bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of servant leadership on the life satisfaction of basic education teachers: the mediating role of satisfaction with job resources
Authors: Quinteros-Durand Ronald , Almanza-Cabe Robinson B. , Morales-García Wilter C. , Mamani-Benito Oscar , Sairitupa-Sanchez Liset Z. , Puño-Quispe Lucy , Saintila Jacksaint , Saavedra-Sandoval Renán , Paredes Alcides Flores , Ramírez-Coronel Andrés Alexis
Published: October 2023

Background

Life satisfaction is essential for teachers’ work performance and student learning. Additionally, servant leadership has been shown to be one of the best leadership practices as it promotes employee well-being and satisfaction. Moreover, satisfaction with job resources acts as a mediator in the relationship between servant leadership and life satisfaction by influencing individual and collective performance in the organization.

Objective

This research aimed to evaluate the mediating role of satisfaction with job resources in the relationship between servant leadership and life satisfaction.

Methods

The study was cross-sectional and explanatory. 620 teachers aged between 20 and 62 years (M = 35 and SD = 9.49) participated in the study. Structural Equation Modeling (SEM) was used to measure life satisfaction, service leadership, and job resource satisfaction through the use of questionnaires.

Results

The results indicated that the model obtained an adequate fit, χ2 = 2,658, df = 551, p < 0.001, CFI = 0.941, TLI = 0.936, RMSEA = 0.079, SRMR = 0.070. The results confirm the positive influence of leadership on satisfaction with resources and life satisfaction. Additionally, a positive influence of satisfaction with job resources on life satisfaction was observed. Moreover, the mediation of job resources in servant leadership and life satisfaction was confirmed.

Conclusion

Servant leadership, supported by satisfaction with job resources, can reduce effort and associated costs, stimulate personal growth and learning, and improve the well-being of teachers.
The prevalence of workaholism: a systematic review and meta-analysis
Authors: Andersen Filip Borgen , Djugum Merjem Emma Torlo , Sjåstad Victoria Steen , Pallesen Ståle
Published: October 2023

The present study represents the first meta-analysis and systematic review on the prevalence of workaholism. It also investigated if sample size, representativeness, and instrument moderated the prevalence estimates. The analysis was pre-registered at PROSPERO (CRD42023395794). We searched Web of Science, PubMed, CINAHL, Embase, PsychInfo. BASE, MedNar, NYAM, OPENGREY, OpenMD and included the first 200 searches on Google scholar as gray literature [search string: “(workaholi* OR “work addict*”) AND (prevalence* OR incident* OR frequen* OR cut-off OR epidem*)]. The search yielded 42 studies to be included, in addition to 11 studies identified using other methods. Two independent raters went through the searches, extracted information and evaluated risk of bias, resulting in agreement ratings of 92.4%, 84.9%, and 87.0%, respectively. The inclusion criteria were studies reporting original data on the prevalence of workaholism written in any European language. Criteria which led to exclusion were conference abstracts, usage of secondary data, purposive sampling of workaholics, qualitative research and pre-determined cut-off based on distribution. Risk of bias of the included articles was evaluated through a checklist. Most of the included studies had a moderate risk of bias. Of the 663 records identified, a total of 53 studies were included, 10 of these being nationally representative with all studies in total amounting to 71,625 participants from 23 countries. The pooled workaholism prevalence was 15.2% (95% CI = 12.4–18.5), which was adjusted to 14.1% (95% CI = 11.2–17.6) following a trim-and-fill adjustment for publication bias. The meta-regression revealed that studies with representative samples reported lower prevalences than those based on non-representative samples, and that studies based on the Dutch Work Addiction Scale yielded higher prevalences than studies employing the Bergen Work Addiction Scale. The regression model explained 29% of the variance implying that a vast amount was still unexplained, and that future research would benefit from the inclusion of other moderators.
Highly sensitised individuals present a distinct Treg signature compared to unsensitised individuals on haemodialysis
Authors: Dudreuilh C. , Basu S. , Shaw O. , Burton H. , Mamode N. , Harris F. , Tree T. , Nedyalko P. , Terranova-Barberio M. , Lombardi G. , Scottà C. , Dorling A.
Published: October 2023

Introduction

Highly sensitised (HS) patients represent up to 30% of patients on the kidney transplant waiting list. When they are transplanted, they have a high risk of acute/chronic rejection and long-term allograft loss. Regulatory T cells (Tregs) (CD4+CD25hiCD127lo) are T cells involved in the suppression of immune alloresponses. A particular subset, called T follicular regulatory T cells (Tfr, CXCR5+Bcl-6+), is involved in regulating interactions between T effectors and B cells within the germinal centre and can be found in peripheral blood. Therefore, we wanted to identify specific subsets of Tregs in the peripheral blood of HS individuals.

Methods

We recruited prospectively healthy volunteers (HV) (n = 9), non-sensitised patients on haemodialysis (HD) (n = 9) and HS individuals, all of whom were on haemodialysis (n = 15).

