bluuebunny/crossref_metadata_2025 huggingface dataset:
Stone Inscriptions as Mirror Images: Historical Details of Tang Dynasty Buddhism in the Luoyang Region
Authors: Song Ting , Wang Yuanlin
Published: November 2023

For a long time, scholarly research on Buddhism in Luoyang during the Tang Dynasty has mainly focused on eminent monks and Buddhist temples. This focus is evident in the recorded literature of ancient times, such as The Continued Biographies of Eminent Monks and The Biographies of Eminent Monks. Based on stone inscriptions, this paper examines the dissemination and development of Buddhism in the Eastern Capital of Luoyang during the Tang Dynasty. This article presents the following viewpoints and findings: Firstly, the epitaphs and pagoda inscriptions provide historical details that are not widely known, such as the names of temples in the suburb, the identities of prominent monks who propagated Dharma in Luoyang, the Buddhist scriptures chanted and learned by the Luoyang people, and the people’s motivation to adopt Buddhism. Secondly, the epitaphs and pagoda inscriptions supplement important historical materials on Chan Buddhism, confirming the widespread popularity of the Northern Sect of Chan Buddhism in the Luoyang region. Thirdly, the epitaphs and pagoda inscriptions reveal that Luoyang Buddhist practice was popular, characterized by the succession of blood-related monastic companions; that is, many families had two or more relatives who became monks or nuns simultaneously or successively, a phenomenon that has not attracted attention from academic circles. Fourthly, the blood-related monastic companions of Buddhist practice affected the mentoring relationships and organizational management of temples and monasteries, promoting communication and interaction between Buddhism and traditional Chinese culture.
Full-Season Crop Phenology Monitoring Using Two-Dimensional Normalized Difference Pairs
Authors: Longchamps Louis , Philpot William
Published: November 2023

The monitoring of crop phenology informs decisions in environmental and agricultural management at both global and farm scales. Current methodologies for crop monitoring using remote sensing data track crop growth stages over time based on single, scalar vegetative indices (e.g., NDVI). Crop growth and senescence are indistinguishable when using scalar indices without additional information (e.g., planting date). By using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only. In a two-dimensional plot of the metrics (ND-space), bare soil, full canopy, and senesced vegetation data all plot in separate, distinct locations regardless of the year. The path traced in the ND-space over the growing season repeats from year to year, with variations that can be related to weather patterns. Senescence follows a return path that is distinct from the growth path.
Satellite Lidar Measurements as a Critical New Global Ocean Climate Record
Authors: Behrenfeld Michael J. , Lorenzoni Laura , Hu Yongxiang , Bisson Kelsey M. , Hostetler Chris A. , Di Girolamo Paolo , Dionisi Davide , Longo Francesco , Zoffoli Simona
Published: November 2023

The year 2023 marked the tenth anniversary of the first published description of global ocean plankton stocks based on measurements from a satellite lidar. Diverse studies have since been conducted to further refine and validate the lidar retrievals and use them to discover new characteristics of plankton seasonal dynamics and marine animal migrations, as well as evaluate geophysical products from traditional passive ocean color sensors. Surprisingly, all of these developments have been achieved with lidar instruments not designed for ocean applications. Over this same decade, we have witnessed unprecedented changes in ocean ecosystems at unexpected rates and driven by a multitude of environmental stressors, with a dominant factor being climate warming. Understanding, predicting, and responding to these ecosystem changes requires a global ocean observing network linking satellite, in situ, and modeling approaches. Inspired by recent successes, we promote here the creation of a lidar global ocean climate record as a key element in this envisioned advanced observing system. Contributing to this record, we announce the development of a new satellite lidar mission with ocean-observing capabilities and then discuss additional technological advances that can be envisioned for subsequent missions. Finally, we discuss how a potential near-term gap in global ocean lidar data might, at least partially, be filled using on-orbit or soon-to-be-launched lidars designed for other disciplinary purposes, and we identify upcoming needs for in situ support systems and science community development.
CycleGAN-Based SAR-Optical Image Fusion for Target Recognition
Authors: Sun Yuchuang , Yan Kaijia , Li Wangzhe
Published: November 2023

