Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the processing of interaural temporal disparities within high frequency stimuli via manipulations of the temporal signatures of their envelopes

During the past several years, we have investigated the processing of interaural temporal disparities (ITDs) conveyed within high-frequency auditory channels. Historically, ITD-processing at high frequencies has been found to be less ef cient than that measured at low frequencies. Using “transposed” stimuli, we have reported that ITD-processing at high frequencies can be enhanced in terms of resolution of ITDs, extents of ITD-based laterality and resistance to the binaural interference found with conventional high-frequency stimuli. Notably and of theoretical import, transposed stimuli provide envelope-based binaural information within high-frequency channels similar to that provided by the waveform within low-frequency channels. More recently, we have utilized “raised-sine” high-frequency stimuli to investigate which particular features of the envelopes of high-frequency waveforms foster enhanced ITD processing. Such raised-sine stimuli permit independent variation of the modulation frequency, modulation depth, and “dead-time/relative peakedness” of the envelope of a high-frequency waveform, while also suitably restricting its spectral content. It will be seen that an interaural correlation-based model including stages mimicking peripheral auditory processing can explain much of the patterning of the results.
Application of Combined Method in Power Load Forecasting

In order to improve the accuracy of load forecasting,a combined forecasting method has been introduced in this paper,which can obtain more accurate results through combining the advantages of single prediction models.Two parameters are introduced so that the combined model can be transformed into extreme value problem and solved by evolutionary programming.At last,the combined forecasting model and single models are used to calculate the historical power supply and maximum load of Yancheng city zone.By comparing the results,it is easy to find that the error of the combined model is less than that of any single model.Furthermore,the combined method is applied to mid-long term load forecasting.
Uncertainty analysis in structured laser illumination planar imaging (SLIPI) applied to non-linear signals: gas-phase phosphor thermometry

Recent studies have used structured laser illumination planar imaging (SLIPI) combined with phosphor thermography to remove multiple scatter effects and near-wall reflections, which lead to biases in temperature measurements and reduced spatial resolution. We show that for the typical non-linear pump-signal range under which thermographic phosphors are used, errors may arise in the reconstruction of the temperature field using SLIPI. In this study, synthetic laser induced phosphorescence (LIP) images are generated numerically by adapting the synthetic PIV image generator for the purpose. The simulations are combined with phosphorescent signal yield functions obtained from experimental data to investigate the application of SLIPI to gas-phase phosphor thermography. We conclude that whilst SLIPI is effective in removing scattering noise for phosphors for which the two-colour signal ratio is insensitive to the laser fluence, it creates a bias in the temperature measurement otherwise. We also show that the extent of multiple scatter in LIP images is always overestimated by SLIPI, owning to the non-linear emission behaviour and particle image diffraction.
A Multi-Criteria Recommender System for Tourism Using Fuzzy Approach

Recommender Systems have been widely used in Information and Communication Technology (ICT). The main reason for this extensive use is to decrease the problem of information explosion. Collaborative Filtering techniques, which attempt to predict what information will meet a user’s needs based on data coming from similar users, are becoming increasingly popular as ways to combat information overload. Collaborative Filtering techniques usage has shown significant advantages in tourism service recommendations. Accuracy improvement of Collaborative Filtering techniques for tourism recommender systems has been an important issue in the previous studies. Therefore, this study aims to improve the recommendation accuracy of Collaborative Filtering techniques for tourism recommender systems. In this study, the method of recommendation is developed using fuzzy C-means algorithm for user-based and item-based models. Two similarity measures, Pearson Correlation and Cosine, are used for similarity calculations of users and items in both user-based and item-based models. Mean Absolute Error (MAE) is then used as an evaluation metric to show the accuracy improvement of proposed method. The experimental results on TripAdvisor dataset with several comparisons are presented to show the enhancement of proposed method predictive accuracy. The experimental results demonstrated that the user-based model of recommendation which uses fuzzy C-means algorithm remarkably improves the recommendation predictive accuracy with MAE=0.72 in relation to the item-based recommendation model with MAE=0.73. Since the proposed recommender system improves the accuracy of Collaborative Filtering techniques, the recommender system will be a promising recommendation method for item recommendation task in tourism domain.
Spectrum allocation algorithm in cognitive radio

