bluuebunny/crossref_metadata_2025 huggingface dataset:
Concentration Retrieval in a Calibration-Free Wavelength Modulation Spectroscopy System Using Particle Swarm Optimization Algorithm
Authors: Zhang Tingting , Sun Yongjie , Wang Pengpeng , Zhu Cunguang
Published: July 2023

This paper develops a concentration retrieval technique based on the particle swarm optimization (PSO) algorithm, which is used for a calibration-free wavelength modulation spectroscopy system. As compared with the commonly used technique based on the Levenberg–Marquardt (LM) algorithm, the PSO-based method is less dependent on the pre-characterization of the laser tuning parameters. We analyzed the key parameters affecting the performance of the PSO-based technique and determined their optimal parameter values through testing. Furthermore, we conducted a comparative analysis of the efficacy of two techniques in detecting C2H2 concentration. The results showed that the PSO-based concentration retrieval technique is about 63 times faster than the LM-based one in achieving the same accuracy. Within 5 s, the PSO-based technique can produce findings that are generally consistent with the values anticipated.
Wireless, Material-Integrated Sensors for Strain and Temperature Measurement in Glass Fibre Reinforced Composites
Authors: Bertram Lukas , Brink Michael , Lang Walter
Published: July 2023

Fiber reinforced plastics (FRP) offer huge potentials for energy efficient applications. Special care must be taken during both FRP fabrication and usage to ensure intended material properties and behavior. This paper presents a novel approach for the monitoring of the strain and temperature of glass fibre reinforced polymer (GFRP) materials in the context of both production process monitoring and structural health monitoring (SHM) applications. The sensor is designed to be integrated into GFRPs during the production process, and the sensor concept includes possibilities of automated placement during textile layup. To minimize sensor impact on GFRP integrity and to simplify vacuum setup and part handling, the sensor operates without the need for either wires or a battery. In the first sections of this work, sensor concept, design and prototype fabrication are presented. Subsequently, it is shown how the sensors can be used for flow front monitoring and cure estimation during GFRP production by measuring local resin temperature. The resulting specimens are then characterized regarding strain measurement capabilities, mechanical influence on the host component and overall system limitations. Average strain sensor accuracy is found to be ≤0.06 mm/m, while a maximum operation temperature of 126.9 °C and a maximum reading distance of 38 mm are measured. Based on a limited number of bending tests, no negative influence of sensor presence on breaking strength could be found. Possible applications include structural components, e.g., wind turbine blades or boat hulls.
Fooling Examples: Another Intriguing Property of Neural Networks
Authors: Zhang Ming , Chen Yongkang , Qian Cheng
Published: July 2023

Neural networks have been proven to be vulnerable to adversarial examples; these are examples that can be recognized by both humans and neural networks, although neural networks give incorrect predictions. As an intriguing property of neural networks, adversarial examples pose a serious threat to the secure application of neural networks. In this article, we present another intriguing property of neural networks: the fact that well-trained models believe some examples to be recognizable objects (often with high confidence), while humans cannot recognize such examples. We refer to these as “fooling examples”. Specifically, we take inspiration from the construction of adversarial examples and develop an iterative method for generating fooling examples. The experimental results show that fooling examples can not only be easily generated, with a success rate of nearly 100% in the white-box scenario, but also exhibit strong transferability across different models in the black-box scenario. Tests on the Google Cloud Vision API show that fooling examples can also be recognized by real-world computer vision systems. Our findings reveal a new cognitive deficit of neural networks, and we hope that these potential security threats will be addressed in future neural network applications.
Video-Based Human Activity Recognition Using Deep Learning Approaches
Authors: Surek Guilherme Augusto Silva , Seman Laio Oriel , Stefenon Stefano Frizzo , Mariani Viviana Cocco , Coelho Leandro dos Santos
Published: July 2023

Due to its capacity to gather vast, high-level data about human activity from wearable or stationary sensors, human activity recognition substantially impacts people’s day-to-day lives. Multiple people and things may be seen acting in the video, dispersed throughout the frame in various places. Because of this, modeling the interactions between many entities in spatial dimensions is necessary for visual reasoning in the action recognition task. The main aim of this paper is to evaluate and map the current scenario of human actions in red, green, and blue videos, based on deep learning models. A residual network (ResNet) and a vision transformer architecture (ViT) with a semi-supervised learning approach are evaluated. The DINO (self-DIstillation with NO labels) is used to enhance the potential of the ResNet and ViT. The evaluated benchmark is the human motion database (HMDB51), which tries to better capture the richness and complexity of human actions. The obtained results for video classification with the proposed ViT are promising based on performance metrics and results from the recent literature. The results obtained using a bi-dimensional ViT with long short-term memory demonstrated great performance in human action recognition when applied to the HMDB51 dataset. The mentioned architecture presented 96.7 ± 0.35% and 41.0 ± 0.27% in terms of accuracy (mean ± standard deviation values) in the train and test phases of the HMDB51 dataset, respectively.
Visual Quantitative Detection of Delamination Defects in GFRP via Microwave
Authors: Yang Xihan , Fang Yang , Wang Ruonan , Li Yong , Chen Zhenmao
Published: July 2023

