bluuebunny/crossref_metadata_2025 huggingface dataset:
Lean and BIM Implementation Barriers in New Zealand Construction Practice
Authors: Likita Ayuba Jerry , Jelodar Mostafa Babaeian , Vishnupriya Vishnupriya , Rotimi James Olabode Bamidele , Vilasini Nimesha
Published: October 2022

The construction sector is lagging behind other industries in terms of efficiency and value achievement. Several building sector initiatives are introduced to improve productivity and project value enhancement. Significant developments such as Lean principles and BIM tools have been applied in the construction sector to achieve efficiency and enhanced productivity while minimizing waste. Lean principles in construction practice are a developing research area, and BIM tools have been widely used in construction project delivery and communications. Although these concepts are beneficial, barriers to their integration and joint implementation have not previously been explored. The paper investigates barriers to implementing Lean and BIM and their interrelationships in the New Zealand construction industry. A three-step triangulation methodology was used in the study to validate the findings. The study used an extensive literature review process, case studies, and expert interviews to consolidate the findings. Barriers to Lean and BIM implementation in construction practice were identified, which include strong cultural resistance, lack of Lean-BIM understanding, resistance to change, lack of knowledge of the Lean-BIM method, and lack of support from senior staff in New Zealand organizations. The effect of implementing Lean-BIM principles is discussed, as are recommendations for implementing the method in construction practice.
Assessing the Emotional Affordance of Brand Image and Foreign Image Based on a Physiological Method Using Examples from Dubai: Exploratory Study
Authors: Albdour Ala’ , Agiel Ahmed , Ghoudi Kilani
Published: October 2022

The United Arab Emirates (UAE) is a country with few restrictions on architectural styles. The main aim of this paper was to investigate a group of participants’ unconscious emotional responses to images of traditional and modern architectural styles in the UAE. All images were from the city of Dubai, but participants were from elsewhere to avoid the influence of familiarity. A physiological method was used to measure the unconscious emotional responses to the images’ visual contexts, specifically the emotions of stress, engagement, interest, focus, excitement, and relaxation. Six architects working in the UAE were then interviewed for their interpretations of both the images themselves and the participants’ emotional responses. A sample of 29 male laypeople (aged 18–45) participated in this study, divided into locals from Al Ain city, locals from the northern emirates, and nonlocals. The results showed that the brand image provided observers with better emotional quality than the foreign image for local participants from Al Ain, and that nonlocal participants showed strong emotional responses to the traditional architecture, but the northern Emiratis remained neutral. The findings of this study contribute to emotion studies in the field of meaning in architecture. It also validates the effectiveness of a physiological method of investigating the emotional responses to architectural styles.
Challenges in the Early Diagnosis of Oral Cancer, Evidence Gaps and Strategies for Improvement: A Scoping Review of Systematic Reviews
Authors: González-Moles Miguel Ángel , Aguilar-Ruiz Manuel , Ramos-García Pablo
Published: October 2022

Oral cancer is a growing problem, accounting for 377,713 worldwide new cases per year, and 177,757 deaths annually and representing a 5-year mortality rate close to 50%, which is a considerable mortality that has not decreased substantially in the last 40 years. The main cause of this high mortality is related to the diagnosis of a high percentage of oral cancers in advanced stages (stages III and IV) in which treatment is complex, mutilating or disabling, and ineffective. The essential cause of a cancer diagnosis at a late stage is the delay in diagnosis, therefore, the achievement of the objective of improving the prognosis of oral cancer involves reducing the delay in its diagnosis. The reasons for the delay in the diagnosis of oral cancer are complex and involve several actors and circumstances—patients, health care providers, and health services. In this paper, we present the results of a scoping review of systematic reviews on the diagnostic delay in oral cancer with the aim to better understand, based on the evidence, and discuss in depth, the reasons for this fact, and to identify evidence gaps and formulate strategies for improvement.
Mining the Immunopeptidome for Antigenic Peptides in Cancer
Authors: León-Letelier Ricardo A. , Katayama Hiroyuki , Hanash Sam
Published: October 2022

Although harnessing the immune system for cancer therapy has shown success, response to immunotherapy has been limited. The immunopeptidome of cancer cells presents an opportunity to discover novel antigens for immunotherapy applications. These neoantigens bind to MHC class I and class II molecules. Remarkably, the immunopeptidome encompasses protein post-translation modifications (PTMs) that may not be evident from genome or transcriptome profiling. A case in point is citrullination, which has been demonstrated to induce a strong immune response. In this review, we cover how the immunopeptidome, with a special focus on PTMs, can be utilized to identify cancer-specific antigens for immunotherapeutic applications.
Rhodium-Catalyzed Dynamic Kinetic Resolution of Racemic Internal Allenes towards Chiral Allylated Triazoles and Tetrazoles
Authors: Sieger Simon V. , Lubins Ilja , Breit Bernhard
Published: October 2022

