bluuebunny/crossref_metadata_2025 huggingface dataset:
Miniaturization of Anthracene-Containing Nonapeptides for Selective Precipitation/Recovery of Metallic Gold from Aqueous Solutions Containing Gold and Platinum Ions
Authors: Tomizaki Kin-ya , Tonoda Tatsuki , Teramura Shungo , Okazaki Haruka , Imai Takahito , Asano Masahiro
Published: November 2021

The separation and recovery of noble metals is increasingly of interest, in particular the recovery of gold nanocrystals, which have applications in medicine and industry. Typically, metal recovery is performed using liquid–liquid extraction or electrowinning. However, it is necessary to develop noble metal recovery systems providing high selectivity in conjunction with a one-pot setup, ready product recovery, and the use of dilute aqueous solutions. In prior work, our group developed a selective gold recovery process using peptides. This previous research showed that RU065, a nonapeptide containing an anthracene moiety (at a concentration of 2.0 × 10−4 M), is capable of selective reduction of HAuCl4 to recover gold from a solution of HAuCl4 and H2PtCl6, each at 5.0 × 10−5 M. However, peptide molecules are generally costly to synthesize, and therefore it is important to determine the minimum required structural features to design non-peptide anthracene derivatives that could reduce operational costs. In this study, we used RU065 together with 23 of its fragment peptides and investigated the selective precipitation/recovery of metallic gold. RU0654–8, a fragment peptide comprising five amino acid residues (having two lysine, one L-isoleusine, and one L-alanine residue (representing six amide groups) along with an L-2-anthrylalanine residue) provided an Au/Pt atomic ratio of approximately 8, which was comparable to that for the full-length original RU065. The structural features identified in this study are expected to contribute to the design of non-peptide anthracene derivatives for low-cost, one-pot selective gold recovery.
A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining
Authors: Shanmugasundar G. , Vanitha M. , Čep Robert , Kumar Vikas , Kalita Kanak , Ramachandran M.
Published: November 2021

Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.
In Situ Observation of the Carbothermic Reduction and Foaming of Slags in Silicomanganese Production
Authors: Canaguier Vincent , Tangstad Merete
Published: November 2021

The carbothermic reduction of slag in silicomanganese production is accompanied by the release of carbon monoxide. This gas can accumulate as bubbles within the slag, leading to foaming and, potentially, disturbances to furnace operation. This study investigated the reduction in the slag together with its foaming using a sessile drop furnace. Five silicomanganese slags produced from industrial raw materials (Assmang ore, Comilog ore, high-carbon FeMn slag with quartz, and FeS additions) were reduced by a graphite substrate at isothermal conditions (i.e., 1540–1660 °C) under CO atmosphere. The reduction reaction was tracked by photographing the slag droplet, and the cyclic expansion and burst of the droplet were used to estimate the gas evolution. The reacted samples were analyzed by wavelength-dispersive X-ray spectroscopy (WDS) to determine MnO and SiO2 reduction. While no foaming was observed using Comilog ore, extensive retention of CO in the slag phase was observed when using Assmang ore or Assmang with high-carbon FeMn slag. The beginning of foaming was attributed to an increase in the reaction rate; the absence of foaming when using Comilog can be attributed to the acidity of the charge. Addition of sulfur to the Comilog-based charge did not influence the reduction.
Local News and Geolocation Technology in the Case of Portugal
Authors: Gonçalves Tatiana Santos , Jerónimo Pedro , Correia João Carlos
Published: November 2021

New projects have recently emerged to develop geolocation technology for the publication of local news in Portugal. These types of new initiatives open the possibility to explore new media perspectives, identifying emerging directions and opportunities to develop more competitive ways to publish local news. In this work, we study these ideas and to what extent they can be used to cope with the challenges that the local Portuguese press is currently facing. We provide local news editors with information to further develop their e-participation and news publishing activities. To this end, we present 10 indicators that measure geolocation technology that has been successful in providing attractive services to local consumers. Lastly, we analyze five Portuguese apps by means of the proposed guideline. Our work shows that the use of geolocation technology has a great potential for local journalism in Portugal but nevertheless we still find flaws in their implementation.
Synthesis of a Poly(3-dodecylthiophene) Bearing Aniline Groups for the Covalent Functionalization of Carbon Nanotubes
Authors: Wasem Klein Felipe , Lamps Jean-Philippe , Paillet Matthieu , Petit Pierre , Mésini Philippe J.
Published: November 2021

