bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Assessment of BDS-3 PPP-B2b/INS Loosely Coupled Integration
Authors: Xu Xiaofei , Nie Zhixi , Wang Zhenjie , Wang Boyang , Du Qinghuai
Published: June 2022

The BeiDou global navigation satellite system (BDS-3) has been officially providing a real-time precise point positioning (PPP) augmentation service, known as the PPP-B2b service, since 2020. Decimeter-level positioning accuracy is expected to be achieved based on the PPP-B2b service. It shows great potential for global navigation satellite system (GNSS) real-time applications, including, for example, vehicle positioning on land. However, the application of the PPP-B2b service is still full of challenges in the urban environment because of GNSS signal blockage. The inertial navigation system (INS) is a popular technology which can provide continuous positions under GNSS challenging scenarios. In this study, we constructed a BDS-3 PPP-B2b/INS loosely coupled integration system for vehicle positioning and evaluated its performance through two automotive experiments. In the first experiment, four periods of 30 s GNSS outages were simulated to evaluate the performance of PPP-B2b/INS loosely coupled integration during GNSS outages. During the simulated GNSS outages, PPP-B2b positioning did not work. Nevertheless, PPP-B2b/INS loosely coupled integration provided continues solution through INS mechanization. The averaged positioning errors at the last epoch of outages were 300.6/498.0/41.0 cm for PPP-B2b/MEMS-IMU and 18.6/21.8/6.1 cm for PPP-B2b/Tactical-IMU loosely coupled integration, in the east, north and up directions, respectively. In the second experiment, we drove the land vehicle in a complex urban environment for 15 min. During this period, two GNSS signal interruptions occurred due to the occlusion of bridges, lasting 15 s and 5 s, respectively. The results show that the improvement of positioning accuracy in the east, north, and up components were 64.1%, 77.8%, and 73.8% respectively for PPP-B2b/MEMS-IMU loosely coupled integration, and 63.9%, 79.5%, and 74.4% respectively for PPP-B2b/Tactical-IMU loosely coupled integration, as compared to the positioning accuracy of PPP-B2b only.
Time Series of Remote Sensing Data for Interaction Analysis of the Vegetation Coverage and Dust Activity in the Middle East
Authors: Namdari Soodabeh , Zghair Alnasrawi Ali Ibrahim , Ghorbanzadeh Omid , Sorooshian Armin , Kamran Khalil Valizadeh , Ghamisi Pedram
Published: June 2022

Motivated by the lack of research on land cover and dust activity in the Middle East, this study seeks to increase the understanding of the sensitivity of dust centers to climatic and surface conditions in this specific region. In this regard, we explore vegetation cover and dust emission interactions using 16-day long-term Normalized Difference Vegetation Index (NDVI) data and daily Aerosol Optical Depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and conduct spatiotemporal and statistical analyses. Eight major dust hotspots were identified based on long-term AOD data (2000–2019). Despite the relatively uniform climate conditions prevailing throughout the region during the study period, there is considerable spatial variability in interannual relationships between AOD and NDVI. Three subsets of periods (2000–2006, 2007–2013, 2014–2019) were examined to assess periodic spatiotemporal changes. In the second period (2007–2013), AOD increased significantly (6% to 32%) across the studied hotspots, simultaneously with a decrease in NDVI (−0.9% to −14.3%) except in Yemen−Oman. Interannual changes over 20 years showed a strong relationship between reduced vegetation cover and increased dust intensity. The correlation between NDVI and AOD (−0.63) for the cumulative region confirms the significant effect of vegetation canopy on annual dust fluctuations. According to the results, changes in vegetation cover have an essential role in dust storm fluctuations. Therefore, this factor must be regarded along with wind speed and other climate factors in Middle East dust hotspots related to research and management efforts.
Distribution and Attribution of Earlier Start of the Growing Season over the Northern Hemisphere from 2001–2018
Authors: Chen Xiaona , Yang Yaping , Du Jia
Published: June 2022