Results

We compared the Treg phenotypes of HV, HD and HS. HS patients had more CD161+ Tregs (p = 0.02) and more CD45RA−CCR7− T effectors (Teffs) (p = 0.04, memory Teffs able to home to the germinal centre) compared to HVs. HS patients had more Bcl-6+ Tregs (p < 0.05), fewer Th1-like Tregs, more Th2-like Tregs (p < 0.001) and more CD161+ (p < 0.05) Tregs compared to HD patients. This population has been described to be highly suppressive. HD had a deficiency in a Th17-like CD161+ effector Treg cluster (cluster iii., CCR6+CCR4+CXCR3− CD39+CD15s+ICOS−CCR7−CD161+) (p < 0.05).

Discussion

This is the first study presenting a deep Treg phenotype in HS patients. We confirmed that HS patients had more of a Th17-like CD161+ effector Treg from population III (CD4+CD25hiCD127loCD45RA−) compared to non-sensitised patients on HD. The clinical relevance of this highly suppressive Tregs population remains to be determined in the context of transplantation.
Use of immune checkpoint inhibitors in solid organ transplant recipients with advanced cutaneous malignancies
Authors: Ji Stephanie , Liu Hao , Pachella Laura , Stephenson Ryan D. , Groisberg Roman , Weiss Sarah A.
Published: October 2023

Background

Immune checkpoint inhibitors (ICI) are standard of care therapy for patients with cutaneous malignancies, the most frequently diagnosed cancers in solid organ transplant (SOT) recipients. The activity and rate of allograft rejection in SOT recipients with advanced skin cancers treated with ICI is understudied.

Methods

We conducted a retrospective analysis of SOT recipients with advanced melanoma, cutaneous squamous cell carcinoma (cSCC), and merkel cell carcinoma (MCC) who were treated with ICI. Unpublished cases from our institution and published cases from the literature were aggregated. Demographics, type of immunosuppressive therapy, type of ICI(s) administered, prior systemic therapies, tumor response to ICI, and evidence of organ rejection and/or failure were recorded. Objective response rates (ORR) and rates of graft rejection and failure are reported.

Results

Ninety patients were identified; four patients from our institution and 86 unique patients from a literature review. ORR to first-line ICI for the entire cohort was 41.1% (37/90). ORR by tumor type was 31% (18/58), 64.3% (18/28), and 25.0% (1/4) for melanoma, cSCC, and MCC, respectively. The rate of graft rejection was 37.8% (34/90) with 61.8% (21/34) of these cases progressing to graft failure. Number of immunosuppressive agents (0, 1, 2, or 3) was inversely associated with rate of graft failure.

Conclusions

In this retrospective analysis, ICIs demonstrate clinical activity in SOT recipients with cutaneous malignancies; however, the rate of graft rejection is high. Treatment plans should be individualized through thorough interdisciplinary discussion. Immunosuppressive modifications may be considered prior to starting treatment, but when feasible, enrollment on clinical trials is preferred.
Colonization of minds: Ukraine between Russian colonialism and Western Orientalism
Authors: Kotliuk Galyna
Published: October 2023

This article focuses on investigating how Russian colonialism has re-invented Ukrainian identity and framed its perception both in Ukraine and in Western societies. Although postcolonial approaches to studying power hierarchies have contributed a lot to our understanding of global inequalities, Ukraine has long been excluded from this interdisciplinary, complex discussion. Such exclusion has created a knowledge gap, which this study aims to address by analyzing the evolution of gender identities and roles in Ukrainian society and critical rethinking of the existing body of knowledge. This article demonstrates the degree to which European societies perceived Ukrainians as Orientalized passive objects, which went in line with the Russian imperialistic ideology of russkii mir, examines its influence on gender identities in Ukraine and, finally, proves that the strengthening of Ukrainian civil society as well as women’s and LGBTQ+ rights is a part of the decolonial processes that were instigated by the Russian war against Ukraine.
Suspension Flux Internal Model Control of Single-Winding Bearingless Flux-Switching Permanent Magnet Motor
Authors: Chen Yao , Yu Wanneng , Yang Rongfeng , Cui Bowen
Published: October 2023

A suspension flux internal model control method is proposed to address the problem of the strong coupling of a single-winding bearingless flux-switching permanent magnet motor leading to a significant ripple of the rotor radial displacement. Firstly, based on air-gap magnetic field modulation theory, the stator flux equation considering rotor dynamic eccentricity is established to reveal the relationship between the eccentric rotor and the magnetic field. Secondly, according to the dynamic characteristics of the motor and the variation law of the air-gap magnetic field, the suspension-plane flux is substituted into the rotor dynamic model, and the suspension flux-dynamics internal model and corresponding output are constructed, respectively. Finally, a complete control strategy is established, and the rotor is stably suspended by PWM control. The simulation and experimental results show that the proposed method has better steady-state and dynamic performance than traditional PID control, and the maximum radial displacement ripples of the rotor are reduced by 53% and 50% in steady-state and dynamic operation.
The Influence of Soil Physical Properties on the Load Factor for Agricultural Tractors in Different Paddy Fields
Authors: Min Yi-Seo , Kim Yeon-Soo , Lim Ryu-Gap , Kim Taek-Jin , Kim Yong-Joo , Kim Wan-Soo
Published: October 2023

