bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Mesoscale Eddy Merging in the Subtropical Northwest Pacific Using Satellite Remote Sensing Data
Authors: Fu Minghan , Dong Changming , Dong Jihai , Sun Wenjin
Published: September 2023

Mesoscale eddies are ubiquitous in the ocean, yet our understanding of their evolutions, particularly eddy merging processes, remains enigmatic. In this study, the merging processes of two cyclonic–cyclonic and two anticyclonic–anticyclonic eddies are analyzed in the Subtropical Northwest Pacific using satellite remote sensing altimetry data. The results reveal that, as eddies approach each other, their contours become connected, leading to the formation of multi-core eddies. Simultaneously, the merging process prompts substantial exchanges of energy and vorticity, resulting in the dissipation of one eddy and the emergence of a more extensive merged eddy. Throughout the merging process, the eddy contours elongate upwards along the centerline (the line connecting eddy centers) and there are distinct changes in both the horizontal and vertical morphology of the eddies. Notably, after the merging, the eddies distinctly exhibit intensified signals of sea surface temperature and vertical temperature anomaly, an outcome of their transformative fusion. The findings of this study significantly enhance our understanding of mesoscale eddy dynamics, particularly in the intricate eddy merging process. However, it is important to note that, due to limitations in vertical observational data, this study does not provide a quantitative portrayal of the vertical mechanisms of eddy merging, which also underscores a pivotal avenue for future research in the field.
Novel UAV Flight Designs for Accuracy Optimization of Structure from Motion Data Products
Authors: Mueller Marlin M. , Dietenberger Steffen , Nestler Maximilian , Hese Sören , Ziemer Jonas , Bachmann Felix , Leiber Julian , Dubois Clémence , Thiel Christian
Published: September 2023

Leveraging low-cost drone technology, specifically the DJI Mini 2, this study presents an innovative method for creating accurate, high-resolution digital surface models (DSMs) to enhance topographic mapping with off-the-shelf components. Our research, conducted near Jena, Germany, introduces two novel flight designs, the “spiral” and “loop” flight designs, devised to mitigate common challenges in structure from motion workflows, such as systematic doming and bowling effects. The analysis, based on height difference products with a lidar-based reference, and curvature estimates, revealed that “loop” and “spiral” flight patterns were successful in substantially reducing these systematic errors. It was observed that the novel flight designs resulted in DSMs with lower curvature values compared to the simple nadir or oblique flight patterns, indicating a significant reduction in distortions. The results imply that the adoption of novel flight designs can lead to substantial improvements in DSM quality, while facilitating shorter flight times and lower computational needs. This work underscores the potential of consumer-grade unoccupied aerial vehicle hardware for scientific applications, especially in remote sensing tasks.
Hierarchical Edge-Preserving Dense Matching by Exploiting Reliably Matched Line Segments
Authors: Yue Yi , Fang Tong , Li Wen , Chen Min , Xu Bo , Ge Xuming , Hu Han , Zhang Zhanhao
Published: September 2023

Image dense matching plays a crucial role in the reconstruction of three-dimensional models of buildings. However, large variations in target heights and serious occlusion lead to obvious mismatches in areas with discontinuous depths, such as building edges. To solve this problem, the present study mines the geometric and semantic information of line segments to produce a constraint for the dense matching process. First, a disparity consistency-based line segment matching method is proposed. This method correctly matches line segments on building structures in discontinuous areas based on the assumption that, within the corresponding local areas formed by two corresponding line pairs, the disparity obtained by the coarse-level matching of the hierarchical dense matching is similar to that derived from the local homography estimated from the corresponding line pairs. Second, an adaptive guide parameter is designed to constrain the cost propagation between pixels in the neighborhood of line segments. This improves the rationality of cost aggregation paths in discontinuous areas, thereby enhancing the matching accuracy near building edges. Experimental results using satellite and aerial images show that the proposed method efficiently obtains reliable line segment matches at building edges with a matching precision exceeding 97%. Under the constraint of the matched line segments, the proposed dense matching method generates building edges that are visually clearer, and achieves higher accuracy around edges, than without the line segment constraint.
Mapping Algal Blooms in Aquatic Ecosystems Using Long-Term Landsat Data: A Case Study of Yuqiao Reservoir from 1984–2022
Authors: Liu Dandan , Ding Hu , Han Xingxing , Lang Yunchao , Chen Wei
Published: September 2023