The spectrum allocation in cognitive radio has bottleneck users. To solve this problem, this paper presents the analyses of the parallel spectrum allocation algorithm and CSGC(color-sensitive graph coloring) algorithm, and proposes that the problem of bottleneck users can be solved by parallel spectrum allocation algorithm in the light of CMMB (collaborative-max-min-bandwidth) criteria. Besides, the performance indicators such as the time consuming and the spectrum utilization rate are analyzed and simulated. The simulation result indicates that the frequency spectrum utilization of the above-mentioned two algorithms is almost the same. However, the time consuming in parallel algorithm is much less than that of CSGC algorithm, which is beneficial to meet the requirement of fast allocation of frequency spectrum in cognitive radio.
A Study on Knowledge-based Context Aware Framework using Machine Learning

In a smart environment, users want to be automatically provided with service, appropriate for user’s situation, by using a large number of devices, existing in the IoT evironment. The use of ontology for expressing context is advantageous to obtain the characteristics expressed in the various kinds of context using the object-based modeling approaches. In order to provide context aware service, context rules must be defined from attributes of context information. Some of these rules are expanded through knowledge base of context rules by being newly added, using the Machine Learning Engine.
A NEW MEDIUM TO HIGH ENTHALPY GEOTHERMAL FIELD IN AEGEAN REGION (AKYAR) MENDERES – SEFERİHİSAR – İZMİR, WESTERN ANATOLIA, TURKEY

Bu calisma, Bati Anadolu’da yer alan yuksek sicaklikli jeotermal sahalarin yaninda dusuk ve orta entalpili jeotermal sahalarin isitma, termal turizm ve tarimsal uygulamalar bakimindan arastirilmasinin onem tasidigi, bunun yani sira yuzey sicakligina gore dusuk sicaklikli olarak gozuken sahalardan da daha yuksek sicaklikta akiskan elde edilebilecegi hedeflenerek yurutulmustur. Calisma alani Izmir’in guney-guneybatisinda Menderes ve Seferihisar ilceleri arasinda kalan bolgede yer alir. Sahanin jeotermal enerji potansiyelinin arastirilmasi ve akiskan elde edilebilmesine yonelik birden fazla meslek disiplinini kapsar. Arazi calismalari, jeolojik prospeksiyon, jeotermal enerji acisindan potansiyel olusabilecek alanlarda detay jeolojik etut, hidrojeoloji, hidrojeokimya, jeofizik etut ve sondaj calismalari yapilarak sonuclandirilmistir. Calismalar sonucunda, Akyar Tepe mevkiinde yapilan 1215,50 m derinliginde bir arastirma sondajinda kuyu ici maksimum sicaklik 141,18 oC olup, kompresorle 104 ton/saat debide, buhar+su seklinde akiskan elde edilmistir. Elde edilen akiskan sodyum klorurlu su sinifinda olup yuzey sularinin yaninda deniz suyu katkili beslenmeye sahip oldugu sonucuna varilmistir. uzun yillar dusuk sicaklikli olarak bilinen bu saha, orta - yuksek entalpili bir jeotermal sistem olarak aciga cikarilmistir
Comprehensive Collaborative Web-based Experimentation: Integration of Teleoperation and Simulation for Supporting Active Learning in Higher Education

