bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study on the Improvement of YOLOv5 and the Quality Detection Method for Cork Discs
Authors: Qu Liguo , Chen Guohao , Liu Ke , Zhang Xin
Published: September 2024

Combining machine vision and deep learning, optical detection technology can achieve intelligent inspection. To address the issues of low efficiency and poor consistency in the quality classification of cork discs used for making badminton heads, research on optimizing the YOLOv5 image-processing algorithm was conducted and applied to cork disc quality detection. Real-time images of cork discs were captured using industrial cameras, and a dataset was independently constructed. A GAN-based defect synthesis algorithm was employed to resolve the lack of defect samples. An attention mechanism was embedded in the YOLOv5 backbone network to enhance feature representation. The number of anchors in the YOLOv5 detection layer was reduced to address similar sample sizes, a center-matching strategy was designed to balance positive samples, and a shortest-distance label assignment algorithm was developed to eliminate ambiguities, improving accuracy and reducing postprocessing complexity. Detection results were integrated into quality classification. Experiments on the NVIDIA RTX3080 GPU demonstrated that the optimized algorithm improved the original YOLOv5 F1 score by 2.4% and mF1 score by 9.0%, achieving a quality classification F1 score of 95.1%, a processing speed of 178.5 FPS, and an mAP of 81.5%. Comparative experiments showed that the improved algorithm achieved the best detection accuracy on the cork disc dataset while maintaining high processing speed.
Increasing Light-Induced Forces with Magnetic Photonic Glasses
Authors: Avalos-Sánchez Hugo , Carmona-Carmona Abraham J. , Palomino-Ovando Martha A. , Desirena Benito Flores , Palomino-Merino Rodolfo , Misaghian Khashayar , Faubert Jocelyn , Toledo-Solano Miller , Lugo Jesus Eduardo
Published: September 2024

In this work, we theoretically and experimentally study the induction of electromagnetic forces in an opal-based magnetic photonic glass, where light normally impinges onto a disordered arrangement of SiO2 spheres by the aggregation of Fe3O4 nanoparticles. The working wavelength is 633 nm. Experimental evidence is presented for the force that results from forced oscillations of the photonic structure. Finite-element method simulations and a theoretical model estimate the magnetic force volumetric density value, peak displacement, and velocity of oscillations. The magnetic force is of the order of 56 microN, which is approximately 500-times higher than forces induced in dielectric optomechanical photonic crystal cavities.
Survey on Optical Wireless Communication with Intelligent Reflecting Surfaces
Authors: Fang Chengwei , Li Shuo , Wang Yinong , Wang Ke
Published: September 2024

Optical Wireless Communication (OWC) technology has gained significant attention in recent years due to its potential for providing high-data-rate wireless connections through the large license-free bandwidth available. A key challenge in OWC systems, similar to high-frequency Radiofrequency (RF) systems, is the presence of dead zones caused by obstacles like buildings, trees, and moving individuals, which can degrade signal quality or disrupt data transmission. Traditionally, relays have been used to mitigate these issues. Intelligent Reflecting Surfaces (IRSs) have recently emerged as a promising solution, enhancing system performance and flexibility by providing reconfigurable communication channels. This paper presents an overview of the application of IRSs in OWC systems. Specifically, we categorize IRSs into two main types: mirror array-based IRSs and metasurface-based IRSs. Furthermore, we delve into modeling approaches of mirror array-based IRSs in OWC and analyze recent advances in IRS control, which are classified into system power or gain optimization-oriented, system link reliability optimization-oriented, system data rate optimization-oriented, system security optimization-oriented, and system energy optimization-oriented approaches. Moreover, we present the principles of metasurface-based IRSs from a physical mechanism perspective, highlighting their application in OWC systems through the distinct roles of light signal refraction and reflection. Finally, we discuss the key challenges and potential future directions for integrating IRS with OWC systems, providing insights for further research in this promising field.
Checklist of Ornamental Trees, Shrubs, and Succulents of Apulia (Southern Italy)
Authors: Venturella Giuseppe , Di Gristina Emilio , Pardi Raimondo , Cirlincione Fortunato , Gargano Maria Letizia
Published: September 2024

