bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Dry Granulation Operating Parameters on Forming Quality
Authors: Xie Xiaowen , Gao Haozheng , Xie Zhongze , Wu Mingge
Published: September 2023

To address the issues of the existing dry granulation process, which mostly relies on manual adjustments based on empirical experience and lacks the analysis of theoretical data, a simplified model of tablet pressing based on a modified Drucker–Prager Cap (DPC) material constitutive model was proposed to simulate and analyze the forming process of tablets, and study the impacts of the feeding speed, roller speed, friction coefficient and roller clearance on the density distribution of the pressed tablets via ABAQUS. The results show that the roller speed significantly impacts the density of tablets, and a lower roller speed and a higher friction coefficient are beneficial to the formation of tablets. As the roller clearance decreases, the average density at the clearance increases, and the unevenness of the lateral density distribution of the tablets increases, showing a distribution trend of larger density at the center and smaller density on both sides.
Open-Source Magnetometer for Characterizing Magnetic Fields in Ultracold Experiments
Authors: Dinçer Koray , Semczuk Mariusz
Published: September 2023

We demonstrate a fully automated open-source magnetometer designed primarily for characterization of magnetic fields produced by coils, permanent magnets or by parasitic sources. It is based on an Arduino Mega microcontroller and a three-axis Hall sensor with a measurement range of ±8 G per axis and the RMS of the field readout below 0.3 mG. For all practical purposes, the sensor displacement during data acquisition is virtually unlimited, which can be particularly useful for characterizing large or extended coils like Helmholtz cages or Zeeman slowers. All components needed for the construction are cheap and widely available off-the-shelf elements or are 3D-printed. The operation of the magnetometer is controlled via a graphical user interface (GUI), which manages all essential functionalities, like data acquisition and plotting. The GUI also incorporates additional features, like data averaging, calibration of the displacement of the Hall sensor or real-time readout of the magnetic field, useful for monitoring magnetic field changes. We have used a pair of rectangular coils constructed for a potassium–cesium 2D magneto-optical trap to benchmark the performance of the magnetometer. We have obtained good agreement with both simulations and measurements acquired with a commercial gaussmeter.
Artificial Neural Network Based Prediction of Long-Term Electric Field Strength Level Emitted by 2G/3G/4G Base Station
Authors: Engiz Begum Korunur
Published: September 2023

Accurate predictions of radio frequency electromagnetic field (RF-EMF) levels can help implement measures to reduce exposure and check regulatory compliance. Therefore, this study aims to predict the RF-EMF levels in the medium using an artificial neural network (ANN). The work was conducted at Ondokuz Mayis University, Kurupelit Campus, where the measurement location has line-of-sight to the base stations. Band selective measurements were also performed to assess the contribution of 2G/3G/4G services to the total RF-EMF level, which was found to be the highest among all services within the total band. Long-term RF-EMF measurements were carried out for 35 days within the frequencies of 100 kHz to 3 GHz. Then, an ANN model with Levenberg–Marquardt (LM) and Bayesian Regulation (BR) algorithms was proposed, which utilized inputs from real-time RF-EMF measurements. The performance of the models was assessed in terms of mean squared error (MSE) and regression performance. The average MSE and regression performances of the models were similar, with the lowest testing MSEs of 2.78 × 10−3 and 3.76 × 10−3 for LM and BR methods, respectively. The analysis of the models showed that the proposed models help to predict the RF-EMF level in the medium with up to 99% accuracy.
A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
Authors: Kamel Menna Maged , Gil-Solla Alberto , Guerrero-Vásquez Luis Fernando , Blanco-Fernández Yolanda , Pazos-Arias José Juan , López-Nores Martín
Published: September 2023

Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.
Strengthening of RC Beams with CFC Panels for Improving Impact Resistance
Authors: Senesavath Souphavanh , Tamai Hiroki , Lu Chi , Sonoda Yoshimi
Published: September 2023

In this study, continuous fiber composite (CFC) panels were used as a strengthening material to improve the impact resistance of reinforced concrete (RC). Both experimental tests and numerical analyses were carried out to investigate the impact resistance of RC beams strengthened with CFC panels. The experiments involved repeated drop-weight impact tests at constant speed. The experimental results confirm that the strengthening of RC beams with CFC panels improves the impact resistance, thereby increasing the number of repeated impacts that can be allowed before a specified residual displacement is reached. In addition, a virtual particle model based on the conventional smoothed particle hydrodynamics (SPH) method, which takes into account the mechanical properties of the adhesive, was introduced as an analytical method to simulate the impact fracture behavior of RC beams strengthened with CFC panels. The analysis results show that the improved SPH method proposed in this study can accurately reproduce the impact behavior of RC beams strengthened with CFC panels and predict the allowable number of repeated impacts. Furthermore, a parametric study was carried out using a validated analytical approach to compare the load-bearing capacity and discuss the impact performance of RC beams with three types of CFC panel reinforcement.
Drone-Based Monitoring and Mapping for LMO Confined Field Management under the Ministry of Environment
Authors: Han Sung Min , Lee Jung Ro , Nam Kyong-Hee
Published: September 2023