The efficiency and accuracy of target recognition in synthetic aperture radar (SAR) imagery have seen significant progress lately, stemming from the encouraging advancements of automatic target recognition (ATR) technology based on deep learning. However, the development of a deep learning-based SAR ATR algorithm still faces two critical challenges: the difficulty of feature extraction caused by the unique nature of SAR imagery and the scarcity of datasets caused by the high acquisition cost. Due to its desirable image nature and extremely low acquisition cost, the simulated optical target imagery obtained through computer simulation is considered a valuable complement to SAR imagery. In this study, a CycleGAN-based SAR and simulated optical image fusion network (SOIF-CycleGAN) is designed and demonstrated to mitigate the adverse effects of both challenges simultaneously through SAR-optical image bidirectional translation. SAR-to-optical (S2O) image translation produces artificial optical images that are high-quality and rich in details, which are used as supplementary information for SAR images to assist ATR. Conversely, optical-to-SAR (O2S) image translation generates pattern-rich artificial SAR images and provides additional training data for SAR ATR algorithms. Meanwhile, a new dataset of SAR-optical image pairs containing eight different types of aircraft has been created for training and testing SOIF-CycleGAN. By combining image-quality assessment (IQA) methods and human vision, the evaluation verified that the proposed network possesses exceptional bidirectional translation capability. Finally, the results of the S2O and O2S image translations are simultaneously integrated into a SAR ATR network, resulting in an overall accuracy improvement of 6.33%. This demonstrates the effectiveness of SAR-optical image fusion in enhancing the performance of SAR ATR.
Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B
Authors: Xiang Ruxuanyi , Xie Tao , Bai Shuying , Zhang Xuehong , Li Jian , Wang Minghua , Wang Chao
Published: November 2023

The monitoring of mesoscale convective systems (MCS) is typically based on satellite infrared data. Currently, there is limited research on the identification of MCS using true color composite cloud imagery. In this study, an MCS dataset was created based on the true color composite cloud imagery from the Fengyun-4B geostationary meteorological satellite. An MCS true color composite cloud imagery identification model was developed based on the Swin-Unet network. The MCS dataset was categorized into continental MCS and oceanic MCS, and the model’s performance in identifying these two different types of MCS was examined. Experimental results indicated that the model achieved a recall rate of 83.3% in identifying continental MCS and 86.1% in identifying oceanic MCS, with a better performance in monitoring oceanic MCS. These results suggest that using true color composite cloud imagery for MCS monitoring is feasible, and the Swin-Unet network outperforms traditional convolutional neural networks. Meanwhile, we find that the frequency and distribution range of oceanic MCS is larger than that of continental MCS, and the area is larger and some parts of it are stronger. This study provides a novel approach for satellite remote-sensing-based MCS monitoring.
ERF-RTMDet: An Improved Small Object Detection Method in Remote Sensing Images
Authors: Liu Shuo , Zou Huanxin , Huang Yazhe , Cao Xu , He Shitian , Li Meilin , Zhang Yuqing
Published: November 2023

A significant challenge in detecting objects in complex remote sensing (RS) datasets is from small objects. Existing detection methods achieve much lower accuracy on small objects than medium and large ones. These methods suffer from limited feature information, susceptibility to complex background interferences, and insufficient contextual information. To address these issues, a small object detection method with the enhanced receptive field, ERF-RTMDet, is proposed to achieve a more robust detection capability on small objects in RS images. Specifically, three modules are employed to enhance the receptive field of small objects’ features. First, the Dilated Spatial Pyramid Pooling Fast Module is proposed to gather more contextual information on small objects and suppress the interference of background information. Second, the Content-Aware Reassembly of Features Module is employed for more efficient feature fusion instead of the nearest-neighbor upsampling operator. Finally, the Hybrid Dilated Attention Module is proposed to expand the receptive field of object features after the feature fusion network. Extensive experiments are conducted on the MAR20 and NWPU VHR-10 datasets. The experimental results show that our ERF-RTMDet attains higher detection precision on small objects while maintaining or slightly enhancing the detection precision on mid-scale and large-scale objects.
Enhancing Path Planning Efficiency for Underwater Gravity Matching Navigation with a Novel Three-Dimensional Along-Path Obstacle Profiling Algorithm
Authors: Zhou Xiaocong , Zheng Wei , Li Zhaowei , Wu Panlong , Sun Yongjin
Published: November 2023