A general Rh-catalyzed addition reaction of nitrogen containing heterocycles to internal allenes is reported. Starting from racemic internal allenes a dynamic kinetic resolution (DKR) provides N-allylated triazoles and tetrazoles. Simultaneous control of N1/Nx-position selectivity, enantioselectivity and olefin geometry gives access to important building blocks of target-oriented synthesis. The synthetic utility is demonstrated by a gram-scale reaction and a broad substrate scope tolerating multiple functional groups. Deuterium labeling experiments and experiments with enantioenriched allenes as starting material support a plausible reaction mechanism.
Cellular Sources and Neuroprotective Roles of Interleukin-10 in the Facial Motor Nucleus after Axotomy
Authors: Runge Elizabeth M. , Setter Deborah O. , Iyer Abhirami K. , Regele Eric J. , Kennedy Felicia M. , Sanders Virginia M. , Jones Kathryn J.
Published: October 2022

Facial motoneuron (FMN) survival is mediated by CD4+ T cells in an interleukin-10 (IL-10)-dependent manner after facial nerve axotomy (FNA), but CD4+ T cells themselves are not the source of this neuroprotective IL-10. The aims of this study were to (1) identify the temporal and cell-specific induction of IL-10 expression in the facial motor nucleus and (2) elucidate the neuroprotective capacity of this expression after axotomy. Immunohistochemistry revealed that FMN constitutively produced IL-10, whereas astrocytes were induced to make IL-10 after FNA. Il10 mRNA co-localized with microglia before and after axotomy, but microglial production of IL-10 protein was not detected. To determine whether any single source of IL-10 was critical for FMN survival, Cre/Lox mouse strains were utilized to selectively knock out IL-10 in neurons, astrocytes, and microglia. In agreement with the localization data reflecting concerted IL-10 production by multiple cell types, no single cellular source of IL-10 alone could provide neuroprotection after FNA. These findings suggest that coordinated neuronal and astrocytic IL-10 production is necessary for FMN survival and has roles in neuronal homeostasis, as well as neuroprotective trophism after axotomy.
The Extracellular Matrix and Neuroblastoma Cell Communication—A Complex Interplay and Its Therapeutic Implications
Authors: Horwacik Irena
Published: October 2022

Neuroblastoma (NB) is a pediatric neuroendocrine neoplasm. It arises from the sympatho-adrenal lineage of neural-crest-derived multipotent progenitor cells that fail to differentiate. NB is the most common extracranial tumor in children, and it manifests undisputed heterogeneity. Unsatisfactory outcomes of high-risk (HR) NB patients call for more research to further inter-relate treatment and molecular features of the disease. In this regard, it is well established that in the tumor microenvironment (TME), malignant cells are engaged in complex and dynamic interactions with the extracellular matrix (ECM) and stromal cells. The ECM can be a source of both pro- and anti-tumorigenic factors to regulate tumor cell fate, such as survival, proliferation, and resistance to therapy. Moreover, the ECM composition, organization, and resulting signaling networks are vastly remodeled during tumor progression and metastasis. This review mainly focuses on the molecular mechanisms and effects of interactions of selected ECM components with their receptors on neuroblastoma cells. Additionally, it describes roles of enzymes modifying and degrading ECM in NB. Finally, the article gives examples on how the knowledge is exploited for prognosis and to yield new treatment options for NB patients.
KRAS G12D Mutation Subtype in Pancreatic Ductal Adenocarcinoma: Does It Influence Prognosis or Stage of Disease at Presentation?
Authors: Shen Henry , Lundy Joanne , Strickland Andrew H. , Harris Marion , Swan Michael , Desmond Christopher , Jenkins Brendan J. , Croagh Daniel
Published: October 2022

Background: KRAS G12D mutation subtype is present in over 40% of pancreatic ductal adenocarcinoma (PDAC), one of the leading global causes of cancer death. This retrospective cohort study aims to investigate whether detection of the KRAS G12D mutation subtype in PDAC patients is a determinant of prognosis across all stages of disease. Methods: We reviewed the medical records of 231 patients presenting with PDAC at a large tertiary hospital, and compared survival using the Kaplan Meier, log-rank test and Cox proportional hazards regression model. Results: KRAS G12D mutation subtype was not significantly associated with poorer survival compared across the whole population of PDAC patients (p = 0.107; HR 1.293 95% CI (0.946–1.767)). However, KRAS G12D patients who were resectable had a shorter median survival time of 356 days compared to all other genotypes (median survival 810 days) (p = 0.019; HR 1.991 95% CI (1.121–3.537)). Conclusions: KRAS G12D patients who were resectable at diagnosis had shorter survival compared to all other PDAC patients. These data suggest that KRAS G12D may be a clinically useful prognostic biomarker of PDAC.
Ethylene Activates the EIN2-EIN3/EIL1 Signaling Pathway in Tapetum and Disturbs Anther Development in Arabidopsis
Authors: Zhu Ben-Shun , Zhu Ying-Xiu , Zhang Yan-Fei , Zhong Xiang , Pan Keng-Yu , Jiang Yu , Wen Chi-Kuang , Yang Zhong-Nan , Yao Xiaozhen
Published: October 2022

