bluuebunny/crossref_metadata_2025 huggingface dataset:
A High-Sensitivity SPR Refractive Index Sensor Based on No-Core Fiber with Ag-Cu Composite Films
Authors: Feng Yuhui , Li Hongyu , Li Shuguang , Liu Yundong , Meng Xiaojian
Published: October 2021

A fiber/Ag-Cu films surface plasmon resonance (SPR) refractive index (RI) sensor composed of multimode fiber-no-core-fiber-multimode fiber (MMF-NCF-MMF) structure is designed. The sensing region length and Cu film deposition time of sensor are gradually optimized by the control variable method, which finally achieves the improvement of sensor properties. We experimentally compared the sensing performance of the fiber/Ag film and fiber/Ag-Cu films sensor. Experimental results show that the fiber/Ag-Cu films sensor has good linearity (R-square = 0.993), and its sensitivity is as high as 3957 nm/RIU in the refractive index detection range of 1.3328–1.3853, which is 1109 nm/RIU higher than the sensitivity of a conventional fiber/Ag film sensor. The sensor presented in this paper adopts the structure with composite metal film, which outperforms the common single-layer metal film in chemical stability such as oxidation resistance and mechanical hardness. Meanwhile, the SPR sensor with MMF-NCF-MMF structure has the advantages of convenient manufacture and compact structure. In conclusion, it can bestow a unique advantage in the field of biological detection or chemical analysis.
Infrastructure as Software in Micro Clouds at the Edge
Authors: Simić Miloš , Sladić Goran , Zarić Miroslav , Markoski Branko
Published: October 2021

Edge computing offers cloud services closer to data sources and end-users, making the foundation for novel applications. The infrastructure deployment is taking off, bringing new challenges: how to use geo-distribution properly, or harness the advantages of having resources at a specific location? New real-time applications require multi-tier infrastructure, preferably doing data preprocessing locally, but using the cloud for heavy workloads. We present a model, able to organize geo-distributed nodes into micro clouds dynamically, allowing resource reorganization to best serve population needs. Such elasticity is achieved by relying on cloud organization principles, adapted for a different environment. The desired state is specified descriptively, and the system handles the rest. As such, infrastructure is abstracted to the software level, thus enabling “infrastructure as software” at the edge. We argue about blending the proposed model into existing tools, allowing cloud providers to offer future micro clouds as a service.
The Role of Noise in Specific Detectivity of InAs/GaSb Superlattice MWIR Bariodes
Authors: Czuba Krzysztof , Ciura Łukasz , Sankowska Iwona , Papis-Polakowska Ewa , Jasik Agata
Published: October 2021

In this paper, the results of the electrical, noise, and optical characterization of p-i-n and p-B-i-n diodes with AlSb and 4 ML AlSb/8 ML GaSb superlattice barriers in High-Operating Temperature conditions, are presented. Experimental and theoretical noise parameters were compared. Both dark current and noise analysis showed that the p-Bp_bulk-i-n bariode had the best performance. P-i-n photodiodes had the highest experimental value of specific detectivity (D*) of 6.16 × 109 Jones at 210 K and zero bias. At about −1 V reverse bias, the bariode with AlSb/GaSb electron barrier caught up to it and both devices achieved D* = (1–1.1) × 108 Jones. Further optimization of the superlattice-based electron barrier should result in the improvement of bariode performance at a smaller bias, at which better noise performance is more pronounced. It was shown that neglecting the low-frequency noise component can lead to a significant overestimation of detectivity. The simple method of incorporation of low-frequency noise contribution in the detectivity calculation, without time-consuming measurements, has been proposed.
Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains
Authors: Molino-Minero-Re Erik , Aguileta Antonio A. , Brena Ramon F. , Garcia-Ceja Enrique
Published: October 2021

Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others’ shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or “architectures” as we call them when they are structurally different, so it is now challenging to prescribe which one is better for a specific collection of sensors and a particular application environment, other than by trial and error. We propose an approach capable of predicting the best fusion architecture (from predefined options) for a given dataset. This method involves the construction of a meta-dataset where statistical characteristics from the original dataset are extracted. One challenge is that each dataset has a different number of variables (columns). Previous work took the principal component analysis’s first k components to make the meta-dataset columns coherent and trained machine learning classifiers to predict the best fusion architecture. In this paper, we take a new route to build the meta-dataset. We use the Sequential Forward Floating Selection algorithm and a T transform to reduce the features and match them to a given number, respectively. Our findings indicate that our proposed method could improve the accuracy in predicting the best sensor fusion architecture for multiple domains.
Comparison of Manual Wheelchair and Pushrim-Activated Power-Assisted Wheelchair Propulsion Characteristics during Common Over-Ground Maneuvers
Authors: Khalili Mahsa , Kryt Garrett , Mortenson W. Ben , Van der Loos Hendrik F. Machiel , Borisoff Jaimie
Published: October 2021

Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that use force sensor data to provide on-demand propulsion assistance to manual wheelchair users. However, available data about kinetic and kinematic of PAPAW use are mainly limited to experiments performed on a treadmill or using a dynamometer. In this work, we performed experiments to gather kinetics of wheelchair propulsion and kinematics of wheelchair motion for a variety of over-ground wheelchair maneuvers with a manual wheelchair with and without PAPAWs. Our findings revealed that using PAPAWs can significantly reduce the propulsion effort and push frequency. Both linear and angular velocities of the wheelchair were significantly increased when using PAPAWs. Less force and push frequency could potentially reduce risk of chronic upper limb injury. Higher linear velocity could be desirable for various daily life activities; however; the increase in the angular velocity could lead to unintended deviations from a desired path. Future research could investigate PAPAW controllers that amplify the desired intentions of users while mitigating any unwanted behaviours.
Multiple Compact Camera Fluorescence Detector for Real-Time PCR Devices
Authors: Koo Seul-Bit-Na , Chi Hyeon-Gyu , Kim Jong-Dae , Kim Yu-Seop , Park Ji-Sung , Park Chan-Young , Lee Deuk-Ju
Published: October 2021

The polymerase chain reaction is an important technique in biological research because it tests for diseases with a small amount of DNA. However, this process is time consuming and can lead to sample contamination. Recently, real-time PCR techniques have emerged which make it possible to monitor the amplification process for each cycle in real time. Existing camera-based systems that measure fluorescence after DNA amplification simultaneously process fluorescence excitation and emission for dozens of tubes. Therefore, there is a limit to the size, cost, and assembly of the optical element. In recent years, imaging devices for high-performance, open platforms have benefitted from significant innovations. In this paper, we propose a fluorescence detector for real-time PCR devices using an open platform camera. This system can reduce the cost, and can be miniaturized. To simplify the optical system, four low-cost, compact cameras were used. In addition, the field of view of the entire tube was minimized by dividing it into quadrants. An effective image processing method was used to compensate for the reduction in the signal-to-noise ratio. Using a reference fluorescence material, it was confirmed that the proposed system enables stable fluorescence detection according to the amount of DNA.
WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
Authors: Álvarez-Merino Carlos S. , Luo-Chen Hao Qiang , Khatib Emil Jatib , Barco Raquel
Published: October 2021

High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.
Rapid Detection of Coronavirus (COVID-19) Using Microwave Immunosensor Cavity Resonator
Authors: Elsheakh Dalia M. , Ahmed Mohamed I. , Elashry Gomaa M. , Moghannem Saad M. , Elsadek Hala A. , Elmazny Waleed N. , Alieldin Nelly H. , Abdallah Esmat A.
Published: October 2021

This paper presents a rapid diagnostic device for the detection of the pandemic coronavirus (COVID-19) using a micro-immunosensor cavity resonator. Coronavirus has been declared an international public health crisis, so it is important to design quick diagnostic methods for the detection of infected cases, especially in rural areas, to limit the spread of the virus. Herein, a proof-of-concept is presented for a portable laboratory device for the detection of the SARS-CoV-2 virus using electromagnetic biosensors. This device is a microwave cavity resonator (MCR) composed of a sensor operating at industrial, scientific and medical (ISM) 2.45 GHz inserted in 3D housing. The changes of electrical properties of measured serum samples after passing the sensor surface are presented. The three change parameters of the sensor are resonating frequency value, amplitude and phase of the reflection coefficient |S11|. This immune-sensor offers a portable, rapid and accurate diagnostic method for the SARS-CoV-2 virus, which can enable on-site diagnosis of infection. Medical validation for the device is performed through biostatistical analysis using the ROC (Receiver Operating Characteristic) method. The predictive accuracy of the device is 63.3% and 60.6% for reflection and phase, respectively. The device has advantages of low cost, low size and weight and rapid response. It does need a trained technician to operate it since a software program operates automatically. The device can be used at ports’ quarantine units, hospitals, etc.
Fabrication and Performance of Graphene Flexible Pressure Sensor with Micro/Nano Structure
Authors: Wu Weibin , Han Chongyang , Liang Rongxuan , Xu Jian , Li Bin , Hou Junwei , Tang Ting , Zeng Zhiheng , Li Jie
Published: October 2021

