bluuebunny/crossref_metadata_2025 huggingface dataset:
Broadband achromatic electromagnetic beam control based on reflective metamaterial
Authors: Zhang Simeng , Li Chenxia
Published: May 2023

The emergence of metasurfaces has shown great potential in planar optics. From visible light to infrared wavelengths, various forms of metasurfaces have been studied to control electromagnetic waves. Due to the dispersive properties of light waves, the application of metasurfaces in broadband devices is limited. We design a metasurface for broadband efficient achromatic transmission in the terahertz band. After optimized design, an achromatic deflector and an achromatic focusing device were obtained. By changing the geometric parameters of the metal ring to meet the phase and dispersion requirements of the metasurface, broadband achromatic metasurfaces and focusing metasurfaces are realized in the range of 1.8–2.3 THz.
Salient Object Detection From Unlabeled Images
Authors: Li Sentan , He Xiaoye , Wang Liyang , Huang Xiaoming
Published: May 2023

Salient object detection via deep neural networks usually needs a large amount of images with human annotation. To avoid laborious and consuming annotation, we propose a robust unsupervised salient object detection method with three stages in this work. Our method first uses unlabeled data to generate an activation map which indicates the coarse location of object. Then one scribble generation method based on the activation map is proposed, which provides foreground scribble and background scribble with high confidence. Finally, a salient object detection model is trained with the supervision of the generated scribble. Performance comparison is carried on four public datasets, showing that our method significantly outperforms state-of-the-art unsupervised methods, and also achieves better performance than some weakly supervised methods.
A Review of Multi-Label Feature Selection
Authors: Wang Xiujuan , Zhou Yuchen
Published: May 2023

Feature selection has become a vital issue in data mining and machine learning. But some challenges have been outstanding when trying to improve the performance of feature selection, such as small sample, uncertain classes, complex features, complementation and redundancy between each feature. In this paper, firstly the background of feature selection is introduced. Then we have presented a new perspective to analyze multi-label feature selection and provided typical papers on different classifications. To further analyze these algorithms, evaluation criterion on results of multi-label feature selection is summarized. Finally, some reflects on research directions, future works and conclusions are organized.
UAV Swarm Cooperative Situation Awareness Theory Model Construction and Scenario Analysis
Authors: Gao Yang , Lv Na
Published: May 2023

The Unmanned Aerial Vehicle (UAV) swarm collaborative operations is one of the disruptive technologies in future information warfare. Considering the characteristics of UAV swarm collaborative operation, we propose two UAV swarm cooperative situation awareness (SA) theory models based on the classical ones and the composition of UAV swarm. Firstly, the improved Endsley three-level SA model is proposed, in which the traditional mental model is changed into human machine intelligent model on the individual level. Then the homogeneous swarm and heterogeneous swarm cooperative SA model are separately built based on the improved team SA model and Distributed SA (DSA) model. Meanwhile, a comparison between the multi-UAV and UAV swarm is carried. Finally, a scenario analysis of swarm cooperative SA in combat situation is presented.
THT: Transfer Head and Tail for Joint Entity Relation Extraction
Authors: Zou Huike , Jiang Jiawei , Fu Yan
Published: May 2023

Extracting entities and relations from unstructured text is the basic task of building knowledge graphs. Various methods based on sequence labeling have been proposed recently, but most of them use uniform token representations to identify entities. It is clear that the first and last words of the head and tail entities should have different representations. For this reason, we propose a method based on word interaction that provides different representations for the first and last words of entities. Specifically, initial token representation is obtained by BERT. Each token interacts with the next k tokens for the entity’s first token recognition, interacts with the previous k tokens for the entity’s last token recognition, and merges them to obtain the head entity. Then recognize the tail entities based on the head entities and form entity pairs. Finally, get the triples based on a simple multi-classification for relation determination. Extensive experimental results on four datasets show that THT performs better than state-of-the-art baselines.
AVC Planning Strategy of Power Grid Based on Genetic Algorithm Improved Neural Network Quantile Regression
Authors: Lv Chenxu
Published: May 2023

Aiming at the security risk of power system instability, this paper proposes a parameter optimization AVC planning strategy based on neural network. Based on a variety of power grid operation modes, the strategy innovatively uses genetic algorithm to improve the neural network quantile regression control variables to obtain state variables. This method can reduce the frequency of invalid operations, make the AVC control strategy more stable, and improve system security. In the actual case, the improved neural network quantile regression of genetic algorithm improves the accuracy by at least 29% compared with multiple linear regression.
AJE: Attention Mechanism for Entity-relation Joint Extraction
Authors: Gui Wei , Cui Aixiang
Published: May 2023