The load factor (LF) of a tractor represents the ratio of actual engine power and rated engine power, and is an important indicator directly used in calculating national air pollutant emissions. Currently, in the Republic of Korea, a fixed value of 0.48 is used for the LF regardless of the working conditions, making it difficult to establish a reliable national air pollutant inventory. Since tractors perform work under soil conditions, soil physical properties directly affect the tractor LF. Therefore, it is expected that more accurate LF estimation will be possible by utilizing soil physical properties. This study was conducted to assess the impact of soil physical properties on the LF. Experimental data were collected in ten different soil conditions. Correlation analysis revealed that the LF exhibited strong correlations with SMC, soil texture, and CI, in that order. The coefficient of determination for the regression model developed using soil variables ranged from 0.678 to 0.926. The developed regression models generally showed higher accuracy when utilizing multiple soil variables, as compared to using a single soil variable. Therefore, an effective estimation of the LF through non-experimental methods can be achieved by measuring various soil properties.
Unravelling the Complexities of Genotype-Soil-Management Interaction for Precision Agriculture
Authors: Christensen Svend , Jensen Signe M.
Published: October 2023

The knowledge of interactions among crop genotypes, soil types, and crop management is essential for precision agriculture. This paper explores these interactions through the analysis of 27 years of winter wheat trials, with 276 unique varieties tested across seven distinct soil types and more than 8000 plots. The study investigates how different winter wheat crop varieties respond to varying soil types and preceding crops. The findings revealed a significant interaction between variety, soil type, and preceding crop. With only a few exceptions, the highest-yielding varieties were predominantly the most recently developed. The ranking of the varieties exhibited inconsistency across the various soil types, implying that a variety yields differently when cultivated in different soil types. Furthermore, the influence of preceding crops on yield varied with soil type. This suggests that taking field-specific soil variation and the preceding crop into account during variety selection may improve the yield potential. Furthermore, the study highlights consistent yield increases due to advancements in breeding programs, with yearly increases ranging from 0.05 to 0.1 t/ha per year across all soil types. Integration of insights from genetics, soil attributes, and management practices demonstrates how farmers can increase productivity.
Effect of X-rays on Seedling Pigment, Biochemical Profile, and Molecular Variability in Astrophytum spp.
Authors: Licznerski Piotr , Lema-Rumińska Justyna , Michałowska Emilia , Tymoszuk Alicja , Winiecki Janusz
Published: October 2023

Cacti are important in agricultural economies and one of the most popular horticultural plant groups. The genus Astrophytum is one of the most valuable and desirable cacti for growers and collectors around the world. By selecting the appropriate breeding methods to induce variations in combination with modern biotechnology tools for rapid change detection, it is possible to meet the challenges of the modern world in creating new variability in plants. However, there exists a lack of research concerning the impact of ionizing radiation on cacti. The aim of the study was to assess the effects of X-rays at different doses (0 Gy—control, 15, 20, 25, and 50 Gy) on the dynamics of seed germination in vitro, changes in the color of seedlings, biochemical changes in the content of metabolites and changes at the molecular level in Astrophytum spp. ‘Purple’. A significant effect of X-rays on the induction of genetic variation was observed. Remarkably high polymorphism rates were observed, ranging from 59.09% for primer S12 to a full 100.0% for S3 and S8, as determined by the SCoT (Start-Codon-Targeted) marker. In addition, a large variation in the content of plant pigments (anthocyanins, carotenoids, chlorophyll a, and chlorophyll b) was noted. Additionally, discernible alterations in the color of the tested cactus seedlings, assessed by the RHSCC catalog, were attributed to the impact of ionizing radiation. These findings hold promise for the application of radiomutation breeding in acquiring new cactus cultivars.
Estimating Chlorophyll Content, Production, and Quality of Sugar Beet under Various Nitrogen Levels Using Machine Learning Models and Novel Spectral Indices
Authors: Elsayed Salah , El-Hendawy Salah , Elsherbiny Osama , Okasha Abdelaziz M. , Elmetwalli Adel H. , Elwakeel Abdallah E. , Memon Muhammad Sohail , Ibrahim Mohamed E. M. , Ibrahim Hazem H.
Published: October 2023