The objective of this study was to devise effective safety management systems for enclosed living modified organism (LMO) fields regulated by the Ministry of Environment (MOE), achieved through an assessment of the impact of LM crops on the surrounding flora. A combination of conventional survey methods and cutting-edge drone-based monitoring systems was employed, with a keen focus on their efficacy. Our investigation spans three distinct zones (forest, non-forest, and enclosed field), involving vegetation surveys, biodiversity index analyses, and drone-powered aerial observations to study topographical shifts. Over time, wild plants adjacent to the enclosed LMO field exhibited stability in terms of species composition. Nevertheless, disparities in growth patterns emerged across various areas. Predominantly, herbs thrived in enclosed and non-forest areas, while trees and shrubs flourished in forested regions. Annual plants predominantly populated the non-forest regions, whereas perennials dominated the forested areas. To this end, drones captured aerial photographs of a 31.65-hectare expanse with 40% coverage overlap, furnishing a real-time vegetation map that transcends the capacities of conventional methods. By combining vegetation surveys, drone-generated vegetation mapping, and dynamic monitoring of topographical changes, our research endeavors to facilitate the formulation of a robust safety management framework for LMO confined fields overseen by the MOE. This holistic approach aspires to prevent ecosystem contamination and establish a resilient, enduring system that averts LMO leakage, thereby safeguarding the environment.
Evaluating Variability in Reflective Photoelasticity: Focus on Adhesives, Light Sources, and Camera Setup
Authors: Kim Seongmin , Nam Boo-Hyun , Jung Young-Hoon
Published: September 2023

This study investigates the impact of various experimental parameters on the reflective photoelastic coating technique used to measure the stress and strain in model soil particles. The focus is directed towards three pivotal parameters: the choice of adhesive for the photoelastic coating, the inherent properties of the light source, and the configuration of the camera for color image analysis. Through a series of uniaxial compression tests on consistently crafted model soil particles, a notable relationship emerges between the adhesive’s shear strength, its viscosity, and the uniformity of the photoelastic response. While the light source’s characteristics and camera adjustments hold significance, their influence on the consistency of the response is comparatively subtle. Consequently, the adhesive’s role is underscored as instrumental in influencing the photoelastic response, offering valuable insights for subsequent research endeavors utilizing the reflective photoelastic technique.
An Improved Method for Optimizing the Timing of Preventive Maintenance of Pavement: Integrating LCA and LCCA
Authors: Guan Xinfang , Zhang Hongchao , Du Xiaobo , Zhang Xiyu , Sun Mutian
Published: September 2023

The optimal preventive maintenance timing of traditional asphalt pavement is based on the maximum benefit-cost ratio. For cost evaluation indicators, mainly agency cost and user cost, use life cycle cost analysis (LCCA) to determine. The environmental impact of pavement preventive maintenance activities is often overlooked. For the benefit evaluation index, only the new area under the performance curve is used to express the maintenance benefit, and the benefit evaluation index is single. This study proposes an integrated approach combining life cycle assessment (LCA) and LCCA to model preventive maintenance timing decisions considering economic, social, and environmental factors. In this paper, LCA is used to quantitatively analyze the energy consumption and emissions in the pavement operation and maintenance process, and the environmental costs are also included in the accounting scope of the costs. The costs that constitute pavement use are analyzed from three aspects: the agency cost, the user cost, and the environmental cost. At the same time, in the process of constructing the cost model, the costs caused by the maintenance construction process to users and the environment are also taken into consideration. Aiming at the benefit index and comprehensively considering the economic benefits, social benefits, and environmental benefits brought by maintenance, a comprehensive evaluation model of maintenance benefits was constructed based on the analytic hierarchy process (AHP). On the basis of accurately calculating the cost and benefit of maintenance in the life cycle, the model of the optimal preventive maintenance timing of asphalt pavement is constructed so as to calculate the optimal timing of preventive maintenance and provide certain information for the formulation of expressway pavement preventive maintenance programs. For reference. Taking a highway project in China as an example to verify the applicability of the model
Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
Authors: Zadgaonkar Akshay , Keskar Ravindra , Kakde Omprakash
Published: September 2023

