bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating the Primary Stability of Implants by two Different Insertion Methodsin Compromised Bone – A Pilot Study
Authors: Bandela Vinod , Munagapati Bharathi , Komala Jayashree , Basany Ram B , Patil Santosh R , Kanaparthi Saraswathi
Published: January 2021

To evaluate the better method of implant insertion into the osteotomy site in compromised quality bone for attaining optimal primary stability and thereby achieving the osseointegration for the ultimate success of implant. A total of 32 specimens (wood blocks) simulating D4 bone, were categorized into two groups. The osteotomy site was prepared as per the protocol and implants were placed till the level of the block. The insertion torque was quantified using digital Kratos torque meter. While the implants were inserted and abutments placed by hand ratcheting in the first group; they were motor-driven in the second group. Later pullout test was carried out in universal testing machine and results were analyzed using IBM SPSS Statistics for Windows Software, version 22 (IBM Corp., Armonk, NY, USA). The mean pull out force values obtained in implants placed by hand driven method were 624.375 N while the force values for implants inserted by motor-driven was 692.625 N. There was a statistically significant difference between hand and motor driven implant into the osteotomy site (p<0.05) between the groups. Of the different methods of implant insertion evaluated in this study, motor-driven imply to have a better primary stability indicating better integration with the bone to become a successful implant.
Construction of a Valid Tool to Assess the Knowledge and Perception of Physiotherapy Students About the Role of Physiotherapy in Depression and Anxiety Due to Chronic Pain Syndrome
Authors: Sureshbabu M Anbupriya , J R Abishek
Published: January 2021

Pain relief is the prior reason of a patient who seeks the health care sector. Effective treatment of pain needs a biopsychosocial approach. To address it, the physiotherapist must have sound knowledge about the psychological aspects of pain.A Qualitative design was utilized to frame questions on the basis of the previously established studies and literature reviews. A 24 item questionnaire under 2 sub domain knowledge and perception was then sent to expert reviews for content validation followed by a face validation.This valid tool can be used as a measurement tool; to assess the knowledge and perception of students; especially students who belong to neurology, rehabilitation and pain relief sectors;about the role of physiotherapy in depression and anxiety in chronic pain syndrome.
Effect of Kele Honey (Trigona Sp) in Malondyaldehide and Superoxide Dismutase Serum and Hepatic Tissue of White Rats (Rattus Norvegicus) Exposed to Cigarettes Smoke
Authors: Dwi-Primayanti I Dewa Ayu Inten , Purnawati Susy , Sukanata Wayan
Published: January 2021

Cigarette smoke contains more than 4000 constituents including tar, nicotine, carbonic monoxide, heavy metals, etc.Cigarette smoke is a source of exogenous free radicals. The accumulation of free radicals causes an imbalance of the endogenous antioxidant body system triggering oxidative stress as a risk factor for various degenerative diseases. The purpose of this study was to determine the effect of giving stingless bee honey to of malondialdehyde (MDA) and superoxide dismutase (SOD)levels in serum and hepatic tissue as a marker of oxidative stress in white rats exposed to cigarette smoke. This study was an experimental study with a posttest only control group design. This research was conducted in vivo at the Integrated Biomedical Laboratory, Faculty of Medicine, Udayana University. The research sample was 30 adult male Wistar rats aged 12-14 weeks, weight 200-400 gr. In this study the sample divided into 2 groups, the control group (exposed to cigarette smoke for 4 weeks of research but not given honey) and the treatment group (exposed to cigarette smoke and given stingless bee honey (Kele-honey) for 4 weeks as well). Statistical analysis using SPSS statistics program, the test used was the average comparative analysis by T-independent test(Mann WhitneyTest for nonparametric) to compare MDA and SOD means of rat serum and hepatic tissue exposed to cigarettes smoke. The results showed that the mean of MDA serum among the control group was 0.2247 ± 0.1192, and among the treatment group was 0.2394 ± 01058, ( p>0.05). The mean of MDA hepatic tissue among the control group was 0.5951 ± 0.1029, and among the treatment group was 0.6721 ± 0.6721, (p>0.05). The results showed that the mean of SOD serum among the control group was 1.0724 ± 0.3446, and among the treatment group was 0.6166 ± 0.2841, (p<0.05). The mean of SOD hepatic among the control group was 1.0174 ± 0.3249, and among the treatment group was 0.6721 ± 0.0847, (p<0.05). The result indicated that mean of MDA both in serum and hepatic, of the treatment group higher than the control group, the differences are not significant. Whereas, the mean of SOD both in serum and hepatic, of the intervention group lower than the control group, the differences are significant.
Assessment of Liver Enzymes Levelamong Obesesudanese Individuals in Khartoum State-Sudan
Authors: Elfaki Elyasa , Hamza Alneil , Ahmed Malak , Ibrahim Mariam , KAbdalhabib Ezedine , Karar Trig
Published: January 2021

