Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Gul, Muhammet(5)
- Yucesan, Melih(3)
- Kaya, Sema Kayapinar(2)
- Ortiz-Barrios, Miguel(2)
- Utku, Anil(2)
- Utku, Anıl(2)
- Akcayol, M. Ali(1)
- Akkuş, Yeliz(1)
- Alfaro-Sarmiento, Isaac(1)
- Arslan Şeker, Sevda(1)
- Aycin, Ejder(1)
- Açar, Mikail(1)
- BARUT, Abdulkadir(1)
- Baltaci, Nazli(1)
- Baskak, Dilber(1)
- Başol Göksülük, Merve(1)
- Beydağ, Kerime Derya(1)
- Borrego-Areyanes, Arlen Alaine(1)
- Caglar, Durdane Kocacoban(1)
- Can, Ümit(1)
- Celik, Erkan(1)
- Ceviz, Ebru(1)
- Dal, Nursel Alp(1)
- De Felice, Fabio(1)
- Dinçer, Kadriye(1)
- Erkuş Küçükkelepçe, Gülhan(1)
- Genç, Harun(1)
- Gómez-Villar, Iván Darío(1)
- Göçmen, Elifcan(1)
- Handhayani, Teny(1)
- Ishizaka, Alessio(1)
- Jiménez-Delgado, Genett(1)
- Karaca, Türkan(1)
- Karacan, Yasemin(1)
- Karasakal, Ömer Faruk(1)
- Karatay, Gülnaz(1)
- Kizar, Oktay(1)
- Koçak, Berna(1)
- Kılınç, Namık(1)
- López, Cristina(1)
Yayınlar
- MakaleYalnız künye2026Scopus SJRQ1Analysis of the impact of the social justice index on the COVID-19 pandemic using artificial intelligence in EU and OECD countries
Utku, Anıl; Can, Ümit; BARUT, Abdulkadir; Pilatin, Abdulmuttalip; Sharabati, Abdel-Aziz Ahmad
The COVID-19 disease has evolved into a pandemic that affects the world in multiple dimensions. While COVID-19 spread rapidly to several nations, distinct patterns of spread emerged in each nation. This spreading behavior is related to various characteristics of countries, including social, political, and economic factors. In this study, an analysis of the COVID-19 pandemic in EU and OECD countries was conducted, taking into account the social justice index for the first time. The impact and significance of the social justice index on the COVID-19 analysis results were attempted to be revealed. As a result, it is understood that the countries with high Social Justice Index scores and sub-dimension scores are more successful in combating the COVID-19 pandemic. In addition, a new LSTM-based model was developed to predict the number of cases and deaths in these countries. The designed LSTM approach was compared with some well-known deep learning and machine learning algorithms. As a result of the experimental studies, the LSTM model obtained the best prediction results. Finally, the similarities between these countries in terms of the characteristics of the COVID-19 pandemic, including death and case spread, were analyzed, and exciting results were disclosed.
- MakaleYalnız künye2025Scopus SJRQ3Infection transmission risk analysis in Covid-19 intensive care nurses
Şahin, Melek; Şenel, Bilgin; Şenel, Mine; Ünlükal, Ceren
The coronavirus pandemic, which affects the whole world, continues to spread rapidly. The pandemic has reached a serious extent and the risks are getting deeper. The sector most affected by the pandemic has been the health sector and health personnel have become an indispensable element of this process. The people at highest risk of being infected are nurses who are in contact with or are responsible for the care of COVID-19 patients. This study was conducted to investigate the transmission risk of COVID-19 infection among nurses who care for patients in intensive care units.In this study, to prevent the transmission of infection from patient to nurse, possible failures in the process were determined by experts selected from nurses, and risk analyses were performed using Failure Mode and Effects Analysis and Entrophy-based Grey Relational Analysis methods. In this study, the three most probable and high-priority failure types were determined in the failures made in the procedures for the transmission of the infection from the patient to the nurse.There is a need to increase the quantity and quality of intensive care unit care personnel to manage new waves of COVID-19 or possible pandemic situations effectively.
