bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial Potential and Phytochemical Profile of Wild and Cultivated Populations of Thyme (Thymus sp.) Growing in Western Romania
Authors: Beicu Rodica , Alexa Ersilia , Obiștioiu Diana , Cocan Ileana , Imbrea Florin , Pop Georgeta , Circioban Denisa , Moisa Cristian , Lupitu Andreea , Copolovici Lucian , Copolovici Dana Maria , Imbrea Ilinca Merima
Published: September 2021

The purpose of this study was to analyze the chemical composition and antimicrobial activity of some thymus populations collected from five different locations in Western Romania. The chemical compositions of the essential oils (EOs) were studied through GC–MS, and the biological activities were evaluated using the microdilution method. The EO yield ranged between 0.44% and 0.81%. Overall, 60 chemical compounds were identified belonging to three chemotypes: thymol (three populations), geraniol (one population) and carvacrol (one population). Thymus vulgaris L. is distinguished by a high content of thymol, while species of spontaneous flora (Th. odoratissimus and Th. pulegioides) contain, in addition to thymol, appreciable amounts of carvacrol and geraniol. The antimicrobial activity of each the five oils was tested on Staphylococcus aureus (ATCC 25923), Streptococcus pyogenes (ATCC 19615), Esherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), Shigella flexneri (ATCC 12022), Salmonella typhimurium (ATCC 14028), Haemophilus influenzae type B (ATCC 10211), Candida albicans (ATCC 10231) and Candida parapsilopsis (ATCC 22019). The EOs showed biological activity on Gram-positive/Gram-negative/fungal pathogens, the most sensitive strains proving to be S. pyogenes, S. flexneri, S. typhimurium and C. parapsilopsis with an MIC starting at 2 µL EO/100 µL. The species sensitive to the action of Thymus sp. from culture or spontaneous flora are generally the same, but it should be noted that T. odoratissimus has a positive inhibition rate higher than other investigated EOs, regardless of the administered oil concentration. To date, there is no research work presenting the chemical and antimicrobial profiling of T. odoratissimus and the correlations between the antimicrobial potential and chemical composition of wild and cultivated populations of thyme (Thymus sp.) growing in Western Romania.
Modeling and Simulation of Fabricated Graphene Nanoplates/Polystyrene Nanofibrous Membrane for DCMD
Authors: Abdullah Ahmad , Al-Qahatani Abdulaziz , Alquraish Mohammed , Bailey Colin , El-Shazly Ahmed , El-Mofty Salah
Published: September 2021

Membrane distillation is an active technique that provides pure water with very good rejection and could be applied to water of extremely high salinity. The low productivity of membrane distillation needs intensive efforts to be competitive with other desalination techniques. In this current study, a composite (PS/GNP) membrane, which is composed of polystyrene (PS) based and 0.25% weight percent graphene nanoplates (GNP) has been fabricated via electrospinning and compared with the blank PS membrane. SEM, FTIR, contact angle and porosity characterization have been performed, and the results show that the validity of the predefined conditions, and the contact angle of the composite membrane, which is found to be 91.68°, proved the hydrophobic nature of the composite membrane. A numerical simulation using Ansys 2020 software has been introduced to study the performance of the fabricated composite membrane when used in direct contact membrane distillation (DCMD). The numerical model has been validated with experimental work from the literature and showed an excellent match. The blank PS and composite PS/GNP membranes have been investigated and compared at different operating conditions, i.e., hot water supply temperature and system flow rate. The results show that the composite PS/GNP membrane outperforms the blank PS membrane at all studied operating conditions.
Analysis of Microplastics Released from Plain Woven Classified by Yarn Types during Washing and Drying
Authors: Choi Sola , Kwon Miyeon , Park Myung-Ja , Kim Juhea
Published: September 2021

