Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel damage detection method based on flexibility identification theory and data fusion technique

An improved flexibility-based method hasbeen proposed in this studyfor damage detection, in which multi-scale convolution is utilized to decrease the interference of the measurementnoise and theDempster-Shafer evidence theory has been adopted to combine all scale information together to amplifythe damage characteristics. Threemain features make theproposed method distinguish with previous study:1)The proposed method is a kind of no-baseline flexibility-based method. Namely, this method can locate the damage with the absence of intact structural flexibility serving as baseline; 2) The flexibilityis estimated without requiring known the structural mass, which is a necessary in traditional method for flexibility estimation; 3) By utilizing multi-scale space theory and data fusion approach, the proposed methodhas a superior noise tolerant ability. Both numerical and experimental examples have been studied to reveal the effectiveness and accuracy of the proposed methodindifferent noise level. The comparison between traditionalmethod and proposed method demonstrates that the latteris well suited to detect damage in beams structure in a noisy environment.
Explored and Critique of Contingency Theory for Management Accounting Research

The paper explored contingency theory as it relates to management accounting and organisational system. It traced the theory to US research on leadership behaviour and interpersonal relationships. Though the previous studies about were faulted because of nonbest fit in organisations leadership. The theory provides an explanation of organisational behaviour base on contingent factors, thus there is no generalised best fit in leadership style. Also, it is relevant to accounting by its effect on technology and environment. Where management accounting research appreciate its impact on budgeting system and control and information disclosure, among others. However, there is a problem with the theory claim on the static behaviour of organisations and fail to address issues of organisational size. Notwithstanding its shortcomings, the theory is acceptable in leadership studies and management accounting and control research.
A Novel Vibration-Based Two-Stage Bayesian System Identification Method *

Model updating aims at making inference on the structural model parameters (e.g., stiffness, mass) based on measured structural response. Based on vibration data, two-stage approach is commonly adopted, i.e., first identify the modal parameters including natural frequencies, mode shapes, and then update the structural parameters utilizing this information by building a finite element model. Identification uncertainty in Stage I is usually not accounted for in traditional methods but this becomes critical for output-only measurement. This paper presents a novel two stages Bayesian system identification method by using the data collected under ambient condition. In Stage I, a Fast Bayesian FFT method is conducted to obtain the most probable value (MPV) of modal parameters and the associated posterior uncertainty. In Stage II, both the MPV and the posterior uncertainty obtained are formulated into the updating process to identify the model parameters. In this method, heuristics that are commonly applied in formulating the likelihood function in the traditional methods are not involved. The proposed method is illustrated by experimental data.
VISITORS’ SATISFACTION OF SERVICES PROVIDED IN BAKO NATIONAL PARK, SARAWAK By

The number of visitors to the Bako National Park has been increasing due to its close location to the city of Kuching and its easy access. The management of the park is facing tremendous pressures in trying to provide the best services and facilities to the visitors. Only satisfied tourists will revisit the park and will not make unwanted publicity especially through the word of mouths. The objective of this study is to measure tourists’ satisfaction on services quality at the park. This study uses the ECOSERVE framework that has been adapted from SERVQUAL models. In the ECOSERVE model the tangible dimension has been re categorized into tangible and ecotangible dimensions. A sample of tourists staying in the park was conveniently selected to respond to the selfadministered questionnaire. Results of the analysis show that most of the tourists in Bako national park are not satisfied with services provided as indicated by the negative values of the gaps. The analysis of the dimensions of the ECOSERVE instrument resulted in detecting the Responsiveness dimension as being the most significant indicator of dissatisfaction level. The European tourists evaluated the service quality in Bako as unsatisfactory with the Western Europeans being more unsatisfied relative to the Eastern Europeans. This dimension relates to the employees willingness to offer services and their ability to indicate exactly the services to be given.
The Prediction of Saint John ’ s Wort Leaves ’ Chlorophyll Concentration Index using Image Processing with Artificial Neural Network

There are several methods for detecting plant nitrogen content including plant analysis like leaf chlorophyll measurement and remote sensing techniques. In this study, image-processing method was used to predict St. John’s wort (Hypericum perforatum L.) leaf chlorophyll concentration from leaves. The experiment was carried out in greenhouse conditions. The Hougland solution was used as a fertilizer. It was applied at 5 different levels to the St. John’s wort grown in pots. SPAD-502 chlorophyll meter was used for measuring the chlorophyll concentration of the leaves. The chlorophyll-a (chl-a) and chlorophyllb (chl-b) of the leaves were measured by UV spectrometer. Artificial Neural Network (ANN) model was developed based on the RGB (red, green, and blue) components of the color image captured with a digital camera for estimating the chlorophyll concentration. According to the obtained results, the neural network model is capable of estimating the St. John’s wort leaf chlorophyll concentration with a reasonable accuracy. The coefficient of determination (R) was 0.99 and mean square error (MSE) was obtained 0.005 from validation.
FREQUENCY OF BENIGN BREAST DISEASE IN WOMEN OF HAZARA DIVISION , PAKISTAN

