Preferred Input Research Articles

A Multi Criteria Decision Making (MCDM) based on TOPSIS and RSM for process improvement in electrical discharge machining of silicon nitride–titanium nitride ceramic composites

This experiment is an attempt to choose the optimal machining parameters for electrical discharge machining (EDM) of silicon nitride–titanium nitride (Si3N4–TiN) ceramic composites based on MCDM approach. The electrical discharge machining have been conducted according to L21 orthogonal array with preferred input parameters like current (I), spark gap voltage (SV), pulse-on duration (Ton) and pulse-off duration (Toff). The response parameters measured are material eradication rate (MRR), electrode eradication rate (EWR) and surface roughness (Ra). The MCDM techniques namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Response Surface Methodology (RSM) are used to find the optimal machining parameters which provide better value of all responses. Also, scanning electron microscopy (SEM) is employed for examining the surface defects like micro-cracks, micro-pores, droplets, surface craters, globules, etc.

Nonlinear Autoregressive Moving Average-L2 Model Based Adaptive Control of Nonlinear Arm Nerve Simulator System

This paper considers the trouble of the usage of approximate strategies for realizing the neural controllers for nonlinear SISO systems. In this paper, we introduce the nonlinear autoregressive moving average (NARMA-L2) model which might be approximations to the NARMA model. The nonlinear autoregressive moving average (NARMA-L2) model is an precise illustration of the input–output behavior of finite-dimensional nonlinear discrete time dynamical systems in a neighborhood of the equilibrium state. However, it isn't always handy for purposes of neural networks due to its nonlinear dependence on the manipulate input. In this paper, nerves system based arm position sensor device is used to degree the precise arm function for nerve patients the use of the proposed systems. In this paper, neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network controller is designed with NARMA-L2 model based model reference adaptive control system. Hence, quite regularly, approximate techniques are used for figuring out the neural controllers to conquer computational complexity. Comparison were made among the neural network controller with NARMA-L2 model, neural network controller with NARMA-L2 model system identification based predictive controller and neural network controller with NARMA-L2 model reference based adaptive control for the preferred input arm function (step, sine wave and random signals). The comparative simulation result shows the effectiveness of the system with a neural network controller with NARMA-L2 model based model reference adaptive control system. Index Terms--- Nonlinear autoregressive moving average, neural network, Model reference adaptive control, Predictive controller DOI: 10.7176/JIEA/10-3-03 Publication date: April 30 th 2020

Group decision making in manufacturing systems: An approach using spatial preference information and indifference zone

Decision making is an essential activity in manufacturing systems when designing production lines, scheduling, etc. Many decision making problems are characterized by multiple conflicting criteria and a large number of alternatives. For these complex decision making problems, it is rational to involve a group of decision makers (DM) for considering different aspects of the problem. This paper proposes an approach for supporting the decision making group to reduce disagreement in the group and obtain a common solution. The proposed approach allows the DMs to specify a region of acceptance, known as indifference zone, in the objective space as preference inputs. This makes the proposed approach applicable to problems with a large number of alternatives. The use of indifference zone concept captures the uncertain nature of preference articulation. Moreover, the indifference zone is shown beneficial in reducing the difficulty of reaching a group common solution. The properties of the proposed method are investigated analytically and with numerical experiments. Finally, the usefulness of the proposed method is shown by tackling a real-world packaging line configuration problem with a large alternative set.

Applying a Multi-Criteria Project Portfolio Tool in Selecting Energy Peat Production Areas

This study demonstrates the characteristics of the new generic project portfolio selection tool YODA (“Your Own Decision Aid”). YODA does not include a mathematical aggregation model. Instead, the decision maker’s preferences are defined by the interactive articulation of acceptance thresholds of project-level decision criteria. Transparency and ease of adopting the method in participatory planning are sought using the method’s simple preference input. The characteristics of the YODA tool are introduced by presenting how it has been applied in participatory land use planning in northern Finland in selecting a combination of peat production sites to attain the goals defined at municipal level. In this process, each stakeholder first constructed a project portfolio that best met his or her preferences. In doing this, acceptance thresholds for project-level decision criteria were defined. In total, eight decision criteria were related to economic value, biodiversity, social impacts, and ecosystem services. Subsequently, the portfolios of different stakeholders were combined in line with the principles of robust portfolio modelling. Core projects were accepted by all stakeholders, while exterior projects were not accepted, and borderline projects by some of the stakeholders. Although the land use planning situation at hand was highly sensitive, because it was related to various aspects of sustainability, the use of YODA provided useful results. The first meeting with stakeholders identified 52 out of 99 sites that none of the stakeholders would use for energy peat production, due to their characteristics, whereas, in the second meeting, a smaller stakeholder group found 18 core projects and 26 borderline projects which could be potential areas for energy peat production. We conclude that YODA—as a generic project portfolio tool—can be used in various planning situations.

