bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Arctic Sea Ice Drift Products Based on FY-3, HY-2, AMSR2, and SSMIS Radiometer Data
Authors: Fang Hailan , Zhang Xi , Shi Lijian , Bao Meng , Liu Genwang , Cao Chenghui , Zhang Jie
Published: October 2022

Different radiometer sensors have different frequencies, spatial resolutions, and time resolutions, which lead to inconsistencies in ice drift products retrieved by radiometer sensors. Based on the continuous maximum cross-correlation method, in this paper, we used China’s FY-3 and HY-2 satellite radiometer data to generate sea ice drift products; we further evaluated the consistency between them and sea ice drift products retrieved from AMSR2 and SSMIS satellite radiometer data, which could help in future retrieval accuracies of more radiometer sea ice drift products. The results show that ice drift products with good reliability can be obtained by retrievals using 37 and 89 GHz channels of FY-3 and HY-2 radiometer bright temperature data. Compared with the buoy data, the root mean square errors (RMSEs) of the 37 GHz HY-2 sea ice drift product (at an interval of 6 days) were 1.40 cm/s and 7.31° for speed and direction, respectively, and the relative errors (REs) were 5.78% and 6.44%, respectively. The RMSEs of the 37 GHz FY-3 sea ice drift product were 0.77 cm/s and 6.49° for speed and direction, respectively, and the REs were 4.38% and 9.23%, respectively. Moreover, comparisons between sea ice drift vectors derived from AMSR2 and SSMIS satellites showed good quantitative agreement.
Synergistic Effect of Atmospheric Boundary Layer and Regional Transport on Aggravating Air Pollution in the Twain-Hu Basin: A Case Study
Authors: Xiong Jie , Bai Yongqing , Zhao Tianliang , Zhou Yue , Sun Xiaoyun , Xu Jiaping , Zhang Wengang , Leng Liang , Xu Guirong
Published: October 2022

The impact of structural variations in the atmospheric boundary layer (ABL) during the regional transport of air pollutants on its local pollution changes deserves attention. Based on multi-source ABL detection and numerical simulation of air pollutants over the Twain-Hu Basin (THB) during 4–6 January 2019, the mechanism of the rapid growth of atmospheric pollutant concentrations in Xianning by the synergistic effect of regional transport and ABL evolution is explored, and the main conclusions are obtained as follows. The vertically stratified atmosphere is noticeable at nighttime, and the heavy humidity of near-surface fog within the stable boundary layer (SBL) promoted the generation and cumulative growth of secondary PM2.5 components during the pollution formation stage. The horizontal transport characteristics of atmospheric pollutant concentration peak were observed in the residual layer (RL) of 500–600 m. At the pollution maintenance stage, the convective boundary layer (CBL) developed during the daytime, and northerly wind transported high-concentration pollutants from the north to the THB. Under the combined action of horizontal transport and turbulent mixing, the high-concentration atmospheric pollutants in the mixing layer (ML) from the ground to the 500 m height were mixed uniformly and maintained accumulation growth. The next day, the strong vertical turbulent mixing caused the downward transport of high-concentration pollutants in the RL during nighttime due to the development of the CBL again, resulting in a doubling of near-surface pollutant concentration in a short time. With the development of ABL turbulence, local pollution dissipated rapidly without the continuous input of pollutants from external regions. This study emphasizes the importance of multi-scale processes impact on pollution variation, that is, regional transport of atmospheric pollutants at the CBL development stage for the rapid growth of PM2.5 concentration in the ML.
A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data
Authors: Dong Shengyue , Cheng Jie , Shi Jiancheng , Shi Chunxiang , Sun Shuai , Liu Weihan
Published: October 2022

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI.
A Continuous PDR and GNSS Fusing Algorithm for Smartphone Positioning
Authors: Zhang Rui , Mi Jing , Li Jing , Wang Qing
Published: October 2022

