bluuebunny/crossref_metadata_2025_split huggingface dataset:
Keterpilihan justin adrian dalam pemilu legislatif DPRD DKI Jakarta 2019 (studi strategi kampanye dan modal sosial)
Authors: Syafrudin Iqbal , Casmana Asep Rudi 
Published: May 2022

Abstract: The purpose of this study is to determine the campaign strategy in the DKI Jakarta Legislative general election held in 2019. The research method used is qualitative with a case study approach. This method is appropriate because it tries to examine the main strategies for the publication process and win Justin Adrian. The data collection technique used was in-depth interviews with 10 people consisting of one elected DPRD member and nine party administrators. The results of this study indicate that there are two main strategies, namely social capital and a door-to-door campaign strategy to direct voters. The conclusion is that in order to win the election, one needs to have a special strategy. This research has an impact on the community to be able to find out about strategies when conducting legislative elections
Abstrak: Tujuan dari penelitian ini adalah untuk mengetahui strategi kampanye dalam pemilihan umum Legislatif DKI Jakarta yang dilaksanakan pada tahun 2019. Metode penelitian yang digunakan adalah Kualitatif dengan pendekatan studi kasus. Metode ini tepat karena mencoba menelaah strategi utama untuk proses publikasi dan memenangkan Justin Adrian. Teknik pengumpulan data yang digunakan adalah dengan wawancara yang mendalam terhadap 10 orang yang terdiri dari satu orang anggota DPRD terpilih dan sembilan orang pengurus partai. Hasil penelitian ini menunjukan bahwa terdapat dua strategi utama, yaitu modal sosial dan strategi kampanye door-to-door terhadap pemilih secara langsung. Kesimpulannya adalah untuk dapat memenangkan pemilu, maka seseorang perlu memiliki strategi khusus. Penelitian ini memiliki impact terhadap masyarakat untuk dapat mengetahui tentang strategi ketika melakukan pemilihan umum legislative.
Construction of an lncRNA–mRNA co-expression regulatory network mediating inflammation and regeneration following acute pancreatitis injury
Authors: Wang Jing , Chen Tianjie , Wang Ruixia , Zhao Shulei 
Published: May 2022

Abstract
Long noncoding RNAs (lncRNAs) play important roles in the occurrence and development of many diseases and can be used as targets for diagnosis and treatment. However, the expression and function of lncRNAs in the injury and repair of acute pancreatitis (AP) are unclear. To decipher lncRNAs’ regulatory roles in AP, we reanalyzed an RNA-seq dataset of 24 pancreatic tissues, including those of normal control mice (BL), those 7 days after mild AP (D7), and those 14 days after mild AP (D14). The results showed significant differences in lncRNA and mRNA expression of D7/D14 groups compared with the control group. Co-expression analysis showed that differentially expressed (DE) lncRNAs were closely related to immunity- and inflammation-related pathways by trans-regulating mRNA expression. The lncRNA–mRNA network showed that the lncRNAs Dancer, Gmm20488, Terc, Snhg3, and Snhg20 were significantly correlated with AP pathogenesis. WGCNA and cis regulation analysis also showed that AP repair-associated lncRNAs were correlated with extracellular and inflammation-related genes, which affect the repair and regeneration of pancreatic injury after AP. In conclusion, the systemic dysregulation of lncRNAs is strongly involved in remodeling AP’s gene expression regulatory network, and the lncRNA–mRNA expression network could identify targets for AP treatment and damage repair.
Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model
Authors: Hwang In-Chang , Choi Dongjun , Choi You-Jung , Ju Lia , Kim Myeongju , Hong Ji-Eun , Lee Hyun-Jung , Yoon Yeonyee E. , Park Jun-Bean , Lee Seung-Pyo , Kim Hyung-Kwan , Kim Yong-Jin , Cho Goo-Yeong 
Published: May 2022