The functionalization of carbon nanotubes by polymers necessitates two steps, first their modification by oxidizing them or by covalently attaching small compounds to them, then the growth of the polymer chains from these anchors or their grafting onto them. In order to better control the process and the rate of functionalization, we develop polymers able to covalently react with the carbon nanotubes by their side chains in one step. We describe the synthesis of a copolymer of dodecylthiophene and its analogue bearing an aniline group at the end of the dodecyl side chain. This copolymer can functionalize single-walled carbon nanotubes (SWNTs) non-covalently and disperse more SWNTs than its hexyl analogues. UV-Vis and fluorescence spectroscopies show that in these non-covalent hybrids, the polymer forms p-stacked aggregates on the SWNTs. The non-covalent hybrids can be transformed into covalent ones by diazonium coupling. In these covalent hybrids the polymer is no longer p-stacked. According to Raman spectroscopy, the conformation of the poly(3-hexylthiophene) backbone is more ordered in the non-covalent hybrids than in the covalent ones.
The King Never Dies: Royal Renunciation and the Fiction of Jain Sovereignty
Authors: Taylor Sarah Pierce
Published: November 2021

To theorize Jain sovereignty, this essay takes up Ernst Kantorowicz’s underlying query of what happens when a king dies. In turning to medieval Jain authors such as Jinasena, we see how sovereignty and renunciation were mutually constituted such that the king’s renunciation completely subverts the problem of the king’s death. If the fiction of Jain kingship properly practiced culminates in renunciation, then such a movement yields up a new figure of the ascetic self-sovereign. Renunciation does not sever sovereignty but extends it into a higher spiritual domain. Worldly and spiritual sovereignty share a metaphorical language and set of techniques that render them as adjacent but hierarchical spheres of authority. In so doing, Jain authors provide a religious answer to a political problem and make the political inbuilt into the religious, thereby revealing their interpenetrating and bounded nature.
Subnational Mortality Modelling: A Bayesian Hierarchical Model with Common Factors
Authors: Lu Qian , Hanewald Katja , Wang Xiaojun
Published: November 2021

We propose a new model in a Bayesian hierarchical framework to project mortality at both national and subnational levels based on sparse or missing data. The new model, which has a country–region–province structure, uses common factors to pool information at the national level and within regions consisting of several provinces or states. We illustrate the model’s use by drawing on a new database containing provincial-level mortality data for China from four censuses conducted during the period 1982–2010. The new model provides good estimates and reasonable forecasts at both the country and provincial levels. The model’s forecast intervals reflect provincial- and regional-level uncertainty. Using subnational data for the period 1999–2018 from the Centers for Disease Control and Prevention (CDC), we also apply the model to the United States. We use mortality forecasts to compute and compare national and subnational life expectancies for China and the United States. The model predicts that, in 2030, China will have a similar national life expectancy at age 60 and a similar heterogeneity in subnational life expectancy as the United States.
Value-Based Financial Risk Prediction Model
Authors: Pospíšil Jiří , Matulayová Nataša , Macháčková Pavla , Jurníčková Pavlína , Olecká Ivana , Pospíšilová Helena
Published: November 2021

The model of financial risk prediction we developed and present in our paper is based on the theoretical assumption that there exists a significant relationship between actual economic situation and values. This assumption confirmed by the research influences the potential risk in financial behaviour and it becomes actual especially in the case of changing life conditions. The concept of the model is based on data received from 3768 respondents questioned across the Czech Republic. Measured variables were indexed, and the cluster and factor analyses were used for multivariate analysis. The model is unique in the combination of personal values projected into six generalized value types and developed economic indexes clustered in four types of economic situations. The primary purpose of the model is to identify the anticipated personal financial risk of clients. The model has fundamental applications as a diagnostic or auto-diagnostic tool in social work, counselling, psychotherapy, and other helping professions, or as a research instrument leading to various hypotheses and to the enhancement of theories concerning economic behaviour.
New Bouguer Anomaly Map for the Territory of the Slovenia
Authors: Medved Klemen , Odalović Oleg , Koler Božo
Published: November 2021