Pedestrian dead reckoning (PDR), used in state-of-the-art smartphones, calculates pedestrian positions by using built-in inertial sensors. However, the complex and changeable usage modes of smartphones have been obstructing the development of PDR in the field of gait detection. Since the measurement of the sensor is affected by noise, position errors will emerge, needing to be corrected periodically via external measurements. To this end, an optimization-based PDR (OBPDR) method for smartphones is proposed in this study. First, an improved finite state machine (IFSM) gait detection method is designed, which can improve the gait recognition rate and stability compared with the traditional peak detection method. Second, the step detection algorithm proposed in this paper is combined with a heading estimation to obtain the PDR dynamic model. Finally, the measurements of GNSS are fused to the PDR model, based on an adaptive extended Kalman filter (AEKF) algorithm, which can enhance the adaptability of the system to the environment in order to reduce the cumulative errors of PDR. Experiments are carried out to evaluate the performance of the proposed method. The results indicate that compared with the gait detection method, based on peak detection, and the integrated positioning method, based on an extended Kalman filter, the proposed method boasts favorable robustness and a high gait recognition rate, the recognition accuracy being kept between 97.5% and 98.5%; the average position error decreased by more than 67.25%.
Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
Authors: Tian Yingze , Xu Tongren , Chen Fei , He Xinlei , Li Shi
Published: October 2022

Data assimilation methods have been used to improve the performances of land surface models by integrating remote sensing and in situ measurements. However, the impact of data assimilation on improving the forecast of land surface variables has not been well studied, which is essential for weather and hydrology forecasting. In this study, a multi-pass land data assimilation scheme (MLDAS) based on the Noah-MP model was used to predict short-term land surface variables (e.g., sensible heat fluxes (H), latent heat fluxes (LE), and surface soil moisture (SM)) by jointly assimilating soil moisture, leaf area index (LAI) and solar-induced chlorophyll fluorescence (SIF). The test was conducted at the Mead site during the growing season (1 May to 30 September) in 2003, 2004, and 2005. Four assimilation-prediction scenarios (assimilating for 15 days, 45 days, 75 days, and 105 days from 1 May, then predicting one future month) are adapted to evaluate the influence of assimilation on subsequent prediction against Noah-MP open-loop simulation (OL). On average, MLDAS produces 28.65%, 27.79%, and 19.15% lower root square deviations (RMSD) for daily H, LE, and SM prediction compared to open-loop run, respectively. The influence of assimilation on prediction can reach around 60 days and 100 days for H (LE) and SM, respectively. Our findings indicate that data assimilation can improve the accuracy of land surface variables in a short-term prediction period.
Scale Effects and Time Variation of Trade-Offs and Synergies among Ecosystem Services in the Pearl River Delta, China
Authors: Liu Wei , Zhan Jinyan , Zhao Fen , Wang Chengxin , Chang Jun , Asiedu Kumi Michael Asiedu , Leng Manman
Published: October 2022

Natural and socioeconomic variables have an impact on ecosystem services (ESs). The ESs trade-offs/synergies are informed by the reality that the same inputs have varying impacts on different ESs. Changing scales and time can alter dominant drivers and biophysical linkages of ESs, affecting their relationships. Although it is often assumed that ES relationships vary across scales, quantitatively testing this assumption with multiple ES is rare. Therefore, this study evaluated the five key ESs in the Pearl River Delta (PRD) from 1990 to 2015. We also employed a statistical approach to investigate the temporal variations, scale dependency, and spatial heterogeneity of ES trade-offs and synergies. The results demonstrated that: (1) The PRD’s synergetic interaction among ESs has been steadily improving over time; (2) The interaction between ESs dramatically altered as the research scale increased; (3) We discovered that the linkages among the soil conservation (SC), carbon sequestration (CS), water yield (WY), and habitat quality (HQ) were primarily synergistic. ESs of SC, CS, WY, and HQ were found to have negative correlations with grain production. This study will strengthen the understanding of the temporal changes and spatial scales of ESs relationships for decision-makers, which is beneficial to ecosystem management.
Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose
Authors: Pulluri Kranthi Kumar , Kumar Vaegae Naveen
Published: October 2022