Ethylene was previously reported to repress stamen development in both cucumber and Arabidopsis. Here, we performed a detailed analysis of the effect of ethylene on anther development. After ethylene treatment, stamens but not pistils display obvious developmental defects which lead to sterility. Both tapetum and microspores (or microsporocytes) degenerated after ethylene treatment. In ein2-1 and ein3-1 eil1-1 mutants, ethylene treatment did not affect their fertility, indicating the effects of ethylene on anther development are mediated by EIN2 and EIN3/EIL1 in vivo. The transcription of EIN2 and EIN3 are activated by ethylene in the tapetum layer. However, ectopic expression of EIN3 in tapetum did not induce significant anther defects, implying that the expression of EIN3 are regulated post transcriptional level. Consistently, ethylene treatment induced the accumulation of EIN3 in the tapetal cells. Thus, ethylene not only activates the transcription of EIN2 and EIN3, but also stabilizes of EIN3 in the tapetum to disturb its development. The expression of several ethylene related genes was significantly increased, and the expression of the five key transcription factors required for tapetum development was decreased after ethylene treatment. Our results thus point out that ethylene inhibits anther development through the EIN2-EIN3/EIL1 signaling pathway. The activation of this signaling pathway in anther wall, especially in the tapetum, induces the degeneration of the tapetum and leads to pollen abortion.
Lack of Retinoblastoma Protein Shifts Tumor Metabolism from Glycolysis to OXPHOS and Allows the Use of Alternate Fuels
Authors: Suresh Babu Vishnu , Dudeja Gagan , SA Deepak , Bisht Anadi , Shetty Rohit , Heymans Stephane , Guha Nilanjan , Ghosh Arkasubhra
Published: October 2022

Mutations in the RB1 locus leading to a loss of functional Rb protein cause intraocular tumors, which uniquely affect children worldwide. These tumors demonstrate rapid proliferation, which has recently been shown to be associated with an altered metabolic signature. We found that retinoblastoma tumors and in-vitro models lack Hexokinase 1 (HK1) and exhibit elevated fatty acid oxidation. We show that ectopic expression of RB1 induces HK1 protein in Rb null cells, and both RB1 and HK1 can mediate a metabolic switch from OXPHOS to glycolysis with increased pyruvate levels, reduced ATP production and reduced mitochondrial mass. Further, cells lacking Rb or HK1 can flexibly utilize glutamine and fatty acids to enhance oxidative phosphorylation-dependent ATP generation, as revealed by metabolic and biochemical assays. Thus, loss of Rb and HK1 in retinoblastoma reprograms tumor metabolic circuits to enhance the glucose-independent TCA (tricarboxylic acid) cycle and the intermediate NAD+/NADH ratios, with a subsequent increase in fatty-acid derived L-carnitine to enhance mitochondrial OXPHOS for ATP production instead of glycolysis dependence. We also demonstrate that modulation of the Rb-regulated transcription factor E2F2 does not result in any of these metabolic perturbations. In conclusion, we demonstrate RB1 or HK1 as critical regulators of the cellular bioenergetic profile and identify the altered tumor metabolism as a potential therapeutic target for cancers lacking functional Rb protein.
Using Near-Infrared Spectroscopy and Stacked Regression for the Simultaneous Determination of Fresh Cattle and Poultry Manure Chemical Properties
Authors: Cobbinah Elizabeth , Generalao Oliver , Lageshetty Sathish Kumar , Adrianto Indra , Singh Seema , Dumancas Gerard G.
Published: October 2022

Excessive use of animal manure as fertilizers can lead to pollution through the introduction of nitrogen, phosphorus, and other mineral compounds to the environment. Wet chemical analytical methods are traditionally used to determine the precise chemical composition of manure to manage the application of animal waste to the soil. However, such methods require significant resources to carry out the processes. Affordable, rapid, and accurate methods of analyses of various chemical components present in animal manure, therefore, are valuable in managing soil and mitigating water pollution. In this study, a stacked regression ensemble approach using near-infrared spectroscopy was developed to simultaneously determine the amount of dry matter, total ammonium nitrogen, total nitrogen, phosphorus pentoxide, calcium oxide, magnesium oxide, and potassium oxide contents in both cattle and poultry manure collected from livestock production areas in France and Reunion Island. The performance of the stacked regression, an ensemble learning algorithm that is formed by collating the well-performing models for prediction was then compared with that of various other machine learning techniques, including support vector regression (linear, polynomial, and radial), least absolute shrinkage and selection operator, ridge regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, and boosted trees. Results show that stack regression performed optimally well in predicting the seven abovementioned chemical constituents in the testing set and may provide an alternative to the traditional partial least squares method for a more accurate and simultaneous method in determining the chemical properties of animal manure.
Polarity-Dependent Twisted Intramolecular Charge Transfer in Diethylamino Coumarin Revealed by Ultrafast Spectroscopy
Authors: Liu Jiawei , Chen Cheng , Fang Chong
Published: October 2022

