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Mode-locked and Q-switched pulsed fiber laser sources with wavelengths of 1.55 μm are widely used in various fields. Gold nanorods (GNRs) have been applied in biomedicine and optics owing to their biocompatibility, easy fabrication, and unique optical properties. This paper presents the analysis of a saturable absorber based on a colloidal gold nanorod (GNR) thin film for dual-function passively mode-locked and Q-switched 1.55-μm fiber lasers. The colloidal GNR thin film possesses superior properties such as a wide operating wavelength range, large nonlinear absorption coefficient, and a picosecond-order recovery time. Its modulation depth and saturation intensity at 1.55 μm are 7.8% and 6.55 MW/cm2, respectively. Passive mode-locked or Q-switched laser operation is achieved by changing the number of GNR thin-film layers. The advantages of these high-quality GNRs in mode-locked and Q-switched fiber lasers with record-high slope efficiency are verified by conducting comprehensive material and laser dynamic analyses. The self-starting mode-locked fiber laser with an efficiency as high as 24.91% and passively Q-switched fiber laser with the maximum energy of 0.403 μJ are successfully demonstrated. This paper presents the novel demonstration of reconfigurable mode-locked and Q-switched all-fiber lasers by incorporating colloidal GNR thin films.

The present paper aims to quantify how human-made changes in the upstream exacerbate climate change impacts on water birds’ habitat in the downstream. To reduce climate change effects and design adaptation policies, it is important to identify whether human activities understate or overstate the effects of climate change in a region on its inhabitants. This paper also shows how human activities may magnify climate change impacts both locally and regionally. Land-use/land-cover change as the important sign of human-made destruction in an ecosystem was detected in the upstream of the Helmand basin over 40 years. Owing to conflicts in Afghanistan, studies on this basin are rare. The water bird’s habitat suitability maps during the study period were created using the maximum entropy model and the multi-criteria evaluation method. The post-classification method was applied to show the land-use/land-cover change over 40 years. These results were compared to the area of suitable habitat for water birds. The findings of these analyses indicated that the irrigated farming was expanded in the upstream despite climate change and water limitation, while the water birds’ habitat in the downstream was declined. These results revealed that the unsustainable pattern of farming and blocking water behind dams in the upstream exacerbated the negative effects of climate change on water birds’ habitat in the downstream. The significance of this study is to demonstrate the role of human in exacerbating climate change impacts both locally and regionally.

Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).

Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.

In this paper using Plasmon Resonances Management (PRM), a bi-functional meta-mirror is proposed in which, the meta-mirror can obtain two opposite properties in two different frequency ranges. In this method, an anisotropic unit cell with polarization conversion property is modified to have two plasmon resonances in both symmetric and anti-symmetric planes in a specific frequency. This allows the unit cell to have the property of unchanged polarization in that frequency. The meta-mirror is composed of this modified unit cell and its mirror as a chessboard arrangement and the incident wave on the meta-mirror is reflected as in-phase in that specific frequency i.e. specular reflection, while as out-of-phase in other frequencies i.e. RCS reduction. The designed meta-mirror in this paper demonstrates the RCS reduction in two side-bands from 4 to 9 GHz and 10.8 to 14.8 GHz while behaving as a specular reflection in the frequency around 10 GHz.

We investigate conducting characteristics of biochar derived from the pyrolysis of a paper at terahertz frequencies. Paper is annealed under temperatures ranging from 600 to 1000 °C to modify structural and electrical properties. We experimentally observe that the terahertz conductivity increases above 102 S/m as the annealing temperature increases up to 800 °C. From structural characterization using energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, Raman spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy, we confirm that more graphitic biochars are produced in high annealing temperature, in agreement with the improvement of terahertz conductivity. Our results show that biochar can be a highly promising candidate to be used in paper-based devices operating at terahertz frequencies.

This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.

