Structural and Electrical Investigation of C 60 –Graphene Vertical Heterostructures

Graphene, with its unique electronic and structural qualities, has become an important playground for studying adsorption and assembly of various materials including organic molecules. Moreover, organic/graphene vertical structures assembled by van der Waals interaction have potential for multifunctional device applications. Here, we investigate structural and electrical properties of vertical heterostructures composed of C60 thin film on graphene. The assembled film structure of C60 on graphene is investigated using transmission electron microscopy, which reveals a uniform morphology of C60 film on graphene with a grain size as large as 500 nm. The strong epitaxial relations between C60 crystal and graphene lattice directions are found, and van der Waals ab initio calculations support the observed phenomena. Moreover, using C60-graphene heterostructures, we fabricate vertical graphene transistors incorporating n-type organic semiconducting materials with an on/off ratio above 3 × 10(3). Our work demonstrates that graphene can serve as an excellent substrate for assembly of molecules, and attained organic/graphene heterostructures have great potential for electronics applications.

Analysis on the connotation of probability in the artificial intelligence algorithm for economic decision making.

This paper analyzes the inner relations between classical sub-scheme probability and statistic probability, subjective probability and objective probability, prior probability and posterior probability, transition probability and probability of utility, and further analysis the goal, method, and its practical economic purpose which represent by these various probability from the perspective of mathematics, so as to deeply understand there connotation and its relation with economic decision making, thus will pave the route for scientific predication and decision making.

Mechanical and Electrical Properties of Single-walled Carbon Nanotubes Synthesized by Chemical Vapor Deposition

Despite the tremendous application potentials of carbon nanotubes (CNTs) proposed by researchers in the last two decades, efficient experimental techniques and methods are still in need for controllable production of CNTs in large scale, and for conclusive characterizations of their properties in order to apply CNTs in high accuracy engineering. In this dissertation, horizontally well-aligned high quality single-walled carbon nanotubes (SWCNTs) have been successfully synthesized on St-cut quartz substrate by chemical vapor deposition (CVD). Effective radial moduli (Eradial) of these straight SWCNTs have been measured by using well-calibrated tapping mode and contact mode atomic force microscopy (AFM). It was found that the measured Eradial decreased from 57 to 9 GPa as the diameter of the SWCNTs increased from 0.92 to 1.91 nm. The experimental results were consistent with the recently reported theoretical simulation data. The method used in this mechanical property test can be easily applied to measure the mechanical properties of other low-dimension nanostructures, such as nanowires and nanodots. The characterized sample is also an ideal platform for electrochemical tests. The electrochemical activities of redox probes Fe(CN)63-/4-, Ru(NH3)63+, Ru(bpy)32+ and protein cytochrome c have been studied on these pristine thin films by using aligned SWCNTs as working electrodes. A simple and high performance electrochemical sensor was fabricated. Flow sensing capability of the device has been tested for detecting neurotransmitter dopamine at physiological conditions with the presence of Bovine serum albumin. Good sensitivity, fast response, high stability and anti-fouling capability were observed. Therefore, the fabricated sensor showed great potential for sensing applications in complicated solution.

An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

Analysis of Dyscalculia Evidences through Artificial Intelligence Systems

Dyscalculia is usually perceived of as a specific learning difficulty for mathematics or, more appropriately, arithmetic. Because definitions and diagnoses of dyscalculia are in their infancy and sometimes are contradictory. However, mathematical learning difficulties are certainly not in their infancy and are very prevalent and often devastating in their impact. Co-occurrence of learning disorders appears to be the rule rather than the exception. Co-occurrence is generally assumed to be a consequence of risk factors that are shared between disorders, for example, working memory. However, it should not be assumed that all dyslexics have problems with mathematics, although the percentage may be very high, or that all dyscalculics have problems with reading and writing. Because mathematics is very developmental, any insecurity or uncertainty in early topics will impact on later topics, hence to need to take intervention back to basics. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work will focus on the development of a Decision Support System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, grounded on a Case-based approach to computing, that allows for the handling of incomplete, unknown, or even self-contradictory information.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

THE SUMMATION OF VOLUNTARY AND ELECTRICAL ELICITED STIMULUS INCREASES JOINT TORQUE?

The aim of this study is to analyze the effect of neuromuscular electrical stimulation (NMES) on myoelectrical activity and on joint torque during isometric plantar flexion contraction. Ten healthy young adult subjects participate in this study. The electrodes for NMES are placed along posterior thigh along ciatic nerve trajectory. It is measured the myoelectrical activity and the isometric torque generated by ankle plantar flexion with an isokinetic dynamometer. The conditions of isometric contractions are maximum isometric voluntary contraction (MIVC), NMES, and association of both (MIVC+NMES). The results show lower torque during NMES and larger SOL activity compare to the others. Besides, in order to keep the same objective task (to produce the same level of torque), neuromuscular adaptations are necessary on the common drive.