Optimization of weldment in bead on plate welding of nickel based superalloy using Activated flux tungsten inert gas welding (A-TIG)

The main objective of this research article is to identify the optimized welding process parameters for a 6.5 mm thick Inconel 625 nickel based superalloy by Activated flux tungsten inert gas welding (A-TIG). The bead on trial experiments was carried out on a L25 orthogonal array with preferable input responses like current, torch speed and arc gap. Output responses considered were welding heat input and heat affected zone width (HAZ). Multi criteria decision making optimization tool, Grey relational analysis (GRA) was used to find the optimum condition. Analysis of variance (ANOVA) was employed to determine the consequence of each individual input’s response. From various experimental trials 300 Amps current, 75 mm/min torch speed and 1 mm arc gap with a heat input of 4.212 kJ/mm were found to be optimal input responses. The optimized sample was examined by Optical microscopy for a detailed study of the interdendritic region of the HAZ and weld zone.

Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.

Analysis of a below-water aggregate mining case study in Ontario, Canada using values-centric online citizen participation

Citizen perspectives on a controversial aggregate mining application amendment are elicited using an online participatory platform P2P-Surveys. The interface uses interactive visual cues to focus the subject’s attention on their social values framework as a decision-aide. Interactive elements encourage learning by providing values feedback and then allowing the subject to adjust their preference input or to change the values-modeling parameters prior to submitting their responses. Individual preference rankings are then aggregated using a Modified Borda Count for subsets of the participants. The resulting group preference rankings provide insights useful to researchers interested in the interface between social values and environmental decision making, and for decision makers who wish to harness citizen input for integrated decision making. A real-life case study for citizen input into a below-water aggregate mining controversy in Southern Ontario, Canada, is utilized to explain this new methodology and to demonstrate the useful information generated to assist in resources management decision making.

An adaptive intelligent system to minimize energy use for a parallel hybrid electric vehicle

This paper elaborates on the synthesis of a neuro-fuzzy controller and also its application to hybrid vehicle powertrain control system. This technique builds a fuzzy inference system for which the parameters can be updated to achieve a preferred input–output mapping. The procedure of learning (guidelines on the parameter updates) is essentially composed of the gradient descent algorithm as well as the least square estimate method. The hybrid vehicle used in this study is the parallel hybrid vehicle. With its small engine, this type of hybrid vehicle is made for steady highway driving. In addition, it composes an electrical device that provides acceleration and deceleration along with a battery management system. A fuzzy logic inference system is used for energy administration. In which, the inference system states the guidelines on recording the nonlinearities in the mapping. The neuro-fuzzy controller is able to accomplish the task of an inverse model with an adaptive fuzzy logic controller. This paper will draw upon the growth and advancement in SOC estimation method and power management system of lithium-ion battery for future high-tech EV applications. It is shown that the intelligent management system proposed in this paper has a significant improve in performance to that of the other existing systems.

The image catalogs of the libraries of RAS Pushchino Science Center and RAS Library for Natural Sciences: Generation and use

The experience of building and use of the image catalogs at the Library of Pushchino Science Center of theRussianAcademyof Sciences and RAS Library for Natural Sciences is examined. The differences between the catalogs are identified: the former comprises the scanned systematic catalog, and the latter – the alphabet catalog of foreign books. Two different approaches toward retroconversion are analyzed. In Pushchino, the Library preferred hand input into the ecatalog to retroconversion on the basis of the completed systematic image catalog. The systematic image catalog is heavily used in the library services and available via the Library’s www-site. Instead, the RAS Library for Natural Sciences after having scanned and edited the alphabet catalog of foreign books, implemented the function of book search and ordering from the image catalog. The prospects and tasks for acquiring retro-entries to the e-catalogs are discussed. The Library of RAS Pushchino Science Center is to complete the retroconvesion of the alphabet card catalog and editing of the systematic image catalog. The RAS Library for Natural Sciences is to generate the image catalog of national book publications.

