bluuebunny/crossref_metadata_2025 huggingface dataset:
A Phytocomplex Obtained from Salvia officinalis by Cell Culture Technology Effectively Controls the Grapevine Downy Mildew Pathogen Plasmopara viticola
Authors: Busato Isabella , Bertaiola Oriana , Tundo Silvio , Guarnerio Chiara , Lucchetta Marco , Sella Luca , Pressi Giovanna , Favaron Francesco
Published: October 2022

The negative impact of using conventional fungicides in plant disease protection has increased the interest in safer alternatives such as plant secondary metabolites, generally having a better toxicological profile. However, cultivation conditions and plant material strongly affect the quality and quantity of secondary metabolites obtained from field grown plants, limiting the standardization needed for industrial production. Plant cell culture technology can provide highly homogeneous biomasses with specific chemical characteristics. A phytocomplex with high rosmarinic acid content (10.12% w/w) was obtained from a selected cell line of Salvia officinalis and was tested against the grapevine downy mildew pathogen, Plasmopara viticola. Grapevine leaf discs were sprayed with the phytocomplex at 5 g/L and then inoculated with P. viticola sporangia. Sporulation level on each disc was assessed after 7 days with an image processing software. The phytocomplex reduced by 95% the sporulation level compared to the control and was also more effective than rosmarinic acid alone, used at the same concentration found in the phytocomplex. Persistence of the phytocomplex was also assessed: when applied 5 days before inoculation, it reduced by 90% the sporulation level compared to the control. These results highlight the possibility to take advantage of cell culture techniques to produce safer pesticides with high quality standards.
The Effect of Leaf Wounding on Basil Plants of Different Developmental Stages
Authors: Konstantis Nikolaos , Koskorellos Konstantinos , Balou Areti , Paravolidaki Athina , Garantziotis George , Koulopoulou Christina Eleni , Koulopoulos Athanasios , Zervoudakis George
Published: October 2022

Leaf wounding is a common stress that triggers a great number of plant mechanisms, while the overall plant status and age could also be critical for these mechanisms. However, there are not sufficient data about plants’ physiological responses after leaf wounding that has been imposed at different developmental stages. In this study, physiological parameters, such as photosynthesis, transpiration, and stomatal conductance, as well as the chlorophyll and anthocyanin leaf contents, of Ocimum basilicum var. minimum L. plants were measured for seven days on wounded plants during three different developmental stages (vegetative, budding, and flowering). All of the measurements were conducted on control and wounded plants, while on the latter they were conducted on both wounded and intact leaves. The physiological parameters mentioned above revealed a remarkable decrease in wounded leaves of the budding and flowering plants, while they seemed to be only partially affected on the leaves of vegetative plants. The physiological parameters’ decrease was not only an immediate plant response that was observed 1–2 h after wounding, but, in general, it was constant (during the seven days of treatments) and diurnal (from 8 a.m. to 8 p.m.). The wounded leaves revealed an immediate and constant anthocyanin content decrease during all of the developmental stages, while the corresponding chlorophyll decrease was mainly evident in the flowering plants. Regarding the intact leaves, they exhibited, in general, a similar profile to that of the control ones. The results above reveal that at the vegetative stage, basil plants are more tolerant to leaf wounding than those at the budding and flowering stages, implying that the plant’s response to wounding is a phenomenon that depends on the plant’s developmental stage.
Role of Antioxidant Enzymes and Glutathione S-Transferase in Bromoxynil Herbicide Stress Tolerance in Wheat Plants
Authors: Gaafar Reda M. , Osman Mohamed El-Anwar H. , Abo-Shady Atef M. , Almohisen Ibrahim A. A. , Badawy Ghada Ahmed , El-Nagar Maysa M. F. , Ismail Gehan A.
Published: October 2022

