bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation on the Impact of Trailing Edge Stacking Styles on Hydraulic Performance in the Multistage Submersible Pump Space Diffuser
Authors: Zhang Hui , Cao Puyu , Ni Dan , Gong Xuran , He Bo , Zhu Rui
Published: December 2023

To investigate the effect of the different wrap angles from the hub to the shroud surface in the space diffuser (i.e., the trailing edge stacking style) on the principle of corner separation vortex flow, a numerical simulation method has been conducted in a multistage submersible pump. Building a linear equation on the profile line of the diffuser trailing edge to optimize the wrap angle on every spanwise from the hub to the shroud, and the mapping response relationship between the wrap angle difference and the hydraulic performance in the space diffuser has been analyzed. Under the variable wrap angle difference (Δϕ=ϕhub−ϕshroud), the secondary flows in different directions, non-uniformity, diffuser efficiency, and pressure recovery are compared. The positive wrap angle difference (i.e., the shroud wrap angle is smaller than the hub one) improves the strength of the secondary flow and partly corner separation vortex in the diffuser, so the hydraulic characteristic of positive cases is better than the negative wrap angle difference. Moreover, in scheme A (in which the hub wrap angle is constant and the shroud wrap angle decreasing), the transversal secondary flow has been weakened, the low-energy fluid located in the corner has been suppressed, the extensional secondary flow has been increased, the diffuser hydraulic performance has been improved, and unidirectionally increases with the wrap angle difference increasing. When the shroud wrap angle is constant, the extensional secondary flow has been enhanced by the increasing hub wrap angle. Meanwhile, the increasing extensional secondary flow has been countered by the deteriorating extensional flow at the diffuser inlet and transversal secondary flow, the diffuser hydraulic performance increases and then decreases as the wrap angle difference increases, with an optimal wrap angle difference is about 20°.
Weed–Crop Segmentation in Drone Images with a Novel Encoder–Decoder Framework Enhanced via Attention Modules
Authors: Khan Sultan Daud , Basalamah Saleh , Lbath Ahmed
Published: December 2023

The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset.
Learn by Yourself: A Feature-Augmented Self-Distillation Convolutional Neural Network for Remote Sensing Scene Image Classification
Authors: Shi Cuiping , Ding Mengxiang , Wang Liguo , Pan Haizhu
Published: December 2023

In recent years, with the rapid development of deep learning technology, great progress has been made in remote sensing scene image classification. Compared with natural images, remote sensing scene images are usually more complex, with high inter-class similarity and large intra-class differences, which makes it difficult for commonly used networks to effectively learn the features of remote sensing scene images. In addition, most existing methods adopt hard labels to supervise the network model, which makes the model prone to losing fine-grained information of ground objects. In order to solve these problems, a feature-augmented self-distilled convolutional neural network (FASDNet) is proposed. First, ResNet34 is adopted as the backbone network to extract multi-level features of images. Next, a feature augmentation pyramid module (FAPM) is designed to extract and fuse multi-level feature information. Then, auxiliary branches are constructed to provide additional supervision information. The self-distillation method is utilized between the feature augmentation pyramid module and the backbone network, as well as between the backbone network and auxiliary branches. Finally, the proposed model is jointly supervised using feature distillation loss, logits distillation loss, and cross-entropy loss. A lot of experiments are conducted on four widely used remote sensing scene image datasets, and the experimental results show that the proposed method is superior to some state-ot-the-art classification methods.
Comparison of GPM IMERG Version 06 Final Run Products and Its Latest Version 07 Precipitation Products across Scales: Similarities, Differences and Improvements
Authors: Wang Yaji , Li Zhi , Gao Lei , Zhong Yong , Peng Xinhua
Published: December 2023

