bluuebunny/crossref_metadata_2025 huggingface dataset:
Engineering a Phosphoketolase Pathway to Supplement Cytosolic Acetyl-CoA in Aspergillus niger Enables a Significant Increase in Citric Acid Production
Authors: Liu Jiao , Zhang Shanshan , Li Wenhao , Wang Guanyi , Xie Zhoujie , Cao Wei , Gao Weixia , Liu Hao
Published: April 2023

Citric acid is widely used in the food, chemical and pharmaceutical industries. Aspergillus niger is the workhorse used for citric acid production in industry. A canonical citrate biosynthesis that occurred in mitochondria was well established; however, some research suggested that the cytosolic citrate biosynthesis pathway may play a role in this chemical production. Here, the roles of cytosolic phosphoketolase (PK), acetate kinase (ACK) and acetyl-CoA synthetase (ACS) in citrate biosynthesis were investigated by gene deletion and complementation in A. niger. The results indicated that PK, ACK and ACS were important for cytosolic acetyl-CoA accumulation and had significant effects on citric acid biosynthesis. Subsequently, the functions of variant PKs and phosphotransacetylase (PTA) were evaluated, and their efficiencies were determined. Finally, an efficient PK-PTA pathway was reconstructed in A. niger S469 with Ca-PK from Clostridium acetobutylicum and Ts-PTA from Thermoanaerobacterium saccharolyticum. The resultant strain showed an increase of 96.4% and 88% in the citrate titer and yield, respectively, compared with the parent strain in the bioreactor fermentation. These findings indicate that the cytosolic citrate biosynthesis pathway is important for citric acid biosynthesis, and increasing the cytosolic acetyl-CoA level can significantly enhance citric acid production.
Tolerance of Rare-Earth Elements in Extremophile Fungus Umbelopsis isabellina from Polar Loparite Ore Tailings in Northwestern Russia
Authors: Shumilov Oleg I. , Kasatkina Elena A. , Kirtsideli Irina Y. , Makarov Dmitry V.
Published: April 2023

In this study, extremophile fungal species isolated from pure loparite-containing sands and their tolerance/resistance to the lanthanides Ce and Nd were investigated. The loparite-containing sands were collected at the tailing dumps of an enterprise developing a unique polar deposit of niobium, tantalum and rare-earth elements (REEs) of the cerium group: the Lovozersky Mining and Processing Plant (MPP), located in the center of the Kola Peninsula (northwestern Russia). From the 15 fungal species found at the site, one of the most dominant isolates was identified by molecular analysis as the zygomycete fungus Umbelopsis isabellina (GenBank accession no. OQ165236). Fungal tolerance/resistance was evaluated using different concentrations of CeCl3 and NdCl3. Umbelopsis isabellina exhibited a higher degree of tolerance/resistance to cerium and neodymium than did the other dominant isolates (Aspergillus niveoglaucus, Geomyces vinaceus and Penicillium simplicissimum). The fungus began to be inhibited only after being exposed to 100 mg L−1 of NdCl3. The toxic effects of Ce were not observed in fungus growth until it was subjected to 500 mg∙L−1 of CeCl3. Moreover, only U. isabellina started to grow after extreme treatment with 1000 mg∙L−1 of CeCl3 one month after inoculation. This work indicates, for the first time, the potential of Umbelopsis isabellina to remove REEs from the loparite ore tailings, making it a suitable candidate for the development of bioleaching methods.
Impact of Hypothermic Oxygenated Machine Perfusion on Hepatocellular Carcinoma Recurrence after Liver Transplantation
Authors: Rigo Federica , De Stefano Nicola , Patrono Damiano , De Donato Victor , Campi Ludovico , Turturica Diana , Doria Teresa , Sciannameo Veronica , Berchialla Paola , Tandoi Francesco , Romagnoli Renato
Published: April 2023

Background: Machine perfusion may be able to mitigate ischemia-reperfusion injury (IRI), which increases hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT). This study aimed to investigate the impact of dual-hypothermic oxygenated machine perfusion (D-HOPE) on HCC recurrence in LT. Methods: A single-center retrospective study was conducted from 2016 to 2020. Pre- and postoperative data of HCC patients undergoing LT were analyzed. Recipients of a D-HOPE-treated graft were compared to those of livers preserved using static cold storage (SCS). The primary endpoint was recurrence-free survival (RFS). Results: Of 326 patients, 246 received an SCS-preserved liver and 80 received a D-HOPE-treated graft (donation after brain death (DBD), n = 66; donation after circulatory death (DCD), n = 14). Donors of D-HOPE-treated grafts were older and had higher BMI. All DCD donors were treated by normothermic regional perfusion and D-HOPE. The groups were comparable in terms of HCC features and estimated 5-year RFS according to the Metroticket 2.0 model. D-HOPE did not reduce HCC recurrence (D-HOPE 10%; SCS 8.9%; p = 0.95), which was confirmed using Bayesian model averaging and inverse probability of treatment weighting-adjusted RFS analysis. Postoperative outcomes were comparable between groups, except for lower AST and ALT peak in the D-HOPE group. Conclusions: In this single-center study, D-HOPE did not reduce HCC recurrence but allowed utilizing livers from extended criteria donors with comparable outcomes, improving access to LT for patients suffering from HCC.
Radiological Influencing Factors in the Diagnosis of Painful Habitual Instability of the Thumb Basal Joint as a Precursor of Carpometacarpal Arthritis of the Thumb—A Retrospective Study
Authors: Winter Raimund , Hasiba-Pappas Sophie , Kamolz Lars-P. , Tschauner Sebastian , Bamer Oskar , Tuca Alexandru Cristian , Luze Hanna , Nischwitz Sebastian P. , Michelitsch Birgit , Friedl Herwig , Lumenta David Benjamin , Girsch Werner
Published: April 2023

