bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Downwelling Far- and Thermal-Infrared Hyperspectral Radiance for Cloud Phase Classification in the Antarctic
Authors: Ren Hong , Liu Lei , Ye Jin , Xie Hailing
Published: December 2023

The cloud phase is one of the most important parameters of clouds. In this paper, we propose a method for cloud phase classification that synergistically utilizes the far- and thermal-infrared bands based on the Atmospheric Emitted Radiance Interferometer (AERI) at the Atmospheric Radiation Measurement West Antarctic Radiation Experiment (AWARE) observatory in 2016. The possible features in the far- and thermal-infrared bands are analyzed based on the differences in the simulated cloud brightness temperature (BT) spectra with different cloud phases. Using the support vector machine (SVM) algorithm, four features are determined to identify the cloud phase, which include the BT at 900 cm−1, the slope of the fitted function of BT in the 900–1000 cm−1 interval, the BT difference (BTD) between 512 cm−1 and 726 cm−1, and the BTD between 550 cm−1 and 726 cm−1. Here, the performance of the proposed method is evaluated with Shupe’s and Turner’s method. The monthly average accuracy of the proposed method, the method without the two far-infrared features, and Turner’s method are about 76%, 36%, and 49%, respectively, which infer the good performance of the proposed method and also indicate that the far-infrared band features can effectively enhance cloud phase classification. It is notable that, compared to Shupe’s method, the accuracy for the proposed method is only 61% during the Antarctic summer, which results from the definitions of cloud phase and radiative effect. In addition, the accuracy is only 44% for Turner’s method in seasons with a low frequency of mixed clouds due to the significant effect of water vapor.
Multi-Dimensional Low-Rank with Weighted Schatten p-Norm Minimization for Hyperspectral Anomaly Detection
Authors: Chen Xi’ai , Wang Zhen , Wang Kaidong , Jia Huidi , Han Zhi , Tang Yandong
Published: December 2023

Hyperspectral anomaly detection is an important unsupervised binary classification problem that aims to effectively distinguish between background and anomalies in hyperspectral images (HSIs). In recent years, methods based on low-rank tensor representations have been proposed to decompose HSIs into low-rank background and sparse anomaly tensors. However, current methods neglect the low-rank information in the spatial dimension and rely heavily on the background information contained in the dictionary. Furthermore, these algorithms show limited robustness when the dictionary information is missing or corrupted by high level noise. To address these problems, we propose a novel method called multi-dimensional low-rank (MDLR) for HSI anomaly detection. It first reconstructs three background tensors separately from three directional slices of the background tensor. Then, weighted schatten p-norm minimization is employed to enforce the low-rank constraint on the background tensor, and LF,1-norm regularization is used to describe the sparsity in the anomaly tensor. Finally, a well-designed alternating direction method of multipliers (ADMM) is employed to effectively solve the optimization problem. Extensive experiments on four real-world datasets show that our approach outperforms existing anomaly detection methods in terms of accuracy.
Pan-Sharpening Network of Multi-Spectral Remote Sensing Images Using Two-Stream Attention Feature Extractor and Multi-Detail Injection (TAMINet)
Authors: Wang Jing , Miao Jiaqing , Li Gaoping , Tan Ying , Yu Shicheng , Liu Xiaoguang , Zeng Li , Li Guibing
Published: December 2023

Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While significant progress was recently achieved in deep-learning-based pan-sharpening techniques, most existing deep learning approaches face two primary limitations: (1) convolutional neural networks (CNNs) struggle with long-range dependency issues, and (2) significant detail loss during deep network training. Moreover, despite these methods’ pan-sharpening capabilities, their generalization to full-sized raw images remains problematic due to scaling disparities, rendering them less practical. To tackle these issues, we introduce in this study a multi-spectral remote sensing image fusion network, termed TAMINet, which leverages a two-stream coordinate attention mechanism and multi-detail injection. Initially, a two-stream feature extractor augmented with the coordinate attention (CA) block is employed to derive modal-specific features from low-resolution multi-spectral (LRMS) images and panchromatic (PAN) images. This is followed by feature-domain fusion and pan-sharpening image reconstruction. Crucially, a multi-detail injection approach is incorporated during fusion and reconstruction, ensuring the reintroduction of details lost earlier in the process, which minimizes high-frequency detail loss. Finally, a novel hybrid loss function is proposed that incorporates spatial loss, spectral loss, and an additional loss component to enhance performance. The proposed methodology’s effectiveness was validated through experiments on WorldView-2 satellite images, IKONOS, and QuickBird, benchmarked against current state-of-the-art techniques. Experimental findings reveal that TAMINet significantly elevates the pan-sharpening performance for large-scale images, underscoring its potential to enhance multi-spectral remote sensing image quality.
Floating Epoch Length Improves the Accuracy of Accelerometry-Based Estimation of Coincident Oxygen Consumption
Authors: Vähä-Ypyä Henri , Husu Pauliina , Vasankari Tommi , Sievänen Harri
Published: December 2023

