bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Efficiency of Generation of Terahertz Surface Plasmon Polaritons by the End-Fire Coupling Technique
Authors: Gerasimov Vasily Valerievich , Nikitin Alexey Konstantinovich , Lemzyakov Alexey Georgievich , Azarov Ivan Aleksandrovich
Published: August 2023

One of the most important problems in the plasmonics of the terahertz (THz) range, which is actively developing now, is the efficient generation of surface plasmon polaritons (SPPs). The simplest and most promising technological technique of photon excitation of THz SPPs is through diffraction of radiation on the edge of the conducting surface of the sample (the end-fire coupling technique). In this paper, we experimentally evaluated the efficiency of the generation of monochromatic THz SPPs (λ0 = 141 μm) by this method with a sample in the form of a cylindrical segment, the convex surface of which has a gold layer coated by zinc sulfide (ZnS) with thickness d = 0–2 µm. Such configuration of the surface supporting the SPPs not only shields the detector from parasitic bulk waves arising during diffraction but also enables one to change the distribution of the SPP field in the air by varying the coating layer thickness d. On an uncoated gold surface, the SPP generation efficiency was η ≈ 20%. In the presence of a ZnS layer on the gold, the SPP generation efficiency gradually increased with d, reached the maximum (ηmax ≈ 60%) at d ≈ 1 μm, and then gradually decreased. Theoretical analysis showed that the efficiency of the SPP generation can be raised up to 80% due to the selection of an optimal SPP field profile via variation of the thickness of the dielectric layer on the metal surface, as well as with optimal incidence of the focused radiation on the edge of the sample.
Heat-Driven Synchronization in Coupled Liquid Crystal Elastomer Spring Self-Oscillators
Authors: Li Kai , Wu Haiyang , Zhang Biao , Dai Yuntong , Yu Yong
Published: August 2023

Self-oscillating coupled machines are capable of absorbing energy from the external environment to maintain their own motion and have the advantages of autonomy and portability, which also contribute to the exploration of the field of synchronization and clustering. Based on a thermally responsive liquid crystal elastomer (LCE) spring self-oscillator in a linear temperature field, this paper constructs a coupling and synchronization model of two self-oscillators connected by springs. Based on the existing dynamic LCE model, this paper theoretically reveals the self-oscillation mechanism and synchronization mechanism of two self-oscillators. The results show that adjusting the initial conditions and system parameters causes the coupled system to exhibit two synchronization modes: in-phase mode and anti-phase mode. The work conducted by the driving force compensates for the damping dissipation of the system, thus maintaining self-oscillation. The phase diagrams of different system parameters are drawn to illuminate the self-oscillation and synchronization mechanism. For weak interaction, changing the initial conditions may obtain the modes of in-phase and anti-phase. Under conditions of strong interactions, the system consistently exhibits an in-phase mode. Furthermore, an investigation is conducted on the influence of system parameters, such as the LCE elastic coefficient and spring elastic coefficient, on the amplitudes and frequencies of the two synchronization modes. This study aims to enhance the understanding of self-oscillator synchronization and its potential applications in areas such as energy harvesting, power generation, detection, soft robotics, medical devices and micro/nanodevices.
Satellite Altimetry for Ocean and Coastal Applications: A Review
Authors: Srinivasan Margaret , Tsontos Vardis
Published: August 2023

More than 30 years of observations from an international suite of satellite altimeter missions continue to provide key data enabling research discoveries and a broad spectrum of operational and user-driven applications. These missions were designed to advance technologies and to answer scientific questions about ocean circulation, ocean heat content, and the impact of climate change on these Earth systems. They are also a valuable resource for the operational needs of oceanographic and weather forecasting agencies that provide information to shipping and fishing vessels and offshore operations for route optimization and safety, as well as for other decision makers in coastal, water resources, and disaster management fields. This time series of precise measurements of ocean surface topography (OST)—the “hills and valleys” of the ocean surface—reveals changes in ocean dynamic topography, tracks sea level variations at global to regional scales, and provides key information about ocean trends reflecting climate change in our warming world. Advancing technologies in new satellite systems allows measurements at higher spatial resolution ever closer to coastlines, where the impacts of storms, waves, and sea level rise on coastal communities and infrastructure are manifest. We review some collaborative efforts of international space agencies, including NASA, CNES, NOAA, ESA, and EUMETSAT, which have contributed to a collection of use cases of satellite altimetry in operational and decision-support contexts. The extended time series of ocean surface topography measurements obtained from these satellite altimeter missions, along with advances in satellite technology that have allowed for higher resolution measurements nearer to coasts, has enabled a range of such applications. The resulting body of knowledge and data enables better assessments of storms, waves, and sea level rise impacts on coastal communities and infrastructure amongst other key contributions for societal benefit. Although not exhaustive, this review provides a broad overview with specific examples of the important role of satellite altimetry in ocean and coastal applications, thus justifying the significant resource contributions made by international space agencies in the development of these missions.
Analysing Pine Disease Spread Using Random Point Process by Remote Sensing of a Forest Stand
Authors: Kosarevych Rostyslav , Jonek-Kowalska Izabela , Rusyn Bohdan , Sachenko Anatoliy , Lutsyk Oleksiy
Published: August 2023

