bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of the Specific Energy of Supercapacitors with Polymeric Materials Using Advanced Molecular Dynamics Simulations
Authors: Ionescu Daniela , Kovaci Maria
Published: December 2024

Supercapacitor/pseudocapacitor structures with electrodes and electrolytes based on conductive polymers, but not only, have been analyzed using advanced molecular dynamics simulation techniques. Results indicated in the literature were used to confirm the results obtained for the specific capacitance and energetic performances of the systems. New material classes like Polymer-MXene electrodes ((PANI)/Ti3C2, PFDs/Ti3C2Tx) present increased capacitance in comparison with simple polymeric composites (PETC or PTh). Combinations of polymers and metallic oxide, like PANI/V2O5, present high capacitance, but new variants can provide improved performance. Different techniques, like electrode doping, adding different salts in the electrolyte (gel electrolyte), and using porous electrodes, can also improve performance. Steps for the non-invasive simulation method with HFSS (Ansys) are defined, and the materials are described at the molecular level as well as the interactions between atomic groups. Macroscopic properties of the system are determined (conductivity, specific energy) and represented on parametric graphs. A complex set of parameters is varied in order to optimize the structures through parameter correlation. Different stages of correlation are considered in order to establish the final sample design and improve energetic performance. An increase of about 8–28% can be obtained concerning the specific energy of the supercapacitor. Prediction, design, atypical behavior, and resonance are addressed using this technique.
Evaluation of the In Vitro Disinfection Potential of the Phytochemicals Linalool and Citronellal Against Biofilms Formed by Escherichia coli and Staphylococcus aureus
Authors: Krapež Patricija , Lunder Manca , Oder Martina , Fink Rok
Published: December 2024

This study aimed to analyze the potential of phytochemicals linalool and citronellal against E. coli and S. aureus biofilms and to compare the results to sodium hypochlorite. We tested the minimal inhibitory concentration, bacterial cell reduction, respiratory chain dehydrogenase activity, cell membrane integrity, and biomass reduction. The results show the lowest inhibitory concentration for both E. coli and S. aureus for sodium hypochlorite, followed by a combination of linalool and citronellal, the sole use of linalool, and the sole use of citronellal, respectively. Furthermore, we found that linalool was effective in biofilm cell reduction, cell respiratory inhibition, membrane integrity, and biomass reduction, while citronellal was less effective. Overall, this indicates that linalool has some benefits in biofilm management, especially with a focus on reducing toxic sodium hypochlorite consumption.
Modeling Investigation on Gas Backflow Performances in Screw Vacuum Pump
Authors: Zhang Li , Liu Yang , Zhang Yongju , Liu Xue , Liu Wenfei , Chen Ziyun
Published: December 2024

Rotor structure has a great influence on the gas backflow in a screw vacuum pump. The characteristics of the gas main flow along the spiral groove of the screw rotor and the gas reverse flow along the tooth-shaped, tooth side, radial, and circumferential clearances are investigated. A new mathematical model of the pumping flow and backflow involved in a flow balance model is proposed to investigate the actions of the shearing force and pressure difference force. The calculated backflow is verified by comparing the experimental measured results. The relationships of the structural parameters of the screw rotor are established. The effects of the rotor parameters, such as pitch, diameter, and compression ratio, on backflow are revealed. The results show that the rotor diameter and compression ratio remain constant and that the influence of pitch on the backflow is slightly weak, with backflow variations of less than 3%, whereas the pitch, rotor length, and compression ratio are constant and the rotor addendum diameter is directly proportional to the backflow. The addendum diameter of rotor #4 is the largest, and its backflow is about 1.5 times larger than that of rotor #1. When the rotor radial sizes and the pitch of the suction end are constant, the compression ratio is inversely proportional to the backflow in the low-pressure region and proportional to the backflow in the high-pressure regions. Therefore, for a vacuum pump operating in low-pressure areas, the use of the compression ratio of 2.2 or higher is favorable for the reduction in backflow.
The Endowment Effect in a Field Study with Risk-Reducing Instruments
Authors: Tomicki Filip , Kuśmierczyk Paweł
Published: December 2024

The endowment effect is a phenomenon whereby ownership increases the perceived value of goods, thereby reducing the willingness to trade. This paper presents the results of a field experiment, designed to assess the robustness of this effect when decisions concern unique, non-tangible instruments of a very high utility. Participating students were unaware of being involved in the experiment, as we monitored their decisions concerning the examination bonuses in a natural academic setting. In contrast to the majority of prior studies that focus on commodities of a known and certain value, this research examines the effect in the context of risky and ambiguous instruments, which are more difficult to evaluate. Our results strongly support the endowment effect hypothesis, proving that the effect is robust, and likely to also be observed in markets concerning high-utility, risky instruments, such as financial ones. Additionally, we find a weak level of support for the gender effect, with female respondents demonstrating a lack of bias when endowed with ambiguous instruments. The study also analyzes the efficiency of these instruments in terms of their utility in risk reduction, and the effort incentives created.
Impact of Macroeconomic Shocks on Financial Performance and Risk Management: A Case Study of LPP SA During the COVID-19 Pandemic and the Ukraine War
Authors: Sokołowska Ewelina , Chmielewski Mariusz , Dziadkiewicz Anna
Published: December 2024