The existing Bouguer anomaly map, which covers the territory of the Republic of Slovenia is a few decades old. Since then, quite a few new gravimetric measurements (data) for the territory of Slovenia as well as high quality digital terrain models that are needed for creating such a map have been made available. The methodology and standards for creating gravity anomaly maps are also changing. Thus, the national Bouguer anomaly map was updated. There were some gross errors detected in the set of old gravimetric data. Additionally, the influence of new updated gravimetric data was analyzed. The comparison of the various maps and the analysis of the influence of input gravimetric data indicates that the new gravimetric data of Slovenia has a significant influence on the creation of the gravimetric anomaly maps for Slovenia (even over 30 mGals at some points).
Using Time Series Optical and SAR Data to Assess the Impact of Historical Wetland Change on Current Wetland in Zhenlai County, Jilin Province, China
Authors: Shi Sixue , Chang Yu , Li Yuehui , Hu Yuanman , Liu Miao , Ma Jun , Xiong Zaiping , Wen Ding , Li Binglun , Zhang Tingshuang
Published: November 2021

Wetlands, as the most essential ecosystem, are degraded throughout the world. Wetlands in Zhenlai county, with the Momoge National Nature Reserve, which was included on the Ramsar list, have degraded by nearly 30%. Wetland degradation is a long-term continuous process with annual or interannual changes in water area, water level, or vegetation presence and growth. Therefore, it requires sufficiently frequent and high-spatial-resolution data to represent its dynamics. This study mapped yearly land-use maps with 30-m resolution from 1985 to 2018 using Landsat data in Google Earth Engine (GEE) to explore the wetland degradation process and mapped 12-day interval land-use maps with 15-m resolution using the Sentinel-1B and Sentinel-2 data in GEE and other assistant platforms to study the characteristics of wetland dynamics in 2018. Four sets of maps were generated using Sentinel-1B (S1), Sentinel-2 (S2), the combination of Sentinel-1B and Sentinel-2 (S12), and S12 with multitemporal remote sensing (S12’). All of the classifications were performed in the Random Forest Classification (RFC) method using remote sensing indicators. The results indicate that S12’ was the most accurate. Then, the impact of the historic land-use degradation process on current wetland change dynamics was discussed. Stable, degradation, and restoration periods were identified according to the annual changes in wetlands. The degraded, stable, restored, and vulnerable zones were assessed based on the transformation characteristics among wetlands and other land-use types. The impact of historical land-use trajectories on wetland change characteristics nowadays is diverse in land-use types and distributions, and the ecological environment quality is the comprehensive result of the effect of historical land-use trajectories and the amount of rainfall and receding water from paddy fields. This study offers a new method to map high-spatiotemporal-resolution land-use (S12’) and addresses the relationship between historic wetland change characteristics and its status quo. The findings are also applicable to wetland research in other regions. This study could provide more detailed scientific guidance for wetland managers by quickly detecting wetland changes at a finer spatiotemporal resolution.
Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation
Authors: Zhao Xin , Guo Jiayi , Zhang Yueting , Wu Yirong
Published: November 2021

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.
Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data
Authors: Kang Yupeng , Hu Xinli , Meng Qingyan , Zou Youfeng , Zhang Linlin , Liu Miao , Zhao Maofan
Published: November 2021

Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring. This is of great significance for guiding agricultural production and formulating agricultural policies. According to the characteristics of the GF-6 satellite’s newly-added red edge bands, wide field view and high-frequency imaging, the time series of vegetation indices about multi-temporal GF-6 WFV data are used for the study of land cover and crop classification. In this study, eight time steps of GF-6 WFV data were selected from March to October 2019 in Hengshui City. The normalized difference vegetation index (NDVI) time series and 10 different red edge spectral indices time series were constructed. Then, based on principal component analysis (PCA), using two feature selection and evaluation methods, stepwise discriminant analysis (SDA) and random forest (RF), the red edge vegetation index of normalized difference red edge (NDRE) was selected. Seven different lengths of NDVI, NDRE and NDVI&NDRE time series were reconstructed by the Savizky-Golay (S-G) smoothing algorithm. Finally, an RF classification algorithm was used to analyze the influence of time series length and red edge indices features on land cover and crop classification, and the planting structure and distribution of crops in the study area were obtained. The results show that: (1) Compared with the NDRE red edge time series, the NDVI time series is more conducive to the improvement of the overall classification accuracy of crops, and NDRE can assist NDVI in improving the crop classification accuracy; (2) With the shortening of NDVI and NDRE time series, the accuracy of crop classification is gradually decreased, and the decline is gradually accelerated; and (3) Through the combination of the NDVI and NDRE time series, the accuracy of crop classification with different time series lengths can be improved compared with the single NDVI time series, which is conducive to improving the classification accuracy and timeliness of crops. This study has fully tapped the application potential of the new red edge bands of GF-6 WFV time series data, which can provide references for crop identification and classification of time series data such as NDVI and red edge vegetation index of different lengths. At the same time, it promotes the application of optical satellite data with red edge bands in the field of agricultural remote sensing.
Sea-Level Variability in the Arabian Gulf in Comparison with Global Oceans
Authors: Al-Subhi Abdullah M. , Abdulla Cheriyeri P.
Published: November 2021

