Search

Rough set theory is a mathematical approach that deals with the problems of uncertainty and ambiguity in knowledge. Neighborhood systems are the most effective instruments for researching rough set theory in general. Investigations on boundary regions and accuracy measures primarily rely on two approximations, namely lower and upper approximations, by using these systems. The concept of the ideal, which is one of the most successful and effective mathematical tools, is used to obtain a better accuracy measure and to decrease the boundary region. Recently, a generalization of Pawlak’s rough set concept has been represented by neighborhood systems of graphs based on rough sets. In this research article, we propose a new method by using the concepts of the ideal and different neighborhoods from graph vertices. We examine important aspects of these techniques and produce accuracy measures that exceed those previously = reported in the literature. Finally, we show that our method yields better results than previous techniques utilized in chemistry.

The image classification problem is a process that many machine learning methods are trying to solve. Graphs, which are combinatorial mathematical structures, are frequently used in machine learning problems. In this study, a method using machine learning based embeddings of weighted regional graphs for image classification problem is proposed.

In 2021, exports of 12.9 billion dollars in the field of textiles in Turkey's exports have an important place in all export income. In the reports published on the textile industry, this figure is expected to increase even more in the coming years. As in other sectors, accurate estimation of sales revenues in the textile sector is very important for the future plans of companies. In this study, a deep learning model has been developed for demand and sales forecasting in the textile industry. In the method used, income will be converted to image data with Gramm Angular Fields of time series. Convolutional neural networks were used to classify these images.

Using networks to analyze time series has become increasingly popular in recent years. Univariate and multivariate time series can be mapped to networks in order to examine both local and global behaviors. Visibility graph-based time series analysis is proposed herein; in this approach, individual time series are mapped to visibility graphs that characterize relevant states. Companies listed on the emerging market index Borsa Istanbul 100 (BIST 100) had their market visibility graphs collected. To further account for the local extreme values of the underlying time series, we constructed a novel kernel function of the visibility graphs. Via the provided novel measure, sector-level and sector-to-sector analyses are conducted using the kernel function associated with this metric. To examine sectoral trends, the COVID-19 crisis period was included in the study’s data set. The findings indicate that an effective strategy for analyzing financial time series has been devised.

One of the most important policies of the European Union is regional development, which comprises measures of enhancing economic growth and citizens’ living standards via strategic investment. Considering that economic growth and wellbeing are intertwined from the perspective of EU policies, this study examines the relationship between wellbeing-related infrastructure and economic growth in 212 NUTS 2 regional subdivisions across the members of Eu-28 during the period 2001–2020. We therefore analyzed data from 151 Western Europe regions and 61 Central and Eastern Europe regions by means of a panel data analysis with the first-difference generalized method of moments estimator. Our main interest was to determine the degree to which Western Europe regions responded to predictors as compared to Central and Eastern Europe regions. According to the empirical results, the predictors with the strongest influence for Western Europe regions were disposable household income, inter-regional mobility, housing indicator, labor force and participation. For Central and Eastern Europe regions, the largest impact was triggered by the housing indicator, internet broadband access and air pollution. In addition, we determined a relational weighted multiplex between all variables of interest by using dynamic time warping and we introduced topological measures in a multilayer multiplex model for both regional subsamples.

Although many studies have shown that deep learning approaches yield better results than traditional methods based on manual features, CADs methods still have several limitations. These are due to the diversity in imaging modalities and clinical pathologies. This diversity creates difficulties because of variation and similarities between classes. In this context, the new approach from our study is a hybrid method that performs classifications using both medical image analysis and radial scanning series features. Hence, the areas of interest obtained from images are subjected to a radial scan, with their centers as poles, in order to obtain series. A U-shape convolutional neural network model is then used for the 4D data classification problem. We therefore present a novel approach to the classification of 4D data obtained from lung nodule images. With radial scanning, the eigenvalue of nodule images is captured, and a powerful classification is performed. According to our results, an accuracy of 92.84% was obtained and much more efficient classification scores resulted as compared to recent classifiers. © 2023 by the authors.