This study analyses the impact of two major macroeconomic shocks—the COVID-19 pandemic and the Ukraine war—on the financial stability of LPP SA, a leading apparel company in Poland and Central and Eastern Europe. This research uses the coefficient of variation to assess changes in financial volatility and evaluates the effectiveness of LPP SA’s risk management strategies. The findings reveal that, while the COVID-19 pandemic had a more destabilising effect on the company’s performance, the Ukraine war’s impact was mitigated by strategic diversification and resource reallocation. The study’s implications are relevant for risk management practices in the apparel industry and other consumer-driven sectors.
Multi-Camera Rig and Spherical Camera Assessment for Indoor Surveys in Complex Spaces
Authors: Perfetti Luca , Bruno Nazarena , Roncella Riccardo
Published: December 2024

This study compares the photogrammetric performance of three multi-camera systems—two spherical cameras (INSTA 360 Pro2 and MG1) and one multi-camera rig (ANT3D)—to evaluate their accuracy and precision in confined environments. These systems are particularly suited for indoor surveys, such as narrow spaces, where traditional methods face limitations. The instruments were tested for the survey of a narrow spiral staircase within Milan Cathedral and the results were analyzed based on different processing strategies, including different relative constraints between sensors, various calibration sets for distortion parameters, interior orientation (IO), and relative orientation (RO), as well as two different ground control solutions. This study also included a repeatability test. The findings showed that, with appropriate ground control, all systems achieved the target accuracy of 1 cm. In partially unconstrained scenarios, the drift errors ranged between 5 and 10 cm. Performance varied depending on the processing pipelines; however, the results suggest that imposing a multi-camera constraint between sensors and estimating both IO and RO parameters during the Bundle Block Adjustment yields the best outcomes. In less stable environments, it might be preferable to pre-calibrate and fix the IO parameters.
Impact Analysis of Orthogonal Circular-Polarized Interference on GNSS Spatial Anti-Jamming Array
Authors: Zhang Ke , Li Xiangjun , Chen Lei , Liu Zengjun , Xie Yuchen
Published: December 2024

With the continuous advancement of electromagnetic countermeasures, new types of interference signals (e.g., multi-polarization suppression interference) pose a significant threat to conventional Global Navigation Satellite System (GNSS) services, even when the receiver employs a right-handed circularly polarized (RHCP) anti-jamming array. This paper proposes a receiving signal model for orthogonal circularly polarized (OCP) interference signals based on conventional arrays, following an analysis of the non-ideal characteristics of actual arrays. Furthermore, the mechanism by which OCP interference signals affect anti-jamming performance is examined. Power inversion (PI) and linear constrained minimum variance (LCMV) techniques, applied to both uniform linear arrays and central circular arrays, are utilized to verify the impact of these interference signals. Simulation and physical testing demonstrate that OCP interference significantly affects the interference subspace of the conventional RHCP array, potentially leading to a reduction in the anti-jamming performance of the receiver. To effectively suppress multi-polarization interference, anti-jamming GNSS receivers must either ensure the consistency of cross-polarization among the elements of the array or adopt polarization-sensitive arrays.
An Extended Omega-K Algorithm for Automotive SAR with Curved Path
Authors: Guo Ping , Li Chao , Li Haolan , Luan Yuchen , Wang Anyi , Wang Rongshu , Tang Shiyang
Published: December 2024

Automotive millimeter-wave (MMW) synthetic aperture radar (SAR) systems can achieve high-resolution images of detection areas, providing environmental perceptions that facilitate intelligent driving. However, curved path is inevitable in complex urban road environments. Non-uniform spatial sampling, brought about by curved path, leads to cross-coupling and spatial variation deteriorates greatly, significantly impacting the imaging results. To deal with these issues, we developed an Extended Omega-K Algorithm (EOKA) for an automotive SAR with a curved path. First, an equivalent range model was constructed based on the relationship between the range history and Doppler frequency. Then, using azimuth time mapping, the echo data was reconstructed with a form similar to that of a uniform linear case. As a result, an analytical two-dimensional (2D) spectrum was easily derived without using of the method of series reversion (MSR) that could be exploited for EOKA. The results from the parking lot, open road, and obstacle experimental scenes demonstrate the performance and feasibility of an MMW SAR for environmental perception.
A Refined Spatiotemporal ZTD Model of the Chinese Region Based on ERA and GNSS Data
Authors: Fan Yongzhao , Xia Fengyu , Sha Zhimin , Jiang Nana
Published: December 2024

