bluuebunny/crossref_metadata_2025 huggingface dataset:
Topography-Mediated Enhancement of Nonviral Gene Delivery in Stem Cells
Authors: Ge Lu , Yang Liangliang , Bron Reinier , van Rijn Patrick
Published: May 2022

Gene delivery holds great promise for bioengineering, biomedical applications, biosensors, diagnoses, and gene therapy. In particular, the influence of topography on gene delivery is considered to be an attractive approach due to low toxicity and localized delivery properties. Even though many gene vectors and transfection systems have been developed to enhance transfection potential and combining it with other forms of stimulations could even further enhance it. Topography is an interesting surface property that has been shown to stimulate differentiation, migration, cell morphology, and cell mechanics. Therefore, it is envisioned that topography might also be able to stimulate transfection. In this study, we tested the hypothesis “topography is able to regulate transfection efficiency”, for which we used nano- and microwave-like topographical substrates with wavelengths ranging from 500 nm to 25 µm and assessed the transfectability of human bone marrow-derived mesenchymal stem cells (hBM-MSCs) and myoblasts. For transfection, Lipofectamine 2000 and a gene encoding plasmid for red-fluorescent protein (m-Cherry) were used and topography-induced cell morphology and transfection efficiency was analyzed. As a result, topography directs cell spreading, elongation, and proliferation as well as the transfection efficiency, which were investigated but were found not to be correlated and dependent on the cell type. A 55% percent improvement of transfection efficiency was identified for hBM-MSCs grown on 2 µm wrinkles (24.3%) as compared to hBM-MSCs cultured on flat controls (15.7%). For myoblast cells, the highest gene-expression efficiency (46.1%) was observed on the 10 µm topography, which enhanced the transfection efficiency by 64% as compared to the flat control (28.1%). From a qualitative assessment, it was observed that the uptake capacity of cationic complexes of TAMRA-labeled oligodeoxynucleotides (ODNs) was not topography-dependent but that the intracellular release was faster, as indicated by the positively stained nuclei on 2 μm for hBM-MSCs and 10 μm for myoblasts. The presented results indicate that topography enhances the gene-delivery capacity and that the responses are dependent on cell type. This study demonstrates the important role of topography on cell stimulation for gene delivery as well as understanding the uptake capacity of lipoplexes and may be useful for developing advanced nonviral gene delivery strategies.
Susceptibility to Degradation in Soil of Branched Polyesterurethane Blends with Polylactide and Starch
Authors: Brzeska Joanna , Jasik Grzegorz , Sikorska Wanda , Mendrek Barbara , Karczewski Jakub , Kowalczuk Marek , Rutkowska Maria
Published: May 2022

A very important method of reducing the amount of polymer waste in the environment is the introduction to the market of polymers susceptible to degradation under the influence of environmental factors. This paper presents the results of testing the susceptibility to degradation in soil of branched polyesterurethane (PUR) based on poly([R,S]-3-hydroxybutyrate) (R,S-PHB), modified with poly([D,L]-lactide) (PLA) and starch (St). Weight losses of samples and changes in surface morphology (SEM, OM and contact angle system) with simultaneously only slight changes in molecular weight (GPC), chemical structure (FTIR and 1HNMR) and thermal properties (DSC) indicate that these materials are subject to enzymatic degradation caused by the presence of microorganisms in the soil. Chemical modification of branched polyesterurethanes with R,S-PHB and their physical blending with small amounts of PLA and St resulted in a slow but progressive degradation of the samples.
A Review of Recent Advances in Natural Polymer-Based Scaffolds for Musculoskeletal Tissue Engineering
Authors: Fan Jingzhi , Abedi-Dorcheh Keyvan , Sadat Vaziri Asma , Kazemi-Aghdam Fereshteh , Rafieyan Saeed , Sohrabinejad Masoume , Ghorbani Mina , Rastegar Adib Fatemeh , Ghasemi Zahra , Klavins Kristaps , Jahed Vahid
Published: May 2022

The musculoskeletal (MS) system consists of bone, cartilage, tendon, ligament, and skeletal muscle, which forms the basic framework of the human body. This system plays a vital role in appropriate body functions, including movement, the protection of internal organs, support, hematopoiesis, and postural stability. Therefore, it is understandable that the damage or loss of MS tissues significantly reduces the quality of life and limits mobility. Tissue engineering and its applications in the healthcare industry have been rapidly growing over the past few decades. Tissue engineering has made significant contributions toward developing new therapeutic strategies for the treatment of MS defects and relevant disease. Among various biomaterials used for tissue engineering, natural polymers offer superior properties that promote optimal cell interaction and desired biological function. Natural polymers have similarity with the native ECM, including enzymatic degradation, bio-resorb and non-toxic degradation products, ability to conjugate with various agents, and high chemical versatility, biocompatibility, and bioactivity that promote optimal cell interaction and desired biological functions. This review summarizes recent advances in applying natural-based scaffolds for musculoskeletal tissue engineering.
In Vitro Biocompatibility and Degradation Analysis of Mass-Produced Collagen Fibers
Authors: Ali Kiran M. , Huang Yihan , Amanah Alaowei Y. , Mahmood Nasif , Suh Taylor C. , Gluck Jessica M.
Published: May 2022

