bluuebunny/crossref_metadata_2025 huggingface dataset:
Critical care nursing role in low and lower middle-income settings: a scoping review
Authors: Macey Andy , O'Reilly Gerard , Williams Ged , Cameron Peter
Published: January 2022

Objectives

A scoping review was conducted to answer the question: How is critical care nursing (CCN) performed in low-income countries and lower middle-income countries (LICs/LMICs)?

Design

Scoping review guided by the JBI Manual for Evidence Synthesis.

Data sources

Six electronic databases and five web-based resources were systematically searched to identify relevant literature published between 2010 and April 2021.

Review methods

The search results received two-stage screening: (1) title and abstract (2) full-text screening. For sources of evidence to progress, agreement needed to be reached by two reviewers. Data were extracted and cross-checked. Data were analysed, sorted by themes and mapped to region and country.

Results

Literature was reported across five georegions. Nurses with a range formal and informal training were identified as providing critical care. Availability of staff was frequently reported as a problem. No reports provided a comprehensive description of CCN in LICs/LMICs. However, a variety of nursing practices and non-clinical responsibilities were highlighted. Availability of equipment to fulfil the nursing role was widely discussed. Perceptions of inadequate resourcing were common. Undergraduate and postgraduate-level preparation was poorly described but frequently reported. The delivery of short format critical care courses was more fully described. There were reports of educational evaluation, especially regarding internationally supported initiatives.

Conclusions

Despite commonalities, CCN is unique to regional and socioeconomic contexts. Nurses work within a complex team, yet the structure and skill levels of such teams will vary according to patient population, resources and treatments available. Therefore, a universal definition of the CCN role in LIC/LMIC health systems is likely unhelpful. Research to elucidate current assets, capacity and needs of nurses providing critical care in specific LIC/LMIC contexts is needed. Outputs from such research would be invaluable in supporting contextually appropriate capacity development programmes.
Trabeculectomy versus Xen gel implant for the treatment of open-angle glaucoma: a 3-year retrospective analysis
Authors: Cappelli Francesca , Cutolo Carlo Alberto , Olivari Sara , Testa Valeria , Sindaco Daniele , Pizzorno Chiara , Ciccione Sara , Traaverso Carlo Enrico , Iester Michele
Published: January 2022

Objective

To compare the two surgical techniques in terms of efficacy, safety, and postoperative management over 36 months of follow-up.

Methods

This retrospective clinical cohort study compared the outcome of trabeculectomy surgery and Xen gel implant in patients having uncontrolled glaucoma. Patients were recruited using the following inclusion criteria: uncontrolled intraocular pressure (IOP) on maximally tolerated medical therapy, healthy conjunctiva freely mobile in the superior sector, open-angle, glaucomatous visual field damage, full follow upfollow-up of at least 36 months. Thirty-four patients were submitted to trabeculectomy and 34 to Xen gel implant. We set the lower limit at 6mmHg mm Hg and the upper limit ≤12 mm Hg for criteria A, upper limit to ≤15 mm Hg for criteria B and upper limit ≤18 mm Hg for criteria C. Criteria for success have been characterizedcharacterised according to whether or not this has been achieved without (complete success) or with IOP -lowering medications (qualified success).

Results

For all survival curves, trabeculectomy was superior to Xen gel implant. When considering complete success, the log-rank test for criteria A was statistically significant (pp=0.006), marginally significant for criteria B (pp=0.065) and not significant for criteria C (pp=0.23). When qualified success was considered, trabeculectomy was superior to Xen gel for criteria A, B, and C (pp=0.012, pp=0.033 and pp=0.025, respectively). Higher number of post-operative flat chamber and bleb leakage was observed in the trabeculectomy group.

Conclusion

Xen gel implant techniques offer a better safety profile but a lower IOP reduction compared to compared with the gold -standard technique.
HOPF BIFURCATION OF A FRACTIONAL TRI-NEURON NETWORK WITH DIFFERENT ORDERS AND LEAKAGE DELAY
Authors: WANG YANGLING , CAO JINDE , HUANG CHENGDAI
Published: January 2022