Estimation of oxygen consumption (VO2) from accelerometer data is typically based on prediction equations developed in laboratory settings using steadily paced and controlled test activities. These equations may not capture the temporary changes in VO2 occurring in sporadic real-life physical activity. In this study, we introduced a novel floating epoch for accelerometer data analysis and hypothesized that an adaptive epoch length provides a more consistent estimation of VO2 in irregular activity conditions than a 6 s constant epoch. Two different activity tests were conducted: a progressive constant-speed test (CS) performed on a track and a 6 min back-and-forth walk test including accelerations and decelerations (AC/DC) performed as fast as possible. Twenty-nine adults performed the CS test, and sixty-one performed the AC/DC test. The data were collected using hip-worn accelerometers and a portable metabolic gas analyzer. General linear models were employed to create the prediction models for VO2 that were cross-validated using both data sets and epoch types as training and validation sets. The prediction equations based on the CS test or AC/DC test and 6 s epoch had excellent performance (R2 = 89%) for the CS test but poor performance for the AC/DC test (31%). Only the VO2 prediction equation based on the AC/DC test and the floating epoch had good performance (78%) for both tests. The overall accuracy of VO2 prediction is compromised with the constant length epoch, whereas the prediction model based on irregular acceleration data analyzed with a floating epoch provided consistent performance for both activities.
Automatic Seizure Detection Based on Stockwell Transform and Transformer
Authors: Zhong Xiangwen , Liu Guoyang , Dong Xingchen , Li Chuanyu , Li Haotian , Cui Haozhou , Zhou Weidong
Published: December 2023

Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.
Aggregation and Oligomerization Characterization of ß-Lactoglobulin Protein Using a Solid-State Nanopore Sensor
Authors: Acharjee Mitu C. , Ledden Brad , Thomas Brian , He Xianglan , Messina Troy , Giurleo Jason , Talaga David , Li Jiali
Published: December 2023

Protein aggregation is linked to many chronic and devastating neurodegenerative human diseases and is strongly associated with aging. This work demonstrates that protein aggregation and oligomerization can be evaluated by a solid-state nanopore method at the single molecule level. A silicon nitride nanopore sensor was used to characterize both the amyloidogenic and native-state oligomerization of a model protein ß-lactoglobulin variant A (βLGa). The findings from the nanopore measurements are validated against atomic force microscopy (AFM) and dynamic light scattering (DLS) data, comparing βLGa aggregation from the same samples at various stages. By calibrating with linear and circular dsDNA, this study estimates the amyloid fibrils’ length and diameter, the quantity of the βLGa aggregates, and their distribution. The nanopore results align with the DLS and AFM data and offer additional insight at the level of individual protein molecular assemblies. As a further demonstration of the nanopore technique, βLGa self-association and aggregation at pH 4.6 as a function of temperature were measured at high (2 M KCl) and low (0.1 M KCl) ionic strength. This research highlights the advantages and limitations of using solid-state nanopore methods for analyzing protein aggregation.
DLUT: Decoupled Learning-Based Unsupervised Tracker
Authors: Xu Zhengjun , Huang Detian , Huang Xiaoqian , Song Jiaxun , Liu Hang
Published: December 2023

Unsupervised learning has shown immense potential in object tracking, where accurate classification and regression are crucial for unsupervised trackers. However, the classification and regression branches of most unsupervised trackers calculate object similarities by sharing cross-correlation modules. This leads to high coupling between different branches, thus hindering the network performance. To address the above issue, we propose a Decoupled Learning-based Unsupervised Tracker (DLUT). Specifically, we separate the training pipelines of different branches to unlock their inherent learning potential so that different branches can fully explore the focused feature regions of interest. Furthermore, we design independent adaptive decoupling-correlation modules according to the characteristics of each branch to obtain more discriminative and easily locatable feature response maps. Finally, to suppress the noise interference brought by unsupervised pseudo-label training and highlight the foreground object, we propose a novel suppression-ranking-based unsupervised training strategy. Extensive experiments demonstrate that our DLUT outperforms state-of-the-art unsupervised trackers.
Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
Authors: Rui Yikang , Zhao Yan , Lu Wenqi , Wang Can
Published: December 2023