Automation and mass-production are two of the many limitations in the tissue engineering industry. Textile fabrication methods such as electrospinning are used extensively in this field because of the resemblance of the extracellular matrix to the fiber structure. However, electrospinning has many limitations, including the ability to mass-produce, automate, and reproduce products. For this reason, this study evaluates the potential use of a traditional textile method such as spinning. Apart from mass production, these methods are also easy, efficient, and cost-effective. This study uses bovine-derived collagen fibers to create yarns using the traditional ring spinning method. The collagen yarns are proven to be biocompatible. Enzymatic biodegradability was also confirmed for its potential use in vivo. The results of this study prove the safety and efficacy of the material and the fabrication method. The material encourages higher cell proliferation and migration compared to tissue culture-treated plastic plates. The process is not only simple but is also streamlined and replicable, resulting in standardized products that can be reproduced.
Graphene-Based Polymer Nanocomposites: Recent Advances
Authors: Díez-Pascual Ana M.
Published: May 2022

Carbon-based nanomaterials are currently attracting a great deal of interest due to their unique chemical, optical, and electronic properties, which make them suitable for a broad range of uses, including supercapacitors, solar cells, fuel cells, lithium batteries, biomedicine, and so forth [...]
Kinetic, Isotherm, and Equilibrium Investigation of Cr(VI) Ion Adsorption on Amine-Functionalized Porous Silica Beads
Authors: Nishino Anzu , Taki Ayane , Asamoto Hiromichi , Minamisawa Hiroaki , Yamada Kazunori
Published: May 2022

The hexavalent chromium (Cr(VI)) ion adsorption properties were conferred to porous silica beads by introducing alkylamine chains through functionalization with an aminosilane coupling agent, [3-(2-aminoethylamino)propyl]triethoxysilane (AEAPTES), or with an epoxysilane coupling agent, (3-glycidyloxypropyl)triethoxysilane (GOPTES), and polyfunctional amine compounds or poly-ethylenimines (PEIs). The presence of amino groups on the silica beads was confirmed by XPS and the amount of amino groups increased to 0.270 mmol/g by increasing the AEAPTES concentration and/or reaction time. The adsorption capacity of the silica beads functionalized with AEAPTES was the maximum at the initial pH value of 3.0 and the initial adsorption rate increased with an increase in the temperature. The adsorption capacity increased with an increase in the amount of amino groups at pH 3.0 and 30 °C. The adsorption behavior obeyed the pseudo-second order kinetic model and was well expressed by the Langmuir isotherm. These results support that Cr(VI) ion adsorption is accomplished through the electrostatic interaction between protonated amino groups and HCrO4− ions. In addition, the adsorption capacity further increased to 0.192–0.320 mmol/g by treating the GOPTES-treated silica beads with triethylenetetramine, pentaethylenehexamine, or PEI. These empirical, equilibria, and kinetic aspects obtained in this study support that the porous silica-based adsorbents prepared in this study can be applied to the removal of Cr(VI) ions.
Ionic Strength of Methylcellulose-Based Films: An Alternative for Modulating Mechanical Performance and Hydrophobicity for Potential Food Packaging Application
Authors: Silva Rafael Resende Assis , Marques Clara Suprani , Arruda Tarsila Rodrigues , Teixeira Samiris Cocco , de Oliveira Taíla Veloso , Stringheta Paulo Cesar , dos Santos Pires Ana Clarissa , de Fátima Ferreira Soares Nilda
Published: May 2022