Accurately estimating crop performance under various fertilizer levels in a rapid and non-destructive manner has become a vital aspect of precision agriculture technology for both economic and environmental benefits. This study aimed to estimate different sugar beet parameters, such as total chlorophyll (Chlt), chlorophyll a (Chla), chlorophyll b (Chlb), root yield (RY), sugar yield (SY), and sugar content (SC) under five nitrogen (N) levels (0, 30, 60, 90, and 120 kg N ha−1). This was achieved by using a combination of the gradient boosting regression (GBR) model with published and newly developed two- and three-band spectral indices (2D- and 3D-SRIs). The results showed that the N levels had the highest proportion of variations (80.4–92.9%) for all parameters, except for SC, which had more variation (59.9%) according to year than the N levels (37.2%). All parameters, except SC, showed a significant increase with gradually increasing N levels. Additionally, the N levels displayed linear and strong positive relationships with the chlorophyll parameters, and linear and strong negative relationships with SC, while these relationships were quadratic and strong with RY and SY. Several published and novel 3D-SRIs exhibited moderate to strong relationships (R2 = 0.65–0.89) with all parameters. The newly developed 3D-SRIs, which involve wavelengths from the visible, near-infrared, and red-edge regions, such as NDI536, 538, 534, NDI738, 750, 542, and NDI448, 734, 398, were effective in accurately estimating all parameters. Combining 2D-SRIs, 3D-SRIs, and the aggregate of all spectral indices (ASRIs) with GBR models could be a robust strategy for estimating the six observed parameters with reasonable precision. The GBR-ASF-6 SRIs and the GBR-ASF-7 SRIs models performed better in predicting Chl content and SC with R2 values of 0.99 and 0.99 (RMSE = 0.073 and 1.568) for the training dataset and R2 values of 0.65 and 0.78 (RMSE = 0.354 and 6.294) for the testing datasets, respectively. The obtained results concluded that published and newly developed 3D-SRIs, GBR based on 2D-SRIs or 3D-SRIs, and the aggregate of all ASRIs can be used in practice to accurately estimate the Chl content, production, and quality of sugar beet across a wide range of N levels under semiarid conditions.
The Susceptibility Trends of Respiratory and Enteric Porcine Pathogens to Last-Resource Antimicrobials
Authors: Vilaró Anna , Novell Elena , Enrique-Tarancon Vicens , Baliellas Jordi , Migura-García Lourdes , Fraile Lorenzo
Published: October 2023

Monitoring the antimicrobial susceptibility of last-resource antimicrobials for veterinary pathogens is urgently needed from a one-health perspective. The objective of this study was to analyze the antimicrobial susceptibility trends of Spanish porcine bacteria to quinolones, cephalosporins, and polymyxins. Isolates of Actinobacillus pleuropneumoniae, Pasteurella multocida, and Escherichia coli were isolated from sick pigs from 2019 to 2022. An antimicrobial susceptibility test was determined based on the minimal inhibitory concentration (MIC) following an internationally accepted methodology. The MIC categorization was based on distributing the range of MIC values in four categories, with category one being the most susceptible (lowest MIC value) and category four the least susceptible (highest MIC value). Moreover, clinical susceptibility (susceptible/non-susceptible) was also determined according to the CLSI and EUCAST clinical breakpoints. A logistic and multinomial logistic regression model was used to analyze the susceptibility data for dichotomized and categorized MIC data, respectively, for any pair of antimicrobial/microorganism. In general terms, the antimicrobial susceptibility of pig bacteria to these antimicrobials remained stable or increased in the last four years in Spain. In the case of A. pleuropneumoniae and quinolones, a significant temporal trend was observed where isolates from 2020 had significantly increased odds of being more susceptible than isolates from 2019. In the case of E. coli and polymyxins, a significant temporal trend was observed where isolates from 2020 and 2021 had significantly increased odds of being more susceptible than isolates from 2019 and 2020, respectively. Finally, significant odds of being less susceptible were only observed for cephalosporins and E. coli for 2020 versus 2019, stagnating for the rest of study period. These results provide sound data on critically important antimicrobials in swine medicine.
XBP1 Modulates the Aging Cardiorenal System by Regulating Oxidative Stress
Authors: Zhang Ji , Zhao Yuanyuan , Gong Nianqiao
Published: October 2023

X-box binding protein 1 (XBP1) is a unique basic-region leucine zipper (bZIP) transcription factor. Over recent years, the powerful biological functions of XBP1 in oxidative stress have been gradually revealed. When the redox balance remains undisturbed, oxidative stress plays a role in physiological adaptations and signal transduction. However, during the aging process, increased cellular senescence and reduced levels of endogenous antioxidants cause an oxidative imbalance in the cardiorenal system. Recent studies from our laboratory and others have indicated that these age-related cardiorenal diseases caused by oxidative stress are guided and controlled by a versatile network composed of diversified XBP1 pathways. In this review, we describe the mechanisms that link XBP1 and oxidative stress in a range of cardiorenal disorders, including mitochondrial instability, inflammation, and alterations in neurohumoral drive. Furthermore, we propose that differing degrees of XBP1 activation may cause beneficial or harmful effects in the cardiorenal system. Gaining a comprehensive understanding of how XBP1 exerts influence on the aging cardiorenal system by regulating oxidative stress will enhance our ability to provide new directions and strategies for cardiovascular and renal safety outcomes.
A Visual Fault Detection Algorithm of Substation Equipment Based on Improved YOLOv5
Authors: Wu Yuezhong , Xiao Falong , Liu Fumin , Sun Yuxuan , Deng Xiaoheng , Lin Lixin , Zhu Congxu
Published: October 2023