Gesture-Based Communication via User Experience Design: Integrating Experience into Daily Life for an Arm-Swing Input Device

Arm swing is a well-known exercise practiced throughout Asia. The benefits of regular exercise affect a person’s gait, shoulder function as well as head and body strength. This paper presents a new study of gesture-based communication by using arm swing as a gestural input command applied to any digital device used by persons aged 21–50 years of age during their workday. In five difference occupations, i.e., student, lecturer, office workers, designer and industrial worker, with different routine tasks were studied. The sampling comprised 30 participants, 6 persons representing each occupation. Data were collected through focus groups. The objective of this study is to provide possibilities to integrate proper user experience of gesture-based communication toward an arm-swing input device onto participants’ routine activities. It is believed this research can have a significant impact on quality on human daily lives. A part of user experience methodology, like a field experiment, is applied to provoke participants to follow the correct sequence case by case. This paper adopts user experience measurement for this qualitative research approach. A proper user experience occurs when one understands how strength and weakness are affected by gestural input. An input gesture, feedback, system and output can then be mapped together into a complete scenario of a person’s daily life. Each participant is encouraged to raise various ideas on how he/she accesses technology rather than on how technologies allow them to access information. Based on the experimental results, this paper will then discuss the possible tasks performed in the five occupations in which an arm-swing input gesture is integrated. The influence of this integration into the participants’ daily activities presents confirmation of a preferable wearable input device for all focus groups. This wearable input device can then be integrated into activities without any interruption in a person’s main actions.

Exploring Animation Styles to Communicate the Effects of Exercise on Gut Microbiota

This study of learning principle-designed scientific animation sought to determine whether animation is an effective teaching tool and what components of animation fulfill that role. It is known that there is a lack of animations that concretely support research-based learning principles. There are qualitative studies describing how different visual styles of animation may affect learning. These studies have provided the visual preferences and opinions of certain audiences. However, there are much fewer quantitative studies that objectively test whether differences in visual style produce different learning outcomes. The limited amount of scientific papers demonstrating how animation design effects comprehension leads to a concern that most scientific animations are crafted according to creator preferences and rely on instinct rather than evidence-based practices. This study analyzed the effects of one component of scientific animation, realism, to quantitatively assess the effects of visual realism on learning and to quantitatively gather viewer preferences and opinions on this subject. One animation was designed using cognitive principles and artistic standards. It was rendered into three distinct visual styles with progressive increase in level of detail: schematic, semi-realistic, realistic. Participants were randomly assigned a level of detail, assessed on the animation material, and given samples of the styles to comment on. There was a positive improvement in test scores before and after viewing one of the three animation styles. The greatest improvement in test scores was seen among participants with low prior knowledge who were shown the simplest visual style (schematic). The vast majority of participants preferred the most detailed version. About ten percent of participants claimed to see "no difference" among the three visual styles when asked to choose a preferred rendering. From these results it was concluded that an animation can effectively fulfill learning design even with simplified visuals. Simplified 3D animations can be specifically beneficial to beginners. This study benefitted from user preference input even though the preferred visual style (realistic) was not linked to a significantly better improvement in scores. The results emphasize the need to integrate learning principles in scientific animation design.

Flexible resonance in prefrontal networks with strong feedback inhibition.

Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.

A Distance-Dependent Distribution of Presynaptic Boutons Tunes Frequency-Dependent Dendritic Integration

SummaryHow presynaptic inputs and neurotransmitter release dynamics are distributed along a dendritic tree is not well established. Here, we show that presynaptic boutons that form onto basal dendrites of CA1 pyramidal neurons display a decrease in active zone (AZ) size with distance from the soma, resulting in a distance-dependent increase in short-term facilitation. Our findings suggest that the spatial distribution of short-term facilitation serves to compensate for the electrotonic attenuation of subthreshold distal inputs during repeated stimulation and fine-tunes the preferred input frequency of dendritic domains.

