bluuebunny/crossref_metadata_2025 huggingface dataset:
Repetitive Cerulein-Induced Chronic Pancreatitis in Growing Pigs—A Pilot Study
Authors: Tomaszewska Ewa , Świątkiewicz Małgorzata , Muszyński Siemowit , Donaldson Janine , Ropka-Molik Katarzyna , Arciszewski Marcin B. , Murawski Maciej , Schwarz Tomasz , Dobrowolski Piotr , Szymańczyk Sylwia , Dresler Sławomir , Bonior Joanna
Published: April 2023

Chronic pancreatitis (CP) is an irreversible and progressive inflammatory disease. Knowledge on the development and progression of CP is limited. The goal of the study was to define the serum profile of pro-inflammatory cytokines and the cell antioxidant defense system (superoxidase dismutase—SOD, and reduced glutathione—GSH) over time in a cerulein-induced CP model and explore the impact of these changes on selected cytokines in the intestinal mucosa and pancreatic tissue, as well as on selected serum biochemical parameters. The mRNA expression of CLDN1 and CDH1 genes, and levels of Claudin-1 and E-cadherin, proteins of gut barrier, in the intestinal mucosa were determined via western blot analysis. The study showed moderate pathomorphological changes in the pigs’ pancreas 43 days after the last cerulein injection. Blood serum levels of interleukin (IL)-1-beta, IL-6, tumor necrosis factor alpha (TNF-alpha), C-reactive protein (CRP), lactate dehydrogenase (LDH), gamma-glutamyl transpeptidase (GGTP), SOD and GSH were increased following cerulein injections. IL-1-beta, IL-6, TNF-alpha and GSH were also increased in jejunal mucosa and pancreatic tissue. In duodenum, decreased mRNA expression of CDH1 and level of E-cadherin and increased D-lactate, an indicator of leaky gut, indicating an inflammatory state, were observed. Based on the current results, we can conclude that repetitive cerulein injections in growing pigs not only led to CP over time, but also induced inflammation in the intestine. As a result of the inflammation, the intestinal barrier was impaired.
Advances in Molecular and Translational Medicine
Authors: Boccellino Mariarosaria
Published: April 2023

Translational medicine is an interdisciplinary field that combines basic research findings with clinical practice to accelerate the development of new diagnostic tools, therapies, and preventive strategies for human diseases [...]
Rapid and Sensitive Detection of Streptococcus iniae in Trachinotus ovatus Based on Multienzyme Isothermal Rapid Amplification
Authors: Wang Yifen , Niu Jingjing , Sun Minmin , Li Ziyi , Wang Xiangyuan , He Yan , Qi Jie
Published: April 2023

Infectious diseases caused by Streptococcus iniae lead to massive death of fish, compose a serious threat to the global aquaculture industry, and constitute a risk to humans who deal with raw fish. In order to realize the early diagnosis of S. iniae, and control the outbreak and spread of disease, it is of great significance to establish fast, sensitive, and convenient detection methods for S. iniae. In the present study, two methods of real-time MIRA (multienzyme isothermal rapid amplification, MIRA) and MIRA-LFD (combining MIRA with lateral flow dipsticks (LFD)) for the simA gene of S. iniae were established, which could complete amplification at a constant temperature of 42 °C within 20 min. Real-time MIRA and MIRA-LFD assays showed high sensitivity (97 fg/μL or 7.6 × 102 CFU/mL), which were consistent with the sensitivity of real-time PCR and 10 times higher than that of PCR with strong specificity, repeatability simplicity, and rapidity for S. iniae originating from Trachinotus ovatus. In summary, real-time MIRA and MIRA-LFD provide effective ways for early diagnosis of S. iniae in aquaculture, especially for units in poor conditions.
Pharmacological Inhibition of the Asparaginyl Endopeptidase (AEP) in an Alzheimer’s Disease Model Improves the Survival and Efficacy of Transplanted Neural Stem Cells
Authors: Cheng Qing , Ma Xiaoli , Liu Jingjing , Feng Xuemei , Liu Yan , Wang Yanxia , Ni Wenwen , Song Mingke
Published: April 2023

