Ikrom KAMBAROV, Matthias BROSSOG, Jorg FRANKE, David KUNZ, Jamshid INOYATKHODJAEV
The interaction between humans and robots has been a rapidly developing technology and a frequently discussed research topic in the last decade because current robots ensure the physical safety of humans during close proximity assembly operations. This interaction promises capability flexibility due to human dexterity skills and capacity flexibility due to robot accuracy. Nevertheless, in these interactions, the humans are marginally outside of the system, while the robots are seen as a crucial component of the assembly activities, which causes the systems to lack flexibility and efficiency. Therefore, this paper presents a study on Human to Robot communication in assembly systems. We conducted a systematic review of related literature and industrial applications involving human and robot interaction modes over the last decade to identify research gaps in the integration of collaborative robots into assembly systems. We believe that we are in a transformation phase from physical interaction mode towards cognitive interaction mode between humans and robots, where humans and robots are able to interact with each other during mutual working conditions and humans are able to guide robots. The main contribution of this paper is to propose a future mode of human-robot interaction in which a skilled operator performs not only physical cooperative tasks with robots but also work aided by smart technologies that allow communication with robots. This interaction mode allows for an increase in the flexibility and productivity of the assembly operation as well as the wellbeing of the human operator in a human-centered manufacturing environment.
{"title":"From Human to Robot Interaction towards Human to Robot Communication in Assembly Systems","authors":"Ikrom KAMBAROV, Matthias BROSSOG, Jorg FRANKE, David KUNZ, Jamshid INOYATKHODJAEV","doi":"10.55549/epstem.1365802","DOIUrl":"https://doi.org/10.55549/epstem.1365802","url":null,"abstract":"The interaction between humans and robots has been a rapidly developing technology and a frequently discussed research topic in the last decade because current robots ensure the physical safety of humans during close proximity assembly operations. This interaction promises capability flexibility due to human dexterity skills and capacity flexibility due to robot accuracy. Nevertheless, in these interactions, the humans are marginally outside of the system, while the robots are seen as a crucial component of the assembly activities, which causes the systems to lack flexibility and efficiency. Therefore, this paper presents a study on Human to Robot communication in assembly systems. We conducted a systematic review of related literature and industrial applications involving human and robot interaction modes over the last decade to identify research gaps in the integration of collaborative robots into assembly systems. We believe that we are in a transformation phase from physical interaction mode towards cognitive interaction mode between humans and robots, where humans and robots are able to interact with each other during mutual working conditions and humans are able to guide robots. The main contribution of this paper is to propose a future mode of human-robot interaction in which a skilled operator performs not only physical cooperative tasks with robots but also work aided by smart technologies that allow communication with robots. This interaction mode allows for an increase in the flexibility and productivity of the assembly operation as well as the wellbeing of the human operator in a human-centered manufacturing environment.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114601","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}
Adnan AHMED, Muhammad AWAIS, Mohammad SIRAJ, Muhammad UMAR
As technology becomes more intertwined with our daily lives, it is increasingly important to protect our data from attackers. Cyber security has become a top priority for individuals, businesses, and governments, as the threat of cybercrime is constantly evolving and becoming more sophisticated. With the rapid increase in cyberattacks, it has become tricky and cumbersome for cybersecurity experts to react to them all, predict new attacks and analyze the impact of damage being done to business. Traditional security measures such as firewalls, anti-virus software, and intrusion detections are no longer adequate in protecting against new vulnerabilities, especially insider and misbehavior attacks. Recently, Artificial Intelligence based techniques have brought tremendous improvements in cybersecurity with the integration of machine learning (ML) algorithms. ML methods have been built upon large volumes of real-time network data to deploy automated security and threat detection systems. Nonetheless, various cyber-attacks still circumvent traditional security mechanisms deployed to detect those attacks. To address the challenge, in this paper, we propose a machine learning-enabled trust-based routing protocol (TrustML-RP) that identifies the attacking nodes responsible for Distributed Denial of Service (DDoS) and packet suppression attacks. The proposed TrustML-RP scheme first adopts a distributed trust model for establishing trust factor among the participating nodes and later employs an effective combination of ML algorithms e.g., Artificial Neural Network (ANN) and Support Vector Machine (SVM) to find an optimal and secure route and identify attacker nodes. A comprehensive performance evaluation of the proposed scheme is carried out to demonstrate the efficiency on a reasonably sized network containing mixed nodes. The results demonstrate the effectiveness of the proposed scheme in building a trusted network environment and improving network security. The research findings suggest that the integration of a trust-based model and ML techniques can improve traditional cybersecurity methods thereby enabling cybersecurity professionals to design more effective cybersecurity systems.
