Pub Date : 2023-06-30DOI: 10.21791/ijems.2023.2.5.
Ákos Pethő, C. Pfau
Magyarországon kiemelt figyelmet és támogatottságot élvez a kézilabda, ez nem csupán hazánkra, hanem Európára is igaz, valamint egyre nagyobb teret hódít magának világszerte is a sportág. Az elmúlt két évtized egyik legnagyobb újdonsága minden a közösségi média, ami megreformálta az információ átadással és a hagyományos marketing tevékenységgel kapcsolatos alap stratégiákat. Ezeken a platformokon gyökeresen változtak meg a tartalomgyártás folyamatai, amire minden sportklubnak érdemes figyelmet fordítania, hiszen jelentős előnyökhöz juthatnak mind gazdasági-, marketing-, közösségépítés terén egyaránt, ha képesek jól kommunikálni. Három egymástól eltérő szinten versenyző sportklub került összehasonlításra kvalitatív vizsgálat során a közösségi média marketing stratégia tevékenységüket tekintve, amelyek név szerint a PICK Szeged, a Balatonfüredi KSE – BFKA Balatonfüred és a DEAC kézilabda csapata. Ezen vizsgálati modellnek az alapkoncepciója arra épült fel, hogy a különböző szinteken, különböző sportszakmai célokért küzdő klubcsapatok online marketingkommunikációs tevékenysége miben tér el egymástól, valamint mik azok a fejlődési lehetőségek, szempontok, amivel lehető leghatékonyabban lehet érvényesülni a social média felületeken. A kézilabda klubcsapatok több kutatási kérdésben is egyetértettek, de felfedezhető volt jelentős különbség is egy-egy területen, amiben a közösségi médiáért felelős szakemberek nem értettek egyet, vagy más nézőpontot képviseltek. Eredményeimet tekintve megállapítható, hogy egy sikeres online marketing stratégia mögött mindig komoly előkészületi és tervező munka áll, valamint a tartalomgyártás diverzifikálása kulcsfontosságú a különböző felületeken, mert a platformok fogyasztói közössége is eltérő egymástól.
{"title":"Közösségi média marketing stratégia elemzése – különböző szinten versenyző kézilabda klubok vizsgálatán keresztül","authors":"Ákos Pethő, C. Pfau","doi":"10.21791/ijems.2023.2.5.","DOIUrl":"https://doi.org/10.21791/ijems.2023.2.5.","url":null,"abstract":"Magyarországon kiemelt figyelmet és támogatottságot élvez a kézilabda, ez nem csupán hazánkra, hanem Európára is igaz, valamint egyre nagyobb teret hódít magának világszerte is a sportág. Az elmúlt két évtized egyik legnagyobb újdonsága minden a közösségi média, ami megreformálta az információ átadással és a hagyományos marketing tevékenységgel kapcsolatos alap stratégiákat. Ezeken a platformokon gyökeresen változtak meg a tartalomgyártás folyamatai, amire minden sportklubnak érdemes figyelmet fordítania, hiszen jelentős előnyökhöz juthatnak mind gazdasági-, marketing-, közösségépítés terén egyaránt, ha képesek jól kommunikálni. Három egymástól eltérő szinten versenyző sportklub került összehasonlításra kvalitatív vizsgálat során a közösségi média marketing stratégia tevékenységüket tekintve, amelyek név szerint a PICK Szeged, a Balatonfüredi KSE – BFKA Balatonfüred és a DEAC kézilabda csapata. Ezen vizsgálati modellnek az alapkoncepciója arra épült fel, hogy a különböző szinteken, különböző sportszakmai célokért küzdő klubcsapatok online marketingkommunikációs tevékenysége miben tér el egymástól, valamint mik azok a fejlődési lehetőségek, szempontok, amivel lehető leghatékonyabban lehet érvényesülni a social média felületeken. A kézilabda klubcsapatok több kutatási kérdésben is egyetértettek, de felfedezhető volt jelentős különbség is egy-egy területen, amiben a közösségi médiáért felelős szakemberek nem értettek egyet, vagy más nézőpontot képviseltek. Eredményeimet tekintve megállapítható, hogy egy sikeres online marketing stratégia mögött mindig komoly előkészületi és tervező munka áll, valamint a tartalomgyártás diverzifikálása kulcsfontosságú a különböző felületeken, mert a platformok fogyasztói közössége is eltérő egymástól.","PeriodicalId":44185,"journal":{"name":"International Journal of Mathematical Engineering and Management Sciences","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85466977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.33889/ijmems.2023.8.3.027
P. Dhiman, Amit Kumar
Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines for the prevention of coronavirus disease 2019 (COVID-19) is a worldwide requirement. Now the next challenge is to store the vaccine in a ULT freezer. It’s become really a big problem to store the vaccine which creates the demand of ULT freezer. The present paper investigates a situational based performance of the ULT freezer with the aim to predict the impact of different component failures as well as human errors on the final performance of the same. For the study, it is not possible to extract the parameters (failure rate and repair time) of the components that never failed before. Thus, to overcome this difficulty, here authors use the possibility theory. Authors present the available data in Right triangular fuzzy number with some tolerance as suggested by system analyst. The lambda-tau methodology and arithmetic operations on right triangular generalized fuzzy numbers (RTrFN) are used to find the various performance parameters namely MTTF, MTTR, MTBF, reliability, availability, maintainability (RAM) and ENOF, under fuzzy environment. The proposed model has been studied using possibility theory under working conditions, preventive maintenance as well as under the rest of conditions. This study reveals the most and least critical component of the ULT freezer which helps maintenance department to plan the maintenance strategy accordingly.
{"title":"A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment","authors":"P. Dhiman, Amit Kumar","doi":"10.33889/ijmems.2023.8.3.027","DOIUrl":"https://doi.org/10.33889/ijmems.2023.8.3.027","url":null,"abstract":"Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines for the prevention of coronavirus disease 2019 (COVID-19) is a worldwide requirement. Now the next challenge is to store the vaccine in a ULT freezer. It’s become really a big problem to store the vaccine which creates the demand of ULT freezer. The present paper investigates a situational based performance of the ULT freezer with the aim to predict the impact of different component failures as well as human errors on the final performance of the same. For the study, it is not possible to extract the parameters (failure rate and repair time) of the components that never failed before. Thus, to overcome this difficulty, here authors use the possibility theory. Authors present the available data in Right triangular fuzzy number with some tolerance as suggested by system analyst. The lambda-tau methodology and arithmetic operations on right triangular generalized fuzzy numbers (RTrFN) are used to find the various performance parameters namely MTTF, MTTR, MTBF, reliability, availability, maintainability (RAM) and ENOF, under fuzzy environment. The proposed model has been studied using possibility theory under working conditions, preventive maintenance as well as under the rest of conditions. This study reveals the most and least critical component of the ULT freezer which helps maintenance department to plan the maintenance strategy accordingly.","PeriodicalId":44185,"journal":{"name":"International Journal of Mathematical Engineering and Management Sciences","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46047026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.33889/ijmems.2023.8.3.026
M. Hasan
Utilizing soft computing, a maximum power point tracking (maximum PPT) control algorithm is developed, and its performance is compared to that of more traditional Lead Acid battery charging methods such as incremental conductance technique-based maximum PPT. Since the power vs voltage graph of a photovoltaic (PV) cell is nonlinear, a suitable control method seeks to obtain the highest power under dynamic conditions. In order to construct a PV cell with the maximum PPT, a fuzzy logic control approach known as soft computing is used. The cell active energy is used to charge the lead acid battery. A fuzzy logic compares its performance with the incremental conductance technique under dynamic conditions. Moreover, dc to dc converter is required to maintain constant output voltage to charge the battery under low level voltage. A zeta converter is taken to maintain output voltage under various insolation. The significance of algorithm is demonstrated by MATLAB Simulation results and hardware results.