The development of artificial intelligence technology provides a new model for substation inspection in the power industry, and effective defect diagnosis can avoid the impact of substation equipment defects on the power grid and improve the reliability and stability of power grid operation. Aiming to combat the problem of poor recognition of small targets due to large differences in equipment morphology in complex substation scenarios, a visual fault detection algorithm of substation equipment based on improved YOLOv5 is proposed. Firstly, a deformable convolution module is introduced into the backbone network to achieve adaptive learning of scale and receptive field size. Secondly, in the neck of the network, a simple and effective BiFPN structure is used instead of PANet. The multi-level feature combination of the network is adjusted by a floating adaptive weighted fusion strategy. Lastly, an additional small object detection layer is added to detect shallower feature maps. Experimental results demonstrate that the improved algorithm effectively enhances the performance of power equipment and defect recognition. The overall recall rate has increased by 7.7%, precision rate has increased by nearly 6.3%, and mAP@0.5 has improved by 4.6%. The improved model exhibits superior performance.
The Catalytic Curing Reaction and Mechanical Properties of a New Composite Resin Matrix Material for Rocket Fuel Storage Tanks
Authors: Li Chuan , Liu Zhengjun , Xue Ke , Huo Yingda , Li Fubao , Zhu Xiaoping
Published: October 2023

In this paper, an equimolar blend of bisphenol A dipropargyl ether and cyanate ester was selected to study the effect of different catalysts on the curing reaction of a bisphenol A dipropargyl ether and cyanate ester blended resin system, and the thermal stability and mechanical properties of the catalytically cured blended resin system were investigated. Acetylacetone salts of transition metals and dibutyl ditin laurate reduced the curing temperature of bisphenol AF-type di cyanate ester, and copper acetylacetonate at a mass fraction of 0.3% significantly reduced the curing temperature of bisphenol AF-type di cyanate ester to less than 473 K. Bisphenol A dipropargyl ether pr-polymerized and equimolarly blended with bisphenol A di cyanate ester and bisphenol E-type di cyanate ester also cured below 473 K under the same conditions. Among the cured compounds of the blended resins of bisphenol A dipropargyl ether with bisphenol AF-type di cyanate ester, bisphenol A-type di cyanate ester and bisphenol E-type di cyanate ester, the blended resins of bisphenol A-type di cyanate ester and bisphenol E-type di cyanate ester have better overall performance. The residual rate of 873 K in air was 38%, and the flexural strength, flexural modulus, and impact strength were 129.4 MPa, 4.3 GPa, and 27.3 kJ·m−2, respectively. This kind of blended resin is expected to be used in the liquid oxygen storage tanks of rockets.
Special Issue on the Application of Analytical Chemistry in Pharmaceutical and Biomedicine
Authors: Iqbal Muzaffar
Published: October 2023

Pharmaceutical and biomedical products that are developed for therapeutic use are either synthesized in the laboratory or naturally derived from living sources [...]
A Graph-Based Approach for Modelling Quantum Circuits
Authors: Alonso Diego , Sánchez Pedro , Álvarez Bárbara
Published: October 2023

A crucial task for the systematic application of model-driven engineering techniques in the development of quantum software is the definition of metamodels, as a first step towards automatic code generation and integration with other tools. The importance is even greater when considering recent work where the first extensions to UML for modelling quantum circuits are emerging and the characterisation of these extensions in terms of their suitability for a model-driven approach becomes unavoidable. After reviewing the related work, this article proposes a unified metamodel for modelling quantum circuits, together with five strategies for its use and some examples of its application. The article also provides a set of constraints for using the identified strategies, a set of procedures for transforming the models between the strategies, and an analysis of the suitability of each strategy for performing common tasks in a model-driven quantum software development environment. All of these resources will enable the quantum software community to speak the same language and use the same set of abstractions, which are key to furthering the development of tools to be built as part of future model-driven quantum software development frameworks.
Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
Authors: Jang Kukjin , Song Taegeon , Kim Dasran , Kim Jinsick , Koo Byeongsoo , Nam Moonju , Kwak Kyungil , Lee Jooyeoun , Chung Myoungsug
Published: October 2023