Why do some smaller states signal to move away from the US-led liberal order? We look at the ruling group survival in smaller allies to answer this pressing puzzle. Despite accepting the merit of systemic explanations, we simply argue that the ruling groups in smaller states engage with revisionist powers in the international system to sustain and enhance their privileged positions in the domestic policy setting. Hungary, a NATO member, and Pakistan, a traditional ally of the United States, have long been showing signs of shifting toward the China/Russia axis. We explain the behavior of Hungary and Pakistan during the 2010s by focusing on the survival strategies of key ruling groups in those countries. We simply argue that relations of competing great powers with the ruling group in smaller states determine the fate of asymmetric alliance.

This research study examines the factors that determined economic growth measured by gross domestic product, gross value added, final consumption expenditure of households, and gross fixed capital formation across a sample of 36 European countries during the time frame Q3 2018−Q3 2021. We conducted a panel data analysis with the first-difference generalized method of moments (GMM) approach and cross-section fixed effects. Empirical results estimated with four econometric models indicated a significant robust impact of the independent variables exports, imports, foreign direct investment inflow, foreign direct investment outflow, social contributions, and wages on economic growth proxies. In addition, multiplex network analysis was used to investigate the connection architectures of the 36 countries, and it yielded statistical measurements for all layers in the multi-layered structure. Relevant policy implications of reported empirical results are also addressed.

It is an inevitable fact that the applications of machine learning and artificial intelligence technologies in agricultural biotechnology approaches, whose applications are increasing rapidly in almost every field, will have an important place in determining the future fate of agriculture. Applications in which artificial intelligence is adapted to biotechnological processes such as breeding, in vitro culture studies, germplasm preservation, disease-free plant production, genetic transformation, and other genetic analyzes are becoming increasingly common. In the current study is highlighted the potential benefits between artificial intelligence and agricultural technologies. As with many plant species, viral diseases have negative effects on fruit yield, life span, and quality of olive varieties, which are important in economy. Elimination of viruses from the plant with traditional methods is quite laborious, takes a lot of time, and often fails to produce successful results. However, new protocols have been developed to eliminate persistent pathogens. These protocols include techniques such as heat application (thermotherapy), chemical therapy (chemotherapy), tissue culture methods (meristem culture). When these techniques are applied alone or together, it may be possible to obtain anti-virus plants. Artificial intelligence technology will make it possible to benefit from the method to be used in the most efficient way by revealing which of these biotechnological methods can be used in the most effective and optimal conditions, and the possible advantages and disadvantages as a result of comparing with others.

Objective: Hepatitis B virus (HBV) and Hepatitis C virus (HCV) infections are a global public health problem. Biological drugs can cause reactivation of HBV infection in patients with chronic HBV carrier or occult HBV infection. Likewise, it is known that it can cause reactivation of HCV infection in patients who are positive for Anti HCV. In our study, we aimed to investigate the prevalence of HBV and HCV infections in rheumatology patients receiving biological therapy. Method: 582 patients who applied to Diyarbakır Health Sciences University Gazi Yaşargil Training and Research Hospital Rheumatology Polyclinic between 01/09/2017 and 01/09/2020 and using biological treatment were included in the study. Demographic and laboratory characteristics were recorded retrospectively. Statistical analysis was performed with SPSS V22.0 (IBM SPSS Statistics, Armonk, NY) software. Results: A total of 582 patients, 342 (58.8%) female and 240 (41.2%) male, were included in our study. Of the patients included in the study, 290 (49.8%) were diagnosed with ankylosing spondylitis (AS), 213 (36.6%) rheumatoid arthritis (RA), and 23 (4%) psoriatic arthritis (PsA). HBsAg 1.4%, Anti-HBs 51.7%, Anti-HBc IgG 31.4% and Anti-HCV 0.3% were positive. Looking according to the diagnosis; In AS; HBsAg 1.7%, Anti-HBs 53.1%, Anti-HBc IgG 38.9%, Anti-HCV 0.0%, in RA; HBsAg was 1.4%, Anti-HBs 51.9%, Anti-HBc IgG 41.7%, Anti-HCV 0.5% were detected. Conclusion: In our country, HBV and HCV infections are still seen at medium endemic levels. Since biological treatments may cause reactivation of HBV and HCV infections, screening, prophylaxis and treatment of these infections before biological treatment still maintains its importance.