The availability of nearly three decades of sea-level estimates from satellite altimetry, for the period from 1993 to the present, is exploited for understanding the climatology of sea level and its long-term variability in the Arabian Gulf (also known as the Persian Gulf) in comparison with global oceans. The Arabian Gulf is characterized by a lower sea level from February to May and a higher sea level from September to December, with a maximum in November and a minimum in April. The variability of sea level in the Arabian Gulf is significantly different and nearly opposite to the pattern of sea-level changes in the adjacent marginal basin, the Red Sea. The analysis of low-passed sea level using the empirical orthogonal function and principal component analysis showed that the first mode of variability explains 87.9% of the long-term variability and the second mode explains nearly half of the remaining variability (5.6%). The linear long-term trends in sea level are 2.58 mm/year for the Northern Arabian Gulf and 3.14 mm/year for its southern part, with an average of 2.92 mm/year for the entire Gulf. The analysis of sea level in the Arabian Sea showed a faster rate of sea level rise in the post-2000 period. The long-term linear trend for the post-2000 period in the Northern Arabian Gulf is 4.06 mm/year, and in the southern Gulf it is 4.44 mm/year, with an average trend of 4.29 mm/year. Under the RCP2.6, RCP4.5, and RCP8.5 scenarios, the numerical projections show an expected rise in sea level in the Arabian Gulf by 8.1, 1.3, and 6.8 cm by 2050, and by 16.9, 17.7, 39.1 cm, respectively by the end of the 21st century.
Numerical Assessment of Downward Incoming Solar Irradiance in Smoke Influenced Regions—A Case Study in Brazilian Amazon and Cerrado
Authors: Casagrande Madeleine S. G. , Martins Fernando R. , Rosário Nilton E. , Lima Francisco J. L. , Gonçalves André R. , Costa Rodrigo S. , Zarzur Maurício , Pes Marcelo P. , Pereira Enio Bueno
Published: November 2021

Smoke aerosol plumes generated during the biomass burning season in Brazil suffer long-range transport, resulting in large aerosol optical depths over an extensive domain. As a consequence, downward surface solar irradiance, and in particular the direct component, can be significantly reduced. Accurate solar energy assessments considering the radiative contribution of biomass burning aerosols are required to support Brazil’s solar power sector. This work presents the 2nd generation of the radiative transfer model BRASIL-SR, developed to improve the aerosol representation and reduce the uncertainties in surface solar irradiance estimates in cloudless hazy conditions and clean conditions. Two numerical experiments allowed to assess the model’s skill using observational or regional MERRA-2 reanalysis AOD data in a region frequently affected by smoke. Four ground measurement sites provided data for the model output validation. Results for DNI obtained using δ-Eddington scaling and without scaling are compared, with the latter presenting the best skill in all sites and for both experiments. An increase in the relative error of DNI results obtained with δ-Eddington optical depth scaling as AOD increases is evidenced. For DNI, MBD deviations ranged from −2.3 to −0.5%, RMSD between 2.3 and 4.7% and OVER between 0 and 5.3% when using in-situ AOD data. Overall, our results indicate a good skill of BRASIL-SR for the estimation of both GHI and DNI.
Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
Authors: Yu Huinan , Yin Gaofei , Liu Guoxiang , Ye Yuanxin , Qu Yonghua , Xu Baodong , Verger Aleixandre
Published: November 2021

We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. We applied it over a test study area of maize crops in northern China using continuous field measurements of LAINet along the year 2019. Sentinel-2 LAI showed an overall accuracy of 0.67 in terms of Root Mean Square Error (RMSE) and it was used, after recalibration, as a benchmark to validate six coarse resolution LAI products: MODIS, Copernicus Global Land Service 1 km Version 2 (called GEOV2) and 300 m (GEOV3), Satellite Application Facility EUMETSAT Polar System (SAF EPS) 1.1 km, Global LAnd Surface Satellite (GLASS) 500 m and Copernicus Climate Change Service (C3S) 1 km V2. GEOV2, GEOV3 and MODIS showed a good agreement with reference LAI in terms of magnitude (RMSE ≤ 0.29) and phenology. SAF EPS (RMSE = 0.68) and C3S V2 (RMSE = 0.41), on the opposite, systematically underestimated high LAI values and showed systematic differences for phenological metrics: a delay of 6 days (d), 20 d and 24 d for the start, peak and the end of growing season, respectively, for SAF EPS and an advance of −4 d, −6 d and −6 d for C3S.
Analysis-Ready Data from Hyperspectral Sensors—The Design of the EnMAP CARD4L-SR Data Product
Authors: Bachmann Martin , Alonso Kevin , Carmona Emiliano , Gerasch Birgit , Habermeyer Martin , Holzwarth Stefanie , Krawczyk Harald , Langheinrich Maximilian , Marshall David , Pato Miguel , Pinnel Nicole , de losReyes Raquel , Schneider Mathias , Schwind Peter , Storch Tobias
Published: November 2021