Twisting intramolecular charge transfer (TICT) is a common nonradiative relaxation pathway for a molecule with a flexible substituent, effectively reducing the fluorescence quantum yield (FQY) by swift twisting motions. In this work, we investigate coumarin 481 (C481) that contains a diethylamino group in solution by femtosecond transient absorption (fs-TA), femtosecond stimulated Raman spectroscopy (FSRS), and theoretical calculations, aided by coumarin 153 with conformational locking of the alkyl arms as a control sample. In different solvents with decreasing polarity, the transition energy barrier between the fluorescent state and TICT state increases, leading to an increase of the FQY. Correlating the fluorescence decay time constant with solvent polarity and viscosity parameters, the multivariable linear regression analysis indicates that the chromophore’s nonradiative relaxation pathway is affected by both hydrogen (H)-bond donating and accepting capabilities as well as dipolarity of the solvent. Results from the ground- and excited-state FSRS shed important light on structural dynamics of C481 undergoing prompt light-induced intramolecular charge transfer from the diethylamino group toward –C=O and –CF3 groups, while the excited-state C=O stretch marker band tracks initial solvation and vibrational cooling dynamics in aprotic and protic solvents (regardless of polarity) as well as H-bonding dynamics in the fluorescent state for C481 in high-polarity protic solvents like methanol. The uncovered mechanistic insights into the molecular origin for the fluorogenicity of C481 as an environment-polarity sensor substantiate the generality of ultrafast TICT state formation of flexible molecules in solution, and the site-dependent substituent(s) as an effective route to modulate the fluorescence properties for such compact, engineerable, and versatile chemosensors.
Application of ANN Weighted by Optimization Algorithms to Predict the Color Coordinates of Cellulosic Fabric in Dyeing with Binary Mix of Natural Dyes
Authors: Vadood Morteza , Haji Aminoddin
Published: October 2022

Cotton is one of the most important fibers used in the textile industry. The dyeing of cotton with synthetic anionic dyes consumes large amounts of salt and alkali, which makes it a challenge for the environment. Furthermore, the relatively high percentage of synthetic dyes remaining in the dyebath is a potential threat for the environment and human health. The application of plant-derived natural dyes has recently been considered as a promising approach to overcome this problem. Optimization of the dyeing process and prediction of the values of the color coordinates of dyed textiles have always been among the most pronounced challenges in the textile industry, especially when a mixture of dyes or mordants is used. In this study, alum was used for mordanting of cotton and two natural dyes—namely, weld and madder—were used for the dyeing. The samples were dyed with various combinations of mordant, weld, and madder for the weight of the fabric and statistical analysis revealed that all three mentioned parameters were effective in determining the color coordinates. To determine the best model to predict the color coordinates of cotton fabrics, the regression method and ANN models weighted with back-propagation (BP) and optimization algorithms, such as the genetic algorithm, particle swarm optimization, gray wolf optimization, FMINCON (a built-in function of MATLAB software) and a combination of particle swarm optimization and FMINCON (PSO-FMIN), were employed and compared based on the mean squared error (MSE). The obtained results revealed that using the PSO-FMIN algorithm for ANN weighting led to higher accuracy in the prediction of color coordinates. The MSEs obtained for ANN outputs and the corresponding actual values reached 2.02, 1.68 and 1.39 for the l*, a* and b* coordinates, which were 44%, 23% and 26% better than the result obtained with BP, respectively.
Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement
Authors: Ntombela Mlungisi , Musasa Kabeya , Leoaneka Moketjema Clarence
Published: October 2022

A number of algorithms that aim to reduce power system losses and improve voltage profiles by optimizing distributed generator (DG) location and size have already been proposed, but they are still subject to several limitations. Hence, new algorithms can be developed or existing ones can be improved so that this important issue can be addressed more appropriately and effectively. This study proposes a reconfiguration methodology based on a hybrid optimization algorithm, consisting of a combination of the genetic algorithm (GA) and the improved particle swam optimization (IPSO) algorithm for minimizing active power loss and maintaining the voltage magnitude at about 1 p.u. The buses at which DGs should be injected were identified based on optimal real power loss and reactive power limit. When applying the proposed optimization algorithm for DGs allocation in power system, the search space or number of iterations was reduced, increasing its convergence rate. The proposed reconfiguration methodology was test in an IEEE-30 bus electrical network system with DGs allocations and the simulations were conducted using MATLAB software compared to other optimization algorithms, such as GA, PSO, and IPSO, the combination of GA and IPSO or Hybrid GA & IPSO (HGAIPSO) method has a smaller number of iterations and is more effective in optimization problems. The effectiveness of the proposed HGAIPSO has been tested on IEEE-30 bus network system with DGs allocations, and the obtained test results have been compared to those from other methods (i.e., GA, PSO, and IPSO). The simulation results show that the proposed HGAIPSO can be an efficient and promising optimization algorithm for distribution network reconfiguration problems. The IEEE-30 bus test system with DGs integrated at various location revealed reductions in overall real power loss of 40.7040%, 36.2403%, and 42.9406% for type 1, type 2, and type 3 DGs allocation, respectively. The highest bus voltage profile goes to 1.01 pu in the IEEE-30 bus.
Endovascular Arteriovenous Fistula Creation—Review of Current Experience
Authors: Mallios Alexandros , Malik Jan , Jennings William C.
Published: October 2022