Glass Fiber reinforced polymers (GFRPs) are widely used and play an important role in modern society. The multilayer structure of GFRPs can lead to delamination defects during production and service, which can have a significant impact on the integrity and safety of the equipment. Therefore, it is important to monitor these delamination defects during equipment service in order to evaluate their effects on equipment performance and lifespan. Microwave imaging testing, with its high sensitivity and noncontact nature, shows promise as a potential method for detecting delamination defects in GFRPs. However, there is currently limited research on the quantitative characterization of defect images in this field. In order to achieve visual quantitative nondestructive testing (NDT), we propose a 2D-imaging visualization and quantitative characterization method for delamination defects in GFRP, and realize the combination of visual detection and quantitative detection. We built a microwave testing experimental system to verify the effectiveness of the proposed method. The results of the experiment indicate the effectiveness and innovation of the method, which can effectively detect all delamination defects of 0.5 mm thickness inside GFRP with high accuracy, the signal-to-background ratio (SBR) of 2D imaging can reach 4.41 dB, the quantitative error of position is within 0.5 mm, and the relative error of area is within 11%.
YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System
Authors: Lindenheim-Locher Wojciech , Świtoński Adam , Krzeszowski Tomasz , Paleta Grzegorz , Hasiec Piotr , Josiński Henryk , Paszkuta Marcin , Wojciechowski Konrad , Rosner Jakub
Published: July 2023

This work is focused on the preliminary stage of the 3D drone tracking challenge, namely the precise detection of drones on images obtained from a synchronized multi-camera system. The YOLOv5 deep network with different input resolutions is trained and tested on the basis of real, multimodal data containing synchronized video sequences and precise motion capture data as a ground truth reference. The bounding boxes are determined based on the 3D position and orientation of an asymmetric cross attached to the top of the tracked object with known translation to the object’s center. The arms of the cross are identified by the markers registered by motion capture acquisition. Besides the classical mean average precision (mAP), a measure more adequate in the evaluation of detection performance in 3D tracking is proposed, namely the average distance between the centroids of matched references and detected drones, including false positive and false negative ratios. Moreover, the videos generated in the AirSim simulation platform were taken into account in both the training and testing stages.
LPO-YOLOv5s: A Lightweight Pouring Robot Object Detection Algorithm
Authors: Zhao Kanghui , Xie Biaoxiong , Miao Xingang , Xia Jianqiang
Published: July 2023

The casting process involves pouring molten metal into a mold cavity. Currently, traditional object detection algorithms exhibit a low accuracy and are rarely used. An object detection model based on deep learning requires a large amount of memory and poses challenges in the deployment and resource allocation for resource limited pouring robots. To address the accurate identification and localization of pouring holes with limited resources, this paper designs a lightweight pouring robot hole detection algorithm named LPO-YOLOv5s, based on YOLOv5s. First, the MobileNetv3 network is introduced as a feature extraction network, to reduce model complexity and the number of parameters. Second, a depthwise separable information fusion module (DSIFM) is designed, and a lightweight operator called CARAFE is employed for feature upsampling, to enhance the feature extraction capability of the network. Finally, a dynamic head (DyHead) is adopted during the network prediction stage, to improve the detection performance. Extensive experiments were conducted on a pouring hole dataset, to evaluate the proposed method. Compared to YOLOv5s, our LPO-YOLOv5s algorithm reduces the parameter size by 45% and decreases computational costs by 55%, while sacrificing only 0.1% of mean average precision (mAP). The model size is only 7.74 MB, fulfilling the deployment requirements for pouring robots.
A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data
Authors: Syed Md Asif Bin , Ahmed Imtiaz
Published: July 2023

In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising the geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel’s location and motion data collected through the automatic identification system (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures, our approach demonstrates a superior tracking performance.
Miniature Integrated 2.4 GHz Rectennas Using Novel Tunnel Diodes
Authors: Walsh Christopher , Muttlak Saad G. , Sadeghi Mohammadreza , Missous Mohamed
Published: July 2023