Virtual and real experimentation play an important role in natural sciences and engineering education. While many simulation packages and laboratory resources are available online, their effective integration into learning environments remains problematic. First, the development of such complex interactive resources is time consuming. Second, their reusability is limited by the development overhead necessary to adapt them to different educational needs. Finally, interoperability with other resources cannot easily be implemented. This paper presents the approach chosen to integrate both simulation and teleoperation Java applets within the eMersion environment dedicated to collaborative Web-based experimentation. The simulation applets were developed using the Easy Java Simulations authoring tool. The teleoperation applets provided remote connection to real laboratory resources. These resources complement each other from a learning point of view. The proposed approach can help educators facing the challenge of integrating Web services or components provided by various academic institutions into comprehensive learning frameworks.
RFID Data Interpolation Algorithm Based on Dynamic Probabilistic Path-Event Model

Missing reads occur frequently during RFID(radio frequency identification) data collection,which will reduce the accuracy of query results in RFID applications.To solve this problem,the existing algorithms mainly take primitive RFID readings as granularity and adopt window smooth strategy based on tag historical readings,which may interpolate data that the query doesn't care about and incur inaccuracy when multiple logic areas are involved.In this paper,data are transformed from data level to logic area level as the interpolation granularity.Then three data interpolating algorithms based on the probabilistic path-event model are proposed,where the incoming events are judged and interpolated by mining the sequence correlation of known area events.Furthermore,the factor of time is considered,and thus probabilistic path-event model is developed.Abundant experiments prove the proposed algorithms have different performance advantages in different conditions and are predominant over the existing strategy in redundancy and accuracy.
Who is a Student: Completion in Coursera Courses at Duke University

Much of the interest in MOOCs centers on questions about who completes them. Duke’s Coursera-based Massive Open Online Courses (MOOCs) confirm many demographic trends previously delineated by researchers at peer institutions. As found in previous research, this study found individuals who speak English as a first language and who already earned at least a bachelor’s degree are the most likely to complete a Coursera course. MOOC researchers to date have not, however, developed clear operational definitions about who constitutes a learner at the outset of the course. This paper proposes some possible definitions to standardize future research. Further, this study looked at factors that predict different learner participation levels and investigated which activities predict Coursera course completion. Study results indicated that viewing online forums and participation in online discussions are both predictive of course completion. The findings suggest that the socio-demographic composition of the group being investigated will depend on how researchers elect to define what a “student” is. Thus, while any of the definitions presented in this paper may be appropriate, depending on what is being studied, the decision of which definition to use should be intentional.
Computation for the Kolmogorov Superposition Theorem

This thesis presents the first known method to compute Lipschitz continuous inner functions for the Kolmogorov Superposition Theorem. While the inner functions of these superpositions can be Lipschitz continuous, previous algorithms that compute inner functions yield only Hölder continuous functions. These Hölder continuous functions suffer from high storage-to-evaluation complexity, thereby rendering the Kolmogorov Superposition Theorem impractical for computation. Once this concern is addressed, the Kolmogorov Superposition Theorem becomes an effective tool in dimension reduction to represent multivariate functions as univariate expressions, with applications in encryption, video compression, and image analysis. In this thesis, I posit sufficient criteria to iteratively construct a Lipschitz continuous inner function. I demonstrate that implementations of the predominant approach for such a Lipschitz construction do not satisfy these conditions. Instead, I manipulate Hölder continuous inner functions to create Lipschitz continuous reparameterizations. I show these reparameterizations meet my sufficient conditions, thereby guaranteeing that these reparameterized functions are inner functions for the Kolmogorov Superposition Theorem.
D-S evidence reasoning and its data fusion application in target recognition

Data fusion based on reasoning method is applied to resolve vehicle target recognition problems. The basic concepts and theory of Dempster Shafer evidence reasoning applied to data fusion are analyzed in detail. The method is applied to multisensor data fusion for vehicle target recognition. Experimental results show that the data fusion result is better than that of a single sensor and that the D S data fusion method is effective.
The H\"ormander multiplier theorem, II: The bilinear local $L^2$ case