Bridge inspection methods using unmanned vehicles have been attracting attention. In this study, we devised an efficient and reliable method for visually inspecting bridges using unmanned vehicles. For this purpose, we developed the BIRD U-Net algorithm, which is an evolution of the U-Net algorithm that utilizes images taken by unmanned vehicles. Unlike the U-Net algorithm, however, this algorithm identifies the optimal function by setting the epoch to 120 and uses the Adam optimization algorithm. In addition, a bilateral filter was applied to highlight the damaged areas of the bridge, and a different color was used for each of the five types of abnormalities detected, such as cracks. Next, we trained and tested 135,696 images of exterior bridge damage, including concrete delamination, water leakage, and exposed rebar. Through the analysis, we confirmed an analysis method that yields an average inspection reproduction rate of more than 95%. In addition, we compared and analyzed the inspection reproduction rate of the method with that of BIRD U-Net after using the same method and images for training as the existing U-Net and ResNet algorithms for validation. In addition, the algorithm developed in this study is expected to yield objective results through automatic damage analysis. It can be applied to regular inspections that involve unmanned mobile vehicles in the field of bridge maintenance, thereby reducing the associated time and cost.
Operation Principles of the Industrial Facility Infrastructures Using Building Information Modeling (BIM) Technology in Conjunction with Model-Based System Engineering (MBSE)
Authors: Bolshakov Nikolai , Rakova Xeniya , Celani Alberto , Badenko Vladimir
Published: October 2023

The current industrial facility market necessitates the digitization of both production and infrastructure to ensure compatibility. This digitization is presently accomplished using Building Information Modeling and digital twin technologies, as well as their integrated usage, which enhances convergence and adds further value to facility assets. However, these technologies primarily focus on the physical components of industrial facilities, neglecting processes, requirements, and functions. To address these gaps, the inclusion of the Model-Based System Engineering approach, a proven benchmark in systems engineering, is essential. This inclusion is the main objective of this research. This article outlines methods and principles for integrating Model-Based System Engineering into the informational modeling of existing industrial facilities to address current market gaps. It offers practical steps for such integration and compares it to other methods, positioning Model-Based System Engineering as a pivotal tool for enhancing the value of industrial facility digital assets. The main findings include the proposal of BIM and MBSE integration, which aims to create a competitive advantage for industrial facilities by improving customer service and operational efficiency, requiring collaboration from various stakeholders.
The Effect of Crowdsourced Police Enforcement Data on Traffic Speed: A Case Study of The Netherlands
Authors: Liu Yutian , Feng Tao
Published: October 2023

The proliferation of smartphones and internet connectivity has provided the opportunity to use crowdsourced data in traffic management. Nowadays, many people use navigation apps such as Google Maps, Waze, and Flitsmeister to obtain real-time travel information and provide feedback on road conditions, such as reporting police speed checks. As an accurate traffic speed prediction is of great significance for road users and traffic managers, different models have been proposed and widely used to predict traffic speed considering the spatio-temporal dependence of traffic data and external factors such as the weather, accidents and points of interest. This study investigates the impact of crowdsourced data about police enforcement from navigation apps on traffic speed. In addition, we examine whether the police enforcement report affects the accuracy of the deep learning prediction model. The authors extract crowdsourced police enforcement information from navigation apps, collect the corresponding historical traffic speed data, and predict traffic speed in several corridors in The Netherlands using a GCN-GRU traffic speed prediction model. The results show that the crowdsourced data for police enforcement cause the average vehicle speed to drop between 1 [km/h] and 3 [km/h] when passing the road segments marked with police activity. Moreover, the prediction performance of the GCN-GRU model during the periods without police enforcement is better than the periods with reported police activity, showing that police speed check reports can decrease the accuracy of speed prediction models.
The Short Time Prediction of the Dst Index Based on the Long-Short Time Memory and Empirical Mode Decomposition–Long-Short Time Memory Models
Authors: Zhang Jinyuan , Feng Yan , Zhang Jiaxuan , Li Yijun
Published: October 2023

The Dst index is the geomagnetic storm index used to measure the energy level of geomagnetic storms, and the prediction of this index is of great significance for geomagnetic storm studies and solar activities. In contrast to traditional numerical modeling techniques, machine learning, which emerged decades ago based on rapidly developing computer hardware and software and artificial intelligence methods, has been unprecedentedly developed in geophysics, especially solar–terrestrial space physics. This study uses two machine learning models, the LSTM (Long-Short Time Memory, LSTM) and EMD-LSTM models (Empirical Mode Decomposition, EMD), to model and predict the Dst index. By building the Dst index data series from 2018 to 2023, two models were built to fit and predict the data. Firstly, we evaluated the influences of the learning rate and the amount of training data on the prediction accuracy of the LSTM model, and finally, 10−3 was thought to be the optimal learning rate. Secondly, the two models were used to predict the Dst index in the solar active and quiet periods, respectively, and the RMSE (Root Mean Square Error) of the LSTM model in the active period was 7.34 nT and the CC (correlation coefficient) was 0.96, and those of the quiet period were 2.64 nT and 0.97. The RMSE and CC of the EMD-LSTM model were 8.87 nT and 0.93 in the active period and 3.29 nT and 0.95 in the quiet period. Finally, the prediction accuracy of the LSTM model in the short time period was slightly better than the EMD-LSTM model. However, there will be a problem of prediction lag, which the EMD-LSTM model can solve and better predict the geomagnetic storm.
Perspective of Using Vertically Oriented Graphene as an Electrochemical Biosensing Platform
Authors: Anghel Elena , Adiaconita Bianca , Avram Andrei , Demetrescu Ioana
Published: October 2023

