Pub Date : 2024-03-05DOI: 10.33140/oajast.02.01.08
In this study, a double-slit experiment was simulated with only classical-mechanics assumptions to demonstrate that the phenomena associated with this experiment can be explained by classical mechanics. The experiment was conducted with the particle nature intact, by performing observations that did not affect the particle motion while recording the particle position at each simulation step. Only one experimental particle was assumed per session to prevent external effects. Nevertheless, wave patterns, which have previously been thought to occur only quantum-mechanically, were observed on the inspection plate. This study focused on proving that classical mechanics can explain the double-slit wave pattern.
{"title":"Alternative Approach to Copenhagen Interpretation","authors":"","doi":"10.33140/oajast.02.01.08","DOIUrl":"https://doi.org/10.33140/oajast.02.01.08","url":null,"abstract":"In this study, a double-slit experiment was simulated with only classical-mechanics assumptions to demonstrate that the phenomena associated with this experiment can be explained by classical mechanics. The experiment was conducted with the particle nature intact, by performing observations that did not affect the particle motion while recording the particle position at each simulation step. Only one experimental particle was assumed per session to prevent external effects. Nevertheless, wave patterns, which have previously been thought to occur only quantum-mechanically, were observed on the inspection plate. This study focused on proving that classical mechanics can explain the double-slit wave pattern.","PeriodicalId":496625,"journal":{"name":"Open Access Journal of Applied Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140264423","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 : 2024-02-19DOI: 10.33140/oajast.02.01.07
The twenty-first century has witnessed significant advancements in informatics, reshaping our understanding of data processing and accessibility. Artificial intelligence (AI), encompassing techniques such as machine learning (ML), deep learning (DP), and neural networks (NN), is poised to revolutionize medicine. AI holds the capability of analyzing vast amounts of data, extracting meaningful insights, and making accurate predictions, thereby empowering industries to make informed decisions, drive innovation, and enhance efficiency. The landscape of medical AI has evolved significantly, demonstrating expert-level disease detection from medical images and promising breakthroughs across various industries. AI revolutionizes medical practice by leveraging advanced algorithms and machine learning capabilities to improve diagnostics, treatment planning, and overall patient care. However, the deployment of medical AI systems in regular clinical practice still needs to be tapped, presenting complex ethical, technical, and human-centered challenges that must be addressed for successful implementation. While AI algorithms have shown efficacy in retrospective medical investigations, their translation into practical medical settings has been limited, raising concerns about their usability and interaction with healthcare professionals. Moreover, the representativeness of retrospective datasets in real-world medical practice is subject to filtering and cleaning biases. Integrating AI into clinical medicine holds great promise for transforming healthcare delivery, improving patient care, and revolutionizing aspects such as diagnosis, treatment planning, drug discovery, personalized treatment, and medical imaging. With advanced algorithms and machine learning capabilities, AI and robotics in Healthcare can analyze large volumes of medical data, extract meaningful insights, and provide accurate predictions, empowering healthcare professionals to make informed decisions and optimize resource allocation. The availability of extensive clinical, genomics, and digital imaging data, coupled with investments from healthcare institutions and technology giants, underscores the potential of AI in healthcare. This review article explores AI's powerful potential to revolutionize healthcare delivery across multiple domains, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.