Abstract
Background: Differential diagnosis of left ventricular hypertrophy (LVH) is often obscure on echocardiography and requires numerous additional tests. We aimed to develop a deep learning algorithm to aid in the differentiation of common etiologies of LVH (i.e., hypertensive heart disease [HHD], hypertrophic cardiomyopathy [HCM], and cardiac amyloidosis [CA]) on echocardiographic images. Methods: Echocardiograms in 5 standard views (parasternal long-axis, parasternal short-axis, apical 4-chamber, apical 2-chamber, and apical 3-chamber) were obtained from 930 subjects: 112 with HHD, 191 with HCM, 81 with ALCA, and 546 normal subjects. The study population was divided into training (n=620), validation (n=155), and test sets (n=155). A convolutional neural network-long short-term memory (CNN-LSTM) algorithm was constructed to independently classify the 3 diagnoses on each view, and the final diagnosis was made by an aggregate network based on the simultaneously predicted probabilities of HCM, HCM, and ALCA. Diagnostic performance of the algorithm was evaluated by the area under the receiver operating characteristic curve (AUC), and accuracy was evaluated by the confusion matrix.Results: The deep learning algorithm was trained and verified using the training and validation sets, respectively. In the test set, the average AUC across the five standard views was 0.962, 0.982, and 0.996 for HHD, HCM, and CA, respectively. The overall diagnostic accuracy was significantly higher for the deep learning algorithm (92.3%) than for echocardiography specialists (80.0% and 80.6%). Conclusions: We developed a deep learning algorithm for the differential diagnosis of 3 common LVH etiologies (HHD, HCM, and ALCA) by applying a hybrid CNN-LSTM model and aggregate network to standard echocardiographic images.
A Simple Scoring Model Based on Machine Learning Predicts Intravenous Immunoglobulin Resistance in Kawasaki Disease
Authors: Sunaga Yuto , Watanabe Atsushi , Katsumata Nobuyuki , Toda Takako , Yoshizawa Masashi , Kono Yosuke , Hasebe Yohei , Koizumi Keiichi , Hoshiai Minako , Kawakami Eiryo , Inukai Takeshi 
Published: May 2022

Abstract
In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions. To establish a simple and accurate scoring model predicting IVIG resistance, we conducted a retrospective cohort study of 996 KD patients that were diagnosed at 11 facilities for 10 years, in which 108 cases (23.5%) were resistant to initial IVIG treatment. We performed machine learning with random forest model using 30 clinical variables at diagnosis in 796 and 200 cases for training and test datasets, respectively. Random forest model accurately predicted IVIG resistance (AUC: 0.75, sensitivity: 0.54, specificity: 0.80). Next, using top five influential features (days of illness at initial therapy, serum levels of C-reactive protein, sodium, total bilirubin, and total cholesterol) in the random forest model, we designed a simple scoring system. In spite of its simplicity, the scoring system predicted IVIG resistance (AUC: 0.73, sensitivity: 0.55, specificity: 0.83) as accurately as the random forest model itself. Moreover, accuracy of our scoring system with five clinical features was almost identical to that of Gunma score with seven clinical features (AUC: 0.73, sensitivity: 0.53, specificity: 0.83), a well-known logistic regression scoring model, and superior to that of two widely used scores (Kurume score: 0.67, 0.46 and 0.76, respectively, and Osaka score: 0.69, 0.33 and 0.84, respectively). Conclusions: Our simple scoring system based on the findings in machine learning, as well as machine learning itself, seems to be useful to accurately predict IVIG resistance in KD patients.
Consequence of Fibonacci quasiperiodic sequences in 1-D photonic crystal refractive index sensor for the blood plasma and cancer cells detections
Authors: Singh Bipin Kumar , Rajput Priyanka S , Dikshit Ashutosh K , Pandey Praveen C , Bambole Vaishali 
Published: May 2022

