bluuebunny/crossref_metadata_2025 huggingface dataset:
Expanding the evidence for cross-sector collaboration in implementation science: creating a collaborative, cross-sector, interagency, multidisciplinary team to serve patients experiencing homelessness and medical complexity at hospital discharge
Authors: Anderson Amanda Joy , Noyes Katia , Hewner Sharon
Published: September 2023

Introduction

Patients with medical and social complexity require care administered through cross-sector collaboration (CSC). Due to organizational complexity, biomedical emphasis, and exacerbated needs of patient populations, interventions requiring CSC prove challenging to implement and study. This report discusses challenges and provides strategies for implementation of CSC through a collaborative, cross-sector, interagency, multidisciplinary team model.

Methods

A collaborative, cross-sector, interagency, multidisciplinary team was formed called the Buffalo City Mission Recuperative Care Collaborative (RCU Collaborative), in Buffalo, NY, to provide care transition support for people experiencing homelessness at acute care hospital discharge through a medical respite program. Utilizing the Expert Recommendations for Implementing Change (ERIC) framework and feedback from cross-sector collaborative team, implementation strategies were drawn from three validated ERIC implementation strategy clusters: 1) Develop stakeholder relationships; 2) Use evaluative and iterative strategies; 3) Change infrastructure.

Results

Stakeholders identified the following factors as the main barriers: organizational culture clash, disparate visions, and workforce challenges related to COVID-19. Identified facilitators were clear group composition, clinical academic partnerships, and strategic linkages to acute care hospitals.

Discussion

A CSC interagency multidisciplinary team can facilitate complex care delivery for high-risk populations, such as medical respite care. Implementation planning is critically important when crossing agency boundaries for new multidisciplinary program development. Insights from this project can help to identify and minimize barriers and optimize utilization of facilitators, such as academic partners. Future research will address external organizational influences and emphasize CSC as central to interventions, not simply a domain to consider during implementation.
Dual mechanical impact of β-escin on model lipid membranes
Authors: Moleiro Lara H. , Martín-Romero María T. , Herráez-Aguilar Diego , Santiago José A. , Caselli Niccolò , Dargel Carina , Geisler Ramsia , Hellweg Thomas , Monroy Francisco
Published: September 2023

Understanding the mechanical behavior of biological membranes is of paramount importance in cell biophysics and in developing new biomaterials for medicine. In this study, we delve into the mechanical impact of β-escin, commonly referred to as escin, a naturally occurring biosurfactant derived from the seeds of the horse chestnut tree. To examine the modulable interaction between escin and dimyristoylphosphatidylcholine (DMPC), which is an archetypical fluid phospholipid and an essential constituent of the cellular fluid membrane, we have used artificial models based on the liquid crystal structure, such as bilayer vesicles and Langmuir monolayers. We have focused on the energetic and kinetic aspects of escin insertion when transversally adsorbed or longitudinally integrated within these model membranes. By employing surface microscopies of epifluorescence and Brewster angle reflectivity, we have elucidated the structural phase behavior of hybrid escin–phospholipid membranes, which exhibit dual mechanical properties characterized by high rigidity and reduced fluidity. Notably, at low temperatures, we observe a soft, glassy rheological behavior reminiscent of liquid crystalline ordered phases, which turns into a fluid-like viscoelasticity resembling more disordered phases at physiological temperatures. The hybrid membranes behave in one way or another as both are driven by an adsorption potential well imposed by escin cohesivity. These intriguing findings are discussed from a physicochemical perspective, highlighting their potential for future pharmacological designs and biomedical applications that exploit the dual mechanical impact of escin on biological membranes.
Perceived support, social and marital challenges in the lives of breast cancer survivors after illness: a self-administered cross-sectional survey
Authors: Ammar-Shehada Walaa , Abusaman Khaled , Bracke Piet
Published: September 2023

Background

Breast cancer (BC) is a traumatic illness. BC is the leading female cancer in Palestine. Complex socio-political dynamics impact patients’ lives, resulting in an increasing need for social support to develop resilience after illness.

Methods

Data was collected through a cross-sectional survey targeting women living in the Gaza Strip who had been diagnosed with BC. The survey was self-administered and distributed to 350 women between 1 March and 30 May 2021. Descriptive statistics and multinomial logistic regression analysis (SPSS, version 28.0) were used to explore perceived support, post-illness social and marital changes and the association between these changes and socio-demographic, illness-related and support-related variables.

Findings

About four-fifth of the women with BC felt supported after illness, either fully or partially, mainly by family members, non-governmental organisations, spouses, and peers. Nevertheless, nearly half of the women perceived negative social changes after illness, and about 40% of married or formerly married women perceived negative changes in their marital life. Survivors’ lived experiences varied by age, marital status, motherhood, prescribed treatment (specifically mastectomy), and the absence of informal support in social life and lack of partner support amongst married or formerly married women.