The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.
Personalized Fair Split Learning for Resource-Constrained Internet of Things
Authors: Chen Haitian , Chen Xuebin , Peng Lulu , Bai Yuntian
Published: December 2023

With the flourishing development of the Internet of Things (IoT), federated learning has garnered significant attention as a distributed learning method aimed at preserving the privacy of participant data. However, certain IoT devices, such as sensors, face challenges in effectively employing conventional federated learning approaches due to limited computational and storage resources, which hinder their ability to train complex local models. Additionally, in IoT environments, devices often face problems of data heterogeneity and uneven benefit distribution between them. To address these challenges, a personalized and fair split learning framework is proposed for resource-constrained clients. This framework first adopts a U-shaped structure, dividing the model to enable resource-constrained clients to offload subsets of the foundational model to a central server while retaining personalized model subsets locally to meet the specific personalized requirements of different clients. Furthermore, to ensure fair benefit distribution, a model-aggregation method with optimized aggregation weights is used. This method reasonably allocates model-aggregation weights based on the contributions of clients, thereby achieving collaborative fairness. Experimental results demonstrate that, in three distinct data heterogeneity scenarios, employing personalized training through this framework exhibits higher accuracy compared to existing baseline methods. Simultaneously, the framework ensures collaborative fairness, fostering a more balanced and sustainable cooperation among IoT devices.
A Triplet Network Fusing Optical and SAR Images for Colored Steel Building Extraction
Authors: Zhang Xiaoyong , Yang Shuo , Yang Xuan , Li Cong , Xu Yue
Published: December 2023

The identification of colored steel buildings in images is crucial for managing the construction sector, environmental protection, and sustainable urban development. Current deep learning methods for optical remote sensing images often encounter challenges such as confusion between the roof color or shape of regular buildings and colored steel structures. Additionally, common semantic segmentation networks exhibit poor generalization and inadequate boundary regularization when extracting colored steel buildings. To overcome these limitations, we utilized the metal detection and differentiation capabilities inherent in synthetic aperture radar (SAR) data to develop a network that integrates optical and SAR data. This network, employing a triple-input structure, effectively captures the unique features of colored steel buildings. We designed a multimodal hybrid attention module in the network that discerns the varying importance of each data source depending on the context. Additionally, a boundary refinement (BR) module was introduced to extract the boundaries of the colored steel buildings in a more regular manner, and a deep supervision strategy was implemented to improve the performance of the network in the colored steel building extraction task. A BR module and deep supervision strategy were also implemented to sharpen the extraction of building boundaries, thereby enhancing the network’s accuracy and adaptability. The results indicate that, compared to mainstream semantic segmentation, this method effectively enhances the precision of colored steel building detection, achieving an accuracy rate of 83.19%. This improvement marks a significant advancement in monitoring illegal constructions and supporting the sustainable development of the Beijing–Tianjin–Hebei metropolitan region.
Direction-of-Arrival Estimation for a Random Sparse Linear Array Based on a Graph Neural Network
Authors: Yang Yiye , Zhang Miao , Peng Shihua , Ye Mingkun , Zhang Yixiong
Published: December 2023

This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes’ aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.
Sim2Real: Generative AI to Enhance Photorealism through Domain Transfer with GAN and Seven-Chanel-360°-Paired-Images Dataset
Authors: Bresson Marc , Xing Yang , Guo Weisi
Published: December 2023

This work aims at providing a solution to data scarcity by allowing end users to generate new images while carefully controlling building shapes and environments. While Generative Adversarial Networks (GANs) are the most common network type for image generation tasks, recent studies have only focused on RGB-to-RGB domain transfer tasks. This study utilises a state-of-the-art GAN network for domain transfer that effectively transforms a multi-channel image from a 3D scene into a photorealistic image. It relies on a custom dataset that pairs 360° images from a simulated domain with corresponding 360° street views. The simulated domain includes depth, segmentation map, and surface normal (stored in seven-channel images), while the target domain is composed of photos from Paris. Samples come in pairs thanks to careful virtual camera positioning. To enhance the simulated images into photorealistic views, the generator is designed to preserve semantic information throughout the layers. The study concludes with photorealistic-generated samples from the city of Paris, along with strategies to further refine model performance. The output samples are realistic enough to be used to train and improve future AI models.
Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed
Authors: Garcia-Molina Gary
Published: December 2023