Background: Benign breast diseases are much common than malignancy however not all of them are reported. The most alarming symptoms that are investigated include breast lumps and bloody nipple discharge while other symptoms like mastalgia and skin induration are commonly ignored by the patients or treated medically. Hence the true incidence of breast diseases is much higher than recorded. In this study the frequency of various benign breast diseases occurring in Hazara division are studied. Methods: This was a retrospective study of breast biopsy specimens received over a period of five years from June 2012 to June 2017 at Histopathology Department at Ayub Teaching Hospital, Abbottabad. All cases diagnosed with ductal carcinoma-in-situ (DCIS), malignancies and lesions in the male breast were excluded from this study. The data was analyzed using SPSS-19. Results: Benign breast disease was diagnosed in 188 (59%) of the total 317 cases. The ages of patients ranged from 13–75 years with a mean age of 29±13 years. The most common lesion was fibroadenoma present in 97 (51%) followed by inflammatory lesions and then fibrocystic disease. Conclusion: Benign breast disease, although more frequent, remains under reported as patients do not seek medical help unless the symptoms are problematic. Fibroadenoma was the most frequent lesion occurring in the second decade.
ON CAGE DYNAMICS IN ROLLING BEARINGS BY TEST AND SIMULATION TRACK OR CATEGORY Rolling Element Bearings III ( 7 A ) AUTHORS AND INSTITUTIONS

Demands on the performance of rolling bearings are constantly increasing. For example, in the process of electrification in the automotive industry and in the bearing assembly of main spindle of machine tools, a guidance system that is as low in vibration as possible is required. A potential cause of vibrations in rolling bearings can be the cage, which under certain operating conditions performs a very high frequency movement, which can be accompanied by strong elastic deformation, that can limit the performance of the bearing. The calculation of the elastic deformation of the cage during an unstable movement represents a challenge, that has not or only approximately been considered, in previous publications on the subject of cage instability. GRILLENBERGER presents a first extension to rigid cage modelling, in which several cage segments allow macro elastic deformation of the cage by spring-damper elements [1]. A more detailed modelling of the elasticity of rolling bearing cages is described by HAHN [2]: a reduction of the cage FE-model according to CRAIG and BAMPTON [3] allows a precise calculation of the cage deformation. The multibody simulation software CABA3D (Computer Aided Bearing Analyzer 3 Dimensional) used for this purpose is also used in this paper to investigate the cage dynamics in a cylindrical roller bearing with respect to different load cases and speeds.
The use of italic fonts refers to changes in the text ]

General Comments: This study is a timely and valuable contribution to our understanding of the budget of OCS. I agree with all of the comments of Referee #2 and will not repeat them here but I would like to bring up a few more points: Overall, there is a lot of discussion of what is higher/lower but not enough quantification of the disagreement nor a discussion of the causes. I wonder if the overall range in OCS emission (which seems unreasonable large) could be better defined by constraining the model to the observation of OCS concentrations that have been mentioned. There are many unphysical emissions discussed in the text and the range seems larger than will be useful in future work.  Thank you for the time and effort spent on this review. Most of your comments could be directly addressed and we believe that the clarity of the paper has been improved, especially that of section 2 (methods). We considerably expanded the section devoted to the comparison of modeled and observed OCS concentrations, despite (as pointed out by the other referee) the small number of sea-surface OCS measurements and the problem associated with the comparison of monthly mean values at a model grid-cell scale to discrete data.
Image Restorations Using Deep Learning Techniques IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES

Conventional methods for solving image restoration problems are typically built on an image degradation model and on some priors of the latent image. The model of the degraded image and the prior knowledge of the latent image are necessary because the restoration is an ill posted inverse problem. However, for some applications, such as those addressed in this thesis, the image degradation process is too complex to model precisely; in addition, mathematical priors, such as low rank and sparsity of the image signal, are often too idealistic for real world images. These difficulties limit the performance of existing image restoration algorithms, but they can be, to certain extent, overcome by the techniques of machine learning, particularly deep convolutional neural networks. Machine learning allows large sample statistics far beyond what is available in a single input image to be exploited. More importantly, the big data can be used to train deep neural networks to learn the complex non-linear mapping between the degraded and original images. This circumvents the difficulty of building an explicit realistic mathematical model when the degradation causes are complex and compounded. In this thesis, we design and implement deep convolutional neural networks (DCNN) for two challenging image restoration problems: reflection removal and joint demosaickingdeblurring. The first problem is one of blind source separation; its DCNN solution requires a large set of paired clean and mixed images for training. As these paired
Lawrence Berkeley National Laboratory Recent Work Title A reservoir of ' historical ' antibiotic resistance genes in remote pristine Antarctic soils Permalink