In this study, we present a model which represents the interaction of financial companies in their network. Since the long time series have a global memory effect, we present our model in the terms of fractional integro-differential equations. This model characterize the behavior of the complex network where vertices are the financial companies operating in XU100 and edges are formed by distance based on Pearson correlation coefficient. This behavior can be seen as the financial interactions of the agents. Hence, we first cluster the complex network in the terms of high modularity of the edges. Then, we give a system of fractional integro-differential equation model with two parameters. First parameter defines the strength of the connection of agents to their cluster. Hence, to estimate this parameter we use vibrational potential of each agent in their cluster. The second parameter in our model defines how much agents in a cluster affect each other. Therefore, we use the disparity measure of PMFGs of each cluster to estimate second parameter. To solve model numerically we use an efficient algorithmic decomposition method and concluded that those solutions are consistent with real world data. The model and the solutions we present with fractional derivative show that the real data of Borsa Istanbul Stock Exchange Market always seek for an equilibrium state.

In this study, we present a mathematical model of socio-economic process of labor force migration. We present our model in the terms of distributed order fractional partial differential equation. The proposed model involves time varying diffusion coefficient. To solve this model we propose an implicit discrete scheme by using graded meshes, and study numerically labor migration flow data from Turkey along with other countries throughout the period of 1966–2014.

Objectives: This study aims to compare the efficacy of the eccentric exercise (EE) and extracorporeal shock wave therapy (ESWT) on chronic midportion Achilles tendinopathy and evaluate the efficacy of these treatment modalities on tendon thickness, vascularity, and elasticity. Patients and methods: In this randomized controlled trial, a total of 63 patients (40 females, 23 males; mean age: 37.3±12.2; range, 18 to 55 years) with chronic midportion Achilles tendinopathy were enrolled between April 2017 and December 2019. The patients were allocated randomly to two groups: the first group was treated with EE every day for three months with the Alfredson protocol, and the second group received four sessions of ESWT at weekly intervals. The study was terminated at the end of three months. Visual Analog Scales (VAS), Victorian Institute of Sports Assessment-Achilles (VISA-A) questionnaires, and ultrasonography measurements were assessed before and after treatment. Patient pain was evaluated at the two-year follow-up. Results: At the three-month follow up, VAS scores decreased, and VISA-A scores increased in both groups (p Conclusion: Eccentric exercise and ESWT are efficient in the short term, whereas EE is efficient on tendon pain in the long term. While EE increases tendon thickness and stiffness, ESWT has no effect on these measures.

In the structural analysis of discrete geometric data, graph kernels have a great track record of performance. Using graph kernel functions provides two significant advantages. First, a graph kernel is capable of preserving the graph’s topological structures by describing graph properties in a high-dimensional space. Second, graph kernels allow the application of machine learning methods to vector data that are rapidly evolving into graphs. In this paper, the unique kernel function for similarity determination procedures of point cloud data structures, which are crucial for several applications, is formulated. This function is determined by the proximity of the geodesic route distributions in graphs reflecting the discrete geometry underlying the point cloud. This research demonstrates the efficiency of this unique kernel for similarity measures and the categorization of point clouds.

Background Beta thalassemia major (β-TM) is a common cause of skeletal morbidity and is associated with increased bone fracture risk, particularly in inadequately transfused children. The aim of this study was to investigate some potential biochemical markers as possible early predictors of BMD variations in children with β-TM. Methods The study included 38 children with β-TM and 40 sex-age matched controls. All patients were subjected to BMD assessment by dual-energy X-ray absorptiometry (DEXA). Serum beta-crosslaps (beta-CTx), osteoprotegerin (OPG), receptor activator of nuclear factor-kappa B ligand (RANKL), urinary deoxypyridinoline (DPD) and ferritin levels were compared between the groups. Results Serum OPG levels were significantly lower in thalassemic children than in controls. The mean ratio of RANKL/OPG was significantly higher in the thalassemic patients than in the control group. Osteoporosis was detected in 10 (3 female and 7 male) of 38 patients (26.3%) according to the femur Z score and in 6 of them (4 male and 2 female) (15.8%) according to the spine Z score. Conclusions Serum OPG concentrations can be used as a biochemical marker in screening patients with beta-thalassemia major for the development of osteoporosis.

