Pub Date : 2021-10-01DOI: 10.1142/9789811238819_0019
{"title":"Trading, Market, and Investment","authors":"","doi":"10.1142/9789811238819_0019","DOIUrl":"https://doi.org/10.1142/9789811238819_0019","url":null,"abstract":"","PeriodicalId":309688,"journal":{"name":"Foundations for Fintech","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126038924","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 : 2021-10-01DOI: 10.1142/9789811238819_0018
{"title":"Cryptocurrencies, Wallet, and Token Economy","authors":"","doi":"10.1142/9789811238819_0018","DOIUrl":"https://doi.org/10.1142/9789811238819_0018","url":null,"abstract":"","PeriodicalId":309688,"journal":{"name":"Foundations for Fintech","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133255331","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 : 2021-10-01DOI: 10.1142/9789811238819_0003
{"title":"Introduction and Probability Distribution","authors":"","doi":"10.1142/9789811238819_0003","DOIUrl":"https://doi.org/10.1142/9789811238819_0003","url":null,"abstract":"","PeriodicalId":309688,"journal":{"name":"Foundations for Fintech","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116809196","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 : 2021-10-01DOI: 10.1142/9789811238819_0007
R. Rodrigo
Gate-level minimization refers to the design task of finding an optimal gate-level implementation of the Boolean function describing a digital circuit. In this section, we will discuss the manual design of dimple circuits. The complexity of a digital logic-gate circuit that implements a Boolean function directly depends on the complexity of the corresponding algebraic expression. Although the truth-table representation of a function is unique, it algebraic form can take many different, but equivalent, forms. Minimization of Boolean function using the algebraic method is awkward. The map method provides a well-structured method of minimizing Boolean functions. The map method is also known as the Karnaugh map or k-map method. The simplified expression produced by the map are always in two standard forms: sum of products or product of sums. We will assume that the simplest algebraic expression is an algebraic expression with a minimum number of terms and with the smallest possible number of literals in each term. This expression produces a circuit diagram with a minimum number of gates and the minimum number of inputs to each gate. However, this simplest expression is not unique: It is possible to sometimes find two or more expressions that satisfy the minimization criteria. In that case, each solution is satisfactory.
{"title":"Boolean Algebra and Logic Gates","authors":"R. Rodrigo","doi":"10.1142/9789811238819_0007","DOIUrl":"https://doi.org/10.1142/9789811238819_0007","url":null,"abstract":"Gate-level minimization refers to the design task of finding an optimal gate-level implementation of the Boolean function describing a digital circuit. In this section, we will discuss the manual design of dimple circuits. The complexity of a digital logic-gate circuit that implements a Boolean function directly depends on the complexity of the corresponding algebraic expression. Although the truth-table representation of a function is unique, it algebraic form can take many different, but equivalent, forms. Minimization of Boolean function using the algebraic method is awkward. The map method provides a well-structured method of minimizing Boolean functions. The map method is also known as the Karnaugh map or k-map method. The simplified expression produced by the map are always in two standard forms: sum of products or product of sums. We will assume that the simplest algebraic expression is an algebraic expression with a minimum number of terms and with the smallest possible number of literals in each term. This expression produces a circuit diagram with a minimum number of gates and the minimum number of inputs to each gate. However, this simplest expression is not unique: It is possible to sometimes find two or more expressions that satisfy the minimization criteria. In that case, each solution is satisfactory.","PeriodicalId":309688,"journal":{"name":"Foundations for Fintech","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114884206","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 : 2021-10-01DOI: 10.1177/00034894880970s211
R. V. Renesse
Objectives: to study the specific features of the symptomatic effect and tolerability of paracetamol (P), glucosamine sulfate (GS), chondroitin sulfate (CS), and meloxicam (M) in patients with knee osteoarthritis (OA). Methods: An 18-month open-label randomized prospective parallel-group trial enrolled 80 patients with knee OA who fulfilled the American College of Rheumatology criteria and signed the informed consent. They had Kellgren and Lawrence grades I-III OA with visual analogue scale pain intensity of > 40mm in the target knee, a body mass index of < 35 rg/m 2 , and no clinical dysfunctions of vital organs and systems. The patients were randomized into 4 groups: 1) P 2g daily; 2) a standard GS regimen; 3) a standard CS regimen; 4) M 15mg daily. The patients were followed up for 18 months, The effectiveness was evaluated by the WOMAC questionnaire, Lequesne index, and OMER-ACT-OARSI (D scenario) during 8 visits. Laboratory and clinical examination as well as electrocardiography were performed. Adverse events were recorded during each visit. Results: After 4 weeks of treatment, symptomatic improvement was noted in all groups; however, the best effect was achieved by the use of M and continued to the end of the study. The percentage of patients reacting to the therapy by the OMERACT-OARSI criteria was highest in M group (100%), reached 90% in GS, 85% in CS groups and 75% in P group. In the groups of P, GS and CS failed to respond to treatment 25, 10, and 15% correspondingly. However, medium narrowing of articular space (NAS) was measured at the end of the study and was significantly lower in GS group (-0.07; p=0,
{"title":"Consensus","authors":"R. V. Renesse","doi":"10.1177/00034894880970s211","DOIUrl":"https://doi.org/10.1177/00034894880970s211","url":null,"abstract":"Objectives: to study the specific features of the symptomatic effect and tolerability of paracetamol (P), glucosamine sulfate (GS), chondroitin sulfate (CS), and meloxicam (M) in patients with knee osteoarthritis (OA). Methods: An 18-month open-label randomized prospective parallel-group trial enrolled 80 patients with knee OA who fulfilled the American College of Rheumatology criteria and signed the informed consent. They had Kellgren and Lawrence grades I-III OA with visual analogue scale pain intensity of > 40mm in the target knee, a body mass index of < 35 rg/m 2 , and no clinical dysfunctions of vital organs and systems. The patients were randomized into 4 groups: 1) P 2g daily; 2) a standard GS regimen; 3) a standard CS regimen; 4) M 15mg daily. The patients were followed up for 18 months, The effectiveness was evaluated by the WOMAC questionnaire, Lequesne index, and OMER-ACT-OARSI (D scenario) during 8 visits. Laboratory and clinical examination as well as electrocardiography were performed. Adverse events were recorded during each visit. Results: After 4 weeks of treatment, symptomatic improvement was noted in all groups; however, the best effect was achieved by the use of M and continued to the end of the study. The percentage of patients reacting to the therapy by the OMERACT-OARSI criteria was highest in M group (100%), reached 90% in GS, 85% in CS groups and 75% in P group. In the groups of P, GS and CS failed to respond to treatment 25, 10, and 15% correspondingly. However, medium narrowing of articular space (NAS) was measured at the end of the study and was significantly lower in GS group (-0.07; p=0,","PeriodicalId":309688,"journal":{"name":"Foundations for Fintech","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122135544","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}
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