bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-Woven Fabrics Based on Nanocomposite Nylon 6/ZnO Obtained by Ultrasound-Assisted Extrusion for Improved Antimicrobial and Adsorption Methylene Blue Dye Properties
Authors: Andrade-Guel M. , Ávila-Orta C. A. , Cabello-Alvarado C. , Cadenas-Pliego G. , Esparza-González S. C. , Pérez-Alvarez M. , Quiñones-Jurado Z. V.
Published: June 2021

Approximately 200,000 tons of water contaminated with dyes are discharged into effluents annually, which in addition to infectious diseases constitute problems that afflict the population worldwide. This study evaluated the mechanical properties, surface structure, antimicrobial performance, and methylene blue dye-contaminant adsorption using the non-woven fabrics manufactured by melt-blowing. The non-woven fabrics are composed of nylon 6 (Ny 6) and zinc oxide nanoparticles (ZnO NPs). The polymer nanocomposites were previously fabricated using variable frequency ultrasound assisted-melt-extrusion to be used in melt-blowing. Energy dispersion spectroscopy (SEM-EDS) images showed a homogeneous dispersion of the ZnO nanoparticles in nylon 6. The mechanical properties of the composites increased by adding ZnO compared to the nylon 6 matrix, and sample Ny/ZnO 0.5 showed the best mechanical performance. All fabric samples exhibited antimicrobial activity against S. aureus and fungus C. albicans, and the incorporation of ZnO nanoparticles significantly improved this property compared to pure nylon 6. The absorption efficiency of methylene blue (MB), during 60 min, for the samples Ny/ZnO 0.05 and Ny/ZnO 0.25 wt%, were 93% and 65%, respectively. The adsorption equilibrium data obeyed the Langmuir isotherm.
Accuracy Improvement of the 14C Method Applied in Biomass and Coal Co-Firing Power Stations
Authors: Tang Yuxing , Luo Zhongyang , Yu Chunjiang
Published: June 2021

The 14C method is an approach used to determine the proportion of carbon derived from biomass and fossil fuel in the co-fired flue gas. Its accuracy is mainly limited by the deviations between the applied biomass fuels’ 14C activity reference value and virtual value. To enrich the theoretical basis of the 14C method when applied to a Chinese biomass and coal co-firing power station, this study performed field sampling experiments and established a new evaluation method based on domestic literature. Unlike previous studies, this study revealed that the 14C activity of biomass far away from fossil carbon sources was 0.7–1.3 pMC lower than the local atmosphere. The 14C activity laws between tree rings and barks, specifically between eucalyptus bark and poplar bark were different, due to different growth models and different bark regeneration cycles, respectively. According to the test results and renewal conclusions, this study proposed a reasonable idea for constructing the prediction equation of referential biomass fuels’ 14C activity. Following this equation, the biomass fuels’ 14C activities of biomass direct-fired power stations at different Chinese cities were obtained.
Limitation of K2CO3 as a Chemical Agent for Upgrading Activated Carbon
Authors: Kim Ji-Hyun , Lee Gibbum , Park Jung-Eun , Kim Seok-Hwi
Published: June 2021

The chemical activation of a carbon precursor with KOH generally results in an activated carbon (AC) with a high specific surface area. However, this process generates a large volume of wastewater that includes dissolved alkali metals, existing mainly as K2CO3. Thus, wastewaters with a high concentration of dissolved K2CO3 can potentially be used in place of KOH as a chemical agent. In the present study, to reduce the thermal stability of K2CO3, which decomposes at temperatures greater than 891 °C, K2CO3 was chemically impregnated into carbon precursors prior to activation of the precursors. The thermochemical properties and activation efficiency of the carbon precursors treated with K2CO3 were compared with those of carbon precursors treated with KOH. Analysis by XPS indicated that C–O–K complexes formed on the surface of the carbon precursors; in addition, their peak intensities were approximately the same irrespective of the chemical agent used. However, the specific surface area of the K2CO3-impregnated AC was 2162 m2/g, which was ~70% of that of the KOH-impregnated AC (3047 m2/g) prepared using the same K/C molar ratio of 0.5. XRD results confirmed that both K2CO3 and KOH transformed into KHCO3 and K4H2(CO3)3·1.5H2O during the impregnation. The peak intensities of these compounds in the XRD pattern of the K2CO3-impregnated carbon precursors were two times greater than those in the pattern of the KOH-impregnated carbon precursors. These compounds eventually transformed into K2CO3, which hardly participated as a chemical agent at the temperature used in the present study (850 °C). Therefore, recrystallisation of K2CO3, even during the impregnation, appeared to adversely affect the degree of activation. Nevertheless, the specific surface area of the K2CO3-activated AC was still ~1.6 times greater than that of the untreated carbon precursor (1378 m2/g), suggesting that the use of wastewater as a chemical agent is feasible for resource recycling.
Universal Chain-End Coupling Conditions for Brominated Polystyrenes, Polyacrylates, and Polymethacrylates
Authors: Andry Joseph J. , Lee Jaenic J. , Wu Jessica , Xia Katherine , Tillman Eric S.
Published: June 2021

