bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning-Based Digital Surface Model Reconstruction of ZY-3 Satellite Imagery
Authors: Zhao Yanbin , Liu Yang , Gao Shuang , Liu Guohua , Wan Zhiqiang , Hu Denghui
Published: July 2024

This study introduces a novel satellite image digital surface model (DSM) reconstruction framework grounded in deep learning methodology. The proposed framework effectively utilizes a rational polynomial camera (RPC) model to establish the mapping relationship between image coordinates and geographic coordinates. Given the expansive coverage and abundant ground object data inherent in satellite images, we designed a lightweight deep network model. This model facilitates both coarse and fine estimation of a height map through two distinct stages. Our approach harnesses shallow and deep image information via a feature extraction module, subsequently employing RPC Warping to construct feature volumes for various angles. We employ variance as a similarity metric to achieve image matching and derive the fused cost volume. Following this, we aggregate cost information across different scales and height directions using a regularization module. This process yields the confidence level of the current height plane, which is then regressed to predict the height map. Once the height map from stage 1 is obtained, we gauge the prediction’s uncertainty based on the variance in the probability distribution in the height direction. This allows us to adjust the height estimation range according to this uncertainty, thereby enabling precise height value prediction in stage 2. After conducting geometric consistency detection filtering of fine height maps from diverse viewpoints, we generate 3D point clouds through the inverse projection of RPC models. Finally, we resample these 3D point clouds to produce high-precision DSM products. By analyzing the results of our method’s height map predictions and comparing them with existing deep learning-based reconstruction methods, we assess the DSM reconstruction performance of our proposed framework. The experimental findings underscore the robustness of our method against discontinuous regions, occlusions, uneven illumination areas in satellite imagery, and weak texture regions during height map generation. Furthermore, the reconstructed digital surface model (DSM) surpasses existing solutions in terms of completeness and root mean square error metrics while concurrently reducing the model parameters by 42.93%. This optimization markedly diminishes memory usage, thereby conserving both software and hardware resources as well as system overhead. Such savings pave the way for a more efficient system design and development process.
Raman-Activated, Interactive Sorting of Isotope-Labeled Bacteria
Authors: Razi Sepehr , Tarcea Nicolae , Henkel Thomas , Ravikumar Ramya , Pistiki Aikaterini , Wagenhaus Annette , Girnus Sophie , Taubert Martin , Küsel Kirsten , Rösch Petra , Popp Jürgen
Published: July 2024

Due to its high spatial resolution, Raman microspectroscopy allows for the analysis of single microbial cells. Since Raman spectroscopy analyzes the whole cell content, this method is phenotypic and can therefore be used to evaluate cellular changes. In particular, labeling with stable isotopes (SIPs) enables the versatile use and observation of different metabolic states in microbes. Nevertheless, static measurements can only analyze the present situation and do not allow for further downstream evaluations. Therefore, a combination of Raman analysis and cell sorting is necessary to provide the possibility for further research on selected bacteria in a sample. Here, a new microfluidic approach for Raman-activated continuous-flow sorting of bacteria using an optical setup for image-based particle sorting with synchronous acquisition and analysis of Raman spectra for making the sorting decision is demonstrated, showing that active cells can be successfully sorted by means of this microfluidic chip.
Explainable Deep-Learning-Based Gait Analysis of Hip–Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression
Authors: Kim Yong-Gyun , Kim Sungjoon , Park Jae Hyeon , Yang Seung , Jang Minkyu , Yun Yeo Joon , Cho Jae-sung , You Sungmin , Jang Seong-Ho
Published: July 2024

Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip–knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.
Development and Assessment of Multiple Illumination Color Fourier Ptychographic Microscopy for High Throughput Sample Digitization
Authors: Gilley Patrik , Zhang Ke , Abdoli Neman , Sadri Youkabed , Adhikari Laura , Fung Kar-Ming , Qiu Yuchen
Published: July 2024

In this study, we proposed a multiplexed color illumination strategy to improve the data acquisition efficiency of Fourier ptychography microscopy (FPM). Instead of sequentially lighting up one single channel LED, our method turns on multiple white light LEDs for each image acquisition via a color camera. Thus, each raw image contains multiplexed spectral information. An FPM prototype was developed, which was equipped with a 4×/0.13 NA objective lens to achieve a spatial resolution equivalent to that of a 20×/0.4 NA objective lens. Both two- and four-LED illumination patterns were designed and applied during the experiments. A USAF 1951 resolution target was first imaged under these illumination conditions, based on which MTF curves were generated to assess the corresponding imaging performance. Next, H&E tissue samples and analyzable metaphase chromosome cells were used to evaluate the clinical utility of our strategy. The results show that the single and multiplexed (two- or four-LED) illumination results achieved comparable imaging performance on all the three channels of the MTF curves. Meanwhile, the reconstructed tissue or cell images successfully retain the definition of cell nuclei and cytoplasm and can better preserve the cell edges as compared to the results from the conventional microscopes. This study initially validates the feasibility of multiplexed color illumination for the future development of high-throughput FPM scanning systems.
A Novel Adversarial Deep Learning Method for Substation Defect Image Generation
Authors: Zhang Na , Yang Gang , Hu Fan , Yu Hua , Fan Jingjing , Xu Siqing
Published: July 2024