The ability to monitor blood pressure unobtrusively and continuously, even during sleep, may promote the prevention of cardiovascular diseases, enable the early detection of cardiovascular risk, and facilitate the timely administration of treatment. Publicly available data from forty participants containing synchronously recorded signals from four force sensors (load cells located under each leg of a bed) and continuous blood pressure waveforms were leveraged in this research. The focus of this study was on using a deep neural network with load-cell data as input composed of three recurrent layers to reconstruct blood pressure (BP) waveforms. Systolic (SBP) and diastolic (DBP) blood pressure values were estimated from the reconstructed BP waveform. The dataset was partitioned into training, validation, and testing sets, such that the data from a given participant were only used in a single set. The BP waveform reconstruction performance resulted in an R2 of 0.61 and a mean absolute error < 0.1 mmHg. The estimation of the mean SBP and DBP values was characterized by Bland–Altman-derived limits of agreement in intervals of [−11.99 to 15.52 mmHg] and [−7.95 to +3.46 mmHg], respectively. These results may enable the detection of abnormally large or small variations in blood pressure, which indicate cardiovascular health degradation. The apparent contrast between the small reconstruction error and the limit-of-agreement width owes to the fact that reconstruction errors manifest more prominently at the maxima and minima, which are relevant for SBP and DBP estimation. While the focus here was on SBD and DBP estimation, reconstructing the entire BP waveform enables the calculation of additional hemodynamic parameters.
Integrated Temperature–Humidity Sensors for a Pouch-Type Battery Using 100% Printing Process
Authors: Oh Gyeongseok , Sim Jae-Ho , Won Mijin , Jung Minhun , Mantry Snigdha Paramita , Kim Dong-Soo
Published: December 2023

The performance, stability, and lifespan of lithium-ion batteries are influenced by variations in the flow of lithium ions with temperature. In electric vehicles, coolants are generally used to maintain the optimal temperature of the battery, leading to an increasing demand for temperature and humidity sensors that can prevent leakage and short circuits. In this study, humidity and temperature sensors were fabricated on a pouch film of a pouch-type battery. IDE electrodes were screen-printed on the pouch film and humidity- and temperature-sensing materials were printed using a dispenser process. Changes in the capacitance of the printed Ag-CNF film were used for humidity sensing, while changes in the resistance of the printed PEDOT:PSS film were used for temperature sensing. The two sensors were integrated into a single electrode for performance evaluation. The integrated sensor exhibited a response of ΔR ≈ 0.14 to temperature variations from 20 °C to 100 °C with 20% RH humidity as a reference, and a response of ΔC ≈ 2.8 to relative humidity changes from 20% RH to 80% RH at 20 °C. The fabricated integrated sensor is expected to contribute to efficient temperature and humidity monitoring applications in various pouch-type lithium-ion batteries.
Enhancing System Safety and Reliability through Integrated FMEA and Game Theory: A Multi-Factor Approach
Authors: Yazdi Mohammad
Published: December 2023

This study aims to address the limitations of traditional Failure Mode and Effect Analysis (FMEA) in managing safety and reliability within complex systems characterized by interdependent critical factors. We propose an integrated framework that combines FMEA with the strategic decision-making principles of Game Theory, thereby enhancing the assessment and mitigation of risks in intricate environments. The novel inclusion of the Best Worst Method (BWM) and Pythagorean fuzzy uncertain linguistic variables refines the accuracy of risk evaluation by overcoming the inherent deficiencies of conventional FMEA approaches. Through sensitivity analysis, the framework’s efficacy in identifying and prioritizing failure modes is empirically validated, guiding the development of targeted interventions. The practical application of our methodology is demonstrated in a comprehensive healthcare system analysis, showcasing its versatility and significant potential to improve operational safety and reliability across various sectors. This research is particularly beneficial for systems engineers, risk managers, and decision-makers seeking to fortify complex systems against failures and their effects.
Recovery of Valuable Metals from Polymetallic Refractory Concentrate by a Sulfuric Acid Curing and Leaching Method
Authors: Jiang Wei , Xue Jilai , Jiang Kaixi , Jiang Xunxiong , Wang Shengdong , Hu Jinping , Northwood Derek O. , Waters Kristian E. , Ma Hao
Published: December 2023