This paper presents a study on enhancing the efficiency of underwater gravity matching navigation path planning in a three-dimensional environment. Firstly, to address the challenges of the computational complexity and prolonged calculation times associated with the existing three-dimensional path planning algorithms, a novel Three-Dimensional Along-Path Obstacle Profiling (TAOP) algorithm is introduced. The principles of the TAOP algorithm are as follows: (1) unfolding obstacles along the path using the path obtained from two-dimensional planning as an axis, interpolating water depth values based on downloaded terrain data, and subjecting obstacles to dilation treatment to construct a dilated obstacle profile for path segments; (2) conducting height direction course planning and a secondary optimization of the path based on the profile contours of the dilated obstacles; and (3) integrating height planning with the path points from two-dimensional planar planning to obtain a complete path containing all turning points in the three-dimensional space. Secondly, gravity anomaly data are utilized to delineate gravity suitability areas, and a three-dimensional planning environment that is suitable for underwater gravity matching navigation is established by integrating seafloor terrain data. Under identical planning environments and parameter conditions, the performance of the TAOP algorithm is compared to that of the RRT* algorithm, Q-RRT* algorithm, and Depth Sorting Fast Search (DSFS) algorithm. The results show that, compared to the RRT* algorithm, Q-RRT* algorithm, and DSFS algorithm, the TAOP algorithm achieves efficiency improvements of 15.6 times, 5.98 times, and 4.04 times, respectively.
Remote Sensing Application in Chinese Medicinal Plant Identification and Acreage Estimation—A Review
Authors: Meng Jihua , You Xinyan , Zhang Xiaobo , Shi Tingting , Zhang Lei , Chen Xingfeng , Zhao Hailan , Xu Meng
Published: November 2023

Chinese Materia Medica Resources (CMMRs) are crucial for developing the tradition and industry of traditional Chinese medicine. Given the increasing demand for CMMRs, an accurate and effective understanding of CMMRs is urgently needed. Chinese medicinal plants (CMPs) are the most important sources of CMMRs. Traditional methods of investigating medicinal plant resources have limitations, including severe subjectivity and poor timeliness, which make it difficult to meet the demand for real-time monitoring of large-scale medicinal plant resources. In recent years, remote sensing technology has become an important means of obtaining information on medicinal plants, and the application of this technology has made up for the shortcomings of traditional methods. This paper first discusses the development of investigation methods of CMMRs; points out the importance of remote sensing technology in the application of spatial distribution and information identification and extraction of Chinese medicinal plant resources (CMPRs); analyzes the characteristics of CMPs in different planting patterns, different habitats, and different regions from the perspective of remote sensing information extraction; and explores the selection of suitable data sources, providing a reference for medicinal plant identification and information extraction. Secondly, according to the existing classification and identification methods, previous studies are summarized from the perspectives of classification scales, classification features, and classification accuracy, and the advantages and disadvantages of the commonly used remote sensing classification methods in the investigation of CMPRs are summarized and compared. Finally, the development trend of remote sensing technology in the identification and information extraction of CMPs is examined, and the key technical problems to be solved in the identification and classification of CMPs and the extraction of area information are summarized so as to provide technical support and experience references for the application of remote sensing in the investigation of CMPRs.
Image Feature Detectors in Agricultural Harvesting: An Evaluation
Authors: Cui Zhihong , Xu Lizhang , Yu Yang , Chai Xiaoyu , Zhang Qian , Liu Peng , Hu Jinpeng , Li Yang , Chen Haiwen
Published: November 2023

Image feature detection serves as the cornerstone for numerous vision applications, and it has found extensive use in agricultural harvesting. Nevertheless, determining the optimal feature extraction technique for a specific situation proves challenging, as the Ground Truth correlation between images is exceedingly elusive in harsh agricultural harvesting environments. In this study, we assemble and make publicly available the inaugural agricultural harvesting dataset, encompassing four crops: rice, corn and soybean, wheat, and rape. We develop an innovative Ground Truth-independent feature detector assessment approach that amalgamates efficiency, repeatability, and feature distribution. We examine eight distinct feature detectors and conduct a thorough evaluation using the amassed dataset. The empirical findings indicate that the FAST detector and ASLFeat yield the most exceptional performance in agricultural harvesting contexts. This evaluation establishes a trustworthy bedrock for the astute identification and application of feature extraction techniques in diverse crop reaping situations.
Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics
Authors: Muksimova Shakhnoza , Umirzakova Sabina , Mardieva Sevara , Cho Young-Im
Published: November 2023

