Miracle-dz/UniFAR-FTUs huggingface dataset:
Pengaruh Metode dan Pendekatan Pembelajaran terhadap Penguasaan Konsep Matematika

Tujuan penelitian ini adalah untuk mengetahui pengaruh metode pembelajaran dan pendekatan pembelajaran terhadap penguasaan konsep matematika siswa. Metode yang digunakan dalam penelitian ini adalah metode eksperimen. Subjek penelitian sebagai populasi seluruh siswa SMP Negeri di Kota Tegal dengan populasi terjangkau siswa di SMP Negeri 17 dan SMP Negeri 18 Kota Tegal dan sampel dipilih secara random 120 orang. Penelitian dilaksanakan mulai 22 Oktober sampai 20 November 2014. Data yang digunakan diperoleh melalui teknik tes sertadianalisis menggunakan analisis kuantitatif dengan teknik anava dua jalan. Dalam hal ini perhitungan hipotesis menggunakan SPSS 20.0. Hasil penelitian diperoleh: (1) Terdapat pengaruh yang sangat signifikan pengaruh metode pembelajaran terhadap penguasaan konsep Matematika siswa. (2) Terdapat pengaruh pendekatan pembelajaran yang signifikan terhadap penguasaan matematika siswa. (3) Tidak terdapat pengaruh yang signifikan interaksi metode pembelajaran dan pendekatan pembelajaran terhadap penguasaan konsep Matematika siswa. Hasil penelitian menunjukkan bahwa penguasaan konsep matematika dengan menggunakan metode pembelajaran Complette Sentence dan pendekatan pembelajaran konsep lebih tinggi dibandingkan dengan pendekatan pembelajaran proses. Kata Kunci . Metode Pembelajaran, Pendekatan Pembelajaran, Penguasaan Konsep Matematika
Spiking response model for uniaxial carbon concrete experimental data

In engineering related tasks, multiple types of neural networks are common methods of solution. Beside the different kinds of artificial neural networks, spiking neural networks (SNN) represent a continuative development in information processing within the computational units of a net. The properties of this neural type is utilized in this contribution in order to evaluate a uniaxial tension test of carbon reinforced specimen regarding the appearance of cracks in the composite structure during the experiment. The crack detection is considered as showcase for further development of evaluation methods based on SNNs with the focal point to engineering related experiments. This contribution is divided into five main parts, whereas the initial brief introduction is devoted to give an overview of neural networks and their computational units, particularly with regard to the classification of spiking neural networks. Since the proposed application of SNNs targets the evaluation of experimental data - especially crack detection - the uniaxial tension test of carbon reinforced concrete specimen is introduced, which is the basis for the experimental data. The utilized spike response model (SRM) is further presented in order to conclusively apply the method to experimental data for the purpose of crack occurrence detection within the data.
The Child Adoption Marketplace

In the United States, child adoption costs vary considerably, ranging from no out-of-pocket expense to US$50,000 or more. What are the causes for the variability in adoption expenses? We administered a survey to a sample of Michigan adoptive families to link adoptive parent characteristics, child characteristics, and adoption-related expenses and subsidies. We then estimated “hedonic” regressions in which adoption cost is a function of child characteristics. The analysis shows that up to 74 percent of the variation in adoption costs is explained by child characteristics. In particular, costs are lower for older children, children of African descent, and special needs children. Findings inform policies regarding the transition of children from foster care to adoptive families.
Channel Density Distributions Explain Spiking Variability in the Globus Pallidus: A Combined Physiology and Computer Simulation Database Approach