Atom transfer radical coupling (ATRC), performed with or without radical traps, has allowed for high extents of coupling (Xc) for a variety of brominated polymers, yet structurally different polymeric chain ends require unique reagents and reaction conditions. Inspired by a similar study that focused on universal conditions for the controlled polymerization of different monomers using atom transfer radical polymerization (ATRP), this work focuses on developing a single set of conditions (or conditions with as little variation as possible) that will achieve extents of coupling greater than 80% or end-brominated chains of polystyrene (PSBr), poly(methyl methacrylate) (PMMABr), and poly(methyl acrylate) (PMABr). The radical traps α-phenyl-tert-butylnitrone (PBN), 2-methyl-2-nitrosopropane (MNP), and nitrosobenzene (NBz) were chosen in this study, along with copper catalysts, reducing agents, and nitrogen-based ligands. Ultimately, a single set of effective reaction conditions was identified with the only difference being the radical trap used: MNP was effective for coupling PSBr and PMABr while NBz was necessary to achieve similarly high extents of coupling for PMMABr.
A Study on Balanced Scorecard and Its Impact on Sustainable Development of Renewable Energy Organizations; A Mediating Role of Political and Regulatory Institutions
Authors: Rafiq Muhammad , Maqbool Saif , Martins José Moleiro , Mata Mário Nuno , Dantas Rui Miguel , Naz Shumaila , Correia Anabela Batista
Published: June 2021

Organizational strategic programs are continuously evolving and gaining the attention of policy makers in order to construct organizations’ ecological and socioeconomic systems. The purpose of this study is to examine the relationship between the balanced scorecard (BSC) and sustainable development involving the mediated effect of political and regulatory influence. To achieve the core objectives of the research, the quantitative (positivism) research method is applied. The goal of the current research is made possible through the quantitative method because of its objective nature of reality. A total of 320 questionnaires were distributed among the different levels of managers; 280 respondents returned the questionnaire. The data are analyzed through a modern statistical tool called Smart-PLS, Partial Least Squares (PLS) is high graphical user interference software that is used to calculate Structural Equation Modeling (SEM) through PLS path modeling. Factor analysis is conducted to eliminate the variables that have no contribution and to reduce the variables to obtain better results in regression. The implications are for energy organizations that are struggling to deal with sustainable development and these tools can help them to achieve their sustainability goals. The study concludes that the adoption of BSC is essential to ensure sustainable development regardless of its challenges. Moreover, consideration of meta-constitutional rules as political influence is important to understand and address in order to mitigate financial loss. In nutshell, the use of BSC is highly recommended to eliminate the routine problems and to ensure environmental sustainability.
Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving
Authors: Chukhrova Nataliya , Johannssen Arne
Published: June 2021

In stochastic claims reserving, state space models have been used for almost 40 years to forecast loss reserves and to compute their mean squared error of prediction. Although state space models and the associated Kalman filter learning algorithms are very powerful and flexible tools, comparatively few articles on this topic were published during this period. Most recently, several articles have been published which highlight the benefits of state space models in stochastic claims reserving and may lead to a significant increase in its popularity for applications in actuarial practice. To further emphasize the merits of these papers, this commentary highlights various additional aspects that are useful for practical applications and offer some fruitful directions for future research.
A Comparison of Optimized Sentinel-2 Super-Resolution Methods Using Wald’s Protocol and Bayesian Optimization
Authors: Armannsson Sveinn E. , Ulfarsson Magnus O. , Sigurdsson Jakob , Nguyen Han V. , Sveinsson Johannes R.
Published: June 2021