Functioning vascular access is an essential element for life-saving hemodialysis therapy. A surgically-created arteriovenous fistula has been considered the best option for many years. Recently, two manufacturers developed systems for percutaneous/endovascular creation of an arteriovenous fistula (WavelinQ and Ellipsys). We provide a review of the available experience with these systems and discuss advantages and disadvantages.
Feasibility of Arterial Spin Labeling Magnetic Resonance Imaging for Musculoskeletal Tumors with Optimized Post-Labeling Delay
Authors: Lin Chien-Hung , Hsieh Tsyh-Jyi , Chou Yi-Chen , Chen Clement Kuen-Huang
Published: October 2022

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is used to perform perfusion imaging without administration of contrast media. However, the reliability of ASL for musculoskeletal tumors and the influence of post-labeling delay (PLD) have not been fully clarified. This study aimed to evaluate the performance of ASL with different PLDs in the imaging of musculoskeletal tumors. Forty-five patients were enrolled and were divided into a malignant group, a hypervascular benign group, a hypovascular benign group and a control group. The tissue blood flow (TBF) of the lesions and normal muscles was measured and the lesion-to-muscle TBF ratio and differences were calculated. The results showed that both the TBF of lesions and muscles increased as the PLD increased, and the TBF of muscles correlated significantly and positively with the TBF of lesions (all p < 0.05). The TBF and lesion-to-muscle TBF differences of the malignant lesions were significantly higher than those of the hypovascular benign lesions and the control group in all PLD groups (all p < 0.0125) and only those of the hypervascular benign lesions in the longest PLD (3025 ms) group (p = 0.0120, 0.0116). In conclusion, ASL detects high TBF in malignant tumors and hypervascular benign lesions, and a longer PLD is recommended for ASL to differentiate musculoskeletal tumors.
An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform
Authors: Yang Zibin , Zhao Yuping , Yu Jiarui , Mao Xiaobo , Xu Huaxing , Huang Luqi
Published: October 2022

To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor–patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.
CBX Family Members in Two Major Subtypes of Renal Cell Carcinoma: A Comparative Bioinformatic Analysis
Authors: Grimaldi Anna Maria , Affinito Ornella , Salvatore Marco , Franzese Monica
Published: October 2022

The biological function and clinical values of Chromobox (CBX) family proteins in renal cell carcinoma (RCC) are still poorly investigated. This study aimed to compare the expression profiles and clinical relevance of CBXs between the two most frequent subtypes of RCC, clear cell renal cell carcinomas (ccRCC) and papillary renal cell carcinomas (pRCC), and to investigate whether CBXs would play a more or less similar role in the pathogenesis and progression of these RCC subtypes. Considering these two RCC populations in the TCGA database, we built a bioinformatics framework by integrating a computational pipeline with several online tools. CBXs showed a similar trend in ccRCC and pRCC tissues but with some features specific for each subtype. Specifically, the relative expressions of CBX3 and CBX2 were, respectively, the highest and lowest among all CBXs in both RCC subtypes. These data also found confirmation in cellular validation. Except for CBX4 and CBX8, all others were deregulated in the ccRCC subtype. CBX1, CBX6, and CBX7 were also significantly associated with the tumor stage. Further, low expression levels of CBX1, CBX5, CBX6, CBX7, and high expression of CBX8 were associated with poor prognosis. Otherwise, in the pRCC subtype, CBX2, CBX3, CBX7, and CBX8 were deregulated, and CBX2, CBX6, and CBX7 were associated with the tumor stage. In addition, in pRCC patients, low expression levels of CBX2, CBX4, and CBX7 were associated with an unfavorable prognosis. Similarly, CBX3, CBX6, and CBX7 presented the highest alteration rate in both subtypes and were found to be functionally related to histone binding, nuclear chromosomes, and heterochromatin. Furthermore, CBX gene expression levels correlated with immune cell infiltration, suggesting that CBXs might reflect the immune status of RCC subtypes. Our results highlight similarities and differences of CBXs within the two major RCC subtypes, providing new insights for future eligible biomarkers or possible molecular therapeutic targets for these diseases.
Machine Learning Models for the Prediction of Renal Failure in Chronic Kidney Disease: A Retrospective Cohort Study
Authors: Su Chuan-Tsung , Chang Yi-Ping , Ku Yuh-Ting , Lin Chih-Ming
Published: October 2022