Microplastics reach the aquatic environment through wastewater. Larger debris is removed in sewage treatment plants, but filters are not explicitly designed to retain sewage sludge’s microplastic or terrestrial soils. Therefore, the effective quantification of filtration system to mitigate microplastics is needed. To mitigate microplastics, various devices have been designed, and the removal efficiency of devices was compared. However, this study focused on identifying different fabrics that shed fewer microplastics. Therefore, in this study, fabric-specific analyses of microplastics of three different fabrics during washing and drying processes were studied. Also, the change in the generation of microplastics for each washing process of standard washing was investigated. The amount of microplastics released according to the washing process was analyzed, and the collected microplastics’ weight, length, and diameter were measured and recorded. According to the different types of yarn, the amount of microplastic fibers produced during washing and drying varied. As the washing processes proceed, the amount of microplastics gradually decreased. The minimum length (>40 µm) of micro-plastics generated were in plain-woven fabric. These results will be helpful to mitigate microplastics in the production of textiles and in selecting built-in filters, and focusing on the strict control of other parameters will be useful for the development of textile-based filters, such as washing bags.
Preparation and Characterization of Cellulose Acetate Film Reinforced with Cellulose Nanofibril
Authors: Cindradewi Azelia Wulan , Bandi Rajkumar , Park Chan-Woo , Park Ji-Soo , Lee Eun-Ah , Kim Jeong-Ki , Kwon Gu-Joong , Han Song-Yi , Lee Seung-Hwan
Published: September 2021

In this study, cellulose acetate (CA)/cellulose nanofibril (CNF) film was prepared via solvent casting. CNF was used as reinforcement to increase tensile properties of CA film. CNF ratio was varied into 3, 5, and 10 phr (parts per hundred rubbers). Triacetin (TA) and triethyl citrate (TC) were used as two different eco-friendly plasticizers. Two different types of solvent, which are acetone and N-methyl-2-pyrrolidone (NMP), were also used. CA/CNF film was prepared by mixing CA and CNF in acetone or NMP with 10% concentration and stirred for 24 h. Then, the solution was cast in a polytetrafluoroethylene (PTFE) dish followed by solvent evaporation for 12 h at room temperature for acetone and 24 h at 80 °C in an oven dryer for NMP. The effect of solvent type, plasticizers type, and CNF amount on film properties was studied. Good dispersion in NMP was evident from the morphological study of fractured surface and visible light transmittance. The results showed that CNF has a better dispersion in NMP which leads to a significant increase in tensile strength and elastic modulus up to 38% and 65%, respectively, compared with those of neat CA. CNF addition up to 5 phr loading increased the mechanical properties of the film composites.
Structural Behavior of High-Strength Concrete Slabs Reinforced with GFRP Bars
Authors: Adam Maher A. , Erfan Abeer M. , Habib Fatma A. , El-Sayed Taha A.
Published: September 2021

In this manuscript, structural testing was conducted on high-strength concrete slab specimens to investigate the behavior of such specimens when reinforced with a locally produced GFRP reinforcement. Subsequently, a finite element model (FEM) was constructed and validated against the experimental results. In the experimental phase, a total of eleven specimens (nine were reinforced with GFRP, while two were reinforced with conventional steel) were constructed and tested. The slabs dimensions are 700 mm × 1750 mm with variable thickness from 100 mm to 150 mm and different reinforcement ratios using different diameters. The structural behavior of the tested slabs was investigated in terms of ultimate load, ultimate deflection, load–deflection relationship, and crack pattern. Additionally, a nonlinear finite element model using the software ANSYS 2019-R1 was constructed to simulate the structural behavior of slabs reinforced with GFRP bars. The results obtained from the finite element analysis are compared with experimental results. The outcomes showed that the contribution of GFRP rebars in concrete slabs improved slab ductility and exhibited higher deflection when compared with traditional steel rebars. Good agreement between experimental and nonlinear analysis was obtained.
Effect of Short Fibres in the Mechanical Properties of Geopolymer Mortar Containing Oil-Contaminated Sand
Authors: Abousnina Rajab , Alsalmi Haifa Ibrahim , Manalo Allan , Allister Rochstad Lim , Alajarmeh Omar , Ferdous Wahid , Jlassi Khouloud
Published: September 2021

