Search results for: neural signal recording

Authors: Amin Aghatabar Roodbary, Mohammad Hassan Bastani

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One of the defects of stepped frequency radar systems is their sensitivity to target motion. In such systems, target motion causes range cell shift, false peaks, Signal to Noise Ratio (SNR) reduction and range profile spreading because of power spectrum interference of each range cell in adjacent range cells which induces distortion in High Resolution Range Profile (HRRP) and disrupt target recognition process. Thus Target Motion Parameters (TMPs) effects compensation should be employed. In this paper, such a method for estimating TMPs (velocity and acceleration) and consequently eliminating or suppressing the unwanted effects on HRRP based on entropy minimization has been proposed. This method is carried out in two major steps: in the first step, a discrete search method has been utilized over the whole acceleration-velocity lattice network, in a specific interval seeking to find a less-accurate minimum point of the entropy function. Then in the second step, a 1-D search over velocity is done in locus of the minimum for several constant acceleration lines, in order to enhance the accuracy of the minimum point found in the first step. The provided simulation results demonstrate the effectiveness of the proposed method.

Keywords: automatic target recognition (ATR), high resolution range profile (HRRP), motion compensation, stepped frequency waveform technique (SFW), target motion parameters (TMPs)

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Authors: Ruphi Naz, Altaf Ahmad, Mohammad Anis

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Sialic acid (5-Acetylneuraminic acid, Neu5Ac) is a common sugar found as a terminal residue on glycoconjugates in many animals. Humans brain and the central nervous system contain the highest concentration of sialic acid (as N-acetylneuraminic acid) where these acids play an important role in neural transmission and ganglioside structure in synaptogenesis. Due to its important biological function, sialic acid is attracting increasing attention. To understand metabolic networks, fluxes and regulation, it is essential to be able to determine the cellular and subcellular levels of metabolites. Genetically-encoded fluorescence resonance energy transfer (FRET) sensors represent a promising technology for measuring metabolite levels and corresponding rate changes in live cells. Taking this, we developed a genetically encoded FRET (fluorescence resonance energy transfer) based nanosensor to analyse the sialic acid level in living cells. Sialic acid periplasmic binding protein (sia P) from Haemophilus influenzae was taken and ligated between the FRET pair, the cyan fluorescent protein (eCFP) and Venus. The chimeric sensor protein was expressed in E. coli BL21 (DE3) and purified by affinity chromatography. Conformational changes in the binding protein clearly confirmed the changes in FRET efficiency. So any change in the concentration of sialic acid is associated with the change in FRET ratio. This sensor is very specific to sialic acid and found stable with the different range of pH. This nanosensor successfully reported the intracellular level of sialic acid in bacterial cell. The data suggest that the nanosensors may be a versatile tool for studying the in vivo dynamics of sialic acid level non-invasively in living cells

Keywords: nanosensor, FRET, Haemophilus influenzae, metabolic networks

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Authors: Riccardo Fratini, Riccardo Santini, Jacopo Serafini, Massimo Gennaretti, Stefano Panzieri

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This paper presents a nonlinear differential model, for a three-bladed horizontal axis wind turbine (HAWT) suited for control applications. It is based on a 8-dofs, lumped parameters structural dynamics coupled with a quasi-steady sectional aerodynamics. In particular, using the Euler-Lagrange Equation (Energetic Variation approach), the authors derive, and successively validate, such model. For the derivation of the aerodynamic model, the Greenbergs theory, an extension of the theory proposed by Theodorsen to the case of thin airfoils undergoing pulsating flows, is used. Specifically, in this work, the authors restricted that theory under the hypothesis of low perturbation reduced frequency k, which causes the lift deficiency function C(k) to be real and equal to 1. Furthermore, the expressions of the aerodynamic loads are obtained using the quasi-steady strip theory (Hodges and Ormiston), as a function of the chordwise and normal components of relative velocity between flow and airfoil Ut, Up, their derivatives, and section angular velocity ε˙. For the validation of the proposed model, the authors carried out open and closed-loop simulations of a 5 MW HAWT, characterized by radius R =61.5 m and by mean chord c = 3 m, with a nominal angular velocity Ωn = 1.266rad/sec. The first analysis performed is the steady state solution, where a uniform wind Vw = 11.4 m/s is considered and a collective pitch angle θ = 0.88◦ is imposed. During this step, the authors noticed that the proposed model is intrinsically periodic due to the effect of the wind and of the gravitational force. In order to reject this periodic trend in the model dynamics, the authors propose a collective repetitive control algorithm coupled with a PD controller. In particular, when the reference command to be tracked and/or the disturbance to be rejected are periodic signals with a fixed period, the repetitive control strategies can be applied due to their high precision, simple implementation and little performance dependency on system parameters. The functional scheme of a repetitive controller is quite simple and, given a periodic reference command, is composed of a control block Crc(s) usually added to an existing feedback control system. The control block contains and a free time-delay system eτs in a positive feedback loop, and a low-pass filter q(s). It should be noticed that, while the time delay term reduces the stability margin, on the other hand the low pass filter is added to ensure stability. It is worth noting that, in this work, the authors propose a phase shifting for the controller and the delay system has been modified as e^(−(T−γk)), where T is the period of the signal and γk is a phase shifting of k samples of the same periodic signal. It should be noticed that, the phase shifting technique is particularly useful in non-minimum phase systems, such as flexible structures. In fact, using the phase shifting, the iterative algorithm could reach the convergence also at high frequencies. Notice that, in our case study, the shifting of k samples depends both on the rotor angular velocity Ω and on the rotor azimuth angle Ψ: we refer to this controller as a spatial repetitive controller. The collective repetitive controller has also been coupled with a C(s) = PD(s), in order to dampen oscillations of the blades. The performance of the spatial repetitive controller is compared with an industrial PI controller. In particular, starting from wind speed velocity Vw = 11.4 m/s the controller is asked to maintain the nominal angular velocity Ωn = 1.266rad/s after an instantaneous increase of wind speed (Vw = 15 m/s). Then, a purely periodic external disturbance is introduced in order to stress the capabilities of the repetitive controller. The results of the simulations show that, contrary to a simple PI controller, the spatial repetitive-PD controller has the capability to reject both external disturbances and periodic trend in the model dynamics. Finally, the nominal value of the angular velocity is reached, in accordance with results obtained with commercial software for a turbine of the same type.