This study assessed the feasibility of five separate machine learning (ML) classifiers for predicting disease progression in patients with pre-dialysis chronic kidney disease (CKD). The study enrolled 858 patients with CKD treated at a veteran’s hospital in Taiwan. After classification into early and advanced stages, patient demographics and laboratory data were processed and used to predict progression to renal failure and important features for optimal prediction were identified. The random forest (RF) classifier with synthetic minority over-sampling technique (SMOTE) had the best predictive performances among patients with early-stage CKD who progressed within 3 and 5 years and among patients with advanced-stage CKD who progressed within 1 and 3 years. Important features identified for predicting progression from early- and advanced-stage CKD were urine creatinine and serum creatinine levels, respectively. The RF classifier demonstrated the optimal performance, with an area under the receiver operating characteristic curve values of 0.96 for predicting progression within 5 years in patients with early-stage CKD and 0.97 for predicting progression within 1 year in patients with advanced-stage CKD. The proposed method resulted in the optimal prediction of CKD progression, especially within 1 year of advanced-stage CKD. These results will be useful for predicting prognosis among patients with CKD.
A Problem-Centered Approach to Designing Blended Courses: Unifying Online and Face-to-Face Modalities
Authors: Hall Jacob Andrew
Published: October 2022

After experimenting with emergency remote teaching during the COVID-19 pandemic, K-12 schools have retained select online instruction by incorporating blended teaching models. In response, teacher education must respond in innovative ways to prepare future educators with blended teaching competencies. This article presents a problem-centered model for designing flipped courses and discusses how this can demonstrate blended teaching practices that pre-service teachers can observe and experience. Applying a descriptive phenomenological research design, the author iteratively prompted 12 pre-service teachers to reflect on their experiences in a flipped, technology integration course, designed according to this problem-centered model. The results indicate that pre-service teachers experienced the online space as a place to experiment with novel technologies; the in-person class as time for practicing challenging skills and reflecting on future possibilities; and the problem-centered nature of the course as a unifying element and scaffold for their learning.
Using Structural Equation Modeling to Assess a Model for Measuring Creative Teaching Perceptions and Practices in Higher Education
Authors: AlAli Rommel , Al-Barakat Ali
Published: October 2022

Considering the differences in academic backgrounds and majors, diversity of faculty members’ perceptions, and complete shift to digital education, energy must be expended toward ensuring that the teaching practices of faculty members are innovative and distinctive by providing advanced methods and models for evaluation operations. Thus, this study aimed to assess a model for measuring perceptions of both the teaching profession and creative teaching practices among faculty members that explains the relationship between faculty members’ perceptions about teaching and their creative practices that was constructed to explain the nature of this relationship and enable the development of the faculty members’ academic and professional performance. Two instruments were developed in this study, and the study sample consisted of 250 faculty members. Structural equation modeling was used to assess the proposed model. The results of the modified construction model showed an improvement in the goodness of fit indicators, which points toward this being the best model for interpreting the study data. The developed assessment model and scales can be used as tools to measure faculty members’ perceptions and predict the improvement of their creative teaching practices as well as for their professional development during distance learning.
Open Lesson as a Means of Teachers’ Learning
Authors: Abdulbakioglu Mustafa , Kolushpayeva Anar , Balta Nuri , Japashov Nursultan , Bae Christine L.
Published: October 2022

Open lesson (OL), similar to the Japanese lesson study in many aspects, is a professional development model regularly used in schools in the Commonwealth of Independent States Countries. The purpose of this study was to examine teachers’ and students’ attitudes and beliefs about OL practices and activities, using both quantitative and qualitative methods. The sample consisted of 72 instructors and 239 university students in Kazakhstan. MANOVA results from teacher data and student data indicated weak positive attitudes and beliefs about OL. Moreover, no differences were found between the gender, discipline, nationality, and teaching experience of teachers, while students learning social sciences showed stronger positive attitudes and beliefs about the effect of OL on learning when compared to students learning natural sciences. Slightly positive attitudes and beliefs of participants imply that OL implementations in schools should be reconsidered.
LASNet: A Light-Weight Asymmetric Spatial Feature Network for Real-Time Semantic Segmentation
Authors: Chen Yu , Zhan Weida , Jiang Yichun , Zhu Depeng , Guo Renzhong , Xu Xiaoyu
Published: October 2022