This work presents the design, fabrication, and measured results of a fully integrated miniature rectenna using a novel tunnel diode known as the Asymmetrical Spacer Layer Tunnel (ASPAT). The term rectenna is an abbreviation for a rectifying antenna, a device with a rectifier and antenna coexisting as a single design. The ASPAT is the centrepiece of the rectifier used for its strong temperature independence, zero bias, and high dynamic range. The antenna is designed to be impedance matched with the rectifier, eliminating the need for a matching network and saving valuable real estate on the gallium arsenide (GaAs) substrate. The antenna is fully integrated with the rectifier on a single chip, thus enabling antenna miniaturisation due to the high dielectric constant of GaAs and spiral design. This miniaturisation enables the design to be fabricated economically on a GaAs substrate whilst being comparable in size to a 15-gauge needle, thus unlocking applications in medical implants. The design presented here has a total die size of 4 × 1.2 mm2, with a maximum measured output voltage of 0.97 V and a 20 dBm single-tone 2.35 GHz signal transmitted 5 cm away from the rectenna.
Rapid and Simultaneous Extraction of Bisabolol and Flavonoids from Gymnosperma glutinosum and Their Potential Use as Cosmetic Ingredients
Authors: Gómez-Patiño Mayra Beatriz , Leyva Pérez Juan Pablo , Alcibar Muñoz Marcia Marisol , Arzate-Vázquez Israel , Arrieta-Baez Daniel
Published: July 2023

Gymnosperma glutinosum is a plant popularly known as “popote”, “tatalencho”, “tezozotla” or “pegajosa”, and it is used in traditional medicine in the region of Tehuacán, Puebla (Mexico), for the treatment of jiotes and acne and to cure diarrhea using the aerial parts in infusions. To analyze the phytochemical composition, we have developed a rapid protocol for the extraction and separation of the components of the aerial parts of G. glutinosum. After a maceration process, chloroformic and methanolic extracts were obtained and analyzed. Extracts were evaluated by GC-MS (gas chromatography-mass spectrometry), and their composition revealed the presence of (−)-α-bisabolol (BIS) as the main component in the chloroformic extract, which was isolated and analyzed by 1H NMR to confirm its presence in the plant. The analysis of methanolic extracts by UPLC-MS (ultra-performance liquid chromatography-mass spectrometry) revealed the occurrence of six methoxylated flavones with m/z 405.08 (C19H18O10), m/z 419.09 (C20H20O10) and m/z 433.11 (C21H22O10), and a group of C20-, C18-hydroxy-fatty acids, which give the plant its sticky characteristic. The presence of BIS, an important sesquiterpene with therapeutic skin effects, as well as some antioxidant compounds such as methoxylated flavones and their oils, could play an important role in cosmetology and dermatology formulations.
Traditional Water Systems Informing Sustainable Contemporary Drylands Design: Documentation, Extraction, and Deployment
Authors: Jiang Yuliang , Arnold Hadley
Published: July 2023

Climate change has become a pressing issue in cities around the globe, especially those in dry regions. Despite these cities’ cultural vitality, water shortages are among the central problems impacting society. The aim of this study was to recapture, record, and rethink the world’s traditional water systems with two objectives. The first objective was to scrutinize the mechanics, social functions, and spatial organization of these systems. The second objective was to develop novel adaptations of these old technologies for new discourses and apply them to the water-stressed urban landscapes of Los Angeles. The intent was to build a greater capacity for resilient landscape and infrastructure design in a post-carbon world by constructing a more robust lexicon of pre-carbon drylands design. Notable similarities surfaced among the systems despite their distinct cultural backgrounds and historical origins, indicating commonalities across the evolution of water infrastructure in human history. The output of this study established the basis for a systematic drylands atlas as a resource for research-informed design of the built environment. The outcomes make fundamental water-centric climate change adaptation strategies accessible through visual communication techniques for professional practices and pedagogic purposes.
Spatial Distribution Characteristics and Influencing Factors of Cultural and Tourism Resources in Xihu District of Hangzhou
Authors: Zhu Tiansong , Yu Kaiping , Wang Bo
Published: July 2023

Exploring the spatial distribution characteristics of regional cultural and tourism resources (CTRs) is crucial to the sustainable development of cultural and tourism industries. Based on 651 CTRs obtained from the latest round of field surveys in the Xihu District of Hangzhou, this article analyzed the spatial distribution of these CTRs from an overall, categorical, and hierarchical multiple perspective using the average nearest neighbor index and kernel density analysis and explored the reasons for the differences in the spatial distribution of the above different types of CTRs using multiple linear regression and Geodetector. The results indicate that the CTRs present a spatially clustered state, and the distribution of CTRs in different categories and grades has diverse characteristics, as natural resources have three high-density areas, humanistic resources have one high-density area, and both general and superior resources have only one high-density area, but the low-density areas are different. In addition, the spatial distribution of the overall, different categories, and different grades of CTRs in this region is influenced by several factors, with the West Lake generating the greatest impact. Additionally, interactive factors can have a greater impact than single factors. These results enrich the research content of Hangzhou’s cultural and tourism industries and provide theoretical support for the high-quality development of Xihu District’s cultural and tourism industries.
The Impact of Accessing Education via Smartphone Technology on Education Disparity—A Sustainable Education Perspective
Authors: Alam Gazi Mahabubul , Forhad Md. Abdur Rahman
Published: July 2023