{"title":"Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare","authors":"","doi":"10.33140/oajast.02.01.07","DOIUrl":"https://doi.org/10.33140/oajast.02.01.07","url":null,"abstract":"The twenty-first century has witnessed significant advancements in informatics, reshaping our understanding of data processing and accessibility. Artificial intelligence (AI), encompassing techniques such as machine learning (ML), deep learning (DP), and neural networks (NN), is poised to revolutionize medicine. AI holds the capability of analyzing vast amounts of data, extracting meaningful insights, and making accurate predictions, thereby empowering industries to make informed decisions, drive innovation, and enhance efficiency. The landscape of medical AI has evolved significantly, demonstrating expert-level disease detection from medical images and promising breakthroughs across various industries. AI revolutionizes medical practice by leveraging advanced algorithms and machine learning capabilities to improve diagnostics, treatment planning, and overall patient care. However, the deployment of medical AI systems in regular clinical practice still needs to be tapped, presenting complex ethical, technical, and human-centered challenges that must be addressed for successful implementation. While AI algorithms have shown efficacy in retrospective medical investigations, their translation into practical medical settings has been limited, raising concerns about their usability and interaction with healthcare professionals. Moreover, the representativeness of retrospective datasets in real-world medical practice is subject to filtering and cleaning biases. Integrating AI into clinical medicine holds great promise for transforming healthcare delivery, improving patient care, and revolutionizing aspects such as diagnosis, treatment planning, drug discovery, personalized treatment, and medical imaging. With advanced algorithms and machine learning capabilities, AI and robotics in Healthcare can analyze large volumes of medical data, extract meaningful insights, and provide accurate predictions, empowering healthcare professionals to make informed decisions and optimize resource allocation. The availability of extensive clinical, genomics, and digital imaging data, coupled with investments from healthcare institutions and technology giants, underscores the potential of AI in healthcare. This review article explores AI's powerful potential to revolutionize healthcare delivery across multiple domains, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.","PeriodicalId":496625,"journal":{"name":"Open Access Journal of Applied Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140450239","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 : 2024-01-22DOI: 10.33140/oajast.02.01.05
Soil is a living, dynamic structure that plays critical roles in terrestrial ecosystems. Soil texture is an important soil property because it influences other important soil qualities such as soil structure, soil moisture, the diversity of living organisms, plant growth, and overall soil quality. Soil texture has an impact on the chemical and physical qualities of the soil, as well as enzyme activity and microbial population. The study's goal was to investigate the chemical characteristics, soil enzymes, and soil respiration of soils with various textures (sandy loam, clay loam, clay, sandy clay loam). Soil samples were collected from eight distinct regions (Kücükkoy, Fethiye, Dinlendik, İçer Çumra, Kuzucu, İnli, Alibeyhüyüğü, and Güvercinlik) at depths ranging from 0 to 30 cm in Konya's Cumra district, and four different texture classes were determined. The varying soil textural classes were found to have different impacts on pH, EC, lime, organic matter, macro and micro components, soil respiration, and various enzyme activities. These textural variations resulted in statistically significant differences. Variations in these factors were also shown to change the activities of specific soil enzymes. The results also show that clay-textured soil contains the highest amounts of micronutrients, soil respiration, catalase enzyme, as well as acidic and alkaline phosphatase enzyme activity
{"title":"Exploring the Physical, Chemical, and Biological Properties of Soils from Different Regions Classified into Different Textural Classes","authors":"","doi":"10.33140/oajast.02.01.05","DOIUrl":"https://doi.org/10.33140/oajast.02.01.05","url":null,"abstract":"Soil is a living, dynamic structure that plays critical roles in terrestrial ecosystems. Soil texture is an important soil property because it influences other important soil qualities such as soil structure, soil moisture, the diversity of living organisms, plant growth, and overall soil quality. Soil texture has an impact on the chemical and physical qualities of the soil, as well as enzyme activity and microbial population. The study's goal was to investigate the chemical characteristics, soil enzymes, and soil respiration of soils with various textures (sandy loam, clay loam, clay, sandy clay loam). Soil samples were collected from eight distinct regions (Kücükkoy, Fethiye, Dinlendik, İçer Çumra, Kuzucu, İnli, Alibeyhüyüğü, and Güvercinlik) at depths ranging from 0 to 30 cm in Konya's Cumra district, and four different texture classes were determined. The varying soil textural classes were found to have different impacts on pH, EC, lime, organic matter, macro and micro components, soil respiration, and various enzyme activities. These textural variations resulted in statistically significant differences. Variations in these factors were also shown to change the activities of specific soil enzymes. The results also show that clay-textured soil contains the highest amounts of micronutrients, soil respiration, catalase enzyme, as well as acidic and alkaline phosphatase enzyme activity","PeriodicalId":496625,"journal":{"name":"Open Access Journal of Applied Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140499374","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 : 2024-01-19DOI: 10.33140/oajast.02.01.04
Machine learning is an area of artificial intelligence that deals with the development of algorithms and models for automatically detecting patterns and making inferences from data. Neural networks are one of the most popular machine learning models that simulate the learning process of the brain and are widely used in various fields such as pattern recognition, prediction and control. Matlab is a popular programming language in the field of machine learning due to its ease of use and numerous libraries that contain the implementation of various machine learning algorithms. In this paper, we will present the simulation of machine learning in neural networks using different algorithms in Matlab. We will describe several algorithms such as feedforward neural network, convolutional neural network and deep neural network. Also, we will show how these algorithms are applied in practice using different datasets. Finally, we will compare the performance of different algorithms and analyze their advantages and disadvantages.