The study focuses on Alzheimer’s and dementia detection using machine learning, acknowledging their impact on cognitive health beyond normal aging. Data markers, rather than biomarkers, are preferred for diagnosis, allowing machine learning to play a role. The objective is to design and test a model for early dementia detection using lifestyle data from the National Health and Ageing Trends Study (NHATS). This could aid in flagging high-risk individuals and understanding aging-related parameter changes. Using NHATS data from 5000 individuals aged 60+, encompassing 1288 parameters over a decade, the study shortlists parameters relevant to dementia. Artificial neural networks and random forest techniques are employed to build a model that identifies key dementia-related parameters. Temporal analysis reveals features that exhibit declining social interactions, quality of life, and increased depression as individuals age. Results show the random forest model achieving an accuracy of 80% for dementia risk prediction, with precision, recall, and F1-score values of 0.76, 1, and 0.86, respectively. Temporal analysis offers insights into aging trends and elderly citizens’ lifestyles, using daily activities as parameters. The study concludes that NHATS data analysed using machine learning techniques aids in understanding aging trends and that machine learning models based on identified parameters can non-intrusively assist in clinical dementia diagnosis and trend-based detection.
High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
Authors: Kim Woosung , Luong Tuan , Ha Yoonwoo , Doh Myeongyun , Yax Juan Fernando Medrano , Moon Hyungpil
Published: September 2023

Drone simulations offer a safe environment for collecting data and testing algorithms. However, the depth camera sensor in the simulation provides exact depth values without error, which can result in variations in algorithm behavior, especially in the case of SLAM, when transitioning to real-world environments. The aerodynamic model in the simulation also differs from reality, leading to larger errors in drag force calculations at high speeds. This disparity between simulation and real-world conditions poses challenges when attempting to transfer high-speed drone algorithms developed in the simulated environment to actual operational settings. In this paper, we propose a more realistic simulation by implementing a novel depth camera noise model and an improved aerodynamic drag force model. Through experimental validation, we demonstrate the suitability of our models for simulating real-depth cameras and air drag forces. Our depth camera noise model can replicate the values of a real depth camera sensor with a coefficient of determination (R2) value of 0.62, and our air drag force model improves accuracy by 51% compared to the Airsim simulation air drag force model in outdoor flying experiments at 10 m/s.
Single-Lens Imaging Spectral Restoration Method Based on Gradient Prior Information Optimization
Authors: He Peidong , Li Zekun , Wang Jianwei , Tang Yinhui , Bai Yang , Lv Qunbo
Published: September 2023

Single-lens imaging systems can capture spectral information, but they are affected by aberrations, defocusing, and other factors, leading to spectral data overlap. It is then necessary to perform restoration on overlapped spectral data. The restoration method should not only deblur image information, but also recover spectral information for each wavelength band. In other words, the restoration process needs to handle both spatial and spectral information in parallel while ensuring that neither is distorted. In this study, while considering the characteristics of capturing overlapped information in a single-lens imaging system, a spectrum restoration method based on gradient prior information optimization is proposed. This method is shown to achieve high-quality restoration of both images and spectra. The feasibility of this algorithm is demonstrated through simulation and experimental verification, which show that the restoration quality of spectra using the proposed algorithm is improved compared to that achieved using the MTV algorithm.
TSNet: Token Sparsification for Efficient Video Transformer
Authors: Wang Hao , Zhang Wenjia , Liu Guohua
Published: September 2023

In the domain of video recognition, video transformers have demonstrated remarkable performance, albeit at significant computational cost. This paper introduces TSNet, an innovative approach for dynamically selecting informative tokens from given video samples. The proposed method involves a lightweight prediction module that assigns importance scores to each token in the video. Tokens with top scores are then utilized for self-attention computation. We apply the Gumbel-softmax technique to sample from the output of the prediction module, enabling end-to-end optimization of the prediction module. We aim to extend our method on hierarchical vision transformers rather than single-scale vision transformers. We use a simple linear module to project the pruned tokens, and the projected result is then concatenated with the output of the self-attention network to maintain the same number of tokens while capturing interactions with the selected tokens. Since feedforward networks (FFNs) contribute significant computation, we also propose linear projection for the pruned tokens to accelerate the model, and the existing FFN layer progresses the selected tokens. Finally, in order to ensure that the structure of the output remains unchanged, the two groups of tokens are reassembled based on their spatial positions in the original feature map. The experiments conducted primarily focus on the Kinetics-400 dataset using UniFormer, a hierarchical video transformer backbone that incorporates convolution in its self-attention block. Our model demonstrates comparable results to the original model while reducing computation by over 13%. Notably, by hierarchically pruning 70% of input tokens, our approach significantly decreases 55.5% of the FLOPs, while the decline in accuracy is confined to 2%. Additional testing of wide applicability and adaptability with other transformers such as the Video Swin Transformer was also performed and indicated its progressive potentials in video recognition benchmarks. By implementing our token sparsification framework, video vision transformers can achieve a remarkable balance between enhanced computational speed and a slight reduction in accuracy.
Development of Fragility and Vulnerability Functions for Reinforced Masonry Structures in Mexico: A Case Study
Authors: Díaz Francisco-Damián , González-Durán Mario , Flores Dora-Luz , López-Lambraño Alvaro , Mena-Hernández Ulises , Villada-Canela Mariana
Published: September 2023