{"title":"Enhancing Cybersecurity with Trust-Based Machine Learning: A Defense against DDoS and Packet Suppression Attacks","authors":"Adnan AHMED, Muhammad AWAIS, Mohammad SIRAJ, Muhammad UMAR","doi":"10.55549/epstem.1368266","DOIUrl":"https://doi.org/10.55549/epstem.1368266","url":null,"abstract":"As technology becomes more intertwined with our daily lives, it is increasingly important to protect our data from attackers. Cyber security has become a top priority for individuals, businesses, and governments, as the threat of cybercrime is constantly evolving and becoming more sophisticated. With the rapid increase in cyberattacks, it has become tricky and cumbersome for cybersecurity experts to react to them all, predict new attacks and analyze the impact of damage being done to business. Traditional security measures such as firewalls, anti-virus software, and intrusion detections are no longer adequate in protecting against new vulnerabilities, especially insider and misbehavior attacks. Recently, Artificial Intelligence based techniques have brought tremendous improvements in cybersecurity with the integration of machine learning (ML) algorithms. ML methods have been built upon large volumes of real-time network data to deploy automated security and threat detection systems. Nonetheless, various cyber-attacks still circumvent traditional security mechanisms deployed to detect those attacks. To address the challenge, in this paper, we propose a machine learning-enabled trust-based routing protocol (TrustML-RP) that identifies the attacking nodes responsible for Distributed Denial of Service (DDoS) and packet suppression attacks. The proposed TrustML-RP scheme first adopts a distributed trust model for establishing trust factor among the participating nodes and later employs an effective combination of ML algorithms e.g., Artificial Neural Network (ANN) and Support Vector Machine (SVM) to find an optimal and secure route and identify attacker nodes. A comprehensive performance evaluation of the proposed scheme is carried out to demonstrate the efficiency on a reasonably sized network containing mixed nodes. The results demonstrate the effectiveness of the proposed scheme in building a trusted network environment and improving network security. The research findings suggest that the integration of a trust-based model and ML techniques can improve traditional cybersecurity methods thereby enabling cybersecurity professionals to design more effective cybersecurity systems.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136113840","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}
Miranda KHAJISHVILI, Jaba SHAINIDZE, Kakha MAKHARADZE, Nugzar GOMIDZE
Our research provides for the analysis of different types of Georgian wine based on 3D fluorescence spectroscopy (3DF) using the Black Comet (200-950 nm) spectrometer manufactured by StellarNet. In this method, the 3D fluorescence signal is divided into a fixed number of statistical components. For each type of wine, a 3D database is strictly defined, which we conventionally call references. The etalon describe the excitation/emission spectra in detail. The advantage of the 3DF method compared to other statistical methods, such as peak component analysis (PCA), lies in the uniqueness of the unfolding of the spectra. The fluorescence spectra of the wine will be further analyzed by peak component analysis (PCA). After performing the PCA analysis, in order to reduce the number of tolerant etalon, we used the tolerant etalon sample (TES) comparison analysis, thus determining how tolerant the researched wine sample is to this or that specific etalon.