The growing environmental concern with the inappropriate disposal of conventional plastics has driven the development of eco-friendly food packaging. However, the intrinsic characteristics of polymers of a renewable origin, e.g., poor mechanical properties, continue to render their practical application difficult. For this, the present work studied the influence of ionic strength (IS) from 0 to 500 mM to modulate the physicochemical properties of methylcellulose (MC). Moreover, for protection against biological risks, Nisin-Z was incorporated into MC’s polymeric matrices, providing an active function. The incorporation of salts (LiCl and MgCl2) promoted an increase in the equilibrium moisture content in the polymer matrix, which in turn acted as a plasticizing agent. In this way, films with a hydrophobic surface (98°), high true strain (85%), and low stiffness (1.6 mPa) can be manufactured by addition of salts, modulating the IS to 500 mM. Furthermore, films with an IS of 500 mM, established with LiCl, catalyzed antibacterial activity against E. coli, conferring synergism and extending protection against biological hazards. Therefore, we demonstrated that the IS control of MC dispersion presents a new alternative to achieve films with the synergism of antibacterial activity against Gram-negative bacteria in addition to flexibility, elasticity, and hydrophobicity required in various applications in food packaging.
An Experimental and Modeling Combined Approach in Preparative Hydrophobic Interaction Chromatography
Authors: Lietta Elena , Pieri Alessandro , Cardillo Antonio G. , Vanni Marco , Pisano Roberto , Barresi Antonello A.
Published: May 2022

Chromatography is a technique widely used in the purification of biopharmaceuticals, and generally consists of several chromatographic steps. In this work, Hydrophobic Interaction Chromatography (HIC) is investigated as a polishing step for the purification of therapeutic proteins. Adsorption mechanisms in hydrophobic interaction chromatography are still not completely clear and a limited amount of published data is available. In addition to new data on adsorption isotherms for some proteins (obtained both by high-throughput and frontal analysis method), and a comparison of different models proposed in the literature, two different approaches are compared in this work to investigate HIC. The predictive approach exploits an in-house code that simulates the behavior of the component in the column using the model parameters found from the fitting of experimental data. The estimation approach, on the other hand, exploits commercial software in which the model parameters are found by the fitting of a few experimental chromatograms. The two approaches are validated on some bind-elute runs: the predictive approach is very informative, but the experimental effort needed is high; the estimation approach is more effective, but the knowledge gained is lower. The second approach is also applied to an in-development industrial purification process and successfully resulted in predicting the behavior of the system, allowing for optimization with a reduction in the time and amount of sample needed.
RETRACTED: Morley et al. A Database for the Extraction, Trade, and Use of Sand and Gravel. Resources 2022, 11, 38
Authors: Morley John , Myers Rupert , Plancherel Yves , Brito-Parada Pablo
Published: May 2022

The published article [...]
TCSPANet: Two-Staged Contrastive Learning and Sub-Patch Attention Based Network for PolSAR Image Classification
Authors: Cui Yuanhao , Liu Fang , Liu Xu , Li Lingling , Qian Xiaoxue
Published: May 2022

Polarimetric synthetic aperture radar (PolSAR) image classification has achieved great progress, but there still exist some obstacles. On the one hand, a large amount of PolSAR data is captured. Nevertheless, most of them are not labeled with land cover categories, which cannot be fully utilized. On the other hand, annotating PolSAR images relies more on domain knowledge and manpower, which makes pixel-level annotation harder. To alleviate the above problems, by integrating contrastive learning and transformer, we propose a novel patch-level PolSAR image classification, i.e., two-staged contrastive learning and sub-patch attention based network (TCSPANet). Firstly, the two-staged contrastive learning based network (TCNet) is designed for learning the representation information of PolSAR images without supervision, and obtaining the discrimination and comparability for actual land covers. Then, resorting to transformer, we construct the sub-patch attention encoder (SPAE) for modelling the context within patch samples. For training the TCSPANet, two patch-level datasets are built up based on unsupervised and semi-supervised methods. When predicting, the classification algorithm, classifying or splitting, is put forward to realise non-overlapping and coarse-to-fine patch-level classification. The classification results of multi-PolSAR images with one trained model suggests that our proposed model is superior to the compared methods.
Calibration and Validation of Polarimetric ALOS2-PALSAR2
Authors: Touzi Ridha , Shimada Masanobu , Motohka Takeshi
Published: May 2022

PALSAR2 polarimetric distortion matrix is measured using corner reflectors deployed in the Amazonian forest. The Amazonian forest near the geomagnetic equator provides ideal sites for the assessment of L-band PALSAR2 antenna parameters, at free Faraday rotation. Corner reflectors (CRs) deployed at free Faraday rotation provide accurate estimation of antenna cross-talks in contrast to the biased measurements obtained with CRs deployed at significant Faraday rotation. The extended Freeman–Van Zyl calibration method introduced and validated for ALOS-PALSAR calibration is used for the assessment of PALSAR-2 calibration parameters. Six datasets collected over the Amazonian rainforests (with CRs) are used to assess PALSAR-2 distortion matrix for five beams (FP6-3 to FP6-7) with incidence angle varying from 25° to 40°. It is shown that the PALSAR2 antenna is highly isolated with very low cross-talks (lower than −40 dB). Finally, the impact of a significant Faraday rotation on antenna cross-talk measurements using CR is discussed.
Identifying High Stranding Risk Areas of the Yangtze Finless Porpoise via Remote Sensing and Hydrodynamic Modeling
Authors: Li Qiyue , Li Wenya , Lai Geying , Liu Ying , Devlin Adam Thomas , Wang Weiping , Zhan Shupin
Published: May 2022