Background: Numerous pesticides and herbicides used in excess cause oxidative stress in plants. These chemicals protect plants from weeds and pests, but they also have very negative side effects, making them common abiotic stressors. One of the most significant nutritional crops in the world is the wheat plant. Conditions of herbicide stress have a negative impact on the plant’s phonological phases and metabolic pathways. Plants primarily make an effort to adjust to the environment and develop oxidative homeostasis, which supports stress tolerance. Methods: When controlling broadleaf weeds that emerge after cereal crop plants have been planted, bromoxynil is frequently used as a selective-contact herbicide. This study looked at the effects of the cyanobacteria Arthrospira platensis and Nostoc muscorum aqueous extracts, tryptophan, and bromoxynil (Bh) alone or in combination on wheat plant growth parameters. Both tryptophan and cyanobacterial extract were used as chemical and natural safeners against Bh application. The antioxidant activity and transcriptome studies using qRT-PCR were assayed after 24, 48, 72, 96 h, and 15 days from Bh application in the vegetation stage of wheat plants (55 days old). Results: In comparison with plants treated with Bh, wheat plants treated with cyanobacteria and tryptophan showed improvements in all growth parameters. Following application of Bh, wheat plants showed reduced glutathione content, as well as reduced antioxidant enzyme activities of superoxide dismutase, catalase, glutathione peroxidase, and glutathione-s-transferase. The combination of different treatments and Bh caused alleviation of the harmful effect induced by Bh on the measured parameters. Additionally, the expression of glutathione synthase and glutathione peroxidase, in addition to those of three genes (Zeta, Tau, and Lambda) of the GST gene family, was significantly upregulated when using Bh alone or in combination with different treatments, particularly after 24 h of treatment. Conclusion: The current study suggests using cyanobacterial extracts, particularly the A. platensis extract, for the development of an antioxidant defense system against herbicide toxicity, which would improve the metabolic response of developed wheat plants.
Investigations of Thermal, Mechanical, and Gas Barrier Properties of PA11-SiO2 Nanocomposites for Flexible Riser Application
Authors: Wen Jihong , Huang Dong , Li Yan , Yu Xichong , Zhang Xinpeng , Meng Xiaoyu , Cong Chuanbo , Zhou Qiong
Published: October 2022

Acidic gas penetration through the internal pressure sheath of a flexible riser tends to cause a corrosive environment in the annulus, reducing the service life of the flexible riser. Nanoparticles can act as gas barriers in the polymer matrix to slow down the gas permeation. Herein, we prepared PA11/SiO2 composites by the melt blending method. The effect of adding different amounts of SiO2 to PA11 on its gas barrier properties was investigated by conducting CO2 permeation tests between 20 °C and 90 °C. As the temperature increased, the lowest value of the permeability coefficient that could be achieved for the PA11 with different contents of SiO2 increased. The composites PA/0.5% SiO2 and PA/1.5% SiO2 had the lowest permeation coefficients in the glassy state (20 °C) and rubbery state (≥50 °C). We believe that this easy-to-produce industrial PA/SiO2 composite can be used to develop high-performance flexible riser barrier layers. It is crucial for understanding riser permeation behavior and enhancing barrier qualities.
Electrical Properties of Polyetherimide-Based Nanocomposites Filled with Reduced Graphene Oxide and Graphene Oxide-Barium Titanate-Based Hybrid Nanoparticles
Authors: Cuenca-Bracamonte Quimberly , Yazdani-Pedram Mehrdad , Aguilar-Bolados Héctor
Published: October 2022

The electrical properties of nanocomposites based on polyetherimide (PEI) filled with reduced graphene oxide (rGO) and a graphene oxide hybrid material obtained from graphene oxide grafted with poly(monomethyl itaconate) (PMMI) modified with barium titanate nanoparticles (BTN) getting (GO-g-PMMI/BTN) were studied. The results indicated that the nanocomposite filled with GO-g-PMMI/BTN had almost the same electrical conductivity as PEI (1 × 10−11 S/cm). However, the nanocomposite containing 10 wt.% rGO and 10 wt.% GO-g-PMMI/BTN as fillers showed an electrical conductivity in the order of 1 × 10−7 S/cm. This electrical conductivity is higher than that obtained for nanocomposites filled with 10% rGO (1 × 10−8 S/cm). The combination of rGO and GO-g-PMMI/BTN as filler materials generates a synergistic effect within the polymeric matrix of the nanocomposite favoring the increase in the electrical conductivity of the system.
Separation of Free Fatty Acid and Triglycerides by Molecular Distillation–Experimental and Simulation Approaches
Authors: Changwatchai Teetach , Nakajima Mitsutoshi , Felipe Lorena de Oliveira , Neves Marcos A.
Published: October 2022

Separation of free fatty acids and triglycerides is important in the edible oil industry. In this study, both experimental and simulation approaches were conducted using falling film molecular distillation. A binary mixture of oleic acid as free fatty acid with refined soybean oil as triglyceride was used. Evaporation temperature range tested was from 110 to 160 °C, while other parameters such as condensation temperature, feeding rate, and vacuum pressure were fixed. The free fatty acid content of retentate was gradually decreased as evaporation temperature increased, while the free fatty acid content of distillate was approximately 100 wt.%, regardless of temperature. Triglycerides had an extremely low vapor pressure compared to oleic acid; therefore, their fraction in distillate was negligible. The mass conservation and transport equations were selected to explain the evaporation mechanism, and analytical solutions were simply introduced under the conditions of low mass fraction of volatile compound in the feeding mixture. Some experimental data are re-quired to determine the coefficient of molecular distillation in the governing equation. Using this obtained parameter, the proposed model could simulate all mass amounts and fractions of each component in retentate and distillate with a good agreement between experimental and simulation data, indicating the reasonable accuracy of the model proposed.
Energy Consumption Analysis of a Diesel Hydrotreating Unit Using an Aspen Simulation
Authors: Tian Ruijie , Xu Weibin , Li Yongchao , Tian Jun , Wu Le
Published: October 2022