Globus pallidus (GP) neurons recorded in brain slices show significant variability in intrinsic electrophysiological properties. To investigate how this variability arises, we manipulated the biophysical properties of GP neurons using computer simulations. Specifically, we created a GP neuron model database with 100,602 models that had varying densities of nine membrane conductances centered on a hand-tuned model that replicated typical physiological data. To test the hypothesis that the experimentally observed variability can be attributed to variations in conductance densities, we compared our model database results to a physiology database of 146 slice recordings. The electrophysiological properties of generated models and recordings were assessed with identical current injection protocols and analyzed with a uniform set of measures, allowing a systematic analysis of the effects of varying voltage-gated and calcium-gated conductance densities on the measured properties and a detailed comparison between models and recordings. Our results indicated that most of the experimental variability could be matched by varying conductance densities, which we confirmed with additional partial block experiments. Further analysis resulted in two key observations: (1) each voltage-gated conductance had effects on multiple measures such as action potential waveform and spontaneous or stimulated spike rates; and (2) the effect of each conductance was highly dependent on the background context of other conductances present. In some cases, such interactions could reverse the effect of the density of one conductance on important excitability measures. This context dependence of conductance density effects is important to understand drug and neuromodulator effects that work by affecting ion channels.
Magnetic Resonance Spectrum Separation Using Sparse Representations and Wavelet Filters

Magnetic Resonance spectroscopy (MRS) provides a “frequency-signal intensity” spectrum of multiple peaks that reflect the biochemical composition of a localized region in the body. The peak intensity or the area under each peak is proportional to the concentration of that assigned metabolite. Accurate quantification of in vivo MRS (measuring peak intensity or area) is very important to diagnose certain metabolic disorders. However, strongly overlapping metabolite peaks, poor knowledge about background component (the baseline), and low signalto- noise ratio (SNR) make the task difficult. In this paper, a novel spectrum separation method using sparse representations and wavelet filters is proposed to separate baseline and spectra of different metabolites and finally achieves an accurate MRS quantification. With the proposed method, the accuracy and the robustness of MRS quantification are improved, from simulation data, compared with a commonly used frequency-domain MRS quantification method. The quantification on tumor metabolism with in vivo brain MR spectra is also demonstrated.
Comparing methods for measuring crime harm/severity

Ensuring police resources are focused where they are most needed requires understanding of the consequences of crime in relation to offenders, victims and places. Most crime analysis is based on counts of crimes, but not all crimes are equivalent to one another. Researchers have recently developed two methods - the Crime Harm Index and the Crime Severity Score - for weighting crime counts according to the severity of different crime types. This article compares these two methods by applying them to two common crime analysis scenarios: focusing resources on the most important types of crime and in the areas most affected by crime. The two measures are found to produce substantially different results when other factors are held constant. The results of severity-weighted crime analysis (and the decision made based on them) could therefore be greatly influenced by the method chosen. The implications of this are discussed and future research avenues outlined.
Generative and Discriminative Latent Variable Grammars

Latent variable grammars take an observed (coarse) treebank and induce more fine-grained grammar categories, that are better suited for modeling the syntax of natural languages. Estimation can be done in a generative or a discriminative framework, and results in the best published parsing accuracies over a wide range of syntactically divergent languages and domains. In this paper we highlight the commonalities and the differences between the two learning paradigms.
Toward a standard for guideline representation: an ontological approach

Guidelines for clinical practice are being introduced in an extensive way in more and more different fields of medicine They have the potentialities of improving the quality and cost-efficiency of care in an increasingly complex health care delivery environment. Computerization may increase the effectiveness of both the information retrieval of guidelines and the management of guideline-based care. The scenario is evolving from stand-alone workstations to telematics applications that enable guidelines development and dissemination. However, such a knowledge sharing requires the definition of formal models for guidelines representation. The models should have a clear semantics in order to avoid ambiguities. The role of ontologies is that of making explicit the conceptualizations behind a model. In this paper we present our library of ontologies and point out its role for integrating existing guideline models and defining standard representations.
Second Order Cone Programming Formulations for Robust Support Vector Ordinal Regression Machine

Support vector ordinal regression machine (SVORM) is an effective method for ordinal regression problem. Up to now, the SVORM implicitly assumes the training data to be known exactly. However, in practice, the training data subject to measurement noise. In this paper, we propose a robust version of SVORM. The robustness of the proposed method is validated by our preliminary numerical experiments.
Elucidating the Influence of Gold Nanoparticles on the Binding of Salvianolic Acid B and Rosmarinic Acid to Bovine Serum Albumin