Precipitation is an essential element in earth system research, which greatly benefits from the emergence of Satellite Precipitation Products (SPPs). Therefore, assessment of the accuracy of the SPPs is necessary both scientifically and practically. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is one of the most widely used SPPs in the scientific community. However, there is a lack of comprehensive evaluation for the performance of the newly released IMERG Version 07, which is essential for determining its effectiveness and reliability in precipitation estimation. In this study, we compare the IMERG V07 Final Run (V07_FR) with its predecessor IMERG V06_FR across scales from January 2016 to December 2020 over the globe (cross-compare their similarities and differences) and a focused study on mainland China (validate against 2481 rain gauges). The results show that: (1) Globally, the annual mean precipitation of V07_FR increases 2.2% compared to V06_FR over land but decreases 5.8% over the ocean. The two SPPs further exhibit great differences as indicated by the Critical Success Index (CSI = 0.64) and the Root Mean Squared Difference (RMSD = 3.42 mm/day) as compared to V06_FR to V07_FR. (2) Over mainland China, V06_FR and V07_FR detect comparable precipitation annually. However, the Probability of Detection (POD) improves by 5.0%, and the RMSD decreases by 3.7% when analyzed by grid cells. Further, the POD (+0%~+6.1%) and CSI (+0%~+8.8%) increase and the RMSD (−11.1%~0%) decreases regardless of the sub-regions. (3) Under extreme rainfall rates, V07_FR measures 4.5% lower extreme rainfall rates than V06_FR across mainland China. But V07_FR tends to detect more accurate extreme precipitation at both daily and event scales. These results can be of value for further SPP development, application in climatological and hydrological modeling, and risk analysis.
Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model
Authors: Wei Wensong , Zhang Fengjuan , Fu Fangting , Sang Shuo , Qiao Zhen
Published: December 2023

The total viable count (TVC) of bacteria is an important index to evaluate the freshness and safety of dishes. To improve the accuracy and robustness of spectroscopic detection of total viable bacteria count in a complex system, a new method based on a near-infrared (NIR) hyperspectral hybrid model and Support Vector Machine (SVM) algorithms was developed to directly determine the total viable count in intact beef dish samples in this study. Diffuse reflectance data of intact and crushed samples were tested by NIR hyperspectral and processed using Multiplicative Scattering Correction (MSC) and Competitive Adaptive Reweighted Sampling (CARS). Kennard–Stone (KS) and Samples Set Partitioning Based on Joint X-Y Distance (SPXY) algorithms were used to select the optimal number of standard samples transferred by the model combined with root mean square error. The crushed samples were transferred into the complete samples prediction model through the Direct Standardization (DS) algorithm. The spectral hybrid model of crushed samples and full samples was established. The results showed that the Determination Coefficient of Calibration (RP2) value of the total samples prediction set increased from 0.5088 to 0.8068, and the value of the Root Mean Square Error of Prediction (RMSEP) decreased from 0.2454 to 0.1691 log10 CFU/g. After establishing the hybrid model, the RMSEP value decreased by 9.23% more than before, and the values of Relative Percent Deviation (RPD) and Reaction Error Relation (RER) increased by 12.12% and 10.09, respectively. The results of this study showed that TVC instewed beef samples can be non-destructively determined based on the DS model transfer method combined with the hybrid model strategy. This study provided a reference for solving the problem of poor accuracy and reliability of prediction models in heterogeneous samples.
AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems
Authors: Felici-Castell Santiago , Segura-Garcia Jaume , Perez-Solano Juan J. , Fayos-Jordan Rafael , Soriano-Asensi Antonio , Alcaraz-Calero Jose M.
Published: December 2023

The proliferation and great variety of low-cost air quality (AQ) sensors, combined with their flexibility and energy efficiency, gives an opportunity to integrate them into Wireless Sensor Networks (WSN). However, with these sensors, AQ monitoring poses a significant challenge, as the data collection and analysis process is complex and prone to errors. Although these sensors do not meet the performance requirements for reference regulatory-equivalent monitoring, they can provide informative measurements and more if we can adjust and add further processing to their raw measurements. Therefore, the integration of these sensors aims to facilitate real-time monitoring and achieve a higher spatial and temporal sampling density, particularly in urban areas, where there is a strong interest in providing AQ surveillance services since there is an increase in respiratory/allergic issues among the population. Leveraging a network of low-cost sensors, supported by 5G communications in combination with Artificial Intelligence (AI) techniques (using Convolutional and Deep Neural Networks (CNN and DNN)) to predict 24-h-ahead readings is the goal of this article in order to be able to provide early warnings to the populations of hazards areas. We have evaluated four different neural network architectures: Multi-Linear prediction (with a dense Multi-Linear Neural Network (NN)), Multi-Dense network prediction, Multi-Convolutional network prediction, and Multi-Long Short-Term Memory (LSTM) network prediction. To perform the training of the prediction of the readings, we have prepared a significant dataset that is analyzed and processed for training and testing, achieving an estimation error for most of the predicted parameters of around 7.2% on average, with the best option being the Multi-LSTM network in the forthcoming 24 h. It is worth mentioning that some pollutants achieved lower estimation errors, such as CO2 with 0.1%, PM10 with 2.4% (as well as PM2.5 and PM1.0), and NO2 with 6.7%.
Balancing Accuracy and Speed in Gaze-Touch Grid Menu Selection in AR via Mapping Sub-Menus to a Hand-Held Device
Authors: Tian Yang , Zheng Yulin , Zhao Shengdong , Ma Xiaojuan , Wang Yunhai
Published: December 2023

