bluuebunny/crossref_metadata_2025 huggingface dataset:
Cisplatin uptake and release in pH sensitive zeolitic imidazole frameworks
Authors: Thompson Megan J. , Wells Stephen A. , Düren Tina
Published: June 2021

Cancer remains hard to treat, partially due to the non-specificity of chemotherapeutics. Metal–organic frameworks (MOFs) are promising carriers for targeted chemotherapy, yet, to date, there have been few detailed studies to systematically enhance drug loading while maintaining controlled release. In this work, we investigate which molecular simulation methods best capture the experimental uptake and release of cisplatin from UiO-66 and UiO-66(NH2). We then screen a series of biocompatible, pH-sensitive zeolitic imidazolate frameworks (ZIFs) for their ability to retain cisplatin in healthy parts of the patient and release it in the vicinity of a tumor. Pure-component GCMC simulations show that the maximum cisplatin loading depends on the pore volume. To achieve this maximum loading in the presence of water, either the pore size needs to be large enough to occupy both cisplatin and its solvation shell or the MOF–cisplatin interaction must be more favorable than the cisplatin–shell interaction. Both solvated and non-solvated simulations show that cisplatin release rates can be controlled by either decreasing the pore limiting diameters or by manipulating framework–cisplatin interaction energies to create strong, dispersed adsorption sites. The latter method is preferable if cisplatin loading is performed from solution into a pre-synthesized framework as weak interaction energies and small pore window diameters will hinder cisplatin uptake. Here, ZIF-82 is most promising. If it is possible to load cisplatin during crystallization, ZIF-11 would outcompete the other MOFs screened as cisplatin cannot pass through its pore windows; therefore, release rates would be purely driven by the pH triggered framework degradation.
The study of the optical phonon frequency of 3C-SiC by molecular dynamics simulations with deep neural network potential
Authors: Chen Wei , Li Liang-Sheng
Published: June 2021

In this work, we implement molecular dynamics (MD) simulations with deep neural network (DNN) potential trained with the datasets from ab initio calculations to determine the dielectric spectra of crystal. The fluctuations of the total dipole moment of crystal, which are obtained from MD, can be directly related to the frequency-dependent permittivity according to the work of Neumann and Steinhauser [Chem. Phys. Lett. 102, 508–513 (1983)]. We generalize their theoretical work to express the permittivity in the form of a tensor and perform MD simulations for cubic silicon carbide (3C-SiC) with 8000 atoms to assess the accuracy. The infrared resonance frequency and the phonon linewidth obtained by the DNN potential are compared with those obtained by the empirical Vashishta potential and experiments. The results of the DNN potential are in good agreement with the experimental measurements. It shows that we can carry out MD simulations for large systems with the accuracy of ab initio calculations to obtain dielectric properties.
Fourier optics with linearly tapered waveguides: Light trapping and focusing
Authors: Gaafar Mahmoud A. , Renner Hagen , Eich Manfred , Petrov Alexander Yu.
Published: June 2021

An optical pulse asymptotically reaching zero group velocity in tapered waveguides can ultimately stop at a certain position in the taper accompanied by a strong spatial compression. This phenomenon can also be observed in spatiotemporal systems where the pulse velocity asymptotically reaches the velocity of a tapered front. The first system is well known from tapered plasmonic waveguides where adiabatic nano-focusing of light is observed. Its counterpart in the spatiotemporal system is the optical push broom effect where a nonlinear front collects and compresses the signal. Here, we use the slowly varying envelope approximation to describe such systems. We demonstrate an analytical solution for the linear taper and the piecewise linear dispersion and show that the solution in this case resembles that of an optical lens in paraxial approximation. In particular, the spatial distribution of the focused light represents the Fourier transform of the signal at the input.
Vortex dynamics of veno-arterial extracorporeal circulation: A computational fluid dynamics study
Authors: Seetharaman Ashwin , Keramati Hamed , Ramanathan Kollengode , E Cove Matthew , Kim Sangho , Chua Kian Jon , Leo Hwa Liang
Published: June 2021

Veno-arterial extra corporeal membrane oxygenation (VA-ECMO) is a modified heart lung machine used for patients with both heart and lung failure. This results in retrograde supply of oxygenated blood through the femoral artery in which the unsteady pulsating antegrade flow from the aorta interacts with a steady, uniform, and retrograde flow from the femoral artery, creating a mixing zone. This work aims to provide a mechanistic interpretation of VA-ECMO by developing an in-silico framework using computational fluid dynamics. We performed several numerical simulations to investigate the effects of aortic geometry on VA-ECMO by implementing two idealized full aorta models and studied the formation of secondary flow features and vortices. We used vortex identification methods to capture the three-dimensional vortical structures formed under various ECMO support levels. Our results show that under pulsatile aortic flow and 80% of ECMO support, the streamwise vorticity and aortic arch geometry strongly influence the mixing zone. Furthermore, we found that pulsatility at the aortic inlet causes oscillation in secondary flow structures at the abdominal aorta leading to unsteadiness in ECMO flow and differential wall shear stress. We also examined the effects of VA-ECMO flow rates on secondary flow and vortical structures. We show that the location and complexity of secondary flows and vortical structures are affected by ECMO support levels and geometry of aortic segments. Together, we believe that this computational framework is a crucial step in understanding flow features and vortical structures formed during VA-ECMO administration, which can improve patient care and ECMO management.
Fully automated spectroscopic ellipsometry analyses: Application to MoOx thin films
Authors: Oiwake Kohei , Nishigaki Yukinori , Fujimoto Shohei , Maeda Sara , Fujiwara Hiroyuki
Published: June 2021