Keywords: wind turbines, aeroelasticity, repetitive control, periodic systems

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Authors: Ajay Kumar Pandey, Mayank Kumar

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White grub (Coleoptera: Scarabaeidae) is a major destructive pest in western Himalayan region of Uttarakhand. Beetles feed on apple, apricot, plum, walnut etc. during night while, second and third instar grubs feed on live roots of cultivated as well as non-cultivated crops. Collection and identification of scarab beetles through light trap was carried out at Crop Research Centre, Govind Ballab Pant University Pantnagar, Udham Singh Nagar (Uttarakhand) during 2018. Field trials were also conducted in 2018 to evaluate pre and post sown application of different insecticides against the white grub infesting soybean. The insecticides like Carbofuran 3 Granule (G) (750 g a.i./ha), Clothianidin 50 Water Dispersal Granule (WG) (120 g a.i./ha), Fipronil 0.3 G (50 g a.i./ha), Thiamethoxam 25 WG (80 g a.i./ha), Imidacloprid 70 WG (300 g a.i./ha), Chlorantraniliprole 0.4% G(100 g a.i./ha) and mixture of Fipronil 40% and Imidacloprid 40% WG (300 g a.i./ha) were applied at the time of sowing in pre sown experiment while same dosage of insecticides were applied in standing soybean crop during (first fortnight of July). Commutative plant mortality data were recorded after 20, 40, 60 days intervals and compared with untreated control. Total 23 species of white grub beetles recorded on the light trap and Holotrichia serrata Fabricious (Coleoptera: Melolonthinae) was found to be predominant species by recording 20.6% relative abundance out of the total light trap catch (i.e. 1316 beetles) followed by Phyllognathus sp. (14.6% relative abundance). H. rosettae and Heteronychus lioderus occupied third and fourth rank with 11.85% and 9.65% relative abundance, respectively. The emergence of beetles of predominant species started from 15th March, 2018. In April, average light trap catch was 382 white grub beetles, however, peak emergence of most of the white grub species was observed from June to July, 2018 i.e. 336 beetles in June followed by 303 beetles in the July. On the basis of the emergence pattern of white grub beetles, it may be concluded that the Peak Emergence Period (PEP) for the beetles of H. serrata was second fortnight of April for the total period of 15 days. In May, June and July relatively low population of H. serrata was observed. A decreasing trend in light trap catch was observed and went on till September during the study. No single beetle of H. serrata was observed on light trap from September onwards. The cumulative plant mortality data in both the experiments revealed that all the insecticidal treatments were significantly superior in protection-wise (6.49-16.82% cumulative plant mortality) over untreated control where highest plant mortality was 17.28 to 39.65% during study. The mixture of Fipronil 40% and Imidacloprid 40% WG applied at the rate of 300 g a.i. per ha proved to be most effective having lowest plant mortality i.e. 9.29 and 10.94% in pre and post sown crop, followed by Clothianidin 50 WG (120 g a.i. per ha) where the plant mortality was 10.57 and 11.93% in pre and post sown treatments, respectively. Both treatments were found significantly at par among each other. Production-wise, all the insecticidal treatments were found statistically superior (15.00-24.66 q per ha grain yields) over untreated control where the grain yield was 8.25 & 9.13 q per ha. Treatment Fipronil 40% + Imidacloprid 40% WG applied at the rate of 300 g a.i. per ha proved to be most effective and significantly superior over Imidacloprid 70WG applied at the rate of 300 g a.i. per ha.

Keywords: bio efficacy, insecticide, soybean, white grub

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Suzanne Ahmed

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Nanomotors, also commonly referred to as nanorobotics or nanomachines, have garnered considerable research attention due to their numerous potential applications in biomedicine, including drug delivery and microsurgery. Nanomotors typically consist of inorganic or polymeric particles that are powered to undergo motion. These artificial, man-made nanoscale motors operate in the low Reynolds number regime and typically have no moving parts. Several methods have been developed to actuate the motion of nanomotors including magnetic fields, electrical fields, electromagnetic waves, and chemical fuel. Since their introduction in 2012, ultrasonically powered nanomotors have been explored in biocompatible fluids and even within living cells. Due to the common use of ultrasound within the biomedical community for both imaging and therapeutics, the introduction of ultrasonically propelled nanomotors holds significant potential for biomedical applications. In this work, metallic nanomotors are electrochemically plated within porous anodic alumina templates to have a diameter of 300 nm and a length that is 2-4 µm. Nanomotors are placed within an acoustic chamber capable of producing bulk acoustic waves in the ultrasonic range. The motion of nanomotors within biomimetic confines is explored. The control over nanomotor motion is exerted by virtue of the properties of the acoustic signal within these biomimetic confines to control speed, modes of motion and directionality of motion. To expand the range of control over nanorod motion within biomimetic confines, external forces from biocompatible magnetic fields, are exerted onto the acoustically propelled nanomotors.