Background: Painful habitual instability of the thumb basal joint (PHIT) is a rarely diagnosed condition that can severely impair hand function. Furthermore, it can increase the risk of developing carpometacarpal arthritis of the thumb (CMAOT). Clinical examination and radiographic imaging provide the foundation for a correct diagnosis, but early detection is still challenging. We investigated two objective, radiographically obtainable parameters as potential risk factors for PHIT. Methods: Clinical data and radiographic images of 33 patients suffering from PHIT were collected and compared to those of 35 people serving as the control group. The two main objectives, the slope angle and the bony offset of the thumb joint, were gathered from the X-rays and statistically analyzed. Results: The analysis showed no differences between the study and the control group concerning the slope angle. Gender and the bony offset, on the other hand, had a significant influence. Female sex and higher offset values were associated with an increased risk of PHIT. Conclusions: The results of this study prove a connection between a high bony offset and PHIT. We believe this information can be valuable in early detection and will allow more efficient treatment of this condition in the future.
Dental Root Injuries Caused by Osteosynthesis Screws in Orthognathic Surgery—Comparison of Conventional Osteosynthesis and Osteosynthesis by CAD/CAM Drill Guides and Patient-Specific Implants
Authors: Pietzka Sebastian , Fink Juliana , Winter Karsten , Wilde Frank , Schramm Alexander , Ebeling Marcel , Kasper Robin , Sakkas Andreas
Published: April 2023

Background/Aim: The primary aim was to evaluate the prevalence and localisation of dental injuries caused by osteosynthesis screws during orthognathic surgery, comparing two different CAD/CAM planning/surgical approaches through retrospective evaluation of post-operative computed tomography. Material and Methods: This study considered all patients who underwent orthognathic surgery from 2010–2019. The examination for dental root injuries between conventional osteosynthesis (Maxilla conventional cohort) and osteosynthesis with patient-specific implant (Maxilla PSI cohort) was performed by evaluating the post-operative CT scans. Results: A total of 126 patients were included in the study. Among the 61 patients of the Maxilla conventional cohort, 10 dental root injuries in 8 patients (13.1%) were detected in the post-operative CT scan, representing 1.5% (n = 10/651) of the osteosynthesis screws inserted in proximity of the alveolar crest. No dental injury occurred following osteosynthesis in the 65 patients of the Maxillary PSI cohort (n = 0/773 screws) (p < 0.001). During a mean follow-up period of 13 months after primary surgery, none of the injured teeth showed evidence of periapical alterations and no endodontic treatments were necessary. Conclusions: Maxillary positioning using CAD/CAM-fabricated drill/osteotomy guide and osteosynthesis with PSI can significantly reduce the risk for dental injury compared to the conventional procedure. However, the clinical significance of the detected dental injuries was rather minor.
Evaluation of the Evolution of Digital Nursing Interventions in an Emergency Unit: An Observational Study
Authors: Medina Martínez Irene , Sánchez-García Juan Carlos , Cortés-Martín Jonathan , Díaz-Rodríguez Lourdes , Limonchi Pérez María Mercedes , Vargas Román Keyla , Rodríguez-Blanque Raquel
Published: April 2023

This study aimed to examine the influence of new ICTs on the recording of nursing interventions in the Emergency Unit of the High Resolution Hospital (HRH) of Loja (Granada), Spain. A descriptive observational study was conducted to analyze the evolution of the Nursing Interventions (NIC) records in the Emergency Unit of the Loja HRH (Granada) from 2017 to 2021. Results showed that 11,076 NIC registrations were exploited, which increased by 51.2% from 2017 to 2021. The linear correlation between the NIC and the years was analyzed with Spearman’s coefficient, obtaining a low level of correlation (p = 0.166), but one that is statistically significant (p < 0.001). The introduction of tablet devices in the emergency room of the Loja HRH (Granada) led to a significant increase in the percentage of NIC recorded and collated during the study period without increasing the number of emergencies attended. However, usability barriers of ICTs were detected, highlighting the need to guide and train health professionals in their use and in the culture of patient safety.
Serum Transforming Growth Factor β1 and Its Genetic Variants Are Associated with Increased Macrophage Inflammatory Protein 1β and Susceptibility to Idiopathic Carpal Tunnel Syndrome
Authors: Fattah Shaimaa A. , Selim Mohamed S. , Abdel Fattah Maha A. , Abo-Elmatty Dina M. , Mesbah Noha M. , Abdel-hamed Asmaa R.
Published: April 2023

