bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new Isomorphism Identification Method for Kinematic Chain Based on Exchange and Compare of Lower-Triangle Matrix with Whole Information
Authors: SUN LIANGBO , Liu Xiaocui , Liu Xin , Hong Xixi , Zhang Deping , Pei Houchang 
Published: May 2023

Abstract
Isomorphism identification is an important step and costs major counting labor (more than 90%) in kinematic chain (KC) structure synthesis, many isomorphism identification methods have been proposed by researchers in past decades. The identification principle is simple and easy to be understood, the result has no objection, and the programming is simple and efficient, these are main criterions for isomorphism identification method. A new Matrix with Whole Information (MWI) has been proposed to describe the structure information of KC, and the principles of determining multiple joints and polygonal links are also proposed. Based on the principle that the Lower Triangular Matrix with Whole Information (LTMWI) can uniquely express the structure of KC, a new isomorphism identification method, based on the exchange and compare of LTMWI is proposed. This method is based on the law of exchange of links’ numbers and key points’ numbers, and two KCs are expressed by LTMWI, in which one LTMWI is selected as the datum matrix, the key points are arranged in different groups and non-zero elements of key points are moved forward, the definite and indefinite key points are obtained, under the assumption that all key points in the same positions have the mapping relationships one-to-one between exchange matrix and datum matrix, a finite number of exchange matrices are generated and compared with datum matrix, then isomorphism can be drawn. This method has the advantages such as simple principle, easy to program and only analysis and comparisons are needed in the identification process. The isomorphism identification results and the mapping relationships of links and even revolute pairs can all be obtained. The isomorphic analysis results of several KCs have shown the above characteristics of this method.
Construction prediction model for mine operation unsafe behavior
Authors: JIANG Xidi , ZHONG Shuheng , Liang jialu 
Published: May 2023

Abstract
Mine accidents are mostly caused by people's unsafe behaviors. To reduce unsafe behaviors and accidents in mine operation, this paper takes mine employees as the research object, analyzes the implementation subject "human" of unsafe behaviors, constructs the human behavior model SMAPP and its simulation process framework from five aspects of human emotion, motivation, ability, personality, and pressure, and simulates different scenarios with fuzzy rules. The simulation results are in agreement with the actual research results, which indicates the reliability and scientificity of the model. In addition, additional events are added in the simulation process to make the model more practical. Through the simulation results, the influence of employees' emotions, motivation, ability, personality, and pressure on the performance of unsafe behavior in mine operations is analyzed, and the measures to reduce the unsafe behavior in mine operations are further proposed.
Contribution to the characterization and valuation of a mixture of clay and dune sand for road construction
Authors: OUSLIMANE Noureddine , BAREBITA Hanane , BENSALAH Jaouad , BELFAQUIR Mustapha 
Published: May 2023

Abstract
In dry and hot regions like Morocco, the most expansive soils are clays or marl whose “normal” state is dry and dense.The problems created by these floors in the field of construction are mainly due to the swelling of the floors.We have chosen for this reason to focus our work on the swelling of clay soils which consists in evaluating the influence of the addition of dune sand and exploiting it in geotechnical studies, to stabilize and modify the physical-chemical characteristics and mechanical at different percentages of compressibility, swelling, and consolidation, through oedometric tests.Treated and untreated samples are compacted to the optimum water content (WOpt) and maximum dry density (γDmax). The results of the compressibility tests show that the reconsolidation stress as well as the consolidation of the treated samples is higher than that of the untreated sample, it was also found that the dune sand reduces the compressibility index (Cc), and swelling (Cg),It can be said that stabilization by dune sand significantly improves the mechanical characteristics of the clay studied. The exploitation of the materials studied and to present a clean and profitable alternative in the field of road construction.
U.S. cities’ integration and evaluation of equity considerations into climate action plans
Authors: Caggiano Holly , Kocakusak Diren , Kumar Pranay , Tier Melissa 
Published: May 2023