Sand contaminated with crude oil is becoming a major environmental issue around the world, while at the same time, fly ash generated by coal-fired power stations is having a detrimental effect on the environment. Previous studies showed that combining these two waste materials can result in an environmentally sustainable geopolymer concrete. Incorporating sand contaminated with crude oil up to a certain level (4% by weight) can improve the mechanical properties of the produced geopolymer concrete but beyond this level can have a detrimental effect on its compressive strength. To overcome this challenge, this study introduces short fibres to enhance the mechanical properties of geopolymer mortar containing fine sand contaminated with 6% by weight of light crude oil. Four types of short fibres, consisting of twisted polypropylene (PP) fibres, straight PP fibres, short glass fibres and steel fibres in different dosages (0.1, 0.2, 0.3, 0.4 and 0.5% by volume of geopolymer mortar) are considered. The optimum strength was obtained when straight PP fibres were used wherein increases of up to 39% and 74% of the compressive and tensile strength, respectively, of the geopolymer mortar were achieved. Moreover, a fibre dosage of 0.5% provided the highest enhancement in the mechanical properties of the geopolymer mortar with 6% crude oil contamination. This result indicates that the reduction in strength of geopolymer due to the addition of sand with 6% crude oil contamination can be regained by using short fibres, making this new material from wastes suitable for building and construction applications.
Olive Sound: A Sustainable Radical Innovation
Authors: Clodoveo Maria Lisa , Crupi Pasquale , Corbo Filomena
Published: September 2021

Olive Sound is the acronym of a Horizon 2020 European Project aimed at the development of a high-flow oil extraction plant, the Sono-Heat-Exchanger, which combines ultrasound and heat exchange in order to break, through a radical innovation model in the oil mill, the historical paradigm that sees as inversely correlated the oil yield and the content of bio-phenols. These compounds are biologically active molecules that transform the product, extra virgin olive oil, from a mere condiment into a functional food. The primary objective of the project, financially supported by the European Union through the “Fast Track to Innovation” program, is the development of a product “ready for the market” (TRL 9) capable of making the involved companies more competitive while increasing the competitiveness of European extra virgin olive oil in the international context.
Potential Use of Lactic Acid Bacteria with Pathogen Inhibitory Capacity as a Biopreservative Agent for Chorizo
Authors: Lucumi-Banguero Ruby Stella , Ramírez-Toro Cristina , Bolívar German A.
Published: September 2021

The biopreservation of meat products is of great interest due to the demand for products with low or minimal chemical additives. Lactic acid bacteria (LAB) have been used as protective cultures for many centuries. The objective of this work was to characterize 10 native LAB isolated from meat masses with biopreservative potential for meat products. The isolates were subjected to viability tests with different concentrations of NaCl, nitrite, and nitrate salts, pHs, and temperature conditions. Antibiotic resistance and type of lactic acid isomer were tested. In addition, the isolates were tested against seven pathogens, and inhibitory substances were identified by diffusion in agar wells. Finally, two isolates, Lb. plantarum (SB17) and Lb. sakei (SB3) were tested as protective cultures of chorizo in a model. As a result, the viability at different concentrations of NaCl and nitrate and nitrate salts were obtained. pH and temperature exerted a negative effect on the growth of some of the isolates. Pathogens were inhibited mainly by the presence of organic acids; P. aurius was the most susceptible, and S. typhimurium and S. marcescens were the most resistant. The strains SB17 and SB3 had similar effects on chorizo, and time exerted a deleterious effect on microbiological quality and pH. The results indicated that the 10 isolates show promising characteristics for the preservation of cooked meat products, with the strain Lb. plantarum (SB17) being the most promising.
Overview of the Benefits and Challenges Associated with Pelletizing Biochar
Authors: Mohammadi Ali
Published: September 2021

Biochar can be derived from a wide variety of organic materials including agricultural wastes and residues, animal wastes, municipal solid wastes, pulp and paper mill wastes, and sewage sludge. Its productivity relies on feedstock type and thermochemical conditions of production. Biochar has many application advantages in several fields and has been widely studied in recent years. However, most of these studies are mainly on the powder form of biochar, while its pelleted form is sparsely reported. Even with the reported studies on biochar pellets, there is still lack of knowledge and awareness of the effects of different feedstock on the densification behavior of biochar. The mechanisms of biochar densification, which appear to be sensitive to the conditions predominating during its thermochemical production, are affected by the material from which the biochar is derived. This partly accounts for why biochar pellets have not been widely adopted in various application fields. Therefore, this paper presents an overview of the benefits associated with the use of biochar pellets and discusses the challenges encountered when pelleting biochars that are derived from different feedstock under various carbonization conditions. Research priority areas needed to overcome the challenges are also identified and discussed. The purpose is to contribute to better understanding on biochar pelletization behavior, and to offer insights useful to comprehend some basic principles that may occur in the pelleting process and to ease further and more thorough investigations.
Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar
Authors: Cota Gizelle , Sagan Vasit , Maimaitijiang Maitiniyazi , Freeman Karen
Published: September 2021