Eye gaze can be a potentially fast and ergonomic method for target selection in augmented reality (AR). However, the eye-tracking accuracy of current consumer-level AR systems is limited. While state-of-the-art AR target selection techniques based on eye gaze and touch (gaze-touch), which follow the “eye gaze pre-selects, touch refines and confirms” mechanism, can significantly enhance selection accuracy, their selection speeds are usually compromised. To balance accuracy and speed in gaze-touch grid menu selection in AR, we propose the Hand-Held Sub-Menu (HHSM) technique.tou HHSM divides a grid menu into several sub-menus and maps the sub-menu pointed to by eye gaze onto the touchscreen of a hand-held device. To select a target item, the user first selects the sub-menu containing it via eye gaze and then confirms the selection on the touchscreen via a single touch action. We derived the HHSM technique’s design space and investigated it through a series of empirical studies. Through an empirical study involving 24 participants recruited from a local university, we found that HHSM can effectively balance accuracy and speed in gaze-touch grid menu selection in AR. The error rate was approximately 2%, and the completion time per selection was around 0.93 s when participants used two thumbs to interact with the touchscreen, and approximately 1.1 s when they used only one finger.
Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography
Authors: Mortier Steven , Turkeš Renata , De Winne Jorg , Van Ransbeeck Wannes , Botteldooren Dick , Devos Paul , Latré Steven , Leman Marc , Verdonck Tim
Published: December 2023

Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an information stream. To this end, we designed an experiment inspired by pip-and-pop but more appropriate for eliciting attention and P3a-event-related potentials (ERPs). In this study, the aim was to distinguish between targets and distractors based on the subject’s electroencephalography (EEG) data. We achieved this objective by employing different machine learning (ML) methods for both individual-subject (IS) and cross-subject (CS) models. Finally, we investigated which EEG channels and time points were used by the model to make its predictions using saliency maps. We were able to successfully perform the aforementioned classification task for both the IS and CS scenarios, reaching classification accuracies up to 76%. In accordance with the literature, the model primarily used the parietal–occipital electrodes between 200 ms and 300 ms after the stimulus to make its prediction. The findings from this research contribute to the development of more effective P300-based brain–computer interfaces. Furthermore, they validate the EEG data collected in our experiment.
Distributed Detour Routing Scheme for Link Failure with Minimized Overhead in LEO Satellite Networks
Authors: Im Jeongju , Youn Jiseung , Kim Soohyeong , Park Joohan , Lee Sejong , Kwon Yongseok , Cho Sunghyun
Published: December 2023

The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the n-hop flooding approach.
CORTO: The Celestial Object Rendering TOol at DART Lab
Authors: Pugliatti Mattia , Buonagura Carmine , Topputo Francesco
Published: December 2023

The Celestial Object Rendering TOol (CORTO) offers a powerful solution for generating synthetic images of celestial bodies, catering to the needs of space mission design, algorithm development, and validation. Through rendering, noise modeling, hardware-in-the-loop testing, and post-processing functionalities, CORTO creates realistic scenarios. It offers a versatile and comprehensive solution for generating synthetic images of celestial bodies, aiding the development and validation of image processing and navigation algorithms for space missions. This work illustrates its functionalities in detail for the first time. The importance of a robust validation pipeline to test the tool’s accuracy against real mission images using metrics like normalized cross-correlation and structural similarity is also illustrated. CORTO is a valuable asset for advancing space exploration and navigation algorithm development and has already proven effective in various projects, including CubeSat design, lunar missions, and deep learning applications. While the tool currently covers a range of celestial body simulations, mainly focused on minor bodies and the Moon, future enhancements could broaden its capabilities to encompass additional planetary phenomena and environments.
Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19
Authors: Kang Jin-Yeong , Bae Ye Seul , Chie Eui Kyu , Lee Seung-Bo
Published: December 2023

Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.
Validity of Inertial Measurement Units to Measure Lower-Limb Kinematics and Pelvic Orientation at Submaximal and Maximal Effort Running Speeds
Authors: Lin Yi-Chung , Price Kara , Carmichael Declan S. , Maniar Nirav , Hickey Jack T. , Timmins Ryan G. , Heiderscheit Bryan C. , Blemker Silvia S. , Opar David A.
Published: December 2023

Inertial measurement units (IMUs) have been validated for measuring sagittal plane lower-limb kinematics during moderate-speed running, but their accuracy at maximal speeds remains less understood. This study aimed to assess IMU measurement accuracy during high-speed running and maximal effort sprinting on a curved non-motorized treadmill using discrete (Bland–Altman analysis) and continuous (root mean square error [RMSE], normalised RMSE, Pearson correlation, and statistical parametric mapping analysis [SPM]) metrics. The hip, knee, and ankle flexions and the pelvic orientation (tilt, obliquity, and rotation) were captured concurrently from both IMU and optical motion capture systems, as 20 participants ran steadily at 70%, 80%, 90%, and 100% of their maximal effort sprinting speed (5.36 ± 0.55, 6.02 ± 0.60, 6.66 ± 0.71, and 7.09 ± 0.73 m/s, respectively). Bland–Altman analysis indicated a systematic bias within ±1° for the peak pelvic tilt, rotation, and lower-limb kinematics and −3.3° to −4.1° for the pelvic obliquity. The SPM analysis demonstrated a good agreement in the hip and knee flexion angles for most phases of the stride cycle, albeit with significant differences noted around the ipsilateral toe-off. The RMSE ranged from 4.3° (pelvic obliquity at 70% speed) to 7.8° (hip flexion at 100% speed). Correlation coefficients ranged from 0.44 (pelvic tilt at 90%) to 0.99 (hip and knee flexions at all speeds). Running speed minimally but significantly affected the RMSE for the hip and ankle flexions. The present IMU system is effective for measuring lower-limb kinematics during sprinting, but the pelvic orientation estimation was less accurate.
An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning
Authors: Venkatapathy Sujitha , Srinivasan Thiruvenkadam , Jo Han-Gue , Ra In-Ho
Published: December 2023

Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra’s algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization.
A Path-Planning Method to Significantly Reduce Local Oscillation of Manipulators Based on Velocity Potential Field
Authors: Wang Xianlun , Wu Qingsong , Wang Tianyu , Cui Yuxia
Published: December 2023

The robotics industry and associated technology applications are a vital support for modern production and manufacturing. With the intelligent development of the manufacturing industry, the application of collaboration robots and human-robot collaboration technology is becoming more and more extensive. In a human-robot collaboration scenario, there are uncertainties such as dynamic impediments, especially in the human upper limb, which puts forward a higher assessment of the manipulator’s route planning technology. As one of the primary branches of the artificial potential field (APF), the velocity potential field (VPF) offers the advantages of good real-time performance and convenient mathematical expression. However, the traditional VPF algorithm is prone to local oscillation phenomena near obstacles, which degrades the smoothness of the movement of the manipulators. An improved velocity potential field algorithm is proposed in this paper. This method solves the problem of sudden velocity change when the manipulator enters and departs the region of the potential field by setting new functions for attraction velocity and repulsion velocity functions. A virtual target point construction method is given to overcome the local oscillation problem of the manipulators near obstacles. The simulation and practical findings of the manipulators reveal that the improved VPF algorithm can not only avoid collision but also effectively reduce the local oscillation problem when dealing with the human upper limb as a dynamic obstacle. The implementation of this algorithm can increase the safety and real-time performance of the human-robot collaboration process and ensure that the collaborative robot is safer and smoother in the working process.
Enhancing the Extinction Efficiency and Plasmonic Response of Bimetallic Nanoparticles of Au-Ag in Robust Thin Film Sensing Platforms
Authors: Meira Diana I. , Rodrigues Marco S. , Borges Joel , Vaz Filipe
Published: December 2023

