivative free optimization methods, and especially a subclass of these referred to as direct search methods. The thrust of our investigation is twofold. First, we implement and evaluate a number of traditional direct search methods according to the premise that they should be suitable as local optimizers when used in a metaheuristic framework. Second, we introduce a new direct search method, based on Scatter Search, designed to remedy the lack of a good derivative free method for solving problems of high dimensions. Our new direct search method has convergence properties comparable to those of existing methods in addition to being able to solve larger problems more effectively. 2008 Elsevier B.V. All rights reserved.
Primary carcinosarcoma of the gallbladder

Carcinosarcoma of gallbladder (CSGB) is a rare malignancy characterized by malignant epithelial and mesenchymal components. Its pathogenesis is unknown and most CSGBs are associated with poor survival because the disease normally presents at an advanced stage, and as a result, curative resection is uncommon. This report describes a case that underwent curative resection. A 77-year-old woman presented with right upper quadrant pain. The preoperative diagnosis was gallbladder (GB) cancer, and thus, curative radical cholecystectomy was performed. However, pathologic examination of the surgical specimen revealed that the tumor was composed of two histologic components of squamous cell carcinoma and spindle cell sarcoma, which was consistent with a diagnosis of carcinosarcoma. The tumor was found to extend to the perimuscular connective tissue and to have metastasized to one lymph node (LN). The prognosis of CSGB remains poor despite curative resection, and thus, the authors recommend that effort be made to improve surgical outcomes.
Antibiotic resistence of Aeromonas hydrophila isolated from Piaractus mesopotamicus (Holmberg, 1887) and Oreochromis niloticus (Linnaeus, 1758)

Um dos maiores problemas envolvendo o tratamento com antibioticos contra Aeromonas hydrophila isolada de peixes confinados e a rapida resistencia ao antibiotico desenvolvida pela bacteria. A atividade antimicrobiana de quimioterapeuticos em isolados a partir de pacu Piaractus mesopotamicus (Holmberg, 1887) e tilapia Oreochromis niloticus (Linnaeus, 1758) foi verificada pelo metodo de difusao de antibiotico em discos de Kirby-Bauer, sobre uma superficie de Agar Mueller-Hinton previamente inoculada com 100 µL de suspensao bacteriana. Apos o periodo de incubacao, os isolados de tilapia e pacu foram uniformemente resistentes a amoxicilina, ampicilina, lincomicina, novobiocina, oxacilina, penicilina e trimetoprim+sulfametoxazol. A cepa tipo para A. hydrophila apresentou resistencia as mesmas substâncias antimicrobianas e tambem contra a rifampicina; o isolado bacteriano de pacu foi a unica linhagem resistente a tetraciclina. Isolados de pacu e tilapia apresentaram resistencia intermediaria frente a eritromicina. O uso de medicamentos em pisciculturas comerciais no Brasil pode favorecer o desenvolvimento de linhagens bacterianas resistentes em especies de peixes nativos, como ja observado em especies exoticas comercialmente produzidas ha mais tempo.
SPADES—a specification and design system and its graphical interface

SPADES is a specification system consisting of a method, a language, and a set of tools. These components are based on a set of concepts, which forms its abstract kernel. SPADES supports the specification of software systems, in particular of real time software. The system to be developed is modelled using the Entity-Relationship-concept. While this seems to be the best way for storing specifications in a computer, it does not automatically lead to representations equally comfortable for humans. This is why SPADES, which has been available for some time, has recently been extended by a graphical interface. This paper gives a brief survey of the system, in particular of its new component.
Gut Microbiota Dysbiosis Is Associated with Inflammation and Bacterial Translocation in Mice with CCl4-Induced Fibrosis