Abstract
We present the consequences of different Fibonacci quasiperiodic sequences on the sensing performance of a one-dimensional (1D) photonic crystal (PC) based sensor to detect the blood plasma and cancer cells. We demonstrate that the sensitivity and figure of merit (FOM) of the sensor can be modulated, respectively, from 427.5 nm/RIU to 530 nm/RIU and from 162.5 RIU− 1 to 2983.33 RIU− 1 by utilizing the pertinent sensor structures with Fibonacci generation and structural parameters for the analyte refractive index from 1.31 to 1.41. We have also shown that the operational sensing wavelengths and sensitivity can be changed significantly by introducing the different Fibonacci sequences in the proposed sensor. The sensing peaks separation, FWHM, and sensitivity can also be tuned by changing the analyte and PC stacked layers' thicknesses. We also introduce the sensing performances for the Blood plasma and Cancer cells detection using the proposed Fibonacci quasiperiodic PC sensor. We observe that the maximum sensitivities and FOM of the proposed sensor are 552.5 nm/RIU and 2986.93 RIU− 1 for cancer cells and 553.55 nm/RIU and 2214.21 RIU− 1 for blood plasma, respectively. The proposed structure reveals the tunable sensing performances and stimulates the fabrication interest.
Airway management in “tubeless” spontaneous-ventilation video-assisted thoracoscopic tracheal surgery: A Retrospective Observational Case Series Study
Authors: liu yuying , Liang Lixia , Yang Hanyu 
Published: May 2022

Abstract
Background: Surgeon and anesthetist need not share the airway in the resection and reconstruction phase of tracheal surgery in spontaneous-ventilation video-assisted thoracoscopic surgery (SV-VATS).Methods: Tubeless SV-VATS to anesthesiologist means tubeless in the resection and reconstruction phase under stable spontaneous ventilation, and unobstructed surgical field to surgeon. What is the ideal airway management strategy during “Visual Field tubeless” SV-VATS for intrathoracic tracheal surgery? Here we retrospectively reviewed the cases of 33 patients whose initial strategy manage with spontaneous ventilation for the common and simpler proximal tracheal resection and reconstruction, but did not discuss sleeve and carina resections, during the study period (2018–2020) in our hospital. To evaluate the device failure rate for tracheal resection in Tubeless SV-VATS, medical records obtained from our institution’s clinical medical records system and reviewed. Results: Between 2018 and 2020, SV-VATS was first attempted in 33 patients who had intrathoracic tracheal surgery, sleeve and carina resections were not included. All patients underwent bronchoscopy (33/33) or partial resection (8/33) before surgery. During the surgery, the airway device comprised either a laryngeal mask airway (n = 27) or single lumen endotracheal tube (n = 6). During the resection and reconstruction phase, Visual Field tubeless SV-VATS failed in 9 patients, and breathing support switched to plan B smoothly: traditional ventilation including a single lumen endotracheal tube for crossfield intubation (n = 4) or a laryngeal mask airway alongside a high-frequency catheter (high-frequency jet ventilation, HFJV) (n = 5) into the distal trachea ventilation. Preoperative respiratory failure or other ventilation-related complications were not observed in this cohort. Conclusion: The choice of a supraglottic airway device or endotracheal tube is an effective strategy for tubeless SV-VATS with appropriate patient selection, and provides breathing support conversion.
Dimensionally Consistent Learning with Buckingham Pi
Authors: Bakarji Joseph , Callaham Jared , Brunton Steven , Kutz J. Nathan 
Published: May 2022

Abstract
In the absence of governing equations, dimensional analysis is a robust technique for extracting insights and finding symmetries in physical systems. Given measurement variables and parameters, the Buckingham Pi theorem provides a procedure for finding a set of dimensionless groups that spans the solution space, although this set is not unique. We propose an automated approach using the symmetric and self-similar structure of available measurement data to discover the dimensionless groups that best collapse this data to a lower dimensional space according to an optimal fit. We develop three data-driven techniques that use the Buckingham Pi theorem as a constraint: (i) a constrained optimization problem with a non-parametric input-output fitting function, (ii) a deep learning algorithm (BuckiNet) that projects the input parameter space to a lower dimension in the first layer, and (iii) a technique based on sparse identification of nonlinear dynamics (SINDy) to discover dimensionless equations whose coefficients parameterize the dynamics. We explore the accuracy, robustness and computational complexity of these methods as applied to three example problems: a bead on a rotating hoop, a laminar boundary layer, and Rayleigh-Benard convection.
Port Scan Identification Through Regression Applying Logistic Testing Methods to Balanced Data
Authors: Tojeiro Carlos Alexandre Carvalho , Reis Carlos De Jesus , Costa Kelton Augusto Pontara Da , Lucas Thiago José 
Published: May 2022