The north of the state of Baja California (BC) is located in one of the regions of Mexico with a high seismic risk. The predominant infrastructure in the city of Tijuana consists of reinforced masonry housing structures, making it crucial to understand the dynamic behavior of this type of structure. Using the “Vulnerability Modeller’sToolKit” (VMTK), fragility functions were obtained for low, medium, and high levels of seismic design. It was found that the probability of achieving or surpassing the different damage states (light, moderate, extensive, and complete) is high in low-height housing (1–3 floors) for an expected value of 0.50 g of PGA and in medium-height housing (4–7 floors) for an expected value of 0.5 g of SA (T = 0.5 s).Vulnerability functions were derived, and it was determined that, for a low seismic design level, low-height and medium-height structures, respectively presented loss probability rates of 30% and 44% for an expected value of 0.50 g of PGA and SA (T = 0.5 s). These fragility and vulnerability functions can be used to estimate damage and losses in future risk scenarios, thereby reducing uncertainty in the risk analysis calculations for the city of Tijuana.
Exploration of Metrics and Datasets to Assess the Fidelity of Images Generated by Generative Adversarial Networks
Authors: Valdebenito Maturana Claudio Navar , Sandoval Orozco Ana Lucila , García Villalba Luis Javier
Published: September 2023

Advancements in technology have improved human well-being but also enabled new avenues for criminal activities, including digital exploits like deep fakes, online fraud, and cyberbullying. Detecting and preventing such activities, especially for law enforcement agencies needing photo profiles for covert operations, is imperative. Yet, conventional methods relying on authentic images are hindered by data protection laws. To address this, alternatives like generative adversarial networks, stable diffusion, and pixel recurrent neural networks can generate synthetic images. However, evaluating synthetic image quality is complex due to the varied techniques. Metrics are crucial, offering objective measures to compare techniques and identify areas for enhancement. This article underscores metrics’ significance in evaluating synthetic images produced by generative adversarial networks. By analyzing metrics and datasets used, researchers can comprehend the strengths, weaknesses, and areas for further research on generative adversarial networks. The article ultimately enhances image generation precision and control by detailing dataset preprocessing and quality metrics for synthetic images.
An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band
Authors: Li Jin , Zheng Tong , Guan Wenyang , Lian Xiaoqin
Published: September 2023

Carrier aggregation (CA) technology in the Fifth-Generation (5G) network can be considered as a major feature for high-rate data services, which can be combined with the concept of 5G in unlicensed band (5G-U) for data offloading to further enhance the efficiency of the scarce licensed spectral resource. However, the introduction of cellular services in an unlicensed spectrum can force unlicensed band users to silent mode. Meanwhile, the coexistence among different radio access technologies operating on the same frequency band is a complex issue, especially considering the requirement diversity of quality of service (QoS) in networking. In this paper, we propose an optimal resource allocation mechanism to maximize system performance satisfaction for multiple types of services with different QoS requirements in the 5G-U network with a CA feature to address the challenge. Additionally, the performance of services in Wi-Fi system is protected by reserving the minimum spectrum resource when offloading for the 5G-U network. Analytical results on the mean ergodic achievable rate of Wi-Fi services and the mean packet delay of 5G services are provided to maximize the system performance satisfaction. Numerical results demonstrate the effectiveness of the proposed algorithm, wherein optimal spectrum and power allocation can contribute to the maximum system satisfaction in a 5G-U network with a CA feature.
The City of Muses Project: Creating a Vibrant and Sensual Metropolitan Landscape through Architecture
Authors: Contin Antonella
Published: September 2023

The Metropolitan Architecture Project aims to create an artistic metropolitan landscape, which captivates visitors. It focuses on the relationship between the form’s image and the surrounding context, emphasising the structural image in architectural design. The project draws inspiration from the City of Muses Project, incorporating a symbolic mediator as a propeller, which represents the connection with the contemporary society’s cultural symbols and bridges the gap between the past, present and future. The methods employed in the Metropolitan Architecture Project involve integrating artistic elements into the metropolitan landscape. This includes incorporating the symbolic mediator and designing the structural image to interact harmoniously with the surrounding environment. The project has successfully introduced a new type of built form characterised by a relational figure and a vibrant and sensual image. By embracing this approach, the architectural design actively engages with the environment and enhances the overall architectural experience. The Metropolitan Architecture Project demonstrates the significance of incorporating an artistic dimension in creating a metropolitan landscape. The project achieves a captivating and interactive architectural design by considering the dynamic relationship between the form, context and structure. This understanding of architecture contributes to a deeper comprehension of the society which constructs it, resulting in a rich and engaging architectural experience.
The Shape of International Art Purchasing—The Shape of Things to Come
Authors: Duke Benjamin
Published: September 2023