The application of a process model to investigate pine tree infestation caused by bark beetles is discussed. The analysis of this disease was carried out using spatial and spatio−temporal models of random point patterns. Spatial point patterns were constructed for remote sensing images of pine trees damaged by the apical bark beetle. The method of random point processes was used for their analysis. A number of known models of point pattern processes with pairwise interaction were fitted to actual data. The best model to describe the real data was chosen using the Akaike information index. The residual K−function was used to check the fit of the model to the real data. According to values of the Akaike information criterion and the residual K−function, two models were found to correspond best to the investigated data. These are the generalized Geyer model of the point process of saturation and the pair interaction process with the piecewise constant potential of a pair of points. For the first time, a spatio−temporal model of the contagious process was used for analysis of tree damage.
An Intra-Class Ranking Metric for Remote Sensing Image Retrieval
Authors: Liu Pingping , Liu Xiaofeng , Wang Yifan , Liu Zetong , Zhou Qiuzhan , Li Qingliang
Published: August 2023

With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAP@K, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval.
Prediction of Seedling Oilseed Rape Crop Phenotype by Drone-Derived Multimodal Data
Authors: Yang Yang , Wei Xinbei , Wang Jiang , Zhou Guangsheng , Wang Jian , Jiang Zitong , Zhao Jie , Ren Yilin
Published: August 2023

In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data and multi-source non-remote sensing data. This study employs Image Guided Filtering Fusion (GFF) to obtain high-resolution multispectral images (HR-MSs) and selects three vegetation indices (VIs) based on correlation analysis and feature reduction in HR-MS for multi-source sensing data. As a supplement to remote sensing data, multi-source non-remote sensing data incorporate two meteorological conditions: temperature and precipitation. This research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD). To validate the monitoring effectiveness of multimodal data, the study constructs four model frameworks based on multimodal data input and employs Support Vector Regression (SVR), Partial Least Squares (PLS), Backpropagation Neural Network (BPNN), and Nonlinear Model Regression (NMR) machine learning models to create winter rapeseed quantitative monitoring models. The findings reveal that the model framework, which integrates multi-source remote sensing data and non-remote sensing data, exhibits the highest average precision (R2 = 0.7454), which is 28%, 14.6%, and 3.7% higher than that of the other three model frameworks, enhancing the model’s robustness by incorporating meteorological data. Furthermore, SVR consistently performs well across various multimodal model frameworks, effectively evaluating the vigor of rapeseed seedlings and providing a valuable reference for rapid, non-destructive monitoring of winter rapeseed.
A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates
Authors: Mumenin Khondoker Mirazul , Biswas Prapti , Khan Md. Al-Masrur , Alammary Ali Saleh , Nahid Abdullah-Al
Published: August 2023