The petrochemical industry is traditionally a high energy consumption industry, thus energy saving in this industry is of great significance in terms of trying to achieve the national goal of “carbon-peak and carbon neutrality” in China. Diesel is one of the main products of petrochemical enterprises. The energy consumption of a diesel hydrotreating (HDT) unit is relatively high due to the high reaction temperature and pressure of HDT reactors. In order to further explore the energy consumption of diesel HDT units under different operating conditions, this paper establishes a full-process simulation based on the kinetics of hydrodesulfurization, hydrodenitrogenation, and aromatics saturation, in Aspen Plus to discuss the effect of different operating conditions on the electricity, steam, and fuel gas consumptions. The results show that with the increase of reaction temperature or reaction pressure, the sulfur content in diesel oil decreases, but the consumption of related utilities and comprehensive energy consumption increases. In addition, this paper also analyzes the energy consumption changes under the operating condition boundaries when the diesel quality is unchanged. When the reaction pressure is 8 MPa and the reaction temperature is 317.5 °C, the comprehensive energy consumption is 5% lower than the energy consumption with the reaction pressure of 6 MPa and the temperature of 320 °C. Therefore, on the premise of satisfying the diesel product quality, the comprehensive energy consumption of the diesel HDT unit can be reduced by increasing the reaction pressure and decreasing the reaction temperature.
Fusion of Clinical and Lived Experiences of Psychosis: Lessons Learned and Implications for Future Clinical Teaching
Authors: Norton Michael John , McLaughlin Mia
Published: October 2022

Psychosis is a clinical syndrome that can cause significant distress leading to hospitalisation/long term stays in psychiatric services. However, limited academic evidence is available examining the lived experience of psychosis. Additionally, no evidence is available looking to combine both learned and experiential knowledge as it pertains to psychosis. As such this article was created to combine both knowledge subsets in order to provide a more complete interpretation of the syndrome itself. This was achieved through academic input from a psychiatrist’s perspective as well as a reflective, autoethnographic input from a service user who has experienced psychosis. Following this collaboration, several recommendations were made to support health professionals to engage appropriately with service users with psychosis. However, the lived experiences of psychosis itself requires further investigation to identify commonalities in experiences that can support clinicians in the diagnosis and co-production of treatment regimens for these service users.
Extirpating Inherently Biased Rote Approaches and Replacing Them with Critical-Thinking-Based Interpretation of Evidence
Authors: Rothschild Bruce
Published: October 2022

Scientific methodology (logos) is predicated upon generating hypotheses and testing them, following where the collected data and evidence lead [...]
Automatic Laboratory Martian Rock and Mineral Classification Using Highly-Discriminative Representation Derived from Spectral Signatures
Authors: Yang Juntao , Kang Zhizhong , Yang Ze , Xie Juan , Xue Bin , Yang Jianfeng , Tao Jinyou
Published: October 2022

The optical properties of rocks and minerals provide a reliable way to measure their chemical and mineralogical composition due to the specific reflection behaviors, which is also the key insight behind most automatic identification and classification approaches. However, the inter-category spectral similarity poses a great challenge to the automatic identification and classification tasks because of the diversity of rocks and minerals. Therefore, this paper develops a recognition and classification approach of rocks and minerals using the highly discriminative representation derived from their raw spectral signatures. More specifically, a transformer-based classification approach integrated with category-aware contrastive learning is constructed and trained in an end-to-end manner, which would force instances of the same category to remain close-by while pushing instances of a dissimilar category far apart in the high-dimensional feature space, in order to produce the highly discriminative feature representation of the rocks and minerals. From both qualitative and quantitative views, experiments are conducted on the laboratory sample dataset with 30 types of rocks and minerals shared from the National Mineral Rock and Fossil Specimens Resource Center, and the spectral information of the laboratory rocks and minerals is captured using a multi-spectral sensor, with a duplicated payload of the counterpart onboard the Zhurong rover. Quantitative results demonstrate that the developed approach can effectively distinguish 30 types of rocks and minerals, with a high overall accuracy of 96.92%. Furthermore, the developed approach is remarkably superior to other existing methods, with average differences of 4.75% in the overall accuracy. Furthermore, we also visualized the derived highly discriminative features of different types of rocks and minerals by projecting them onto a two-dimensional map, where the same categories tend to be modeled by nearby locations and the dissimilar categories by distant locations with high probability. It can be observed that, compared with those in the raw spectral feature space, the clusters are formed better in the derived highly discriminative feature space, which further confirms the promising representation capability.
Assessing Nitrogen Variability at Early Stages of Maize Using Mobile Fluorescence Sensing
Authors: Siqueira Rafael , Mandal Dipankar , Longchamps Louis , Khosla Raj
Published: October 2022