Empirical tropospheric models can improve the performance of GNSS precise point positioning (PPP) by providing a priori zenith tropospheric delay (ZTD) information. However, existing models experience insufficient ZTD profile refinement, inadequate correction for systematic bias between the ZTD used in empirical modelling and the GNSS ZTD, and low time efficiency in model updating as more data become available. Therefore, a refined spatiotemporal empirical ZTD model was developed in this study on the basis of the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) data and GNSS data. First, an ENM-R profile model was established by refining the modelling height of the negative exponential function model (ENM). Second, a regression kriging interpolation method was designed to model the systematic bias correction between the ERA5 ZTD and the GNSS ZTD. Last, the final refined ZTD model, ENM-RS, was established by introducing systematic bias correction into ENM-R. Experiments suggest that, compared with the ENM-R and GPT3 models, ENM-RS can effectively suppress systematic bias and improve ZTD modelling accuracy by 10~17%. To improve model update efficiency, the idea of updating an empirical model with sequential least square (SLSQ) adjustment is proposed for the first time. When ENM-RS is modelled via 12 years of ERA data, our method can reduce the time consumption to one-fifth of that of the traditional method. The benefits of our ENM-RS model are evaluated with PPP. The results show that relative to PPP solutions with ENM-R- and GPT3-derived ZTD constraints as well as no constraint, the ENM-RS ZTD constraint can decrease PPP convergence time by approximately 10~30%.
Patch-Based Surface Accuracy Control for Digital Elevation Models by Inverted Terrestrial Laser Scanning (TLS) Located on a Long Pole
Authors: Reinoso-Gordo Juan F. , Ariza-López Francisco J. , García-Balboa José L.
Published: December 2024

Currently, many digital elevation models (DEMs) are derived from airborne LiDAR data acquisition flights. The vertical accuracy of both products has typically been evaluated using methods based on randomly sampled control points. However, due to the superficial nature of the DEM, logic suggests that it is more appropriate to use a superficial object as an evaluation and control element, that is, a “control surface” or “control patch”. Our approach proposes a method for obtaining each patch from a georeferenced point cloud (PC) measured with a terrestrial laser scanner (TLS). In order to reduce the dilution of precision due to very acute angles of incidence that occur between the terrain and the scanner′s rays when it is stationed on a conventional tripod, a system has been created that allows the scanner to be placed face down at a height of up to 7 m. Stationing the scanner at that height also has the advantage of reducing shadow areas in the presence of possible obstacles. In our experiment, the final result is an 18 m × 18 m PC patch which, after resampling, can be transformed into a high-density (10,000 points/m2) and high-quality (absolute positional uncertainty < 0.05 m) DEM patch, that is, with a regular mesh format. This DEM patch can be used as the ground truth to assess the surface accuracy of DEMs (DEM format) or airborne LiDAR data acquisition flights (PC format).
The Spectrum Difference Enhanced Network for Hyperspectral Anomaly Detection
Authors: Liu Shaohua , Guo Huibo , Gao Shiwen , Zhang Wuxia
Published: December 2024

Most deep learning-based hyperspectral anomaly detection (HAD) methods focus on modeling or reconstructing the hyperspectral background to obtain residual maps from the original hyperspectral images. However, these methods typically do not pay enough attention to the spectral similarity in the complex environment, resulting in inadequate distinction between background and anomalies. Moreover, some anomalies and background are different objects, but they are sometimes recognized as the objects with the same spectrum. To address the issues mentioned above, this paper proposes a Spectrum Difference Enhanced Network (SDENet) for HAD, which employs variational mapping and Transformer to amplify spectrum differences. The proposed network is based on the encoder–decoder structure, which contains a CSWin-Transformer encoder, Variational Mapping Module (VMModule), and CSWin-Transformer decoder. First, the CSWin-Transformer encoder and decoder are designed to supplement image information by extracting deep and semantic features, where a cross-shaped window self-attention mechanism is designed to provide strong modeling capability with minimal computational cost. Second, in order to enhance the spectral difference characteristics between anomalies and background, a randomly sampling VMModule is presented for feature space transformation. Finally, all fully connected mapping operations are replaced with convolutional layers to reduce the model parameters and computational load. The effectiveness of the proposed SDENet is verified on three datasets, and experimental results show that it achieves better detection accuracy and lower model complexity compared with existing methods.
An Integrated Algorithm Fusing UWB Ranging Positioning and Visual–Inertial Information for Unmanned Vehicles
Authors: Li Shuang , Wang Lihui , Yu Baoguo , Liang Xiaohu , Du Shitong , Li Yifan , Yang Zihan
Published: December 2024

During the execution of autonomous tasks within sheltered space environments, unmanned vehicles demand highly precise and seamless continuous positioning capabilities. While the existing visual–inertial-based positioning methods can provide accurate poses over short distances, they are prone to error accumulation. Conversely, radio-based positioning techniques could offer absolute position information, yet they encountered difficulties in sheltered space scenarios. Usually, three or more base stations were required for localization. To address these issues, a binocular vision/inertia/ultra-wideband (UWB) combined positioning method based on factor graph optimization was proposed. This approach incorporated UWB ranging and positioning information into the visual–inertia system. Based on a sliding window, the joint nonlinear optimization of multi-source data, including IMU measurements, visual features, as well as UWB ranging and positioning information, was accomplished. Relying on visual inertial odometry, this methodology enabled autonomous positioning without the prerequisite for prior scene knowledge. When UWB base stations were available in the environment, their distance measurements or positioning information could be employed to institute global pose constraints in combination with visual–inertial odometry data. Through the joint optimization of UWB distance or positioning measurements and visual–inertial odometry data, the proposed method precisely ascertained the vehicle’s position and effectively mitigated accumulated errors. The experimental results indicated that the positioning error of the proposed method was reduced by 51.4% compared to the traditional method, thereby fulfilling the requirements for the precise autonomous navigation of unmanned vehicles in sheltered space.
Burned Olive Trees Identification with a Deep Learning Approach in Unmanned Aerial Vehicle Images
Authors: Vasilakos Christos , Verykios Vassilios S.
Published: December 2024