Electroencephalography (EEG) is increasingly being used in pediatric neurology and provides opportunities to diagnose various brain illnesses more accurately and precisely. It is thought to be one of the most effective tools for identifying newborn seizures, especially in Neonatal Intensive Care Units (NICUs). However, EEG interpretation is time-consuming and requires specialists with extensive training. It can be challenging and time-consuming to distinguish between seizures since they might have a wide range of clinical characteristics and etiologies. Technological advancements such as the Machine Learning (ML) approach for the rapid and automated diagnosis of newborn seizures have increased in recent years. This work proposes a novel optimized ML framework to eradicate the constraints of conventional seizure detection techniques. Moreover, we modified a novel meta-heuristic optimization algorithm (MHOA), named Aquila Optimization (AO), to develop an optimized model to make our proposed framework more efficient and robust. To conduct a comparison-based study, we also examined the performance of our optimized model with that of other classifiers, including the Decision Tree (DT), Random Forest (RF), and Gradient Boosting Classifier (GBC). This framework was validated on a public dataset of Helsinki University Hospital, where EEG signals were collected from 79 neonates. Our proposed model acquired encouraging results showing a 93.38% Accuracy Score, 93.9% Area Under the Curve (AUC), 92.72% F1 score, 65.17% Kappa, 93.38% sensitivity, and 77.52% specificity. Thus, it outperforms most of the present shallow ML architectures by showing improvements in accuracy and AUC scores. We believe that these results indicate a major advance in the detection of newborn seizures, which will benefit the medical community by increasing the reliability of the detection process.
A Residual UNet Denoising Network Based on Multi-Scale Feature Extraction and Attention-Guided Filter
Authors: Liu Hualin , Li Zhe , Lin Shijie , Cheng Libo
Published: August 2023

In order to obtain high-quality images, it is very important to remove noise effectively and retain image details reasonably. In this paper, we propose a residual UNet denoising network that adds the attention-guided filter and multi-scale feature extraction blocks. We design a multi-scale feature extraction block as the input block to expand the receiving domain and extract more useful features. We also develop the attention-guided filter block to hold the edge information. Further, we use the global residual network strategy to model residual noise instead of directly modeling clean images. Experimental results show our proposed network performs favorably against several state-of-the-art models. Our proposed model can not only suppress the noise more effectively, but also improve the sharpness of the image.
Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation
Authors: Neethirajan Suresh
Published: August 2023

This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in ‘shy feeder’ cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.
A Comprehensive Methodology for Optimizing Read-Out Timing and Reference DAC Offset in High Frame Rate Image Sensing Systems
Authors: Jun Jaehoon
Published: August 2023

This paper presents a comprehensive timing optimization methodology for power-efficient high-resolution image sensors with column-parallel single-slope analog-to-digital converters (ADCs). The aim of the method is to optimize the read-out timing for each period in the image sensor’s operation, while considering various factors such as ADC decision time, slew rate, and settling time. By adjusting the ramp reference offset and optimizing the amplifier bandwidth of the comparator, the proposed methodology minimizes the power consumption of the amplifier array, which is one of the most power-hungry circuits in the system, while maintaining a small color linearity error and ensuring optimal performance. To demonstrate the effectiveness of the proposed method, a power-efficient 108 MP 3-D stacked CMOS image sensor with a 10-bit column-parallel single-slope ADC array was implemented and verified. The image sensor achieved a random noise of 1.4 e−rms, a column fixed-pattern noise of 66 ppm at an analog gain of 16, and a remarkable figure-of-merit (FoM) of 0.71 e−·nJ. This timing optimization methodology enhances energy efficiency in high-resolution image sensors, enabling higher frame rates and improved system performance. It could be adapted for various imaging applications requiring optimized performance and reduced power consumption, making it a valuable tool for designers aiming to achieve optimal performance in power-sensitive applications.
A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles
Authors: Chen Zhihao , Zhao Zhiyao , Xu Jiping , Wang Xiaoyi , Lu Yang , Yu Jiabin
Published: August 2023

A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target’s path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.
The Development and Validation of a Targeted LC-HRAM-MS/MS Methodology to Separate and Quantify p-Synephrine and m-Synephrine in Dietary Supplements and Herbal Preparations
Authors: Vanhee Celine , Barhdadi Sophia , Kamugisha Angélique , Van Mulders Tanika , Vanbrusselen Kevin , Willocx Marie , Deconinck Eric
Published: August 2023