On-chip technology continuously explores and provides novel methods for high-throughput drug screening, such as droplet-array sandwiching technology. Droplet-array sandwiching technology involves upper and lower substrates with a droplet-array designed for a one-step process for drug screening, including cell-based screening. This technology is, however, limited to batch manipulation of the droplet-array. This paper proposes selective control of individual droplets, which allows different conditions for individual droplet pairs. Electrowetting-on-dielectric (EWOD) technology is introduced to control the height of the droplets so that the contact between droplet-pairs can be individually controlled. Circular patterns 4 mm in diameter composed of electrodes for EWOD and hydrophilic-hydrophobic patterns for droplet formation 4 μl in volume were developed. This paper presents and examines the selective control of the droplet height by EWOD for an applied voltage up to 160 V, followed by successful results of selective control of the contact and transport of substances.

Antimicrobial resistance (AMR) is a global health emergency. Complementary to developing new drugs, AMR can be monitored and controlled through cost-effective active surveillance of resistance. As an initiative to monitor resistance, countries all across the globe are collecting data using a variety of surveillance tools. Moreover, hospitals routinely collect the AMR data for treatment which is being stored in their Laboratory and Hospital Information systems (LIS-HIS). The generated clinical data is collected & stored in various formats, making it very difficult to analyze and generate national reports. To integrate the stored clinical data for predictive modeling and analysis, there is an immediate need for a one-stop data repository capable of importing and exporting data in simple data exchange formats (CSV/Excel). The paper highlights the design & development of i-DIA, a python-based web API to facilitate the interoperability of AMR data by automatically importing the bulk of medical data from CSV files into generic data management and analysis system. The i-DIA has been integrated and tested with the ICMR’s AMR surveillance network on in-house developed software, i-AMRSS. The i-AMRSS is presently collecting data from 31 laboratories across India and i-DIA has been used to import data generated from LIS & HIS of a few hospitals directly into the system. The paper also proposes the complete web-based framework (an extension of i-DIA) integrated with peer-to-peer system architecture.

Due to the audio information of different types of vehicle models are distinct, the vehicle information can be identified by the audio signal of vehicle accurately. In real life, in order to determine the type of vehicle, we do not need to obtain the visual information of vehicles and just need to obtain the audio information. In this paper, we extract and stitching different features from different aspects: Mel frequency cepstrum coefficients in perceptual characteristics, pitch class profile in psychoacoustic characteristics and short-term energy in acoustic characteristics. In addition, we improve the neural networks classifier by fusing the LSTM unit into the convolutional neural networks. At last, we put the novel feature to the hybrid neural networks to recognize different vehicles. The results suggest the novel feature we proposed in this paper can increase the recognition rate by 7%; destroying the training data randomly by superimposing different kinds of noise can improve the anti-noise ability in our identification system; and LSTM has great advantages in modeling time series, adding LSTM to the networks can improve the recognition rate of 3.39%.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Eucalyptus plantations around the world have been largely used by the paper industry. Optimizing the management of resources is a common practice in this highly competitive industry and new forest growth models may help to understand the impact of climate change on the decisions of the optimization processes. Current optimized management plans use empirical equations to predict future forest stands growth, and it is currently impractical to replace these empirical equations with physiological models due to data input requirements. In this paper, we present a different approach, by first carrying out a preliminary assessment with the process-based physiological model 3PG to evaluate the growth of Eucalyptus stands under climate change predictions. The information supplied by 3PG was then injected as a modifier in the projected yield that feeds the management plan optimizer allowing the interpretation of climate change impacts on the management plan. Modelling results show that although a general increase of rain with climate change is predicted, the distribution throughout the year will not favor the tree growth. On the contrary, rain will increase when it is less needed (summer) and decrease when it is most needed (winter), decreasing forest stand productivity between 3 and 5%, depending on the region and soil. Evaluation of the current optimized plan that kept constant the relation between wood price/cellulose ton shows a variation in different strategic management options and an overall increase of costs in owned areas between 2 and 4%, and a decrease of cumulated net present value, initially at 15% with later stabilization at 6–8%. This is a basic comparison to observe climate change effects; nevertheless, it provides insights into how the entire decision-making process may change due to a reduction in biomass production under future climate scenarios. This work demonstrates the use of physiological models to extract information that could be merged with existing and already implemented empirical models. The methodology may also be considered a preliminary alternative to the complete replacement of empirical models by physiological models. Our approach allows some insight into forest responses to different future climate conditions, something which empirical models are not designed for.