The knowledge graph as the default data model for learning on heterogeneous knowledge

In modern machine learning, raw data is the pre-ferred input for our models. Where a decade ago data scien-tists were still engineering features, manually picking out the details they thought salient, they now prefer the data in their raw form. As long as we can assume that all relevant and ir-relevant information is present in the input data, we can de-sign deep models that build up intermediate representations to sift out relevant features. However, these models are often domain specific and tailored to the task at hand, and therefore unsuited for learning on heterogeneous knowledge: informa-tion of different types and from different domains. If we can develop methods that operate on this form of knowledge, we can dispense with a great deal of ad-hoc feature engineering and train deep models end-to-end in many more domains. To accomplish this, we first need a data model capable of ex-pressing heterogeneous knowledge naturally in various do-mains, in as usable a form as possible, and satisfying as many use cases as possible. In this position paper, we argue that the knowledge graph is a suitable candidate for this data model. This paper describes current research and discusses some of the promises and challenges of this approach.

Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback

AbstractIn this work we propose a framework for incorporating occupant feedback towards temperature control of multi‐occupant spaces, and analyze it using singular perturbation theory. Such a system would typically have to accommodate occupants with different temperature preferences, and incorporate that with thermal correlation among multiple zones to obtain optimal control for minimization of occupant discomfort and energy cost. In current practice, an acceptable temperature set point for the occupancy level of the zone is estimated, and the control law is designed to maintain temperature at the corresponding set point irrespective of the changes in occupancy and the preferences of multiple occupants. Proposed algorithm incorporates active occupant feedback to minimize aggregate user discomfort and total energy cost. Occupant binary feedback in the form of hot/cold or thermal comfort preference input is used by the control algorithm. The control algorithm also takes the energy cost into account, trading it off optimally with the aggregate occupant discomfort. For convergence to the optimal, sufficient separation between the occupant feedback frequency and the temperature dynamics of system is necessary; in absence of which, the occupant feedback provided do not correctly reflect the effect of current control input value on occupant discomfort. Under sufficient time scale separation, using singular perturbation theory, we establish the stability condition of the system and show convergence of the proposed solution to the desired temperature that minimizes energy cost plus occupant discomfort. The occupants are only assumed to be rational in that they choose their own comfort range to minimize individual thermal discomfort. Optimization for a multi‐zone building also takes into account the thermal correlation among different zones. Simulation study with parameters based on our test facility, and experimental study conducted in the same building demonstrates performance of the algorithm under different occupancy and ambient conditions.

Practical issues related to the application of piezoelectric based wave propagation technique in monitoring of concrete curing

Smart piezoelectric material, namely, the Lead Zirconate Titanate (PZT) has recently found its application in concrete curing and strength development monitoring using the wave propagation (WP) technique. Real-life application of this technique is sometimes questioned as various practical issues have not been attended. In this paper, a series of experimental studies are performed to investigate a range of practical issues related to the application of this technique, in an attempt to reduce the gap between laboratory studies and real-life applications. Issues such as the consistency of wave velocities and repeatability of the sensor’s electrical signatures, the optimum frequency of actuation, the effect of PZT transducer’s spacing and sizes, the waveform of actuation signatures, the effect of input voltage amplification, the effect of different surface roughness, the environmental effect and the effect of different types of coarse aggregates are experimentally studied. Results showed that 5-peaks sinusoidal tone burst actuated at 30kHz or 120kHz are preferred input voltage. Spacing of transducers should be approximately 90mm. Smooth and even surface of the host structure is preferred for bonding. The WP technique is found to be reliable and its performance is consistent across different specimens up to a period of 365days. It is also capable of differentiating the strength concrete with different types of coarse aggregate. Studies conducted in this paper provide basic understanding into various practical issues, which is expected to serve as guidelines to future development, design, optimization and commercialization of a more effective PZT based WP technique in monitoring of concrete curing.

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex

Studies of multisensory integration by single neurons have traditionally emphasized empirical principles that describe nonlinear interactions between inputs from two sensory modalities. We previously proposed that many of these empirical principles could be explained by a divisive normalization mechanism operating in brain regions where multisensory integration occurs. This normalization model makes a critical diagnostic prediction: a non-preferred sensory input from one modality, which activates the neuron on its own, should suppress the response to a preferred input from another modality. We tested this prediction by recording from neurons in macaque area MSTd that integrate visual and vestibular cues regarding self-motion. We show that many MSTd neurons exhibit the diagnostic form of cross-modal suppression, whereas unisensory neurons in area MT do not. The normalization model also fits population responses better than a model based on subtractive inhibition. These findings provide strong support for a divisive normalization mechanism in multisensory integration.