Characterizing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Practitioners of precision N management require determination of in-season plant N status in real-time in the field to enable the most efficient N fertilizer management system. The objective of this study was to assess if mobile in-field fluorescence sensor measurements can accurately quantify the variability of nitrogen indicators in maize canopy early in the crop growing season. A Multiplex®3 fluorescence sensor was used to collect crop canopy data at the V6 and V9 maize growth stages. Multiplex fluorescence indices were successful in discriminating variability among N treatments with moderate accuracies at V6, and higher at the V9 stage. Fluorescence-based indices were further utilized with a machine learning (ML) model to estimate canopy nitrogen indicators i.e., N concentration and above-ground biomass at the V6 and V9 growth stages independently. Parameter estimation using the Support Vector Regression (SVR)-based ML mode indicated a promising accuracy in estimation of N concentration and above-ground biomass at the V6 stage of maize with the moderate range of correlation coefficient (r = 0.72 ± 0.03) and Root Mean Square Error (RMSE). The retrieval accuracies (r = 0.90 ± 0.06) at the V9 stage were better than those of the V6 growth stage with a reasonable range of error estimates and yielding the lowest RMSE (0.23 (%N) and 12.37 g (biomass)) for all canopy N indicators. Mobile fluorescence sensing can be used with reasonable accuracies for determining canopy N variability at early growth stages of maize, which would help farmers in optimal management of nitrogen.
Towards Forest Condition Assessment: Evaluating Small-Footprint Full-Waveform Airborne Laser Scanning Data for Deriving Forest Structural and Compositional Metrics
Authors: Sumnall Matthew J. , Hill Ross A. , Hinsley Shelley A.
Published: October 2022

Spatial data on forest structure, composition, regeneration and deadwood are required for informed assessment of forest condition and subsequent management decisions. Here, we estimate 27 forest metrics from small-footprint full-waveform airborne laser scanning (ALS) data using a random forest (RF) and automated variable selection (Boruta) approach. Modelling was conducted using leaf-off (April) and leaf-on (July) ALS data, both separately and combined. Field data from semi-natural deciduous and managed conifer plantation forests were used to generate the RF models. Based on NRMSE and NBias, overall model accuracies were good, with only two of the best 27 models having an NRMSE > 30% and/or NBias > 15% (Standing deadwood decay class and Number of sapling species). With the exception of the Simpson index of diversity for native trees, both NRMSE and NBias varied by less than ±4.5% points between leaf-on only, leaf-off only and combined leaf-on/leaf-off models per forest metric. However, whilst model performance was similar between ALS datasets, model composition was often very dissimilar in terms of input variables. RF models using leaf-on data showed a dominance of height variables, whilst leaf-off models had a dominance of width variables, reiterating that leaf-on and leaf-off ALS datasets capture different aspects of the forest and that structure and composition across the full vertical profile are highly inter-connected and therefore can be predicted equally well in different ways. A subset of 17 forest metrics was subsequently used to assess favourable conservation status (FCS), as a measure of forest condition. The most accurate RF models relevant to the 17 FCS indicator metrics were used to predict each forest metric across the field site and thresholds defining favourable conditions were applied. Binomial logistic regression was implemented to evaluate predicative accuracy probability relative to the thresholds, which varied from 0.73–0.98 area under the curve (AUC), where 11 of 17 metrics were >0.8. This enabled an index of forest condition (FCS) based on structure, composition, regeneration and deadwood to be mapped across the field site with reasonable certainty. The FCS map closely and consistently corresponded to forest types and stand boundaries, indicating that ALS data offer a feasible approach for forest condition mapping and monitoring to advance forest ecological understanding and improve conservation efforts.
Mapping Forest Stock Volume Based on Growth Characteristics of Crown Using Multi-Temporal Landsat 8 OLI and ZY-3 Stereo Images in Planted Eucalyptus Forest
Authors: Liu Zhaohua , Ye Zilin , Xu Xiaodong , Lin Hui , Zhang Tingchen , Long Jiangping
Published: October 2022