Documenting the impacts of climate change and human activities on tropical rainforests is imperative for protecting tropical biodiversity and for better implementation of REDD+ and UN Sustainable Development Goals. Recent advances in very high-resolution satellite sensor systems (i.e., WorldView-3), computing power, and machine learning (ML) have provided improved mapping of fine-scale changes in the tropics. However, approaches so far focused on feature extraction or the extensive tuning of ML parameters, hindering the potential of ML in forest conservation mapping by not using textural information, which is found to be powerful for many applications. Additionally, the contribution of shortwave infrared (SWIR) bands in forest cover mapping is unknown. The objectives were to develop end-to-end mapping of the tropical forest using fully convolution neural networks (FCNNs) with WorldView-3 (WV-3) imagery and to evaluate human impact on the environment using the Betampona Nature Reserve (BNR) in Madagascar as the test site. FCNN (U-Net) using spatial/textural information was implemented and compared with feature-fed pixel-based methods including Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). Results show that the FCNN model outperformed other models with an accuracy of 90.9%, while SVM, RF, and DNN provided accuracies of 88.6%, 84.8%, and 86.6%, respectively. When SWIR bands were excluded from the input data, FCNN provided superior performance over other methods with a 1.87% decrease in accuracy, while the accuracies of other models—SVM, RF, and DNN—decreased by 5.42%, 3.18%, and 8.55%, respectively. Spatial–temporal analysis showed a 0.7% increase in Evergreen Forest within the BNR and a 32% increase in tree cover within residential areas likely due to forest regeneration and conservation efforts. Other effects of conservation efforts are also discussed.
Conversion of Natural Wetland to Farmland in the Tumen River Basin: Human and Environmental Factors
Authors: Liu Yuyan , Jin Ri , Zhu Weihong
Published: September 2021

Wetlands play an important role in the terrestrial ecosystem. However, agricultural activities have resulted in a significant decrease in natural wetlands around the world. In the Tumen River Basin (TRB), a border area between China, the Democratic People’s Republic of Korea (DPRK), and Russia, natural wetlands have been reclaimed and converted into farmland, primarily due to the migration practices of Korean-Chinese. To understand the spatial and temporal patterns of this conversion from wetlands to farmland, Landsat remote sensing images from four time periods were analyzed. Almost 30 years of data were extracted using the object-oriented classification method combined with random forest classification. In addition, statistical analysis was conducted on the conversion from natural wetland to farmland and from farmland to wetland, as well as on the relationship between the driving factors. The results revealed that a loss of 49.2% (12,540.1 ha) of natural wetlands in the Chinese portion of the TRB was due to agricultural encroachment for grain production. At the sub-basin scale, the largest area of natural wetland converted into farmland in the past 30 years was in the Hunchun River Basin (HCH), which accounts for 22.0% (2761.2 ha) of the total. Meanwhile, 6571.4 ha of natural wetlands, mainly in the Gaya River Basin (GYH), have been restored from farmland. These changes are closely related to the migration of the agricultural populations.
Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night
Authors: Li Siyuan , Jiao Jiannan , Wang Chi
Published: September 2021

The monitoring of vegetation via remote sensing has been widely applied in various fields, such as crop diseases and pests, forest coverage and vegetation growth status, but such monitoring activities were mainly carried out in the daytime, resulting in limitations in sensing the status of vegetation at night. In this article, with the aim of monitoring the health status of outdoor plants at night by remote sensing, a polarized multispectral low-illumination-level imaging system (PMSIS) was established, and a fusion algorithm was proposed to detect vegetation by sensing the spectrum and polarization characteristics of the diffuse and specular reflection of vegetation. The normalized vegetation index (NDVI), degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to better detect the health status of plants in the night environment. Based on NDVI, DoLP and AOP fusion images (NDAI), a new index of night plant state detection (NPSDI) was proposed. A correlation analysis was made for the chlorophyll content (SPAD), nitrogen content (NC), NDVI and NPSDI to understand their capabilities to detect plants under stress. The scatter plot of NPSDI shows a good distinction between vegetation with different health levels, which can be seen from the high specificity and sensitivity values. It can be seen that NPSDI has a good correlation with NDVI (coefficient of determination R2 = 0.968), PSAD (R2 = 0.882) and NC (R2 = 0.916), which highlights the potential of NPSDI in the identification of plant health status. The results clearly show that the proposed fusion algorithm can enhance the contrast effect and the generated fusion image will carry richer vegetation information, thereby monitoring the health status of plants at night more effectively. This algorithm has a great potential in using remote sensing platform to monitor the health of vegetation and crops.
Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture
Authors: Xu Rui , Li Changying , Bernardes Sergio
Published: September 2021

Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.
Observations by Ground-Based MAX-DOAS of the Vertical Characters of Winter Pollution and the Influencing Factors of HONO Generation in Shanghai, China
Authors: Xu Shiqi , Wang Shanshan , Xia Men , Lin Hua , Xing Chengzhi , Ji Xiangguang , Su Wenjing , Tan Wei , Liu Cheng , Hu Qihou
Published: September 2021

Analyzing vertical distribution characters of air pollutants is conducive to study the mechanisms under polluted atmospheric conditions. Nitrous acid (HONO) is a kind of crucial species in photochemical cycles. Exploring the influence and sources of HONO in air pollution at different altitudes offers some insights into the research of tropospheric oxidation chemistry processes. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements were conducted in Shanghai, China, from December 2017 to March 2018 to investigate vertical distributions and diurnal variations of trace gases (NO2, HONO, HCHO, SO2, and water vapor) and aerosol extinction coefficient in the boundary layer. Aerosol and NO2 showed decreasing profile exponentially, SO2 and HCHO concentrations were observed relatively high values in the middle layer. SO2 was caused by industrial emissions, while HCHO was from secondary sources. As for HONO, below 0.82 km, the heterogeneous reactions of NO2 impacted on forming HONO, while in the upper layers, vertical diffusion might be the dominant source. The contribution of OH production from HONO photolysis at different altitudes was mainly controlled by the concentration of HONO. MAX-DOAS measurements characterize the vertical structure of air pollutants in Shanghai and provide further understanding for HONO formation, which can help deploy advanced measurement platforms of regional air pollution over eastern China.
Systematic Approach for Tunnel Deformation Monitoring with Terrestrial Laser Scanning
Authors: Jia Dongfeng , Zhang Weiping , Liu Yanping
Published: September 2021

The use of terrestrial laser scanning (TLS) point clouds for tunnel deformation measurement has elicited much interest. However, general methods of point-cloud processing in tunnels are still under investigation, given the high accuracy and efficiency requirements in this area. This study discusses a systematic method of analyzing tunnel deformation. Point clouds from different stations need to be registered rapidly and with high accuracy before point-cloud processing. An orientation method of TLS in tunnels that uses a positioning base made in the laboratory is proposed for fast point-cloud registration. The calibration methods of the positioning base are demonstrated herein. In addition, an improved moving least-squares method is proposed as a way to reconstruct the centerline of a tunnel from unorganized point clouds. Then, the normal planes of the centerline are calculated and are used to serve as the reference plane for point-cloud projection. The convergence of the tunnel cross-section is analyzed, based on each point cloud slice, to determine the safety status of the tunnel. Furthermore, the results of the deformation analysis of a particular shield tunnel site are briefly discussed.
CCT: Conditional Co-Training for Truly Unsupervised Remote Sensing Image Segmentation in Coastal Areas
Authors: Fang Bo , Chen Gang , Chen Jifa , Ouyang Guichong , Kou Rong , Wang Lizhe
Published: September 2021