Conclusion

This study shows how BC undermines the social status of women and further exacerbates existing social vulnerabilities. Nevertheless, it is possible to manage and potentially overturn this circumstance by enveloping patients with social support. Guiding partners, families, and friends on providing emotional and instrumental support will help survivors to cope better during recovery.
The effects of differential feeding on ileum development, digestive ability and health status of newborn calves
Authors: Wang Jie , Chen Yang , Li Mianying , Xia Siqi , Zhao Kaisen , Fan Huimei , Ni Jiale , Sun Wenqiang , Jia Xianbo , Lai Songjia
Published: September 2023

Pre-weaning is the most important period for the growth and development of calves. Intestinal morphology, microbial community and immunity are initially constructed at this stage, and even have a lifelong impact on calves. Early feeding patterns have a significant impact on gastrointestinal development and microbial communities. This study mainly analyzed the effects of three feeding methods on the gastrointestinal development of calves, and provided a theoretical basis for further improving the feeding mode of calves. it is very important to develop a suitable feeding mode. In this study, we selected nine newborn healthy Holstein bull calves were randomly selected and divided into three groups (n = 3), which were fed with starter + hay + milk (SH group), starter + milk (SF group), total mixed ration + milk (TMR group). After 80 days of feeding Feeding to 80 days of age after, the ileum contents and blood samples were collected, and the differences were compared and analyzed by metagenomic analysis and serum metabolomics analysis. Results show that compared with the other two groups, the intestinal epithelium of the SH group was more complete and the goblet cells developed better. The feeding method of SH group was more conducive to the development of calves, with higher daily gain and no pathological inflammatory reaction. The intestinal microbial community was more conducive to digestion and absorption, and the immunity was stronger. These findings are helpful for us to explore better calf feeding patterns. In the next step, we will set up more biological replicates to study the deep-seated reasons for the differences in the development of pre-weaning calves. At the same time, the new discoveries of neuro microbiology broaden our horizons and are the focus of our future attention.
Privacy Threats on Social Networking Websites
Authors: Shahid Ayesha , Abdullah Umair
Published: September 2023

Widespread use of social networking sites has increased the privacy threat for every individual. Privacy and security problem are two major issues associated with social networks, as the majority of the social network users are not cautious about the usability of the social websites. Social media sites have become latent target regarding offenders because of the occurrence of sensitive information and lack of user awareness of privacy settings. The overall aim of this paper is to enhance awareness about privacy and security issues associated with social networks and to provide guidelines to users for secure usage of social websites. Descriptive research has been conducted as it takes up the majority of online surveying and because of its quantitative nature, it is considered as conclusive. The survey results show that most of the users have their real information on social networking sites and they don’t change privacy settings of their accounts on regular basis. Moreover, as per survey findings, most users accept friend requests and invitations of unknown persons on social networking sites. Results of this research study will be helpful to bring awareness among users about privacy setting and they will learn how to control the privacy settings of their accounts and what type of content should be uploaded on social networking sites.
Assessing Research Collaboration in Database Systems and Computer Networks by Analysis of Coauthorship Network
Authors: Ahmed Adeel , Ahmed Tanveer
Published: September 2023

Community detection is a fundamental problem in social networks. These networks detect communities based on link analysis and strong connection strengths, but cannot reflect Author’s from different research areas. To address the problem of community detection, we have done a study for “Analyzing patterns of collaboration in co-authorship network using Modularity and Centrality Measures”. This analysis study uses combine features of Modularity with centrality measure to effectively detect community of different author’s having different research collaboration with different interests in domain of Computer Networks and Database Systems. Experiment of Dataset shown that this approach is better detect best authors from specific domain having high collaboration with other coauthors and presents information to the researcher’s that have relative interest in relative author’s community.
Applying Centrality Measures for Impact Analysis in Coauthorship Network
Authors: Ahmed Adeel , Shabbir Riqza , Afzal Atifa , Akmal Muhammad , Fatimah Sahar
Published: September 2023

Nowadays social networking is an essential part of everyone’s life to communicate with different people around the globe. Due to improvement in expertise networks are growing rapidly and becoming more complex. Through social networking, we can identify different communities that help us to get information about different people and their work in different fields. In social networks, community detection is one of the hot areas. In this paper, we have analyzed a co-authorship network of political science and ranked the authors on the basis of common centrality measures. Finding reveals that these common centrality measures can be useful indicators for impact analysis.
Kobe Braynt Shot Prediction using Machine Learning
Authors: Shahzad Taimur , Rehman Zahoor Ur , Batool Eliza , Ullah Zain
Published: September 2023