Labeled as a fast-growing tree species, eucalyptus has outstanding carbon sequestration capacity. Forest stock volume (FSV) is regarded as an important parameter for evaluating the quality of planted eucalyptus forests. However, it is an intractable problem to map FSV of planted eucalyptus forests using optical images because of growth characteristics of the crown and low saturation levels. To improve the accuracy of FSV in planted eucalyptus forests, time series Landsat 8 OLI (LC8) images and ZY-3 stereo images were acquired in the study area. Additionally, then, three composite images were proposed using acquired Landsat 8 OLI images based on the size and shape of eucalyptus crowns, and several spectra variables were extracted from these composite images. Furthermore, corrected canopy height model (CCHM) was also extracted from ZY-3 stereo images. Meanwhile, four models (random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and multiple linear regression (MLR)) were used to estimate the FSV with various variable sets using the importance of the alternative variables ranked by RF. The results show that the sensitivity between proposed spectral variables and FSV is significantly improved using proposed composed images based on the growth characteristics of the crown, especially for young eucalyptus forests. After adding CCHM and stand age to the optimal variable set, the average relative root mean square error (rRMSE) of estimated FSV decreased from 41.01% to 29.94% for single LC8 images and from 32.64% to 26.47% for proposed composite LC8 images, respectively. After using the variable set extracted from composite LC8 images, the number of samples with overestimated FSV was significantly decreased for the young forest. Furthermore, forest height plays an important role in improving the accuracy of mapping FSV, whether young or mature eucalyptus forest. It was also proved that composite images related to crown close and CCHM have great potential to delay the saturation phenomenon for mapping FSV in planted eucalyptus forest.
Ship Classification and Anomaly Detection Based on Spaceborne AIS Data Considering Behavior Characteristics
Authors: Yan Zhenguo , Song Xin , Zhong Hanyang , Yang Lei , Wang Yitao
Published: October 2022

With the establishment of satellite constellations equipped with ship automatic identification system (AIS) receivers, the amount of AIS data is continuously increasing, and AIS data have become an important part of ocean big data. To further improve the ability to use AIS data for maritime surveillance, it is necessary to explore a ship classification and anomaly detection method suitable for spaceborne AIS data. Therefore, this paper proposes a ship classification and anomaly detection method based on machine learning that considers ship behavior characteristics for spaceborne AIS data. In view of the characteristics of different types of ships, this paper introduces the extraction and analysis of ship behavior characteristics in addition to traditional geometric features and discusses the ability of the proposed method for ship classification and anomaly detection. The experimental results show that the classification accuracy of the five types of ships can reach 92.70%, and the system can achieve better results in the other classification evaluation metrics by considering the ship behavior characteristics. In addition, this method can accurately detect anomalous ships, which further proves the effectiveness and feasibility of the proposed method.
Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification
Authors: Wei Yinyin , Zhang Xiangyang , Zeng Aijun , Huang Huijie
Published: October 2022

Biometric recognition technology has been widely used in various fields of society. Iris recognition technology, as a stable and convenient biometric recognition technology, has been widely used in security applications. However, the iris images collected in the actual non-cooperative environment have various noises. Although mainstream iris recognition methods based on deep learning have achieved good recognition accuracy, the intention is to increase the complexity of the model. On the other hand, what the actual optical system collects is the original iris image that is not normalized. The mainstream iris recognition scheme based on deep learning does not consider the iris localization stage. In order to solve the above problems, this paper proposes an effective iris recognition scheme consisting of the iris localization and iris verification stages. For the iris localization stage, we used the parallel Hough circle to extract the inner circle of the iris and the Daugman algorithm to extract the outer circle of the iris, and for the iris verification stage, we developed a new lightweight convolutional neural network. The architecture consists of a deep residual network module and a residual pooling layer which is introduced to effectively improve the accuracy of iris verification. Iris localization experiments were conducted on 400 iris images collected under a non-cooperative environment. Compared with its processing time on a graphics processing unit with a central processing unit architecture, the experimental results revealed that the speed was increased by 26, 32, 36, and 21 times at 4 different iris datasets, respectively, and the effective iris localization accuracy is achieved. Furthermore, we chose four representative iris datasets collected under a non-cooperative environment for the iris verification experiments. The experimental results demonstrated that the network structure could achieve high-precision iris verification with fewer parameters, and the equal error rates are 1.08%, 1.01%, 1.71%, and 1.11% on 4 test databases, respectively.
A Data-Driven OBE Magnetic Interference Compensation Method
Authors: Wang Yizhen , Han Qi , Zhan Dechen , Li Qiong
Published: October 2022

Aeromagnetic compensation is a technology used to reduce aircraft magnetic interference, which plays an important role in aeromagnetic surveys. In addition to maneuvering interferences, the onboard electronic (OBE) interference has been proven to be a significant part of aircraft interference, which must be reduced before further interpretation of aeromagnetic data. In the past, most researchers have focused on establishing linear models to compensate for OBE magnetic interference. However, such methods can only work using accurate reference sensors. In this paper, we propose a data-driven OBE interference compensation method, which can reduce OBE interference without relying on any other reference sensor. This network-based method can integrally detect and repair the OBE magnetic interference. The proposed method builds a prediction model by combining wavelet decomposition with a long short-term memory (LSTM) network to detect and predict OBE interference, and then estimates the local variation of the magnetic field to remove the drift of the interference. In our tests, we construct 10 semi-real datasets to quantitatively evaluate the performance of the proposed method. The F1 score of the proposed method for OBE interference detection is over 0.79, and the RMSE of the compensated signal is less than 0.009 nT. Moreover, we also test our method on real signals, and the results show that our method can detect all interference and significantly reduce the standard deviation of the magnetic field.
A Particleboard Surface Defect Detection Method Research Based on the Deep Learning Algorithm
Authors: Zhao Ziyu , Ge Zhedong , Jia Mengying , Yang Xiaoxia , Ding Ruicheng , Zhou Yucheng
Published: October 2022