Stem-cell-based therapy is very promising for Alzheimer’s disease (AD), yet has not become a reality. A critical challenge is the transplantation microenvironment, which impacts the therapeutic effect of stem cells. In AD brains, amyloid-beta (Aβ) peptides and inflammatory cytokines continuously poison the tissue microenvironment, leading to low survival of grafted cells and restricted efficacy. It is necessary to create a growth-supporting microenvironment for transplanted cells. Recent advances in AD studies suggest that the asparaginyl endopeptidase (AEP) is a potential intervention target for modifying pathological changes. We here chose APP/PS1 mice as an AD model and employed pharmacological inhibition of the AEP for one month to improve the brain microenvironment. Thereafter, we transplanted neural stem cells (NSCs) into the hippocampus and maintained therapy for one more month. We found that inhibition of AEPs resulted in a significant decrease of Aβ, TNF-α, IL-6 and IL-1β in their brains. In AD mice receiving NSC transplantation alone, the survival of NSCs was at a low level, while in combination with AEP inhibition pre-treatment the survival rate of engrafted cells was doubled. Within the 2-month treatment period, implantation of NSCs plus pre-inhibition of the AEP significantly enhanced neural plasticity of the hippocampus and rescued cognitive impairment. Neither NSC transplantation alone nor AEP inhibition alone achieved significant efficacy. In conclusion, pharmacological inhibition of the AEP ameliorated brain microenvironment of AD mice, and thus improved the survival and therapeutic efficacy of transplanted stem cells.
Extracellular Vesicles in Adipose Tissue Communication with the Healthy and Pathological Heart
Authors: Michel Lauriane Y. M.
Published: April 2023

Adipose tissue and its diverse cell types constitute one of the largest endocrine organs. With multiple depot locations, adipose tissue plays an important regulatory role through paracrine and endocrine communication, particularly through the secretion of a wide range of bioactive molecules, such as nucleic acids, proteins, lipids or adipocytokines. Over the past several years, research has uncovered a myriad of interorgan communication signals mediated by small lipid-derived nanovesicles known as extracellular vesicles (EVs), in which secreted bioactive molecules are stably transported as cargo molecules and delivered to adjacent cells or remote organs. EVs constitute an essential part of the human adipose secretome, and there is a growing body of evidence showing the crucial implications of adipose-derived EVs in the regulation of heart function and its adaptative capacity. The adipose tissue modifications and dysfunction observed in obesity and aging tremendously affect the adipose-EV secretome, with important consequences for the myocardium. The present review presents a comprehensive analysis of the findings in this novel area of research, reports the key roles played by adipose-derived EVs in interorgan cross-talk with the heart and discusses their implications in physiological and pathological conditions affecting adipose tissue and/or the heart (pressure overload, ischemia, diabetic cardiomyopathy, etc.).
Mesenchymal Stem Cell Behavior under Microgravity: From Stress Response to a Premature Senescence
Authors: Pala Renzo , Cruciani Sara , Manca Alessia , Garroni Giuseppe , EL Faqir Mohammed Amine , Lentini Veronica , Capobianco Giampiero , Pantaleo Antonella , Maioli Margherita
Published: April 2023

Mesenchymal stem cells are undifferentiated cells able to acquire different phenotypes under specific stimuli. Wharton’s jelly is a tissue in the umbilical cord that contains mesenchymal stromal cells (MSCs) with a high plasticity and differentiation potential. Their regeneration capability is compromised by cell damage and aging. The main cause of cell damage is oxidative stress coming from an imbalance between oxidant and antioxidant species. Microgravity represents a stressing condition able to induce ROS production, ultimately leading to different subcellular compartment damages. Here, we analyzed molecular programs of stemness (Oct-4; SOX2; Nanog), cell senescence, p19, p21 (WAF1/CIP1), p53, and stress response in WJ-MSCs exposed to microgravity. From our results, we can infer that a simulated microgravity environment is able to influence WJ-MSC behavior by modulating the expression of stress and stemness-related genes, cell proliferation regulators, and both proapoptotic and antiapoptotic genes. Our results suggest a cellular adaptation addressed to survival occurring during the first hours of simulated microgravity, followed by a loss of stemness and proliferation capability, probably related to the appearance of a molecular program of senescence.
Genome-Wide Analysis of the Expansin Gene Family in Populus and Characterization of Expression Changes in Response to Phytohormone (Abscisic Acid) and Abiotic (Low-Temperature) Stresses
Authors: Yin Zhihui , Zhou Fangwei , Chen Yingnan , Wu Huaitong , Yin Tongming
Published: April 2023

Expansins are a group of cell wall enzyme proteins that help to loosen cell walls by breaking hydrogen bonds between cellulose microfibrils and hemicellulose. Expansins are essential plant proteins that are involved in several key processes, including seed germination, the growth of pollen tubes and root hairs, fruit ripening and abscission processes. Currently, there is a lack of knowledge concerning the role of expansins in woody plants. In this study, we analyzed expansin genes using Populus genome as the study target. Thirty-six members of the expansin gene family were identified in Populus that were divided into four subfamilies (EXPA, EXPB, EXLA and EXLB). We analyzed the molecular structure, chromosome localization, evolutionary relationships and tissue specificity of these genes and investigated expression changes in responses to phytohormone and abiotic stresses of the expansin genes of Populus tremula L. (PtEXs). Molecular structure analysis revealed that each PtEX protein had several conserved motifs and all of the PtEXs genes had multiple exons. Chromosome structure analysis showed that the expansin gene family is distributed on 14 chromosomes. The PtEXs gene family expansion patterns showed segmental duplication. Transcriptome data of Populus revealed that 36 PtEXs genes were differently expressed in different tissues. Cis-element analysis showed that the PtEXs were closely associated with plant development and responses to phytohormone and abiotic stress. Quantitative real-time PCR showed that abscisic acid (ABA) and low-temperature treatment affected the expression of some PtEXs genes, suggesting that these genes are involved in responses to phytohormone and abiotic stress. This study provides a further understanding of the expansin gene family in Populus and forms a basis for future functional research studies.
microRNA Detection via Nanostructured Biochips for Early Cancer Diagnostics
Authors: Martino Sara , Tammaro Chiara , Misso Gabriella , Falco Michela , Scrima Marianna , Bocchetti Marco , Rea Ilaria , De Stefano Luca , Caraglia Michele
Published: April 2023