The presence of defects in substation equipment is a major factor affecting the safety of power transmission. Therefore, timely and accurate detection of these defects is crucial. As intelligent inspection robots advance, using mainstream object detection models to diagnose surface defects in substation equipment has become a focal point of current research. However, the lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. To address the issue of insufficient training data for defect images with complex backgrounds, such as rust and surface oil leakage in substation equipment, which leads to the poor performance of detection models, this paper proposes a novel adversarial deep learning model for substation defect image generation: the Abnormal Defect Detection Generative Adversarial Network (ADD-GAN). Unlike existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model uses a joint discriminator for both overall images and defect images to address the issue of low attention to local defect areas, thereby reducing the loss of image features. This approach enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves a mean average precision (mAP) of 81.5% on the test dataset, and outperforms other image data augmentation and generation methods. This confirms that the ADD-GAN method can generate a high-fidelity image dataset of substation equipment defects.
Generalization Enhancement of Visual Reinforcement Learning through Internal States
Authors: Yang Hanlin , Zhu William , Zhu Xianchao
Published: July 2024

Visual reinforcement learning is important in various practical applications, such as video games, robotic manipulation, and autonomous navigation. However, a major challenge in visual reinforcement learning is the generalization to unseen environments, that is, how agents manage environments with previously unseen backgrounds. This issue is triggered mainly by the high unpredictability inherent in high-dimensional observation space. To deal with this problem, techniques including domain randomization and data augmentation have been explored; nevertheless, these methods still cannot attain a satisfactory result. This paper proposes a new method named Internal States Simulation Auxiliary (ISSA), which uses internal states to improve generalization in visual reinforcement learning tasks. Our method contains two agents, a teacher agent and a student agent: the teacher agent has the ability to directly access the environment’s internal states and is used to facilitate the student agent’s training; the student agent receives initial guidance from the teacher agent and subsequently continues to learn independently. From another perspective, our method can be divided into two phases, the transfer learning phase and traditional visual reinforcement learning phase. In the first phase, the teacher agent interacts with environments and imparts knowledge to the vision-based student agent. With the guidance of the teacher agent, the student agent is able to discover more effective visual representations that address the high unpredictability of high-dimensional observation space. In the next phase, the student agent autonomously learns from the visual information in the environment, and ultimately, it becomes a vision-based reinforcement learning agent with enhanced generalization. The effectiveness of our method is evaluated using the DMControl Generalization Benchmark and the DrawerWorld with texture distortions. Preliminary results indicate that our method significantly improves generalization ability and performance in complex continuous control tasks.
Using Machine Learning Multiclass Classification Technique to Detect IoT Attacks in Real Time
Authors: Alrefaei Ahmed , Ilyas Mohammad
Published: July 2024

This paper presents a real-time intrusion detection system (IDS) aimed at detecting the Internet of Things (IoT) attacks using multiclass classification models within the PySpark architecture. The research objective is to enhance detection accuracy while reducing the prediction time. Various machine learning algorithms are employed using the OneVsRest (OVR) technique. The proposed method utilizes the IoT-23 dataset, which consists of network traffic from smart home IoT devices, for model development. Data preprocessing techniques, such as data cleaning, transformation, scaling, and the synthetic minority oversampling technique (SMOTE), are applied to prepare the dataset. Additionally, feature selection methods are employed to identify the most relevant features for classification. The performance of the classifiers is evaluated using metrics such as accuracy, precision, recall, and F1 score. The results indicate that among the evaluated algorithms, extreme gradient boosting achieves a high accuracy of 98.89%, while random forest demonstrates the most efficient training and prediction times, with a prediction time of only 0.0311 s. The proposed method demonstrates high accuracy in real-time intrusion detection of IoT attacks, outperforming existing approaches.
Risk Factors Associated with Musculoskeletal Injuries within the Crew of the Leopard 2 A6 Main Battle Tank Using Inertial Movement Unit Sensors: A Pilot Study
Authors: Pedro Bruno , Assunção Ana , Carnide Filomena , Damião Beatriz , Lucena Rui , Almeida Nuno , Simões Paula , Veloso António P.
Published: July 2024