The realm of medical imaging is a critical frontier in precision diagnostics, where the clarity of the image is paramount. Despite advancements in imaging technology, noise remains a pervasive challenge that can obscure crucial details and impede accurate diagnoses. Addressing this, we introduce a novel teacher–student network model that leverages the potency of our bespoke NoiseContextNet Block to discern and mitigate noise with unprecedented precision. This innovation is coupled with an iterative pruning technique aimed at refining the model for heightened computational efficiency without compromising the fidelity of denoising. We substantiate the superiority and effectiveness of our approach through a comprehensive suite of experiments, showcasing significant qualitative enhancements across a multitude of medical imaging modalities. The visual results from a vast array of tests firmly establish our method’s dominance in producing clearer, more reliable images for diagnostic purposes, thereby setting a new benchmark in medical image denoising.
Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages
Authors: Nie Lei , Zhang Junjie , Bao Haizhou , Huo Yiming
Published: November 2023

Efficient routing in urban vehicular networks is essential for timely and reliable safety message transmission, and the selection of paths and relays greatly affects the quality of routing. However, existing routing methods usually face difficulty in finding the globally optimal transmission path due to their greedy search strategies or the lack of effective ways to accurately evaluate relay performance in intricate traffic scenarios. Therefore, we present a vehicular safety message routing method based on heuristic path search and multi-attribute decision-making (HMDR). Initially, HMDR utilizes a heuristic path search, focusing on road section connectivity, to pinpoint the most favorable routing path. Subsequently, it employs a multi-attribute decision-making (MADM) technique to evaluate candidate relay performance. The subjective and objective weights of the candidate relays are determined using ordinal relationship analysis and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting methods, respectively. Finally, the comprehensive utility values of the candidate relays are calculated in combination with the link time and the optimal relay is selected. In summary, the proposed HMDR method is capable of selecting the globally optimal transmission path, and it comprehensively considers multiple metrics and their relationships when evaluating relays, which is conducive to finding the optimal relay. The experimental results show that even if the path length is long, the proposed HMDR method gives preference to the path with better connectivity, resulting in a shorter total transmission delay for safety messages; in addition, HMDR demonstrates faster propagation speed than the other evaluated methods while ensuring better one-hop distance and one-hop delay. Therefore, it helps to improve the performance of vehicular safety message transmission in intricate traffic scenarios, thus providing timely data support for secure driving.
An Improved Orthogonal Matching Pursuit Algorithm for CS-Based Channel Estimation
Authors: Si Lu , Xu Weizhang , Yu Xinle , Yin Hang
Published: November 2023

Wireless broadband transmission channels usually have time-domain-sparse properties, and the reconstruction of these channels using a greedy search-based orthogonal matching pursuit (OMP) algorithm can effectively improve channel estimation performance while decreasing the length of the reference signal. In this research, the improved OMP and SOMP algorithms for compressed-sensing (CS)-based channel estimation are proposed for single-carrier frequency domain equalization (SC-FDE) systems, which, in comparison with conventional algorithms, calculate the path gain after obtaining the path delay and updating the observation matrices. The reliability of the communication system is further enhanced because the channel path gain is calculated using longer observation vectors, which lowers the Cramér–Rao lower bound (CRLB) and results in better channel estimation performance. The developed method can also be applied to time-domain-synchronous OFDM (TDS-OFDM) systems, and it is applicable to the improvement of other matching pursuit algorithms.
Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study
Authors: Espinosa Miguel A. , Ponce Pedro , Molina Arturo , Borja Vicente , Torres Martha G. , Rojas Mario
Published: November 2023

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea–hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.
A Policy Gradient Algorithm to Alleviate the Multi-Agent Value Overestimation Problem in Complex Environments
Authors: Yang Yang , Li Jiang , Hou Jinyong , Wang Ye , Zhao Huadong
Published: November 2023

Multi-agent reinforcement learning excels at addressing group intelligent decision-making problems involving sequential decision-making. In particular, in complex, high-dimensional state and action spaces, it imposes higher demands on the reliability, stability, and adaptability of decision algorithms. The reinforcement learning algorithm based on the multi-agent deep strategy gradient incorporates a function approximation method using discriminant networks. However, this can lead to estimation errors when agents evaluate action values, thereby reducing model reliability and stability and resulting in challenging convergence. With the increasing complexity of the environment, there is a decline in the quality of experience collected by the experience playback pool, resulting in low efficiency of the sampling stage and difficulties in algorithm convergence. To address these challenges, we propose an innovative approach called the empirical clustering layer-based multi-agent dual dueling policy gradient (ECL-MAD3PG) algorithm. Experimental results demonstrate that our ECL-MAD3PG algorithm outperforms other methods in various complex environments, demonstrating a remarkable 9.1% improvement in mission completion compared to MADDPG within the context of complex UAV cooperative combat scenarios.
Flexible Measurement of High-Slope Micro-Nano Structures with Tilted Wave Digital Holographic Microscopy
Authors: Ma Xinyang , Xiong Rui , Wang Wei , Zhang Xiangchao
Published: November 2023