Dietary supplements containing Citrus aurantium or p-synephrine remain very popular in Europe and the United States of America (USA). They are primarily sold as weight loss enhancers, although their efficacy and the safety are still under scrutiny. To this end, several countries have set maximum threshold levels of p-synephrine that are permitted in dietary supplements. Moreover, there have also been reports of possible chemical adulteration of these supplements with the synthetic positional isomer, m-synephrine, known to be used as a medicinal product. Therefore, it is pivotal for regulatory agencies to be able to discriminate between the two positional isomers and also quantify the amount of each when encountered in dietary supplements. Here, we present the development and the validation of a simple and fast “dilute and shoot” procedure, employing liquid chromatographic (LC) separation in combination with high-resolution accurate mass (HRAM) tandem mass spectroscopy (LC-HRAM-MS/MS) to separate these two isomers and subsequently quantify them. The quantification methodology has been validated using the “total error approach”, applying accuracy profiles, and is consequently compliant with ISO17025. Moreover, ten real-life samples, either purchased online or encountered by Belgian regulatory agencies, were analyzed using the described procedure. Startlingly, only one sample out of ten was compliant with Belgian legislation in terms of labeling, the presence of a batch number, expiration date and dosage (with a tolerated error of ±20%). Moreover, three samples also contained banned substances such as yohimbine and sibutramine.
High-Degree Oxidative Desulfurization of a Commercial Marine Fuel Using Deep Eutectic Solvents and Their Recycling Process
Authors: Thoda Olga , Svinterikos Efstratios , Sakkas Konstantinos Miltiadis , Moschovi Anastasia Maria , Yakoumis Iakovos
Published: August 2023

Escalating environmental concerns have dictated the need to develop innovative methods for efficiently desulfurizing marine fuels (heavy fuel oils). In this work, the oxidative desulfurization method using deep eutectic solvents (DESs) was applied to reduce the sulfur content in a commercially available heavy fuel oil (HFO) below 0.5 wt.%, as current regulations demand. Initially, the S-compounds in the fuel were oxidized using an oxidative mixture of H2O2 with carboxylic acid (either acetic or formic acid). Subsequently, the oxidized S-compounds were extracted from the fuel using a series of environmentally friendly deep eutectic solvents (DESs), the best of which was proven to be a mixture of choline chloride with ethylene glycol at a 1/2 molar ratio. The process was optimized by investigating the effect of several process parameters on the desulfurization efficiency, namely, the H2O2/S molar ratio, the H2O2/acid molar ratio, the acid type, the oxidation temperature and oxidation time, the solvent/fuel mass ratio, the extraction time, and the extraction temperature. A desulfurization efficiency of 75.7% was achieved under the optimized conditions, reducing the S content in the fuel to 0.33 wt.%. Furthermore, different methods to recycle the DESs were investigated, and consecutive desulfurization and solvent regeneration cycles were performed. The most efficient recycling method was determined to be the anti-solvent addition of excess water, which resulted in 89.5% DES purification by causing precipitation of the dissolved solids. After three cycles of desulfurization and regeneration using different recycling routes, it was found that the regeneration degree declines gradually; however, it is more than 79.3% in all cases.
Redesign and Implementation of the Electromagnetism Course for Engineering Students Using the Backward Design Methodology
Authors: González Jesús , Martínez Liliana , Aguas Roberto , De La Hoz Jhon , Sánchez Henry
Published: August 2023

In this study, the redesign and implementation of an electromagnetism course for engineering students are presented using the methodology of backward design (BD), recognized for its attributes for the development of active learning and its possibility of specifying the fundamentals and principles of engineering education in the development of higher education syllabus. The purposes of this study were as follows: (1) to implement the backward design (BD) methodology to develop argumentative, purposeful, and interpretive skills in an electromagnetism course; (2) to design and apply rubrics to evaluate tests and laboratory reports and determine the level that best suits the knowledge, skills, and competencies of students; (3) to assess the perception of students regarding the use of the methodology and their contributions to the development and understanding of the concepts of the course. The results of this educational research experience showed that the BD teaching approach was pedagogical and significantly superior to conventional models for improving knowledge and skills of electromagnetism based on Hake’s statistic (h) of 0.73, which led us to conclude that there was a learning gain in the students. This paper focuses on Sustainable Development Goal 4 (SDG 4) of the United Nations Agenda, which is aimed at “Quality education”. The study’s results reveal that fully integrated and sustainable solutions can be envisioned for higher education entities via the implementation of BD methodology, which can support the educational transformation of the higher education sector based on SDG 4, which promotes inclusive and equitable quality education for all. This can mitigate the risk associated with university dropouts due to subjects that are highly complex for many students like electromagnetism.
Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management
Authors: Ndiaye Junior Bruno , Obour Augustine K. , Harmoney Keith , Diouf Doudou , Faye Aliou , Diamé Lamine , Fall Dioumacor , Assefa Yared
Published: August 2023

