bluuebunny/crossref_metadata_2025 huggingface dataset:
Feeling like the enemy: the emotion management and alienation of hospital doctors
Authors: Byrne John-Paul , Creese Jennifer , McMurray Robert , Costello Richard W. , Matthews Anne , Humphries Niamh
Published: August 2023

Introduction

Globally, an epidemic of psychological distress, burnout, and workforce attrition signify an acute deterioration in hospital doctors' relationship with their work—intensified by COVID-19. This deterioration is more complicated than individual responses to workplace stress, as it is heavily regulated by social, professional, and organizational structures. Moving past burnout as a discrete “outcome,” we draw on theories of emotion management and alienation to analyze the strategies through which hospital doctors continue to provide care in the face of resource-constraints and psychological strain.

Methods

We used Mobile Instant Messaging Ethnography (MIME), a novel form of remote ethnography comprising a long-term exchange of digital messages to elicit “live” reflections on work-life experiences and feelings.

Results

The results delineate two primary emotion-management strategies—acquiescence and depersonalization—used by the hospital doctors to suppress negative feelings and emotions (e.g., anger, frustration, and guilt) stemming from the disconnect between professional norms of expertise and self-sacrifice, and organizational realities of impotence and self-preservation.

Discussion

Illustrating the continued relevant of alienation, extending its application to doctors who disconnect to survive, we show how the socio-cultural ideals of the medical profession (expertise and self-sacrifice) are experienced through the emotion-management and self-estrangement of hospital doctors. Practically, the deterioration of hospital doctors' relationship with work is a threat to health systems and organizations. The paper highlights the importance of understanding the social structures and disconnects that shape this deteriorating relationship and the broad futility of self-care interventions embedded in work contexts of unrealized professional ideals, organizational resource deficits and unhappy doctors, patients, and families.
Does Land Transfer Enhance the Sustainable Livelihood of Rural Households? Evidence from China
Authors: Yang Hui , Huang Zeng , Fu Zhuoying , Dai Jiayou , Yang Yan , Wang Wei
Published: August 2023

Land transfer and its socio-economic impact are key areas of research interest. Such an examination can help to enhance the sustainability of farming livelihoods, maximise livelihood strategies, and achieve sustainable development. This paper establishes a sustainable livelihood evaluation index for rural households based on sustainable livelihood theory. It measures the degree of sustainability in the livelihoods of farmers based on field research data from 650 rural households in Hubei Province, China, and analyses the impact of land transfer using a multiple linear regression model. A number of control variables were identified and introduced into the analysis. It also uses the regression decomposition approach to investigate the impact of each factor on the sustainable livelihoods of rural households. The findings revealed that (1) land transfer can significantly increase the sustainability of rural households’ livelihoods; (2) livelihood sustainability increases with the size of the land transfer area; and (3) the primary elements determining the ability of rural households to maintain sustainable living are land transfers, the amount of land transferred, and the size of the family. Based on these findings, this study argues for the need to improve land transfer management, accelerate agricultural industrialisation and promote the transfer of land contract management rights to improve the livelihoods of rural households.
Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology
Authors: Pazhanivelan Sellaperumal , Kumaraperumal Ramalingam , Shanmugapriya P. , Sudarmanian N. S. , Sivamurugan A. P. , Satheesh S.
Published: August 2023

New agronomic opportunities for more informed agricultural decisions and enhanced crop management have been made possible by drone-based near-ground remote sensing. Obtaining precise non-destructive information regarding crop biophysical characteristics at spatial and temporal scales is now possible. Drone-mounted multispectral and thermal sensors were used to assess crop phenology, condition, and stress by profiling spectral vegetation indices in crop fields. In this study, vegetation indices, viz., Atmospherically Resistant Vegetation Index (ARVI), Modified Chlorophyll Absorption Ratio Index (MCARI), Wide Dynamic Range Vegetation Index (WDRVI), Normalized Red–Green Difference Index (NGRDI), Excess Green Index (ExG), Red–Green Blue Vegetation Index (RGBVI), and Visible Atmospherically Resistant Index (VARI) were generated. Furthermore, Pearson correlation analysis showed a better correlation between WDRVI and VARI with LAI (R = 0.955 and R = 0.982) ground truth data. In contrast, a strong correlation (R = 0.931 and R = 0.844) was recorded with MCARI and NGRDI with SPAD chlorophyll ground truth data. Then, the best-performing indices, WDRVI and MCARI in cotton, and VARI and NGRDI in rice, were further used to generate the yield model. This study for determining LAI and chlorophyll shows that high spatial resolution drone imageries are accurate and fast. As a result, finding out the LAI and chlorophyll and how they affect crop yield at a regional scale is helpful. The widespread use of unmanned aerial vehicles (UAV) and yield prediction were technical components of large-scale precision agriculture.
Agricultural Product Price Forecasting Methods: A Review
Authors: Sun Feihu , Meng Xianyong , Zhang Yan , Wang Yan , Jiang Hongtao , Liu Pingzeng
Published: August 2023