The start of the growing season (SOS) is a vital ecological indicator for climate change and the terrestrial ecosystem. Previous studies have reported that the SOS over the Northern Hemisphere (NH) has experienced remarkable changes in the past few decades. However, because of the different spatial and temporal coverages of existing SOS studies, a coherent and robust account for SOS changes in the NH has been lacking. Using satellite-retrieved vegetation-phenology datasets, ground observations, and several auxiliary datasets, this study evaluated the performance of the latest MODIS vegetation-dynamics dataset (MCD12Q2-C6) and explored the distribution and attribution of the SOS to climate change over the NH for the period 2001–2018. The validation results using the Chinese Ecosystem Research Network (CERN) and Lilac-leafing observations (Lilac) displayed that the MCD12Q2-C6 has a good performance in SOS monitoring over the NH mid-latitudes. Meanwhile, evidence from MCD12Q2-C6 pointed out that the SOS was advanced by 2.08 days on average over the NH during 2001–2018, especially for Europe, China, and Alaska, United States. In addition, detailed-sensitivity analysis showed that the increased surface air temperature (Ts) (−1.21 ± 0.34 days °C−1) and reduced snow-cover fraction (Sc) (0.62 ± 0.29 days%−1) were the key driving factors of the observed SOS changes over the NH during 2001–2018. Compared with Ts and Sc, the role of total precipitation (Pt) was minor in dominating the spring vegetation-phenology changes at the same period. The findings of this study contribute to our understanding of the responses of SOS to the competing changes of Ts, Pt, and Sc over the NH.
Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm
Authors: Chen Yuanlin , Fan Meng , Li Mingyang , Li Zhongbin , Tao Jinhua , Wang Zhibao , Chen Liangfu
Published: June 2022

Due to the advantage of geostationary satellites, Himawari-8/AHI can provide near-real-time air quality monitoring over China with a high temporal resolution. Satellite-based aerosol optical depth (AOD) retrieval over land is a challenge because of the large surface contribution to the top of atmosphere (TOA) signal and the uncertainty of aerosol modes. Here, by combining satellite TOA reflectance, sun-sensor geometries, meteorological factors and vegetation information, we propose a data-driven AOD detection algorithm based on a deep neural network (DNN) model for Himawari-8/AHI. It is trained by sample data of 2018 and 2019 and is applied to derive hourly AODs over China in 2020. By comparison with ground-based AERONET measurements, R2 for DNN-estimated AOD is up to 0.8702, which is much higher than that for the AHI AOD product with R2 = 0.4869. The hourly AOD results indicate that the DNN model has a good potential in improving the performance of AOD retrieval in the early morning and in the late afternoon, and the spatial distribution is reliable for capturing the variation of aerosol pollution on the regional scale. By analyzing different DNN modeling strategies, it is found that seasonal modeling can hardly increase the accuracy of AOD retrieval to a certain extent, and R2 increases from 0.7394 to 0.8168 when meteorological features, especially air pressure, are involved in the model training.
Community Scale Assessment of the Effectiveness of Designed Discharge Routes from Building Roofs for Stormwater Reduction
Authors: Fu Xiaoran , Wang Dong , Luan Qinghua , Liu Jiahong , Wang Zhonggen , Tian Jiayu
Published: June 2022

Urban flooding is increasing due to climate change and the expansion of impervious land surfaces. Green roofs have recently been identified as effective solutions for mitigating urban stormwater. However, discharge routes that involve receiving catchments of stormwater runoff from roofs to mitigate high flows have been limited. Thus, a hydrological model was constructed to investigate the effects of changing discharge routes on stormwater flow. Three hypothetical scenarios were assessed using various combinations of discharge routes and roof types. The reduction effects on outflow and overflow were identified and evaluated across six return periods of designed rainstorms in the Tai Hung Tulip House community in Beijing. The results showed that green roofs, together with the discharge routes connecting to pervious catchments, were effective in reducing peak flow (13.9–17.3%), outflow volume (16.3–27.3%), drainage overflow frequency, and flood duration. Although mitigation can be improved by considering discharge routes, it is limited compared to that achieved by the effects of green roofs. However, integrating green roofs and discharge routes can improve community resilience to rainstorms with longer return periods. These results provide useful information for effective design of future stormwater mitigation and management strategies in small-scale urban areas.
Three-Dimensional Imaging with Bistatic Vortex Electromagnetic Wave Radar
Authors: Liang Jia , Zhang Qun , Luo Ying , Yuan Hang , Chen Yijun
Published: June 2022

The vortex electromagnetic wave radar based on monostatic geometry can realize the imaging of a radar target in both range dimension and azimuth dimension by adjusting vortex electromagnetic wave modes, but it has no ability to distinguish the elevation of the radar target. To achieve three-dimensional imaging of the radar target, a three-dimensional imaging method of vortex electromagnetic wave based on bistatic geometry is proposed. The echo model of the target based on vortex electromagnetic wave in bistatic geometry is constructed firstly. The elevation resolution of the target is realized, and then the three-dimensional coordinate of the target is reconstructed. Moreover, the elevation resolution and the computational complexity of the proposed method are also analyzed. Simulation results demonstrate the effectiveness of the proposed method. The results also illustrate that the array element requirements and the computational complexity of presented algorithm are less than Multiple Signal Classification (MUSIC) algorithm.
Particle Swarm Optimization-Based Variational Mode Decomposition for Ground Penetrating Radar Data Denoising
Authors: Liu Sixin , Chen Yuhan , Luo Chaopeng , Jiang Hejun , Li Hong , Li Hongqing , Lu Qi
Published: June 2022