This study aims to assess the musculoskeletal risk of military personnel on a Leopard 2 A6 main battle tank crew and to identify associated factors for future prevention and mitigation strategies. A sample of 57 Portuguese military personnel, who are or were part of the Leopard 2 A6 main battle tank crew, answered a questionnaire on their perception of task performance, considering muscle demands, comfort, posture, movements, and associated symptoms. A subsample of four soldiers from the Armoured Squadron of the Portuguese Mechanized Brigade were assessed using an inertial measurement unit system and underwent a whole-body kinematic analysis coupled with a Rapid Entire Body Assessment during a simulated two-hour mission. The results indicate that soldiers accurately perceive their roles within the crew and that, overall, there is a high risk of musculoskeletal injuries in all tasks. However, tasks directly related to the crew’s primary duties carry consistently high risk when considering the time spent on their tasks. This study highlights the need for targeted preventive measures to reduce the incidence and severity of injuries among the crew of the Leopard 2 A6 main battle tank.
The Impact of Profitability Sustainability on Innovation in Dairy Companies: The Multiple Moderating Effects of Corporate Social Responsibility
Authors: Wan Xiangrong , Pan Fanghui , Liu Chenyang , Zhao Jing , Li Cuixia
Published: July 2024

Although previous studies have extensively explored the relationship between corporate profitability and innovation, the specific impact of profitability sustainability on corporate innovation has not received sufficient attention. Furthermore, while Corporate Social Responsibility (CSR) has been recognized as significantly influencing corporate innovation, its moderating role between profitability sustainability and innovation remains underexplored. This study fills these research gaps by empirically analyzing the impact of profitability sustainability on corporate innovation and examining in detail the multiple moderating effects of CSR. This paper employs Ordinary Least Squares (OLS) and Instrumental Variables Two-Stage Least Squares (IV-2SLS) methods, using data from dairy companies listed on China’s A-share and H-share markets from 2016 to 2021, to empirically analyze the impact of profitability sustainability on corporate innovation and to examine in detail the multiple moderating effects of CSR. The results indicate that profitability sustainability significantly promotes corporate innovation. CSR directly moderates this relationship, and along with other moderating variables (financing constraints, executive compensation), it plays a complex role in this interaction, potentially inhibiting the positive connection between profitability sustainability and innovation when acting alone, but significantly enhancing innovation when interacting with CSR. Heterogeneity analysis shows that non-state-owned and H-share listed dairy companies exhibit a more significant positive effect of profitability sustainability on innovation compared to state-owned and A-share listed companies. These findings highlight the key moderating role of CSR in promoting innovation within the dairy industry and offer new perspectives on how profitability sustainability can drive corporate innovation.
Switching to Reuse: The Impact of Information on Consumers’ Choices for Reusable Food Packaging
Authors: Mastria Serena , Vezzil Alessandro , De Cesarei Andrea
Published: July 2024

In recent years, there has been a shift toward sustainability in several aspects of our lives, including food packaging. In line with a circular economy model, several organizations are adopting industrial reusable solutions for food packaging, which are designed to be used multiple times throughout their life cycle. Despite an overall positive impression, many consumers lack clarity on reusable food packaging, with concerns regarding safety and cost, for example, that affect the actual adoption of reusable packaging. This study aimed to assess the impact of information regarding specific characteristics of reuse that could encourage consumers to choose reusable packaging over other sustainable (compostable and recyclable) alternatives when purchasing agrifoods. Through an online survey involving 104 participants, the study compared preferences and willingness to pay for reusable packaging while delivering (or not delivering) information as to the safety and production costs of reusable packaging. Information concerning the safety of reusable packaging, but not that regarding production costs, increased consumers’ preferences for reusable packaging. At the same time, willingness to pay was not affected by either type of information. These results highlight the crucial role that delivering appropriate information plays in addressing consumers’ psychological concerns and in fostering the acceptance of reusable packaging when purchasing agrifoods.
Integrated Assessment of Methane Production from the Co-Digestion of Swine Wastewater and Other Organic Wastes
Authors: Sousa Izabelle de Paula , Rosa André Pereira , Almeida Guilherme Kurtemback , Rocha Dilson Novais , Neves Thiago de Alencar , Borges Alisson Carraro
Published: July 2024

The study aimed to evaluate and compare the co-digestion of swine wastewater (SW) and other co-substrates: grass residue (GR), food waste (FW), and poultry litter (PL). The comparisons were performed using the biochemical methane potential (BMP) test. The maximum accumulated methane (CH4) production was submitted to a joint analysis of variance. Tukey’s test (α = 0.05) was used to compare the results of the treatments, and Dunnett’s test (α = 0.05) was used to compare the ratios (100:0, 75:25, 50:50, 25:75, and 0:100) (based on volatile solids—VS). In addition, both the synergistic effect and kinetic adjustment of some models were evaluated. The results indicated that the co-digestion of all substrates (GR, FW, and PL) with SW improved the methane production yield in comparison with mono-digestion (GR, FW, and PL). A positive synergistic effect was observed for the FW:SW (25:75 and 75:25). According to both Tukey’s and Dunnett’s tests (α = 0.05), the FW:SW ratio of 25:75 did not show statistical difference compared with the mono-digestion (SW), which exhibited the largest CH4 production. Among the models evaluated, the modified Gompertz function presented the best fit. For the co-digestion treatments, the ratio of FW:SW of 25:75 exhibited the most promising potential for integrated management, demonstrating the best synergistic effect among the substrates. In this context, methane production from co-digestion equalled that of mono-digestion, while enabling integrated residue management.
The Power of Digitalization, the Hainan Free Trade Port, and Regulations for Modern Economic Development in Turkmenistan Are Significant
Authors: Wang Shumin , Maysa Odenyazova , Khaskheli Muhammad Bilawal , Yang Wensheng
Published: July 2024