Keywords: nanomotors, nanomachines, nanorobots, ultrasound

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Authors: Kishor C. Poria, Deepak Adroja, Arvind Bajaj

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During the last few decades, the growth of single crystals has attained enormous importance for both academic research and technology. Single crystals are pillars of modern technology. In recent emerging trends of photonics and optoelectronics technology, there has been increased need for organic and semi organic materials for Non-Linear Optical (NLO) applications. The paper dealt with the initiation of good single crystals of thiourea and metal doped thiourea. The authors have successfully grown thiourea (pure) and metal doped thiourea crystals using relatively simple and inexpensive slow evaporation of aqueous solution technique. Pure thiourea crystals were grown with different light intensities and frequencies as there growth conditions. Metals (Cu, Co, Ni, Fe) doped crystals were grown using a simple evaporation technique. The paper explains growth methods and associated grown parameters in detail. The average size of the crystal is varied in size from 40 mm x 1mm to 1.5 mm x 1.5 mm to 0.5 mm. Crystals obtained are hexagonal, tetragonal, and rectangular in shape with different optical qualities. All grown crystals are characterized using X-Ray Diffraction Analysis (XRD), Ultra Violet Visible analysis, and Fourier Transform Infrared Spectrometry. Their non-linear optical characteristics were determined by Second Harmonic Generation (SHG) and their Laser Dispersive analysis. The grown crystals are characterized using Nd:YAG laser and the highest conversion efficiency of the signal pass light are calculated. It shows 58 % of standard values for KDP crystals. All results are summarized in this work.

Keywords: crystal, metal-doped thiourea, non-linear optical, NLO, thiourea

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Authors: Carolin Lutz, Jörg Matthes, Patrick Waibel, Ulrich Precht, Krassimir Garbev, Günter Beuchle, Uwe Schweike, Peter Stemmermann, Hubert B. Keller

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Online measurement of the product quality is a challenging task in cement production, especially in the production of Celitement, a novel environmentally friendly hydraulic binder. The mineralogy and chemical composition of clinker in ordinary Portland cement production is measured by X-ray diffraction (XRD) and X ray fluorescence (XRF), where only crystalline constituents can be detected. But only a small part of the Celitement components can be measured via XRD, because most constituents have an amorphous structure. This paper describes the development of algorithms suitable for an on-line monitoring of the final processing step of Celitement based on NIR-data. For calibration intermediate products were dried at different temperatures and ground for variable durations. The products were analyzed using XRD and thermogravimetric analyses together with NIR-spectroscopy to investigate the dependency between the drying and the milling processes on one and the NIR-signal on the other side. As a result, different characteristic parameters have been defined. A short overview of the Celitement process and the challenging tasks of the online measurement and evaluation of the product quality will be presented. Subsequently, methods for systematic development of near-infrared calibration models and the determination of the final calibration model will be introduced. The application of the model on experimental data illustrates that NIR-spectroscopy allows for a quick and sufficiently exact determination of crucial process parameters.

Keywords: calibration model, celitement, cementitious material, NIR spectroscopy

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Authors: Nada Poropat Jeletic, Gordana Hrzica

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Bilingual conversational corpora represent a meaningful and the most comprehensive data source for investigating the genuine contact phenomena in non-monitored bi-lingual speech productions. They can be particularly useful for bilingual research since some features of bilingual interaction can hardly be accessed with more traditional methodologies (e.g., elicitation tasks). The method of language sampling provides the resources for describing language interaction in a bilingual community and/or in bilingual situations (e.g. code-switching, amount of languages used, number of languages used, etc.). To capture these phenomena in genuine communication situations, such sampling should be as close as possible to spontaneous communication. Bilingual spoken corpus design is methodologically demanding. Therefore this paper aims at describing the methodological challenges that apply to the corpus design of the conversational corpus design of the Istrian Istrovenetian-Croatian Bilingual Corpus. Croatian is the first official language of the Croatian-Italian officially bilingual Istria County, while Istrovenetian is a diatopic subvariety of Venetian, a longlasting lingua franca in the Istrian peninsula, the mother tongue of the members of the Italian National Community in Istria and the primary code of informal everyday communication among the Istrian Italophone population. Within the CLARIN infrastructure, TalkBank is being used, as it provides relevant procedures for designing and analyzing bilingual corpora. Furthermore, it allows public availability allows for easy replication of studies and cumulative progress as a research community builds up around the corpus, while the tools developed within the field of corpus linguistics enable easy retrieval and analysis of information. The method of language sampling employed is kept at the level of spontaneous communication, in order to maximise the naturalness of the collected conversational data. All speakers have provided written informed consent in which they agree to be recorded at a random point within the period of one month after signing the consent. Participants are administered a background questionnaire providing information about the socioeconomic status and the exposure and language usage in the participants social networks. Recording data are being transcribed, phonologically adapted within a standard-sized orthographic form, coded and segmented (speech streams are being segmented into communication units based on syntactic criteria) and are being marked following the CHAT transcription system and its associated CLAN suite of programmes within the TalkBank toolkit. The corpus consists of transcribed sound recordings of 36 bilingual speakers, while the target is to publish the whole corpus by the end of 2020, by sampling spontaneous conversations among approximately 100 speakers from all the bilingual areas of Istria for ensuring representativeness (the participants are being recruited across three generations of native bilingual speakers in all the bilingual areas of the peninsula). Conversational corpora are still rare in TalkBank, so the Corpus will contribute to BilingBank as a highly relevant and scientifically reliable resource for an internationally established and active research community. The impact of the research of communities with societal bilingualism will contribute to the growing body of research on bilingualism and multilingualism, especially regarding topics of language dominance, language attrition and loss, interference and code-switching etc.