{"title":"A Soft Computing Intelligent Control Algorithm to Extract Maximum Energy from Solar Panel","authors":"M. Hasan","doi":"10.33889/ijmems.2023.8.3.026","DOIUrl":"https://doi.org/10.33889/ijmems.2023.8.3.026","url":null,"abstract":"Utilizing soft computing, a maximum power point tracking (maximum PPT) control algorithm is developed, and its performance is compared to that of more traditional Lead Acid battery charging methods such as incremental conductance technique-based maximum PPT. Since the power vs voltage graph of a photovoltaic (PV) cell is nonlinear, a suitable control method seeks to obtain the highest power under dynamic conditions. In order to construct a PV cell with the maximum PPT, a fuzzy logic control approach known as soft computing is used. The cell active energy is used to charge the lead acid battery. A fuzzy logic compares its performance with the incremental conductance technique under dynamic conditions. Moreover, dc to dc converter is required to maintain constant output voltage to charge the battery under low level voltage. A zeta converter is taken to maintain output voltage under various insolation. The significance of algorithm is demonstrated by MATLAB Simulation results and hardware results.","PeriodicalId":44185,"journal":{"name":"International Journal of Mathematical Engineering and Management Sciences","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41530454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.33889/ijmems.2023.8.3.021
The machine learning model has become a critical consideration in the supply chain. Most of the companies have experienced vari-ous supply chain risks over the past three years. Earlier risk prediction has been performed by supply chain risk management. In this study, an integrated supply chain operations reference (ISCOR) model has been used to evaluate the organization's supply chain risk. Machine learning (ML) has become a hot topic in research and industry in the last few years. With this motivation, we have moved in the direction of a machine learning-based pathway to predict the supply chain risk. The great attraction of this research is that suppliers will understand the associated risk in the activity. This research includes data pre-processing, feature extraction, data transformation, and missing value replacement. The proposed integrated model involves the support vector machine (SVM), k near-est neighbor (k-NN), random forest (RF), decision tree (DT), multiple linear regression (MLR) algorithms, measured performance, and prediction of supply chain risk. Also, these algorithms have performed a comparative analysis under different aspects. Among the other algorithms, the random forest algorithm achieves an accuracy of 99% and has accomplished superior results with a maxi-mum precision of 0.99, recall of 0.99, and F-score of 0.99 with 1% error rate. The model’s prediction indicates that it can be used to find the supply chain risk. Finally, the limitation and the challenges discussed also provide an outlook for future research direction to perform effective management to mitigate the risk.
{"title":"Integrated Model for Predicting Supply Chain Risk Through Machine\u0000Learning Algorithms","authors":"","doi":"10.33889/ijmems.2023.8.3.021","DOIUrl":"https://doi.org/10.33889/ijmems.2023.8.3.021","url":null,"abstract":"The machine learning model has become a critical consideration in the supply chain. Most of the companies have experienced vari-ous supply chain risks over the past three years. Earlier risk prediction has been performed by supply chain risk management. In this study, an integrated supply chain operations reference (ISCOR) model has been used to evaluate the organization's supply chain risk. Machine learning (ML) has become a hot topic in research and industry in the last few years. With this motivation, we have moved in the direction of a machine learning-based pathway to predict the supply chain risk. The great attraction of this research is that suppliers will understand the associated risk in the activity. This research includes data pre-processing, feature extraction, data transformation, and missing value replacement. The proposed integrated model involves the support vector machine (SVM), k near-est neighbor (k-NN), random forest (RF), decision tree (DT), multiple linear regression (MLR) algorithms, measured performance, and prediction of supply chain risk. Also, these algorithms have performed a comparative analysis under different aspects. Among the other algorithms, the random forest algorithm achieves an accuracy of 99% and has accomplished superior results with a maxi-mum precision of 0.99, recall of 0.99, and F-score of 0.99 with 1% error rate. The model’s prediction indicates that it can be used to find the supply chain risk. Finally, the limitation and the challenges discussed also provide an outlook for future research direction to perform effective management to mitigate the risk.","PeriodicalId":44185,"journal":{"name":"International Journal of Mathematical Engineering and Management Sciences","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43954982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}