Olive tree orchards are suffering from wildfires in many Mediterranean countries. Following a wildfire event, identifying damaged olive trees is crucial for developing effective management and restoration strategies, while rapid damage assessment can support potential compensation for producers. Moreover, the implementation of real-time health monitoring in olive groves allows producers to carry out targeted interventions, reducing production losses and preserving crop health. This research examines the use of deep learning methodologies in true-color images from Unmanned Aerial Vehicles (UAV) to detect damaged trees, including withering and desiccation of branches and leaf scorching. More specifically, the object detection and image classification computer vision techniques area applied and compared. In the object detection approach, the algorithm aims to localize and identify burned/dry and unburned/healthy olive trees, while in the image classification approach, the classifier categorizes an image showing a tree as burned/dry or unburned/healthy. Training data included true color UAV images of olive trees damaged by fire obtained by multiple cameras and multiple flight heights, resulting in various resolutions. For object detection, the Residual Neural Network was used as a backbone in an object detection approach with a Single-Shot Detector. In the image classification application, two approaches were evaluated. In the first approach, a new shallow network was developed, while in the second approach, transfer learning from pre-trained networks was applied. According to the results, the object detection approach managed to identify healthy trees with an average accuracy of 74%, while for trees with drying, the average accuracy was 69%. However, the optimal network identified olive trees (healthy or unhealthy) that the user did not detect during data collection. In the image classification approach, the application of convolutional neural networks achieved significantly better results with an F1-score above 0.94, either in the new network training approach or by applying transfer learning. In conclusion, the use of computer vision techniques in UAV images identified damaged olive trees, while the image classification approach performed significantly better than object detection.
Fusion Unbiased Pseudo-Linear Kalman Filter-Based Bearings-Only Target Tracking
Authors: Cai Zhihao , Xing Shiqi , Meng Weize , Wang Junpeng , Su Xinyuan , Quan Sinong
Published: December 2024

In the realm of bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) has garnered significant interest, due to its low computational demands and robust stability. However, the interrelation between the measurement matrix and noise introduces bias into the PLKF’s target state estimation. To address this issue, we introduce a fusion unbiased PLKF (FUBKF) algorithm. This algorithm initiates with a global pseudo-linear treatment of the measurement equation, subsequently isolating the noise within the measurement matrix. By employing the unscented Kalman filter (UKF), the algorithm achieves precise estimation of the measurement matrix, thereby mitigating the estimation error stemming from the correlation between the measurement matrix and noise. Simulation outcomes demonstrate that the proposed algorithm substantially enhances tracking accuracy and sustains high stability in both 2D and 3D bearings-only target tracking scenarios, encompassing both non-maneuvering and maneuvering conditions.
Improving Performance of Uncombined PPP-AR Model with Ambiguity Constraints
Authors: Liu Yichen , Hugentobler Urs , Duan Bingbing
Published: December 2024

With the advancement of multi-frequency and multi-constellation GNSS signals and the introduction of observable-specific bias (OSB) products, the uncombined precise point positioning (PPP) model has grown more prevalent. However, this model faces challenges due to the large number of estimated parameters, resulting in strong correlations between state parameters, such as clock errors, ionospheric delays, and hardware biases. This can slow down the convergence time and impede ambiguity resolution. We propose two methods to improve the triple-frequency uncombined PPP-AR model by integrating ambiguity constraints. The first approach makes use of the resolved ambiguities from dual-frequency ionosphere-free combined PPP-AR processing and incorporates them as constraints into triple-frequency uncombined PPP-AR processing. While this approach requires the implementation of two filters, increasing computational demands and thereby limiting its feasibility for real-time applications, it effectively reduces parameter correlations and facilitates ambiguity resolution in post-processing. The second approach incorporates fixed extra-wide-lane (EWL) and wide-lane (WL) ambiguities directly, allowing for rapid convergence, and is well suited for real-time processing. Results show that, compared to the uncombined PPP-AR model, integrating N1 and N2 constraints reduces averaged convergence time from 8.2 to 6.4 min horizontally and 13.9 to 10.7 min vertically in the float solution. On the other hand, integrating EWL and WL ambiguity constraints reduces the horizontal convergence to 5.9 min in the float solution and to 4.6 min for horizontal and 9.7 min for vertical convergence in the fixed solution. Both methods significantly enhance the ambiguity resolution in the uncombined triple-frequency PPP model, increasing the validated fixing rate from approximately 80% to 89%.
Mapping Rural Household Vulnerability to Flood-Induced Health Risks in Disaster-Stricken Khyber Pakhtunkhwa, Pakistan
Authors: Shah Ashfaq Ahmad , Ullah Wahid , Khan Nasir Abbas , Alotaibi Bader Alhafi , Xu Chong
Published: December 2024