Kobe Bryant was one of the best players of Basketball. Data regarding his 20 years played games is available on the Kaggle. We transform the categorical features by PCA and normalize the data by minmax normalization technique. Machine learning techniques such as logistic regression, Random Forest, Linear Discriminant Analysis, Naïve bayes, Gradient Boosting, Adaboost and Neural Network are applied on pre-processed data to classify whether he made shot or not.  The prediction accuracy of LR, RF, LDA, NB, GB, ABC and ANN is 67.84%, 64.22%, 67.82%, 61%, 67.8%, 68% and 67% respectively on hold an out method.  The experimental results shows that Adaboost has highest prediction accuracy as compared to others method with 5 cross validations. Finally, we have got satisfactory results as compared to our benchmark (Kaggle).
Facial Based Gender Classification for Real Time Applications
Authors: Haider Anmol , Imran Muhammad
Published: September 2023

Appearance and facial features play an important role in gender recognition through images. For gender classification, multiple techniques were presented to acquire better results in which preprocessing part is one of the major and very important for gender classification as it removes noise, enhances, images, and eliminates any unnatural colors from an image. Another major aspect is the efficient feature extraction method. If features extracted accurately then the result of classification will improve. Over the past few years, gender classification techniques work perfectly for a controlled environment. However, challenges occurred for real-time applications due to low resolution, off-angle poses, faces with occlusion, and various expressions. The main focus of this study is to overcome existing challenges and propose a method that can be implemented in real-time applications. This research work proposed a novel method in which CNN has been used for classification of gender for real-time application. To assess the performance of proposed method experiments were conducted on static images and video data sets. The proposed research work achieved 98% of accuracy during the experiments.
A Novel Digital Twin Framework for Aeroengine Performance Diagnosis
Authors: Wang Zepeng , Wang Ye , Wang Xizhen , Yang Kaiqiang , Zhao Yongjun
Published: September 2023

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path diagnosis methods. To address these issues, this study proposes a novel digital twin framework for aeroengines that achieves the digitalization of physical systems. The mechanism model is constructed at the component level. The data-driven model is built using a particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused using the low-rank multimodal fusion method (LWF) and combined with the sparse stacked autoencoder (SSAE) to form a digital twin framework of the engine for performance diagnosis. Compared to methods that are solely based on mechanism or data, the proposed digital twin framework can effectively use mechanism and data information to improve the accuracy and reliability. The research results show that the proposed digital twin framework has an error rate of 0.125% in predicting gas path parameters and has a gas path fault diagnosis accuracy of 98.6%. Considering that the degradation cost of a typical flight mission for only one aircraft engine after 3000 flight cycles is approximately USD 209.5, the proposed method has good economic efficiency. This framework can be used to improve engine reliability, availability, and efficiency, and has significant value in engineering applications.
Characteristics of Vortices around Forward Swept Wing at Low Speeds/High Angles of Attack
Authors: Kanazaki Masahiro , Setoguchi Nao
Published: September 2023

The forward-swept wing (FSW), one of the wing planforms used in aircraft, is known for its high performance in reducing wave drag. Additionally, a study has shown that this wing planform can mitigate sonic booms, which pose a significant challenge to achieving supersonic transport (SST). Therefore, FSW is expected to find applications in future SST aircraft owing to aerodynamic advantages at high speeds. However, there is a lack of sufficient knowledge and systematization to improve aerodynamic performance at low speeds and high angles of attack during takeoff and landing. These are crucial for practical implementation. Although the aerodynamic benefits of an FSW in high-speed flight can be harnessed using advanced structural and control technologies, the realization of SST using an FSW is challenging without enhanced research on low-speed aerodynamics. This study explores the practical aerodynamic knowledge of FSWs. We utilized a numerical simulation based on the Navier–Stokes equation and focused on investigating wake vortex phenomena. Our simulation included various wing planforms, including backward-swept wings (BSWs). The results revealed the presence of vortices with lateral axes emanating from the FSW, while longitudinal vortices were observed in the BSW. Based on these results, we developed a theoretical hypothesis for the vortex structure around an FSW.
Hyperspectral Estimation of SPAD Value of Cotton Leaves under Verticillium Wilt Stress Based on GWO–ELM
Authors: Yuan Xintao , Zhang Xiao , Zhang Nannan , Ma Rui , He Daidi , Bao Hao , Sun Wujun
Published: September 2023