Background Gut is the major source of endogenous bacteria causing infections in advanced cirrhosis. Intestinal barrier dysfunction has been described in cirrhosis and account for an increased bacterial translocation rate. Hypothesis and Aims We hypothesize that microbiota composition may be affected and change along with the induction of experimental cirrhosis, affecting the inflammatory response. Animals and Methods Progressive liver damage was induced in Balb/c mice by weight-controlled oral administration of carbon tetrachloride. Laparotomies were performed at weeks 6, 10, 13 and 16 in a subgroup of treated mice (n = 6/week) and control animals (n = 4/week). Liver tissue specimens, mesenteric lymph nodes, intestinal content and blood were collected at laparotomies. Fibrosis grade, pro-fibrogenic genes expression, gut bacterial composition, bacterial translocation, host's specific butyrate-receptor GPR-43 and serum cytokine levels were measured. Results Expression of pro-fibrogenic markers was significantly increased compared with control animals and correlated with the accumulated dose of carbon tetrachloride. Bacterial translocation episodes were less frequent in control mice than in treated animals. Gram-positive anaerobic Clostridia spp count was decreased in treated mice compared with control animals and with other gut common bacterial species, altering the aerobic/anaerobic ratio. This fact was associated with a decreased gene expression of GPR43 in neutrophils of treated mice and inversely correlated with TNF-alpha and IL-6 up-regulation in serum of treated mice along the study protocol. This pro-inflammatory scenario favoured blood bacterial translocation in treated animals, showing the highest bacterial translocation rate and aerobic/anaerobic ratio at the same weeks. Conclusions Gut microbiota alterations are associated with the development of an inflammatory environment, fibrosis progression and bacterial translocation in carbon tetrachloride-treated mice.
Climate Change-induced High Temperature Stress on Global Crop Production

Exposure to high temperatures during the reproductive period of crops decreases their productivity. The Intergovernmental Panel on Climate Change’s (IPCC) fifth Assessment Report predicts that the frequency of high temperatures will continue to increase in the future, resulting in significant impacts on the world’s food supply. This study evaluate climate change-induced heat stress on four major agricultural crops (rice, maize, soybean, and wheat) at a global level, using the coupled atmosphere-ocean model of Hadley Centre Global Environmental Model version 2 (HadGEM2-AO) and FAO/IIASA Global Agro-Ecological Zone (GAEZ) model data. The maximum temperature rise (1.8-3.5°C) during the thermal-sensitive period (TSP) from the baseline (1961-1990) to the future (2070-2090) is expected to be larger under a Representative Concentration Pathway (RCP) 8.5 climate scenario than under a RCP2.6 climate scenario, with substantial heat stress-related damage to productivity. In particular, heat stress is expected to cause severe damage to crop production regions located between 30 and 50°N in the Northern Hemisphere. According to the RCP8.5 scenario, approximately 20% of the total cultivation area for all crops will experience unprecedented, extreme heat stress in the future. Adverse effects on the productivity of rice and soybean are expected to be particularly severe in North America. In Korea, grain demands are heavily dependent on imports, with the share of imports from the U.S. at a particularly high level today. Hence, it is necessary to conduct continuous prediction on food security level following the climate change, as well as to develop adaptation strategy and proper agricultural policy.
Oblique Photogrammetric Analysis of Dome Growth at Mount St. Helens Volcano, 2004 – 2007

This project is an oblique photogrammetric survey of the 2004-2007 dome-building eruption of Mount St. Helens volcano with two primary objectives: (1) to evaluate the potential of a new rapid and low-cost technique to create digital elevation models (DEMs) and subsequently calculate dome volumes and extrusion rates at Mount St. Helens; and (2) to attempt to understand mechanics associated with lava dome extrusion and collapse by analyzing volumetric and extrusion rate measurements in the context of dome height measurements, seismicity data, and migration of the locus of dome growth. The new method uses sets of oblique aerial photographs, acquired from a helicopter with a standard digital camera, and commercially available photogranunetry software to create DEMs. Twelve sets of overlapping digital images from November 2004 to April 2007 of the growing dome inside the crater of Mount St. Helens were used to produce DEMs. Analyses of the digital images were carried out using PhotoModeler software, which produces three dimensional coordinates of points identified in multiple photos. The DEMs were used for qualitative and quantitative descriptions of the 2004-2007 emption. The DEMs were used to calculate height, volume, and extrusion rate, and to track changes in these quantities through time. These quantities show a decreasing extrusion rate through time that has remained lower than 1 m^/s since October 2005. Total dome volume had reached 94x10^ m^ as of April 2007, a larger volume than that of the 1980-1986 Dome. Quantitative evaluation of the DEMs also shows a relationship between dome height and extrusion rate, which varies with the character of eruptive activity. Results were validated by comparing volume measurements derived from traditional aerophotogrammetric surveys run by the USGS Cascades Volcano Observatory. The new oblique photogrammetric technique yields estimates of eruptive volume consistently within 5% of the volumes estimated with traditional surveys. The end result of this project is a new technique that provides inexpensive, rapid assessment of volcanic activity that can be an important supplement to volcano monitoring worldwide.
Meteorological Drivers and Large-Scale Climate Forcing of West Antarctic Surface Melt

