Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Principle Component Analysis in Identifying Synoptic Patterns of Wet Periods in Central Iran

Atmosphere circulation patterns have an important role in appearance of natural events. For this purpose, in the present study in order to identify the atmosphere circulation patterns which cause humid periods, the principle components analysis method and cluster analysis were used. Therefore by emphasis on peripheral circulation approach, wet periods during a 30 years statistic period (1982-2011) in 6 synoptic stations in Kerman, Yazd and Isfahan province which have long term common statistic period were calculated using standard precipitation index. The findings of principle component analysis showed that by eight components it is possible to explain 94 percent of the variations in geo-potential height data. Therefore, the first component with 62.3 percent appearance shows the dominance of polar and Siberia high pressure in cold period of the year, and the remaining components show atmosphere instabilities which penetrate the region from Mediterranean sea, Black sea and Red sea. The results of cluster inspections show that there are two patterns with 28 percent frequency and 15 percent probability of precipitation in the region. Therefore, the circulation pattern of middle level of the atmosphere shows that by establishment of a deep trough at the east of Mediterranean sea and red sea and locating the east of trough on the researched area, and simultaneously a low pressure centre become dominant at the sea level on the region which cause precipitation event. This arrangement of circulation pattern causes Iran to exit from drought.
An Efficient ΣΔ-STAP Detector for Radar Seeker using RPCA Post-processing

Adaptive detection of moving targets in sea clutter environment is considered as one of the crucial tasks for radar seekers. Due to the severe spreading of the sea clutter spectrum, the ability of space-time adaptive processing with sum and difference beams (ΣΔ-STAP) algorithms to suppress the sea clutter is very limited. This paper, investigated the low-rank property of the range-Doppler data matrix according to the eigenvalue distribution from the eigen spectrum, and proposed an efficient ΣΔ-STAP detector based on the robust principle component analysis (RPCA) algorithm to detect moving targets, which meets the low-rank matrix recovery conditions. The proposed algorithm first adopts ΣΔ-STAP algorithm to preprocess the sea clutter, then separates the sparse matrix of target component from the range-Doppler data matrix through the RPCA algorithm, and finally, effectively detects moving targets in the range-Doppler plane. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm in the low signal-to-noise ratio scenarios. Defence Science Journal, Vol. 64, No. 4, July 2014, pp. 344-349, DOI:http://dx.doi. org/10.14429/dsj.64. 4862
Influence of cutting height and plant spacing on Sesbania (Sesbania aegyptiaca [Poir]) productivity under hyper-arid conditions in El-kharga Oasis, El-Wadi El-Gaded, Egypt

Search for an adapted forage species for feed production under existing marginal lands and harsh conditions is needed to overcome sever feed shortage in Egypt. Sesbania, Sesbania aegyptiaca (Sesbania sesban, L) has shown potential for forage production and being included in grazing systems, especially on marginal lands and salt–affected soils. An experiment was conducted in the summer of 2004 and 2005 under artesian irrigated water conditions where four cutting heights (10, 20, 30, and 40 cm above ground level) and three plant spacing (10, 20 and, 30 cm between plants) were investigated. The highest average accumulated fresh and dry forage yield obtained from cutting at 10 cm from ground surface whereas the 40 cm cutting level produced the lowest yield and no significant difference was observed between 20 and 30 cm cutting levels. Highest yield was obtained from second cutting. The CP% in forage harvested from 10 cm spacing was highest followed by 20 and 30 cm spacing, respectively. Forage from 1 st cutting had the highest CP% followed by 2 nd and 3 rd cutting. The response of CF% in Sesbania aegyptiaca to cutting level and time was opposite to those of CP %.
Impacts of future urban expansion on urban heat island effects during heatwave events in the city of Melbourne in southeast Australia

The city of Melbourne in southeast Australia is planning to expand urban areas substantially by the year 2050 and this expansion has the potential to alter the Urban Heat Island (UHI), that is, higher temperatures in urban areas as compared to surrounding rural areas. Moreover, Melbourne has been experiencing more frequent heatwaves for last two decades, and the intensity and duration of heatwaves is expected to increase in the future, which could exacerbate the UHI. This study evaluates the potential impacts of future urban expansion on the urban meteorology in Melbourne city during four of the most severe heatwave events during the period 2000–2009. Urban expansion is implemented as high‐density urban with a high urban fraction of 0.9 to investigate the maximum possible impact. Simulations are carried out using the Weather Research and Forecasting model coupled with the Single‐Layer Urban Canopy Model with current land‐use and future urban expansion scenarios. Urban expansion increases the near‐surface (2‐m) UHI (UHI2) by 0.75 to 2.80 °C and the skin‐surface UHI (UHIsk) by 1.9 to 5.4 °C over the expanded urban areas during the night, with no changes in existing urban areas. No substantial changes in UHI2 and UHIsk occur during the day over both existing and expanded urban areas. This is largely driven by changes in the storage heat flux, with an increase in storage heat at night and a decrease during the day; that is, excess storage heat accumulated during the day is released at night, which causes a slower decrease of near‐surface temperature and increase in the UHI. Urban expansion did not affect human thermal comfort (HTC) in existing urban areas and there were no marked differences in HTC between existing and expanded urban areas.
Enzymatic and catalystic enchancement of methane production from corn silage and grain residues