Neuromyelitis Optica and Multiple Sclerosis are idiopathic inflammatory demyelinating diseases of the central nervous system that currently are considered distinct autoimmune diseases, so differences in genetic susceptibility would be expected. This study aimed to investigate the HLA association with Neuromyelitis Optica by a systematic review with meta-analysis. The STROBE instrument guided research paper assessments. Thirteen papers published between 2009 and 2020 were eligible. 568 Neuromyelitis Optica patients, 41.4% Asians, 32.4% Latin Americans and 26.2% Europeans were analyzed. Only alleles of the DRB1 locus were genotyped in all studies. Neuromyelitis Optica patients have 2.46 more chances of having the DRB1*03 allelic group than controls. Ethnicity can influence genetic susceptibility. The main HLA association with Neuromyelitis Optica was the DRB1*03:01 allele in Western populations and with the DPB1*05:01 allele in Asia. Differences in the Multiple Sclerosis and Neuromyelitis Optica genetic susceptibility was confirmed in Afro descendants. The DRB1*03 allelic group associated with Neuromyelitis Optica has also been described in other systemic autoimmune diseases.

In the multi-effect evaporation salt making process, the smooth operation of the salt making process is crucial. As the salt production process continues, many unstable factors will cause the salt production process not to proceed smoothly. These factors can be discovered in advance by predicting the salt production data, thus, it is of great significance to predict the multi-effect evaporation salt production data. In the process of multi-effect evaporation and salt production, the multiple salt-making devices make the influence between the parameters closer, and the influence of a single parameter on itself is sometimes ductile. Therefore, the data of multi-effect evaporation and salt production have the characteristics of high dimensions, high complexity and temporal information. If the historical salt production data is used for data prediction directly, the prediction model will take a long time and the prediction effect is not good. Thus, how to predict the multi-effect evaporation salt production data is the main research problem of this paper. In view of the above problems, according to the characteristics of multi-effect evaporation salt production data, this paper analyzes and improves the self encoder for feature extraction of multi effect-evaporation salt production data, so as to solve the problem of high dimensions and high complexity of salt production data. On this basis, combined with the time-series information contained in the salt production data, a multi-effect evaporation salt production data prediction model is proposed based on long-term and short-term memory cycle neural network to solve the prediction problem of time-series salt production data. Experiments show that the prediction model can predict and prevent the problems in salt production line in advance. It has a certain theoretical research value and application value in the intelligent production process and production line optimization of salt chemical industry.

PM2.5 pollution has become a serious urban environmental problem, especially in developing countries with increasing urbanization. Understanding the proportion of PM2.5 generation sources has laid a foundation for better PM2.5 concentration reduction This paper used Point of Interesting (POI)data, building profile data of Xi’an, PM2.5 concentration and wind monitoring data of five provinces near Xi’an as the basic data. And this paper studied the spatial distribution of various buildings in Xi’an, the temporal and spatial distribution of PM2.5 in Xi’an and the five provinces, and found that the spatial distribution of PM2.5 concentration in Xi’an and the building distribution in Xi’an does not match. Based on this, a quantitative model of PM2.5 concentration in Xi’an, energy consumption, wind, and other factors is established through the qualitative and quantitative analysis of PM2.5 concentration in Xi’an. Entropy theory and dissipative structure theory are applied to analyze this phenomenon. The results show PM2.5 in Xi’an mainly comes from the spread of PM2.5 in the five provinces. The PM2.5 generated by energy consumption in Xi’an is not enough to cause serious PM2.5 pollution. And further suggestions on how to reduce PM2.5 concentration in Xi’an are put forward.