- MakaleAçık Erişim2024Scopus SJRQ1Spread patterns of COVID-19 in European countries: hybrid deep learning model for prediction and transmission analysis
Utku, Anıl; Akcayol, M. Ali
The COVID-19 pandemic has profoundly impacted healthcare systems and economies worldwide, leading to the implementation of travel restrictions and social measures. Efforts such as vaccination campaigns, testing, and surveillance have played a crucial role in containing the spread of the virus and safeguarding public health. There needs to be more research exploring the transmission dynamics of COVID-19, particularly within European nations. Therefore, the primary objective of this research was to examine the spread patterns of COVID-19 across various European countries. Doing so makes it possible to implement preventive measures, allocate resources, and optimize treatment strategies based on projected case and mortality rates. For this purpose, a hybrid prediction model combining CNN and LSTM models was developed. The performance of this hybrid model was compared against several other models, including CNN, k-NN, LR, LSTM, MLP, RF, SVM, and XGBoost. The empirical findings revealed that the CNN-LSTM hybrid model exhibited superior performance compared to alternative models in effectively predicting the transmission of COVID-19 within European nations. Furthermore, examining the peak of case and death dates provided insights into the dynamics of COVID-19 transmission among European countries. Chord diagrams were drawn to analyze the inter-country transmission patterns of COVID-19 over 5-day and 14-day intervals.
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İncele - BildiriYalnız künye2024An Analysis of Dependence Relationships Between Covid-19 Pandemic and Environmental Conditions Cases Study in Indonesia, England, and Türkiye
Handhayani, Teny; Caglar, Durdane Kocacoban; Wibowo, F.W.
This study explores the causal relationships between environmental conditions and Coronavirus disease 2019 (COVID-19) using Bayesian networks in three geographically distinct cities: Jakarta (Indonesia), London (England), and Istanbul (Türkiye). The datasets collected from these cities span from January 2020 to October 2022, encompassing a variety of environmental variables (Particulate Matter (PM2.5), temperature, humidity, precipitation, wind speed, and Ultraviolet (UV) index) and COVID-19 variables (new cases, deaths, and population density). By using the PC algorithm and Greedy Equivalence Search (GES) to generate causal graphs, and applying the Intersection-Validation (InterVal) method to derive agreement graphs, this research reveals significant geographical differences in how environmental factors influence COVID-19 dynamics. The Peter-Clark (PC) algorithm identifies conditional dependencies, while GES optimizes the structure of the causal graph. The study highlights the significant impact of temperature, UV index, and humidity on COVID-19 dynamics, emphasizing the importance of localized public health strategies tailored to specific environmental conditions. These findings contribute to the understanding of environmental influences on COVID-19 and offer valuable insights for future research and policy-making aimed at mitigating the impact of pandemics in diverse geographical settings.
AtıfScopus3OpenAlex2İncele - MakaleAçık Erişim2023DETERMINATION OF ANXIETY LEVELS AND KNOWLEDGE LEVELS ABOUT COVID-19 DURING THE COVID-19 OUTBREAK OF PREGNANTS: SURVEY IN TURKEY
Yüksekol, Ozlem Dogan; Nazik, Feyza; Baltaci, Nazli; Ulucan, Mihriban
Purpose: To determine the anxiety and knowledge levels of pregnant women about outbreaks during the COVID-19 pandemic. Material and Methods: This study was conducted as a descriptive study with 276 pregnant women presenting to obstetrics and gynecology outpatient clinics of hospitals in eastern Turkey. The data were collected using a personal information form, a form for assessing the knowledge levels of the participants about COVID-19, and the COVID-19 Anxiety Scale. Results: It was found that 75.4% of the participants were worried that they would catch COVID-19 in the hospital during or after childbirth. The mean COVID-19 Anxiety Scale score of the participants was 6.7 & PLUSMN;2.4 (min: 5, max: 14). The anxiety levels of the participants who were in the last trimester of pregnancy, those who did not receive information about COVID-19, those who changed their mode of delivery due to the pandemic, those who received inadequate prenatal care, those whose social support decreased, and those who felt vulnerable were significantly higher. Conclusion: The results of this study revealed that anxiety in pregnant women is affected by various factors. It is important to provide care and support for these groups.