Laser-induced graphene (LIG) has been widely used in flexible sensors due to its excellent mechanical properties and high conductivity. In this paper, a flexible pressure sensor prepared by bionic micro/nanostructure design and LIG mass fraction regulation is reported. First, prepared LIG and conductive carbon paste (CCP) solutions were mixed to obtain a conductive polymer. After the taro leaf structure was etched on the surface of the aluminum alloy plate by Nd:YAG laser processing, the conductive polymer was evenly coated on the template. Pressure sensors were packaged with a stencil transfer printing combined with an Ecoflex flexible substrate. Finally, the effects of different laser flux and the proportion of LIG in the composite on the sensitivity of the sensor are discussed. The results show that when the laser flux is 71.66 J·cm−2 and the mass fraction of LIG is 5%, the sensor has the best response characteristics, with a response time and a recovery time of 86 ms and 101 ms, respectively, with a sensitivity of 1.2 kPa−1 over a pressure range of 0–6 kPa, and stability of 650 cycle tests. The LIG/CCP sensor with a bionic structure demonstrates its potential in wearable devices.
Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach
Authors: Alonso Marcos , Maestro Daniel , Izaguirre Alberto , Andonegui Imanol , Graña Manuel
Published: October 2021

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.
A Hybrid Speech Enhancement Algorithm for Voice Assistance Application
Authors: Gnanamanickam Jenifa , Natarajan Yuvaraj , K. R. Sri Preethaa
Published: October 2021

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.
Live-Cell Systems in Real-Time Biomonitoring of Water Pollution: Practical Considerations and Future Perspectives
Authors: Wlodkowic Donald , Karpiński Tomasz M.
Published: October 2021

Continuous monitoring and early warning of potential water contamination with toxic chemicals is of paramount importance for human health and sustainable food production. During the last few decades there have been noteworthy advances in technologies for the automated sensing of physicochemical parameters of water. These do not translate well into online monitoring of chemical pollutants since most of them are either incapable of real-time detection or unable to detect impacts on biological organisms. As a result, biological early warning systems have been proposed to supplement conventional water quality test strategies. Such systems can continuously evaluate physiological parameters of suitable aquatic species and alert the user to the presence of toxicants. In this regard, single cellular organisms, such as bacteria, cyanobacteria, micro-algae and vertebrate cell lines, offer promising avenues for development of water biosensors. Historically, only a handful of systems utilising single-cell organisms have been deployed as established online water biomonitoring tools. Recent advances in recombinant microorganisms, cell immobilisation techniques, live-cell microarrays and microfluidic Lab-on-a-Chip technologies open new avenues to develop miniaturised systems capable of detecting a broad range of water contaminants. In experimental settings, they have been shown as sensitive and rapid biosensors with capabilities to detect traces of contaminants. In this work, we critically review the recent advances and practical prospects of biological early warning systems based on live-cell biosensors. We demonstrate historical deployment successes, technological innovations, as well as current challenges for the broader deployment of live-cell biosensors in the monitoring of water quality.
The Impact of Artificial Intelligence on Data System Security: A Literature Review
Authors: Raimundo Ricardo , Rosário Albérico
Published: October 2021

Diverse forms of artificial intelligence (AI) are at the forefront of triggering digital security innovations based on the threats that are arising in this post-COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control, and web domain, to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods, and outage management systems in an effort to understand the interplay between AI and those issues. the dependence on the emergence of AI in running industries and shaping the education, transports, and health sectors is now well known in the literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system security within the current digital society is opportune. This paper aims at identifying research trends in the field through a systematic bibliometric literature review (LRSB) of research on AI and system security. the review entails 77 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. the LRSB results were synthesized across current research subthemes. Findings are presented. the originality of the paper relies on its LRSB method, together with an extant review of articles that have not been categorized so far. Implications for future research are suggested.
Neural Network Analysis for Microplastic Segmentation
Authors: Lee Gwanghee , Jhang Kyoungson
Published: October 2021