Joint extraction of entities and relations is an significant issue of information extraction, which is very helpful for many downstream tasks, including knowledge base construction, question answering, and biomedical text diagnosis[1], etc. The common approach of existing models is to extract the subject and the relation first, then compute the subject and the relation to obtain the object, and finally, the triplet is judged. However, such an approach cannot efficiently handle of information extraction, and the results are not very good for Subject-Object Overlap (SOO) case. In this paper, a joint entity-relation extraction method AJE is proposed based on dot-product attention mechanism. The method first maps subject, object and relationship into three matrices of Q, K and V. After that, the attention weighting is achieved on these three matrices and the output vector is used to determine whether the triple is correct or not. The F1-score is used in experiments to show that the proposed model is more efficient than the current existing ones. It also has better results in handling other cases such as SOO, multi-triple problem, etc.
Intelligent Temperature Control of Pressure Gauge Calibration Based on ANT-LION Neural Network
Authors: Yang Longyu , Yang Jiejie , Xie Yanhui , Chai Yi , Ma Le
Published: May 2023

Aiming at the actual industrial requirements of high accuracy and high efficiency in the industrial process of pressure gauge calibration, this paper studies the intelligent temperature control of the pressure gauge calibration system. Based on the physical mechanism of the calibration system, the controlled object model is established. According to the characteristics of large time delay and nonlinearity of the calibration industrial process, a neural network PID control method based on ANT-LION optimization algorithm is proposed. The experimental results show that the method is effective and reliable, which improves the speed and stability of the temperature control of the pressure gauge calibration system, and realizes the intelligent control of the temperature of the calibration system.
Meta-Learned User Preference Estimator with Attention Network for Cold-Start Recommendation
Authors: Liu Shilong , Liu Yang , Zhang Xiaotong , Xu Cheng , He Jie , Qi Yue
Published: May 2023

One crucial challenge in the recommendation research field is the cold-start problem. Meta-learning is a feasible algorithm to reduce the error of cold-start recommendation because it can adjust to new tasks rapidly through relatively few updates. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. We proposed a recommendation model called attentional meta-learned user preference estimator that combines attention network and meta-learning. This method enhances the ability of modelling the personalized user interest by learning the weights between users and items based on attention mechanism, then improves the performance of cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%.
Deep metric learning based approach for network intrusion detection
Authors: Fu Xingbing , Zhang Xuewen , Fu Jianfeng , Wu Bingjin , Zhang Jianwu
Published: May 2023

Today, intrusion detection systems are the way to defend network intrusion flows. In this paper, to categorize network traffic data, we proposed a novel method for detecting network intrusions. It builds intrusion detection models using a deep metric learning (DML) strategy that incorporates two multi-scale convolutional neural networks (MSCNN) and a Triplet network. During the phase of training MSCNN networks, the network traffic data are divided into attack network traffic data and normal data, and we train two distinct MSCNN networks on the basis of these two datasets. To determine the distance between the network traffic data, a Triplet network is trained to learn a mapping space which preserves the relationships of attacks and normal network flows. Soft-margin triplet loss is used as a loss function to train the Triplet network. In the prediction stage, each new flow passes through two MSCNN networks, which reconstruct the network flows, and then the Triplet network measures the distance between the network flow and the reconstructed flow. To verify the model’s advantages in intrusion detection, we compare the performance of the MSCNN+Triplet network, the CNN+LSTM, and the shallow neural network. Experimental results reveal the proposed scheme has superior detection performance compared to other intrusion detection schemes. The code will be available at https://gitee.com/wbsk/mscnn_triplet/tree/master/.
Health Assessment of Flow Meter Calibration Device Based on Fuzzy Comprehensive Evaluation
Authors: Yang Jiejie , Yang Longyu , Chai Yi , Ma Le , Liu Qie
Published: May 2023

In view of the rapid development of related technologies in the field of computer and artificial intelligence at home and abroad, the health maintenance mode of the existing flowmeter calibration device is still very lagging behind. The actual composition structure and principle of the device are studied to determine the multi-level evaluation index of the device. Using the actual operation data of the calibration pipeline, the parameter feature vector is extracted as a healthy sample. By calculating the combined weight and fuzzy membership matrix of evaluation indexes at all levels, the membership matrix of health status is solved, and the health status of equipment is classified and evaluated according to fuzzy logic. The example analysis shows that the method can reflect the real health status of the equipment to a certain extent and improve the timeliness of the health assessment of the calibration device.
Hybrid Encoding Evolutionary Algorithm for Reversible Logic Synthesis
Authors: Wang Xiaoxiao , Wang Ziheng , Su Shengqiang , Wang Jinghao , Ji Yingchao
Published: May 2023

Variable-length encoding evolutionary algorithm has been proved effective in small and medium-sized reversible logic synthesis. Variable-length chromosome is adopted because the length of optimal reversible circuit is unknown. However, it leads to huge search space. The improved two-stage hybrid encoding synthesis is proposed to reduce the search space and enhance the feasible ratio. Fixed and variable-length encoding are applied in the two stages respectively. Estimated maximum length is obtained through heuristic information from the positive polarity Reed Muller spectra and the fixed chromosome length in the first stage is set to a value less than the maximum one. When the population converges in the first stage and the feasible solution has not found, the second stage starts to work. It employs the original variable-length encoding algorithm, but the optimization problem is simplified through the reduced positive polarity Reed Muller spectra of the best solution from the first stage. Experimental results show that both the fixed-length encoding and the simplified fitness function in the two stages both can decrease the search space and save the synthesis time.
Data Fusion Algorithm For Wireless Sensor Networks Based On Cluster Routing And Deep Learning
Authors: Hu Ting
Published: May 2023