Rapid and non-destructive estimation of the chlorophyll content in cotton leaves is of great significance for the real-time monitoring of cotton growth under verticillium wilt (VW) stress. The spectral reflectance of healthy and VW cotton leaves was determined using hyperspectral technology, and the original spectra were processed using Savitzky–Golay (SG) smoothing, and on its basis through mean centering, standard normal variate (SG-SNV), multiplicative scatter correction (SG-MSC), reciprocal second-order differentiation, and logarithmic second-order differentiation ([lg(SG)]″) preprocessing operations. The characteristic bands were selected based on the correlation coefficient, vegetation index, successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS). The single-factor model, back propagation neural network of particle swarm optimization algorithm, and extreme learning machine (ELM) of a grey wolf optimizer (GWO) algorithm were constructed to compare and explore the ability of each model to estimate the soil plant analysis development (SPAD) value of cotton under VW stress. The results showed that spectral pretreatment could improve the correlation between characteristic bands and SPAD values. SG-MSC and SG-SNV showed better changes in the five pretreatments, and the maximum correlation coefficients of healthy and VW cotton leaves were higher than 0.74. Compared with SPA, the accuracy of model estimation based on CARS-extracted characteristic bands was higher, and the estimation accuracy of the multi-factor model was better than that of the single-factor model under each pretreatment. For healthy cotton leaves, [lg(SG)]″–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.956 and 0.887, respectively. For VW cotton leaves, SG-MSC–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.832 and 0.824, respectively. Therefore, the GWO–ELM model constructed under different pretreatments combined with characteristic extraction methods can be used for the estimation of leaf SPAD values under VW stress to dynamically monitor VW stress in cotton and provide a theoretical reference for precision agriculture.
Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests
Authors: Yu Yue , Yu Haiye , Li Xiaokai , Zhang Lei , Sui Yuanyuan
Published: September 2023

The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R2 values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R2 and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.
Is the Hippo Pathway Effector Yes-Associated Protein a Potential Key Player of Dairy Cattle Cystic Ovarian Disease Pathogenesis?
Authors: Dos Santos Esdras Corrêa , Boyer Alexandre , St-Jean Guillaume , Jakuc Natalia , Gévry Nicolas , Price Christopher A. , Zamberlam Gustavo
Published: September 2023

Cystic ovarian disease (COD) in dairy cattle is characterized by preovulatory follicles that become cysts, fail to ovulate and persist in the ovary; consequently, interfering with normal ovarian cyclicity. The intraovarian key players that orchestrate the alterations occurring in the preovulatory follicle and that culminate with cyst formation and persistence, however, remain uncertain. Interestingly, the Hippo pathway effector yes-associated protein (YAP) has been described in humans and mice as a key player of anovulatory cystic disorders. To start elucidating if YAP deregulation in ovarian follicle cells can be also involved in the pathogenesis of COD, we have generated a series of novel results using spontaneously occurring cystic follicles in cattle. We found that mRNA and protein levels of YAP are significantly higher in granulosa (GCs) and theca cells (TCs) isolated from cystic follicles (follicular structures of at least 20 mm in diameter) in comparison to respective cell types isolated from non-cystic large follicles (≥12 mm). In addition, immunohistochemistry and Western blot analyses used to determine YAP phosphorylation pattern suggest that YAP transcriptional activity is augmented is cystic GCs. These results were confirmed by a significant increase in the mRNA levels encoding for the classic YAP-TEAD transcriptional target genes CTGF, BIRC5 and ANKRD1 in GCs from follicle cysts in comparison to non-cystic large follicles. Taken together, these results provide considerable insight of a completely novel signaling pathway that seems to play an important role in ovarian cystic disease pathogenesis in dairy cattle.
The Anti-Muscle Atrophy Effects of Ishige sinicola in LPS-Induced C2C12 Myotubes through Its Antioxidant and Anti-Inflammatory Actions
Authors: Kim Mi-Bo , Lee Hyeju , Lee Chaehyeon , Tan Yuqing , Lee Sang Gil
Published: September 2023

Inflammation and oxidative stress are known to be major factors in muscle atrophy. The objective of this study was to evaluate whether the antioxidant activity of Ishige sinicola ethanol extract (ISE) and fractions from ISE could prevent lipopolysaccharide (LPS)-induced muscle atrophy in C2C12 myotubes. IS was extracted with ethanol and fractionated with five organic solvents. Then, ISE and five fractions from ISE were used to evaluate the total antioxidant activity and the protective effect of LPS-induced muscle atrophy in C2C12 myotubes. The ISE and butanol (BuOH) fraction showed higher total antioxidant activity and higher total phenol content than other fractions of ISE. The ISE and BuOH fraction significantly attenuated the LPS-induced diameter of C2C12 myotubes as well as the mRNA and protein expression levels of the muscle-specific E3 ubiquitin ligases. The mRNA expression of forkhead box O type 3α was stimulated by LPS, which was suppressed by the BuOH fraction but not ISE. Furthermore, ISE and the BuOH fraction significantly reduced LPS-stimulated gene expression of pro-inflammatory cytokines and inflammation-inducible enzymes, which was mediated by through the inhibition of the p38/extracellular signal-regulated kinase signaling pathway. Thus, ISE exerts a protective effect against muscle atrophy in LPS-induced C2C12 myotubes through the antioxidant activity and anti-inflammatory effects of ISE.
Anthropometric Measurements, Physical Fitness Performance and Specific Throwing Strength in Adolescent Track-and-Field Throwers: Age, Sex and Sport Discipline
Authors: Zhao Yifan , Zhao Kewei
Published: September 2023