Keywords: conversational corpora, bilingual corpora, code-switching, language sampling, corpus design methodology

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Authors: Maha Saliba Foster

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Speech perception has been recognized as a bi-sensory process involving the auditory and visual channels. Compared to the auditory modality, the contribution of the visual signal to speech perception is not very well understood. Studying how the visual modality affects speech recognition can have pedagogical implications in second language learning, as well as clinical application in speech therapy. The current investigation explores the potential effect of speech visual cues on the perception of Arabic emphatics (AEs). The corpus consists of 36 minimal pairs each containing two contrasting consonants, an AE versus a non-emphatic (NE). Movies of four Lebanese speakers were edited to allow perceivers to have partial view of facial regions: lips only, lips-cheeks, lips-chin, lips-cheeks-chin, lips-cheeks-chin-neck. In the absence of any auditory information and relying solely on visual speech, perceivers were above chance at correctly identifying AEs or NEs across vowel contexts; moreover, the models were able to predict the probability of perceivers’ accuracy in identifying some of the COIs produced by certain speakers; additionally, results showed an overlap between the measurements selected by the computer and those selected by human perceivers. The lack of significant face effect on the perception of AEs seems to point to the lips, present in all of the videos, as the most important and often sufficient facial feature for emphasis recognition. Future investigations will aim at refining the analyses of visual cues used by perceivers by using Principal Component Analysis and including time evolution of facial feature measurements.

Keywords: Arabic emphatics, machine learning, speech perception, visual speech perception

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Authors: Shilpa Khobragade, Carlos Da Silva Granja, Niklas Sandström, Igor Efimov, Victor P. Ostanin, Wouter van der Wijngaart, David Klenerman, Sourav K. Ghosh

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Rapid, label-free and direct detection of pathogenic bacteria is critical for the prevention of disease outbreaks. This paper for the first time attempts to probe the nonlinear acoustic response of quartz crystal resonator (QCR) functionalized with specific DNA aptamers for direct detection and quantification of viable E. coli KCTC 2571 bacteria. DNA aptamers were immobilized through biotin and streptavidin conjugation, onto the gold surface of QCR to capture the target bacteria and the detection was accomplished by shift in amplitude of the peak 3f signal (3 times the drive frequency) upon binding, when driven near fundamental resonance frequency. The developed nonlinear acoustic aptasensor system demonstrated better reliability than conventional resonance frequency shift and energy dissipation monitoring that were recorded simultaneously. This sensing system could directly detect 10⁽⁵⁾ cells/mL target bacteria within 30 min or less and had high specificity towards E. coli KCTC 2571 bacteria as compared to the same concentration of S.typhi bacteria. Aptasensor response was observed for the bacterial suspensions ranging from 10⁽⁵⁾-10⁽⁸⁾ cells/mL. Conclusively, this nonlinear acoustic aptasensor is simple to use, gives real-time output, cost-effective and has the potential for rapid, specific, label-free direction detection of bacteria.

Keywords: acoustic, aptasensor, detection, nonlinear

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Authors: Thi Thao Ninh, Yuri Trusov, José Ramón Botella

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Heterotrimeric G proteins, comprised of three subunits, α, β and γ, are involved in signal transduction pathways that mediate a vast number of processes across the eukaryotic kingdom. 23 Gα subunits are present in humans whereas most plant genomes encode for only one canonical Gα. The disparity observed between Arabidopsis, rice, and maize Gα-deficient mutant phenotypes suggest that Gα functions have diversified between eudicots and monocots during evolution. Alternatively, since the only Gα mutations available in dicots have been produced in Arabidopsis, the possibility exists that this species might be an exception to the rule. In order to test this hypothesis, we studied the G protein α subunit (TGA1) in tomato. Four tga1 knockout lines were generated in tomato cultivar Moneymaker using CRISPR/Cas9. The tga1 mutants exhibit a number of auxin-related phenotypes including changes in leaf shape, reduced plant height, fruit size and number of seeds per fruit. In addition, tga1 mutants have increased sensitivity to abscisic acid during seed germination, reduced sensitivity to exogenous auxin during adventitious root formation from cotyledons and excised hypocotyl explants. Our results suggest that Gα mutant phenotypes in tomato are very similar to those observed in monocots, i.e. rice and maize, and cast doubts about the validity of using Arabidopsis as a model system for plant G protein studies.