In the context of earth observation and remote sensing, super-resolution aims to enhance the resolution of a captured image by upscaling and enhancing its details. In recent years, numerous methods for super-resolution of Sentinel-2 (S2) multispectral images have been suggested. Most of those methods depend on various tuning parameters that affect how effective they are. This paper’s aim is twofold. Firstly, we propose to use Bayesian optimization at a reduced scale to select tuning parameters. Secondly, we choose tuning parameters for eight S2 super-resolution methods and compare them using real and synthetic data. While all the methods give good quantitative results, Area-To-Point Regression Kriging (ATPRK), Sentinel-2 Sharpening (S2Sharp), and Sentinel-2 Symmetric Skip Connection convolutional neural network (S2 SSC) perform markedly better on several datasets than the other methods tested in this paper.
Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds
Authors: Li Shiming , Ge Xuming , Li Shengfu , Xu Bo , Wang Zhendong
Published: June 2021

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.
Effects of Day/Night Factor on the Detection Performance of FY4A Lightning Mapping Imager in Hainan, China
Authors: Sun Hao , Yang Jing , Zhang Qilin , Song Lin , Gao Haiyang , Jing Xiaoqin , Lin Guo , Yang Kang
Published: June 2021

In this study, the effect of day/night factor on the detection performance of the FY4A lightning mapping imager (LMI) is evaluated using the Bayesian theorem, and by comparing it to the measurements made by a ground-based low-frequency magnetic field lightning location system. Both the datasets were collected in the summers of 2019–2020 in Hainan, China. The results show that for the observed summer thunderstorms in Hainan, the daytime detection efficiencies of LMI (DELMI) were 20.41~35.53% lower than the nighttime DELMI. Compared to other space-based lightning sensors (lightning imaging sensors/optical transient detectors (LIS/OTD) and geostationary lightning mapper (GLM)), the detection performance of LMI is more significantly influenced by the day/night factor. The DELMI rapidly dropped within about four hours after sunrise while it increased before sunset. For the storms that formed at night and lasted for an entire day, the DELMI remained relatively low during the daytime, even as the thunderstorms intensified. The poor detection performance of LMI during daytime is probably because of the sunlight reflection by clouds and atmosphere, which results in larger background radiative energy density (RED) than that at night. During night, LMI captured the lightning signals well with low RED (8.38~10.63 μJ sr−1 m−2 nm−1). However, during daytime, signals with RED less than 77.12 μJ sr−1 m−2 nm−1 were filtered, thus lightning groups could rarely be identified by LMI, except those with extremely high RED. Due to the limitations of the Bayesian theorem, the obtained DE in this study was “relative” DE rather than “absolute” DE. To obtain the absolute DE of LMI, the total lightning density is necessary but can hardly be measured. Nonetheless, the results shown here clearly indicate the strong impact of day/night factor on the detection performance of LMI, and can be used to improve the design and post-processing method of LMI.
The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia
Authors: Shen Jianxiu , Evans Fiona H.
Published: June 2021

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.
Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records
Authors: Zhu Zhihua , Yang Yueying , Cai Yanpeng , Yang Zhifeng
Published: June 2021

Analyzing flooding in urban areas is a great challenge due to the lack of long-term rainfall records. This study hereby seeks to propose a modeling framework for urban flood analysis in ungauged drainage basins. A platform called “RainyDay” combined with a nine-year record of hourly, 0.1° remotely sensed rainfall data are used to generate extreme rainfall events. These events are used as inputs to a hydrological model. The comprehensive characteristics of urban flooding are reflected through the projection pursuit method. We simulate runoff for different return periods for a typical urban drainage basin. The combination of RainyDay and short-record remotely sensed rainfall can reproduce recent observed rainfall frequencies, which are relatively close to the design rainfall calculated by the intensity-duration-frequency formula. More specifically, the design rainfall is closer at high (higher than 20-yr) return period or long duration (longer than 6 h). Contrasting with the flood-simulated results under different return periods, RainyDay-based estimates may underestimate the flood characteristics under low return period or short duration scenarios, but they can reflect the characteristics with increasing duration or return period. The proposed modeling framework provides an alternative way to estimate the ensemble spread of rainfall and flood estimates rather than a single estimate value.
CPS-Det: An Anchor-Free Based Rotation Detector for Ship Detection
Authors: Yang Yi , Pan Zongxu , Hu Yuxin , Ding Chibiao
Published: June 2021