This paper focuses on the Hopf bifurcation of a fractional tri-neuron network with both leakage delay and communication delay under different fractional orders. By applying fractional Laplace transform, the stability theorem of linear autonomous system and Hopf bifurcation theorem, we obtain a class of asymptotic stability criterion of zero solution as well as delay-induced Hopf bifurcation conditions for the considered system. Simultaneously, the stability and Hopf bifurcation for tri-neuron network with single fractional order are also discussed as a special case of our proposed neural network model. Finally, a simulation example is given to illustrate the efficiency of the presented theoretical results in this paper.
GUDERMANNIAN NEURAL NETWORKS TO INVESTIGATE THE LIÉNARD DIFFERENTIAL MODEL
Authors: WANG BO , WANG YE , GÓMEZ-AGUILAR J. F. , SABIR ZULQURNAIN , RAJA MUHAMMAD ASIF ZAHOOR , JAHANSHAHI HADI , ALASSAFI MADINI O. , ALSAADI FAWAZ E.
Published: January 2022

The aim of this study is to present the numerical solutions of the Liénard nonlinear model by designing the structure of the computational Gudermannian neural networks (GNNs) along with the global/local search efficiencies of genetic algorithms (GAs) and interior-point algorithm (IPA), i.e. GNNs–GAs–IPA. A merit function in terms of differential system and its boundary conditions is designed and optimization is performed by using the proposed computational procedures of GAs–IPA to solve the Liénard nonlinear differential system. Three different highly nonlinear examples based on the Liénard differential system have been tested to check the competence, exactness and proficiency of the proposed computational paradigm of GNNs–GAs–IPA. The statistical performances in terms of different operators have been provided to check the reliability, consistency and stability of the computational GNNs–GAs–IPA. The plots of the absolute error, performance measures, results comparison, convergence analysis based on different operators, histograms and boxplots are also illustrated. Moreover, statistical gauges using minimum, mean, maximum, semi-interquartile range, standard deviation and median are also provided to authenticate the optimal performance of the GNNs–GAs–IPA.
COMPLEX NETWORKS MODELED ON A KIND OF SIERPIŃSKI-LIKE CARPET
Authors: HUANG LIANG , PENG LI
Published: January 2022

In this paper, we use a kind of Sierpiński-like carpet to construct a special evolving network, whose nodes are all the solid squares in the construction of the carpet up to stage t. In our network, two nodes are neighbors if and only if the intersection of their corresponding squares is a line segment. Using the encoding method in fractal geometry, we show that our evolving network is scale-free and has the small-world effect, but is not fractal scaling.
Shooting Down the Server Front-End Bottleneck
Authors: Kumar Rakesh , Grot Boris
Published: January 2022

The front-end bottleneck is a well-established problem in server workloads owing to their deep software stacks and large instruction footprints. Despite years of research into effective L1-I and BTB prefetching, state-of-the-art techniques force a trade-off between metadata storage cost and performance. Temporal Stream prefetchers deliver high performance but require a prohibitive amount of metadata to accommodate the temporal history. Meanwhile, BTB-directed prefetchers incur low cost by using the existing in-core branch prediction structures but fall short on performance due to BTB’s inability to capture the massive control flow working set of server applications. This work overcomes the fundamental limitation of BTB-directed prefetchers, which is capturing a large control flow working set within an affordable BTB storage budget. We re-envision the BTB organization to maximize its control flow coverage by observing that an application’s instruction footprint can be mapped as a combination of its unconditional branch working set and, for each unconditional branch, a spatial encoding of the cache blocks around the branch target. Effectively capturing a map of the application’s instruction footprint in the BTB enables highly effective BTB-directed prefetching that outperforms the state-of-the-art prefetchers by up to 10% for equivalent storage budget.
PAVEL: Decorative Patterns with Packed Volumetric Elements
Authors: Fanni Filippo Andrea , Pellacini Fabio , Scateni Riccardo , Giachetti Andrea
Published: January 2022

Many real-world hand-crafted objects are decorated with elements that are packed onto the object’s surface and deformed to cover it as much as possible. Examples are artisanal ceramics and metal jewelry. Inspired by these objects, we present a method to enrich surfaces with packed volumetric decorations. Our algorithm works by first determining the locations in which to add the decorative elements and then removing the non-physical overlap between them while preserving the decoration volume. For the placement, we support several strategies depending on the desired overall motif. To remove the overlap, we use an approach based on implicit deformable models creating the qualitative effect of plastic warping while avoiding expensive and hard-to-control physical simulations. Our decorative elements can be used to enhance virtual surfaces, as well as 3D-printed pieces, by assembling the decorations onto real surfaces to obtain tangible reproductions.
Environmental Factors Influencing COVID-19 Incidence and Severity
Authors: Weaver Amanda K. , Head Jennifer R. , Gould Carlos F. , Carlton Elizabeth J. , Remais Justin V.
Published: January 2022