In order to make full use of node energy and extend the network’s lifetime, a data fusion algorithm for wireless sensor networks is proposed in this paper. The algorithm improves the traditional clustering routing protocol and introduces a deep learning model to build the data fusion algorithm based on the optimized clustering architecture. When optimizing cluster routing protocol, the cluster head is selected by comprehensively considering such factors as node density, residual energy of nodes and the reasonable cluster size. The deep learning model and classification model are introduced in the data fusion process. The optimal parameters of the network are obtained by training the deep learning model of the sink node, then the sink nodes transfer these parameters to the corresponding sensor nodes. The cluster head extracts, classifies and fuses similar features of the data. The nodes in the cluster use the deep learning model to fit the collected raw data. Simulation results show that the data fusion algorithm proposed in this paper can reduce the redundant data transmitted to the sink node of wireless sensor networks, reduce the network energy consumption and prolong the network’s life. The overall performance of the network is improved.
A novel Tunnel Positioning Approach via Long Term Evolution Cellular Signal
Authors: Jia Huiqiang , Jia Kebin , Tian Xiuchen , Xiong Daoquan
Published: May 2023

Currently, Radio Frequency Identification System (RFIS), ZigBee and Ultra-Wide Band (UWB) methods are mainly used to positioning in enclosed space. But they require complex hardware layout and high hardware costs, resulting in the inability to meet the positioning needs of complex environments. Therefore, we designed a novel tunnel positioning approach via Long Term Evolution (LTE) cellular signal. This approach includes: signal acquisition, data preprocessing, feature database construction, model training and real-time positioning. In the data preprocessing stage, we adopt 3sigma and Kalman filtering to filter outliers and noise, and use information gain and information gain rate to select effective features. In the real-time positioning phase, a combination of K-Weighted Nearest Neighbor (KWNN) and Support Vector Regression (SVR) is used for positioning in the tunnel. To verify the designed approach, we did an experiment using data from the actual tunnel. The experimental results show that this approach has better positioning accuracy than FK-NN [1] and TSVR [2].
Exploring hand gesture recognition using micro-Doppler radar data based on vision transformers
Authors: Qu Chongxiao , Zhang Yongjin , Jin Lei , Fan Changjun , Liu Shuo , Chen Xiayan
Published: May 2023

Nowadays, radar technology is undergoing rapid development. Contrary to the old stereotype of radar systems being large, complex and mainly used for detecting targets at a long distance, small and compact radio frequency products are getting flourishing and widely applied. Benefiting from this, hand gesture recognition using a low-cost, low-power consuming radar is becoming an area of wide interest and study. In this paper, we explore using low-cost FMCW (Frequency Modulated Continuous Wave) radar modules for gesture recognition based on vision transformers. First, we pre-process the radar data and convert the 1-dimensional spectrogram complex samples into 2-dimensional matrices just like images. Then, we adopt an existing model based on vision transformers to classify them, which leverages existing state-of-the-art technologies for vision recognition problems to address our issue. Experimental results show that our scheme yields good performance and it is a promising method.
Natural Gas Load forecasting based on the fusion of VMD-SE and CNN-BiLSTM-Attention
Authors: Bian Genqing , Yang Yang , Guo Xusen , Qu Wenjing , Xi Wenjing
Published: May 2023

The rational dispatch and steady supply of natural gas resources can be effectively supported by accurate forecasting of the natural gas load. The accuracy of natural gas load forecasting is, however, low due to the fundamental characteristics of the non-stationary and non-linear natural gas load. Based on this, this paper proposes a new natural gas load forecasting model based on the fusion of VMD-SE and CNN-BiLSTM-Attention. First, the average instantaneous frequency method is used to determine the optimal K value of VMD, and then the sample entropy method is introduced to reconstruct the sub-sequence of VMD decomposition, which effectively reduces the computational overhead of the algorithm. Then, using CNN to mine the level information of multi-dimensional time series variables, using BiLSTM to mine the time series feature information, and introducing the Attention mechanism to give corresponding weight to different feature factors, highlighting the influence of key factors, the anticipated values of each reconstructed sub-series are superimposed to provide the final predicted value of the natural gas load. The model proposed in this paper has lower error than other forecasting models, can effectively improve the precision of natural gas load forecasting, and has a better forecasting effect, according to the results of establishing various forecasting models using the natural gas load data of one region of China as an example.
A Novel Disease Prediction Model Based on Blockchain and Federated Learning
Authors: Wang Bowen , Cheng Ziwen , Yi Liu , Cheng Zhu
Published: May 2023