Carpal tunnel syndrome (CTS) is a common entrapment neuropathy in which one of the body’s peripheral nerves becomes pinched or crushed. Transforming growth factor beta 1 (TGF-β1) plays an important role in the pathogenesis of CTS. An association between TGF-β1 polymorphisms and the susceptibility or progression of a number of diseases has been reported. In this study, three TGF-β1 single nucleotide polymorphisms (SNPs), serum TGF-β1, and macrophage inflammatory protein 1 beta (MIP-1β) were investigated as potential diagnostic markers for the progression of CTS in Egyptian patients. One hundred CTS patients and 100 healthy controls were recruited for the study. TGF-β1 SNPs +915G/C, −509C/T and −800G/A were determined by TaqMan genotyping assay. Serum TGF-β1 and MIP-1β levels were measured by ELISA. Serum TGF-β1 and MIP-1β levels increased significantly and were strongly correlated with the occurrence of CTS. The C allele of +915G/C, the T allele of −509C/T, and the G allele of −800G/A occurred more frequently in patients from CTS than in controls. The serum levels of TGF-β1 and MIP-1β in the group of carriers of the genotypes +915G/C GC and CC, the genotype −509C/T TT and the genotype −800G/A GA and AA were significantly higher in CTS patients. TGF-β1 and its +915G/C, −509C/T, and −800G/A SNPs and MIP-1β could be useful prognostic markers for the occurrence of CTS.
The Generalised Extreme Value Distribution Approach to Comparing the Riskiness of BitCoin/US Dollar and South African Rand/US Dollar Returns
Authors: Chikobvu Delson , Ndlovu Thabani
Published: April 2023

In this paper, the generalised extreme value distribution (GEVD) model is employed to estimate financial risk in the form of return levels and the value at risk (VaR) for the two exchange rates, BitCoin/US dollar (BTC/USD) and the South African rand/US dollar (ZAR/USD). The Basel Committee on Banking Supervision (BCBS) responsible for developing supervisory guidelines for banks and financial trading desks recommended that VaR be computed and reported. The maximum likelihood estimation (MLE) method is used to estimate the parameters of the GEVD. The estimated risk values are used to compare the riskiness of the two exchange rates and help both traders and investors to define their position in forex trading. This is to helping understanding the risk they are taking when they convert their savings/investments to BitCoin instead of the South African currency, the rand. The high extreme value index associated with the BTC/USD compared to the ZAR/USD implies that BitCoin is riskier than the rand. The BTC/USD has higher values of expected extreme/tail losses of 13.44%, 18.02%, and 23.41% at short (6 months), medium (12 months), and long (24 months) terms, compared to the ZAR/USD expected extreme/tail losses of 2.40%, 2.84%, and 3.28%, respectively. The computed VaR estimates for losses of USD 0.17, USD 0.22, and USD 0.38 per dollar invested in BTC/USD at 90%, 95%, and 99%, compared to ZAR/USD’s USD 0.03, USD 0.03, and USD 0.04 at the respective confidence levels, confirm the high risk associated with BitCoin. The conclusion drawn from this study is that BTC/USD is riskier than ZAR/USD, despite the rand being a developing country’s currency, hence perceived as being risky. The perception is that the rand is riskier than BitCoin and perceptions do influence exchange rates. Kupiec’s backtest results confirmed the model’s adequacy. These findings are helpful to investors, traders, and risk managers when deciding on trading positions for the two currencies.
The Results of Digitizing Historical Maps: Comparison of Lithuanian Land-Use Structure in the 19th and 21st Centuries
Authors: Piškinaitė Eglė , Veteikis Darijus
Published: April 2023

Studies of long-term land-use changes can reveal significant results about land use in the past and lead to the main causes of these changes being revealed. We georeferenced 27 selected sheets from 1846–1872 topographical maps of the Russian Empire, covering the territory of the modern Republic of Lithuania. The georeferencing was based on using ground control points. We discuss the overall insignificant errors obtained from joining the georeferenced sheets of this historical map. Manual digitizing (vectorizing) was preferred over automated means because of the specifications of the map. All the data were merged into five land-use categories: (1) forests; (2) wetlands; (3) built-up areas; (4) water bodies; and (5) other (mostly including arable land, grassland, and pastures). The reconstructed land-use structure of the 19th century was assessed for its compatibility with the spatial data of the CORINE land cover in 2018. The results showed that forest land use increased from 26.57% to 33.52%, built-up areas increased from 4.35% to 3.23%, and water bodies increased from 2.24% to 5.78%. Meanwhile, wetlands decreased from 4.35% to 0.84%, and other land use decreased from 66.56% to 56.63%. The main LULC change trends were determined to have resulted from political and economic decisions. The reconstructed LULC situation and identified LULC changes can provide the background for land management and future studies in various academic fields.
Modifiable Risk Factors and Preventative Strategies for Severe Retinopathy of Prematurity
Authors: Prasad Minali , Ingolfsland Ellen C. , Christiansen Stephen P.
Published: April 2023