This study examines the effects of digitization on economic growth in Turkmenistan, emphasizing the legal framework and real-world applications. The definition of “digitalization”, an analysis of its impact on the nation’s economy, and a focus on its unique features are presented first. It then explores several critical issues regarding Turkmenistan’s digital economy’s progress, such as assessing its current economic situation, significant trends, innovation, and challenges. It examines the financial results of implementing the Concept for the Development of the Digital Economy for 2019–2025 and the impacts of these policies. The report’s conclusion offers some recommendations and a breakdown of the challenges Turkmenistan faces as its digital economy expands, and how technology developments, digitization, and regulatory agendas affect Turkmenistan’s and the Hainan Free Trade Port’s economic growth in Turkmenistan. It examines the possible benefits of using digital technologies to promote trade facilitation and regional economic expansion. The potential and state of the country’s digital economy are better understood. The investigation also emphasizes how vital digital technologies are to advancing society and creating jobs, and this research emphasizes the need for digitization as a successful tool for economic growth and highlights the need for a market economy based on a solid legal framework. One of the central tenets of Turkmenistan’s economic strategy is the development of emerging digital technology. Considering China’s experience, the paper examines Turkmenistan’s digital economy’s legal approach to technology use. Turkmenistan can benefit from this experience just by starting its digital transformation journey.
Urban Disparity Analytics Using GIS: A Systematic Review
Authors: Malaker Tanmoy , Meng Qingmin
Published: July 2024

Urban disparity has been extensively studied using geospatial technology, yet a comprehensive review of GIS applications in this field is essential to address the current research status, potential challenges, and future trends. This review combines bibliometric analysis from two databases, Web of Science (WOS) and Scopus, encompassing 145 articles from WOS and 80 from Scopus, resulting in a final list of 201 articles after excluding 24 duplicates. This approach ensures a comprehensive understanding of urban disparities and the extensive applications of GIS technology. The review highlights and characterizes research status and frontiers into research clusters, future scopes, and gaps in urban disparity analysis. The use of both WOS and Scopus ensures the review’s credibility and comprehensiveness. Findings indicate that most research has focused on accessibility analysis of urban services and facilities. However, there is a recent paradigm shift toward environmental justice, demonstrated by increasing GIS applications in analyzing pollution exposure, urban heat islands, vegetation distribution, disaster vulnerability, and health vulnerability.
Rainfall and Extreme Drought Detection: An Analysis for a Potential Agricultural Region in the Southern Brazilian Amazon
Authors: Silva Rogério De Souza , Dallacort Rivanildo , Maciel Junior Ismael Cavalcante , Carvalho Marco Antonio Camillo De , Yamashita Oscar Mitsuo , Santana Dthenifer Cordeiro , Teodoro Larissa Pereira Ribeiro , Teodoro Paulo Eduardo , Silva Junior Carlos Antonio da
Published: July 2024

In recent decades, the main commercial crops of Mato Grosso, such as soybeans, corn, and cotton, have been undergoing transformations regarding the adoption of new technologies to increase production. However, regardless of the technological level, the climate of the region, including the rainfall regime, can influence the success of crops and facilitate, or not, the maximum production efficiency. This study aimed to define the behavior of the variability in monthly and annual rainfall and its probability of monthly occurrence and calculate the drought index for the northwestern region of Mato Grosso, in the southern region of the Brazilian Amazon. To carry out the study, daily rainfall records were collected, calculating the totals for each month of the historical series for each of the four National Water and Sanitation Agency (ANA) rain gauge stations, Aripuanã (1985–2020), Colniza (2001–2020), Cotriguaçu (2004–2020), and Juína (1985–2020), representing the northwestern region. The annual distribution of rainfall during the periods studied ranged from 1376.2 to 3017.3 mm. The monthly distribution indicated a typical water shortage in the months of June, July, and August. The probability of rainfall near the average for each month was more than 50%. The monthly SPI-1 index revealed a total of 56 months affected by very dry events and 34 extreme dry events. The annual SPI-12 index pointed to seven very dry years and five extremely dry years. Therefore, the region presented high rainfall rates in most years; however, a significant process of drought was also observed, including in rainy months, which are the periods with the greatest demand for the main agricultural crops.
Short-Term Wind Power Prediction Based on a Modified Stacking Ensemble Learning Algorithm
Authors: Yang Yankun , Li Yuling , Cheng Lin , Yang Shiyou
Published: July 2024