- MakaleAçık Erişim2023Respiratory function rehabilitation in individuals with Covid-19: swimming exercise
Genç, Harun; Ceviz, Ebru; Kizar, Oktay; Dinçer, Kadriye
Background and Study Aim Material and Methods Understanding the impact of swimming exercises on post-Covid-19 respiratory function is crucial for optimizing recovery strategies in affected individuals. The aim of this study is to determine the effect of 8-week swimming exercises on respiratory function parameters in adult individuals who have had Covid-19 disease. Sixteen male students, aged 19-21, voluntarily participated in the study. Eight participants were assigned to the experimental group (EG), and eight to the control group (CG), all of whom were students of the Faculty of Sports Sciences at the University. The EG underwent an 8-week swimming exercise program conducted at the Bingol Youth and Sports swimming pool (25 meters), three days a week. The CG was not involved in any exercise regimen. Pulmonary function tests (FVC, FEV1, FEV1/FVC, PEF) were conducted using a Cosmed Pony FXdel 2016 spirometer, one day before and one day after the exercise program for both EG and CG. Data analysis was performed using Independent Samples and Paired Samples T tests in SPSS 25, with a significance level set at p < 0.05. Results A statistically significant increase in respiratory FVC, FEV1, FEV1/FVC, and PEF function values was observed in the experimental group (EG) after the exercise intervention (p < 0.05). This increase was observed when comparing these values to their pre-exercise measurements. In contrast, there was no statistically significant difference in respiratory function results before and after exercise in the control group (CG) (p > 0.05). Conclusions The findings of this study highlight the therapeutic potential of an 8-week swimming exercise program in significantly enhancing respiratory function among adult individuals who have previously contracted Covid-19. These results offer valuable insights for the development of post-Covid-19 rehabilitation protocols, emphasizing the importance of regular exercise, such as swimming, in the recovery process.
AtıfWoS1OpenAlex2İncele - MakaleAçık Erişim2023Scopus SJRQ1COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model
Baskak, Dilber; Ozbey, Sumeyye; Yucesan, Melih; Gul, Muhammet
The fight against the COVID-19 pandemic, which has affected the whole world in recent years and has had devastating effects on all segments of society, has been one of the most important priorities. The Turkish Standards Institution has determined a checklist to contribute to developing safe and clean environments in higher education institutions in Turkey and to follow-up on infection control measures. However, this study is only a checklist that makes it necessary for decision-makers to make a subjective evaluation during the evaluation process, while the need to develop a more effective, systematic framework that takes into account the importance levels of multiple criteria has emerged. Therefore, this study applies the best-worst method under interval type-2 fuzzy set concept (IT2F-BWM) to determine the importance levels of criteria affecting the “COVID-19 safe campus” evaluation of universities in the context of global pandemic. A three-level hierarchy consisting of three main criteria, 11 sub-criteria, and 58 sub-criteria has been created for this aim. Considering the hierarchy, the most important sub-criterion was determined as periodic disinfection. The high contribution of the interval-valued type-2 fuzzy sets in expressing the uncertainty in the decision-makers’ evaluations and the fact that BWM provides criterion weights with a mathematical optimization model that produces less pairwise comparisons and higher consistency are the main factors in choosing this approach. Simple additive weighting (SAW) has also been injected into the IT2F-BWM to determine the safety level of any university campus regarding COVID-19. Thus, decision-makers will be better prepared for the devastating effects of the pandemic by first improving the factors that are relatively important in the fight against the pandemic. In addition, a threshold value will be determined by considering all criteria, and it will prepare the ground for a road map for campuses. A case study is employed to apply the proposed model, and a comparison study is also presented with the Bayesian BWM to validate the results of the criteria weights.
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İncele - MakaleAçık Erişim2023Scopus SJRQ1Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries
Utku, Anil
COVID-19 has a disease and health phenomenon and has sociological and economic adverse effects. Accurate prediction of the spread of the epidemic will help in the planning of health management and the development of economic and sociological action plans. In the literature, there are many studies to analyse and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyse the cross-country spread in the world's most populous countries. In this study, it was aimed to predict the spread of the COVID-19 epidemic. The motivation of this study is to reduce the workload of health workers, take preventive measures and optimize health processes by predicting the spread of the COVID-19 epidemic. A hybrid deep learning model was developed to predict and analyse COVID-19 cross-country spread and a case study was carried out for the world's most populous countries. The developed model was tested extensively using RMSE, MAE and R2. The experimental results showed that the developed model was more successful in predicting and analysis of COVID-19 cross-country spread in the world's most populous countries than LR, RF, SVM, MLP, CNN, GRU, LSTM and base CNN-GRU. In the developed model, CNN performs convolution and pooling operations to extract spatial features from the input data. GRU provides learning of long-term and non-linear relationships inferred by CNN. The developed hybrid model was more successful than the other models compared, as it enabled the effective features of the CNN and GRU models to be used together. The prediction and analysis of the cross-country spread of COVID-19 in the world's most populated countries can be presented as a novelty of this study.