Today, the ground segments of the Landsat and Sentinel missions provide a wealth of well-calibrated, characterized datasets which are already orthorectified and corrected for atmospheric effects. Initiatives such as the CEOS Analysis Ready Data (ARD) propose and ensure guidelines and requirements so that such datasets can readily be used, and interoperability within and between missions is a given. With the increasing availability of data from operational and research-oriented spaceborne hyperspectral sensors such as EnMAP, DESIS and PRISMA, and in preparation for the upcoming global mapping missions CHIME and SBG, the provision of analysis ready hyperspectral data will also be of increasing interest. Within this article, the design of the EnMAP Level 2A Land product is illustrated, highlighting the necessary processing steps for CEOS Analysis Ready Data for Land (CARD4L) compliant data products. This includes an overview of the design of the metadata, quality layers and archiving workflows, the necessary processing chain (system correction, orthorectification and atmospheric correction), as well as the resulting challenges of this procedure. Thanks to this operational approach, the end user will be provided with ARD products including rich metadata and quality information, which can readily be integrated in analysis workflows, and combined with data from other sensors.
Knowledge Distillation of Grassmann Manifold Network for Remote Sensing Scene Classification
Authors: Tian Ling , Wang Zhichao , He Bokun , He Chu , Wang Dingwen , Li Deshi
Published: November 2021

Due to device limitations, small networks are necessary for some real-world scenarios, such as satellites and micro-robots. Therefore, the development of a network with both good performance and small size is an important area of research. Deep networks can learn well from large amounts of data, while manifold networks have outstanding feature representation at small sizes. In this paper, we propose an approach that exploits the advantages of deep networks and shallow Grassmannian manifold networks. Inspired by knowledge distillation, we use the information learned from convolutional neural networks to guide the training of the manifold networks. Our approach leads to a reduction in model size, which addresses the problem of deploying deep learning on resource-limited embedded devices. Finally, a series of experiments were conducted on four remote sensing scene classification datasets. The method in this paper improved the classification accuracy by 2.31% and 1.73% on the UC Merced Land Use and SIRIWHU datasets, respectively, and the experimental results demonstrate the effectiveness of our approach.
A General Framework of Remote Sensing Epipolar Image Generation
Authors: Wang Xuanqi , Wang Feng , Xiang Yuming , You Hongjian
Published: November 2021

Epipolar images can improve the efficiency and accuracy of dense matching by restricting the search range of correspondences from 2-D to 1-D, which play an important role in 3-D reconstruction. As most of the satellite images in archives are incidental collections, which do not have rigorous stereo properties, in this paper, we propose a general framework to generate epipolar images for both in-track and cross-track stereo images. We first investigate the theoretical epipolar constraints of single-sensor and multi-sensor images and then introduce the proposed framework in detail. Considering large elevation changes in mountain areas, the publicly available digital elevation model (DEM) is applied to reduce the initial offsets of two stereo images. The left image is projected into the image coordinate system of the right image using the rational polynomial coefficients (RPCs). By dividing the raw images into several blocks, the epipolar images of each block are parallel generated through a robust feature matching method and fundamental matrix estimation, in which way, the horizontal disparity can be drastically reduced while maintaining negligible vertical disparity for epipolar blocks. Then, stereo matching using the epipolar blocks can be easily implemented and the forward intersection method is used to generate the digital surface model (DSM). Experimental results on several in-track and cross-track images, including optical-optical, SAR-SAR, and SAR-optical pairs, demonstrate the effectiveness of the proposed framework, which not only has obvious advantages in mountain areas with large elevation changes but also can generate high-quality epipolar images for flat areas. The generated epipolar images of a ZiYuan-3 pair in Songshan are further utilized to produce a high-precision DSM.
Investigation of Advanced Robotized Polymer Sheet Incremental Forming Process
Authors: Ostasevicius Vytautas , Eidukynas Darius , Jurenas Vytautas , Paleviciute Ieva , Gudauskis Marius , Grigaliunas Valdas
Published: November 2021