This study maps the rural household vulnerability to flood-induced health risks in flood-affected Khyber Pakhtunkhwa (KPK), Pakistan, focusing on the devastating 2022 flood. Using data from 600 households in the severely impacted districts of Khyber Pakhtunkhwa province (including Charsadda and Nowshera), this research examines the influence of demographic, socioeconomic, and infrastructural factors on household vulnerability. This study assesses household vulnerability to flooding and health issues using logistic regression. The current study findings revealed that female-headed households, those with younger heads, and families with lower educational levels are particularly vulnerable. Income disparities significantly shape coping capacity, with wealthier households more likely to adopt effective risk-mitigation strategies. Proximity to functioning healthcare facilities emerged as a crucial factor in reducing vulnerability, as these households faced fewer health hazards. Conversely, households in areas where health and water infrastructure were damaged experienced higher risks of disease outbreaks, including cholera and malaria, due to water contamination and inadequate sanitation. This study highlights the urgent need for resilient infrastructure, strengthened public health systems, improved health education, and enhanced water and sanitation services to mitigate flood-induced health risks. Policymakers are urged to sustainable development practices by adopting gender-sensitive disaster management strategies, prioritizing educational initiatives, and fostering community support networks to enhance resilience to future flood events in KPK.
The Impact of Host–Guest Interactions Among Young People on Cultural Sustainability: A Case Study of Dialects
Authors: Geng Songtao , Li Danyang
Published: December 2024

Dialects represent significant cultural factors and play an essential role in sustainable cultural development. Therefore, identifying and addressing the negative impacts of dialects is necessary for promoting cultural sustainability. Guided by the sensory marketing theory, this study targeted young tourists and conducted an empirical study. The study aimed to explore the impact of destination dialect perceptions on tourists’ emotions and behaviors. The results indicated that their dialect perceptions influence tourists’ recommendations and feedback behaviors. Exclusive perceptions and negative emotions were found to negatively affect tourists’ recommendations and feedback behaviors, acting as sequential mediators between destination dialect perceptions and tourists’ recommendations and feedback behaviors. This study extends the theoretical framework on destination dialect perceptions and provides practical insights for managers seeking to enhance destination development.
Fostering Purchase Intentions Through CSR and Service Quality: The Role of Customer Satisfaction, Brand Loyalty, and Admiration
Authors: Irshad Omer , Ahmad Sohail , Mahmood Shahid
Published: December 2024

A management idea known as corporate social responsibility encourages businesses to incorporate social and environmental considerations into their daily operations and relationships with stakeholders. The purpose of this research is to identify the aspects that influence customers’ perceptions of a brand’s commitment to social responsibility and their subsequent intentions to purchase. The theoretical model proposed draws on numerous theories of consumer behavior, including the perceived value theory, the corporate identity theory, and the theory of planned behavior. A survey was then given to 280 users in Pakistan afterwards. Later, structural equation modeling was used to evaluate the suggested model. It was discovered that customer satisfaction, brand loyalty, and brand admiration mediated the relationship between CSR and CSR-related purchase intent. In conjunction with service quality, CSR practices increase customer satisfaction. The effect of customer satisfaction on CSR purchasing intent is also mediated by brand loyalty and brand admiration. This study backs consumer behavior theories by elucidating the lagged and immediate indicators of CSR purchasing intent. CSR may result in customer satisfaction if implemented intelligently to meet its objectives. Customer satisfaction is a requirement for customers to be ready to pay for CSR.
Effects of Different Straw Incorporation Amounts on Soil Organic Carbon, Microbial Biomass, and Enzyme Activities in Dry-Crop Farmland
Authors: Zhang Xinyi , Ren Xiaoyan , Cai Liqun
Published: December 2024

The direct input of straw to the field can increase the source and supply of soil carbon and nitrogen, change the soil microbial biomass and enzyme activity, and affect the soil organic carbon sequestration, which in turn affects soil fertility and quality. In this study, a three-year field orientation experiment was conducted to investigate the effects of straw input on soil microbial biomass, enzyme activity, and soil organic carbon at different straw incorporation amounts (0, 3000, 7000, and 14,000 kg/hm2). The results showed that soil microbial biomass, enzyme activity, and organic carbon content increased with the increase in straw amount, and the increase in 4-fold straw input (T4) treatment was significantly larger than that of other treatments; the microbial biomass, enzyme activity, and SOC (soil organic carbon) in different soil layers were 0–10 cm > 10–20 cm > 20–30 cm; and straw incorporation amounts had a significant effect on soil microbial biomass, enzyme activity, and SOC. The amount of straw input to the field had a highly significant positive effect on soil microbial carbon and nitrogen and SOC (p < 0.001). In conclusion, four times the amount of straw input to the field had the most obvious effect on enhancing soil organic carbon content, microbial biomass, and enzyme activity. This has important implications for the development of sustainable agriculture.
The Determinants of Green Purchasing in the Hospitality Sector: A Study on the Mediation Effect of LOHAS Orientation
Authors: Sözer Edin Güçlü , Civelek Mustafa Emre , Ertemel Adnan Veysel , Pehlivanoğlu Mustafa Çağrı
Published: December 2024