Electrochemical sensors based on vertically oriented graphene (VG) have gained attention in recent years due to the unique properties of VG, such as its large surface area, biocompatibility, and high electrical conductivity. In this paper, we studied an electrochemical sensor with interdigitated electrodes modified with VG as an essential interface for the identification of two types of human colon adenocarcinoma cells: SW403 (high invasiveness) and HT29 (low invasiveness). Both cell lines have epithelial morphology, and we tested the electrochemical sensor on different concentrations of SW403 and HT29 cells. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used for morphological characterization of VG deposited on the working interdigitated electrodes, Raman spectroscopy was used to evaluate the graphitic nature of the VG growth on electrodes, and atomic force microscopy (AFM) was used to study the rugosity of the VG. Fourier-transform infrared spectrometry (FTIR) was used to study the configurations of the chemical bonds in the VG used for the working electrode of the electrochemical sensor. Vertically oriented graphene improves the sensor’s response on the cell lines, as evaluated by electrochemical impedance spectroscopy (EIS).
Identifying the Influence of the Dietary Polyunsaturated Fatty Acid Content on Milk Quality Traits in Najdi Sheep at Different Stages of Lactation
Authors: Matar Abdulkareem M. , Abdelrahman Mutassim M. , Ayadi Moez , Aljummah Riyadh S.
Published: October 2023

The quality of milk is fundamental to producing safe milk and dairy products that are suitable for consumption. The objective of this study was to determine the influence that the content of dietary polyunsaturated fatty acids (PUFA) has on the total mixed ration (TMR) and lactation stage of milk’s composition and the fatty acid profile (FA) of the dairy Najdi breed. During the lactation period, 48 Najdi ewes used the following four treatments: traditional feed (TR) and three total mixed rations (TMR1, TMR2, and TMR3) with different PUFA content. Milk samples were taken from whole milk in the morning. The chemical composition and FA profile were analyzed. The results show that the ewes fed TMR1 with a PUFA content (25.20%) produced high-quality milk as they contained high levels of polyunsaturated fatty acids (PUSF), linoleic acid (LA), α-linolenic acid (ALA) and omega-3 (n3). On the other hand, the milk of ewes fed TMR2 with PUFA (15.06%) contained high levels of unsaturated fatty acids (USF) and monounsaturated fatty acids (MUFA) and had significantly reduced hypercholesterolemia (HFA). It can be concluded that milk fat, protein, and total solids in the milk of Najdi sheep increased as lactation progressed, while lactose remained constant. Furthermore, as lactation progressed, saturated fatty acids (SFA), stearic acids, and oleic acids (OA) increased while USF and MUFA decreased. TMR diets can directly benefit the producer and livestock by producing high-quality milk while reducing its economic value.
Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation
Authors: Kim Sungjun , Azad Muhammad Muzammil , Song Jinwoo , Kim Heungsoo
Published: October 2023

As laminated composites are applied more commonly, Prognostics and Health Management (PHM) techniques for the maintenance of composite systems are also attracting attention. However, applying PHM techniques to a composite system is challenging due to the data imbalance problem from the lack of failure data and unpredictable failure cases. Despite numerous studies conducted to address this limitation, including techniques like data augmentation and transfer learning, significant challenges remain. In this study, the Wasserstein Generative Adversarial Network (WGAN) model using a time-series data augmentation technique is proposed as a solution to the data imbalance problem. To ensure the performance of the WGAN model, time-series data augmentation of experimental data is executed with a frequency analysis. After that, a One-Dimensional Convolutional Neural Network (1D CNN) is used for fault diagnosis in laminated composites, validating the performance improvement after data augmentation. The proposed data augmentation significantly elevated the performance of the 1D CNN classification model compared to its non-augmented counterpart. Specifically, the accuracy increased from 89.20% to 91.96%. The precision improved remarkably from 29.76% to 74.10%, and its sensitivity rose from 33.33% to 94.39%. Collectively, these enhancements highlight the vital role of data augmentation in improving fault diagnosis performance.
Predicting the Composition and Mechanical Properties of Seaweed Bioplastics from the Scientific Literature: A Machine Learning Approach for Modeling Sparse Data
Authors: Ibarra-Pérez Davor , Faba Simón , Hernández-Muñoz Valentina , Smith Charlene , Galotto María José , Garmulewicz Alysia
Published: October 2023

The design of biodegradable polymeric materials is of increasing scientific interest due to accelerating levels of plastics pollution. One area of increasing interest is the design of biodegradable polymer films based on seaweed as a raw material. The goal of the study is to explore whether machine learning techniques can be used to predict the properties of unknown compositions based on existing data from the literature. Clustering algorithms are used, which show how some ingredients components at certain concentration levels alter the mechanical properties of the films. Robust regression algorithms with three popular models, namely decision tree, random forest, and gradient boosting. Their predictive capabilities are compared, resulting in the random forest algorithm being the most stable with the greatest predictive capacity. These analyses offer a decision support system for biomaterials manufacturing and experimentation. The results and conclusions of the study indicate that bioplastics made from seaweed have promising potential as a sustainable alternative to traditional plastics, discovering interesting additives to improve the performance of biopolymers. In addition, the machine learning approaches used provide effective tools for analyzing and predicting the properties of these materials in structured but highly sparse data.
Ergonomic Evaluation of Different Surgeon Positions for Total Knee Arthroplasty Surgery
Authors: Sánchez-Robles Marina , Díaz-Martínez Francisco J. , León-Muñoz Vicente J. , Marín-Martínez Carmelo , Murcia-Asensio Antonio , Moreno-Cascales Matilde , Lajara-Marco Francisco
Published: October 2023