Salvianolic acid B and rosmarinic acid are two main water-soluble active ingredients from Salvia miltiorrhiza with important pharmacological activities and clinical applications. The interactions between salvianolic acid B (or rosmarinic acid) and bovine serum albumin (BSA) in the presence and absence of gold nanoparticles (Au NPs) with three different sizes were investigated by using biophysical methods for the first time. Experimental results proved that two components quenched the fluorescence of BSA mainly through a static mechanism irrespective of the absence or presence of Au NPs. The presence of Au NPs decreased the binding constants of salvianolic acid B with BSA from 27.82% to 10.08%, while Au NPs increased the affinities of rosmarinic acid for BSA from 0.4% to 14.32%. The conformational change of BSA in the presence of Au NPs (caused by a noncompetitive binding between Au NPs and drugs at different albumin sites) induced changeable affinity and binding distance between drugs and BSA compared with no Au NPs. The competitive experiments revealed that the site I (subdomain IIA) of BSA was the primary binding site for salvianolic acid B and rosmarinic acid. Additionally, two compounds may induce conformational and micro-environmental changes of BSA. The results would provide valuable binding information between salvianolic acid B (or rosmarinic acid) and BSA, and also indicated that the Au NPs could alter the interaction mechanism and binding capability of drugs to BSA, which might be beneficial to understanding the pharmacokinetics and biological activities of the two drugs.
Hot spot contention and message combining in the simple serial synchronized multistage interconnection network

Simple Serial Synchronized (SSS) Multistage Interconnection Network (MIN) is a novel MIN architecture for connecting processors and memory modules in multiprocessors. Synchronized bit-serial communication simplifies the structure/control, and permits the bit-serial message combining mechanism. From the theoretical analysis and empirical results, it appears that the influence of the hot spot contention is not catastrophic in the small size SSS-MIN (16/spl times/16) when some parallel programs from SPLASH benchmark run. In this situation, the effect of the message combining is small. However, in a large system (256 processors), the pass through ratio improves by 11% when a parallel program with relatively large access ratio (r=0.4) runs. Since the additional hardware for the message combining is only 20% in the SSS-MIN, the message combining is useful in large systems.
The 'subsequent artificial neural network' (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses

MOTIVATION Human decisions often proceed in two steps. Initially those most preferred are chosen followed by a subsequent choice of these preferences. Applying one artificial neural network (ANN), a classification is limited to the preselection process. The final categorization is only possible by a subsequent ANN that distinguishes the pre-chosen classes. Existing strategies using coupled ANNs are discussed and a new approach particularly suited for multiclass classification problems is introduced ('Subsequent ANN', SANN). RESULTS Evaluating a simulated data base comprising 3 classes, classification results of SANN were obviously superior to those achieved by ANN. To evaluate a real-world data base the microarray benchmark GCM (14 classes) was chosen. The ANN results reached 72%, comparable to previous results. Using SANN, up to 81% of the tumors were correctly classified. AVAILABILITY Programs used in this work and numerical results are available upon request.
Using context and similarity for face and location identification