The objective of this study was to identify the determinants of personal norm and to measure the effect of personal norm on green purchasing behavior as well as a possible mediating role of consumer LOHAS orientation in the effect of personal norm on green purchasing behavior in the hospitality context. A field study was designed to measure the hypothesized effects, and 418 consumers who regularly purchase summer holidays in hotels were included in the survey. Structural Equation Modeling (SEM) was employed in order to test the proposed hypotheses. The results confirm that social norm, the ascription of responsibility, and the attitude towards green purchasing behavior are the determinants of personal norm. Personal norm is found to affect both the green purchasing behavior and the LOHAS orientation of consumers. The results of the study also confirmed that LOHAS orientation does not mediate the effect of personal norm on green purchasing behavior due to the dominance of personal norm’s effect on LOHAS orientation. Academic as well as managerial implications are provided in the Discussion. The Conclusion provides the most important academic and practical contributions of the study, limitations related to the generalizability of the findings, and recommendations for future studies.
Tourism Policymaking in Troubling Times: Sustainability-Driven Challenges, Implemented Policies, and Goals for Sustainable Development
Authors: Kuščer Kir , Peters Mike , Schönherr Sarah
Published: December 2024

Tourism’s vulnerability to crises underscores the need for new or adapted tourism policies that prioritize sustainable tourism development across ecological, economic, and stakeholder dimensions. While past research has highlighted the relevance of sustainable policies for tourism crisis recovery, the research gap lies in the actual implementation of these policies in response to crises. This study examines tourism policymaking in Europe by conducting a quantitative study with 181 tourism organizations responsible for sustainable tourism and its policy implementation. Exploratory factor analysis and structural equation modeling were employed to reveal how tourism policymakers perceive challenges, the subsequent impact on implemented policies, and the intended sustainability-driven goals. Specifically, policies addressing environmental, technological, promotional, and personnel challenges have been enacted due to the climate change crisis, the COVID-19 pandemic, and financial recessions. These policies not only aim to pursue economic recovery but also contribute to the sustainable development of tourism, engaging key stakeholders and aligning with broader sustainability goals such as climate action and governance. This study addresses the underdeveloped area of sustainomics by contributing to the discourse on political implementation for sustainable development, providing a critical exploration of how tourism governance can enhance sustainability practices in crisis contexts.
Evaluating Artificial Intelligence Models for Resource Allocation in Circular Economy Digital Marketplace
Authors: Sheikh Arifuzzaman (Arif) , Simske Steven J. , Chong Edwin K. P.
Published: December 2024

This study assesses the application of artificial intelligence (AI) algorithms for optimizing resource allocation, demand-supply matching, and dynamic pricing within circular economy (CE) digital marketplaces. Five AI models—autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), random forest (RF), gradient boosting regressor (GBR), and neural networks (NNs)—were evaluated based on their effectiveness in predicting waste generation, economic growth, and energy prices. The GBR model outperformed the others, achieving a mean absolute error (MAE) of 23.39 and an R2 of 0.7586 in demand forecasting, demonstrating strong potential for resource flow management. In contrast, the NNs encountered limitations in supply prediction, with an MAE of 121.86 and an R2 of 0.0151, indicating challenges in adapting to market volatility. Reinforcement learning methods, specifically Q-learning and deep Q-learning (DQL), were applied for price stabilization, resulting in reduced price fluctuations and improved market stability. These findings contribute a conceptual framework for AI-driven CE marketplaces, showcasing the role of AI in enhancing resource efficiency and supporting sustainable urban development. While synthetic data enabled controlled experimentation, this study acknowledges its limitations in capturing full real-world variability, marking a direction for future research to validate findings with real-world data. Moreover, ethical considerations, such as algorithmic fairness and transparency, are critical for responsible AI integration in circular economy contexts.
Sustainable Cycling: Boosting Commuting and Tourism Opportunities in Istria
Authors: Šobot Aleksandar , Gričar Sergej , Šugar Violeta , Bojnec Štefan
Published: December 2024

This study explores the evolution of cycling in Istria, a region located in parts of Slovenia and Croatia along the Northern Adriatic coast, recognised for its vibrant tourism sector. The research aims to evaluate the potential of cycling for both daily commuting and tourism while examining its economic, environmental, and social impacts. The study uses secondary data from 2010 to 2023, econometric methods, and interviews to assess how cycling contributes to economic growth, regional collaboration, employment rates, and greenhouse gas (GHG) emissions. In Slovenia, regression analysis indicates that bicycle imports positively correlate with tourist arrivals, which positively influences GDP. However, VAR analysis shows that employment has a negative impact on GHG emissions. In contrast, the increase in bicycles contributes to higher GHG emissions, suggesting the possibility of greenwashing or double pollution. In Croatia, regression coefficients were found to be insignificant, aligning with insights gained from interviews. Nonetheless, VAR analysis reveals that tourist arrivals and GDP positively reinforce each other, while GHG emissions negatively affect employment. Bicycles are shown to reduce GHG emissions, and tourist arrivals are also associated with lowered GHG emissions. The study concludes that cycling holds significant potential for fostering sustainable tourism and regional development; however, the differing impacts observed in Slovenia and Croatia necessitate tailored policy interventions. The hypothesis is partially confirmed in Slovenia, where bicycle imports positively impact tourist arrivals and GDP but are challenged by increased GHG emissions, while in Croatia, the hypothesis is not supported by direct economic gains from cycling. However, bicycles contribute to reducing GHG emissions.
How Does Environmental Regulation Affect Corporate Environmental, Social, and Governance (ESG) Greenwashing? Evidence from China
Authors: Yin Bitao , Li Zongting , Xiong Zhiqiao , Shi Daqian
Published: December 2024

