bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Knowledge of Hepatitis E Virus (HEV) Epidemiology in Ruminants
Authors: Di Profio Federica , Sarchese Vittorio , Palombieri Andrea , Fruci Paola , Lanave Gianvito , Robetto Serena , Martella Vito , Di Martino Barbara
Published: September 2022

Hepatitis E virus (HEV) infection represents an emerging public health concern worldwide. In industrialized countries, increasing numbers of autochthonous cases of human HEV infection are caused by zoonotic transmission of genotypes 3 and 4, mainly through the consumption of contaminated raw or undercooked meat of infected pigs and wild boars, which are considered the main reservoirs of HEV. However, in the last few years, accumulating evidence seems to indicate that several other animals, including different ruminant species, may harbor HEV. Understanding the impact of HEV infection in ruminants and identifying the risk factors affecting transmission among animals and to humans is critical in order to determine their role in the epidemiological cycle of HEV. In this review, we provide a summary of current knowledge on HEV ecology in ruminants. A growing body of evidence has revealed that these animal species may be potential important hosts of HEV, raising concerns about the possible implications for public health.
Unlocking the Hidden Genetic Diversity of Varicosaviruses, the Neglected Plant Rhabdoviruses
Authors: Bejerman Nicolas , Dietzgen Ralf G. , Debat Humberto
Published: September 2022

The genus Varicosavirus is one of six genera of plant-infecting rhabdoviruses. Varicosaviruses have non-enveloped, flexuous, rod-shaped virions and a negative-sense, single-stranded RNA genome. A distinguishing feature of varicosaviruses, which is shared with dichorhaviruses, is a bi-segmented genome. Before 2017, a sole varicosavirus was known and characterized, and then two more varicosaviruses were identified through high-throughput sequencing in 2017 and 2018. More recently, the number of known varicosaviruses has substantially increased in concert with the extensive use of high-throughput sequencing platforms and data mining approaches. The novel varicosaviruses have revealed not only sequence diversity, but also plasticity in terms of genome architecture, including a virus with a tentatively unsegmented genome. Here, we report the discovery of 45 novel varicosavirus genomes which were identified in publicly available metatranscriptomic data. The identification, assembly, and curation of the raw Sequence Read Archive reads has resulted in 39 viral genome sequences with full-length coding regions and 6 with nearly complete coding regions. The highlights of the obtained sequences include eight varicosaviruses with unsegmented genomes, which are linked to a phylogenetic clade associated with gymnosperms. These findings have resulted in the most complete phylogeny of varicosaviruses to date and shed new light on the phylogenetic relationships and evolutionary landscape of this group of plant rhabdoviruses. Thus, the extensive use of sequence data mining for virus discovery has allowed us to unlock of the hidden genetic diversity of varicosaviruses, the largely neglected plant rhabdoviruses.
Recent Advances in Ophthalmic Drug Delivery
Authors: Chauhan Anuj , Fitzhenry Laurence , Serro Ana Paula
Published: September 2022

Due to population aging and to the increasing prevalence of diseases such as diabetes, chronic eye disorders such as glaucoma, age-related macular degeneration, and diabetic retinopathy have increased significantly, becoming responsible for a high percentage of blindness and vision impairment cases at a global level [...]
Antimicrobial Peptides Active in In Vitro Models of Endodontic Bacterial Infections Modulate Inflammation in Human Cardiac Fibroblasts
Authors: Marianantoni Giulia , Meogrossi Giada , Tollapi Eva , Rencinai Alessandro , Brunetti Jlenia , Marruganti Crystal , Gaeta Carlo , Pini Alessandro , Bracci Luisa , Ferrari Marco , Grandini Simone , Falciani Chiara
Published: September 2022