AtıfScopus23WoS20OpenAlex31İncele - MakaleAçık Erişim2023Scopus SJRQ1DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES
Utku, Anil
The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries.
AtıfScopus9OpenAlex11İncele - MakaleYayıncıda açık erişim2023Scopus SJRQ2The Symptom Experience and Functioning of Non-Hospitalized COVID-19 Patients Within the First 20 days
Tütün Yümin, Eylem; Sürmeli, Mahmut; Topcuoğlu, Ceyhun; Başol Göksülük, Merve; Yümin, Murat
The adverse physical, psychological, and mental health consequences associated with COVID-19 illness are well-documented. However, how specific symptoms change over time and how COVID-19 affects one’s day-to-day activities of daily living (ADL), Quality of Life (QoL), sleep quality, and fatigue severity are not well described. This longitudinal and descriptive study examined the changes in COVID-19 symptoms, ADL, QoL, sleep quality, and fatigue severity within the first 20 days. A convenience sample (n = 41) of non-hospitalized SARS-CoV-2 positive patients were recruited and followed for 20 days. Participants completed self-report measures: COVID-19 symptoms, ADL, QoL, sleep quality, and fatigue severity at days: 1, 10, and 20 following a diagnosis. Findings revealed that symptoms decreased over 20 days (p <.001). In parallel with the decrease in symptoms, QoL and ADL improved over 20 days (p <.05). However, sleep quality and fatigue severity did not improve within 20 days (p >.05). Our findings contribute to the growing evidence that COVID-19 symptoms can linger, especially fatigue and sleep quality, that affect overall day-to-day functioning for at least 20 days after diagnosis. To mitigate the effect of COVID-19 on QOL and ADL, findings underscore the need for clinicians to work collaboratively with patients to develop a symptom management plan for a variety of symptoms including fatigue and sleep quality. Beginning to repurpose existing self-management strategies for the longer term COVID-19 symptoms could be beneficial and help to optimize patient outcomes. Future work should examine these variables over a longer timeframe and among different samples of non-hospitalized patients.
AtıfScopus1WoS1İncele - MakaleAçık Erişim2022Scopus SJRQ2A New Hybrid Fuzzy Multi-Criteria Decision Methodology for Prioritizing the Antivirus Mask over COVID-19 Pandemic
Kaya, Sema Kayapinar; Pamucar, Dragan; Aycin, Ejder
During the COVID-19 pandemic, masks have become essential items for all people to protect themselves from the virus. Because of considering multiple factors when selecting an antivirus mask, the decision-making process has become more complicated. This paper proposes an integrated approach that uses F-BWM-RAFSI methods for antivirus mask selection process with respect to the COVID-19 pandemic. Finally, sensitivity analysis was demonstrated by evaluating the effects of changing the weight coefficients of the criterion on the ranking results, simulating changes in Heronian operator parameters, and comparing the obtained solution to other MCDM approaches to ensure its robustness.