In spectroscopic ellipsometry, the optical properties of materials are obtained indirectly by generally assuming dielectric function and optical models. This ellipsometry analysis, which typically requires numerous model parameters, has essentially been performed by a trial-and-error approach, making this method as a rather time-consuming characterization technique. Here, we propose a fully automated spectroscopic ellipsometry analysis method, which can be applied to obtain dielectric functions of light absorbing materials in a full measured energy range without any prior knowledge of model parameters. The developed method consists of a multiple-step grid search and the following non-linear regression analysis. Specifically, in our approach, the analyzed spectral region is gradually expanded toward a higher energy, while incorporating an additional optical transition peak whenever the root mean square error of the fitting analysis exceeds a critical value. In particular, we have established a unique algorithm that could be employed for the ellipsometry analyses of different types of optical materials. The proposed scheme has been applied successfully for the analyses of MoOx transparent oxides and the complex dielectric function of a MoOx layer that exhibits dual optical transitions due to band-edge and deep-level absorptions has been determined. The developed method can drastically reduce a time necessary for an ellipsometry analysis, eliminating a serious drawback of a traditional spectroscopic ellipsometry analysis method.
A path integral ground state approach for asymmetric top rotors with nuclear spin symmetry: Application to water chains
Authors: Sahoo Tapas , Serwatka Tobias , Roy Pierre-Nicholas
Published: June 2021

A path integral ground state (PIGS) approach for the simulation of asymmetric top rotors is presented. The method is based on Monte Carlo sampling of angular degrees of freedom. A symmetry-adapted rotational density matrix is used to account for nuclear spin statistics. To illustrate the method, ground-state properties of collections of para-water molecules confined to a one-dimensional lattice are computed. Those include energetic and structural observables. An advantage of the PIGS method is that expectation values can be obtained directly since the square of the wavefunction is sampled during a simulation. To benchmark the method, ground state energies and orientational distributions are computed using exact diagonalization for a single para-water molecule in an external field using a finite basis of symmetric top eigenfunctions. Benchmark results are also provided for N = 2 para-water molecules pinned to lattice sites at various distances to sample the crossover from hydrogen bonding to the dipole–dipole interaction regime. Excellent agreement between the PIGS results and the finite basis set calculations is observed. A thorough analysis of the convergence in terms of the imaginary time propagation length and systematic Trotter error is performed. The PIGS approach is then applied to a chain of N = 11 water molecules, and an equation of state is constructed in terms of the intermolecular separation. Ordering effects are also studied, and a transition between hydrogen bonding to dipole–dipole alignment is observed. The method is scalable and can also be applied in higher dimensions.
Magnetization dynamics of antiferromagnetic metals of PtMn and IrMn driven by a pulsed spin-transfer torque
Authors: Kang Kyuhwe , Lee Won-Bin , Lee Dong-Kyu , Lee Kyung-Jin , Choi Gyung-Min
Published: June 2021

Antiferromagnets are promising materials for memory devices owing to their ultrafast spin dynamics. For efficient control of antiferromagnets, a direct interaction between the spin current and local magnetization of the sublattice is required. In this study, we demonstrated that a short-pulsed spin current can induce fast dynamics of metallic antiferromagnets of PtMn and IrMn via spin-transfer torque on the magnetization of sublattices. We employed two methods to generate a short-pulsed spin current, namely ultrafast demagnetization of a ferromagnet and optical spin polarization of a heavy metal. The magnetization dynamics were measured using the time-resolved magneto-optical Kerr effect and were analyzed using the Landau–Lifshitz–Gilbert equation. Our results provide important evidence of the direct interaction between the magnetization of antiferromagnets and spin current.
Monitoring the COVID-19 epidemic with nationwide telecommunication data
Authors: Persson Joel , Parie Jurriaan F. , Feuerriegel Stefan
Published: June 2021

Significance

To manage the current epidemic, policymakers need tools that help them in evidence-based decision making. In particular, decision support is needed to assess policy measures by their ability to enforce social distancing. A solution is offered by our work: We use mobility data derived from telecommunication metadata as a proxy for social distancing, and, based on this, we demonstrate how the effect of policy measures can be monitored in a nationwide setting. Compared to the status quo, this provides a clear benefit: Monitoring policy measures through case counts has a substantial time lag, whereas our approach allows for monitoring in near real time.
How Saudi migrants’ metapragmatic judgments of Arabic L1 nonverbal greetings change after intense and prolonged exposure to English
Authors: Alshahrani Hessa , Dewaele Jean-Marc
Published: June 2021