Ground Penetrating Radar (GPR) has become a widely used technology in geophysical prospecting. The Variational Mode Decomposition (VMD) method is a fully non-recursive signal decomposition method with noise robustness for GPR data processing. The VMD algorithm determines the central frequency and bandwidth of each Intrinsic Mode Function (IMF) by iteratively searching for the optimal solution of the variational mode and is capable of adaptively and effectively dividing the signal in the frequency domain into the many IMFs. However, the penalty parameter α and the number of IMFs K in VMD processing are determined depending on manual experience, which are important parameters affecting the decomposition results. In this paper, we propose a method to automatically search the parameters α and K optimally by Particle Swarm Optimization (PSO) algorithm. Then the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) are used to judge the best superposition of the IMFs for data reconstruction, and the process is data-driven without human subjective intervention. The proposed method is used to process the field data, and the reconstruction data show that this innovative VMD processing can effectively improve the SNR and highlight the target reflections, even some targets not found in pre-processing are also revealed.
Analysis of the First Optical Detection of a Meteoroidal Impact on the Lunar Surface Recorded from Brazil
Authors: Duarte Cavalcante Pinto David , Yanagisawa Masahisa , Zurita Marcelo Luiz do Prado Villarroel , Caldas Romualdo Arthur Alencar , Domingues Marcelo , Costa Rafaela Lisboa , da Rocha Júnior Rodrigo Lins , dos Santos Silva Fabrício Daniel , Gomes Heliofábio Barros , Gomes Helber Barros , de Melo Maria Luciene Dias , de Morais Teixeira Lucas , da Silva Júnior Ernande Roberto , Junior Neftali Dias Cavalcante , Herdies Dirceu Luís
Published: June 2022

Two lunar flashes are reported and fully analyzed, with one of them fulfilling every criterion preconized in the literature for the characterization of an impact, including confirmation by two simultaneous observations. It happened at 07:13:46 UT on 14 December 2017, at the selenographic coordinates of 9.79° (±0.06°)N and 45.42 (±0.07°)E. The peak magnitudes in the R and V bands vary from 6.3 to 7.9 and from 7.4 to 9.0, respectively, depending on the observatory, as the cameras’ exposure times were considerably different. The impactor mass is estimated to be between 1.6 and 2.0 kg, with a diameter of 10 to 11 cm, having produced a crater of 8.4 to 8.9 m in diameter. Results for the second flash are also presented and discussed, although the confirmation of an impact was not possible due to a pause in the recordings at one of the sites. The observations took place as part of an inaugural observing campaign in Brazil for lunar impact flash (LIF) detection conceived by the Brazilian Meteor Observation Network (BRAMON) and were carried out by two teams located in different states in the Northeast Region of Brazil, about 353 km apart from each other, at a time when the Moon was crossing the densest part of the Geminid meteoroid stream in 2017. The observing setups included 0.13 m and 0.2 m telescopes, both equipped with sensitive cameras. The Maceió setup probably delivered the finest definition ever reported in the literature for lunar impact monitoring, resulting in high-accuracy positioning. This will certainly aid in finding the associated crater from orbiter images, which will substantiate another work, aimed at performing a comparative analysis between the results from our photometry and the data retrieved by the LRO images. These observations were also very likely the first and the only one so far made by a normal framerate camera and a long-exposure camera simultaneously. The associated benefits are commented on. The source of the impactors is also discussed. In view of the successful results of this experience, national observing campaigns of this kind will be given continuation.
Severity Estimation for Interturn Short-Circuit and Demagnetization Faults through Self-Attention Network
Authors: Lee Hojin , Jeong Hyeyun , Kim Seongyun , Kim Sang Woo
Published: June 2022

This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based on a self-attention module. The self-attention mechanism enhances the high-dimensional feature extraction and regression ability of the network by concentrating on specific sequence representations, thereby supporting the estimation of the fault severities. The proposed strategy can diagnose a hybrid fault in which the ISCF and DF occur simultaneously and does not require the exact model and parameters essential for the existing method for estimating the fault severity. The effectiveness and feasibility of the proposed fault diagnosis strategy are demonstrated through experimental results based on various fault cases and load torque conditions.
FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN)
Authors: Bazangani Farideh , Richard Frédéric J. P. , Ghattas Badih , Guedj Eric
Published: June 2022

Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due to the difficulties of data collection and data privacy, finding an appropriate dataset (balanced, enough samples, etc.) is quite a challenge. Although image synthesis could be beneficial to overcome this issue, synthesizing 3D images is a hard task. The main objective of this paper is to generate 3D T1 weighted MRI corresponding to FDG-PET. In this study, we propose a separable convolution-based Elicit generative adversarial network (E-GAN). The proposed architecture can reconstruct 3D T1 weighted MRI from 2D high-level features and geometrical information retrieved from a Sobel filter. Experimental results on the ADNI datasets for healthy subjects show that the proposed model improves the quality of images compared with the state of the art. In addition, the evaluation of E-GAN and the state of art methods gives a better result on the structural information (13.73% improvement for PSNR and 22.95% for SSIM compared to Pix2Pix GAN) and textural information (6.9% improvements for homogeneity error in Haralick features compared to Pix2Pix GAN).
Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia
Authors: Ye Chen , Xiao Yuhao , Li Ruoyu , Gu Hongkai , Wang Xinyu , Lu Tianyang , Jin Lingjing
Published: June 2022

Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD.
Recent Advances in Flexible Sensors and Their Applications
Authors: Zazoum Bouchaib , Batoo Khalid Mujasam , Khan Muhammad Azhar Ali
Published: June 2022

Flexible sensors are low cost, wearable, and lightweight, as well as having a simple structure as per the requirements of engineering applications. Furthermore, for many potential applications, such as human health monitoring, robotics, wearable electronics, and artificial intelligence, flexible sensors require high sensitivity and stretchability. Herein, this paper systematically summarizes the latest progress in the development of flexible sensors. The review briefly presents the state of the art in flexible sensors, including the materials involved, sensing mechanisms, manufacturing methods, and the latest development of flexible sensors in health monitoring and soft robotic applications. Moreover, this paper provides perspectives on the challenges in this field and the prospect of flexible sensors.
Marine Application Evaluation of Monocular SLAM for Underwater Robots
Authors: Zhang Yang , Zhou Li , Li Haisen , Zhu Jianjun , Du Weidong
Published: June 2022

With the development of artificial intelligence technology, visual simultaneous localization and mapping (SLAM) has become a cheap and efficient localization method for underwater robots. However, there are many problems in underwater visual SLAM, such as more serious underwater imaging distortion, more underwater noise, and unclear details. In this paper, we study these two problems and chooses the ORB-SLAM2 algorithm as the method to obtain the motion trajectory of the underwater robot. The causes of radial distortion and tangential distortion of underwater cameras are analyzed, a distortion correction model is constructed, and five distortion correction coefficients are obtained through pool experiments. Comparing the performances of contrast-limited adaptive histogram equalization (CLAHE), median filtering (MF), and dark channel prior (DCP) image enhancement methods in underwater SLAM, it is found that the DCP method has the best image effect evaluation, the largest number of oriented fast and rotated brief (ORB) feature matching, and the highest localization trajectory accuracy. The results show that the ORB-SLAM2 algorithm can effectively locate the underwater robot, and the correct distortion correction coefficient and DCP improve the stability and accuracy of the ORB-SLAM2 algorithm.
Exploration of Mobile Device Behavior for Mitigating Advanced Persistent Threats (APT): A Systematic Literature Review and Conceptual Framework
Authors: Jabar Thulfiqar , Mahinderjit Singh Manmeet
Published: June 2022

During the last several years, the Internet of Things (IoT), fog computing, computer security, and cyber-attacks have all grown rapidly on a large scale. Examples of IoT include mobile devices such as tablets and smartphones. Attacks can take place that impact the confidentiality, integrity, and availability (CIA) of the information. One attack that occurs is Advanced Persistent Threat (APT). Attackers can manipulate a device’s behavior, applications, and services. Such manipulations lead to signification of a deviation from a known behavioral baseline for smartphones. In this study, the authors present a Systematic Literature Review (SLR) to provide a survey of the existing literature on APT defense mechanisms, find research gaps, and recommend future directions. The scope of this SLR covers a detailed analysis of most cybersecurity defense mechanisms and cutting-edge solutions. In this research, 112 papers published from 2011 until 2022 were analyzed. This review has explored different approaches used in cybersecurity and their effectiveness in defending against APT attacks. In a conclusion, we recommended a Situational Awareness (SA) model known as Observe–Orient–Decide–Act (OODA) to provide a comprehensive solution to monitor the device’s behavior for APT mitigation.
A Simulation-Based Approach to Aid Development of Software-Based Hardware Failure Detection and Mitigation Algorithms of a Mobile Robot System
Authors: Sini Jacopo , Passarino Andrea , Bonicelli Stefano , Violante Massimo
Published: June 2022