Freshwater cetaceans that inhabit river basins with seasonally changing hydrological regimes have a higher risk of stranding, leading to increased mortality and population decline. In Poyang Lake, the stranding risk of the critically endangered Yangtze finless porpoise are high, due to the significant differences in hydrological and landscape conditions between the flood and dry seasons. However, this stranding information is not well recorded, resulting in poorly guided investigation and rescue efforts. We here employ remote sensing and hydrodynamic modeling to obtain four evaluation indicators in dry, normal, and flood scenarios in Poyang Lake. Results show that nearly 50% of the largest habitat range of the porpoises in the flood season will be land areas during the dry season, and that landscape fragmentation between land and water has increased over the past three decades. In all scenarios, the mean water depth of the habitat varied from 1.77 to 4.89 m from September–February. In the dry scenario, about 59% of the habitat experiences a water depth of <1 m within 15 days. The high stranding risk area is 284.54 km2 in the dry scenario, >251.04 km2 in the normal scenario, and >90.12 km2 in the flood scenario. High-stranding risk areas are located within city boundaries, where porpoise stranding cases have been recorded, especially in Duchang, which has the most high-risk areas in all scenarios. In all scenarios, the high stranding risk area has an average bathymetry of 7.81 m and an average water depth between 1.75 and 5.54 m. Our results can guide future investigations to establish sound stranding networks, and the methods proposed here are also applicable to studies of other freshwater cetaceans facing severe stranding risk.
Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data
Authors: Wang Weiyan , Ma Yingying , Meng Xiaoliang , Sun Lin , Jia Chen , Jin Shikuan , Li Hui
Published: May 2022

The leaf area index (LAI), a key parameter used to characterize the structure and function of the vegetation canopy, is crucial to simulations of the carbon, nitrogen, and water cycles of Earth’s system. In this paper, a neural network (NN) method coupled with vegetation canopy and atmospheric radiative transfer (RT) simulations is proposed to realize LAI retrieval without prior data support and complex atmospheric corrections. The look-up table (LUT) of the top-of-atmosphere (TOA) reflectance and associated input variables was simulated by 6S (6S simulation) based on the top-of-canopy (TOC) reflectance LUT simulated by PROSAIL. This was then used to train the NN to obtain the LAI inversion model. This method has been successfully applied to MODIS L1B data (MOD021KM), and the LAI retrieval of the vegetation canopy was realized. The estimated LAI was compared with the MODIS LAI (MOD15A2H) using mid-latitude summer data from 2000 to 2017 in the DIRECT 2.0 ground database. The experiments indicated that the LAI retrieved by the TOA reflectance (r = 0.7852, RMSE = 0.5191) was not much different from the LAI retrieved by the TOC reflectance (r = 0.8063, RMSE = 0.7669), and the accuracy was better than the MODIS LAI (r = 0.7607, RMSE = 0.8239), which proves the feasibility of this method.
Forest Fire Monitoring and Positioning Improvement at Subpixel Level: Application to Himawari-8 Fire Products
Authors: Xu Haizhou , Zhang Gui , Zhou Zhaoming , Zhou Xiaobing , Zhou Cui
Published: May 2022

Forest fires are among the biggest threats to forest ecosystems and forest resources, and can lead to ecological disasters and social crises. Therefore, it is imperative to detect and extinguish forest fires in time to reduce their negative impacts. Satellite remote sensing, especially meteorological satellites, has been a useful tool for forest-fire detection and monitoring because of its high temporal resolution over large areas. Researchers monitor forest fires directly at pixel level, which usually presents a mixture of forest and fire, but the low spatial resolution of such mixed pixels cannot accurately locate the exact position of the fire, and the optimal time window for fire suppression can thus be missed. In order to improve the positioning accuracy of the origin of forest fire (OriFF), we proposed a mixed-pixel unmixing integrated with pixel-swapping algorithm (MPU-PSA) model to monitor the OriFFs in time. We then applied the model to the Japanese Himawari-8 Geostationary Meteorological Satellite data to obtain forest-fire products at subpixel level. In this study, the ground truth data were provided by the Department of Emergency Management of Hunan Province, China. To validate the positioning accuracy of MPU-PSA for OriFFs, we applied the model to the Himawari-8 satellite data and then compared the derived fire results with fifteen reference forest-fire events that occurred in Hunan Province, China. The results show that the extracted forest-fire locations using the proposed method, referred to as forest fire locations at subpixel (FFLS) level, were far closer to the actual OriFFs than those from the modified Himawari-8 Wild Fire Product (M-HWFP). This improvement will help to reduce false fire claims in the Himawari-8 Wild Fire Product (HWFP). We conducted a comparative study of M-HWFP and FFLS products using three accuracy-evaluation indexes, i.e., Euclidean distance, RMSE, and MAE. The mean distances between M-HWFP fire locations and OriFFs and between FFLS fire locations and OriFFs were 3362.21 m and 1294.00 m, respectively. The mean RMSEs of the M-HWFP and FFLS products are 1225.52 m and 474.93 m, respectively. The mean MAEs of the M-HWFP and FFLS products are 992.12 m and 387.13 m, respectively. We concluded that the newly proposed MPU-PSA method can extract forest-fire locations at subpixel level, providing higher positioning accuracy of forest fires for their suppression.
Assessment of IRNSS-Only Data Processing: Availability, Single-Frequency SPP and Short-Baseline RTK
Authors: Pan Lin , Pei Gen , Yu Wenkun , Zhang Zhehao
Published: May 2022