Overweight and obesity prevalence continues to increase and worldwide growing epidemic health problems. Obesity imposes a significant effect on liver functions that may be associated with a substantial risk factor for the development of several non-communicable diseases, considerable disability, and premature death. To estimate and compare serum activity of ALT, ALP, and GGT among healthy and obese Sudanese individuals. Materials and methods: a total number of 80 participants were enrolled in this cross-sectional study, and they were categorized into 40 obese and 40 normal-weight individuals. Serum liver enzyme activity was measured for each participant.Results: A significant increase was found in ALT, ALP, and GGT levels among the obese group. There is a statistical difference of GGT level mean between gender within the obese group. A positive correlation was observed between the BMI and serum level of ALT (r=0.343, p =0.002), ALP (r=0.503, p <0.001) and GGT (r=0.237, p= 0.034) and positive correlation between obesity duration and GGT level (r=0.461, p =0.003).Conclusion: Study results revealed that obesity was associated with a substantial elevation in liver enzymes that considered risk factors for liver diseases in Sudanese individuals.
Hemorheological Indices and Glycated Hemoglobin in Type 2 Diabetes Mellitus
Authors: Pangaluri Renuka , Bag Somnath , V. M Vinodhini
Published: January 2021

The use of HbA1c in diagnosis of diabetes mellitus by WHO and ADA has come under scrutiny. Aim: To study the erythrocyte index alterations and their relationship with HbA1c for proper interpretation of HbA1c values in diabetic subjects. Materials and methods: Data from 301 diabetic patients was collected. The parameters included were fasting plasma glucose, red blood cell count, hemoglobin level, hematocrit, glycated Hemoglobin (HbA1c), MCV, MCH, MCHC and RDW. Results: A negative correlation was found between HbA1c and MCV, MCH and MCHC and positive correlation with RDW. Conclusion: Hematological parameters like MCH, MCV and MCHC should be taken into account in interpreting HbA1c levels in diagnosis and management of pre diabetes and diabetes.
Evaluation of Leptin and HOMA-Adiponectin in Hypothyroid Infertile Females
Authors: Senghor K. A. Arul , Shivasekar Meera , Vinodhini V. M
Published: January 2021

Introduction:Thyroid hormones and leptin is a perfect pair that maintains homeostasis of metabolic profile and abnormality in thyroid hormone levels potentiates insulin resistant state.The intriguing relationship between obesity and hypothyroidism is related by the key element leptin leading to consequent clinical implications. The severity of insulin resistance in hypothyroid and adipocytokine levels relate to the chance of pregnancy in infertile women. Aim:To assess the Leptin, insulin resistance and HOMA-adiponectin in Hypothyroid infertile women. Settings and Design:It is an Observational Cross-sectional work conducted in women with 20 to 40 years of age. About 180 infertile females were screened at the Obstetric& Gynecology OPD. 86 married women were categorized as groups: Euthyroid (A) –included fertile women who had TSH and FT4 within normal reference range. Subclinical Hypothyroidism (B) - infertile women with raised TSH ranging from 4.6 – 20 mIU/ml and normal FT4 Overt hypothyroid (C)- includedinfertile women with elevated TSH greater than 20 mIU/ml and decreased free T4 level. Methods and Material:Measurement of adiposity indices that includes calculated parameters such as BMI, WHR, waist circumference / height ratio and body roundness index. Fasting serum sample was collected and analysed for FT3, FT4, TSH, prolactin, insulin. Using Biovendor and DRG Human kit based on ELISA; Adiponectin and Leptin levels was estimated respectively. HOMA-IR and HOMA-adiponectin were calculated. Results:Mean Leptin concentrations and HOMA-adiponectin were significantly increased (p < 0.01) in Overt hypothyroid infertile women as compared to euthyroid and subclinical hypothyroid infertile women. Leptin had significantly positive correlation with TSH, FT3/FT4 ratio whereas negatively correlated with FT4. Significant positive correlation was observed with HOMA-IR and HOMA- adiponectin in hypothyroid infertile women. Conclusions:The study concludes that in hypothyroid infertile women having high leptin levels with reduced insulin sensitivity as related with increased HOMA-adiponectin. Analysis of thyroid profile, insulin sensitivity state along with leptin is mandatory to regulate ovulatory process in infertile women.
Serum Brain-Derived Neurotrophic Factor (BDNF)Level may Predict the Functional Outcome of Acute Ischemic Stroke Patients
Authors: Astuti Astuti , Sutarni Sri , Setyopranoto Ismail
Published: January 2021