The extinction efficiency of noble metal nanoparticles (NPs), namely gold (Au) and silver (Ag), are dependent on their size and surrounding dielectric. Exploiting the Localized Surface Plasmon Resonance (LSPR) phenomenon, the composition and structure of the NPs might be tailored to achieve a configuration that optimizes their response (sensitivity) to environmental changes. This can be done by preparing a bimetallic system, benefiting from the chemical stability of Au NPs and the higher scattering efficiency of Ag NPs. To enhance the LSPR sensing robustness, incorporating solid supports in the form of nanocomposite thin films is a suitable alternative. In this context, the NPs composed of gold (Au), silver (Ag), and their mixture in bimetallic Au-Ag NPs, were grown in a titanium dioxide (TiO2) matrix using reactive DC magnetron sputtering. Thermal treatment at different temperatures (up to 700 °C) tuned the LSPR response of the films and, consequently, their sensitivity. Notably, the bimetallic film with Au/Ag atomic ratio 1 exhibited the highest refractive index sensitivity (RIS), with a value of 181 nm/RIU, almost one order of magnitude higher than monometallic Au-TiO2. The nanostructural analysis revealed a wide NP size distribution of bimetallic NPs with an average size of 31 nm, covering about 20% of the overall surface area. These findings underscore the significant potential of bimetallic film systems, namely AuAg-TiO2, in LSPR sensing enhancement.
Resource and Energy Utilization of Swine Wastewater Treatment: Recent Progress and Future Directions
Authors: Meng Xiangwei , Jin Ming , Feng Qianzi , Sha Aiqi , Bai Shunwen , Zhao Xinyue
Published: December 2023

Livestock and poultry farming, as a crucial component of agricultural production, poses a substantial threat to the ecological environment due to the discharge of wastewater. In recent years, researchers have proposed various resource treatment technologies for livestock and poultry breeding wastewater. However, a comprehensive discussion regarding the limitations and avenues for optimizing resource utilization technologies for livestock and poultry farming wastewater treatment is notably absent in existing literature. This paper takes swine wastewater as an illustrative case and undertakes a review of the advantages, disadvantages, and optimization directions of resource treatment technologies, including physical and chemical technology, microbial metabolism, microbial electrochemistry, constructed wetlands, and microalgae-based techniques. Based on mass balance, the recovery rates of various treatment technologies are estimated, and it was found that microbial electrochemistry and constructed wetland techniques may become the mainstream for resource utilization in the future. Furthermore, this paper emphasizes that in addition to resource efficiency, the optimization of resource utilization technologies for swine wastewater should also focus on the following aspects: (1) striking a balance between environmental impact and economic benefits; (2) reducing the cost of resource and energy utilization; and (3) safeguarding environmental and ecological security.
Linguistic Integration of Adult Migrants in Greece and Italy: Language Requirements and Learning Opportunities in L2 Greek and L2 Italian
Authors: Mouti Anna , Rocca Lorenzo
Published: December 2023

Migration has almost always been accompanied by language-related processes and concerns. Integration dimensions interact with numerous language-related issues, such as language requirements and learning opportunities, and the purpose of our paper is situated in the broader field of linguistic integration of adult refugees and migrants in Greece and Italy. Greece and Italy share a double role both as host and transition countries, as two of the main EU entry points for refugees and migrants since the 2015 refugee crisis, and therefore they have been selected as two suitable cases to be further explored. This paper aims to give an overview of the language requirements and language-learning opportunities in the migration context in Greece and Italy. Through our comprehensive review of language requirements and language-learning opportunities in Greece and Italy, we have undertaken an examination of the two contexts employing a comparative approach to scrutinize the processes of linguistic integration. The information presented has shown that similar linguistic requirements are set in both countries although the use and implementation of the Knowledge of Society (KoS) tests seem to discriminate between the two contexts. The results agree with similar findings through indexes such as the Language Policy Index for Migrants (LAPIM) and the Migrant Integration Policy Index (MIPEX).
Utilization of an Information System for the Efficient Implementation of the Integrated Environmental Permit System in South Korea
Authors: Park Jae-Hong , Seok Hee-Jeong , Kang Phil-Goo , Ahn Hyangsig
Published: December 2023