Agricultural price prediction is a hot research topic in the field of agriculture, and accurate prediction of agricultural prices is crucial to realize the sustainable and healthy development of agriculture. It explores traditional forecasting methods, intelligent forecasting methods, and combination model forecasting methods, and discusses the challenges faced in the current research landscape of agricultural commodity price prediction. The results of the study show that: (1) The use of combined models for agricultural product price forecasting is a future development trend, and exploring the combination principle of the models is a key to realize accurate forecasting; (2) the integration of the combination of structured data and unstructured variable data into the models for price forecasting is a future development trend; and (3) in the prediction of agricultural product prices, both the accuracy of the values and the precision of the trends should be ensured. This paper reviews and analyzes the methods of agricultural product price prediction and expects to provide some help for the development of research in this field.
Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns
Authors: Li Cuiling , Wang Xiu , Chen Liping , Zhao Xueguan , Li Yang , Chen Mingzhou , Liu Haowei , Zhai Changyuan
Published: August 2023

This study adopted hyperspectral imaging technology combined with machine learning to detect the disease severity of stem blight through the canopy of asparagus mother stem. Several regions of interest were selected from each hyperspectral image, and the reflection spectra of the regions of interest were extracted. There were 503 sets of hyperspectral data in the training set and 167 sets of hyperspectral data in the test set. The data were preprocessed using various methods and the dimension was reduced using PCA. K−nearest neighbours (KNN), decision tree (DT), BP neural network (BPNN), and extreme learning machine (ELM) were used to establish a classification model of asparagus stem blight. The optimal model depended on the preprocessing methods used. When modeling was based on the ELM method, the disease grade discrimination effect of the FD−MSC−ELM model was the best with an accuracy (ACC) of 1.000, a precision (PREC) of 1.000, a recall (REC) of 1.000, an F1-score (F1S) of 1.000, and a norm of the absolute error (NAE) of 0.000, respectively; when the modeling was based on the BPNN method, the discrimination effect of the FD−SNV−BPNN model was the best with an ACC of 0.976, a PREC of 0.975, a REC of 0.978, a F1S of 0.976, and a mean square error (MSE) of 0.072, respectively. The results showed that hyperspectral imaging of the asparagus mother stem canopy combined with machine learning methods could be used to grade and detect stem blight in asparagus mother stems.
Quantifying Soil Particle Settlement Characteristics through Machine Vision Analysis Utilizing an RGB Camera
Authors: Kim Donggeun , Song Jisu , Park Jaesung
Published: August 2023

Soil particle size distribution is a crucial factor in determining soil properties and classifying soil types. Traditional methods, such as hydrometer tests, have limitations in terms of time required, labor, and operator dependency. In this paper, we propose a novel approach to quantify soil particle size analysis using machine vision analysis with an RGB camera. The method aims to overcome the limitations of traditional techniques by providing an efficient and automated analysis of fine-grained soils. It utilizes a digital camera to capture the settling properties of soil particles, eliminating the need for a hydrometer. Experimental results demonstrate the effectiveness of the machine vision-based approach in accurately determining soil particle size distribution. The comparison between the proposed method and traditional hydrometer tests reveals strong agreement, with an average deviation of only 2.3% in particle size measurements. This validates the reliability and accuracy of the machine vision-based approach. The proposed machine vision-based analysis offers a promising alternative to traditional techniques for assessing soil particle size distribution. The experimental results highlight its potential to revolutionize soil particle size analysis, providing precise, efficient, and cost-effective analysis for fine-grained soils.
Effect of Rural Black-Gray Water Treatment by Subsurface Wastewater Infiltration System on Soil Environment of Vegetable Crop Field
Authors: Wang Songmin , Liu Liyuan , Zhang Chunxue , Wei Xiaocheng , Zheng Xiangqun
Published: August 2023

A field trial was conducted in Tianjin to assess the impact of “three-compartment septic tank (SPT) + soil wastewater infiltration system (SWIS)” on vegetable crop soil, determine the SPT effluent quality, and establish the consumer safety of vegetables grown above the SWIS. The effluent total nitrogen (TN), total phosphorus (TP), ammonium-N (NH4+-N), chemical oxygen demand (COD), and 5-d biochemical oxygen demand (BOD5) levels all varied largely every month. The average COD failed to meet the criteria of the Standard for Irrigation Water Quality (No. GB5084-2021) but significantly influenced bacterial community distribution. Hierarchical clustering disclosed seasonal variation in SPT effluent. SWIS treatment of rural black-grey water significantly affected both the vegetable soil TN and TP content, and it promoted microbial community diversity and richness in deep soil. The treatment also increased the relative abundances of the beneficial bacterial genera Thiobacillus and Arthrobacter by more than 320% and decreased the relative abundance of the pathogenic bacterial genus Streptomyces in vegetable soil by more than 20.33%. The faecal coliform levels and ascaris egg mortality rates in the vegetable crop soils lay within published human health and safety thresholds both before and after SWIS treatment. All vegetable crops grown above the SWIS were fit for human consumption. The VC level in the vegetables planted in experimental households were higher than those for the vegetables planted in ordinary households. The present work provides reasonable theoretical and empirical bases for optimising the “SPT + SWIS” process and SPT discharge standards in rural areas.
Changes in the Soil Phosphorus Supply with Rice Straw Return in Cold Region
Authors: Yan Shuangshuang , Liu Chunxue , Li Jianan , Li Jinwang , Cui Can , Fan Jinsheng , Gong Zhenping , Zhang Zhongxue , Yan Chao
Published: August 2023