This study investigates the impact of spending more than three years in an English environment on Saudi migrants’ metapragmatic judgments of Arabic L1 nonverbal greetings and their personality traits. Participants are 437 adults comprising three groups: Saudi L2 speakers of English in the UK, Saudis in Saudi Arabia, and British L1 speakers of English in the UK. They observed and rated an audiovisual stimulus illustrating Saudi L1 nonverbal greeting behaviours of handshake and cheek-to-cheek kiss. Statistical analyses revealed that appropriateness ratings by Saudi migrants in the UK diverged from those by Saudis in Saudi Arabia and approximated those of English L1 users in the UK. Moreover, appropriateness ratings by Saudi migrants were differently associated with personality profiles, which differed for three traits between the two Saudi groups. These findings suggest change in L1 metapragmatic judgements as well as personality as a result of prolonged and intense exposure to an L2. The results are interpreted in the light of Cook’s (2012) concept of multicompetence.
Tanaka–Ito α-continued fractions and matching
Authors: Carminati Carlo , Langeveld Niels , Steiner Wolfgang
Published: June 2021

Two closely related families of α-continued fractions were introduced in 1981: by Nakada on the one hand, by Tanaka and Ito on the other hand. The behavior of the entropy as a function of the parameter α has been studied extensively for Nakada’s family, and several of the results have been obtained exploiting an algebraic feature called matching. In this article we show that matching occurs also for Tanaka–Ito α-continued fractions, and that the parameter space is almost completely covered by matching intervals. Indeed, the set of parameters for which the matching condition does not hold, called the bifurcation set, is a zero measure set (even if it has full Hausdorff dimension). This property is also shared by Nakada’s α-continued fractions, and yet there also are some substantial differences: not only does the bifurcation set for Tanaka–Ito continued fractions contain infinitely many rational values, it also contains numbers with unbounded partial quotients.
Global dynamics of a quintic Liénard system with

Z2

-symmetry I: saddle case
Authors: Chen Hebai , Tang Yilei , Xiao Dongmei
Published: June 2021

In the paper we deal with a quintic Liénard system of the form

x

̇

=
y
−

(

a

1

x
+

a

2

x

3

+

a

3

x

5

)

,

y

̇

=

b

1

x
+

b

2

x

3

with a

Z

2

-symmetry, where

a

1

,

a

2

,

b

1

∈
R

and a
3
b
2 ≠ 0. A complete study of this system with b
2 > 0, called the saddle case, is finished, showing that the system exhibits at most two limit cycles, and the necessary and sufficient conditions are obtained on the existence of two limit cycles and a two-saddle heteroclinic loop. We also present a global bifurcation diagram and the corresponding phase portraits of this system, including Hopf bifurcation, Bautin bifurcation, two-saddle heteroclinic loop bifurcation and double limit cycle bifurcation.
Compressible Navier–Stokes equations with heterogeneous pressure laws
Authors: Bresch Didier , Jabin Pierre-Emmanuel , Wang Fei
Published: June 2021

This paper concerns the existence of global weak solutions à la Leray for compressible Navier–Stokes equations with a pressure law which depends on the density and on time and space variables t and x. The assumptions on the pressure contain only locally Lipschitz assumption with respect to the density variable and some hypothesis with respect to the extra time and space variables. It may be seen as a first step to consider heat-conducting Navier–Stokes equations with physical laws such as the truncated virial assumption. The paper focuses on the construction of approximate solutions through a new regularized and fixed point procedure and on the weak stability process taking advantage of the new method introduced by the two first authors with a careful study of an appropriate regularized quantity linked to the pressure.
Digital synthesis of programmable photonic integrated circuits
Authors: Zhang Juan , Ji Zhengyong , Ding Yipeng , Wang Yang
Published: June 2021

Programmable photonic waveguide meshes can be programmed into many different circuit topologies and thereby provide a variety of functions. Due to the complexity of the signal routing in a general mesh, a particular synthesis algorithm often only accounts for a specific function with a specific cell configuration. In this paper, we try to synthesize the programmable waveguide mesh to support multiple configurations with a more general digital signal processing platform. To show the feasibility of this technique, photonic waveguide meshes in different configurations (square, triangular and hexagonal meshes) are designed to realize optical signal interleaving with arbitrary duty cycles. The digital signal processing (DSP) approach offers an effective pathway for the establishment of a general design platform for the software-defined programmable photonic integrated circuits. The use of well-developed DSP techniques and algorithms establishes a link between optical and electrical signals and makes it convenient to realize the computer-aided design of optics–electronics hybrid systems.
Adaptive Identification of Control Objects in Systems with Standard Controllers
Authors: Yakovis L.M. , Strongin P.Ya.
Published: June 2021