Endodontic and periodontal disease are conditions of infectious origin that can lead to tooth loss or develop into systemic hyperinflammation, which may be associated with a wide variety of diseases, including cardiovascular. Endodontic and periodontal treatment often relies on antibiotics. Since new antimicrobial resistances are a major threat, the use of standard antibiotics is not recommended when the infection is only local. Antimicrobial peptides were recently demonstrated to be valid alternatives for dental treatments. The antimicrobial peptide M33D is a tetrabranched peptide active against Gram-negative and Gram-positive bacteria. It has a long life, unusual for peptides, because its branched form provides resistance to proteases. Here the efficacy of M33D and of its analog M33i/l as antibiotics for local use in dentistry was evaluated. M33D and M33i/l were active against reference strains and multidrug-resistant clinical isolates of Gram-negative and Gram-positive species. Their minimum inhibitory concentration against different strains of dental interest was between 0.4 and 6.0 μM. Both peptides acted rapidly on bacteria, impairing membrane function. They also disrupted biofilm effectively. Disinfection of the root canal is crucial for endodontic treatments. M33D and M33i/l reduced E. faecalis colonies to one-twentieth in a dentin slices model reproducing root canal irrigation. They both captured and neutralized lipopolysaccharide (LPS), a bacterial toxin responsible for inflammation. The release of IL-1β and TNFα by LPS-stimulated murine macrophages was reduced by both peptides. Human cardiac fibroblasts respond to different insults with the release of proinflammatory cytokines, and consequently, they are considered directly involved in atherogenic cardiovascular processes, including those triggered by infections. The presence of M33D and M33i/l at MIC concentration reduced IL6 release from LPS- stimulated human cardiac fibroblasts, hence proving to be promising in preventing bacteria-induced atherogenesis. The two peptides showed low toxicity to mammalian cells, with an EC50 one order of magnitude higher than the average MIC and low hemolytic activity. The development of antimicrobial peptides for dental irrigations and medication is a very promising new field of research that will provide tools to fight dental infections and their severe consequences, while at the same time protecting standard antibiotics from new outbreaks of antimicrobial resistance.
Proposal to Consider Chemical/Physical Microenvironment as a New Therapeutic Off-Target Approach
Authors: Giuliani Alessandro , Fais Stefano
Published: September 2022

The molecular revolution could lead drug discovery from chance observation to the rational design of new classes of drugs that could simultaneously be more effective and less toxic. Unfortunately, we are witnessing some failure in this sense, and the causes of the crisis involve a wide range of epistemological and scientific aspects. In pharmacology, one key point is the crisis of the paradigm the “magic bullet”, which is to design therapies based on specific molecular targets. Drug repurposing is one of the proposed ways out of the crisis and is based on the off-target effects of known drugs. Here, we propose the microenvironment as the ideal place to direct the off-targeting of known drugs. While it has been extensively investigated in tumors, the generation of a harsh microenvironment is also a phenotype of the vast majority of chronic diseases. The hostile microenvironment, on the one hand, reduces the efficacy of both chemical and biological drugs; on the other hand, it dictates a sort of “Darwinian” selection of those cells armed to survive in such hostile conditions. This opens the way to the consideration of the microenvironment as a convenient target for pharmacological action, with a clear example in proton pump inhibitors.
A Systematic Review of Independent and Chain Pharmacies Effects on Medication Adherence
Authors: Nind James , Smith Alesha , Scahill Shane , Marra Carlo A.
Published: September 2022

As the last step in the care pathway, pharmacies can significantly impact a patient’s medication adherence and the success of treatment. The potential impact of patient’s pharmacy choice on their medication adherence has yet to be established. This study aims to review the impact a pharmacies ownership model, either independent or chain, has on its users’ medication adherence. As a generalisation, independent pharmacies offer a more personal service and chain pharmacies offer medications at lower prices. A keyword search of EMBASE and MEDLINE databases in March 2022 identified 410 studies, of which 5 were deemed to meet our inclusion criteria. The studies mostly took place in North America, measured medication adherence using pharmacy records over a 12-month period. This review was unable to substantiate a difference in the rate of medication adherence between the users of independent and chain pharmacies. However, those with a lower income, greater medication burden, and increased age appeared to use an independent pharmacy more than a chain pharmacy and to have greater medication adherence when doing so. Establishing the differences in service provision between types of pharmacies and why people choose a pharmacy to frequent should be a focus of future research.
Introducing Quantum Technologies at Secondary School Level: Challenges and Potential Impact of an Online Extracurricular Course
Authors: Bondani Maria , Chiofalo Maria Luisa , Ercolessi Elisa , Macchiavello Chiara , Malgieri Massimiliano , Michelini Marisa , Mishina Oxana , Onorato Pasquale , Pallotta Filippo , Satanassi Sara , Stefanel Alberto , Sutrini Claudio , Testa Italo , Zuccarini Giacomo
Published: September 2022

Stimulated by the European project “QTEdu CSA”, within the flagship “Quantum Technologies”, a community of researchers active in the fields of quantum technologies and physics education has designed and implemented an extracurricular course on quantum physics concepts and quantum technologies applications for high school. The course, which featured eight interactive lectures, was organized online between March and May 2021 and attended by about 250 students from all over Italy. In this paper, we describe the main tenets and activities of the course. Moreover, we report on the effectiveness of the course on students’ knowledge of the basic concepts of quantum physics and students’ views about epistemic aspects and applications of quantum technologies. Results show that the designed activities were effective in improving students’ knowledge about fundamental aspects of quantum mechanics and familiarizing them with quantum technology applications.
Effects of Nitrogen Deficiency on the Metabolism of Organic Acids and Amino Acids in Oryza sativa
Authors: Chen Ling-Hua , Cheng Zu-Xin , Xu Ming , Yang Zhi-Jian , Yang Lin-Tong
Published: September 2022