Most phosphorus (P) in soil exists in nonlabile forms, leading to poor soil P supply capacity and limiting crop growth. This study evaluated the effect of 10 years of rice straw return on rice yield, soil P budget, P fractions, and phosphatase activity to establish the relationship between soil P fractions and related microbial communities. Four treatments, i.e., no rice straw return (S0), low amount of rice straw return (S1), high amount of rice straw return (S2), and abandoned farmland (AL), were used in the evaluation. The results showed that rice straw return had no effect on the rice yield and P uptake, and the P budget was positive in the S2 treatment. Rice straw return increased the phosphatase activity and content of soil Olsen-P, total P, NaHCO3-, and NaOH-extractable P, and the phosphatase activity and P fractions were both increased with the amount of straw returned. There was a positive correlation between most soil P fractions and active organic carbon fractions. Rice straw return changed the composition and abundance of soil phosphate-solubilizing bacteria (PSB). The findings showed that straw return decreased the proportion of soil nonlabile P, enhancing the soil P supply capacity, and they further showed that the abundance of PSB was not consistent with soil P content.
Integrated miRNA and mRNA Transcriptome Analysis Reveals Eggplant’s (Solanum melongena L.) Responses to Waterlogging Stress
Authors: Jiang Zheng , Xia Xin , Liu Yiyong , He Jie , Yang Xu
Published: August 2023

Waterlogging stress poses a significant threat to eggplants (Solanum melongena L.), causing root oxygen deficiency and subsequent plant damage. This study aims to explore the morphological changes and chlorophyll and lignin indicators of eggplant seedlings under different time points (0, 3, 6, 12, 24, 48 h) of waterlogging stress. High-throughput sequencing was used to identify differentially expressed miRNAs and mRNAs in response to waterlogging stress in eggplants. The results showed that the content of chlorophyll a significantly decreased during the early stage of waterlogging stress, while the degradation of chlorophyll b intensified with prolonged stress, and carotenoid content remained relatively stable. Additionally, this study investigated changes in root lignin, indicating its role in enhancing cell wall stability and tolerance to cope with hypoxic stress. Using DESeq2, 246 differentially expressed miRNAs were identified, among which significant changes were observed in the miR156, miR166, miR167, and miR399 families. These miRNAs may play a crucial regulatory role in eggplant’s adaptation to the hypoxic environment after waterlogging stress. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that differentially expressed genes were mainly related to cellular physiological processes, metabolic processes, and the biosynthesis of secondary metabolites, influencing the seedlings’ stress resistance under different waterlogging conditions. Furthermore, by constructing a regulatory miRNA–target gene network that pertains to eggplant’s response to waterlogging stress, we have laid the foundation for revealing the molecular mechanisms of eggplant’s response to waterlogging stress.
Prevalence of Carbapenem Resistance Genes among Acinetobacter baumannii Isolated from a Teaching Hospital in Taiwan
Authors: Su Pai-Wei , Yang Emirlyn Cheng , Moi Sin-Hua , Yang Cheng-Hong , Chuang Li-Yeh
Published: August 2023

