Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92% of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50. 0 and CAMERA version 1.30. 0.
Isolation and Characterization of Marine Bacterium Producing Arylsulfatase

A bacterial strain capable of hydrolyzing sulfate ester bonds in p-nitrophenyl sulfate and agar was isolated from the Southeast coast of Korea. The isolated strain (AS6330) is aerobic, Gram-negative, rod-shaped, and motile. Octadecanoic acid was the major cellular fatty acid in the isolate. An almost complete 16S rDNA sequence of the isolate was determined and the sequence similarity of the 16S rDNA with those of known Sphingomonas spp. was found to be at most 96.4%, implying-that the isolate was a new Sphingomonas species. The organism was grown optimally at NaCI concentration of 1.5-3.5%. Optimum culture conditions were determined to be 30°C and pH 7.0 for 48 h fermentation using a laboratory fermentor under constant culture conditions. Partially purified arylsulfatase through Q-Sepharose and phenyl-Sepharose chromatographies catalyzed hydrolysis of sulfate ester bonds in agar, and 97% of sulfates in agar were removed after 4 h reaction at 45C and pH 7.0. The arylsulfatase from the isolated bacterium might be useful for the removal of sulfate groups in agar.
Watermarks and Text Transformations in Visual Document Authentication

Integrity of digital documents is a very important issue if they are to be legally binding. Common solutions, like digital signatures or message authentication codes, are based on cryptography and require computers or similar hardware to be produced. They can be trusted only as long as the employed hardware can be trusted. We present a complementary scheme for document authentication in untrusted environment. The scheme combines visual authentication, human- recognizable watermarks, and image transformations, and is suitable also for larger text document. Because it relies on visual cryptography, it requires practically no computational power on the receiver side. To prevent potential attackers from obtaining signatures without author’s knowledge, we propose using a simple challenge-response protocol.
An improved FRUC algorithm based on unilateral and bidirectional motion estimation

Most Traditional algorithms using only unilateral estimation or bidirectional estimation usually produce poor visual quality because of the fact that the unilateral motion estimation suffers from holes and overlaps and the bidirectional motion estimation suffers from inaccurate motion vector. This paper presents a new improved Frame rate up conversion(FRUC) scheme which combines the unilateral estimation and the bidirectional estimation smartly to avoid holes and inaccurate motion vector problem. The algorithm proposed in this paper outperforms other traditional methods both in objective and subjective performances. The superior performance of the proposed algorithm has been tested extensively and representative examples are given in this work.
Power flow control with bidirectional dual active bridge battery charger in low-voltage microgrids

This paper describes a power conversion system that implements bidirectional power flow between the electric grid and a battery, based on a full bridge inverter associated with a dual active bridge. A method for power flow control based on the agreement between battery pack and low voltage grid requirements has been developed to allow the large penetration of both, electric vehicles (EV) and renewable microgeneration. A laboratory prototype containing all functions required to implement a vehicle-to-grid (V2G) power interface has been built. The hardware implementation is reported, including control scheme and high-frequency transformer design details. Converter key waveforms are presented to evaluate the power quality on batteries and grid, as well as its dynamic behavior. An experimental laboratory setup is described and results are shown to demonstrate the technical benefits for low-voltage (LV) microgrids, if electric vehicle charger includes a power flow control based on frequency-voltage droop concept.
Activation-Dependent Rapid Postsynaptic Clustering of Glycine Receptors in Mature Spinal Cord Neurons

Abstract Inhibitory synapses are established during development but continue to be generated and modulated in strength in the mature nervous system. In the spinal cord and brainstem, presynaptically released inhibitory neurotransmitter dominantly switches from GABA to glycine during normal development in vivo. While presynaptic mechanisms of the shift of inhibitory neurotransmission are well investigated, the contribution of postsynaptic neurotransmitter receptors to this shift is not fully elucidated. Synaptic clustering of glycine receptors (GlyRs) is regulated by activation-dependent depolarization in early development. However, GlyR activation induces hyperpolarization after the first postnatal week, and little is known whether and how presynaptically released glycine regulates postsynaptic receptors in a depolarization-independent manner in mature developmental stage. Here we developed spinal cord neuronal culture of rodents using chronic strychnine application to investigate whether initial activation of GlyRs in mature stage could change postsynaptic localization of GlyRs. Immunocytochemical analyses demonstrate that chronic blockade of GlyR activation until mature developmental stage resulted in smaller clusters of postsynaptic GlyRs that could be enlarged upon receptor activation for 1 h in the mature stage. Furthermore, live cell-imaging techniques show that GlyR activation decreases its lateral diffusion at synapses, and this phenomenon is dependent on PKC, but neither Ca2+ nor CaMKII activity. These results suggest that the GlyR activation can regulate receptor diffusion and cluster size at inhibitory synapses in mature stage, providing not only new insights into the postsynaptic mechanism of shifting inhibitory neurotransmission but also the inhibitory synaptic plasticity in mature nervous system.
Internal Returns to Scale as a