Keywords: auxin-related phenotypes, CRISPR/Cas9, G protein α subunit, heterotrimeric G proteins, tomato

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Authors: Hatim Laalej, Salvador Sumohano-Verdeja, Thomas McLeay

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Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.

Keywords: machining, milling operation, tool condition monitoring, tool wear prediction

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Authors: David A. Harness

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Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: automated theorem proving, constructive quantum field theory, information theory, neural networks

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Authors: J. DeBoard, R. Dietrich, J. Hughes, K. Yurko, G. Harms

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Alzheimer’s disease (AD) is a predominant type of dementia and is likely a major cause of neural network impairment. The pathogenesis of this neurodegenerative disorder has yet to be fully elucidated. There are currently no known cures for the disease, and the best hope is to be able to detect it early enough to impede its progress. Beyond age and genetics, another prevalent risk factor for AD might be traumatic brain injury (TBI), which has similar neurodegenerative hallmarks. Our research focuses on obtaining information and methods to be able to predict when neurodegenerative effects might occur at a clinical level by observation of events at a cellular and molecular level in model mice. First, we wish to introduce our evidence that brain damage can be observed via brain imaging prior to the noticeable loss of neuromuscular control in model mice of AD. We then show our evidence that some blood biomarkers might be able to be early predictors of AD in the same model mice. Thus, we were interested to see if we might be able to predict which mice might show long-term neurodegenerative effects due to differing degrees of TBI and what level of TBI causes further damage and earlier death to the AD model mice. Upon application of TBIs via an apparatus to effectively induce extremely mild to mild TBIs, wild-type (WT) mice and AD mouse models were tested for cognition, neuromuscular control, olfactory ability, blood biomarkers, and brain imaging. Experiments are currently still in process, and more results are therefore forthcoming. Preliminary data suggest that neuromotor control diminishes as well as olfactory function for both AD and WT mice after the administration of five consecutive mild TBIs. Also, seizure activity increases significantly for both AD and WT after the administration of the five TBI treatment. If future data supports these findings, important implications about the effect of TBI on those at risk for AD might be possible.

Keywords: Alzheimer's disease, blood biomarker, neurodegeneration, neuromuscular control, olfaction, traumatic brain injury

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Wesley Preßler, Lucie Schmidt

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Digital transformation is playing an increasingly important role in the development of smart residential quarters. In order to accompany and steer this process and ultimately make the success of the transformation efforts measurable, it is helpful to use an appropriate maturity model. However, conventional maturity models for digital transformation focus primarily on the evaluation of processes and neglect the information and power imbalances between the stakeholders, which affects the validity of the results. The Multi-Generation Smart Community (mGeSCo) research project is developing an interdisciplinary maturity model that integrates the dimensions of digital literacy, interpretive patterns, and technology acceptance to address this gap. As part of the mGeSCo project, the technological development of selected dimensions in the Smart Quarter Jena-Lobeda (Germany) is being investigated. A specific maturity model, based on Cohen's Smart Cities Wheel, evaluates the central dimensions Working, Living, Housing and Caring. To improve the reliability and relevance of the maturity assessment, the factors Digital Literacy, Interpretive Patterns and Technology Acceptance are integrated into the developed model. The digital literacy dimension examines stakeholders' skills in using digital technologies, which influence their perception and assessment of technological maturity. Digital literacy is measured by means of surveys, interviews, and participant observation, using the European Commission's Digital Literacy Framework (DigComp) as a basis. Interpretations of digital technologies provide information about how individuals perceive technologies and ascribe meaning to them. However, these are not mere assessments, prejudices, or stereotyped perceptions but collective patterns, rules, attributions of meaning and the cultural repertoire that leads to these opinions and attitudes. Understanding these interpretations helps in assessing the overarching readiness of stakeholders to digitally transform a/their neighborhood. This involves examining people's attitudes, beliefs, and values about technology adoption, as well as their perceptions of the benefits and risks associated with digital tools. These insights provide important data for a holistic view and inform the steps needed to prepare individuals in the neighborhood for a digital transformation. Technology acceptance is another crucial factor for successful digital transformation to examine the willingness of individuals to adopt and use new technologies. Surveys or questionnaires based on Davis' Technology Acceptance Model can be used to complement interpretive patterns to measure neighborhood acceptance of digital technologies. Integrating the dimensions of digital literacy, interpretive patterns and technology acceptance enables the development of a roadmap with clear prerequisites for initiating a digital transformation process in the neighborhood. During the process, maturity is measured at different points in time and compared with changes in the aforementioned dimensions to ensure sustainable transformation. Participation, co-creation, and co-production are essential concepts for a successful and inclusive digital transformation in the neighborhood context. This interdisciplinary maturity model helps to improve the assessment and monitoring of sustainable digital transformation processes in smart residential quarters. It enables a more comprehensive recording of the factors that influence the success of such processes and supports the development of targeted measures to promote digital transformation in the neighborhood context.

