Search results for: data aggregation

Authors: Parvaneh Eskandari, Rachel O'Reilly

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In the last years, controlling the self-assembly behavior of stimuli-responsive nano-objects, including micelles, vesicles, worm-like, etc., at different conditions is considered a pertinent challenge in the polymer community. The aim of the project was to synthesize aggregation-induced emission (AIE)-active stimuli-responsive polymeric nano-objects to control the self-assemblies morphologies of the prepared nano-objects. Two types of nanoobjects, micelle and vesicles, including PDMAEMA-b-P(BzMA-TPEMA) [PDMAEMA: poly(N,Ndimethylaminoethyl methacrylate); P(BzMA-TPEMA): poly[benzyl methacrylate-co- tetraphenylethene methacrylate]] were synthesized by using reversible addition−fragmentation chain-transfer (RAFT)- mediated polymerization-induced self-assembly (PISA), which combines polymerization and self-assembly in a single step. Transmission electron microscope and dynamic light scattering (DLS) analysis were used to confirm the formed self-assemblies morphologies. The controlled self-assemblies were applied as nitrophenolic compounds (NPCs) adsorbents from wastewater, thanks to their CO2-responsive part, PDMAEMA. Moreover, the fluorescence-active part of the prepared nano-objects, P(BzMA-TPEMA), played a key role in the detection of the NPCs at the aqueous solution. The optical properties of the prepared nano-objects were studied by UV/Vis and fluorescence spectroscopies. For responsivity investigations, the hydrodynamic diameter and Zeta-potential (ζ-potential) of the sample's aqueous solution were measured by DLS. In the end, the prepared nano-objects were used for the detection and adsorption of different NPCs.

Keywords: aggregation-induced emission polymers, stimuli-responsive polymers, reversible addition−fragmentation chain-transfer polymerization, polymerization-induced self-assembly, wastewater treatment

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Authors: Teo Hao Jing, Shane Ho Ken Wae, Lee Jin Yu, Burra Venkata Durga Kumar

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Current distributed healthcare systems face the challenge of interoperability of health data. Storing electronic health records (EHR) in local databases causes them to be fragmented. This problem is aggravated as patients visit multiple healthcare providers in their lifetime. Existing solutions are unable to solve this issue and have caused burdens to healthcare specialists and patients alike. Blockchain technology was found to be able to increase the interoperability of health data by implementing digital access rules, enabling uniformed patient identity, and providing data aggregation. Consortium blockchain was found to have high read throughputs, is more trustworthy, more secure against external disruptions and accommodates transactions without fees. Therefore, this paper proposes a blockchain-based model for data management applications. In this model, a consortium blockchain is implemented by using a delegated proof of stake (DPoS) as its consensus mechanism. This blockchain allows collaboration between users from different organizations such as hospitals and medical bureaus. Patients serve as the owner of their information, where users from other parties require authorization from the patient to view their information. Hospitals upload the hash value of patients’ generated data to the blockchain, whereas the encrypted information is stored in a distributed cloud storage.

Keywords: blockchain technology, data management applications, healthcare, interoperability, delegated proof of stake

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Authors: Bhat Geetalaxmi Jairam, D. V. Ashoka

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Wireless Sensor Networks are used in many applications to collect sensed data from different sources. Sensed data has to be delivered through sensors wireless interface using multi-hop communication towards the sink. The data collection in wireless sensor networks consumes energy. Energy consumption is the major constraints in WSN .Reducing the energy consumption while increasing the amount of generated data is a great challenge. In this paper, we have implemented two data gathering protocols with multiple mobile sinks/elements to collect data from sensor nodes. First, is Energy-Efficient Data Gathering with Tour Length-Constrained Mobile Elements in Wireless Sensor Networks (EEDG), in which mobile sinks uses vehicle routing protocol to collect data. Second is An Intelligent Agent-based Routing Structure for Mobile Sinks in WSNs (IAR), in which mobile sinks uses prim’s algorithm to collect data. Authors have implemented concepts which are common to both protocols like deployment of mobile sinks, generating visiting schedule, collecting data from the cluster member. Authors have compared the performance of both protocols by taking statistics based on performance parameters like Delay, Packet Drop, Packet Delivery Ratio, Energy Available, Control Overhead. Authors have concluded this paper by proving EEDG is more efficient than IAR protocol but with few limitations which include unaddressed issues likes Redundancy removal, Idle listening, Mobile Sink’s pause/wait state at the node. In future work, we plan to concentrate more on these limitations to avail a new energy efficient protocol which will help in improving the life time of the WSN.