Following the fourth target of the United Nations’ Sustainable Development Goals (SDGs), education disparity is one of the graver concerns delaying substantial economic development, especially in emerging market-based nations. Despite numerous efforts to address this disparity, it has been a long-standing concern for many communities. It is important to note that the recent pandemic has changed teaching and learning approaches worldwide, where disparity is one of the most challenging issues. Although similar and/or equal access to technology has always been challenging, online learning practices were widely implemented with varying levels of success during the pandemic. Using purposive sampling on administrative data between 2020 and 2022 on undergraduate programs in public universities in Bangladesh, this study examines the effects of technology access on academic achievement. This study employs the difference in differences (DID) technique to analyze the data and finds that access to technology improves students’ learning. Findings strongly suggest that technology access reduces disparity, mainly due to socioeconomic status (SES). Findings also imply that ensuring access to technology for individuals from poorer SES backgrounds should be prioritized to address education inequality-related challenges.
Synthesis of Biomass Corridor in Peninsular Malaysia via Hybrid Mathematical and Graphical Framework
Authors: Lam Hon Loong , Ang Jia Chun , Heng Yi Peng , Lee Ho Yan , Loy Adrian Chun Minh , How Bing Shen
Published: July 2023

The valorisation of biomass by synthesising a multi-biomass corridor can be an optimistic pathway to solving the growing waste management problem. However, the supply chain problem usually involves a massive number of variables, including the connectivity of the sink source and the selection of a technology pathway. In this work, a “Decomposition Approach” was utilised, wherein a P-graph was incorporated with a conventional mathematical model to reduce the number of variables. Although this type of approach is well established with respect to solving biomass supply chain problems, no previous works have comprehensively considered the effect of the maximum allowable travel distance (MATD) on a supply chain model. A case study in Peninsular Malaysia involving oil palm, paddy, and coconut biomass was conducted using the proposed approach. Moreover, a multiple linear regression (MLR) tool for formulating the cost-correlated function based on the best technology pathway obtained from a P-Graph was incorporated. As a result, the net profit of the biomass corridor was estimated to be USD 0.87 billion, with 1.45 × 107 tonnes per year of biomass being sent to 39 processing hubs over a 20-year lifespan. Furthermore, a sensitivity analysis was also conducted to investigate the impact of several cost-related parameters on the net profit.
Insect-Based Feed Acceptance amongst Consumers and Farmers in Ireland: A Pilot Study
Authors: Ranga Leocardia , Noci Francesco , Vale Ana P. , Dermiki Maria
Published: July 2023

The potential of insect-based feed (IBF) as a sustainable alternative to conventional animal feed is widely reported, yet there is extremely limited information on its acceptance in Ireland, a country with a strong farming background. Therefore, this study aims to provide baseline data on factors affecting acceptance of IBF amongst a segment of consumers and farmers in Ireland. Quantitative and qualitative data were collected amongst 233 consumers, 73 of which were farmers. Non-parametric statistical tests revealed that the willingness to consume foods from animals fed with IBF depends on the type of food and is affected by a combination of consumer- and product-related factors. Consumers’ age, gender, diet, and education level, the foods’ packaging information, safety, and price, and whether insects are part of an animal’s natural diet or environmentally friendly had a significant effect. Safety concern regarding use of IBF was the main factor affecting farmers’ willingness to use it. Qualitative findings revealed concerns emanating from the bovine spongiform encephalopathy outbreak and a general need for more information. Accordingly, information on the benefits of using IBF increased its acceptance. Thus, IBF acceptance might depend on dedicated educational interventions which include addressing the safety aspect of the feed even among those with higher level of education.
Multiresponse Performance Evaluation and Life Cycle Assessment for the Optimal Elimination of Pb (II) from Industrial Wastewater by Adsorption Using Vine Shoot Activated Carbon
Authors: Sabando-Fraile Celia , Corral-Bobadilla Marina , Lostado-Lorza Rubén , Somovilla-Gomez Fátima
Published: July 2023

Excess Pb (II) concentrations in wastewater have raised concerns of a risk to health and the environment due to their toxicity. This has contributed to the need for sustainable technology to remove heavy metals from wastewater. Biosorption provides a potential contribution to a solution. This study proposes a cost-effective method to remove lead ions from wastewater through the use of activated carbon from vine shoots as a biosorbent. However, economic cost and environmental impact are aspects that are necessary to study. This research suggests the use of a life cycle assessment and multiresponse surface method with desirability functions to improve and optimize the biosorption process. The experiments were conducted using a Box–Behnken design of experiments (BBD) combined with the multiresponse surface method. Three input variables were considered. They are initial lead concentration, pH, and the amount of activated carbon from vine shoots. These are the most significant adsorption process variables. The final lead concentration was considered as a process output variable. Human toxicity, global warming, abiotic depletion (fossil fuel), marine aquatic ecotoxicity, and freshwater ecotoxicity were regarded as process environmental impacts. Four optimization scenarios were proposed using these methods. The maximum removal of lead was 92.12%, whereas 92.09% of lead was removed when the minimum dose of vine shoot activated carbon was used. In contrast, 52.62% of lead was removed in the case of minimal environmental impact.
Agroforestry as a Driver for the Provisioning of Peri-Urban Socio-Ecological Functions: A Trans-Disciplinary Approach
Authors: Dal Borgo Alice Giulia , Chiaffarelli Gemma , Capocefalo Valentina , Schievano Andrea , Bocchi Stefano , Vagge Ilda
Published: July 2023