We use wavelets of tensor product type to obtain the boundedness of bilinear multiplier operators on $\mathbb R^n\times \mathbb R^n$ associated with H\"ormander multipliers on $\mathbb R^{2n}$ with minimal smoothness. We focus on the local $L^2$ case and we obtain boundedness under the minimal smoothness assumption of $n/2$ derivatives. We also provide counterexamples to obtain necessary conditions for all sets of indices.
A classification of taut, Stein surfaces with a proper $\R$-action

We present a classification of 2-dimensional, taut, Stein manifolds with a proper $\R$-action. For such manifolds the globalization with respect to the induced local $\C$-action turns out to be Stein. As an application we determine all 2-dimensional taut, non-complete, Hartogs domains over a Riemann surface.
Observational and theoretical validation of longitudinal type surface acoustic wave devices

The authors have proposed longitudinal type surface acoustic wave (SAW) devices comprising a LiNbO3 thin plate, acoustic mirror, handle substrate and interdigital transducers (IDT). This paper describes the observational and theoretical validation of the proposed devices by using a laser probe system and finite element analysis (FEA). It is shown that the laser probe system based on the Sagnac interferometer is effective for direct observation of the devices operated at even 2 GHz range through the observation of SAW field distribution of the existence and lack of acoustic mirror, their comparison with FEA results and changes of the transverse mode pattern. In addition, a phenomenon of SAW radiation normal to the busbars from IDT is found both observationally and theoretically, and a possible radiation mechanism explaining the results is also discussed.
An Investigation of Hydrometeor Latent Cooling upon Convective Cold Pool Formation, Sustainment, and Properties

Downdrafts extending from convective clouds can produce cold pools that propagate outward, sometimes initiating new convection along their leading edges. Models operating at scales requiring convective parameterizations usually lack representation of this detail, and thus fail to predict this convective regeneration and longer episodes of convective activity. Developing such parameterizations requires an improved understanding of the physical drivers of cold pools, and detailed studies of the roles of all the contributing microphysical processes have been lacking. This study utilizes a set of 12 simulations conducted within a single convective environment, but with variability in the microphysical fields produced by varying parameters influencing warm-rain or ice processes. Time-integrated microphysical budgets quantify the contribution of each hydrometeor type to the total latent cooling occurring in the downdrafts that form and sustain the cold pool. The timing of the onset of the cold pool is earlier in cases with a stronger warm rain process, but both graupel and rain were equally as likely to be the dominant hydrometeor in the downdraft first forming the cold pool. Graupel sublimation is the dominant term in sustaining the cold pool in all simulations, but the evaporation of rain has the strongest correlation to the cold pool expansion rate, depth, and intensity. Reconciling the current results with past studies elucidates the importance of considering: graupel sublimation, the latent cooling only in downdrafts contributing to the cold pool, and latent cooling in those downdrafts at altitudes that may be significantly higher than the melting level.
Two-Photon Imaging of Brain Regions in Fissures and Learning Manifolds from Neural Dynamics

Progress in systems neuroscience requires effective tools and techniques for probing neural circuits, and for analyzing the resulting data in ways that drive theoretical insight. This thesis consists of three parts, aimed broadly toward furthering the measurement and analysis of neural circuits. In the first part, we present methods for two-photon imaging of brain regions situated in deep fissures, enabling the use of cellular resolution optical tools for probing areas such as the medial prefrontal cortex (mPFC) and medial entorhinal cortex (MEC). We demonstrate recordings of population activity in the mPFC and grid cells in the MEC in behaving mice. In the second part, we present an optical approach for measuring dopaminergic input to the mPFC with high spatiotemporal resolution, which has not been feasible using traditional methods. We demonstrate recordings of mPFC dopamine signals in behaving mice, and present preliminary evidence for fine-scale heterogeneity across individual dopaminergic axons. In the third part, we present a new unsupervised learning algorithm for inferring underlying, nonlinear structure in neuronal population activity. We use this algorithm to characterize the geometric properties of hippocampal activity and their relationship to behavior. And, we propose a conceptual model explaining how neural coding and trial-to-trial variability both arise from movement along a low dimensional, nonlinear activity manifold, driven by internal cognitive processes.
Application of partial least squares as a complementary and preliminary receptor model for source apportionment of ambient aerosol based on molecular organic markers