A high proportion of new energy has become a prominent feature of modern power systems. Due to the intermittency, volatility, and strong randomness in wind power generation, an accurate and reliable method for the prediction of wind power is required. This paper proposes a modified stacking ensemble learning method for short-term wind power predictions to reduce error and improve the generalization performance of traditional single networks in tackling the randomness of wind power. Firstly, the base learners including tree-based models and neural networks are improved based on the Bagging and Boosting algorithms, and a method for determining internal parameters and iterations is provided. Secondly, the linear integration and stacking integration models are combined to obtain deterministic prediction results. Since the modified stacking meta learner can change the weight, it will enhance the strengths of the base learners and optimize the integration of the model prediction to fit the second layer prediction, compared to traditional linear integration models. Finally, a numerical experiment showed that the modified stacking ensemble model had a decrease in MAPE from about 8.3% to 7.5% (an absolute decrease of 0.8%) compared to a single learner for the 15 min look-ahead tests. Changing variables such as the season and predicting the look-ahead time showed satisfactory improvement effects under all the evaluation criteria, and the superiority of the modified stacking ensemble learning method proposed in this paper regarding short-term wind power prediction performance was validated.
Seismic Risk Assessment and Analysis of Influencing Factors in the Sichuan–Yunnan Region
Authors: Wan Jia , Dou Ruiyin , Ma Tao
Published: July 2024

Investigating the distribution characteristics of earthquake disaster risks in the Sichuan–Yunnan region is of great importance for enhancing government emergency response capabilities and achieving sustainable regional development. This study, based on disaster systems theory, constructs a seismic risk evaluation index system for the Sichuan–Yunnan region and employs the entropy method to determine the comprehensive risk index for earthquake disasters across 37 prefecture-level cities. The findings reveal the following: (1) High-risk areas for disaster-causing factors are located in the Hengduan Mountain region and the North–South Mountain Range Valley Region; medium-risk areas are distributed along the northwestern edge of the Sichuan Basin; low-risk areas are situated in the eastern part of the Sichuan Basin and the Yunnan Plateau. (2) High-risk disaster-prone environments are found in the Hengduan Mountain region; medium-risk areas are present on the Yunnan Plateau and the western part of the North–South Mountain Range Valley Region; low-risk areas are in the Sichuan Basin. (3) High-vulnerability areas include the central Sichuan Basin and Kunming on the Yunnan Plateau; medium-vulnerability areas are located in the eastern and western parts of the Sichuan Basin; low-vulnerability areas are in the less developed parts of the Yunnan Plateau, the North–South Mountain Range Valley Region, and the Hengduan Mountain region. (4) High-risk seismic disaster areas are concentrated in the developed regions of the Sichuan Basin and the Yunnan Plateau; medium-risk areas are concentrated in the western part of the North–South Mountain Range Valley Region; low-risk areas are sporadically distributed in the eastern parts of the Sichuan–Yunnan region. (5) The vulnerability of the population, economy, and lifeline systems significantly explain the variation in seismic risk levels, all exceeding 0.70; the synergistic effects of disaster-causing factor danger, disaster-prone environment stability, and disaster-prone environment sensitivity are the most pronounced, with explanatory power exceeding 0.85 after factor interaction.
Sustainable Processes of Biosorption of Pb(II) Ions from Synthetic Wastewater Using Waste Biomass from Mullein Leaves
Authors: Tasić Žaklina , Nujkić Maja , Savić Gajić Ivana , Medić Dragana , Milić Snežana
Published: July 2024

The aim of this study is to evaluate mullein (Verbascum thapsus) as a cost-effective and sustainable adsorbent for the biosorption of Pb(II) ions from synthetic wastewater samples. Biosorption of mullein was investigated as a function of initial Pb(II) concentration (25–400 mg L−1), biosorbent dosage (2–20 g L−1), solution pH (3–7), and contact time (10–120 min). Mullein as a material with a high affinity for Pb(II) ions had a biosorption efficiency of 98.56% under the optimal conditions: pH 6, initial concentration of Pb(II) at 100 mg L−1, contact time of 90 min, and biosorbent dosage of 20 g L−1. The FTIR spectra of mullein leaves showed that oxygen-containing functional groups on the surface are potentially active sites for the biosorption of Pb(II) ions. EDS analysis and the pHPZC value confirmed the adsorption of Pb(II) ions at the active sites of the mullein. Kinetic and isotherm data enabled insights into the modes of Pb(II) biosorption on the mullein surface which were best explained with the pseudo-second-order kinetic model and the Freundlich adsorption isotherm. Biosorption occurs on the mullein surface via multilayer adsorption. The reusability of mullein showed that the native biosorbents can be reused five times, showing the economic and sustainable benefit of this low-cost biosorbent material.
Research on the Impact Mechanism of Smart City Construction on Economic Growth—An Analysis Based on the Schumpeterian Innovation Theory Framework
Authors: Ma Ruiguang , Sun Jingwen , Yin Jiangbin
Published: July 2024