With the advent of the era of big data, the model architecture of disease prediction has been transformed from cloud computing architecture to edge computing architecture to alleviate problems, such as single point of failure, huge burden of computing and long communication delays.However, due to the lack of trust among edge computing participants (herein referred to as medical institutions with data resources or data storage nodes), the patients’ personal privacy and security issues are still challenging, and there is an urgent need to propose new solutions with higher security. In this paper, we proposed a new disease prediction model with the edge computing framework based on blockchain and federated learning technology to realise full mining of data value, as well as protecting local data sharing security and privacy. In the process of federated learning with blockchain as the underlying platform working environment, we propose the concept of model accuracy as the selection indicator of data nodes in federated learning, which improves the accuracy of the federated learning model and optimizes the training delay.
Research and Design of Real Time Big Data Visualization Analysis Platform Based on Flink
Authors: Zhang Hang
Published: May 2023

Real-time big data analysis provides a better value than standard offline big data analysis. Academic groups are still researching and exploring the technologies of real-time big data analysis. Due to the vast scale and poor transmission rate of big data, there are several impediments in the research process, making it hard to fully access the potential value of data.A real-time large data visualization analysis platform based on Flink is presented to overcome this challenge.The platform first collects data in real time and sends it to the message middleware Kafka, and then the big data analysis platform reads the data stream in Kafka in real time and transmits it to the multidimensional operator developed with Flink as the framework for real-time Calculate, and uses WebSocket technology to push the calculation results to the front end in real time, and perform visual display, enabling real-time monitoring and analysis of data flow.The experimental findings suggest that the real-time big data analysis platform based on the integration of Flink and Kafka can accurately, promptly, and clearly portray the real-time data flow process and visual analysis.
Sentiment Analysis of Social Network Text Based on HDBSCAN and SO-PMI
Authors: Yang Yuxuan , Wei Yingmei , Gao Min , Ran Zanxi , Wang Qi
Published: May 2023

With the rapid development of the Internet, the length and themes of text content in social network environments are becoming more and more abundant. The probability and degree of semantic similarity between different words in different texts are also increasing. It is difficult to effectively identify text topics and their sentiment tendencies. This paper takes the text data of the Ford Forum of the PCauto as the research object, builds a word vector model based on Word2vec, uses HDBSCAN to classify social network context, and calculates the sentiment value of different topic through SO-PMI. It aims to explore the complexity of the semantic evolution and feature extraction process and its generating text topics, based on which we further analyze the variability of sentiment tendencies between words and topics and then summarize their characteristics.
Application of VMD-LSTM in Water Quality Prediction
Authors: Tao Dongqing , Yang Yun , Cai Zhi , Duan Junlong , Lan Hao
Published: May 2023

Water is an indispensable prerequisite and essential factor for human life and social development. Change in water quality is closely related to water pollution. Water quality prediction is the basic work to control water environmental pollution. Therefore, the establishment of effective models is of great value to social economy, environmental protection and human health. This paper aims at the problem that the prediction error of relative maximum and relative minimum values of Long Short-Term Memory (LSTM) in dealing with time series is large, combined with the advantages of Variational Mode Decomposition(VMD) to extract signal fluctuation characteristics and reduce noise interference, Taking the Dissolved oxygen(DO) and the Total phosphorus(TP) as the main research objects, A combined model based on VMD-LSTM is proposed. The three indexes of Mean Absolute Error(MAE), Root Mean Square Error(RMSE) and Mean Absolute Percentage Error(MAPE) were used to judge. Experiments show that the VMD-LSTM model has improved all indicators compared with the LSTM model. DO prediction results respectively reduced by 38.8%, 41.4% and 44.4%, TP prediction results respectively reduced by 30.4%, 34.4% and 32.2%, the validity of the model is verified.
Graph-Clustering Anonymity Privacy Protection Algorithm With Fused Distance-Attributes
Authors: Wang Zhenxin , Liu Tao , Wang Yujie , Bao Xianglin , Xu Xiaofeng , Huang Xiangxiang , Cheng Bin
Published: May 2023

Clustering anonymity is a common social network data privacy protection scheme, which is based on graph-clustering. Many existing graph clustering methods mainly focus on the relationship between the structure and attributes of nodes, and the difference between them due to the metric usually causes the problem of poor clustering results. To address the shortcomings in the above graph-clustering methods, a graph-clustering anonymity method implemented with fused distance-attributes (GCA-DA) is proposed. Firstly, the algorithm quantifies the distance and attribute similarity between nodes separately and balances the metric differences between them to calculate the integrated similarity. Then all the nodes in the graph are clustered into clusters according to the integrated similarity between two nodes, each of which contains no fewer than k nodes. Finally, all the clusters are anonymized. In this method, the attribute generalization for every cluster can prevent attacks by the background knowledge of structure and attributes. In addition, the attributes are divided into numerical and non-numerical attributes to measure them separately, therefore can maintain the usability of the data better. Experiment results demonstrate the effectiveness in improving the quality of clustering and reducing information loss.
Index Correlation Analysis in Water Quality Monitoring Big Data
Authors: Li Hao , Yu Ziwen , Song Lingyu , Chen Xia , Tan WenWu , Li Peilun , Zhang Jianjun , Xiao Ke , Wang Li , Lin Haijun
Published: May 2023