Keywords: aggregation, consumption, data gathering, efficiency

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Authors: Renato Morbidelli, Carla Saltalippi, Alessia Flammini, Tommaso Picciafuoco, Corrado Corradini

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A considerable part of rainfall data to be used in the hydrological practice is available in aggregated form within constant time intervals. This can produce undesirable effects, like the underestimate of the annual maximum rainfall depth, Hd, associated with a given duration, d, that is the basic quantity in the development of rainfall depth-duration-frequency relationships and in determining if climate change is producing effects on extreme event intensities and frequencies. The errors in the evaluation of Hd from data characterized by a coarse temporal aggregation, ta, and a procedure to reduce the non-homogeneity of the Hd series are here investigated. Our results indicate that: 1) in the worst conditions, for d=ta, the estimation of a single Hd value can be affected by an underestimation error up to 50%, while the average underestimation error for a series with at least 15-20 Hd values, is less than or equal to 16.7%; 2) the underestimation error values follow an exponential probability density function; 3) each very long time series of Hd contains many underestimated values; 4) relationships between the non-dimensional ratio ta/d and the average underestimate of Hd, derived from continuous rainfall data observed in many stations of Central Italy, may overcome this issue; 5) these equations should allow to improve the Hd estimates and the associated depth-duration-frequency curves at least in areas with similar climatic conditions.

Keywords: central Italy, extreme events, rainfall data, underestimation errors

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Authors: Lin Li, P. G. R. N. I. Pussella

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When representing spatial data and their attributes on different types of maps, the scale plays a key role in the process of map generalization. The process is consisted with two main operators such as selection and omission. Once some data were selected, they would undergo of several geometrical changing processes such as elimination, simplification, smoothing, exaggeration, displacement, aggregation and size reduction. As a result of these operations at different levels of data, the geometry of the spatial features such as length, sinuosity, orientation, perimeter and area would be altered. This would be worst in the case of preparation of small scale maps, since the cartographer has not enough space to represent all the features on the map. What the GIS users do is when they wanted to analyze a set of spatial data; they retrieve a data set and does the analysis part without considering very important characteristics such as the scale, the purpose of the map and the degree of generalization. Further, the GIS users use and compare different maps with different degrees of generalization. Sometimes, GIS users are going beyond the scale of the source map using zoom in facility and violate the basic cartographic rule 'it is not suitable to create a larger scale map using a smaller scale map'. In the study, the effect of map generalization for GIS analysis would be discussed as the main objective. It was used three digital maps with different scales such as 1:10000, 1:50000 and 1:250000 which were prepared by the Survey Department of Sri Lanka, the National Mapping Agency of Sri Lanka. It was used common features which were on above three maps and an overlay analysis was done by repeating the data with different combinations. Road data, River data and Land use data sets were used for the study. A simple model, to find the best place for a wild life park, was used to identify the effects. The results show remarkable effects on different degrees of generalization processes. It can see that different locations with different geometries were received as the outputs from this analysis. The study suggests that there should be reasonable methods to overcome this effect. It can be recommended that, as a solution, it would be very reasonable to take all the data sets into a common scale and do the analysis part.

Keywords: generalization, GIS, scales, spatial analysis

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Authors: Imed Feki, Faouzi Msahli

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Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones.

Keywords: data sensitivity, feature selection, fuzzy logic, OWA operators, percolation technique

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Authors: Purva Mishra, Rajesh Potlia, Kuljeet Singh Sandhu

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The role of glycyl residues in the protein structure has lingered within the research community for the last several decades. Glycyl residue is the only amino acid that is achiral due to the lack of a side chain and can, therefore, exhibit Ramachandran conformations that are disallowed for L-amino acids. The structural and functional significance of glycyl residues with L-disallowed conformation, however, remains obscure. Through statistical analysis of various datasets, we found that the glycyls with L-disallowed conformations are over-represented at disease-associated sites and tend to be evolutionarily conserved. The mutations of L-disallowed glycyls tend to destabilize the native conformation, reduce protein solubility, and promote inter-molecular aggregations. We uncovered a structural motif referred to as “β-crescent” formed around the L-disallowed glycyl, which prevents β-sheet aggregation by disrupting the alternating pattern of β-pleats. The L-disallowed conformation of glycyls also holds predictive power to infer the pathogenic missense variants. Altogether, our observations highlight that the L-disallowed conformation of glycyls is selected to facilitate native folding and prevent inter-molecular aggregations. The findings may also have implications for designing more stable proteins and prioritizing the genetic lesions implicated in diseases.

Keywords: Ramachandran plot, β-sheet, protein stability, protein aggregation

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Authors: Auxeeliya Jesudoss, Ashraph Sulaiman, Ratnakar Kotnana

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V2V communication brings up several questions of scalability issues although message sharing in vehicular ad-hoc networks comprises of both Vehicle-to-Vehicle communications (V2V) and Vehicle to Infrastructure communication (V2I). It is not an easy task for a vehicle to verify all signatures of the messages sent by its neighboring vehicles in a timely manner, without resulting in message loss. Moreover, the communication overhead of a vehicle to authenticate another vehicle would increase together with the security of the system. Another issue to be addressed is the continuous mobility of vehicles which requires at least some information on the node’s own position to be revealed to the neighboring vehicles. This may facilitate the attacker to congregate information on a node’s position or its mobility patterns. In order to tackle these issues, this paper introduces a RSU aggregated message deliverance scheme called RAMeD. With RAMeD, roadside units (RSUs) are responsible for verifying the identity of the vehicles entering in its range, collect messages from genuine vehicles and to aggregate similar messages into groups before sending them to all the vehicles in its communication range. This aggregation will tremendously improve the rate of message delivery and reduce the message lose ratio by avoiding similar messages being sent to the vehicles redundantly. The proposed protocol is analyzed extensively to evaluate its merits and efficiency for vehicular communication.