MicroRNA (miRNA) are constituted of approximately 22 nucleotides and play an important role in the regulation of many physiological functions and diseases. In the last 10 years, an increasing interest has been recorded in studying the expression profile of miRNAs in cancer. Real time-quantitative polymerase chain reaction (RT-qPCR), microarrays, and small RNA sequencing represent the gold standard techniques used in the last 30 years as detection methods. The advent of nanotechnology has allowed the fabrication of nanostructured biosensors which are widely exploited in the diagnostic field. Nanostructured biosensors offer many advantages: (i) their small size allows the construction of portable, wearable, and low-cost products; (ii) the large surface–volume ratio enables the loading of a great number of biorecognition elements (e.g., probes, receptors); and (iii) direct contact of the recognition element with the analyte increases the sensitivity and specificity inducing low limits of detection (LOD). In this review, the role of nanostructured biosensors in miRNA detection is explored, focusing on electrochemical and optical sensing. In particular, four types of nanomaterials (metallic nanoparticles, graphene oxide, quantum dots, and nanostructured polymers) are reported for both detection strategies with the aim to show their distinct properties and applications.
Serpentine and Vermiform Are Produced Autonomously to Fulfill Their Function in Drosophila Wings
Authors: Zhang Xubo , Ji Yanan , Moussian Bernard , Yang Shumin , Zhang Jianzhen , Zhang Tingting , Zhang Min
Published: April 2023

Group I chitin deacetylases (CDAs), CDA1 and CDA2, play an essential role in cuticle formation and molting in the process of insect wing development. A recent report showed that trachea are able to take up a secreted CDA1 (serpentine, serp) produced in the fat body to support normal tracheal development in the fruit fly Drosophila melanogaster. However, whether CDAs in wing tissue were produced locally or derived from the fat body remains an open question. To address this question, we applied tissue-specific RNAi against DmCDA1 (serpentine, serp) and DmCDA2 (vermiform, verm) in the fat body or the wing and analyzed the resulting phenotypes. We found that repression of serp and verm in the fat body had no effect on wing morphogenesis. RT-qPCR showed that RNAi against serp or verm in the fat body autonomously reduced their expression levels of serp or verm in the fat body but had no non-autonomous effect on the expression in wings. Furthermore, we showed that inhibition of serp or verm in the developing wing caused wing morphology and permeability deficiency. Taken together, the production of Serp and Verm in the wing was autonomous and independent of the fat body.
Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma
Authors: Yang Xinggang , Wu Juan , Chen Xiyang
Published: April 2023

Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human−computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC.
Machine Learning and Case-Based Reasoning for Real-Time Onboard Prediction of the Survivability of Ships
Authors: Louvros Panagiotis , Stefanidis Fotios , Boulougouris Evangelos , Komianos Alexandros , Vassalos Dracos
Published: April 2023

The subject of damaged stability has greatly profited from the development of new tools and techniques in recent history. Specifically, the increased computational power and the probabilistic approach have transformed the subject, increasing accuracy and fidelity, hence allowing for a universal application and the inclusion of the most probable scenarios. Currently, all ships are evaluated for their stability and are expected to survive the dangers they will most likely face. However, further advancements in simulations have made it possible to further increase the fidelity and accuracy of simulated casualties. Multiple time domain and, to a lesser extent, Computational Fluid dynamics (CFD) solutions have been suggested as the next “evolutionary” step for damage stability. However, while those techniques are demonstrably more accurate, the computational power to utilize them for the task of probabilistic evaluation is not there yet. In this paper, the authors present a novel approach that aims to serve as a stopgap measure for introducing the time domain simulations in the existing framework. Specifically, the methodology presented serves the purpose of a fast decision support tool which is able to provide information regarding the ongoing casualty utilizing prior knowledge gained from simulations. This work was needed and developed for the purposes of the EU-funded project SafePASS.
Parameter Prediction of the Non-Linear Nomoto Model for Different Ship Loading Conditions Using Support Vector Regression
Authors: Lan Jiafen , Zheng Mao , Chu Xiumin , Ding Shigan
Published: April 2023