During the past 15 years many trade theorists have incorporated scale economies and imperfect competition into their models. This abandonment of perfect markets has profoundly changed the way that economists think about the determinants of trade flows and the effect of trade policies on performance. Below I selectively review the empirical evidence on both issues, focusing on the role of increasing internal returns to scale (IIRS). 1 To conserve space, I refer the reader to the working-paper version of this document (Tybout, 1993) for more extensive references.
Stock selection with full-scale optimization and differential evolution

When composing stock portfolios, managers frequently choose among hundreds of stocks. The stocks' risk properties are analyzed with statistical tools, and managers try to combine these to meet the investors' risk profiles. A recently developed tool for performing such optimization is called full-scale optimization (FSO). This methodology is very flexible for investor preferences, but because of computational limitations it has until now been infeasible to use when many stocks are considered. We apply the artificial intelligence technique of differential evolution to solve FSO-type stock selection problems of 97 assets. Differential evolution finds the optimal solutions by self-learning from randomly drawn candidate solutions. We show that this search technique makes large scale problem computationally feasible and that the solutions retrieved are stable. The study also gives further merit to the FSO technique, as it shows that the solutions suit investor risk profiles better than portfolios retrieved from traditional methods.
Beyond best practice

The importance of implementing best management practices is widely understood. However, the authors argue, best practice alone is not enough. They use examples of three high-performing companies to show that those companies not only use standard best practices but also embrace internally developed idiosyncratic "signature processes" that reflect the history and values of the organization and executive team. Such "signature processes" ? a daily morning meeting of senior executives at the Royal Bank of Scotland Group, an easily reconfigured organizational structure at Nokia that involves modular teams and a "peer assist" program where business-unit heads help one another at BP Plc ? help drive high performance because they engender passion and energy within an organization. The mechanisms by which signature processes develop differ from those associated with best-practice ideas, however. The latter are often adapted from shared knowledge originating outside the company, whereas signature processes start with the values that internal executives champion.
Waiting for Returns: Using Space-Time Duality to Calibrate Financial Diffusions

In this brief paper we propose an alternative methodology for testing and calibrating diffusion processes for financial time-series. The methodology focuses on the duality that exists between time and space for any given stochastic process. More specifically, we use the First Passage Time (FPT) which is the amount of time required by a stochastic process to travel a pre-specified distance. Thus, for example, we demonstrate that testing the hypothesis that (logarithmic) investment returns are independent and normally distributed is equivalent to testing the hypothesis that the FPT is Inverse Gaussian distributed. We apply this idea to calibrate geometric Brownian motion (GBM) parameters for the S&P 500 index over the period 1952 - 2004.
Adaptive dynamic inversion for nonaffine-in-control systems via time-scale separation: part II

This paper presents an adaptive dynamic inversion method for uncertain nonaffine-in-control dynamical systems. Online approximation of the adaptive dynamic inversion controller is performed using time-scale separation. The resulting control signal is sought as a solution of a "fast" dynamical equation, which inverts a series parallel model, whose state is shown to track the state of the original nonaffine-in-control system. Numerically verifiable sufficient conditions on the system parameters are given. A simulation example illustrates the theoretical results.
Building Ontology for Adaptive Collaboration in a Dynamic Environment

Building new knowledge-based systems usually involves constructing or modelling new knowledge bases systems. Current knowledge modelling methodologies tend to focus on the subsystem, viewing domain knowledge as strongly dependent on the particular task at hand. Recently, however, the potential value of taskindependent knowledge bases (or "ontologies") suitable to large-scale integration has been underlined in many ways. This article examined three broad methods for building ontologies and uses one of them to build ontology for adaptive collaboration in a dynamic environment for knowledge sharing.
A Layered Network Coding Solution for Incentives in Peer-to-Peer Live Streaming