In this study we focused on the need to fill a knowledge gap among Italian botanical studies namely that of ornamental species census. In particular, we addressed one of the regions in southern Italy with less knowledge in the field of such studies and with an obvious presence of non-native species. A widespread census of the Apulian territory was carried out between 2021 and 2024 in both urban and suburban areas including street trees, parks, and private and historic gardens. The inventory of ornamental trees, shrubs, and succulents of Apulia (southern Italy) was carried out in six provinces, i.e., Bari, Barletta-Andria-Trani, Brindisi, Foggia, Lecce, and Taranto. The checklist comprises 287 taxa (including 265 species, 6 varieties, 5 subspecies, and 11 forms) included in 179 genera belonging to 78 families. We evaluated the number of taxa per families and genera, the presence of each taxa in the provinces of Apulia, the number of taxa per occurrence status, growth forms, geographical origin, and the number of native and alien taxa and also the artificial hybrids. Remarks of the most significant taxa and evaluation of geographical distribution in Italy were also taken into consideration. A large number of surveyed taxa (51.74%) are comprised in the list reported in the recent study on allochthonous vascular flora in Italy with a marked prevalence of Neophyte Casual Alien and Neophyte Naturalized Alien species.
Correction: Bibi et al. Valorization of Agricultural Waste as a Chemiresistor H2S-Gas Sensor: A Composite of Biodegradable-Electroactive Polyurethane-Urea and Activated-Carbon Composite Derived from Coconut-Shell Waste. Polymers 2023, 15, 685
Authors: Bibi Aamna , Santiago Karen S. , Yeh Jui-Ming , Chen Hsiu-Hui
Published: September 2024

References [42,43] have been removed as they have been identified as being irrelevant to our study due to a scope mismatch [...]
The Use of Natural Minerals as Reinforcements in Mineral-Reinforced Polymers: A Review of Current Developments and Prospects
Authors: Fajdek-Bieda Anna , Wróblewska Agnieszka
Published: September 2024

Natural minerals play a key role in the burgeoning field of mineral-reinforced polymers, providing an important element in strengthening and toughening the properties of composite materials. This article presents a comprehensive overview of the use of minerals in mineral-reinforced polymers, covering various aspects of their applications and impact on the final properties of these materials. The potential of various types of natural minerals (for example talc, montmorillonite, halloysite, diatomite) as reinforcements in mineral-reinforced polymers is discussed. Techniques for producing mineral-reinforced polymers using minerals, including the mixing method, impregnation, and coating application, are presented in detail. In addition, the effects of process parameters and component ratios on the final properties of mineral-reinforced polymers are discussed. The latest research on the use of minerals in mineral-reinforced polymers is also presented, including their effects on the strength, stiffness, resistance to environmental conditions, and biodegradation of the materials. Finally, the development prospects and potential applications of mineral-reinforced polymers with minerals in various industrial sectors, including packaging, automotive, construction, and medicine, are discussed.
Recent Advancements in Gel Polymer Electrolytes for Flexible Energy Storage Applications
Authors: Nguyen Thi Khanh Ly , Pham-Truong Thuan-Nguyen
Published: September 2024

Since the last decade, the need for deformable electronics exponentially increased, requiring adaptive energy storage systems, especially batteries and supercapacitors. Thus, the conception and elaboration of new deformable electrolytes becomes more crucial than ever. Among diverse materials, gel polymer electrolytes (hydrogels, organogels, and ionogels) remain the most studied thanks to the ability to tune the physicochemical and mechanical properties by changing the nature of the precursors, the type of interactions, and the formulation. Nevertheless, the exploitation of this category of electrolyte as a possible commercial product is still restrained, due to different issues related to the nature of the gels (ionic conductivity, evaporation of filling solvent, toxicity, etc.). Therefore, this review aims to resume different strategies to tailor the properties of the gel polymer electrolytes as well as to provide recent advancements in the field toward the elaboration of deformable batteries and supercapacitors.
A Novel Composite Material UiO-66-Br@MBC for Mercury Removal from Flue Gas: Preparation and Mechanism
Authors: Zhang Zhen , Li Zikuo , Feng Youxiang , Yu Jingxiang , Zhang Xikai , Wen Jinchao , Nie Haotian , Yu Yue , Jia Li
Published: September 2024