Introduction:Due to the high prevalence, disability, and cost incurred by acute ischemic stroke, several parameters to predict the functional outcome of stroke have been developed. Brain-Derived Neurotrophic Factor (BDNF) is a protein that plays an important role in neuroplasticity after a stroke attack. Lower BDNF level was associated with increased risk of developing stroke and poor prognostic functional outcome in stroke patients. Here, we measured serum BDNF levels in the acute and chronic phases of acute ischemic stroke patients to see whether the level was changing and affecting the functional outcome. Methods: A hospital-based prospective cohort study was conducted in the three largest governmental hospitals in Yogyakarta, Indonesia. Acute ischemic stroke patients were consecutively recruited fromJune 2018 until July 2019. Serum BDNF level measurements using enzyme-linked immunosorbent assay (ELISA) and functional outcome assessments using Barthel Index (BI) were performed on the 5th and 30th days after stroke onset, representing the acute and chronic phases of stroke, respectively. Results: Sixty-eight patients completed the study and were categorized into dependent (n=22) and independent (n=46) groups according to BI score on the 30th day after stroke onset. The mean serum BDNF level in the acute phase of the independent group was significantly higher than the dependent group (27,152.28 vs 23,143.41; p=0.044). Similar results were also found in the measurement of serum BDNF levels in the chronic phase in which the mean serum BDNF level of the independent group was found to be significantly higher than the dependent group (27,526.48 vs 22,818.91; p=0.036). There were no significant changes in the measurement of serum BDNF level between the acute and chronic phases in both dependent and independent groups. Conclusions: Serum BDNF level, either in the acute or chronic phase of stroke onset, may predict the functional outcome of the acute ischemic stroke patients.
Serum Carboxy Methyl Lysine, Insulin Resistance and Sensitivity in Type 2 Diabetes Mellitus and Diabetic Nephropathy Cases; an Observational Study
Authors: Deepika R Sai , KN Shashidhar , A Raveesha , C Muninarayana
Published: January 2021

Objective: Diabetes Mellitus (DM) and one of its types; type 2 diabetes mellitus (T2DM) is more prevalent from adolescent across the globe, invariable of heredity and age. Diet restriction shall cope up and help body metabolism to absorb the required nutrition and eliminate the junk out of body under healthy diet. Masking of insulin action on target cells leads to insulin resistance (IR) and decreased insulin sensitivity (IS) resulting in increased glycated products such, glycated hemoglobin, glycated albumin and other glycated macromolecules called Advanced Glycation End products. Along with AGE and diabetic profiling, BMI, insulin and lipid profiling may help elucidate the correlation between CML and glucose metabolism in diabetics and diabetic nephropathy cases. CML is formed by Glycoxidation and lipoxidation. Thereby, main objective of this study includes basic renal profiling was also performed to assess kidney functioning and finding its relation with CML. Results: Correlation of CML with important diabetic and metabolic syndrome parameters were performed, giving a supportive evidence for labeling CML as harmful molecule. CML positively correlated with HbA1c, TG and HOMA IR and negatively correlated with QUICKI in group 2 signifying increased damage to tissues due to collective action of glucose, lipid and insulin resistance. Monitoring plasma CML regularly during follow up along with HbA1c may help keep track on plasma glucose status and its deleterious effects on tissues thereby preventing erosion of tissue and vascular lining.
DigitalWatering Hole Attack Detection Using Sequential Pattern
Authors: Subburaj T. , Suthendran K.
Published: January 2021

Internet plays a vital role in day to day communication, business transactions etc and thus unavoidable. But many of the users are lagging in using the same in a secured manner which increases the possibility of attack. In 2017, Attackers had targeted dozens of global banks with new malware.Watering hole attacks attempt to infect more than 100 organizations in 31 different countries. It is becoming very difficult to detect and prevent the cyber attacks, since new attacks are increasing day by day.Awatering hole attack is a computer attack in which the attacker aims to victim the kind of websites that the target group go to often and checks these websites for vulnerabilities. After that by injecting Java Script or HTMLredirects the victim to a separate site hosting the exploit code for the chosen vulnerability. In this paper, a novel method is proposed to detect the possibility of watering hole attack using support count and confidence of the sequence pattern mining. Further by analysing the website URL links, alarming the users about the watering hole attack.
Boosting Based Implementation of Biometric Authentication in IoT
Authors: Thilagavathi B. , Suthendran K.
Published: January 2021

In security and control application the biometric authentication played a specific and important role to identify the person. Analysis of face recognization is the prerequisite process for the entire authentication. This paper focuses an automatic real-time implementation of face recognization system by highlevel description language such as python. Comparing the biometrics where still images are used, video based biometric holds ample information than a single image. It provides the innovative solution for automatic real time face recognition from the video by the following algorithms like Adaboost, Haar cascade classifier and local binary pattern Histogram (LBPH). From the video stream, the input images are trained by adaboost algorithm which is implemented in cascade classifier. AdaBoost is called as adaptive boosting algorithm. It is a learning algorithm which has been combined with a weak classifier in order to form a strong classifier. Here, the real time testing image is compared with the 627 frames of trained images which are obtained from 47.7 MB (56088648 bytes) video. The hardware implementation of face recognization is obtained and the result can be stored and viewed in http://169.254.108.24. Thus by the above said procedures are authenticated by the combinational performance of Adaboost and Cascade classifier.
Handling Selfishness over Collaborative Mechanism in a Mobile Ad hoc Network
Authors: Yamini K. Anish Pon , Kannan Suthendran , Thangadurai Arivoli
Published: January 2021

