bluuebunny/crossref_metadata_2025 huggingface dataset:
Colloidal TiO2 Nanorod Films Deposited Using the MAPLE Technique: Role of the Organic Capping and Absence of Characteristic Surface Patterns
Authors: Cesaria Maura , Taurino Antonietta , Cozzoli Pantaleo Davide , Arima Valentina , Caricato Anna Paola
Published: August 2023

Thin films of titanium dioxide (TiO2) nanocrystals, widely acknowledged for their unique physical-chemical properties and functionalities, are used in disparate technological fields, including photovoltaics, sensing, environmental remediation and energy storage. In this paper, the preparation of thin films consisting of anatase-phase TiO2 nanorods deposited using the matrix-assisted pulsed laser evaporation (MAPLE) technique and their characterization in terms of morphology, elemental composition and wettability are presented and discussed. Particular attention is paid to the effects of the laser fluence, varied over a broad range (F = 25, 50, 100 mJ/cm2), and to the role of the capping surfactants bound to the surface of the nanorod precursors. Whereas increasing fluence favored a partial removal of the surface-bound surfactants, a post-growth UV-light-driven photocatalytic treatment of the films was found to be necessary to reduce the incorporated fraction of organics to a further substantial extent. It was noteworthy that, under our experimental conditions, the distinctive surface patterns and roughness that commonly degrade the morphology of films deposited using the MAPLE technique were not observable. This previously unreported experimental evidence was rationalized on the basis of the interaction dynamics between solvent/solute droplets ejected from the laser-irradiated target and the rough surfaces of the growing film.
Effect of Cavitating Hydraulic Elements on Pump Characteristics
Authors: Jablonská Jana , Kozubková Milada , Drábková Sylva , Blejchař Tomáš
Published: August 2023

The effective and reliable performance of any pump can be significantly impacted by the piping system design. One of the essential points is the pump suction pipe. Poor design and dimension of the suction piping can lead to cavitation in the pump which affects its head and efficiency. The primary objective is to reduce the hydraulic losses of the suction piping in order to maintain a Net Positive Suction Head required by the pump. Suction piping is recommended to be short and straight, and branch connections, valves and elbows should be avoided, which is not always possible. In addition, cavitation can occur in the actual hydraulic elements installed on the pump suction. This work is focused on the investigation of cavitating hydraulic element in the suction pipe on the pump performance. A converging-diverging nozzle with a circular cross-section was used for this purpose. A straight pipe segment of constant diameter and the same length was used for comparison. Both elements were characterized by the loss coefficient and the cavitation number. Their influence on the pump head and the Net Positive Suction Head Available (NPSHA) was investigated.
Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure
Authors: Wang Jun , Sun Lun , Li Heng , Ding Ruoxi , Chen Ning
Published: August 2023

Heat exchangers in operation often experience scaling, which can lead to a decrease in heat exchange efficiency and even safety accidents when fouling accumulates to a certain thickness. To address this issue, manual intervention is currently employed to monitor fouling thickness in advance. In this study, we propose a two-layer LSTM neural network model with an attention mechanism to effectively learn fouling thickness data under different working conditions. The model accurately predicts the scaling thickness of the heat exchanger during operation, enabling timely human intervention and ensuring that the scaling remains within a safe range. The experimental results demonstrate that our proposed neural network model (TA-LSTM) outperforms both the traditional BP neural network model and the LSTM neural network model in terms of accuracy and stability. Our findings provide valuable technical support for future research on heat exchanger descaling and fouling growth detection.
Properties of High-Flowability Liquefied Stabilized Soil Made of Recycled Construction Sludge
Authors: Shigematsu Yuji , Inazumi Shinya , Chaiprakaikeow Susit , Nontananandh Supakij
Published: August 2023

This paper focuses on the development of high-flowability liquefied stabilized soils (HFLSS) made of recycled construction sludge (RCS) to enhance their application in construction work. Liquefied stabilized soils (LSSs) have already found widespread use in construction sites, particularly for filling long-distance structures and dealing with complex underground spaces. However, to further optimize their performance, the development of high-flowability liquefied stabilized soils (HFLSSs) with superior flowability is required. This study experimentally investigates the basic properties, including mechanical characteristics and flowability performance, of the newly developed HFLSS made of RCS. The results confirm that the developed HFLSS made of RCS meets the quality requirements expected from LSSs and exhibits enhanced flowability, making it a promising material for construction applications. The advanced development of LSSs in this paper expects to promote recycling construction-generated soils, including construction-generated sludges in the construction industry.
The Possibility of Asking about Dao: On the Philosophical Significance of Dialogue in the Zhuangzi
Authors: Wang Yiming
Published: August 2023