Keywords: digital transformation, interdisciplinary, maturity model, neighborhood

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Authors: Mohammad Bagher Heidari, Hefzollah Mohammadian

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TETRA (Terrestrial Trunked Radio) is a digital radio communication standard, which has been implemented in several different parts of the gas refinery ninth (phase 12th) by South Pars Gas Complex. Studies on possible impacts on the users' health considering different exposure conditions are missing. Objectives: To investigate possible acute effects of electromagnetic fields (EMF) of two different levels of TETRA hand-held transmitter signals on cognitive function and well-being in healthy young males. Methods: In the present double-blind cross-over study possible effects of short-term (2.5 h) EMF exposure of handset-like signals of TETRA (450 - 470 MHz) were studied in 30 healthy male participants (mean ± SD: 25.4 ±2.6 years). Individuals were tested on nine study days, on which they were exposed to three different exposure conditions (Sham, TETRA 1.5 W/kg and TETRA 10.0 W/kg) in a randomly assigned and balanced order. Participants were tested in the afternoon at a fixed timeframe. Results: Attention remained unchanged in two out of three tasks. In the working memory, significant changes were observed in two out of four subtasks. Significant results were found in 5 out of 35 tested parameters, four of them led to an improvement in performance. Mood, well-being and subjective somatic complaints were not affected by TETRA exposure. Conclusions: The results of the present study do not indicate a negative impact of a short-term EMF- effect of TETRA on cognitive function and well-being in healthy young men.

Keywords: TETRA (terrestrial trunked radio), electromagnetic fields (EMF), mobile telecommunication health research (MTHR), antenna

Procedia PDF Downloads 287

Authors: Vasundhara H. Krishnani, Ajay Saini, Dibesh Karmacharya, Salit Kark

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Illegal Wildlife Trade is one of the most pressing global conservation challenges. Unregulated wildlife trade can threaten biodiversity, contribute to habitat loss, limit sustainable development efforts, and expedite species declines and extinctions. In low-income and middle-income countries, such as Nepal and other countries in Asia and Africa, many of the people engaged in the early stages of illegal wildlife trade, which includes the hunting and transportation of wildlife, belong to Indigenous tribes and local communities.These countries primarily rely on punitive measures to prevent and suppress Illegal Wildlife Trade. For example, in Nepal, people involved in wildlife crimes can often be sentenced to incarceration and a hefty fine and serve up to 15 years in prison. Despite these harsh punitive measures, illegal wildlife trade remains a significant conservation challenge in many countries. The aim of this study was to examine factors affecting the participation of Indigenous communities in Illegal Wildlife Trade while recording the experiences of members of the Indigenous Chepang community, some of whom were imprisoned for their alleged involvement in rhino poaching. Chepangs, belonging to traditionally a hunter-gatherer community, are often considered an isolated and marginalized Indigenous community, some of whom live around the Chitwan National Park in Nepal. Established in 1973, Chitwan National Park is situated in the Chitwan Valley of Nepal and was one of the first regions that was declared as a protected area in Nepal, aiming to protect the one-horned rhinoceros as a flagship species. Conducted over a period of three years, this study used semi-structured interviews and focus group discussions to collect data from Illegal Wildlife Trade offenders, family members of offenders, community Elders, NGO personnel, community forest representatives, Chepang community representatives, and Government school teachers from the region surrounding Chitwan National Park. The study also examined the social, cultural, health, and financial impacts that the imprisonment of offenders had on the families of the community members, especially women and children. The results suggest that involvement of the members of the Chepang community living around Chitwan National Park in the poaching of the one-horned rhinoceros (Rhinoceros unicornis) can be attributed to a range of factors, some of which include: lack of livelihood opportunities, lack of awareness regarding wildlife rules and regulations and poverty.This work emphasises the need for raising awareness and building programs to enhance alternative livelihood training and empower indigenous and marginalised communities that provide sustainable alternatives. Furthermore, the issue needs to be addressed as a community solution which includes all community members. We suggest this multi-pronged approach can benefit wildlife conservation by reducing illegal poaching and wildlife trade, as well as community conservation in regions with similar challenges. By actively involving and empowering local communities, the communities become key stakeholders in the conservation process. This involvement contributes to protecting wildlife and natural ecosystems while simultaneously providing sustainable livelihood options for local communities.

Keywords: alternative livelihoods, chepang community, illegal wildlife trade, low-and middle-income countries, nepal, one-horned rhinoceros

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Authors: Khamael M. Abualnaja, Lidija Šiller, Ben R. Horrocks

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Surface enhanced Raman spectroscopy (SERS) of Silicon nano crystals (SiNCs) were obtained using two different laser excitations: 488 nm and 514.5 nm. Silver nano particles were used as plasmonics metal nano particles due to a robust SERS effect that observed when they mixed with SiNCs. SiNCs have been characterized by scanning electron microscopy (SEM), high resolution transmission electron microscopy (HRTEM), atomic force microscopy (AFM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). It is found that the SiNCs are crystalline with an average diameter of 65 nm and FCC lattice. Silver nano particles (AgNPs) of two different sizes were synthesized using photo chemical reduction of AgNO3 with sodium dodecyl sulfate (SDS). The synthesized AgNPs have a polycrystalline structure with an average particle diameter of 100 nm and 30 nm, respectively. A significant enhancement in the SERS intensity was observed for AgNPs100/SiNCs and AgNPs30/SiNCs mixtures increasing up to 9 and 3 times respectively using 488 nm intensity; whereas the intensity of the SERS signal increased up to 7 and 2 times respectively, using 514.5 nm excitation source. The enhancement in SERS intensities occurs as a result of the coupling between the excitation laser light and the plasmon bands of AgNPs; thus this intense field at AgNPs surface couples strongly to SiNCs. The results provide good consensus between the wavelength of the laser excitation source and surface plasmon resonance absorption band of silver nano particles consider to be an important requirement in SERS experiments.