It is necessary to locate microplastic particles mixed with beach sand to be able to separate them. This paper illustrates a kernel weight histogram-based analytical process to determine an appropriate neural network to perform tiny object segmentation on photos of sand with a few microplastic particles. U-net and MultiResUNet are explored as target networks. However, based on our observation of kernel weight histograms, visualized using TensorBoard, the initial encoder stages of U-net and MultiResUNet are useful for capturing small features, whereas the later encoder stages are not useful for capturing small features. Therefore, we derived reduced versions of U-net and MultiResUNet, such as Half U-net, Half MultiResUNet, and Quarter MultiResUNet. From the experiment, we observed that Half MultiResUNet displayed the best average recall-weighted F1 score (40%) and recall-weighted mIoU (26%) and Quarter MultiResUNet the second best average recall-weighted F1 score and recall-weighted mIoU for our microplastic dataset. They also require 1/5 or less floating point operations and 1/50 or a smaller number of parameters over U-net and MultiResUNet.
WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities
Authors: Gehlot Anita , Singh Rajesh , Kuchhal Piyush , Kumar Adesh , Singh Aman , Alsubhi Khalid , Ibrahim Muhammad , Villar Santos Gracia , Brenosa Jose
Published: October 2021

Currently, two-wheelers are the most popular mode of transportation, driven by the majority the people. Research by the World Health Organization (WHO) identifies that most two-wheeler deaths are caused due to not wearing a helmet. However, the advancement in sensors and wireless communication technology empowers one to monitor physical things such as helmets through wireless technology. Motivated by these aspects, this article proposes a wireless personal network and an Internet of Things assisted system for automating the ignition of two-wheelers with authorization and authentication through the helmet. The authentication and authorization are realized with the assistance of a helmet node and a two-wheeler node based on 2.4 GHz RF communication. The helmet node is embedded with three flex sensors utilized to experiment with different age groups and under different temperature conditions. The statistical data collected during the experiment are utilized to identify the appropriate threshold value through a t-test hypothesis for igniting the two-wheelers. The threshold value obtained after the t-test is logged in the helmet node for initiating the communication with the two-wheeler node. The pairing of the helmet node along with the RFID key is achieved through 2.4 GHZ RF communication. During real-time implementation, the helmet node updates the status to the server and LABVIEW data logger, after wearing the helmet. Along with the customization of hardware, a LABVIEW data logger is designed to visualize the data on the server side.
Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
Authors: Loh Hui Wen , Hong Wanrong , Ooi Chui Ping , Chakraborty Subrata , Barua Prabal Datta , Deo Ravinesh C. , Soar Jeffrey , Palmer Elizabeth E. , Acharya U. Rajendra
Published: October 2021

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
Authors: Chung Youngbeen , Jin Jie , Jo Hyun In , Lee Hyun , Kim Sang-Heon , Chung Sung Jun , Yoon Ho Joo , Park Junhong , Jeon Jin Yong
Published: October 2021

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.
Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning
Authors: Althnian Alhanoof , Almanea Nada , Aloboud Nourah
Published: October 2021

Neonatal jaundice is a common condition worldwide. Failure of timely diagnosis and treatment can lead to death or brain injury. Current diagnostic approaches include a painful and time-consuming invasive blood test and non-invasive tests using costly transcutaneous bilirubinometers. Since periodic monitoring is crucial, multiple efforts have been made to develop non-invasive diagnostic tools using a smartphone camera. However, existing works rely either on skin or eye images using statistical or traditional machine learning methods. In this paper, we adopt a deep transfer learning approach based on eye, skin, and fused images. We also trained well-known traditional machine learning models, including multi-layer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF), and compared their performance with that of the transfer learning model. We collected our dataset using a smartphone camera. Moreover, unlike most of the existing contributions, we report accuracy, precision, recall, f-score, and area under the curve (AUC) for all the experiments and analyzed their significance statistically. Our results indicate that the transfer learning model performed the best with skin images, while traditional models achieved the best performance with eyes and fused features. Further, we found that the transfer learning model with skin features performed comparably to the MLP model with eye features.
An Efficient Electronic Cash System Based on Certificateless Group Signcryption Scheme Using Conformable Chaotic Maps
Authors: Meshram Chandrashekhar , Imoize Agbotiname Lucky , Aljaedi Amer , Alharbi Adel R. , Jamal Sajjad Shaukat , Barve Sharad Kumar
Published: October 2021