Emerging evidence supports a link between environmental factors—including air pollution and chemical exposures, climate, and the built environment—and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and coronavirus disease 2019 (COVID-19) susceptibility and severity. Climate, air pollution, and the built environment have long been recognized to influence viral respiratory infections, and studies have established similar associations with COVID-19 outcomes. More limited evidence links chemical exposures to COVID-19. Environmental factors were found to influence COVID-19 through four major interlinking mechanisms: increased risk of preexisting conditions associated with disease severity; immune system impairment; viral survival and transport; and behaviors that increase viral exposure. Both data and methodologic issues complicate the investigation of these relationships, including reliance on coarse COVID-19 surveillance data; gaps in mechanistic studies; and the predominance of ecological designs. We evaluate the strength of evidence for environment–COVID-19 relationships and discuss environmental actions that might simultaneously address the COVID-19 pandemic, environmental determinants of health, and health disparities.
Designing for Dissemination and Sustainability to Promote Equitable Impacts on Health
Authors: Kwan Bethany M. , Brownson Ross C. , Glasgow Russell E. , Morrato Elaine H. , Luke Douglas A.
Published: January 2022

Designing for dissemination and sustainability (D4DS) refers to principles and methods for enhancing the fit between a health program, policy, or practice and the context in which it is intended to be adopted. In this article we first summarize the historical context of D4DS and justify the need to shift traditional health research and dissemination practices. We present a diverse literature according to a D4DS organizing schema and describe a variety of dissemination products, design processes and outcomes, and approaches to messaging, packaging, and distribution. D4DS design processes include stakeholder engagement, participatory codesign, and context and situation analysis, and leverage methods and frameworks from dissemination and implementation science, marketing and business, communications and visualarts, and systems science. Finally, we present eight recommendations to adopt a D4DS paradigm, reflecting shifts in ways of thinking, skills and approaches, and infrastructure and systems for training and evaluation.
Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network
Authors: Mohiyuddin Aqsa , Basharat Asma , Ghani Usman , Peter Veselý , Abbas Sidra , Naeem Osama Bin , Rizwan Muhammad
Published: January 2022

Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.
Study on the Applicability of Elastic Recovery (Resilience) Experiment for Asphalt‐Rubber
Authors: Yao Zechen , Yang Renfeng , Kang Jian , Zhang Zhigang
Published: January 2022

In this paper, the applicability of the elastic recovery (resilience) experiment for asphalt‐rubber (AR) binders has been quantitatively assessed. The mechanical model, based on the viscoelastic constitutive relation and particle inclusion theory, was developed. The interfacial detachment between crumb rubber (CR) particles and asphalt caused by stress concentration was analyzed with Weibull statistical equations. Based on the road roughness excitation, the vehicle‐road coupling vibration model was established to analyze the impact of vehicle loading on road surface deformation. AR binders with different CR particle sizes were assessed using scanning electron microscope (SEM) imaging and prepared for testing the elastic recovery (resilience). The results showed that the greater internal stress caused by the longer stretch length of AR binders in the elastic recovery experiment was ten times higher than that obtained from the resilience experiment, leading to the interfacial detachment between asphalt and the CR particles. Hence, the elastic property of some of the CR particles with high modulus was not reflected, resulting in the test values being lower than actual values. With the reduction of CR particle size, the interfacial detachment was improved in the elastic recovery experiment due to intense material interchange and the enhancement of interfacial bond strength. The millimeter‐scale compression deformation of the AR binder in the resilience experiment was closer to the actual deformation of the road surface. The experimental time of resilience (120 min) has been reported less than that for elastic recovery (200 min–230 min). This study shows that the resilience experiment has a significant advantage in assessing the elastic property of the AR binder.
Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model
Authors: Deng Huijian , Cao Shijian , Tang Jingen
Published: January 2022

In the process of sports, athletes often have aggressive behaviors because of their emotional fluctuations. This violent sports behavior has caused many serious bad effects. In order to reduce and solve this kind of public emergencies, this paper aims to create a swarm intelligence model for predicting people's sports attack behavior, takes the swarm intelligence algorithm as the core technology optimization model, and uses the Internet of Things and other technologies to recognize emotions on physiological signals, predict, and intervene sports attack behavior. The results show the following: (1) After the 50-fold cross-validation method, the results of emotion recognition are good, and the accuracy is high. Compared with other physiological electrical signals, EDA has the worst classification performance. (2) The recognition accuracy of the two methods using multimodal fusion is improved greatly, and the result after comparison is obviously better than that of single mode. (3) Anxiety, anger, surprise, and sadness are the most detected emotions in the model, and the recognition accuracy is higher than 80%. Sports intervention should be carried out in time to calm athletes' emotions. After the experiment, our model runs successfully and performs well, which can be optimized and tested in the next step.
MSIS: Multispectral Instance Segmentation Method for Power Equipment
Authors: Shu Jun , He Juncheng , Li Ling
Published: January 2022

Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.
Throughput-Oriented Full-Duplex Cognitive Radio Network Parameter Optimization
Authors: Jiang Haibin , Yu Zhiyong , Yang Jian , Kang Kai
Published: January 2022

Full-duplex cooperative spectrum sensing (FD-CSS) is an important research field in the field of spectrum sensing. In the FD-CSS network, the secondary user (SU) senses the usage status of the authorized spectrum by the primary user (PU) through the sensing channel and then reports the perceived data to the fusion center (FC) through the reporting channel. The FC makes a comprehensive judgment after summarizing the data through the fusion algorithm. In the secondary network with SU, throughput is an important index to measure the performance of the network. Taking throughput as the optimization goal, this paper theoretically deduces and verifies the optimal data fusion algorithm in cooperative spectrum sensing (CSS), the threshold of optimal energy detection, and the optimal transmission power of SU in the secondary network. The simulation results show the correctness of the results in this paper.
Computer Intelligent Algorithm in the Recovery of the Elbow Joint Sports Injury Model
Authors: Duan Huiyu , Xun Shenglong , Bao Yichen , Zhang Gong
Published: January 2022

In this study, the inverse kinematics mathematics computer intelligent algorithm model is used to study the sports injuries of the elbow joint of adolescents. At the same time, we simulated the movement parameter changes during the rehabilitation training of the patient’s wrist and proposed a joint angular velocity function based on cubic fitting. Research has found that when the training scene changes greatly or the target task is changed, the smoothness of the elbow joint movement will change. The research conclusions of this article provide a theoretical basis for the selection of man-machine action points and the formulation of rehabilitation training methods. This article establishes the degree-of-freedom simulation model of the operating arm, which is the number of independent position variables in the operating arm, and these position variables determine the positions of all parts in the mechanism.
Identification of Immune-Related Prognostic mRNA and lncRNA in Patients with Hepatocellular Carcinoma
Authors: Chen Dan , Li Xiaoting , Li Hui , Wang Kai , Tian Xianghua
Published: January 2022

Background. As the most common hepatic malignancy, hepatocellular carcinoma (HCC) has a high incidence; therefore, in this paper, the immune-related genes were sought as biomarkers in liver cancer. Methods. In this study, a differential expression analysis of lncRNA and mRNA in The Cancer Genome Atlas (TCGA) dataset between the HCC group and the normal control group was performed. Enrichment analysis was used to screen immune-related differentially expressed genes. Cox regression analysis and survival analysis were used to determine prognostic genes of HCC, whose expression was detected by molecular experiments. Finally, important immune cells were identified by immune cell infiltration and detected by flow cytometry. Results. Compared with the normal group, 1613 differentially expressed mRNAs (DEmRs) and 1237 differentially expressed lncRNAs (DElncRs) were found in HCC. Among them, 143 immune-related DEmRs and 39 immune-related DElncRs were screened out. These genes were mainly related to MAPK cascade, PI3K-AKT signaling pathway, and TGF-beta. Through Cox regression analysis and survival analysis, MMP9, SPP1, HAGLR, LINC02202, and RP11-598F7.3 were finally determined as the potential diagnostic biomarkers for HCC. The gene expression was verified by RT-qPCR and western blot. In addition, CD4 + memory resting T cells and CD8 + T cells were identified as protective factors for overall survival of HCC, and they were found highly expressed in HCC through flow cytometry. Conclusion. The study explored the dysregulation mechanism and potential biomarkers of immune-related genes and further identified the influence of immune cells on the prognosis of HCC, providing a theoretical basis for the prognosis prediction and immunotherapy in HCC patients.
Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network
Authors: Li Xiaorong , Zhang Lijun , Li Dongchen , Guo Dan
Published: January 2022