This paper describes a new approach to the automatic detection of human faces and places depicted in photographs taken on cameraphones. Cameraphones offer a unique opportunity to pursue new approaches to media analysis and management: namely to combine the analysis of automatically gathered contextual metadata with media content analysis to fundamentally improve image content recognition and retrieval. Current approaches to content-based image analysis are not sufficient to enable retrieval of cameraphone photos by high-level semantic concepts, such as who is in the photo or what the photo is actually depicting. In this paper, new methods for determining image similarity are combined with analysis of automatically acquired contextual metadata to substantially improve the performance of face and place recognition algorithms. For faces, we apply Sparse-Factor Analysis (SFA) to both the automatically captured contextual metadata and the results of PCA (Principal Components Analysis) of the photo content to achieve a 60% face recognition accuracy of people depicted in our database of photos, which is 40% better than media analysis alone. For location, grouping visually similar photos using a model of Cognitive Visual Attention (CVA) in conjunction with contextual metadata analysis yields a significant improvement over color histogram and CVA methods alone. We achieve an improvement in location retrieval precision from 30% precision for color histogram and CVA image analysis, to 55% precision using contextual metadata alone, to 67% precision achieved by combining contextual metadata with CVA image analysis. The combination of context and content analysis produces results that can indicate the faces and places depicted in cameraphone photos significantly better than image analysis or context analysis alone. We believe these results indicate the possibilities of a new context-aware paradigm for image analysis.
Comparison of Nuclear Starburst Luminosities between Seyfert 1 and 2 Galaxies Based on Near-Infrared Spectroscopy

We report on infrared K- (2-2.5 μm) and L-band (2.8-4.1 μm) slit spectroscopy of 23 Seyfert 1 galaxies in the CfA and 12 μm samples. A polycyclic aromatic hydrocarbon (PAH) emission feature at 3.3 μm in the L band is primarily used to investigate nuclear star-forming activity in these galaxies. The 3.3 μm PAH emission is detected in 10 sources (=43%), demonstrating that detection of nuclear star formation in a significant fraction of Seyfert 1 galaxies is now feasible. For the PAH-detected nuclei, the surface brightness values of the PAH emission are as high as those of typical starbursts, suggesting that the PAH emission probes the putative nuclear starbursts in the dusty tori around the central active galactic nuclei (AGNs). The magnitudes of the nuclear starbursts are quantitatively estimated from the observed 3.3 μm PAH emission luminosities. The estimated starburst luminosities relative to some indicators of AGN powers in these Seyfert 1 galaxies are compared with 32 Seyfert 2 galaxies in the same samples that we have previously observed. We find that there is no significant difference in nuclear starburst to AGN luminosity ratios of Seyfert 1 and 2 galaxies and that nuclear starburst luminosity positively correlates with AGN power in both types. Our results favor a slightly modified AGN unification model, which predicts that nuclear starbursts occurring in the dusty tori of Seyfert galaxies are physically connected to the central AGNs, rather than the classical unification paradigm, in which the dusty tori simply hide the central AGNs of Seyfert 2 galaxies and reprocess AGN radiation as infrared dust emission in Seyfert galaxies. No significant differences in nuclear star formation properties are recognizable between Seyfert 1 galaxies in the CfA and 12 μm samples.
A Survey on Energy Efficient Wireless Communication in Cellular Networks

The explosive growth of high data rate in wireless networks has resulted in a tremendous increase in energy consumption. Energy efficiency in wireless networks has drawn increasing attention of the research communities. High energy consumption has a significant impact on the environment due to the associated CO2 emission. Moreover energy costs can account for as much as half of a mobile service provider’s annual operating expenses. Energy consumption of a cellular network is becoming important interms of both operational costs and environmental impacts. This paper presents a brief survey on some of the works that has been already done to achieve energy efficiency in cellular networks. This paper discuss the techniques such as cell zooming, internetwork cooperation, IDLE mode procedure for femtocell base stations, DBSOS with CoMP and RTP, three stage stackelberg game using backward induction method and also compares the performance metrices of these techniques.
The Prevalence of Dyslipidemia in Patients with Spinal Cord Lesion in Thailand