Cowpea fodder has been one of the favored livestock forages for centuries in sub-Saharan Africa, particularly in Senegal. However, little research has been conducted on quantifying the nutritional quality of cowpea fodder because of the costly wet chemistry analysis. The main objective of this study was to develop predictive equations for a sustainable quantification of the nutritional quality of dual-purpose cowpea fodder using near infrared spectroscopy (NIRS) and to investigate the influence of cropping system, fertilizer, genotype, and their interaction on biomass yield and cowpea forage nutritional value. In this study, 120 samples from a dual-purpose cowpea variety trial were used to develop NIRS equations to estimate forage quality parameters including concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), calcium (Ca), phosphorus (P), potassium (K), and iron (Fe). Partial least squares (PLS) regression generated prediction equations using NIRS wavelength measurements, and reference wet chemistry analysis from calibration samples were developed. The PLS prediction equations for the different forage quality parameters had an R2 of calibration 0.94, 0.93, 0.88, 0.63, 0.69, 0.87, and 0.94 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. Using these prediction equations, correlation of the predicted values of the calibration subset and the prediction test subset resulted in significant positive relationships, with R2 of 0.83, 0.74, 0.70, 0.63, 0.59, 0.75, and 0.83 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The corresponding RMSE of these relationships was 0.91, 2.68, 3.45, 0.23, 0.06, 0.11, and 100 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The range and mean concentrations of the calibration subset overlapped with that of the prediction subset for all parameters evaluated. Cross-validation procedures indicated good correlations between wet chemistry analysis and NIRS forage quality estimates. Results of the second experiment showed that the cropping system had no significant effect on cowpea forage yield and nutritive value. However, cowpea variety and fertilizer, both individually and their interaction, had a significant effect on fodder yield and cowpea forage quality. We conclude that the NIRS calibration equations developed can be used to accurately predict the cowpea forage quality parameters evaluated in this study.
Distribution and Source Resolution of Heavy Metals in an Electroplating Site and Their Health Risk Assessment
Authors: Fan Zikai , Xu Xiaoyun , Wang Rong , Meng Zhi , Wang Luochun , Cao Xinde , Lou Ziyang
Published: August 2023

It is very important to understand the distribution and sources of typical potentially toxic elements in industrial sites in order to provide essential information for risk assessment and the process of land reclamation selection. Here, around 29 soil column samples of 6 m depth were collected using a geoprobe drill rig from a typical electroplating site located in the Yangtze River Delta, which has been operating for more than 20 years. Analysis in the laboratory, including measurement of elemental concentrations using ICP-OES, was carried out. The distribution and sources of typical heavy metals were investigated, and correlated risks were assessed using positive matrix factorization. As, Pb, and Cr were the dominant heavy metal pollutants, with ranges of 3.20–154 mg/kg, 13.9–9271 mg/kg, and 27.2–2970 mg/kg, which were 1.28 times, 11.6 times, and 3.71 times higher than the Chinese national standard, respectively. Pb was found to be accumulated in the top 0–2 m and As in the top 0–3 m due to the presence of a typical clay and loamy soil. Additionally, Cr could be transferred into the groundwater, with a maximum concentration of 497 mg/kg, due to frequent interaction between the groundwater and soil. A PMF model showed that the dominant sources of pollution were the electroplating process section, the glass melting process section, the production process section, and the electroplating wastewater. Pb, As, and Cr were mainly generated from the industrial production process, glass melting process, and electroplating process. The pH and CEC appeared to influence the chemical speciation greatly, with higher content observed bound to carbonates as a result of exchange processes in the case of high CEC and low pH conditions. Both the Pb and As observed could contribute to non-carcinogenic and carcinogenic health risks, respectively, based on PMF-HRA analysis, which should receive greater attention in risk management strategies for polluted sites. Identification of the main sources of heavy metals in a site could provide a basis for potential land reclamation.
A Comparative Analysis of the Oral Bioaccessibility of Metals/Metalloids Determined Using the Unified Bioaccessibility Research Group of Europe Method and 0.07 M HCl Single Extraction Method
Authors: Baek Dong-Jun , Moon Deok Hyun , Kwon Jung-Hwan , An Jinsung
Published: August 2023