Signcryption schemes leveraging chaotic constructions have garnered significant research interest in recent years. These schemes have proffered practical solutions towards addressing the vast security vulnerabilities in Electronic Cash Systems (ECS). The schemes can seamlessly perform message confidentiality and authentication simultaneously. Still, their applications in emerging electronic cash platforms require a higher degree of complexity in design and robustness, especially as billions of online transactions are conducted globally. Consequently, several security issues arise from using open wireless channels for online business transactions. In order to guarantee the security of user information over these safety-limited channels, sophisticated security schemes are solely desired. However, the existing signcryption schemes cannot provide the required confidentiality and authentication for user information on these online platforms. Therefore, the need for certificateless group signcryption schemes (CGSS) becomes imperative. This paper presents an efficient electronic cash system based on CGSS using conformable chaotic maps (CCM). In our design, any group signcrypter would encrypt information/data with the group manager (GM) and send it to the verifier, who confirms the authenticity of the signcrypted information/data using the public criteria of the group. Additionally, the traceability, unforgeability, unlinkability, and robust security of the proposed CGSS-CCM ECS scheme have been built leveraging computationally difficult problems. Performance evaluation of the proposed CGSS-CCM ECS scheme shows that it is secure from the Indistinguishably Chosen Ciphertext Attack. Finally, the security analysis of the proposed technique shows high efficiency in security-vulnerable applications. Overall, the scheme gave superior security features compared to the existing methods in the preliminaries.
PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems
Authors: Zhang Zhong , Yum Dae Hyun , Shin Minho
Published: October 2021

Crowdsensing systems have been developed for wide-area sensing tasks because humancarried smartphones are prevailing and becoming capable. To encourage more people to participate in sensing tasks, various incentive mechanisms were proposed. However, participating in sensing tasks and getting rewards can inherently risk the users’ privacy and discourage their participation. In particular, the rewarding process can expose the participants’ sensor data and possibly link sensitive data to their identities. In this work, we propose a privacy-preserving reward system in crowdsensing using the blind signature. The proposed scheme protects the participants’ privacy by decoupling contributions and rewarding claims. Our experiment results show that the proposed mechanism is feasible and efficient.
On the Performance Evaluations of Cooperative Retransmission Scheme for Cell-Edge Users of URLLC in Multi-Carrier Downlink NOMA Systems
Authors: Ryu Won-Jae , Kim Jae-Woo , Shin Soo-Young , Kim Dong-Seong
Published: October 2021

Non-orthogonal multiple access (NOMA) has a key feature that the cell-center user (CCU) has prior information about the messages of the cell-edge user (CEU) in the same user-pair. It means that CCU can be used for retransmission when the CEU requests retransmission. As ultra-reliability and low-latency communication (URLLC) requires high-reliability constraints (e.g., 99.999%), using CCU for retransmission can be useful to satisfy the reliability constraint. In this study, to ensure the reliability of CEU, cooperative retransmission (CR) scheme for downlink NOMA systems is proposed. And the CR scheme is evaluated with Block error rate (BLER) considering reliability and with packet loss rate (PLR) in terms of reliability and latency constraints. And the evaluation results showed that the proposed CR scheme can satisfy the target BLER for URLLC low SNR compared to the conventional retransmission scheme, and showed the improved PLR compared to the conventional retransmission scheme in low SNRs.
Importance of Diakonoff’s Research in Recognition of the Median Period in Iran
Authors: MORADI Maryam , ALILOO Sara
Published: October 2021

The history of the Median period written by Igor Mikhailovich Diakonoff (published in 1956) was translated to Persian by Karim Keshavarz in 1345 Solar year. This book was the product of profound and insightful research of this author that had been done by the request of Azerbaijan Science Academy in that time and its importance at the time of its publication and translation was immense as there had been no single book or other research publication directly covering this period in the Persian literature before. The available historical or archeological information on this period was only limited to the notes of Greek historians and some new western resources. Although those sources were valuable, they could not fill the vacant place of rigidly and accurately written resources for other researchers of this period because they were not based on direct data i.e. scripts, tablets and other archaeological data found during relevant excavations. The difference between Diakonoff’s research and other resources is that he first gives comprehensive information concerning resources from this period, then fully assesses them, and additionally describes the geographical and historical aspects. This makes Diakonoff’s Median book an exceptionally accurate resource in this regard and therefore even after several decades it still remains so important in Iranology and cognition of Iranians. In fact, one can see the immense impact of Diakonoff’s research on the field of the Median history since the translation time until the present time. It has always been one of the main sources cited in historical, linguistic, ethnological, archaeological and interdisciplinary studies by Iranian researchers.
Process Evaluation on Medical Record Reporting and Information Usage Iskandar Muda Hospital Nagan Raya Regency
Authors: Sahrana Oka , Safrizal Safrizal , Husna Arfah , Fera Dian
Published: October 2021