Effective lepton flavor violating Hℓiℓj vertex from right-handed neutrinos within the mass insertion approximation

In this work we present a new computation of the lepton flavor violating Higgs boson decays that are generated radiatively to one-loop from heavy right-handed neutrinos. We work within the context of the inverse seesaw model with three $\nu_R$ and three extra singlets $X$, but the results could be generalized to other low scale seesaw models. The novelty of our computation is that it uses a completely different method by means of the mass insertion approximation which works with the electroweak interaction states instead of the usual 9 physical neutrino mass eigenstates of the inverse seesaw model. This method also allows us to write the analytical results explicitly in terms of the most relevant model parameters, that are the neutrino Yukawa coupling matrix $Y_\nu$ and the right-handed mass matrix $M_R$, which is very convenient for a phenomenological analysis. This $Y_\nu$ matrix, being generically nondiagonal in flavor space, is the only responsible for the induced charged lepton flavor violating processes of our interest. We perform the calculation of the decay amplitude up to order ${\cal O}(Y_\nu^2+Y_\nu^4)$. We also study numerically the goodness of the mass insertion approximation results. In the last part we present the computation of the relevant one-loop effective vertex $H\ell_i\ell_j$ for the lepton flavor violating Higgs decay which is derived from a large $M_R$ mass expansion of the form factors. We believe that our simple formula found for this effective vertex can be of interest for other researchers who wish to estimate the $H \to \ell_i \bar \ell_j$ rates in a fast way in terms of their own preferred input values for the relevant model parameters $Y_\nu$ and $M_R$.

A multi criteria decision making approach for process improvement in friction stir welding of aluminium alloy

This work is an attempt to select the optimum process parameters for friction stir welding of aluminium 2024 alloy based on multiple criteria decision making approach. The friction stir welding experiments have been conducted according to L9 orthogonal array with preferred input parameters are tool rotational speed, welding speed and tool offset. The response parameters measured are ultimate tensile strength, impact toughness and hardness of welded joint that determines quality of joint. The multi criteria decision making techniques namely Grey Relational Analysis and Technique for Order Preference by Similarity to Ideal Solution are used to find the optimum process parameter which provides maximum value of all responses. Same result is obtained through both techniques and the optimum conditions are tool rotational speed of 1000rpm, welding speed of 80mm/min and tool offset at 0mm.

Abstract B39: Asparagine drives translational adaptation of cancer cells to glutamine deficit

Abstract Most proliferating cells consume exogenous glutamine to fuel biosynthesis and cell growth, which leads to its marked depletion from the microenvironment. Glutamine provides carbon and nitrogen for a wide array of biomolecules, among which are non-essential amino acids, glucosamine and nucleotides; in addition, carbon backbone of glutamine is used as a preferred anaplerotic input into the TCA cycle. However, as recent in vivo metabolomic studies indicate, a number of tumor types are capable of utilizing alternative anaplerotic routes and, in fact, synthesize glutamine de novo. In addition, select cancer cell lines are able to proliferate in the absence of exogenous glutamine in culture medium. However, the metabolic and regulatory underpinnings that enable the cellular adaptation to glutamine deficit and de novo glutamine production need further investigation. In this work, we identified the non-essential amino acid asparagine as essential for the survival and proliferation of cancer cells in the absence of exogenous glutamine supply. Our data indicate that asparagine, though structurally related to glutamine, is not catabolized in the same manner. Instead, asparagine acts by restoring protein translation, which becomes profoundly compromised in the absence of glutamine. This, in turn, enables the post-transcriptional adaptive upregulation of glutamine synthetase (GLUL), which is required for glutamine-independent growth. Taken together, these findings show that the cellular adaptation to glutamine-limiting conditions relies upon the availability of asparagine, which may represent an important therapeutic vulnerability. Citation Format: Natalya N. Pavlova, Ji Zhang, Craig B. Thompson. Asparagine drives translational adaptation of cancer cells to glutamine deficit. [abstract]. In: Proceedings of the AACR Special Conference on Translational Control of Cancer: A New Frontier in Cancer Biology and Therapy; 2016 Oct 27-30; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2017;77(6 Suppl):Abstract nr B39.