Abstract
While cities in the United States play an active role developing and implementing climate policy, urban centers are often sites of socio-spatial inequity. Thus, we explore how cities grapple with these inequities in their Climate Action Plans (CAPs). While CAPs can empower cities to address Sustainable Development Goals and engage in equitable planning practices that prioritize marginalized communities, little empirical research examines how equity goals are measured and evaluated. We find that among large U.S. cities with CAPs, less than one third include measurable indicators to evaluate progress towards achieving equity goals. Across climate adaptation and mitigation planning, nineteen cities consider equity goals as they relate to ten thematic areas, outcomes, and five dimensions of equity. We suggest ways forward for cities to develop, implement, and measure a diverse and holistic set of equity indicators to use in their climate planning efforts and beyond.
A Machine Learning Approach to Seizure Detection in a Rat Model of Post-Traumatic Epilepsy
Authors: Kotloski Robert J. 
Published: May 2023

Abstract
Epilepsy is a common neurologic condition frequently investigated with rodent models, with seizures identified by electroencephalography (EEG). Given technological advances, large datasets of EEG amenable to machine learning approaches for identification of seizures are widespread. While such approaches have been explored for human EEGs, machine learning approaches to identifying seizures in rodent EEG are limited. We utilized a predesigned deep convolutional neural network (DCNN), GoogLeNet, to classify images for seizure recognition. Training images were generated through multiplexing spectral content (scalograms), kurtosis, and entropy for two-second EEG segments. Over 2200 hours of EEG data were scored for the presence of seizures, with 95.6% of seizures identified by the DCNN and a false positive rate of 34.2% (1.52/hr), as compared to visual scoring. Multiplexed images were superior to scalograms alone and a DCNN trained specifically for the individual animal was superior to using DCNNs across animals. For this dataset the DCNN approach is superior to an algorithm utilizing total variation following wavelet decomposition. We demonstrate the novel use of a predesigned DCNN constructed to classify images, utilizing multiplexed images of EEG spectral content, kurtosis, and entropy, to rapidly and objectively identifies seizures in a large dataset of rat EEG with high sensitivity.
Concurrent occurrence of a de novo MACF1 mutation and an inherited 16p13.11 microduplication in a preterm neonate with multiple congenital malformations, hypotonia, excessive joint activity, feeding difficulties and respiratory problems：a case report
Authors: Mi Lanlan , Yao Ruen , Guo Weiwei , Wang Jian , Zhang Guoqing , Ye Xiuxia 
Published: May 2023

Abstract

Background Microtubule action crossing factor 1 (MACF1) is a large specific protein that plays an important role in regulating cytoskeleton dynamics, cell migration, growth and differentiation. 16p13.11 microduplication syndrome has various manifestations, mainly including neurodevelopmental and physiological phenotypic abnormalities caused by 16p13.11 chromosome copy number duplication. There are currently no reports of a patient experiencing both a de novo MACF1 mutation and a hereditary 16p13.11 microduplication simultaneously.
Case presentation In the present report, we describe a female preterm neonate Chinese patient who demonstrated features of neonates with duplication in this region included developmental delay, respiratory problems, feeding difficulties, hypotonia, excessive joint activity and multiple congenital malformations. Using whole-exome sequencing, we identified a pathogenic mutation in MACF1(c.15266T>C / p. Met5089Thr) isolated from peripheral blood DNA. 16p13.11 microduplication(chr16:14,916,289-16,315,688) was also revealed by chromosomal microarray analysis, which was inherited from mother. The right hand of the patient was a penetrating palm, which was first reported in 16p13.11 microduplication syndrome.
Conclusions The patient's global developmental delay, severe hypotonia, respiratory problems, feeding difficulties are mainly attributed to de novo MACF1 and the involvement of maternal 16p13.11 duplication may exacerbate these conditions. The patient's multiple malformations are believed to be the result of 16p13.11 duplication. Gene testing for patients with complex phenotypes can provide clues for clinical diagnosis and a reference for patients and their families for genetic counseling.
Remodeling Periodontal Osteoimmune Microenvironment Through MAPK/NFκB Phosphorylation Pathway of Macrophage via Intelligent ROS-scavenging
Authors: Qiu Xinyi , Peng Haoran , Zhao Yue , Yu Yijun , Yang Jie , Liu Chao , Ren Shuangshuang , Miao Leiying 
Published: May 2023