Too many pre-clinical experiments are giving results which cannot be reproduced. This may be because the experiments are incorrectly designed. In “Completely randomized” (CR) and “Randomised block” (RB) experimental designs, both the assignment of treatments to experimental subjects and the order in which the experiment is done, are randomly determined. These designs have been used successfully in agricultural and industrial research and in clinical trials for nearly a century without excessive levels of irreproducibility. They must also be used in pre-clinical research if the excessive level of irreproducibility is to be eliminated. A survey of 100 papers involving mice and rats was used to determine whether scientists had used the CR or RB designs. The papers were assigned to three categories “Design acceptable”, “Randomised to treatment groups”, so of doubtful validity, or “Room for improvement”. Only 32 ± 4.7% of the papers fell into the first group, although none of them actually named either the CR or RB design. If the current high level of irreproducibility is to be eliminated, it is essential that scientists engaged in pre-clinical research use “Completely randomised” (CR), “Randomised block” (RB), or one of the more specialised named experimental designs described in textbooks on the subject.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Ground deformation can cause serious environmental issues such as infrastructure damage, ground compaction, and reducing the ground capacity to store water. Mashhad, as one of the largest and most populated cities in the Middle East, has been suffering from extreme subsidence. In the last decade, some researchers have been interested in measuring land subsidence rates in the Mashhad valley by InSAR techniques. However, most of those studies were based on inaccurate measurements introducing uncertainties in the resulting subsidence rates. These researches used a small number of EnviSat data with long perpendicular and inhomogeneous temporal baseline. This paper seeks to determine the subsidence rate in urban areas of Mashhad in recent years, the threat that was neglected by the city managers and decision-makers. For this purpose, the Persistent Scatterer InSAR technique was applied in the study area using two time-series of descending and ascending Sentinel-1A acquisitions between 2014 and 2017. The results demonstrated the maximum line-of-sight deformation rate of 14.6 cm/year and maximum vertical deformation (subsidence) rate about 19.1 cm/year which could have irreversible consequences. The results were assessed and validated using piezometric data, GPS stations, and geotechnical properties. This assessment confirms that the main reason for subsidence in the interested area is groundwater over-extraction. Also, investigation of geotechnical properties shows that thick fine-grained layers in the northwest of the city could strongly affect the results. At the end of this paper, a new simplified method was proposed to estimate specific storage in special cases to predict the subsidence rate.

Findings on the effects of probiotics on salivary cytokines and immunoglobulines have been conflicting. We aimed to perform a systematic review and meta-analysis on clinical trials that examined the effects of oral intake and local administration of probiotics on salivary cytokines and immunoglobulines in adults. We searched PubMed, MEDLINE, SCOPUS, EMBASE, and Google Scholar up to April 2020 for all relevant published papers assessing probiotic intakes and salivary cytokines and immunoglobulines. We included all randomized clinical trials that investigated the effect of oral probiotic supplementation or lozenges tablets on inflammatory biomarkers in adults. Studies that reported their effect sizes as mean ± SD or mean ± SEM were included. After excluding non-relevant papers, 8 studies remained in this review. Combining findings from 3 studies with 4 effect sizes, we found no significant reduction in salivary IgA concentrations after oral probiotic supplementation [weighted mean difference (WMD): −0.26; 95% CI: (−0.86, 0.35)]. A significant increase in salivary IL-1β concentrations reached after local probiotic supplementation (WMD: 28.21; 95% CI: 18.42, 38.01); however, no significant changes in salivary IL-6 concentrations after local probiotic supplementation was found (WMD: 0.36; 95% CI: −0.85, 1.56). We observed a significant increase in salivary IL-8 concentrations after local probiotic supplementation (WMD: 31.82; 95% CI: 27.56, 36.08). In case of salivary IL-10 concentrations after local probiotic administration, no significant reduction was seen (WMD: −0.02; 95% CI: −0.10, 0.06). we found that oral and local administrations of probiotics might influence some of salivary cytokines. However, additional clinical trials are required to examine these effects on further pro- and anti-inflammatory cytokines and immunoglobulines.