Major constraint of Mobile ad hoc networks is unpredictable mobility and limited resources like energy consumption which may ultimately end in failure of network connectivity and performance deterioration. Almost all of the suggested techniques presume that every nodes share their memory space with full collaboration. But in realism, few nodes selfishly decide either to cooperate partially only with other nodes, or not at all. Thus, a advanced collaborative mechanism based on dissemination of selfish node awareness is suggested. Numerical results reveal that such mechanism results in better improvement in performance.
A Secured MANET Using Multicast Routing Protocols and Semi Markov Process
Authors: M Maragatharajan , S. P. Balakannan
Published: January 2021

Reliable data delivery is essential in the mobile ad hoc network (MANET) and the devices change their positions very frequently since they do not have any fixed infrastructure. In this paper, we have proposed multicast routing protocols for military communications. The military communications with MANET require data security. We using one of the most widely used algorithms Neighbor supporting Ad hoc Multicast routing protocol which is Mesh-based routing algorithm. Semi Markov process is used to develop the node behavior model for network survivability. In this work, reliable data delivery is obtained for the MANET by estimating the present performance of the network through isolating the forwarder node in Semi Markov process.
Survey on Access Control Mechanisms in Cloud Computing
Authors: Karatas Gozde , Akbulut Akhan
Published: January 2021

The benefits that Internet-based applications and services have given to the end user with today’s cloud computing technology are very remarkable. The distributed services instantly scaled over the Internet provided by cloud computing can be achieved by using some mechanisms in the background. It is a critical task for end users to control access to resources because lack of control often leads to security risks. In addition, this may cause systems to fail. This paper describes seven different access control mechanisms used in cloud computing platforms for different purposes. Besides, the advantages and disadvantages of various models developed from previous service-based architectures and used for cloud computing are detailed and classified. During the assessments, NIST’s metrics were taken as a reference, and in the study, 109 articles from the past decade were examined.We also compared our research with the existing survey papers.
A Hybrid Approach of Secret Sharing with Fragmentation and Encryption in Cloud Environment for Securing Outsourced Medical Database: A Revolutionary Approach
Authors: Le Dac-Nhuong , Seth Bijeta , Dalal Surjeet
Published: January 2021

Cloud Computing is observed as the greatest paradigm change in Information technology. Data outsourcing is an inventive representation with the intention of trustworthy storage and proficient query execution to customers. Data stored on the cloud is showing great attention. However, the security issues allied with data storage over the cloud is a chief daunting cause for potential adopters. Hence the focus is to find techniques that will offer more security. Many diseases fighting organizations are working together io implement cloud as a data sharing vehicle. It is obligatory to build up innovative solutions with the intention of amalgamate diverse approaches in order to generate flexible and adaptable systems, particularly for achieving elevated levels of utilization of developed algorithms. In this document, we suggest an innovative model based on fragmentation, secret sharing and encryption for medical databases which will divide the data amongst several cloud service providers. We develop a systematic structure exploiting the sensitive nature of information and results in enhanced security level. A database for medical system is represented as Entity association and Relational model. A cloud based model is proposed to offer secure patient centric right to access PHR in a competent way. The simulation results implemented in NetBeans Java for performance evaluation of existing cryptographic techniques are shown. Our security model is evaluated using CrypTool 1.4.30 considering the entropy of algorithms. The future work includes development of a computerized system retrieving, storing and maintaining data efficiently and quickly.
Enhanced Matrix Chain Multiplication
Authors: Suvarna B. , Padmaja T. Maruthi
Published: January 2021

Let A1, A2,....An be the given sequence of n matrices, generally matrix chain multiplication algorithm is used to obtain its-product with minimum cost(lowest cost). However the matrix chain multiplication is a dynamic programming paradigm and takes O(n3) computational complexity. Here we present improved algorithm for matrix chain multiplication with minimum space and time complexities. The viability of this new algorithm is demonstrated using few examples and the performance is computationally verified. This algorithm does not take O(n3) if any two of the S values are not same and O(n3) when the two values of S are same in the worst case.
Trustworthy Vehicular Communication Employing Multidimensional Diversification for Moving-target Defense
Authors: Ghourab Esraa M. , Samir Effat , Azab Mohamed , Eltoweissy Mohamed
Published: January 2021