Sulfuric acid curing and leaching is a promising technology for treating refractory ores. In this work, a refractory concentrate containing 3191 ppm uranium (U), 2135 ppm niobium (Nb), and 0.7% rare earth minerals (REMs) went through two stages: curing by high-concentration H2SO4 and leaching by low-concentration H2SO4. We investigated the behavior of those valuable metals during the two stages. For both curing and leaching, the operating parameters include the acid-to-solid ratio, time, temperature, and H2SO4 concentration. The recovery for U, Nb, and REMs was as high as 95%, 86%, and 73.5% using a curing acid-to-solid ratio of 1:1, curing temperature of 200 °C, curing time of 1 h, H2SO4 concentration of 98%, leaching liquid-to-solid ratio of 4:1, leaching time of 2 h, leaching temperature of 60 °C, and leaching H2SO4 concentration of 5 g/L. A “sulfuric acid curing–leaching-U extraction by N235–Nb recovery by resin adsorption–REMs’ recovery by resin adsorption” method was implemented, where the overall U, Nb, and REMs’ recovery reached 93.1%, 84.5%, and 69.6%, respectively.
Immobilisation of Molybdenum in a Sulphate-Reducing Bioreactor
Authors: Kousi Pavlina , Strongyli Dimitra-Artemis , Tsakiridis Petros E. , Hatzikioseyian Artin , Remoundaki Emmanouella
Published: December 2023

This work presents a biological remediation process for molybdenum-bearing wastewater which may lead to the fabrication of biogenic Mo chalcogenide particles with (photo)catalytic properties. The process is based on dissimilatory sulphate reduction, utilising sulphate-reducing bacteria (SRB), and reductive precipitation of molybdate which is the predominant species of molybdenum in oxygenated water/wastewater. The SRB culture was established in a biofilm reactor which was fed with synthetic solutions containing sulphate (17.7 mM), molybdate molybdenum (2 mM), divalent iron (1.7 mM) and ethanol as the carbon/electron donor. The performance of the bioreactor was monitored in terms of pH, sulphate and molybdenum (Mo(VI) and total) content. The presence of thiomolybdate species was studied by scanning UV-Vis absorbance of samples from the reactor outflow while the reactor precipitates were studied via electron microscopy coupled with energy dispersive spectrometry, X-ray diffractometry and laser light scattering. A molar molybdate/sulphate ratio of 1:12.5 proved effective for molybdate reduction and recovery by 76% in 96 h, whereas sulphate was reduced by 57%. Molybdenum was immobilised in the sulphidic precipitates of the bioreactor, presumably via two principal mechanisms: (i) microbially mediated reduction and precipitation, and (ii) thiomolybdate formation and sorption/incorporation into iron sulphides.
Socio-Demographic Differences in Positive Youth Development in Spanish Undergraduates
Authors: Gomez-Baya Diego , Muñoz-Parralo Maria , Martin-Barrado Antonio David , Trejos-Castillo Elizabeth , Gaspar de Matos Margarida , Wiium Nora
Published: December 2023

The positive youth development (PYD) model is a strength-based conceptualization of the transition to adulthood, in which the relationships that individuals establish within this context are essential. These adaptive contextual–individual interactions allow youths to acquire better psychological adjustment and greater social participation. The PYD model differentiates five areas to be nurtured (the 5Cs) in young people: confidence, competence, connection, caring, and character. The present work aimed to examine differences in overall PYD and its respective 5Cs among Spanish university students based on their socio-demographic characteristics. A cross-sectional study with 1038 students between 18 and 28 years old from 11 Spanish universities was conducted. In order to assess the 5Cs, the Spanish adaptation of the 5Cs of the PYD Short Form was used. When analyzing the variability of the 5Cs according to socio-demographic variables, some relevant differences were found, though with small effect sizes. Students with better scores on the 5Cs included those who were living in the countryside or big cities, studied Social Sciences, were enrolled in small universities, had parents with a high education level, and those who reported high socio-economic status. Women presented higher scores in terms of connection, caring, and character, whereas men reported more perceived competence and confidence, which may be due to gender socialization during adolescence and youth. Families with parents with a higher level of education and of a higher socio-economic level may offer more resources and support greater positive youth development. These social inequalities in PYD may suggest the need for the promotion of equal opportunities in education, health promotion, and social participation, which may allow young people from all backgrounds to develop their individual potential.
Right-Wing Leftists, Left-Wing Rightists, and Traditionalist Liberals: Core Political Values and Ideological Inconsistency at the Party-Elite Level in Bulgaria
Authors: Konstantinov Martin
Published: December 2023

