bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021
Authors: Ebrahim Oumer Abdulkadir , Derbew Getachew 
Published: May 2023

AbstractEthiopia has been challenged by the growing magnitude of diabetes in general and type-2 diabetes in particular. Knowledge extraction from stored dataset can be an important base for better decision on diabetes rapid diagnosis, suggestive on prediction for early intervention. Thus, this study was addressed these problem by application of supervised machine learning algorithms for classification and prediction of type 2 diabetes disease status and might provide context-specific information to program planners and policy makers so that, priority will be given to the more affected groups. To apply supervised machine learning algorithms; compare these algorithms and select the best algorithm based on their performance for classification and prediction of type-2 diabetic disease status (positive or negative) in public hospitals of Afar regional state, Northeastern Ethiopia. This study was conducted at Afar regional state from February to June of 2021. Decision tree; pruned J 48, Artificial neural network, K-nearest neighbor, Support vector machine, Binary logistic regression, Random forest, and Naïve Bayes supervised machine learning algorithms were applied using secondary data from the medical database record review. A total of 2239 sample Dataset diagnosed for diabetes from 2012 to April 22/2020 (1523 with type-2 diabetes and 716 without type-2 diabetes) was checked for its completeness prior to analysis. For all algorithms, WEKA3.7 tool was used for analysis purposes. Moreover, all algorithms were compared based on their correctly classification rate, kappa statistics, confusion matrix, area under the curve, sensitivity, and specificity. From the seven major supervised machine learning algorithms, the best classification and prediction results were obtained from random forest [correctly classified rate (93.8%), kappa statistics (0.85), sensitivity (0.98), area under the curve (0.97) and confusion matrix (out of 454 actual positive prediction for 446)] which was followed by decision tree pruned J 48 [correctly classified rate (91.8%), kappa statistics (0.80), sensitivity (0.96), area under the curve (0.91) and confusion matrices (out of 454 actual positive prediction for 438)] and k-nearest neighbor [correctly classified rate (89.8%), kappa statistics (0.76), sensitivity (0.92), area under the curve (0.88) and confusion matrices (out of 454 actual positive prediction for 421)]. Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting type-2 diabetes disease status. Therefore, based on this performance, random forest algorithm can be judged as suggestive and supportive for clinicians at the time of type-2 diabetes diagnosis.
The ancestral haplotype markers HLA -A3 and B7 do not influence the likelihood of advanced hepatic fibrosis or cirrhosis in HFE hemochromatosis
Authors: Olynyk John K. , Grainger Richard , Currie Helen , Ramm Louise E. , Ramm Grant A. 
Published: May 2023

AbstractAdvanced hepatic fibrosis occurs in up to 25% of individuals with C282Y homozygous hemochromatosis. Our aim was to determine whether human leukocyte antigen (HLA)-A3 and B7 alleles act as genetic modifiers of the likelihood of advanced hepatic fibrosis. Between 1972 and 2013, 133 HFE C282Y homozygous individuals underwent clinical and biochemical evaluation, HLA typing, liver biopsy for fibrosis staging and phlebotomy treatment. Hepatic fibrosis was graded according to Scheuer as F0–2 (low grade hepatic fibrosis), F3–4 (advanced hepatic fibrosis), and F4 cirrhosis. We analysed associations between the severity of fibrosis and HLA-A3 homozygosity, heterozygosity or absence, with or without the presence of HLA-B7 using categorical analysis. The mean age of HLA-A3 homozygotes (n = 24), heterozygotes (n = 65) and HLA-A3 null individuals (n = 44) was 40 years. There were no significant differences between the groups for mean(± SEM) serum ferritin levels (1320 ± 296, 1217 ± 124, 1348 ± 188 $$\upmu$$
μ
g/L), hepatic iron concentration (178 ± 26, 213 ± 22, 199 ± 29 $$\upmu$$
μ
mol/g), mobilizable iron stores (9.9 ± 1.5, 9.5 ± 1.5, 11.5 ± 1.7 g iron removed via phlebotomy), frequency of advanced hepatic fibrosis (5/24[12%], 13/63[19%], 10/42[19%]) or cirrhosis (3/24[21%], 12/63[21%], 4/42[24%]), respectively. The presence or absence of HLA-B7 did not influence the outcome. Thus, HLA-A3 and HLA-B7 alleles are not associated with the risk of advanced hepatic fibrosis or cirrhosis in C282Y hemochromatosis.
Vascular cell-adhesion molecule 1 (VCAM-1) regulates JunB-mediated IL-8/CXCL1 expression and pathological neovascularization
Authors: Kaur Geetika , Sharma Deepti , Bisen Shivantika , Mukhopadhyay Chandra Sekhar , Gurdziel Katherine , Singh Nikhlesh K. 
Published: May 2023