Food adulteration is the most serious problem found in the food industry as it harms people’s healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.
Security Concerns in MMO Games—Analysis of a Potent Application Layer DDoS Threat
Authors: Gavrić Nikola , Bojović Živko
Published: October 2022

The application layer in the Internet protocol suite offers a significant degree of freedom regarding the orchestration of distributed denial-of-service attacks due to many different and unstandardized protocols. The primary focus of defending against application-layer distributed denial-of-service attacks has traditionally been Hypertext Transfer Protocols oriented while observing individual users’ actions independently from one another. In this paper, we present and analyze a novel application-layer DDoS attack in massively multiplayer online games that utilize the cooperative efforts of the attackers to deplete the server’s or players’ bandwidth. The attack exploits in-game dependencies between players to cause a massive spike in bandwidth while the attackers’ traffic remains legitimate. We introduce a multiplayer-relations graph to model user behavior on a game server. Additionally, we demonstrate the attack’s devastating capabilities on an emulated World of Warcraft server. Lastly, we discuss flaws of the existing defense mechanisms and possible approaches for the detection of these attacks using graph theory and multiplayer-relations graphs.
Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors
Authors: Cheng Xiao , Yao Daojin , Yang Lin , Dong Wentao
Published: October 2022

With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways.
Noninvasive Non-Contact SpO2 Monitoring Using an Integrated Polarization-Sensing CMOS Imaging Sensor
Authors: Sarkar Mukul , Assaad Maher
Published: October 2022

Background:In the diagnosis and primary health care of an individual, estimation of the pulse rate and blood oxygen saturation (SpO2) is critical. The pulse rate and SpO2 are determined by methods including photoplethysmography (iPPG), light spectroscopy, and pulse oximetry. These devices need to be compact, non-contact, and noninvasive for real-time health monitoring. Reflection-based iPPG is becoming popular as it allows non-contact estimation of the heart rate and SpO2. Most iPPG methods capture temporal data and form complex computations, and thus real-time measurements and spatial visualization are difficult. Method:In this research work, reflective mode polarized imaging-based iPPG is proposed. For polarization imaging, a custom image sensor with wire grid polarizers on each pixel is designed. Each pixel has a wire grid of varying transmission axes, allowing phase detection of the incoming light. The phase information of the backscattered light from the fingertips of 12 healthy volunteers was recorded in both the resting as well as the excited states. These data were then processed using MATLAB 2021b software. Results: The phase information provides quantitative information on the reflection from the superficial and deep layers of skin. The ratio of deep to superficial layer backscattered phase information is shown to be directly correlated and linearly increasing with an increase in the SpO2 and heart rate. Conclusions: The phase-based measurements help to monitor the changes in the resting and excited state heart rate and SpO2 in real time. Furthermore, the use of the ratio of phase information helps to make the measurements independent of the individual skin traits and thus increases the accuracy of the measurements. The proposed iPPG works in ambient light, relaxing the instrumentation requirement and helping the system to be compact and portable.
Automatic Placement of Visual Sensors in a Smart Space to Ensure Required PPM Level in Specified Regions of Interest
Authors: Khutornoi Iaroslav , Kobyzhev Aleksandr , Vatamaniuk Irina
Published: October 2022

This work is devoted to a cost-effective method for the automatic placement of visual sensors within a smart room to ensure the requirements for its design. Various unique conditions make the process of manually placing sensors time consuming and can also lead to a decrease in system efficiency. To automate the design process, we solve a multi-objective optimization problem known as the art gallery problem in 3D, modified as follows. For the specified regions of interest within a smart room, the required pixels per meter level (PPM) should be ensured. The optimization criteria are visibility of the room and the cost of equipment. To meet these criteria, we describe a room model with doors, windows, and obstacles represented in such a way as to consider their impact on the field of view of the sensors. To model sensor placement, a genetic algorithm is used. The optimal solution is selected from the Pareto front by means of the technique for order of preference by similarity to ideal solution (TOPSIS). The developed method’s effectiveness has been tested on modeling real premises of various types. The method is flexible because of the assignment of weights to certain aspects when placing sensors. Further, it can be scalable to other types of sensors.
A Fast Adaptive Multi-Scale Kernel Correlation Filter Tracker for Rigid Object
Authors: Zheng Kaiyuan , Zhang Zhiyong , Qiu Changzhen
Published: October 2022