The report is devoted to the study of the problem of parametric identification of controlled dynamic systems in their normal operation mode using a model with adjustable parameters. The task of determining parameters of test signals providing the specified accuracy of identification is considered. The peculiarity of the work consists in imparting adaptive properties to the identification procedure, which makes it possible to minimize the negative effect of “swinging” of the controlled object on its output when special test signals are applied.
Designing Online Healthcare Using DDD in Microservices Architecture
Authors: Rizki M , Fajar A N , Retnowardhani A
Published: June 2021

Today, the system development had been transformed to service-oriented architecture. It more specific in microservices architecture to responds the agility and flexibility in software maintenance. This transformation is an important part that promoting independence, speed and security, while still kept balanced with the company’s condition through comprehensive and effective steps. Culture of the development cycle of a company highly affected by the adaptation of microservices architecture. Thus, in order to achieve flexibility, speed and security, which are the main goal of adapting microservices, changes must be deliver well and sustainably. This study focused on designing of microservices architecture for health system using domain driven design (DDD) approach.
Prognosis of Diabetes Mellitus with Transfer Learning-Based Naïve Bayes Method
Authors: Andriani A , Hartati S
Published: June 2021

Early detection of diabetes mellitus (DM) prognosis against several diseases cannot be done medically in the short term. Supervised Learning method can be used to classify diabetic patient data to detect the prognosis of DM. There are several problems in the classification of patient medical record data. First, medical record data is not always good, namely structured and complete. Second, DM patient medical record data from various sources do not necessarily have the same parameters. The impact of supervised learning on a dataset of DM patients is not necessarily applicable to a dataset of DM patients from different sources, so that the learning outcomes are not long-lived learning. The proposed method in this study is data classification with the Naïve Bayes method based on transfer learning by applying learning outcomes in the source domain to the target domain as a starting point for learning in the target domain. The method applied uses parameter-based transfer learning so that it can be used to overcome parameter differences in two different datasets.
Neural Network Enhancement Forecast of Dengue Fever Outbreaks in Coastal Region
Authors: Harumy THF , Ginting Dewi Sartika Br.
Published: June 2021

Dengue Fever is among the world’s fastest-spreading mosquito-borne illnesses. In Indonesia, more than 33 percent of the world community is in danger. The Coastal Zone is one of the region most at risk of contracting dengue fever, particularly from the social and environmental sectors, so an early diagnostic study must deal with this efficiently and effectively. This research aimed to predict and identify the coastal areas with the most severe dengue fever potential to avoid dengue fever. The methodology used is a neural network-based sensitivity Analysis and multiple linear regression. Bagan Deli, Sibolga, Tapanuli Tengah, Langkat, Medan are samples of the coastal regions used in this study. The reports used was secondary data for dengue fever patients suffering and meteorological parameters, the model used in [5-5-1] for prediction, for five years from 2014-2019. The results showed which Langkat (0.4936), Serdang Bedagai (0.4695), The Middle Of Tapanuli (0.4399), Medan (0.4313), and Sibolga (0.3133) are perhaps the most prevalent areas affected by dengue fever with a value of 89.8 percent. The Result is temperature and humidity are the conditions that most affect the transmission of dengue fever.
Thermal Analysis on Heat Pump Chili Drying Chamber with variations in the direction of air flow using Computational Fluid Dynamics
Authors: Siburian A H , Silalahi P G , Romauli N D M , Ambarita H
Published: June 2021

Drying chilies using a heat pump includes drying at low temperatures so that the temperature distribution and flow direction in the drying chamber need to be considered. The distribution of temperature and flow direction in the heat pump drying room with a drying room size of 1 m × 0.8 m × 0.75 m to dry 1 kg of chilies built in the Sustainable Energy and Research Center (SERC) laboratory is not good enough to make the drying time longer. The procedure has been established with the aim of evaluating the speed distribution and temperature of the drying air in the drying chamber to determine the need for redesign. Computational Fluid Dynamics (CFD) is used to analyze flow patterns that occur in the drying chamber where in this study two types of flow directions that enter the heat pump chili drying chamber are compared. CFD has replaced the classical numerical analysis method of drying processes based on experimental models. Analytical methods and experimental methods are limited in describing the distribution of temperature and air flow in the drying chamber. In this case, the finite element method is used in the form of CFD analysis where the geometry of the drying chamber is divided into several analyzed elements. After doing the CFD simulation, it is obtained that the heat pump chili drying chamber model with the flow direction from below the temperature increases faster than the heat pump chili drying chamber model with the flow direction from the side. The simulation results show that the drying room with the direction of hot air flow from below, the temperature of the dried chilies increases faster and the temperature distribution is better than the drying room with the flow direction from the side. In both models of the drying chamber, the temperature value is obtained in the timestep 600, namely in the drying chamber model with hot air flow from the side is 305,569 K and in the drying chamber model with hot air flow from below is 313,697 K. For the drying model in this heat pump chili drying room it is advisable to use the flow direction from below because it is better. This solution can also be offered to get the right drying chamber model.
Performance Analysis Similarity Matrix, Responsibility Matrix, Availability Matrix, Criterion Matrix of Affinity Propagation
Authors: Dhany Hanna Willa
Published: June 2021