Ship detection is a significant and challenging task in remote sensing. At present, due to the faster speed and higher accuracy, the deep learning method has been widely applied in the field of ship detection. In ship detection, targets usually have the characteristics of arbitrary-oriented property and large aspect ratio. In order to take full advantage of these features to improve speed and accuracy on the base of deep learning methods, this article proposes an anchor-free method, which is referred as CPS-Det, on ship detection using rotatable bounding box. The main improvements of CPS-Det as well as the contributions of this article are as follows. First, an anchor-free based deep learning network was used to improve speed with fewer parameters. Second, an annotation method of oblique rectangular frame is proposed, which solves the problem that periodic angle and bounded coordinates in conjunction with the regression calculation can lead to the problem of loss anomalies. For the annotation scheme proposed in this paper, a scheme for calculating Angle Loss is proposed, which makes the loss function of angle near the boundary value more accurate and greatly improves the accuracy of angle prediction. Third, the centerness calculation of feature points is optimized in this article so that the center weight distribution of each point is suitable for the rotation detection. Finally, a scheme combining centerness and positive sample screening is proposed and its effectiveness in ship detection is proved. Experiments on remote sensing public dataset HRSC2016 show the effectiveness of our approach.
A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution
Authors: Xu Shuo , Cheng Jie , Zhang Quan
Published: June 2021

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.
Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry
Authors: Wang Wenyue , Hocke Klemens , Mätzler Christian
Published: June 2021

Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.
MDCwFB: A Multilevel Dense Connection Network with Feedback Connections for Pansharpening
Authors: Li Weisheng , Xiang Minghao , Liang Xuesong
Published: June 2021

In most practical applications of remote sensing images, high-resolution multispectral images are needed. Pansharpening aims to generate high-resolution multispectral (MS) images from the input of high spatial resolution single-band panchromatic (PAN) images and low spatial resolution multispectral images. Inspired by the remarkable results of other researchers in pansharpening based on deep learning, we propose a multilevel dense connection network with a feedback connection. Our network consists of four parts. The first part consists of two identical subnetworks to extract features from PAN and MS images. The second part is a multilevel feature fusion and recovery network, which is used to fuse images in the feature domain and to encode and decode features at different levels so that the network can fully capture different levels of information. The third part is a continuous feedback operation, which refines shallow features by feedback. The fourth part is an image reconstruction network. High-quality images are recovered by making full use of multistage decoding features through dense connections. Experiments on different satellite datasets show that our proposed method is superior to existing methods, through subjective visual evaluation and objective evaluation indicators. Compared with the results of other models, our results achieve significant gains on the multiple objective index values used to measure the spectral quality and spatial details of the generated image, namely spectral angle mapper (SAM), relative global dimensional synthesis error (ERGAS), and structural similarity (SSIM).
Design and Implementation of a Wireless Sensor Network for Seismic Monitoring of Buildings
Authors: Jornet-Monteverde Julio Antonio , Galiana-Merino Juan José , Soler-Llorens Juan Luis
Published: June 2021

This article presents a new wireless seismic sensor network system, especially design for building monitoring. The designed prototype allows remote control, and remote and real-time monitoring of the recorded signals by any internet browser. The system is formed by several Nodes (based on the CC3200 microcontroller of Texas Instruments), which are in charge of digitizing the ambient vibrations registered by three-component seismic sensors and transmitting them to a central server. This server records all the received signals, but also allows their real-time visualization in several remote client browsers thanks to the JavaScript’s Node.js technology. The data transmission uses not only Wi-Fi technology, but also the existing network resources that nowadays can be found usually in any official or residential building (lowering deployment costs). A data synchronization scheme was also implemented to correct the time differences between the Nodes, but also the long-term drifts found in the internal clock of the microcontrollers (improving the quality of records). The completed system is a low-cost, open-hardware and open-software design. The prototype was tested in a real building, recording ambient vibrations in several floors and observing the differences due to the building structure.
Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN
Authors: Naglah Ahmed , Khalifa Fahmi , Khaled Reem , Abdel Razek Ahmed Abdel Khalek , Ghazal Mohammad , Giridharan Guruprasad , El-Baz Ayman
Published: June 2021

Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the proposed system would enable the learning of more advanced radiomics with an additional advantage of visualizing the texture patterns after learning. We evaluated the proposed system using data collected from a cohort of 49 patients with pathologically proven thyroid nodules. The accuracy of the proposed system has also been compared against recent CNN models as well as multiple machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among all compared methods with a diagnostic accuracy of 0.87, specificity of 0.97, and sensitivity of 0.69. The results suggest that texture features extracted using deep learning can contribute to the protocols of cancer diagnosis and treatment and can lead to the advancement of precision medicine.
Ammonia Gas Sensor Based on Graphene Oxide-Coated Mach-Zehnder Interferometer with Hybrid Fiber Structure
Authors: Fan Xiaofeng , Deng Shuying , Wei Zhongchao , Wang Faqiang , Tan Chunhua , Meng Hongyun
Published: June 2021

A graphene oxide-coated in-fiber Mach-Zehnder interferometer (MZI) formed with a multimode fiber-thin core fiber-multimode fiber (MMF-TCF-MMF) is proposed and experimentally demonstrated for ammonia gas (NH3) sensing. The MZI structure is composed of two segments of MMF of length 2 mm, with a flame-tapered TCF between them as the sensing arm. The MMFs act as mode couplers to split and recombine light owing to the core diameter mismatch with the other fibers. A tapered TCF is formed by the flame melting taper method, resulting in evanescent wave leakage. A layer of graphene oxide (GO) is applied to the tapered region of the TCF to achieve gas adsorption. The sensor operates on the principle of changing the effective refractive index of the cladding mode of a fiber through changing the conductivity of the GO coating by adsorbed NH3 molecules, which gives rise to a phase shift and shows as the resonant dip shifts in the transmission spectrum. So the concentration of the ammonia gas can be obtained by measuring the dip shift. A wavelength-shift sensitivity of 4.97 pm/ppm with a linear fit coefficient of 98.9% is achieved for ammonia gas concentrations in the range of 0 to 151 ppm. In addition, we performed a repetitive dynamic response test on the sensor by charging/releasing NH3 at concentration of 200 ppm and a relative humidity test in a relative humidity range of 35% to 70%, which demonstrates the reusability and stability of the sensor.
Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap
Authors: Dhirani Lubna Luxmi , Armstrong Eddie , Newe Thomas
Published: June 2021

Industrial IoT (IIoT) is a novel concept of a fully connected, transparent, automated, and intelligent factory setup improving manufacturing processes and efficiency. To achieve this, existing hierarchical models must transition to a fully connected vertical model. Since IIoT is a novel approach, the environment is susceptible to cyber threat vectors, standardization, and interoperability issues, bridging the gaps at the IT/OT ICS (industrial control systems) level. IIoT M2M communication relies on new communication models (5G, TSN ethernet, self-driving networks, etc.) and technologies which require challenging approaches to achieve the desired levels of data security. Currently there are no methods to assess the vulnerabilities/risk impact which may be exploited by malicious actors through system gaps left due to improper implementation of security standards. The authors are currently working on an Industry 4.0 cybersecurity project and the insights provided in this paper are derived from the project. This research enables an understanding of converged/hybrid cybersecurity standards, reviews the best practices, and provides a roadmap for identifying, aligning, mapping, converging, and implementing the right cybersecurity standards and strategies for securing M2M communications in the IIoT.
Optimum PZT Patch Size for Corrosion Detection in Reinforced Concrete Using the Electromechanical Impedance Technique
Authors: Hire Jaamac Hassan , Hosseini Seyedsina , Moradi Farshad
Published: June 2021