Keywords: silicon nanocrystals (SiNCs), silver nanoparticles (AgNPs), surface enhanced raman spectroscopy (SERS)

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Authors: Susan Diamond

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Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare. 

Keywords: deep learning, machine learning, cognitive computing, model training

Procedia PDF Downloads 196

Authors: M. Rutkowski, J. Cwiklak, M. Grzegorzewski, M. Adamski

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All class A Airplanes have to be equipped with Mod. S transponder for ATC surveillance purposes. This technology was designed to provide a robust and dependable solution to localize, identify and exchange data with the airplane. The purpose of this paper is to analyze potential hazards that are a result of lack of any security or encryption on a design level. Secondary Surveillance Radars rely on an active response from an airplane. SSR radar installation is broadcasting a directional interrogation signal to the planes in range on 1030MHz frequency with DPSK modulation. If the interrogation is correctly received by the transponder located on the plane, a proper answer is sent on 1090MHz with PPM modulation containing plane’s SQUAWK, barometric altitude, GPS coordinates and 24bit unique address code. This technology does not use any kind of encryption. All of the specifications from the previous chapter can be found easily on the internet. Since there is no encryption or security measure to ensure the credibility of the sender and message, it is highly hazardous to use such technology to ensure the safety of the air traffic. The only thing that identifies the airplane is the 24-bit unique address. Most of the planes have been sniffed by aviation enthusiasts and cataloged in web databases. In the moment of writing this article, The PoFung Technologies has announced that they are planning to release all band SDR transceiver – this device would be more than enough to build your own Mod. S Transponder. With fake transponder, a potential terrorist can identify as a different airplane. By replacing the transponder in a poorly controlled airspace, hijackers can enter another airspace identifying themselves as another plane and land in the desired area.

Keywords: flight safety, hijack, mod S transponder, security analysis

Procedia PDF Downloads 282

Authors: Haruna Maama

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Environmental, social and governance (ESG) reporting is gaining considerable attention despite being voluntary. Meanwhile, it consumes resources to provide ESG reporting, raising a question of its value relevance. The study examined the impact of ESG reporting on the market value of listed firms in SSA. The annual and integrated reports of 276 listed sub-Sahara Africa (SSA) firms. The integrated reporting scores of the firm were analysed using a content analysis method. A multiple regression estimation technique using a GMM approach was employed for the analysis. The results revealed that ESG has a positive relationship with firms’ market value, suggesting that investors are interested in the ESG information disclosure of firms in SSA. This suggests that extensive ESG disclosures are attempts by firms to obtain the approval of powerful social, political and environmental stakeholders, especially institutional investors. Furthermore, the market value analysis evidence is consistent with signalling theory, which postulates that firms provide integrated reports as a signal to influence the behaviour of stakeholders. This finding reflects the value placed on investors' social, environmental and governance disclosures, which affirms the views that conventional investors would care about the social, environmental and governance issues of their potential or existing investee firms. Overall, the evidence is consistent with the prediction of signalling theory. In the context of this theory, integrated reporting is seen as part of firms' overall competitive strategy to influence investors' behaviour. The findings of this study make unique contributions to knowledge and practice in corporate reporting.

Keywords: environmental accounting, ESG accounting, signalling theory, sustainability reporting, sub-saharan Africa

Procedia PDF Downloads 57

Authors: Jin Fan, M. Ala Saadeghvaziri

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Unmanned aerial vehicles (UAVs), also referred to as drones, equipped with various kinds of advanced detecting or surveying systems, are effective and low-cost in data acquisition, data delivery and sharing, which can benefit the building of infrastructures. This paper will give an overview of applications of drones in planning, designing, construction and maintenance of infrastructures. The drone platform, detecting and surveying systems, and post-data processing systems will be introduced, followed by cases with details of the applications. Challenges from different aspects will be addressed. Opportunities of drones in infrastructure include but not limited to the following. Firstly, UAVs equipped with high definition cameras or other detecting equipment are capable of inspecting the hard to reach infrastructure assets. Secondly, UAVs can be used as effective tools to survey and map the landscape to collect necessary information before infrastructure construction. Furthermore, an UAV or multi-UVAs are useful in construction management. UVAs can also be used in collecting roads and building information by taking high-resolution photos for future infrastructure planning. UAVs can be used to provide reliable and dynamic traffic information, which is potentially helpful in building smart cities. The main challenges are: limited flight time, the robustness of signal, post data analyze, multi-drone collaboration, weather condition, distractions to the traffic caused by drones. This paper aims to help owners, designers, engineers and architects to improve the building process of infrastructures for higher efficiency and better performance.