To reduce the mercury content in flue gas from coal-fired power plants and to obtain high-performance, low-cost mercury adsorbents, a novel composite material was prepared by structural design through the in situ growth method. Functionalization treatments such as the modification of functional groups and multilayer loading of polymetallic were conducted. These materials include the MOF material UiO-66 and modified biochar doped with Fe/Ce polymetallic, both of which contain unsaturated metal centrals and oxygen-containing functional groups. On the basis of obtaining the effects of adsorption temperature and composite ratio on the Hg0 removal characteristics, coupling and synergistic mechanisms between the various types of active centers included were investigated by using a variety of characterization and analysis tools. The active adsorption sites and oxidation sites were identified during this process, and the constitutive relationship between the physicochemical properties and the performance of Hg0 removal was established. The temperature-programmed desorption technique, Grand Canonical Monte Carlo simulation, and adsorption kinetic model were employed to reveal the mechanism of Hg0 removal. The results showed that the UiO-66-Br@MBC composite adsorbent possessed an excellent Hg0 removal performance at adsorption temperatures ranging from 50 to 250 °C, and targeted construction of adsorption and oxidation sites while maintaining thermal stability. The Hg0 removal by the composites is the result of both adsorption and oxidation. The micropores and small pore mesopores in the samples provide physical adsorption sites. The modified biochar acts as a carrier to facilitate the full exposure of the central metal zirconium ions, the formation of more active sites, and the process of electron transfer. The doping modification of the Br element can enhance the overall redox ability of the sample, and the introduced Fe and Ce polymetallic ions can work in concert to promote the oxidation process of Hg0. The excellent regulation of the ratio between adsorption and oxidation sites on the surface of the composite material finally led to a significant boost in the samples’ capacity to remove Hg0.
Study on Wind Farm Flow Field Characteristics Based on Boundary Condition Optimization of Complex Mountain Numerical Simulation
Authors: Wang Xiuru , Hu Jianliang , Deng Kai , Zhang Mingjie , Shen Shizhao , Shen Yunshan , Chen Sheng , Pan Weijie , Wen Ruifeng , Kang Weiwei , Pan Zihang , Xu Zhang
Published: September 2024

The accurate prediction of the flow field characteristics of complex mountains is of great practical significance for the development and construction of wind farms, but it is not yet fully understood. The main purpose of this study is to propose a method for the study of flow field characteristics under complex mountain conditions, which can optimize the boundary conditions required for numerical simulation through the wind acceleration ratio and, at the same time, couple the numerical simulation and wind measurement data to reflect the real mountain flow field distribution. The results show that the proposed method has good applicability in complex mountain wind farms, can reproduce the real flow field distribution, and has a certain practical value. Wind speed distribution and turbulence intensity are greatly affected by boundary conditions such as wind speed and wind direction and are also affected by the shielding effect brought by terrain changes. The contrast between 120° and 150° wind direction is more obvious. When the incoming wind moves to the top of a mountain or the ridgeline, it will form a low-speed wake area behind it, resulting in reduced wind speed, increased turbulence intensity, and an unstable flow field.
Teaching Catholic Religion in Croatian Public Schools: Legal Frame and Challenges
Authors: Quirós-Fons Antonio
Published: September 2024

This paper explores the Croatian framework of collaboration between the state and the Catholic Church in the educational field, specifically the teaching of Catholic religion in public schools. First, the statute of the subject of religion was analyzed, following what is established in the Agreement with the Holy See and its Development Agreement with the Croatian Conference of Bishops. Some challenges to this peculiar model of cooperation were revised, mainly the ones regarding the enrollment and the co-responsibility of both ecclesiastical and state authorities in the approval and control of programs and contents. Second, the status of religion teachers was also evaluated. The same ecclesiastical and state authorities also have competence over the teaching staff, from their training to the control of their work, and must confront inconvenient situations related to the suitability of these professionals. In the study of each section and its corresponding rules and practices, it was possible to outline singular aspects, in some cases recognized—or rejected—judicially as discriminatory. This approximation to the right to religious education intends to examine the practice observed by the respective authorities in applying such provisions. The obtained findings and proposals about the singular Croatian model provide a useful contribution for comparative purposes.
Thinned and Sparse Beamforming for Semicircular FDAs in the Transmit–Receive Domain
Authors: Wang Anyi , Huang Xiao , Xu Yanhong , Meng Xiao , Lu Yumeng
Published: September 2024