This paper proposes the use of a 1-dimensional (1-D) electromechanical impedance model to extract proper design guidelines when selecting patch-size and frequency range for corrosion detection in reinforced concrete structures using the electromechanical impedance (EMI) technique. The theoretical results show that the sensitivity mainly lies in the peak frequencies of the impedance spectrum, while outside resonant frequencies the sensitivity levels are low, and are prone to natural variation. If the mechanical impedance ratio between the host structure and patch is too large, the peaks and thereby the sensitivity decreases. This can be counteracted by increasing the patch thickness. Tests were carried out in reinforced concrete structures, where lead zirconate titanate (PZT) patches were attached to the rebars. Patches measuring 10 × 10 mm in length and width, with thicknesses of 0.3, 0.5 and 1.5 mm, were used. The results show that only the 10 × 10 × 1.5 mm patch, was able to generate a clear peak in the 50 kHz to 400 kHz impedance spectrum. Furthermore, a reinforced concrete structure with the 1.5 mm patch attached was induced significant corrosion damages, resulting in cracking of the structure. Due to this, a leftward shift of the main peak, and creation of new peaks in the spectrum was observed.
Mechanically Rotating Intravascular Ultrasound (IVUS) Transducer: A Review
Authors: Sung Jin-Ho , Chang Jin-Ho
Published: June 2021

Intravascular ultrasound (IVUS) is a valuable imaging modality for the diagnosis of atherosclerosis. It provides useful clinical information, such as lumen size, vessel wall thickness, and plaque composition, by providing a cross-sectional vascular image. For several decades, IVUS has made remarkable progress in improving the accuracy of diagnosing cardiovascular disease that remains the leading cause of death globally. As the quality of IVUS images mainly depends on the performance of the IVUS transducer, various IVUS transducers have been developed. Therefore, in this review, recently developed mechanically rotating IVUS transducers, especially ones exploiting piezoelectric ceramics or single crystals, are discussed. In addition, this review addresses the history and technical challenges in the development of IVUS transducers and the prospects of next-generation IVUS transducers.
Analysis of Postural Instability in the Upright Position on Narrow Platforms and the Interactions with Postural Constraints
Authors: Sugama Atsushi , Seo Akihiko
Published: June 2021

Background: Loss of balance is a considerable risk factor for workers while using ladders, because they are required to maintain static postural balance on platforms of a restricted size. This study observed center of mass (CoM) and center of pressure (CoP) behaviors and evaluated the effects of the platform depth (anterior–posterior length) and working postures. Methods: Eleven male participants stood on four platforms with depths ranging from 6 to 15 cm and maintained their positions for 60 s while performing or not performing other tasks (object holding, upward viewing, or both simultaneously). The kinematics were analyzed on the sagittal plane based on the inverse pendulum model. Results: The absolute moving range for the CoP–CoM linearly increased with the decreasing platform depth, and the working postures affected the slopes of the linear fits. The relative range of CoP–CoM displacement on narrow platforms was highly correlated with the subjective sense of instability. Conclusions: Monitoring the CoP is effective for a better understanding and evaluation of static postural balance. This study’s findings contribute to improving the design of work equipment through the use of wider platforms that are robust against the effects of working postures.
Application of Laser-Induced, Deep UV Raman Spectroscopy and Artificial Intelligence in Real-Time Environmental Monitoring—Solutions and First Results
Authors: Post Claudia , Brülisauer Simon , Waldschläger Kryss , Hug William , Grüneis Luis , Heyden Niklas , Schmor Sebastian , Förderer Aaron , Reid Ray , Reid Michael , Bhartia Rohit , Nguyen Quoc , Schüttrumpf Holger , Amann Florian
Published: June 2021

Environmental monitoring of aquatic systems is the key requirement for sustainable environmental protection and future drinking water supply. The quality of water resources depends on the effectiveness of water treatment plants to reduce chemical pollutants, such as nitrates, pharmaceuticals, or microplastics. Changes in water quality can vary rapidly and must be monitored in real-time, enabling immediate action. In this study, we test the feasibility of a deep UV Raman spectrometer for the detection of nitrate/nitrite, selected pharmaceuticals and the most widespread microplastic polymers. Software utilizing artificial intelligence, such as a convolutional neural network, is trained for recognizing typical spectral patterns of individual pollutants, once processed by mathematical filters and machine learning algorithms. The results of an initial experimental study show that nitrates and nitrites can be detected and quantified. The detection of nitrates poses some challenges due to the noise-to-signal ratio and background and related noise due to water or other materials. Selected pharmaceutical substances could be detected via Raman spectroscopy, but not at concentrations in the µg/l or ng/l range. Microplastic particles are non-soluble substances and can be detected and identified, but the measurements suffer from the heterogeneous distribution of the microparticles in flow experiments.
Influence of Vortex Finder Structure on Separation Performance of Double-Overflow Three-Product Hydrocyclones
Authors: Zhang Yuekan , Ge Jiangbo , Jiang Lanyue , Wang Hui , Yang Junru , Chen Bo
Published: June 2021