Mechatronic systems, like mobile robots, are fairly complex. They are composed of electromechanical actuation components and sensing elements supervised by microcontrollers running complex embedded software. This paper proposes a novel approach to aid mobile robotics developers in adopting a rigorous development process to design and verify the robot’s detection and mitigation capabilities against random hardware failures affecting its sensors or actuators. Unfortunately, assessing the interactions between the various safety/mission-critical subsystem is quite complex. The failure mode effect analysis (FMEA) alongside an analysis of the failure detection capabilities (FMEDA) are the state-of-the-art methodologies for performing such an analysis. Various guidelines are available, and the authors decided to follow the one released by AIAG&VDA in June 2019. Since the robot’s behavior is based on embedded software, the FMEA has been integrated with the hardware/software interaction analysis described in the ECSS-Q-ST-30-02C manual. The core of this proposal is to show how a simulation-based approach, where the mechanical and electrical/electronic components are simulated alongside the embedded software, can effectively support FMEA. As a benchmark application, we considered the mobility system of a proof-of-concept assistance rover for Mars exploration designed by the D.I.A.N.A. student team at Politecnico di Torino. Thanks to the adopted approach, we described how to develop the detection and mitigation strategies and how to determine their effectiveness, with a particular focus on those affecting the sensors.
Design of a Smart IoT-Based Control System for Remotely Managing Cold Storage Facilities
Authors: Mohammed Maged , Riad Khaled , Alqahtani Nashi
Published: June 2022

Cold storage is deemed one of the main elements in food safety management to maintain food quality. The temperature, relative humidity (RH), and air quality in cold storage rooms (CSRs) should be carefully controlled to ensure food quality and safety during cold storage. In addition, the components of CSR are exposed to risks caused by the electric current, high temperature surrounding the compressor of the condensing unit, snow and ice accumulation on the evaporator coils, and refrigerant gas leakage. These parameters affect the stored product quality, and the real-time sending of warnings is very important for early preemptive actionability against the risks that may cause damage to the components of the cold storage rooms. The IoT-based control (IoT-BC) with multipurpose sensors in food technologies presents solutions for postharvest quality management of fruits during cold storage. Therefore, this study aimed to design and evaluate a IoT-BC system to remotely control, risk alert, and monitor the microclimate parameters, i.e., RH, temperature, CO2, C2H4, and light and some operating parameters, i.e., the temperature of the refrigeration compressor, the electrical current, and the energy consumption for a modified CSR (MCSR). In addition, the impacts of the designed IoT-BC system on date fruit quality during cold storage were investigated compared with a traditional CSR (TCSR) as a case study. The results showed that the designed IoT-BC system precisely controlled the MCSR, provided reliable data about the interior microclimate atmosphere, applied electrical current and energy consumption of the MCSR, and sent the necessary alerts in case of an emergency based on real-time data analytics. There was no significant effect of the storage time on the most important quality attributes for stored date fruit in the MCSR compared with the TCSR. As a result, the MCSR maintained high-quality attributes of date fruits during cold storage. Based on the positive impact of the designed IoT-BC system on the MCSR and stored fruit quality, this modification seems quite suitable for remotely managing cold storage facilities.
ROSEBUD: A Deep Fluvial Segmentation Dataset for Monocular Vision-Based River Navigation and Obstacle Avoidance
Authors: Lambert Reeve , Chavez-Galaviz Jalil , Li Jianwen , Mahmoudian Nina
Published: June 2022

Obstacle detection for autonomous navigation through semantic image segmentation using neural networks has grown in popularity for use in unmanned ground and surface vehicles because of its ability to rapidly create a highly accurate pixel-wise classification of complex scenes. Due to the lack of available training data, semantic networks are rarely applied to navigation in complex water scenes such as rivers, creeks, canals, and harbors. This work seeks to address the issue by making a one-of-its-kind River Obstacle Segmentation En-Route By USV Dataset (ROSEBUD) publicly available for use in robotic SLAM applications that map water and non-water entities in fluvial images from the water level. ROSEBUD provides a challenging baseline for surface navigation in complex environments using complex fluvial scenes. The dataset contains 549 images encompassing various water qualities, seasons, and obstacle types that were taken on narrow inland rivers and then hand annotated for use in semantic network training. The difference between the ROSEBUD dataset and existing marine datasets was verified. Two state-of-the-art networks were trained on existing water segmentation datasets and tested for generalization to the ROSEBUD dataset. Results from further training show that modern semantic networks custom made for water recognition, and trained on marine images, can properly segment large areas, but they struggle to properly segment small obstacles in fluvial scenes without further training on the ROSEBUD dataset.
Warranty Seal Deformation Identification for Product Warranty Violation
Authors: Awal  Md Rabiul , Zakaria Nurul Ain , Jusoh Muzammil , Abdelghany  Mahmoud A. , Yahya Muhammad Syarifuddin , Zakaria Hidayatul Aini
Published: June 2022