Severe ROP is characterized by the development of retinal fibrovascular proliferation that may progress to retinal detachment. The purpose of this report is to review five of the most common and well-studied perinatal and neonatal modifiable risk factors for the development of severe ROP. Hyperoxemia, hypoxia, and associated prolonged respiratory support are linked to the development of severe ROP. While there is a well-established association between clinical maternal chorioamnionitis and severe ROP, there is greater variability between histologic chorioamnionitis and severe ROP. Neonatal sepsis, including both bacterial and fungal subtypes, are independent predictors of severe ROP in preterm infants. Although there is limited evidence related to platelet transfusions, the risk of severe ROP increases with the number and volume of red blood cell transfusions. Poor postnatal weight gain within the first six weeks of life is also strongly tied to the development of severe ROP. We also discuss preventative strategies that may reduce the risk of severe ROP. Limited evidence-based studies exist regarding the protective effects of caffeine, human milk, and vitamins A and E.
Niosomal Curcumin Suppresses IL17/IL23 Immunopathogenic Axis in Skin Lesions of Psoriatic Patients: A Pilot Randomized Controlled Trial
Authors: Kolahdooz Hanieh , Khori Vahid , Erfani-Moghadam Vahid , Livani Fatemeh , Mohammadi Saeed , Memarian Ali
Published: April 2023

Psoriasis (PS) is characterized by hyperplasia of epidermis and infiltration of immune cells in the dermis. A negligible susceptibility of hypodermic permeation for local anti-inflammatory remedies is one of the major causes of medication failures. Although curcumin (CUR) has indicated effectiveness in treatment of inflammation, its successful permeation through the stratum corneum is yet a challenging issue. Therefore, niosome (NIO) nanoparticles were used as curcumin carriers to enhance its delivery and anti-inflammatory effects. Curcumin-niosome (CUR-NIO) formulations were constructed by the thin-film-hydration (TFH) technique and were added to hyaluronic acid and Marine-collagen gel-based formulation. Five mild-to-moderate PS patients (18–60 years) with PASI scores < 30 with symmetrical and similar lesions were included in the study. The prepared formulation (CUR 15 µM) was topically administered for 4 weeks on the skin lesions, in comparison to the placebo. Clinical skin manifestations were monitored and skin punches were obtained for further gene expression analyses. There was a significant reduction in redness, scaling, and an apparent improvement in CUR-NIO-treated group in comparison to the placebo-treated counterpart. The gene expression analyses resulted in significantly downregulation of IL17, IL23, IL22, and TNFα, S100A7, S100A12, and Ki67 in CUR-NIO-treated lesions. Consequently, CUR-NIO could provide therapeutic approaches for the patients with mild-to-moderate PS by suppressing the IL17/IL23 immunopathogenic axis.
In Memory of an Exceptional Limnologist, Dariusz Borowiak (1962–2022)
Authors: Rzymski Piotr , Klimaszyk Piotr , Nowiński Kamil , Marszelewski Włodzimierz
Published: April 2023

Dariusz Borowiak, a respected Polish limnologist and geographist, died on 7th November 2022, at the age of 60, in Gdańsk, the city where the ideas of Polish companionship and solidarity were born [...]
A Simulation Independent Analysis of Single- and Multi-Component cw ESR Spectra
Authors: Sinha Roy Aritro , Dzikovski Boris , Dolui Dependu , Makhlynets Olga , Dutta Arnab , Srivastava Madhur
Published: April 2023

The accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understanding their structure–function relationships and electrochemical properties. The current methods of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly/partially resolved and unresolved spectra, which is common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper–nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.
Base Station Planning Based on Region Division and Mean Shift Clustering
Authors: Chen Jian , Shi Yongkun , Sun Jiaquan , Li Jiangkuan , Xu Jing
Published: April 2023

The problem of insufficient signal coverage of 5G base stations can be solved by building new base stations in areas with weak signal coverage. However, due to construction costs and other factors, it is not possible to cover all areas. In general, areas with high traffic and weak coverage should be given priority. Although many scientists have carried out research, it is not possible to make the large-scale calculation accurately due to the lack of data support. It is necessary to search for the central point through continuous hypothesis testing, so there is a large systematic error. In addition, it is difficult to give a unique solution. In this paper, the weak signal coverage points were divided into three categories according to the number of users and traffic demand. With the lowest cost as the target, and constraints such as the distance requirement of base station construction, the proportion of the total signal coverage business, and so on, a single objective nonlinear programming model was established to solve the base station layout problem. Through traversal search, the optimal threshold of the traffic and the number of base stations was obtained, and then, a kernel function was added to the mean shift clustering algorithm. The center point of the new macro station was determined in the dense area, the location of the micro base station was determined from the scattered and abnormal areas, and finally the unique optimal planning scheme was obtained. Based on the assumptions made in this paper, the minimum total cost is 3752 when the number of macro and micro base stations were determined to be 31 and 3442 respectively, and the signal coverage rate can reach 91.43%. Compared with the existing methods, such as K-means clustering, K-medoids clustering, and simulated annealing algorithms, etc., the method proposed in this paper can achieve good economic benefits; when the traffic threshold and the number of base stations threshold are determined, the unique solution can be obtained.
Mathematical Model to Understand the Dynamics of Cancer, Prevention Diagnosis and Therapy
Authors: Alzahrani Ebraheem , El-Dessoky M. M. , Khan Muhammad Altaf
Published: April 2023

In the present study, we formulate a mathematical model to understand breast cancer in the population of Saudi Arabia. We consider a mathematical model and study its mathematical results. We show that the breast cancer model possesses a unique system of solutions. The stability results are shown for the model. We consider the reported cases in Saudi Arabia for the period 2004–2016. The data are given for the female population in Saudi Arabia that is suffering from breast cancer. The data are used to obtain the values of the parameters, and then we predict the long-term behavior with the obtained numerical results. The numerical results are obtained using the proposed parameterized approach. We present graphical results for the breast cancer model under effective parameters such as τ1, τ2, and τ3 that cause decreasing future cases in the population of stages 3 and 4, and the disease-free condition. Chemotherapy generally increases the risk of cardiotoxicity, and, hence, our model result shows this fact. The combination of chemotherapy stages 3 and 4 and the parameters τ1 and τ2 together at a low-level rate and also treating the patients before the chemotherapy will decrease the population of cardiotoxicity. The findings of this study are intended to reduce the number of cardiotoxic patients and raise the number of patients who recover following chemotherapy, which will aid in public health decision making.
CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps
Authors: Wu Tsu-Yang , Li Haonan , Chu Shu-Chuan
Published: April 2023

The Phasmatodea Population Evolution (PPE) algorithm, inspired by the evolution of the phasmatodea population, is a recently proposed meta-heuristic algorithm that has been applied to solve problems in engineering. Chaos theory has been increasingly applied to enhance the performance and convergence of meta-heuristic algorithms. In this paper, we introduce chaotic mapping into the PPE algorithm to propose a new algorithm, the Chaotic-based Phasmatodea Population Evolution (CPPE) algorithm. The chaotic map replaces the initialization population of the original PPE algorithm to enhance performance and convergence. We evaluate the effectiveness of the CPPE algorithm by testing it on 28 benchmark functions, using 12 different chaotic maps. The results demonstrate that CPPE outperforms PPE in terms of both performance and convergence speed. In the performance analysis, we found that the CPPE algorithm with the Tent map showed improvements of 8.9647%, 10.4633%, and 14.6716%, respectively, in the Final, Mean, and Standard metrics, compared to the original PPE algorithm. In terms of convergence, the CPPE algorithm with the Singer map showed an improvement of 65.1776% in the average change rate of fitness value, compared to the original PPE algorithm. Finally, we applied our CPPE to stock prediction. The results showed that the predicted curve was relatively consistent with the real curve.
Phi, Fei, Fo, Fum: Effect Sizes for Categorical Data That Use the Chi-Squared Statistic
Authors: Ben-Shachar Mattan S. , Patil Indrajeet , Thériault Rémi , Wiernik Brenton M. , Lüdecke Daniel
Published: April 2023

In both theoretical and applied research, it is often of interest to assess the strength of an observed association. Existing guidelines also frequently recommend going beyond null-hypothesis significance testing and reporting effect sizes and their confidence intervals. As such, measures of effect sizes are increasingly reported, valued, and understood. Beyond their value in shaping the interpretation of the results from a given study, reporting effect sizes is critical for meta-analyses, which rely on their aggregation. We review the most common effect sizes for analyses of categorical variables that use the χ2 (chi-square) statistic and introduce a new effect size—פ (Fei, pronounced “fay”). We demonstrate the implementation of these measures and their confidence intervals via the effectsize package in the R programming language.
On Construction of Partially Dimension-Reduced Approximations for Nonstationary Nonlocal Problems of a Parabolic Type
Authors: Čiegis Raimondas , Starikovičius Vadimas , Suboč Olga , Čiegis Remigijus
Published: April 2023