Digital holographic microscopy is an important measurement method for micro-nano structures. However, when the structured features are of high-slopes, the interference fringes can become too dense to be recognized. Due to the Nyquist’s sampling limit, reliable wavefront restoration and phase unwrapping are not feasible. To address this problem, the interference fringes are proposed to be sparsified by tilting the reference wavefronts. A data fusion strategy including region extraction and tilt correction is developed for reconstructing the full-area surface topographies. Experimental results of high-slope elements demonstrate the validity and reliability of the proposed method.
Modified Nested Barker Codes for Ultra-Wideband Signal–Code Constructions
Authors: Nenashev Vadim A. , Bestugin Aleksandr R. , Rabin Alexey V. , Solenyi Sergei V. , Nenashev Sergey A.
Published: November 2023

Currently, various applications of ultra-wideband signal–code constructions are among the most vibrant technologies, being implemented in very different fields. The purpose of this research consists of analyzing Barker codes and searching for the optimal nested representations of them. We also aim to synthesize signal–code constructions based on the tenets of nesting of alternative modified Barker codes, which employ an asymmetric alphabet. The scientific merit of the paper is as follows: on the basis of new analytic expressions, modified nested codes and signal–code constructions were obtained, applicable for the establishment of the unambiguous association of the component values of the nested codes with any lobes of the normalized autocorrelation function. With these analytical expressions, we are, hence, able to determine the values of the binary asymmetrical components of the nested codes related to the side lobes of the normalized autocorrelation function. In this way, we clearly obtain better (low) levels for these lobes than by using the autocorrelation function, as established by the equivalent conventional Barker codes, including the nested constructions. Practical application of these modulated ultra-wideband signals ensures improved correlational features, high-fidelity probabilistic detection, and more precise positional detection of physical bodies depending on the range coordinate.
Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty
Authors: Wu Wenbo , Zou Tianji , Zhang Lu , Wang Ke , Li Xuzhi
Published: November 2023

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.
Adapting to Change: Investigating the Influence of Distance Learning on Performance in Italian Conservatories
Authors: Giffi Veronica , Fantinelli Stefania , Galanti Teresa
Published: November 2023

During the COVID-19 pandemic, the sudden switch from face-to-face learning to distance learning generated many critical issues in music institutes. Specifically, conservatories found themselves using a didactic methodology that had never been considered before to ensure the continuation of students’ education. In particular, the adoption of distance learning has had a greater impact on those classes characterized by a significant practical–experiential component. This study aims to investigate the phenomenon of distance learning in Italian conservatories to explore how this experience affects students’ performance through their satisfaction with distance learning. A cross-sectional study was conducted on 328 students of 41 Italian conservatories, using an online self-report questionnaire to investigate conservatory students’ experience of distance learning and its impact on performance. To test the hypotheses, a mediation model was tested using SPSS version 26. The results show that the positive experience of distance learning has a positive impact on perceived performance and that satisfaction with distance learning, as a mediator role, further reinforces this relationship. This study is the first known one to explore the relationship between the experience of distance learning and student performance in the context of conservatories and music teaching.
Osmotic Stress Alleviation in Saccharomyces cerevisiae for High Ethanol Fermentations with Different Wort Substrates
Authors: Douradinho Rafael , Sica Pietro , Tonoli Fernando , Mattos Eduardo , Oliveira Matheus , Pinto Alana , Mota Layna , Faria Tamires , Costa Vitória Franco , Leite Gabriela , Arthur Valter , Coelho Suani , Baptista Antonio
Published: November 2023