Water eutrophication poses a dual threat to ecological and human well-being. Gaining insight into the intricate dynamics of phytoplankton bloom phenology holds paramount importance in comprehending the complexities of aquatic ecosystems. Remote sensing technologies have gained attention for mapping algal blooms (ABs) effectively, but distinguishing them from aquatic vegetation (AV) remains challenging due to their similar spectral characteristics. To address this issue, we propose a meticulous three-step methodology for AB mapping employing long-term Landsat imagery. Initially, a multi-index decision tree model (DTM) is deployed to identify the vegetation signal (VS) encompassing both AV and ABs. Subsequently, the annual maximum growth range of AV is precisely delineated using vegetation presence frequency (VPF) in conjunction with normal and low water level imagery. Lastly, ABs are accurately extracted by inversely intersecting VS and AV. The performance of our approach is thoroughly validated using the interclass correlation coefficient (ICC) based on a Gaofen-2 Panchromatic Multi-spectral (GF-2 PMS) image, demonstrating strong consistency with notable values of 0.822 longitudinally, 0.771 latitudinally, and 0.797 overall. The method is applied to Landsat images from 1984 to 2022 to quantify the spatial distribution and temporal variations of ABs in Yuqiao Reservoir—a significant national water body spanning a vast area of 135 km2 in China. Our findings reveal a pervasive and uneven dispersion of ABs, predominantly concentrated in the northern sector. Notably, the intensity of ABs experienced an initial surge from 1984 to 2008, followed by a subsequent decline from 2014 to 2022. Importantly, anthropogenic activities, such as fish cage culture, alongside pollution stemming from nearby industrial and agricultural sources, exert a profound influence on the dynamics of water eutrophication. Fortunately, governmental initiatives focused on water purification exhibit commendable efficacy in mitigating the ecological burden on reservoirs and upholding water quality. The methodological framework presented in this study boasts remarkable precision in AB extraction and exhibits considerable potential in addressing the needs of aquatic ecosystems.
Optical Remote Sensing Ship Recognition and Classification Based on Improved YOLOv5
Authors: Jian Jun , Liu Long , Zhang Yingxiang , Xu Ke , Yang Jiaxuan
Published: September 2023

Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area of ship targets is small, and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed and false detection. To solve these problems, this paper proposes an improved-YOLOv5 algorithm mainly including: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce a cross-layer connection channel and lightweight GSConv structures into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) Use the Wise-IoU loss function to calculate the localization loss in the Output, and assign reasonable gradient gains to cope with differences in image quality. In addition, during the preprocessing stage of experimental data, a median+bilateral filter method was used to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Improved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; compared to the original YOLOv5s, the mean Average Precision (mAP) improved by 3.2% and the Frames Per Second (FPN) accelerated by 8.7%.
Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network
Authors: Khoshkhabar Maryam , Meshgini Saeed , Afrouzian Reza , Danishvar Sebelan
Published: September 2023

Segmenting the liver and liver tumors in computed tomography (CT) images is an important step toward quantifiable biomarkers for a computer-aided decision-making system and precise medical diagnosis. Radiologists and specialized physicians use CT images to diagnose and classify liver organs and tumors. Because these organs have similar characteristics in form, texture, and light intensity values, other internal organs such as the heart, spleen, stomach, and kidneys confuse visual recognition of the liver and tumor division. Furthermore, visual identification of liver tumors is time-consuming, complicated, and error-prone, and incorrect diagnosis and segmentation can hurt the patient’s life. Many automatic and semi-automatic methods based on machine learning algorithms have recently been suggested for liver organ recognition and tumor segmentation. However, there are still difficulties due to poor recognition precision and speed and a lack of dependability. This paper presents a novel deep learning-based technique for segmenting liver tumors and identifying liver organs in computed tomography maps. Based on the LiTS17 database, the suggested technique comprises four Chebyshev graph convolution layers and a fully connected layer that can accurately segment the liver and liver tumors. Thus, the accuracy, Dice coefficient, mean IoU, sensitivity, precision, and recall obtained based on the proposed method according to the LiTS17 dataset are around 99.1%, 91.1%, 90.8%, 99.4%, 99.4%, and 91.2%, respectively. In addition, the effectiveness of the proposed method was evaluated in a noisy environment, and the proposed network could withstand a wide range of environmental signal-to-noise ratios (SNRs). Thus, at SNR = −4 dB, the accuracy of the proposed method for liver organ segmentation remained around 90%. The proposed model has obtained satisfactory and favorable results compared to previous research. According to the positive results, the proposed model is expected to be used to assist radiologists and specialist doctors in the near future.
Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization
Authors: Xu Shuchen , Sun Yongrong , Zhao Kedong , Fu Xiyu , Wang Shuaishuai
Published: September 2023

Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem. However, visual odometry relies on dead-reckoning technology, where a slight positioning error can accumulate over time, resulting in a catastrophic positioning error. Thus, this paper proposes a road-network-map-assisted vehicle positioning method based on the theory of pose graph optimization. This method takes the dead-reckoning result of visual odometry as the input and introduces constraints from the point-line form road network map to suppress the accumulated error and improve vehicle positioning accuracy. We design an optimization and prediction model, and the original trajectory of visual odometry is optimized to obtain the corrected trajectory by introducing constraints from map correction points. The vehicle positioning result at the next moment is predicted based on the latest output of the visual odometry and corrected trajectory. The experiments carried out on the KITTI and campus datasets demonstrate the superiority of the proposed method, which can provide stable and accurate vehicle position estimation in real-time, and has higher positioning accuracy than similar map-assisted methods.
A Long Skip Connection for Enhanced Color Selectivity in CNN Architectures
Authors: Sanchez-Cesteros Oscar , Rincon Mariano , Bachiller Margarita , Valladares-Rodriguez Sonia
Published: September 2023