The Indian Regional Navigation Satellite System (IRNSS) currently can provide independent positioning services with eight in-orbit satellites. This study provides a comprehensive assessment of IRNSS-only data processing, including the availability of satellite constellation, the performance of single-frequency single point positioning (SPP), and the performance of single-frequency short-baseline real-time kinematic (RTK) positioning. Regarding the availability of IRNSS-only case in its primary service areas, the average number of visible satellites is 6–8, and the average Position Dilution of Precision (PDOP) value falls between 3.3 and 6.2, under a service rate of nearly 100.0%. The datasets from 14 stations located in the IRNSS service areas spanning a week are used for position determination. The results show that under the IRNSS single-system case, the positioning accuracy of the SPP is 6.031, 6.015, and 9.668 m in the east, north, and up directions, respectively, and the mean positioning bias of short-baseline RTK is 5.4, −21.1, and −0.2 mm with a standard deviation (STD) error of 7.8, 19.2, and 29.0 mm in the three directions, respectively. For comparative analysis, the results of the GPS single-system and GPS/IRNSS dual-system combination cases are also presented. The positioning performance of IRNSS is inferior to that of GPS, and the performance improvement of GPS/IRNSS dual-system integrated solutions over GPS single-system solutions is not significant. Furthermore, based on the GPS/IRNSS dual-system solutions, the inter-system bias estimates from SPP, the code observation residuals from SPP, and the carrier phase observation residuals from short-baseline RTK are characterized.
Towards Automated Detection and Localization of Red Deer Cervus elaphus Using Passive Acoustic Sensors during the Rut
Authors: Avots Egils , Vecvanags Alekss , Filipovs Jevgenijs , Brauns Agris , Skudrins Gundars , Done Gundega , Ozolins Janis , Anbarjafari Gholamreza , Jakovels Dainis
Published: May 2022

Passive acoustic sensors have the potential to become a valuable complementary component in red deer Cervus elaphus monitoring providing deeper insight into the behavior of stags during the rutting period. Automation of data acquisition and processing is crucial for adaptation and wider uptake of acoustic monitoring. Therefore, an automated data processing workflow concept for red deer call detection and localization was proposed and demonstrated. The unique dataset of red deer calls during the rut in September 2021 was collected with four GPS time-synchronized microphones. Five supervised machine learning algorithms were tested and compared for the detection of red deer rutting calls where the support-vector-machine-based approach demonstrated the best performance of −96.46% detection accuracy. For sound source location, a hyperbolic localization approach was applied. A novel approach based on cross-correlation and spectral feature similarity was proposed for sound delay assessment in multiple microphones resulting in the median localization error of 16 m, thus providing a solution for automated sound source localization—the main challenge in the automation of the data processing workflow. The automated approach outperformed manual sound delay assessment by a human expert where the median localization error was 43 m. Artificial sound records with a known location in the pilot territory were used for localization performance testing.
Volcanic Eruption of Cumbre Vieja, La Palma, Spain: A First Insight to the Particulate Matter Injected in the Troposphere
Authors: Sicard Michaël , Córdoba-Jabonero Carmen , Barreto Africa , Welton Ellsworth J. , Gil-Díaz Cristina , Carvajal-Pérez Clara V. , Comerón Adolfo , García Omaira , García Rosa , López-Cayuela María-Ángeles , Muñoz-Porcar Constantino , Prats Natalia , Ramos Ramón , Rodríguez-Gómez Alejandro , Toledano Carlos , Torres Carlos
Published: May 2022