AtıfScopus21WoS15OpenAlex23İncele - MakaleAçık Erişim2022Job Satisfaction, Intent to Stay at Work, and Burnout Levels of Nurses Working in Coronavirus Disease 2019 Clinics: A Cross-Sectional Study
Erkuş Küçükkelepçe, Gülhan; Arslan Şeker, Sevda; Karaca, Türkan
Objective: It is thought that the fact that coronavirus disease 2019 pandemic process has been continuing for a long time, and the level of job satisfaction of nurses may have an impact on their intention to experience burnout and stay at work. This study aimed to determine the relationship between job satisfaction, intention to stay, and burnout levels of nurses working in coronavirus disease 2019 clinics. Methods: This descriptive and correlational study was conducted with 325 nurses who were working in coronavirus disease 2019 clinics. Data were collected using the sociodemographic characteristics form, the Minnesota Satisfaction Questionnaire, the Maslach Burnout Inventory, and the Intent to Leave Job Scale. Descriptive statistics, correlation analysis, and regression analysis were used in the analysis of the research data. Results: Emotional exhaustion, depersonalization, personal achievement, and intent to leave explain 45% of job satisfaction (F = 67.911, p =.000); job satisfaction and intent to leave explain 40% of emotional exhaustion (F = 111.470, p =.000), 16% of depersonalization (F = 33.785, p =.000), and 20% of personal achievement (F = 43.102, p =.000); job satisfaction, emotional exhaustion, depersonalization, and personal achievement explain 24% of intent to leave (F = 27.190, p =.000). Conclusion: The prolongation of the coronavirus disease 2019 pandemic, the uncertainty of the process, and the conditions related to the working environment of the nurses have an effect on job satisfaction, burnout, and intent to leave. The continuity of the well-being of employees is an important issue that needs to be emphasized and evaluated, as it will have a multi-faceted effect to be ready for different pandemics in the near/far future.
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İncele - MakaleAçık Erişim2022Scopus SJRQ3Electrochemical Detection of Ivermectin Used for the Treatment of COVID-19 with Glutardialdehyde-Modified Glassy Carbon Electrode
Koçak, Berna; İpek, Yeliz
In this study, the surface of the glassy carbon electrode was modified with glutardialdehyde. The modified glassy carbon electrode showed electrocatalytic activity against ivermectin. The glassy carbon electrode modified with glutardialdehyde showed high sensitivity, selectivity, and stability in the determination of ivermectin. The peak current of glutardialdehyde oxidation obtained by differential pulse voltammetry decreased inversely with the ivermectin concentration. Ivermectin inhibited the oxidation reaction of glutardialdehyde and caused a decrease in current. This change made the analysis of ivermectin electrochemically possible. In order to demonstrate the applicability of the developed method in real samples, recovery studies were carried out in tap water and urine. The highest sensitivity (0.45 µA/((µmol·L−1)(cm2))) was achieved with urine sample and the lowest detection limit as 2.66 × 10−6 mol·L−1 was obtained with BRT solution sample. Graphical abstract: [Figure not available: see fulltext.]
AtıfScopus14WoS13OpenAlex13İncele - MakaleAçık Erişim2022Scopus SJRQ1A fuzzy hybrid decision-making framework for increasing the hospital disaster preparedness: The colombian case
Ortiz-Barrios, Miguel; Gul, Muhammet; Yucesan, Melih; Alfaro-Sarmiento, Isaac; Navarro-Jiménez, Eduardo; Jiménez-Delgado, Genett
The recent increase in the number of disasters over the world has once again brought to the agenda the question of preparedness of the hospitals, which are the most necessary units of healthcare pillar to resist these disasters. The COVID-19 epidemic disease, which has affected the whole world, has caused a large number of people to die in some countries simply because of the inadequate and incomplete planning and lack of readiness of hospitals. For this reason, determining the disaster preparedness level of hospitals is an important issue that needs to be studied and it is important in terms of disaster damage reduction. In this study, a fuzzy hybrid decision-making framework is proposed to assess hospital disaster preparedness. The framework covers three important decision-making methods. For the first phase, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is used to assign relative weights for several disaster preparedness criteria considering uncertainty. Secondly, Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) is applied to identify interrelations among these criteria and feedback. Finally, via the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, priorities of hospitals regarding disaster readiness are obtained. A case study involving the participation of 10 Colombian tertiary hospitals is carried out to show the applicability of this fuzzy hybrid approach.