This article is about the role of cryptocurrencies, for example, decentralized autonomous organisations (DAOs) and non-fungible tokens (NFTs), in the international art market. These are cryptocurrencies which can be used to work with local governments to deliver non-state-funded consultancy in, for example, funding bid writing or community risk assessment. Self-polycentric and cause-based DAOs typically focus on actively listening to their token owners, utilizing the group’s skills under a transparent incentive structure fostering trust. This article delivers a critical evaluation of DAOs as an organisational management structure and business operations vehicle. This evaluation considers DAOs’ utility in supplying goods and services, through the critical lens of facilitating the international art market. The objective of this article is to raise wider awareness and understanding of DAOs as a legal entity. This paper acts to introduce the uninitiated to the business, societal value and legal uncertainties of DAOs and NFTs. DAOs are internet-based organisations built upon a set of instructions presented in and controlled by a computer programme, i.e., a smart contract. Effectively, DAOs are an artificial, electronic, online, digital technology entity, with no physical form.
Characterization of the Cultivable Microbiota Components of Marine Bioaerosols in the North Tropical Atlantic
Authors: Moura Gabriela Cristina Chagas , Ayres Yasmin Marques , Brito Anna Luisa de Carvalho , Júnior Edmilson Ferreira de Souza , Rocha Rafael dos Santos , De Sousa Paulo Miguel Vieira , Ferreira Antônio Geraldo , Sousa Oscarina Viana de , Veleda Doris
Published: September 2023

Microorganisms are key to balancing marine ecosystems and have complex interactions at the ocean–atmosphere interface, affecting global climate and human health. This research investigated the diversity of cultivable bacteria and fungi in marine bioaerosols in the North Tropical Atlantic Ocean. Using the technique of spontaneous sedimentation in selective culture media, samples were collected during oceanographic expeditions. After isolation and purification, microbial strains were identified by phenotypic and genetic analyses. Fungi isolated included Acrophialophora, Aspergillus, Chrysosporium, Cladosporium, Fonsecaea, Mucor, Rhodotorula, Schizophyllum, Stemphylium, Candida, Curvularia, Cystobasidium, Exophiala, Neotestudina, Penicillium, Pestalotiopsis, and Preussia. The bacterial isolates belonged to the Bacillota, Pseudomonadota, Enterobacteriaceae family, Bacillus genus, and Serratia liquefaciens groups. About 40% of bacteria and 42% of fungi were identified as potential human pathogens, suggesting a relationship between human actions and the microbiota present in bioaerosols on the high seas. Sea surface temperature (SST) and wind speed influenced microorganisms. More studies and analyses in different scenarios should be conducted considering environmental and climate variables in order to deepen knowledge and generate information on the subject, so that standards can be established, and quality parameters determined.
Investigating Willingness to Invest in Renewable Energy to Achieve Energy Targets and Lower Carbon Emissions
Authors: Karasmanaki Evangelia , Galatsidas Spyridon , Ioannou Konstantinos , Tsantopoulos Georgios
Published: September 2023

There is a keen interest in renewable energy sources (RES) as a key aspect of reducing the emissions of greenhouse gases (GHG). Supporting policies have facilitated citizen investments in renewable energy, as such investments can make a substantial contribution to emissions reduction. The problem, however, is that the factors affecting citizen willingness- to invest in renewable energy are still uncertain and tend to constantly change, highlighting the need to perform studies on the subject more frequently. As citizen investments in RES can contribute to emissions reduction, the aim of this study is to understand the factors that affect the willingness of citizens to invest in renewable energy. Using simple random sampling, a representative sample of 1536 citizens in an EU country was administered structured questionnaires, and the results were analyzed using logistic regression. It was shown that willingness to invest is affected by both financial and non-financial factors, such as citizens’ agreement with the construction of renewable facilities near their residence, information sources for obtaining information about environmental and energy topics, satisfaction with the media’s coverage of renewable investments, and their occupation. Results from this study raise substantial policy implications and may be used to improve the design of strategies for attracting citizen investments.
Investigating Vertical Distributions and Driving Factors of Black Carbon in the Atmospheric Boundary Layer Using Unmanned Aerial Vehicle Measurements in Shanghai, China
Authors: Wang Hanyu , Huang Changhai
Published: September 2023