The thinned and sparse beamforming for semicircular FDAs were investigated, where the excitation amplitudes were also considered in thinned semicircular FDAs, and only the elements’ positions were incorporated into the sparse semicircular FDA. Firstly, the transmit–receive model was introduced to handle the inherent time-varying issue of FDA, followed by the thinned and sparse implementations successively. Note that three types of non-linearly varying frequency offsets (FO), i.e., log-FO, sin-FO, and tanh-FO, were adopted during the investigations. Under the same assumption that 50% of the elements should be saved, the sidelobe levels (SLLs) of the thinned semicircular -FDAs were reduced by 5.8 dB, 4.4 dB, and 4.4 dB, and the widths of the mainlobes were all widened by 3° in their angle dimension. Compared with the thinned semicircular FDAs, the phenomenon of mainlobe widening was alleviated in the sparse semicircular FDAs where the SLLs were reduced by 2.2 dB, 3.7 dB and 3.5 dB, and the mainlobes’ widths in the angle dimension were widened by 1°, 0° and 1°, respectively. It should be highlighted that the sparse semicircular FDA with sin-FO did not broaden the mainlobe in the angle dimension. Therefore, it can be concluded that a sparse semicircular FDA is superior over a thinned semicircular FDA, since it can reduce the same cost with a higher array resolution.
Framework for Regional to Global Extension of Optical Water Types for Remote Sensing of Optically Complex Transitional Water Bodies
Authors: Atwood Elizabeth C. , Jackson Thomas , Laurenson Angus , Jönsson Bror F. , Spyrakos Evangelos , Jiang Dalin , Sent Giulia , Selmes Nick , Simis Stefan , Danne Olaf , Tyler Andrew , Groom Steve
Published: September 2024

Water quality indicator algorithms often separate marine and freshwater systems, introducing artificial boundaries and artifacts in the freshwater to ocean continuum. Building upon the Ocean Colour- (OC) and Lakes Climate Change Initiative (CCI) projects, we propose an improved tool to assess the interactions across river–sea transition zones. Fuzzy clustering methods are used to generate optical water types (OWT) representing spectrally distinct water reflectance classes, occurring within a given region and period (here 2016–2021), which are then utilized to assign membership values to every OWT class for each pixel and seamlessly blend optimal in-water algorithms across the region. This allows a more flexible representation of water provinces across transition zones than classic hard clustering techniques. Improvements deal with expanded sensor spectral band-sets, such as Sentinel-3 OLCI, and increased spatial resolution with Sentinel-2 MSI high-resolution data. Regional clustering was found to be necessary to capture site-specific characteristics, and a method was developed to compare and merge regional cluster sets into a pan-regional representative OWT set. Fuzzy clustering OWT timeseries data allow unique insights into optical regime changes within a lagoon, estuary, or delta system, and can be used as a basis to improve WQ algorithm performance.
A Texture-Considerate Convolutional Neural Network Approach for Color Consistency in Remote Sensing Imagery
Authors: Qian Xiaoyuan , Su Cheng , Wang Shirou , Xu Zeyu , Zhang Xiaocan
Published: September 2024

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products.
An Algorithm to Retrieve Range Ocean Current Speed under Tropical Cyclone Conditions from Sentinel-1 Synthetic Aperture Radar Measurements Based on XGBoost
Authors: Zhou Yuhang , Shao Weizeng , Nunziata Ferdinando , Wang Weili , Li Cheng
Published: September 2024

In this study, a novel algorithm to retrieve the current speed along the range direction under extreme sea states is developed from C-band synthetic aperture radar imagery. To this aim, a Sentinel-1 (S-1) dual-polarized synthetic aperture radar (SAR) dataset consisting of 2300 images is collected during 200 tropical cyclones (TCs). The dataset is complemented with collocated wave simulations from the Wavewatch-III (WW3) model and reanalysis currents from the HYbrid Coordinate Ocean Model (HYCOM). The corresponding TC winds are officially released by IFRMER, while the Stokes drift following the wave propagation direction is estimated from the waves simulated by WW3. In this study, first the dependence of wind, Stokes drift, and range current on the Doppler centroid anomaly is investigated, and then the extreme gradient boosting (XGBoost) machine learning model is trained on 87% of the S-1 dataset for range current retrieval purposes. The rest of the dataset is used for testing the retrieval algorithm, showing a root mean square error (RMSE) and a correlation coefficient (r) of 0.11 m/s and 0.97, respectively, with the HYCOM outputs. A validation against measurements collected from two high-frequency (HF) phased-array radars is also performed, resulting in an RMSE and r of 0.12 m/s and 0.75, respectively. Those validation results are better than the 0.22 m/s RMSE and 0.28 r achieved by the empirical CDOP model. Hence, the experimental results confirm the soundness of the XGBoost, exhibiting a certain improvement over the empirical model.
Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition
Authors: Gao Fei , Luo Xin , Lang Rongling , Wang Jun , Sun Jinping , Hussain Amir
Published: September 2024

Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, thereby necessitating an open set recognition (OSR) challenge for SAR-ATR. The crux of OSR lies in establishing distinct decision boundaries between known and unknown classes to mitigate confusion among different classes. To address this issue, we introduce a novel framework termed reinforced class separability for SAR target open set recognition (RCS-OSR), which focuses on optimizing prototype distribution and enhancing the discriminability of features. First, to capture discriminative features, a cross-modal causal features enhancement module (CMCFE) is proposed to strengthen the expression of causal regions. Subsequently, regularized intra-class compactness loss (RIC-Loss) and intra-class relationship aware consistency loss (IRC-Loss) are devised to optimize the embedding space. In conjunction with joint supervised training using cross-entropy loss, RCS-OSR can effectively reduce empirical classification risk and open space risk simultaneously. Moreover, a class-aware OSR classifier with adaptive thresholding is designed to leverage the differences between different classes. Consequently, our method can construct distinct decision boundaries between known and unknown classes to simultaneously classify known classes and identify unknown classes in open scenarios. Extensive experiments conducted on the MSTAR dataset demonstrate the effectiveness and superiority of our method in various OSR tasks.
An Agent-Based Method for Feature Recognition and Path Optimization of Computer Numerical Control Machining Trajectories
Authors: Li Purui , Chen Meng , Ji Chuanhao , Zhou Zheng , Lin Xusheng , Yu Dong
Published: September 2024

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models and analyze the key geometric information in the G-code generated during CNC machining, and it uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bézier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. The effectiveness of the proposed method is validated through the optimization of process files for gear models, pentagram bosses, and maple leaf models. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths and reduce machining defects, offering substantial application value.
Brain Neuroplasticity Leveraging Virtual Reality and Brain–Computer Interface Technologies
Authors: Drigas Athanasios , Sideraki Angeliki
Published: September 2024

This study explores neuroplasticity through the use of virtual reality (VR) and brain–computer interfaces (BCIs). Neuroplasticity is the brain’s ability to reorganize itself by forming new neural connections in response to learning, experience, and injury. VR offers a controlled environment to manipulate sensory inputs, while BCIs facilitate real-time monitoring and modulation of neural activity. By combining VR and BCI, researchers can stimulate specific brain regions, trigger neurochemical changes, and influence cognitive functions such as memory, perception, and motor skills. Key findings indicate that VR and BCI interventions are promising for rehabilitation therapies, treatment of phobias and anxiety disorders, and cognitive enhancement. Personalized VR experiences, adapted based on BCI feedback, enhance the efficacy of these interventions. This study underscores the potential for integrating VR and BCI technologies to understand and harness neuroplasticity for cognitive and therapeutic applications. The researchers utilized the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method to conduct a comprehensive and systematic review of the existing literature on neuroplasticity, VR, and BCI. This involved identifying relevant studies through database searches, screening for eligibility, and assessing the quality of the included studies. Data extraction focused on the effects of VR and BCI on neuroplasticity and cognitive functions. The PRISMA method ensured a rigorous and transparent approach to synthesizing evidence, allowing the researchers to draw robust conclusions about the potential of VR and BCI technologies in promoting neuroplasticity and cognitive enhancement.
Analysis of the Spatial Distribution and Common Mode Error Correlation in a Small-Scale GNSS Network
Authors: Li Aiguo , Wang Yifan , Guo Min
Published: September 2024

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network’s spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods.
Turnover Intention among Staff Who Support Older Adults Living Alone in Japan: A Cross-Sectional Study
Authors: Nakai Hisao , Ishii Kuniko , Sagino Takako
Published: September 2024

Older adults in Japan traditionally rely on family members for care. However, the growing number of older people living alone has increased staff burden in community general support centers (CGSCs), which provide care for older people in Japan. This study aimed to identify factors linked to turnover intention among CGSC staff. Of 1002 staff invited to participate, 283 completed a survey (response rate: 28.2%). Logistic regression analysis of 183 valid questionnaires examined demographic, job-related, and attitudinal factors associated with turnover intention. The results showed that the perceived difficulty of supporting older adults living alone was the main predictor of turnover intention. Staff reporting high perceived difficulty levels were more likely to consider leaving. Negative attitudes toward supporting this population also increased turnover risk. These findings highlight a pressing need for targeted interventions to strengthen the ability of CGSC staff to manage the challenges of caring for isolated older individuals. Addressing staff perceptions and providing essential training and resources could reduce turnover rates and increase the quality of care for this vulnerable group.
Racial Disinformation, Populism and Associated Stereotypes across Three European Countries during the COVID-19 Pandemic
Authors: D’Errico Francesca , Cicirelli Paolo Giovanni , Lops Angelica , Scardigno Rosa
Published: September 2024