Some recent studies show that filters in convolutional neural networks (CNNs) have low color selectivity in datasets of natural scenes such as Imagenet. CNNs, bio-inspired by the visual cortex, are characterized by their hierarchical learning structure which appears to gradually transform the representation space. Inspired by the direct connection between the LGN and V4, which allows V4 to handle low-level information closer to the trichromatic input in addition to processed information that comes from V2/V3, we propose the addition of a long skip connection (LSC) between the first and last blocks of the feature extraction stage to allow deeper parts of the network to receive information from shallower layers. This type of connection improves classification accuracy by combining simple-visual and complex-abstract features to create more color-selective ones. We have applied this strategy to classic CNN architectures and quantitatively and qualitatively analyzed the improvement in accuracy while focusing on color selectivity. The results show that, in general, skip connections improve accuracy, but LSC improves it even more and enhances the color selectivity of the original CNN architectures. As a side result, we propose a new color representation procedure for organizing and filtering feature maps, making their visualization more manageable for qualitative color selectivity analysis.
Vibration Reduction and Explosion Control Investigation for an Ultra-Shallow Buried Tunnel under Crossing Buildings Based on HHT Analysis
Authors: Xu Rui , Zhang Jichun , Wu Bian , Zhang Feng-Liang
Published: September 2023

With the rapid development of underground space utilization, the excavation of new tunnels with ultra-shallow under crossing buildings using the drilling and blasting method is gradually increasing. The blasting vibration will undoubtedly affect the surrounding buildings. Reducing the impact of blasting vibration on ground buildings has become an important technical challenge faced by tunnel blasting technicians. The inlet end of the Xi’an-Chengdu High-Speed Railway Xiannvyan Tunnel passes below a village through an ultra-shallow buried section; as a result, blasting vibration control is a major concern. A design scheme for a 0.6 m footage in tunnel was proposed and verified through field tests. A 0.8 m footage scheme and 1.8 m footage millisecond interference vibration reduction scheme were proposed, respectively. Based on the HHT analysis, by comparing the surface vibration velocities and instantaneous energy obtained from the millisecond delay detonation of cutting holes and the detonation of different charging schemes, we found that the free surface, mass of single dynamite charges, and tunnel burial depth had significant influences on the surface vibration.
The Development of the Pooled Rideshare Acceptance Model (PRAM)
Authors: Gangadharaiah Rakesh , Brooks Johnell O. , Rosopa Patrick J. , Su Haotian , Boor Lisa , Edgar Ashley , Kolodge Kristin , Jia Yunyi
Published: September 2023

Due to the advancements in real-time information communication technologies and sharing economies, rideshare services have gained significant momentum by offering dynamic and/or on-demand services. Rideshare service companies evolved from personal rideshare, where riders traveled solo or with known individuals, into pooled rideshare (PR), where riders can travel with one to multiple unknown riders. Similar to other shared economy services, pooled rideshare is beneficial as it efficiently utilizes resources, resulting in reduced energy usage, as well as reduced costs for the riders. However, previous research has demonstrated that riders have concerns about using pooled rideshare, especially regarding personal safety. A U.S. national survey with 5385 participants was used to understand human factor-related barriers and user preferences to develop a novel Pooled Rideshare Acceptance Model (PRAM). This model used a covariance-based structural equation model (CB-SEM) to identify the relationships between willingness to consider PR factors (time/cost, privacy, safety, service experience, and traffic/environment) and optimizing one’s experience of PR factors (vehicle technology/accessibility, convenience, comfort/ease of use, and passenger safety), resulting in the higher-order factor trust service. We examined the factors’ relative contribution to one’s willingness/attitude towards PR and user acceptance of PR. Privacy, safety, trust service, and convenience were statistically significant factors in the model, as were the comfort/ease of use factor and the service experience, traffic/environment, and passenger safety factors. The only two non-significant factors in the model were time/cost and vehicle technology/accessibility; it is only when a rider feels safe that individuals then consider the additional non-significant variables of time, cost, technology, and accessibility. Privacy, safety, and service experience were factors that discouraged the use of PR, whereas the convenience factor greatly encouraged the acceptance of PR. Despite the time/cost factor’s lack of significance, individual items related to time and cost were crucial when viewed within the context of convenience. This highlights that while user perceptions of privacy and safety are paramount to their attitude towards PR, once safety concerns are addressed, and services are deemed convenient, time and cost elements significantly enhance their trust in pooled rideshare services. This study provides a comprehensive understanding of user acceptance of PR services and offers actionable insights for policymakers and rideshare companies to improve their services and increase user adoption.
Ethnobotanical Review of Selected Medicinal Plants in Guam for the Treatment of Urinary Tract Ailments and Their Pharmacological Properties
Authors: Ferdosh Sahena
Published: September 2023