Purpose: The aims of this study were: (1) to profile anthropometric, physical fitness, and specific throwing strength characteristics among 14–18 years boys and girls throwers; (2) to evaluate which factors vary with age, and which correlate with specific throwing strength; (3) to identify the measured variables that best predict specific throwing strength. Methods: Anthropometric, physical fitness, and specific throwing strength of 154 boys and 104 girls, who participated in track-and-field throw (Shot put, Javelin, Discus and Hammer throw) from four age categories (U15, U16, U17, U18), were measured in September 2022. The differences and correlations in parameters among different age, sex and throwing groups were analyzed using parametric and non-parametric testing. Multivariate linear regression analysis was used to identify the variables that best explain the specific throwing strength. Results: Disparities in height between boys and girls of the same age have consistently existed, however, the dissimilarity in weight tends to diminish as they grow older. Boys and girls of identical age groups exhibit noteworthy disparities in terms of speed, agility, and jumping prowess. These disparities tend to amplify as they advance in age. Significant differences were observed among boys of different ages in Height (p = 0.038), Body Mass (p = 0.02), BMI (p = 0.025), sit and reach test (p = 0.035), standing long jump (p = 0.012), standing triple jump (p < 0.01), forward overhead medicine ball throw (p = 0.002) and the hexagon agility test (p < 0.01). No differences were found in anthropometric measurements among girls, but differences were found in the hexagon agility test (p = 0.017) and plank test (p = 0.041). Specific throwing strength exhibits variations due to differences in events, age, and gender. Additionally, physical fitness performance, especially lower limb power, linear sprint speed, forward overhead medicine ball throw and backward overhead shot throw, have a high correlation with specific throwing strength. Conclusions: These findings broaden the existing knowledge base for coaches and practitioners, enabling them to discern the distinctive attributes of track and field throwers and capture the crucial physical markers that are pivotal for nurturing the progression of track-and-field throwers. The study suggests that throwers aged 14 to 18 should strive to comprehensively cultivate their athletic abilities.
Intelligent Simulation Technology Based on RCS Imaging
Authors: Hao Jiaxing , Wang Xuetian , Yang Sen , Gao Hongmin
Published: September 2023

The target simulation of airplanes is an important research topic. It is particularly important to find the right balance between high performance and low cost. In order to balance the contradictions between realistic target simulations and controllable costs, the scientific formulation of the performance parameters of target simulation is the key to achieving high performance. This paper proposes an intelligent simulation technology based on RCS imaging simulation through the combination of 60° variation corner reflector and a Luneberg lens reflector. It is designed to simulate several important RCS characteristics of the aircraft. At the same time, the different RCS images are automatically shifted to the corresponding gear position to achieve the purpose of simulation, and the price is low and the performance is good. It can be used for the training of radar target searching.
Research on Optimization of Medical Waste Emergency Disposal Transportation Network for Public Health Emergencies in the Context of Intelligent Transportation
Authors: Zhao Fei , Wang Xi , Liu Beibei , Sun Wenzhuo , Liu Zheng
Published: September 2023

In order to build a more comprehensive emergency disposal and transportation network system for medical waste, it is necessary to consider various uncertain factors and data characteristics. Therefore, in the context of intelligent transportation, this article considers the uncertainty of the quantity and regional population density of infectious medical waste generation as well as the emergency disposal of infectious medical waste under multi-cycle and multi-objective conditions, and it constructs a multi-cycle emergency disposal logistics network optimization model for infectious medical waste under uncertain conditions. Through deterministic transformation of the model and data mining of the medical waste disposal logistics network in Wuhan, China, the multi-objective model under uncertain conditions was also solved and sensitivity analyzed using the MOPSO-NSGA2 intelligent algorithm, verifying the effectiveness and superiority of the algorithm.
A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
Authors: Han Sangchul , Paik Woojin , Ko Myeong-Cheol , Park Minkyu
Published: September 2023