Within the realm of disinformation, across all media platforms, a crucial subject of interest seems to be immigration, which produces the so-called ‘racial hoaxes’. Racial hoaxes are closely linked to the spread of populist ideologies and ethnic stereotypes, both of which are psychosocial processes that, during health crises, could acquire particular features based on cultural differences. This study analysed the main contents of 239 racial hoaxes in relation to three main features: the country of origin (i.e., France, Italy and Spain), the stereotypical contents, and the COVID-19 pandemic period. The results highlight some similarities across these three countries but also peculiarities in terms of topics and stereotypical contents that were magnified during the pandemic period. The peculiarities of emerging racial hoaxes are discussed in relation to the stereotype content model and the literature on populist discourses, providing valuable information for use in psycho-educational intervention, policymaking and social integration efforts.
Comparison of Inter-Rater and Intra-Rater Reliability of Raters with Different Levels of Experience When Using Landing Error Scoring System (LESS) in Field-Based Screening of Professional Football Players
Authors: Liveris Nikolaos I. , Tsarbou Charis , Xergia Sofia A. , Papadopoulos Angelos , Tsepis Elias
Published: September 2024

It is essential for physical sports therapists to use reliable field-based tests to identify potential injury risk factors in athletes. The purpose of this study was to compare the inter- and intra-rater reliability of experienced and novice raters during use of the Landing Error Scoring System (LESS) in a field-based examination of professional football athletes. Thirty-seven male football athletes underwent pre-season LESS assessment. Two raters independently evaluated the recorded landing techniques at two separate intervals, two months apart, following the LESS standard protocol. Inter-and intra-rater values were calculated for the LESS total scores and individual scoring items. The overall LESS scores had excellent intra-rater reliability values for both the experienced (interclass correlation coefficient (ICC) = 0.95, 95% CI, 0.89–0.97; p < 0.001) and novice rater (ICC = 0.95, 95% CI, 0.90–0.97; p < 0.001), and very good to excellent inter-rater values for the first (ICC = 0.90, 95% CI, 0.77–0.95; p < 0.001) and second (ICC = 0.86, 95% CI, 0.71–0.93; p < 0.001) evaluation. Most of the individual scoring items ranged from moderate to perfect agreement. In conclusion, sports physical therapists, regardless of experience, can reliably use the LESS’s total score, through video analysis of the regime. Individual scoring items can inform clinicians about impairments in the landing mechanism but data should be interpreted cautiously.
Enhancing User Participation in Cultural Heritage through Serious Games: Combining Perspectives from the Experience Economy and SOR Theory
Authors: Zhang Ru , Huang Qianghong , Luo Jiacheng , Xu Junping , Pan Younghwan
Published: September 2024

The dissemination and preservation of cultural heritage has attracted much attention globally, and serious games play an important role in cultural heritage dissemination and preservation; however, it is not clear which experiences of serious games are effective in increasing people’s attention to cultural heritage. This study combines the experience economy and stimulus–organism–response (SOR) theory to construct a theoretical model to explore which experiences of serious games influence users’ behavioral intention to care about cultural heritage. We surveyed 302 users who experienced serious games and analyzed the data and measurement model using SPSS 27 and partial least squares structural equation modeling (PLS-SEM). The results showed that the four experiences of serious games (educational, entertaining, escapist, and esthetic) had a significant effect on users’ perceived value and memory and that perceived value and memory significantly influenced users’ participation. In addition, this study found that the entertainment experience was not only a key factor influencing perceived value and memory, but also the most significant factor directly affecting user participation. This finding provides specific recommendations for game developers and cultural heritage organizations to enhance user engagement. Enhancing the four experience dimensions and strengthening users’ perceived value and memory can effectively encourage users to pay attention to cultural heritage through serious games and contribute to the preservation and sustainable development of cultural heritage.
This Is Not a ‘Drill’: Young People’s Understandings of and Hopes for Sustainability Education in England
Authors: Wheeler Kathryn
Published: September 2024

Global surveys show that young people are concerned about sustainability and climate change and how it will affect their future. Education for Sustainability is seen as a key approach that can equip young people with the knowledge, skills, and competencies to act for climate justice. Despite much policy attention on Education for Sustainable Development (ESD), this paper argues that current educational approaches inadequately prepare young people to understand and engage with climate change and sustainability issues. Drawing on a unique survey of 1655 young people in England, this study identifies significant knowledge gaps in understandings of sustainability concepts and a strong demand for educational reforms. Quantitative data reveal variations in sustainability understandings across age, gender, and income, while qualitative insights uncover young people’s feelings about the gaps in their education, exposing their fears, anger, powerlessness, and hopes for change. This study highlights the dominance of consumer choice in young people’s perceptions of sustainability, emphasizing the need to shift from individualistic to collective actions. This paper concludes with a call to integrate sustainability education into the English National Curriculum to promote holistic and action-oriented learning and create opportunities for nurturing hope through transformative education.
The Role of Video Cameras and Emerging Technologies in Disaster Response to Increase Sustainability of Societies: Insights on the 2023 Türkiye–Syria Earthquake
Authors: Oliveira Carlos Sousa , Ferreira Mónica Amaral , O’Neill Hugo
Published: September 2024