Medical records are all records and documents about the patient's identities, examinations, treatments, actions and other services provided to the patient. Reporting medical records at Iskandar Muda hospital still does not follow standards. This is due to the lack of discipline of officers in filling out medical records, lack of medical records of officers and related health workers, then also influenced by the Hospital Management Information System that does not yet exist. The purpose of the study was to evaluate the reporting of medical records at Sultan Iskandar Muda hospital. This study uses qualitative research. The results showed that Sultan Iskandar Muda Hospital has been processing medical record data. The procedure of making a report that is not appropriate is the completion of resumes and daily census pain hospitalization. While the proper methods are a recapitulation of outpatient visits, making reports of hospital activities and making morbidity reports of inpatients and outpatients. The medical records unit has produced internal and external reports following the guidelines, and middle-level hospital management has fully used medical record information. It can be concluded that in processing medical record data, there are some obstacles. The procedure of making a report is not following the guidelines, and medical record information has been fully utilized.
Penggunaan Teknologi Biorock® Terhadap Pertumbuhan dan Tingkat Kelangsungan Hidup Karang Acropora di Perairan Karang Jeruk Kabupaten Tegal
Authors: Nugroho Beni Sabdo , Zuhry Noor , Budhiati Retno
Published: October 2021

Perairan Karang Jeruk merupakan salah satu destinasi ekosistem terumbu karang yang terdapat di Kabupaten Tegal. Tujuan penelitian ini adalah untuk mengetahui pertumbuhan dan laju pertumbuhan serta tingkat kelangsungan hidup karang bercabang Acropora yang ditransplantasi dengan metode biorock® di Perairan Karang Jeruk Kabupaten Tegal. Metode yang digunakan dalam penelitian ini adalah metode eksperimen dan observasi. Kemudian dianalisis melalui uji t-Students dengan menggunakan SPSS versi 16. Berdasarkan hasil penelitian, pertumbuhan mutlak karang Acropora pada stasiun biorock® lebih besar dibandingkan dengan pertumbuhan mutlak pada stasiun kontrol, baik untuk tinggi maupun untuk diameter. Dapat dilihat pertumbuhan mutlak tinggi pada stasiun biorock® 44,9750 ± 15,3901 mm sedangkan pada stasiun kontrol 2,1675 ± 0,6726 mm serta pertumbuhan mutlak diameter pada stasiun biorock® 0,8350 ± 0,2177 mm sedangkan pada stasiun kontrol 0,2025 ± 0,0835 mm. Laju pertumbuhan karang Acropora pada stasiun biorock® lebih cepat dibandingkan dengan laju pertumbuhan pada stasiun kontrol, baik untuk tinggi maupun untuk diameter. Dapat dilihat laju pertumbuhan tinggi pada stasiun biorock® 11,2438 ± 3,8475 mm bulan-1 sedangkan pada stasiun kontrol 0,5419 ± 0,1682 mm bulan-1 serta laju pertumbuhan diameter pada stasiun biorock® 0,2088 ± 0,0544 mm bulan-1 sedangkan pada stasiun kontrol 0,0506 ± 0,0209 mm bulan-1. Tingkat kelangsungan hidup karang pada stasiun biorock® dan stasiun kontrol 100%.
TIC aplicadas a la educación en el Grado de Pedagogía y Educación Primaria. La pizarra digital interactiva.
Authors: Llorente Cejudo María del Carmen , Barroso Osuna Julio Manuel , Palacios Rodríguez Antonio de Padua , Martín Párraga Lorena
Published: October 2021