Metals and metalloids in consumer products can be ingested by humans and cause health problems. The unified Bioaccessibility Research Group of Europe (BARGE) method (UBM, i.e., ISO 17924), with complex digestive ingredients, and the 0.07 M HCl single extraction method, as a simplified means, have been widely used to assess oral bioaccessibility in vitro. Herein, the bioaccessible concentrations of metals and metalloids in 13 certified reference materials acting as surrogates for consumer products were determined using the UBM and the 0.07 M HCl single extraction method and compared. The bioaccessible concentrations of metals and metalloids evaluated using the UBM and the 0.07 M HCl single extraction method ranged from 0.002 to 17,449 mg/kg and from 0.003 to 20,283 mg/kg, respectively; their bioaccessibility ranged from 0.00002 to 26.9% and from 0.00002 to 36.6%, respectively. The 0.07 M HCl single extraction method showed higher concentrations, as the bioaccessible concentrations of metals and metalloids differed by 1.38 times (i.e., the slope of the linear regression), indicating its potential for conservative assessment. However, the Student’s t-test results for the 12 metals and metalloids showed no significant differences (p-value ≥ 0.05). It demonstrated that the relatively simple 0.07 M HCl single extraction method can be used as an in vitro test method to assess the oral bioaccessibility of metals and metalloids in various consumer products by replacing the UBM and/or through its use as a screening method prior to the application of the UBM, thereby moving towards green analytical chemistry.
Reduction in Soil Compaction by Utilization of Waste Tire Rubber
Authors: Pattanawanidchai Sirichai , Sae-Oui Pongdhorn , Na-Lumpang Thipjak , Loykulnant Surapich , Kuankhamnuan Thirapong
Published: August 2023

Hardpan is one of the major problems found in many parts of Thailand where monocropping and repeated plowing at the same depth are conducted. This work aimed to study the feasibility of using crumb rubber produced from waste tires to alleviate the soil compaction problem. Effects of particle size and loading of crumb rubber on the degree of soil compaction were investigated. The quality of water extracted from crumb rubber was also analyzed to determine the severity of environmental contamination. The scope of work was further extended into field tests. The results reveal that the addition of crumb rubber into sandy loam subsoil significantly reduced the bulk density of the compacted soil and permitted greater water permeation through the compacted soil layer. The effects were more pronounced with either increasing crumb rubber loading or reducing crumb rubber particle size. For the field test, the addition of small crumb rubber (S-crumb) at 30%wt. resulted in a significant reduction in penetration force in conjunction with a considerable increase in water permeability. The results clearly confirm the reduction in soil compaction by the addition of crumb rubber. From a quality analysis of the extracted water, most poisonous heavy metals were below the detection limits of the test equipment, except zinc.
Sustainability of Entrepreneurship: An Empirical Study on the Impact Path of Corporate Social Responsibility Based on Internal Control
Authors: Guan Xiao , Yao Chunli , Zhang Weimin
Published: August 2023

Effective internal control of enterprises can increase their social responsibility by improving financial performance, forming a sustainable cycle of enterprise development. This article uses relevant data from Chinese listed companies to explore the relationship between internal control, financial performance, and corporate social responsibility, as well as the differences in the impact of internal control on corporate social responsibility under the heterogeneity of property rights. We found that the three have a good promoting effect on each other; at the same time, financial performance plays a part in the media effect in corporate internal control and corporate social responsibility, and this effect is stronger in non-state-owned holding enterprises than in state-owned holding enterprises. This article suggests the following: (1) establish an internal control system for socially responsible enterprises and internalize corporate responsibility awareness; (2) strengthen the internal control and independent third-party supervision systems and form a joint internal and external supervision pattern; and (3) improve the top-level design of social responsibility and combine incentive and punishment measures. This study provides constructive suggestions for the sustainable development of Chinese listed companies and future research directions.
Numerical Simulation Study of Pressure Transfer Based on the Integration of Fracturing, Shut-in and Production in Tight Reservoirs
Authors: Gu Tuan , Yan Le , Fan Tao , Guo Xiaochao , Fan Feng , Zhang Yanjun
Published: August 2023