Particleboard surface defects have a significant impact on product quality. A surface defect detection method is essential to enhancing the quality of particleboard because the conventional defect detection method has low accuracy and efficiency. This paper proposes a YOLO v5-Seg-Lab-4 (You Only Look Once v5 Segmentation-Lab-4) model based on deep learning. The model integrates object detection and semantic segmentation, which ensures real-time performance and improves the detection accuracy of the model. Firstly, YOLO v5s is used as the object detection network, and it is added into the SELayer module to improve the adaptability of the model to receptive field. Then, the Seg-Lab v3+ model is designed on the basis of DeepLab v3+. In this model, the object detection network is utilized as the backbone network of feature extraction, and the expansion rate of atrus convolution is reduced to the computational complexity of the model. The channel attention mechanism is added onto the feature fusion module, for the purpose of enhancing the feature characterization capabilities of the network algorithm as well as realizing the rapid and accurate detection of lightweight networks and small objects. Experimental results indicate that the proposed YOLO v5-Seg-Lab-4 model has mAP (Mean Average Precision) and mIoU (Mean Intersection over Union) of 93.20% and 76.63%, with a recognition efficiency of 56.02 fps. Finally, a case study of the Huizhou particleboard factory inspection is carried out to demonstrate the tiny detection accuracy and real-time performance of this proposed method, and the missed detection rate of surface defects of particleboard is less than 1.8%.
Embodying Language through Gestures: Residuals of Motor Memories Modulate Motor Cortex Excitability during Abstract Words Comprehension
Authors: De Marco Doriana , De Stefani Elisa , Vecchiato Giovanni
Published: October 2022

There is a debate about whether abstract semantics could be represented in a motor domain as concrete language. A contextual association with a motor schema (action or gesture) seems crucial to highlighting the motor system involvement. The present study with transcranial magnetic stimulation aimed to assess motor cortex excitability changes during abstract word comprehension after conditioning word reading to a gesture execution with congruent or incongruent meaning. Twelve healthy volunteers were engaged in a lexical-decision task responding to abstract words or meaningless verbal stimuli. Motor cortex (M1) excitability was measured at different after-stimulus intervals (100, 250, or 500 ms) before and after an associative-learning training where the execution of the gesture followed word processing. Results showed a significant post-training decrease in hand motor evoked potentials at an early processing stage (100 ms) in correspondence to words congruent with the gestures presented during the training. We hypothesized that traces of individual semantic memory, combined with training effects, induced M1 inhibition due to the redundancy of evoked motor representation. No modulation of cortical excitability was found for meaningless or incongruent words. We discuss data considering the possible implications in research to understand the neural basis of language development and language rehabilitation protocols.
Development and Reliability Review of an Assessment Tool to Measure Competency in the Seven Elements of the Risk Management Process: Part Two—Evaluation of the Individual Results from the RISKometric Tool
Authors: Marling Garry , Horberry Tim , Harris Jill
Published: October 2022

This study aims to test further the RISKometric previously developed by the authors. This paper is the second of three studies: it compares individuals’ RISKometric results in the first study with their performance in a risk scenario exercise in this second study; so, providing a reliability review for the RISKometric. A risk scenario exercise was developed that required participants to individually undertake a risk management process on a realistic, potentially hazardous event involving working at heights during simultaneous operations. Two observers assessed their responses, rating the participants’ competence in each of the seven risk management process elements. Twenty-six participants individually undertook the risk scenario exercise, known as round one. Analyses found that participants’ individual competence ratings given to them (by observers) when undertaking the risk scenario exercise were strongly and positively associated with the competence ratings given to them by their peers and downline colleagues in the RISKometric in an earlier study; for each of the seven elements in this second study. This finding supports the RISKometric as a useful tool for rating the competency of individuals in the seven elements of the risk management process. Work was also undertaken in preparation for a planned future third study whereby eight participants of the original 26 were selected to individually undertake the risk scenario exercise again to determine any difference in ratings, e.g., if there was a learning effect. The analysis found no significant difference over the two rounds.
Big Five Personality Factors in Relation to Coping with Contact Restrictions during the COVID-19 Pandemic: A Small Sample Study
Authors: Weiß Martin , Rodrigues Johannes , Hewig Johannes
Published: October 2022