The construction and operation of information systems are valuable for the implementation of an integrated environmental permit system (IEPS). This study introduces an IEPS and examines its implementation in South Korea. Using operational cases from the IEPS, we demonstrate how the system is structured for each permit application procedure and the mechanisms by which permits are processed. In South Korea, the operation of an IEPS has made it easy to track the progress of permits and immediately notify the relevant authorities regarding permit applications and supplementary requests, thereby reducing the time required for permit reviews and amendments. Furthermore, disclosing the review results and other permit information ensures the reliability of permits. Additionally, the regular input of data, such as pollutant monitoring data from facilities and compliance with permit conditions, into the IEPS facilitates the stable management of all data related to facility permits and operations. Regulatory agencies can utilize the database within the IEPS to gain insight into facility conditions before conducting on-site inspections, ultimately reducing the time required for such inspections. Moreover, by accessing past inspection records through this system, facilities requiring more focused management can be identified, and data from the system can be used in planning future inspections.
A Review of Research on the Impact Mechanisms of Green Development in the Transportation Industry
Authors: Mao Yumeng , Li Xuemei
Published: December 2023

Green development in the transportation industry is a new type of development. As the huge energy consumption and carbon emissions generated by the transportation industry have caused many environmental problems, the healthy and environmentally friendly mode of industrial development has received more and more attention. However, the quantification of green development in the transportation industry varies in terms of boundaries, scope, and methods. Due to digital empowerment, the degree of influence and direction of the factors affecting the green development are not fixed. The prediction of future development prospects is relatively single-minded, lacking a comprehensive simulation scenario setting from multiple perspectives. This paper systematically reviews the research progress of green development of the transportation industry from three aspects: development performance assessment, influence mechanism analysis, and development path exploration. After a critical analysis, this study concludes that (1) a clear methodology is needed to assess the direct and indirect non-desired output results of the transportation industry; (2) considering the endogenization of the level of technology, the influence of the interaction between the influencing factors, etc., on the degree and direction of the role of the factors, a more scientific econometric model should be established for in-depth discussion; (3) resident travel options are an important factor affecting environmental issues in transportation. Carbon emission projections and analyses of emission reduction scenarios should integrate the multiple possibilities of residential preferences and policy incentives. The findings of this paper provide valuable references to the energy saving and emission reduction goals of the transportation industry, and the coordinated development of the industry and the economy.
The Influence of Groves on Aboveground Arthropod Diversity and Evolution in a Vineyard in Southern Romania
Authors: Vizitiu Diana Elena , Sardarescu Ionela-Daniela , Buciumeanu Elena Cocuta , Guta Ionela-Cătălina , Dincă Lucian , Bălăcenoiu Flavius , Toma Dragoș , Crișan Vlad , Din Alin
Published: December 2023

This paper investigates the biodiversity of adult arthropods in two grapevine plantations influenced by two adjacent groves over a three-year period (2020–2022) in the viticultural center of Stefănești Argeș, located in southern Romania. The study holds significant implications for introducing parasitoid/predatory insect species into vineyards to control grapevine pests. A total of 164 arthropod species were identified, including 27 beneficial species. Additionally, two moth species, Lobesia botrana and Sparganothis pilleriana, were identified. L. botrana was consistently observed throughout the study, while S. pilleriana was only observed in 2022. The research reveals that the location with the highest number of identified species was in a grove near a black field, with 103 species. Other areas with notable species diversity included a vineyard maintained as a black field (89 species), a grove near permanent natural grassland (88 species), and a vineyard with intervals between rows of grapevines maintained as natural permanent grassland (81 species). Introducing beneficial organisms, such as the predator Crysoperla carnea, is recommended to control grapevine moths in this ecosystem.
Pyrolysis of Tyre Waste in a Fixed-Bed Reactor
Authors: Papuga Saša , Djurdjevic Milica , Tomović Goran , Vecchio Ciprioti Stefano
Published: December 2023

This paper presents the results of investigations on the pyrolysis of tyre waste in a laboratory fixed-bed batch reactor. The results regarding the influence of either the reaction temperature (425, 450, 475, and 500 °C) and the flow of the inert gas (0, 100, 300, and 500 mL/min) on product yield (referred to as pyrolysis of waste tyres) are also considered and discussed. On the basis of the abovementioned findings, the most appropriate experimental conditions were selected to contribute to a higher yield of pyrolysis oil. The sample of pyrolysis oil obtained from the experiments carried out in the selected optimal conditions (reaction time 120 min, temperature 450 °C and the inert gas flow of 100 mL/min) was subjected to calorimetric and infrared spectroscopy analysis.
A Compact Microwave Quadrature Hybrid Coupler Using Capacitive Composite Lines and Meandered Stubs
Authors: Roshani Sobhan , Yahya Salah I. , Assaad Maher , Chaudhary Muhammad Akmal , Hazzazi Fawwaz , Ghadi Yazeed Yasin , Ali Sarmad M. , Roshani Saeed
Published: December 2023