The problem of antibiotic-resistant strains has become a global public issue; antibiotic resistance not only limits the choice of treatments but also increases morbidity, mortality and treatment costs. The multi-drug resistant Acinetobacter baumannii is occurring simultaneously in hospitals and has become a major public health issue worldwide. Although many medical units have begun to control the use of antibiotics and paid attention to the issue of drug resistance, understanding the transmission pathways of clinical drug-resistant bacteria and drug-resistant mechanisms can be effective in real-time control and prevent the outbreak of antibiotic-resistant pathogens. In this study, a total of 154 isolates of Acinetobacter baumannii obtained from Chia-Yi Christian Hospital in Taiwan were collected for specific resistance genotyping analysis. Ten genes related to drug resistance, including blaOXA-51-like, blaOXA-23-like, blaOXA-58-like, blaOXA-24-like, blaOXA-143-like, tnpA, ISAba1, blaPER-1, blaNDM and blaADC, and the repetitive element (ERIC2) were selected for genotyping analysis. The results revealed that 135 A. baumannii isolates (87.6%) carried the blaOXA-51-like gene, 4.5% of the isolates harbored the blaOXA-23-like gene, and 3.2% of the isolates carried the blaOXA-58-like gene. However, neither the blaOXA-24-like nor blaOXA-143-like genes were detected in the isolates. Analysis of ESBL-producing strains revealed that blaNDM was not found in the test strains, but 38.3% of the test isolates carried blaPER-1. In addition, blaADC, tnpA and ISAba1genes were found in 64.9%, 74% and 93% of the isolates, respectively. Among the carbapenem-resistant strains of A. baumannii, 68% of the isolates presenting a higher antibiotic resistance carried both tnpA and ISAba1 genes. Analysis of the relationship between their phenotypes (antibiotic resistant and biofilm formation) and genotypes (antibiotic-resistant genes and biofilm-related genes) studied indicated that the bap, ompA, ISAba1and blaOXA-51 genes influenced biofilm formation and antibiotic resistance patterns based on the statistical results of a hierarchical clustering dendrogram. The analysis of the antibiotic-resistant mechanism provides valuable information for the screening, identification, diagnosis, treatment and control of clinical antibiotic-resistant pathogens, and is an important reference pointer to prevent strains from producing resistance.
Berry Extracts and Their Bioactive Compounds Mitigate LPS and DNFB-Mediated Dendritic Cell Activation and Induction of Antigen Specific T-Cell Effector Responses
Authors: Upadhaya Puja , Lamenza Felipe F. , Shrestha Suvekshya , Roth Peyton , Jagadeesha Sushmitha , Pracha Hasan , Horn Natalie A. , Oghumu Steve
Published: August 2023

Berries have gained widespread recognition for their abundant natural antioxidant, anti-inflammatory, and immunomodulatory properties. However, there has been limited research conducted thus far to investigate the role of the active constituents of berries in alleviating contact hypersensitivity (CHS), the most prevalent occupational dermatological disease. Our study involved an ex vivo investigation aimed at evaluating the impact of black raspberry extract (BRB-E) and various natural compounds found in berries, such as protocatechuic acid (PCA), proanthocyanidins (PANT), ellagic acid (EA), and kaempferol (KMP), on mitigating the pathogenicity of CHS. We examined the efficacy of these natural compounds on the activation of dendritic cells (DCs) triggered by 2,4-dinitrofluorobenzene (DNFB) and lipopolysaccharide (LPS). Specifically, we measured the expression of activation markers CD40, CD80, CD83, and CD86 and the production of proinflammatory cytokines, including Interleukin (IL)-12, IL-6, TNF-α, and IL-10, to gain further insights. Potential mechanisms through which these phytochemicals could alleviate CHS were also investigated by investigating the role of phospho-ERK. Subsequently, DCs were co-cultured with T-cells specific to the OVA323-339 peptide to examine the specific T-cell effector responses resulting from these interactions. Our findings demonstrated that BRB-E, PCA, PANT, and EA, but not KMP, inhibited phosphorylation of ERK in LPS-activated DCs. At higher doses, EA significantly reduced expression of all the activation markers studied in DNFB- and LPS-stimulated DCs. All compounds tested reduced the level of IL-6 in DNFB-stimulated DCs in Flt3L as well as in GM-CSF-derived DCs. However, levels of IL-12 were reduced by all the tested compounds in LPS-stimulated Flt3L-derived BMDCs. PCA, PANT, EA, and KMP inhibited the activated DC-mediated Interferon (IFN)-γ and IL-17 production by T-cells. Interestingly, PANT, EA, and KMP significantly reduced T-cell proliferation and the associated IL-2 production. Our study provides evidence for differential effects of berry extracts and natural compounds on DNFB and LPS-activated DCs revealing potential novel approaches for mitigating CHS.
Plasma Ion Bombardment Induced Heat Flux on the Wafer Surface in Inductively Coupled Plasma Reactive Ion Etch
Authors: Cho Sung Il , Park Hyun Keun , An Surin , Hong Sang Jeen
Published: August 2023

Plasma plays an important role in semiconductor processes. With the recent miniaturization and integration, the control of plasma became essential for success in the critical dimension of a few nanometers and etch narrow and deep holes with their high aspect ratios. Recently, the etching process has reached physical limitations due to a significant increase in wafer surface temperature under the elevated amount of RF power, affecting not only the warpage phenomenon, but also etching uniformity and etching profiles. Therefore, the plasma characteristics are identified using an invasive single Langmuir probe (SLP) for wafer temperature diagnosis. Optical data is obtained through a non-invasive optical emission spectroscopy (OES) and the plasma parameters are derived to compare and verify with the SLP. Two variables, electron temperature and electron density, are substituted for the heat flux formula to derive the heat flux according to the location. Using a wafer-type temperature sensor, the trends of the derived heat flux values towards wafer chuck were investigated. This study presents a method to calculate heat flux values in real time, anticipate wafer temperatures, and potentially illuminate existing ion heating problems.
Analysis of Mechanical Properties of Air-Ribbed Skeleton Membrane Structure
Authors: Zheng Lei , Xu Jiannan , Li Feng , Zhang Jun , Su Ronghua , Wang Jing , Liu Ying , Ru Yi
Published: August 2023