Keywords: vehicular ad-hoc networks, V2V, V2I, VANET communication, scalability, message aggregation

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Authors: Abdel Ghany F. Shoair, Mai M. A. H. Shanab

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The basic aqueous systems NiSO4.6H₂O / K₂S₂O₈ (PH= 14) or NiSO₄.6H₂O / KBrO₃ (PH = 11.5) were ‎investigated ‎for the ‎catalytic conversion benzyl alcohol and ‎some para-substituted benzyl ‎alcohols to their ‎corresponding ‎acids in 75-97 % yield at room ‎temperature. The active species ‎was isolated and characterized by scanning ‎electron ‎microscopy (SEM), ‎‎transmission electron microscopy (TEM), X-ray ‎powder diffraction, EDX and ‎‎FT-IR ‎techniques and identified as NiO₂ nanoparticles (NPNPs). The SEM and ‎TEM images of nickel peroxide samples show a fine spherical-like ‎aggregation of ‎NiO₂ molecules with a nearly homogeneous partial size and confirm the ‎aggregation's size ‎to ‎be in the range of 2-3 nm. The yields, turnover (TO) and turn ‎over frequencies (TOF) were calculated. ‎It was noticed ‎that the aromatic alcohols ‎containing para-substituted electron donation groups gave better ‎‎yields than ‎those having electron-withdrawing groups. The optimum conditions for this ‎‎catalytic reaction ‎were studied using benzyl alcohol as a model. The mechanism ‎of the ‎catalytic conversion reaction was ‎suggested, in which the produced ‎(NPNPs) convert alcohols ‎to acids in two steps through the formation of the ‎‎corresponding aldehyde. The produced ‎NiO, because of this conversion, is ‎converted again to (NPNPs) by ‎an excess of K₂S₂O₈ or KBrO₃. This ‎catalytic cycle continues ‎until all the substrate is oxidized.

Keywords: Nickel, oxidation, catalysts, benzyl alcohol

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Authors: Sofia Barbosa, Mariana Pinto, José António Almeida, Edgar Carvalho, Catarina Diamantino

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The aim of this work was to test a methodology able to generate spatial-temporal maps that can synthesize simultaneously the trends of distinct hydrochemical indicators in an old radium-uranium tailings dam deposit. Multidimensionality reduction derived from principal component analysis and subsequent data aggregation derived from clustering analysis allow to identify distinct hydrochemical behavioural profiles and to generate synthetic evolutionary hydrochemical maps.

Keywords: Contamination plume migration, K-means of PCA scores, groundwater and mine water monitoring, spatial-temporal hydrochemical trends

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Authors: Wahizatul Afzan Azmi, Nur Ain Farhah Ros Saidon Khudri, Mohamad Haris Hussain, Tse Seng Chuah

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Malaysia houses a broad range of palm trees species and some of these palm trees are very crucial for the country’s social and economic development, especially the oil palm trees. However, the destructive pest of the various palms species, Rhynchophorus ferrugineus (Coleoptera: Curculionidae) or known as Red Palm Weevil (RPW) was first detected in Terengganu in 2007. Recently, the pattern of infestation has move from coastal lines toward inland areas. After the coconut plantations, it is presumed that the RPW will be a serious threat to the oil palm plantations in Malaysia. Thus, this study was carried out to detect the presence and distribution of Red Palm Weevil (RPW) in selected oil palm plantations of Terengganu. A total of 42 traps were installed in the three oil palm plantations in Terengganu and were inspected every week for two months. Oil palm plantation A collected significantly higher adults RPW compared to the other locations. Generally, females of RPW were significantly higher than male individuals. Females were collected more as the synthetic aggregation pheromone used, ferrugineol was synthesized from the male aggregation pheromone of adult RPW. Oil palm plantation A collected the highest number of RPW might be due to the abundance of soft part in the host plant as the oil palm trees age ranged between 6 to 10 years old. As a conclusion, RPW presence was detected in some oil palm plantations of Terengganu and immediate action is crucially needed before it is too late.