Peri-urban rural system rehabilitation is pivotal to the socio-ecological balanced functioning of urban systems. In this paper, we investigate the performance of agroforestry participative practices in rehabilitating peri-urban belts (in-field productive agroforestry; between-field landscape features). We test a new trans-disciplinary, multi-level analytical framework for the ecosystem services (ESs) assessment based on site-specific socio-ecological information. We parallelly analyse ecological and cultural traits: 1. agroecosystem components (flora–vegetation; human community); 2. their organization at the landscape level (landscape eco-mosaic; cultural landscape); and 3. their socio-ecological functions/processes. We compare the current state with a transformation scenario. The first application to the “Milano Porta Verde” agroecology hub, Italy, outlined: 1. the agro-eco-mosaic structuring and diversification improvement consequent to the agroforestry model spread (higher natural components percentage, agricultural patch shape complexity, landscape heterogeneity, landscape structural diversity, connectivity and circuitry); and 2. the cultural functions provided by participative practices (40 initiatives; 1860 people involved; 10 stakeholder types), enabling cultural landscape rehabilitation processes (higher accessibility, citizen empowerment, community and knowledge building, cultural values building). These results qualitatively inform the ES analysis. The potential ES supply matrices and maps showed an increase, through a transformation scenario, in the total ESs delivered by natural components (+44% support ESs; +36% regulating ESs) and agricultural components (+21% cultural ESs; +15% regulating ESs).
Spatial–Temporal Change Analysis and Multi-Scenario Simulation Prediction of Land-Use Carbon Emissions in the Wuhan Urban Agglomeration, China
Authors: Zhang Junxiang , Zhang Chengfang , Dong Heng , Zhang Liwen , He Sicong
Published: July 2023

In the context of global warming, the Wuhan Urban Agglomeration is actively responding to China’s carbon peak and carbon neutrality goals and striving to achieve a reduction in carbon sources and an increase in carbon sinks. Therefore, it is critical to investigate carbon emissions from land use. This study uses the carbon emission coefficient method to calculate carbon emissions from land use in the Wuhan Urban Agglomeration, analyzes its temporal and spatial changes and differences in urban structure, and couples with the Markov–PLUS model to simulate and predict the carbon emissions of four scenarios of land use in 2035. The research found the following: (1) during the Wuhan “1+8” City Circle stage, carbon sources and emissions increased steadily, with average annual growth rates of 1.92% and 1.99%, respectively. Carbon sinks remained stable and then decreased, with an average annual growth rate of −0.46%. (2) During the Wuhan Metropolitan Area stage—except for 2020 and 2021, which were affected by COVID-19—carbon sources, sinks, and emissions continued to grow in general, and the average annual growth rates increased to 4.46%, 1.58%, and 4.51%, respectively. (3) In terms of urban structure differences, Wuhan is a high-carbon optimization zone; Xianning, Huangshi, and Huanggang are ecological protection zones; other cities, such as Ezhou, Xiaogan, and Xiantao are comprehensive optimization zones; and there is no low-carbon development zone. (4) The multi-scenario simulation results show that carbon sources and emissions are the highest under the economic development scenario, with values of 100.2952 and 9858.83 million tons, respectively, followed by cropland protection, natural development, and low-carbon development scenarios. Under low-carbon development, carbon sinks were the highest, with values of 1.9709 million tons, followed by natural development, economic development, and cropland protection scenarios. The research results are conducive to the formulation of carbon peak and neutrality goals as well as low-carbon development plans for the Wuhan Urban Agglomeration.
A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree
Authors: Xing Feng , Song Xiaoyu , Wang Yubo , Qin Caiyan
Published: July 2023

Wind power is an essential component of renewable energy. It enables the conservation of conventional energy sources such as coal and oil while reducing greenhouse gas emissions. To address the stochastic and intermittent nature of ultra-short-term wind power, a combined prediction model based on variational mode decomposition (VMD) and gradient boosting regression tree (GBRT) is proposed. Firstly, VMD is utilized to decompose the original wind power signal into three meaningful components: the long-term component, the short-term component, and the randomness component. Secondly, based on the characteristics of these three components, a support vector machine (SVM) is selected to predict the long-term and short-term components, while gated recurrent unit-long short-term memory (GRU-LSTM) is employed to predict the randomness component. Particle swarm optimization (PSO) is utilized to optimize the structural parameters of the SVM and GRU-LSTM combination for enhanced prediction accuracy. Additionally, a GBRT model is employed to predict the residuals. Finally, the rolling predicted values of the three components and residuals are aggregated. A deep learning framework using TensorFlow 2.0 has been built on the Python platform, and a dataset measured from a wind farm has been utilized for learning and prediction. The comparative analysis reveals that the proposed model exhibits superior short-term wind power prediction performance, with a mean squared error, mean absolute error, and coefficient of determination of 0.0244, 0.1185, and 0.9821, respectively.
Biobased Natural Sapindus mukorossi–Carvacrol Emulsion for Sustainable Laundry Washing
Authors: Lunder Manca , Tomšič Brigita , Fink Rok
Published: July 2023