Many biogas plants used corn silage and grain processing (dryers) residues as a raw material. Hydraulic retention time working with such raw materials is relatively long and requires large volumes of bioreactors. Variety of additives can be used to improve the anaerobic digestion process. This article shows the results of the study where the enzyme mixture and catalyst Metaferm are used for the digestion process echancement. Corn silage and grain processing residues were digested in 0.72 l bioreactors at temperature 38 o C in the batch mode process. 2 biorectors were for control purposes and contained inoculums only. The other 14 biorectors contain biomass substrates without/with added enzyme mixture or catalyst Metaferm. Addition of enzyme mixture (0.5 g) in bioreactors with corn silage or grain processing residues increases the average methane yield by 7.76 % or 4.48 % respectively. Addition of catalyst Metaferm (1 ml) into bioreactors with corn silage or grain processing residues increases the average methane yield by 1.79 % or 6.72 % respectively.
Methods to Estimate the Channel Delay Profile and Doppler Spectrum of Shallow Underwater Acoustic Channels

In this paper, we present the methods to detect the channel delay profile and the Doppler spectrum of shallow underwater acoustic channels (SUAC). In our channel sounding methods, a short impulse in form of a sinusoid function is successively sent out from the transmitter to estimated the channel impulse response (CIR). A bandpass filter is applied to eliminate the interference from out-of-band (OOB). A threshould is utilized to obtain the maximum time delay of the CIR. Multipath components of the SUAC are specified by correlating the received signals with the transmitted sounding pulse with its shifted phases from 0 to 2Π. We show the measured channel parameters, which have been carried out in some lakes in Hanoi. The measured results illustrate that the channel is frequency selective for a narrow band transmission. The Doppler spectrum can be obtained by taking the Fourier transform of the time correlation of the measured channel transfer function. We have shown that, the theoretical maximum Doppler frequency fits well to that one obtained from measurement results.
High-Resolution FTIR and Diode Laser Spectroscopy of Supersonic Jets

Fourier transform infrared (FTIR) spectrometers and tunable diode lasers in combination with a supersonic molecular beam expansion are a perfect tool for the investigation of molecules, ions, and radicals at low temperatures. The internal degrees of freedom of the molecules are adiabatically cooled to very low temperatures and thus only low-lying energy levels are populated. The reduction of the number of populated levels at low temperatures makes the assignment of the spectra much easier as compared to the congested room-temperature spectra. Under certain conditions, the Doppler linewidths are greatly reduced, corresponding to very low effective translational temperatures. Supersonic expansion also provides a suitable method for producing and investigating van der Waals clusters and hydrogen-bonded complexes. Unstable species such as radicals and ions can be efficiently produced and studied in a molecular beam. The low rotational temperature allows for the study of nuclear spin symmetry conservation or conversion between nuclear spin isomers. A molecular beam expansion can be obtained by expanding gas through a slit or a circular nozzle. Both expansion geometries can be used in combination with a multipass optical setup and with cavity ring down spectroscopy, which enhances the effective absorption path length. Cooling of the molecules can be promoted by seeding in noble gases. This article summarizes the general aspects of the experimental technique as well as current developments. To demonstrate how powerful the combination of a molecular beam expansion with tunable diode laser and FTIR spectroscopy can be, we report results on some important current examples. Keywords: FTIR; supersonic jet; diode laser; molecular beam; clusters; van der Waals complexes; hydrogen bonds; nuclear spin symmetry conservation; infrared spectroscopy; isotopes
DESTİNASYON İMAJININ ZİYARETÇİ MEMNUNİYETİ ÜZERİNDEKİ ETKİSİ: MARDİN’E GELEN YERLİ TURİSTLER ÜZERİNDE BİR ARAŞTIRMA

Bir destinasyona gelen turistlerin destinasyona iliskin memnuniyetlerini etkilemekte olan cok cesitli faktorler bulunmaktadir. Bunlardan birisi de destinasyonun sahip oldugu olumlu imajdir. Bu arastirmanin temel amaci, destinasyon imajinin ziyaretci memnuniyeti uzerindeki etkisinin Mardin destinasyonunda incelenmesidir. Arastirmanin evrenini Mardin’i ziyaret eden yerli turistler meydana getirmektedir. Arastirmada tesadufi ornekleme yonteminden yararlanilmistir. Bu dogrultuda Şubat 2018 ile Agustos 2018 tarihleri arasinda Mardin’i ziyaret etmis olan 403 yerli turistten anket formu araciligiyla gerekli veriler toplanmistir. Veriler SPSS 22.0 bilgisayar programiyla analiz edilmistir. Arastirma sonucunda destinasyon imajinin ziyaretci memnuniyetini anlamli ve pozitif bicimde etkiledigi tespit edilmistir.
An emerging signature pedagogy for sustainable food systems education