The efficient and accurate tracking of a target in complex scenes has always been one of the challenges to tackle. At present, the most effective tracking algorithms are basically neural network models based on deep learning. Although such algorithms have high tracking accuracy, the huge number of parameters and computations in the network models makes it difficult for such algorithms to meet the real-time requirements under limited hardware conditions, such as embedded platforms with small size, low power consumption and limited computing power. Tracking algorithms based on a kernel correlation filter are well-known and widely applied because of their high performance and speed, but when the target is in a complex background, it still can not adapt to the target scale change and occlusion, which will lead to template drift. In this paper, a fast multi-scale kernel correlation filter tracker based on adaptive template updating is proposed for common rigid targets. We introduce a simple scale pyramid on the basis of Kernel Correlation Filtering (KCF), which can adapt to the change in target size while ensuring the speed of operation. We propose an adaptive template updater based on the Mean of Cumulative Maximum Response Values (MCMRV) to alleviate the problem of template drift effectively when occlusion occurs. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods based on a kernel correlation filter.
Predictive Diagnosis Based on Predictor Symptoms for Isolated Photovoltaic Systems Using MPPT Charge Regulators
Authors: García Emilio , Quiles Eduardo , Correcher Antonio , Morant Francisco
Published: October 2022

In this work, new results are presented on the implementation of predictive diagnosis techniques on isolated photovoltaic (PV) systems and installations. The novelties introduced in this research focus on the additional advantages obtained from the point of view of predictive diagnosis of faults caused by partial shading in isolated PV installations using maximum power point tracking (MPPT) regulators. MPPT regulators are comparatively more appropriate than pulse width modulation (PWM) solar regulators in order to implement fault diagnosis systems. MPPT regulators have a physical separation between the electrical parameters belonging to the part of the solar panel with respect to the batteries part. Therefore, these electrical parameters can be used to obtain early predictive symptoms of the effects of partial shading with a greater level of observation and sensitivity. Additionally, modifications are proposed in the PV system assembly to obtain greater homogeneity of all the panels regarding the solar irradiance reception angle.
Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces
Authors: Mirza Irfan Baig , Georgakopoulos Dimitrios , Yavari Ali
Published: October 2022

Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces.
A Force-Feedback Methodology for Teleoperated Suturing Task in Robotic-Assisted Minimally Invasive Surgery
Authors: Ehrampoosh Armin , Shirinzadeh Bijan , Pinskier Joshua , Smith Julian , Moshinsky Randall , Zhong Yongmin
Published: October 2022

With robotic-assisted minimally invasive surgery (RAMIS), patients and surgeons benefit from a reduced incision size and dexterous instruments. However, current robotic surgery platforms lack haptic feedback, which is an essential element of safe operation. Moreover, teleportation control challenges make complex surgical tasks like suturing more time-consuming than those that use manual tools. This paper presents a new force-sensing instrument that semi-automates the suturing task and facilitates teleoperated robotic manipulation. In order to generate the ideal needle insertion trajectory and pass the needle through its curvature, the end-effector mechanism has a rotating degree of freedom. Impedance control was used to provide sensory information about needle–tissue interaction forces to the operator using an indirect force estimation approach based on data-based models. The operator’s motion commands were then regulated using a hyperplanar virtual fixture (VF) designed to maintain the desired distance between the end-effector and tissue surface while avoiding unwanted contact. To construct the geometry of the VF, an optoelectronic sensor-based approach was developed. Based on the experimental investigation of the hyperplane VF methodology, improved needle–tissue interaction force, manipulation accuracy, and task completion times were demonstrated. Finally, experimental validation of the trained force estimation models and the perceived interaction forces by the user was conducted using online data, demonstrating the potential of the developed approach in improving task performance.
Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images
Authors: Elgafi Mahmoud , Sharafeldeen Ahmed , Elnakib Ahmed , Elgarayhi Ahmed , Alghamdi Norah S. , Sallah Mohammed , El-Baz Ayman
Published: October 2022

Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images.
Design and Analysis of Multi-User Faster-Than-Nyquist-DCSK Communication Systems over Multi-Path Fading Channels
Authors: Dawa Mohamed , Herceg Marijan , Kaddoum Georges
Published: October 2022

In this paper, we present a new multi-user chaos-based communication system using Faster-than-Nyquist sampling to achieve higher data rates and lower energy consumption. The newly designed system, designated Multi-user Faster Than Nyquist Differential Chaos Shift Keying (MU-FTN-DCSK), uses the traditional structure of Differential Chaos Shift Keying (DCSK) communication systems in combination with a filtering system that goes below the Nyquist limit for data sampling. The system is designed to simultaneously enable transmissions from multiple users through multiple sampling rates resulting in semi-orthogonal transmissions. The design, performance analysis, and experimental results of the MU-FTN-DCSK system are presented to demonstrate the utility of the newly proposed system in enabling multi-user communications and enhancing the spectral efficiency of the basic DCSK design without the addition of new blocks. The MU-FTN-DCSK system presented in this paper demonstrates spectral gains for one user of up to 23% and a combined gain of 25% for four (U=4) users. In this paper, we present a proof of concept demonstrating a new degree of freedom in the design of Chaos-based communication systems and their improvement in providing wireless transmissions without complicated signal processing tools or advanced hardware designs.
Setup and Analysis of a Mid-Infrared Stand-Off System to Detect Traces of Explosives on Fabrics
Authors: Dreier Lisa B. , Kölbl Christoph , Jeuk Vincent , Beleites Claudia , Köhntopp Anja , Duschek Frank
Published: October 2022

The increasing number of terrorist attacks within the last decade has demonstrated that taking preventive protective measures is highly important. In addition to existing measures, automated detection systems for fast and reliable explosive detection are required. A sensitive spectroscopic system based on mid-infrared spectroscopy has been developed and applied to explosive samples on different types of fabric under various geometric conditions. Using this system, traces of TNT, RDX, PETN and ammonium nitrate can be detected in less than a second. Various approaches for data pretreatment (wavelength calibration) and subsequent analysis (normalization, removal of atmospheric water absorption lines) are presented and the remaining challenges on the road to a fully automated system, including a robust classification algorithm, are discussed.
Distributed Supervision Model for Enterprise Data Asset Trading Based on Blockchain Multi-Channel in Industry Alliance
Authors: Zhang Jianxiong , Guo Bing , Ding Xuefeng , Hu Dasha , Jiang Yuming
Published: October 2022

Compared with traditional physical commodities, data are intangible and easy to leak, and the related trading process has problems, such as complex participating roles, lengthy information flow, poor supervisory coverage and difficult information traceability. To handle these problems, we construct a distributed supervision model for data trading based on blockchain, and conduct multi-party hierarchical and multi-dimensional supervision of the whole process of data trading through collaborative supervision before the event, at present and after the event. First, the characteristics of information flow in the data trading process are analyzed, and the main subject and key supervision information in the data trading process are sorted out and refined. Secondly, combined with the actual business process of data trading supervision, a multi-channel structure of distributed supervision is proposed by adopting an access–verification–traceability strategy. Finally, under the logical framework of the supervision model, the on-chain hierarchical structure and the data hybrid storage method of “on-chain + off-chain” are designed, and multi-supervisor-oriented hierarchical supervision and post-event traceability are realized through smart contracts. The results show that the constructed blockchain-based distributed supervision model of data trading can effectively isolate and protect sensitive and private information between data trading, so as to realize the whole process, multi-subject and differentiated supervision of key information of data trading, and provide an effective and feasible method for the controllable and safe supervision of data trading.
Optimal Achievable Transmission Capacity Scheme for Full-Duplex Multihop Wireless Networks
Authors: Khun Aung Thura Phyo , Lim Yuto , Tan Yasuo
Published: October 2022