Significant changes in the load of cargo ships make it difficult to simulate and control their motion. In this work, a parameter prediction method for a ship maneuvering motion model is developed based on parameter identification and support vector regression (SVR). First, the effects of least-squares (LS) and multi-innovation least-squares (MILS) parameter identification methods for the non-linear Nomoto model are investigated. The MILS method is then used to identify the parameters of the non-linear Nomoto model under various load conditions, and model training datasets are established. On this basis, SVR is used to predict the parameters of the non-linear Nomoto model. The results reveal that the MILS method converges faster than the LS method. The SVR method achieves lower accuracy than the MILS method, but exhibits reasonable prediction accuracy for zigzag motions, and the maneuvering motion model can be predicted as navigation conditions change.
Genome Re-Annotation and Transcriptome Analyses of Sanghuangporus sanghuang
Authors: Shen Zi-Qi , Jiang Ji-Hang , Li Chang-Tian , Li Yu , Zhou Li-Wei
Published: April 2023

Sanghuangporus sanghuang, the generic type of Sanghuangporus belonging to Hymenochaetaceae, is a precious medicinal wood-inhabiting macrofungus with high commercial potential. To facilitate the medicinal utilization of this fungal resource, transcriptome sequences are newly generated from S. sanghuang strain MS2. In association with the previously generated genome sequences from the same strain by our lab and all available fungal homologous protein sequences in the UniProtKB/Swiss-Prot Protein Sequence Database, a new methodology was employed for genome assembly and annotation. A total of 13,531 protein-coding genes were identified from the new version of the genome of S. sanghuang strain MS2 with a complete BUSCOs of 92.8%, which indicates a remarkable improvement in the accuracy and completeness of the genome assembly. In general, more genes involved in medicinal functions were annotated compared with the original version of the genome annotation, and most of these genes were also found in the transcriptome data of the currently sampled growth period. Given the above, the current genomic and transcriptomic data provides valuable insights into the evolution and metabolites analysis of S. sanghuang.
Metabolomic Analysis of Extracellular Vesicles from the Cereal Fungal Pathogen Fusarium graminearum
Authors: Garcia-Ceron Donovan , Truong Thy T. , Ratcliffe Julian , McKenna James A. , Bleackley Mark R. , Anderson Marilyn A.
Published: April 2023

Fusarium graminearum (F. graminearum) is a filamentous fungus that infects cereals such as corn, wheat, and barley, with serious impact on yield as well as quality when the grain is contaminated with mycotoxins. Despite the huge impact of F. graminearum on food security and mammalian health, the mechanisms used by F. graminearum to export virulence factors during infection are not fully understood and may involve non-classical secretory pathways. Extracellular vesicles (EVs) are lipid-bound compartments produced by cells of all kingdoms that transport several classes of macromolecules and are implicated in cell–cell communication. EVs produced by human fungal pathogens carry cargo that facilitate infection, leading us to ask whether plant fungal pathogens also deliver molecules that increase virulence via EVs. We examined the metabolome of the EVs produced by F. graminearum to determine whether they carry small molecules that could modulate plant–pathogen interactions. We discovered that EVs from F. graminearum were produced in liquid medium-containing inducers of trichothecene production, but in lower quantities compared to other media. Nanoparticle tracking analysis and cryo-electron microscopy revealed that the EVs were morphologically similar to EVs from other organisms; hence, the EVs were metabolically profiled using LC-ESI-MS/MS. This analysis revealed that EVs carry 2,4-dihydroxybenzophenone (BP-1) and metabolites that have been suggested by others to have a role in host–pathogen interactions. BP-1 reduced the growth of F. graminearum in an in vitro assay, suggesting that F. graminearum might use EVs to limit metabolite self-toxicity.
Characteristics Mode Analysis-Inspired Compact UWB Antenna with WLAN and X-Band Notch Features for Wireless Applications
Authors: Kumar Praveen , MM Manohara Pai , Kumar Pradeep , Ali Tanweer , Alsath M. Gulam Nabi , Suresh Vidhyashree
Published: April 2023

A compact circular structured monopole antenna for ultrawideband (UWB) and UWB dual-band notch applications is designed and fabricated on an FR4 substrate. The UWB antenna has a hybrid configuration of the circle and three ellipses as the radiating plane and less than a quarter-lowered ground plane. The overall dimensions of the projected antennas are 16 × 11 × 1.6 mm3, having a −10 dB impedance bandwidth of 113% (3.7–13.3 GHz). Further, two frequency band notches were created using two inverted U-shaped slots on the radiator. These slots notch the frequency band from 5–5.6 GHz and 7.3–8.3 GHz, covering IEEE 802.11, Wi-Fi, WLAN, and the entire X-band satellite communication. A comprehensive frequency and time domain analysis is performed to validate the projected antenna design’s effectiveness. In addition, a circuit model of the projected antenna design is built, and its performance is evaluated. Furthermore, unlike the traditional technique, which uses the simulated surface current distribution to verify functioning, characteristic mode analysis (CMA) is used to provide deeper insight into distinct modes on the antenna.
Scrutinizing the Hydrological Responses of Chennai, India Using Coupled SWAT-FEM Model under Land Use Land Cover and Climate Change Scenarios
Authors: Preetha Pooja , Hasan Mahbub
Published: April 2023