Among the water quality indicators, permanganate and turbidity are important indicators to reflect the pollution status of water bodies. In order to study the correlation between the two, the water quality monitoring data of relevant water areas were obtained by designing a web crawler, and the water quality monitoring data set was constructed. After the data was cleaned, the correlation analysis was carried out. The experimental results show that there is a big difference in the correlation coefficient between the two indicators at different periods of the same monitoring point. The correlation between the two indicators in the abundant-water season is greater than that in the flat-water season, and the correlation between them in the flat-water season is greater than that in the poor-water season. Among them, there was a high positive correlation between the two indicators during the abundant-water season, and there is little correlation between them during the poor-water season.
A Multi-Objective Cloud Workflow Scheduling Strategy Based on the Modified HEFT Algorithm
Authors: Wang Heng , Deng Jianguang
Published: May 2023

Aiming at the issues of poor system resource utilization and the challenge of ensuring the quality of service (QoS) in scientific workflow scheduling in the current cloud computing environment. By combining the existing IaaS cloud resource model with the scientific workflow task model, the article proposed a cloud workflow scheduling model framework. Firstly, proposed multi-objective QoS constraints based on the execution time and costs in scientific workflow scheduling. Then, modified the HEFT algorithm which was a typical scientific workflow scheduling algorithm. Based on the modified HEFT algorithm makes full use of the idle time of resources and maximizes the multi-objective QoS requirements of users. The comparative experiments show that the scheduling strategy of the modified HEFT algorithm can effectively optimize the costs and execution time of scientific workflow scheduling and achieve higher system resource utilization.
Differential-Linear Cryptanalsis on SIMECK32/64 and SIMON32/64
Authors: Zhang Feng , Li Feng , Zhang Wenzheng
Published: May 2023

In this paper, we give differential-linear cryptanalysis of SIMON, which is a family of lightweight block ciphers published by the National Security Agency, and SIMECK, which is a family of lightweight block ciphers proposed by Yang et al. Firstly, all input difference and output masks with one active bit are traversed to obtain a 9-round SIMON32/64 differential-linear distinguisher and a 10-round SIMECK32/64 differential-linear distinguisher. Then, a 12-round SIMON32/64 differential-linear distinguisher with bias 2−12.69 and a 13-round SIMECK32/64 differential-linear distinguisher with bias 2−14.03 can be obtained by searching one round of differential characteristics forward and two rounds of linear approximations backward. The dynamic key guessing technique proposed by Wang et al. has excellent advantages in the SIMON-like cipher key recovery process. Therefore, we have applied it to differential-linear cryptanalysis. Then, the 12-round SIMON32/64 differential-linear distinguisher is extended forward by four rounds and backward by four rounds to attack the 20-round SIMON32/64 with time complexity 255.68 and data complexity 228. And the 13-round SIMECk32/64 differential-linear distinguisher is extended forward by four rounds and backward by four rounds to attack the 21-round SIMECK32/64 with time complexity 250.67 and data complexity 230. These are the best differential-linear cryptanalysis results for SIMON32/64 and SIMECK32/64 in the open literature.
Design of a composite collimator for the hard X-ray camera in HL-2M tokamak
Authors: Chen Hao , Zhao Chaoyang , Wang Zhonghai , Li Lingli , Zhou Yanhan , Zhang Jie , Xu Hongbing , Cheng Shikui , Zhu Yuxuan , Zhang Yipo
Published: May 2023

A new hard X-ray (HXR) camera is being designed for the
HuanLiu-2M (HL-2M) tokamak to measure the spatial-temporal evolution
of the fast electrons in the energy range of 20–200 keV during
radio frequency (RF) drive or heating experiments. The new camera
integrates 63 channels of independent detectors consisting of 63
lutetium yttrium oxyorthosilicate scintillators (LYSO) coupled to
silicon photomultipliers (SiPMs). A composite collimator (pinhole +
collimating tube) is designed for the HXR camera to arrange the 63
channels in a compact space and reduce background rays. This paper
introduces the design of composite collimator and the calculation of
geometric parameters of the pinhole and collimating tube. The
geometric factor uniformity (GFU) and effective count
rate (ECR) of the composite collimator were evaluated by
Geant4 simulation for different pinhole inner wall thicknesses
 T
1. The results show that when the energy is 200 keV,
 GFU increases and ECR increases and then
decreases when reducing T
1. The optimal T
1 is 2 mm, and
the resulting GFU is 0.83 and ECR is 78.2%
for 200 keV.
Free radical scavenging effects of grapefruit essential oil nanoemulsion stabilized with carrageenan and its cytotoxicity assay on HeLa cell line
Authors: Watcharin Waralee , Gupta Suman , Saning Amonrada , Laodheerasiri Sireerat , Chuenchom Laemthong
Published: May 2023