Smart city construction aims to promote the digital transformation of cities, support the modernization of urban governance, and stimulate urban innovation and development. In this paper, we analyze the role of smart city construction on economic growth and the impact mechanism driving this. In terms of theoretical analysis, we discuss the role of smart city construction in economic growth based on Schumpeterian innovation theory. In terms of empirical analysis, we evaluate the impact of smart city construction on economic growth with panel data of Chinese prefecture-level cities from 2011 to 2019, using time-varying difference-in-differences models and the event study method. The findings of the research indicate that the construction of smart cities has a positive effect on regional economic growth and the results can be confirmed through a series of robustness tests. Smart city construction generates mediating effects by enhancing urban innovation capability and entrepreneurship, thereby promoting urban economic growth. Furthermore, the impact of smart city construction on economic growth exhibits heterogeneous effects due to variations in the degree of marketization. In regions with a higher level of marketization, the promotional effect of smart city construction on economic growth is stronger. Consequently, it is imperative to intensify the implementation of smart city construction and persistently pursue market-oriented reforms.
Ultrasound-Guided Botulinum Neurotoxin Injection for Alleviating Cricopharyngeus Muscle Spasticity: A Cadaveric Feasibility Study with Nerve Ending Analysis
Authors: Lee Ji-Hyun , Lee Hyung-Jin , Kim Bo Hae
Published: July 2024

Botulinum neurotoxin (BNT) injection into the cricopharyngeus muscle (CPM) under ultrasound (US) guidance is a minimally invasive technique performed to relieve cricopharyngeal dysphagia by reducing CPM spasticity. This technique is basically accessible only to both lateral sides of the CPM. This cadaveric study aimed to evaluate whether US-guided injection could effectively deliver BNT to abundant areas of gross nerve endings within the CPM. We utilized a newly modified Sihler’s staining method to identify regions with abundant neural endings within the CPM while preserving the three-dimensional morphology of the muscle in 10 sides of 5 fresh cadavers. A mixture of 0.2 mL dye was injected into the 16 sides of CPM under US guidance in 8 cadavers. Nerve endings were abundant in posterolateral areas of the CPM; the injected dye was identified at the posterolateral area on 12 sides (12/16 side, 75%) without diffusion into the posterior cricoarytenoid muscle. The injection failed on four sides (two sides of the prevertebral fascia and two sides of the esophagus below the CPM). These results suggest that US-guided injection could be a feasible technique as it can deliver BNT to the most abundant nerve distribution areas within the CPM in most cases.
From Small to Mega: Evaluating Urban Scale
Authors: Talen Emily
Published: July 2024

Scale is a significant although understudied dimension of cities. To better understand scale and flesh out the range of issues and debates involved in its conception, we conducted thirty-three in-depth interviews with a wide range of urban actors: developers, planners, policymakers, leaders of non-profit community organizations, and scholars. We wanted to know how contemporary urban actors in the U.S. conceive of scale as a factor in development? How is “small-scale” vs. “large-” or “mega-scale” differentiated, and are they inter-related? What are the pros and cons of both small- vs. large-scale projects? Four themes emerged from our interviews: scale definition, barriers to small-scale development, scale evaluation, and solutions for overcoming barriers to small-scale development. The point of this survey was to better understand possible responses and attitudes about scale among those involved in the urban development process.
Clinical Study and Serological Diagnosis of Vector-Borne Pathogens in Sardinian Dogs
Authors: Chisu Valentina , Tanda Antonio , Sechi Sara , Pinna Parpaglia Maria Luisa , Masu Gabriella , Loi Federica , Masala Giovanna
Published: July 2024