New technologies are being used to facilitate the recognition process during and after earthquakes. These advanced tools are essential to keep track of what is left from of the destruction suffered by the built stock. Among the new technologies are video recordings captured during seismic events, footage from drones, and satellite imagery acquired before and after the event. This review paper presents a series of examples collected from the 2023 Türkiye–Syria earthquakes to illustrate how these new technologies offer a unique and efficient way to capture, document, and transfer information among experts in seismology, earthquake engineering, and disaster management. Whenever possible, these examples are accompanied by simple qualitative explanations to enhance understanding. To demonstrate the potential of video cameras and drone imagery for quantitative analysis, in addition to the various simple examples provided, two case studies are provided—one on road blockages, and another on intensity assessment and wave attenuation as observed in video cameras. These technologies are critical and merit considerable focus, particularly video cameras, which have not received much attention recently, on helping to understand seismic wave passage and their impact on the built environment. Enhancing our use of video cameras in this context can significantly contribute to the sustainability and resilience of our society. With the rapid advancement of image analysis, we advocate for a collaborative platform for accessing and utilizing imagery materials, aiding current and future generations in analysing the causes of such tragedies.
The Relationship Between Three-Dimensional Spatial Structure and CO2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China
Authors: Pan Banglong , Dong Doudou , Diao Zhuo , Wang Qi , Li Jiayi , Feng Shaoru , Du Juan , Li Jiulin , Wu Gen
Published: September 2024

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of urban socio-economic activities, urban land use changes, and urban morphology, neglecting the importance of the three-dimensional spatial structure of cities. This study takes 30 cities in East China as an example. By using urban building data and carbon emission datasets, four machine learning algorithms, BP, RF, CNN, and CNN-RF, are established to build a CO2 emission prediction model based on three-dimensional spatial structure, and the main influencing factors are further studied. The results show that the CNN-RF model performed optimally in both the testing and validation phases, with the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of 0.85, 0.82; 10.60, 22.32; and 2.53, 1.92, respectively. Meanwhile, in the study unit, S, V, NHB, AN, BCR, SCD, and FAR have a greater impact on CO2 emissions. This indicates a strong correlation between urban three-dimensional spatial structure and carbon emissions. The CNN-RF model can effectively evaluate the relationship between them, providing strategic support for spatial optimization of low-carbon cities.
Green Web Meter: Structuring and Implementing a Real-Time Digital Sustainability Monitoring System
Authors: Sala Antonello , Barbetti Lorenzo , Rosini Andrea
Published: September 2024

The central aim of this paper is to present the Green Web Meter software, which is designed to support the measurement of the digital ESG performance of websites and web apps, as well as providing sound-based analytic tools for improving their overall quality. In this work, we will discuss the process our research team adhered to in order to provide a basic structure to the general framework of our tool, select a consistent set of KPIs that are suitable for automated real-time tracking, and determine the proper calculation methods to compute the final scores on which the ESG assessment is based. Specifically, we began from the analysis of the UNI PdR 147 guidelines on digital sustainability, highlighting the relevance of the quality construct, and subsequently selected three quality assessment models (E-S Qual, Sitequal, and Webqual 4.0) to define the core references to structure the basic framework. Starting from this general perspective, we proceeded to define the eight related KPIs and the individual scores based on the automatic calculations.
Feasibility Study and Results from a Baseline Multi-Tool Active Seismic Acquisition for CO2 Monitoring at the Hellisheiði Geothermal Field
Authors: Meneghini Fabio , Poletto Flavio , Bellezza Cinzia , Farina Biancamaria , Draganov Deyan , Van Otten Gijs , Stork Anna L. , Böhm Gualtiero , Schleifer Andrea , Janssen Martijn , Travan Andrea , Zgauc Franco , Durucan Sevket
Published: September 2024