The existing large inflatable membrane structure is complex in structure and takes a long time to be extended. In this paper, an air-ribbed skeleton membrane structure is proposed to meet the requirement of camp and emergency tents needing to be set up quickly and easily. Ten single-arch air ribs are arranged side by side to support the tarpaulin, and wind ropes are added to the end and side faces of the tarpaulin to remain stable. The finite element model of the air-ribbed skeleton membrane structure is established to analyze the stress and the displacement of the membrane structure under combined wind and snow loads. The maximum displacement (439.4 mm) and maximum stress (29.94 MPa) are both within the safe standard. The stress and the displacement of the membrane structure in the wind load case is affected by the angle of the wind. The value of maximum stress and displacement at the wind angle of 90° are both lower than those at 0° and 45° in the wind load case. It is advisable to align the site layout of the membrane structure at the wind angle of 90°. The effect of the angle of wind ropes on the stress and displacement of the membrane structure is also studied. The maximum stress and displacement in the case when the angle of wind rope is 30° is smaller than those in the case when the angle of wind ropes is 45°. It is recommended that the wind rope should be laid at 30° to reinforce the membrane structure.
Review of sEMG for Robot Control: Techniques and Applications
Authors: Song Tao , Yan Zhe , Guo Shuai , Li Yuwen , Li Xianhua , Xi Fengfeng
Published: August 2023

Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.
Parameter-Optimal-Gain-Arguable Iterative Learning Control for Linear Time-Invariant Systems with Quantized Error
Authors: Liu Yan , Ruan Xiaoe
Published: August 2023

In this paper, a parameter optimal gain-arguable iterative learning control algorithm is proposed for a class of linear discrete-time systems with quantized error. Based on the lifting model description for ILC systems, the iteration time-variable derivative learning gain in the algorithm is optimized by resolving a minimization problem regarding the tracking error energy and the learning effort amplified by a weighting factor. Further, the tracking error can be monotonically convergent to zero when the condition is guaranteed and the rate of convergence can be adjusted by scaling the weighting factor of an optimization problem. This algorithm is more innovative when compared with the existing iterative learning control algorithm for quantization systems. The innovations of this algorithm are as follows: (i) this optimization-based strategy for selecting learning gains can improve the active learning ability of the control mechanism and avoid the passivity of existing selective learning gains; (ii) the algorithm of POGAILC with data quantization can improve the convergence performance of tracking errors and reduce the negative effects of data quantization on the control performance of the logarithmic quantizer; and (iii) we provide a rigorous algorithm convergence analysis by deriving the existence of the unique solution for the optimal learning-gain vector under a singular and nonsingular tracking-error diagonalized matrix. Finally, numerical simulations are used to demonstrate the effectiveness of the algorithm.
Review of Soft Computing Techniques in Monitoring Cardiovascular Disease in the Context of South Asian Countries
Authors: Thakur Gajendra Singh , Sahu Sunil Kumar , Swamy N. Kumar , Gupta Manish , Jan Tony , Prasad Mukesh
Published: August 2023

The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models.
Research and Application of Coupled Mechanism and Data-Driven Prediction of Blast Furnace Permeability Index
Authors: Tan Kangkang , Li Zezheng , Han Yang , Qi Xiwei , Wang Wei
Published: August 2023

In order to ensure the stable operation of blast furnace production, it is necessary to keep abreast of the trends in the gas permeability index of the blast furnace. As one of the key parameters to be monitored in the process of blast furnace smelting, the gas permeability index directly reflects the performance of the blast furnace in the actual production of the furnace. Continuous monitoring of the permeability index is required in the actual production of the blast furnace in order to effectively guarantee the stable and smooth operation of the blast furnace. The aim of this study is to accurately predict the trend in the blast furnace gas permeability index by constructing an intelligent prediction model and utilizing a data-driven approach to monitor the gas permeability index and ensure the stable operation of the blast furnace. First, based on the actual production data of a #2 blast furnace of an iron and steel enterprise, an isolated forest algorithm is applied to detect and eliminate the outliers in the original data, and then a data driver set is constructed after normalization of the deviation. Second, by analyzing the coupling mechanism between the blast furnace permeability and gas flow, as well as Spearman correlation analysis and MIC maximum information coefficient (MIC) analysis, key parameters are screened out as feature variables from the data-driven set. Finally, a wavelet neural network algorithm is used to construct an intelligent prediction model of the blast furnace gas permeability index. Compared with a BP neural network (BP), a particle swarm-optimized BP neural network (PSO-BP), and XGBoost, the wavelet neural network shows obvious advantages when the error is controlled in the range of ±0.1, and the prediction accuracy can reach 95.71%. The model is applied to the actual production of a #2 blast furnace of an iron and steel enterprise, and the results show that the predicted value of the blast furnace permeability index is highly consistent with the actual value of real-time blast furnace production, which verifies its excellent characteristics.
Thermodynamic and Economic Analysis of a Liquid Air Energy Storage System with Carbon Capture and Storage for Gas Power Plants
Authors: Qin Xiaoqiao , Tan Hongbo , Wen Na , Liu Weiming
Published: August 2023