Each process in classifying data into several clusters or grouping so that the data in one cluster has a maximum similarity level and between clusters has a minimum similarity is called clustering. Clustering is divided into 2 approaches in its development, namely the partitioning and hierarchical approach to clustering[1]. The Water Quality Status dataset has 8 attributes, 4 classes and 120 instances, Class distribution is good condition (30 instances), lightly polluted (30 instances), medium polluted (30 instances) and heavily polluted (30 instances). 70% of the data will be used as training data and 30% of the data will be used as randomized test data. The simplify the process of completing the performance calculation of the clustering model, the research implementation was carried out using the MATLAB function. That iteration is carried out with the number of clusters generated from 100 to 2,500 iterations with the results of the number of clusters as many as 10 clusters. In the experiment, iteration amounted to 5000 and there was a change in the results of the number of clusters by 9 clusters. After re-testing using the number of iterations of 10,000-50,000 iterations, but the number of clusters produced did not change anything at all. So that the conclusion in testing the AP method produces the most optimal number of clusters of 10 clusters.
Research Review on Big Data Usage for Learning Analytics and Educational Data Mining: A Way Forward to Develop an Intelligent Automation System
Authors: Yunita A , Santoso H B , Hasibuan Z A
Published: June 2021

Digitalization and the development of information technology, especially Artificial Intelligence, have been embraced in all fields. At the same time, data has grown mostly from the digital footprints or any technology information system. The development of technology and big data offers enormous opportunities to conduct big data analytics in any field, including education. This study aims to review current research related to big data analytics in education and explain future research direction. Using Kitchenham’s technique, we selected and clustered the literature into the types of data, methods, type of data analytics and learning analytics application used. The results show that research of big data learning analytics generally aims to improve the learning process, analyze learner behaviour for student profiling, improve student retention and evaluate student feedback in the context of MOOCs and Learning Management System. Several future directions for this topic are: 1) building a big open dataset including data pre-processing and addressing the problem of imbalanced dataset, 2) process mining for learning log activity to gain knowledge and insights from online behaviour, not only from the perspective of the learner but also from the activities of the teacher, 3) designing an automated framework which uses big data and allows descriptive, predictive, prescriptive analytical learning to be carried out. To summarize, embracing big data to learning analytics and educational data mining is an open research area that seems very powerful in education.
Adhd Classification From FMRI Data Using Fine Tunining in SVM
Authors: Anitha S , Thomas Geroge S
Published: June 2021

ML (Machine learning) is a subset of AI and also improved learning technique has different performance in result over the conventional ML determining the complexity in structures of dimensional data. ADHD is one of the most important neurological disorders and it is represented by different symptoms and we can extract useful the information from FMRI time series. In this paper the ADHD identification and classification is obtained by machine learning techniques. This paper explores an artificial intelligence in unsupervised learning is appropriate to learn features from raw data. The proposed system presented with two stage approaches for ADHD diagnosis which associated SoftMax Regression and SVM fine tuning approach. In the implementation part used FMRI brain images are data sets. The two stage approach shows the high accuracy in performance by using the learning techniques.
Side Effects of Mercury Based Clinical Thermometer and India’s Trade Performance to Select Countries – an Analysis
Authors: Kalpana Sai B. , Senith S. , Anthony Raj S.
Published: June 2021

A clinical thermometer is a device that tests the temperature of a person’s body. Thermometers are now commonplace not only in hospitals and clinics, but even in offices, institutes, and even several households. Though the invention of digital thermometer and other temperature measuring have been launched, sold in the market, and shrink the local and global demand, still Galileo’s Mercurian is wanted by a specific market. In this article, a detailed analysis is performed about India’s export and import of clinical thermometer for select countries.
Literature Review on Estimation of Drug Parameters Using Physiologically-Based Pharmacokinetic Modeling
Authors: Ambikadevi B. , Subha Hency Jose P.
Published: June 2021

Physiologically based pharmacokinetic (PBPK) modelling is a computing technique used to identify the pharmacokinetic properties of humans and other animal species. This technique is used to identify various drug parameters. One of the parameters is bioavailability. Bioavailability is defined as the fraction of substances being absorbed by the body. Contrasts in bioavailability, realizing whether drug formulations are identical is necessary. The area under the plasma concentration time curve which is AUC determines the bioavailability of the drug. PBPK model is a significant modelling to determine the pharmacokinetic parameters. The goal of this paper is to review various literature which uses PBPK model in different pharmaceutical applications in different populations evaluating, verifying, predicting and identifying different pharmacokinetic and drug parameters.
Pre-Diabetic Retinopathy identification using hybridGenetic Algorithm-Neural Network classifier
Authors: Mohamed Jebran P , Gupta Shweta
Published: June 2021