AbstractVascular adhesion molecules play an important role in various immunological disorders, particularly in cancers. However, little is known regarding the role of these adhesion molecules in proliferative retinopathies. We observed that IL-33 regulates VCAM-1 expression in human retinal endothelial cells and that genetic deletion of IL-33 reduces hypoxia-induced VCAM-1 expression and retinal neovascularization in C57BL/6 mice. We found that VCAM-1 via JunB regulates IL-8 promoter activity and expression in human retinal endothelial cells. In addition, our study outlines the regulatory role of VCAM-1-JunB-IL-8 signaling on retinal endothelial cell sprouting and angiogenesis. Our RNA sequencing results show an induced expression of CXCL1 (a murine functional homolog of IL-8) in the hypoxic retina, and intravitreal injection of VCAM-1 siRNA not only decreases hypoxia-induced VCAM-1-JunB-CXCL1 signaling but also reduces OIR-induced sprouting and retinal neovascularization. These findings suggest that VCAM-1-JunB-IL-8 signaling plays a crucial role in retinal neovascularization, and its antagonism might provide an advanced treatment option for proliferative retinopathies.
Adaptive observer‐based control for fractional‐order chaotic MEMS system
Authors: Yaghoubi Zahra , Adeli Mahdieh 
Published: May 2023

AbstractThis paper proposes an adaptive observer‐based scheme to control fractional‐order chaotic micro‐electro‐mechanical‐systems (MEMS). The fractional‐order model of a system exhibits the dynamic behaviour of a physical system more accurately than a similar integer‐order one. An observer‐based control considering the unknown parameters is presented for a fractional‐order chaotic micro‐electro‐mechanical‐system. To handle the existence of the unknown parameters, the control law consists of both estimated parameters and also estimated states that have been designed. First, a fractional‐order observer is designed to estimate the state variables. The asymptotic convergence to zero of the estimation state error is proved using the Lyapunov scheme. After that, with the designed observer as a foundation, adaptive control is expanded for the closed‐loop system to ensure its asymptotic stability. An evaluation of the proposed method shows its efficacy via simulations for two case studies.
GIGAN: Self‐supervised GAN for generating the invisible using cycle transformation and conditional normalization
Authors: Quan Fengnan , Lang Bo 
Published: May 2023

AbstractObjects in a real scene often occlude each other and inferring a complete appearance from the visible part is an important and challenging task. In this paper, the authors propose a self‐supervised generative adversarial network GIGAN (GAN for generating the invisible), which can generate the complete appearance of objects without labelled invisible part information. The authors build two cycle transformation networks CycleIncomplete (CycleI) and CycleComplete (CycleC) that share parameters to improve the accuracy of mask completion. This design does not require well‐matched training images and can make better use of the limited labelled samples. In addition, the authors propose a conditional normalization module and combine it with the inferred complete mask output. The combination not only enhances the content recovery ability and obtains more realistic outputs, but also improves the efficiency of the generation process. Experimental results show that compared with existing self‐supervised learning models, our method achieves l1 error, mean intersection‐over‐union (mIOU), and Fréchet inception distance (FID) improvements on the COCOA and KINS datasets.
Design and implementation of power factor corrected circuits without using inductor‐current sensors
Authors: Liu Tian‐Hua , Hsu Chih‐Pin , Wang Shao‐Ming , Song Yu‐Cheng 
Published: May 2023