The growth of heterodox ideological configurations, or ideological inconsistencies, among the electorate of Western countries, has been offered as one explanation for recent momentous political events, such as Brexit or the election of Donald Trump as US President. Previous research, however, suggests that ideological inconsistency has been typical for Central and Eastern European (CEE) states for the past thirty years after the fall of the Socialist regimes there. Based on a survey of 102 active members of local and national party structures in Bulgaria, followed by in-depth interviews with the same respondents, I develop a conceptual and methodological approach aimed at objectively measuring Bulgarians’ political values and ideological orientations. Building on previous research on the statistical independence of the social and economic dimensions of ideology, this study identifies three main models of ideological inconsistency at the party-elite level in Bulgaria, offering evidence of the “homogeneity in ideological inconsistency” in this post-Socialist country, with party elites and electorate following the same patterns of inconsistency. The existence of a conservative value complex, integrating traditionalist, statist, and nationalist attitudes regarding the social sphere, is another major finding of the study. I discuss the specific historical and socio-cultural background contributing to ideological inconsistency in Bulgaria and potential implications for the wider CEE region.
A Longitudinal Investigation of a Partial SMEC in a Sample of University Students
Authors: Poškus Mykolas Simas
Published: December 2023

A part of the Simple Model of Environmental Citizenship (SMEC) was explored in a two-wave longitudinal study in a non-intervention setting with approximately one month between measurements. The variables of environmental citizenship, environmental literacy, concrete environmental knowledge, and environmental (self-)education were chosen as potentially the most malleable parts of the SMEC which could naturally change over time in an educational setting. A total of 171 university students participated in the first measurement wave of the study, and 93 participants were retained in the second wave. The mean age of participants was 23 years of age (SD = 4), and 76.6% of participants were women, 21.6% were men, and 1.8% chose not to answer. Results uncovered that, in one month’s time, participants’ concrete environmental knowledge as well as environmental literacy significantly increased, while environmental (self-)education and environmental citizenship had positive yet insignificant effects. Path analysis revealed that measurements at the second measurement wave did not add any new variance that could not be explained through first wave measurements, highlighting that the development of the investigated variables requires more time.
The Effect of Alternative Dryland Crops on Soil Microbial Communities
Authors: Dangi Sadikshya R. , Allen Brett L. , Jabro Jay D. , Rand Tatyana A. , Campbell Joshua W. , Calderon Rosalie B.
Published: December 2023

The composition of a soil microbial community that is associated with novel rotation crops could contribute to an increased yield of subsequent crops and is an important factor influencing the composition of the rhizosphere microbiome. However, the effect of alternative dryland crops on soil microbial community composition is not clear in the northern Great Plains (NGP). The objective of this study, therefore, was to evaluate the effects of the oilseed crops Ethiopian mustard (Brassica carinata A.) or camelina (Camelina sativa L.) or a 10-species forage/cover crop (CC) mix and fallow on soil biological health. Phospholipid fatty acid (PLFA) analysis was used to characterize the microbial community structure. The results showed that the total bacterial PLFA proportion was significantly higher in camelina and fallow compared to CCs and carinata, whereas the total fungal proportion was significantly higher under a CC mix compared to camelina and fallow. The fungal-to-bacterial ratio was significantly higher in CCs (0.130) and carinata (0.113) compared to fallow (0.088). Fungi are often considered a good indicator of soil health, while bacteria are crucial in soil functions. The changes in specific microbial communities due to crop-related alterations might play a key role in the yield of subsequent crops. This study provides valuable insights into the effect of oilseeds, CCs, and fallow on microbial communities.
Sustainable Recovery of Antioxidant Compounds from Rossa Di Tropea Onion Waste and Application as Ingredient for White Bread Production
Authors: Imeneo Valeria , Piscopo Amalia , Santacaterina Simone , De Bruno Alessandra , Poiana Marco
Published: December 2023