The volcanic eruption of Cumbre Vieja (La Palma Island, Spain), started on 19 September 2021 and was declared terminated on 25 December 2021. A complete set of aerosol measurements were deployed around the volcano within the first month of the eruptive activity. This paper describes the results of the observations made at Tazacorte on the west bank of the island where a polarized micro-pulse lidar was deployed. The analyzed two-and-a-half months (16 October–31 December) reveal that the peak height of the lowermost and strongest volcanic plume did not exceed 3 km (the mean of the hourly values is 1.43 ± 0.45 km over the whole period) and was highly variable. The peak height of the lowermost volcanic plume steadily increased until week 11 after the eruption started (and 3 weeks before its end) and started decreasing afterward. The ash mass concentration was assessed with a method based on the polarization capability of the instrument. Two days with a high ash load were selected: The ash backscatter coefficient, aerosol optical depth, and the volume and particle depolarization ratios were, respectively, 3.6 (2.4) Mm−1sr−1, 0.52 (0.19), 0.13 (0.07) and 0.23 (0.13) on 18 October (15 November). Considering the limitation of current remote sensing techniques to detect large-to-giant particles, the ash mass concentration on the day with the highest ash load (18 October) was estimated to have peaked in the range of 800–3200 μg m−3 in the lowermost layer below 2.5 km.
Polymer Optical Fiber Plantar Pressure Sensors: Design and Validation
Authors: Safarloo Sahar , Núñez-Cascajero Arántzazu , Sanchez-Gomez Ruben , Vázquez Carmen
Published: May 2022

The proper measurement of plantar pressure during gait is critical for the clinical diagnosis of foot problems. Force platforms and wearable devices have been developed to study gait patterns during walking or running. However, these devices are often expensive, cumbersome, or have boundary constraints that limit the participant’s motions. Recent advancements in the quality of plastic optical fiber (POF) have made it possible to manufacture a low-cost bend sensor with a novel design for use in plantar pressure monitoring. An intensity-based POF bend sensor is not only lightweight, non-invasive, and easy to construct, but it also produces a signal that requires almost no processing. In this work, we have designed, fabricated, and characterized a novel intensity POF sensor to detect the force applied by the human foot and measure the gait pattern. The sensors were put through a series of dynamic and static tests to determine their measurement range, sensitivity, and linearity, and their response was compared to that of two different commercial force sensors, including piezo resistive sensors and a clinical force platform. The results suggest that this novel POF bend sensor can be used in a wide range of applications, given its low cost and non-invasive nature. Feedback walking monitoring for ulcer prevention or sports performance could be just one of those applications.
LAG: Layered Objects to Generate Better Anchors for Object Detection in Aerial Images
Authors: Wan Xueqiang , Yu Jiong , Tan Haotian , Wang Junjie
Published: May 2022

You Only Look Once (YOLO) series detectors are suitable for aerial image object detection because of their excellent real-time ability and performance. Their high performance depends heavily on the anchor generated by clustering the training set. However, the effectiveness of the general Anchor Generation algorithm is limited by the unique data distribution of the aerial image dataset. The divergence in the distribution of the number of objects with different sizes can cause the anchors to overfit some objects or be assigned to suboptimal layers because anchors of each layer are generated uniformly and affected by the overall data distribution. In this paper, we are inspired by experiments under different anchors settings and proposed the Layered Anchor Generation (LAG) algorithm. In the LAG, objects are layered by their diagonals, and then anchors of each layer are generated by analyzing the diagonals and aspect ratio of objects of the corresponding layer. In this way, anchors of each layer can better match the detection range of each layer. Experiment results showed that our algorithm is of good generality that significantly uprises the performance of You Only Look Once version 3 (YOLOv3), You Only Look Once version 5 (YOLOv5), You Only Learn One Representation (YOLOR), and Cascade Regions with CNN features (Cascade R-CNN) on the Vision Meets Drone (VisDrone) dataset and the object DetectIon in Optical Remote sensing images (DIOR) dataset, and these improvements are cost-free.
Improved Active Disturbance Rejection Double Closed-Loop Control of a Rotary-Type VCM in a Moving Mirror Control System
Authors: Zhi Liangjie , Huang Min , Han Wei , Wang Zhanchao , Lu Xiangning , Bai Yang , Gao Han
Published: May 2022