Organic acids metabolism and nitrogen (N) metabolism in rice seedlings and the relationship between them are not fully understood. In this study, rice (Oryza sativa L. ssp. Indica) variety “Huanghuazhan” was used as the experimental material, and three N levels (5 mM, 1 mM, and 0 mM NH4NO3) were set by the hydroponic method for different levels of N treatment. Our results showed that the increased content of malate in rice leaves caused by reducing N level was related to the increased synthesis of malate (the activity of leaf PEPC increased)and the decreased degradation of malate (the activity of leaf NADP-ME decreased), while the increased contents of citrate and isocitrate in rice leaves caused by reducing N level might not be caused by the increased biosynthesis, but due to the decrease in degradation of citrate and isocitrate (the activities of leaf CS, ACO, and NADP-IDH decreased). The increased content of malate in rice roots caused by reducing N level might be related to the increased biosynthesis and the decreased degradation of root malate (the activities of root NAD-MDH and PEPC increased, while the activity of NADP-ME decreased). Compared to the control (5 mM NH4NO3), the increased content of citrate in rice roots caused by reducing N level might be related to the increased biosynthesis rather than the decreased degradation of citrate, due to the higher activities of CS and ACO in rice roots under 0 mM N and 1mM N treatment when compared to that of the control ones. At the same time, the increased content of isocitrate in roots was related to the increased isomerization of isocitrate (the activity of root ACO increased) and the decreased degradation of isocitrate (the activity of root NADP-IDH decreased). With the reducing N level, the activities of N metabolism-related enzymes, such as nitrate reductase (NR), glutamine synthetase (GS), and glutamate synthase (GOGAT), decreased in rice leaves and roots, resulting in the decreased contents of total free amino acids (TFAAs) and soluble proteins in rice seedlings, and finally led to the growth inhibition. Our results showed that the dynamics of organic acids metabolism caused by reducing N level were different in rice leaves and roots. In conclusion, there was a close correlation between organic acids metabolism and N metabolism in rice leaves and roots under N-limited conditions; furthermore, such a correlation was more obvious in rice leaves than that of roots.
Geometry Optimization of the Runner Insert for Improving the Appearance Quality of the Injection Molded Auto Part
Authors: Ma Zhiguo , Li Zheng , Huang Ming , Liu Chuntai , Shen Changyu , Wang Xinyu
Published: September 2022

Appearance quality is one of the most important indexes for many injection-molded products, like optical parts, automotive parts etc., especially at the area near the injection gate. Different from the work that focused on designing the dimensions of the runner, this work proposed a method which is based on the insert technology to improve the appearance quality of a standard automotive part. An insert was introduced into the runner system which located before the gate. Three different shapes of this insert (circular, rectangular and diamond) were used to study the effect of geometrical factors on the appearance quality in this paper. All inserts were parameterized to describe their location and dimensions. Based on the geometrical design parameters, expected improvement optimization problem about the appearance quality were solved by using sequential approximate optimization method. The appearance qualities of three cases are improved by 13.77%, 21.56%, 14.37% respectively. Results showed that the best geometrical design scheme of the insert is rectangular with the optimal geometrical location and dimensions. The reasons were discussed by investigating the flow and thermal history in detail. Compared with the design case without any insert, the heat was absorbed and the velocity field was changed by the insert before the polymer melts ran into the cavity. It changed the complicated thermo-mechanical history inside the material during the entire processing history, which improved the final appearance quality of this auto part.
Thermo-Hydro-Glycol Ageing of Polyamide 6,6: Microstructure-Properties Relationships
Authors: Laügt Clément , Bouvard Jean-Luc , Robert Gilles , Billon Noëlle
Published: September 2022

The microstructural evolutions occurring during the thermo-hydro-glycol ageing of an injection molded PA66 were studied. They were correlated to the evolutions of its mechanical properties. The aged samples were immersed in an antifreeze fluid—mainly composed of water and ethylene glycol—at varying times and temperatures. The aim was to combine an as exhaustive as possible microstructural investigation and a rigorous mechanical analysis. Consequently, the microstructure of the aged and unaged PA66 was assessed through the average molar mass, the diameter of the spherulites, the lamellae thickness, the crystallite’s apparent size, a crystal perfection index, and a crystallinity index. Moreover, a core-skin approach was set up. The mechanical consequences of the microstructural changes were investigated by DMA and tensile testing. The local true strain fields were measured with a digital image correlation system. The temperatures and strain rates of the tests were chosen by referring to the time-temperature superposition principle. It is concluded that the water and ethylene glycol intake resulted in an intense plasticization, the loss of the molar mass resulted in the embrittling of the polymer, and finally, it was identified that the changes of the crystalline structure have an influence on the stiffness of PA66.
“Non-Action” and “Assistance”: Laozi’s Thoughts on How to Treat Others
Authors: Ye Shuxun
Published: September 2022