The aim of this work is to evaluate the possibility of inexpensively producing small-batch polymer sheet components using robotized single point incremental forming (SPIF) without backing plate support. An innovative method of thermal and ultrasound assisted deformation of a polymer sheet is proposed using a tool with a sphere mounted in a ring-shaped magnetic holder, the friction of which with the tool holder is reduced by ultrasound, and the heating is performed by a laser. The heated tool moving on the sheet surface locally increases the plasticity of the polyvinyl chloride (PVC) polymer in the contact zone with less deforming force does not reducing the stiffness of the polymer around the tool contact area and eliminating the need for a backing plate. The free 3D rotating ball also changes the slip of the tool on the surface of the polymer sheet by the rolling, thereby improving the surface quality of the product. The finite element method (FEM) allowed the virtual evaluation of the deformation parameters of the SPIF. Significant process parameters were found, and the behavior of the heated polymer sheet was determined.
Digital Biomarkers for the Objective Assessment of Disability in Neurogenic Thoracic Outlet Syndrome
Authors: Najafi Bijan , Zahiri Mohsen , Wang Changhong , Momin Anmol , Paily Paul , Burt Bryan M.
Published: November 2021

Neurogenic thoracic outlet syndrome (nTOS) is a musculoskeletal disorder in which compression of the brachial plexus between the scalene muscles of the neck and the first rib results in disabling upper extremity pain and paresthesia. Currently there are no objective metrics for assessing the disability of nTOS or for monitoring response to its therapy. We aimed to develop digital biomarkers of upper extremity motor capacity that could objectively measure the disability of nTOS using an upper arm inertial sensor and a 20-s upper extremity task that provokes nTOS symptoms. We found that digital biomarkers of slowness, power, and rigidity statistically differentiated the affected extremities of patients with nTOS from their contralateral extremities (n = 16) and from the extremities of healthy controls (n = 13); speed and power had the highest effect sizes. Digital biomarkers representing slowness, power, and rigidity correlated with patient-reported outcomes collected with the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire and the visual analog scale of pain (VAS); speed had the highest correlation. Digital biomarkers of exhaustion correlated with failure of physical therapy in treating nTOS; and digital biomarkers of slowness, power, and exhaustion correlated with favorable response to nTOS surgery. In conclusion, sensor-derived digital biomarkers can objectively assess the impairment of motor capacity resultant from nTOS, and correlate with patient-reported symptoms and response to therapy.
EGM Toolbox—Interface for Controlling ABB Robots in Simulink
Authors: Obal Paweł , Gierlak Piotr
Published: November 2021

The development of industrial robotics requires the use of increasingly sophisticated control algorithms. In modern tasks posed by industry, it is not sufficient for the manipulator to move along a programmed path, reaching individual points with the greatest accuracy. There is a need for solutions that can allow detection and avoidance of obstacles appearing on the robot’s path and that can compensate the path for low-repetitive workpieces, adjust the strength of the impact of manipulator tools on the workpiece or enable safe cooperation of manipulators with people. To support this development, this work proposes an interface for controlling industrial robots in the Simulink environment. With its use, we can easily test our control algorithms using an external controller without the need to write an extensive program in the RAPID language. The robot controller’s task is to control the drives to achieve the set trajectory.
Applying MMD Data Mining to Match Network Traffic for Stepping-Stone Intrusion Detection
Authors: Yang Jianhua , Wang Lixin
Published: November 2021

A long interactive TCP connection chain has been widely used by attackers to launch their attacks and thus avoid detection. The longer a connection chain, the higher the probability the chain is exploited by attackers. Round-trip Time (RTT) can represent the length of a connection chain. In order to obtain the RTTs from the sniffed Send and Echo packets in a connection chain, matching the Sends and Echoes is required. In this paper, we first model a network traffic as the collection of RTTs and present the rationale of using the RTTs of a connection chain to represent the length of the chain. Second, we propose applying MMD data mining algorithm to match TCP Send and Echo packets collected from a connection. We found that the MMD data mining packet-matching algorithm outperforms all the existing packet-matching algorithms in terms of packet-matching rate including sequence number-based algorithm, Yang’s approach, Step-function, Packet-matching conservative algorithm and packet-matching greedy algorithm. The experimental results from our local area networks showed that the packet-matching accuracy of the MMD algorithm is 100%. The average packet-matching rate of the MMD algorithm obtained from the experiments conducted under the Internet context can reach around 94%. The MMD data mining packet-matching algorithm can fix the issue of low packet-matching rate faced by all the existing packet-matching algorithms including the state-of-the-art algorithm. It is applicable to network-based stepping-stone intrusion detection.
Spectral Characteristics of EEG during Active Emotional Musical Performance
Authors: Pousson Jachin Edward , Voicikas Aleksandras , Bernhofs Valdis , Pipinis Evaldas , Burmistrova Lana , Lin Yuan-Pin , Griškova-Bulanova Inga
Published: November 2021