Abstract
Due to the large volume of software and applications required for data communication which works with different ports, technicians maintain a wide range of open ports on the network. With this, they can open vulnerabilities that are exploited by criminals. Thus, network administrators increasingly need information that can assist them in discovering future attacks. In this work, we used logistic regression to identify Port Scan attacks and did some tests with data balancing techniques to seek better results. Keywords: port scan; network-security; data balancing; intrusion; data visualization; logistic regression
Contribution to Improving hydrodynamics method for hot strip rolling application
Authors: mimoune derrez , Zaaf Mohamed , AMIRAT Abdelaziz 
Published: May 2022

Abstract
The present work is a contribution to the improvement of the hydrodynamic method proposed by Si Li et al [2016] used to predict the pressures and the rolling speeds during hot rolling of aluminum strips. The hydrodynamic model gives good prediction however it is based on empirical coefficients that must be identified for each rolling case. Therefore, a critical analysis of the method has been first made and then a methodology for improving it has been presented. The improvement consists in coming out of the empiric coefficients and considering the variation of viscosity as a function of pressures. Results are much better in good agreement with the experimental data. The validated model can then be admitted as an improved hydrodynamic model for predicting pressures and velocities during hot rolling of thin strips.
Disaster chain effects on landscape evolution following earthquakes in mountainous areas
Authors: Li Congrong , Chen Fang , Wang Lei 
Published: May 2022

Abstract
Landslides and debris flows frequently occur in mountainous areas following earthquakes and, in turn, affect the evolution of the terrain. Following an earthquake, geological disasters no longer occur individually or sporadically and are instead typically clustered and chain-like. However, little attention has been given to the long-term effects of these disaster chains on landscape evolution processes following earthquakes. In this study, an earthquake-stricken area was selected as an example to study the disaster chain effects on landscape evolution. We predicted landslide occurrences in the long-term landscape evolution process and analyzed mass movements in response to future rainfall scenarios under disaster chain effects. The results showed that disaster chain effects are expected to directly affect the spatial material redistribution pattern under extreme rainfall conditions with increased sediment yields compared to the results derived without landslide effects under the same rainfall scenario; the difference in the cumulative sediment yield showed a positive linear correlation with time (\({R}^{2}\) = 0.8688) over the 50-year landscape evolution process. Regarding the landslide risk, the landslide-susceptible area tended to move upwards to the relatively high-elevation area, and the rainfall intensity expected to lead to great risks in the study area was the 20-year-return-period extreme rainfall amount; this finding was expected to remain unchanged over a long period of time. Thus, disaster chain effects greatly alter the locations at which landslides are most likely to occur. The results obtained in this research can provide scientific information for hazard management and mitigation.
Electronic structure and optical properties of B-, N-, and BN-doped black phosphorene using the First-Principles
Authors: He Jianlin , Liu GuiLi , Li Xinyue , Zhang Guoying 
Published: May 2022

Abstract
The structural stability, electronic structure, and optical properties of BN-doped black phosphorene systems at different concentrations were investigated using a density generalized theory approach based on the first principles. It was found that BN-doped black phosphorene was more stable compared with B and N atom doping. With the increase of doping concentration, the stability of the structure gradually decreases, and the structure of the system with 25% doping concentration is the most stable. The intrinsic and N-doped black phosphorene are direct bandgap semiconductors, and B and BN doping make the black phosphorene change from direct bandgap to indirect bandgap. The total density of states is mainly contributed by the p-state electrons of the B and P atoms, and the N atoms have a role in the local density of states with little contribution to the overall one. The black phosphorene undergoes charge transfer between the B and N atoms. The amount of charge transfer increases with the increase of doping concentration. The BN-doped black phosphorene system is blue-shifted at the absorption and reflection peaks compared to the intrinsic black phosphorene system. The doping makes the dielectric function peaks are shifted to the high energy direction, which leads to an increase in the intensity of the electric field generated by light, which is beneficial to improve the efficiency of photovoltaic power generation, and the peak of photoconductivity is slightly reduced and shifted to the low energy direction, which is more favorable to the leap of photons, with the most obvious performance when the BN doping concentration is 12.5% and 25%.
Modeling Spatio-temporal Assessment of Land Use Land Cover of Lahore and its impact on Land surface Temperature Using Multi-Spectral Remote Sensing Data
Authors: Tariq Aqil , Mumtaz Faisal 
Published: May 2022