From a sustainability and circular economy point of view, this study evaluated the possibility of recovering antioxidant compounds from Rossa di Tropea onion waste for application in the food industry as natural ingredients. In particular, the aim was to investigate the effect of adding natural antioxidants recovered from ‘Rossa di Tropea’ onion waste to maintain/improve the functional and qualitative characteristics of white bread. Total phenolic content, antioxidant activity, and sensorial aspects were studied on the different enriched samples during the storage period. The ‘Rossa di Tropea’ onion skins proved to be a good source of natural polyphenols, and their use in white bread production has resulted in a significant increase in bioactive compound content and antioxidant activity (ABTS and DPPH assays). Moreover, the enriched bread showed acceptable quality attributes in terms of odor, colour, and taste, despite the increase in firmness during the storage time compared to the control sample. The obtained results suggest the possibility of applying the antioxidants recovered by ‘Rossa di Tropea’ onion waste as ingredients in the formulation of bakery products to obtain new food with functional characteristics.
A Multi-Objective Optimization Method for the Design of a Sustainable House in Ecuador by Assessing LCC and LCEI
Authors: Chen Yuan , Gallardo Stephanie
Published: December 2023

The building industry significantly contributes to global warming, driving the demand for sustainable construction and green buildings. However, barriers like cost concerns and limited knowledge persist. Previous studies have used multi-objective optimization (MOO) to minimize life cycle cost and environmental impact, often emphasizing energy efficiency. In equatorial climates, unique factors like material selection must be considered. This study assesses the cost-effectiveness of sustainable materials, focusing on envelope materials in Ecuador. The case study is a single-family house in the equatorial climate, optimized using Building Information Modeling (BIM), Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA). In this study, a MOO process using the weighted sum approach (WSA) identifies sustainable house designs. The sustainable houses achieve a 98% decrease in Ozone Depletion Potential, a 75% reduction in Global Warming Potential, and a 45% drop in Primary Energy Demand, although they still incur a 30% increased cost. The results offer a foundation for cost-effective, eco-friendly housing solutions. Bamboo emerges as a promising material with local acceptance. This research highlights the significance of material selection in sustainable construction and provides a replicable approach for diverse settings. It aims to promote sustainable housing solutions in Ecuador and beyond.
Business Confidence in the Sustainable Manufacturing Sector in the Context of Production, Production Prices, and Interest Rates
Authors: Suhányiová Alžbeta , Suhányi Ladislav , Kočišová Michaela
Published: December 2023

Business confidence can be a very important predictor of future development and sustainability in a selected industry. It is one of the economic sentiment indicators belonging to the most important indicators of the health of the economy. This paper examines the relationships between the Business Confidence Indicator (BCI) of the manufacturing sector and the production, production prices, and interest rates of two neighbouring countries, the Slovak Republic and Hungary. These relationships are examined within the manufacturing sector because it has long been the driving force of sustainable economic development and has significant spill-over effects on the other sectors of the economy. A cluster analysis is performed with the aim of positioning the selected countries into clusters within the OECD countries in relation to the examined variables. Multiple regression models were used to verify the mentioned relationships between variables, separately for each of the two analysed countries. The coefficients of determination are not too high (Slovakia 0.5 and Hungary 0.38), but in the case of sentiment, they reach higher values than in comparable research. Data from the freely available OECD database was used. It can be concluded that the results show positive relationships between the BCI and the production and long-term interest rates, and negative relationships between the BCI and the Producer Prices Index (PPI) and short-term interest rates. The relationship with the short-term interest rates cannot be confirmed in the case of Hungary. Other differences are also shown in that a higher PPI can negatively affect the BCI more intensively in Hungary (regression coefficient 0.18), and that in the case of the Slovak Republic, it appears that the level of influence of the long-term interest rates on the BCI is higher than that in Hungary (the difference between the regression coefficients is 0.27). Practical implications are stated for relevant institutions in the country in three areas: (1) in support of increasing production; (2) in the regulation and subsidization of input production costs; (3) in the stabilization of the lending behaviour of financial institutions.
Film-Induced Tourism, Destination Branding and Game of Thrones: A Review of the Peñíscola de Cine Project
Authors: Huerta-Viso Pablo Jesús , Llorca Abad Germán , Canós-Darós Lourdes
Published: December 2023