Keywords: red palm weevil, pest, oil palm, pheromone

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Authors: Jessica Chorostecki, Aashka Shah, Fen Bao, Ginny Bao, Edwin George, Navid Seraji-Bozorgzad, Veronica Gorden, Christina Caon, Elliot Frohman

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Background: Parkinson’s Disease (PD) is a neuro degenerative disorder associated with the loss of dopaminergic cells and the presence α-synuclein (AS) aggregation in of Lewy bodies. Both dopaminergic cells and AS are found in the retina. Optical coherence tomography (OCT) allows high-resolution in-vivo examination of retinal structure injury in neuro degenerative disorders including PD. Methods: We performed a cross-section OCT study in patients with definite PD and healthy controls (HC) using Spectral Domain SD-OCT platform to measure the peripapillary retinal nerve fiber layer (pRNFL) thickness and total macular volume (TMV). We performed intra-retinal segmentation with fully automated segmentation software to measure the volume of the RNFL, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), and the outer nuclear layer (ONL). Segmentation was performed blinded to the clinical status of the study participants. Results: 101 eyes from 52 PD patients (mean age 65.8 years) and 46 eyes from 24 HC subjects (mean age 64.1 years) were included in the study. The mean pRNFL thickness was not significantly different (96.95 μm vs 94.42 μm, p=0.07) but the TMV was significantly lower in PD compared to HC (8.33 mm3 vs 8.58 mm3 p=0.0002). Intra-retinal segmentation showed no significant difference in the RNFL volume between the PD and HC groups (0.95 mm3 vs 0.92 mm3 p=0.454). However, GCL, IPL, INL, and ONL volumes were significantly reduced in PD compared to HC. In contrast, the volume of OPL was significantly increased in PD compared to HC. Conclusions: Our finding of the enlarged OPL corresponds with mRNA expression studies showing localization of AS in the OPL across vertebrate species and autopsy studies demonstrating AS aggregation in the deeper layers of retina in PD. We propose that the enlargement of the OPL may represent a potential biomarker of AS aggregation in PD. Longitudinal studies in larger cohorts are warranted to confirm our observations that may have significant implications in disease monitoring and therapeutic development.

Keywords: Optical Coherence Tomography, biomarker, Parkinson's disease, alpha-synuclein, retina

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Authors: Sakina Fatima, Joseph-Patrick W. E. Clarke, Patricia A. Thibault, Subha Kalyaanamoorthy, Michael Levin, Aravindhan Ganesan

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Heteronuclear ribonucleoprotein (hnRNPA1 or A1) is associated with the pathology of different diseases, including neurological disorders and cancers. In particular, the aggregation and dysfunction of A1 have been identified as a critical driver for neurodegeneration (NDG) in Multiple Sclerosis (MS). Structurally, A1 includes a low-complexity domain (LCD) and two RNA-recognition motifs (RRMs), and their interdomain coordination may play a crucial role in A1 aggregation. Previous studies propose that RNA-inhibitors or nucleoside analogs that bind to RRMs can potentially prevent A1 self-association. Therefore, molecular-level understanding of the structures, dynamics, and nucleotide interactions with A1 RRMs can be useful for developing therapeutics for NDG in MS. In this work, a combination of computational modelling and biochemical experiments were employed to analyze a set of RNA-A1 RRM complexes. Initially, the atomistic models of RNA-RRM complexes were constructed by modifying known crystal structures (e.g., PDBs: 4YOE and 5MPG), and through molecular docking calculations. The complexes were optimized using molecular dynamics simulations (200-400 ns), and their binding free energies were computed. The binding affinities of the selected complexes were validated using a thermal shift assay. Further, the most important molecular interactions that contributed to the overall stability of the RNA-A1 RRM complexes were deduced. The results highlight that adenine and guanine are the most suitable nucleotides for high-affinity binding with A1. These insights will be useful in the rational design of nucleotide-analogs for targeting A1 RRMs.

Keywords: hnRNPA1, molecular docking, molecular dynamics, RNA-binding proteins

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Authors: Trapti Pathak

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Fishes can change their body colour according to their surroundings by. They do so by either aggregation or dispersion of melanosomes within the skin. These movements can regulate by means of sympathetic nerves with the help of cytoskeleton. Employing the melanophores on isolated scales of the fingerling of teleost fish, it is attempted to characterise the concerned nerves and the receptors located on melenocytes along with implication of microtubules a part of cytoskeleton in the pigmentary translocation in the fish. The scales from dorso-lateral trunk of the fish represented the sympathetic– neuromelanophore preparations which were stimulated by chemical means, such as adrenergic agonist, antagonist and the microtubule-disrupting drugs such as yuhombine, dopamine, colchicine etc. Adrenaline is an adrenergic agonist which is strongly induced the dorse-dependent concentration of pigment in innervated melanophores while Yohimbine is an adrenergic antagonist which is known to block effectively the α2-adrenoceptors inhibited the action of adrenaline. Colchicine effectively interferes with melanosome aggregating action of adrenaline. From these results it is concluded that the chromatic fibres of adrenergic nature innervate the melanophores and these cells do possess α2-adrenoceptors which mediate the melanosome aggregation and the movements of pigment granules through microtubules means of transport within the cell. These movements of pigment are linked to paling or darkening achieved of teleost fish respectively when they approach to their background.