Black carbon (BC) is a significant component of fine particulate matter (PM2.5, with aerodynamic diameters ≤ 2.5 μm), and its spatial distribution greatly affects the global radiation budget. However, the vertical distributions and key driving factors of BC in the atmospheric boundary layer, where BC is mostly concentrated, remain unclear. In this study, gradient measurements of BC were made using an unmanned aerial vehicle (UAV) platform from ground level to 500 m above ground level (AGL) during and after the 2016 G20 control period in Shanghai. Generally, vertical profiles of BC from local time (LT) 9 to 17 on all experimental days demonstrated an upward trend with increasing height. The BC emitted from chimneys was initially released at higher altitudes, resulting in the positive gradients of vertical BC profiles. Furthermore, with the progressive development of the boundary layer height from LT 9 to 15, the average concentration of BC per vertical profile decreased. However, meteorological conditions unfavorable for dispersions caused by particularly high temperatures, low wind speed, unfavorable boundary layer conditions, or especially high relative humidity, and hygroscopic growth owing to the extremely high relative humidity, led to an overall increase in vertical BC and ground-based PM2.5 and BC. Despite the impact of adverse meteorological conditions, emission control measures during the control period not only effectively decreased the BC concentration but also reduced the proportion of BC in PM2.5 in the atmospheric boundary layer. The results of this study can provide valuable observations for evaluating numerical model results and important implications for making control strategies of BC in the future.
A Review of Infrasound and Seismic Observations of Sample Return Capsules since the End of the Apollo Era in Anticipation of the OSIRIS-REx Arrival
Authors: Silber Elizabeth A. , Bowman Daniel C. , Albert Sarah
Published: September 2023

Advancements in space exploration and sample return technology present a unique opportunity to leverage sample return capsules (SRCs) towards studying atmospheric entry of meteoroids and asteroids. Specifically engineered for the secure transport of valuable extraterrestrial samples from interplanetary space to Earth, SRCs offer unexpected benefits that reach beyond their intended purpose. As SRCs enter the Earth’s atmosphere at hypervelocity, they are analogous to naturally occurring meteoroids and thus, for all intents and purposes, can be considered artificial meteors. Furthermore, SRCs are capable of generating shockwaves upon reaching the lower transitional flow regime, and thus can be detected by strategically positioned geophysical instrumentation. NASA’s OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC is one of only a handful of artificial objects to re-enter the Earth’s atmosphere from interplanetary space since the end of the Apollo era and it will provide an unprecedented observational opportunity. This review summarizes past infrasound and seismic observational studies of SRC re-entries since the end of the Apollo era and presents their utility towards the better characterization of meteoroid flight through the atmosphere.
Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China
Authors: Guo Yakai , Shao Changliang , Su Aifang
Published: September 2023

In this study, a rainstorm of the type experienced on 20 July 2021 over central East China was simulated using the first-generation Chinese Reanalysis datasets and Global Land Data Assimilation System datasets, and the Noah land surface model coupled with the advanced weather research and forecasting model. Based on this, the gridded planetary boundary layer (PBL) profiles and ensemble states within soil perturbations were collected to investigate the typical land–atmosphere coupling chain during this modeled rainstorm by using various local coupling metrics and introduced ensemble statistical metrics. The results show that (1) except for the stratospheric thermodynamics and the surface temperature over mountain areas, the main characteristics of the mid-low atmospheric layers and the surface have been well captured in this modeled rainstorm; (2) the typical coupling intensity is characterized by the dominant morning moistening, an early afternoon weak PBL warming factor of around 2, a noontime buoyant mixing temperature deficit around 274 K, daytime PBL and surface latent flux contributions of around 100 and 280 W/m2, respectively, and significant afternoon soil-surface latent flux coupling; and (3) an overall negative soil–rainfall relationship can be identified from the ensemble metrics in which the moist static energy is more significant than PBL height, and this is consistent with the significance of daytime surface moistening indicated by local coupling metrics. Taking the multi-process chain in chronological order, the wet soil contributes greatly to daytime moisture evaporation, which then increases the early noon PBL warming and enhances the noon period buoyant mixing within weak moist heating; however, this is suppressed by large-scale forcing such as the upper southwestern inflows of rainstorms, which further significantly shapes the spatial distribution of the statistical metrics. These quantitatively described local daytime couplings highlight the potential local application of promoting public weather forecasting efforts, while the high spatial differences in the coupling indicate the more applicable threshold diagnoses within finer-scale spatial investigations.
A Novel Combined Model for Air Quality Index Forecasting in Changchun
Authors: Chen Feng , Wang Lei , Deng Hongyu
Published: September 2023

With the rapid development of the economy and continuous improvement in people’s living standards, the predictions of the air quality index have attracted wide attention. In this paper, a new feature selection method (Pearson-MI) and a combined model construction method (modified inverse variance method) were proposed to study the air quality index (AQI) and its influencing factors in Changchun. The Pearson-MI method selects the factors that affect the AQI of Changchun City from many influencing factors. This method reduces the RMSE of the LSTM model and XGBoost model by 27% and 5% and the MAE by 41% and 5%, respectively. A model that combines XGBoost, SVR, RF, and LSTM was constructed using the inverse variance method to predict the air quality index of Changchun City. The modified combined model resulted in a 2% reduction in RMSE and a 0.6% reduction in MAE compared with the unmodified combined model. The numerical results of our study show that the prediction accuracy of the modified combined model is obviously higher than that of the basic model, and the prediction accuracy is further improved under the Pearson-MI feature selection.
Fire Weather Conditions in Plantation Areas in Northern Sumatra, Indonesia
Authors: Hayasaka Hiroshi
Published: September 2023