This study implemented a coupled SWAT-FEM simulation model to evaluate the impacts of land use land cover and climate change scenarios (LCS) on the water resources of river catchments in Chennai, India. The land use land cover data were obtained by merging the source data from National Remote Sensing Centre (NRSC) and International Water Management Institute (IWMI). Climate change simulations were obtained from four global climate models (GCM), including GFDL Baseline Scenario (1981–2000), GFDL A1B Scenario (2081–2100), CCSM4 Baseline Scenario (1986–2005), and CCSM4 A1B Scenario (2081–2100). The LCS predicted temperature increases of 2.32 °C and 1.74 °C for GFDL and CCSM4, respectively, by the end of the century. The water use predictions suggested increases above 20% in the utilization of water by 2100, inferring the noticeable dynamics of inter-annual as well as inter-month variability in water resources in the river basins of Chennai soon. The study is novel through its implementation of a coupled modeling approach to improve the practicality of the SWAT-FEM model and to deliver useful projections of land and climate change impacts on hydrological responses. The results provide useful insights into how the variability in climate conditions alters the spatiotemporal water responses in catchment scales.
Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China
Authors: Ding Lilian , Liao Yan , Zhu Congmou , Zheng Qiwei , Wang Ke
Published: April 2023

Identifying the trade-offs and synergies (TOSs) of ecosystem services (ESs) and their responses to landscape patterns at various scales, especially in mountainous areas, could benefit the strategies of ES management and landscape optimization. In this study, the southern Zhejiang Province, a hilly region in eastern China, was chosen as the study area. Five ESs, including food production (FP), carbon sequestration (CS), flood mitigation (FM), water conservation (WC), and soil retention (SR) in 2020 were quantified. The TOSs of these ESs were identified at four spatial scales (i.e., grid, watershed, town, and county scales) through Pearson correlation analysis and the spatial overlay method. The effects of landscape patterns on the TOSs of ESs were analyzed by applying a logistic regression model. Results showed that FP and other ESs were trade-offs, while the other ES pairs were synergies. Spatial overlay results showed that weak synergies increased significantly, while strong synergies decreased significantly with the increase of the scale. The direction of the influence of landscape pattern on TOSs did not change, but the magnitudes of the impacts were scale-dependent. Landscape composition (i.e., cropland%, forest%, construction land%) had more significant effects on the trade-offs of ESs than spatial configuration (i.e., LSI, PD, COHE, and SHDI). The magnitudes of impact of landscape composition were strengthened at larger scales, while the effects of landscape configuration on the TOSs of ESs became complex as the scale changed. The results of this study could contribute to understanding how landscape patterns affect TOSs across scales, which will promote the hierarchical governance of ESs in mountainous areas.
Speech Rate and Turn-Transition Pause Duration in Dutch and English Spontaneous Question-Answer Sequences
Authors: Hoogland Damar , White Laurence , Knight Sarah
Published: April 2023

The duration of inter-speaker pauses is a pragmatically salient aspect of conversation that is affected by linguistic and non-linguistic context. Theories of conversational turn-taking imply that, due to listener entrainment to the flow of syllables, a higher speech rate will be associated with shorter turn-transition times (TTT). Previous studies have found conflicting evidence, however, some of which may be due to methodological differences. In order to test the relationship between speech rate and TTT, and how this may be modulated by other dialogue factors, we used question-answer sequences from spontaneous conversational corpora in Dutch and English. As utterance-final lengthening is a local cue to turn endings, we also examined the impact of utterance-final syllable rhyme duration on TTT. Using mixed-effect linear regression models, we observed evidence for a positive relationship between speech rate and TTT: thus, a higher speech rate is associated with longer TTT, contrary to most theoretical predictions. Moreover, for answers following a pause (“gaps”) there was a marginal interaction between speech rate and final rhyme duration, such that relatively long final rhymes are associated with shorter TTT when foregoing speech rate is high. We also found evidence that polar (yes/no) questions are responded to with shorter TTT than open questions, and that direct answers have shorter TTT than responses that do not directly answer the questions. Moreover, the effect of speech rate on TTT was modulated by question type. We found no predictors of the (negative) TTT for answers that overlap with the foregoing questions. Overall, these observations suggest that TTT is governed by multiple dialogue factors, potentially including the salience of utterance-final timing cues. Contrary to some theoretical accounts, there is no strong evidence that higher speech rates are consistently associated with shorter TTT.
Application of the TRIZ Innovation System Method to Bicycle Handlebars
Authors: Yao Kai-Chao , Huang Wei-Tzer , Xu Jing-Ran , Huang Shu-Hua , Tsai Chin-Tang , Ho Wei-Sho , Liao Chun-Chung
Published: April 2023

