Clays Can Decrease Gaseous Nutrient Losses from Soil-Applied Livestock Manures

Clays could underpin a viable agricultural greenhouse gas (GHG) abatement technology given their affinity for nitrogen and carbon compounds. We provide the first investigation into the efficacy of clays to decrease agricultural nitrogen GHG emissions (i.e., N2O and NH3). Via laboratory experiments using an automated closed-vessel analysis system, we tested the capacity of two clays (vermiculite and bentonite) to decrease N2O and NH3 emissions and organic carbon losses from livestock manures (beef, pig, poultry, and egg layer) incorporated into an agricultural soil. Clay addition levels varied, with a maximum of 1:1 to manure (dry weight). Cumulative gas emissions were modeled using the biological logistic function, with 15 of 16 treatments successfully fitted (P < 0.05) by this model. When assessing all of the manures together, NH3 emissions were lower (×2) at the highest clay addition level compared with no clay addition, but this difference was not significant (P = 0.17). Nitrous oxide emissions were significantly lower (×3; P < 0.05) at the highest clay addition level compared with no clay addition. When assessing manures individually, we observed generally decreasing trends in NH3 and N2O emissions with increasing clay addition, albeit with widely varying statistical significance between manure types. Most of the treatments also showed strong evidence of increased C retention with increasing clay additions, with up to 10 times more carbon retained in treatments containing clay compared with treatments containing no clay. This preliminary assessment of the efficacy of clays to mitigate agricultural GHG emissions indicates strong promise.

Legume pastures can reduce N2O emissions intensity in subtropical cereal cropping systems

Alternative sources of N are required to bolster subtropical cereal production without increasing N2O emissions from these agro-ecosystems. The reintroduction of legumes in cereal cropping systems is a possible strategy to reduce synthetic N inputs but elevated N2O losses have sometimes been observed after the incorporation of legume residues. However, the magnitude of these losses is highly dependent on local conditions and very little data are available for subtropical regions. The aim of this study was to assess whether, under subtropical conditions, the N mineralised from legume residues can substantially decrease the synthetic N input required by the subsequent cereal crop and reduce overall N2O emissions during the cereal cropping phase. Using a fully automated measuring system, N2O emissions were monitored in a cereal crop (sorghum) following a legume pasture and compared to the same crop in rotation with a grass pasture. Each crop rotation included a nil and a fertilised treatment to assess the N availability of the residues. The incorporation of legumes provided enough readily available N to effectively support crop development but the low labile C left by these residues is likely to have limited denitrification and therefore N2O emissions. As a result, N2O emissions intensities (kg N2O-N yield−1 ha−1) were considerably lower in the legume histories than in the grass. Overall, these findings indicate that the C supplied by the crop residue can be more important than the soil NO3− content in stimulating denitrification and that introducing a legume pasture in a subtropical cereal cropping system is a sustainable practice from both environmental and agronomic perspectives.

Machine recognition of printed Kannada text

This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.

A stochasticity threshold in holography and the instability of AdS

We give strong numerical evidence that a self-interacting probe scalar field in AdS, with only a few modes turned on initially, will undergo fast thermalization only if it is above a certain energetic threshold. Below the threshold the energy stays close to constant in a few modes for a very long time instead of cascading quickly. This indicates the existence of a Strong Stochasticity Threshold (SST) in holography. The idea of SST is familiar from certain statistical mechanical systems, and we suggest that it exists also in AdS gravity. This would naturally reconcile the generic nonlinear instability of AdS observed by Bizon and Rostworowski, with the Fermi-Pasta-Ulam-Tsingou-like quasiperiodicity noticed recently for some classes of initial conditions. We show that our simple setup captures many of the relevant features of the full gravity-scalar system.

Valores de referência para níveis séricos do Fator de crescimento semelhante à insulina tipo I (IGF-I) numa população adulta do Estado do Rio de Janeiro, Brasil