As the fastest growing trend in big data analysis, deep learning technology has proven to be both an unprecedented breakthrough and a powerful tool in many fields, particularly for image segmentation tasks. Nevertheless, most achievements depend on high-quality pre-labeled training samples, which are labor-intensive and time-consuming. Furthermore, different from conventional natural images, coastal remote sensing ones generally carry far more complicated and considerable land cover information, making it difficult to produce pre-labeled references for supervised image segmentation. In our research, motivated by this observation, we take an in-depth investigation on the utilization of neural networks for unsupervised learning and propose a novel method, namely conditional co-training (CCT), specifically for truly unsupervised remote sensing image segmentation in coastal areas. In our idea, a multi-model framework consisting of two parallel data streams, which are superpixel-based over-segmentation and pixel-level semantic segmentation, is proposed to simultaneously perform the pixel-level classification. The former processes the input image into multiple over-segments, providing self-constrained guidance for model training. Meanwhile, with this guidance, the latter continuously processes the input image into multi-channel response maps until the model converges. Incentivized by multiple conditional constraints, our framework learns to extract high-level semantic knowledge and produce full-resolution segmentation maps without pre-labeled ground truths. Compared to the black-box solutions in conventional supervised learning manners, this method is of stronger explainability and transparency for its specific architecture and mechanism. The experimental results on two representative real-world coastal remote sensing datasets of image segmentation and the comparison with other state-of-the-art truly unsupervised methods validate the plausible performance and excellent efficiency of our proposed CCT.
Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery
Authors: Ibrahim Esther Shupel , Rufin Philippe , Nill Leon , Kamali Bahareh , Nendel Claas , Hostert Patrick
Published: September 2021

Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops—maize and potato—and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize–cereals and potato–maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize–legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services.
Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets
Authors: Cavallo Carmela , Papa Maria Nicolina , Gargiulo Massimiliano , Palau-Salvador Guillermo , Vezza Paolo , Ruello Giuseppe
Published: September 2021

Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. With multispectral data, the frequency of observation is limited by the possible presence of clouds. To overcome this problem, the data acquired by the two missions, Landsat-8 and Sentinel-2, were jointly used, thus roughly halving the revisit time. The varied types of land cover were grouped into four classes: (1) open water, (2) mosaic of water, mud and vegetation, (3) bare soil and (4) vegetated soil. The automatic classification of the four classes was obtained through a rule-based method that combined the NDWI, MNDWI and NDVI indices. Point information, provided by geo-located ground pictures, was spatially extended with the help of a very high-resolution image (GeoEye-1). In this way, surfaces with known land cover were obtained and used for the validation of the classification method. The overall accuracy was found to be 0.96 and 0.98 for Landsat-8 and Sentinel-2, respectively. The consistency evaluation between Landsat-8 and Sentinel-2 was performed in six days, in which acquisitions by both missions were available. The observed dynamics of the land cover were highly variable in space. For example, the presence of the open water condition lasted for around 60–80 days in the areas closest to the Albufera lake and progressively decreased towards the boundaries of the park. The study demonstrates the feasibility of using moderate-resolution multispectral images to monitor land cover changes in wetland environments.
Restoration of Remote PPG Signal through Correspondence with Contact Sensor Signal
Authors: Kim So-Eui , Yu Su-Gyeong , Kim Na Hye , Suh Kun Ha , Lee Eui Chul
Published: September 2021

Photoplethysmography (PPG) is an optical measurement technique that detects changes in blood volume in the microvascular layer caused by the pressure generated by the heartbeat. To solve the inconvenience of contact PPG measurement, a remote PPG technology that can measure PPG in a non-contact way using a camera was developed. However, the remote PPG signal has a smaller pulsation component than the contact PPG signal, and its shape is blurred, so only heart rate information can be obtained. In this study, we intend to restore the remote PPG to the level of the contact PPG, to not only measure heart rate, but to also obtain morphological information. Three models were used for training: support vector regression (SVR), a simple three-layer deep learning model, and SVR + deep learning model. Cosine similarity and Pearson correlation coefficients were used to evaluate the similarity of signals before and after restoration. The cosine similarity before restoration was 0.921, and after restoration, the SVR, deep learning model, and SVR + deep learning model were 0.975, 0.975, and 0.977, respectively. The Pearson correlation coefficient was 0.778 before restoration and 0.936, 0.933, and 0.939, respectively, after restoration.
Daily Life Upper Limb Activity for Patients with Match and Mismatch between Observed Function and Perceived Activity in the Chronic Phase Post Stroke
Authors: Essers Bea , Coremans Marjan , Veerbeek Janne , Luft Andreas , Verheyden Geert
Published: September 2021