Background: Soil bacteria naturally produce antibiotics as a competitive mechanism, with a concomitant evolution, and exchange by horizontal gene transfer, of a range of antibiotic resistance mechanisms. Surveys of bacterial resistance elements in edaphic systems have originated primarily from human-impacted environments, with relatively little information from remote and pristine environments, where the resistome may comprise the ancestral gene diversity. Methods: We used shotgun metagenomics to assess antibiotic resistance gene (ARG) distribution in 17 pristine and remote Antarctic surface soils within the undisturbed Mackay Glacier region. We also interrogated the phylogenetic placement of ARGs compared to environmental ARG sequences and tested for the presence of horizontal gene transfer elements flanking ARGs. Results: In total, 177 naturally occurring ARGs were identified, most of which encoded single or multi-drug efflux pumps. Resistance mechanisms for the inactivation of aminoglycosides, chloramphenicol and β-lactam antibiotics were also common. Gram-negative bacteria harboured most ARGs (71%), with fewer genes from Gram-positive Actinobacteria and Bacilli (Firmicutes) (9%), reflecting the taxonomic composition of the soils. Strikingly, the abundance of ARGs per sample had a strong, negative correlation with species richness (r = − 0.49, P < 0.05). This result, coupled with a lack of mobile genetic elements flanking ARGs, suggests that these genes are ancient acquisitions of horizontal transfer events. Conclusions: ARGs in these remote and uncontaminated soils most likely represent functional efficient historical genes that have since been vertically inherited over generations. The historical ARGs in these pristine environments carry a strong phylogenetic signal and form a monophyletic group relative to ARGs from other similar environments.
Cluster Interconnection of Offshore Wind Farms Using Direct AC High Frequency Links

Cluster connection of wind turbines is a promising topology which will enable to exploit the advantages of series and conventional shunt connection for offshore wind farms. This paper discusses the advantages and disadvantages of this kind of connection for high voltage direct current transmission and presents five different possible configurations for conversion system based on high frequency AC links. A hierarchical control strategy is proposed: in the first level, each turbine is controlled by using PI controls in rotor flux oriented frame while in the second level an optimal dispatch based on a non linear optimization model is used for coordinated on-shore/off-shore control. A case study of one of the configurations is presented with simulation results using Matlab and PSIM. Although only one of the proposed configurations is tested by simulations, the concept is general and can be easily extended to all the proposed topologies. NOMENCLATURE β blade pinch angle λ tip speed ratio Φ blade diameter Ψr flux in rotor Ψs flux in stator ρ air density Aw rotor swept area Cp performance coefficient f switching frequency IDC Current in the HVDC transmission line IkDC Current in the cluster k at the offshore grid Ikm DC voltage in the conversion system m and cluster k Ir current in rotor Is current in stator Jm inertia in the generator and the wind turbine m duty cycle np number of pairs of poles PM output mechanical power PT total generated power TE electrical torque Tm mechanical torque Us voltages in stator VOn−sh DC voltage in the converter on-shore Vw wind velocity wm mechanical rotational speed in the turbine wr mechanical rotational speed in the generator ws electrical speed
The Relationship Between Blood Antioxidant Enzyme Levels and Genotype During Migraine Attack and Initial Periods in Migraine Patients

1 Yuzuncu Yil University, Faculty of Veterinary, Department of Genetics, 65080 Van, TURKEY 2 Firat University, Faculty of Medicine, Department of Neurology, 23119 Elazig, TURKEY 3 Bitlis Eren University, Faculty of Health of Science, Department of Biochemistry, Bitlis, TURKEY 4 Firat University, Faculty of Veterinary, Department of Biochemistry, 23119, Elazig, TURKEY 5 Adıyaman State Hospital, Department of Neurology, Adıyaman, TURKEY
Predicting Machining Centre Geometric Tolerance Thresholds with Support Vector Machines