AbstractThis paper investigates the design and implementation of a new single‐phase sensorless power factor corrected (PFC) circuit. Here, the inductor‐current sensor and its sensing circuit are removed. As a result, the cost and volume of the PFC circuit are effectively reduced. Two different methods, including an inductor‐voltage estimation method and an inductor‐current estimation method, are proposed and compared. To improve the dynamic responses of the PFC circuit, predictive voltage control and predictive current control are implemented here. A digital signal processor, type TMS‐320F‐2808, manufactured by Texas Instruments, is used here as the control centre to execute the sensorless methods and control algorithms. A 400 W, 50 kHz switching frequency, single‐phase alternating current (AC) 110 V /DC 300 V PFC circuit is implemented. The measured results validate the feasibility and correctness of the proposed methods. This proposed sensorless PFC circuit can be widely applied in industry due to its simplicity.
Multi‐type power generation planning method for power systems based on complex adaptive system theory
Authors: Suo Xun , Zhao Shuqiang , Ma Yanfeng , Dong Ling 
Published: May 2023

AbstractAiming at the problem of multi‐point power source layout planning for power systems, the output characteristics of a power system composed of wind power, photovoltaic power, hydropower, traditional thermal power, concentrated solar power and electrochemical energy storage are comprehensively analyzed. A power source multi‐point layout planning model for a power system based on complex adaptive system theory is proposed with a focus on the complementarity among these different energies and the combination of power optimization planning and complex adaptive system theory. With the minimum construction unit of various types of power sources as the ‘agent’, considering the interactions among agents and the accumulation of experiences, the behaviour rules of the model are constantly changed, the grid‐connected positions of various types of power sources are adjusted, and the optimal layout schemes for all kinds of power capacities for each node are obtained. In addition, an agent modelling method based on complex phenomena emergence with simple rules is proposed, which reveals the core idea of the complex adaptive system theory: adaptability creates complexity. Taking the new energy construction base in Northwest China as an example, the proposed method is verified to have a significant effect on improving energy consumption in the new system. Based on the current power capacity layout and the future grid structure in this area, the future power optimization scheme is determined, and it provides guiding significance for actual engineering construction.
Underwater acoustic target recognition based on automatic feature and contrastive coding
Authors: Sun Baogui , Luo Xinwei 
Published: May 2023

AbstractUnderwater acoustic target recognition (UATR) technology based on deep learning and automatic encoding has become an important research direction in the underwater acoustic field in recent years. However, the existing methods do not have favourable self‐adaptability for different data because of the complex and changeable underwater environment, which easily leads to an unsatisfactory recognition effect. The concept of contrastive learning is introduced into UATR and a model named Contrastive Coding for UATR (CCU) is proposed. Based on the unsupervised contrastive learning framework, the model has been modified for the underwater acoustic field. Thus, the CCU can generate adaptable automatic features according to different data. The experimental test shows that the model is superior to other automatic encoding models and has achieved excellent recognition performance on different underwater acoustic datasets.
A fast Wigner Hough transform algorithm for parameter estimation of low probability of intercept radar polyphase coded signals
Authors: Yang Jincheng , Chen Shiwen , Dong Jinpeng , Han Xiao 
Published: May 2023

