Miracle-dz/UniFAR-FTUs huggingface dataset:
MoMoRo: Providing Mobility Support for Low-Power Wireless Applications

Recently, mobile devices have been introduced in various wireless sensor network (WSN) applications in order to solve complex tasks or to increase the data collection efficiency. However, the current generation of low-power WSN protocols is mainly designed to support data collection and address application-specific challenges without any particular considerations for mobility. In this paper, we introduce MoMoRo, a mobility support layer that can be easily applied to existing data collection protocols, thereby enabling mobility support in the network. MoMoRo robustly collects neighborhood information and uses a fuzzy estimator to make link quality estimations. This fuzzy estimator continuously reconfigures its thresholds for determining the fuzzy sets, allowing MoMoRo to easily adapt to changing channel environments. Furthermore, MoMoRo includes an active destination search scheme that allows disconnected mobile nodes with sparse traffic to quickly reconnect if there are packets in the network destined to this mobile node. We evaluate MoMoRo both indoor and outdoor and show that a continuously moving device in a MoMoRo-enabled RPL (i.e., IPv6 Routing Protocol for Low-Power and Lossy Networks) network can achieve a high packet reception ratio of up to 96% and stay connected in areas where RPL alone cannot and with less than half the packet overhead needed by the well-known Ad hoc On-Demand Distance Vector routing protocol.
Joining a carbon nanotube and a graphene sheet

This paper is a synopsis of the two least squares approaches developed in the work of Baowan et al. (2007) for the perpendicular joining of a flat graphene sheet with a carbon nanotube. The two least squares approaches are the variation in the bond length and the variation in the bond angle. These are used to examine the joined structure of a zigzag (8,0) carbon nanotube with a flat graphene sheet. There are sixteen possible distinct defects corresponding to the number of atoms at the (8,0) tube open end, and therefore, in total sixteen joining structures need to be investigated. Moreover, the polygons that occur at the junction are determined and are shown to be consistent with Eulerpsilas theorem. Assuming that only pentagons, hexagons and heptagons are acceptable, the number of possible structures is greatly reduced, but there is only one structure that is physically meaningful. These purely geometrical approaches can be formally related directly to a certain numerical energy minimization method used by a number of authors (Cornell et al., 1995).
A Small-Molecule-Inducible Nrf2-Mediated Antioxidant Response Provides Effective Prophylaxis against Cerebral Ischemia In Vivo

The transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) coordinates expression of genes required for free radical scavenging, detoxification of xenobiotics, and maintenance of redox potential. Previously, activation of this pleiotropic response was neuroprotective in cell culture models that simulate components of stroke damage. However, the role of Nrf2 in limiting stroke damage in vivo remained unclear. We report that Nrf2 activation protects the brain from cerebral ischemia in vivo. Acute (1-3 d) intracerebroventricular or intraperitoneal pretreatment with tert-butylhydroquinone (tBHQ), an Nrf2 activity inducer, reduced cortical damage and sensorimotor deficit at 24 h and even 1 month after ischemia-reperfusion in rats. Cortical glutathione levels robustly increased with tBHQ administration to rats and Nrf2-expressing mice, but not Nrf2-/- mice. Basal and inducible activities of antioxidant/detoxification enzymes in Nrf2-/- mice were reduced when compared with Nrf2+/+ controls. Interestingly, larger infarcts were observed in Nrf2-/- mice at 7 d after stroke, but not at 24 h, suggesting that Nrf2 may play a role in shaping the penumbra well after the onset of ischemia. Neuronal death caused by a “penumbral” model of stroke, using intracortical endothelin-1 microinjection, was attenuated by tBHQ administration to Nrf2+/+, but not to Nrf2-/- mice, confirming the Nrf2-specific action of tBHQ in vivo. We conclude that Nrf2 plays a role in modulating ischemic injury in vivo. Accordingly, Nrf2 activation by small molecule inducers may be a practical preventative treatment for stroke-prone patients.
Secure Resource Allocation for OFDMA Two-Way Relay Wireless Sensor Networks Without and With Cooperative Jamming

We consider secure resource allocations for orthogonal frequency division multiple access (OFDMA) two-way relay wireless sensor networks (WSNs). The joint problem of subcarrier (SC) assignment, SC pairing and power allocations, is formulated under scenarios of using and not using cooperative jamming (CJ) to maximize the secrecy sum rate subject to limited power budget at the relay station (RS) and orthogonal SC allocation policies. The optimization problems are shown to be mixed integer programming and nonconvex. For the scenario without CJ, we propose an asymptotically optimal algorithm based on the dual decomposition method and a suboptimal algorithm with lower complexity. For the scenario with CJ, the resulting optimization problem is nonconvex, and we propose a heuristic algorithm based on alternating optimization. Finally, the proposed schemes are evaluated by simulations and compared with the existing schemes.
HF-EPR, Raman, UV/VIS Light Spectroscopic, and DFT Studies of the Ribonucleotide Reductase R2 Tyrosyl Radical from Epstein-Barr Virus