Peatland fires in Indonesia tend to be more active during El Niño-related droughts, with the exception of fires in North Sumatra. As North Sumatra is located north of the equator and is affected by the winter and summer monsoons, fires tend to be more active not only during the dry main season from January to March, but also in June and August due to short-term droughts. Due to these complex fire trends, no appropriate fire-related indices have been found in North Sumatra. In this paper, 20 years of fire (hotspot (HS) data from 2003 to 2022, weather data (hourly and daily), and various satellite data were used to analyze fire weather conditions in Dumai plantation areas. Analysis results of 20 fire incidents (largest fires (HSs) of each year) showed the following fire weather conditions: high wind speeds (>19 km h−1), high temperatures (>33 °C), and low relative humidity (<50%). Based on the results of fire and weather analyses, several fire-related indices selected from various satellite-measured data were examined. Precipitable water vapor has the highest negative correlation with fires. It is hoped that this new fire index will be used for fire prevention not only Sumatra but also in other areas in Indonesia.
Modeling and Experimental Investigation of the Interaction between Pressure-Dependent Aging and Pressure Development Due to the Aging of Lithium-Ion Cells
Authors: Avdyli Arber , Fill Alexander , Birke Kai Peter
Published: September 2023

In order to meet the increasing demands of the battery in terms of range, safety and performance, it is necessary to ensure optimal operation conditions of a lithium-ion cell. In this thesis, the influence of mechanical boundary conditions on the cell is investigated theoretically and experimentally. First, fundamental equations are derived that lead to coupled models that can be parameterized based on specific cell measurements and predict the pressure evolution due to capacity aging and vice versa. The model is used to derive optimal operating points of the cell, which can be considered in the module design.
Cloud-Based Optimization of a Battery Model Parameter Identification Algorithm for Battery State-of-Health Estimation in Electric Vehicles
Authors: Di Rienzo Roberto , Nicodemo Niccolò , Roncella Roberto , Saletti Roberto , Vennettilli Nando , Asaro Salvatore , Tola Roberto , Baronti Federico
Published: September 2023

Connectivity and cloud computing are key elements in the future of electric mobility. They allow manufacturers to provide advanced fleet management and predictive diagnostic services. In particular, cloud computing dramatically enhances data availability and enables the use of more complex and accurate state estimation algorithms for electric vehicle lithium-ion batteries. A tuning procedure for a moving window least squares algorithm to estimate the parameters of a 2-RC equivalent circuit battery model is presented in this paper. The tuning procedure uses real data collected from a test vehicle and uploaded to the Stellantis-CRF cloud. The tuned algorithm was applied to eight months of road tests and showed very small estimation errors. The errors are comparable to other literature data, even when the literature results were obtained in laboratory tests. The estimated model parameters are tracked through time and seem accurate enough to show the first signs of battery aging.
Native Agarose Gels and Contact Blotting as Means to Optimize the Protocols for the Formation of Antigen–Ligand Complexes
Authors: D’Ercole Claudia , de Marco Ario
Published: September 2023

Background: Protein complexes provide valuable biological information, but can be difficult to handle. Therefore, technical advancements designed to improve their manipulation are always useful. Methods: We investigated the opportunity to exploit native agarose gels and the contact blot method for the transfer of native proteins to membranes as means for optimizing the conditions for obtaining stable complexes. As a simple model of protein–protein interactions, an antigen–ligand complex was used in which both proteins were fused to reporters. Results: At each step, it was possible to visualize both the antigen, fused to a fluorescent protein, and the ligand, fused to a monomeric ascorbate peroxidase (APEX) and, as such, a way to tune the protocol. The conditions for the complex formation were adapted by modifying the buffer conditions, the concentration of the proteins and of the cross-linkers. Conclusions: The procedure is rapid, inexpensive, and the several detection opportunities allow for both the monitoring of complex stability and the preservation of the functionality of its components, which is critical for understanding their biomedical implications and supporting drug discovery. The overall protocol represents a handy alternative to gel filtration, uses very standard and ubiquitous equipment, and can be implemented rapidly and without specific training.
Functional Near-Infrared Spectroscopy-Based Evidence of the Cerebral Oxygenation and Network Characteristics of Upper Limb Fatigue
Authors: Li Feng , Bi Jiawei , Liang Zhiqiang , Li Lu , Liu Yu , Huang Lingyan
Published: September 2023