Liquid air energy storage (LAES) technology is helpful for large-scale electrical energy storage (EES), but faces the challenge of insufficient peak power output. To address this issue, this study proposed an efficient and green system integrating LAES, a natural gas power plant (NGPP), and carbon capture. The research explores whether the integration design is theoretically feasible for future adoption in operating the LAES system and NGPP. The effect of the charging pressure, the number of air expansion stages, and electricity prices on the overall thermodynamic and economic characteristics are investigated. The round-trip efficiency and the exergy round-trip efficiency of the proposed system are 47.72% and 69.74%, respectively. The calculations show that the minimum dynamic payback period for such a project is 3.72 years, and the lowest levelized cost of electricity is 0.0802 USD·kWh−1. This work provides a reference for peak-shaving power stations with energy storage and carbon capture.
Fuzzy Adaptive PSO-ELM Algorithm Applied to Vehicle Sound Quality Prediction
Authors: Wang Chenlin , Yang Gongzhuo , Li Junyu , Huang Qibai
Published: August 2023

When dealing with specific tasks, the hidden layer output matrix of an extreme learning machine (ELM) may change, largely due to the random assigned weight matrix of the input layer and the threshold matrix of the hidden layer, which sequentially leads to the corresponding change to output weights. The unstable fluctuations of the output weights increase the structural risk and the empirical risk of ELM. This paper proposed a fuzzy adaptive particle swarm optimization (PSO) algorithm to solve this problem, which could nonlinearly control the inertia factor during the iteration by fuzzy control. Based on the fuzzy adaptive PSO-ELM algorithm, a sound quality prediction model was developed. The prediction results of this model were compared with the other three sound quality prediction models. The results showed that the fuzzy adaptive PSO-ELM model was more precise. In addition, in comparison with two other adaptive inertia factor algorithms, the fuzzy adaptive PSO-ELM model was the fastest model to reach goal accuracy.
Advances in Pulp and Paper Technologies
Authors: Kim Chul-Hwan
Published: August 2023

It is of great honor to present this Editorial for the Special Issue “Advances in Pulp and Paper Technologies” [...]
Special Issue: “Novel Approaches and Applications in Ergonomic Design II”
Authors: You Heecheon
Published: August 2023

The appropriate design of human–system interactions is crucial for ensuring safety, usability, productivity, and well-being [...]
The Impact of Salinity and Nutrient Regimes on the Agro-Morphological Traits and Water Use Efficiency of Tomato under Hydroponic Conditions
Authors: Madugundu Rangaswamy , Al-Gaadi Khalid A. , Tola ElKamil , Patil Virupakshagouda C. , Sigrimis Nick
Published: August 2023

The effects of saline water on three greenhouse tomato cultivars (Feisty-Red, Ghandowra-F1, and Valouro-RZ) under three salinity concentrations (S1, ~2.5 dS m−1; S2, ~6.0 dS m−1; and ~9.0 dS m−1) and four nutrient regimes (N1–N4) were studied by evaluating the vegetative growth, chlorophyll content, leaf area, water use efficiency (WUE), and fruit yield of the cultivars. Vegetative growth parameters, such as plant height, leaf area, and stem diameter, were negatively correlated with increased levels of salinity. Also, the lowest WUE was noted for the high-salinity (~9.0 dS m−1) treatments. The Valouro-RZ cultivar performed better in terms of vegetative growth parameters when compared to both the Ghandowra-F1 and Feisty-Red cultivars. The plants grafted onto Maxifort rootstock showed more tolerance to salinity stress, with significant differences in plant growth, tomato yield, and WUE when compared with the non-grafted plants. The use of a modified nutrient solution (N2) in combination with moderately saline water (S2, ~6.0 dS m−1) resulted in a high mean yield (30.7 kg m−2), with a reduction of about ~1.6% compared with the mean yield of the control (i.e., the combination of S1 and N1), which was estimated to be about 31.2 kg m−2. High salinity significantly affected the mean WUE, which was the highest at 31.3 kg m−3 for the control plants (low salinity—S1), followed by the moderate-salinity (S2) plants at 30.4 kg m−3, and the lowest mean WUE was recorded for the high-salinity (S3) plants at 17.7 kg m−3. These results indicate that a combination of grafting onto rootstocks and using an appropriate nutrient recipe (i.e., N2 in this study) can mitigate the negative effects of salt stress on tomato plants grown under hydroponic conditions.
Long Term Skeletal, Alveolar, and Dental Expansion Effects of the Midfacial Skeletal Expander
Authors: Ding Catherine , Paredes Ney , Wu Ben , Moon Won
Published: August 2023