AtıfScopus27WoS24OpenAlex34İncele - MakaleAçık Erişim2022Scopus SJRQ1How Covid-19 pandemic and partial lockdown decisions affect air quality of a city? The case of Istanbul, Turkey
Celik, Erkan; Gul, Muhammet
The world is currently struggling with a new type of coronavirus (2019-nCoV) pandemic that first appeared in Wuhan, China, and then spread to almost all countries. As in other countries of the world, public authorities in Turkey are implementing many preventive and mitigating partial lockdown (PL) actions against the virus's effects. Some decisions and policies implemented before and after March 11, 2020, when the first virus case has been identified, have reduced people and traffic circulation, which has also turned into some improvements in air quality. At this point, this study aims to investigate how this pandemic affects the air quality of a metropolis. A case study of the city of Istanbul, the most affected city with more than half of Turkey's cases, is performed. In our analysis, we observe, compare, and discuss the impact of the COVID-19 pandemic and PL decisions on Istanbul city's air quality. We consider the particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NO), nitrogen oxides (NOx), and ozone (O3) concentrations. We used data from 19 air monitoring stations (AMSs) and obtained improvements in the air quality for the pandemic period. In summary, the concentration levels in PM10, NO2, NO, and NOx result in a clear decline in pandemic times compared to the normal times in Istanbul. On the other hand, a non-homogenous trend for SO2 and CO concentrations is observed for different AMSs. A partial increase in O2 concentration is obtained in the comparison of before and during the PL period.
AtıfScopus15WoS13OpenAlex22İncele - MakaleYalnız künye2022Scopus SJRQ2Linear programming with fuzzy parameters for inventory routing problem in effective management of personal protective equipment: a case study of corona virus disease 2019
Göçmen, Elifcan
Coronavirus disease 2019 (COVID-19) brings about severe disruptions for human living, economics, and environmental balance. This pandemic has boosted the demand for personal protective equipment (PPE) and thus, medical wastes. Thus, a research hypothesis is stated whether it would be possible to provide more efficient distribution and inventory planning to optimize the PPE availability in a fuzzy environment. To confirm the hypothesis, in addition to addressing a scientific novelty considering integration of linear programming with fuzzy parameters, named as the Jimenez’s method and clustering heuristic methods, the practicality of these approaches is provided on a real case study and generated instances from Turkey. The computational results reveal that clustering the 32% of the hospitals ensures optimum total cost and solution time while feasibility degree changes between 0, 4, and 1 values of the Jimenez are not more effective on the results. Improved cost results reveal the economic importance of the healthcare supply chain management and optimal PPE distribution and inventory planning can break down the disease propagation and reduce the detrimental impacts on human life. The proposed approach, for the first time, ensures practical and theoretical insights of distribution and storage decisions across disciplines, namely, engineering and health-care organization authorities.
AtıfScopus19WoS12OpenAlex19İncele - MakaleYalnız künye2022Scopus SJRQ1A calibrated Fuzzy Best-Worst-method to reinforce supply chain resilience during the COVID 19 pandemic
López, Cristina; Ishizaka, Alessio; Gul, Muhammet; Yücesan, Melih; Valencia, Daniela
Making decisions require increasingly more sophisticated methods. The Best-Worst method was developed recently to find a good balance between the amount of information required and its consistency. The Fuzzy Best-Worst method was then proposed to integrate the uncertainty and imprecision of evaluations. In this paper, we propose the Calibrated Fuzzy Best-Worst Method, which enables us to define personalized fuzzy numbers. This new method was applied to the real case study of a global fashion supply chain suffering a dramatic drop of demand due to the COVID-19 shocks. Since its upstream was leading to a ripple effect on the rest of the supply chain stages, our study focused on evaluating the viability of its distribution channels based on weighted resilience capabilities and performance indicators. The proposed method allowed being more precise than the traditional Best-Worst method because it personalizes the uncertainty and subjectivity. The results revealed that cash flow, revenues, and inventory turnover were the most important performance indicators in the supply chain resilience. Financial strength, adaptability, and market position were shown to be the most critical resilience capabilities in preserving supply chain survivability with low-demand items and longer disruptions. Their effects explain why e-commerce was the most viable distribution channel.