Enabling trustworthy Vehicle to Vehicle (V2V) communication given the wireless medium and the highly dynamic nature of the vehicular environment is a hard challenge. Eavesdropping and signal jamming in such highly dynamic environment is a real problem. This paper proposes a nature inspired multidimensional Moving-Target Defense (MTD) that employs real time spatiotemporal diversity to obfuscate wireless signals against attacker reach. In space: the mechanism manipulates the wireless transmission pattern and configuration to confuse eavesdroppers. In Time: we manipulate the transmission payload, by intentionally injecting some fake data into the real transmission. Further, the mechanism changes the data transmission granularity over time from fine to coarse grained data chunks. As a case study, we assumed the direct transmission model across dynamic multi-paths relayed communication via vehicles traveling on a multi-lane road. The system is evaluated based on a complete analysis of the system model and comprehensive simulated scenarios. Results showed the effectiveness of the presented approach with an increased confusion factor, a massive reduction in the intercept probability and clear increase in the channel secrecy.
ScaleNet: Scalable and Hybrid Framework for Cyber Threat Situational Awareness Based on DNS, URL, and Email Data Analysis
Authors: Vinayakumar R. , Soman K. P. , Poornachandran Prabaharan , Mohan Vysakh S. , Kumar Amara Dinesh
Published: January 2021

A computer virus or malware is a computer program, but with the purpose of causing harm to the system. This year has witnessed the rise of malware and the loss caused by them is high. Cyber criminals have continually advancing their methods of attack. The existing methodologies to detect the existence of such malicious programs and to prevent them from executing are static, dynamic and hybrid analysis. These approaches are adopted by anti-malware products. The conventional methods of were only efficient till a certain extent. They are incompetent in labeling the malware because of the time taken to reverse engineer the malware to generate a signature. When the signature becomes available, there is a high chance that a significant amount of damage might have occurred. However, there is a chance of detecting the malicious activities quickly by analyzing the events of DNS logs, Emails, and URLs. As these unstructured raw data contains rich source of information, we explore how the large volume of data can be leveraged to create cyber intelligent situational awareness to mitigate advanced cyber threats. Deep learning is a machine learning technique largely used by researchers in recent days. It avoids feature engineering which served as a critical step for conventional machine learning algorithms. It can be used along with the existing automation methods such as rule and heuristics based and machine learning techniques. This work takes the advantage of deep learning architectures to classify and correlate malicious activities that are perceived from the various sources such as DNS, Email, and URLs. Unlike conventional machine learning approaches, deep learning architectures don’t follow any feature engineering and feature representation methods. They can extract optimal features by themselves. Still, additional domain level features can be defined for deep learning methods in NLP tasks to enhance the performance. The cyber security events considered in this study are surrounded by texts. To convert text to real valued vectors, various natural language processing and text mining methods are incorporated. To our knowledge, this is the first attempt, a framework that can analyze and correlate the events of DNS, Email, andURLsat scale to provide situational awareness against malicious activities. The developed framework is highly scalable and capable of detecting the malicious activities in near real time. Moreover, the framework can be easily extended to handle large volume of other cyber security events by adding additional resources. These characteristics have made the proposed framework stand out from any other system of similar kind.
Feature Extraction and Classification Using Deep Convolutional Neural Networks
Authors: Bodapati Jyostna Devi , Veeranjaneyulu N.
Published: January 2021

The impressive gain in performance obtained using deep neural networks (DNN) for various tasks encouraged us to apply DNN for image classification task. We have used a variant of DNN called Deep convolutional Neural Networks (DCNN) for feature extraction and image classification. Neural networks can be used for classification as well as for feature extraction. Our whole work can be better seen as two different tasks. In the first task, DCNN is used for feature extraction and classification task. In the second task, features are extracted using DCNN and then SVM, a shallow classifier, is used to classify the extracted features. Performance of these tasks is compared. Various configurations ofDCNNare used for our experimental studies.Among different architectures that we have considered, the architecture with 3 levels of convolutional and pooling layers, followed by a fully connected output layer is used for feature extraction. In task 1 DCNN extracted features are fed to a 2 hidden layer neural network for classification. In task 2 SVM is used to classify the features extracted by DCNN. Experimental studies show that the performance of υ-SVM classification on DCNN features is slightly better than the results of neural network classification on DCNN extracted features.
The Need for Steganalysis in Image Distribution Channels
Authors: Steinebach Martin , Liu Huajian , Ester Andre
Published: January 2021

The rise of social networks during the last 10 years has created a situation in which up to 100 million new images and photographs are uploaded and shared by users every day. This environment poses an ideal background for those who wish to communicate covertly by the use of steganography. It also creates a new set of challenges for steganalysts, who have to shift their field of work away from a purely scientific laboratory environment and into a diverse real-world scenario, while at the same time having to deal with entirely new problems, such as the detection of steganographic channels or the impact that even a low false positive rate has when investigating the millions of images which are shared every day on social networks. We evaluate how to address these challenges with traditional steganographic and statistical methods, rather than using high performance computing and machine learning. To achieve this we first analyze the steganographic algorithm F5 applied to images with a high degree of diversity, as would be seen in a typical social network.We show that the biggest challenge lies in the detection of images whose payload is less then 50% of the available capacity of an image.We suggest new detection methods and apply these to the problem of channel detection in social network. We are able to show that using our attacks we are able to detect the majority of covert F5 channels after a mix containing 10 stego images has been classified by our scheme.
Hardware Random Number Generator Using FPGA
Authors: Devi D. Indhumathi , Chithra S. , Sethumadhavan M.
Published: January 2021