Getting access to sufficient funding is the keystone for the development of any business, but especially for small and medium enterprises (SMEs). These economic entities are crucial players in the global economy since they include almost 90% of companies, provide jobs for nearly 50% of the global workforce, and enhance long-term economic growth. In this context, our study explores important sources concerning the financing of small and medium enterprises and their impact on economic growth during the period 2005–2020 with data from SMEs covering the 28 countries belonging to the European Union. The set of predictors included Strength of legal rights index, Days sales outstanding, Bad debt loss, Interest rate, Bank support, Business angels, Private lenders, and Public support. The set of dependent variables included Cost of loans, Equity fund, GDP growth rate, and Value added growth rate. Our methodological approach was complex, it considered a panel data analysis with a first-difference generalized method of moments estimator and a multiplex time series analysis. The novelty of the study resides in combining the two methods in order to investigate significant drivers of economic growth across the EU. Empirical results showed that economic growth was mainly triggered by predictors such as Interest rate, Business angels, Bank support, and Public support. Moreover, the valuable mathematical insights elicited by the multiplex time series analysis suggested that European economies cooperated intensively through SME activities. Based on our empirical results, national and regional authorities should enact adequate policies to support business endeavors of small and medium enterprises.

To compare the clinical features, laboratory findings, and prognosis of Behçet's disease (BD) patients with and without Budd-Chiari syndrome (BCS).This multicenter retrospective study investigated 61 (M/F: 41/20) patients with BD, having coexistent BCS, and 169 (M/F:100/69) BD patients as the control group without BCS from 22 different centers of Turkey diagnosed between 1990 and 2017.Of the total 61 BD patients with BCS, the onset of the first symptom and the median age of diagnosis were earlier in contrast to BD patients without BCS (p = 0.005 and p = 0.007). Lower extremity deep vein and inferior vena cava (IVC) thrombosis were more common in patients with BCS (all; p 0.01) compared to the control group. Mortality was significantly higher in BD-BCS patients with IVC thrombosis than in the controls (p = 0.004). Since most of the cases in our cohort had chronic and silent form of BCS, mortality rate was 14.8%, which was on the lower range of mortality rate reported in literature (14-47%). While all BD-BCS patients received immunosuppressive (IS) agents, only half of them received additional anticoagulant treatments. Among IS agents, interferon treatment was more frequently used in this cohort (19%), compared to other series reported in literature (2.3%).To our knowledge, this is the largest series of BD patients with BCS. Our patients had earlier disease onset and diagnosis, higher frequency of IVC thrombosis, and higher mortality rate, compared to BD patients without BCS. Mortality was significantly higher in BD-BCS patients with IVC thrombosis compared to controls. Key Points • Mortality rate is higher in BD-associated BCS patients with IVC involvement. • Chronic and silent form of BD-associated BCS has a better prognosis. • The main treatment options are corticosteroids and immunosuppressive agents, whereas anticoagulant treatment remains controversial.

In the network analysis, vulnerability plays key role. Similarly, Laplacian matrices are also effective tools in network analysis. In this study, we examine correlations between those two concepts. We first calculate the well-known vulnerability measures called edge connectivity, vertex connectivity, and solitude number. Then, we find correlation between vulnerability measures and energies of Laplacian matrices. As a result, we find strong correlations between Laplacian energies and vertex connectivity of a network.

Forecasting price changes is very important for the process of estimating and managing market risk in financial markets. Price changes in financial markets may also depend on non-market factors. Considering this situation, the study investigates the effect of the COVID-19 pandemic on Borsa Istanbul. It tackles changes in the fractal dimensions of the time series obtained with the daily closing prices of stocks traded on Borsa Istanbul (BIST). According to the results of the sector-based analysis, we found that fractal dimension changes were quite effective in price estimation.