This study investigated the application of the TRIZ innovation system method to bicycle handlebars and used a satisfaction survey to evaluate whether the bicycle handlebars met the users’ requirements, in order to reduce the cost and time needed for product development. First, we conducted a literature search and expert consultation, to explore the improvement requirements for handlebar design and bicycle motion injury; then, we used a contradiction matrix and the invention principle of the TRIZ method for analysis and design; and finally we used a human–machine system and the human measurement method of human factor engineering to analyze the user and bicycle handlebar operation and related dimensional definitions, as a reference for the design and to make a prototype bicycle handlebar. The SERVQUAL questionnaire was developed to compare the differences between the designed bicycle handlebar prototype and commercially available bicycle handlebars, and a practical analysis of the design was conducted using a two-dimensional quality model (Kano model) and the important–performance analysis method (IPA). Research results: 1. Innovative design of the bicycle handlebars: the development of height adjustment slots, front and rear angle design, front and rear swivel left and right extension mechanism, the user can clearly determine the current position and adjust to the most comfortable position to avoid waist, cervical vertebrae, and arm injuries. 2. Satisfaction assessment of bicycle handlebar innovation design: analyse the importance and satisfaction of bicycle handlebar innovation design, determine the production time and mass production value of the product, use questionnaires to conduct IPA and Kano model cross-analysis matrix, in order to reduce the time and cost required for product development.
Development and Performance Test of a Simulation-Based Tractor-Attachable Wind-Blast-Type Onion Stem Cutting Machine
Authors: Lee Sangyoon
Published: April 2023

Stem cutting process of many steps in onion farming involves the least supply of machines. Machines developed in foreign countries are not suitable owing to the geographic characteristics of Korea; it is necessary to develop customized machines. Therefore, in this study, a tractor-attachable blast-type onion stem cutting machine was developed to address these issues. To determine the installation angle of a detachable blade with a 300-mm length, 115-mm width, 5-mm thickness, and a cutting angle of 45° at the tip, we compared the simulation results with the analysis results obtained by fabricating a simple experimental device. Despite small differences in values, the R2 between the two results was found to be 0.99; if using a regression equation, they could be adequate for use in estimation. Moreover, by measuring the motor power consumption, wind speed, and high-speed camera shooting, the stem was raised, regardless of its water content, when the installation angle was greater than 30°. Motor power consumption could also be reduced when the installation angle was 30° rather than 40°. Therefore, the optimal cutter blade installation angle for the tractor-attachable blast-type onion stem cutting machine was confirmed to be 30°. The developed machine was tested at various rotating speeds. The best performance was obtained when the test was repeated thrice at a driving speed of 0.4 m/s. The remaining onion stem length averaged 9.98 cm, the standard deviation was 4.72 cm and the stem cutting rate was 96.8%.
Application of an Intensive Longitudinal Functional Model with Multiple Time Scales in Objectively Measured Children’s Physical Activity
Authors: Zahed Mostafa , Lalonde Trent , Skafyan Maryam
Published: April 2023

This study proposes an intensive longitudinal functional model with multiple time-varying scales and subject-specific random intercepts through mixed model equivalence that includes multiple functional predictors, one or more scalar covariates, and one or more scalar covariates. An estimation framework is proposed for estimating a time-varying coefficient function that is modeled as a linear combination of time-invariant functions with time-varying coefficients. The model takes advantage of the information structure of the penalty, while the estimation procedure utilizes the equivalence between penalized least squares estimation and linear mixed models. A number of simulations are conducted in order to empirically evaluate the process. In the simulation, it was observed that mean square errors for functional coefficients decreased with increasing sample size and level of association. Additionally, sample size had a greater impact on a smaller level of association, and level of association also had a greater impact on a smaller sample size. These results provide empirical evidence that ILFMM estimates of functional coefficients are close to the true functional estimate (basically unchanged). In addition, the results indicated that the AIC could be used to guide the choice of ridge weights. Moreover, when ridge weight ratios were sufficiently large, there was minimal impact on estimation performance. Studying two time scales is important in a wide range of fields, including physics, chemistry, biology, engineering, economics, and more. It allows researchers to gain a better understanding of complex systems and processes that operate over different time frames. Consequently, studying physical activities with two time scales is critical for advancing our understanding of human performance and health and for developing effective strategies to optimize physical activity and exercise programs. Therefore, the proposed model was applied to analyze the physical activity data from the Active Schools Institute of the University of Northern Colorado to determine what kind of time-structure patterns of activities could adequately describe the relationship between daily total magnitude and kids’ daily and weekly physical activity.
Design and Analysis of a New COVID-19 Model with Comparative Study of Control Strategies
Authors: Butt Azhar Iqbal Kashif , Batool Saira , Imran Muhammad , Al Nuwairan Muneerah
Published: April 2023