How to treat others is a key topic in Laozi’s thought. Laozi not only advocates “non-action” (wuwei 無爲), or not interfering with others, but also hopes that actors will take some positive responsibilities for others. He expects that actors can implement the instructions of Dao 道 and fulfill the role of “assisting others to achieve their self-so-ness but not daring to interfere” (chapter 64). He believes that “one person will become more abundant when he contributes to others” (chapter 81); that is to say, actors and others are always in the process of mutual attainment. What he claims can be summarized by the dual assertion “to do V1, but not to do V2” (V means a type of action), containing two kinds of responsibilities: the situations represented by “not to do V2” are concrete manifestations of “non-action,” which refers to the negative responsibility of non-interference with others, while the situations signified by “to do V1” are essentially a kind of action of assisting others, which is a positive responsibility for others. There is a subtle cooperative relationship between “assistance” and “non-action.” In a nutshell, what Laozi expects is a responsibility to support others to actualize their authentic self on the premise of earnestly respecting the spontaneity and autonomy of others. When the focus on “non-action” is broken through, we can grasp Laozi’s thinking deeply and expand our understanding of his thought.
Predicting Rock Bursts in Rock Mass Blocks Using Acoustic Emission
Authors: Nosov Viktor V. , Borovkov Alexey I. , Artyushchenko Artem P.
Published: September 2022

Geophysical methods for local rock burst prediction are currently being developed along two lines: improving recording equipment and improving data processing methods. Progress in developing processing methods is constrained by the lack of informative prognostic models that describe the condition of rock mass, the process of rock mass fracturing, and the phenomena that can substantiate the choice of both criteria and test parameters of the condition of rock mass and give an estimate of the time remaining until rock pressure manifestation. In particular, despite achievements in hardware design, researchers using the seismo-acoustic method to predict rock bursts measure the acoustical activity or energy capacity of elastic wave scattering after a man-made explosion and are faced with the dependence of forecast results on destabilizing factors. To solve this problem, we applied an information and kinetic approach to forecasting. In this article, we discuss the principles of selecting test parameters that are resistant to destabilizing factors. We propose a micromechanical model of fracture accumulation in a rock mass block that reflects the dependence of acoustic emission (AE) parameters on time, which makes it possible to detect the influence of various factors on forecast data and filter the signals. We also propose criteria and a methodology for rock burst risk assessment. The results were tested in analyzing the seismo-acoustic phenomena caused by man-made explosions at the Taimyrsky and Oktyabrsky mines in Norilsk. The article gives examples of using the proposed criteria. The effectiveness of their application is compared with traditional methods for assessing rock burst risks and evaluating the stress–strain parameters of rock mass in terms of their being informative, stable, and representative by means of statistical processing of experimental data.
Selection of Lunar South Pole Landing Site Based on Constructing and Analyzing Fuzzy Cognitive Maps
Authors: Jia Yutong , Liu Lei , Wang Xingchen , Guo Ningbo , Wan Gang
Published: September 2022

The Permanently Shadowed Regions (PSRs) of the lunar south pole have never been directly sampled. To explore and discover lunar resources, the Chinese lunar south pole exploration mission is scheduled to land in direct sunlight near the PSR, where sampling and analysis will be carried out. The selection of sites for lunar landing sampling sites is one of the key steps of the mission. The main factors affecting the site selection are the distribution of PSRs, lunar surface slopes, rock distribution, light intensity, and maximum temperature. In this paper, the main factors affecting site selection are analyzed based on lunar multi-source remote sensing data. Combined with previous engineering constraints, we then propose a comprehensive multi-factor fuzzy cognition and selection model for the lunar south site selection. An analytical model based on a fuzzy cognitive map algorithm is also established. Furthermore, to make a preliminary landing area selection, we determine the evaluation index for the candidate landing areas using fuzzy reasoning. Using the proposed model and combined scoring index, we also verify and analyze the prominent impact craters at the lunar south pole. The scores of de Gerlache (88.48°S 88.34°W), Shackleton (89.67°S 129.78°E), and Amundsen (84.5°S, 82.8°E) craters are determined using fuzzy interference as 0.816, 0.814, and 0.784, respectively. Moreover, using our proposed approach, we identify feasible landing sites around the de Gerlache crater close to the PSR to facilitate discovery of water ice exposures in future missions. The proposed method is capable of evaluating alternative landing zones subject to multiple engineering constraints on the Moon or Mars based on the existing data.
Detection of Carbon Use Efficiency Extremes and Analysis of Their Forming Climatic Conditions on a Global Scale Using a Remote Sensing-Based Model
Authors: Wang Miaomiao , Zhao Jian , Wang Shaoqiang
Published: September 2022