Random numbers are employed in wide range of cryptographic applications. Output of an asynchronous sampling of ring oscillators can be used as the source of randomness and Linear Hybrid CellularAutomata is used to improve the quality of random data. FPGA is an ideal platform for the implementation of random number generator for cryptographic applications. The circuit described in this paper has been implemented on a highly efficient FPGA board which generated a 32-bit random number at a frequency of 125 MHz. The generated sequence of random numbers were subjected to Diehard test and NIST test for testing randomness and found to pass these tests. These tests are a battery of statistical tests for measuring the quality of a random number generator.
A Decentralized Blockchain-based Architecture for a Secure Cloud-Enabled IoT
Authors: Marwan Mbarek , Temghart Abdelkarim Ait , Sifou Fatima , AlShahwan Feda
Published: January 2021

The integration of cloud computing and Internet of Things (IoT) offers a promising, rich platform for data collection and analysis in smart healthcare. In such a model, IoT devices collect data about patient health status through multiple intelligent sensors, whereas cloud offers scalable resources to quickly meet workload demands. Despite these remarkable improvements, the current architectures do not sufficiently address the security needs for patient medical records. In this perspective, and bearing in mind the specific characteristics of each technology, we propose a distributed security mechanism in a way that fits with IoT and cloud constraints. Our contribution to secure cloud-enabled IoT is twofold. First, we rely on OM-AM (Objective, Model, Architecture and Mechanism) for modeling and analysing the security and privacy requirements of smart healthcare. Second, we use blockchain architecture along with Attribute-Based Access Control (ABAC) model as a decentralized flexible system to support access control decisions. In particular, we rely on XACML (eXtensible Access Control Markup Language) to easily build and implement robust policies required for maintaining a secure IoT-based environment. The novelty of the proposed framework lies at smartly leveraging the recent technologies to keep health information confidential. In fact, putting blockchain and IoT together would undoubtedly create a totally new solution for remote patient monitoring. The simulation results show that the proposal is an efficient way of implementing ubiquitous and cognitive tools for smart healthcare systems.
An Application of CHNN for FANETs Routing Optimization
Authors: Wei Xing , Yang Hua
Published: January 2021

Routing algorithm has a decisive influence on routing quality, and routing quality has a direct impact on network performance. For FANETs, the highly dynamically changing topology poses a challenge to the design of routing algorithms. The paper studies the characteristics of FANETs, and uses a CHNN to search for FANETs routing to form CHNNR. Using NS3 as a simulation tool, a highly dynamic simulation scheme in the background of the network topology of the air flight platform was designed, making the simulation scene closer to the dynamic performance of the FANETs highly dynamic mobile node. By comparing parameters such as network delay, normalized network throughput, routing load and data transmission success rate, the performance of CHNNR and passive routing algorithms is analyzed and compared. The simulation results show that the comprehensive performance of CHNNR is better than other passive routing algorithms, and it is more suitable for FANETs networks where nodes move at a high speed and the network topology changes frequently, and lay the foundation for the next research.
Sentimental Analysis using Capsule Network with Gravitational Search Algorithm
Authors: Diviya Prabha V , Rathipriya R
Published: January 2021

Day by day the recent development of communication and the data on the web is increasing tremendously. Moreover, the use of social media among people to express their opinion has greatly increased. Therefore, analyzing this textual data using sentimental analysis techniques can be very helpful in capturing and categorizing people’s opinions. This work aims to propose an algorithm which is combination of Capsule Network (CN) with Gravitational Search Algorithm (GSA) to analyze people’s sentiments from twitter data. In text data mining, CN works to an excessive extent for sentiment analysis compared with other models. The performance of the proposed approach is studied using existing benchmark datasets and COVID-19 twitter posts. The results showed that the proposed approach could automatically classify the sentiments with high performance. It works better compared to other algorithms and results also encourage further research.
Robust Optimization of Best-worst Multi-criteria Decision-making Method
Authors: Qu Deqiang , Wu Zhong , Qu Shaojian , Zhang Fan , Li Ping
Published: January 2021

The Best-worst multi-criteria decision-making method can determine optimal weight value of each criteria. It uses two vectors for pairwise comparisons in multi-criteria decision-making problem. This paper improves the original method from the perspective of robust optimization. Four robust counterpart constraints instead of two linear constraints in original optimization model are proposed. The decision-making problem can divide into full consistent and non-full consistent problems by classifying parameter value. We can achieve a unique set of interval solution in full consistent decision-making problem. Non-full consistent problem can result in multiple sets of optimal interval solution. The result which we get from this method is more effective than the original method. Each criterion can achieve optimal weight interval value. Then we take quantile value of each interval as optimal weight. This is effectively illustrated in the numerical test at the end of the paper.
A new construction of anticode-optimal Grassmannian codes
Authors: DELA CRUZ Ben Paul , LAMPOS John Mark , PALİNES Herbert , SİSON Virgilio
Published: January 2021