In contrast to the aphoristic style of the Daodejing, Zhuangzi is known for its abundant use of dialogues. These dialogues, especially those found in chapters 21 and 22, are consciously organized around the theme of “asking about Dao”. They bring together the diverse propositions about the Dao that are found independently throughout the Daodejing. The purpose of this paper is to highlight the fact that Zhuangzi consciously orders these propositions in terms of the different levels of knowledge of Dao. Within this theoretical framework, Zhuangzi further ranks the questions and answers regarding these propositions. Certain dialogues are partly dismissed because both participants demonstrate a flawed and shallow understanding of the Dao, while other questions and answers are acknowledged and appreciated for their correct and profound understanding of it. There is a strict corresponding relationship between levels of knowledge of Dao and different attitudes toward dialogues in it. Therefore, the examination of the dialogues about the Dao reveals that Zhuangzi places explicit emphasis on the knowledge of the Dao compared to the Daodejing, which also sheds light on Zhuangzi’s distinctive awareness of the problems surrounding this key concept.
In Vitro Efficacy of Hungarian Propolis against Bacteria, Yeast, and Trichomonas gallinae Isolated from Pigeons—A Possible Antibiotic Alternative?
Authors: Kerek Ádám , Csanády Péter , Tuska-Szalay Barbara , Kovács László , Jerzsele Ákos
Published: August 2023

The spread of antimicrobial resistance is one of the most serious human and animal health problems of our time. Propolis is a natural substance with antibacterial, antifungal, and antiparasitic activity, the most active components of which are polyphenols and terpenoids. In the present study, the authors investigated the efficacy of propolis against Staphylococcus spp., Enterococcus spp., Escherichia coli and Salmonella enterica, Candida albicans fungi, and Trichomonas gallinae isolated from pigeons. For each pathogen, the minimum inhibitory concentration (MIC) and minimum eradication concentration (MEC) of eight isolates were determined for 96%, 90%, 80%, 70%, and 60% ethanolic extracts of propolis from the region of Észak-Alföld. Propolis was shown to be effective in inhibiting the growth of Gram-positive bacteria, Candida albicans, and Trichomonas gallinae strains. Propolis showed a much better efficacy against Gram-positive bacteria (1.56–400 µg/mL) than against Gram-negative bacteria (>13,000 µg/mL). For Staphylococcus spp., MIC values ranged within 1.56–400 µg/mL and MEC values within 12.5–3260 µg/mL, while for Enterococcus spp. MIC values ranged within 1.56–400 µg/mL and MEC values within 12.5–800 µg/mL. MIC values > 13,000 µg/mL were found for Escherichia coli and Salmonella enterica species. For Candida albicans, MIC values ranging from 1.56 to 400 µg/mL and MEC values ranging from 3.125 to 800 µg/mL were effective. MEC values between 2.5 and 5 mg/mL were observed for three Trichomonas gallinae strains. The effectiveness against Gram-positive bacteria has, in some cases, approached that of antibiotics, making propolis a potential alternative in the treatment of wound infections. Its outstanding efficacy against Trichomonas gallinae holds promise as a potential alternative for treating this widespread infection in pigeons.
Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach
Authors: Kayastha Miraj B. , Liu Tao , Titze Daniel , Havens Timothy C. , Huang Chenfu , Xue Pengfei
Published: August 2023

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at a spatial resolution of ~1 km. In our dataset, the Great Lakes are represented by ~33,000 unstructured grid points and the LSTM training incorporated the information from each grid point. The LSTM was trained with seven meteorological variables from reanalysis data as feature variables and the LST from a historical satellite-derived dataset as the target variable. The LSTM was able to capture the spatial heterogeneity of LST in the Great Lakes well and exhibited high correlation (≥0.92) and low bias (limited to ±1.5 °C) for the temporal evolution of LST during the training (1995–2020) and testing (1979–1994) periods.
Black Marble Nighttime Light Data for Disaster Damage Assessment
Authors: Zhang Danrong , Huang Huili , Roy Nimisha , Roozbahani M. Mahdi , Frost J. David
Published: August 2023

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.
Correction: de Michele et al. Shallow Bathymetry from Multiple Sentinel 2 Images via the Joint Estimation of Wave Celerity and Wavelength. Remote Sens. 2021, 13, 2149
Authors: de Michele Marcello , Raucoules Daniel , Idier Deborah , Smai Farid , Foumelis Michael
Published: August 2023