Keywords: melenophores, agonists, antagonist, colour change

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Authors: Patrick Cummings, Laura Cassani, Deirdre Kelliher

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In this paper we present findings from a research effort to apply a hybrid collaborative-context approach for a system focused on Marine Corps civil affairs data collection, aggregation, and analysis called the Marine Civil Information Management System (MARCIMS). The goal of this effort is to provide operators with information to make sense of the interconnectedness of entities and relationships in their area of operation and discover existing data to support civil military operations. Our approach to build a recommendation engine was designed to overcome several technical challenges, including 1) ensuring models were robust to the relatively small amount of data collected by the Marine Corps civil affairs community; 2) finding methods to recommend novel data for which there are no interactions captured; and 3) overcoming confirmation bias by ensuring content was recommended that was relevant for the mission despite being obscure or less well known. We solve this by implementing a combination of collective matrix factorization (CMF) and graph-based random walks to provide recommendations to civil military operations users. We also present a method to resolve the challenge of computation complexity inherent from highly connected nodes through a precomputed process.

Keywords: Recommendation engine, collaborative filtering, context based recommendation, graph analysis, coverage, civil affairs operations, Marine Corps

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Cedillo Ortiz Cesar Isaac, Marañón-Ruiz Virginia-Francisca, Lozano-Alvarez Juan Antonio, Jáuregui-Rincón Juan, Roger Chiu Zarate

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Introduction: The contamination of water with the azo-dye is a problem worldwide as although wastewater contaminate is treated in a municipal sewage system, still contain a considerable amount of dyes. In the present, there are different processes denominated tertiary method in which it is possible to lower the concentration of the dye. One of these methods is by adsorption onto various materials which can be organic or inorganic materials. The xanthan is a biomaterial as removal agents to decrease the dye content in aqueous solution. The Zimm-Bragg model described the experimental isotherms obtained when this biopolymer was used in the removal of textile dyes. Nevertheless, it was not established if a possible correlation between dye structure and removal efficiency exists. In this sense, the principal objective of this report is to propose a qualitative relationship between the structure of three azo-dyes (Congo Red (CR), Methyl Red (MR) and Methyl Orange (MO)) and their removal efficiency from aqueous environment when xanthan are used as dye sequestering agents. Methods: The dyes were subjected to different pH and ionic strength values to obtain the conditions of maximum dye removal. Afterward, these conditions were used to perform the adsorption isotherm as was reported in the previous study in our group. The Zimm-Bragg model was used to describe the experimental data and the parameters of nucleation (Ku) and cooperativity (U) were obtained by optimization using the R statistical software. The spectra from UV-Visible (aqueous solution), Infrared absorption and Raman spectroscopies (dry samples) were obtained from the biopolymer-dye complex. Results: The removal percent with xanthan in each dye are as follows: with CR had 99.98 % when the pH is 12 and ionic strength is 10.12, with MR had 84.79 % when the pH is 9.5 and ionic strength is 43 and finally the MO had 30 % in pH 4 and 72. It can be seen that when xanthan is used to remove the dyes, exists a lower dependence between structure and removal efficiency. This may be due to the different tendency to form aggregates of each dye. This aggregation capacity and the charge of each dye resulting from the pH and ionic strength values of aqueous solutions are key factors in the dye removal. The experimental isotherm of MR was only that adequately described by Zimm-Bragg model. Because with the CR had the 100 % of remove thus is very difficult obtain de experimental isotherm and finally MO had results fluctuating and therefore was impossible get the accurate data. Conclusions: The study of the removal of three dyes with xanthan as dye sequestering agents suggests that aggregation capacity of dyes and the charge resulting from structural characteristics such as molecular weight and functional groups have a relationship with the removal efficiency. Acknowledgements: We are gratefully acknowledged support for this project by Consejo Nacional de Ciencia y Tecnología, México (CONACyT, Grant No. 632694.)

Keywords: adsorption, azo dyes, xanthan gum, Zimm Bragg theory

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Authors: Reema K. AlEssa, Sahar Alshomer, Abdullah Alfaleh, Sultan ALkhenaizan, Mohammed Albalwi