Canine vector-borne diseases (CVBDs) comprise a group of infectious diseases caused by a wide range of pathogens transmitted by arthropod vectors. Clinical signs commonly involve symptoms such as fever, anorexia, weight loss, blood disorders, hepatosplenomegaly, and others that can lead to death in dogs with comorbidities. Some pathogens responsible for CVBDs constitute a serious threat to human health due to their zoonotic transmission. This study aimed to determine the prevalence of zoonotic vector-borne diseases (Rickettsia rickettsii, Anaplasma phagocytophilum, Ehrlichia canis, Bartonella henselae, and Leishmania infantum) in domestic Sardinian dogs with and without clinical signs of these pathogens. Blood serum samples were collected from 142 dogs and examined through serological analysis. Clinical signs suggestive of these pathogens were also evaluated. The results obtained showed that 33 (33/140; 23.6%), 22 (22/134; 16.4%), 14 (14/142; 9.9%), 20 (20/66; 30.3%), and 26 (26/108; 24.1%) dogs were seropositive for Rickettsia sp., Anaplasma sp., Ehrlichia sp., Bartonella sp., and Leishmania sp. antibodies, respectively. Among these dogs, 12 dogs presented with at least one clinical sign (8.5%), while 18 (12.7%) showed more than two symptoms at the same time. Furthermore, among the asymptomatic dogs (93/142; 65.5%), 13% (n = 12) tested positive for A. phagocytophilum, 12% (n = 11) tested positive for B. henselae, 9% (n = 8) tested positive for E. canis, 12% (n = 11) tested positive for L. infantum, and 19% (n = 18) tested positive for R. rickettsii. This survey represents the first study assessing different canine vector-borne pathogens in dogs from North Sardinia. Since the pathogens detected here represent emerging zoonotic diseases, these results highlight the need to undertake further studies to increase the knowledge of these under-reported vector-borne pathogens in Sardinia.
Experimental Study on Structure Optimization and Dynamic Characteristics of Articulated Steering for Hydrogen Fuel Cell Engineering Vehicles
Authors: Zhang Qinguo , Wang Xiaoyang , Tong Zheming , Cheng Zhewu , Liu Xiaojian
Published: July 2024

The prominent problem of articulated steering structure of engineering vehicle is that there is pressure oscillation in the hydraulic system during steering, which seriously affects the performance of steering system. To solve this problem, the maximum stroke difference of left and right cylinders and the minimum maximum cylinder pressure are the optimization objectives, and the position of cylinder hinge point is the design variable. The multi-objective optimization design of articulated steering system is carried out by using the particle swarm optimization algorithm. After optimization, the maximum pressure of the steering system is reduced by 13.5%, and the oscillation amplitude is reduced by 16%, so the optimization effect is obvious. The dynamic characteristics of the hydraulic steering system under different loads, such as pressure and flow rate, are obtained through field steering tests of wheel loaders. The results show that the load has an important effect on the pressure response of the system, and the causes and influencing factors of pressure and flow fluctuation are determined. The relationship between mileage and hydrogen consumption is obtained, which provides data support for vehicle control strategy. The high-pressure overflow power consumption accounts for 60% of the total work, and the work lost on the steering gear reaches 36 kJ. The test results verify the rationality and correctness of the optimization method of steering mechanism and provide data support for the improvement in steering hydraulic system.
Development and Measurement of a Very Thick Aerodynamic Profile for Wind Turbine Blades
Authors: Schaffarczyk Alois Peter , Lobo Brandon Arthur , Balaresque Nicholas , Kremer Volker , Suhr Janick , Wang Zhongxia
Published: July 2024

We designed 60% thick airfoil to improve the aerodynamic performance in the root region of wind turbine rotor blades, taking into account current constraints. After an extensive literature review and patent research, a design methodology (including the considerations of simple manufacturing) was set up, and extensive 2D- and 3D-CFD investigations with four codes (Xfoil, MSES, ANSYS fluent, and DLR-tau) were performed, including implementation inside a generic 10 MW test-blade (CIG10MW). Comparison with results from Blade Element Momentum (BEM) methods and the estimation of 3D effects due to the rotating blade were undertaken. One specific shape (with a pronounced flat-back) was selected and tested in the Deutsche WindGuard aeroacoustic Wind Tunnel (DWAA), in Bremerhaven, Germany. A total of 34 polars were measured, included two trailing edge shapes and aerodynamic devices such as vortex generators, gurney flaps, zig-zag tape, and a splitter plate. Considerable changes in lift and drag characteristics were observed due to the use of aerodynamic add-ons. With the studies presented here, we believe we have closed an important technological gap.
MATHEMATICAL MODELING OF A TETHERED PLATFORM'S CONTROLLED VERTICAL FLIGHT
Authors: Bezmen P. , Emel'yanova O. , Yacun S.
Published: July 2024

There is a problem of quickly obtaining reliable and sufficient information about the environment, including in hard-to-reach areas where there is no communication or long-term stationary monitoring is required in order to ensure the safety or security of important objects. The paper discusses the issues of mathematical modeling of automated cable parking of an unmanned aerial vehicle (platform) in a vertical plane.
The aim of the research is to create tools for the design of tethered flying platforms equipped with an automated cable restraint system, taking into account external random atmospheric disturbances during vertical flight. To achieve this aim, it is necessary to carry out mathematical modeling of the flight of tethered unmanned aerial platforms, taking into account external active and reactive forces, as well as disturbing forces – random air flows, which can be significant in strength and variable in direction, which undoubtedly affects the stable position tethered platform. The use of the extended Kalman filter algorithm as an observer of the tethered flying platform state in mathematical modeling of the platform's flight makes it possible to take into account random disturbances of the atmosphere as a partially deterministic environment in order to reduce the position error of the aircraft during its control.
To solve the problem, methods of classical mechanics, motion stability theory, optimal control, estimation theory, and parametric optimization were used.
The results of mathematical modeling of a tethered flying platform – a tethered copter – flight are presented, taking into account random atmospheric disturbances. The results of computational experiments made it possible to establish that random atmospheric disturbances can have a significant impact on the position control of an aircraft.
ANALISYS OF PERLITE ADDITIVE AS INTERNAL CURE AGENT IN CEMENT CONCRETE SYSTEM
Authors: Sidorova A.
Published: July 2024