In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in‐depth analysis of HR strategic decision‐making and an HR strategic decision model is constructed for simulation. The four evaluation dimensions of index screening are extracted, the generalized gray correlation analysis is applied to screen the initial selection indexes of HR strategic decision‐making, and then the HR strategic decision‐making index system of power supply enterprises is constructed. On this basis, the applicability of the BP neural network in HR strategic decision‐making is analysed and demonstrated, a BP neural network‐based HR strategic decision‐making model for power supply enterprises is constructed, and the rationality of the model is illustrated through the model training and testing. Finally, an empirical study is conducted with S power supply company as an example to illustrate the operability of the BP network model for HR strategic decision‐making. The results of this paper provide a scientific basis for human resource decision‐making in power supply enterprises and provide theoretical support for promoting the healthy development of power supply enterprises. Improving the level of human resource management can make fuller use of human resources and realize the strategic goals of the enterprise. A strategy for optimizing the training of hybrid convolutional neural networks is proposed, using an exponential linear unit activation function to solve the problem of neuron dead zones, a hybrid pooling strategy is used to improve the problem of feature information loss in maximum pooling processing, and an improved cross‐entropy loss function is used to solve the problem of insufficient learning of difficult classification samples. The optimization of the model training process is finally completed, and the recommendation quality is improved. The Bayesian probability distribution table is learned to be filled in the expert data set; finally, the accuracy and effectiveness of the model are tested on the experimental platform. The results of the experiments show that the established Bayesian model beats the platform’s built‐in intelligent method with a 78.2% win rate. That is, the model can make intelligent recommendations for strategies in staffed and unmanned platform decision‐making and command‐and‐control combat units to execute tactical actions to achieve the best operational effectiveness.
Recent Progress on the Implementation of Renewable Biodiesel Fuel for Automotive and Power Plants: Raw Materials Perspective
Authors: Sentanuhady Jayan , Hasan Wisnu Hozaifa , Muflikhun Muhammad Akhsin
Published: January 2022

The issue of energy availability and the impact of global warming has become a specific concern for researchers in various countries in the world. This condition opens up space for potential alternative energy sources that are newer and more sustainable. Biodiesel as a sustainable alternative energy can be applied directly to industrial and automotive fields and even as a fuel source for power plants. This study provides a specific overview of the use and development of biodiesel as a fuel source for renewable power generation based on material sources. An essential part of this study discusses the development of various combinations of biodiesel mixtures and their applications in various types of industries. The physical and chemical characteristics of various types of biodiesel have been studied for their use in various parts of the world. The use of biodiesel has a positive effect on reducing emissions and pollutants, but a particular method is needed to optimize the efficiency and effectiveness, both technically and nontechnically. However, the utilization accompanied by optimizing the characteristics and parameters of biodiesel shows that this alternative energy is feasible and can be applied as a renewable fuel source for automotive industry and power generation in the future.
Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management
Authors: Chew Michael Yit Lin , Yan Ke
Published: January 2022

Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.
[Retracted] Correlation Analysis of Acute Coronary Syndrome with Serum IL‐18, MMP‐9, hs‐CRP, and Plasma FIB
Authors: Yang Yuexia , Li Guoming , Zhang Ruiqin
Published: January 2022

Aim. This study attempted to investigate the diagnostic value of interleukin‐18 (IL‐18), matrix metalloproteinase‐9 (MMP‐9), high‐sensitivity C‐reactive protein (hs‐CRP), and fibrinogen (FIB) in acute coronary syndrome (ACS) and their correlation with the degree of vascular lesions. Materials and Methods. Altogether 206 patients with coronary heart disease admitted to our hospital were selected as research objects, including 136 patients with ACS (group A), 70 patients with stable angina pectoris (SAP) (group B), and 60 patients with noncoronary heart disease who had normal coronary angiography during the same period were selected as group C. The levels of IL‐18, MMP‐9, and hs‐CRP in the serum were detected by enzyme‐linked immunosorbent assay (ELISA), and the level of FIB in plasma was detected by automatic coagulation analyzer. Results. Serum IL‐18, MMP‐9, hs‐CRP, and plasma FIB levels in group A were significantly higher than those in group B and group C (p < 0.05). ROC curve and multivariate logistic regression showed that the sensitivity and specificity of combined diagnosis of ACS with serum IL‐18, MMP‐9, hs‐CRP, and plasma FIB were 86.03% and 95.71%, respectively. Serum IL‐18, MMP‐9, hs‐CRP, and plasma FIB were positively correlated with Gensini grading (p < 0.001). Serum IL‐18, MMP‐9, hs‐CRP, and plasma FIB levels were positively correlated (p < 0.001). Conclusion. The combined detection of serum IL‐18, MMP‐9, hs‐CRP, and plasma FIB has good diagnostic value for ACS, and these index levels are positively correlated with the degree of vascular lesions.
Neural Network Based Estimation of Service Life of Different Metal Culverts in Arkansas
Authors: Hossain Zahid , Hasan MdAriful , Ghabchi Rouzbeh
Published: January 2022