NC-Checker is a tool developed by metrology software products ltd. for data collection and benchmarking of geometric performance in CNC machining centres. It runs a series of probing cycles on a calibrated datum point within a machine volume, revealing the errors inherent in the system. It is then possible to compare these errors against both nominal positions and the typical operational conditions, using this data to assess the likelihood of quality part production but also to track any changes in geometric performance over time. Herein, geometric performance is defined as the tool tip or probe’s deviation from nominal position as understood by the controller, affecting finished workpiece quality as a result of inaccurately machined dimensions. Due to a variety of error sources [1] inherent in machining operations, it is not possible to guarantee identical geometric performance at all times, and it is observed that geometric performance tends to drift over time. As such, machining centres undergo regular maintenance to ensure they are performing within the acceptable tolerances. One key functionality of NC-Checker is the ability to set threshold limits on each tested parameter, providing the operator with a simple system to determine whether the machining centre is in a satisfactory state for operation or not. This is presented in the form of a benchmark report. Generally, the ability to edit these thresholds is restricted to the Manufacturing or Maintenance Engineer and is determined by the required precision of the part in current production. Through interference with the tolerance thresholds, there is the possibility of distorting the machining centre’s actual health state as perceived in the NC-Checker benchmark report. This can have implications for finished part quality if the operator is using NC-Checker as a ‘go/no-go’ system, as is often the application of the software. Thus, in order to achieve effective monitoring of machining centre geometric performance, it is pertinent to explore the issue.
Survivability Evaluation in Wireless Sensor Network

Wireless Sensor Network (WSN) has the potential to gather information and send it to a base station. These networks are unattended and have the ability to work in military and civil applications. Wireless sensor nodes are combining the wireless communication infrastructure with the sensing technology. Instead of transmitting the perceived data to the control center through wired links, ad hoc communication methods are utilized, and data packets are transmitted using multi-hop connections. The survivability performance of WSN networks is an important issue. Network survivability is defined as the ability of a network to maintain or restore an acceptable level of performance in the event of deterministic or random failures. We perceive that packet loss, packet delay and network lifetime are integral components of WSN survivability. Therefore, we propose a composite model for survivability performance evaluation of WSN that consists of these parameters. Simulation results are used to validate the proposed model. The simulation results agree very well with the model.
Primary ambient extraction for random sign Hilbert filtering decorrelation

The primary ambient extraction (PAE) decomposes a stereo mixture into separated primary and ambient components, providing a crucial step in spatial audio analysis and reproduction. The necessary spatial assumptions of PAE include (i) uncorrelated balanced ambient components, (ii) uncorrelated ambient and primary components, and (iii) primary components with the only difference in the panning factor. Hence, different PAE methods can be developed for different decorrelation processes that are used to produce the uncorrelated ambient components. This paper investigates a new decorrelation process, namely the random sign Hilbert filtering decorrelation process. This decorrelation process ensures that the ambient components consistently have ±90° phase difference between the left and right channels. The PAE method catering for the random sign Hilbert filtering decorrelation process is called the uncorrelated-ambient PAE (UAPAE). This paper expounds the UAPAE and demonstrate a comparison between the UAPAE and existing PAE methods.
Major element rock analysis by X-ray fluorescence – a simple fusion method

One part of powdered rock is mixed with seven parts by weight of anhydrous sodium tetraborate and 0.185 parts of anhydrous sodium carbonate. The mixture is melted in a gold plated platinum crucible over an Amal burner. The melt is stirred occasionally with a platinum wire. When it is homogeneous the melt is poured into the middle of a copper wire ring which rests on a hot stainless steel disc. The polished end of a cold aluminium cylinger is brought down to press out the molten blob to fill the ring. The chilled glass disc needs no annealing. The disc is analsed for magnesium, aluminium, silicon, phosphorus, potassium, calcium, titanium, manganese and iron. The primary X-ray beam is generated by a silver target. Intensities of the Ka X-rays are measured. For most rocks no background measurements are necessary. A vitreous enamel disc containing all the analysed elements is used as a permanent reference standard to record instrument drift. Synthetic standards are used to calibrate the instrument. The results are corrected to take account of absorption of the characteristic X-rays by the glass. This is a digital version of the article first published in Philips Analytical Equipment Bulletin FS35, August 1971 Each page is reproduced as a picture.
GROUP BASED LOCALIZED MULTIPLE KERNEL LEARNING ALGORITHM WITH lp-NORM

Localized multiple kernel learning is a promising strategy for combining multiple features or kernels in terms of their discriminative power. However, learning specific combination kernel for each sample generally leads to expensive computation and less reliability caused by some dominant samples. In addition, traditional sparse constraint generally causes that one of the best kernels is selected, while some useful kernels may have not been used efficiently. In this paper, we proposed a groupbased non-sparse localized multiple kernel learning algorithm to tackle the issues above. In our algorithm, the samples are divided into groups and then the kernel weights are optimized for each group. Because the sparse constraint may lose useful kernels, we use an lp-norm constraint on the kernels and obtain non-sparse results to avoid losing useful kernels. The advantage of each kernel is adopted in various local spaces. Since some datasets consist of multiple features, we propose a multiple kernel clustering method to make the clustering result more reliable. Experiments state that our method performs better than other excellent MKL algorithms.