AbstractIt is difficult for a receiver to intercept the signals from a radar system that can emit low probability of intercept (LPI) polyphase coded signals. The traditional Wigner Hough transform (WHT) algorithm requires a large amount of computation and takes a long time to estimate the parameters of the LPI radar polyphase coded signals. To address this problem, an iterative angle search (IAS) algorithm, which when used in combination with the WHT algorithm significantly reduces the computational cost is proposed. When the signal‐to‐noise ratio is in the range of −4 to 20 dB, the carrier frequency, number of subcodes, and number of cycles of the carrier frequency per subcode of five polyphase coded signals, namely, Frank, P1, P2, P3, and P4, are accurately estimated in simulation experiments. Based on the selected IAS algorithm parameters, the estimation accuracy of the proposed method is the same as that of the traditional WHT algorithm. However, the operation time is only 5.14% of that of the traditional method. The IAS algorithm has certain application prospects. Experiments indicate that the proposed algorithm provides excellent performance and can rapidly and accurately estimate the parameters of LPI polyphase codes.
Metabolomic Profile of Insulin Resistance Women with Systemic Lupus
Erythematosus
Authors: Munguía-Realpozo Pamela , Mendoza-Pinto Claudia , Etchegaray-Morales Ivet , Osorio-Peña Angel David , Vélez-Pelcastre Sandra , Escamilla-Márquez Marco Antonio , Ayón-Aguilar Jorge , Méndez-Martínez Socorro , García-Carrasco Mario 
Published: May 2023

AbstractThe aims of this study were in systemic lupus erythematosus (SLE) patients: 1) to
compare the metabolomic profile of insulin resistance (IR) with controls and 2)
to correlate the metabolomic profile with other IR surrogates and SLE disease
variables and vitamin levels. In this cross-sectional study, serum samples were
collected from women with SLE (n=64) and gender- and age-matched
controls (n=71), which were not diabetic. Serum metabolomic profiling
was performed using UPLC-MS-MS (Quantse score). HOMA and QUICKI were carried
out. Serum 25(OH)D concentrations were measured by chemiluminescent immunoassay.
In women with SLE, the metabolomic Quantose score significantly correlated with
HOMA-IR, HOMA2-IR, and QUICKI. Although concentrations of IR metabolites were
not different between SLE patients and controls, fasting plasma insulin levels
were higher and insulin sensitivity lower in SLE women. Interestingly, the
Quantose IR score was significantly correlated with complement C3 levels
(r=0.7; p=0.001). 25 (OH)D did not correlate with any metabolite
or the Quantose IR index. Quantose IR may be a useful tool for IR assessment.
There was a possible correlation between the metabolomic profile and complement
C3 levels. The implementation of this metabolic strategy may help develop
biochemical insight into metabolic disorders in SLE.
Disengaged or raising voices? An analysis of the relationship between individual risk perception and non-institutionalised political participation
Authors: Weiss Julia 
Published: May 2023

AbstractThe economic crisis of 2008 resulted in rising levels of unemployment, which were accompanied by protests in many countries. This led to a scientific debate on the influence of grievances on political participation. Previous studies have shown that individual affectedness leads to ‘exiting’, whereas in times of crisis grievances lead to increased political participation. What all these studies have in common is that they solely investigate actual deprivation. The present study adds a new aspect and examines if the individual risk perception of becoming deprived influences political participation. Does the individual risk perception of becoming deprived influence political participation and, if so, does this depend on the economic context? Based on grievance theory and using data from the European Social Survey and the World Bank, this study shows that the personal risk of becoming deprived increases the likelihood of participation. However, the participation-enhancing effect of one’s own risk perception is diminished when individuals live in an economically weak country and thus see no chance of improvement. This underlines that context plays a role and that economically deteriorating situations in the country can reduce participation rates.
How mixture of plant and prey diets affects long-term rearing of predatory mite Neoseiulus cucumeris (Acari: Phytoseiidae)
Authors: Yazdanpanah Shima , Fathipour Yaghoub 
Published: May 2023