To retrospectively evaluate the effect of anti-tumor necrosis factor-alpha (TNF-α) drugs on hepatic and renal functions in patients with ankylosing spondylitis (AS).A total of 148 patients (89 male, 59 female) who were followed up for a minimum duration of 1 year on newly started anti TNF-α therapy were included. Patients were divided into 5 groups based on the TNF-α treatment received. Initially, pre-treatment BASDAI (Bath Ankylosing Spondylitis Disease Activity) scores and laboratory results were compared between the groups before the treatment. Also, ESR (erythrocyte sedimentation rate), C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea, and creatinine values were compared before treatment and at 3, 6, and 12 months after treatment. Also presence of hematuria and proteinuria was examined.Of the overall group, 68 (45%), 33 (22%), 23 (15%), 18 (12%), and 6 (4%) received golimumab, certolizumab, etanercept, adalimumab, and infliximab. Baseline demographic characteristics, disease activity scores, and laboratory parameters were comparable between the groups (P.05). There was a significant decline in BASDAI scores from baseline at 12 months (pre-treatment 5.24 ± 0.5, 3.01 ± 0.48 post-treatment at 12 months, P.001). Although there was an increase in AST and ALT from baseline to 3, 6, and 12 months of treatment, the values remained within normal range (P.05). Also, there were no significant changes in mean creatinine levels (P.05). There were no correlations between disease activity parameters (ESR, CRP, and BASDAI) and hepatic and renal functions (P.05).No hepatotoxicity or nephrotoxicity were found in association with the use of anti-TNF-α agents over a 1 year period. However, hepatotoxicity and nephrotoxicity are among known adverse effects of these agents. Based on the existing literature data, routine monitoring of patients in terms of potential hepatic and renal toxicity before and after treatment remains a valid recommendation in clinical practice.

WOS: 000551602200001 Nowadays, increase of analyzing stock markets as complex systems lead graph theory to play a key role. For instance, detecting graph communities is an important task in the analysis of stocks, and as planar maximally filtered graphs let us to get important information for the topology of the market. In this study, we first obtain correlation network representation of UK's leading stock market network by using a novel threshold method. Then, we determine vertex clusters by using modularity and analyze clusters in planar maximally filtered graph substructures. Our analyze include a new measure called weighted Gini index for measuring the sparsity. The main goal of this paper is to study the hierarchical evolution of the market communities throughout the Brexit referendum, which is known as the stress period for the stock market. Hence, the overall sample is divided into two sub-periods of pre-referendum, and post-referendum to obtain communities and hierarchical structures. Our results indicate that financial companies are leading elements of the clusters. Moreover, the significant changes within the network topologies are observed for insurance, consumer goods, consumer services, mining, and technology sectors whereas oil and gas and health care sectors have not been affected by Brexit stress.

We analyze the daily returns of stock market indices of NASDAQ and S&P 500 over the period of 2004–2015. We build network models by using a threshold method that captures strong relations amongst the agents. We also subdivide the whole time scale into 20 parts to capture structural changes in networks throughout the global economic crisis of 2008. To determine the dynamics of the structural changes, we use the mimetic discretization of the curvatures on boundaries of communities. We observe different geometric behavior of the boundary throughout the economic crisis. Our findings suggest that the mimetic discretization of boundary curvature is an efficient tool to capture granular structural changes in financial networks.

Tezcan, Mehmet Engin/0000-0002-1753-4936; TUFAN, Abdurrahman/0000-0001-6244-9362; SARI, Ismail/0000-0001-7737-4180; YILMAZ, Sedat/0000-0002-4691-3417 WOS: 000436403100017 PubMed: 28992387 ObjectiveApproximately 30-45% of patients with familial Mediterranean fever (FMF) have been reported to have attacks despite colchicine treatment. Currently, data on the treatment of colchicine-unresponsive or colchicine-intolerant FMF patients are limited; the most promising alternatives seem to be anti-interleukin-1 (anti-IL-1) agents. Here we report our experience with the off-label use of anti-IL-1 agents in a large group of FMF patients. MethodsIn all, 21 centers from different geographical regions of Turkey were included in the current study. The medical records of all FMF patients who had used anti-IL-1 treatment for at least 6 months were reviewed. ResultsIn total, 172 FMF patients (83 [48%] female, mean age 36.2 years [range 18-68]) were included in the analysis; mean age at symptom onset was 12.6 years (range 1-48), and the mean colchicine dose was 1.7 mg/day (range 0.5-4.0). Of these patients, 151 were treated with anakinra and 21 with canakinumab. Anti-IL-1 treatment was used because of colchicine-resistant disease in 84% and amyloidosis in 12% of subjects. During the mean 19.6 months of treatment (range 6-98), the yearly attack frequency was significantly reduced (from 16.8 to 2.4; P < 0.001), and 42.1% of colchicine-resistant FMF patients were attack free. Serum levels of C-reactive protein, erythrocyte sedimentation rate, and 24-hour urinary protein excretion (5,458.7 mg/24 hours before and 3,557.3 mg/24 hours after) were significantly reduced. ConclusionAnti-IL-1 treatment is an effective alternative for controlling attacks and decreasing proteinuria in colchicine-resistant FMF patients.