This study investigated the impact of China’s new environmental protection law on the greenwashing of A-share listed companies. By employing the difference-in-differences (DID) method, this study reveals that the EPL exerts a significant inhibitory effect on corporate greenwashing behavior. To ensure the robustness of the results, several tests are conducted, including parallel trend tests, placebo tests, and propensity score matching DID (PSM-DID), all of which confirm the reliability of the findings. This paper explores the influence mechanism of the EPL through constructing the institutional deterrence mechanism, cost effect mechanism, and financing constraint mechanism. It is found that the EPL has a more significant inhibitory effect on economically developed regions, non-high-tech industries, and non-state-owned enterprises. Finally, specific policy recommendations are put forward from macro and micro perspectives.
Imaginique Expressions: Tailoring Personalized Short-Text-to-Image Generation Through Aesthetic Assessment and Human Insights
Authors: Wan Yitian , Xiao Luwei , Wu Xingjiao , Yang Jing , He Liang
Published: December 2024

The text-to-image task, a critical branch of computer vision and image processing, has witnessed remarkable advancements fueled by the abundance of realistic data and rapid AI innovation. However, existing research often overlooks scenarios involving sparse textual input and fails to incorporate human personalized preferences into the generative process. To address these gaps, we propose a novel AI methodology: personalized short-text-to-image generation through aesthetic assessment and human insights. Our approach introduces a symmetry between personalized aesthetic preferences and the generated images by leveraging a data-driven personality encoder (PE) to extract personal information and embed it into a Big Five personality trait-based image aesthetic assessment (BFIAA) model. This model harmonizes aesthetic preferences with the generative process by adapting the stable diffusion framework to align with personalized assessments. Experimental results demonstrate the effectiveness of our method: the PE module achieves an accuracy of 98.1%, while the BFIAA model surpasses the baseline by 13% on the PLCC metric, accurately reflecting human aesthetic preferences. Furthermore, our adapted generation model improves convergence loss by over 10% compared to the base model, consistently producing personalized images that are more aligned with human preferences.
Expansion Direction Selection of the Coal-to-Olefin Industrial Chain in Inner Mongolia from the Value Chain Perspective
Authors: Xu Desheng , Liu Chen , Du Qing , Duan Wei , Zhang Chunyan
Published: December 2024

Inner Mongolia is a key region for China’s clean energy production and a demonstration base for modern coal chemical industry production. However, modern coal chemical industry development in this region faces many problems, such as complex downstream product structures, low value-added products, and severe environmental pollution. With limited future coal allowance in the coal chemical industry, the scarce available coal will likely be allocated to fields with strong product competitiveness to enhance industrial chain value. Given this, the Inner Mongolia coal-to-olefin industrial chain is selected to explore its strategic expansion directions. An improved value chain accounting method is proposed to account for the value of each segment of the coal-to-olefin industrial chain in Inner Mongolia from 2011 to 2022. The improved value chain accounting method is then combined with system dynamics to construct a model for selecting the expansion directions of the coal-to-olefin industrial chain based on the value chain perspective. Finally, the differences in the value of each future expansion direction are analyzed through scenario simulation and comparison. The most important result is that the higher the extension of the coal-to-olefin industry chain in Inner Mongolia, the higher the value of the industry chain. Carbon punishment is the most important factor affecting the value of the industry chain, and enterprises and governments should increase investment in innovation and renewable energy. Accordingly, this study provides a decision-making method for selecting the optimal expansion direction of the modern coal chemical industrial chain in Inner Mongolia.
The Association Between Serum Trimethylamine N-Oxide and Arterial Stiffness in Chronic Peritoneal Dialysis Patients: A Cross-Sectional Study
Authors: Huang Po-Yu , Lin Yu-Li , Chen Yi-Hsin , Hung Szu-Chun , Liou Hung-Hsiang , Tsai Jen-Pi , Hsu Bang-Gee
Published: December 2024