High-gravity fermentation, used for ethanol production from sugarcane, corn, and mixed substrates, offers several benefits. Yeast, a rapidly multiplying unicellular microorganism, can be adapted for high sugar and ethanol tolerance on a lab scale. However, different substrates can enhance fermentation efficiency. Our study consisted of two experiments. In the first, we compared simple batch feeding with a fed-batch system for yeast selection in high-gravity fermentation. We ran eight cycles with increasing initial sugar contents (50 to 300 g L−1). No significant differences were observed in the first seven cycles, but in the eighth, the fed-batch system showed lower glycerol and fructose contents and higher cell viability than the simple batch system. In the second experiment, we used the fed-batch system with 300 g L−1 from sugarcane, corn, and mixed wort. The results showed that mixed wort produced higher ethanol contents and greater fermentation efficiency compared to corn and sugarcane as substrates. In conclusion, our findings indicate that the fed-batch system is more suitable for high-gravity fermentation on a lab scale, and the combination of sugarcane juice and corn can enhance fermentation efficiency, paving the way for integrating these substrates in industrial ethanol production.
Eye-Tracking Studies on Sustainable Food Consumption: A Systematic Literature Review
Authors: Ruppenthal Tonia
Published: November 2023

In recent decades, sustainable products have been increasingly investigated using eye-tracking. Eye-tracking is applied to the record eye movements and visual attention of consumers as they search for food, and from this, conclusions can be drawn about their sustainable food consumption. To obtain a comparative overview of the previous utilization of eye-tracking in studies on sustainable food and consumption, a systematic literature search following the guidelines of the PRISMA statement was applied. In total, 38 eye-tracking studies were identified, with six main areas of investigation emerging: eye-tracking application, labeling, consumer attention, consumer choice and preference, consumer attitude and behavior, and willingness-to-pay. The review is aimed at both researchers and managers. In future research, the sample size of eye-tracking should be increased or focused on certain age groups in order to uncover sustainable consumer habits. In addition, field and not just laboratory studies with eye-tracking need to be conducted. To obtain comparable results, it is necessary that researchers apply the same eye-tracking metrics and terms. Organic labels can influence consumer attention and purchase decisions if bottom-up factors of the labels are better aligned with other product information. Top-down factors, i.e., consumer attitude and choice, must also be taken into account.
The Effects of the Biophilic Atmosphere on Hedonic Value, Utilitarian Value, and Customer Loyalty
Authors: Ortegón-Cortázar Leonardo , Royo-Vela Marcelo
Published: November 2023

Current literature emphasizes the benefits of the biophilic atmosphere on people’s lives. However, there is a shortage of in-depth research pertaining to its effects on variables such as perceived value and customer loyalty. Therefore, the objective of this study is to evaluate the positive effects of the biophilic atmosphere both in the hedonic and utilitarian dimensions of perceived value and on customer loyalty. An analysis conducted using structural equations applied to 385 customers in shopping malls revealed that hedonic and utilitarian values mediate the relationship between this new retail atmosphere and loyalty responses. Additionally, results indicate that hedonic value can positively influence utilitarian value in a physical setting that includes natural elements. These results improve the understanding of the effect of biophilia on the promotion of sustainable environmental practices of protection and preservation of nature; moreover, they offer alternative information to encourage attraction and loyalty towards modern commercial settings.
Delving into the Digital Twin Developments and Applications in the Construction Industry: A PRISMA Approach
Authors: Afzal Muhammad , Li Rita Yi Man , Shoaib Muhammad , Ayyub Muhammad Faisal , Tagliabue Lavinia Chiara , Bilal Muhammad , Ghafoor Habiba , Manta Otilia
Published: November 2023

Construction 4.0 is witnessing exponential growth in digital twin (DT) technology developments and applications, revolutionizing the adoption of building information modelling (BIM) and other emerging technologies used throughout the built environment lifecycle. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, the DT enhances this with real-time data for integrating cyber-physical systems, enabling live asset monitoring and better decision making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study provides a holistic review of the existing research focusing on the critical components responsible for developing the applications of DT technology in the construction industry. It highlights five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, non-technical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices. It also pinpoints the latest developments in AI, namely the large language model (LLM) and retrieval-augmented generation (RAG)’s implications for DT education, policies, and the construction industry’s practices.
Fungal and Mycotoxin Contamination of Green Leaf Spices Commercialized in Romania: A Food Choice Perspective
Authors: Cighir Anca , Curticăpean Augustin , Mare Anca Delia , Cighir Teodora , Gabor Manuela Rozalia , Toma Felicia , Man Adrian
Published: November 2023