In view of the difficulty of traditional hydrocyclones to meet the requirements of fine classification, a double-overflow three-product (internal overflow, external overflow and underflow) hydrocyclone was designed in this study. Numerical simulation and experimental research methods were used to investigate the effects of double-overflow flow field characteristics and structural parameters (i.e., internal vortex finder diameter and insertion depth) on separation performance. The research results showed that the larger the diameter of the internal vortex finder, the greater the overflow yield and the larger the cut size. The finest internal overflow product can be obtained when the internal vortex finder is 30 mm longer than the external vortex finder. The separation efficiency is highest when the internal vortex finder is 30 mm shorter than the external vortex finder.
Multivariate Analysis of the Offensive Phase in High-Performance Women’s Soccer: A Mixed Methods Study
Authors: Iván-Baragaño Iyán , Maneiro Rubén , Losada José L. , Ardá Antonio
Published: June 2021

Currently, there are still few studies on the tactical and contextual criteria that determine offensive success in women’s soccer. The objectives of this study were to discover contextual and tactical criteria that show an association with success in elite women’s soccer and to establish a multivariate predictive model for the offensive phase. For this, 2323 ball possessions were analyzed in FIFA Women’s World Cup 2019 via direct observation. In addition, eight semi-structured interviews were conducted with women’s soccer coaches and players. For direct observation, a bivariate analysis between the analysed criteria and possession’s outcome was suggested. Additionally, a multivariate predictive analysis was proposed via a decision tree method. To analyze the interviews, a sequential analysis of delays and polar coordinates analysis were carried out. It was established a multi-variant model of offensive success based on possession zone (middle offensive), initial offensive intention (progress) and start zone (preoffensive or offensive) criteria. The probability of offensive success was 75.2% interactively between those criteria. In addition, the analysis of the interviews allowed us to conclude that criteria related to technical–tactical performance, decision-making and physical capacities of the players will be decisive in increasing the probabilities of success.
Advanced Modelling Tools to Support Planning for Sand/Gravel Quarries
Authors: Pavesi Filippo Carlo , Richiedei Anna , Pezzagno Michele
Published: June 2021

Sand and gravel quarry planning must guarantee the public interest in the procurement of raw materials while ensuring environmental sustainability. An Analyzing Planning Support System for sand and gravel quarry plan can assist decision-makers during the planning process. The proposed Analyzing PSS uses integrating geologic, economic, environmental, and geographic information to quantify raw materials and the size of quarries. This kind of tool is useful to support public authority decisions. The study provides the results of an experience conducted in the province of Brescia (NUT 3 in Northern Italy).
Nature-Based Solutions Tools for Planning Urban Climate Adaptation: State of the Art
Authors: Voskamp Ilse M. , de Luca Claudia , Polo-Ballinas Monserrat Budding , Hulsman Helena , Brolsma Reinder
Published: June 2021

Despite the recognized potential of nature-based solutions (NBSs) to support climate adaptation, there are still wide barriers for a wider uptake of such NBS in urban areas. While tailored NBS tools could facilitate and accelerate this process, a comprehensive mapping of their availability and capacity to respond to cities’ challenges is missing. This research aims to provide an overview of tools that intend to facilitate the uptake of NBS for urban climate adaptation supporting cities in overcoming their challenges. To do so, this paper (i) presents the results of interviews and workshops with municipal officers and decision-makers from different European cities that identified the challenges they experience with NBS uptake; (ii) selects and reviews NBS tools and (iii) analyzes them on their capacity to address these implementation challenges. Our research revealed four key challenges that municipal officers experience: resources availability; level of expertise, know-how or competence; the institutional setting, and collaborative governance and planning. The results from the tools’ review show that existing tools can support overcoming a lack of expertise (31), but, to a smaller extent, can also be of use when experiencing the institutional setting (13), availability of resources (11), and collaborative governance and planning (10) as a challenge. This work provides researchers and tool developers with insights into potential market saturation as well as scarcity of certain types of tools that would match cities’ challenges, highlighting needs and opportunities for new tool development.
Sustainable Management of Organic Waste and Recycling for Bioplastics: A LCA Approach for the Italian Case Study
Authors: Vinci Giuliana , Ruggieri Roberto , Billi Andrea , Pagnozzi Carmine , Di Loreto Maria Vittoria , Ruggeri Marco
Published: June 2021