In this paper, we consider the well-known unital embedding from $\FF_{q^k}$ into $M_k(\FF_q)$ seen as a map of vector spaces over $\FF_q$ and apply this map in a linear block code of rate $\rho/\ell$ over $\FF_{q^k}$. This natural extension gives rise to a rank-metric code with $k$ rows, $k\ell$ columns, dimension $\rho$ and minimum distance $k$ that satisfies the Singleton bound. Given a specific skeleton code, this rank-metric code can be seen as a Ferrers diagram rank-metric code by appending zeros on the left side so that it has length $n-k$. The generalized lift of this Ferrers diagram rank-metric code is a Grassmannian code. By taking the union of a family of the generalized lift of Ferrers diagram rank-metric codes, a Grassmannian code with length $n$, cardinality $\frac{q^n-1}{q^k-1}$, minimum injection distance $k$ and dimension $k$ that satisfies the anticode upper bound can be constructed.
INFLUENCE OF NUMERICAL SOIL MODEL TYPE ON DYNAMIC RESPONSE OF REINFORCED CONCRETE FRAME STRUCTURES
Authors:   Josip Juraj Strossmayer University of Osijek, Faculty of Civil Engineering and Architecture Osijek, Ravlić Patricija , Kraus Ivan ,   Josip Juraj Strossmayer University of Osijek, Faculty of Civil Engineering and Architecture Osijek
Published: January 2021

In engineering practice, numerical models of structures are typically supported using nondeformable media. However, the compliance and stratification of the underlying soil influence the seismic response of the structure. Its influence on structures subjected to dynamic loads has been investigated using various approaches to soil foundation modeling. This study was conducted for 70 different cases. Three actual soil profiles and two structures were analyzed, and the soil was modeled in three ways. Dynamic analysis was performed using five records of previous earthquakes. Finally, the relationship between the magnitude of the top displacement of the structure and the frequency content of ground motions was established.
NUMERICAL ANALYSIS OF WELDED BEAM-COLUMN JOINTS IN ALUMINUM STRUCTURES
Authors:   University of Split, Faculty of Civil Engineering, Architecture and Geodesy, Hržić Tin , Boko Ivica ,   University of Split, Faculty of Civil Engineering, Architecture and Geodesy, Torić Neno ,   University of Split, Faculty of Civil Engineering, Architecture and Geodesy
Published: January 2021

The objective of this study is to compare the performance for welded beam-column joints in aluminum structures using moment-rotation diagrams and to evaluate the influence of welding on joint rigidity. A nonlinear numerical calculation of welded beam–column joints is performed using the finite element method. Material and geometric nonlinearities were included in the model. The connection was analyzed for two cases: variants with and without welded stiffener plates. Two material models were used. The first was derived from the applicable standard for aluminum structures, while the other was based on the results of a previous material study and calibrated using a separate numerical calculation. From the results of the calculations, moment-rotation diagrams for different geometries and material properties of joints were constructed and compared. The obtained moment-rotation diagrams for the stiffened and unstiffened joints demonstrate that stiffening of the joints in aluminum structures improves the properties of the joint in terms of higher rigidity, resistance, and ductility, regardless of the existence of the heat affected zone.
PROVIDING THE THERMAL RELIABILITY OF WINDOW JUNCTIONS DURING THE THERMAL MODERNIZATION OF CIVIL BUILDINGS
Authors:   Department of building, road machines and construction, Central Ukrainian National Technical University, Kropyvnytskyi, Pashynskyi Mykola , Dzhyrma Stanislav ,   Department of building, road machines and construction, Central Ukrainian National Technical University, Kropyvnytskyi, Pashynskyi Victor ,   Department of building, road machines and construction, Central Ukrainian National Technical University, Kropyvnytskyi, Nastoyashchiy Vladislav ,   Department of building, road machines and construction, Central Ukrainian National Technical University, Kropyvnytskyi
Published: January 2021

In order to assess the possibility of condensation on an internal window jamb surface, the temperature fields of window junctions to brick and precast concrete walls of civil buildings were analyzed, which were massively produced in the second half of the twentieth century. This study obtained the dependences of the jamb temperature on the thickness of the additional insulation layer and the outdoor temperature. Additionally, it showed the possibility of adjusting the jamb temperature by shifting the window frame inside the building. Outside air temperatures at which no condensation will occur in the initial junction structures and after thermal modernization by additional facade insulation were determined. The results can be used to replace worn-out windows and during the thermal modernization of buildings.
MODIFICATION OF ASPHALT MIXES USING WHITE CEMENT DUST AND IRON FILINGS AS A FILLER
Authors:   Department of Civil Engineering, Faculty of Engineering, Al-Balqa Applied University, Al-Salt, Jordan, Khliefat Ibrahim , Msallam Majed ,   Department of Civil Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
Published: January 2021