Grapefruit peel essential oil exhibited antioxidant and antibacterial activities attributed to their phenolic and flavonoid compounds. In this study, nanoemulsion of grapefruit peel essential oil and carrageenan was investigated for their antioxidant properties. The oil in water (o/w) emulsion was formulated with 0.5% and 1.0% (w/v) grapefruit peel essential oil and carrageenan. Carrageenan was utilised as an encapsulating agent to decrease volatility of essential oil and increase its stability. The resulting nanoemulsion was characterised using transmission electron microscopes (TEM), ultraviolet–visible (UV–vis) spectrophotometry and Fourier-transform infrared spectroscopy (FTIR). Nanoemulsion formulated with 0.5% grapefruit peel essential oil exhibited strong radical scavenging 2,2-diphenyl-1-picrylhydrazyl (DPPH) effects showing the highest half maximal effective concentration value (EC50) at 83.4 ± 1.4 mg l−1 and revealed the highest total phenolic content (80.1 ± 11.8 mg GAE/g) in comparison with 1.0% grapefruit peel essential oil-nanoemulsion and free grapefruit peel essential oil (2.0% w/v). The cytotoxicity of nanoemulsion prepared from grapefruit peel essential oil against HeLa cells also exhibited higher than 90% of cell viability, which was not affected by the formulation of nanoemulsion. The incorporation of grapefruit peel essential oil into nanoemulsion improved its stability while retaining its bioactivity and non-toxicity.
Stretchable kirigami-inspired conductive polymers for strain sensors applications
Authors: Abbasipour Mina , Kateb Pierre , Cicoira Fabio , Pasini Damiano
Published: May 2023

Kirigami metamaterials can be exploited in stretchable electronics owing to their architecture, which can be leveraged to amplify stretchability, bendability and deformability. Herein, we report a stretchable kirigami-structured poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS)/polydimethylsiloxane (PDMS) polymer composite. The electromechanical response and mechanical behavior of kirigami PEDOT:PSS-coated PDMS and polymer composite specimens were investigated and compared with their non-kirigami counterparts. The kirigami structure exhibited improved electromechanical properties owing to its characteristic architecture. This study illustrates the application of a kirigami polymer composite as a strain sensor for human motion detection.
Temperature-induced hysteresis in amplification and attenuation of surface-plasmon-polariton waves
Authors: Mackay Tom G , Son Tran Vinh , Haché Alain , Lakhtakia Akhlesh
Published: May 2023

The propagation of surface-plasmon-polariton (SPP) waves at the planar interface of a metal and a dielectric material was investigated for a dielectric material with strongly temperature-dependent constitutive properties. The metal was silver and the dielectric material was vanadium multioxide impregnated with a combination of active dyes. Depending upon the volume fraction of vanadium multioxide, either attenuation or amplification of the SPP waves may be achieved; the degree of attenuation or amplification is strongly dependent on both the temperature and whether the temperature is increasing or decreasing. At intermediate volume fractions of vanadium multioxide, for a fixed temperature, a SPP wave may experience attenuation if the temperature is increasing but experience amplification if the temperature is decreasing.
Climate concern elasticity of carbon footprint
Authors: Leferink Enar Kornelius , Heinonen Jukka , Ala-Mantila Sanna , Árnadóttir Áróra
Published: May 2023

The income elasticity of carbon footprint is a summary variable often used to describe the relationship between income and carbon footprints. Previous studies primarily calculate this elasticity using emissions intensities per monetary unit. However, this study is based on a survey conducted in Nordic countries which allows us to directly calculate carbon footprints from responses about quantity and types of activities instead of from spending. As a result, we curtail an inbuilt relationship between income and carbon footprints. As a result, our method produces an income elasticity estimate that is approximately one-fourth of the highest estimates that exist, and 30% lower than the smallest current estimate. Furthermore, we introduce a new summary variable called the climate concern elasticity of carbon footprint. This variable provides a simple method to analyze the relationship between pro-climate attitudes, pro-climate behavior, and carbon footprints. This new parameter can serve as a framework that identifies key areas where the investigation of the relationship between people’s climate concern and their carbon footprint may be most useful. This framework and improved knowledge of income elasticities can guide policymakers and future research and provide new methods to estimate carbon footprint distributions.
Robotic in situ bioprinting for cartilage tissue engineering
Authors: Wang Yaxin , Pereira Rúben F , Peach Chris , Huang Boyang , Vyas Cian , Bartolo Paulo
Published: May 2023