Abstract
Periodontitis is an inflammatory disorder which leads to the defect of tooth-supporting tissue, especially in alveolar bone. During this process, the polarization behavior of macrophages affects immune inflammation and bone regeneration in which reactive oxygen species(ROS) play an essential role. ROS level should be regulated to the physiological level to protect stem cells from the inflammatory immune microenvironment. Our previous study constructed a ROS-responsive nanoplatform (Pssl-NAC), which possessed ROS-responsive antioxidative effect and could be potentially applied in periodontitis. However, the connection among bone regeneration, inflammation and oxidative stress remained in osteoimmune regulation is not clear. To further investigate the mechanism of the way how Pssl-NAC works in the treatment of periodontitis would be meaningful. Here, we investigated the effect of PssL-NAC in the regulation of the osteoimmune microenvironment through macrophage polarization. Results show PssL-NAC regulated the macrophage polarization direction in an inflammatory environment by maintaining an appropriate level of intracellular ROS, in which the MAPK/NFκB phosphorylation pathway is particularly important. In the macrophage-hPDLSCs co-culture system, PssL-NAC treatment significantly enhanced the osteogenic differentiation of hPDLSCs. In vivo experiment further confirmed the M2 macrophages increased in the periodontal tissue of rats, and the expression of iNOS and p65 decreased after PssL-NAC treatment. In conclusion, PssL-NAC regulates the osteoimmune microenvironment and protects stem cells from oxidative stress injury for bone regeneration, which provides a strategy for the treatment of periodontitis.
Freshwater fungi from southern Australia: Microvesuvius unicellularis gen. et. sp. nov. and Achrochaeta rivulata sp. nov.
Authors: Fryar Sally , Catcheside David E.A. 
Published: May 2023

Abstract
During a survey of freshwater fungi in temperate southern Australia, two previously unknown anamorphic ascomycetes were found. The coelomycetous ascomycete was placed in the family Morosphaeriaceae (Pleosporales) as a new genus based on molecular data. We introduce the new genus and species Microvesuvius unicellularis with morphological and molecular data. The Dictyochaeta-like hyphomycete was placed in Achrochaetabased on both morphological characters and phylogenetic analyses using ITS, 28S, and tef1 sequences. Achrochaeta rivulata is the second species described within this genus.
Prediction of hepatocellular carcinoma and Edmondson-Steiner grade using an integrated workflow of multiple machine learning algorithms
Authors: Han Likai , Peng Xiaojun , Hu Xingen , Ma Tianshi , Shu Zhenyu , Ru Guoqing , Gao Junshun , Yu Lili 
Published: May 2023

Abstract
Early diagnosis of hepatocellular carcinoma (HCC) is indeed a great challenge. Based on traditional methods, the specificity and sensitivity of US/AFP are insufficient to detect the early onset of HCC. In this study, we constructed a prediction model for HCC diagnosis and Edmondson-Steiner (ES) grade using machine learning algorithms. The prediction model was constructed based on CT/MRI images, blood AFP, and pathological diagnosis datasets of 171 patients from Zhejiang Provincial People's Hospital. First, the automatic liver segmentation method of deep learning algorithm is used to locate the region of interest, and then PyRadiomics (engineering hard-coded feature algorithm) and Boruta (random forest algorithm) are used to extract and screen disease-related image features. By comparing the performance of various algorithms, we choose "plr" as the optimal algorithm for the HCC diagnosis model with AUC of 0.990, Kappa of 0.893 and accuracy of 0.952. "gbm" is the optimal algorithm for the ES grade prediction model with AUC 0.941, Kappa 0.777, and accuracy rate 0.902 in the TCGA-LIHC dataset. Compared with traditional diagnostic models based on clinical features, our model significantly improves the predictive performance. AUC increased from 0.733 to 0.933. This study shows that processing image data using deep learning methods can yield important features compared to conventional methods. Choosing an appropriate machine learning algorithm to build a predictive model can significantly improve the performance of disease diagnosis.
Latency Reduction in Heterogeneous 5g Networks Integrated With Reinforcement Algorithm
Authors: Kodavati Baburao , Ramarakula Madhu 
Published: May 2023