CO2 capture and underground storage, combined with geothermal resource exploitation, are vital for future sustainable and renewable energy. The SUCCEED project explores the feasibility of re-injecting CO2 into geothermal fields to enhance production and store CO2 for climate change mitigation. This integration requires novel time-lapse monitoring approaches. At the Hellisheiði geothermal power plant in Iceland, seismic surveys utilizing conventional geophones and a permanent fiber-optic helically wound cable (HWC) for Distributed Acoustic Sensing (DAS) were designed to provide subsurface information and CO2 monitoring. This work details the feasibility study and active seismic acquisition of the baseline survey, focusing on optical fiber sensitivity, seismic modeling, acquisition parameters, source configurations, and quality control. Post-acquisition signal analysis using a novel electromagnetic vibrating source is discussed. The integrated analysis of datasets from co-located sensors improved quality-control performance and geophysical interpretation. The study demonstrates the advantages of using densely sampled DAS data in space by multichannel processing. This experimental work highlights the feasibility of using HWC DAS cables in active surface seismic surveys with an environmentally friendly electromagnetic source, providing also a unique case of joint signal analysis from different types of sensors in high-temperature geothermal areas for energy and CO2 storage monitoring in a time-lapse perspective.
A New Approach to Detect Driver Distraction to Ensure Traffic Safety and Prevent Traffic Accidents: Image Processing and MCDM
Authors: Alemdar Kadir Diler , Çodur Muhammed Yasin
Published: September 2024

One of the factors that threaten traffic safety and cause various traffic problems is distracted drivers. Various studies have been carried out to ensure traffic safety and, accordingly, to reduce traffic accidents. This study aims to determine driver-distraction classes and detect driver violations with deep learning algorithms and decision-making methods. Different driver characteristics are included in the study by using a dataset created from five different countries. Weight classification in the range of 0–1 is used to determine the most important classes using the AHP method, and the most important 9 out of 23 classes are determined. The YOLOv8 algorithm is used to detect driver behaviors and distraction action classes. The YOLOv8 algorithm is examined according to performance-measurement criteria. According to mAP 0.5:0.95, an accuracy rate of 91.17% is obtained. In large datasets, it is seen that a successful result is obtained by using the AHP method, which is used to reduce transaction complexity, and the YOLOv8 algorithm, which is used to detect driver distraction. By detecting driver distraction, it is possible to partially avoid traffic accidents and the negative situations they create. While detecting and preventing driver distraction makes a significant contribution to traffic safety, it also provides a significant improvement in traffic accidents and traffic congestion, increasing transportation efficiency and the sustainability of cities. It also serves sustainable development goals such as energy efficiency and reducing carbon emissions.
Investigating ‘Land-Use Trajectories’ in Mediterranean Rural Areas with Official Statistics and a Multiway Factor Analysis
Authors: Maialetti Marco , Ciaschini Clio , Quaranta Giovanni , Salvia Rosanna , Scarpitta Donato , Bigiotti Stefano , Chelli Francesco Maria , Salvati Luca
Published: September 2024

While displaying diversified economic values, agro-ecosystems remain a relevant component of rural landscapes in Southern Europe. Coupled with the expansion of ecologically fragile areas because of climate warming, intensification and simplification of rural landscapes—e.g., in lowland—represent frequent trajectories of rural land-use change (RLUC) in the Mediterranean region and demonstrated to harm ecosystem functionality and ecological quality, especially in a context of socioeconomic transformations of landscapes. Additionally, an incipient ‘extensivation’ of geographically remote and economically marginal agricultural systems was also observed, likely following depopulation and land abandonment. The present study elaborated in this direction, providing an integrated RLUC evaluation scheme based on a multivariate analysis of land-use indicators derived from official statistics. This approach was applied to a continuous, long-term RLUC monitoring of a rural landscape in an ecologically complex Mediterranean region (Latium, Central Italy) experiencing multiple trends (e.g., lowland urbanization, crop intensification in gently sloping areas, and land abandonment/depopulation in steep zones) with diversified environmental implications. Based on administrative inventories, such evaluation was carried out over fifty years (1970–2020) in the study area, considering trends over time in selected crop surfaces at the municipal scale as inputs of a multiway factor analysis (MFA). This analysis quantifies stability in the main rural land-use types and possible changes in the dominant farming systems. Simplified indicators of land configuration (namely a crop intensity index and a rural woodland index reflecting ‘intensification’ or ‘extensivation’ of local farming systems) made available at the same spatial and temporal scales completed the informative picture corroborating MFA results. Taken together, our findings delineate multiple changes in rural landscapes, discriminating land-use trajectories in coastal and inland districts. These development trajectories were basically dependent on processes of (i) land abandonment in relict and remote areas, (ii) crop intensification in dynamic rural districts, and (iii) fringe urbanization along the coastal strip.