Keywords: bridge, construction, drones, infrastructure, information

Procedia PDF Downloads 108

Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

Procedia PDF Downloads 54

Authors: Hu-Jiang Shi, Li-Juan Zhu

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The implication of 5-hydroxytryptamine 2C receptor (5-HT2CR) in affective behaviors is a topic of debate, and the underlying mechanisms remain largely unclear. Here, we elucidate that the interaction between hippocampal neuronal nitric oxide synthase (nNOS) and carboxy-terminal PDZ ligand of nNOS (CAPON) contributes to the disruption of hippocampal excitation-inhibition (E/I) balance, which is responsible for the anxiety- and depressive-like behaviors caused by chronic stress-related 5-HT2CR signaling deficiency. In detail, activation or inhibition of 5-HT2CR by CP809101 or SB242084 modulates nNOS-CAPON interaction by influencing intracellular Ca²⁺ release. Notably, the dissociation of nNOS-CAPON abolishes SB242084-induced anxiety- and depressive-like behaviors, as well as the reduction in extracellular signal-regulated kinase (ERK)/cAMP-response element binding protein (CREB)/synapsin signaling and SNARE complex assembly. Furthermore, nNOS-CAPON blockers restore the impairments caused by SB242084, including the reduction in SNARE assembly-mediated γ-aminobutyric acid (GABA) vesicle release and a consequent shift of the E/I balance toward excitation in CA3 pyramidal neurons. Conclusively, our findings disclose the regulatory role of 5-HT2CR in anxiety- and depressive-like behaviors and highlight the hippocampal nNOS-CAPON coupling-triggered E/I imbalance as a pivotal cellular event underpinning the behavioral consequences of 5-HT2CR inhibition.

Keywords: 5-HT2CR, anxiety, depression, nNOS-CAPON coupling, excitation-inhibition balance, neurotransmitter release

Procedia PDF Downloads 48

Authors: Abubakar Uba Ibrahim, Ibrahim Haruna Shanono

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Smart metering and demand response are gaining ground in industrial and residential applications. Smart Appliances have been given concern towards achieving Smart home. The success of Smart grid development relies on the successful implementation of Information and Communication Technology (ICT) in power sector. Smart Appliances have been the technology under development and many new contributions to its realization have been reported in the last few years. The role of ICT here is to capture data in real time, thereby allowing bi-directional flow of information/data between producing and utilization point; that lead a way for the attainment of Smart appliances where home appliances can communicate between themselves and provide a self-control (switch on and off) using the signal (information) obtained from the grid. This paper depicts the background on ICT for smart appliances paying a particular attention to the current technology and identifying the future ICT trends for load monitoring through which smart appliances can be achieved to facilitate an efficient smart home system which promote demand response program. This paper grouped and reviewed the recent contributions, in order to establish the current state of the art and trends of the technology, so that the reader can be provided with a comprehensive and insightful review of where ICT for smart appliances stands and is heading to. The paper also presents a brief overview of communication types, and then narrowed the discussion to the load monitoring (Non-intrusive Appliances Load Monitoring ‘NALM’). Finally, some future trends and challenges in the further development of the ICT framework are discussed to motivate future contributions that address open problems and explore new possibilities.

Keywords: communication technology between appliances, demand response, load monitoring, smart appliances, smart grid

Procedia PDF Downloads 597

Authors: Ciaran Friel, Jerry Suls, Patrick Robles, Frank Vicari, Joan Duer-Hefele, Karina W. Davidson

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Physical activity guidelines focus on increasing moderate intensity activity for older adults, but adherence to recommendations remains low. This is despite the fact that scientific evidence supports that any increase in physical activity is positively correlated with health benefits. Behavior change techniques (BCTs) have demonstrated effectiveness in reducing sedentary behavior and promoting physical activity. This pilot study uses a Personalized Trials (N-of-1) design to evaluate the efficacy of using four BCTs to promote an increase in low-intensity physical activity (2,000 steps of walking per day) in adults aged 45-75 years old. The 4 BCTs tested were goal setting, action planning, feedback, and self-monitoring. BCTs were tested in random order and delivered by text message prompts requiring participant response. The study recruited health system employees in the target age range, without mobility restrictions and demonstrating interest in increasing their daily activity by a minimum of 2,000 steps per day for a minimum of five days per week. Participants were sent a Fitbit Charge 4 fitness tracker with an established study account and password. Participants were recommended to wear the Fitbit device 24/7, but were required to wear it for a minimum of ten hours per day. Baseline physical activity was measured by the Fitbit for two weeks. Participants then engaged with a clinical research coordinator to review comprehension of the text message content and required actions for each of the BCTs to be tested. Participants then selected a consistent daily time in which they would receive their text message prompt. In the 8 week intervention phase of the study, participants received each of the four BCTs, in random order, for a two week period. Text message prompts were delivered daily at a time selected by the participant. All prompts required an interactive response from participants and may have included recording their detailed plan for walking or daily step goal (action planning, goal setting). Additionally, participants may have been directed to a study dashboard to view their step counts or compare themselves with peers (self-monitoring, feedback). At the end of each two week testing interval, participants were asked to complete the Self-Efficacy for Walking Scale (SEW_Dur), a validated measure that assesses the participant’s confidence in walking incremental distances and a survey measuring their satisfaction with the individual BCT that they tested. At the end of their trial, participants received a personalized summary of their step data in response to each individual BCT. Analysis will examine the novel individual-level heterogeneity of treatment effect made possible by N-of-1 design, and pool results across participants to efficiently estimate the overall efficacy of the selected behavioral change techniques in increasing low-intensity walking by 2,000 steps, 5 days per week. Self-efficacy will be explored as the likely mechanism of action prompting behavior change. This study will inform the providers and demonstrate the feasibility of N-of-1 study design to effectively promote physical activity as a component of healthy aging.

Keywords: aging, exercise, habit, walking

Procedia PDF Downloads 119