This paper presents the design, simulation and low-cost fabrication of microfluidic channel for biomedical application. Channel is fabricated using soft lithography technique. Printed Circuit Board (PCB) is used to make the master for the channel. Channel pattern is transferred on PCB plate using toner transfer technique followed by ferric chloride etching. Paper also discusses, the issues involved in PCB based master fabrication and their viable solutions. Glass is used as substrate material and the channel is made of Sylgard 184 Polydimethylsiloxane (PDMS). Channel is interfaced with a syringe pump to observe the fluid flow. To predict the behavior of the channel, FEM simulation is performed using COMSOL Multiphysics 5.2a. There is a good match between the theoretical, simulation and test results. Finally, to test the biocompatibility of the channel, genomic DNA is passed through the channel and gel electrophoresis analysis is performed.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Contains fulltext : 220691.pdf (Publisher’s version ) (Open Access) Invasive motor Cortex Stimulation (iMCS) was introduced in the 1990's for the treatment of chronic neuropathic orofacial pain (CNOP), although its effectiveness remains doubtful. However, CNOP is known to be a heterogeneous group of orofacial pain disorders, which can lead to different responses to iMCS. Therefore, this paper investigated (1) whether the effectiveness of iMCS is significantly different among different CNOP disorders and (2) whether other confounding factors can be impacting iMCS results in CNOP. A systematic review and meta-analysis using a linear mixed-model was performed. Twenty-three papers were included, totaling 140 CNOP patients. Heterogeneity of the studies showed to be 55.8%. A visual analogue scale (VAS) measured median pain relief of 66.5% (ranging from 0-100%) was found. Linear mixed-model analysis showed that patients suffering from trigeminal neuralgia responded significantly more favorable to iMCS than patients suffering from dysfunctional pain syndromes (p = 0.030). Also, patients suffering from CNOP caused by (supra)nuclear lesions responded marginally significantly better to iMCS than patients suffering from CNOP due to trigeminal nerve lesions (p = 0.049). No other confounding factors were elucidated. This meta-analysis showed that patients suffering from trigeminal neuralgia and patients suffering from (supra)nuclear lesions causing CNOP responded significantly more favorable than others on iMCS. No other confounding factors were found relevant.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

This paper proposes a visual management scheme of medical things with a color-change radio frequency identification (RFID) tag. The color-change RFID tag employs a specific RFID tag integrated circuit (IC) and a laminated pH-indicating paper. The IC has energy harvesting and switched ground functions, which enable it to generate electricity to the laminated pH-indicating paper. This phenomenon causes electrolysis of NaCl solution absorbed in the laminated pH-indicating paper. Electrolysis generates alkaline matter to change the color of the pH-indicating paper. This paper gives a new and sensitive structure of the laminated pH-indicating paper. The proposed advanced color-change RFID tag with new laminated pH-indicating paper succeeds in changing its color noticeably at a 1 m distance using an RFID reader radiating 1 W radio waves. The color change was observed 3–5 s after starting radio wave irradiation. The results of this experiment also confirm that the changed color can be held for over 24 h. Furthermore, two demonstrations of the visual management system of medical things (patient clothes and sanitizers) are presented.