The Arkansas Department of Transportation (ARDOT) uses different types of metal culverts and cross‐drains. Service lives of these culverts are largely influenced by the corrosion of the metals used in these culverts. Corrosion of metallic parts in any soil‐water environment is governed by geochemical and electrochemical properties of the soils and waters. Many transportation agencies including ARDOT primarily focus on investigating the physical and mechanical properties of soils rather than their chemical aspects. The main objective of this study is to analyze the geotechnical and geochemical properties of soils in Arkansas to estimate the service lives of different metal pipes in different conditions. Soil resistivity values were predicted after analyzing the United States Department of Agriculture (USDA) soil survey data using neural network (NN) models. The developed NN models were trained and verified by using laboratory test results of soil samples collected from ARDOT, and survey data were obtained from the USDA. The service lives of metal culverts were then estimated based on the predicted soil properties and water quality parameters extracted from the data acquired from the Arkansas Department of Environmental Quality (ADEQ). Finally, Geographic Information System‐based corrosion risk maps of three different types of metal pipes were developed based on their estimated service lives. The developed maps will help ARDOT engineers to assess the corrosion potential of the metal pipes before starting the new construction and repair projects and will allow using proper culvert materials to maximize their life spans.
MiR-218 Promotes Adriamycin-Induced H9C2 Apoptosis by Inhibiting Stress-Associated Endoplasmic Reticulum Protein 1
Authors: Chen Qinghua , Chen Gang , Zhao Shuofang
Published: January 2022

Objective. Adriamycin is a clinically important chemotherapeutic drug, but its use is restricted due to its myocardial toxicity. Therefore, it is especially important to explore the toxicity mechanism of Adriamycin (ADR) to cardiomyocytes. Methods. The myocardial toxicity model of ADR was constructed in vitro, and the effect of miR-218 inhibitor and sh-Serp1 on the activity of H9C2 cells induced by ADR was detected by MTT method. Also, flow cytometry, real-time polymerase chain reaction (RT-PCR), and TUNEL staining were used to detect the cell apoptosis. The activity of LDH was detected by colorimetry, and the interaction of miR-218 with Serp1 was detected by double-luciferase reporter gene assay. Western blotting technique was used to detect the expression level of caspase3 and p38 MAPK signal pathway. Results. miR-218 inhibitor can obviously inhibit ADR-induced decrease in cell activity of H9C2 cells, inhibit cell apoptosis, and inhibit p38 MAPK signaling pathway activation. Conversely, sh-Serp1 aggravated the decrease in H9C2 cell activity and promoted cell apoptosis. Conclusion. Upregulation of miR-218 expression will promote ADR-induced apoptosis of H9C2 cells. At the same time, we confirmed that the mechanism by which miR-218 promotes myocardial apoptosis was through the Serp1/p38 MAPK/caspase-3 signaling pathway.
Performance of HCV Antigen Testing for the Diagnosis and Monitoring of Antiviral Treatment: A Systematic Review and Meta‐Analysis
Authors: Flores Geane Lopes , Mota Jurema Corrêa , da Silva Andrade Larissa Tropiano , Lopes Renata Serrano , Bastos Francisco Inácio , Villar Livia Melo
Published: January 2022

Background and Aims. Active hepatitis C virus (HCV) infection is based on the detection of HCV RNA that it is effective but presents high cost and the need to hire trained personnel. This systematic review and meta‐analysis is aimed at evaluating the diagnostic accuracy of HCV Ag testing to identify HCV cases and to monitor antiviral treatment including DAA treatment. Methods. The studies were identified through a search in PubMed, Lilacs, and Scopus from 1990 through March 31, 2020. Cohort, cross‐sectional, and randomized controlled trials were included. Two independent reviewers extracted data and assessed quality using an adapted Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) tool. Our primary outcome was to determine the accuracy of HCV Ag detection for the diagnosis, which we estimated using random‐effects meta‐analysis. Results. Of 3,062 articles identified, 54 met our eligibility criteria. The studies described cohorts from 20 countries, including 14,286 individuals with chronic HCV individuals. Studies for ECLIA technology demonstrated highest quality compared to studies that used ELISA. The pooled sensitivity and specificity (95% CI) for HCV Ag detection of active HCV infection were 98.82% (95%CI = 98.04%; 99.30%) and 98.95% (95%CI = 97.84%; 99.49%), respectively. High concordance was found between HCV Ag testing and HCV RNA detection 89.7% and 95% to evaluate antiviral treatment. Conclusions. According to our findings, HCV Ag testing could be useful to identify HCV active cases in low‐resource areas. For antiviral treatment, HCV Ag testing will be useful at the end of treatment.
Orthodontic Treatment of Bilateral Transposition of Maxillary Canines and Lateral Incisors
Authors: Nyakale Mandla Dominic
Published: January 2022