In this paper, a new structure of the quadrature hybrid coupler (QHC) with compact size is proposed using capacitive composite lines and meandered open stubs. The proposed coupler works at 1.6 GHz with a 0.4 GHz bandwidth, which shows 25% fractional bandwidth (FBW). The proposed QHC occupies only 15 mm × 15 mm (0.12 λ × 0.12 λ), while the typical QHC size is 32 mm × 32 mm (0.25 λ × 25 λ) at the same working frequency. In the designed structure, two symmetric meandered stubs and two symmetric π-shaped composite networks including capacitors and microstrip lines are applied together. The designed QHC has a small size and occupies only 22% of the area of the conventional QHC, resulting in a 78% size reduction. The designed prototype has been analyzed, fabricated and tested, and the experimental results verify the simulated and analysis results. The results show a better than 27 dB return loss, more than 28 dB isolation between the output ports and less than 0.4 dB insertion loss at the working frequency of 1600 MHz. With the achieved desirable specifications, the fabricated QHC is a suitable choice for wireless microwave applications.
Algebraic Structures on Smooth Vector Fields
Authors: Alkinani Amnah A. , Alghamdi Ahmad M.
Published: December 2023

The aim of this work is to investigate some algebraic structures of objects which are defined and related to a manifold. Consider L to be a smooth manifold and Γ∞(TL) to be the module of smooth vector fields over the ring of smooth functions C∞(L). We prove that the module Γ∞(TL) is projective and finitely generated, but it is not semisimple. Therefore, it has a proper socle and nonzero Jacobson radical. Furthermore, we prove that this module is reflexive by showing that it is isomorphic to its bidual. Additionally, we investigate the structure of the Lie algebra of smooth vector fields. We give some questions and open problems at the end of the paper. We believe that our results are important because they link two different disciplines in modern pure mathematics.
Snowmobiling and Climate Change: Exploring Shifts in Snowmobile Activity Using a Temporal Analogue Approach in Ontario (Canada)
Authors: Rutty Michelle , Cardwell Francesca , Gunn Grant
Published: December 2023

The multi-billion-dollar snowmobile industry is predicated on natural snowfall and cold temperatures, with a near absence of research that examines industry response to climatic variability and change. Using a temporal analogue approach, this study examines 30 years of climate data (1989–2019), along with operational (grooming hours) and performance (permit sales) indicators, to provide insight into the vulnerability and adaptive capacity of the Ontario snowmobile industry in a medium (RCP4.5) and high (RCP8.5) mid-century (2046–2060) emission scenario. The results underscore important temporal and spatial variability across Ontario’s 16 snowmobile districts, indicating that snowmobilers are highly resilient to marginal conditions, changing districts and switching from seasonal to daily permits in response to warming temperatures. The findings from this study can inform risk assessments in other major snowmobile markets (e.g., Canada, Europe, USA), with future research needs discussed.
A Second Career for p53 as A Broad-Spectrum Antiviral?
Authors: Harford Joe B.
Published: December 2023

As the world exits the global pandemic caused by the previously unknown SARS-CoV-2, we also mark the 30th anniversary of p53 being named “molecule of the year” by Science based on its role as a tumor suppressor. Although p53 was originally discovered in association with a viral protein, studies on its role in preventing carcinogenesis have far overshadowed research related to p53′s role in viral infections. Nonetheless, there is an extensive body of scientific literature demonstrating that p53 is a critical component of host immune responses to viral infections. It is striking that diverse viruses have independently developed an impressive repertoire of varied mechanisms to counter the host defenses that are mediated by and through p53. The variety of ways developed by viruses to disrupt p53 in their hosts attests to the protein’s importance in combatting viral pathogens. The present perspective aims to make the case that p53 ought to be considered a virus suppressor in addition to a tumor suppressor. It is hoped that additional research aimed at more fully understanding the role of p53 in antiviral immunity will result in the world being better positioned for the next pandemic than it was when SARS-CoV-2 emerged to produce COVID-19.
Emerging Zoonotic Diseases among Pastoral Communities of Caia and Búzi Districts, Sofala, Mozambique: Evidence of Antibodies against Brucella, Leptospira, Rickettsia, and Crimean-Congo Hemorrhagic Fever Virus
Authors: Oludele John , Alho Pascoal , Chongo Inocêncio , Maholela Plácida , Magaia Vlademiro , Muianga Argentina , Melchior Bibiana , Isaías Telma , Gatambire Aline , Zimba Edna , Nhavoto Emídio , Notiço Paulo , Inguana Pedro , Cantoria Juma , António Virgílio , Monteiro Vanessa , Ali Sádia , Inlamea Osvaldo , Samo Gudo Eduardo
Published: December 2023