To slow down the spread of the SARS-Cov-2 virus, countries worldwide severely restricted public and social life. In addition to the physical threat posed by the viral disease (COVID-19), the pandemic also has implications for psychological well-being. Using a small sample (N = 51), we examined how Big Five personality traits relate to coping with contact restrictions during three consecutive weeks in the first wave of the COVID-19 pandemic in Germany. We showed that extraversion was associated with suffering from severe contact restrictions and with benefiting from their relaxation. Individuals with high neuroticism did not show a change in their relatively poor coping with the restrictions over time, whereas conscientious individuals seemed to experience no discomfort and even positive feelings during the period of contact restrictions. Our results support the assumption that neuroticism is a vulnerability factor in relation to psychological wellbeing but also show an influence of contact restrictions on extraverted individuals.
Impact of the Digital Economy in the High-Quality Development of Tourism—An Empirical Study of Xinjiang in China
Authors: Zhao Xinna , Mei Xuehui , Xiao Zhengqing
Published: October 2022

In the era of big data, the digital economy has become a key driving force for the high-quality development of tourism. Based on the annual panel data of 14 prefectures in Xinjiang from 2008 to 2018, this study proves the positive effect of the digital economy on the high-quality development of tourism. Through the construction of an evaluation index system for the high-quality development, a fixed effects model is used to investigate the relationship between them. Furthermore, mediating effect analysis is employed to study the mechanism. The robustness testing and heterogeneity analysis show the validity and rationality of the model. The results show that (1) The digital economy is an important driving force in the high-quality development of tourism in Xinjiang; (2) The digital economy promotes high-quality development by stimulating the upgrading of the tourism structure; (3) The impact of the digital economy on the high-quality development of tourism in different regions in Xinjiang presents great heterogeneity. The provincial capital presents a more significant effect.
American and Australian Tariff Policies: Do They Rock or Tango or Roll?
Authors: Cassette Aurélie , Farvaque Etienne
Published: October 2022

How can two countries’ trade policies be related to each other? A first possibility is that they are not related at all and that each country’s tariffs are defined under national considerations (the “rock” hypothesis). A second is that each country adapts its tariffs in reaction to what the other does (the “tango” hypothesis). A third is that both countries react to events happening in the rest of the world (the “roll” possibility). This paper examines the determinants of Australia’s and the US’ average tariff levels. Relying on historical data that cover a century (1904 to 2005), the three hypotheses are examined. The results indicate a strong long-run relation between the US and the Australian tariffs. Interrelations are also exhibited, with the US decisions influencing more strongly the Australian tariffs than the opposite. The results are important to assess the sustainability and stability of the regional trade agreements in the Pacific area.
Regional Differences and Key Influencing Factors of Fertilizer Integrated Efficiency in China
Authors: Liu Qinpu , Tu Wei , Pu Lijie , Zhou Li
Published: October 2022

Overuse and low efficiency of chemical fertilizers have caused severe non-point source pollution in China. The investigation of regional difference and the key influencing factors of fertilization intensities (FI) and efficiency can provide references for decision-makers to establish efficient policies for fertilizer use. Using simple models of fertilizer allocation efficiency (FAE) and fertilizer integrated efficiency (FIE), it was found that the east of China excessively used fertilizers, and both the middle and west showed both excessive and insufficient fertilizer use. The average values of the FIE in the east, middle and west of China were 0.69, 0.68 and 0.64, respectively, all of which were at low efficiency. The inter-provincial differences of FIE throughout the country ranged from 0.47 in Shannxi to 0.94 in Shanghai. The population aging rate (PAR), effective irrigation rate (EIR), natural disasters affected rate (DAR) and disaster damaged rate (DDR) are considered the key factors influencing the FIE, based on the new concept of cumulative weight (CW). PAR and EIR are the positive factors, while DAR and DDR are negative. The average FIE is now 0.67 in China, which implies that the increase of chemical fertilizer use efficiency or the reduction of chemical fertilizer amount has a potential of approximate 33%, with the current grain yield and other inputs unchanged. The increase of fertilizer use efficiency should be conducted under local conditions. Optimized intensification of grain production should be given more attention in the east, and implementing disaster prevention and reduction technologies and water-saving irrigation technologies are the preference in the middle and west of China.
Are Non-Special Dimensions of Proximity in Local Clusters Related? An Analysis of 99 European Clusters
Authors: Royo-Vela Marcelo , Mazandarani Mohammad-Reza
Published: October 2022

The destructive effects of industries on the environment are the most crucial reason for the need for firms’ proximity to developing innovative activities such as corporate sustainability. However, most prior efforts have focused on the role of different types of proximity in cluster performances, not the relationship among them. Therefore, this study intends to go one step back, discuss the inter-relationships between different types of proximity, and propose them in a conceptual model as the antecedents of corporate sustainability. These factors are known as the non-specific dimensions of proximity within local clusters: cognitive, organisational, institutional, and social, which received less attention as a theoretical model. To this end, this study invited all members of the European Cluster Cooperation Platform (ECCP) to participate. Among some 1080 European cluster organisations, 113 organisations participated, of which 99 of them were usable for this research. Contrary to existing conceptual theories, the results revealed no strong evidence for significant interrelations among all dimensions of non-specific proximity. Moreover, organisational proximity does not strongly relate to cognitive and social proximities, and there is no meaningful relationship between institutional and social proximities.
Electric Resistance Sintering of Al-TiO2-Gr Hybrid Composites and Its Characterization
Authors: Ansari Salman , Arif Sajjad , Ansari Akhter H. , Samad Abdul , Hadidi Haitham , Muaz Muhammed
Published: October 2022