As multiprocessor (or multicore) real-time systems become popular, there has been much research on multiprocessor real-time scheduling algorithms. This work evaluates EDZL (Earliest Deadline until Zero Laxity), a scheduling algorithm for real-time multiprocessor systems. First, we compare the performance of EDZL schedulability tests. We measure and compare the ratio of task sets admitted by each test. We also investigate the dominance between EDZL schedulability tests and discover that the union of the demand-based test and the utilization-based test is an effective combination. Second, we compare the schedulability of EDZL and EDF(k). We prove that the union of the EDZL schedulability tests dominates the EDF(k) schedulability test, i.e., the union of the EDZL schedulability tests can admit all task sets admitted by the EDF(k) schedulability test. We also compare the schedulability of EDZL and EDF(k) through scheduling simulation by measuring the ratio of successfully scheduled task sets. EDZL can successfully schedule 7.0% more task sets than EDF(k).
Fractional p-Laplacian Coupled Systems with Multi-Point Boundary Conditions
Authors: Samadi Ayub , Ntouyas Sotiris K. , Tariboon Jessada
Published: September 2023

This article is allocated to the existence and uniqueness of solutions for a system of nonlinear differential equations consisting of the Caputo fractional-order derivatives. Our main results are proved via standard tools of fixed point theory. Finally, the presented results are clarified by constructing some examples.
Finite Element Analysis of Head–Neck Kinematics in Rear-End Impact Conditions with Headrest
Authors: Wang Yuan , Jiang Hanhui , Teo Ee Chon , Gu Yaodong
Published: September 2023

A detailed three-dimensional (3D) head–neck (C0–C7) finite element (FE) model was developed and used to dictate the motions of each cervical spinal segment under static physiological loadings of flexion and extension with a magnitude of 1.0 Nm and rear-end impacts. In this dynamic study, a rear-end impact pulse was applied to C7 to create accelerations of 4.5 G and 8.5 G. The predicted segmental motions and displacements of the head were in agreement with published results under physiological loads of 1.0 Nm. Under rear-end impact conditions, the effects of peak pulse acceleration and headrest angles on the kinematic responses of the head–neck complex showed rates of increase/decrease in the rotational motion of various cervical spinal segments that were different in the first 200 ms. The peak flexion rotation of all segments was lower than the combined ROM of flexion and extension. The peak extension rotation of all segments showed variation compared to the combined ROM of flexion and extension depending on G and the headrest angle. A higher acceleration of C7 increased the peak extension angle of lower levels, but the absolute increase was restricted by the distance between the head and the headrest. A change in the headrest angle from 45° to 30° resulted in a change in extension rotation at the lower C5–C6 segments to flexion rotation, which further justified the effectiveness of having distance between the head and the headrest. This study shows that the existing C0-C7 FE model is efficient at defining the gross reactions of the human cervical spine under both physiological static and simulated whiplash circumstances. The fast rate of changes in flexion and extension rotation of various segments may result in associated soft tissues and bony structures experiencing tolerances beyond their material characteristic limits. It is suggested that a proper location and angle of the headrest could effectively prevent the cervical spine from injury in traumatic vehicular accidents.
Characterization of the Intracellular Acidity Regulation of Brain Tumor Cells and Consequences for Therapeutic Optimization of Temozolomide
Authors: Tafech Alaa , Jacquet Pierre , Beaujean Céline , Fertin Arnold , Usson Yves , Stéphanou Angélique
Published: September 2023

A well-known feature of tumor cells is high glycolytic activity, leading to acidification of the tumor microenvironment through extensive lactate production. This acidosis promotes processes such as metastasis, aggressiveness, and invasiveness, which have been associated with a worse clinical prognosis. Moreover, the function and expression of transporters involved in regulation of intracellular pH might be altered. In this study, the capacity of tumor cells to regulate their intracellular pH when exposed to a range of pH from very acidic to basic was characterized in two glioma cell lines (F98 and U87) using a new recently published method of fluorescence imaging. Our results show that the regulation of acidity in tumors is not the same for the two investigated cell lines; U87 cells are able to reduce their intracellular acidity, whereas F98 cells do not exhibit this property. On the other hand, F98 cells show a higher level of resistance to acidity than U87 cells. Intracellular regulation of acidity appears to be highly cell-dependent, with different mechanisms activated to preserve cell integrity and function. This characterization was performed on 2D monolayer cultures and 3D spheroids. Spatial heterogeneities were exhibited in 3D, suggesting a spatially modulated regulation in this context. Based on the corpus of knowledge available in the literature, we propose plausible mechanisms to interpret our results, together with some new lines of investigation to validate our hypotheses. Our results might have implications on therapy, since the activity of temozolomide is highly pH-dependent. We show that the drug efficiency can be enhanced, depending on the cell type, by manipulating the extracellular pH. Therefore, personalized treatment involving a combination of temozolomide and pH-regulating agents can be considered.
AAV-Mediated Targeting of the Activin A-ACVR1R206H Signaling in Fibrodysplasia Ossificans Progressiva
Authors: Yang Yeon-Suk , Lin Chujiao , Ma Hong , Xie Jun , Kaplan Frederick S. , Gao Guangping , Shim Jae-Hyuck
Published: September 2023

Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare genetic disorder characterized by progressive disabling heterotopic ossification (HO) at extra-skeletal sites. Here, we developed adeno-associated virus (AAV)-based gene therapy that suppresses trauma-induced HO in FOP mice harboring a heterozygous allele of human ACVR1R206H (Acvr1R206H/+) while limiting the expression in non-skeletal organs such as the brain, heart, lung, liver, and kidney. AAV gene therapy carrying the combination of codon-optimized human ACVR1 (ACVR1opt) and artificial miRNAs targeting Activin A and its receptor ACVR1R206H ablated the aberrant activation of BMP-Smad1/5 signaling and the osteogenic differentiation of Acvr1R206H/+ skeletal progenitors. The local delivery of AAV gene therapy to HO-causing cells in the skeletal muscle resulted in a significant decrease in endochondral bone formation in Acvr1R206H/+ mice. These mice showed little to no expression in a major AAV-targeted organ, the liver, due to liver-abundant miR-122-mediated repression. Thus, AAV gene therapy is a promising therapeutic strategy to explore in suppressing HO in FOP.
Biological Performance of Primary Dental Pulp Stem Cells Treated with Gold Nanoparticles
Authors: Abuarqoub Duaa , Mahmoud Nouf , Alshaer Walhan , Mohammad Marwa , Ibrahim Abed Alqader , Al-Mrahleh Mairvat , Alnatour Mohammad , Alqudah Dana A. , Esawi Ezaldeen , Awidi Abdalla
Published: September 2023

Gold nanoparticles (AuNPs) are one of the most stable nanoparticles that have been prevalently used as examples for biological and biomedical applications. Herein, we evaluate the effect of AuNPs on the biological processes of dental pulp stem cells derived from exfoliated deciduous teeth (SHED). Two different shapes of PEGylated AuNPs, rods (AuNR-PEG) and spheres (AuNS-PEG), were prepared and characterized. SHED cells were treated with different concentrations of AuNR-PEG and AuNS-PEG to determine their effect on the stemness profile of stem cells (SCs), proliferation, cytotoxicity, cellular uptake, and reactive oxygen species (ROS), for cells cultured in media containing-fetal bovine serum (FBS) and serum-free media (SFM). Our results showed that both nanoparticle shapes maintained the expression profile of MSC surface markers. Moreover, AuNS-PEG showed a stimulatory effect on the proliferation rate and lower toxicity on SHED, compared to AuNR-PEG. Higher concentrations of 0.5–0.125 nM of AuNR-PEG have been demonstrated to cause more toxicity in cells. Additionally, cells treated with AuNPs and cultured in FBS showed a higher proliferative rate and lower toxicity when compared to the SFM. For cellular uptake, both AuNS-PEG and AuNR-PEG were uptaken by treated cells efficiently. However, cells cultured in SFM media showed a higher percentage of cellular uptake. For ROS, AuNR-PEG showed a significant reduction in ROS at lower concentrations (<0.03 nM), while AuNS-PEG did not show any significant difference compared to the control untreated cells. Thus, our results give evidence about the optimum concentration and shape of AuNPs that can be used for the differentiation of stem cells into specific cell lineages in tissue engineering and regenerative medicine.
Molecular and Cellular Mechanisms of Bone and Cartilage Diseases
Authors: Terkawi M. Alaa
Published: September 2023

Bones and cartilage, the two most important parts of the musculoskeletal system, provide mobility and maintain the body’s posture [...]
Editorial of the Special Issue: Cellular Mechanisms of Cardiovascular Disease
Authors: Martins-Marques Tânia , Coutinho Gonçalo , Kiss Attila
Published: September 2023

Cardiovascular diseases (CVD) remain the major cause of mortality and disability worldwide, having contributed to 19 [...]
Otsu Multi-Threshold Image Segmentation Based on Adaptive Double-Mutation Differential Evolution
Authors: Guo Yanmin , Wang Yu , Meng Kai , Zhu Zongna
Published: September 2023