Low-temperature laundry washing prompted the formulation of a new biobased detergent that meets sustainability criteria. A soapnut extract conferred good cleaning performance on the standard soiling agents EMPA 101 and EMPA 114 but showed rather weak performance on EMPA 116 and EMPA 160. The results indicated the good disinfection properties of the soapnut extract–Carvacrol emulsion (>6 log CFU/carriers), whereas the commercial detergent and sole soapnut extract were less effective. Comparable results were observed for cross-contamination assessment and wash water, whereas total elimination was achieved only for the soapnut extract–Carvacrol emulsion. We demonstrated significantly decreased water surface tension for all three compounds. The microorganism cell membrane integrity assessment showed the highest number of dead cells on cotton carriers washed with the soapnut extract–Carvacrol emulsion. All this indicated that the newly formulated biobased laundry detergent made of soapnut extract and Carvacrol could effectively remove standard soiling agents and ensure good disinfection.
Numerical Investigation of Scour Downstream of Diversion Barrage for Different Stilling Basins at Flood Discharge
Authors: Zaffar Muhammad Waqas , Hassan Ishtiaq , Latif Umair , Jahan Shah , Ullah Zeeshan
Published: July 2023

The hydraulic performance of stilling basins is affected by their size and geometry, which can be predicted by local scour. In 2008, based on a rigid bed study, the stilling basin of Taunsa barrage was remodeled, in which the old friction and baffle blocks were replaced with chute blocks and end sills. However, the study did not consider the effects of the remodeled basin on the erodible bed and only investigated hydraulic jumps. Therefore, this study developed FLOW-3D scour models for a designed flow of 24.28 m3/s/m to investigate the flow field and local scouring downstream of old and remodeled basins. The results showed that as compared to Large Eddy Simulation (LES) and Standard K-ε models, the Renormalization Group (RNG-K-ε) model predicted the scour profiles with better accuracy, for which the coefficient of determination (R2) reached 0.736, 0.823, and 0.747 for bays 33, 34, and 55, respectively. Downstream of the remodeled basin, the net change in sediment bed was 88%, 91%, and 95% in the LES, Standard, and RNG-K-ε models, respectively. However, downstream of the old basin, the net change in sediment bed reached 51%. Conclusively, based on the results, the study suggests investigating scour downstream of Taunsa Barrage using other discharges and sediment transport rate equations.
Accounting for the Logic and Spatiotemporal Evolution of the Comprehensive Value of Cultivated Land around Big Cities: Empirical Evidence Based on 35 Counties in the Hefei Metropolitan Area
Authors: Wang Bingyi , Chen Tong , Liu Wangbing
Published: July 2023

Exploring the value of cultivated land resources around big cities is an important prerequisite for when realizing the “trinity” of cultivated land protection in terms of quantity, quality, and ecology. At present, the value of cultivated land resources faces the problem of having a low comparative efficiency of economic output, inadequate visualisation of asset value, and serious undervaluation. The manifestation of social, ecological, and cultural values in cultivated land plays an important role in accurately calculating the value of cultivated land. Therefore, this study attempted to clarify the composition and account for the logic of the comprehensive value of cultivated land around big cities. Taking the Hefei metropolitan area as an example, we used mathematical modeling and geostatistical analysis to measure the integrated value of cultivated land around big cities from 2010 to 2020 and analyse the spatial and temporal evolution characteristics and influencing factors. The results revealed a 2:7:1 ratio of economic, social, ecological, and cultural values for cultivated land in the metropolitan area. The comprehensive value of cultivated land was higher in 2020 than in 2010, was approximately 7.7 times the current compensation standard, and was significantly affected by the natural geography, economic development, cultivated land conditions, and protection policies. Furthermore, the comprehensive value showed spatial differentiation characteristics of ‘high in the east and south and low in the west and north’, and a spatial agglomeration effect was evident in some areas. A significant increase was observed in the social value of cultivated land, followed by an increase in the ecological and cultural values, whereas a slow downward trend was observed for the economic value. The economic value of cultivated land around big cities showed a downward trend, whereas the social, ecological, and cultural values showed upward trends. In the future, differentiated policy tools should be adopted based on the spatial heterogeneity of the comprehensive value of cultivated land in the Hefei metropolitan area to enhance their comprehensive value. In addition, scientific and reasonable compensation standards should be established, and high-quality agricultural development with the high-level protection of cultivated land should be promoted in metropolitan areas.
Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
Authors: Manzoor Maria , Kamboh Usman Rauf , Gulshan Sumaira , Tomforde Sven , Gul Iram , Siddiqui Alighazi , Arshad Muhammad
Published: July 2023