AtıfScopus26WoS24OpenAlex29İncele - MakaleYalnız künye2022Scopus SJRQ3Identification of Potential Inhibitors for Severe Acute Respiratory Syndrome-related Coronavirus 2 (SARS-CoV-2) Angiotensin-converting Enzyme 2 and the Main Protease from Anatolian Traditional Plants
Kılınç, Namık; Açar, Mikail; Tuncay, Salih; Karasakal, Ömer Faruk
Background: The 2019 novel coronavirus disease (COVID-19) has caused a global health catastrophe by affecting the human population around the globe. Unfortunately, there is no specific medi-cation or treatment currently available for COVID-19. Objective: It is extremely important to find effective drug treatment in order to put an end to this pandemic period and return to normal daily life. In this context and considering the urgency, rather than focusing on the discovery of novel compounds, it is critical to explore the effects of existing herbal agents with proven antiviral properties on the virus. Methods: Molecular docking studies were carried out employing three different methods, Glide extra precision (XP) docking, induced fit docking (IFD), and molecular mechanics/generalized born surface area (MM/GBSA), to determine the potential antiviral and antibacterial effects of 58 phytochemicals present in Rosmarinus officinalis, Thymbra spicata, Satureja thymbra, and Stachys lavandulifolia plants against the main protease (Mpro) and angiotensin-converting enzyme 2 (ACE2) enzymes. Results: 7 compounds stood out among all the molecules, showing very high binding affinities. Accord-ing to our findings, the substances chlorogenic acid, rosmarinic acid, and rosmanol exhibited extremely significant binding affinities for both Mpro and ACE2 enzymes. Furthermore, carnosic acid and alpha-cadinol showed potent anti-Mpro activity, whereas caffeic acid and carvacrol exhibited promising anti-ACE2 activity. Conclusion: Chlorogenic acid, rosmarinic acid, rosmanol, carnosic acid, alpha-cadinol, caffeic acid, and carvacrol compounds have been shown to be powerful anti-SARS-CoV-2 agents in docking simulations against Mpro and ACE2 enzymes, as well as ADME investigations.
AtıfScopus32WoS30OpenAlex31İncele - MakaleAçık Erişim2021Scopus SJRQ1Attitudes of Married Muslim Women Regarding Family Planning Methods During the COVID-19 Pandemic in Western Turkey
Dal, Nursel Alp; Beydağ, Kerime Derya
COVID-19 had negative effects upon family planning. Women cannot visit healthcare facilities because of a fear of curfew-related sanctions or exposure to COVID-19. It is seen that religious beliefs are influential especially in terms of family planning method preference and having abortions. This study was conducted to determine the attitudes of married Muslim women regarding family planning methods during the COVID-19 pandemic period. The descriptive and cross-sectional study was conducted with 611 married Muslim women who presented to the family planning outpatient clinic of a public hospital in Western Turkey between 1 September 2020 and 1 March 2021. The mean Family Planning Attitude Scale score of the women was found to be 137.53 ± 27.11. It was observed that, as the mean age of the women increased, their family planning attitudes were more positive, and as their number of pregnancies, abortions and living children increased, their attitudes were more negative (p < 0.05). Women do not want to get pregnant during the pandemic process. This is because the effects of COVID-19 on both themselves and the baby to be born are not known, which worries women. Therefore, in order to transform positive attitudes regarding family planning into behavior, it is recommended to provide effective training and consultancy services during the pandemic period.
AtıfScopus5WoS4OpenAlex11İncele - MakaleAçık Erişim2021Scopus SJRQ1A multiple criteria decision-making approach for increasing the preparedness level of sales departments against COVID-19 and future pandemics: A real-world case
Ortiz-Barrios, Miguel; Borrego-Areyanes, Arlen Alaine; Gómez-Villar, Iván Darío; De Felice, Fabio; Petrillo, Antonella; Gul, Muhammet; Yucesan, Melih
The impact of the pandemic and the lockdown has been more devastating than expected on the world economy. It is essential to formulate strategies in real-time. In this research, a multicriteria decision-making model for increasing the preparedness level of sales departments when facing COVID-19 waves and future pandemics is proposed. The model is comprised of 8 criteria, 29 sub-criteria, and 7 alternatives. The study is based on the integration of the AHP and TOPSIS techniques. AHP is used for calculating the criteria and sub-criteria weights. While, TOPSIS is used for calculating the preparedness level, ranking the companies, and identifying the weaknesses that should be addressed for increasing their effectiveness in the current market scenario. The model is developed with the aid of an experts’ group from the electrical appliance sector and studies from the reported literature. This application is completely novel in the literature and has been applied in the wild with remarkable companies in Colombia. A case study in the electrical appliance sector is presented as a pilot study but it should be noted that the methodology is flexible and scalable in any scenario.
AtıfScopus18OpenAlex21İncele