The research on neural correlates of intentional emotion communication by the music performer is still limited. In this study, we attempted to evaluate EEG patterns recorded from musicians who were instructed to perform a simple piano score while manipulating their manner of play to express specific contrasting emotions and self-rate the emotion they reflected on the scales of arousal and valence. In the emotional playing task, participants were instructed to improvise variations in a manner by which the targeted emotion is communicated. In contrast, in the neutral playing task, participants were asked to play the same piece precisely as written to obtain data for control over general patterns of motor and sensory activation during playing. The spectral analysis of the signal was applied as an initial step to be able to connect findings to the wider field of music-emotion research. The experimental contrast of emotional playing vs. neutral playing was employed to probe brain activity patterns differentially involved in distinct emotional states. The tasks of emotional and neutral playing differed considerably with respect to the state of intended-to-transfer emotion arousal and valence levels. The EEG activity differences were observed between distressed/excited and neutral/depressed/relaxed playing.
Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0
Authors: Peppes Nikolaos , Daskalakis Emmanouil , Alexakis Theodoros , Adamopoulou Evgenia , Demestichas Konstantinos
Published: November 2021

The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.
Study on LT Accuracy Improvement by Calibration Based on Network Measurements
Authors: Velázquez Jesús , Conte Javier , Majarena Ana Cristina , Santolaria Jorge
Published: November 2021

Laser trackers (LT) are widely used to calibrate other machines. Nevertheless, very little is known about calibrating an LT. There are some standards that allow us to evaluate the LT performance. However, they require specialized equipment. A calibration procedure to improve the LT accuracy in an easy and fast way is presented in this paper. This method is based on network measurements where a set of reflectors were measured from different LT positions in a working environment. The methodology proposed deal with the lack of nominal data of the reflector mesh. A measurement scenario was defined, based on error parameter dependence on distances and angles, thus, obtaining those positions more sensitive to errors. The influence of the incidence angle of the laser beam on the reflector was characterized, revealing that its contribution to the LT measurement error can be up to 13 µm. Error kinematic parameters were identified to provide the optimum value of an objective function, where the reflector mesh nominal data were unknown. The calibration procedure was validated with nominal data, by measuring a set of reflectors located on a coordinate measuring machine. The findings of this study suggested that the LT accuracy can be improved up to 25%. Moreover, the method can be carried out by the LT user without requiring specialized equipment.
A Dual-Band Wide-Input-Range Adaptive CMOS RF–DC Converter for Ambient RF Energy Harvesting
Authors: Heo Bo-Ram , Kwon Ickjin
Published: November 2021

In this paper, a dual-band wide-input-range adaptive radio frequency-to-direct current (RF–DC) converter operating in the 0.9 GHz and 2.4 GHz bands is proposed for ambient RF energy harvesting. The proposed dual-band RF–DC converter adopts a dual-band impedance-matching network to harvest RF energy from multiple frequency bands. To solve the problem consisting in the great degradation of the power conversion efficiency (PCE) of a multi-band rectifier according to the RF input power range because the available RF input power range is different according to the frequency band, the proposed dual-band RF rectifier adopts an adaptive configuration that changes the operation mode so that the number of stages is optimized. Since the optimum peak PCE can be obtained according to the RF input power, the PCE can be increased over a wide RF input power range of multiple bands. When dual-band RF input powers of 0.9 GHz and 2.4 GHz were applied, a peak PCE of 67.1% at an input power of −12 dBm and a peak PCE of 62.9% at an input power of −19 dBm were achieved. The input sensitivity to obtain an output voltage of 1 V was −17 dBm, and the RF input power range with a PCE greater than 20% was 21 dB. The proposed design achieved the highest peak PCE and the widest RF input power range compared with previously reported CMOS multi-band rectifiers.
A Gyroless Algorithm with Multi-Hypothesis Initialization for Projectile Navigation
Authors: Jardak Nabil , Adam Ronan , Changey Sébastien
Published: November 2021