The Chamorro healers of Guam have more than a thousand years of history of using herbs and medicinal plants for the treatment of common ailments. The objective of this study is to review the bioactive compounds and pharmacological properties of medicinal plants which are used for urinary tract health by local healers. Literature searches were performed using Google Scholar, ScienceDirect, PubMed, and SpringerLink, by using several keywords, including “medicinal plants in Guam”, “traditional uses”, “bioactive compounds”, “pharmacological properties”, and “urinary tract infections”. This review highlights the traditional uses, bioactive compounds, and pharmacological properties of five medicinal plant species, namely Euphorbia hirta, Phyllanthus amarus, Premna serratifolia, Psidium guajava, and Urena lobata. Phenolics, alkaloids, terpenoids, essential oils, and polypeptides are the leading secondary metabolites reported in different plant extracts, which have been found to have significant antimicrobial, antioxidant, anti-inflammatory, antidiabetic, and anticancer properties. The therapeutic claims made about medicinal plants in Guam are well supported by the literature, having similar applications and pharmacological properties in other regions of the world. These medicinal plants have a lot of unexplored potential that might be utilized to develop more potent drugs for the treatment of infectious diseases, as well as food and herbal supplements.
Community Reintegration of Offenders at an Overcrowded Rural Correctional Facility: Work Experiences of Correctional Officials
Authors: Legodi Refiloe , Dube Misheck
Published: September 2023

Community reintegration of offenders is one of South Africa’s methods of rehabilitating offenders who have served a portion of their sentence in a correctional centre. However, overcrowding in the rural correctional facilities that have limited resources have exposed the correctional officials to enormous work-related challenges. This paper discusses the work-related challenges experienced by correctional officials at Rooigrond Correctional Centre, a rural correctional centre in North West Province in South Africa. This study employed a qualitative research approach and a descriptive research design to capture the challenges experienced by correctional officials during reintegration of offenders at Rooigrond Correctional Centre. Due to the different ranks of the correctional officials who participated in the study, a quota sampling technique was used to involve ten correctional officials in face-to-face interviews. Thematic data analysis was used, which showed that correctional officials experience a plethora of challenges which have an impact on work and personal levels. This paper makes significant conclusions and recommendations to improve the work-related experiences of the correctional officials in overcrowded, low-resourced rural correctional facilities.
A New Index of Perceived Job Quality in 116 Countries: Associations with Working Hours and Other National Characteristics
Authors: Joshanloo Mohsen
Published: September 2023

The main purpose of this study was to create a global index of perceived job quality that assesses individuals’ perceptions of enjoyment, meaning, and engagement at work, as well as freedom of choice in job selection. The study also explored the correlation between weekly working hours and perceived job quality. A sample of 121,207 individuals from 116 countries was used, sourced from the Gallup World Poll. Additionally, variables from other sources were incorporated to establish the nomological net of the new index. Perceived job quality was highest in South and North America, while it was lowest in East Asia, the Middle East, and North Africa. Perceived job quality was weakly associated with cultural characteristics at the national level, while it was more strongly associated with experienced positive affect, psychosocial well-being, and optimism. No evidence was found that countries with higher levels of wealth have higher average levels of perceived job quality. The number of hours worked per week was not significantly related to perceived job quality at the national level. Working hours were found to be longer in collectivist, hierarchical, and less free countries, as well as in countries where work is valued over leisure. Weekly working hours was largely unrelated to economic indicators at the national level.
Optimization of Urban Road Green Belts under the Background of Carbon Peak Policy
Authors: Li Weijia , Wang Yuejiao
Published: September 2023

To explore the reasonable width of urban road green belts under the background of carbon peak policy and provide quantitative design guidance for urban green-belt planning, a closed-loop design that integrates urban traffic, carbon emissions, urban greening, and carbon absorption was established at the road network level. First, the factors affecting urban transport carbon emissions were analyzed from the aspects of environment, economy, population, and technology. A carbon emission prediction model was constructed based on the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The path of the carbon peak in the transportation sector was simulated. A scenario under the carbon peak target was designed to determine the development trend of each factor. The layout forms and design requirements of urban road greening were then summarized. The annual carbon sequestration amounts of different plant types were calculated. An objective optimization model was constructed with the lowest cost of greening as the objective function. The constraint conditions specify that the carbon absorption be greater than the carbon emissions, in addition to specifying the basic greening design requirements. Finally, an empirical study was conducted on a road network area in Xi’an. According to the results, the traffic carbon emissions of Xi’an City will be 2.71 Mt in 2025, with road traffic accounting for 1.94 Mt. Without considering the road-measurement green-width constraint, the proportions of the road green belt for six road classes and the red-line width under the carbon peak target are 0.31, 0.33, 0.40, 0.22, 0.21, and 0.23. These research results provide a quantitative and reliable basis for designing the width of an urban green belt under the background of carbon peak policy. Under proportion restrictions, road greening yields better performance by considering both aesthetics and road characteristics.
Multistep Extraction Transformation of Spent Coffee Grounds to the Cellulose-Based Enzyme Immobilization Carrier
Authors: Brekalo Mirna , Rajs Blanka Bilić , Aladić Krunoslav , Jakobek Lidija , Šereš Zita , Krstović Saša , Jokić Stela , Budžaki Sandra , Strelec Ivica
Published: September 2023