The possibility of using white cement dust and iron filings as fillers to modify asphalt mix properties was investigated. Numerous aggregate tests were conducted, including sieve analysis, specific gravity, absorption, and abrasion tests. Several bitumen tests were also conducted, including penetration, softening point, ductility, flash point, and specific gravity tests. Marshall properties were used to evaluate the physical performance of the mixtures. The construction of pavements with iron filings in hot asphalt mixtures is not recommended, but white cement dust powder or limestone powder can be beneficial. A 5% content was observed to be the best white cement dust content. There is no clear relationship between air voids and the percentages of white cement dust and iron filings, as the curve fluctuates. The use of white cement dust as a filler always exhibits better results; therefore, using white cement dust widely is recommended over using iron filings.
Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo
Authors: Schleusener Johannes , Guo Shuxia , Darvin Maxim E. , Thiede Gisela , Chernavskaia Olga , Knorr Florian , Lademann Jürgen , Popp Jürgen , Bocklitz Thomas W.
Published: January 2021

Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.
Alternative Method to Design Null-screens for Testing Freeform Surfaces
Authors: Gonzalez-Utrera D. , Aguirre-Aguirre D. , Díaz-Uribe R.
Published: January 2021

New null-screens are presented to test freeform surfaces by reflection. The design starts with a uniform and/or partial sampling over the surface; afterwards, rays fulfilling reflection law are traced to the screen and the detector.
Exploring the design space of 3-mirror freeform imagers
Authors: Bauer Aaron , Schiesser Eric M. , Rolland Jannick P.
Published: January 2021

Variations on a large aperture three-mirror freeform imager are explored. The tradeoff of optical performance with design parameters such as F-number and FOV aspect ratio is quantified. Reimaging and wide-FOV systems are also studied.
A Space Compression Optic
Authors: DelMastro Michael P. , Reshef Orad , Bearne Katherine K. M. , Alhulaymi Ali H. , Giner Lambert , Boyd Robert W. , Lundeen Jeff S.
Published: January 2021

We theoretically introduce a new optic, a plate that reduces propagation length for image formation. We experimentally demonstrate it advances a beam’s focus and shifts an obliquely incident beam identically to a longer propagation distance.
Active Modulation of Valley Excitons in a Monolayer WSe2 via Chiral Metamaterials
Authors: Wu Zilong , Zheng Yuebing
Published: January 2021

Active modulation of handedness-selective valley dynamics in a monolayer WSe2 is achieved using plasmonic moiré chiral metamaterials (MCMs) with strong and dynamically tunable optical chirality.
Engineering and Controlling Optical Tamm States Confined at Plasmonic Metasurfaces
Authors: Buchnev Oleksandr , Belosludtsev Alexandr , Reshetnyak Victor , Evans Dean R. , Fedotov Vassili A.
Published: January 2021

We demonstrate experimentally that optical Tamm states corresponding to Tamm plasmons can be supported by a dielectric mirror interfaced with a metasurface, a discontinuous thin metal film periodically patterned on the sub-wavelength scale. Not only do such states survive the nano-patterning of the metal film, but they also become sensitive to external perturbations, as a result. In particular, by depositing a nematic liquid crystal on the outer side of the metasurface we were able to red-shift Tamm plasmons by 35 nm, while electrical switching of the liquid crystal enabled us to tune the wavelength of these notoriously inert excitations within a 10 nm range.
Model on Higher Odd Harmonic Generation from Electronic Self-Phase Modulation under Extreme High Intense Pumping of Rare Gas Molecules
Authors: Alfano Robert R. , Mazhar Shah Faisal B. , Shi Lingyan
Published: January 2021

A new theoretical model is presented on the underlying mechanism for odd higher harmonics generation (HHG) produced by ultrafast laser pulses from the nonlinear optical-electronic response n2 for SPM effects in rare gas molecules.
Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting interferometry based on deep learning
Authors: Xu Xiaoqing , Xie Ming , Ji Ying , Wang Yawei
Published: January 2021

In dual-wavelength interferometry, the key issue is how to efficiently retrieve the phases at each wavelength using the minimum number of wavelength-multiplexed interferograms. To address this problem, a new dual-wavelength interferogram decoupling method with the help of deep learning is proposed in this study. This method requires only three randomly phase-shifted dual-wavelength interferograms. With a well-trained deep neural network, one can obtain three interferograms with arbitrary phase shifts at each wavelength. Using these interferograms, the wrapped phases of a single wavelength can be extracted, respectively, via an iterative phase retrieval algorithm, and then the phases at different synthetic beat wavelengths can be calculated. The feasibility and applicability of the proposed method are demonstrated by simulation experiments of the spherical cap and red blood cell, respectively. This method will provide a solution for the problem of phase retrieval in multiwavelength interferometry.