Articular cartilage damage caused by trauma or degenerative pathologies such as osteoarthritis can result in significant pain, mobility issues, and disability. Current surgical treatments have a limited capacity for efficacious cartilage repair, and long-term patient outcomes are not satisfying. Three-dimensional bioprinting has been used to fabricate biochemical and biophysical environments that aim to recapitulate the native microenvironment and promote tissue regeneration. However, conventional in vitro bioprinting has limitations due to the challenges associated with the fabrication and implantation of bioprinted constructs and their integration with the native cartilage tissue. In situ bioprinting is a novel strategy to directly deliver bioinks to the desired anatomical site and has the potential to overcome major shortcomings associated with conventional bioprinting. In this review, we focus on the new frontier of robotic-assisted in situ bioprinting surgical systems for cartilage regeneration. We outline existing clinical approaches and the utilization of robotic-assisted surgical systems. Handheld and robotic-assisted in situ bioprinting techniques including minimally invasive and non-invasive approaches are defined and presented. Finally, we discuss the challenges and potential future perspectives of in situ bioprinting for cartilage applications.
Strain localization in wearable integrated electronics using dot pattern
Authors: Hajizadeh Pouria , Naserifar Naser
Published: May 2023

Sensors and actuators can now achieve high levels of stretchability and functionality owning to the recent development of stretchable electronics. One main factor in creating high-performance stretchable electronics that can be adequately transferred to curved surfaces, such as human skin, is the conformal design of the island-bridge model. In island-bridge models, interconnect conductors (bridges) are the most vulnerable. Thus, it is necessary to preserve the durability and functionality of interconnect conductors under high strains. In this study, by transferring dots to the main substrate, the interconnect conductors can be protected. This causes the strain to become localized in the region between the two neighboring islands. This region is known as safe region. In the safe region, the strains bypass the other side of the stretchable electronic from the upper and lower parts. This objective was achieved using the experimental tensile test and finite element analysis. These results displayed a 4% − 9% reduction in the average strain of safe region while applying 10% − 30% strain. The design parameters were optimized with energy release rate measurement. Additionally, the interconnect conductors’ path in the safe region was optimized improving this value to at least 50% below its applied strain. This design made the stretchable electronic significantly durable while maintaining its functionality.
Shape sensing of optical fiber Bragg gratings based on deep learning
Authors: Manavi Roodsari Samaneh , Huck-Horvath Antal , Freund Sara , Zam Azhar , Rauter Georg , Schade Wolfgang , Cattin Philippe C
Published: May 2023

Continuum robots in robot-assisted minimally invasive surgeries provide adequate access to target anatomies that are not directly reachable through small incisions. Achieving precise and reliable shape estimation of such snake-like manipulators necessitates an accurate navigation system, that requires no line-of-sight and is immune to electromagnetic noise. Fiber Bragg grating (FBG) shape sensing, particularly eccentric FBG (eFBG), is a promising and cost-effective solution for this task. However, in eFBG sensors, the spectral intensity of the Bragg wavelengths that carries the strain information can be affected by undesired bending-induced phenomena, making standard characterization techniques less suitable for these sensors. We showed in our previous work that a deep learning model has the potential to extract the strain information from the eFBG sensor’s spectrum and accurately predict its shape. In this paper, we conducted a more thorough investigation to find a suitable architectural design of the deep learning model to further increase shape prediction accuracy. We used the Hyperband algorithm to search for optimal hyperparameters in two steps. First, we limited the search space to layer settings of the network, from which, the best-performing configuration was selected. Then, we modified the search space for tuning the training and loss calculation hyperparameters. We also analyzed various data transformations on the network’s input and output variables, as data rescaling can directly influence the model’s performance. Additionally, we performed discriminative training using the Siamese network architecture that employs two convolutional neural networks (CNN) with identical parameters to learn similarity metrics between the spectra of similar target values. The best-performing network architecture among all evaluated configurations can predict the shape of a 30 cm long sensor with a median tip error of 3.11 mm in a curvature range of 1.4 m−1 to 35.3 m−1.
Low-dimensional optoelectronic synaptic devices for neuromorphic vision sensors
Authors: Lv Chengzhai , Zhang Fanqing , Li Chunyang , Li Zhongyi , Zhao Jing
Published: May 2023

Neuromorphic systems represent a promising avenue for the development of the next generation of artificial intelligence hardware. Machine vision, one of the cores in artificial intelligence, requires system-level support with low power consumption, low latency, and parallel computing. Neuromorphic vision sensors provide an efficient solution for machine vision by simulating the structure and function of the biological retina. Optoelectronic synapses, which use light as the main means to achieve the dual functions of photosensitivity and synapse, are the basic units of the neuromorphic vision sensor. Therefore, it is necessary to develop various optoelectronic synaptic devices to expand the application scenarios of neuromorphic vision systems. This review compares the structure and function for both biological and artificial retina systems, and introduces various optoelectronic synaptic devices based on low-dimensional materials and working mechanisms. In addition, advanced applications of optoelectronic synapses as neuromorphic vision sensors are comprehensively summarized. Finally, the challenges and prospects in this field are briefly discussed.
Iterative solution of spatial network models by subspace decomposition
Authors: Görtz Morgan , Hellman Fredrik , Målqvist Axel
Published: May 2023