Dental transposition is a severe disturbance of tooth position and its eruptive sequence. It may affect any tooth, and it may occur at any location within the dental arch, although some teeth may be more frequently affected than others. There are several types of dental transposition, and their classification depends on the type of teeth involved. The aetiology of transposition is not very clear and has always been the subject of controversies, and it is still not completely understood. The prevalence of dental transposition and the factors related to this dental anomaly have been well documented in the literature. When treating dental transposition, many factors which may affect the treatment results must be considered, such as aesthetics, occlusion, treatment duration, patient comfort, patient cooperation, and periodontal support. Various treatment options are currently available for the treatment of dental transposition. Successful treatment outcomes depend on the patient’s age, concern about facial and dental aesthetics, motivation of the patient, functional requirements, type of malocclusion, and the position of the root apices. The present case report is aimed at outlining the orthodontic treatment of a young adult patient diagnosed with bilateral maxillary canine-lateral incisor transposition.
The Social and Economic Significance of Natural Gum and Resin in the Woodlands of South Omo Zone, Southern Ethiopia
Authors: Hido Alemayehu , Alemayehu Asabeneh
Published: January 2022

The social, economic, and ecological role of woodlands forests in dry and lowland areas are more imperative than anywhere else. Most of the dry lands of Ethiopia host woodlands that produce various commercial gum-resin products. The objective of this study was to investigate the social and economic importance of commercial gum-resin products focusing on their roles in the local livelihoods. Four kebeles, namely, Luka and Enchete from Bena-Tsemay district, and Erbore and Asele from Hamer district of South Omo Zone, were purposively selected for the collection of socioeconomic data. Semistructured questionnaire and checklists were used for households and key informant interviews and focus group discussions, respectively. In addition, market assessments and field observation were conducted to collect the primary data and supplemented with secondary data. A total of 80 households were randomly selected for one-to-one interviews from the total number of 425 households in the selected kebeles. A total of 10 key informant interviews with elders, forestry experts, local merchants, and enterprise members involved in gum-resins marketing and 4 focus group discussions with 12 members including men, women, and youth were conducted. Simple descriptive statistical tools were used for the data analysis. The results of the study showed that the mean annual income earned from the sale of gum-resins was 5670 and 4571 ETB per household at Bena-Tsemay and Hamer study sites, respectively. About 84% of the respondents indicated that gum-resin collection was the simplest and fastest means to earn income for school children and women. Gum and resin collection was the third most important livelihood option in the study area following animal husbandry and crop and honey production. The majority (84%) of respondents recognized the income made from gum-resin products as a safety net during recurring famine eras. Moreover, respondents recognized the benefit of gum arabic as food during famine time, as chewing gum, and as nutritious fodder. It was also used as folk medicine for both human and livestock diseases. Communities used this resource as a constituent to treat eye and skin infections, bleeding, wounds, ulcers, stomachache, gastrointestinal infections, etc. In addition, 88.33% of shrubs/trees were used as source of cash income for local communities and 11.67% as fodder for animal husbandry. Despite this significance, various constraints hindered the utilization of the resource in the study area, and these included lack of appropriate tapping techniques, market access, market information, cooperatives, infrastructure facilities, and appropriate institutions as well as poor local communities’ awareness of land management. The present investigation has provided valuable information for overcoming the major constraints by devising strategies to maximize gum-resin production and commercialization in the study area.
An Empirical Study on the Precise Employment Situation-Oriented Analysis of Digital-Driven Talents with Big Data Analysis
Authors: Li Lin , Tsai Sang-Bing
Published: January 2022