A healthy, sustainable diet contributes massively to the general well-being of a population. Nowadays, people have started to significantly improve their diet by reducing processed products, as well as including a higher number of fruits, vegetables, cereals, and spices to flavor their food. However, making the right food choice, without any harmful pathogens that pose a risk to human health, can remain quite challenging. Despite prioritizing food safety in its processing, production, and distribution, food contamination remains a prevalent and undesirable occurrence. This study aims to detect the fungal load of commonly used green leaf spices commercialized in Romania and the impact of those microorganisms and their secondary metabolites on consumers. Six (28.57%) out of the twenty-one tested samples showed different degrees of fungal contamination, mostly with Aspergillus section Flavi, followed by Aspergillus section Nigri and other fungi in lower amounts. Three main fungal secondary metabolites with potential impact on consumers, namely mycotoxins, were determined with high-performance liquid chromatography (HPLC): Aflatoxin B1, Ochratoxin A, and Zearalenone. Moreover, their legal limits (5 μg/kg, 15 μg/kg, and 50 μg/kg, respectively) were exceeded by 95.24%, 100%, and 85.71%, respectively. Environmental factors that affect the processing and packaging of these spices did not show any relation to fungal contamination, conversely to price, which significantly correlates with the mycological quality of the products.
The Importance of the New Silk Road in the Hungarian Automotive Supply Chain
Authors: Pató Beáta Sz. G. , Heizler Gábor , Herczeg Márk , Pató Gábor Bálint , Rahmat Al Fauzi , Dávid Lóránt Dénes , Varga Imre , Csiszárik-Kocsir Ágnes
Published: November 2023

The connection between Europe and Asia has always been important for the flow of materials and intellectual goods. Hungary is in an important position in Central Europe to connect different key ports and logistics hubs. Therefore, this article aims to analyse the opportunities and impacts of the New Silk Road initiative on the supply chain and transportation network in Hungary. This result showed that the existence of the New Silk Road gives huge opportunities for different supply-chain-related activities and initiatives, especially for the Hungarian automotive industry. Specifically, the New Silk Road has the potential to enable the advantages and positive impact of rail transport activities in Hungarian automotive supply chains by providing a long-term competitive solution as an alternative to maritime transport while mitigating several related risks and increasing the domestic supply chain’s resilience. Consequentially, the New Silk Road initiative may affect the supply chains and transportation route networks of several European and Asian nations, including Hungary.
Identification of the Pollution Mechanisms and Remediation Strategies for Abandoned Wells in the Karst Areas of Northern China
Authors: Li Huayao , Zhang Fawang , Du Xinqiang , Tian Dezhi , Jiao Shan , Zhu Jiliang , Dai Fenggang
Published: November 2023

Abandoned well pollution is a critical component of global environmental issues and a historical legacy issue of national development. Despite this, the specific mechanisms by which abandoned wells pollute groundwater remain unclear, particularly in the karst regions of Northern China, where no scientifically effective remediation methods exist. To address this gap, this study focuses on Yangquan City in Shanxi Province and employs field investigations, the analytic hierarchy process, high-definition deep-well logging technology, and qualitative analysis to assess the pollution risk of all abandoned wells in the study area, identifying those with high pollution risk. Through the analysis of extensive image and video data for these high-risk wells, we propose a conceptual model of cross-strata channels in abandoned wells and elucidate the mechanisms by which they pollute groundwater. The results show that, from a single-well perspective, the pollution mechanism is cross-strata pollution. From a regional perspective, the pollution mechanisms are hydraulic connectivity and solute migration and transformation. Based on these findings, we present a scientifically effective remediation strategy tailored to the typical characteristics of abandoned wells in the study area, offering a viable solution to the abandoned well pollution problem in Yangquan City. This research not only augments the theoretical framework in the domain of groundwater pollution but also advances sustainable groundwater security and management strategies. Moreover, the study furnishes theoretical foundations and pragmatic solutions for the remediation of abandoned wells in Yangquan City, which are crucial for the sustainability of the groundwater ecosystem.
Linking Irrational Beliefs with Well-Being at Work: The Role of Fulfilling Performance Expectations
Authors: Santarpia Ferdinando Paolo , Bodoasca Emma , Cantonetti Giulia , Ferri Donato , Borgogni Laura
Published: November 2023