Trimethylamine N-oxide (TMAO), a gut microbiome-derived metabolite, participates in the atherogenesis and vascular stiffening that is closely linked with cardiovascular (CV) complications and related deaths in individuals with kidney failure undergoing peritoneal dialysis (PD) therapy. In these patients, arterial stiffness (AS) is also an indicator of adverse CV outcomes. This study assessed the correlation between serum TMAO concentration quantified with high-performance liquid chromatography and mass spectrometry and central AS measured by carotid–femoral pulse wave velocity (cfPWV) in patients with chronic PD. Of the 160 participants included, 23.8% had a cfPWV of ≥10 m/s, which fulfilled the AS criteria. Multivariable logistic regression analysis revealed that TMAO, age, and waist circumference were positively associated with AS. Multivariable stepwise linear regression showed that underlying diabetes, advanced age, waist circumference, systolic blood pressure, and logarithmic-transformed TMAO were independently correlated with cfPWV. The area under the receiver operating characteristic curve for TMAO in differentiating AS from non-AS was 0.737. In conclusion, serum TMAO level was significantly independently correlated with central AS among participants undergoing PD for end-stage kidney failure.
The Impact of Electron Phase Shifts on ββ-Decay Kinematics
Authors: Niţescu Ovidiu , Ghinescu Stefan , Šimkovic Fedor
Published: December 2024

We reexamine the angular correlation between the emitted electrons in the double beta decay (DBD) of 100Mo, with particular attention to the impact of electronic wave function phase shifts. In the two-neutrino mode, the angular correlation factor increases modestly compared to calculations without phase shifts. However, a more detailed analysis of the angular correlation energy distributions uncovered a striking feature: electrons are most likely emitted in the same direction when one of them is below a certain energy threshold. We show that this feature is absent in previous Standard Model (SM) predictions and that phase shifts could also influence the angular correlations predicted by new physics models in two-neutrino DBD. For the neutrinoless mode, the direction flip is also present when phase shifts are included in the calculation. However, the angular correlation factor does not change much when phase shifts are taken into account, though our analysis is limited to the light neutrino exchange as the dominant mechanism. These findings highlight the subtle yet significant role that phase shifts can play in shaping electron emission patterns, influencing both SM and new physics predictions in DBD.
Immunogenicity and Safety According to Immunosuppressive Drugs and Different COVID-19 Vaccine Platforms in Immune-Mediated Disease: Data from SAFER Cohort
Authors: Machado Ketty Lysie Libardi Lira , Burian Ana Paula Neves , Martins-Filho Olindo Assis , Mill José Geraldo , Ferreira Lunara Baptista , Tapia Karina Rosemarie Lallemand , Moulin Anna Carolina Simões , Moulaz Isac Ribeiro , Ribeiro Priscila Dias Cardoso , Magalhães Vanessa de Oliveira , Biegelmeyer Erika , Peixoto Flávia Maria Matos Melo Campos , Ribeiro Sandra Lúcia Euzébio , Telles Camila Maria Paiva França , Bühring Juliana , Sartorio Natalia Sarzi , Hax Vanessa , Rezende Rodrigo Poubel Vieira de , Baptista Katia Lino , Melo Ana Karla Guedes de , Cruz Vitor Alves , Vieira Rejane Maria Rodrigues de Abreu , Azevedo Renata Henriques de , Azevedo Valderilio Feijó , Pinheiro Marcelo de Medeiros , Monticielo Odirlei André , Reis Neto Edgard Torres Dos , Teixeira-Carvalho Andréa , Xavier Ricardo Machado , Sato Emilia Inoue , de Souza Viviane Angelina , Ferreira Gilda Aparecida , Pileggi Gecilmara Salviato , Valim Valeria
Published: December 2024

Background/Objectives: The effectiveness of COVID-19 vaccine in patients with immune-mediated inflammatory diseases (IMID) depends on the underlying disease, immunosuppression degree and the vaccine regimens. We evaluate the safety and immunogenicity of different COVID-19 vaccine schedules. Methods: The SAFER study: “Safety and effectiveness of the COVID-19 Vaccine in Rheumatic Disease”, is a Brazilian multicentric prospective observational phase IV study in the real-life. Data were analyzed after 2 or 3 doses of COVID-19 vaccines: adenoviral vectored vaccine (ChAdOx1 nCoV-19, Astrazeneca), mRNA vaccine (BNT162b2, Pfizer–BioNTech) or inactivated SARS-COV-2 vaccine (CoronaVac, Sinovac Biotech). IgG antibody against SARS-CoV-2 spike (IgG-S) receptor-binding domain level were quantified at baseline (T1) and 28 days after the first (T2), 2nd (T3) and 3rd (T4) doses by chemiluminescence (SARS-CoV-2-IgG-II Quant-assay, Abbott-Laboratories). Results: 721 patients with IMID were included in the analysis. The median titers of IgG-S (BAU/mL) increased progressively over the times: at baseline was 6.26 (5.41–7.24), T2: 73.01 (61.53–86.62), T3: 200.0 (174.36–229.41) and T4: 904.92 (800.49–1022.97). The multivariate linear regression showed that greater IgG-S titers were associated with pre-exposure to COVID-19 (p < 0.001) and BNT162b2 booster vaccine (p < 0.001). Rituximab and immunosuppressant drugs were independent factors for low titers (p = 0.002, p < 0.001, respectively). No serious adverse event was reported. Conclusions: All platforms were safe and induced an increase in IgG-S antibodies. COVID-19 pre-exposure and BNT162b2 booster regimens were predictors of higher humoral immune responses, which is relevant in immunosuppressed populations. Immunosuppressants (mainly rituximab) predicted the lowest antibodies.