Aiming to address the problem of moving mirror speed fluctuations in moving mirror control systems, an improved active disturbance rejection double closed-loop controller (IADR-DCLC) is proposed and verified by simulation to realize the high-performance control of a moving mirror control system. First, the mathematical model of a rotary-type voice coil motor (RT VCM) is established, and the relationship between the angular velocity of the RT VCM and the optical path scanning velocity is analyzed. Second, in order to suppress the model uncertainty and external disturbance of the system, an improved active disturbance rejection controller (IADRC) is proposed. Compared with a conventional ADRC, the tracking differentiator of the proposed IADRC is replaced with desired signal optimization (DSO), and the actual speed is introduced to the extended state observer (ESO). The IADRC is used in the position–speed double closed-loop control model. Finally, the simulation results show that the IADR-DCLC has not only a good tracking effect but also a good anti-interference ability and can meet the requirements of the moving mirror control system for the uniformity of optical-path scanning speed and accurate control of the position of the moving mirror.
An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques
Authors: Muthukumar Krishnan Arumugasamy , Bouazizi Mondher , Ohtsuki Tomoaki
Published: May 2022

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.
Enabling Real-Time Quality-of-Service and Fine-Grained Aggregation for Wireless TSN
Authors: Zhang Litianyi , Gu Yifan , Wang Rui , Yu Kan , Pang Zhibo , Li Yonghui , Vucetic Branka
Published: May 2022

Wireless Time-Sensitive Networking (WTSN) has emerged as a promising technology for Industrial Internet of Things (IIoT) applications. To meet the latency requirements of WTSN, wireless local area network (WLAN) such as IEEE 802.11 protocol with the time division multiple access (TDMA) mechanism is shown to be a practical solution. In this paper, we propose the RT-WiFiQA protocol with two novel schemes to improve the latency and reliability performance: real-time quality of service (RT-QoS) and fine-grained aggregation (FGA) for TDMA-based 802.11 systems. The RT-QoS is designed to guarantee the quality-of-service requirements of different traffic and to support the FGA mechanism. The FGA mechanism aggregates frames for different stations to reduce the physical layer transmission overhead. The trade-off between the reliability and FGA packet size is analyzed with numerical results. Specifically, we derive a critical threshold such that the FGA can achieve higher reliability when the aggregated packet size is smaller than the critical threshold. Otherwise, the non-aggregation scheme outperforms the FGA scheme. Extensive experiments are conducted on the commercial off-the-shelf 802.11 interface. The experiment results show that compared with the existing TDMA-based 802.11 system, the developed RT-WiFiQA protocol can achieve deterministic bounded real-time latency and greatly improves the reliability performance.
Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation
Authors: Lin Meiyan , Zhang Xiaoxu , Tian Ye , Huang Yonghui
Published: May 2022

Multi-signal detection is of great significance in civil and military fields, such as cognitive radio (CR), spectrum monitoring, and signal reconnaissance, which refers to jointly detecting the presence of multiple signals in the observed frequency band, as well as estimating their carrier frequencies and bandwidths. In this work, a deep learning-based framework named SigdetNet is proposed, which takes the power spectrum as the network’s input to localize the spectral locations of the signals. In the proposed framework, Welch’s periodogram is applied to reduce the variance in the power spectral density (PSD), followed by logarithmic transformation for signal enhancement. In particular, an encoder-decoder network with the embedding pyramid pooling module is constructed, aiming to extract multi-scale features relevant to signal detection. The influence of the frequency resolution, network architecture, and loss function on the detection performance is investigated. Extensive simulations are carried out to demonstrate that the proposed multi-signal detection method can achieve better performance than the other benchmark schemes.
Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
Authors: Kumar Manoj , Kumar Sushil , Kashyap Pankaj Kumar , Aggarwal Geetika , Rathore Rajkumar Singh , Kaiwartya Omprakash , Lloret Jaime
Published: May 2022

Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters popsize=60, number of rooster Nr=0.3, number of hen’s Nh=0.6 and swarm updating frequency θ=10. Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.
Digital RMB, RMB Internationalization and Sustainable Development of the International Monetary System
Authors: Shen Chunming
Published: May 2022

The sound and sustainable development of the international monetary system is the cornerstone of the sound and stable development of the global economy. This paper takes digital currency in China as its research object and utilizes a regime-switching transition auto-regression (STAR) model and nonlinear time-varying parameter–stochastic volatility–vector auto regression (TVP-SV-VAR) model to empirically analyze the relationship between digital RMB, RMB internationalization, and the development of the international monetary system. The results show that the relationship between DC/EP and the internationalization of the RMB is time-varying, with the above relationship being significantly different in various economic situations. DC/EP can boost the internationalization of the RMB, and thereby contribute to the diversification of the international monetary system. The results have important policy implications for the sound and sustainable development of economic and financial markets.
Challenges for Implementing Environmental Management Plans in Construction Projects: The Case of Malaysia
Authors: Asnor Asri Syahmi , Al-Mohammad Mohammad S. , Wan Ahmad Saffuan , Almutairi Saud , Rahman Rahimi A.
Published: May 2022