At the present stage of development of materials science, there are a large number of ways to reduce shrinkage in cement composite systems. However, not every of these methods can be classified as reliable, capable of providing the effect of non-shrinkage under any conditions. This problem becomes especially relevant when it comes to concretes and mortars that are intended for work in difficult conditions, for example, repair mortars.
The main methods of curing for freshly molded concrete are analyzed, and the use of perlite sand as an internal maintenance agent to reduce shrinkage in the manufacture of repair mortars is justified. A method for measuring shrinkage using the TEREM 4.0 measuring complex is described.
The effect of replacing part of the fine aggregate (quartz sand) with light aggregate (perlite sand) is considered. Graphs of the dependences of the shrinkage value were obtained for different contents of perlite sand and under conditions of low and high environmental humidity. It has been established that the introduction of fine fraction perlite into the cement-sand mortar, as a replacement for part of the sand, in an amount of no more than 5 % by volume is an effective way to reduce shrinkage deformations in dry conditions (φ ≤ 40 %).
THE SOCIAL AND ADMINISTRATIVE CENTER OF THE VOLODARSKY (NEVSKY) DISTRICT OF ST. PETERSBURG. PROJECTS AND ACTUAL IMPLEMENTATION. FEATURES OF URBAN PLANNING AND SPATIAL SOLUTIONS
Authors: Kakunets K.
Published: July 2024

The urban planning feature of the development of the socio-administrative district center of the Volodarsky (Nevsky) district consists in the ensemble construction of its composition. Despite numerous changes in design decisions and the long-term implementation of the project, the urban planning potential inherent in the design of this center can be traced in its modern structure, as well as in the structure of the entire Nevsky district and the city as a whole. This urban planning composition is relevant for the study and formation of proposals for the preservation of the public center, as a single urban planning composition. The availability of design materials from different years, including General Plans of Leningrad and design proposals for the development of the territory of the Shchemilovka district in 1930-1960, as well as field studies allow us to analyze the conceived and implemented urban planning and spatial features of this complex and identify the features of the formation of the public administrative center of the Volodarsky (Nevsky) district in the city of St. Petersburg. The purpose of this study is to identify the key urban planning and spatial features that influenced the development of the public administrative center of the Volodarsky (Nevsky) district. This study is the basis for further work on improving security items for the public administrative district center of Volodarsky (Nevsky) district.
A Novel Role for the Longevity-Associated Protein SLC39A11 as a Manganese Transporter
Authors: Xia Zhidan , Tang Biyao , Li Xiaopeng , Li Xinran , Jia Yangfan , Jiang Jianwei , Chen Jingyao , Song Jingshu , Liu Siyi , Min Junxia , Wang Fudi
Published: July 2024

The identification of aging- and longevity-associated genes is important for promoting healthy aging. By analyzing a large cohort of Chinese centenarians, we previously found that single-nucleotide polymorphisms (SNPs) in the
SLC39A11
gene (also known as
ZIP11
) are associated with longevity in males. However, the function of the SLC39A11 protein remains unclear. Here, we found that
SLC39A11
expression is significantly reduced in patients with Hutchinson–Gilford progeria syndrome (HGPS). In addition, we found that zebrafish with a mutation in
slc39a11
that significantly reduces its expression have an accelerated aging phenotype, including a shortened average lifespan, muscle atrophy and reduced swimming, impaired muscle regeneration, gut damage, and abnormal morphology in the reproductive system. Interestingly, these signs of premature aging were more pronounced in male zebrafish than in females. RNA-sequencing analysis revealed that cellular senescence may serve as a potential mechanism for driving this
slc39a11
deficiency-induced phenotype in mutant zebrafish. Moreover, immunofluorescence showed significantly increased DNA damage and reactive oxygen species signaling in
slc39a11
mutant zebrafish. Using inductively coupled plasma mass spectrometry (ICP-MS), we found that manganese significantly accumulates in
slc39a11
mutant zebrafish, as well as in the serum of both global
Slc39a11
knockout and hepatocyte-specific
Slc39a11
knockout mice, suggesting that this metal transporter regulates systemic manganese levels. Finally, using cultured human fibroblasts, we found that both knocking down
SLC39A11
and exposure to high extracellular manganese increased cellular senescence. These findings provide compelling evidence that SLC39A11 serves to protect against the aging process, at least in part by regulating cellular manganese homeostasis.