There was an error in the original publication [...]
Impervious Surface Area Patterns and Their Response to Land Surface Temperature Mechanism in Urban–Rural Regions of Qingdao, China
Authors: Pan Tao , Li Baofu , Ning Letian
Published: August 2023

The expansion of impervious surface area (ISA) in megacities of China often leads to land surface temperature (LST) aggregation effects, which affect living environments by impacting thermal comfort levels, thus becoming an issue of public concern. However, from an urban–rural synchronous comparison perspective, the study of LST responses to ISA changes is still lacking in the central coastal megalopolises of China. To solve this issue, a collaborative methodology of artificial digitization—fully constrained least squares mixed pixel decomposition—split-window algorithm—PCACA model was established for Qingdao using land use dataset and remote sensing images. The conclusions are below. Long time series of land use monitoring indicated that the expansion ratios of urban and rural areas were 131.29% and 43.42% in the past 50 years (i.e., from 1970 to 2020). Within urban and rural areas, a synchronous ISA increase was observed, with ratios of +9.14% (140.55 km2) and +7.94% (28.04 km2), respectively. Higher ratios and area changes were found in the urban regions, and a similar ISA change pattern in both urban and rural regions was captured by the ISA horizontal epitaxial expansion and vertical density enhancement. Further, the horizontal gradient effect displayed that the mean LSTs were 28.75 °C, 29.77 °C and 31.91 °C in the urban areas and 28.73 °C, 29.66 °C and 31.65 °C in the rural areas in low-, medium-, and high-density ISAs. The vertical density effect showed that the LST change was 1.02 °C and 2.14 °C in the urban areas but 0.93 °C and 1.99 °C in the rural areas during the ISA-density transition from low- to medium- and from medium- to high-density, respectively. Potential surface thermal indicators were assessed, and the urban regions displayed higher sensible heat flux (280.13 W/m2) compared to the rural regions (i.e., 274.76 W/m2). The mechanism effect of the ISA changes on LST in the urban and rural regions was revealed. These findings form a new comparative perspective of the urban–rural synchronous change in the central coastal megalopolis of China and can provide a practical reference for relevant studies.
High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland
Authors: Zhong Guangrui , Chen Jianjun , Huang Renjie , Yi Shuhua , Qin Yu , You Haotian , Han Xiaowen , Zhou Guoqing
Published: August 2023

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) > Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.
Spectrum Allocation and User Scheduling Based on Combinatorial Multi-Armed Bandit for 5G Massive MIMO
Authors: Dou Jian , Liu Xuan , Qie Shuang , Li Jiayi , Wang Chaoliang
Published: August 2023

As a key 5G technology, massive multiple-input multiple-output (MIMO) can effectively improve system capacity and reduce latency. This paper proposes a user scheduling and spectrum allocation method based on combinatorial multi-armed bandit (CMAB) for a massive MIMO system. Compared with traditional methods, the proposed CMAB-based method can avoid channel estimation for all users, significantly reduce pilot overhead, and improve spectral efficiency. Specifically, the proposed method is a two-stage method; in the first stage, we transform the user scheduling problem into a CMAB problem, with each user being referred to as a base arm and the energy of the channel being considered a reward. A linear upper confidence bound (UCB) arm selection algorithm is proposed. It is proved that the proposed user scheduling algorithm experiences logarithmic regret over time. In the second stage, by grouping the statistical channel state information (CSI), such that the statistical CSI of the users in the angular domain in different groups is approximately orthogonal, we are able to select one user in each group and allocate a subcarrier to the selected users, so that the channels of users on each subcarrier are approximately orthogonal, which can reduce the inter-user interference and improve the spectral efficiency. The simulation results validate that the proposed method has a high spectral efficiency.
Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model
Authors: Liu Hanlin , Lin Nan , Zhang Honghong , Liu Yongji , Bai Chenzhao , Sun Duo , Feng Jiali
Published: August 2023