Carbon use efficiency (CUE) represents the proficiency of plants in transforming carbon dioxide (CO2) into carbon stock in terrestrial ecosystems. CUE extremes represent ecosystems’ extreme proficiency in carbon transformation. Studying CUE extremes and their forming climate conditions is critical for enhancing ecosystem carbon storage. However, the study of CUE extremes and their forming climate conditions on the global scale is still lacking. In this study, we used the results from the daily Boreal Ecosystem Productivity Simulator (BEPS) model to detect the positive and negative CUE extremes and analyze their forming climatic conditions on a global scale. We found grasslands have the largest potential in changing global CUE, with the contribution being approximately 32.4% to positive extremes and 30.2% to negative extremes. Spring in the Northern Hemisphere (MAM) contributed the most (30.5%) to positive CUE extremes, and summer (JJA) contributed the most (29.7%) to negative CUE extremes. The probabilities of gross primary production (GPP) extremes resulted in CUE extremes (>25.0%) being larger than autotrophic respiration (Ra), indicating CUE extremes were mainly controlled by GPP rather than Ra extremes. Positive temperature anomalies (0~1.0 °C) often accompanied negative CUE extreme events, and positive CUE extreme events attended negative temperature anomalies (−1.0~0 °C). Moreover, positive (0~20.0 mm) and negative precipitation (−20.0~0 mm) anomalies often accompanied positive and negative CUE extremes, respectively. These results suggest that cooler and wetter climate conditions could be beneficial to enhance carbon absorptions of terrestrial ecosystems. The study provides new knowledge on proficiency in carbon transformation by terrestrial ecosystems.
Source-Independent Waveform Inversion Method for Ground Penetrating Radar Based on Envelope Objective Function
Authors: Liu Xintong , Liu Sixin , Luo Chaopeng , Jiang Hejun , Li Hong , Meng Xu , Feng Zhihui
Published: September 2022

For the full waveform inversion, it is necessary to provide an accurate source wavelet for forwarding modeling in the iteration. The source wavelet estimation method based on deconvolution technology can solve this problem to some extent, but we find that the estimated source wavelet is not accurate and needs to be manually corrected repeatedly in the iteration. This process is highly operator-intensive, and the update process is time-consuming and increases the potential for errors. We propose a source-independent waveform inversion (SIEWI) scheme for cross-hole GPR data, and use the envelope objective function combined with this method to effectively reduce the nonlinearity of inversion. The residual field used by SIEWI to construct the gradient inherits the characteristics of the envelope wavefield. Compared with full waveform inversion (FWI), SIEWI is more robust and less sensitive to frequency components and inaccurate source wavelet. To avoid cycle jumping, the multi-scale strategy effectively utilizes the properties of convolutional wavefields. In one iteration, the wavefield is decomposed into multiple frequency bands through multiple convolutions in the time domain to construct a multi-scale inversion strategy that preferentially inverts low-frequency information.
BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning
Authors: Okey Ogobuchi Daniel , Maidin Siti Sarah , Adasme Pablo , Lopes Rosa Renata , Saadi Muhammad , Carrillo Melgarejo Dick , Zegarra Rodríguez Demóstenes
Published: September 2022

Following the recent advances in wireless communication leading to increased Internet of Things (IoT) systems, many security threats are currently ravaging IoT systems, causing harm to information. Considering the vast application areas of IoT systems, ensuring that cyberattacks are holistically detected to avoid harm is paramount. Machine learning (ML) algorithms have demonstrated high capacity in helping to mitigate attacks on IoT devices and other edge systems with reasonable accuracy. However, the dynamics of operation of intruders in IoT networks require more improved IDS models capable of detecting multiple attacks with a higher detection rate and lower computational resource requirement, which is one of the challenges of IoT systems. Many ensemble methods have been used with different ML classifiers, including decision trees and random forests, to propose IDS models for IoT environments. The boosting method is one of the approaches used to design an ensemble classifier. This paper proposes an efficient method for detecting cyberattacks and network intrusions based on boosted ML classifiers. Our proposed model is named BoostedEnML. First, we train six different ML classifiers (DT, RF, ET, LGBM, AD, and XGB) and obtain an ensemble using the stacking method and another with a majority voting approach. Two different datasets containing high-profile attacks, including distributed denial of service (DDoS), denial of service (DoS), botnets, infiltration, web attacks, heartbleed, portscan, and botnets, were used to train, evaluate, and test the IDS model. To ensure that we obtained a holistic and efficient model, we performed data balancing with synthetic minority oversampling technique (SMOTE) and adaptive synthetic (ADASYN) techniques; after that, we used stratified K-fold to split the data into training, validation, and testing sets. Based on the best two models, we construct our proposed BoostedEnsML model using LightGBM and XGBoost, as the combination of the two classifiers gives a lightweight yet efficient model, which is part of the target of this research. Experimental results show that BoostedEnsML outperformed existing ensemble models in terms of accuracy, precision, recall, F-score, and area under the curve (AUC), reaching 100% in each case on the selected datasets for multiclass classification.
Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut
Authors: Duan Fuzhou , Wu Yanyan , Guan Hongliang , Wu Chenbo
Published: September 2022