Product warranty seals or stickers are criteria for after-sale warranty services. The unauthorized removal or modification of a seal will void the warranty. So far, there is no detection method to confirm the warranty, other than the visual inspection of the deformation of the seal. Hence, a system to detect, read, and record the ’warranty’ seal deformation is presented in this paper. A flexible piezoelectric sensor was used to determine the mechanical impacts of the seal. Three major impacts are discussed and evaluated in this paper—partial removal, complete removal, and drop deformations of the seal. These impacts were compared with the ambient responses to distinguish the conditions. All three impact cases show distinct characteristics in terms of sensor values, pulses, and pulse widths. For partial removal and complete removal of the seal, both cases exhibited maximum sensor values but differed in pulse and pulse width. A partially removed seal experienced the maximum number of pulses while complete removal experienced the maximum pulse width. However, if the seal experienced a drop impact, it showed lower sensor values, with the lowest pulse and pulse width. Hence, an algorithm was applied to generalize the conditions and decisions of warranty violations.
A New Axial Stress Measurement Method for High-Strength Short Bolts Based on Stress-Dependent Scattering Effect and Energy Attenuation Coefficient
Authors: Fu Tong , Chen Ping , Yin Aijun
Published: June 2022

The accurate estimation of axial stresses is a major problem for high-strength bolted connections that needs to be overcome to improve the assembly quality and safety of aviation structures. However, the conventional acoustoelastic effect based on velocity-stress dependence is very weak for short bolts, which leads to large estimation errors. In this article, the effect of axial stress on ultrasonic scattering attenuation is investigated by calculating the change in the energy attenuation coefficient of ultrasonic echoes after applying axial preload. Based on this effect, a stress-dependent attenuation estimation model is developed to measure the bolt axial stress. In addition, the spectrum of the first and second round-trip echoes is divided into several frequency bands to calculate the energy attenuation coefficients, which are used to select the frequency band sensitive to the axial stress changes. Finally, the estimation model between axial stress and energy attenuation coefficients in the sensitive frequency band is established under 20 steps of axial preloads. The experimental results show that the energy attenuation coefficient in the sensitive band corresponds well with axial stress. The average relative error of the predicted axial stress is 6.28%, which is better than that of the conventional acoustoelastic effect method. Therefore, the proposed approach can be used as an effective method to measure the axial stress of short bolts in the assembly of high-strength connections.
Main Metabolites of Pseudomonas aeruginosa: A Study of Electrochemical Properties
Authors: Schneider Sylvia , Ettenauer Jörg , Pap Ildiko-Julia , Aspöck Christoph , Walochnik Julia , Brandl Martin
Published: June 2022

Pseudomonas aeruginosa is a ubiquitously distributed soil and water bacterium and is considered an opportunistic pathogen in hospitals. In cystic fibrosis patients, for example, infections with P. aeruginosa can be severe and often lead to chronic or even fatal pneumonia. Therefore, rapid detection and further identification are of major importance in hospital hygiene and infection control. This work shows the electrochemical properties of five P. aeruginosa key metabolites considering their potential use as specific signaling agents in an electrochemical sensor system. The pure solutes of pyocyanin (PYO), Pseudomonas quinolone signal (PQS), pyochelin (PCH), 2-heptyl-4-hydroxyquinoline (HHQ), and 2-heptyl-4-hydroxyquinoline N-oxide (HQNO) were analyzed by different electrochemical techniques (cyclic and square wave voltammetry) and measured using a Gamry Reference 600+ potentiostat. Screen-printed electrodes (DropSens DRP110; carbon working and counter, silver reference electrode) were used to determine signal specificities, detection limits, as well as pH dependencies of the substances. All of the compounds were electrochemically inducible with well-separated oxidation and/or reduction peaks at specific peak potentials relative to the reference electrode. Additionally, all analytes exhibited linear concentration dependency in ranges classically reported in the literature. The demonstration of these properties is a promising step toward direct multiplexed detection of P. aeruginosa in environmental and clinical samples and thus, can make a significant contribution to public health and safety.
Enriching Facial Anti-Spoofing Datasets via an Effective Face Swapping Framework
Authors: Yang Jiachen , Lan Guipeng , Xiao Shuai , Li Yang , Wen Jiabao , Zhu Yong
Published: June 2022

In the era of rapid development of the Internet of things, deep learning, and communication technologies, social media has become an indispensable element. However, while enjoying the convenience brought by technological innovation, people are also facing the negative impact brought by them. Taking the users’ portraits of multimedia systems as examples, with the maturity of deep facial forgery technologies, personal portraits are facing malicious tampering and forgery, which pose a potential threat to personal privacy security and social impact. At present, the deep forgery detection methods are learning-based methods, which depend on the data to a certain extent. Enriching facial anti-spoofing datasets is an effective method to solve the above problem. Therefore, we propose an effective face swapping framework based on StyleGAN. We utilize the feature pyramid network to extract facial features and map them to the latent space of StyleGAN. In order to realize the transformation of identity, we explore the representation of identity information and propose an adaptive identity editing module. We design a simple and effective post-processing process to improve the authenticity of the images. Experiments show that our proposed method can effectively complete face swapping and provide high-quality data for deep forgery detection to ensure the security of multimedia systems.
Assessment of income in healthcare facilities in accordance with the requirements of the Federal Standard Accounting of the public sector and IPSAS
Authors:   Basic Department of Financial Control, analysis and audit of the Main Control of Moscow, Federal State Budgetary Educational Institution higher education Plekhanov Russian University of Economics, Trofimova Liudmila Borisovna
Published: June 2022