The main aim of this article is to propose an adaptive method to solve multidimensional parabolic problems with fractional power elliptic operators. The adaptivity technique is based on a very efficient method when the multidimensional problem is approximated by a partially dimension-reduced mathematical model. Then in the greater part of the domain, only one-dimensional problems are solved. For the first time such a technique is applied for problems with nonlocal diffusion operators. It is well known that, even for classical local diffusion operators, the averaged flux conjugation conditions become nonlocal. Efficient finite volume type discrete schemes are constructed and analysed. The stability and accuracy of obtained local discrete schemes is investigated. The results of computational experiments are presented and compared with theoretical results.
Novel Approach of Airfoil Shape Representation Using Modified Finite Element Method for Morphing Trailing Edge
Authors: Lendraitis Martynas , Lukoševičius Vaidas
Published: April 2023

This study presents a novel approach to parameterize the geometry of a morphing trailing-edge flap that allows its aerodynamics to be optimized while capturing the expected structural behavior of the flap. This approach is based on the finite frame element method, whereby the initial flap surface is defined as a structure with constraints that are similar to those of a morphing flap with passive skin. The initial shape is modified by placing a series of distributed loads on the surface. The finite frame element method is modified with rigid rotation corrections to maintain the initial element length without requiring nonlinear calculations and to achieve accurate surface-length results by only solving the linear FEM equations twice. The proposed method enables the shape of the morphing flaps to be rapidly formulated while maintaining the initial upper surface-length and trailing-edge angle. The constraints are inherently integrated into the algorithm, eliminating the need for unnecessary feasibility checks during the aerodynamic optimization. By using the proposed airfoil parameterization method, a case study was conducted by using a genetic algorithm to optimize the lift-to-drag ratio of the NACA 23012 airfoil flap starting at 0.7c with 10 degrees of deflection. The optimizer resulted in a structurally feasible morphing flap that achieved a 10% increase in the lift-to-drag ratio in the optimized angle of attack range.
Dynamic Learning Rate of Template Update for Visual Target Tracking
Authors: Li Da , Li Song , Wei Qin , Chai Haoxiang , Han Tao
Published: April 2023

The trackers based on discriminative correlation filter (DCF) have achieved remarkable performance in visual target tracking in recent years. Since the targets are usually affected by various factors such as deformation, rotation, motion blur and so on, the trackers have to update the templates for tracking online. The purpose of template update is to adapt to the target changes, the magnitude of which is closely related to the motion state of the target. Actually, the learning rate of template update indicates the weight of the historical sample, and its value is fixed in most existing trackers, which will decrease the precision of the tracker or make the tracker unstable. In this study, a new dynamic learning rate method for template update is proposed for visual target tracking. The motion state of the target is defined by the difference in target center position between the frames. Then, the learning rate is adjusted dynamically according to the motion state of the target instead of the fixed value, which could achieve better performance. Experiments on the popular datasets OTB100 and UAV123 show that with the proposed dynamic learning rate for template update, the DCF-based trackers can improve tracking accuracy and obtain better tracking stability in scenarios such as fast movement and motion blur.
Research on Signal Detection of OFDM Systems Based on the LSTM Network Optimized by the Improved Chameleon Swarm Algorithm
Authors: Sun Yunshan , Cheng Yuetong , Liu Ting , Huang Qian , Guo Jianing , Jin Weiling
Published: April 2023

In order to improve the signal detection capability of orthogonal frequency-division multiplexing systems, a signal detection method based on an improved LSTM network for OFDM systems is proposed. The LSTM network is optimized by the Chameleon Swarm Algorithm (CLCSA) with the coupling variance and lens-imaging learning. The signal detection method based on the traditional LSTM network has the problem of a complex manual tuning process and insufficient stability. To solve the above problem, the improved Chameleon Swarm Algorithm is used to optimize the initial hyperparameters of the LSTM network and obtain the optimal hyperparameters. The optimal hyperparameters initialize the CLCSA-LSTM network model and the CLCSA-LSTM network model is trained. Finally, the trained CLCSA-LSTM network model is used for signal detection in the OFDM system. The simulation results show that the signal detection performance of the OFDM receiver has been significantly improved, and the dependence on CP and pilot overhead can be reduced. Under the same channel environment, the proposed method in this paper has better performance than other signal detection methods, and is close to the performance of the MMSE method, but it does not need prior statistical characteristics of the channel, so it is easy to implement.
Stability Analysis and Hopf Bifurcation of a Delayed Diffusive Predator–Prey Model with a Strong Allee Effect on the Prey and the Effect of Fear on the Predator
Authors: Xie Yining , Zhao Jing , Yang Ruizhi
Published: April 2023