Abstract Concerns are growing over the ability of the modern food system to simultaneously achieve food security and environmental sustainability in the face of global change. Yet, the dominant tendency within university settings to conceptualize and address diverse food system challenges as separate, disconnected issues is a key barrier to food system transformation. To address this fragmented approach, educators in North American institutes of higher education have begun new degree programs, specializations and certificates related to food systems. These programs, which we term sustainable food system education (SFSE) programs, have a common goal: to support post-secondary students across a range of disciplines in developing the knowledge, skills and dispositions to effectively address complex challenges in the food system. Graduates of these programs will be able to engage in collective action towards transforming the food system. As educators participating in flagship SFSE programs, we identify common pedagogical themes evident in SFSE programs, including our own. We then propose a signature pedagogy (SP) for sustainable food systems education. Signature pedagogies are conceptual models that identify the primary elements by which professional education in a specific field is designed, structured and implemented. On the basis of our analysis of SFSE programs, we identified systems thinking, multi-, inter- and trans-disciplinarity, use of experiential learning approaches and participation in collective action projects as central themes within a SFSE SP. By making these themes and their function explicit within a pedagogical framework, we seek to spur critical and creative thought regarding challenges of professional education in the field of sustainable food systems. Scholars and practitioners are encouraged to review, critique and implement our framework to advance the dialogue on SFSE theory and practice.
A C-Tesla Protocol for Sensor Networks

The present invention is to manufacture a low hydrogen-concentration silicon crystal having less micro defects caused from oxygen precipitation generated during an annealing process. Particularly, a silicon crystal including hydrogen concentration lower than 0.55x1011 cm-3, where the hydrogen concentration dependency is small and the micro defect density is less, may be used for a substrate of semiconductor devices. The low hydrogen-concentration silicon substrate is manufactured by measuring the hydrogen concentrations in a silicon crystal and in a hydrogen-doped silicon crystal having a known hydrogen concentration, where both the silicon crystals have been annealed at an equal condition so as to generate thermal donors therein, and by comparing thus measured hydrogen concentrations. The known hydrogen-doped silicon crystal has been prepared by a hydrogen-diffusing annealing in a hydrogen containing atmosphere, where the hydrogen concentration is calculated from the hydrogen partial pressure and the temperature of the annealing.
A Survey on Prediction of Bus Arrival Time using Global Positioning System (GPS)

Buses are an affordable means of public transport used by majority of the population in cities. Bus services are provided in many cities and have been proved to be an excellent means of transport; however, the commuters are uncertain about the arrival time of the buses, which leads to usage of private vehicles or taxis, thus leading to an increase in fuel consumption and pollution. Rather than waiting for buses it would be beneficial for passengers to know the tentative arrival times of the buses. Thus, for the convenience of citizens this application is proposed, which tracks the locations of the user as well as the bus using GPS sensors, and then calculate the approximate time required by the bus to reach the stop including the traffic analysis and various other parameters. Thus, the commuters can be aware about the waiting time for their respective buses, helping them in planning their journey accordingly.
Diversity controlled rotating machinery fault detection

Classifier ensembles are more and more often applied for technical diagnostic problems. When dealing with vibration signals a lot of point features can be extracted. In this situation there is the problem of how to choose the best classifiers in the ensemble. One solution is the use of measures that quantify diversities amongst the classifier outputs. While there is no general diversity definition and method of calculation, the selection of the correct measure is a vital task. In this paper research is presented on the application of classifier ensembles built with Bagging for the detection of rotating machinery faults. It was found that there is a relationship between classification accuracy and the diversity measures.
EDUCATION LEVEL AND KNOWLEDGE LEVEL IN OPEN DEFECATION BEHAVIOR

Open defecation behavior is a problem for developing countries including Indonesia. This can cause a dirty environment and a source of transmission of diseases such as diarrhea and other infectious diseases. So there needs to be research that analyzes the risk factors for these behaviors. This study aims to analyze the relationship of knowledge and education level to defecation behavior in all areas. A total of 110 family heads were examined and selected using the proportional random sampling technique. The statistical analysis used is Chi Square. The results of the analysis found that there was a relationship between the level of education (value 0.041; OR = 2.576; 95% CI = 1.118-5,934) and knowledge (pvalue 0.001 OR = 4,000; 95% CI = 1.794-8,920) towards open defecation behavior. There needs to be an effort from various parties to be able to increase knowledge through periodic education and the use of health promotion media that can be accessed anytime and anywhere.
Minimum-dissipation models for large-eddy simulation