Background: The Midfacial Skeletal Expander (MSE) produces a greater skeletal effect than its dentoalveolar side effects. The aim of this study was to quantify the stability of the different components of MSE expansion post-orthodontic treatment. Methods: Fourteen subjects (mean age of 20.4 ± 3.5 years) were treated with the MSE. The pre-expansion (T0), post-expansion (T1), and post-treatment (T2) CBCT records were superimposed and compared. The rotational fulcrum of the zygomaticomaxillary complex was identified, and angular measurements were generated to assess changes in the zygomaticomaxillary complex (skeletal expansion), dentoalveolar bone (alveolar bone bending), and maxillary first molars (dental tipping). The stability of all three components after orthodontic treatment was also assessed by comparing changes between T0–T1 and T0–T2. Results: Post-expansion, angular measurements showed that skeletal expansion accounted for 87.50% and 88.56% of total expansion, alveolar bone bending for 7.09% and 5.23%, and dental tipping for 5.41% and 6.21% on the right and left sides, respectively. Post-treatment skeletal expansion relapsed by 11.20% and 13.28% on the right and left sides, respectively. Conclusions: The MSE mainly produces skeletal changes with insignificant and negligible dentoalveolar changes immediately after expansion. In the long term, the majority of skeletal expansion was maintained.
Construction Control and Monitoring Platform of a Large-Segment Steel Box Girder with Hoisting Installation
Authors: Wen Feng , Liang Xu , Chen Chunlei , Xu Linghua , Feng Qian
Published: August 2023

The large-segment hoisting construction technology for bridges is increasingly widely used due to its flexibility and efficiency, although it also poses challenges to construction monitoring. Traditional monitoring technology is unitary with low data processing efficiency, making it difficult to meet the accuracy requirements of large-segment hoisting. The application of digital technology has brought about an opportunity for innovation in bridge construction monitoring technology. To address existing challenges and explore digital applications, this paper takes the integral hoisting construction control of the large-segment steel box girder in a large cross-sea bridge as an example, developing an alignment, stress, and temperature monitoring scheme by taking the key points of hoisting construction control into consideration. A monitoring platform was developed, and the workflow of large-segment hoisting construction monitoring is systematically summarized from the viewpoint of practical engineering, which provides a valuable reference for achieving precise and efficient construction monitoring and control in similar projects.
Investigation of the Motion Characteristics of Parts on a Platform Subjected to Planar Oscillations
Authors: Kilikevičius Sigitas , Liutkauskienė Kristina , Česnavičius Ramūnas , Keršys Artūras , Makaras Rolandas
Published: August 2023

Positioning applications are very important in a variety of industrial processes, including automatic assembly. This paper proposes a technique for positioning applications that involves employing a platform subjected to planar oscillations along circular, elliptical, and complex trajectories. Dynamic and mathematical models of the motion of a part on the platform were developed to investigate the motion characteristics of the part. The research showed that when the platform was excited in two perpendicular directions by sinusoidal waves, different trajectories of the part’s motion could be obtained by controlling excitation parameters such as the frequencies and amplitudes of the waves and the phase shift between the waves. Furthermore, by adjusting these parameters, the average displacement velocity of the part could be controlled. The results demonstrate that the part can be moved in any direction at a given velocity and can be subjected to complex dense positioning trajectories. Therefore, such a platform can be applied in feeding, positioning, and manipulation tasks.
Product Styling Cognition Based on Kansei Engineering Theory and Implicit Measurement
Authors: Zhang Qinwei , Liu Zhifeng , Yang Bangqi , Wang Caixia
Published: August 2023

Effective product styling designs must increasingly address users’ emotional requirements. This study introduces a product styling design method combining electroencephalography (EEG) and eye tracking for multimodal measurement based on the Kansei engineering theory. The feasibility of determining a target image using a similarity calculation model is verified. An experimental paradigm based on implicit measures is presented for product styling cognition research. This paradigm involves determining the target image, sample selection, target image matching experiments, and product styling cognition experiments. Based on the combined EEG and eye-tracking measurements, insights into product-form cognition are deduced to provide a scientific basis for product-form innovation design. Notably, variations in event-related potential during user cognition of product styling are more evident in the product-styling perception phase than in the evaluation phase. In the styling perception phase, samples with “high match” with the target image elicit more pronounced EEG responses than those with “low match”. These findings demonstrate the viability of understanding product-form cognition through multimodal implicit measurements, addressing issues such as the pronounced subjectivity inherent in traditional methods. Furthermore, this approach provides a pioneering technique for Kansei engineering research and offers a methodology for multimodal implicit measurements of product innovation design.
Effectiveness of Immature Asian Pear Extract on Pulmonary Injury Caused by Particulate Matter in Mice
Authors: Kim Mi-Ran , Lee Jin-Hwa , Ku Mo-Un , Kim Ki-Young , Shin Su , Hong Eun-Jin , Ku Sae-Kwang , Choi Jae-Suk
Published: August 2023