As an important replacement resource for conventional oil and gas, tight oil and gas are quite abundant. Long horizontal wells and multi-stage fracturing have become key technologies for developing tight oil and gas, and reasonable shut-in measures can improve the utilization efficiency of fracturing fluid. Therefore, it is especially critical to master the pressure transfer law during the shut-in process in tight reservoirs to further improve the energy efficiency of fracturing fluid. However, many studies have mostly focused on the separate design of fracturing, shut-in and production, and have not yet revealed the pressure transfer law during shutting in well based on the integration of fracturing, shut-in and production, which makes it difficult to realize the efficient development of tight oil and gas. Taking the tight oil reservoir in Block M as an example, the geological model of the target block was established using an integrated fracturing development software platform, on which the simulation of fracture extension, well shut-in and production was carried out. The changes in the reservoir pressure field during shutting in well were analyzed, and the influence law of fracturing fluid volume, shut-in time, reservoir original formation pressure and fracture network complexity on the effect of well shut-in were studied to optimize the shut-in system. It was found that the retained fluid increases, the pore pressure of the near-fracture matrix increases, and the diffusion distance of fracturing fluid to the distant matrix increases. The tight oil production increased after shutting in well, and the optimal retained fluid volume of 9600 m3 was actually preferred based on the model. The pore pressure of the near-fracture matrix decreases as the shut-in time increases, the diffusion distance of fracturing fluid to the distant matrix increases, and the pore pressure decreases with an increase in diffusion distance. The tight oil production increased after shutting in well, and the optimal shut-in time was actually preferred to be 90 days based on the model. The increase in formation pressure on abnormal low pressure formation is larger, and the production can be significantly improved after shutting in well. The more complex the fracture network is, the more obvious the non-uniform variation in matrix pore pressure during shutting in well. The research is of great significance for the optimal design of a shut-in system for tight reservoirs and the sustainable development of oil and gas resources in China.
Land-Use-Change-Induced Cooling and Precipitation Reduction in China: Insights from CMIP6 Models
Authors: Tian Peizhi , Jian Binyang , Li Jianrui , Cai Xitian , Wei Jiangfeng , Zhang Guo
Published: August 2023

In the 21st century, the effect of land use/land cover change (LULCC) on climate has become an area of active research. To explore the effects of LULCC on temperature and precipitation in China, we used outputs from the BCC-CSM2-MR, CESM2, IPSL-CM6A-LR, and UKESM1 models, which participated in the Land Use Model Intercomparison Project (LUMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on these models, we identified temporal variations in precipitation and near-surface air temperature (hereinafter temperature) with and without historical land use changes and their relation with LULCC in China during 1850–2014. We then determined the significant changing period (1972–2012) and revealed the relation between the spatial distribution of historical change in vegetation cover types, precipitation, and temperature. The results showed that annual historical precipitation decreased faster (132.23 mm/(1000 a) faster), while annual historical temperature increased slower (2.70 °C/(1000 a) slower) than that without LULCC during 1850–2014. LULCC not only influenced surface properties to change local precipitation and temperature distributions and mean values, but also affected other components through atmospheric circulations due to typical monsoon characteristics in China. The relative contribution of grassland change to precipitation variation was the largest, while relatively, cropland change contributed the most to temperature variation. Our study innovatively used new model outputs from LUMIP to analyze the impacts of LULCC on precipitation and temperature, which can help to guide and improve future land use management and predictions of precipitation and temperature.
Role of ChatGPT and Skilled Workers for Business Sustainability: Leadership Motivation as the Moderator
Authors: Vrontis Demetris , Chaudhuri Ranjan , Chatterjee Sheshadri
Published: August 2023

ChatGPT is an AI tool that was primarily developed by OpenAI with the support of its GPT-3.5 and GPT-4 platforms and large language models. ChatGPT has been fine-tuned with both supervised and reinforcement learning technology. Various types of organizations are interested in adopting applications supported by GPT-3.5 and GPT-4 platforms, but there is an acute shortage of trained workers in this field. Research is needed to investigate the role of this very new technology and skilled workers in maintaining business sustainability. Also, few studies have investigated the role of leadership motivation in accelerating business sustainability. Therefore, this study aims to examine the role of ChatGPT and skilled employees in business sustainability. It also investigates the moderating impact of leadership motivation on business sustainability. With the help of resource-based view (RBV) and the extant literature, a theoretical model was developed, which was then validated using the PLS-SEM technique on feedback from 209 respondents. The study finds that both ChatGPT and skilled workers have significant impact on improving business sustainability. The study also demonstrates that leadership motivation significantly impacts business sustainability.
Towards a More Sustainable and Less Invasive Approach for the Investigation of Modern and Contemporary Paintings
Authors: Raicu Teodora , Zollo Fabiana , Falchi Laura , Barisoni Elisabetta , Piccolo Matteo , Izzo Francesca Caterina
Published: August 2023