Globally, natural wetlands have suffered severe ecological degradation (vegetation, soil, and biotic community) due to multiple factors. Understanding the spatiotemporal dynamics and driving forces of natural wetlands is the key to natural wetlands’ protection and regional restoration. In this study, we first investigated the spatiotemporal evolutionary trends and shifting characteristics of natural wetlands in the Northeast Plain of China from 1990 to 2020. A dataset of driving-force evaluation indicators was constructed with nine indirect (elevation, temperature, road network, etc.) and four direct influencing factors (dryland, paddy field, woodland, grassland). Finally, we built the driving force analysis model of natural wetlands changes to quantitatively refine the contribution of different driving factors for natural wetlands’ dynamic change by introducing the sparrow search algorithm (SSA) and extreme gradient boosting algorithm (XGBoost). The results showed that the total area of natural wetlands in the Northeast Plain of China increased by 32% from 1990 to 2020, mainly showing a first decline and then an increasing trend. Combined with the results of transfer intensity, we found that the substantial turn-out phenomenon of natural wetlands occurred in 2000–2005 and was mainly concentrated in the central and eastern parts of the Northeast Plain, while the substantial turn-in phenomenon of 2005–2010 was mainly located in the northeast of the study area. Compared with a traditional regression model, the SSA-XGBoost model not only weakened the multicollinearity of each driver but also significantly improved the generalization ability and interpretability of the model. The coefficient of determination (R2) of the SSA-XGBoost model exceeded 0.6 in both the natural wetland decline and rise cycles, which could effectively quantify the contribution of each driving factor. From the results of the model calculations, agricultural activities consisting of dryland and paddy fields during the entire cycle of natural wetland change were the main driving factors, with relative contributions of 18.59% and 15.40%, respectively. Both meteorological (temperature, precipitation) and topographic factors (elevation, slope) had a driving role in the spatiotemporal variation of natural wetlands. The gross domestic product (GDP) had the lowest contribution to natural wetlands’ variation. This study provides a new method of quantitative analysis based on machine learning theory for determining the causes of natural wetland changes; it can be applied to large spatial scale areas, which is essential for a rapid monitoring of natural wetlands’ resources and an accurate decision-making on the ecological environment’s security.
CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective
Authors: Guo Ningbo , Jiang Mingyong , Gao Lijing , Tang Yizhuo , Han Jinwei , Chen Xiangning
Published: August 2023

Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the “summation” way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a contextual, relational attention-based recognition network (CRABR-Net) was presented, which extracts different convolutional feature maps from CNN, focuses important feature content by using a simple, parameter-free attention module (SimAM), fuses the adjacent feature maps by using the complementary relationship feature map calculation, improves the feature learning ability by using the enhanced relationship feature map calculation, and finally uses the concatenated feature maps from different layers for RSSOR. Experimental results show that CRABR-Net exploits the relationship between the different CNN layers to improve recognition performance, achieves better results compared to several state-of-the-art algorithms, and the average accuracy on AID, UC-Merced, and RSSCN7 can be up to 96.46%, 99.20%, and 95.43% with generic training ratios.
Application of Cost Effective and Real-Time Resistivity Sensor to Study Early Age Concrete
Authors: Tenório Filho José Roberto , Abbas Yawar , Oudenhoven Jos , Matthys Stijn
Published: August 2023

Concrete is a widely used construction material, demanding strict quality control to maintain its integrity. The durability and lifespan of concrete structures rely heavily, amongst other factors, on the characteristics of fresh and early age concrete, which are strongly dependent on the curing process. To ensure long-term durability, it is crucial to assess concrete properties throughout construction and verify compliance with design specifications. Currently, electrical resistivity-based sensors are available and used for quality control and monitoring, however, these sensors tend to be costly or only measure at a single location within the concrete cover. This study introduces a printed circuit board (PCB)-based array of electrodes capable of measuring concrete resistivity profiles across the concrete cover, from its fresh state to early age development. In this work, the feasibility of such resistivity PCB-sensors, novel for concrete, is evaluated under laboratory conditions. The sensors exhibit a promising performance in monitoring the efficiency of concrete curing under various conditions. Additionally, they successfully evaluate the effectiveness of internal curing (in our study, promoted by superabsorbent polymers) during the initial stages of hardening. This sensor array provides a valuable tool for monitoring the curing of concrete at early age, and showcases a preliminary solution that could be further developed to ensure long-term performance of concrete infrastructure.
Nanoribbon Biosensor-Based Detection of microRNA Markers of Prostate Cancer
Authors: Ivanov Yuri D. , Malsagova Kristina A. , Goldaeva Kristina V. , Kapustina Svetlana I. , Pleshakova Tatyana O. , Popov Vladimir P. , Kozlov Andrey F. , Galiullin Rafael A. , Shumov Ivan D. , Enikeev Dmitry V. , Potoldykova Natalia V. , Ziborov Vadim S. , Petrov Oleg F. , Dolgoborodov Alexander Y. , Glukhov Alexander V. , Novikov Sergey V. , Grabezhova Victoria K. , Yushkov Evgeniy S. , Konev Vladimir A. , Kovalev Oleg B. , Archakov Alexander I.
Published: August 2023