The present study investigated the possibility of spent coffee ground (SCG) transformation to a cellulose-based enzyme immobilization carrier using a multistep extraction procedure. In the first step, SCGs were extracted with n-hexane by Soxhlet extraction in order to obtain SCG oil, while the remaining solid residue was subjected to continuous solvent flow sequential subcritical extraction with 96% and 50% ethanol and water. Afterwards, the obtained solid residue was subjected to alkaline liquefaction with 8% NaOH in order to remove lignin and produce cellulose-enriched SCGs as a potential enzyme immobilization carrier. Multistep extraction transformation of SCGs was monitored by chemical analysis of extracts and obtained solid residues. Soxhlet extraction of 100 g of SCGs yielded 10.58 g of SCG oil rich in linoleic and palmitic acid, while continuous solvent flow sequential subcritical extraction of 100 g of defatted SCGs yielded a total of 1.63 g of proteins, 5.58 g of sugars, 204 mg of caffeine, 76 mg of chlorogenic acid, and 11.97 mg of 5-(hydroxymethyl)furfural. Alkaline liquefaction of 100 g of sequentially extracted defatted SCGs by 8% NaOH yielded 7.45 g of proteins, 8.63 g of total polyphenols, 50.73 g of sugars, and 20.83 g of cellulose-enriched SCGs. Based on the characteristics of cellulose-enriched SCGs including a volume-weighted mean particle size of 277 μm, relative narrow particle size distribution with a span value of 1.484, water holding capacity of 7.55 mL/g, and a lack of carrier leakage, it could be safely concluded that produced cellulose-enriched SCGs fulfills criteria to be used as potential enzyme immobilization carrier. Overall, it seems that the proposed multistep extraction transformation of SCGs has great potential to be used for the production of several high-value added products.
Exploring the Spatiotemporal Dynamics of CO2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data
Authors: Li Yongxing , Guo Wei , Li Peixian , Zhao Xuesheng , Liu Jinke
Published: September 2023

Climate change caused by CO2 emissions is posing a huge challenge to human survival, and it is crucial to precisely understand the spatial and temporal patterns and driving forces of CO2 emissions in real time. However, the available CO2 emission data are usually converted from fossil fuel combustion, which cannot capture spatial differences. Nighttime light (NTL) data can reveal human activities in detail and constitute the shortage of statistical data. Although NTL can be used as an indirect representation of CO2 emissions, NTL data have limited utility. Therefore, it is necessary to develop a model that can capture spatiotemporal variations in CO2 emissions at a fine scale. In this paper, we used the nighttime light and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), and proposed a normalized urban index based on combination variables (NUI-CV) to improve estimated CO2 emissions. Based on this index, we used the Theil–Sen and Mann–Kendall trend analysis, standard deviational ellipse, and a spatial economics model to explore the spatial and temporal dynamics and influencing factors of CO2 emissions over the period of 2000–2020. The experimental results indicate the following: (1) NUI-CV is more suitable than NTL for estimating the CO2 emissions with a 6% increase in average R2. (2) The center of China’s CO2 emissions lies in the eastern regions and is gradually moving west. (3) Changes in industrial structure can strongly influence changes in CO2 emissions, the tertiary sector playing an important role in carbon reduction.
Reflective and Cooperative Learning for Understanding Sustainability through an Eco-Innovation Strategy in Rural Travel and Hospitality: A STEAM Case Study
Authors: Hung Chin-Lien , Yu Tien-Fu , Lin Yun-Hui , Lin Yi-Chien , Chen Yi-Hsuan , Lo Wei-Shuo
Published: September 2023

Eco-innovation denotes developing green practices using environmentally friendly innovative approaches or technologies. Although eco-innovation has been broadly applied in different industries, such as low-carbon production and manufacturing, how to implement such innovation in education for sustainable development (ESD) has rarely been studied. Therefore, this research considered a reflective and cooperative learning approach to science, technology, engineering, arts, and math (STEAM) education. A case study examined Wanluan Township of Pingtung County in southern Taiwan. Four departments’ students and lecturers at Meiho University were involved in situated learning. Hospitality management students played farm owners who engaged in organic agriculture to produce food and beverages. These agricultural products were farm-to-table, cooked, and served to customers in a natural dining setting through the students’ teamwork. Tourism, sports and leisure management, and food science and nutrition students played tourists in a self-guided travel context, who engaged in acts such as visiting buildings to understand heritage while observing that the houses were still in use as dwellings. This encouraged reflection on the importance of cultural preservation. The results showed that eco-innovation can represent a sustainable marketing strategy for improving the local community economy and can be implemented in a practical situation in STEAM. The ESD goal for 2030—societal transformation—is to foster students’ responsible behavior and attitudes in a personally authentic manner, thereby fostering sustainable learning and understanding.
Loyalty to World Heritage Cities: A Case Study of the Historic Ibn Ṭūlūn Mosque in Cairo (Egypt)
Authors: Elassal Ibrahim Mohamed Ibrahim , Hernandez-Rojas Ricardo David , Moreno Quispe Luz Arelis
Published: September 2023