Fine particulate matter is of great concern in urban areas because it is one of the most heavily emitted pollutants by numerous emission sources. Several receptor models have been developed to estimate contributions of emission sources to the observed concentrations of air pollutants. Most of these tools require source profiles and uncertainties of the measured concentrations of the pollutants in the atmosphere. In addition, these receptor models have been widely applied using a trace element approach. However, a quick overview of the source contributions and key chemical species is desirable before selecting and applying a robust receptor model. Here, partial least squares (PLS) was applied as a preliminary source apportionment step for particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) attribution based on molecular organic markers. Given that PLS is an unweighted uncertainty model and that the uncertainties for organic species are not easy to quantify, Monte Carlo sampling (MC‐S) was performed as a complement to PLS to assess the variation of source contributions on the total PM2.5. These methods were performed using 43 samples collected during the spring and fall season monitoring campaigns. In general, source contributions obtained from the MC‐S results were not statistically different from the PLS original results, and uncertainties in ambient measurements did not have a significant influence on the source contributions. Finally, the source contributions were compared with those obtained by positive matrix factorization and chemical mass balance showing insignificant differences. The PLS and MC‐S fit well as preliminary and complementary tools for source apportionment based on organic molecular markers.
A Review: Color Feature Extraction Methods for Content Based Image Retrieval

For more than a decade Content Based Image Retrieval is topic of interest for researcher. The three primitive visual features, namely, color, texture and shape refers to the term ‘content’ in content based image retrieval. Although visual features cannot be completely determined by semantic features, but still semantic features are used because they are easier to integrate into mathematical formulations. Therefore good visual feature extraction is one of the important task for representing image compactly. Among the visual features, colors is the most vital, reliable and widely used feature. This paper reviews various methods, namely Global Color Histogram, Histogram Intersection, Image Bitmap, Local Color Histogram, Color Correlogram, etc., employed to extract the color feature. This paper briefly elaborates these different methods of color feature extraction and then presents a comparative study for selection of these methods in various applications.
Optically Switched Frequency for Reconfigurable Dipole Antenna Using Photodiode Switches

The design, fabrication, and measurement of reconfigurable antenna using photodiode switches has been investigated. The antenna had dimension 104 mm x 41 mm x 1.6 mm and FR4 substrate with relative permittivity4.8. It structure was Coplanar Stripline (CPS) dipole. Two photodiodeshad assembled on gap in both dipole arms, which was activated by 600 nm red laser. The unilluminated state, antenna had resonant frequency 800 MHz with RL -32 dB, 2225 MHz with RL -12.5 dB, 3320 with RL -14.1 dB. Meanwhile for the illuminated state, antennahad resonant frequency and return loss shifting to 800 MHz with RL -32 dB, 2225 MHz with RL -12.5 dB, 3320 MHz with RL -14.1 dB. This condition caused low impedance and longer dipole arm . If dimension size increases, resonant frequency will decrease. So, there was shifting frequency and return loss at under illuminated by laser.
Ξ (1690) as a K- Σ molecular state

We show that a $\Xi ^{\ast}$ pole can be dynamically generated near the $\bar{K} \Sigma$ threshold as an $s$-wave $\bar{K} \Sigma$ molecular state in a coupled-channels unitary approach with the leading-order chiral interaction. This $\Xi ^{\ast}$ state can be identified with the $\Xi (1690)$ resonance with $J^{P} = 1/2^{-}$. We find that the experimental $\bar{K}^{0} \Lambda$ and $K^{-} \Sigma ^{+}$ mass spectra are qualitatively reproduced with the $\Xi ^{\ast}$ state. Moreover we theoretically investigate properties of the dynamically generated $\Xi ^{\ast}$ state.