This paper addresses an alternative perspective on tourism success, emphasising sustainability over traditional quantitative metrics such as arrival numbers. It explores the impact of fiction films and TV series on individuals’ mental representations of destinations featured on screen, as well as the capacity of film discourse to construct a brand aligned with local stakeholders’ interests. Qualitative methods have been employed, conducting a literature review on sustainable film tourism and destination branding. Local news and an interview with the head of the Peñíscola Film Office complemented academic insights. The primary goal is to examine the “Peñíscola de Cine” project as a paradigm of success, initiated by the city council of Peñíscola, Spain. This project positions the municipality as a natural film set through productions like Game of Thrones (2011–2019), illustrating how film can contribute to destination branding and community engagement. The study highlights the positive contribution of film tourism to sustainability by diversifying and de-seasonalising a territory’s offerings. It also attracts a more educated and environmentally conscious audience. However, it cautiously discusses the potential risks, as evidenced by misapplications in Goathland, England, and Skellig Michael, Ireland, following their appearances in Heartbeat (1992–2010) and Star Wars (1977–2019), respectively. The paper concludes by suggesting film-friendly measures for destination management organizations (DMOs), emphasising the pivotal role of film commissions and film offices in crafting effective marketing strategies and capturing the interest of audiovisual production companies.
Effective Surface Structure Changes and Characteristics of Activated Carbon with the Simple Introduction of Oxygen Functional Groups by Using Radiation Energy
Authors: Yang So Yeong , Bai Byong Chol , Kim Yong Ryeol
Published: December 2023

In recent years, research has aimed to enhance the environmental friendliness of activated carbon by modifying its surface properties to effectively capture specific harmful gases. This study’s primary goal is to swiftly introduce oxygen functional groups to activated carbon surfaces using microwave and plasma techniques and evaluate their characteristics. In the microwave method, we varied nitric acid concentrations and treatment durations for surface modification. Additionally, plasma treatment was used to introduce oxygen functional groups for comparative purposes. Surface characteristics were assessed through SEM, BET, XPS, and FT-IR analyses. The results indicate that in the microwave method, the quantity of oxygen functional groups increased with longer reaction times. Specifically, the sample treated for 20 min with 8 moles of nitric acid displayed an oxygen content of 14.11 at%, and higher nitric acid concentrations led to a reduced specific surface area. In the case of plasma treatment, higher oxygen flow rates resulted in an O1s content of 17.1 at%, and an increase in oxygen flow rate introduced more oxygen functional groups but decreased the specific surface area.
Calogero-like Model without Rearrangement Symmetry
Authors: Znojil Miloslav
Published: December 2023

Reinterpretation of mathematics behind the exactly solvable Calogero’s A-particle quantum model is used to propose its generalization. Firstly, it is argued that the strongly singular nature of Calogero’s particle–particle interactions makes the original permutation-invariant Hamiltonian tractable as a direct sum H=⨁Ha of isospectral components, which are mutually independent. Secondly, after the elimination of the center-of-mass motion, the system is reconsidered as existing in the reduced Euclidean space RA−1 of relative coordinates and decaying into a union of subsets Wa called Weyl chambers. The mutual independence of the related reduced forms of operators Ha enables us to makes them nonisospectral. This breaks the symmetry and unfolds the spectral degeneracy of H. A new multiparametric generalization of the conventional A-body Calogero model is obtained. Its detailed description is provided up to A=4.
Enhancing Multi-Modal Perception and Interaction: An Augmented Reality Visualization System for Complex Decision Making
Authors: Chen Liru , Zhao Hantao , Shi Chenhui , Wu Youbo , Yu Xuewen , Ren Wenze , Zhang Ziyi , Shi Xiaomeng
Published: December 2023

Visualization systems play a crucial role in industry, education, and research domains by offering valuable insights and enhancing decision making. These systems enable the representation of complex workflows and data in a visually intuitive manner, facilitating better understanding, analysis, and communication of information. This paper explores the potential of augmented reality (AR) visualization systems that enhance multi-modal perception and interaction for complex decision making. The proposed system combines the physicality and intuitiveness of the real world with the immersive and interactive capabilities of AR systems. By integrating physical objects and virtual elements, users can engage in natural and intuitive interactions, leveraging multiple sensory modalities. Specifically, the system incorporates vision, touch, eye-tracking, and sound as multi-modal interaction methods to further improve the user experience. This multi-modal nature enables users to perceive and interact in a more holistic and immersive manner. The software and hardware engineering of the proposed system are elaborated in detail, and the system’s architecture and preliminary function testing results are also included in the manuscript. The findings aim to aid visualization system designers, researchers, and practitioners in exploring and harnessing the capabilities of this integrated approach, ultimately leading to more engaging and immersive user experiences in various application domains.
An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
Authors: Hassan Noha , Fernando Xavier , Woungang Isaac
Published: December 2023

Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSU)s are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies.