bluuebunny/crossref_metadata_2025 huggingface dataset:
PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization
Authors: Liu Huaiqin , Yu Qinghe , Wu Qu
Published: April 2023

In processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics. However, real-time implementation and high reliability require the adjustment of specific model parameters. Therefore, this paper proposes a PID controller that combines a back-propagation neural network (BPNN) and adversarial learning-based grey wolf optimization (ALGWO). To enhance the unpredictable behavior and capacity for exploration of the grey wolf, this study develops a new parameter-learning technique. Alpha gray wolves use the random walk of levy flight as their hunting method. In beta and delta gray wolves, a search strategy centering on the top gray wolf is employed, and in omega gray wolves, the decision wolves handle the confrontation strategy. A fair balance between exploration and exploitation can be achieved, as evidenced by the success of the adversarial learning-based grey wolf optimization technique in ten widely used benchmark functions. The effectiveness of different activation functions in conjunction with ALGWO were evaluated in resolving the parameter adjustment issue of the BPNN model. The results demonstrate that no unique activation function outperforms others in different controlled systems, but their fitnesses are significantly inferior to those of the conventional PID controller.
Three-Dimensional Temperature Field Simulation and Analysis of a Concrete Bridge Tower Considering the Influence of Sunshine Shadow
Authors: Zou Shuai , Xiao Jun , Xian Jianping , Zhang Yongshui , Zhang Jingfeng
Published: April 2023

This paper forms a set of three-dimensional temperature field simulation methods considering the influence of sunshine shadow based on the DFLUX subroutine and FILM subroutine interface provided by the Abaqus platform to simulate the three-dimensional temperature field of concrete bridge towers and study its distribution law. The results show that the method has high accuracy for shadow recognition and temperature field calculation. The maximum difference between the shadow recognition results and the theoretical calculation value was only 19.1 mm, and the maximum difference between the simulated temperature and the measured temperature was 3.3 °C. The results of analyzing the temperature field of the concrete bridge tower using this algorithm show that the temperature difference between the opposite external surface of the tower column can reach 11.6 °C, which is significantly greater than the recommended temperature difference value of 5 °C in the specifications. For the concrete bridge tower, in the thickness direction of the tower wall, the temperature change was obvious only at a range of 0.3 m from the external surface of the tower wall, and the temperature change in the remaining range was small. In addition, the temperature gradient distribution of the sunshine temperature field in the direction of wall thickness conformed to the exponential function T(x) = T0e−αx + C. Additionally, the data fitting results indicate that using the temperature data at a distance of 0.8 m from the external surface as the calculation parameter in the function can achieve the ideal fitting result.
A Maturity Model for Trustworthy AI Software Development
Authors: Cho Seunghwan , Kim Ingyu , Kim Jinhan , Woo Honguk , Shin Wanseon
Published: April 2023

Recently, AI software has been rapidly growing and is widely used in various industrial domains, such as finance, medicine, robotics, and autonomous driving. Unlike traditional software, in which developers need to define and implement specific functions and rules according to requirements, AI software learns these requirements by collecting and training relevant data. For this reason, if unintended biases exist in the training data, AI software can create fairness and safety issues. To address this challenge, we propose a maturity model for ensuring trustworthy and reliable AI software, known as AI-MM, by considering common AI processes and fairness-specific processes within a traditional maturity model, SPICE (ISO/IEC 15504). To verify the effectiveness of AI-MM, we applied this model to 13 real-world AI projects and provide a statistical assessment on them. The results show that AI-MM not only effectively measures the maturity levels of AI projects but also provides practical guidelines for enhancing maturity levels.
Experimental Investigation and Theoretical Prediction Model of Flexural Bearing Capacity of Pre-Cracked RC Beams
Authors: Zhou Shuming , Yan Donghuang
Published: April 2023

Cracking is one of the main diseases of small- and medium-span reinforced concrete (RC) bridges. It is a key problem to determine the change in mechanical properties of RC beams after cracking in bridge-performance evaluation. The present study performs static loading tests on seven simply supported T-beams with different crack damage conditions. The influences of crack location, crack depth and steel-bar diameter at a prefabricated crack on the stress, deflection and crack distribution pattern of pre-cracked test beams are investigated. The failure mode and mechanism of pre-cracked beams are revealed. Based on the experimental results, a finite element model of a pre-cracked beam is developed and validated. Following this, a theoretical prediction method is proposed to calculate the ultimate load of pre-cracked RC beams. The results indicate that the direct damage to mid-span section size can significantly affect the stiffness of the RC beam. The local damage of the tensile steel section has insignificant influence on the overall stiffness of the beam. The stiffness degradation of the pre-cracked beam at the quarter span is smaller than that of the pre-cracked beam at mid-span. The strain of the T-beam section in the pre-cracked test conformed to the assumption of the flat section. The experimental observations are in good agreement with the theoretical predictions, which can provide a theoretical basis for the performance evaluation of existing RC beams.
Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin
Authors: Wang Yi , Sun Wenlei , Liu Liqiang , Wang Bingkai , Bao Shenghui , Jiang Renben
Published: April 2023

Aiming at the problems of the traditional planetary gear fault diagnosis method of wind turbines, such as the poor timeliness of data transmission, weak visualization effect of state monitoring, and untimely feedback of fault information, this paper proposes a planetary gear fault diagnosis method for wind turbines based on a digital twin. The method was used to build the digital twin model of wind turbines and analyze the wind turbines’ operating state utilizing virtual and real data. Empirical mode decomposition (EMD) was used, and an atom search optimization–support vector machine (ASO-SVM) model was established for planetary gear fault diagnosis. The digital twin model diagnoses faults and constantly revises the model based on the diagnostic results. The digital twin fault diagnosis system was implemented in the Unity3D platform. The experimental results demonstrate the feasibility of the proposed early-warning system for the real-time diagnosis of planetary gear faults in wind turbines.
Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources
Authors: Li Menglong , Ni Zehao , Tian Le , Hu Yuxiang , Shen Juan , Wang Yu
Published: April 2023

In the existing medical knowledge graphs, there are problems concerning inadequate knowledge discovery strategies and the use of single sources of medical data. Therefore, this paper proposed a research method for multi-data-source medical knowledge graphs based on the data, information, knowledge, and wisdom (DIKW) system to address these issues. Firstly, a reliable data source selection strategy was used to assign priorities to the data sources. Secondly, a two-step data fusion strategy was developed to effectively fuse the processed medical data, which is conducive to improving the quality of medical knowledge graphs. The proposed research method is for the design of a multi-data-source medical knowledge graph based on the DIKW system. The method was used to design a set of DIK three-layer knowledge graph architectures according to the DIKW system in line with the medical knowledge discovery strategy, employing a scientific method for expanding and updating knowledge at each level of the knowledge graph. Finally, question and answer experiments were used to compare the two different ways of constructing knowledge graphs, validating the effectiveness of the two-step data fusion strategy and the DIK three-layer knowledge graph.
Analysis of Size Distribution, Chemical Composition, and Optical Properties of Mineral Dust Particles from Dry Deposition Measurement in Tenerife: Determined by Single-Particle Characterization
Authors: Waza Andebo , Schneiders Kilian , Heuser Johannes , Kandler Konrad
Published: April 2023

In this paper, individual particle analysis by automated scanning electron microscopy (SEM) coupled with energy-dispersive X-ray (EDX) was used to assess the size-resolved information of composition, size distribution, complex refractive index, and mixing state of mineral dust aerosol particles collected using different passive and active samplers. In the study, over 120,000 particles from 53 samples were analyzed. Results show that dust particles are the dominating mineral particle type during this campaign, comprising different classes of silicates, Si-rich (quartz-like), Ca-rich (calcite-like), CaMg-rich (dolomite-like), and CaS-rich (gypsum-like). The results also show that there is no significant difference in composition between suspended and deposited dust particles. By using the particle composition, the size-resolved complex refractive index of dust particles was calculated. The real part of the refractive index varied between 1.71 and 1.53 for wavelengths in the range of 370 to 950 nm. The imaginary part of the refractive index, determined mostly by iron oxide, varied between 3.28×10−4 and 7.11×10−5 for wavelengths ranging from 250 nm to 1640 nm. In addition, the refractive index values showed a slight decrease with increasing particle size. We also analyzed the potential for buffering of the acid mobilization of iron by other dust compounds. For particles which contain both iron (Fe) and (unprocessed) calcium (Ca), acids that are able to dissolve insoluble Fe particles can react with the Ca particles before reacting with Fe, but eventually, with longer processing time, the Fe particles could be processed. By analyzing the ratio of sulfate mass to the total aerosol mass of individual particles, the mixing state of sulfate particles to the total dust particles was investigated. The analysis showed that the finer dust particles were associated with higher content of sulfate, while the coarse dust particles correspond to lower sulfate contents, revealing that only fine mode sulfate is more internally mixed with mineral dust aerosol particles.
Predictive Model of Clothing Insulation in Naturally Ventilated Educational Buildings
Authors: de la Hoz-Torres María L. , Aguilar Antonio J. , Costa Nélson , Arezes Pedro , Ruiz Diego P. , Martínez-Aires Mª Dolores
Published: April 2023

Providing suitable indoor thermal conditions in educational buildings is crucial to ensuring the performance and well-being of students. International standards and building codes state that thermal conditions should be considered during the indoor design process and sizing of heating, ventilation and air conditioning systems. Clothing insulation is one of the main factors influencing the occupants’ thermal perception. In this context, a field survey was conducted in higher education buildings to analyse and evaluate the clothing insulation of university students. The results showed that the mean clothing insulation values were 0.60 clo and 0.72 clo for male and female students, respectively. Significant differences were found between seasons. Correlations were found between indoor and outdoor air temperature, radiant temperature, the temperature measured at 6 a.m., and running mean temperature. Based on the collected data, a predictive clothing insulation model, based on an artificial neural network (ANN) algorithm, was developed using indoor and outdoor air temperature, radiant temperature, the temperature measured at 6 a.m. and running mean temperature, gender, and season as input parameters. The ANN model showed a performance of R2 = 0.60 and r = 0.80. Fifty percent of the predicted values differed by less than 0.1 clo from the actual value, whereas this percentage only amounted to 32% if the model defined in the ASHRAE-55 Standard was applied.
Silent Tears of Midwives: ‘I Want Every Mother Who Gives Birth to Have Her Baby Alive’—A Narrative Inquiry of Midwives Experiences of Very Early Neonatal Death from Tanzania
Authors: Becker Jan , Becker Chase , Abeysekera Rachel , Moir James , Gray Marion , Shimwela Meshack , Oprescu Florin
Published: April 2023

Background: Midwives working in settings with limited clinical resources experience high rates of very early neonatal deaths. Midwives manage the impact of this grief and trauma almost daily, which may affect patient care and their own well-being. Research Aims: To explore how midwives are impacted by and cope with high rates of very early neonatal deaths. To document midwives’ insights and local solutions that may reduce very early neonatal deaths in limited resource settings. To document the stories of midwives in order to create awareness and garner support for midwives and their critical work in low resource settings. Methods: Narrative inquiry utilizing semi structured interviews. Twenty-one midwives with at least six months experience who had experienced or witnessed very early neonatal death were interviewed. Data were audio recorded and transcribed, and reflexive thematic analysis of transcripts was conducted. Results and Discussion: Three themes were identified: (1) deep sadness resulting from very early neonatal deaths leading to internal struggles; (2) use of spirituality, including prayer and occasional beliefs that unexplainable deaths were ‘God’s plan’; and (3) development of resilience by seeking solutions, educating themselves, taking accountability and guiding mothers. Participating midwives noted that inadequate staff and high caseloads with limited basic supplies hindered their clinical practice. Participants articulated that they concentrated on active solutions to save babies during labour, such as vigilant foetal rate heart monitoring and partogram. Further, reduction and prevention of very early neonatal death is a complex problem requiring multidisciplinary teams and woman-centred care approaches to address issues contributing to the health of mothers and their new-borns. Conclusions: Midwives’ narratives highlighted ways of coping with grief and deep sadness, through prayer, and further education of both mothers and fellow colleagues to achieve better antenatal and intrapartum care and outcomes. This study gave midwives an opportunity for their voices to be heard and to generate solutions or insights that can be shared with colleagues in similar low-resource settings.
Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images
Authors: Mohammed Abdul Sami , Hasanaath Ahmed Abul , Latif Ghazanfar , Bashar Abul
Published: April 2023

One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren–Lawrence (KL) system. This requires the physician’s expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.
Automatic Identification of Lung Opacities Due to COVID-19 from Chest X-ray Images—Focussing Attention on the Lungs
Authors: Arias-Londoño Julián D. , Moure-Prado Álvaro , Godino-Llorente Juan I.
Published: April 2023

Due to the primary affection of the respiratory system, COVID-19 leaves traces that are visible in plain chest X-ray images. This is why this imaging technique is typically used in the clinic for an initial evaluation of the patient’s degree of affection. However, individually studying every patient’s radiograph is time-consuming and requires highly skilled personnel. This is why automatic decision support systems capable of identifying those lesions due to COVID-19 are of practical interest, not only for alleviating the workload in the clinic environment but also for potentially detecting non-evident lung lesions. This article proposes an alternative approach to identify lung lesions associated with COVID-19 from plain chest X-ray images using deep learning techniques. The novelty of the method is based on an alternative pre-processing of the images that focuses attention on a certain region of interest by cropping the original image to the area of the lungs. The process simplifies training by removing irrelevant information, improving model precision, and making the decision more understandable. Using the FISABIO-RSNA COVID-19 Detection open data set, results report that the opacities due to COVID-19 can be detected with a Mean Average Precision with an IoU > 0.5 (mAP@50) of 0.59 following a semi-supervised training procedure and an ensemble of two architectures: RetinaNet and Cascade R-CNN. The results also suggest that cropping to the rectangular area occupied by the lungs improves the detection of existing lesions. A main methodological conclusion is also presented, suggesting the need to resize the available bounding boxes used to delineate the opacities. This process removes inaccuracies during the labelling procedure, leading to more accurate results. This procedure can be easily performed automatically after the cropping stage.
A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA
Authors: Srinivasan Saravanan , Gunasekaran Subathra , Mathivanan Sandeep Kumar , Jayagopal Prabhu , Khan Muhammad Attique , Alasiry Areej , Marzougui Mehrez , Masood Anum
Published: April 2023

We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.
Osteoarthritis and Diabetes: Where Are We and Where Should We Go?
Authors: Alenazi Aqeel M. , Alhowimel Ahmed S. , Alshehri Mohammed M. , Alqahtani Bader A. , Alhwoaimel Norah A. , Segal Neil A. , Kluding Patricia M.
Published: April 2023

Diabetes mellitus (DM) and osteoarthritis (OA) are chronic noncommunicable diseases that affect millions of people worldwide. OA and DM are prevalent worldwide and associated with chronic pain and disability. Evidence suggests that DM and OA coexist within the same population. The coexistence of DM in patients with OA has been linked to the development and progression of the disease. Furthermore, DM is associated with a greater degree of osteoarthritic pain. Numerous risk factors are common to both DM and OA. Age, sex, race, and metabolic diseases (e.g., obesity, hypertension, and dyslipidemia) have been identified as risk factors. These risk factors (demographics and metabolic disorder) are associated with DM or OA. Other possible factors may include sleep disorders and depression. Medications for metabolic syndromes might be related to the incidence and progression of OA, with conflicting results. Given the growing body of evidence indicating a relationship between DM and OA, it is vital to analyze, interpret, and integrate these findings. Therefore, the purpose of this review was to evaluate the evidence on the prevalence, relationship, pain, and risk factors of both DM and OA. The research was limited to knee, hip, and hand OA.
The Effect of Daily Meditative Practices Based on Mindfulness and Self-Compassion on Emotional Distress under Stressful Conditions: A Randomized Controlled Trial
Authors: Gutiérrez-Hernández María Elena , Fanjul Rodríguez Luisa Fernanda , Díaz Megolla Alicia , Oyanadel Cristián , Peñate Castro Wenceslao
Published: April 2023

Intervention programs based on self-compassion have demonstrated their efficacy both in reducing psychological distress and increasing well-being. The goal of this study was to test the efficacy of an online intervention to increase mindfulness and self-compassion levels in a non-clinical sample in a highly stressful context: the ten weeks of lockdown imposed in the early stages of the COVID-19 pandemic. The intervention sessions consisted of thirty-minute guided meditations followed by thirty minutes of inquiry. Sixty-one participants completed two thirds of the sessions or more, and 65 individuals participated in a waiting-list (WL) control group. Self-compassion, anxiety, depression and stress levels were assessed. The analysis of pre-post results suggests that the interventions increased self-compassion levels and decreased anxiety, depression and stress levels, whereas the WL group did not show any significant changes. The emotional changes in the intervention group were associated with the increase in self-compassion. However, at follow-up, the scores of emotional distress variables returned to the initial pre-intervention scores. These data can be interpreted in line with previous results that have shown the efficacy of self-compassion-based intervention programs. Given that this efficacy was not maintained at follow-up, data are discussed according to the pervasive role of a highly stressful context and—as described in other studies—the need for regular practice to maintain the benefits obtained.
Age and Growth Characteristics of Okamejei kenojei in the West Sea of South Korea According to Coronal Vertebral Microstructure
Authors: Youn Byoung-Il , Choi Dong-Hyuk , Roh Tae-Hyoung , Lee Seung-Hwan , Han Kyeong-Ho , Kwon Dae-Hyeon , Kim Maeng-Jin
Published: April 2023

In this study, the growth and age characteristics of Okamejei kenojei in the West Sea of South Korea were examined, and specimens were collected by bottom trawls from January to December 2019. The relationship between disc width (L) and body weight (W) was W = 6.1 × 10−3 L3.40. Age was determined by measuring annuli on the vertebral centrum surface, which forms around June every year. The spawning period was extrapolated from monthly changes of the gonadosomatic index in June–July. The relationship between the vertebral centrum radius and disc width, which was analyzed separately for females and males, was determined as L = 16.159R0.6145 and L = 15.543R0.6851, respectively. Finally, O. kenojei growth, that is, the disc width (L) at a certain age (t), was expressed using the von Bertalanffy growth equation as Lt = 58.70(1 − e−0.21(t+0.12)) for females and Lt = 53.94(1 − e−0.26(t+0.05)) for males. This study provides basic data on the age characteristics of O. kenojei for future research and more efficient fish stock management.
Highly Selective Polyene-Polyyne Resistive Gas Sensors: Response Tuning by Low-Energy Ion Irradiation
Authors: Zavidovskiy Ilya A. , Streletskiy Oleg A. , Nuriahmetov Islam F. , Nishchak Olesya Yu. , Savchenko Natalya F. , Tatarintsev Andrey A. , Pavlikov Alexander V.
Published: April 2023

The formation of polyene-polyyne-based nanocomposites by dehydrohalogenation of the drop-cast-deposited polyvinylidene fluoride, assessment and ion-induced tailoring of their gas sensing properties are reported. The investigated structure was analyzed by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy, transmission electron microscopy and Fourier-transform infrared spectroscopy, revealing the thickness-dependent incomplete dehydrofluorination of the structure and its porosity induced by KOH treatment. The polyene-polyyne structures modified by low-energy Ar+ were studied by SEM and Raman spectroscopy, which showed the morphology variation, the shortening of chains and the graphitization of samples. The resistive gas sensing properties of the samples were analyzed at room temperature, revealing selective sensing of ammonia vapor by non-irradiated sample and the enhancement of the sensing properties for ethanol and water vapor after ion irradiation. With the ion dose enlargement, the change in the sensing response from electrical conductivity increase to decrease was observed for ammonia and ethanol, allowing us to discuss the origin and tunability of the sensing mechanism of the samples.
Current Status of Peri-Implant Diseases: A Clinical Review for Evidence-Based Decision Making
Authors: Scarano Antonio , Khater Ahmad G. A. , Gehrke Sergio Alexandre , Serra Paola , Francesco Inchingolo , Di Carmine Mariastella , Tari Sergio Rexhep , Leo Lucia , Lorusso Felice
Published: April 2023

Background: the prevalence of peri-implant diseases is constantly growing, particularly with the increasing use of dental implants. As such, achieving healthy peri-implant tissues has become a key challenge in implant dentistry since it considers the optimal success paradigm. This narrative review aims to highlight the current concepts regarding the disease and summarize the available evidence on treatment approaches clarifying their indications for usage following the World Workshop on the Classification of Periodontal and Peri-implant Diseases (2017). Methods: we reviewed the recent literature and conducted a narrative synthesis of the available evidence on peri-implant diseases. Results: scientific evidence on case definitions, epidemiology, risk factors, microbiological profile, prevention, and treatment approaches for peri-implant diseases were summarized and reported. Conclusions: although there are numerous protocols for managing peri-implant diseases, they are diverse and nonstandardized, with no consensus on the most effective, leading to treatment confusion.
REAC Antalgic Neuro Modulation in Chronic Post Herpetic Neuralgia
Authors: Barcessat Ana Rita Pinheiro , Nunes Lucas dos Santos , Gonçalves Rebeca Góes , Darienso Danyela
Published: April 2023

Background: Chronic post-herpetic neuralgia (CPHN) is a symptomatic condition that afflicts adults and elderly individuals. The chronicity of this symptomatology can be conditioned by the epigenetic modifications induced by the virus on the processes of neurotransmission and sensitivity to pain. The aim of this study is to investigate whether manipulating endogenous bioelectrical activity (EBA), responsible for neurotransmission processes and contributing to the induction of epigenetic modifications, can alleviate pain symptoms. Methods: This manipulation was carried out with the antalgic neuromodulation (ANM) treatment of radioelectric asymmetric conveyer (REAC) technology. Pain assessment before and after treatment was performed using a numerical analog scale (NAS) and a simple descriptive scale (SDS). Results: The results of the analysis showed an over four-point decrease in NAS scale score and over one point decrease in SDS scale score, with a statistical significance for both tests of p < 0.005. Conclusions: The results obtained in this study demonstrate how REAC ANM manipulation of EBA can lead to improvement in epigenetically conditioned symptoms such as CPHN. These results should prompt further research to expand knowledge and ensure optimized therapeutic outcomes.
Impact of Preoperative Ureteral Stenting in Retrograde Intrarenal Surgery for Urolithiasis
Authors: Jeong Jae Yong , Cho Kang Su , Jun Dae Young , Moon Young Joon , Kang Dong Hyuk , Jung Hae Do , Lee Joo Yong
Published: April 2023

Background and Objectives: Ureteral stent insertion passively dilates the ureter. Therefore, it is sometimes used preoperatively before flexible ureterorenoscopy to make the ureter more accessible and facilitate urolithiasis passage, especially when ureteroscopic access has failed or when the ureter is expected to be tight. However, it may cause stent-related discomfort and complications. This study aimed to assess the effect of ureteral stenting prior to retrograde intrarenal surgery (RIRS). Materials and Methods: Data from patients who underwent unilateral RIRS for renal stone with the use of a ureteral access sheath from January 2016 to May 2019 were retrospectively analyzed. Patient characteristics, including age, sex, BMI, presence of hydronephrosis, and treated side, were recorded. Stone characteristics in terms of maximal stone length, modified Seoul National University Renal Stone Complexity score, and stone composition were evaluated. Surgical outcomes, including operative time, complication rate, and stone-free rate, were compared between two groups divided by whether preoperative stenting was performed. Results: Of the 260 patients enrolled in this study, 106 patients had no preoperative stenting (stentless group), and 154 patients had stenting (stenting group). Patient characteristics except for the presence of hydronephrosis and stone composition were not statistically different between the two groups. In surgical outcomes, the stone-free rate was not statistically different between the two groups (p = 0.901); however, the operation time for the stenting group was longer than that of the stentless group (44.8 ± 24.2 vs. 36.1 ± 17.6 min; p = 0.001). There were no differences in the complication rate between the two groups (p = 0.523). Conclusions: Among surgical outcomes for RIRS with a ureteral access sheath, preoperative ureteral stenting does not provide a significant advantage over non-stenting with respect to the stone-free rate and complication rate.
A Comprehensive Survey on the Expediated Anti-COVID-19 Options Enabled by Metal Complexes—Tasks and Trials
Authors: Gopal Judy , Muthu Manikandan , Sivanesan Iyyakkannu
Published: April 2023

Contemporary pharmacology dating back to the late 19th/early 20th centuries has benefitted largely from the incorporation of metal complexes. Various biological attributes have been successfully realized using metal/metal complex-based drugs. Among anticancer, antimicrobial, and antiviral applications, anticancer applications have extracted the maximum benefit from the metal complex, Cisplatin. The following review has compiled the various antiviral benefits harnessed through inputs from metal complexes. As a result of exploiting the pharmacological aspects of metal complexes, the anti-COVID-19 deliverables have been summarized. The challenges ahead, the gaps in this research area, the need to improvise incorporating nanoaspects in metal complexes, and the need to test metal complex-based drugs in clinical trials have been discussed and deliberated. The pandemic shook the entire world and claimed quite a percentage of the global population. Metal complex-based drugs are already established for their antiviral property with respect to enveloped viruses and extrapolating them for COVID-19 can be an effective way to manipulate drug resistance and mutant issues that the current antiCOVID-19 drugs are facing.
Inhibition of Acetylcholinesterase by Novel Lupinine Derivatives
Authors: Schepetkin Igor A. , Nurmaganbetov Zhangeldy S. , Fazylov Serik D. , Nurkenov Oralgazy A. , Khlebnikov Andrei I. , Seilkhanov Tulegen M. , Kishkentaeva Anarkul S. , Shults Elvira E. , Quinn Mark T.
Published: April 2023

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive memory loss and cognitive impairment due in part to a severe loss of cholinergic neurons in specific brain areas. AD is the most common type of dementia in the aging population. Although several acetylcholinesterase (AChE) inhibitors are currently available, their performance sometimes yields unexpected results. Thus, research is ongoing to find potentially therapeutic AChE inhibitory agents, both from natural and synthetic sources. Here, we synthesized 13 new lupinine triazole derivatives and evaluated them, along with 50 commercial lupinine-based esters of different carboxylic acids, for AChE inhibitory activity. The triazole derivative 15 [1S,9aR)-1-((4-(4-(benzyloxy)-3-methoxyphenyl)-1H-1,2,3-triazol-1-yl)methyl)octahydro-2H-quinolizine)] exhibited the most potent AChE inhibitory activity among all 63 lupinine derivatives, and kinetic analysis demonstrated that compound 15 was a mixed-type AChE inhibitor. Molecular docking studies were performed to visualize interaction between this triazole derivative and AChE. In addition, a structure-activity relationship (SAR) model developed using linear discriminant analysis (LDA) of 11 SwissADME descriptors from the 50 lupinine esters revealed 5 key physicochemical features that allowed us to distinguish active versus non-active compounds. Thus, this SAR model could be applied for design of more potent lupinine ester-based AChE inhibitors.
Antibacterial Activity of the Green Synthesized Plasmonic Silver Nanoparticles with Crystalline Structure against Gram-Positive and Gram-Negative Bacteria
Authors: Hassan Afandy Hemn , Sabir Dana Khdr , Aziz Shujahadeen B.
Published: April 2023

Nanoparticles (NPs) have attracted considerable interest in numerous fields, including agriculture, medicine, the environment, and engineering. The use of green synthesis techniques that employ natural reducing agents to reduce metal ions and form NPs is of particular interest. This study investigates the use of green tea (GT) extract as a reducing agent for the synthesis of silver NPs (Ag NPs) with crystalline structure. Several analytical techniques, including UV-visible spectrophotometry, Fourier transform infrared (FTIR) spectroscopy, high-resolution transmission electron microscopy (HR-TEM), and X-ray diffraction (XRD), were used to characterize the synthesized Ag NPs. The results of UV-vis revealed that the biosynthesized Ag NPs exhibited an absorbance plasmonic resonance peak at 470 nm. According to FTIR analyses, the attachment of Ag NPs to polyphenolic compounds resulted in a decrease in intensity and band shifting. In addition, the XRD analysis confirmed the presence of sharp crystalline peaks associated with face-centered cubic Ag NPs. Moreover, HR-TEM revealed that the synthesized particles were spherical and 50 nm in size on average. The Ag NPs demonstrated promising antimicrobial activity against Gram-positive (GP) bacteria, Brevibacterium luteolum and Staphylococcus aureus, and Gram-negative (GN) bacteria, Pseudomonas aeruginosa and Escherichia coli, with a minimal inhibitory concentration (MIC) of 6.4 mg/mL for GN and 12.8 mg/mL for GP. Overall, these findings suggest that Ag NPs can be utilized as effective antimicrobial agents.
Effect of Graphite Nanoplatelet Size and Dispersion on the Thermal and Mechanical Properties of Epoxy-Based Nanocomposites
Authors: Agustina Elsye , Goak Jeung Choon , Lee Suntae , Kim Yongse , Hong Sung Chul , Seo Yongho , Lee Naesung
Published: April 2023

This study investigated the effect of graphite nanoplatelet (GNP) size and dispersion on the thermal conductivities and tensile strengths of epoxy-based composites. GNPs of four different platelet sizes, ranging from 1.6 to 3 µm, were derived by mechanically exfoliating and breaking expanded graphite (EG) particles using high-energy bead milling and sonication. The GNPs were used as fillers at loadings of 0–10 wt%. As the GNP size and loading amount increased, the thermal conductivities of the GNP/epoxy composites increased, but their tensile strengths decreased. However, interestingly, the tensile strength reached a maximum value at the low GNP content of 0.3% and thereafter decreased, irrespective of the GNP size. Our observations of the morphologies and dispersions of the GNPs in the composites indicated that the thermal conductivity was more likely related to the size and loading number of fillers, whereas the tensile strength was more influenced by the dispersion of fillers in the matrix.
Why Are Conservative Young Evangelicals in Norway Avoiding Right-Wing Politics?
Authors: Zeiffert Roald
Published: April 2023

Drawing on new survey data, this article elaborates on how young evangelicals in Norway navigate between a secular majority and evangelical subgroups. It shows how they combine pro-fertility norms with liberal attitudes towards migration. Explaining why they avoid both left- and right-wing politics, the article elaborates on the central role the Christian Democratic party plays for young evangelicals in Norway.
The InflateSAR Campaign: Developing Refugee Vessel Detection Capabilities with Polarimetric SAR
Authors: Lanz Peter , Marino Armando , Simpson Morgan David , Brinkhoff Thomas , Köster Frank , Möller Matthias
Published: April 2023

In the efforts to mitigate the ongoing humanitarian crisis at the European sea borders, this work builds detection capabilities to help find refugee boats in distress. For this paper, we collected dual-pol and quad-pol synthetic aperture radar (SAR) data over a 12 m rubber inflatable in a test-bed lake near Berlin, Germany. To consider a real scenario, we prepared the vessel so that its backscattering emulated that of a vessel fully occupied with people. Further, we collected SAR imagery over the ocean with different sea states, categorized by incidence angle and by polarization. These were used to emulate the conditions for a vessel located in ocean waters. This setup enabled us to test nine well-known vessel-detection systems (VDS), to explore the capabilities of new detection algorithms and to benchmark different combinations of detectors (detector fusion) with respect to different sensor and scene parameters (e.g., the polarization, wind speed, wind direction and boat orientation). This analysis culminated in designing a system that is specifically tailored to accommodate different situations and sea states.
Predefined Time Synchronization of Multi-Agent Systems: A Passivity Based Analysis
Authors: Pandey Vinay , Taslima Eram , Singh Bhawana , Kamal Shyam , Dinh Thach Ngoc
Published: April 2023

This paper deals with the predefined-time synchronization for a class of nonlinear multi-agent systems. The notion of passivity is exploited to design the controller for predefined-time synchronization of a nonlinear multi-agent system, where the time of synchronization can be preassigned. Developed control can be used to synchronize large-scale, higher-order multi-agent systems as passivity is an important property in designing control for complex control systems, where the control inputs and outputs are considered in determining the stability of the system in contrast to other approaches, such as state-based Control We introduced the notion of predefined-time passivity and as an application of the exposed stability analysis, static and adaptive predefined-time control algorithms are designed to study the average consensus problem for nonlinear leaderless multiagent systems in predefined-time. We provide a detailed mathematical analysis of the proposed protocol, including convergence proof and stability analysis. We discussed the tracking problem for a single agent, and designed state feedback and adaptive state feedback control scheme to make tracking error predefined-time passive and then showed that in the absence of external input, tracking error reduces to zero in predefined-time. Furthermore, we extended this concept for a nonlinear multi-agent system and designed state feedback and adaptive state feedback control scheme which ensure synchronization of all the agents in predefined-time. To further strengthen the idea, we applied our control scheme to a nonlinear multi-agent system by taking the example of Chua’s circuit. Finally, we compared the result of our developed predefined-time synchronization framework with finite-time synchronization scheme available in literature for the Kuramoto model.
Improved Discriminative Object Localization Algorithm for Safety Management of Indoor Construction
Authors: Hwang Jungeun , Lee Kanghyeok , Ei Zan May Mo , Jang Minseo , Shin Do Hyoung
Published: April 2023

Object localization is a sub-field of computer vision-based object recognition technology that identifies object classes and locations. Studies on safety management are still in their infancy, particularly those aimed at lowering occupational fatalities and accidents at indoor construction sites. In comparison to manual procedures, this study suggests an improved discriminative object localization (IDOL) algorithm to aid safety managers with visualization to improve indoor construction site safety management. The IDOL algorithm employs Grad-CAM visualization images from the EfficientNet-B7 classification network to automatically identify internal characteristics pertinent to the set of classes evaluated by the network model without the need for further annotation. To evaluate the performance of the presented algorithm in the study, localization accuracy in 2D coordinates and localization error in 3D coordinates of the IDOL algorithm and YOLOv5 object detection model, a leading object detection method in the current research area, are compared. The comparison findings demonstrate that the IDOL algorithm provides a higher localization accuracy with more precise coordinates than the YOLOv5 model over both 2D images and 3D point cloud coordinates. The results of the study indicate that the IDOL algorithm achieved improved localization performance over the existing YOLOv5 object detection model and, thus, is able to assist with visualization of indoor construction sites in order to enhance safety management.
Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
Authors: Cai Bowen , Zhang Qianqian , Ge Jungang , Xie Weiliang
Published: April 2023

Due to the characteristics of global coverage, on-demand access, and large capacity, the low earth orbit (LEO) satellite communication (SatCom) has become one promising technology to support the Internet-of-Things (IoT). However, due to the scarcity of satellite spectrum and the high cost of designing satellites, it is difficult to launch a dedicated satellite for IoT communications. To facilitate IoT communications over LEO SatCom, in this paper, we propose the cognitive LEO satellite system, where the IoT users act as the secondary user to access the legacy LEO satellites and cognitively use the spectrum of the legacy LEO users. Due to the flexibility of code division multiple access (CDMA) in multiple access and the wide use of CDMA in LEO SatCom, we apply CDMA to support cognitive satellite IoT communications. For the cognitive LEO satellite system, we are interested in the achievable rate analysis and resource allocation. Specifically, considering the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratios (SINRs) and accordingly obtain the achievable rates for both legacy and IoT systems. The power of the legacy and IoT transmissions at the receiver are jointly allocated to maximize the sum rate of the IoT transmission subject to the legacy satellite system performance requirement and the maximum received power constraints. We prove that the sum rate of the IoT users is quasi-concave over the satellite terminal receive power, based on which the optimal receive powers for these two systems are derived. Finally, the resource allocation scheme proposed in this paper has been verified by extensive simulations.
Model Selection with Missing Data Embedded in Missing-at-Random Data
Authors: Takai Keiji , Hayashi Kenichi
Published: April 2023

When models are built with missing data, an information criterion is needed to select the best model among the various candidates. Using a conventional information criterion for missing data may lead to the selection of the wrong model when data are not missing at random. Conventional information criteria implicitly assume that any subset of missing-at-random data is also missing at random, and thus the maximum likelihood estimator is assumed to be consistent; that is, it is assumed that the estimator will converge to the true value. However, this assumption may not be practical. In this paper, we develop an information criterion that works even for not-missing-at-random data, so long as the largest missing data set is missing at random. Simulations are performed to show the superiority of the proposed information criterion over conventional criteria.
Samarkand in the cultural space of Samara
Authors:   Samara, Russia, Zhigalov A.V.
Published: April 2023

On February 28, at the initiative of the Samara Municipal Information and Library System, the recording of the video installation "Samarkand: Expanding the boundaries of the universal and uniting the world through culture" took place in the library No. 8 of the city of Samara within the framework of the project "Longrid: Dialogues about modern Literature".