In the present work, Al-TiO2-Gr hybrid composites were fabricated through a sustainable manufacturing approach, i.e., ERS (Electric Resistance Sintering) technique. In this experimental work, sintering is performed in a high-density graphite die, which also works as a heating element. The green compacts kept in the graphite die are sintered in two ways simultaneously (conduction and resistance heating). This facilitated the accomplishment of the sintering at a lower current (300–500 A). The aluminum (Al) was reinforced with 9 wt. % TiO2 (rutile) nanoparticles and 3 wt. % graphite microparticles to synthesize a self-lubricated high wear resistance material. Mechanical properties such as density, hardness, and wear loss of the Al-TiO2-Gr hybrid composite were investigated. Scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) were performed for microstructural investigation. The experiments were performed according to the Taguchi design of the experiment, where three input process parameters (temperature, holding time, and sintering load) were taken to fabricate the Al-TiO2-Gr composite. The sintering temperature of 550 °C resulted in the maximum value of mean sintered density (approx. 2.45 gm/cm3). The holding time of 10 min for the sintering resulted in the maximum value of mean sintered density and mean hardness (HRB 53.5). The mean value of wear loss was found to be minimum for the composites sintered at 600 °C for 10 min. The maximum value of the sintering load (800 N) revealed better density and hardness. Worn surfaces and wear debris were also analyzed with the help of SEM images. The sintering temperature of 600 °C resulted in imparting more wear resistance which was proved by smooth surfaces, micro-cutting, and fewer crates, grooves, and smaller pits.
Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning
Authors: Mansouri Ehsan , Manfredi Maeve , Hu Jong-Wan
Published: October 2022

In order to reduce the adverse effects of concrete on the environment, options for eco-friendly and green concretes are required. For example, geopolymers can be an economically and environmentally sustainable alternative to portland cement. This is accomplished through the utilization of alumina-silicate waste materials as a cementitious binder. These geopolymers are synthesized by activating alumina-silicate minerals with alkali. This paper employs a three-step machine learning (ML) approach in order to estimate the compressive strength of geopolymer concrete. The ML methods include CatBoost regressors, extra trees regressors, and gradient boosting regressors. In addition to the 84 experiments in the literature, 63 geopolymer concretes were constructed and tested. Using Python language programming, machine learning models were built from 147 green concrete samples and four variables. Three of these models were combined using a blending technique. Model performance was evaluated using several metric indices. Both the individual and the hybrid models can predict the compressive strength of geopolymer concrete with high accuracy. However, the hybrid model is claimed to be able to improve the prediction accuracy by 13%.
Challenging Examples of the Wise Use of Computer Tools for the Sustainability of Knowledge and Developing Active and Innovative Methods in STEAM and Mathematics Education
Authors: Körtesi Péter , Simonka Zsolt , Szabo Zsuzsanna Katalin , Guncaga Jan , Neag Ramona
Published: October 2022

The rapid changes in information and communication technology (ICT), the increasing availability of processing power, and the complexity of mathematical software demand a radical re-thinking of science, technology, engineering, arts, and mathematics (STEAM), as well as mathematics education. In the transition to technology-based classrooms, the constant use of educational software is a requirement for sustainable STEAM and mathematics education. This software supports a collaborative and actionable learning environment, develops 21st-century skills, and promotes the adoption of active and innovative methodologies. This paper focuses on learning and teaching mathematics and analyzes the role and utility of ICT tools in education as computer algebra systems (CAS) and dynamic geometry systems (DGS) in implementing active and innovative teaching methodologies related to sustainable STEAM education. Likewise, it highlights the necessity for learners to have extensive knowledge of mathematical theory, an essential asset to ensure the reliable and effective use of mathematical software. Through a practical experiment, this study aims to highlight that a mixed teaching method can significantly improve the sustainability of math knowledge. It provides various solid examples of CAS and DGS applications to emphasize its usage rooted in a mathematical background to enable learners to identify when the computer solution is unreliable. The study highlights that the proper use of CAS and DGS is an efficient method of deepening our understanding of mathematical notions and solving tasks in STEAM subjects and real-life applications. This paper’s goal is to direct our attention to the proper and intelligent use of computer tools, especially symbolic calculators, such as CAS and DGS, without providing an in-depth analysis of the challenges of these technologies. The outcomes of the paper should offer educators and learners new elements of active strategies and innovative learning models that can be immediately applied in education.