Objective. To assess the prevalence of dyslipidemia in 90 patients with spinal cord lesion (SCL) of duration greater than 2 years. The study was carried out from November 2007 to September 2008. Methods. Clinical history, physical examination, and lipid profiles were recorded and analyzed. Dyslipidemia was assessed using guidelines from the National Cholesterol Education Project Adult Treatment Panel III (ATP III). Results. The prevalence of dyslipidemia in at least one lipid parameter was 76.7%. The most frequent finding was low HDL-C (58.9%). Hypertriglyceridemia, hypercholesterolemia and high LDL-C were found in 28.9%, 26.7% and 21.1% of patients, respectively. The factors sex = male and age ≥45 years were associated with high LDL-C (P < 0.05 and P < 0.01). Patients who exercised less than 30 minutes per day had associated hypercholesterolemia (P < 0.01), hypertriglyceridemia (P < 0.01), and higher LDL-C (P < 0.05). Patients with BMI ≥23 kg/m2 had associated hypercholesterolemia and high LDL-C. Age was a significant determinant of high LDL-C. BMI was the most powerful and significant determinant of hypercholesterolemia and high LDL-C. Conclusion. SCL patients should have a regular lipid checkup, especially those patients having the following risk factors: males, age ≥45 years, BMI ≥23 kg/m2 and exercise duration <30 minutes per day.
A Parallel Bidirectional DC/DC Converter Topology for Energy Storage Systems in Wind Applications

Integration of an energy storage system (ESS) improves wind energy system performance. Due to its simplicity and robustness, the bidirectional IGBT buck/boost converter is often selected to interface the supercapacitor or battery ESS with the DC voltage bus. For conventional single-unit topologies, the converter has discontinuous current on the high voltage side and other limitations. This paper proposes a parallel structure of bidirectional buck/boost converters and its power-tracking control scheme for wind ESS applications. The converter performance is studied and compared to the single-unit topologies. The proposed converter has smooth currents and fast dynamics which allow reduction of converter switching frequency and DC filtering capacitor. Other advantages of this topology include reduction of switching losses, improved reliability and flexibility.
Advances in Nonlinear Blind Source Separation

In this paper, we briefly review recent advances in blind source separation (BSS) for nonlinear mixing models. After a general introduction to the nonlinear BSS and ICA (indepen- dent Component Analysis) problems, we discuss in more detail uniqueness issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they are nonunique without extra constraints, which are often implemented by using a suitable regularization. Post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed. I. THE NONLINEAR ICA AND BSS PROBLEMS Consider samples of the observed data vector , modeled by (1)
Characterization of Diazotrophs Containing Mo-Independent Nitrogenases, Isolated from Diverse Natural Environments

ABSTRACT Molybdenum-independent nitrogenases were first described in the nitrogen-fixing bacterium Azotobacter vinelandii and have since been described in other diazotrophic bacteria. Previously, we reported the isolation of seven diazotrophs with Mo-independent nitrogenases from aquatic environments. In the present study, we extend these results to include diazotrophs isolated from wood chip mulch, soil, “paraffin dirt,” and sediments from mangrove swamps. Mo-deficient, N-free media under both aerobic and anaerobic conditions were used for the isolations. A total of 26 isolates were genetically and physiologically characterized. Their phylogenetic placement was determined using 16S rRNA gene sequence analysis. Most of the isolates are members of the gamma subdivision of the class Proteobacteria and appear to be specifically related to fluorescent pseudomonads and azotobacteria. Two other isolates, AN1 and LPF4, are closely related to Enterobacter spp. and Paenibacillus spp. , respectively. PCR and/or Southern hybridization were used to detect the presence of nitrogenase genes in the isolates. PCR amplification of vnfG and anfG was used to detect the genetic potential for the expression of the vanadium-containing nitrogenase and the iron-only nitrogenase in the isolates. This study demonstrates that diazotrophs with Mo-independent nitrogenases can be readily isolated from diverse natural environments.
Sub-hertz relative frequency stabilization of two-diode laser-pumped Nd:YAG lasers locked to a Fabry-Perot interferometer

Two-diode laser-pumped Nd:YAG lasers have been frequency stabilized to a commercial 6.327-GHz free spectral range Fabry-Perot interferometer yielding a best-case beatnote linewidth of 330 mHz. In addition, a Fabry-Perot interferometer with a free spectral range of 680 MHz, a linewidth of 25 kHz, and a finesse of 27500 has been built, and when substituted in place of the commercial interferometer produced a robust and easily repeatable beatnote linewidth of 700 MHz. >