Diabetic retinopathy (DR) is one of the main prevalent diabetes problems, causing blurry vision and degeneration amongst adults of working age. The first symptoms of DR include Microaneurysms (MA). A Genetic Algorithm-Artificial Neural Network (GA-NN) technique is developed for early diagnosis of DR. There are five steps of the proposed framework. Image pre-processing is achieved using r-polynomial transformation. In the extraction, the K-means algorithm is used to segment blood vessels, and candidate patches were generated. Shape attributes, GLCM and LBP features have been derived from excluded blood vessel image and from patches separately. To achieve independent classification, GA-NN classifiers is employed. The ultimate decision system projects the MA or non-MA class labels by plurality voting for eachclassifier. This methodology was tested on two databases: e-Ophtha-MA and DIARETDB1. The e-ophtha-MA and DIARETDB1 datasets had AUCs of 0.89 and 0.87, respectively, on the receiver operating characteristic (ROC) curve.
Automatic Indoor Air Quality Control of COVID -19 Patient Facilities Using Type-2 Fuzzy Controller
Authors: Naskar Indrajit , Pal A K
Published: June 2021

Big facilities can be used to set up cohort wards for severe and critical COVID-19 patients. The purpose of carbon dioxide and humidity control is to provide healthy air for breathing by both diluting the pollutants originating in the building and removing the pollutants from it. In this paper, for proper mechanical ventilation to maintain indoor air quality (IAQ), a Self-tuned Interval Type-2 Fuzzy PI controller (SIT2FPIC) is proposed. 49 fuzzy if-then rules are used to tune the Interval Type-2 Fuzzy PI controller (IT2FPIC) for proper functioning and to reduce the parameter variations. Correlations of CO2 concentration and air humidity are studied to maintain the air quality in COVID-19 facilities. Mathematical models of CO2 concentration and air humidity in hospitals or temporary COVID-19 facilities are developed considering the presence of COVID-19 patients, doctors, nurses, etc in the room. The control and monitoring of room air quality are very much essential for any infectious diseases like coronavirus. To maintain the air quality in terms of humidity and carbon dioxide, the proposed SIT2FPIC is applied to the mathematical model of humidity and carbon dioxide for the simulation study. The effectiveness of the proposed controller in mechanical ventilation in the treatment facilities is shown by a comparative study of different transient parameters. Effects of load variations in the model are also studied to check the usefulness of the proposed scheme.
Lipid Data Acquisition for devices Treatment of Coronary Diseases Health stuff on the Internet of Medical Things
Authors: Pradeep Raj M S , Manimegalai P , Ajay P , Amose John
Published: June 2021

Recently, the widespread deployment of smart phones globally, biomedical diagnostics. Smartphone-based devices are expected to be commonly used Intelligent Health Tracking Point-of-Care (PoC) Internet of Medical Things (IoMT) applications. As a result, this paper presents a smartphone-based blood lipid data acquisition dongle for measuring blood lipid levels such as TC, HDL-C, and triglycerides (TG). Blood loss in the fingertip to manage coronary artery disease. A thin photochemical during detection. The test strip, composed of LEDs and detectors, is plugged into a small dongle where the colour switches. The intensities of the calculated reflective coefficient of the lipide determinant are indicated. The product of chromogenics. Such photochemical data acquisition data dongle was focused on smart phones. Validated and achieved a correlation coefficient above 0.843 by reference to a clinical analyzer and summed. 5.017 per unit variance coefficient (CV) for 93 blood lipid patients assessment. The photochemistry Therefore, dongle is promising for the potential treatment of IoMT chronic diseases.
A Noval Method for Gait And Tremor Quantification In Parkinson’s Disease Patients
Authors: Sai Sri Lakshmi M , Mridula Rukmini , Borgohain Rupam
Published: June 2021

Tremor and gait disturbances are one of the key features in Parkinson’s Disease. There is an unmet need to evaluate the complex motor disturbances, especially tremor, gait and balance disturbances, that manifest and progress with the duration of disease. Till date there exists no validated system which can quantify gait and tremor, the values of which can be used clinically to assess patients. To analyze the tremor, we used MEMS based triaxial accelerometers attachable to the region of interest, mainly knuckles. And for gait assessment, we used snug fit shoes attached with switches at prefixed points. Data was collected and analyzed offline in MATLAB. With this instrument we were able to successfully measure gait and tremor in the patients. Standardization with complete removal of human intervention in this whole process of assessment must be studied further.
Smart Shopping Trolley System using IoT
Authors: Muralidharan J. , Muthukumaran N. , Ranjith Kumar R. , Rubika M.
Published: June 2021