Impact of Commercial Search Engines and International Databases on Engineering Teaching and Research

For the last three decades, the engineering higher education and professional environments have been completely transformed by the "electronic/digital information revolution" that has included the introduction of personal computer, the development of email and world wide web, and broadband Internet connections at home. Herein the writer compares the performances of several digital tools with traditional library resources. While new specialised search engines and open access digital repositories may fill a gap between conventional search engines and traditional references, these should be not be confused with real libraries and international scientific databases that encompass textbooks and peer-reviewed scholarly works. An absence of listing in some Internet search listings, databases and repositories is not an indication of standing. Researchers, engineers and academics should remember these key differences in assessing the quality of bibliographic "research" based solely upon Internet searches.

Reliability of the input-output properties of the cortico-spinal pathway obtained from transcranial magnetic and electrical stimulation

The purpose of this experiment was to assess the test-retest reliability of input-output parameters of the cortico-spinal pathway derived from transcranial magnetic (TMS) and electrical (TES) stimulation at rest and during muscle contraction. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle of eight individuals on three separate days. The intensity of TMS at rest was varied from 5% below threshold to the maximal output of the stimulator. During trials in which the muscle was active, TMS and TES intensities were selected that elicited MEPs of between 150 and 300 X at rest. MEPs were evoked while the participants exerted torques up to 50% of their maximum capacity. The relationship between MEP size and stimulus intensity at rest was sigmoidal (R-2 = 0.97). Intra-class correlation coefficients (ICC) ranged between 0.47 and 0.81 for the parameters of the sigmoid function. For the active trials, the slope and intercept of regression equations of MEP size on level of background contraction were obtained more reliably for TES (ICC = 0.63 and 0.78, respectively) than for TMS (ICC = 0.50 and 0.53, respectively), These results suggest that input-output parameters of the cortico-spinal pathway may be reliably obtained via transcranial stimulation during longitudinal investigations of cortico-spinal plasticity. (C) 2001 Elsevier Science B.V. All rights reserved.

Lead stimulus modality change and the attentional modulation of the acoustic and electrical blink reflex

Two experiments investigated the effects of the sensory modality of the lead and of the blink-eliciting stimulus during lead stimulus modality change on blink modulation at lead intervals of 2500 and 3500 ins. Participants were presented with acoustic, visual, or tactile change stimuli after habituation training with lead stimuli from the same or a different sensory modality. In Experiment 1, latency and magnitude of the acoustic blink were facilitated during a change to acoustic or visual lead stimuli, but not during a change to tactile lead stimuli. After habituation to acoustic lead stimuli, blink magnitude was smaller during tactile change stimuli than during habituation stimuli. The latter finding was replicated in Experiment 2 in which blink was elicited by electrical stimulation of the trigeminal nerve. The consistency of the findings across different combinations of lead stimulus and blink-eliciting stimulus modalities does not support a modality-specific account of attentional blink modulation. Rather, blink modulation during generalized orienting reflects modality non-specific processes, although modulation may not always be found during tactile lead stimuli. (C) 2002 Elsevier Science B.V. All rights reserved.

Quadriceps concentric and eccentric exercise 1: Changes in contractile and electrical activity following eccentric and concentric exercise

The purpose of this study was to determine whether or not losses of strength or endurance following eccentric and concentric exercise are associated with reduced excitation. The effects of eccentric and concentric work on maximal voluntary isometric contraction (MVC) and surface electromyogram (EMG) of the quadriceps were studied in 10 healthy male subjects following bench-stepping for 20 min with a constant leading leg. Prior to stepping and at 0, 0.25, 0.50, 0.75, 1, 3. 24 and 48 h afterwards the subjects performed a 30 s leg extension MVC with each leg during which the isometric force and the root mean square voltage of the EMG were recorded. In the eccentrically exercised muscles (ECC), MVC0-3 (force during the first 3 s of contraction) fen immediately after the bench-stepping exercise to 88 +/- 2% (mean SE) of the pre-exercise value and remained significantly lower than the concentrically exercised muscles (p < 0.05). The muscle weakness in the ECC could not be attributed to central fatigue as surface EMG amplitude at MVC0-3 increased during the recovery period. Muscle weakness after eccentric exercise appears to be due to contractile failure, which is not associated with a reduction in excitation as assessed by surface EMG. Muscular fatigue over 30 s did not change in the two muscle groups after exercise (p = 0.79), indicating that the ECC were weaker but not more fatiguable after exercise.

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.