Prostate cancer (PC) is one of the major causes of death among elderly men. PC is often diagnosed later in progression due to asymptomatic early stages. Early detection of PC is thus crucial for effective PC treatment. The aim of this study is the simultaneous highly sensitive detection of a palette of PC-associated microRNAs (miRNAs) in human plasma samples. With this aim, a nanoribbon biosensor system based on “silicon-on-insulator” structures (SOI-NR biosensor) has been employed. In order to provide biospecific detection of the target miRNAs, the surface of individual nanoribbons has been sensitized with DNA oligonucleotide probes (oDNA probes) complementary to the target miRNAs. The lowest concentration of nucleic acids, detectable with our biosensor, has been found to be 1.1 × 10−17 M. The successful detection of target miRNAs, isolated from real plasma samples of PC patients, has also been demonstrated. We believe that the development of highly sensitive nanotechnology-based biosensors for the detection of PC markers is a step towards personalized medicine.
6G—Enabling the New Smart City: A Survey
Authors: Murroni Maurizio , Anedda Matteo , Fadda Mauro , Ruiu Pietro , Popescu Vlad , Zaharia Corneliu , Giusto Daniele
Published: August 2023

Smart cities and 6G are technological areas that have the potential to transform the way we live and work in the years to come. Until this transformation comes into place, there is the need, underlined by research and market studies, for a critical reassessment of the entire wireless communication sector for smart cities, which should include the IoT infrastructure, economic factors that could improve their adoption rate, and strategies that enable smart city operations. Therefore, from a technical point of view, a series of stringent issues, such as interoperability, data privacy, security, the digital divide, and implementation issues have to be addressed. Notably, to concentrate the scrutiny on smart cities and the forthcoming influence of 6G, the groundwork laid by the current 5G, with its multifaceted role and inherent limitations within the domain of smart cities, is embraced as a foundational standpoint. This examination culminates in a panoramic exposition, extending beyond the mere delineation of the 6G standard toward the unveiling of the extensive gamut of potential applications that this emergent standard promises to introduce to the smart cities arena. This paper provides an update on the SC ecosystem around the novel paradigm of 6G, aggregating a series of enabling technologies accompanied by the descriptions of their roles and specific employment schemes.
Algorithm for Automatic Rod Feeding and Positioning Error Compensation for Underground Drilling Robots in Coal Mines
Authors: Lu Qianhai , Kong Lingfei , Peng Guangyu , Jia Wang , Jin Sun , Dai Chenyu , Zhu Qianxiang
Published: August 2023

In the pursuit of automating the entire underground drilling process in coal mines, the automatic rod feeding technology of drilling robots plays a crucial role. However, the current lack of positional accuracy in automatic rod feeding leads to frequent accidents. To address this issue, this paper presents an algorithm for compensating positioning errors in automatic rod feeding. The algorithm is based on a theoretical mathematical model and manual teaching methods. To enhance the positioning accuracy, we first calibrate the pull rope sensor to correct its measurement precision. Subsequently, we establish a theoretical mathematical model for rod feeding positions by employing spatial coordinate system transformations. We determine the target rod feeding position using a manual teaching-based approach. Furthermore, we analyze the relationship between the theoretical rod delivery position and the target rod delivery position and propose an anisotropic spatial difference compensation technique that considers both distance and direction. Finally, we validate the feasibility of our proposed algorithm through automatic rod feeding tests conducted on a coal mine underground drilling robot. The results demonstrate that our algorithm significantly improves the accuracy of rod feeding positions for coal mine underground drilling robots.
Understanding the Spatiotemporal Impacts of the Built Environment on Different Types of Metro Ridership: A Case Study in Wuhan, China
Authors: Yang Hong , Peng Jiandong , Zhang Yuanhang , Luo Xue , Yan Xuexin
Published: August 2023

As the backbone of passenger transportation in many large cities around the world, it is particularly important to explore the association between the built environment and metro ridership to promote the construction of smart cities. Although a large number of studies have explored the association between the built environment and metro ridership, they have rarely considered the spatial and temporal heterogeneity between metro ridership and the built environment. Based on metro smartcard data, this study used EM clustering to classify metro stations into five clusters based on the spatiotemporal travel characteristics of the ridership at metro stations. And the GBDT model in machine learning was used to explore the nonlinear association between the built environment and the ridership of different types of stations during four periods in a day (morning peak, noon, evening peak, and night). The results confirm the obvious spatial heterogeneity of the built environment’s impact on the ridership of different types of stations, as well as the obvious temporal heterogeneity of the impact on stations of the same type. In addition, almost all built environment factors have complex nonlinear effects on metro ridership and exhibit obvious threshold effects. It is worth noting that these findings will help the correct decisions be made in constructing land use measures that are compatible with metro functions in smart cities.
Impact of the COVID-19 Pandemic on the Developmental Outcomes among Korean Kinship Foster Care Children: Gender Differences
Authors: Kang Hyunah , Chung Ick-Joong , Oh Sehyeon
Published: August 2023