Background: Emerging zoonotic diseases are an increasing threat to public health. There is little data on the seroprevalence of zoonotic diseases among pastoralists in the country. We aim to carry out a cross-sectional study on the prevalence of major zoonotic diseases among pastoral communities in the Caia and Búzi districts. Methods: Between January and December 2018, a questionnaire was used to solicit socio-demographic data from consenting pastoralists with the collection of blood samples in the Caia and Búzi districts of the Sofala province. All samples were tested using ELISA commercial reagents for the detection of IgM antibodies against Brucella and Leptospira. Likewise, IgM and IgG antibodies against Rickettsia and CCHFV were determined using ELISA kits. Results: A total of 218 samples were tested, of which 43.5% (95/218) were from the district of Caia and 56.4% (123/218) from the Búzi district. Results from both districts showed that the seroprevalence of IgM antibodies against Brucella and Leptospira was 2.7% (6/218) and 30.3% (67/218), respectively. Positivity rates for IgM and IgG anti-Rickettsia and CCHFV were 8.7% (19/218), 2.7% (6/218), 4.1% (9/218), and 0.9% (2/218), respectively. Conclusions: Results from our study showed evidence of antibodies due to exposure to Brucella, Leptospira, Rickettsia, and CCHFV with antibodies against Leptospira and Rickettsia being the most prevalent. Hence, laboratory diagnosis of zoonotic diseases is essential in the early detection of outbreaks, the identification of silent transmission, and the etiology of non-febrile illness in a pastoral community. There is a need to develop public health interventions that will reduce the risk of transmission.
The Group A Streptococcal Vaccine Candidate VAX-A1 Protects against Group B Streptococcus Infection via Cross-Reactive IgG Targeting Virulence Factor C5a Peptidase
Authors: McCabe Sinead , Bjånes Elisabet , Hendriks Astrid , Wang Zhen , van Sorge Nina M. , Pill-Pepe Lucy , Bautista Leslie , Chu Ellen , Codée Jeroen D. C. , Fairman Jeff , Kapoor Neeraj , Uchiyama Satoshi , Nizet Victor
Published: December 2023

Group B Streptococcus (Streptococcus agalactiae or GBS) is the leading infectious cause of neonatal mortality, causing roughly 150,000 infant deaths and stillbirths annually across the globe. Approximately 20% of pregnant women are asymptomatically colonized by GBS, which is a major risk factor for severe fetal and neonatal infections as well as preterm birth, low birth weight, and neurodevelopmental abnormalities. Current clinical interventions for GBS infection are limited to antibiotics, and no vaccine is available. We previously described VAX-A1 as a highly effective conjugate vaccine against group A Streptococcus that is formulated with three antigens, SpyAD, streptolysin O, and C5a peptidase (ScpA). ScpA is a surface-expressed, well-characterized GAS virulence factor that shares nearly identical sequences with the lesser studied GBS homolog ScpB. Here, we show that GBS C5a peptidase ScpB cleaves human complement factor C5a and contributes to disease severity in the murine models of pneumonia and sepsis. Furthermore, antibodies elicited by GAS C5a peptidase bind to GBS in an ScpB-dependent manner, and VAX-A1 immunization protects mice against lethal GBS heterologous challenge. These findings support the contribution of ScpB to GBS virulence and underscore the importance of choosing vaccine antigens; a universal GAS vaccine such as VAX-A1 whose formulation includes GAS C5a peptidase may have additional benefits through some measure of cross-protection against GBS infections.