In Heritage Science, sampling is frequently performed for the subsequent diagnostics of modern and contemporary paintings using invasive analytical techniques. However, it endangers the integrity of artworks, and thus, it should be carefully planned and carried out only as a last resort by specialists. Pigment mixtures have commonly been employed by modern and contemporary artists due to the ease of combining paints on the color palette. Hence, a painting might include both primary/secondary paints and mixtures of those. Therefore, obtaining a sample from a mixture might be sufficient for the identification of the individual primary-colored paints. This study focused on the creation of a user-friendly computational workflow for the analysis of images of paintings for the identification of mixtures using cluster analysis (K-means and Fuzzy C-means clustering). Sixteen modern and contemporary paintings that belong to the International Gallery of Modern Art Ca’ Pesaro in Venice have been selected: seven of them by Guido Cadorin (1892–1976), six by Andreina Rosa (1924–2019), and three by Boris Brollo (b. 1944), and the artworks of the latter being examined for the first time in this study (using Raman and ER–FTIR spectroscopies). It was found that mixtures can be identified in unvarnished paintings that consist of both non-overlapping and vibrant-colored paint layers, like those of Boris Brollo, and overlapping paint layers, like those of Andreina Rosa. Moreover, K-means clustering performs better in the case of non-overlapping colors, whereas Fuzzy C-means in the case of overlapping colors. In contrast, paintings that have been rendered with dark colors and that present a varnish layer, like those of Guido Cadorin, cannot be preliminary investigated in the proposed manner.
Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination
Authors: Pula Michal , Kucharczyk Emilia , Zdanowicz Agata , Guzinski Maciej
Published: August 2023

In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patients were subjected to both objective and subjective evaluation of image quality. The objective evaluation was based on the SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) of the brain’s gray and white matter. The regions of interest for our study were set in the BGA (basal ganglia area) and PCF (posterior cranial fossa). Simultaneously, a subjective assessment of image quality, based on brain structure visibility, was conducted by experienced radiologists. In the assessed scans, we obtained up to a 54% increase in SNR for gray matter and a 60% increase for white matter using DLIR in comparison to ASIR-V. Moreover, we achieved a CNR increment of 58% in the BGA structures and 50% in the PCF. In the subjective assessment of the obtained images, DLIR had a mean rating score of 2.8, compared to the mean score of 2.6 for ASIR-V images. In conclusion, DLIR shows improved image quality compared to the standard iterative reconstruction of CT images of the head.
Lepton–Nucleus Interactions within Microscopic Approaches
Authors: Lovato Alessandro , Nikolakopoulos Alexis , Rocco Noemi , Steinberg Noah
Published: August 2023

This review paper emphasizes the significance of microscopic calculations with quantified theoretical error estimates in studying lepton–nucleus interactions and their implications for electron scattering and accelerator neutrino oscillation measurements. We investigate two approaches: Green’s Function Monte Carlo and the extended factorization scheme, utilizing realistic nuclear target spectral functions. In our study, we include relativistic effects in Green’s Function Monte Carlo and validate the inclusive electron scattering cross section on carbon using available data. We compare the flux-folded cross sections for neutrino-carbon scattering with T2K and MINERνA experiments, noting the substantial impact of relativistic effects in reducing the theoretical curve strength when compared to MINERνA data. Additionally, we demonstrate that quantum Monte Carlo-based spectral functions accurately reproduce the quasi-elastic region in electron scattering data and T2K flux-folded cross sections. By comparing results from Green’s Function Monte Carlo and the spectral function approach, which share a similar initial target state description, we quantify errors associated with approximations in the factorization scheme and the relativistic treatment of kinematics in Green’s Function Monte Carlo.
Beyond the Syndemic of Opioid Use Disorders and HIV: The Impact of Opioids on Viral Reservoirs
Authors: Trunfio Mattia , Chaillon Antoine , Beliakova-Bethell Nadejda , Deiss Robert , Letendre Scott L. , Riggs Patricia K. , Higgins Niamh , Gianella Sara
Published: August 2023

People with HIV are more likely to have opioid use disorder and to be prescribed opioids for chronic pain than the general population; however, the effects of opioids on the immune system and HIV persistence have not been fully elucidated. Opioids may affect HIV reservoirs during their establishment, maintenance, and reactivation by enhancing HIV infectivity and replication due to upregulation of co-receptors and impairment of innate antiviral responses. Opioids may also modulate immune cell functioning and microbial translocation and can reverse viral latency. In this review, we summarize the current findings for and against the modulating effects of opioids on HIV cellular and anatomical reservoirs, highlighting the current limitations that affect in vitro, ex vivo, and in vivo studies in the field. We propose further research targets and potential strategies to approach this topic.