In this paper, we propose a diffusive predator–prey model with a strong Allee effect and nonlocal competition in the prey and a fear effect and gestation delay in the predator. We mainly study the local stability of the coexisting equilibrium and the existence and properties of Hopf bifurcation. We provide bifurcation diagrams with the fear effect parameter (s) and the Allee effect parameter (a), showing that the stable region of the coexisting equilibrium increases (or decreases) with an increase in the fear effect parameter (s) (or the Allee effect parameter (a)). We also show that gestation delay (τ) can affect the local stability of the coexisting equilibrium. When the delay (τ) is greater than the critical value, the coexistence equilibrium loses its stability, and bifurcating periodic solutions appear. Whether the bifurcated periodic solution is spatially homogeneous or inhomogeneous depends on the fear effect parameter (s) and the Allee effect parameter (a). These results show that the fear effect parameter (s), the Allee effect parameter (a), and gestation delay (τ) can be used to control the growth of prey and predator populations.
Distributed Observers for State Omniscience with Stochastic Communication Noises
Authors: Chen Kairui , Zhu Zhangmou , Zeng Xianxian , Wang Junwei
Published: April 2023

The focus of this paper is on solving the state estimation problem for general continuous-time linear systems through the use of distributed networked observers. To better reflect the communication environment, stochastic noises are considered when observers exchange information. In the networked observers, each local observer measures only part of the system output, and the state estimation can not be accomplished within a single observer. Then, all observers communicate through a pre-specified graph to make up information in the remaining system output. By solving a parametric algebraic Riccati equation (ARE), a simple method to calculate parameters in the observers is proposed. Furthermore, using the stability theory of stochastic differential equations, state omniscience is discussed in almost sure sense and in the mean square sense for the cases of state-dependent noises and non-state-dependent noises, respectively. It is shown that, for observable linear systems, the resulting observers work in a coordinated mode to reach state omniscience under the connected graph. Illustrative examples are provided to show the effectiveness of the distributed observers.
A Stochastic Convergence Result for the Nelder–Mead Simplex Method
Authors: Galántai Aurél
Published: April 2023

We prove that the Nelder–Mead simplex method converges in the sense that the simplex vertices converge to a common limit point with a probability of one. The result may explain the practical usefulness of the Nelder–Mead method.
Nonlinear Dynamics of Perturbations of a Plane Potential Fluid Flow: Nonlocal Generalization of the Hopf Equation
Authors: Zubarev Nikolay M.
Published: April 2023

In this paper, we analytically study the two-dimensional unsteady irrotational flow of an ideal incompressible fluid in a half-plane whose boundary is assumed to be a linear sink. It is shown that the nonlinear evolution of perturbations of the initial uniform flow is described by a one-dimensional integro-differential equation, which can be considered as a nonlocal generalization of the Hopf equation. This equation can be reduced to a system of ordinary differential equations (ODEs) in the cases of spatially localized or spatially periodic perturbations of the velocity field. In the first case, ODEs describe the motion of a system of interacting virtual point vortex-sinks/sources outside the flow domain. In the second case, ODEs describe the evolution of a finite number of harmonics of the velocity field distribution; this is possible due to the revealed property of the new equation that the interaction of initial harmonics does not lead to generation of new ones. The revealed reductions made it possible to effectively study the nonlinear evolution of the system, in particular, to describe the effect of nonlinearity on the relaxation of velocity field perturbations. It is shown that nonlinearity can significantly reduce the relaxation rate by more than 1.5 times.
Estimations of Covering Functionals of Convex Bodies Based on Relaxation Algorithm
Authors: Yu Man , Lv Yafang , Zhao Yanping , He Chan , Wu Senlin
Published: April 2023

Estimating covering functionals of convex bodies is an important part of Chuanming Zong’s program to attack Hadwiger’s covering conjecture, which is a long-standing open problem from convex and discrete geometry. In this paper, we transform this problem into a vertex p-center problem (VPCP). An exact iterative algorithm is introduced to solve the VPCP by making adjustments to the relaxation-based algorithm mentioned by Chen and Chen in 2009. The accuracy of this algorithm is tested by comparing numerical and exact values of covering functionals of convex bodies including the Euclidean disc, simplices, and the regular octahedron. A better lower bound of the covering functional with respect to 7 of 3-simplices is presented.
Learning Bilateral Clipping Parametric Activation for Low-Bit Neural Networks
Authors: Ding Yunlong , Chen Di-Rong
Published: April 2023

Among various network compression methods, network quantization has developed rapidly due to its superior compression performance. However, trivial activation quantization schemes limit the compression performance of network quantization. Most conventional activation quantization methods directly utilize the rectified activation functions to quantize models, yet their unbounded outputs generally yield drastic accuracy degradation. To tackle this problem, we propose a comprehensive activation quantization technique namely Bilateral Clipping Parametric Rectified Linear Unit (BCPReLU) as a generalized version of all rectified activation functions, which limits the quantization range more flexibly during training. Specifically, trainable slopes and thresholds are introduced for both positive and negative inputs to find more flexible quantization scales. We theoretically demonstrate that BCPReLU has approximately the same expressive power as the corresponding unbounded version and establish its convergence in low-bit quantization networks. Extensive experiments on a variety of datasets and network architectures demonstrate the effectiveness of our trainable clipping activation function.
Statistical Inference of Two Gamma Distributions under the Joint Type-II Censoring Scheme
Authors: Ding Leijia , Gui Wenhao
Published: April 2023