Full-duplex (FD) communication has been attractive as the breakthrough technology for improving attainable spectral efficiency since the 5G mobile communication system. Previous research focused on self-interference cancellation and medium access control (MAC) protocol to realize the FD system in wireless networks. This paper proposes an optimal achievable transmission capacity (OATC) scheme for capacity optimization in the FD multihop wireless networks. In this paper, the proposed OATC scheme considers the temporal reuse for spectral efficiency and the spatial reuse with transmit power control scheme for interference mitigation and capacity optimization. OATC scheme controls the transmit power to mitigate interference and optimizes the transmission capacity, which leads to the optimal achievable network capacity. We conduct the performance evaluation through numerical simulations and compare it with the existing FD MAC protocols. The numerical simulations reveal that considering only the concurrent transmissions in the FD system does not guarantee optimal transmission capacity. Moreover, the hybrid mechanism, including the sequential transmissions, is also crucial because of the interference problem. Besides, numerical simulation validates that the proposed OATC scheme accomplishes the optimal achievable network capacity with lower interference power and higher achievable throughput than the existing MAC protocols.
Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities
Authors: Aslam Nida , Khan Irfan Ullah , Bashamakh Asma , Alghool Fatima A. , Aboulnour Menna , Alsuwayan Noorah M. , Alturaif Rawa’a K. , Brahimi Samiha , Aljameel Sumayh S. , Al Ghamdi Kholoud
Published: October 2022

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.
The Impact of Atmospheric Parameters on the Dielectric Permittivity Values of SikaBlock®-M150 and Other Rigid Polyurethane Foams Measured with a Capacitive One-Side Access Sensor
Authors: Beverte Ilze , Gaidukovs Sergejs , Andersons Janis , Skruls Vilis
Published: October 2022

A shortage of research on the impact of atmospheric parameters on the measured dielectric permittivity values of rigid polyurethane (PU) foams was identified. Therefore, the impact of temperature, pressure, and relative humidity of air in the test room on the measured values of dielectric permittivity of rigid PU foams of different densities as well as monolithic polyurethane was investigated in a year-long experimental research study with a capacitive one-side access sensor. It was shown that relative humidity has the highest correlation with the dielectric permittivity values of rigid PU materials. The detected values of parameters were linked to the water vapour mass in ambient air and its correlation with permittivity of the investigated materials was determined. The warm-up drift and warm-up time of the spectrometer were estimated experimentally. A novel methodology was demonstrated to determine the true permittivity spectrum of rigid PU foams without any involvement of the environmental chamber, desiccators, or saturated salt/water solutions. A relative increase in the measured dielectric permittivity value was estimated numerically for the entire density range of rigid PU foams, i.e., 33–1280 kg/m3 (including monolithic PU).
Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information
Authors: Ko Kabseok , Cho Seokheon , Rao Ramesh R.
Published: October 2022

Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numerical approaches, as well as data-driven forecasting approaches, have been studied for ozone forecasting. Data-driven forecasting models, in particular, have gained momentum with the introduction of machine learning advancements. We consider planetary boundary layer (PBL) height as a new input feature for data-driven ozone forecasting models. PBL has been shown to impact ozone concentrations, making it an important factor in ozone forecasts. In this paper, we investigate the effectiveness of utilization of PBL height on the performance of surface ozone forecasts. We present both surface ozone forecasting models, based on multilayer perceptron (MLP) and bidirectional long short-term memory (LSTM) models. These two models forecast hourly ozone concentrations for an upcoming 24-h period using two types of input data, such as measurement data and PBL height. We consider the predicted values of PBL height obtained from the weather research and forecasting (WRF) model, since it is difficult to gather actual PBL measurements. We evaluate two ozone forecasting models in terms of index of agreement (IOA), mean absolute error (MAE), and root mean square error (RMSE). Results showed that the MLP-based and bidirectional LSTM-based models yielded lower MAE and RMSE when considering forecasted PBL height, but there was no significant changes in IOA when compared with models in which no forecasted PBL data were used. This result suggests that utilizing forecasted PBL height can improve the forecasting performance of data-driven prediction models for surface ozone concentrations.
Study of an Energy-Harvesting Damper Based on Magnetic Interaction
Authors: Aberturas Susana , Hernando Antonio , Olazagoitia José Luis , García Miguel Ángel
Published: October 2022