The article examines approaches to assessing income in accordance with the requirements of the Federal Standard Accounting and IPSAS on the example of a budgetary healthcare institution of the City Clinical Hospital No. N.I. Pirogov. The income of a budgetary institution in the reporting is reflected in terms of all types of activities carried out by institutions of this organizational and legal form. The author proposes and substantiates the approach of dividing income received into income from exchange and non-exchange transactions in the statement of financial results in order to facilitate subsequent activities for the assessment of accounting objects related to the procedure for transforming financial statements into the format IPSAS.
The role of information and psychological operations in the escalation of the Ukrainian crisis
Authors:   Diplomatic Academy of the Ministry of Foreign Affairs of the Russian Federation, Surma Ivan V.
Published: June 2022

The article shows the importance and increasing role of the information and psychological warfare factor as the Ukrainian crisis escalates. The role of the Special Operations Forces of Ukraine in the informational and psychological confrontation is noted. The structure and functionality of the 16th, 72nd, 74th and 83rd centers of information and psychological operations of the Special operations Forces of Ukraine are considered. The main functions of these centers are shown, such as intelligence and subversive activities, identification of internal opponents, disinformation of the population, information terrorism, demoralization of citizens of Donbass, etc. The tools used by the centers of information and psychological operations in their activities are considered, in addition to the official media, several thousand Internet resources, information and news sites, coordinated groups of users of social networks and individual "platforms" in social networks are used. The role of the NATO Coordination Center for Responding to Computer Incidents (NCIRC — NATO Computer Incident Response Capability), the NATO Center of Excellence for Joint Cyber Defense (NATO CCD COE — NATO Cooperative Cyber Defense Center of Excellence — Tallinn) and the cyber operations center in Mons (Belgium) is noted.
Basic Department of Trade Policy, Plekhanov Russian University of Economics
Authors:   Department of Economic Theory, Plekhanov Russian University of Economics, Russia, Moscow, Dubovik Mayya Valerianovna , Kozlova Daria Vladimirovna ,   Department of Economics, Customs, Information Technology and Natural Science Disciplines of the Bryansk Branch of the Plekhanov Russian University of Economics, Russia, Bryansk, Kipen Irina Stepanovna ,   Department of Economics, Customs, Information Technology and Natural Science Disciplines of the Bryansk Branch of the Plekhanov Russian University of Economics, Russia, Bryansk
Published: June 2022

The article presents the results of a study of the dynamics of lending carried out by regional banks of legal entities and individuals in the regions of the Central Federal District of Russia. Our correlation analysis did not show a significant relationship between the number of regional banks and the level of development of lending in the studied subjects of the Federation. We also compared the number of regional banks and the share of unprofitable organizations, but the results of this analysis showed no relationship between these variables. Thus, there is no statistical evidence that regional banks contribute to the development of the region. We believe that the centralization of the Russian banking sector leads to monopolization and slows down economic growth.
Development of the green economy in Sweden
Authors:   Plekhanov Russian University of Economics, Russia, Moscow,, Murtuzaliev Sergey Stanislavovich , Murtuzalieva Svetlana Yuryevna ,   Higher School of Industrial Policy and Entrepreneurship of the RUDN University, Russia
Published: June 2022

In the last decade, the importance of the concept of green economy has increased both in the political and economic context. This concept covers a number of policy measures aimed at reducing the negative impact of economic activity on the environment. Sweden is one of the most ambitious economies actively working towards the development of a green economy. It showed other countries that it is possible to combine economic growth with a reduction in the carbon footprint. The country has the most comprehensive carbon tax, actively invests in the development of electric vehicles, 50 % of its electricity comes from renewable sources. While some countries are hesitating and lagging behind in their environmental programs, Sweden’s response to the COVID-19 pandemic has been to redouble efforts to transition to a green economy: increased spending aimed at decarbonization, energy efficiency, the creation of green technologies and environmental protection. Eco-innovation is a key element of Sweden’s national environmental policy strategy, and the country is the flagship among the countries of the world in the development of new technologies in such areas as bioenergy, smart power grids, eco-friendly construction, waste recycling, environmentally friendly transport, water management and solar energy. Also, the Swedish government actively subsidizes the green industry and helps polluting industries to reduce the negative impact on the environment. As a long-term climate goal, Sweden intends to achieve zero emissions into the atmosphere by 2045. In this article, the author analyzes the development of Sweden’s green economy in the economic and political spectrum.
Analisis Terhadap PenegakAnalisis Penegakan Hukum Pemilu Dalam Perspektif UU No 7 Tahun 2017
Authors: Octavia Vanessa vanessa
Published: June 2022