Abstract
Heterogeneous network is considered as key technologies involved in significant utilization of available unused spectrum. Through the implementation of heterogeneous network energy utilization is achieved through effective spectral efficiency with higher throughput. However, this heterogeneous network environment is subjected to the challenge of latency. To derive the complete potential of heterogeneous networks machine learning algorithms need to be adopted for a dynamic environment. In the case of a practical scenario, it is difficult to reduce the network latency due to the complex network nature. To overcome those limitations, this paper proposed a Q-learning Reinforcement learning (QleaRL) for reducing latency. The proposed QleaRL utilizes Cooperative Q - learning based on consideration of state, action, and reward. Through optimal policy, reinforcement learning is computed based on Q -values. The performance of the proposed QleaRL is evaluated for latency. Simulation of proposed QleaRL is examined in terms of numerical analysis. The performance of the proposed QleaRL exhibits superior performance than the fixed power allocation (FPA) and tabular Q learning.
Theoretical and Experimental Observations on Empowered EMI Shielding of Rare Earth Ion Infused α-MnO 2 Nano-rods
Authors: Mondal Dheeraj , Bhattacharya Debopriya , Mondal Tanmoy , Kundu Manisha , Sarkar Sujata , Mondal Indrajit , Mandal Tapas Kumar , Paul Biplab Kumar , Das Sukhen 
Published: May 2023

Abstract

In this communication, for the first time rare earth elements like Gd3+ and Er3+ ions are impregnated into the α-MnO2 tunneled structure involving the modified chemical synthesis route followed by hydrothermal technique. X-ray diffraction patterns and Raman spectroscopy confirm the proper phase formation without any impurity. A theoretical in-depth DFT study for the electronic bands and variation of density of states with the infusion of rare earth elements into the host structure confirms the experimental findings and depicts the suitability of the material towards the EMI shielding application. The novel synthesis technique helps to achieve uniform nanorod formation with a high specific surface area. Doping-induced defects, high surface-to-volume ratio, Maxwell-Wagner–Siller interfacial polarization, etc lead to achieving high dielectric response with moderate tangent loss at frequency range 40Hz to 1MHz. Temperature-dependent dielectric behavior indicates the paramagnetic to ferromagnetic transition at 120 \(℃\) and 70 \(℃\) for the Gd- and Er-doped α-MnO2 samples respectively. The electromagnetic interference (EMI) shielding effectiveness (SE) has achieved a maximum of − 43 dB at 15.3 GHz and − 46 dB at 15.3 GHz for Gd- and Er-doped α-MnO2 thin layer of thickness ∼600 µm respectively. Higher dipolar polarization, loss tangent, conductive pathways, and defects inside the crystal lead to attaining a high EMI shielding efficiency. This result reveals > 99.999% EMI SE against the hazardous electromagnetic waves in the microwave/GHz frequency region.
Evaluating the facilitators and constraints that informed the adaptation of Family Integrated Care to a Ugandan neonatal hospital unit: a qualitative study
Authors: Kabajassi Olive , Reiter Anna , Tagoola Abner , Kenya-Mugisha Nathan , O’Brien Karel , Wiens Matthew O. , Feeley Nancy , Duby Jessica 
Published: May 2023