(1) Background: This study explored changes before and during the COVID-19 pandemic in terms of developmental outcomes among kinship foster care children in the Republic of Korea: and gender differences in the changes; (2) Methods: The study analyzed the data of 217 kinship care children who participated in both the first- and second-wave surveys of the Panel Study of Korean Foster Care Children. As the main statistical methods, we utilized repeated-measures ANOVA and analysis of covariance (ANCOVA); (3) Results: Analysis of developmental outcomes measured before and during the pandemic showed no significant changes. However, significant interaction effects existed between time (before and during the pandemic) and gender, indicating that boys and girls recorded different patterns of change before and during the COVID-19 pandemic; (4) Discussion:During the COVID-19 pandemic, girls experienced negative changes in most areas of development, whereas boys experienced positive changes. The policy and practical implications for foster care children in Korea were discussed.
Gender-Related Violence: What Can a Concept Do?
Authors: Alldred Pam
Published: August 2023

This paper explains the logic for using the concept of gender-related violence (GRV) as a broad category that problematises homophobia, transphobia and the policing of gender norms and the gender binary, as well as gender-based violence—understood as primarily violence against women and girls (VAWG). It then evaluates the utility of this concept and its capacity to introduce theoretical refinement to the study of gendered violence, by reviewing its reception within a large international feminist project on gendered violence in the lives of children and young people. The aim of this study was to improve knowledge and understanding of forms of violence and discrimination among practitioners who have everyday contact with general populations of children and young people. It sought to improve their ability to identify and challenge sexist, sexualising, homophobic or transphobic language or behaviour, and their knowledge of how and when to refer children and young people to appropriate support. The paper reports my view of the contribution that the concept of gender-related violence made in each of the four project sites: Ireland, Italy, Spain and the UK. The findings are mixed: in Spain and the UK the concept seemed helpful, but in Italy and Ireland it was initially expected to be helpful but, in practice, a conceptualisation closer to gender-based violence plus homophobia was employed. It is tentatively concluded that where LGBTQIA+ rights were well-established as well as the problematisation of VAWG, this framework was successful, but that it was less successful in more heteronormative sites where homophobia was less problematised. It is suggested that, as a concept, GRV can make a valuable intervention in sites like the former.
Framing the Residential Patterns of Asian Communities in Three Italian Cities: Evidence from Milan, Rome, and Naples
Authors: Bitonti Francesca , Benassi Federico , Mazza Angelo , Strozza Salvatore
Published: August 2023

Today, the interplay between economic inequality, international migration, and urban transformation has raised awareness about segregation and its social implications on a global and European scale. As cities become home to diverse populations with various backgrounds including social, racial, ethnic, and cultural, the proximity of these groups becomes more pronounced. This article explores the residential segregation of four Asian immigrant groups in three major Italian cities: Milan, Rome, and Naples. Using data from the 2011 Italian General Population Census and employing an areal weighted interpolation procedure, the study measures segregation using both traditional two-group indices and multi-group indices that account for the complexities of contemporary societies. The results indicate a north–south disparity, with Naples exhibiting the highest levels of residential segregation. Among the analysed immigrant groups, Bangladeshis and Chinese tended to be more self-segregated, while Filipinos and Sri Lankans were relatively more dispersed. This research underscores the necessity for a nuanced understanding of segregation dynamics and the adoption of appropriate approaches to address the challenges and opportunities presented by the coexistence of diverse groups in urban areas. By contributing to the existing literature on residential segregation in Southern Europe, this study sheds light on the spatial patterns and social dynamics of different ethnic groups in Italian cities.
Patriarchal Challenges for Women Empowerment in Neoliberal Agricultural Development: A Study in Northwestern Bangladesh
Authors: Rahman Md. Mizanur , Huq Hamidul , Hossen M. Anwar
Published: August 2023