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Ichthyosis is a disorder of abnormal keratinization, characterized by excessive scaling, and consists of more than twenty subtypes varied in severity, mode of inheritance, and the genes involved. There is insufficient data in the literature about the epidemiology and characteristics of ichthyosis locally. Our aim is to identify the histopathological features and genetic profile of ichthyosis. Method: It is an observational retrospective case series study conducted in March 2020, included all patients who were diagnosed with Ichthyosis and confirmed by histological and molecular findings over the last 20 years in King Abdulaziz Medical City (KAMC), Riyadh, Saudi Arabia. Molecular analysis was performed by testing genomic DNA and checking genetic variations using the AmpliSeq panel. All disease-causing variants were checked against HGMD, ClinVar, Genome Aggregation Database (gnomAD), and Exome Aggregation Consortium (ExAC) databases. Result: A total of 60 cases of Ichthyosis were identified with a mean age of 13 ± 9.2. There is an almost equal distribution between female patients 29 (48%) and males 31 (52%). The majority of them were Saudis, 94%. More than half of patients presented with general scaling 33 (55%), followed by dryness and coarse skin 19 (31.6%) and hyperlinearity 5 (8.33%). Family history and history of consanguinity were seen in 26 (43.3% ), 13 (22%), respectively. History of colloidal babies was found in 6 (10%) cases of ichthyosis. The most frequent genes were ALOX12B, ALOXE3, CERS3, CYP4F22, DOLK, FLG2, GJB2, PNPLA1, SLC27A4, SPINK5, STS, SUMF1, TGM1, TGM5, VPS33B. Most frequent variations were detected in CYP4F22 in 16 cases (26.6%) followed by ALOXE3 6 (10%) and STS 6 (10%) then TGM1 5 (8.3) and ALOX12B 5 (8.3). The analysis of molecular genetic identified 23 different genetic variations in the genes of ichthyosis, of which 13 were novel mutations. Homozygous mutations were detected in the majority of ichthyosis cases, 54 (90%), and only 1 case was heterozygous. Few cases, 4 (6.6%) had an unknown type of ichthyosis with a negative genetic result. Conclusion: 13 novel mutations were discovered. Also, about half of ichthyosis patients had a positive history of consanguinity.

Keywords: ichthyosis, genetic profile, molecular characterization, congenital ichthyosis

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Authors: Isra Elfatih Salih Edrees, Mehmet Serdar Ufuk Türeli

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In this paper, an energy-aware method is presented, integrating energy-efficient relay-augmented techniques for correlated data routing with the goal of optimizing bottleneck throughput in wireless sensor networks. The system tackles the dual challenge of throughput optimization while considering sensor network energy consumption. A unique routing metric has been developed to enable throughput maximization while minimizing energy consumption by utilizing data correlation patterns. The paper introduces a game theoretic framework to address the NP-complete optimization problem inherent in throughput-maximizing correlation-aware routing with energy limitations. By creating an algorithm that blends energy-aware route selection strategies with the best reaction dynamics, this framework provides a local solution. The suggested technique considerably raises the bottleneck throughput for each source in the network while reducing energy consumption by choosing the best routes that strike a compromise between throughput enhancement and energy efficiency. Extensive numerical analyses verify the efficiency of the method. The outcomes demonstrate the significant decrease in energy consumption attained by the energy-efficient relay-augmented bottleneck throughput maximization technique, in addition to confirming the anticipated throughput benefits.

Keywords: correlated data aggregation, energy efficiency, game theory, relay-augmented routing, throughput maximization, wireless sensor networks

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Authors: Stephanie Boue, Nicolas Sierro, Julia Hoeng, Manuel C. Peitsch

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Systems biology relies on large numbers of data points and sophisticated methods to extract biologically meaningful signal and mechanistic understanding. For example, analyses of transcriptomics and proteomics data enable to gain insights into the molecular differences in tissues exposed to diverse stimuli or test items. Whereas the interpretation of endpoints specifically measuring a mechanism is relatively straightforward, the interpretation of big data is more complex and would benefit from comparing results obtained with diverse analysis methods. The sbv IMPROVER project was created to implement solutions to verify systems biology data, methods, and conclusions. Computational challenges leveraging the wisdom of the crowd allow benchmarking methods for specific tasks, such as signature extraction and/or samples classification. Four challenges have already been successfully conducted and confirmed that the aggregation of predictions often leads to better results than individual predictions and that methods perform best in specific contexts. Whenever the scientific question of interest does not have a gold standard, but may greatly benefit from the scientific community to come together and discuss their approaches and results, datathons are set up. The inaugural sbv IMPROVER datathon was held in Singapore on 23-24 September 2016. It allowed bioinformaticians and data scientists to consolidate their ideas and work on the most promising methods as teams, after having initially reflected on the problem on their own. The outcome is a set of visualization and analysis methods that will be shared with the scientific community via the Garuda platform, an open connectivity platform that provides a framework to navigate through different applications, databases and services in biology and medicine. We will present the results we obtained when analyzing data with our network-based method, and introduce a datathon that will take place in Japan to encourage the analysis of the same datasets with other methods to allow for the consolidation of conclusions.

Keywords: big data interpretation, datathon, systems toxicology, verification

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Salam K. Al-Dawery

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Despite of few research works on municipal sludge, still there is a lack of actual data. Thus, this work was focused on the conditioning and rheology of fresh activated sludge. The effect of cationic polyelectrolyte has been investigated at different concentrations and pH values in a comparative fashion. Yield stress is presented in all results indicating the minimum stress that necessary to reach flow conditions. Connections between particle-particle is the reason for this yield stress, also, the addition of polyelectrolyte causes strong bonds between particles and water resulting in the aggregation of particles which required higher shear stress in order to flow. The results from the experiments indicate that the cationic polyelectrolytes have significant effluence on the sludge characteristic and water quality such as turbidity, SVI, zone settling rate and shear stress.