Epstein-Barr virus (EBV) belongs to the gamma subfamily of herpes viruses, among the most common pathogenic viruses in humans worldwide. The viral ribonucleotide reductase small subunit (RNR R2) is involved in the biosynthesis of nucleotides, the DNA precursors necessary for viral replication, and is an important drug target for EBV. RNR R2 generates a stable tyrosyl radical required for enzymatic turnover. Here, the electronic and magnetic properties of the tyrosyl radical in EBV R2 have been determined by X-band and high-field/high-frequency electron paramagnetic resonance (EPR) spectroscopy recorded at cryogenic temperatures. The radical exhibits an unusually low g1-tensor component at 2.0080, indicative of a positive charge in the vicinity of the radical. Consistent with these EPR results a relatively high C-O stretching frequency associated with the phenoxyl radical (at 1508 cm−1) is observed with resonance Raman spectroscopy. In contrast to mouse R2, EBV R2 does not show a deuterium shift in the resonance Raman spectra. Thus, the presence of a water molecule as a hydrogen bond donor moiety could not be identified unequivocally. Theoretical simulations showed that a water molecule placed at a distance of 2.6 Å from the tyrosyl-oxygen does not result in a detectable deuterium shift in the calculated Raman spectra. UV/VIS light spectroscopic studies with metal chelators and tyrosyl radical scavengers are consistent with a more accessible dimetal binding/radical site and a lower affinity for Fe2+ in EBV R2 than in Escherichia coli R2. Comparison with previous studies of RNR R2s from mouse, bacteria, and herpes viruses, demonstrates that finely tuned electronic properties of the radical exist within the same RNR R2 Ia class.
Optimal Control with Adaptive Internal Dynamics Models

Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. The optimal feedback control law for systems with non-linear dynamics and non-quadratic costs can be found by iterative methods, such as the iterative Linear Quadratic Gaussian (iLQG) algorithm. So far this framework relied on an analytic form of the system dynamics, which may often be unknown, difficult to estimate for more realisti c control systems or may be subject to frequent systematic changes. In this paper, we present a novel combination of learning a forward dynamics model within the iLQG framework. Utilising such adaptive internal models can compensate for complex dynamic perturbations of the controlled system in an online fashion . The specific adaptive framework introduced lends itself to a computationally more efficient implementation o f the iLQG optimisation without sacrificing control accuracy - allowing the method to scale to large DoF systems.
Gallager-type bounds for non-binary linear block codes over memoryless symmetric channels

The performance analysis of non-binary linear block codes is studied under ML decoding where it is assumed that the transmission takes place over memoryless symmetric channels. Gallager-type bounds are derived, and the proposed bounds are exemplified for expurgated regular ensembles of non-binary low-density parity-check (LDPC) codes. These bounds are also compared with classical and recent improved sphere-packing bounds, indicating that these bounding techniques are informative for the performance evaluation of coded communication systems which incorporate non-binary coding techniques.
Machine Learning of Phonologically Conditioned Noun Declensions For Tamil Morphological Generators

This paper presents machine learning solutions to a practical problem of Natural Language Generation (NLG), particularly the word formation in agglutinative languages like Tamil, in a supervised manner. The morphological generator is an important component of Natural Language Processing in Artificial Intelligence. It generates word forms given a root and affixes. The morphophonemic changes like addition, deletion, alternation etc., occur when two or more morphemes or words joined together. The Sandhi rules should be explicitly specified in the rule based morphological analyzers and generators. In machine learning framework, these rules can be learned automatically by the system from the training samples and subsequently be applied for new inputs. In this paper we proposed the machine learning models which learn the morphophonemic rules for noun declensions from the given training data. These models are trained to learn sandhi rules using various learning algorithms and the performance of those algorithms are presented. From this we conclude that machine learning of morphological processing such as word form generation can be successfully learned in a supervised manner, without explicit description of rules. The performance of Decision trees and Bayesian machine learning algorithms on noun declensions are discussed.
A Constructivist Approach for Digital Learning: Malaysian Schools Case Study