Space information networks is network systems that can receive, transmit, and process spatial information lively. It uses satellites, stratosphere airships, Unmanned Aerial Vehicles, and other platforms as the carrier. It supports high-dynamic, real-time broadband transmission of earth observations and ultra-long-distance, long-delay reliable transmission of deep space exploration. The deeper the network integration, the higher the system’s security concerns and the more likely SINs will be controlled and destroyed in terms of cybersecurity. How to integrate new IT technologies such as artificial intelligence, digital twins, and blockchain to diverse application scenarios of SINs while maintaining SIN cybersecurity will be a long-term critical technical issue. This study is a review of the security issues for space information networks. First, this paper examines space information networks’ security issues and figures out the relationship between the main security threats, services, and mechanisms. Then, this article selects secure routing and anomaly detection from many security technologies to conduct a detailed overview from two perspectives of traditional methods and artificial intelligence. Subsequently, this paper investigates anomaly detection schemes for spatial information networks and proposes a deep learning-based anomaly detection scheme. Finally, we suggest the potential research directions and opening problems of space information network security. Overall, this paper aims to give readers an overview of the newly emerging technologies in space information networks’ security issues and provide inspiration for future exploration.

Particleboard surface defect detection technology is of great significance to the automation of particleboard detection, but the current detection technology has disadvantages such as low accuracy and poor real-time performance. Therefore, this paper proposes an improved lightweight detection method of You Only Live Once v5 (YOLOv5), namely PB-YOLOv5 (Particle Board-YOLOv5). Firstly, the gamma-ray transform method and the image difference method are combined to deal with the uneven illumination of the acquired images, so that the uneven illumination is well corrected. Secondly, Ghost Bottleneck lightweight deep convolution module is added to Backbone module and Neck module of YOLOv5 detection algorithm to reduce model volume. Thirdly, the SELayer module of attention mechanism is added into Backbone module. Finally, replace Conv in Neck module with depthwise convolution (DWConv) to compress network parameters. The experimental results show that the PB-YOLOv5 model proposed in this paper can accurately identify five types of defects on the particleboard surface: Bigshavings, SandLeakage, GlueSpot, Soft and OliPollution, and meet the real-time requirements. Specifically, recall, F1 score, mAP@.5, mAP@.5:.95 values of pB-Yolov5s model were 91.22%, 94.5%, 92.1%, 92.8% and 67.8%, respectively. The results of Soft defects were 92.8%, 97.9%, 95.3%, 99.0% and 81.7%, respectively. The detection of single image time of the model is only 0.031 s, and the weight size of the model is only 5.4 MB. Compared with the original YOLOv5s, YOLOv4, YOLOv3 and Faster RCNN, the PB-Yolov5s model has the fastest Detection of single image time. The Detection of single image time was accelerated by 34.0%, 55.1%, 64.4% and 87.9%, and the weight size of the model is compressed by 62.5%, 97.7%, 97.8% and 98.9%, respectively. The mAP value increased by 2.3%, 4.69%, 7.98% and 13.05%, respectively. The results show that the PB-YOLOV5 model proposed in this paper can realize the rapid and accurate detection of particleboard surface defects, and fully meet the requirements of lightweight embedded model.

In this paper, the hybrid combination of genetic algorithm and particle swarm optimization (GAPSO) is used to optimize the phase distribution (PD) of beam-scanning reflectarray. The GAPSO takes advantage of both conventional algorithms and it could cover their weaknesses. Two novel PDs are proposed in this paper which constant phase elements (CPEs) and ordinary elements (OEs) are two basic kinds of elements used in them. The phases of CPEs are fixed and it is not changed during beam scanning and only OEs’ phase could be adjusted to scan the main beam. In this work GAPSO and two novel PDs are applied to array factor’s PD of a 30 × 30 reflectarray antenna to displace the main beam electronically in the vertical plane from − 40° to 40°. Also, in these two novel PDs, 28.8% of total elements are selected as CPEs. In the first one with only CPEs, the phase of OEs (71.2% of total elements) could adjust, but in the second novel PD with CPEs and phase symmetry plane 35.5% of the total elements’ phase could be changed to scan the beam. Optimization results show that the novel PD and hybrid algorithm have appropriate performance in the electronically beam scanning of reflectarrays.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