The joint Type-II censoring scheme is a useful model when carrying out comparative lifecycle tests of units from various production lines. This article takes into account the estimation problem of the joint Type-II censoring data coming from two Gamma distributions with the same shape parameter but various scale parameters. The maximum likelihood estimators of the parameters from Gamma populations and asymptotic confidence intervals based on the observed Fisher information matrix are obtained. Bootstrap methods are also applied to construct confidence intervals. The Metropolis–Hastings algorithm is considered to draw Markov Chain Monte Carlo samples when computing Bayesian estimates as well as establishing the corresponding credible intervals. Monte Carlo simulations are adopted to compare the performance of the estimates. Finally, two real engineering datasets are analyzed.
Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients
Authors: Dai Xiaowen , Huang Shidan , Jin Libin , Tian Maozai
Published: April 2023

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location. Smoothing methods are employed for approximating varying coefficients, including B-spline and local polynomial approximation. A fixed-effects quantile regression (FEQR) estimator is typically biased in the presence of the spatial lag variable. The wild bootstrap method is employed to attenuate the estimation bias. Simulations are conducted to study the performance of the proposed method and show that the proposed methods are stable and efficient. Further, the estimators based on the B-spline method perform much better than those of the local polynomial approximation method, especially for location-varying coefficients. Real data about economic development in China are also analyzed to illustrate application of the proposed procedure.
Optical Solitons for the Concatenation Model with Differential Group Delay: Undetermined Coefficients
Authors: Biswas Anjan , Vega-Guzman Jose , Yıldırım Yakup , Moraru Luminita , Iticescu Catalina , Alghamdi Abdulah A.
Published: April 2023

In the current study, the concatenation model of birefringent fibers is explored for the first time, and we present optical soliton solutions to the model. The integration algorithm used to achieve this retrieval is the method of undetermined coefficients, which yields a wide range of soliton solutions. The parameter constraints arise naturally during the derivation of the soliton solutions, which are essential for such solitons to exist.
Enhancing Computational Accuracy in Surrogate Modeling for Elastic–Plastic Problems by Coupling S-FEM and Physics-Informed Deep Learning
Authors: Zhou Meijun , Mei Gang , Xu Nengxiong
Published: April 2023

Physics-informed neural networks (PINNs) provide a new approach to solving partial differential equations (PDEs), while the properties of coupled physical laws present potential in surrogate modeling. However, the accuracy of PINNs in solving forward problems needs to be enhanced, and solving inverse problems relies on data samples. The smoothed finite element method (S-FEM) can obtain high-fidelity numerical solutions, which are easy to solve for the forward problems of PDEs, but difficult to solve for the inverse problems. To the best of the authors’ knowledge, there has been no prior research on coupling S-FEM and PINN. In this paper, a novel approach that couples S-FEM and PINN is proposed. The proposed approach utilizes S-FEM to synthesize high-fidelity datasets required for PINN inversion, while also improving the accuracy of data-independent PINN in solving forward problems. The proposed approach is applied to solve linear elastic and elastoplastic forward and inverse problems. The computational results demonstrate that the coupling of the S-FEM and PINN exhibits high precision and convergence when solving inverse problems, achieving a maximum relative error of 0.2% in linear elasticity and 5.69% in elastoplastic inversion by using approximately 10,000 data points. The coupling approach also enhances the accuracy of solving forward problems, reducing relative errors by approximately 2–10 times. The proposed coupling of the S-FEM and PINN offers a novel surrogate modeling approach that incorporates knowledge and data-driven techniques, enabling it to solve both forward and inverse problems associated with PDEs with high levels of accuracy and convergence.
A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering
Authors: Dey Alokananda , Bhattacharyya Siddhartha , Dey Sandip , Konar Debanjan , Platos Jan , Snasel Vaclav , Mrsic Leo , Pal Pankaj
Published: April 2023

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algorithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits.
Iterative Schemes Involving Several Mutual Contractions
Authors: Navascués María A. , Jha Sangita , Chand Arya K. B. , Mohapatra Ram N.
Published: April 2023

In this paper, we introduce the new concept of mutual Reich contraction that involves a pair of operators acting on a distance space. We chose the framework of strong b-metric spaces (generalizing the standard metric spaces) in order to add a more extended underlying structure. We provide sufficient conditions for two mutually Reich contractive maps in order to have a common fixed point. The result is extended to a family of operators of any cardinality. The dynamics of iterative discrete systems involving this type of self-maps is studied. In the case of normed spaces, we establish some relations between mutual Reich contractivity and classical contractivity for linear operators. Then, we introduce the new concept of mutual functional contractivity that generalizes the concept of classical Banach contraction, and perform a similar study to the Reich case. We also establish some relations between mutual functional contractions and Banach contractivity in the framework of quasinormed spaces and linear mappings. Lastly, we apply the obtained results to convolutional operators that had been defined by the first author acting on Bochner spaces of integrable Banach-valued curves.