Understanding the drivers of surface melting in West Antarctica is crucial for understanding future ice loss and global sea level rise. This study identifies atmospheric drivers of surface melt on West Antarctic ice shelves and ice sheet margins and relationships with tropical Pacific and high-latitude climate forcing using multidecadal reanalysis and satellite datasets. Physical drivers of ice melt are diagnosed by comparing satellite-observed melt patterns to anomalies of reanalysis near-surface air temperature, winds, and satellite-derived cloud cover, radiative fluxes, and sea ice concentration based on an Antarctic summer synoptic climatology spanning 1979–2017. Summer warming in West Antarctica is favored by Amundsen Sea (AS) blocking activity and a negative phase of the southern annular mode (SAM), which both correlate with El Niño conditions in the tropical Pacific Ocean. Extensive melt events on the Ross–Amundsen sector of the West Antarctic Ice Sheet (WAIS) are linked to persistent, intense AS blocking anticyclones, which force intrusions of marine air over the ice sheet. Surface melting is primarily driven by enhanced downwelling longwave radiation from clouds and a warm, moist atmosphere and by turbulent mixing of sensible heat to the surface by föhn winds. Since the late 1990s, concurrent with ocean-driven WAIS mass loss, summer surface melt occurrence has increased from the Amundsen Sea Embayment to the eastern Ross Ice Shelf. We link this change to increasing anticyclonic advection of marine air into West Antarctica, amplified by increasing air–sea fluxes associated with declining sea ice concentration in the coastal Ross–Amundsen Seas.
Evaluating Upgrade Options under Sequential Technology Innovations

This study employs a real option pricing approach to conduct an evaluation of the optimal investment strategy for a firm confronted with sequential technology innovations. Stochastic assumptions of innovations in their arrival times and their profitability are added to the model, so as to conform to the most important characteristics of real-world technology markets. Furthermore, this study incorporates a dividend-like payout yield which stands for the opportunity cost of waiting to invest. Simulations and sensitivity analysis of the derived model are presented. It is hoped that the results of this study will help predict actual firm policy.
Robust road detection from a single image

Road detection from images is a challenging task in computer vision. Previous methods are not robust, because their features and classifiers cannot adapt to different circumstances. To overcome this problem, we propose to apply unsupervised feature learning for road detection. Specifically, we develop an improved encoding function and add a feature selection process to obtain robust and discriminative road features. Besides, a road segmentation algorithm is proposed to extract road regions from the learned feature maps, in which a tree structure is established to represent the hierarchical relations of various regions segmented by multiple thresholds, and a two-loop optimization is then employed to select the most stable regions as road areas. Experimental results on several challenging datasets justify the effectiveness of our method.
Proposal : Developing a Decision-Theoretic User Model for Automatic Customization

This thesis focuses on the development of a decision-theoretic approach to user modeling in the context of automatic interface customization. The modeling aspects of this work are grounded in artificial intelligence (AI), while the empirical methodology is grounded in human-computer interaction (HCI). The publications produced as part of this thesis have focused mostly on developing new models that are populated and tested with real user data. As such, the contributions of this thesis lie in the intersection of AI and HCI.