Minimum-dissipation eddy-viscosity models are a class of sub-filter models for large-eddy simulation that give the minimum eddy dissipation required to dissipate the energy of sub-filter scales. A previously derived minimum-dissipation model is the QR model. This model is based on the invariants of the resolved rate-of-strain tensor and has many desirable properties. It appropriately switches off for laminar and transitional flows, has low computational complexity, and is consistent with the exact sub-filter tensor on isotropic grids. However, the QR model proposed in the literature gives insufficient eddy dissipation. It is demonstrated that this can be corrected by increasing the model constant. The corrected QR model gives good results in simulations of decaying grid turbulence on an isotropic grid. On anisotropic grids the QR model is not consistent with the exact sub-filter tensor and requires an approximation of the filter width. It is demonstrated that the results of the QR model on anisotropic grids are primarily determined by the used filter width approximation, and that no approximation gives satisfactory results in simulations of both a temporal mixing layer and turbulent channel flow. A new minimum-dissipation model for anisotropic grids is proposed. This anisotropic minimum-dissipation (AMD) model generalizes the desirable practical and theoretical properties of the QR model to anisotropic grids and does not require an approximation of the filter width. The AMD model is successfully applied in simulations of decaying grid turbulence on an isotropic grid and in simulations of a temporal mixing layer and turbulent channel flow on anisotropic grids.
Ergodic Measure Preserving Flows

Probabilistic modelling is a general and elegant framework to capture the uncertainty, ambiguity and diversity of data. Probabilistic inference is the core technique for developing training and simulation algorithms on probabilistic models. However, the classic inference methods, like Markov chain Monte Carlo (MCMC) methods and mean-field variational inference (VI), are not computationally scalable for the recent developed probabilistic models with neural networks (NNs). This motivates many recent works on improving classic inference methods using NNs, especially, NN empowered VI. However, even with powerful NNs, VI still suffers its fundamental limitations. In this work, we propose a novel computational scalable general inference framework. With the theoretical foundation in ergodic theory, the proposed methods are not only computationally scalable like NN-based VI methods but also asymptotically accurate like MCMC. We test our method on popular benchmark problems and the results suggest that our methods can outperform NN-based VI and MCMC on deep generative models and Bayesian neural networks.
ANALYSIS OF LOCAL EXPERTS IN SOCIAL MEDIA A Thesis by SINDHUJA VENKATESH

Recent popular social services (e.g., Foursquare, Twitter, Instagram) are creating a comprehensive geo-social overlay of the planet through geo-located posts, images, and other user-generated content. These public, voluntarily shared footprints provide a potentially rich source for uncovering the landscape of users’ interests and topical expertise, which has important implications for social search engines, recommender systems, and other geo and socially-aware applications. This thesis presents the first large-scale investigation of local interests and expertise through an analysis of a unique 13 million user geo-coded list dataset sampled from Twitter. Twitter lists encode a “known for” relationship between a labeler and a labelee. In the small, these lists are helpful for individual users to organize friends or contacts. In the aggregate, however, these lists reveal global patterns of interest and expertise. Concretely, this thesis presents a qualitative and quantitative analysis on the relationships between user locations, interests, and topic expertise as revealed through these Twitter lists. Through thorough analysis this thesis examines the (i) impact of geo-location on topic expertise and users’ topic interests in Twitter; (ii) the degree of “locality” of topics; and (iii) the concentration and dispersion of expertise.
CLUSTERING DOCUMENT TREES BASED ON SIMILARITY MEASURE

With rapid change in technology, Data Mining and Warehousing is gaining a lot of prominence in the field of computers. Retrieval of information in large intra organizations is becoming a tedious task. Data mining is now offering many powerful and innovative techniques for solving the problem of information retrieval. This paper introduces a novelapproach for clustering text documents based on frequent subtrees. Document trees are constructed by extracting noun hypernyms relationship for each and every word in the text document using Wordnet 2.1 lexical reference. This technique sweeps over the traditional text mining approaches which are based on frequent keyword occurrences. The aim of this technique is that it can cluster documents even if the documents do not have words in common. The key idea behind this paper is to automate the clustering mechanism by discovering frequent subtrees from various document trees. To identify the frequent sub trees occurrences in the constructed document trees, the closed frequent substructure mining approach is employed. This approach explores the depth first search in frequent subtree mining to discover all frequent subtrees without candidate generation and false positive pruning to accelerate the mining process. Clusters are formed based on similarity measure. Hence, this paper accentuates on the concept of frequent subtree mining based on noun hypernyms to form clusters resulting in the automation of system for easy searching, organizing and maintaining of voluminous text documents