The purpose of this study was to investigate the influences of Constructivist Learning Environments (CLEs) through the use of laptops supported within 1:1 e-learning education in Malaysian schools. The main objectives of this study were to investigate (a) different possible gaps between constructivist theory and classroom practices in Malaysian schools, (b) success, if any, of the classroom students who would undertake Classmate PC (CMPC) classroom putting while into practice a constructivist approach to learning, and (c) develop a conceptual framework model based on students’ communication in a constructivist learning environment. Yet, (d) there is a strong need to address Constructivist Learning Environments (CLEs) practices on local Malaysian settings. A modified Constructivist eLearning Environment Questionnaire (CLEQ) survey used in this study was multiply regressed against student Perceived Learning Outcomes (PRCVD). Findings revealed different aspects of students’ learning outcomes and enforcement to use creative thinking in building students’ knowledge within constructivism learning context.
An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.
Best monitoring functions

We propose a method for a static adaptive grid method for solving partial differential equations (PDEs) using splines. The principle is based on the observation that very good grids for spline approximations can be obtained by equidistributing the error. The efficiency of the resulting adaptive method following the same principle is tested. It works quite well for all PDEs that we investigated, even when the solution develops shocks and other phenomena. The method is also applicable for PDEs whose solutions are vector-valued. In the case that the number of space dimensions is larger than one, we restrict ourselves to tensor-product splines. As to be expected, the proposed method is only efficient when the physical phenomena, {\em i.e.} shocks, are more or less aligned with (one of) the axes of the domain.
64 65 SplicingTypesAnno : annotating and quantifying alternative splicing events for RNA-Seq data

Alternative splicing plays a key role in the regulation of the central dogma. Four major types of alternative splicing have been classified as intron retention, exon skipping, alternative 5 splice sites or alternative donor sites, and alternative 3 splice sites or alternative acceptor sites. A few algorithms have been developed to detect splice junctions from RNA-Seq reads. However, there are few tools targeting at the major alternative splicing types at the exon/intron level. This type of analysis may reveal subtle, yet important events of alternative splicing, and thus help gain deeper understanding of the mechanism of alternative splicing. This paper describes a user-friendly R package, extracting, annotating and analyzing alternative splicing types for sequence alignment files from RNA-Seq. SplicingTypesAnno can: 1) provide annotation for major alternative splicing at exon/intron level. By comparing the annotation from GTF/GFF file, it identifies the novel alternative splicing sites; 2) o↵er a convenient two-level analysis: genome-scale annotation for users with high performance computing environment, and gene-scale annotation for users with personal computers; 3) generate a user-friendly web report and additional BED files for IGV visualization. SplicingTypesAnno is a user-friendly R package for extracting, annotating and analyzing alternative splicing types at exon/intron level for sequence alignment files from RNA-Seq. It is publically available at https://sourceforge.net/projects/splicingtypes/files/ or http://genome.sdau. edu.cn/research/software/SplicingTypesAnno.html.
CREEP OF INDIUM, LEAD, AND SOME OF THEIR ALLOYS WITH VARIOUS METALS

>High-temperature creep experiments were carried out on indium, lead, and on binary substitutional alloys of In-Pb, In-Sn, In-Tl, In-Cd, In-Hg, Pb-Bi, Pb-Sn, Pb-In, and Pb-Cd. The stress at which the power law creep equation breaks down was found to be much smaller for indium and lead than for metals with larger elastic moduli. The exponent over the stress in the power law creep equation was found to decrease upon alloying from values close to 4.5 to values near 3. (auth)
Does the prognosis of colorectal cancer vary with tumor site?

Aim The aim of this study was to compare prognostic factors between colon and rectal cancers. Background Prognosis of Colorectal Cancer (CRC) may depend on the anatomical site. Patients and methods A total of 1219 patients with CRC diagnosis according to the pathology report of RCGLD cancer registry, from 1 January 2002 to 1 October 2007, were entered into the study. Demographic and clinico-pathological factors were analyzed using survival analysis. Results From age at diagnosis, colon cancer had significantly better survival than rectal cancer (Multivariate Hazard Ratio (MVHR)=0.24; 95% Confidence Interval (CI) =(0.074-0.77)). Other factors, including marital status (MVHR =1.78; 95% CI =(0.33-9.62)), body mass index (BMI) (MVHR =1.21 and 1.54; 95% CI =(.30-4.85) and (.44-5.4) respectively for < 18.5 and >30 BMI groups), pathologic stage (MVHR =.64; 95% CI =(.21-1.98)) and alcohol history (MVHR =4.86; 95% CI =(.67-35.14)) were not significantly different between the two patient group but suggested a possible effect upon prognosis. Overall survival in rectum was better than that of colon. Conclusion Our findings support this hypothesis that prognosis of CRC varies with tumor site.
Evaluation of resting-state magnetoencephalogram complexity in Alzheimer's disease with multivariate multiscale permutation and sample entropies