Keywords: rheology, polyelectrolyte, settling volume index, turbidity

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Authors: Wang Yang

Abstract:

Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map

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Authors: Hossein Gitinavard, Mohammad Hossein Fazel Zarandi

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In the last decades, concerns about the environmental issues lead to professional and academic efforts on green supplier selection problems. In this sake, one of the main issues in evaluating the green supplier selection problems, which could increase the uncertainty, is the preferences of the experts' judgments about the candidate green suppliers. Therefore, preparing an expert system to evaluate the problem based on the historical data and the experts' knowledge can be sensible. This study provides an expert evaluation system to assess the candidate green suppliers under selected criteria in a multi-period approach. In addition, a ranking approach under interval-valued hesitant fuzzy set (IVHFS) environment is proposed to select the most appropriate green supplier in planning horizon. In the proposed ranking approach, the IVHFS and the last aggregation approach are considered to margin the errors and to prevent data loss, respectively. Hence, a comparative analysis is provided based on an illustrative example to show the feasibility of the proposed approach.

Keywords: green supplier selection, expert system, ranking approach, interval-valued hesitant fuzzy setting

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Authors: Jianuo Sun, Jinghan Wang, Sirui Zhang, Chenhan Xu, Hongxia Hao, Hong Zhou

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In recent years, the diversification and industrialization of drug-related crimes have posed significant threats to public health and safety globally. The widespread and increasingly younger demographics of drug users and the persistence of drug-impaired driving incidents underscore the urgency of this issue. Drug detection, a specialized forensic activity, is pivotal in identifying and analyzing substances involved in drug crimes. It relies on pharmacological and chemical knowledge and employs analytical chemistry and modern detection techniques. However, current drug detection methods are limited by their inability to perform semi-quantitative, real-time field analyses. They require extensive, complex laboratory-based preprocessing, expensive equipment, and specialized personnel and are hindered by long processing times. This study introduces an alternative approach using nucleic acid aptamers and Aggregation-Induced Emission (AIE) technology. Nucleic acid aptamers, selected artificially for their specific binding to target molecules and stable spatial structures, represent a new generation of biosensors following antibodies. Rapid advancements in AIE technology, particularly in tetraphenyl ethene-based luminous, offer simplicity in synthesis and versatility in modifications, making them ideal for fluorescence analysis. This work successfully synthesized, isolated, and purified an AIE molecule and constructed a probe comprising the AIE molecule, nucleic acid aptamers, and exonuclease for cocaine detection. The probe demonstrated significant relative fluorescence intensity changes and selectivity towards cocaine over other drugs. Using 4-Butoxytriethylammonium Bromide Tetraphenylethene (TPE-TTA) as the fluorescent probe, the aptamer as the recognition unit, and Exo I as an auxiliary, the system achieved rapid detection of cocaine within 5 mins in aqueous and urine, with detection limits of 1.0 and 5.0 µmol/L respectively. The probe-maintained stability and interference resistance in urine, enabling quantitative cocaine detection within a certain concentration range. This fluorescent sensor significantly reduces sample preprocessing time, offers a basis for rapid onsite cocaine detection, and promises potential for miniaturized testing setups.

Keywords: drug detection, aggregation-induced emission (AIE), nucleic acid aptamer, exonuclease, cocaine

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Authors: Claudeline D. Cellan

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Development is multifaceted, and efforts to hasten growth in all these facets have been gaining traction in recent years. Thus, producing a composite index that is reflective of these multidimensional impacts captures the interests of policymakers. The problem lies in going through a mixture of theoretical, methodological and empirical decisions and complexities which, when done carelessly, can lead to inconsistent and unreliable results. This study looks into index computation from a different and less complex perspective. Borrowing the idea of archetypes or ‘pure types’, archetypal analysis looks for points in the convex hull of the multivariate data set that captures as much information in the data as possible. The archetypes or 'pure types' are estimated such that they are convex combinations of all the observations, which in turn are convex combinations of the archetypes. This ensures that the archetypes are realistically observable, therefore achievable. In the sense of composite indices, we look for the best among these archetypes and use this as a benchmark for index computation. Its straightforward and simplistic approach does away with aggregation and substitutability problems which are commonly encountered in index computation. As an example of the application of archetypal analysis in index construction, the country data for the Human Development Index (HDI 2017) of the United Nations Development Programme (UNDP) is used. The goal of this exercise is not to replicate the result of the UNDP-computed HDI, but to illustrate the usability of archetypal analysis in index construction. Here best is defined in the context of life, education and gross national income sub-indices. Results show that the HDI from the archetypal analysis has a linear relationship with the UNDP-computed HDI.