Projectiles are subjected to a high acceleration shock at launch (20,000 g and higher) and can spin very fast. Thus, the components of onboard navigation units must therefore withstand such constraints in addition to being inexpensive. This makes only a few inertial sensors suitable for projectiles navigation. Particularly, rate gyroscopes which are gun-hardened and have an appropriate operating range are not widely available. On the other hand, magneto-resistive sensors are inexpensive and can satisfy both gun-hardening and operating range requirements, making them an alternative for angular estimation in guided projectiles. This paper presents a gyroless navigation algorithm for projectiles. The lack of gyroscope is handled by the usage of attitude kinematics computed over past attitude estimates of the filter, coupled with a measurement model based on magnetometer and GPS observations of the attitude. The observability of the attitude when considering non-calibrated magnetometers and its dependency on the initialization is addressed. Then, to cope with the initialization dependency of the filter, we proposed a multi-hypothesis initialization algorithm. In terms of performance, the algorithm is shown to provide a high-rate navigation solution with an interesting performance.
Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks
Authors: Wei Xijia , Wei Zhiqiang , Radu Valentin
Published: November 2021

Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localization system, MM-Loc, relying on zero hand-engineered features, but learning automatically from data instead. This is achieved by using modality-specific neural networks to extract preliminary features from each sensing modality, which are then combined by cross-modality neural structures. We show that our choice of modality-specific neural architectures can estimate the location independently. But for better accuracy, a multimodal neural network that fuses the features of early modality-specific representations is a better proposition. Our proposed MM-Loc system is tested on cross-modality samples characterised by different sampling rate and data representation (inertial sensors, magnetic and WiFi signals), outperforming traditional approaches for location estimation. MM-Loc elegantly trains directly from data unlike conventional indoor positioning systems, which rely on human intuition.
The Accuracy and Reliability of Tooth Shade Selection Using Different Instrumental Techniques: An In Vitro Study
Authors: Sirintawat Nattapong , Leelaratrungruang Tanyaporn , Poovarodom Pongsakorn , Kiattavorncharoen Sirichai , Amornsettachai Parinya
Published: November 2021

This study aimed to investigate and compare the reliability and accuracy of tooth shade selection in the model using 30 milled crowns via five methods: (1) digital single-lens reflex (DSLR) camera with twin flash (TF) and polarized filter (DSLR + TF), (2) DSLR camera with a ring flash (RF) and polarized filter (DSLR + RF), (3) smartphone camera with light corrector and polarized filter (SMART), (4) intraoral scanner (IOS), and (5) spectrophotometer (SPEC). These methods were compared with the control group or manufacturer’s shade. The CIE Lab values (L, a, and b values) were obtained from five of the methods to indicate the color of the tooth. Adobe Photoshop was used to generate CIE Lab values from the digital photographs. The reliability was calculated from the intraclass correlation based on two repetitions. The accuracy was calculated from; (a) ΔE calculated by the formula comparing each method to the control group, (b) study and control groups were analyzed by using the Kruskal–Wallis test, and (c) the relationship between study and control groups were calculated using Spearman’s correlation. The reliability of the intraclass correlation of L, a, and b values obtained from the five methods showed satisfactory correlations ranging from 0.732–0.996, 0.887–0.994, and 0.884–0.999, respectively. The ΔE from all groups had statistically significant differences when compared to the border of clinical acceptance (ΔE = 6.8). The ΔE from DSLR + TF, DSLR + RF, SMART, and SPEC were higher than clinical acceptance (ΔE > 6.8), whereas the ΔE from IOS was 5.96 and all of the L, a, and b values were not statistically significantly different from the manufacturer’s shade (p < 0.01). The ΔE of the DSLR + RF group showed the least accuracy (ΔE = 19.98), whereas the ∆E of DSLR + TF, SMART, and SPEC showed similar accuracy ∆E (ΔE = 10.90, 10.57, and 11.57, respectively). The DSLR camera combined with a ring flash system and polarized filter provided the least accuracy. The intraoral scanner provided the highest accuracy. However, tooth shade selection deserves the combination of various techniques and a professional learning curve to establish the most accurate outcome.
Sensor Selection and State Estimation for Unobservable and Non-Linear System Models
Authors: Devos Thijs , Kirchner Matteo , Croes Jan , Desmet Wim , Naets Frank
Published: November 2021

To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case.