Women are recognized as proactive catalysts for societal change in rural Bangladesh because of their significant socio-economic contributions to agricultural practices. Historically, they have been closely associated with various agricultural activities ranging from seed preservation to crop harvesting. Despite their vital role in the rural economy, women farmers have encountered a number of obstacles resulting from the adaptation of neoliberal agrarian policies. In recent decades, women’s participation in the agricultural sector has declined, which can be attributed to the influence of capitalist patriarchy on agricultural practices. Grounded on this argument, this present study aims to investigate a research question: What are the major challenges to women’s participation in agricultural practices in Bangladesh? In order to answer this question, data has been collected through in-depth interviews and household surveys from three villages of Jaldhaka Upazila in the Nilphamari district of northwestern Bangladesh. The findings of this paper argue that women’s participation in agriculture is challenged by the effects of the neoliberal policy perspective, such as the mechanization of agriculture, commercial land use, and land inequality, which continue to ratify patriarchy in agriculture. In such a situation, this study suggests women’s empowerment could be effectively facilitated by adopting agricultural policies that are specifically tailored to address gender dynamics, emphasizing the active engagement and meaningful representation of women in the processes of policy formulation and execution.
The Role of Personal Resources in Buffering College Student Technostress during the Pandemic: A Study Using an Italian Sample
Authors: Ingusci Emanuela , Ciavolino Enrico , Signore Fulvio , Zito Margherita , Russo Vincenzo , Ghislieri Chiara
Published: August 2023

Given the upheavals that characterize the world of higher education and the recent literature on the subject, the examination of what can improve student well-being has become critical. The JD-R model, originally developed to explain the implementation of motivational processes and the simultaneous unfolding of mechanisms that impact health, is used to contextualize the processes that occur in higher education systems. Objective. The purpose of this paper is to examine the role of personal resources such as optimism and self-efficacy in increasing academic engagement and as protective factors against technostress. Method. A SEM model was implemented using MPLUS 7 and Jamovi on a sample of 421 university Italian students. They completed an online self-report questionnaire during the height of COVID-19 (May–November 2021) while taking online courses and were predominantly female (64.4%) and full-time academic students (87.6%) with a mean age of 24.6 years. Direct and indirect effects were estimated, accounting for the mediating role of academic engagement. Results. The results indicate that both self-efficacy and optimism have direct and negative effects on technostress. Self-efficacy, in turn, significantly increases academic engagement, whereas optimism has no effect on it. Finally, academic engagement appears to reduce the impact of technostress on the lives of students involved in the study, confirming its mediating role in reducing technostress. Conclusions. This study provides numerous important clues and insights into improving academic performance and well-being, as the use of personal resources can have important implications for avoiding the negative consequences of technology.
Influence of Practice Periodization and Sleep Duration on Oxidative Stress in High School Judo Athletes
Authors: Yoshida Ena , Hayashida Harumi
Published: August 2023

Numerous research studies have investigated the relationship between exercise, oxidative stress level, and condition in athletes who engage in intense training on a daily basis. However, it is known that oxidative stress is affected by exercise, sleep, and the psychological state, but there are only a few studies that have comprehensively examined oxidative stress based on the actual practice periods and living conditions of athletes. Therefore, our study aimed to explore the influence of three distinct training periods (short training period, intensive training period, and pre-competition periods) as well as life situations (sleep and number of steps) on oxidative stress levels (diacron reactive oxygen metabolites: d-ROMs) in high school judo athletes. The results showed that, among the three periods, the level of oxidative stress increased the most during the pre-competition period, and the value was higher than during the training period, when the intensity of training was highest. The levels of the d-ROMs values during the pre-competition period were negatively correlated with the amount of sleep on the previous day. The findings suggest that, besides the exercise intensity, factors such as sleep duration and other life situations should be regarded as critical considerations for high school judo athletes.
Experiment Study of Stemming Length and Stemming Material Impact on Rock Fragmentation and Dynamic Strain
Authors: Shi Xiuzhi , Zhang Zongguo , Qiu Xianyang , Luo Zhihua
Published: August 2023

Stemming length and stemming materials are crucial factors in blasting design, which affect the sustainability of mining. This study investigates the influence of stemming length and stemming material on rock fragmentation, stemming recoil, and surface strain response through 15 small-scale model blasting tests. The results indicate that when using clay as a stemming material, increasing the stemming length facilitates rock fragmentation and reduces the stemming recoil area. The strain measurements show that both tensile and compressive strain peaks on the blasting crater surface increase with the growth of stemming length, while the strain peaks on the upper surface decrease. A comparative analysis of different stemming materials reveals that clay performs the best, exhibiting the highest total weight of fragments, blasting crater size, and fragmentation energy utilization. Strain results indicate that clay stemming generates more significant strain peaks and higher strain loading rates on the blasting crater surface, favoring a more concentrated application of explosive energy on the crater surface and improving rock fragmentation. Sand + clay stemming yields fragments more concentrated in medium-sized particles than clay stemming. If the blasting goal is to increase the utilization efficiency of explosive energy and reduce the hazards of stemming recoil, it is recommended to use clay stemming. In addition, if uniform fragmentation is desired (reducing large and fine particles), a combination of sand + clay stemming can be used. These findings have practical implications for optimizing blasting design and engineering applications.
Efficient Degradation of Rhodamine B Dye through Hand Warmer Heterogeneous Activation of Persulfate
Authors: Ye Tiantian , Liu Lihong , Wang Yilin , Zhang Jianqiang , Wang Zhenxing , Li Cong , Luo Haoyu
Published: August 2023