In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation.
WSNEAP: An Efficient Authentication Protocol for IIoT-Oriented Wireless Sensor Networks
Authors: Yi Fumin , Zhang Lei , Xu Lijuan , Yang Shumian , Lu Yanrong , Zhao Dawei
Published: September 2022

With the development of the Industrial Internet of Things (IIoT), industrial wireless sensors need to upload the collected private data to the cloud servers, resulting in a large amount of private data being exposed on the Internet. Private data are vulnerable to hacking. Many complex wireless-sensor-authentication protocols have been proposed. In this paper, we proposed an efficient authentication protocol for IIoT-oriented wireless sensor networks. The protocol introduces the PUF chip, and uses the Bloom filter to save and query the challenge–response pairs generated by the PUF chip. It ensures the security of the physical layer of the device and reduces the computing cost and communication cost of the wireless sensor side. The protocol introduces a pre-authentication mechanism to achieve continuous authentication between the gateway and the cloud server. The overall computational cost of the protocol is reduced. Formal security analysis and informal security analysis proved that our proposed protocol has more security features. We implemented various security primitives using the MIRACL cryptographic library and GMP large number library. Our proposed protocol was compared in-depth with related work. Detailed experiments show that our proposed protocol significantly reduces the computational cost and communication cost on the wireless sensor side and the overall computational cost of the protocol.
Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition
Authors: Tang Jingwei , Chien Ying-Ren
Published: September 2022

Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power development. To promote the utilization of wind energy and improve the accuracy of wind power prediction, we comprehensively consider the influence of wind farm environmental factors and historical power on wind power generation. This paper presents a short-term wind power prediction model based on time convolution neural network (TCN) and variational mode decomposition (VMD). First, due to the non-smooth characteristics of the wind farm environmental data, this paper uses VMD to decompose the data of each environmental variable to reduce the influence of the random noise of the data on the prediction model. Then, the modal components with rich feature information are extracted according to the Pearson correlation coefficient and Maximal information coefficient (MIC) between each modal component and the power. Thirdly, a prediction model based on TCN is trained according to the preferred modal components and historical power data to achieve accurate short-term wind power prediction. In this paper, the model is trained and tested with a public wind power dataset provided by the Spanish Power Company. The simulation results show that the model has higher prediction accuracy, with MAPE and R2 are 2.79% and 0.9985, respectively. Compared with the conventional long short-term neural network (LSTM) model, the model in this paper has good prediction accuracy and robustness.
An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature Decoupling
Authors: Hu Zhongxu , Zhang Yiran , Xing Yang , Li Qinghua , Lv Chen
Published: September 2022

Multi-state driver monitoring is a key technique in building human-centric intelligent driving systems. This paper presents an integrated visual-based multi-state driver monitoring framework that incorporates head rotation, gaze, blinking, and yawning. To solve the challenge of head pose and gaze estimation, this paper proposes a unified network architecture that tackles these estimations as soft classification tasks. A feature decoupling module was developed to decouple the extracted features from different axis domains. Furthermore, a cascade cross-entropy was designed to restrict large deviations during the training phase, which was combined with the other features to form a heterogeneous loss function. In addition, gaze consistency was used to optimize its estimation, which also informed the model architecture design of the gaze estimation task. Finally, the proposed method was verified on several widely used benchmark datasets. Comprehensive experiments were conducted to evaluate the proposed method and the experimental results showed that the proposed method could achieve a state-of-the-art performance compared to other methods.
A New Bolt Defect Identification Method Incorporating Attention Mechanism and Wide Residual Networks
Authors: Liu Liangshuai , Zhao Jianli , Chen Ze , Zhao Baijie , Ji Yanpeng
Published: September 2022