An environmental management plan (EMP) can reduce the impact of construction projects on the environment. While there is an increasing trend to promote sustainability, effective EMP implementation in Malaysia is limited. In this study, we aim to investigate the challenges for implementing EMP at construction sites in Malaysia, identify the main issues among those challenges, and determine the interrelationships between the main challenges. We identified 30 potential challenges for implementing EMP through a systematic literature review of 41 papers and semi-structured interviews with 20 construction industry professionals. Subsequently, a survey was used to solicit opinions on the challenges. The collected data were analyzed using mean score, standard deviation, normalization, factor analysis, and analysis of variance (ANOVA). The results illustrate that 21 main challenges exist for implementing EMP in Malaysia. From these, eight main challenges can be grouped into two major components: people- and project-related challenges. ANOVA test results suggest different perceptions of the challenges between project owners, contractors, and consultants. The theoretical implications of this study include a profound understanding of the challenges in implementing EMP at construction sites in Malaysia and their underlying relations.
Multiple Smart Cities: The Case of the Eco Delta City in South Korea
Authors: Han Dongho , Kim Ji Hyun
Published: May 2022

This paper follows the urban development process of the Eco Delta City (EDC) in South Korea, a new waterfront development demonstrating the concept of a smart city. The investigation focuses on the mobilisation process under the framework of assemblage thinking: the way in which the smart city concept was applied to the project and the relational moments that delayed and stopped the process. This qualitative research with the case study and ethnographical tradition of analysis was conducted with data from diverse archival sources and interviews. By dissecting the network of EDC development, the analysis finds that the smart city mobilisation emerged from the complex actor-relations rather than from the top-down policy, and the initially brought smartness framed by the government was not accepted intactly but was contested, affiliated and compounded by the actor-relations. This study also verifies that the assemblage approach is a suitable tool in managing and evaluating policy mobilisation because it is affected by the local context and actor-relations rather than just imitation and direct application.
Recent Advances in Water Harvesting: A Review of Materials, Devices and Applications
Authors: Chen Kuanfu , Tao Yujie , Shi Weiwei
Published: May 2022

Water is essential for life. However, water scarcity is becoming one of the most severe issues worldwide in terms of its potential impacts. There are diverse forms of water on earth and water harvesting from them is quite feasible to access more fresh water for drinking, sanitation and irrigation. In this review, we summarize the recent technologies of various water harvesters, based on different forms of water resources, aiming to improve the water harvesting systems. We mainly address three points: forming principles of different water circumstance, working mechanism of typical water harvesters, and the challenges and future research orientations. This systemic review on recent technologies in water harvesting provides insight into the sustainable water resources, water supply, and water collecting systems for the future.
Anti-Erosion Influences of Surface Roughness on Sloping Agricultural Land in the Loess Plateau, Northwest China
Authors: Li Taotao , Wang Yu , Wu Faqi
Published: May 2022

The roughness of surface soil and the benefits produced by tillage for slope runoff and sediment reduction have attracted considerable interest; however, there are inconsistencies in existing research results. In this study, we have studied the anti-erosion influences of several typical tillage practices on both runoff and sediment generation in areas of sloping farmland in the Loess Plateau of northwest China. Rough surfaces were prepared manually, according to the surface microtopography of the plateau’s sloping farmland, using four tillage practices; a smooth surface was used as a control. Rainfall simulation experiments were performed using three rainfall intensities and five slope gradients. A path analysis was used to analyze the interactive effects of the slope gradient, rainfall intensity, and the surface roughness on the sediment yield and runoff volume. According to our findings, the gradient of a slope and the intensity of the rainfall both had a positive effect, while the surface roughness had a negative effect; the rate of 40.8% and 21.0% was lower than the values under CK on sediment yield and runoff volume. The interaction between the rainfall intensity and surface roughness always had a runoff reduction effect. Conversely, there was a critical slope gradient between 5° and 10° for sediment yield. The interaction between the slope gradient and surface roughness also had a runoff reduction effect, which was diminished by increasing the rainfall intensity. However, their interactive influence on sediment yield was inconsistent, with a critical slope gradient between 10° and 15°. Based on the comprehensive interactive effects among all three factors, we concluded that rainfall intensity, slope gradient, and surface roughness collectively played a crucial role in promoting runoff and sediment generation under tillage. The results support soil and water conservation by tillage on the sloping farmlands of the Loess Plateau.
Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System
Authors: Arshad Jehangir , Rehman Ateeq Ur , Othman Mohamed Tahar Ben , Ahmad Muhammad , Tariq Hassaan Bin , Khalid Muhammad Abdullah , Moosa Muhammad Abdul Rehman , Shafiq Muhammad , Hamam Habib
Published: May 2022

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.