Abstract
Urban sprawl, also widely known as urbanization, is one of the significant problems in the world. This research aims to assess and predict the urban growth and impact on Land Surface Temperature (LST) of Lahore as well as Land-Use and Land-cover (LULC) with a Cellular–Automata–Markov–Chain (CA–Markov–Chain). LULC and LST distributions were mapped using Landsat (5, 7, and 8) data from 1990, 2004, and 2018. Long–term changes to the landscape were simulated using a CA-Markov model at 14-year intervals from 2018 to 2046. Results indicate that the built-up area was increased from 342.54 (18.41%) km2 to 720.31 (38.71%) km2. Meanwhile, barren land, water and vegetation area was decreased from 728.63 (39.16%) km2 to 544.83 (29.28%) km2, from 64.85 (3.49%) km2 to 34.78 (1.87%) km2 and from 724.53 (38.94%) km2 to 560.63 (30.13%) km2 respectively. In addition, Urban Index, a non-vegetation index, accurately predicted LST, showing the maximum correlation R2 = 0.87 with respect to retrieved LST. According to CA–Markov Chain analysis, we can predict the growth of built-up area from 830.22 km2 to 955.53 km2 between 2032 and 2046, based on the development from 1990 to 2018. As Urban Index as the predictor anticipated that the LST 20–23°C, and 24–27°C, regions would all decline in coverage from 5.30–4.79% and 15.79–13.77% in 2032 and 2046, while the temperature 36–39°C regions would all grow in coverage from 15.60–17.21% of the city. These findings are significant for the planning and development division to ensure the long-term usage of land resources for urbanization expansion projects in the future.
Improving working relationships with families in German early childhood intervention home visitors: A quasi-experimental training study
Authors: Georg Anna , Hauschild Sophie , Schröder-Pfeifer Paul , Kasper Lea , Taubner Svenja 
Published: May 2022

Abstract
Background: Home visitation services within the early childhood intervention program (ECI) for families with a child aged 0-3 in Germany are mainly provided by frontline pediatric nurses and family midwifes. Home visitors are often challenged by difficult interactions with families. Mentalizing, the ability to understand mental states of oneself and others, is a key skill for building effective working relationships, which in turn positively affect intervention outcomes. The aim of this study was to investigate if a mentalizing skills training offered to home visitors active in German ECI contributes to continued professional development. We investigated, whether the training positively affected the quality of the working relationships with families as well as home visitors’ empathy, self-efficacy, and mentalizing. Methods: To test the effects of a single day mentalizing skills training on the working relationship in N = 73 ECI home visitors, we used a quasi-experimental design with repeated measures (T0, T1, T2, T3) across seven weeks in order to assess immediate change from baseline (T0) after the training (T2) and stability of changes at follow up (T3). A literature-based intervention was implemented before the training to estimate possible repeated measurement and expectational effects (T1). Primary outcome was the quality of the working relationship experienced by the home visitors. Secondary outcome criteria were empathy, work-related self-efficacy, self-reported and observer-rated mentalizing. Results: Significant positive change in the working relationship quality was observed at T2 and at T3. Results on the secondary outcomes were less consistent, with data indicating improvement in empathy and increase on some but not all components of mentalizing. Conclusions: This study provides preliminary evidence that brief mentalizing skills trainings may be an effective method for continuous professional qualification in frontline ECI home visitors who afterwards, experience better working relationships with families. Thus, training participation may positively impact efficacy and implementation of home visitations in ECI.