We investigated actual daily life upper limb (UL) activity in relation to observed UL motor function and perceived UL activity in chronic stroke in order to better understand and improve UL activity in daily life. In 60 patients, we collected (1) observed UL motor function (Fugl-Meyer Assessment (FMA-UE)), (2) perceived UL activity (hand subscale of the Stroke Impact Scale (SIS-Hand)), and (3) daily life UL activity (bilateral wrist-worn accelerometers for 72 h) data. Data were compared between two groups of interest, namely (1) good observed (FMA-UE >50) function and good perceived (SIS-Hand >75) activity (good match, n = 16) and (2) good observed function but low perceived (SIS-Hand ≤75) activity (mismatch, n = 15) with Mann–Whitney U analysis. The mismatch group only differed from the good match group in perceived UL activity (median (Q1–Q3) = 50 (30–70) versus 93 (85–100); p < 0.001). Despite similar observed UL motor function and other clinical characteristics, the affected UL in the mismatch group was less active in daily life compared to the good match group (p = 0.013), and the contribution of the affected UL compared to the unaffected UL for each second of activity (magnitude ratio) was lower (p = 0.022). We conclude that people with chronic stroke with low perceived UL activity indeed tend to use their affected UL less in daily life despite good observed UL motor function.
SFPD: Simultaneous Face and Person Detection in Real-Time for Human–Robot Interaction
Authors: Fiedler Marc-André , Werner Philipp , Khalifa Aly , Al-Hamadi Ayoub
Published: September 2021

Face and person detection are important tasks in computer vision, as they represent the first component in many recognition systems, such as face recognition, facial expression analysis, body pose estimation, face attribute detection, or human action recognition. Thereby, their detection rate and runtime are crucial for the performance of the overall system. In this paper, we combine both face and person detection in one framework with the goal of reaching a detection performance that is competitive to the state of the art of lightweight object-specific networks while maintaining real-time processing speed for both detection tasks together. In order to combine face and person detection in one network, we applied multi-task learning. The difficulty lies in the fact that no datasets are available that contain both face as well as person annotations. Since we did not have the resources to manually annotate the datasets, as it is very time-consuming and automatic generation of ground truths results in annotations of poor quality, we solve this issue algorithmically by applying a special training procedure and network architecture without the need of creating new labels. Our newly developed method called Simultaneous Face and Person Detection (SFPD) is able to detect persons and faces with 40 frames per second. Because of this good trade-off between detection performance and inference time, SFPD represents a useful and valuable real-time framework especially for a multitude of real-world applications such as, e.g., human–robot interaction.
Design and Integrated Analysis of a Flexible Support Microstructure with a Honeycomb Sandwich for the Optical Window of a Hypersonic Remote Sensor
Authors: Zhang Mingqiang , Li Yaobin , Ding Yalin , Sun Jianjun , Li Jing
Published: September 2021

In order to reduce the influence of the optical window on the image quality of a hypersonic visible light optical remote sensor, we propose a method of adding a double-layer semicircular honeycomb core microstructure with flexible support of a high temperature elastic alloy between a window glass and a frame to reduce the influence of complex thermal stress on the surface accuracy of the optical window. An equivalent model of a semicircular honeycomb structure was established, the elastic parameters of the semicircular honeycomb sandwich microstructure were derived by an analytical method, and a numerical verification and finite element simulation were carried out. The results show that the equivalent model is in good agreement with the detailed model. The optical-mechanical-thermal integrated simulation analysis of the optical window assembly with flexible supporting microstructure proves that the semicircular honeycomb sandwich flexible supporting structure has a positive effect on stress attenuation of the window glass and ensures the wave aberration accuracy of the transmitted optical path difference of the optical window (PV < 0.665 λ, RMS < 0.156 λ, λ = 632.8 nm). Combined with the actual optical system, the optical performance of the window assembly under the flexible support structure is verified. The simulation results show that the spatial frequency of the modulation transfer function (MTF) of the optical system after focusing is not less than 0.58 in the range of 0–63 cycle/mm and the relative decline of MTF is not more than 0.01, which meet the imaging requirements of a remote sensor. The study results show that the proposed metal-based double-layer semicircular honeycomb sandwich flexible support microstructure ensures the imaging quality of the optical window under ultra-high temperature conditions.
A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques
Authors: Muresan Cristina I. , Birs Isabela R. , Dulf Eva H. , Copot Dana , Miclea Liviu
Published: September 2021