The COVID-19 pandemic has become a worldwide concern and has caused great frustration in the human community. Governments all over the world are struggling to combat the disease. In an effort to understand and address the situation, we conduct a thorough study of a COVID-19 model that provides insights into the dynamics of the disease. For this, we propose a new LSHSEAIHR COVID-19 model, where susceptible populations are divided into two sub-classes: low-risk susceptible populations, LS, and high-risk susceptible populations, HS. The aim of the subdivision of susceptible populations is to construct a model that is more reliable and realistic for disease control. We first prove the existence of a unique solution to the purposed model with the help of fundamental theorems of functional analysis and show that the solution lies in an invariant region. We compute the basic reproduction number and describe constraints that ensure the local and global asymptotic stability at equilibrium points. A sensitivity analysis is also carried out to identify the model’s most influential parameters. Next, as a disease transmission control technique, a class of isolation is added to the intended LSHSEAIHR model. We suggest simple fixed controls through the adjustment of quarantine rates as a first control technique. To reduce the spread of COVID-19 as well as to minimize the cost functional, we constitute an optimal control problem and develop necessary conditions using Pontryagin’s maximum principle. Finally, numerical simulations with and without controls are presented to demonstrate the efficiency and efficacy of the optimal control approach. The optimal control approach is also compared with an approach where the state model is solved numerically with different time-independent controls. The numerical results, which exhibit dynamical behavior of the COVID-19 system under the influence of various parameters, suggest that the implemented strategies, particularly the quarantine of infectious individuals, are effective in significantly reducing the number of infected individuals and achieving herd immunity.
Digital Twin Simulation Tools, Spatial Cognition Algorithms, and Multi-Sensor Fusion Technology in Sustainable Urban Governance Networks
Authors: Nica Elvira , Popescu Gheorghe H. , Poliak Milos , Kliestik Tomas , Sabie Oana-Matilda
Published: April 2023

Relevant research has investigated how predictive modeling algorithms, deep-learning-based sensing technologies, and big urban data configure immersive hyperconnected virtual spaces in digital twin cities: digital twin modeling tools, monitoring and sensing technologies, and Internet-of-Things-based decision support systems articulate big-data-driven urban geopolitics. This systematic review aims to inspect the recently published literature on digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks. We integrate research developing on how blockchain-based digital twins, smart infrastructure sensors, and real-time Internet of Things data assist urban computing technologies. The research problems are whether: data-driven smart sustainable urbanism requires visual recognition tools, monitoring and sensing technologies, and simulation-based digital twins; deep-learning-based sensing technologies, spatial cognition algorithms, and environment perception mechanisms configure digital twin cities; and digital twin simulation modeling, deep-learning-based sensing technologies, and urban data fusion optimize Internet-of-Things-based smart city environments. Our analyses particularly prove that virtual navigation tools, geospatial mapping technologies, and Internet of Things connected sensors enable smart urban governance. Digital twin simulation, data visualization tools, and ambient sound recognition software configure sustainable urban governance networks. Virtual simulation algorithms, deep learning neural network architectures, and cyber-physical cognitive systems articulate networked smart cities. Throughout January and March 2023, a quantitative literature review was carried out across the ProQuest, Scopus, and Web of Science databases, with search terms comprising “sustainable urban governance networks” + “digital twin simulation tools”, “spatial cognition algorithms”, and “multi-sensor fusion technology”. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow diagram was generated using a Shiny App. AXIS (Appraisal tool for Cross-Sectional Studies), Dedoose, MMAT (Mixed Methods Appraisal Tool), and the Systematic Review Data Repository (SRDR) were used to assess the quality of the identified scholarly sources. Dimensions and VOSviewer were employed for bibliometric mapping through spatial and data layout algorithms. The findings gathered from our analyses clarify that Internet-of-Things-based smart city environments integrate 3D virtual simulation technology, intelligent sensing devices, and digital twin modeling.
Three-Dimensional Path Planning of UAV Based on Improved Particle Swarm Optimization
Authors: Deng Lixia , Chen Huanyu , Zhang Xiaoyiqun , Liu Haiying
Published: April 2023

The traditional particle swarm optimization algorithm is fast and efficient, but it is easy to fall into a local optimum. An improved PSO algorithm is proposed and applied in 3D path planning of UAV to solve the problem. Improvement methods are described as follows: combining PSO algorithm with genetic algorithm (GA), setting dynamic inertia weight, adding sigmoid function to improve the crossover and mutation probability of genetic algorithm, and changing the selection method. The simulation results show that the improved PSO algorithm solves better route results and is faster and more stable.
Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder
Authors: Ali Abdullah Marish , Ghaleb Fuad A. , Mohammed Mohammed Sultan , Alsolami Fawaz Jaber , Khan Asif Irshad
Published: April 2023

Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate model. Most of the existing solutions rely solely on content-based features that are insufficient and overlapping. Moreover, most of the models used for classification are constructed with the concept of a dense features vector unsuitable for short news sentences. To address this problem, this study proposed a Web-Informed-Augmented Fake News Detection Model using Stacked Layers of Convolutional Neural Network and Deep Autoencoder called ICNN-AEN-DM. The augmented information is gathered from web searches from trusted sources to either support or reject the claims in the news content. Then staked layers of CNN with a deep autoencoder were constructed to train a probabilistic deep learning-base classifier. The probabilistic outputs of the stacked layers were used to train decision-making by staking multilayer perceptron (MLP) layers to the probabilistic deep learning layers. The results based on extensive experiments challenging datasets show that the proposed model performs better than the related work models. It achieves 26.6% and 8% improvement in detection accuracy and overall detection performance, respectively. Such achievements are promising for reducing the negative impacts of fake news on communities.
Receiving Routing Approach for Virtually Coupled Train Sets at a Railway Station
Authors: Zhang Yinggui , Xu Qianying , Yu Runchuan , Zhao Minghui , Liu Jiachen
Published: April 2023

Elaborated in several forms before being formally defined, virtually coupled train sets (VCTS) have become an issue for capacity increase with obvious shorter train intervals. As the station organization strategy is still ambiguous due to the lack of literature, the receiving routing problem for VCTS is studied in particular. First, the existing concept of VCTS is explained, which refers to the virtual connection of trains through safe and reliable communication technology, allowing short-interval collaborative operations without the need for physical equipment. Subsequently, the operating characteristics and receiving requirements are analyzed. With a summary of factors affecting receiving operations, a mathematical model is proposed with the objectives of minimizing operation duration and maximizing effectiveness, which is solved by an improved genetic algorithm (GA) with an elitist and adaptive strategy. Numerical tests are carried out 250 times based on a practical station and EMU parameters. The macro results show the valid pursuit of designed objectives with an average duration of 204.95 s and an efficiency of 91.76%. Microevolution of an optimal scheme indicates that safety requirements are met while the process duration is only 35.83% of the original CTCS-3 mode.
Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion
Authors: Dui Hongyan , Zhang Yulu , Zhang Songru , Zhang Yun-An
Published: April 2023

Urban road networks have promoted high-quality travel for residents by increasing connectivity and intelligence. But road congestion has not been effectively alleviated, causing a loss of time and energy. At present, the recovery of urban road networks mainly considers removing the failed edges. Considering the recovery cost and time, it is important to take active maintenance behavior to restore these networks. One of the key problems is dispatching traffic workers reasonably to achieve timely maintenance. In this paper, a flow-distribution-based process and execution (FD-PE) model is established for solving congestion. The maintenance centers (MC) study the reasons for and spread of congestion by edge flow. Based on the genetic algorithm (GA), two models of maintenance for urban road networks are developed, which include a single MC-centered dispatching plan and the co-scheduling of MCs. Both models aim at minimizing recovery time and allocating maintenance resources. The road network in Zhengzhou is borrowed as a case to explain the feasibility of the proposed models. The results show that on the premise of dividing network regions, it is reasonable to take a single MC to recover congestion. Compared with a single MC, the co-scheduling of MCs may save more time.
Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification
Authors: Alsubai Shtwai , Alqahtani Abdullah , Binbusayyis Adel , Sha Mohemmed , Gumaei Abdu , Wang Shuihua
Published: April 2023

Diabetic retinopathy seems to be the cause of micro-vascular retinal alterations. It remains a leading reason for blindness and vision loss in adults around the age of 20 to 74. Screening for this disease has become vital in identifying referable cases that require complete ophthalmic evaluation and treatment to avoid permanent loss of vision. The computer-aided design could ease this screening process, which requires limited time, and assist clinicians. The main complexity in classifying images involves huge computation, leading to slow classification. Certain image classification approaches integrating quantum computing have recently evolved to resolve this. With its parallel computing ability, quantum computing could assist in effective classification. The notion of integrating quantum computing with conventional image classification methods is theoretically feasible and advantageous. However, as existing image classification techniques have failed to procure high accuracy in classification, a robust approach is needed. The present research proposes a quantum-based deep convolutional neural network to avert these pitfalls and identify disease grades from the Indian Diabetic Retinopathy Image Dataset. Typically, quantum computing could make use of the maximum number of entangled qubits for image reconstruction without any additional information. This study involves conceptual enhancement by proposing an optimized structural system termed an optimized multiple-qbit gate quantum neural network for the classification of DR. In this case, multiple qubits are regarded as the ability of qubits in multiple states to exist concurrently, which permits performance improvement with the distinct additional qubit. The overall performance of this system is validated in accordance with performance metrics, and the proposed method achieves 100% accuracy, 100% precision, 100% recall, 100% specificity, and 100% f1-score.