This paper presents a block-chain enabled inkjet-printed ultrahigh frequency radiofrequency identification (UHF RFID) system for the supply chain management, traceability and authentication of hard to tag bottled consumer products containing fluids such as water, oil, juice, and wine. In this context, we propose a novel low-cost, compact inkjet-printed UHF RFID tag antenna design for liquid bottles, with 2.5 m read range improvement over existing designs along with robust performance on different liquid bottle products. The tag antenna is based on a nested slot-based configuration that achieves good impedance matching around high permittivity surfaces. The tag was designed and optimized using the characteristic mode analysis. Moreover, the proposed RFID tag was commercially tested for tagging and billing of liquid bottle products in a conveyer belt and smart refrigerator for automatic billing applications. With the help of block-chain based product tracking and a mobile application, we demonstrate a real-time, secure and smart supply chain process in which items can be monitored using the proposed RFID technology. We believe the standalone system presented in this paper can be deployed to create smart contracts that benefit both the suppliers and consumers through the development of trust. Furthermore, the proposed system will paves the way towards authentic and contact-less delivery of food, drinks and medicine in recent Corona virus pandemic.

In the field of intelligent manufacturing, robot grasping and sorting is important content. However, there are some disadvantages in the traditional single-view-based manipulator grasping methods by using a 2D camera, where the efficiency and the accuracy of grasping are both low when facing the scene of stacking and occlusion for the reason that there is information missing by single-view 2D camera-based methods while acquiring scene information, and the methods of grasping only can’t change the difficult-to-grasp scene which is stack and occluded. Regarding the issue above, a pushing-grasping collaborative method based on the deep Q-network in dual viewpoints is proposed in this paper. This method in this paper adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, which solved the problem of lack of information missing. What’s more, it combines the pushing and grasping actions with the deep Q-network, which make it have the ability of active exploration, so that the trained manipulator can make the scenes less stacking and occlusion, and with the help of that, it can perform well in more complicated grasping scenes. In addition, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before.

The importance of virus disease outbreaks and its prevention is of growing public concern but our understanding of virus transmission routes is limited by adequate sampling strategies. While conventional swabbing methods provide merely a microbial snapshot, an ideal sampling strategy would allow reliable collection of viral genomic data over longer time periods. This study has evaluated a new, paper-based sticker approach for collection of reliable viral genomic data over longer time periods up to 14 days and after implementation of different hygiene measures. In contrast to swabbing methods, which sample viral load present on a surface at a given time, the paper-based stickers are attached to the surface area of interest and collect viruses that would have otherwise been transferred onto that surface. The major advantage of one-side adhesive stickers is that they are permanently attachable to a variety of surfaces. Initial results demonstrate that stickers permit stable recovery characteristics, even at low virus titers. Stickers also allow reliable virus detection after implementation of routine hygiene measures and over longer periods up to 14 days. Overall, results for this new sticker approach for virus genomic data collection are encouraging, but further studies are required to confirm anticipated benefits over a range of virus types.