The saving and re-use of energy has acquired great relevance in recent years, being of great importance in the automotive sector. In the literature, it is possible to find different proposals for energy-harvesting damper systems (EHSA)—the electromagnetic damper being a highly recurrent but still poorly defined proposal. This article specifically focuses on studying the concept and feasibility of an electromagnetic suspension system that is capable of recovering energy, using a damper formed by permanent magnets and a system of coils that collect the electromotive force generated by the variation of the magnetic field. To study the feasibility of the system, it is necessary to know the maximum energy that can be recovered through the winding system; however, the difficulties in obtaining the derivative of the magnetic flux and its derivative for each position make the analytical method very tedious. This paper presents an experimental method with which to maximize energy recovery by defining the optimum relative position between magnet and coil.
Optical Polarization Sensor Based on a Metalens
Authors: Kotlyar Victor , Nalimov Anton , Kovalev Alexey , Stafeev Sergey
Published: October 2022

We investigated an optical microsensor of the polarization state of a laser light based on a metalens. In contrast to known polarization sensors based on metasurfaces that deflect different polarization types using various angles to the optical axis, the studied polarization sensor generated different patterns in the metalens focus to realize varied polarization states: left circular polarization generated a light ring in the focus, right circular polarization generated a circular focal spot, and linear polarization generated an elliptic spot with two sidelobes. Moreover, the tilt angle of the linear polarization matched the tilt angle of the elliptic focal spot. The simulation results were consistent with the theoretical predictions. A metalens with a diameter of several tens of microns was designed and fabricated in a thin amorphous silicon film with a thickness of 120 μm and a low aspect ratio, high numerical aperture, and short focal distance equal to a wavelength of 633 nm.
Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification
Authors: Manisha  , Li Chang-Tsun , Lin Xufeng , Kotegar Karunakar A.
Published: October 2022

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.
μ-FTIR, μ-Raman, and SERS Analysis of Amide I Spectral Region in Oral Biofluid Samples during Orthodontic Treatment
Authors: Camerlingo Carlo , Portaccio Marianna , d’Apuzzo Fabrizia , Nucci Ludovica , Perillo Letizia , Lepore Maria
Published: October 2022

Gingival crevicular fluid (GCF) is a site-specific exudate deriving from the epithelium lining of the gingival sulcus. GCF analysis provides a simple and noninvasive diagnostic procedure to follow-up periodontal and bone remodeling in response to diseases or mechanical stimuli such as orthodontic tooth movement (OTM). In recent years, the use of vibrational spectroscopies such as Fourier Transform Infrared and Raman microspectroscopy and Surface-Enhanced Raman spectroscopy contributed to characterizing changes in GCF during fixed orthodontic treatment. Amide I band plays a relevant role in the analysis of these changes. The aim of this study was to investigate the spectroscopy response of Amide I depending on the OTM process duration. A model based on Gaussian–Lorentzian curves was used to analyze the infrared spectra, while only Lorentzian functions were used for Raman and SERS spectra. Changes induced by the OTM process in subcomponents of the Amide I band were determined and ascribed to secondary structure modification occurring in proteins. The vibrational spectroscopies allow us to efficiently monitor the effects of the orthodontic force application, thus gaining increasing attention as tools for individual patient personalization in clinical practice.
Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things
Authors: Saba Tanzila , Rehman Amjad , Haseeb Khalid , Bahaj Saeed Ali , Damaševičius Robertas
Published: October 2022