Esta colección supone un total de 26 píldoras formativas, a modo de objetos de aprendizaje, fundamentadas en los contenidos que componen la materia de Tecnologías de la Información y la Comunicación Aplicadas a la educación y Tecnología Educativa de los Grados de Educación Primaria y de Pedagogía de la Facultad de Ciencias de la Educación de la Universidad de Sevilla.
La colección es una producción audiovisual con carácter didáctico, y estará compuesta por 26 videos cortos a modo de objeto de aprendizaje, de no más de 4 minutos de duración como máximo cada uno. Los videos se incorporarán en la plataforma de enseñanza telemática Blackboard en los diferentes temas que el alumno irá abordando a lo largo de la asignatura, y el objetivo fundamental que se persigue es:

- Profundizar, reforzar y ejemplificar los contenidos de aprendizaje que los alumnos han trabajado a lo largo de los diferentes bloques temáticos de la asignatura.
- Diseñar y producir un material multimedia online y offline que le permita a los alumnos reforzar los contenidos trabajados en la asignatura.
- Hacer accesible contenido relevante para la docencia que no puede estar presente en el aula de otra manera.
The effect of cryopreservation on the parameters of mononuclear apoptosis during extracorporeal photopheresis
Authors: Vasilyeva V. A. , Kuzmina L. A. , Petinati N. A. , Drize N. I. , Kamelskikh D. V. , Gaponova T. V. , Nikiforova K. A. , Davidova Yu. O. , Kapranov N. M. , Galtceva I. V. , Parovichnikova E. N. , Savchenko V. G.
Published: October 2021

Introduction. The mechanism of action of extracorporeal photopheresis (ECP) is associated with the induction of apoptosis of lymphocytes, which cause the activation of antigen-presenting cells (APC). As a result of the use of ECF, the number of T-regulatory lymphocytes increases, which induces an immunosuppressive effect. The actual problem lies in the use of cryopreserved and photo-irradiated mononuclear cells.

Aim — to analyze the functional features of cryopreserved mononuclear cells after ECP.

Materials and methods. The study analyzed the indicators of early and late stages of apoptosis in the concentrate of mononuclear cells of different groups, depending on the time of collection and the type of exposure on these concentrates of mononuclear cells: immediately after apheresis (groups 1.1 and 1.2), and after ECP (groups 1.3, 2.1 and 2.2), groups 1.2 and 2.2 were after cryopreservation, and 1.3 — after cryopreservation, and then after ECP. 113 samples of mononuclear cell concentrates obtained from 12 patients with chronic graft-versus-host reaction (GVHD) were analyzed. All groups of samples were cultured to determine the dynamics of changes in indicators of early and late stages of apoptosis over time.

Results. The proportion of lymphocytes in the late stage of apoptosis after 2 days cultivation, after the standard ECP procedure, was comparable to the proportion of lymphocytes in the late stage of apoptosis during ECP followed by cryopreservation of the leukoconcentrate.

Conclusion. It is advisable to collect mononuclear cells, perform their extracorporeal photo-irradiation, and then divide the photo-irradiated mononuclear cells into several parts both for cryopreservation and for the return of non-cryopreserved photo-irradiated mononuclear cells to the patient.
Kaposi’s sarcoma in HIV-negative patient with primary myelofi brosis
Authors: Egorova E. K. , Melikyan A. L. , Subortseva I. N. , Domonova E. A. , Silveystrova O. Y. , Chebotarev D. I. , Kovrigina A. M.
Published: October 2021

Introduction. Kaposi’s sarcoma (КS) is a multi-focal malignant tumor of vascular origin with a predominant lesion of the skin, involving internal organs and lymph nodes. Epidemic and immunosuppressive variants of the disease are considered to be the most studied and common. The main reason for the development of the immunosuppressive variant is the use of immunosuppressive drugs in patients after transplantation of solid organs. Descriptions of the development of Kaposi’s sarcoma in patients with diseases of the hematopoietic system are rare.

Aim — to describe a rare combination of a malignant tumor of vascular origin (Kaposi’s sarcoma) and a chronic myeloproliferative disease: primary myelofi brosis.