Lead (Pb) is well known for the containment of soil surfaces. In the last few decades, phytoremediation has been the most ideal technology to extract Pb from soil, involving numerous chemical reactions and cost analysis. The aim of this study is to model and to optimize Pb extraction from the contaminated soil via Pelargonium hortorum by comparing two modeling approaches: response surface methodology (RSM) and artificial neural networks (ANNs) with the genetic algorithm (GA). To determine the significance of the proposed solution, in vitro essays were performed to check the Pb tolerance of bacterial strains (NCCP 1844, 1848, 1857, and 1862), followed by the co-application of bacteria and citric acid on a Pb hyperaccumulator (Pelargonium hortorum L.) on Murashige and Skoog (MS) agar medium. Afterwards, a pot culture experiment was performed to optimize Pb extraction competency from Pb-spiked (0 mg kg−1, 500 mg kg−1, 1000 mg kg−1, and 1500 mg kg−1) soil by Pelargonium hortorum L., to which citric acid (5 and 10 mmol L−1) and Microbacterium paraoxydance (1 and 1.5 OD) were applied. Plants were harvested at 30, 60, and 90 day intervals, and they were analyzed for dry biomass and Pb uptake characteristics. The maximum Pb extraction efficiency of 86.0% was achieved with 500 mg kg−1 soil Pb for 60 days. Furthermore, RSM, based on the Box–Behnken design (BBD) and the ANN-based Levenberg–Marquardt Algorithm (LMA), were applied to model Pb extraction from the soil. The significance of the predicted values from RSM and LMA were close to 36.0% and 86.05%, respectively, compared to the laboratory values. The comprehensive evaluation of these findings encouraged the accuracy, reliability, and efficiency of the ANN for the optimization process. Therefore, experimental results showed that ANN is an accurate technique to optimize an integrated phytoremediation system for sustainable Pb removal, besides being environmentally friendly and potentially cost-effective.
On a Measure of Tail Asymmetry for the Bivariate Skew-Normal Copula
Authors: Yoshiba Toshinao , Koike Takaaki , Kato Shogo
Published: July 2023

Asymmetry in the upper and lower tails is an important feature in modeling bivariate distributions. This article focuses on the log ratio between the tail probabilities at upper and lower corners as a measure of tail asymmetry. Asymptotic behavior of this measure at extremely large and small thresholds is explored with particular emphasis on the skew-normal copula. Our numerical studies reveal that, when the correlation or skewness parameters are around at the boundary values, some asymptotic tail approximations of the skew-normal copulas proposed in the literature are not suitable to compute the measure of tail asymmetry with practically extremal thresholds.
On Edge Detection Algorithms for Water-Repellent Images of Insulators Taking into Account Efficient Approaches
Authors: Ding Yizhuo , Nan Xiaofei
Published: July 2023

Computer vision has become an essential interdisciplinary field that aims to extract valuable information from digital images or videos. To develop novel concepts in this area, researchers have employed powerful tools from both pure and applied mathematics. Recently, the use of fractional differential equations has gained popularity in practical applications. Moreover, symmetry is a critical concept in digital image processing that can significantly improve edge detection. Investing in symmetry-based techniques, such as the Hough transform and Gabor filter, can enhance the accuracy and robustness of edge detection algorithms. Additionally, CNNs are incredibly useful in leveraging symmetry for image edge detection by identifying symmetrical patterns for improved accuracy. As a result, symmetry reveals promising applications in enhancing image analysis tasks and improving edge detection accuracy. This article focuses on one of the practical aspects of research in computer vision, namely, edge determination in image segmentation for water-repellent images of insulators. The article proposes two general structures for creating fractional masks, which are then calculated using the Atangana–Baleanu–Caputo fractional integral. Numerical simulations are utilized to showcase the performance and effectiveness of the suggested designs. The simulations’ outcomes reveal that the fractional masks proposed in the study exhibit superior accuracy and efficiency compared to various widely used masks documented in the literature. This is a significant achievement of this study, as it introduces new masks that have not been previously used in edge detection algorithms for water-repellent images of insulators. In addition, the computational cost of the suggested fractional masks is equivalent to that of traditional masks. The novel structures employed in this article can serve as suitable and efficient alternative masks for detecting image edges as opposed to the commonly used traditional kernels. Finally, this article sheds light on the potential of fractional differential equations in computer vision research and the benefits of developing new approaches to improve edge detection.
An Evaluation of the Accuracy and Precision of Ceramic Orthodontic Bracket Slot Dimensions Utilizing Micro-Computed Tomography (Micro-CT)
Authors: Garrett Antonio , Alghilan Maryam A. , Ash Simon , Awawdeh Mohammed , Singh Parmjit
Published: July 2023