In this study, an innovative method for RhB (Rhodamine B) degradation in a persulfate (PS) and hand warmer heterogeneous activation system was investigated. The hand warmer showed better catalytic performance and excellent reusability in terms of PS activation. The reaction rate constants of RhB removal in the hand warmer/PS process (0.354 min−1) were much faster than those in other PS- and Fe-related processes (0.010–0.233 min−1) at pH 7. The iron in the hand warmer is the main active ingredient to catalyze PS, and activated carbon, salt, and H+/OH− accelerate the activated reaction due to the formation of micro-batteries in the solution. Moreover, the catalyst of the hand warmer showed excellent stability and reusability with a low level of iron leaching. This new, effective, inexpensive, repeatable, and environmentally friendly catalyst combined with PS has promising prospects for the removal of dyes from industrial wastewater.
An Integrated Strategy for Rescheduling High-Speed Train Operation under Single-Direction Disruption
Authors: Han Chang , Zhou Leishan , Guo Bin , Yue Yixiang , Zhao Wenqiang , Wang Zeyu , Zhou Hanxiao
Published: August 2023

Comparing to other modes of transportation, high-speed railway has the advantages of energy saving, environment friendly, safety and convenience for passengers, and has been more and more popular. However, unforeseen emergencies may disrupt the normal train operation. In this paper, an integrated dispatch strategy (IDS) is proposed to synergistically reschedule the train timetable and rolling stock circulation plan under single-direction disruptions. A two-objective model is formulated, aiming at minimizing both the delay time of passengers and the operation costs of railway companies, to reschedule the train operation efficiently and economically. An algorithm based on Non-dominated Sorting Genetic Algorithms-II (NSGA-II) is designed to solve the model. To accelerate the solving process, we propose a quick method to generate an assignment plan to serve disrupted passengers, and based on the practical experiences, the algorithm acceleration strategy (AAS) is proposed to improve the quality of initial solutions. The model and algorithm are tested on real-world instances of the Beijing-Shanghai high-speed railway line. The results indicate that the average minimized delay time of passengers is 6,012,386 min and the average minimized additional operation costs (operation mileage of standby rolling stocks) are 1623 km, with a decrease of 28.5% and 18.3%, respectively, indicating the model and algorithm are adaptable to handle single-direction disruptions on the railway line, and AAS can further accelerate the computing speed and improve the solutions quality. Finally, the characteristics of disrupted sections of railway lines are well studied and analyzed.
Decision Support Systems Based on Gaseous Emissions and Their Impact on the Sustainability Assessment at the Livestock Farm Level: An Evaluation from the User’s Side
Authors: Alexandropoulos Evangelos , Anestis Vasileios , Dragoni Federico , Hansen Anja , Cummins Saoirse , O’Brien Donal , Amon Barbara , Bartzanas Thomas
Published: August 2023

To achieve national and global air quality and climate change objectives, the agricultural sector increasingly requires dependable decision support tools for gaseous emissions at the farm level. We evaluated thirteen greenhouse gas (GHG)-based decision support systems (DSS), considering criteria such as not only the accessibility, user-friendliness, stakeholder involvement, sustainability methodology, and modeling aspects, but also the input parameters and outputs provided, all crucial for decision making. While most DSSs provide information for facilitating their use, only four are suitable for inexperienced users, and stakeholder participation in DSS development is infrequent. The dominant methodology for farm-level GHG estimation is IPCC 2006, with quantitative models primarily used for indicators’ assessment. Scenario and contribution analyses are the prevailing decision support approaches. Soil, crop, and fertilizer types are the most implemented non-livestock-related inputs, while climate- and feed-related costs are the least required. All DSSs assess farm-level mitigation measures, but less than half offer sustainability consultation. These tools promote environmental sustainability by evaluating mitigation strategies, disseminating farm sustainability information, and guiding sustainable farm management. Yet, challenges such as disparate estimation methods, result variations, comparison difficulties, usability concerns, steep learning curves, the lack of automation, the necessity for multiple tools, the limited integration of the results, and changing regulations hinder their wider adoption.