Nearly 70 years old dream of incorporating molecule as the device element is still challenged by competing defects in almost every experimentally tested molecular device approach. This paper focuses on the magnetic tunnel junction (MTJ) based molecular spintronics device (MTJMSD) method. An MTJMSD utilizes a tunnel barrier to ensure a robust and mass-producible physical gap between two ferromagnetic electrodes. MTJMSD approach may benefit from MTJ's industrial practices; however, the MTJMSD approach still needs to overcome additional challenges arising from the inclusion of magnetic molecules in conjunction with competing defects. Molecular device channels are covalently bonded between two ferromagnets across the insulating barrier. An insulating barrier may possess a variety of potential defects arising during the fabrication or operational phase. This paper describes an experimental and theoretical study of molecular coupling between ferromagnets in the presence of the competing coupling via an insulating tunnel barrier. We discuss the experimental observations of hillocks and pinhole-type defects producing inter-layer coupling that compete with molecular device elements. We performed theoretical simulations to encompass a wide range of competition between molecules and defects. Monte Carlo Simulation (MCS) was used for investigating the defect-induced inter-layer coupling on MTJMSD. Our research may help understand and design molecular spintronics devices utilizing various insulating spacers such as aluminum oxide (AlOx) and magnesium oxide (MgO) on a wide range of metal electrodes. This paper intends to provide practical insights for researchers intending to investigate the molecular device properties via the MTJMSD approach and do not have a background in magnetic tunnel junction fabrication.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Electromagnetic spectrum detection is the basis of the next generation wireless communication technology. Wireless signal identification is an important part of electromagnetic spectrum detection and management activities. This paper proposes to extract the distribution features of different modulated signals from the signal I/Q data. A two-dimensional gradient matrix is used to describe the characteristics of the signal classification. The minimum gradient cumulative distance (GCD) estimate between the sample and the model is used as the decision criterion for the signal classification. According to the result of the confusion matrix, the weight of the model is adjusted. Experiments show that the recognition rate of the modulated signal mentioned in this paper can reach 82.75%. The I/Q data sample was extracted under actual engineering conditions involving random noise, and the recognition rate dropped to approximately 79%. Based on the initial model gradient matrix, a reasonable algorithm is set to adjust the weight of the model, which can effectively improve the recognition rate of the modulated signal.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Crop variety identification is an essential link in seed detection, phenotype collection and scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The traditional image processing method of peanut variety identification needs to extract many features, which has defects such as intense subjectivity and insufficient generalization ability. Based on the deep learning technology, this paper improved the deep convolutional neural network VGG16 and applied the improved VGG16 to the identification and classification task of 12 varieties of peanuts. Firstly, the peanut pod images of 12 varieties obtained by the scanner were preprocessed with gray-scale, binarization, and ROI extraction to form a peanut pod data set with a total of 3365 images of 12 varieties. A series of improvements have been made to VGG16. Remove the F6 and F7 fully connected layers of VGG16. Add Conv6 and Global Average Pooling Layer. The three convolutional layers of conv5 have changed into Depth Concatenation and add the Batch Normalization(BN) layers to the model. Besides, fine-tuning is carried out based on the improved VGG16. We adjusted the location of the BN layers. Adjust the number of filters for Conv6. Finally, the improved VGG16 model's training test results were compared with the other classic models, AlexNet, VGG16, GoogLeNet, ResNet18, ResNet50, SqueezeNet, DenseNet201 and MobileNetv2 verify its superiority. The average accuracy of the improved VGG16 model on the peanut pods test set was 96.7%, which was 8.9% higher than that of VGG16, and 1.6–12.3% higher than that of other classical models. Besides, supplementary experiments were carried out to prove the robustness and generality of the improved VGG16. The improved VGG16 was applied to the identification and classification of seven corn grain varieties with the same method and an average accuracy of 90.1% was achieved. The experimental results show that the improved VGG16 proposed in this paper can identify and classify peanut pods of different varieties, proving the feasibility of a convolutional neural network in variety identification and classification. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

This paper investigates how the topological structure of the technological spillover network among agents affects the adoption of a new clean technology and the reduction of system’s carbon emissions. Through building a systematic technology adoption model with technological spillover effect among agents from the network perspective, this paper first illustrates how the new technology diffuses from the earlier adopters to the later adopters under different network topological structures. Further, this paper examines how the carbon emission constraints imposed on pilot agents affect the carbon emissions of other agents and the entire system under different network topological structures. Simulation results of our study suggest that, (1) different topological structures of the technological spillover network have great influence on the adoption and diffusion of a new advanced technology; (2) imposing carbon emission constraints on pilot agents can reduce carbon emissions of other agents and thereby the entire system. However, the effectiveness of the carbon emission constraints is also largely determined by the network topological structures. Our study implies that the empirical research of the network topological structure among the participating entities is a pre-requisite to evaluate the real effectiveness of a carbon emission reduction policy from the system perspective.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.