O nível sérico do Fator de crescimento semelhante à insulina tipo I (IGF-I) é fundamental para auxiliar no dignóstico e controle terapêutico dos transtornos relacionados à secreção do Hormônio de Crescimento (GH), bem como no diagnóstico e seguimento de outras doenças. Estabelecer valores de referência para as dosagens séricas de IGF-I por um ensaio imunoquimioluminométrico (ICMA), utilizando o sistema automatizado Immulite 2000/Diagnostic Products Corporation (DPC), e por um ensaio imunoradiométrico (IRMA), utilizando o kit comercial ACTIVE IGF-I/Diagnostic System Laboratories (DSL)-5600, numa população brasileira adulta da cidade do Rio de Janeiro. Este estudo, aprovado pelo Comitê de Ética do Instituto Estadual de Hematologia Arthur de Siqueira Cavalcanti, Rio de Janeiro, Brasil, incluiu amostras de 484 indivíduos saudáveis (251 homens e 233 mulheres) com idades entre 18 e 70 anos. As amostras foram estudadas por ICMA- Immulite 2000/DPC and IRMA- ACTIVE IGF-I/DSL-5600. Para análise dos dados foram utilizados modelos específicos para idade e sexo, após transformação dos dados de IGF-I. Foi observada uma lenta diminuição dos níveis de IGF-I com a idade usando ambos os ensaios. Os níveis de IGF-I foram signicativamente (p=0,0181) mais elevados em mulheres do que em homens, quando as amostras foram analisadas usando ICMA. Não houve diferença significativa dos níveis de IGF-I entre homens e mulheres quando as amostras foram analisadas usando IRMA. Este estudo estabeleceu valores de referência de IGF-I específicos para idade e sexo, determinados com o sistema automatizado ICMA-Immulite 2000/DPC, e valores de referência de IGF-I específicos para idade, determinados com o kit comercial IRMA- ACTIVE IGF-I/DSL-5600, em uma população adulta brasileira, da cidade do Rio de Janeiro.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.

黄土旱塬区冬小麦不同施肥处理的土壤呼吸及土壤碳动态

依据黄土旱塬区黑垆土上中国科学院长武站长期定位试验(始于1984年),于2008年3月到6月,测定了冬小麦连作系统中返青期、拔节期、抽穗期、灌浆期和收获期土壤呼吸日变化、生育期变化以及土壤可溶性有机碳(Dissolved organic C,DOC)和微生物量碳(Soil microbial biomass C,MBC),研究了施肥措施对土壤呼吸、DOC和MBC的影响以及土壤呼吸与碳组分之间的关系。研究涉及6个处理:休闲地(F)、不施肥(CK)、有机肥(M)、氮肥(N)、氮磷肥(NP)和氮磷有机肥(NPM)。结果表明,冬小麦连作系统中土壤呼吸的日变化格局呈单峰曲线,最高值出现在12:00左右(拔节期)和14:30左右(成熟期),最小值出现在0:00～3:00之间或6:00左右;冬小麦土壤呼吸速率拔节期最高,其次是灌浆后期,抽穗期最低;不同施肥条件下,各生育期土壤呼吸速率大小顺序:NPM>M>NP>N>CK>F。土壤水分亏缺是导致抽穗期和灌浆期土壤呼吸速率降低的重要原因。各施肥处理DOC含量高低顺序为灌浆期>抽穗期>成熟期>返青期>拔节期;除M,NPM处理MBC含量拔节期>灌浆期外,各施肥处理MBC含量高低顺序...

东海天然气水合物地球物理特征及全波形反演方法研究

地球物理方法是目前海域天然气水合物和游离气识别与预测分析的重要手段，并且已经由早期主要研究地层的速度和振幅信息发展到利用波形特征进行叠前反演，提取多种属性、多种弹性参数进行综合分析的阶段，因此对水合物地层进行综合地球物理属性研究具有重要的理论意义和实践意义。 本文通过对2001年东海973航次在冲绳海槽取得的多道地震数据进行有针对性的特殊处理，并通过精细地地震地层解释，发现海槽南部存在大量的泥底辟构造并伴生天然气水合物。针对DMS01-5测线上的泥底辟构造，分别从叠加速度分析、砂泥岩比分析、计算海底热流与实测海底热流对比分析、流体势能分析和波阻抗反演分析等几方面探讨了泥底辟型天然气水合物的地球物理特征，并对该处底辟顶部和其周围岩层中似海底反射（BSR）的成因进行了探讨，认为这里的BSR并不代表天然气水合物稳定带的底界，而分别对应于天然气水合物生成带的底界和游离气的顶界。 基于波动方程的一维半空间叠前全波形反演可以求取多个弹性参数，同时可以获得水合物沉积层精细的速度结构，这对天然气水合物和游离气的识别进而估算天然气水合物和游离气的含量至关重要。本文系统讨论了在Kennett广义反射透射系数矩阵正演基础上叠前全波形反演的遗传算法。Kennett广义反射透射系数矩阵正演算法包含了自由表面反射、薄互层层间多次反射波、透射反射波以及P-SV波之间的相互转换波，适合水合物层精细速度结构研究。采用遗传算法进行全波形反演克服了传统局部线性最优化方法依赖初始模型，需要利用目标函数导数信息的不足之处，算法收敛速度较快并且具有一定的稳定性。