Main findings. To verify the diagnosis of Kaposi’s sarcoma in a patient with primary myelofi brosis, the following were used: histological, immunohistochemical studies of skin biopsies and the polymerase chain reaction method with detection of a fl uorescent signal in “real time” in biological material samples (peripheral blood, smear from the mucous membrane of the oropharynx, skin biopsies from lesions) with quantitative determination of the activity of the human herpes virus 8. A comprehensive examination made it possible to establish the fi nal diagnosis of multiple malignant neoplasms and recommend treatment.
Trzy horyzonty znaczeniowe „edukacji dla bezpieczeństwa”
Authors: Rojek Marcin , Matusiak-Rojek Magdalena
Published: October 2021

CEL NAUKOWY: Celem artykułu jest ustalenie znaczenia coraz częściej używanej konstrukcji słownej – „edukacja dla bezpieczeństwa”, której przypisywane są intuicyjne znaczenia i tymczasowe sensy, zależące od kontekstu użycia i wpisania w dany kadr referencyjny. „Edukacja dla bezpieczeństwa” może oznaczać wszystko, przysparzając kłopotliwej wieloznaczności, albo nic, co ogranicza możliwość porozumienia w obrębie środowisk nią zainteresowanych. PROBLEM I METODY BADAWCZE: Główny problem badawczy zawiera się w pytaniu: Czym obecnie w pedagogice jest „edukacja dla bezpieczeństwa? Zastosowano metodę analizy literatury przedmiotu, aktów prawnych oraz działalności Zespołu Edukacji dla Bezpieczeństwa KNP PAN, którego autorzy są członkami. PROCES WYWODU: Na podstawie analizy wyróżniono trzy horyzonty znaczeniowe (prekonceptualizacje) „edukacji dla bezpieczeństwa” i dokonano ich charakterystyki. WYNIKI ANALIZY NAUKOWEJ: Z przeprowadzonej analizy wynika, że „edukacja dla bezpieczeństwa” może być obecnie w pedagogice rozumiana jako: (1) całożyciowa praktyka edukacyjna, (2) przedmiot szkolny oraz (3) przedmiot badań. Wyznaczone tu horyzonty znaczeniowe są jednocześnie możliwymi konceptualizacjami „edukacji dla bezpieczeństwa” w najbliższej przyszłości. WNIOSKI, INNOWACJE, REKOMENDACJE: Współistnienie wskazanych tu prekonceptualizacji sprzyja ich wzajemnym oddziaływaniom wspierających dalszy rozwój perspektywicznie skutkujący wyłonieniem się nowej subdyscypliny – pedagogiki bezpieczeństwa. Bezpieczeństwo podlega dynamicznym zmianom i nie posiada stałego poziomu zorganizowania, dlatego „edukacja dla bezpieczeństwa” powinna być traktowana jako proces całożyciowy, składowa idei life long learning. W „edukacji dla bezpieczeństwa” jako przedmiocie szkolnym dominują treści bezpieczeństwa cywilnego, a przedmiot ten traktowany jest w szkołach jako drugorzędny. W dyskursie pedagogicznym „edukacja dla bezpieczeństwa” coraz częściej zastępuje popularne dotąd „wychowanie dla/do pokoju”
Effectiveness of Synthetic Images in Violence Detection
Authors: Nadeem Muhammad Shahroz , Kurugollu Fatih
Published: October 2021

This paper shows that using synthetic images for violence detection is a feasible option for training deep models, they aid in quick convergence and perform better.
Han-era Funerary Art Objects — Works of Bronze. Han-era Bronze Aesthetics
Authors: Mubing Qiu
Published: October 2021

Speaking of bronze here we refer to various objects made of bronze which is an alloy composed of pure copper and tin, which contains a small fraction of lead. Because bronze is an alloy, its melting point is at a lower temperature than that of pure copper, but the material itself is harder. Ji jin 1 (吉金)ritual metal vessels, mentioned in ancient Chinese sources are in fact works of bronze. The invention of bronze alloys was an event of epochal historical importance. Thus, the period from the invention of bronze to ubiquitous use of iron is called the Bronze Age. The Bronze Age in China began with the birth of the Chinese civilization. It lasted until the end of Han dynasty and had several stages of development. Works of bronze first appeared in China in slave-owning society of XiaShang (夏商2070 BC), and reached its zenith at the peak of slave-owning system at the end of Shang (商)and the beginning of Zhou. After the end of Chunqiu period (春 秋475 BC) the slave-owning system began to decline, and the production of bronze items dwindled. In comparison to Shang and Zhou (周朝) bronze, Han objects are much smaller in size because during this period their primary purpose was to be used in common everyday life. Mystical, solemn and even somewhat barbaric bronze of Shang and Zhou was replaced by modest, practical and convenient household utensils.