We consider the design of an efﬁcient streaming system for live video over peer-to-peer networks. Such a system must accommodate for large populations of heterogeneous users, behave robustly irrespective of user dynamics and ensure prescribed levels of quality of experience. Seeking a solution capable of addressing also the lack of incentives for peer-to-peer collaboration and the scarcity of certain video segments, we present a network coding based scheme for layered peer-to-peer live streaming. The key idea is to use layered coding as a tool to generate incentives and combine it with a speciﬁc network coding scheme to achieve efﬁcient scheduling and delay minimization. The proposed solution is shown to achieve a short decoding delay, which further enables a smooth playback, while ensuring that users' decoding rate is proportional to their contribution to the system.
Packet drop probability analysis of ARQ and HARQ-CC with energy harvesting transmitters and receivers

In this paper, we analyze the packet drop probability (PDP) of dual energy harvesting (EH) links, i.e., links whose both transmitter and receiver are EH nodes. The link employs either an automatic repeat request (ARQ) protocol or a hybrid ARQ protocol with chase combining (HARQ-CC) at the receiver. We present the performance analysis for both slow and fast fading channels. We model the system evolution as a discrete-time Markov chain (DTMC) and obtain approximate closed-form expressions for the PDP. Simulations show that the presented approximate expressions closely match the actual PDP. The results capture the relative impact of the energy harvesting rates at the transmitter and receiver, and the effect of the underlying protocol and power policy, on the PDP.
Experimental teaching, Perceived usefulness, Ease of use, Learning Interest and Science Achievement of Taiwan 8th Graders in TIMSS 2007 Database

the data of Taiwanese 8 grader in the 4th cycle of Trends in International Mathematics and Science Study (TIMSS) are analyzed to examine the influence of the science teachers’ preference in experimental teaching on the relationships between the affective variables ( the perceived usefulness of science, ease of using science and science learning interest) and the academic achievement in science. After dealing with the missing data, 3711 students and 145 science teacher’s data were analyzed through a Hierarchical Linear Modeling technique. The major objective of this study was to determine the role of the experimental teaching moderates the relationship between perceived usefulness and achievement. Keywords—TIMSS database, Science achievement, Experimental teaching, Perceived Usefulness, Perceived Ease of Use
Synthesized Approaches: Expanding the Perspectives and Impact of Qualitative and Quantitative Evaluation.

Qualitative evaluation and quantitative evaluation are discussed, examining how the perspective of each can and should create a critical debate that encourages inquiry instead of hostility. To achieve the major goal of linking evaluation information to the organization's culture, the evaluator needs to use multiple methods in a responsive, issue-raising, contextually sensitive approach. While some evaluators and researchers propose that the qualitative and quantitative approaches cannot be synthesized because of their polar epistemological stances, others develop compatibility of methods as their main focus. Arguments that the incompatibility thesis presents cannot be dismissed. However, discrepancies should prompt the researcher to probe particular issues in greater depth. This process may expand knowledge about evaluation in terms of increasing the likelihood of audiences engaging in their own knowledge expansion. In the practice of evaluation, the evaluator should be aware of the various qualitative and quantitative methods to make the evaluation a process of identifying, educating, and communicating the perspectives and concerns of the evaluator and the stakeholders to maximize the relevance and success of the evaluation and the program. (SLD) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** U.S. DEPARTMENT OF EDUCATION Office of Educahonal Research and improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) (D/11,5 document has been reproduced as received from the person or organization or,9,.una 1' MnO r changes have been made to Improve reproduction quality Pants 01 v(ew or opmlons slated rn )his dOCu ment do not necessarily represent olfictat OE RI Posthon or policy -PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY /141,Sy P/0/1) TEA/ TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC). " SYNTHESIZED APPROACHES: EXPANDING THE PERSPECTIVES AND IMPACT OF QUALITATIVE AND QUANTITATIVE EVALUATION
User Position Detection In An Indoor Environment

Various techniques that employ Global Positioning System(GPS) signals such as A-GPS and GPS transmitters, have been introduced with the hope to provide a solution for indoor positioning detection. Indoor positioning system (IPS) is a term that is used for network devices used to wirelessly locate objects or people inside building. The study is based on the issue in order to determine the position of an object in indoor environment or inside a building. The problems arise when the position of an object inside a building cannot be determined using GPS. We proposed the implementation of trilateration technique to determine the position of users in indoor areas based on Wi-Fi signal strengths from access points (AP) within the indoor vicinity. In this paper, percentage of signal strengths obtained from Wi-Fi analyzer in a smartphone were converted into distance between users and each AP. A user’s indoor position could then be determined using a formula proposed based on trilateration technique.
A Novel Audio Watermarking Algorithm for Copyright Protection Based on DCT Domain