地震波传播模拟的边界元―体积元积分方程法及其近似求解

To improve the efficiency of boundary-volume integral equation technique, this paper is involved in the approximate solutions of boundary-volume integral equation technique. Firstly, based on different interpretations of the self-interaction and extrapolation operators of the resulting boundary integral equation matrix, two different hybrid BEM+Born series modeling schemes are formulated and validated through comparisons with the full-waveform BE numerical solutions for wave propagation simulation in a semicircular alluvial valley and a complex fault model respectively. Numerical experiments indicate that both the BEM+Born series modeling schemes are suitable for complex geological structures and significantly improve computational efficiency especially for the cases of high frequencies and multisource seismic survey. Then boundary-volume integral equation technique is illuminated in detail and verified by modeling wave propagation in complex media. Furthermore, the first-order and second-order Born approximate solutions for the volume-scattering waves are studied and quantified by numerical simulation in different random medium models. Finally, preconditioning generalized minimal residual method is applied to solve boundary-volume integral equation and compared with Gaussian elimination method. Numerical experiments indicate this method makes the calculations more efficient.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Auditory gist: Recognition of very short sounds from timbre cues

Sounds such as the voice or musical instruments can be recognized on the basis of timbre alone. Here, sound recognition was investigated with severely reduced timbre cues. Short snippets of naturally recorded sounds were extracted from a large corpus. Listeners were asked to report a target category (e.g., sung voices) among other sounds (e.g., musical instruments). All sound categories covered the same pitch range, so the task had to be solved on timbre cues alone. The minimum duration for which performance was above chance was found to be short, on the order of a few milliseconds, with the best performance for voice targets. Performance was independent of pitch and was maintained when stimuli contained less than a full waveform cycle. Recognition was not generally better when the sound snippets were time-aligned with the sound onset compared to when they were extracted with a random starting time. Finally, performance did not depend on feedback or training, suggesting that the cues used by listeners in the artificial gating task were similar to those relevant for longer, more familiar sounds. The results show that timbre cues for sound recognition are available at a variety of time scales, including very short ones.

A novel method to count the red blood cells in thin blood films

This paper describes a novel idea to identify the total number of red blood cells (RBCs) as well as their location in a Giemsa stained thin blood film image. This work is being undertaken as a part of developing an automated malaria parasite detection system by scanning a photograph of thin blood film in order to evaluate the parasitemia of the blood. Not only will this method eliminates the segmentation procedures that are normally used to segment the cells in the microscopic image, but also avoids any image pre-processing to deal with non uniform illumination prior to cell detection. The method utilizes basic knowledge on cell structure and brightness of the components due to Giemsa staining of the sample and detects and locates the RBCs in the image.

Visualizing control systems performance: A fractional perspective

This article presents a novel method for visualizing the control systems behavior. The proposed scheme uses the tools of fractional calculus and computes the signals propagating within the system structure as a time/frequency-space wave. Linear and nonlinear closed-loop control systems are analyzed, for both the time and frequency responses, under the action of a reference step input signal. Several nonlinearities, namely, Coulomb friction and backlash, are also tested. The numerical experiments demonstrate the feasibility of the proposed methodology as a visualization tool and motivate its extension for other systems and classes of nonlinearities.

Model Selection in Summary Evaluation

A difficulty in the design of automated text summarization algorithms is in the objective evaluation. Viewing summarization as a tradeoff between length and information content, we introduce a technique based on a hierarchy of classifiers to rank, through model selection, different summarization methods. This summary evaluation technique allows for broader comparison of summarization methods than the traditional techniques of summary evaluation. We present an empirical study of two simple, albeit widely used, summarization methods that shows the different usages of this automated task-based evaluation system and confirms the results obtained with human-based evaluation methods over smaller corpora.

Ventilation rates in schools and pupil’s performance using computerized assessment tests

Research shows that poor indoor air quality (IAQ) in school buildings can cause a reduction in the students’ performance assessed by short-term computer-based tests; whereas good air quality in classrooms can enhance children's concentration and also teachers’ productivity. Investigation of air quality in classrooms helps us to characterise pollutant levels and implement corrective measures. Outdoor pollution, ventilation equipment, furnishings, and human activities affect IAQ. In school classrooms, the occupancy density is high (1.8–2.4 m2/person) compared to offices (10 m2/person). Ventilation systems expend energy and there is a trend to save energy by reducing ventilation rates. We need to establish the minimum acceptable level of fresh air required for the health of the occupants. This paper describes a project, which will aim to investigate the effect of IAQ and ventilation rates on pupils’ performance and health using psychological tests. The aim is to recommend suitable ventilation rates for classrooms and examine the suitability of the air quality guidelines for classrooms. The air quality, ventilation rates and pupils’ performance in classrooms will be evaluated in parallel measurements. In addition, Visual Analogue Scales will be used to assess subjective perception of the classroom environment and SBS symptoms. Pupil performance will be measured with Computerised Assessment Tests (CAT), and Pen and Paper Performance Tasks while physical parameters of the classroom environment will be recorded using an advanced data logging system. A total number of 20 primary schools in the Reading area are expected to participate in the present investigation, and the pupils participating in this study will be within the age group of 9–11 years. On completion of the project, based on the overall data recommendations for suitable ventilation rates for schools will be formulated.