In recent years, deep learning models have achieved great success in the field of semantic segmentation, which achieve satisfactory performance by introducing a large number of parameters. However, this achievement usually leads to high computational complexity, which seriously limits the deployment of semantic segmented applications on mobile devices with limited computing and storage resources. To address this problem, we propose a lightweight asymmetric spatial feature network (LASNet) for real-time semantic segmentation. We consider the network parameters, inference speed, and performance to design the structure of LASNet, which can make the LASNet applied to embedded devices and mobile devices better. In the encoding part of LASNet, we propose the LAS module, which retains and utilize spatial information. This module uses a combination of asymmetric convolution, group convolution, and dual-stream structure to reduce the number of network parameters and maintain strong feature extraction ability. In the decoding part of LASNet, we propose the multivariate concatenate module to reuse the shallow features, which can improve the segmentation accuracy and maintain a high inference speed. Our network attains precise real-time segmentation results in a wide range of experiments. Without additional processing and pre-training, LASNet achieves 70.99% mIoU and 110.93 FPS inference speed in the CityScapes dataset with only 0.8 M model parameters.
Machine Learning to Predict Pre-Eclampsia and Intrauterine Growth Restriction in Pregnant Women
Authors: Gómez-Jemes Lola , Oprescu Andreea Madalina , Chimenea-Toscano Ángel , García-Díaz Lutgardo , Romero-Ternero María del Carmen
Published: October 2022

The use of artificial intelligence in healthcare in general and in obstetrics and gynecology in particular has great potential. Specifically, machine learning methods could help improve the health and well-being of pregnant women, closely monitoring their health parameters during pregnancy, or reducing maternal and perinatal morbidity and mortality with early detection of pathologies. In this work, we propose a machine learning model to predict risk events in pregnancy, in particular the prediction of pre-eclampsia and intrauterine growth restriction, using Doppler measures of the uterine artery, sFlt-1, and PlGF values. For this purpose, we used a public dataset from a study carried out by the University Medical Center of Ljubljana, in which data were collected from 95 pregnant women with pre-eclampsia and intrauterine growth restriction. We adopted a multi-label approach to accomplish the prediction task. Different classifiers were evaluated and compared. The performance of each model was tested in terms of accuracy, precision, recall, F1 score, Hamming loss, and AUC-ROC. On the basis of these parameters, a variation of the decision tree classifier was found to be the best performing model. Our model had a robust recall metric (0.89) and an AUC ROC metric (0.87), taking into account the size of the data and the unbalance of the class.
JN-Logo: A Logo Database for Aesthetic Visual Analysis
Authors: Tian Nannan , Liu Yuan , Sun Ziruo
Published: October 2022

Data are an important part of machine learning. In recent years, it has become increasingly common for researchers to study artificial intelligence-aided design, and rich design materials are needed to provide data support for related work. Existing aesthetic visual analysis databases contain mainly photographs and works of art. There is no true logo database, and there are few public and high-quality design material databases. Facing these challenges, this paper introduces a larger-scale logo database named JN-Logo. JN-Logo provides 14,917 logo images from three well-known websites around the world and uses the votes of 150 graduate students. JN-Logo provides three types of annotation: aesthetic, style and semantic. JN-Logo’s scoring system includes 6 scoring points, 6 style labels and 11 semantic descriptions. Aesthetic annotations are divided into 0–5 points to evaluate the visual aesthetics of a logo image: the worst is 0 points; the best is 5 points. We demonstrate five advantages of the JN-Logo database: logo images as data objects, rich human annotations, quality scores for image aesthetics, style attribute labels and semantic description of style. We establish a baseline for JN-Logo to measure the effectiveness of its performance on algorithmic models of people’s choices of logo images. We compare existing traditional handcrafted and deep-learned features in both the aesthetic scoring task and the style-labeling task, showing the advantages of deep learning features. In the logo attribute classification task, the EfficientNet _B1 model achieved the best results, reaching an accuracy of 0.524. Finally, we describe two applications of JN-Logo: generating logo design style and similarity retrieval of logo content. The database of this article will eventually be made public.
Biosensors Fabricated by Laser-Induced Metallization on DLP Composite Resin
Authors: Zhang Ran , Wang Qinyi , Chen Ya , Jiao Chen , Liu Fuxi , Xu Junwei , Zhang Qiuwei , Zhao Jiantao , Shen Lida , Wang Changjiang
Published: October 2022

With the growing emphasis on medical testing, people are seeking more technologies to detect indexes of the human body quickly and at a low cost. The electrochemical biosensors became a research hotspot due to their excellent properties. In this study, dicopper hydroxide phosphate (Cu2(OH)PO4) was incorporated in resin, and the resin sheets were prepared by digital light processing (DLP). The copper base points were activated on the resin sheet surface by Nd: YAG laser and then covered by the electroless copper plating and the electroless silver plating. The laser could effectively activate copper base points on the resin surface. Furthermore, silver electrodes on the detection chips could distinguish glucose solutions of different concentrations well. Finally, a novel detection kit with a three-electrode chip was designed for rapid health testing at home or in medical institutions in the future.
An Application-Oriented Method Based on Cooperative Map Matching for Improving Vehicular Positioning Accuracy
Authors: Zhou Nanhao , Chen Wei , Li Changzhen , Du Luyao , Zhang Donghua , Zhang Ming
Published: October 2022