The city of Cairo is famous worldwide for its distinct heritage sites. Its uniqueness, its call effect, and its history are significant. So, it is essential to improve its management of tourism. The main objective of this research is to analyze the satisfaction of visitors to historic mosques in the context of heritage tourism. The study focuses on tourist loyalty from several perspectives: expectations, satisfaction with the city itself and satisfaction with the mosque. The fieldwork was carried out in Cairo (Egypt), specifically in the Ibn Ṭūlūn Mosque. It is more than 2000 years old and is considered the oldest mosque in Cairo and one of the most amazing mosques in the world. There are few studies on mosques from a tourism perspective, which makes this study innovative and a substantial contribution to the literature. The methodology used was based on partial least squares (PLS) structural equation modeling. A structured questionnaire was used to achieve the proposed objective. The results obtained confirmed that satisfaction during their visit to the Ibn Ṭūlūn Mosque positively influenced tourists’ loyalty, and therefore they recommended revisiting it.
Effect of Moisture Content on the Permanent Strain of Yellow River Alluvial Silt under Long-Term Cyclic Loading
Authors: Du Zibo , Zhang Zheng , Wang Lei , Zhang Jingwei , Li Yonghui
Published: September 2023

The Yellow River alluvial silt has unique engineering properties and is unstable when encountering moisture. The mechanical properties of silt subgrade can be impaired by the increase in moisture content due to rainwater infiltration, which has a negative effect on traffic safety. To further reveal the influence of moisture content on the deformation characteristics of silt, a series of monotonic and cyclic triaxial tests were conducted on the alluvial silt with different moisture contents. The development law of cyclic accumulative permanent strain and the effects of moisture content, cyclic deviator stress and confining pressure on the axial permanent strain of silt were explored. The study shows that the static strength of silt decreases with the increase in moisture content, and the attenuation of static strength is mainly caused by the decrease in cohesion due to the reduction in matric suction. The permanent strain rises linearly with the increase in moisture content and cyclic deviator stress, and decreases with the increase in confining pressure. An empirical model for predicting the permanent strain of silt under long-term cyclic loading considering the effect of moisture content was established. Compared with the test data and other existing models, the established model has easier obtained parameters, higher prediction accuracy and better applicability.
Knowledge-Based Faultlines and Corporate Social Irresponsibility: Evidence from Chinese High-Polluting Companies
Authors: Ma Jingchen , Huang Xu
Published: September 2023

Government requests and societal expectations have pressured high-polluting companies to focus on corporate social responsibility strategies. Using the upper echelons theory as a theoretical framework, we investigated how top management team (TMT) faultlines influence corporate social performance (CSP) based on data from 212 high-polluting companies. The results showed that CSP can be improved by reducing corporate social irresponsibility (CSiR), knowledge-based faultlines have a U-shaped effect on CSiR, and there is a knowledge-based faultline critical point. This implies that knowledge-based faultlines can improve CSiR before reaching this critical point. Additionally, medium-strength knowledge-based faultlines are more conducive to improving irresponsible behavior. CEO power plays a significant moderating role in the relationship between TMT faultlines and CSiR and slows the U-shaped effect of knowledge-based faultlines on CSiR. These findings could help enterprises optimize team structures, adjust corporate social responsibility strategies, and maintain sustainable development in high-polluting sectors.
Livelihoods and Perceptions of Climate Change among Dairy Farmers in the Andes: Implications for Climate Education
Authors: Vargas-Burgos Julio C. , Heredia-R Marco , Torres Yenny , Puhl Laura , Heredia Biviana N. , Cayambe Jhenny , Hernán-González Julio , Torres Alexandra , Luna Marcelo , Toulkeridis Theofilos , Torres Bolier
Published: September 2023

Climate change mainly affects the production and consumption systems associated with food, livelihoods, production (e.g., reduced milk production), water, and land use. The role of local knowledge is recognized as important for decision-making under changing circumstances. This study was conducted in the northern part of the Ecuadorian Andes using a sample of 170 dairy-cattle-farming households. The objectives were to: (i) characterize the rural livelihoods of dairy cattle farmers; (ii) evaluate access to climate information and perceptions of climate change; and (iii) determine the relationship between livelihoods and perceptions of climate change. Significant differences were identified between the groups evaluated in relation to the dairy farmers’ livelihoods. In addition, 85.29% of the respondents indicated that climate information is important, but 67.83% did not trust the sources of information. It was found that there is a significant relationship between the level of education and age with the variables of climate change perceptions. This combined knowledge will allow people to promote agri-environmental and educational policies to achieve climate literacy at a rural level.
A Conflict-Based Safety Diagnosis of SCI Roundabouts Using a Surrogate Safety Measure Model
Authors: Hasanvand Mehrzad , Nasiri Amir Saman Abdollahzadeh , Rahmani Omid , Shaaban Khaled , Samadi Hossein
Published: September 2023