Objective: The objective of this research is to better understand the effects of upper limb fatigue on the cerebral cortex. The aim of this study was to investigate the characteristics of cerebral oxygenation and cortical functional connectivity in healthy adults after upper limb fatigue using functional near-infrared spectroscopy (fNIRS). Methods: Nineteen healthy adults participated in this study. The participants began exercising on an arm crank ergometer with no load, which was then increased by 0.2 kg per minute, maintaining a speed of at least 90 revolutions per minute during the exercise. Functional near-infrared spectroscopy covering the prefrontal cortex and motor area was used to monitor brain activity during rest and exercise. Heart rate and RPE were monitored during exercise to evaluate the degree of fatigue. Paired-sample t-tests were used to examine differences in the concentration of oxygenated hemoglobin (HbO2) and functional connectivity before and after fatigue. Results: All participants completed the exercise test that induced fatigue. We observed a significant decrease in HbO2 levels in the prefrontal and motor areas after exercise. In addition, brain network features showed a significant decrease in functional connectivity between the left and right motor cortices, between the motor and prefrontal cortices, and between both prefrontal cortices after fatigue. Conclusion: This study demonstrates that, in healthy adults, exercise-induced fatigue in the upper limbs significantly affects brain function. In particular, it leads to reduced functional connectivity between the motor cortex and the prefrontal cortex.
Application of Escherichia coli-Derived Recombinant Human Bone Morphogenic Protein-2 to Unstable Spinal Fractures
Authors: Kim Young-Hoon , Lee Jun-Seok , Ha Kee-Yong , Kim Sang-Il , Jung Ho-Young , Kim Geon-U , Joh Yongwon , Park Hyung-Youl
Published: September 2023

(1) Background: Recently, Escherichia coli-derived recombinant human bone morphogenetic protein-2 (E. coli-derived rhBMP-2) has been increasingly applied to different types of spinal surgeries and reported to achieve successful fusion. This pilot study aimed to evaluate the clinical efficacy and safety of rhBMP-2 in patients undergoing posterior instrumented fusions for unstable spinal fractures. (2) Methods: This study included ten consecutive patients undergoing spinal surgery using E. coli-derived rhBMP-2 with more than one year of follow-up. Radiologic outcomes were compared, including the average fracture healing period, local kyphosis correction, and clinical outcomes between preoperative and the last follow-up. (3) Results: The average time of radiographic union was 99.9 ± 45.4 (62–192) days, with an average use of 5.2 ± 3.9 months of anabolic agents. Radiologic parameters such as anterior vertebral height and vertebral wedge angle were significantly corrected postoperatively and at the last follow-up. Clinical outcomes other than leg pain were significantly improved after the surgery. In addition, four patients with preoperative neurologic deficits showed improved neurologic status. (4) Conclusions: Combined with the anabolic agents, applying E. coli-derived rhBMP-2 to the fractured vertebral body could be an effective surgical treatment for unstable spinal fractures. Further trials are needed to validate this result.
Using Geometric Morphometric Analysis of Magnetic Resonance Imaging to Assess the Anatomy of the Eustachian Tube in Children with and without Otitis Media
Authors: Fricano Ellen E. , Gremba Allison P. , Teixeira Miriam S. , Swarts J. Douglas , Alper Cuneyt M.
Published: September 2023

Otitis media (OM) is among the most common of childhood illnesses. It has long been hypothesized that children under age two are predisposed to OM due to differences in the anatomy of the Eustachian tube (ET), including the angle of the ET. OM in later childhood is less common but does occur, begging the question, are there shape differences in the ET that persist underlying later occurrences of OM? To answer this question, a novel method, which applied geometric and morphometric shape analysis to landmarks obtained from MRI data, was used. MRI scans were performed on 16 children (5 control, 3 cOME, and 8 rAOM) between 2011 and 2015. Sixteen landmarks representing the shape of the ET, cranial base, and palate were analyzed. The results of a Procrustes ANOVA indicate that the shape of the ET varies significantly (p < 0.01) between the OM and control groups. The shape differences between the OM group and the control are a medial and low attachment site of the tensor veli palatini (TVP) muscle, a posterior and high torus tubarius, and an anteriorly projected palate. These results support previous findings that a relatively horizontal ET is associated with a predisposition for OM. This study used a novel approach to examine anatomical differences in children with and without OM. First, the data set is unique in that it includes MRI scans of children with a confirmed OM diagnosis. Second, the use of MRI scans in craniofacial anatomy OM research is novel and allows for the collection of soft tissue landmarks and the visualization of soft tissue structures. Third, geometric morphometric shape analysis is a statistical method that captures shape differences, offering a more universal picture of nuanced changes within the entire set of landmarks, in contrast to more traditional linear and angular measurements used in prior OM studies examining craniofacial anatomy.