A quick and effective way of segmenting images is the Otsu threshold method. However, the complexity of time grows exponentially as the number of thresolds rises. The aim of this study is to address the issues with the standard threshold image segmentation method’s low segmentation effect and high time complexity. The two mutations differential evolution based on adaptive control parameters is presented, and the twofold mutation approach and adaptive control parameter search mechanism are used. Superior double-mutation differential evolution views Otsu threshold picture segmentation as an optimization issue, uses the maximum interclass variance technique as the objective function, determines the ideal threshold, and then implements multi-threshold image segmentation. The experimental findings demonstrate the robustness of the enhanced double-mutation differential evolution with adaptive control parameters. Compared to other benchmark algorithms, our algorithm excels in both image segmentation accuracy and time complexity, offering superior performance.
Unique Brain Network Identification Number for Parkinson’s and Healthy Individuals Using Structural MRI
Authors: Samantaray Tanmayee , Gupta Utsav , Saini Jitender , Gupta Cota Navin
Published: September 2023

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson’s disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject’s brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node (i), weighted by a factor of 2−(i−1). The numerical representation (UBNIN) was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. UBNIN ranges observed for PD were 15,360 to 17,768,936,615,460,608, and HC ranges were 12,288 to 17,733,751,438,064,640. This model may be implemented as a neural signature of a person’s unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts: A: ≤32 years (n1 = 4, n2 = 5), B: 33–42 years (n1 = 18, n2 = 14), C: 43–52 years (n1 = 42, n2 = 23), D: 53–62 years (n1 = 69, n2 = 22), and E: ≥63 years (n1 = 46, n2 = 6), where n1 and n2 are the number of individuals in PD and HC, respectively, to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox (Version 2019-03-03)-based MATLAB (R2020a) functions. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age-related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study.
Status of Typical Artificial Lighting Environments in Different Public Buildings in China, and Requirements for Their Improvement
Authors: Liang Qingxuan , Jin Ling , Luo Tao , Shi Jiaxin , Xue Peng , Liu Jiaping , Wang Bin , Jin Xuan
Published: September 2023

The luminous environment is an essential factor that affects people’s working and living experiences in buildings. In order to clarify the building luminous environment parameters that are required for occupant satisfaction, we collected 2553 completed questionnaires while conducting field measurements of new and updated luminous comfort metrics in 15 contexts in four cities in China. By developing a five-step approach to identifying the luminous environment needs of different building occupants, including data cleaning, correlation analysis, and nonparametric testing, we determined the thresholds for all key metrics for each scenario. The research results show that different public building environments have unique luminous environment improvement requirements, and this conclusion can guide future lighting design, LED technology and daylighting integration technology.
Constitutive Model of FRP Tube-Confined Alkali-Activated Slag Lightweight Aggregate Concrete Columns under Axial Compression
Authors: Zhang Xinyu , Chen Pang , Wang Hui , Xu Changchun , Wang Hao , Zhang Longliang
Published: September 2023

Fiber reinforced polymer (FRP), as a novel type of composite material, has been extensively employed in structural strengthening and composite structures. The FRP tube-confined alkali-activated slag lightweight aggregate concrete column (FRP-AASLAC) can effectively improve the utilization rate of slag, reduce carbon emissions, reduce structural self-weight, and improve structural ductility. Therefore, the axial compressive properties of FRP-AASLAC were studied in this paper. The influences of the type of FRP, FRP thickness and the content of lightweight aggregate on the failure modes, bearing capacities, deformation properties and constitutive relationships of FRP-AASLAC were revealed. The results indicate that the constitutive relationships of FRP-AASLAC show double broken line patterns without obvious softening sections. The restraining effect of FRP on lightweight aggregate concrete is higher than that on ordinary concrete as lightweight aggregate concrete has lower strength and more easily undergoes lateral expansion under external loads. Models for compressive strength, peak compressive strain and constitutive relationship for FRP-AASLAC are proposed.
Using Artificial Intelligence to Generate Master-Quality Architectural Designs from Text Descriptions
Authors: Chen Junming , Wang Duolin , Shao Zichun , Zhang Xu , Ruan Mengchao , Li Huiting , Li Jiaqi
Published: September 2023

The exceptional architecture designed by master architects is a shared treasure of humanity, which embodies their design skills and concepts not possessed by common architectural designers. To help ordinary designers improve the design quality, we propose a new artificial intelligence (AI) method for generative architectural design, which generates designs with specified styles and master architect quality through a diffusion model based on textual prompts of the design requirements. Compared to conventional methods dependent on heavy intellectual labor for innovative design and drawing, the proposed method substantially enhances the creativity and efficiency of the design process. It overcomes the problem of specified style difficulties in generating high-quality designs in traditional diffusion models. The research results indicated that: (1) the proposed method efficiently provides designers with diverse architectural designs; (2) new designs upon easily altered text prompts; (3) high scalability for designers to fine-tune it for applications in other design domains; and (4) an optimized architectural design workflow.