Abstract
Background: Family Integrated Care (FICare) is a model of care developed in a Canadian Neonatal Intensive Care Unit that engages parents to be active participants in their infant’s care team. FICare has the potential to have the greatest impact in low-income countries, where the neonatal mortality rate is disproportionately high and the health workforce is severely strained.
This manuscript details the facilitators and constraints that informed the adaptation of FICare to a neonatal hospital unit in Uganda
Methods: Focus groups of ten mothers and interviews of eight workers were conducted to identify facilitators and constraints to the implementation of FICare in Uganda. Transcripts were analyzed using inductive content analysis. An adaptation team of key stakeholders developed Uganda FICare in the Special Care Nursery in Jinja Regional Referral Hospital based on the results from the focus groups and interviews.
Results: The potential to reduce the healthcare provider workload, the desire to empower mothers and the pursuit to improve neonatal outcomes were identified as key facilitators. Maternal difficulty in learning new skills, lack of trust from healthcare providers and increased maternal stress were cited as potential barriers. Uganda FICare focused on task-shifting important but often neglected patient care tasks from healthcare providers to mothers. Healthcare providers were taught how to respond to maternal concerns. All intervention material was adapted to prioritize images over text. Mothers familiar with FICare were encouraged to provide peer-to-peer support and guidance to mothers with newly hospitalized infants.
Conclusions: Engaging stakeholders to identify the facilitators and constraints to local implementation is a key step in adapting an intervention to a new context. Uganda FICare shares the core values of the original FICare but is adapted to enhance its feasibility in low-resource settings.
Barriers and enablers to implementing Interprofessional Collaborative Teams in Primary Care: A narrative review of the literature using the Consolidated Framework for Implementation Research
Authors: Grant Amy , Kontak Julia , Jeffers Elizabeth , Lawson Beverley , MacKenzie Adrian , Burge Fred , Boulos Leah , Lackie Kelly , Marshall Emily Gard , Mireault Amy , Philpott Susan , Sampalli Tara , Sheppard-LeMoine Debbie , Martin-Misener Ruth 
Published: May 2023

Abstract
Background
Interprofessional collaborative teams (teams) have been introduced across Canada to improve access to and quality of primary care. However, the quality and speed of team implementation has been challenging and has not kept pace with increasing access issues. The aim of this research was to use an implementation framework to categorize and describe barriers and enablers to team implementation in primary care.
Methods
A narrative review that prioritized systematic reviews and evidence syntheseswas conducted. A search using pre-defined terms was conducted using Ovid MEDLINE, and potentially relevant grey literature was identified through ad hoc Google searches and hand searching of health organization websites. The Consolidated Framework for Implementation Research (CFIR) was used to categorize barriers and enablers into five domains: (1) Features of Team Implementation; (2) Government, Health Authorities and Health Organizations; (3) Characteristics of the Team; (4) Characteristics of Team Members; and (5) Process ofImplementation.
Results
Data were extracted from 19 of 435 articles that met inclusion/exclusion criteria. Most barriers and enablers were categorized into two domains of the CFIR: Characteristics of the Team and Government, Health Authorities, and Health Organizations. Key themes identified within the Characteristics of the Team domain were team-leadership, including designating a manager responsible for day-to-day activities and facilitating collaboration; clear governance structures, technology supports and tools that facilitate information sharing and communication; and a combination of formal and informal methods of communication. Key themes within the Government, Health Authorities, and Health Organizations domain were professional remuneration plans, regulatory policy, and interprofessional education that encourage and incorporate interprofessional competencies and values.
Conclusions
Barriers and enablers to implementing teams using the CFIR were identified, which enables stakeholders and teams to tailor implementation of teams at the local level to impact the accessibility and quality of primary care.
A Novel Application of Artificial intelligence technology for Outdoor High-Voltage Composite Insulators
Authors: BELHOUCHET Khaled , OUCHEN Lyamine , BAYADI Abdelhafid , ALTI Nadjim 
Published: May 2023

Abstract

This paper reports on a new approach for optimal design of corona rings on composite insulator. Finite element method combined with the experimental design methodology and Bat optimization method were used in order to minimize the maximum E-field along the insulator length, with the objective of increasing its long-term performance. Corona ring height (H), radius (R) and tube diameter (Dr) are the most important parameters in the design corona rings. They have been evaluated using different techniques and their optimization was achieved by minimizing the objective function using Bat algorithm which is one of the most recently developed nature-inspired optimization approaches. Simulation studies cover potential and electrical field distributions using COMSOL 4.3, which uses a numerical analysis technique based on Finite Element Method (FEM) were done. Reported results show that the optimized corona ring decreases significantly the electric field near its end fittings. That is, potential distribution will be more uniform with the presence of the optimized corona ring. By installing corona ring, the electric field improves about 58.6% compared with the threshold value. The results show that optimization using Bat algorithm as new methodology is efficient for designing corona rings on transmission line composite insulators and it presents more accurate result in finding the best solutions for corona discharges problems.