Ergonomics and risk factors for work-related musculoskeletal disorders have been studied extensively in various industry fields. However, only a few decades ago, these issues became a concern in the healthcare sector. Total knee arthroplasty (TKA) is one of the most common procedures performed by orthopaedic surgeons, and it would be desirable to perform it with an ergonomically safer technique. This study evaluated the ergonomic risk of different surgeon positions when performing contralateral TKA using the dominant hand. After the authors defined the four possible surgeon positions according to the most common positions used by surgeons in our environment (position A, on the opposite side of the knee to be operated on; position B, on the same side as the knee to be operated on; position C, with the patient’s legs separated and the surgeon standing between them; and position D, facing the knee to be operated on, at the patient’s feet), we performed an ergonomic analysis using the Rapid Entire Body Assessment (REBA) method. The overall REBA scores (lower score values indicate better ergonomics than higher) were between 7 and 6.5 for position A, between 6.17 and 5.5 for position B, between 5.92 and 5.5 for position C, and between 3.75 and 3.42 for position D. The test–retest and inter-rater reliability values ranged from substantial agreement to almost perfect agreement. Based on the results, we can conclude that the most ergonomic position for a right-handed surgeon to perform a left TKA is facing the left knee, at the patient’s feet (position D).
Exploring a Multidisciplinary Assessment of Organisational Maturity in Business Continuity: A Perspective and Future Research Outlook
Authors: Russo Nelson , Mamede Henrique São , Reis Leonilde , Martins José , Branco Frederico
Published: October 2023

In a competitive business landscape heavily reliant on information and communication technology, organisations must be prepared to address disruptions in their business operations. Business continuity management involves effective planning for the swift reestablishment of business processes in the short term. However, there are still obstacles to implementing business continuity plans, which can be justified by various factors. The purpose of this study is to present the perspectives and future research paths based on a systematic literature review from the peer-reviewed literature published from 1 January 2000 to 31 December 2021. This systematic literature review adheres to the guidelines established by evidence-based software engineering and leverages the Parsifal online tool. The primary research results identify and establish connections between the common components and activities of business continuity management as defined in international standards and frameworks to identify gaps in the existing knowledge. These findings will contribute to the development of a framework that provides a practical approach applicable to organisations of all sizes, taking into account each aspect of business continuity management, with a particular emphasis on information and communication technology systems. This paper’s contribution lies in offering insights from a systematic literature review regarding the strategic principles for designing and implementing a business continuity plan, along with a comprehensive overview of related research. Furthermore, it presents a path forward to guide future research efforts aimed at addressing the gaps in the literature concerning continuity planning.
Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China
Authors: Zhou Shu , Guo Zhen , Huang Gang , Liu Kanglin
Published: October 2023

Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km2 area of Xiaojin county, eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20°–30° and 30°–40°, respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil–rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions.
Laboratory Tests and Numerical Simulation of the Thermal–Mechanical Response of a Fiber-Reinforced Phase Change Concrete Pile
Authors: Bao Xiaohua , Shi Jiaxin , Chen Guancong , Li Yingpeng , Hu Jinxin , Cui Hongzhi
Published: October 2023

The critical problem restricting the development and application of phase change energy piles is that adding phase change materials to concrete generally reduces its thermal conductivity. Therefore, exploring a scheme to improve the heat transfer performance of phase change energy piles is necessary. In this study, steel fibers were added to energy piles to enhance the heat exchange capacity between the pile and the surrounding soil. The model tests were conducted on two types of energy piles: a fiber-reinforced pile and a fiber-reinforced phase change pile. Based on laboratory tests, a three-dimensional thermo–hydro–mechanical coupled finite-element model was established to characterize the phase transformation process of FRPC piles accurately. Then, the thermal parameters of the phase change concrete pile were optimized and analyzed to explore the feasibility of improving the application of the phase change pile. The results reveal that the cooling condition where the initial ground temperature was higher than the phase change temperature was more suitable for the FRPC pile. When the flow rate was increased by 50%, the peak heat power of the FRPC pile increased by 25.7%. There is an optimal economic flow rate to balance the system’s energy consumption and heat power in different conditions. Increasing thermal conductivity and specific heat capacity are effective solutions to improve the heat transfer capacity of concrete piles. The energy pile that was enhanced with the high-thermal-conductivity PCM is a good choice to improve long-term operation performance.