We present and analyze a preconditioned conjugate gradient method (PCG) for solving spatial network problems. Primarily, we consider diffusion and structural mechanics simulations for fiber based materials, but the methodology can be applied to a wide range of models, fulfilling a set of abstract assumptions. The proposed method builds on a classical subspace decomposition into a coarse subspace, realized as the restriction of a finite element space to the nodes of the spatial network, and localized subspaces with support on mesh stars. The main contribution of this work is the convergence analysis of the proposed method. The analysis translates results from finite element theory, including interpolation bounds, to the spatial network setting. A convergence rate of the PCG algorithm, only depending on global bounds of the operator and homogeneity, connectivity and locality constants of the network, is established. The theoretical results are confirmed by several numerical experiments.
The Borel partition spectrum at successors of singular cardinals
Authors: Brian Will
Published: May 2023

Assuming that

0
†

0^\dagger

does not exist, we prove that if there is a partition of

R

\mathbb R

into

ℵ
ω

\aleph _\omega

Borel sets, then there is also a partition of

R

\mathbb R

into

ℵ

ω
+
1

\aleph _{\omega +1}

Borel sets.
Parenteral medication considerations for the ketogenic diet
Authors: Abu-Sawwa Renad , Busque Katherine , Cokley Jon
Published: May 2023

Purpose

This initiative conducted a needs assessment regarding the extent of potential risk for accidental carbohydrate exposure in patients on the ketogenic diet in acute care settings at 2 academic medical centers.

Medications used in the emergency department, intensive care unit, or operating room can contain carbohydrates or be diluted in carbohydrate-containing fluids. Use of these medications can shift patients on the ketogenic diet out of ketosis, causing breakthrough seizures. Despite standard clinical practices, there are no consensus guidelines to date for the logistical management of these patients during hospital admissions. This lack of standardized management increases the risk for parenteral medication errors during transitions within the healthcare system. A review of the literature demonstrates increased medication safety errors compounded by this lack of systemwide endeavors. Initiatives enhancing provider education and quality improvement safety measures have been reported; however, the extent of the potential risk with regard to medication formulation has not been assessed. Fifty medications were evaluated for their potential risk for carbohydrate exposure in a real-world quality improvement needs assessment conducted at 2 academic medical centers.

Conclusion

Because of increased exposure to carbohydrate-containing medications and medication safety errors, the authors recommend developing institutional protocols, an order set in the electronic medical record, and a multidisciplinary approach for patients on the ketogenic diet. Further research is warranted to assess the impact of these quality improvement measures on safety and clinical outcomes and to justify the development and implementation of consensus guidelines in centers of excellence that serve these patients.
Developmental dynamics of crassulacean acid metabolism (CAM) in Opuntia ficus-indica
Authors: Niechayev Nicholas A , Mayer Jesse A , Cushman John C
Published: May 2023

Background and Aims

The relative contributions of C3 photosynthesis and crassulacean acid metabolism (CAM) during the earliest stages of development were investigated to assess how much each might contribute to cactus pear (Opuntia ficus-indica) productivity.

Methods

The developmental progression of C3 photosynthesis and CAM was assessed in seedlings and daughter cladodes of mature plants by titratable acidity, δ13C isotopic values and diel gas exchange measurements.

Key Results

Nocturnal acidification was observed in seedling cladodes and cotyledons at the earliest stages of development and became highly significant by 75 days of development. Seedling cotyledons showed mean δ13C values of −21.4 and −17.1 ‰ at 30 and 100 days of age, respectively. Seedling cladodes showed mean δ13C values of −19.4 and −14.5 ‰ at 30 and 100 days of age, respectively. These values are typical of CAM plants. Net CO2 assimilation was negative, then occurred in both the day and the night, with nighttime fixation becoming predominant once the primary cladode reached 5 cm in size. Emergent daughter cladodes growing on mature plants showed nocturnal titratable acidity at the earliest stages of development, which became significant when daughter cladodes were >2.5–5 cm in height. Emergent daughter cladodes showed mean δ13C values of −14.5 to −15.6 ‰, typical of CAM plants. CO2 assimilation studies revealed that net CO2 uptake was negative in daughter cladodes <12 cm in length, but then exhibited net positive CO2 assimilation in both the day and the night, with net nocturnal CO2 assimilation predominating once the daughter cladode grew larger.

Conclusions

Developing O. ficus-indica primary and daughter cladodes begin as respiring sink tissues that transition directly to performing CAM once net positive CO2 fixation is observed. Overall, these results demonstrate that CAM is the primary form of photosynthetic carbon assimilation for O. ficus-indica even at the earliest stages of seedling or daughter cladode development.