Abstract

The title of this is article is "Analysis of Election Law Enforcement in the Perspective of Law No. 7 of 2017" The main problem we raised is how the KPU faces changes to Law No. 15 of 2011 concerning election administrators and Law Number 8 of 2012 concerning the election of DPR, DPD and DPRD Members into Law Number 7 of 2017 and what are the challenges faced by KPU in the Implementation of Laws Number 7 of 2017. In this paper the method we used is quantitative analysis that gives an overview of an object through certain analytical techniques. The ways that can be done by the KPU in dealing with changes to Law Number 15 Year 2011 concerning election administrators and Law Number 8 of 2012 concerning the election of DPR, DPD and DPRD Members become Law Number 7 Year 2017. Something that can be made are involving electoral institutions, which in this case is the Bawaslu (Election Supervisory Body) and its ranks.  Therefore, Bawaslu must increase strict supervision in the administration of elections.

Keywords: how to deal with changing laws, challenges faced by the KPU, Law Number 7 of 2017
Sosyo-Psikolojik Faktörlerin Seçmen Davranışlarına Etkileri: Pamukkale Üniversitesi Örneği
Authors: FİLİZ Uğur , POLAT Ferihan
Published: June 2022

Siyasal davranış ve siyasal katılım çalışmaları içinde gelişen seçmen davranışı araştırmaları, bu gün hem siyaset bilimcilerin hem de kamu oyunun en çok dikkatini çeken konulardan biridir. Seçmen davranışı çok boyutlu bir kavram olup, birçok faktörün etkisi altında gerçekleşmektedir. Bu çalışma, seçmen davranışına etki eden sosyo-psikolojik faktörlere ilişkin bir alan araştırmasına dayanmaktadır. Bu amaçla, Pamukkale Üniversitesine kayıtlı öğrencilerden, 410 denek üzerinde 37 soruluk anket çalışması yapılmıştır. Genç seçmenlerin kendilerini tanımladıkları siyasi kimlikler ile elde edilen veriler çaprazlanarak, oy vermelerine etki eden psikolojik faktörler ortaya konmaya çalışılmıştır.
Settlement Of Conflict Utilization Of Plantation Land Above Ulayat Rights Of Indigenous Communities
Authors: Sahari Alpi
Published: June 2022

Conflict resolution disputes that are carried out through the courts often experience obstacles, including the absence of legal benefits in resolving disputes between communities cultivating customary land rights and plantation company authorities. The development of this conflict will certainly have an impact on national development plans related to the welfare of the entire community. One example of a dispute occurring in North Sumatra Province is the control of plantation land by a company which is claimed by the community as customary land of the Deli Sultanate, based on historically before the independence of the Republic of Indonesia, it was the land of the local community which was handed over to the Sultan to be managed. Keselutanan Deli then collaborated with the Dutch Colonial to manage the land handed over by the community based on the Van Consessie Deed which was signed by Sultan Maimon Al Rasyid Perkasa Alam and Deli Cultur Administrator Maatschaappij J.G. A. Godenhart and approved by Resident der Outkust Van Sumatra P.J Kooreman. The results showed that the revitalization of plantations in the agricultural, trade and manufacturing industries could be a strategy for improving community welfare and avoiding land problems between plantation companies that were granted management rights to customary law communities.
Molecular docking and simulation studies to prove the antimicrobial property of cowpea extract
Authors: Kingsley Danie , Abraham Jayanthi
Published: June 2022

This study is undertaken to prove the antimicrobial potential of the methanolic extracts of the seeds of cowpea (Vigna unguiculata) for the antimicrobial assay and biochemical characterization. The knowledge about the type of compound and its nature were obtained using characterizations including UV-Visible analysis and GC-MS. The extracts of cowpea displayed a zone of inhibition which is 13-17 mm, against Staphylococcus aureus being the most sensitive. Thin-layer chromatography, GC-MS and UV-Vis results showed the presence of methyl esters in higher amounts in the extracts with small amounts of sterols, suggesting that methyl esters can be the cause of the biological activity seen in the present work. SwissADME tool helped in screening the absorption, distribution, metabolism and excretion parameters of the identified phytoligands. Molecular docking results were following the in vitro studies performed. Protein-ligand docking using AutoDock 4.0 software was used for the validation of the wet-lab results. Binding energies were calculated for each ligand and its interaction with amino acids in the active site of the target protein. Among all the ligands tested 9-Octadecenoic Acid has emerged most promising against DNA topoisomerase of Staphylococcus aureus. The molecular dynamic simulation was also done to access the stability of the docked complex concerning temperature, hydrogen bonds, and hydrophobic interactions. Cowpea could be used as an effective antimicrobial agent in human welfare.