Bolts are important components on transmission lines, and the timely detection and exclusion of their abnormal conditions are imperative to ensure the stable operation of transmission lines. To accurately identify bolt defects, we propose a bolt defect identification method incorporating an attention mechanism and wide residual networks. Firstly, the spatial dimension of the feature map is compressed by the spatial compression network to obtain the global features of the channel dimension and enhance the attention of the network to the vital information in a weighted way. After that, the enhanced feature map is decomposed into two one-dimensional feature vectors by embedding a cooperative attention mechanism to establish long-term dependencies in one spatial direction and preserve precise location information in the other direction. During this process, the prior knowledge of the bolts is utilized to help the network extract critical feature information more accurately, thus improving the accuracy of recognition. The test results show that the bolt recognition accuracy of this method is improved to 94.57% compared with that before embedding the attention mechanism, which verifies the validity of the proposed method.
ArbGaze: Gaze Estimation from Arbitrary-Sized Low-Resolution Images
Authors: Kim Hee Gyoon , Chang Ju Yong
Published: September 2022

The goal of gaze estimation is to estimate a gaze vector from an image containing a face or eye(s). Most existing studies use pre-defined fixed-resolution images to estimate the gaze vector. However, images captured from in-the-wild environments may have various resolutions, and variation in resolution can degrade gaze estimation performance. To address this problem, a gaze estimation method from arbitrary-sized low-resolution images is proposed. The basic idea of the proposed method is to combine knowledge distillation and feature adaptation. Knowledge distillation helps the gaze estimator for arbitrary-sized images generate a feature map similar to that from a high-resolution image. Feature adaptation makes creating a feature map adaptive to various resolutions of an input image possible by using a low-resolution image and its scale information together. It is shown that combining these two ideas improves gaze estimation performance substantially in the ablation study. It is also demonstrated that the proposed method can be generalized to other popularly used gaze estimation models through experiments using various backbones.
Vector Representations of Idioms in Conversational Systems
Authors: Adewumi Tosin , Liwicki Foteini , Liwicki Marcus
Published: September 2022

In this study, we demonstrate that an open-domain conversational system trained on idioms or figurative language generates more fitting responses to prompts containing idioms. Idioms are a part of everyday speech in many languages and across many cultures, but they pose a great challenge for many natural language processing (NLP) systems that involve tasks such as information retrieval (IR), machine translation (MT), and conversational artificial intelligence (AI). We utilized the Potential Idiomatic Expression (PIE)-English idiom corpus for the two tasks that we investigated: classification and conversation generation. We achieved a state-of-the-art (SoTA) result of a 98% macro F1 score on the classification task by using the SoTA T5 model. We experimented with three instances of the SoTA dialogue model—the Dialogue Generative Pre-trained Transformer (DialoGPT)—for conversation generation. Their performances were evaluated by using the automatic metric, perplexity, and a human evaluation. The results showed that the model trained on the idiom corpus generated more fitting responses to prompts containing idioms 71.9% of the time in comparison with a similar model that was not trained on the idiom corpus. We have contributed the model checkpoint/demo/code to the HuggingFace hub for public access.
Antimycobacterial Activity of Rosmarinus officinalis (Rosemary) Extracted by Deep Eutectic Solvents
Authors: Dheyab Ali Sami , Kanaan Mohammed Qahtan , Hussein Nabeel Abood , AlOmar Mohamed Khalid , Sabran Siti Fatimah , Abu Bakar Mohd Fadzelly
Published: September 2022

Tuberculosis (TB) is a massive problem for public health and is the leading cause of illness and death worldwide. Rosemary (Rosmarinus officinalis) is used traditionally to treat many diseases, such as infections of the lungs including pulmonary TB. R. officinalis was collected from Al Anbar Governorate, Iraq, and was extracted with deep eutectic solvents (DESs) of many different kinds and with conventional water solvent. The antimycobacterial activities of the R. officinalis extracts were tested against multidrug-resistant (MDR) Mycobacterium tuberculosis by agar disc diffusion assay. Minimum inhibitory concentrations were measured spectrophotometrically at 570 nm. Then, a time-kill assay and cell membrane integrity analysis were conducted to investigate the effects of the most active extracts on cell growth. The in vitro cytotoxicity of the most active extracts was evaluated against Rat Embryonic Fibroblasts (REF) cell line by MTT assay. Liquid chromatography-mass spectrometry (LC-MS) was conducted to analyze the chemical components of the most active extracts. At 200 mg/mL concentration, a significant inhibition activity was seen in DES2: Tailor (DIZ = 17.33 ± 1.15 mm), followed by DES3: ChGl, DES1: LGH and DES4: ChXl. The best result was DES2: Tailor, which had a MIC of 3.12 mg/mL and an MBC of 12.5 mg/mL. The DES2 extract exhibited a high drop in the number of colonies over time, killing more than 80 colonies. The main phytochemical compounds of the R. officinalis extract were camphene, camphenilol, α-pinene, limonene, apigenin, camphor, carnosol, linalool and myrcene. R. officinalis extracts obtained by DESs have shown evident power in treating tuberculosis, and extraction by DES is a greener procedure than the methods involving conventional extraction solvents. As a result, additional research into the application of DES should be considered.
Injuries and Strength Training Practices in Collegiate Tennis
Authors: Vasenina Ecaterina , Hammert William B. , Kataoka Ryo , Dankel Scott J. , Buckner Samuel L.
Published: September 2022