Currently, billions of consumers shop at large in shopping mall stores for their everyday necessities. In today’s life, purchasing has become a part of everyday life. On holidays and weekends, malls experience massive crowds. When there are exclusive sales and discounts, the urgency is intensified. Here we will use IOT based technology for people buy various things and place them in a trolley. When they have completed their order, they must pay at the billing desk. The Bills generated by a RFID reader which is a time-consuming procedure that leads to long queues at billing counters. The key objective is to build a framework that can be used in shopping malls to fix the issue described above. Both trolleys would be fitted with the system. It will require an RFID reader; RFID tags would be found in many of the mall’s goods. When a consumer places items in the trolley, the code is read, and the price of certain items is saved in the admin portal. When we add items to the cart, the device depicts the item’s name, number, expiration date, and rate, which is then applied to the overall bill. As a result, billing will take place within the trolley itself. The complete bill data will be passed to online portal Wi-Fi Module with trolley id, and we will be able to pay the balance either online or at bill counter by scanning the RFID tag placed in the trolley. In-season inventory reductions are often applied in transactions through admin, and item stock may be recorded. Intelligent Carts and automated recognition systems are being utilized by merchants to enhance the efficiency of their offerings and to boost market demand, enabling them to save time and money.
Stage of lung cancer and toxic content determination in the lung walls using X-Ray image
Authors: Kumaraguru S , Deepa AR , Anil Kumaar K M , Bhuvaneswari M.
Published: June 2021

Massive global, developing nations depend earlier detection of disease. Automated analysis of both the lung domains is a vital part in the machine diagnosis of digital Chest radiography. In this report, using image sequence patient specific adaptive lung structures that identify lung parameters, outpacing nation performance, we introduce a semi verification comprehensive lungs segmentation method. The goal of this study is to examine an effective and efficient image segmentation technique to calculate the analysis of computerized tomography (CT) application of analytical by clinicians. Current methodologies of diagnostic imaging produce massive images that are incredibly unpleasant to manually analyse. The consequences of segmentation algorithms improve the accuracy and also the duration of converge. There seems to be a strong need anyway period and develop to apply new developmental algorithms for solving problems. Image classification segmentation associated with. Lung cancer among men is the most commonly diagnosed cancer globally. In order to protect human lives, premature diagnosis of lung cancer changes to felicitous therapy.
The role of EMI/EMC for the medical devices according to IEC – 60601-1 standards
Authors: Siddeeq Mohammad Abubakar , Mythili A , Ahamed Syed Junaid
Published: June 2021

This paper gives information about the important role of the EMI/EMC for the medical devices according to the standard IEC-60601-1. PRE-Compliance testing is mandatory for all medical devices. The main objective of pre-compliance tests is all about whether the device is affected by electromagnetic interference. Electromagnetic interference is the disruption of an electronic device when in the vicinity of electromagnetic energy of an external source either man-made or naturally occurring
A Novel Eye Cataract Diagnosis and Classification Using Deep Neural Network
Authors: Jayachitra S. , Nitheesh Kanna K. , Pavithra G. , Ranjeetha T.
Published: June 2021

Eye Cataract is one of the main causes of blindness. It affects mostly people at the age of 60.In India, half of the aged people have cataract or have already treated by a surgery. The cataract identification is highly complicated in an early stage. To achieve this, experts chose the concepts of Deep Learning. In this paper we proposed Dense-Net and U-Net to detect and classify the eye cataract. Further, we took 200 samples of eye image to determine the presence of cataract with its severity. Finally, the comparison of Dense-Net and U-Net are tabulated interms of accuracy, sensitivity, and specificity. Hence, it proven that U- Net gives 10% accurate results than Dense Net.
Medical Image Denoising Using Two-Stage Iterative Down-Up CNN and SURF Features
Authors: Jospine Shiney M.R. , Hepsiba D , Vijay Anand L. D.
Published: June 2021

This paper proposes a Two-Stage Deep Iterative Down-Up Convolutional Neural Network for denoising the medical images, which more often increase and decrease the feature map resolution. The function, Speeded Up Robust Features (SURF) is employed in the proposed system to handle the problem of gradient vanishing. The SURF is a patented local feature detector and descriptor. Experiment is conducted using several different medical noise images like Computed Tomography (CT) and Ultrasound Image and found that the proposed system outperforms the current state-of-the-art consistently in image denoising methodologies.
An Early Prediction of Parkinson’s Disease Using Facial Emotional Recognition
Authors: Anusri U , Dhatchayani G , Princely Angelinal Y , Kamalraj S.
Published: June 2021

Parkinson’s disease (PD) is a neurodegenerative disorder which challenges the population due to its uncertainty in prediction of the disease. It is a progressive disorder of the nervous system marked by tremor, muscular rigidity and slow, imprecise movement, chiefly affecting middle-aged and elderly people. In our proposed work, we utilize the facial emotions of PD patients and normal person to identify their facial emotions like sad, happy, anger, and depression. For this predictive analysis, the datasets are acquired from Parkinson’s Progression Markers Initiative (PPMI) which consists of 188 PD patients and 50 normal people for testing and training process. By utilize this dataset, we applying the CNN architecture of Alex Net, and Vgg 16 to achieve their performance in terms of accuracy, sensitivity, specificity, F1 score and area under curve. Finally, it proven that Vgg 16 gives 10% more accurate results than Alex Net. This research outcome will be very useful in diagnosis of early-stage Parkinson’s disease in healthcare.
A robust Kalman filter time scale algorithm with data anomaly
Authors: Song H. , Dong S. , Qu L. , Wang X. , Guo D.
Published: June 2021