The collection of the organic fraction in Italy recorded significant growth in the decade 2010–2019 (+74%) due to both the increase in the spread of separate waste collection as well as the increase in the biological treatment plants of municipal waste. However, within the organic fraction there remains a share of non-compostable material (NCM) (plastic, glass, aluminum, etc.), equal to ~5% of the total, which affects the efficiency of composting practices as well as decreasing both the yield and the quality of the final compost, causing a portion of organic material to be subtracted from composting and ending up in landfills. Therefore, the purpose of this work is to evaluate how the sustainability of the organic fraction collection and the amount of compost obtained in the composting plants could improve, following the use of biodegradable and compostable bioplastic bags (shoppers), in replacement for conventional plastic ones. The Life Cycle Assessment (LCA) and Carbon Footprint (CF) methodology was used for the assessment, comparing two different scenarios based on data relating to the collection of the organic fraction in Italy in 2019. Scenario 1 relates to the composting of organic material that also contains plastics, bioplastics, and non-compostable materials, while in scenario 2 the share of plastic material in the first scenario has been entirely replaced by bioplastics. The results show that scenario 2 assumes the lowest values for 15 impact categories out of the 18 analyzed, and, among these, in three categories it assumes negative values: ozone formation-terrestrial ecosystems (−1.64 × 10−2 kg NOX eq), ozone formation-human health (−8.50 × 10−3 kg NOX eq), and fossil resource scarcity (−4.91 × 102 kg oil eq). Furthermore, scenario 2 has a negative carbon footprint (−3.80 kg CO2 eq) compared to scenario 1 (79.71 kg CO2 eq), and in general it is the most sustainable scenario as a direct consequence of the greater amount of compost obtained (307.4 kg vs. 269.2 kg).
A Framework for Evaluating Agricultural Ontologies
Authors: Goldstein Anat , Fink Lior , Ravid Gilad
Published: June 2021

An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.
Specified Dosages of Biochar Application Not Impact Native Organic Carbon but Promote a Positive Effect on Native Humic Acid in Humicryepts Soil
Authors: Li Qiao , Liu Songjian , Gao Shangzhi , Zhou Xin , Liu Riyue , Guan Song , Dou Sen
Published: June 2021

Biochar is considered to have potential use in carbon (C) sequestration and has been widely used in soil amendment. Humic substances (HSs), assigned as the stable organic C, have obvious agronomic benefits. However, the response mechanisms of these carbonaceous substances to biochar are unclear in biochar-amended soils. In a two-year experiment, the δ13C technique was employed to trace the fate of the biochar-derived C in HSs and evaluate the effects of four treatments, including no biochar control (CK) and biochar addition at dosages of 6 t ha−1 (BC6), 12 t ha−1 (BC12), and 24 t ha−1 soil (BC24), on soil organic carbon (SOC) and HSs. Compared to CK, biochar application significantly improved total SOC contents and the C pool index, whereas the C labile index declined. Humic acid (HA) and humin were distinctly enhanced in bulk soil. Moreover, the aliphaticity was intensified in the chemical composition of HA. In particular, native HA contents substantially increased by 16.30–55.95%. Biochar-applied C of 4.08–6.43% was finitely involved in HA formation over the two years, which resulted in a genetic relationship between soil HA and biochar to some extent. The low dosages of biochar at 6 t ha−1, 12 t ha−1 and 24 t ha−1 did not obviously affect native SOC contents. Moreover, BC24 had less of an effect on HA formation compared to BC6 and BC12, but had the highest SOC. These results demonstrate that biochar application can improve SOC stocks, reduce C instability and promote HSs formation, and they suggest that determining and keeping an optimum dosage of biochar application can represent an effective strategy (i.e., not only sequestrate C, but also improve soil quality), which is beneficial to sustainability in the ecological environment and agriculture.