Audio digital watermarking technology is the new means of audio reproduction, which can pre-implant the special data as a watermark to the audio signal which pass the information on behalf of the owner of identity and prove the user's legitimacy. The novel technology of embedding image data into the audio signal and additive audio watermarking algorithm based on DCT domain is proposed. AC DCT coefficients play different influence in robust and inaudibility. First, we decompose the audio into no overlapping blocks (eight data per block). Then every block performs the DCT operation: watermark is embedded by modulating AC DCT coefficient. Every block is embedded one bit message. Experimental results demonstrate that the watermark is inaudible and this algorithm is robust to common operations of digital audio signal processing, such as low-pass filtering, smoothing, adding noise and so on. The extracted watermark image quality is showed by considering normalize correlation (NC) value with a suitable scaling parameter for embedding.
Sparse distributed memory: understanding the speed and robustness of expert memory

How can experts, sometimes in exacting detail, almost immediately and very precisely recall memory items from a vast repertoire? The problem in which we will be interested concerns models of theoretical neuroscience that could explain the speed and robustness of an expert's recollection. The approach is based on Sparse Distributed Memory, which has been shown to be plausible, both in a neuroscientific and in a psychological manner, in a number of ways. A crucial characteristic concerns the limits of human recollection, the “tip-of-tongue” memory event—which is found at a non-linearity in the model. We expand the theoretical framework, deriving an optimization formula to solve this non-linearity. Numerical results demonstrate how the higher frequency of rehearsal, through work or study, immediately increases the robustness and speed associated with expert memory.
On the distributed approaches with imperfect channel estimation for MIMO interference channel

This paper considers distributed approaches for the downlink of multi-user MIMO interference network where all nodes iteratively and distributedly update their precoders or receive filters. Those include the distributed interference alignment and minimum sum mean-squared error schemes. We analyse these algorithms in presence of effective imperfect channel estimation at each iteration and we present the pre-coders and receive filters updates in this case. The numerical results show that the algorithms converge in average. However, the sum-rate performance is degraded with respect to the estimation errors. Based on these results, we show that for a useful design in practice it is preferable to ameliorate the quality of the estimators and reduce the number of iterations.
MLK-Means-A Hybrid Machine Learning based K-Means Clustering Algorithms for Document Clustering

Document clustering is useful in many information retrieval tasks such as document browsing, organization and viewing of retrieval results. They are very much and currently the subject of significant global research. Generative models based on the multivariate Bernoulli and multinomial distributions have been widely used for text classification. In this work, address a new hybrid algorithm called MLK-Means for clustering TMG format document data, in which, the normal Euclidean distance based metric of the k-mean process is replaced by a machine learning technique. The results of the proposed algorithm were compared with the probabilistic model namely, von MisesFisher model-based clustering (vMF-based k-means) and the standard k-mean with L-2 normalized data method. In this proposed work, the MLK-Means algorithm has been implemented and its performance is compared with other algorithms mentioned above. The improvements in the proposed algorithm are more significant and comparable.
THE ROAD AHEAD: AGRICULTURAL POLICY REFORM IN THE WTO -- SUMMARY REPORT

Agricultural trade barriers and producer subsidies inflict real costs, both on the countriesvthat use these policies and on their trade partners. Trade barriers lower demand for trade partners' products, domestic subsidies can induce an oversupply of agricultural products which depresses world prices, and export subsidies create increased competition for producers in other countries. Eliminating global agricultural policy distortions would result in an annual world welfare gain of $56 billion. High protection for agricultural commodities in the form of tariffs continues to be the major factor restricting world trade. In 2000, World Trade Organization (WTO) members continued global negotiations on agricultural policy reform. To help policymakers and others realize what is at stake in the global agricultural negotiations, this report quantifies the costs of global agricultural distortions and the potential benefits of their full elimination. It also analyzes the effects on U.S. and world agriculture if only partial reform is achieved in liberalizing tariffs, tariff-rate quotas (limits on imported goods), domestic support, and export subsidies.