Kalman filtering is utilized in many fields because of its
capability to separate data from white phase noise. In time and
frequency domain, employing Kalman filter is particularly important
because of its use in building time scales. The studied time scale
algorithms have been usually based on an ensemble of clocks without
data anomaly, or the anomaly data is processed in advance to secure
the reliability of the data used in Kalman filter algorithm. This
increases the amount of computation and affects the real-time
performance of the algorithm. In this study a robust Kalman filter
is employed to present a method of time scale calculation. It
extends a previously published Kalman filter algorithm that is
useful for an ensemble of clocks without phase anomalies. In the
algorithm, the inflation factor and the optimal adaptive factor are
applied to the clock ensemble. The introduced algorithm may be
useful for an ensemble of clocks with measurement outliers and phase
jumps. The effectiveness of the proposed method can be verified
through simulation and experimental analysis. The analysis result
shows that the robust Kalman filter algorithm can resist the
influence of measurement outliers and phase jumps on time scale
performance. So, the accuracy and the stability of time scale can be
improved.
First attempt of global-scale assimilation of subdaily scale soil moisture estimates from CYGNSS and SMAP into a land surface model
Authors: Kim Hyunglok , Lakshmi Venkataraman , Kwon Yonghwan , Kumar Sujay V
Published: June 2021

Soil moisture performs a key function in the hydrologic process and understanding the global-scale water cycle. However, estimations of soil moisture taken from current sun-synchronous orbit satellites are limited in that they are neither spatially nor temporally continuous. This limitation creates discontinuous soil moisture observation from space and hampers our understanding of the fundamental processes that control the surface hydrologic cycle across both time and space domains. Here, we propose to use frequent soil moisture observations from NASA’s constellation of eight micro-satellites called the Cyclone Global Navigation Satellite System (CYGNSS) together with the Soil Moisture Active Passive (SMAP) to assimilate subdaily scale soil moisture into a land surface model (LSM). Our results, which are based on triple collocation analysis (TCA), show how current scientific advances in satellite systems can fill previous gaps in soil moisture observations in subdaily scale by past observations, and eventually adds value to improvements in global scale soil moisture estimates in LSMs. Overall, TCA-based fractional mean square errors of LSM soil moisture are improved by 61.3% with the synergetic assimilation of CYGNSS data with SMAP soil moisture observations. However, assimilating satellite-based soil moisture over dense vegetation areas can degrade the performance of LSMs as these areas propagate erroneous soil moisture information to LSMs. To our knowledge, this study is the first global assimilation of GNSS-based soil moisture observations in LSMs.
Air quality assessment in three East African cities using calibrated low-cost sensors with a focus on road-based hotspots
Authors: Singh Ajit , Ng’ang’a David , Gatari Michael J , Kidane Abel W , Alemu Zinabu A , Derrick Ndawula , Webster Mbujje J , Bartington Suzanne E , Thomas G Neil , Avis William , Pope Francis D
Published: June 2021

Poor air quality is a development challenge. Urbanization and industrial development along with increased populations have brought clear socio-economic benefits to Low-and Middle-Income Countries (LMICs) but can also bring disadvantages such as decreasing air quality. A lack of reliable air quality data in East African cities makes it difficult to understand air pollution exposure and to predict future air quality trends. This work documents urban air quality and air pollution exposure in the capital cities of Kampala (Uganda), Addis Ababa (Ethiopia) and Nairobi (Kenya). We build a situational awareness of air pollution through repeated static and dynamic mobile monitoring in a range of urban locations, including urban background, roadside (pavement and building), rural background, and bus station sites, alongside vehicle-based measurements including buses and motorcycle taxis. Data suggest that the measured particulate matter mass concentrations (PM2.5, PM10) in all studied cities was at high concentrations, and often hazardous to human health, as defined by WHO air quality guidelines. Overall, the poorest air quality was observed in Kampala, where mean daily PM2.5 and PM10 concentrations were significantly above the WHO limits at urban background locations by 122% and 69% and at roadside locations by 193% and 215%, respectively. Traffic is clearly a major contributor to East African urban air pollution; monitoring in Kampala and Addis Ababa, on motorcycle taxis, in buses and at bus stations indicated that drivers and commuters were exposed to poor air quality throughout their commute. Road-related air pollution can also impact indoor locations near roads. Using one exemplar building located within Nairobi’s Central Business District, it is shown that measured outdoor PM concentrations significantly correlate with the indoor air quality (r = 0.84). This link between roadside emissions and indoor air pollution within buildings located close to the road should be explored more fully. This study, through a series of case studies, provides clear evidence that roads and traffic need to be a focus for mitigation strategies to reduce air pollution exposure in East African cities.