Alzheimer's disease (AD) is one of the fastest growing neurological diseases in the world. We evaluate multivariate multiscale sample entropy (mvMSE) and multivariate multiscale permutation entropy (mvMPE) approaches to distinguish resting-state magnetoencephalogram (MEG) signals of 36 AD patients from those of 26 normal controls. We also discuss about choosing the appropriate embedding dimension value as an effective parameter for mvMPE and MPE for the first time. The results illustrate that both the mvMPE and mvMSE can be useful in the diagnosis of AD, although with different running times and abilities. In addition, our findings show that the MEG complexity analysis performed on deeper time scales by mvMPE and mvMSE may be a useful tool to characterize AD. In most scale factors, the average of the mvMPE and mvMSE values of AD patients are lower than those of controls.
The Sigma Ontology Development Environment

Abstract: We discuss the development of an environment for formal knowledge engineering. The Sigma system is an advance over previously developed systems in that it integrates a number of modern ontology development tools, which has motivated a number of research issues. Primary components include an ontology browsing and editing environment, a first order logic inference system and a natural language to logic translator. Although largely independent of any particular ontology, it supports a number of publicly available formal ontologies. We discuss the development of an environment for formal knowledge engineering. The Sigma system is an advance over previously developed systems in that it integrates a number of modern ontology development tools, which has motivated a number of research issues. Primary components include an ontology browsing and editing environment, a first order logic inference system and a natural language to logic translator. Although largely independent of any particular ontology, it supports a number of publicly available formal ontologies.
Electrophoretic behaviour of oligonucleotides and mono‐, di‐ and triphosphate nucleotides by capillary zone electrophoresis

A systematic investigation has been made into the mechanisms of the capillary zone electrophoresis (CZE) separation of 12 common nucleotides (mono‐, di‐ and triphosphorylated) and polydeoxythymidylic acid oligonucleotides (pd(T)5‐18) using electrophoretic mobility values calculated from migration time data. Relationships between electrophoretic mobility and the physicochemical characteristics of the analytes (charge, dissociation constants, charge‐to‐mass ratio) and the background electrolyte conditions (buffer strength, percentage organic modifier and buffer pH) were characterised. Nucleotide migration was dominated by the negatively charged phosphate groups. Additionally, there were important contributions to migration behaviour from the ionised amide groups of the nucleobases guanine and uracil at higher buffer pH values or with the presence of methanol in the electrolyte. Calculated electrophoretic mobility values for the nucleotides showed a substantially improved (5‐fold) inter‐run repeatability compared with migration time data. These studies show the value of representing nucleotide migration data as electrophoretic mobility in CZE for obtaining a more thorough analysis of separation mechanisms and to compensate for variation in migration time data caused by small changes in electrosmotic flow. Oligonucleotides pd(T)5‐11 could be adequately resolved from their nearest neighbour, but the limit of single‐base separation was pd(T)10 from pd(T)11 under the conditions used. It was calculated that a difference in charge‐to‐mass ratio of 2.64×10–5 was required for resolution under the CZE conditions used.
The Role of Polarity in Inferring Acceptance and Rejection in Dialogue

We study the role that logical polarity plays in determining the rejection or acceptance function of an utterance in dialogue. We develop a model inspired by recent work on the semantics of negation and polarity particles and test it on annotated data from two spoken dialogue corpora: the Switchboard Corpus and the AMI Meeting Corpus. Our experiments show that taking into account the relative polarity of a proposal under discussion and of its response greatly helps to distinguish rejections from acceptances in both corpora.
Fuzzy Clustering With Partial Supervision in Organization and Classification of Digital Images

In a Web-oriented society, organization, retrieval, and classification of digital images have become one of the major endeavors. In this paper, we study the mechanisms of fuzzy clustering and fuzzy clustering with partial supervision in the analysis and classification of images. It is demonstrated that the main features of fuzzy clustering become essential in revealing the structure in a collection of images and supporting their classification. The discussed operational framework of fuzzy clustering is realized by means of fuzzy c-means (FCM). When dealing with the mode of partial supervision, we augment an original objective function guiding the clustering process by an additional component expressing a level of coincidence between the membership degrees produced by the FCM and class allocation supplied by the user(s). The study also contrasts the use of the technology of fuzzy sets in image clustering with other approaches studied in this area. A suite of experiments deals with two collections of images, namely, Columbia object image library (COIL-20) and a database composed of 2000 outdoor images.