The development of smart applications has benefited greatly from the expansion of wireless technologies. A range of tasks are performed, and end devices are made capable of communicating with one another with the support of artificial intelligence technology. The Internet of Things (IoT) increases the efficiency of communication networks due to its low costs and simple management. However, it has been demonstrated that many systems still need an intelligent strategy for green computing. Establishing reliable connectivity in Green-IoT (G-IoT) networks is another key research challenge. With the integration of edge computing, this study provides a Sustainable Data-driven Secured optimization model (SDS-GIoT) that uses dynamic programming to provide enhanced learning capabilities. First, the proposed approach examines multi-variable functions and delivers graph-based link predictions to locate the optimal nodes for edge networks. Moreover, it identifies a sub-path in multistage to continue data transfer if a route is unavailable due to certain communication circumstances. Second, while applying security, edge computing provides offloading services that lower the amount of processing power needed for low-constraint nodes. Finally, the SDS-GIoT model is verified with various experiments, and the performance results demonstrate its significance for a sustainable environment against existing solutions.
GC-MS Analysis of the Phytochemical Constituents, Safety Assessment, Wound Healing and Anti-Inflammatory Activities of Cucurbita pepo Leaf Extract in Rats
Authors: Akubugwo Emmanuel Iroha , Emmanuel Okezie , Ekweogu Celestine Nwabu , Ugbogu Ositadinma Chinyere , Onuorah Tochukwu Remigius , Egeduzu Ozioma Glory , Ugbogu Eziuche Amadike
Published: October 2022

In traditional medicine, Cucurbita pepo L. is used for the treatment of rheumatism, diabetes, inflammations, and wound injuries. This study was conducted to evaluate the phytochemical constituents, safety profile, wound healing, and anti-inflammatory activities of Cucurbita pepo leaf extract in rats. The phytochemical analysis of C. pepo extract was carried out using gas chromatography-mass spectrometry (GC-MS). In acute toxicity tests, the rats orally received a single dose of 5 g/kg extract of C. pepo. In a subacute toxicity study, the rats received 200, 400, and 800 mg/kg of the C. pepo extract via daily gavage for 14 days. Bioactive compounds 1-octen-3-ol, nonanal, trans-β-ionone, phytol, trans-farnesol, and squalene were identified. There were no toxic effects detected in any of the evaluated parameters, namely liver, kidney, haematological, lipid, and antioxidant enzymes. In wound healing, C. pepo extract showed greater % wound contraction and tensile strength, as well as reduced wound healing time (12 days) and epithelialization when compared to the control (normal saline) and povidone-iodine treated groups. Rats treated with C. pepo extract elicit anti-inflammatory activity. The findings of this study revealed that the C. pepo extract has wound healing and anti-inflammatory properties with a wide margin of safety.
Appreciatively Efficient Sorption Achievement to U(VI) from the El Sela Area by ZrO2/Chitosan
Authors: Negm Sameh H. , Abd El-Magied Mahmoud O. , El Maadawy Wael M. , Abdel Aal Mostafa M. , Abd El Dayem Samia M. , Taher Mahmoud A. , Abd El-Rahem Khaled A. , Rashed Mohamed N. , Cheira Mohamed F.
Published: October 2022

The need to get uranium out of leaching liquid is pushing scientists to come up with new sorbents. This study uses the wet technique to improve the U(VI) sorption properties of ZrO2/chitosan composite sorbent. To validate the synthesis of ZrO2/CS composite with Zirconyl-OH, -NH, and -NH2 for U(VI) binding, XRD, FTIR, SEM, EDX, and BET are used to describe the ZrO2/chitosan wholly formed. To get El Sela leaching liquid, it used 150 g/L H2SO4, 1:4 S:L ratio, 200 rpm agitation speed, four hours of leaching period, and particle size 149–100 µm. In a batch study, the sorption parameters are evaluated at pH 3.5, 50 min of sorbing time, 50 mL of leaching liquid (200 mg/L U(VI)), and 25 °C. The sorption capability is 175 mg/g. Reusing ZrO2/CS for seven cycles with a slight drop in performance is highly efficient, with U(VI) desorption using 0.8 M acid and 75 min of desorption time. The selective U(VI) recovery from El Sela leachate was made possible using ZrO2/CS. Sodium diuranate was precipitated and yielded a yellow cake with a purity level of 94.88%.