Strength and conditioning practices may influence injury rates in the sport of tennis. Methods: Coaches reported the number injuries over the past year. Coaches were also surveyed on whether their training program included training related to upper-body or lower-body strength, power, muscle growth, and eccentric exercise. Separate regression analyses were run in the upper and lower body to examine the relationship between injuries and participation in training focused on strength, power, growth, and maximal eccentric exercise. A total of 111 coaches were surveyed. The most frequent injuries observed were ankle sprains (144 injures), followed by paraspinal muscle strains (126 injuries). When pooled, there were a total of 355 lower-body and 260 upper-body injuries. Strength and conditioning practices explained 9.9% of the variance of injury rates in the upper body (R2 = 0.099). The only significant predictor of upper-body injury was participation in upper-body muscle growth training (β = 1.613, p = 0.013). In addition, training practices explained 11.1% of the variance of injury in the lower body (R2 = 0.111). Coaches value injury prevention exercise, sports-specific training and flexibility and mobility training the most, with muscle growth and maximal power ranked lowest. Additionally, the most frequent injuries observed in collegiate tennis players were ankle sprains (144 injures), followed by paraspinal muscle strains (126 injuries).
Research on Urban Greenway Alignment Selection Based on Multisource Data
Authors: Lu Jiahang , Wu Xiaohua
Published: September 2022

At present, people’s growing need for a beautiful ecological environment needs to be met. As a high-quality ecological product, greenways help improve the living environment and promote green and coordinated development. How to scientifically and rationally plan greenway routes and expand the green ecological network has become an important issue for the sustainable development of high-density built-up areas. Therefore, this study determines the suitability evaluation system of urban greenway alignment selection based on spatial constructiveness, greenway accessibility, resource attractiveness and public demand through factor analysis. Taking Xixi Wetland to South Lake in Yuhang District as an example, combining urban multi-source data and GIS spatial analysis methods, all routes suitable for greenway construction in the study area were determined, and four excellent greenway routes were initially identified through manual correction. Subsequently, the PSO-BP neural network model was trained using 172 greenway samples to obtain a greenway alignment suitability evaluation model with an accuracy of more than 96%, which was used for subsequent greenway alignment optimization. The above four preliminary greenway routes are substituted into the PSO-BP model to obtain the optimal greenway routes in the study area. The results show that there are 17 indicators in 4 levels and 8 dimensions in the urban greenway route selection potential model, among which two indicators, public cycling and running demand, have a greater weight and should be highly considered in the route selection assessment. The optimal planned route for the greenway start from Guangfu Temple entrance of Xixi Wetland, follow the roadside green belt and Yuhangtang River greenway to South Lake. The study obtained the best planning route for the greenway from Xixi Wetland to South Lake in Yuhang District, which is a new practice of landscape garden planning and design supported by multisource data and new technologies, with a view to providing a reference for the preferential selection of urban greenway routes in similar high-density built-up areas.
Household Vulnerability to Flood Disasters among Tharu Community, Western Nepal
Authors: Pangali Sharma Til Prasad , Zhang Jiahua , Khanal Narendra Raj , Nepal Pashupati , Pangali Sharma Bishnu Prasad , Nanzad Lkhagvadorj , Gautam Yograj
Published: September 2022

Monsoon floods are frequent in the Tarai region of Nepal and claim thousands of lives and substantial numbers of properties every year. Certain human activities are more affected than others in the case of the same hazard. This study analyzes vulnerability to flooding among Tharu households. Data were collected by employing household surveys, group discussions, and key informant interviews in the Thapapur Village Development Committee (VDC) of Kailali district, western Tarai, Nepal. The analysis presented in this study is based on the theory that underpins the pressure and release (PAR) and access models. The results show that Tharu people are the major inhabitants in the study area and they prefer to live within their community; many ex-bonded laborers (marginalized people) choose this location for residence. Human causalities have been reduced in recent years due to easy access to cell phones, which has facilitated effective flood warnings with suitable lead times, but agriculture production loss and other losses are still high. Agricultural land is not only an important natural asset but is also considered a financial asset due to its high price and private ownership. The study concludes that subsistence agriculture-based households with small landholding sizes and less income diversification are highly vulnerable to flooding. Improper resettlement of ex-bonded laborers and land fragmentation due to separation of family members are the most prominent factors resulting in small landholdings. The results can guide government authorities to develop proper flood management strategies for the people living in the lowlands (particularly the Tarai region) of Nepal.