{"title":"Analyzing Neural Network Algorithms for Improved Performance: A Computational Study","authors":"","doi":"10.33140/oajast.02.01.04","DOIUrl":"https://doi.org/10.33140/oajast.02.01.04","url":null,"abstract":"Machine learning is an area of artificial intelligence that deals with the development of algorithms and models for automatically detecting patterns and making inferences from data. Neural networks are one of the most popular machine learning models that simulate the learning process of the brain and are widely used in various fields such as pattern recognition, prediction and control. Matlab is a popular programming language in the field of machine learning due to its ease of use and numerous libraries that contain the implementation of various machine learning algorithms. In this paper, we will present the simulation of machine learning in neural networks using different algorithms in Matlab. We will describe several algorithms such as feedforward neural network, convolutional neural network and deep neural network. Also, we will show how these algorithms are applied in practice using different datasets. Finally, we will compare the performance of different algorithms and analyze their advantages and disadvantages.","PeriodicalId":496625,"journal":{"name":"Open Access Journal of Applied Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140503055","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 : 2024-01-17DOI: 10.33140/oajast.02.01.03
This research delves into the synergy between fuzzy numbers and neural networks, presenting a novel perspective on interpreting neural network functionality. Fuzzy numbers offer a flexible framework to capture uncertainties and imprecisions, enriching the interpretability of neural network outputs. By integrating fuzzy number theory into the analysis, our study seeks to enhance the transparency and reliability of neural network models, contributing to a more nuanced understanding of their inner
{"title":"Fuzzy Numbers Unraveling the Intricacies of Neural Network Functionality","authors":"","doi":"10.33140/oajast.02.01.03","DOIUrl":"https://doi.org/10.33140/oajast.02.01.03","url":null,"abstract":"This research delves into the synergy between fuzzy numbers and neural networks, presenting a novel perspective on interpreting neural network functionality. Fuzzy numbers offer a flexible framework to capture uncertainties and imprecisions, enriching the interpretability of neural network outputs. By integrating fuzzy number theory into the analysis, our study seeks to enhance the transparency and reliability of neural network models, contributing to a more nuanced understanding of their inner","PeriodicalId":496625,"journal":{"name":"Open Access Journal of Applied Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140505279","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 : 2024-01-04DOI: 10.33140/oajast.02.01.01
This paper presents theoretical and pedagogical considerations as well as the basic results of research on the evaluation and assessment of student achievements in mathematics. The research was carried out in class and subject classes. The paper is based on the hypothesis that descriptive assessment is more successful and increases students' motivation for mathematics as a science. The aim of the paper is to investigate, analyze and interpret the attitudes of students and teachers about descriptive and numerical assessment. The problem of this paper is which type of evaluation has a greater influence on students' motivation towards mathematics. Analytical, theoretical and deductive methods were used in the work. Research techniques for proving the views of this work are a survey. As for the instruments, we made distinctions about the attitudes of students and teachers. The paper ends with a concluding discussion of the problem.
{"title":"Topic: Grade as a Motivation for Learning Mathematics","authors":"","doi":"10.33140/oajast.02.01.01","DOIUrl":"https://doi.org/10.33140/oajast.02.01.01","url":null,"abstract":"This paper presents theoretical and pedagogical considerations as well as the basic results of research on the evaluation and assessment of student achievements in mathematics. The research was carried out in class and subject classes. The paper is based on the hypothesis that descriptive assessment is more successful and increases students' motivation for mathematics as a science. The aim of the paper is to investigate, analyze and interpret the attitudes of students and teachers about descriptive and numerical assessment. The problem of this paper is which type of evaluation has a greater influence on students' motivation towards mathematics. Analytical, theoretical and deductive methods were used in the work. Research techniques for proving the views of this work are a survey. As for the instruments, we made distinctions about the attitudes of students and teachers. The paper ends with a concluding discussion of the problem.","PeriodicalId":496625,"journal":{"name":"Open Access Journal of Applied Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450585","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：