The use of natural products in developing respiratory-function-protective pharmaceuticals is actively progressing. However, in this context, the improvement effects of young Asian pear (Pyrus pyrifolia Nakai) extracts have not been evaluated yet. Thus, this study investigated the anti-inflammatory and lung damage improvement effects of immature Asian pear extract (IAP; 400, 200, and 100 mg/kg) using a particulate matter 2.5 μm (PM2.5)-induced sub-acute lung injury mouse model. The experimental results were compared with dexamethasone (0.75 mg/kg), used as a control drug. After two intranasal instillations of PM2.5 and ten doses of IAP extract for eight days, changes in macroscopic lung autopsy, leukocyte fractionation from bronchoalveolar lavage fluid, lung antioxidant defense system, lung histopathology, and mRNA expression in lung tissue were confirmed. Stress-induced inflammatory lung damage through the increased expression of PM2.5-induced PI3K/Akt and p38 MAPK mRNA was significantly suppressed via the administration of IAP extract (400–100 mg/kg). Furthermore, IAP extract administration promoted serous fluid production in lung tissue, increased substance P and ACh levels, and decreased mucus-production-related expression of MUC5AC and MUC5B mRNA. Interestingly, the observed effects showed a dose-dependent manner without serious hepatotoxicity. The results of this study indicate that a proper oral administration of IAP extract could be helpful in protecting against lung diseases, positioning IAP extract as a potential candidate for an alternative agent to safeguard the respiratory system.
Ground Deformation Associated with Deep Excavations in Beijing, China
Authors: Li Shu , Ge Chenhe , Li Pengfei , Yang Meng
Published: August 2023

A performance study is considered to be reliable method for comprehending deformations associated with deep excavation. To gain insight into the laws governing ground deformations that are associated with deep excavation, details of 88 cases were collected and analyzed in Beijing. The results were compared with worldwide case histories. Field data were selected to survey the ground behavior and to examine the correlation between deformation and excavation. The position and magnitude of the final ground deformation (δv), as well as the maximum deformation (δvm), the correlations between δvm and excavation depth (H), the length–width ratio, embedded depth ratio (EDR), and the stiffness of the support system, were assessed. The clear evolution process, influence zone, and final deformation pattern are illustrated. Our study revealed the following: (1) the groove pattern is detected in the final deformation of the ground surface, δvm occurred when positioned approximately 0.42H~0.62H off the wall, when the 1st~2nd supports on the bottom were removed; (2) δvm increases with an increase in H, and it ranges from 0.04% to 0.12% when H has an average value of approximately 0.089%; (3) EDR has an observable effect on reducing the δvm, as there a slight impact was observed until the ratios exceeded 0.4; (4) the deformation value of the 75% monitoring points ranged from −25 mm to 0 mm; (5) excavation could cause minor upheaval in some areas, but the upheaval reduces with increasing levels of excavation, so both deformation magnitude and the number of points are low; (6) deformation exhibits clear temporal–spatial characteristics, the settlement rate gradually increased over time, especially after drainage started or consolidation appeared, and when the internal structure is completed, δvm decreases with the rise in support system stiffness, ranging from 7000 to 11,000, and deformation becomes stable; and (7) transverse sections near the excavation center experienced larger deformations than others and the smallest deformations were near the corners, a significant increase occurs with the removal of the lowest 1–2 struts, particularly on the long side where ∆δv reaches 2.8 ± 0.75 mm, and the influence zones extend from approximately 2.5H to 3H beyond the excavated face. These findings have valuable implications for designing and constructing similar projects in Beijing and other regions, as they can help prevent accidents and minimize resource wastage.
Solar Sail Transfer Trajectory Design for Comet 29P/Schwassmann–Wachmann 1 Rendezvous
Authors: Quarta Alessandro A. , Abu Salem Karim , Palaia Giuseppe
Published: August 2023

The goal of this paper is to analyze the optimal transfer towards the periodic comet 29P/Schwassmann–Wachmann 1 of a solar sail-based spacecraft. This periodic and active comet is an interesting and still unexplored small body that has been regarded as an object of the Centaurs group. In this work, a classical (heliocentric) orbit-to-orbit transfer is studied from an optimal viewpoint, by finding the spacecraft trajectories that minimize the flight time for a given value of the solar sail characteristic acceleration, that is, the typical performance parameter of a photonic sail. In particular, the optimal Earth–comet transfer is studied both in a typical three-dimensional mission scenario and with a simplified two-dimensional approach, whose aim is to rapidly obtain an accurate estimation of the minimum flight time with a reduced computation cost. The numerical simulations illustrate the mission performance, in terms of the characteristics of the rapid transfer trajectory, as a function of the typical propulsive parameter and the solar sail thrust model.