Accurate vehicular positioning is important for intelligent and connected vehicles (ICVs). However, in urban canyons, vehicles that rely solely on global navigation satellite system (GNSS) are susceptible to factors such as signal blocking and multi-path, reducing the positioning performance. In this paper, an application-oriented cooperative map matching (CMM) method is proposed, and a low-cost Global Positioning System (GPS)/BeiDou navigation satellite system (BDS) integrated positioning system is designed. The road constraints of a real traffic environment, which simplifies the computational complexity and facilitates practical applications, are modeled. The positioning system is designed to collect and store the positioning data for experimental analysis. Static and dynamic experiments are conducted to verify the effectiveness of the CMM method. From the experimental results, the mean absolute error (MAE) and root mean square error (RMSE) of the positioning with CMM correction in the static experiment are reduced by 9.0% and 4.9%, respectively. In the dynamic experiment, compared with the original positioning error, the MAE is reduced by 44.2% while the RMSE is reduced by 24.3%. The results show that the proposed method can improve vehicular positioning accuracy effectively in both static and dynamic environments.
Salt Deposits and Brine Blowout: Development of a Cross-Linking Composition for Blocking Formations and Methodology for Its Testing
Authors: Dvoynikov Mikhail , Sidorov Dmitry , Kambulov Evgeniy , Rose Frederick , Ahiyarov Rustem
Published: October 2022

Uncontrolled inflow of formation fluid (brine) into a well adversely affects the cation–anion bonds in solutions and leads to their dissociation and loss of aggregative stability. Blow-out significantly complicates the drilling process and leads to an increase in non-productive time and in financial costs for problem solving. It is necessary to create a blocking screen that allows separation of the layer from the well and prevents brine flow. This article is devoted to the development of polymeric-blocking compositions that work due to the crystallization reaction of divalent salts of calcium and magnesium chlorides. More than 14 components were detected in the formation fluid on the atomic emission spectrometer. Based on the study of the compatibility of components with brine and the study of rheology and filtration processes through a real core under HPHT conditions, the optimal component polymer composition was selected. The reason for the increase in the rheology of composition during its thickening was established. With the help of tomographic studies, the depth of penetration of the filtrate into the core of layers was determined. For further studies, an experimental stand was designed for physical simulation of the isolation process under HPHT conditions and backpressure from the formation.
Supplier Evaluation Considering Green Production Based on Probabilistic Linguistic Information
Authors: Yuan Shuailei , Liu Aijun , Li Zengxian , Yang Yun , Liu Jing , Su Yue
Published: October 2022

The evaluation of manufacturing component suppliers is focused on economic indicators, with insufficient emphasis on green indicators and no consideration of the correlation between indicators. Firstly, indicators related to green production are incorporated into the supplier evaluation system. Then, for the problem that attributes in decision making can be divided into different categories and there are interrelationships between attributes of the same category, a multi-attribute decision-making (MADM) method based on the partitioned Maclaurin symmetric mean operator (PMSM) is proposed. Finally, the proposed MADM method was applied to the evaluation of component suppliers considering green production. Comparing popular decision methods with the newly proposed method for validation, it was demonstrated that the proposed multi-attribute decision method is highly flexible and versatile. Furthermore, the newly proposed aggregation operator can not only handle the correlation between multiple attributes, but also be converted to other general aggregation operators through parameter adjustment.
An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis
Authors: Xia Rui , Gao Yunpeng , Zhu Yanqing , Gu Dexi , Wang Jiangzhao
Published: October 2022

Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675).
On the Possibility of Cleaning Producer Gas Laden with Large Quantities of Tars through Using a Simple Fixed-Bed Activated Carbon Adsorption Process
Authors: Kochel Mateusz , Szul Mateusz , Iluk Tomasz , Najser Jan
Published: October 2022

The study presents the results of research on using fixed-bed, activated carbon (AC) adsorbers in the cleaning of heavily tar-laden producer gas from the gasification of biomass. The efficiency of removal of organic compounds as well as the remaining adsorption capacity of the bed were determined using a spectrum of commonly applied diagnostic methods, including qualitative and quantitative analyses of the adsorbed compounds and changes in the pore volume of the bed material (IN, MN). The authors compare these lab quantifications with a simple technique which is based on the analysis of the changes in the position of temperature front in the bed. The main benefit of the latter is the possibility of performing the diagnostics of the bed “online” and using low-cost temperature measurements. The test was performed using a commercially available AC Desotec AIRPEL 10-3 and real producer gas obtained through the gasification of alder chips. For tar, VOC and C2–C5 compounds, the removal efficiencies reached respectively 74.5%-wt., 52.8%-wt., and 85.5%-wt. Obtained results indicate that depending on the final application of the gas, the use of dry adsorption systems is an interesting alternative to the well-established but complicated, cumbersome, and costly wet scrubbers. Moreover, a concept for in situ regeneration of the adsorbent, coupled with direct reforming of the tars, is presented and discussed.