Recently, the surrogate safety assessment model has been considered for the safety performance analysis of roundabouts. This model can perform a detailed analysis of conflicts based on the trajectory data of vehicles inside the roundabout. The Separated Central Island (SCI) roundabout, as one of the roundabouts with distinct geometrical characteristics, holds the potential for safety evaluation using the conflict-based method. Therefore, in this study, a safety diagnosis of an SCI roundabout was conducted for the first time. In this study, a safety diagnosis procedure for SCI roundabout was first defined; for this purpose, vehicle trajectory data were obtained using an Unnamed Aerial Vehicle (UAV) and then entered into Surrogate Safety Measure Model software (SSAM 3.0). A case study was conducted on the only SCI roundabout in Iran, located in Mashhad. The parameters of Time-to-Collision (TTC), maximum speed difference of two vehicles involved in a collision (MaxΔV), and maximum speed of vehicles in collision (MaxS) were utilized to determine the type and severity of conflicts and risk analysis. The results of roundabout risk analysis showed that the severity level of conflicts is mainly of the injury type and that the lowest severity of conflicts is related to fatality. In addition, the highest frequency of injury conflicts is associated with lane-change conflict and the lowest frequency is linked to rear-end conflict. The highest and lowest frequency of damage conflicts are related to crossing and lane-change conflicts, respectively. After overall risk scoring, the severity level of conflicts is mainly related to injury type and the lowest severity of conflicts is associated with fatality; 31% of the total conflicts obtained are of the damaging type, and 69% are of the injury type. Finally, comparing the results of the conflict data with the 8-year crash data in such roundabouts confirms that in the absence of crash data in such roundabouts traffic engineers can use the roundabout analysis based on this study to predict the safety situation of such roundabouts before implementing engineering processes.
How to Keep Investors’ Confidence after Being Labeled as Polluting Firms: The Role of External Political Ties and Internal Green Innovation Capabilities
Authors: Lu Liangdong , Wang Mengyao , Xu Jia
Published: September 2023

This study delves into investors’ perceptions of the polluting label attached to listed manufacturing firms, emphasizing the interplay between external political ties and internal green innovative capability in influencing these perceptions. Drawing on a longitudinal analysis of listed manufacturing firms in China from 2010 to 2020 and employing a difference-in-differences (DID) approach, we treat firms identified under the National Specially Monitored (NSM) program as the treated group, while non-NSM firms form the control group. The time variable captures the period post the introduction of the NSM program. Our findings highlight that the polluting label created a loss prospect for investors, signifying diminishing returns over time. Interestingly, firms with closer connections to local governments experienced amplified negative investor perceptions. In contrast, strong affiliations with the central government and robust green innovative capabilities cushioned these adverse reactions. Notably, central ties proved even more beneficial when complemented by green innovative capability. By melding signal theory with the literature on sense-making, this research adds nuance to the discourse on the role of resources in determining firm success amidst environmental controversies.
Air Quality Index (AQI) Prediction in Holy Makkah Based on Machine Learning Methods
Authors: Almaliki Abdulrazak H. , Derdour Abdessamed , Ali Enas
Published: September 2023

Makkah draws millions of visitors during Hajj and Ramadan, establishing itself as one of Saudi Arabia’s most bustling cities. The imperative lies in maintaining pristine air quality and comprehending diverse air pollutants to effectively manage and model air pollution. Given the capricious and variably spatiotemporal nature of pollution, predicting air quality emerges as a notably intricate endeavor. In this study, we confronted this challenge head-on by harnessing sophisticated machine learning techniques, encompassing the fine decision tree (FDT), ensemble boosted tree (EBOT), and ensemble bagged tree (EBAT). These advanced methodologies were enlisted to project air quality index (AQI) levels, focusing specifically on the Makkah region. Constructed and trained on air quality data spanning 2016 to 2018, our forecast models unearthed noteworthy insights. The outcomes revealed that EBOT exhibited unparalleled accuracy at 97.4%, astutely predicting 75 out of 77 samples. On the other hand, FDT and EBAT achieved accuracies of 96.1% and 94.8%, respectively. Consequently, the EBOT model emerges as the epitome of reliability, showcasing its prowess in forecasting the air quality index. We believe that the insights garnered from this research possess universal applicability, extending their potential to regions worldwide.
Phosphorus Fertilization Affects Morphological, Physiological and Agronomic Characteristics of Faba Bean Cultivars
Authors: Papakaloudis Paschalis , Dordas Christos
Published: September 2023