This study’s aim is to determine the accuracy and precision of the bracket slot height in MBT 0.022″ ceramic brackets. Five brackets from 11 different systems (n = 55) were scanned using micro-computed tomography (micro-CT). The slot height was measured at the face (external) and base (internal) of the slot. Data were analyzed using a One-Sample t-Test, and a Paired t-Test. The lowest external height was seen in OrthoCare Purity® at 0.02264″ (2.9%), and the greatest in TOC Ghost Advanced® at 0.02736″ (24.4%). The lowest internal height was seen in Forestadent Glam® at 0.02020″ (−8.2%), and the greatest in TOC Ghost Advanced® at 0.2547″ (15.8%). OrthoCare Purity® measurements corresponded most closely with the expected measurements. TP InVu® was found to be the most precise bracket for external height (range = 0.00043″) and American Orthodontics 20/40® for internal height (range = 0.00028″). In assessing slot geometry, all brackets demonstrated a higher mean external slot height compared to the internal measurements at the base of the bracket. Orthodontic bracket slots are larger than expected and slot parallelism was not observed in any bracket brand tested. Similarly, slot dimensions are imprecise where two ‘identical’ brackets have different slot sizes. The clinician should, therefore, assume that play is most likely higher than expected.
Whole-Transcriptome Analysis on the Leaves of Rosa chinensis Jacq. under Exposure to Polycyclic Aromatic Hydrocarbons
Authors: Tian Shili , Liu Qingyang , Qu Jingming , Yang Ming , Ma Qiaoyun , Liu Jia , Shao Peng , Liu Yanju
Published: July 2023

The leaves of plants can be recommended as a cheap and sustainable environmental protection tool to mitigate PAHs with high toxicity in the ambient environment because they can serve as a reactor to remove ambient PAHs. Although previous studies have demonstrated that PAHs exhibit toxicological features, our knowledge about how ambient PAHs influence the leaves of plants is limited regarding the leaves of plants reducing ambient PAHs as a reactor. In this study, 1-year-old Rosa chinensis Jacq. with good growth potential was selected as a model plant. The leaves of Rosa chinensis Jacq. were exposed to 16 types of PAHs in the environmental concentration exposure group (0.1 μg L−1) and high-concentration exposure group (5 μg L−1) for seven days. In comparison, the leaves of Rosa chinensis Jacq. were exposed to de-ionized water and were chosen as the control group. During the exposure periods, the physiological parameters of leaves including, chlorophyll value, water content, temperature and nitrogen, were monitored using a chlorophyll meter. After 7 days of exposure, the leaves in the control and exposure groups were collected and used for whole-transcriptome analysis. Our results demonstrate that significant differentially expressed genes were observed in the leaves of Rosa chinensis Jacq. exposed to individual PAHs at 5 μg L−1 compared to the control group. These differentially expressed genes were involved in seven main pathways using bioinformatic analyses. In contrast, the levels of PAHs at environmentally relevant concentrations had negligible impacts on the physiological parameters and the gene transcription levels of the leaves of Rosa chinensis Jacq. Our results may provide direct evidence to remove ambient PAHs using terrestrial trees without considering the risk of PAHs at environmentally relevant concentrations on the leaves of terrestrial plants.
Uncertainties in Pollution and Risk Assessments of Heavy Metals in Lake Sediments Using Regional Background Soils in China
Authors: Wan Dejun , Gao Jiapeng , Song Ruiting , Song Lei , Ning Dongliang
Published: July 2023

Background soils are frequently utilized as a surrogate to assess pollution levels and environmental risks of heavy metals in Chinese lakes. However, there remains a lack of understanding regarding the reliability and uncertainty of such assessments. Here, we determined heavy metals (As, Cd, Co, Cr, Cu, Hg, Ni, Pb, and Zn) in sediment cores from five rural lakes in North China to evaluate the reliability and uncertainty of the assessments using background soils by comparing them with assessments based on background sediments. Comparative studies reveal large uncertainties in the assessments using background soils. Among these metals, uncertainties for Hg and Cd are relatively large, whereas those for the other metals are minor. This discrepancy is due to the considerably higher natural variability of Hg and Cd in soils and sediments in comparison to the other metals. Generally, assessments utilizing background soils underestimate pollution levels and risks of Hg but overestimate those of Cd in these lakes. Despite limited human activities around the lakes, they still received a considerable influx of heavy metals via regional atmospheric transport. Assessments of the nine metals indicate moderate to considerable ecological risks in these lakes. The risks are contributed primarily (78–89%) by Hg and Cd. This study underscores the substantial uncertainties in assessing heavy metal pollution and risks using regional background soils and emphasizes the importance of controlling atmospheric emissions of Hg and Cd to mitigate pollution in rural and remote water bodies in China.