This study explored the association between irrational beliefs—i.e., rigid, unrealistic, and illogical convictions that people hold—and well-being at work. In detail, we tested whether secondary irrational beliefs (i.e., self-depreciation, low frustration tolerance, and awfulizing) displayed both common and unique associations with well-being. Furthermore, we investigated whether the perceived degree of performance expectations’ fulfillment mediate such associations. Data were collected on a sample of 3576 employees from companies providing business and consulting services. Results showed that the general irrationality factor and awfulizing were negatively associated with well-being, both directly and indirectly (via a lower degree of performance expectations’ fulfillment). Low frustration tolerance was positively related with the performance expectations’ fulfillment, which, in turn, fully mediated its association with well-being. Self-depreciation did not relate to our outcomes. The study contributes to the advancement of irrational beliefs literature as it is the first to disentangle the common and unique associations they have with well-being at work and identified the perceived fulfillment of performance expectations as a relevant mediating mechanism in the workplace. Theoretical and practical implications are discussed.
Causality in the Relationship between Economic Growth and Compensation
Authors: Przekota Grzegorz , Janowski Andrzej , Szczepanska-Przekota Anna
Published: November 2023

Compensation of labour and economic growth are two economic variables of particular interest to researchers. There have been many theories linking these quantities in causal relationships. Similarly, some studies suggest that changes in wages lead to economic growth, while others contradict this and suggest that economic growth is the cause of changes in wages. It is important to determine which of these quantities is the cause and which is the effect, as this allows for a more effective implementation of fiscal policy. The research presented in this article addresses this issue. They are based on data from OECD countries for the years 2003–2021. Correlation and cointegration analysis were used in the description. Both general dependencies, i.e., based on annual averages obtained for each country, and specific dependencies, i.e., for each country separately, have been examined. The general conclusion is that current compensation acts as a brake on economic growth, while current economic growth stimulates future compensation. Such results can be the basis for designing government programmes aimed at stimulating the economy rather than regulating wages. However, the specifics of some countries differ from the general conclusion. However, these countries are in the minority.
Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications
Authors: Kumar Arun , Chakravarthy Sumit , Nanthaamornphong Aziz
Published: November 2023

Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance.
Multi Polar q-Rung Orthopair Fuzzy Graphs with Some Topological Indices
Authors: Kausar Andleeb , Abughazalah Nabilah , Yaqoob Naveed
Published: November 2023

The importance of symmetry in graph theory has always been significant, but in recent years, it has become much more so in a number of subfields, including but not limited to domination theory, topological indices, Gromov hyperbolic graphs, and the metric dimension of graphs. The purpose of this monograph is to initiate the idea of a multi polar q-rung orthopair fuzzy graphs (m-PqROPFG) as a fusion of multi polar fuzzy graphs and q-rung orthopair fuzzy graphs. Moreover, for a vertex of multi polar q-rung orthopair fuzzy graphs, the degree and total degree of the vertex are defined. Then, some product operations, inclusive of direct, Cartesian, semi strong, strong lexicographic products, and the union of multi polar q-rung orthopair fuzzy graphs (m-PqROPFGs), are obtained. Also, at first we define some degree based fuzzy topological indices of m-PqROPFG. Then, we compute Zareb indices of the first and second kind, Randic indices, and harmonic index of a m-PqROPFG.
Editorial Summary: Mathematical Models and Methods in Various Sciences
Authors: Ferreira Dário
Published: November 2023

The Special Issue of Symmetry, “Mathematical Models and Methods in Various Sciences”, aimed to bring together innovative papers on the theory, methodology, and applications of symmetric/asymmetric mathematical models and methods in various areas of science [...]
Computing Topological Descriptors of Prime Ideal Sum Graphs of Commutative Rings
Authors: Öztürk Sözen Esra , Alsuraiheed Turki , Abdioğlu Cihat , Ali Shakir
Published: November 2023

Let n≥1 be a fixed integer. The main objective of this paper is to compute some topological indices and coindices that are related to the graph complement of the prime ideal sum (PIS) graph of Zn, where n=pα,p2q,p2q2,pqr,p3q,p2qr, and pqrs for the different prime integers p,q,r, and s. Moreover, we construct M-polynomials and CoM-polynomials using the PIS-graph structure of Zn to avoid the difficulty of computing the descriptors via formulas directly. Furthermore, we present a geometric comparison for representations of each surface obtained by M-polynomials and CoM-polynomials. Finally, we discuss the applicability of algebraic graphs to chemical graph theory.