Faba bean (Vicia faba L.) is an important legume crop grown worldwide, especially under rainfed conditions. Faba beans require phosphorus (P) fertilization to maintain high N2 fixation rates and to obtain high yields. However, farmers in many countries use low quantities of P because of its cost and the risk of drought, which reduces the crop’s response to P fertilization. The objective of the present study was to determine the effect of P fertilization on two faba bean cultivars, examining several key traits to identify the most efficient genotype. Seed yield was influenced by the main effect of variety, the two-way interaction between year and variety, and the three-way interaction among year, treatment, and variety. In the KK-14 cultivar, seed yield increased by 99% during the first year. Similar trends were observed for the yield components. Additionally, the morphological and physiological characteristics were affected by the fertilization treatments and the interaction between cultivars and year. Therefore, the use of appropriate cultivars, along with proper management in cropping systems, can significantly impact growth, biomass yield, and productivity under different conditions, leading to higher yields and greater economic returns for farmers.
Multivariate Statistical Methods and GIS-Based Evaluation of Potable Water in Urban Children’s Parks Due to Potentially Toxic Elements Contamination: A Children’s Health Risk Assessment Study in a Developing Country
Authors: Ghani Junaid , Nawab Javed , Ullah Zahid , Rafiq Naseem , Hasan Shah Zaib , Khan Sardar , Shah Muddaser , Almutairi Mikhlid H.
Published: September 2023

Contamination of potentially toxic elements (PTEs) has received widespread attention in urban children’s parks (UCPs) worldwide in the past few decades. However, the risk assessment of PTEs in drinking water sources of UCPs is still unknown particularly in developing countries. Hence, the present study investigated the spatial distribution, sources for PTEs (Cd, Cr, Pb, Ni, and Cu), and health risk assessment in drinking water sources of UCPs in Khyber Pakhtunkhwa, Pakistan. Among PTEs, Cd, Cr, and Pb had low to high concentrations and exceeded the safe limits of WHO and PAK-EPA in most UCPs. PCA results showed high anthropogenic and low natural sources, contributing to the release of PTEs in all UCPs. Heavy-metal pollution index (PTE-PI) results showed low to high pollution levels for all UCPs, with the highest values of 113 and 116 for Sardaryab Park Charsadda (SPC) and Zoo Park Peshawar (ZPP), respectively. Heavy-metal evaluation index (PTE-EI) results also showed low to high pollution levels for all UCPs. UCPs samples (50%) showed low pollution levels in PTE-PI results. To the contrary, UCPs samples (50%) exhibited high pollution levels in PTE-EI results. The non-carcinogenic risk of HQ and HI values of all PTEs were below the permissible limit (<1) for adults and children via ingestion and dermal contact. CR and TCR results showed that PTEs (Cr, Cd, Pb, and Ni) had the highest carcinogenic risk (>1.00 × 10−4) for both adults and children in all UCPs, except Cd and Ni for adults via the ingestion route, while Cr values (>1.00 × 10−4) were exceeded for children in some of the UCPs via the dermal route. Consequently, long-term exposure to toxic PTEs could pose a carcinogenic risk to the local population. Thus, the present study suggests that the government should implement enforcement with firm protocols and monitoring guidelines of environmental regulations to mitigate PTEs originating from anthropogenic sources in order to reduce health risks and improve public health safety in urban areas.
Developing Chatbots for Cyber Security: Assessing Threats through Sentiment Analysis on Social Media
Authors: Arora Amit , Arora Anshu , McIntyre John
Published: September 2023

In recent years, groups of cyber criminals/hackers have carried out cyber-attacks using various tactics with the goal of destabilizing web services in a specific context for which they are motivated. Predicting these attacks is a critical task that assists in determining what actions should be taken to mitigate the effects of such attacks and to prevent them in the future. Although there are programs to detect security concerns on the internet, there is currently no system that can anticipate or foretell whether the attacks will be successful. This research aims to develop sustainable strategies to reduce threats, vulnerability, and data manipulation of chatbots, consequently improving cyber security. To achieve this goal, we develop a conversational chatbot, an application that uses artificial intelligence (AI) to communicate, and deploy it on social media sites (e.g., Twitter) for cyber security purposes. Chatbots have the capacity to consume large amounts of information and give an appropriate response in an efficient and timely manner, thus rendering them useful in predicting threats emanating from social media. The research utilizes sentiment analysis strategy by employing chatbots on Twitter (and analyzing Twitter data) for predicting future threats and cyber-attacks. The strategy is based on a daily collection of tweets from two types of users: those who use the platform to voice their opinions on important and relevant subjects, and those who use it to share information on cyber security attacks. The research provides tools and strategies for developing chatbots that can be used for assessing cyber threats on social media through sentiment analysis leading to a global sustainable development of businesses. Future research may utilize and improvise on the tools and strategies suggested in our research to strengthen the knowledge domain of chatbots, cyber security, and social media.