Abstract
The phytoseiid mite Neoseiulus cucumeris (Oudemans) (Acari: Phytoseiidae) is one of the well-known natural enemies across the globe which can feed on different types of pests and pollen grains. This predator was reared on the mixture of the stored products mite, Tyrophagus putrescentiae (Schrank) (Acari: Acaridae) (as prey diet) along with different plant pollens (as plant diet) including almond (TA), cattail (TC), castor-bean (TCb), date (TD), saffron (TS), and mixed pollens of almond, cattail, castor bean, and date (TP) for 20 generations (G1–G20). The effects of the mixed plant and prey diets on biological parameters of N. cucumeris were evaluated under laboratory conditions at 25 ± 1°C, 60 ± 5% RH, and a photoperiod of 16:8 (L: D) h every 10 generations (G1, G10, and G20). N. cucumeris showed the highest performance on the mixed pollens + T. putrescentiae (TP diet) among the diets tested. Our findings revealed almost stable performance of the predator by long-term feeding on a mixture of pollen and prey diet and this combination can use as a suitable alternative food for mass rearing of N. cucumeris.
Impact of alcohol home delivery and other methods of obtaining alcohol in young adults
Authors: Noel Jonathan K , Rosenthal Samantha R 
Published: May 2023

Abstract
Aims: To estimate the use of home alcohol delivery and other methods of obtaining alcohol, rates of ID checking for home alcohol delivery purchases, and associations with alcohol-related consequences. Methods: Surveillance data from 784 lifetime drinkers participating in the 2022 Rhode Island Young Adult Survey were used. The method of obtaining alcohol (e.g. type of purchase, gifted, theft) was assessed. The Alcohol Use Disorders Identification Test, Brief Young Adults Alcohol Consequences Questionnaire, and a drinking and driving question were used to measure high-risk drinking behaviors, experiencing negative alcohol questions, and history of drinking and driving. Logistic regression models adjusting for sociodemographic variables were used to estimate main effects. Results: About 7.4% of the sample purchased alcohol through a home delivery or to-go purchase; 12.1% of participants who obtained alcohol this way never had their ID checked during the purchase attempt, and 10.2% of these purchases were completed by participants under the legal purchase age. Home delivery/to-go purchases were associated with high-risk drinking. Alcohol theft was associated with high-risk drinking, experiencing negative alcohol consequences, and drinking and driving. Conclusions: Home alcohol delivery and to-go purchases may provide an opportunity for underage access to alcohol, but their current use as a method of obtaining alcohol is rare. Stronger ID checking policies are needed. Alcohol theft was linked to several negative alcohol outcomes, and home-based preventive interventions should be considered.
Effect of PDYN and OPRK1 polymorphisms on the risk of alcohol use disorder and the intensity of depressive symptoms
Authors: Özkan-Kotiloğlu Selin , Kaya-Akyüzlü Dilek , Yurdakul Rabia , Yıldırım Mukaddes Asena , Özgür-İlhan İnci 
Published: May 2023

Abstract
Aims: The dynorphin (DYN)/Kappa Opioid Receptor (KOR) system has been suggested to be involved in both negative affective states and the action of alcohol. The present study was undertaken to explore whether the DYN/KOR system genes, PDYN and OPRK1, influence on individual differences in the intensity of depressive symptoms at admission as well as the risk of alcohol use disorder (AUD) risk in a sample of 101 individuals with AUD and 100 controls. Methods: PDYN (rs2281285, rs2225749 and rs910080) and OPRK1 (rs6473797, rs963549 and rs997917) polymorphisms were analyzed by PCR-RFLP. The intensity of depressive and anxiety symptoms and craving were measured by the Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and Penn Alcohol Craving Scale, respectively. Results: A significant association between the risk of AUD and OPRK1 rs6473797 (P &lt; 0.05) at the gene level. OPRK1 rs6473797 CC genotype was found to lead to a 3.11 times greater alcohol dependence risk. In addition, the BDI-II score of the OPRK1 rs963549 CC genotype was found to be significantly lower (20.9 ± 11.2, min: 1.0, max: 48.0) than that of the CT + TT genotypes (27.04 ± 12.7, min: 0.0, max: 49.0) (t: −2.332, P = 0.022). None of the PDYN polymorphisms were associated with BDI-II score. Conclusion: Variations in the KOR are associated with the risk of AUD and the intensity of depressive symptoms at admission at the gene level in Turkish males. On the other hand, PDYN gene seemed not to be associated with AUD, depression, anxiety, and craving.