Keywords: archetypes, composite index, convex combination, development

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Authors: K. B. M. Islam, Syeeda Shiraj-Um-Mahmuda, Afroj Jahan, A. A. Bhuiyan

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In Bangladesh, the use of imported probiotics in poultry is gradually being increased. But surprisingly, no probiotic bacteria have been isolated yet in Bangladesh despite the existence of scavenging native poultry as potential source that is seemingly more resistant to GIT infection as well as other diseases. Therefore, the study was undertaken to isolate, identify and characterize the potential probiotic Lactobacillus and Bifidobacteria strains from Bangladeshi indigenous poultry, and to evaluate their suitability to use in poultry industry. Crop and cecal samples from 61 healthy indigenous birds were used to isolate potential probiotics strains following conventional cultural methods. A total of 216 isolates were identified following physical, biochemical and molecular methods that belonged to the genus Lactobacillus and Bifidobacteria. An auto-aggregation test was performed for 180 and 136 isolated lactobacilli and bifidobacteria strains, respectively. Twelve lactobacilli isolates and 7 bifidobacteria isolates were selected because of their convenient aggregation. In vitro tests including antibacterial activity, resistance to low pH, hemolytic activities etc. were performed for evaluation of probiotic potential of each strain. Under the in vitro conditions and with respects to the probiotic traits, three lactobacilli; LS16, LS45, LS133 and two bifidobacteria, BS21 and BS90 were found to be potential probiotic strains. Thus, they are proposed to be evaluated for their in vivo probiotic properties. If the proposed strains are found suitable as the probiotics to be used in commercial poultry industry, it is expected that the local probiotics would be more beneficial and would save the huge amount of money that Bangladesh spends every year for the importation of such materials from abroad.

Keywords: Bangladeshi poultry, gut microbiota, lactic acid bacteria, scavenging chicken, GIT health

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Alazar Nebyou, Sujata Pandit

Abstract:

Lactic acid bacteria (LAB) are essential ingredients in probiotic foods, intestinal microflora, and dairy products that are capable of coping up with harsh gastrointestinal tract conditions and are available in a variety of environments. The objective of this study is to evaluate the probiotic property of LAB isolated from bovine milk. Milk samples were collected from local dairy farms. Samples were obtained using sterile test tubes and transported to a laboratory in the icebox for further biochemical characterization. Preliminary physiological and biochemical identification of LAB isolates was conducted by growing on MRS agar after ten-fold serial dilution. Seven of the best isolates were selected for the evaluation of the probiotic property. The LAB isolates were checked for resistance to antibiotics and their antimicrobial activity by disc diffusion assay and agar well diffusion assay respectively. Bile salt hydrolase activity of isolates was studied by growing isolates in a BSH medium with bile salt. Cell surface property of isolates was assayed by studying their autoaggregation and coaggregation percentage with S. aerues. All isolates were found BSH positive. In addition, BCM2 and BGM1 were susceptible to all antibiotic disks except BBM1 which was resistant to all antibiotic disks. BCM1 and BGM1 had the highest autoaggregation and coaggregation potential respectively. Since all LAB isolates showed gastrointestinal tolerance and good cell surface property they could be considered as good potential probiotic candidates for treatment and probiotic starter culture preparation.

Keywords: probiotic, aggregation, lactic acid bacteria, antimicrobial activity

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Authors: S. Agheb, G. Ledwich, G.Walker, Z.Tong

Abstract:

Frequency control has become more of concern for reliable operation of interconnected power systems due to the integration of low inertia renewable energy sources to the grid and their volatility. Also, in case of a sudden fault, the system has less time to recover before widespread blackouts. Electric Vehicles (EV)s have the potential to cooperate in the Emergency Frequency Regulation (EFR) by a nonlinear control of the power system in case of large disturbances. The time is not adequate to communicate with each individual EV on emergency cases, and thus, an aggregate model is necessary for a quick response to prevent from much frequency deviation and the occurrence of any blackout. In this work, an aggregate of EVs is modelled as a big virtual battery in each area considering various aspects of uncertainty such as the number of connected EVs and their initial State of Charge (SOC) as stochastic variables. A control law was proposed and applied to the aggregate model using Lyapunov energy function to maximize the rate of reduction of total kinetic energy in a two-area network after the occurrence of a fault. The control methods are primarily based on the charging/ discharging control of available EVs as shunt capacity in the distribution system. Three different cases were studied considering the locational aspect of the model with the virtual EV either in the center of the two areas or in the corners. The simulation results showed that EVs could help the generator lose its kinetic energy in a short time after a contingency. Earlier estimation of possible contributions of EVs can help the supervisory control level to transmit a prompt control signal to the subsystems such as the aggregator agents and the grid. Thus, the percentage of EVs contribution for EFR will be characterized in the future as the goal of this study.

Keywords: emergency frequency regulation, electric vehicle, EV, aggregation, Lyapunov energy function

Procedia PDF Downloads 70