This paper conducts an in-depth research analysis on the precise employment of college graduates in the context of big data using a number-driven approach. The textual information of the study is obtained by using in-depth interviews, and the evaluation index system of college students’ employment quality is constructed by combining the step-by-step coding method with rooting theory. The research on the current situation of employment recommendation platform research and the application status of big data in the employment recommendation platform is explored by using a bibliometric approach. And the innovative use of web crawler technology is used to comprehensively understand the recommendation function and status quo of the same type of recommendation platform, which provides a reference for the research of this platform. Based on the preliminary analysis of platform requirements and overall design, the overall design and functional implementation of the big data employment recommendation platform are carried out by using big data crawler technology, big data architecture technology, text mining technology, database technology, etc. The construction of a recommendation module based on user history information, a recommendation based on real-time user online behavior data, and hybrid recommendation carried out on the recommendation module to grasp all-round the platform is built based on a stakeholder perspective. Based on the platform construction, the initial platform operation and maintenance management mechanism was established from the stakeholder’s perspective. The Pearson correlation coefficient is used to objectively evaluate the current situation of talent supply in universities and talent demand in enterprises from the perspective of image and data. In the research on the development status of the big data education industry, the Lorenz curve and Gini coefficient are used to match the status of new big data majors with their college construction volume in each province and provide data support for the reasonable adjustment of majors setting in each province according to the education level.
The Effect of Inner Engineering Online (IEO) Program on Reducing Stress for Information Technology Professionals: A Randomized Control Study
Authors: Upadhyay P. , Chang T. F. H. , Hariri S. , Rallabandi S. , Yathavakilla Santha , Novack V. , Subramaniam B.
Published: January 2022

In recent years, mindfulness-based interventions (MBIs) are rapidly growing in the workplace. Several meta-analyses conclude that overall MBIs have a moderate effect of alleviating deficit-based experiences, such as burnout and stress, but a small to no effect of promoting asset-based experiences, such as positive affect and well-being. While workplace MBIs vary greatly in their content, format, and duration, the dominant format is still face to face in a group setting, which limits scalability. Our study introduces an emerging workplace intervention called Inner Engineering Online (IEO) and evaluates its effect on reducing stress, burnout, depression, and anxiety and increasing mindfulness and joy. Drawing on the classical yogic science, IEO is a comprehensive web-based multicomponent intervention that utilizes dialectic discourse, meditation, and yogic practices designed to improve physical, mental, and emotional health. Utilizing a randomized active control cross-over experimental design with a sample of 71 employees of an Information Technology company, we tested our hypothesis that IEO training and regular daily yogic practice are likely to lower the stress levels, prevent burnout, and alleviate anxiety and depression, while at the same time promotes positive affect for employees. The results show that IEO program significantly reduces stress only among those who adhere to recommended daily yogic practices. The study is limited by its small sample size. Future research using a large sample is recommended to reexamine the effect of IEO training on occupational health. This trial is registered with NCT04126564.
Effects of Neuromuscular Electrical Stimulation Combined with Repetitive Transcranial Magnetic Stimulation on Upper Limb Motor Function Rehabilitation in Stroke Patients with Hemiplegia
Authors: Du Junqiu , Wang Shouyong , Cheng Yun , Xu Jiang , Li Xuejing , Gan Yimin , Zhang Liying , Zhang Song , Cui Xiaorui
Published: January 2022

Objective. To investigate the effect of neuromuscular electrical stimulation (NMES) combined with repetitive transcranial magnetic stimulation (rTMS) on upper limb motor dysfunction in stroke patients with hemiplegia. Methods. A total of 240 stroke patients with hemiplegia who met the inclusion criteria were selected and randomly divided into 4 groups (60 cases in each group): control group, NMES group, rTMS group, and NMES + rTMS group. Before treatment and 4 weeks after treatment, we evaluated and compared the results including Fugl-Meyer assessment of upper extremity (FMA-UE) motor function, modified Barthel index (MBI), modified Ashworth scale (MAS), and motor nerve electrophysiological results among the 4 groups. Results. Before treatment, there was no significant difference in the scores of FMA-UE, MBI, MAS, and motor nerve electrophysiological indexes among the four groups, with comparability. Compared with those before treatment, the scores of the four groups were significantly increased and improved after treatment. And the score of the NMES + rTMS group was notably higher than those in the other three groups. Conclusion. NMES combined with rTMS can conspicuously improve the upper extremity motor function and activities of daily life of stroke patients with hemiplegia, which is worthy of clinical application and promotion.
Independent Drug Action in Combination Therapy: Implications for Precision Oncology
Authors: Plana Deborah , Palmer Adam C. , Sorger Peter K.
Published: January 2022

Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy.

Significance:

The model of independent drug action represents an effective means to predict the magnitude of benefit likely to be observed in new clinical trials for combination therapies. The “bet-hedging” strategy implicit in independent action suggests that individual patients often benefit from only a subset—sometimes one—of the drugs in a combination. Personalized, targeted combination therapy, consisting of agents likely to be active in a particular patient, will increase, perhaps substantially, the magnitude of therapeutic benefit. Precision approaches of this type will require a better understanding of variability in drug response and new biomarkers, which will entail preclinical research on diverse panels of cancer models rather than studying drug synergy in unusually sensitive models.