The present manuscript aims at raising awareness of the endless possibilities of fractional calculus applied not only to system identification and control engineering, but also into sensing and filtering domains. The creation of the fractance device has enabled the physical realization of a new array of sensors capable of gathering more information. The same fractional-order electronic component has led to the possibility of exploring analog filtering techniques from a practical perspective, enlarging the horizon to a wider frequency range, with increased robustness to component variation, stability and noise reduction. Furthermore, fractional-order digital filters have developed to provide an alternative solution to higher-order integer-order filters, with increased design flexibility and better performance. The present study is a comprehensive review of the latest advances in fractional-order sensors and filters, with a focus on design methodologies and their real-life applicability reported in the last decade. The potential enhancements brought by the use of fractional calculus have been exploited as well in sensing and filtering techniques. Several extensions of the classical sensing and filtering methods have been proposed to date. The basics of fractional-order filters are reviewed, with a focus on the popular fractional-order Kalman filter, as well as those related to sensing. A detailed presentation of fractional-order filters is included in applications such as data transmission and networking, electrical and chemical engineering, biomedicine and various industrial fields.
Device-Free Human Identification Using Behavior Signatures in WiFi Sensing
Authors: Zhang Ronghui , Jing Xiaojun
Published: September 2021

Wireless sensing can be used for human identification by mining and quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric (DFB) system, WirelessID, that explores the joint human fine-grained behavior and body physical signatures embedded in channel state information (CSI) by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to the identification performance. Inspired by the success of the attention mechanism in computer vision (CV), thus, to extract more robust features, we introduce the spatiotemporal attention function into our system. To evaluate the performance, commercial WiFi devices are used for prototyping WirelessID in a real laboratory environment with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.
A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture
Authors: Kalyani Yogeswaranathan , Collier Rem
Published: September 2021

Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades. This article aims to provide a systematic literature review of current works that have been done in Cloud, Fog, and Edge Computing applications in the smart agriculture domain between 2015 and up-to-date. The key objective of this review is to identify all relevant research on new computing paradigms with smart agriculture and propose a new architecture model with the combinations of Cloud–Fog–Edge. Furthermore, it also analyses and examines the agricultural application domains, research approaches, and the application of used combinations. Moreover, this survey discusses the components used in the architecture models and briefly explores the communication protocols used to interact from one layer to another. Finally, the challenges of smart agriculture and future research directions are briefly pointed out in this article.
Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test
Authors: Mendoza Tomas , Lee Chia-Hsuan , Huang Chien-Hua , Sun Tien-Lung
Published: September 2021

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.
Reliability and Validity of the CORE Sensor to Assess Core Body Temperature during Cycling Exercise
Authors: Verdel Nina , Podlogar Tim , Ciuha Urša , Holmberg Hans-Christer , Debevec Tadej , Supej Matej
Published: September 2021

Monitoring core body temperature (Tc) during training and competitions, especially in a hot environment, can help enhance an athlete’s performance, as well as lower the risk for heat stroke. Accordingly, a noninvasive sensor that allows reliable monitoring of Tc would be highly beneficial in this context. One such novel non-invasive sensor was recently introduced onto the market (CORE, greenTEG, Rümlang, Switzerland), but, to our knowledge, a validation study of this device has not yet been reported. Therefore, the purpose of this study was to evaluate the validity and reliability of the CORE sensor. In Study I, 12 males were subjected to a low-to-moderate heat load by performing, on two separate occasions several days apart, two identical 60-min bouts of steady-state cycling in the laboratory at 19 °C and 30% relative humidity. In Study II, 13 males were subjected to moderate-to-high heat load by performing 90 min of cycling in the laboratory at 31 °C and 39% relative humidity. In both cases the core body temperatures indicated by the CORE sensor were compared to the corresponding values obtained using a rectal sensor (Trec). The first major finding was that the reliability of the CORE sensor is acceptable, since the mean bias between the two identical trials of exercise (0.02 °C) was not statistically significant. However, under both levels of heat load, the body temperature indicated by the CORE sensor did not agree well with Trec, with approximately 50% of all paired measurements differing by more than the predefined threshold for validity of ≤0.3 °C. In conclusion, the results obtained do not support the manufacturer’s claim that the CORE sensor provides a valid measure of core body temperature.