Gida aglari, bilimsel alintilar, sosyal aglar, haberlesme aglari, Internet ve borsa aglari gibi interaktif aktorleri iceren sistemler, karmasik sistemlerin icerigi kapsami altinda pek cok arastirmaci tarafindan incelenmistir. Bu tur sistemler agirlikli aglar tarafindan temsil edilir. Aktorler arasindaki yogun baglantilar ve iliskiler, tahmin veya risk analizinde onemli bir rol oynamaktadir. Bu calismada, aktif kuresel borsa aginin hiyerarsik yapisini olcmek icin yeni bir yaklasim onerilmistir. Onerdigimiz bu yaklasimda, 21 farkli dunya borsa piyasalarinin birbirleriyle iliskisi Pearson iliskileri tarafindan belirlenmektedir. Ilgili hisse senedi agi belli bir esik degerine dayanmaktadir. Ayni zamanda, borsa graf topluluklarinin tepelerinin etkilesimini karakterize etmek icin yeni bir topolojik olcum kullanilmaktadir ve bu olcu 2008 yili kuresel ekonomik krizin zaman dilimleri icin incelenmektedir.

In this study we present a new model of neoclassical economic growth by considering that workers move from regions with lower density of capital to regions with higher density of capital. Since the labor migration and capital flow involves self-similarities in long range time, we use the fractional order derivatives for the time variable. To solve this model we proposed Variational Iteration Method, and studied numerically labor migration flow data from Turkey along with other countries throughout the period of 1966–2014.

Dermatomyositis (DM) is a rare disease that may affect the skeletal muscles and the skin. Literature data on its incidence and prevalence are limited. There are no data on its incidence or prevalence in Turkey. Patients diagnosed with DM at the Trakya University Medical Faculty, Department of Rheumatology from November 2004 to November 2014 were reviewed retrospectively. Patients' clinical and demographic features, laboratory data, treatment modalities, follow-up durations, disease courses, outcomes, and complications were evaluated. Our study included 23 patients with DM; 14 were females and 9 were males (female/male: 1.55). Over the course of the study, the annual incidence of DM was 3.7 per million (95% CI 0-18.8) person years, and the overall prevalence was 32.2 per million (95% CI 18.1-46.3). Incidence in women was higher (4.6/1,000,000 person years) compared to men (2.9/1,000,000 person years). The frequencies of most common findings were as follows: heliotrope rash (82.6%), Gottron papules (87%), proximal myopathy (78.3%), and facial erythema (60.9%). In our hospital-based study, the frequency of DM was lower than those reported in North America; however, they were similar to European countries.

In the simple task assignment problem, at most one task should be assigned to each agent; this constraint is relaxed in the multiple task assignment problems. The goal of the well-known Generalized Assignment Problem is to assign tasks to agents such that the capacity of the agent does not exceed its limits as it minimizes the total cost. In this study we present a novel approach to solve the general assignment problem by using the hypergraphs. Hypergraphs can be considered as the generalization of the graphs in such way that an edge can connect any number of vertices, and can be seen as the set systems. In the hypergraph multi-assignment problem, we are looking for a cost minimizing solution to tasks assignment to the agents which are the individual hyperedges. For this purpose, we first determine the tasks as hyperedges and then obtain the vertex cover of the simple graph representation of a hypergraph. Amongst the all possible covers, we choose the cost minimizing one as the solution.

The main purpose of this paper is to present a numerical method to solve the linear Fredholm integro-differential difference equations with constant argument under initial-boundary conditions. The proposed method is based on the Euler polynomials and collocation points and reduces the integro-differential difference equation to a system of algebraic equations. For the given method, we develop the error analysis related with residual function. Also, we present illustrative examples to demonstrate the validity and applicability of the technique.