{"title":"On the Development of the Fluorescence Excitation-Emission Etalon Matrix Algorithm of Wine","authors":"Miranda KHAJISHVILI, Jaba SHAINIDZE, Kakha MAKHARADZE, Nugzar GOMIDZE","doi":"10.55549/epstem.1361727","DOIUrl":"https://doi.org/10.55549/epstem.1361727","url":null,"abstract":"Our research provides for the analysis of different types of Georgian wine based on 3D fluorescence spectroscopy (3DF) using the Black Comet (200-950 nm) spectrometer manufactured by StellarNet. In this method, the 3D fluorescence signal is divided into a fixed number of statistical components. For each type of wine, a 3D database is strictly defined, which we conventionally call references. The etalon describe the excitation/emission spectra in detail. The advantage of the 3DF method compared to other statistical methods, such as peak component analysis (PCA), lies in the uniqueness of the unfolding of the spectra. The fluorescence spectra of the wine will be further analyzed by peak component analysis (PCA). After performing the PCA analysis, in order to reduce the number of tolerant etalon, we used the tolerant etalon sample (TES) comparison analysis, thus determining how tolerant the researched wine sample is to this or that specific etalon.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114441","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}
This work describes a longitudinal fracture analysis of beam structure of circular cross-section under periodic strains. The material whose properties vary in radial direction has non-linear viscoelastic behaviour. The beam is loaded in torsion so that the twist angle represents a periodical function. Time-dependent behaviour under periodic strains is dealt with a model having a non-linear spring and a linear dashpot. The complementary strain energy in the beam is considered to determine the strain energy release rate. The balance of energy is examined to verify the strain energy release rate. The ascendency of various parameters over strain energy release rate is assessed.
{"title":"Non-linear Viscoelastic Beams Under Periodic Strains: An Approach for Analyzing of Longitudinal Fracture","authors":"Victor RIZOV","doi":"10.55549/epstem.1365805","DOIUrl":"https://doi.org/10.55549/epstem.1365805","url":null,"abstract":"This work describes a longitudinal fracture analysis of beam structure of circular cross-section under periodic strains. The material whose properties vary in radial direction has non-linear viscoelastic behaviour. The beam is loaded in torsion so that the twist angle represents a periodical function. Time-dependent behaviour under periodic strains is dealt with a model having a non-linear spring and a linear dashpot. The complementary strain energy in the beam is considered to determine the strain energy release rate. The balance of energy is examined to verify the strain energy release rate. The ascendency of various parameters over strain energy release rate is assessed.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114461","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}
Fatlume ZHUJANI, Georgi TODOROV, Konstantin KAMBEROV
The majority of work in cutting machining processes is focused on choosing the parameters that will result in the greatest rate of material removal and the least amount of surface roughness, cutting temperatures, cutting pressures, vibrations, etc., which are the main quality responses. Surface roughness is one of the most precise quality criteria that affects how machined parts work. By choosing the appropriate cutting settings, it should be possible to get a greater surface finish and a longer service life for the machined parts. The link between changes in surface roughness caused by turning operations with respect to different machining settings is investigated in this study using the Taguchi method L9(33). The orthogonal array, signal-to-noise ratio, and analysis of variance are utilized to examine the performance characteristics when turning Inconel 718 bars with CCMT09T308N-SU coated cemented carbide insert tools. The optimal setting of cutting parameters to lessen surface roughness is chosen using the Taguchi method. Speed, feed rate, and cut depth are the three cutting parameters. Experimental results are given to illustrate the effectiveness of this strategy.
{"title":"Single Objective Optimization of Cutting Parameters for Surface Roughness in Turning of Inconel 718 Using Taguchi Approach","authors":"Fatlume ZHUJANI, Georgi TODOROV, Konstantin KAMBEROV","doi":"10.55549/epstem.1368279","DOIUrl":"https://doi.org/10.55549/epstem.1368279","url":null,"abstract":"The majority of work in cutting machining processes is focused on choosing the parameters that will result in the greatest rate of material removal and the least amount of surface roughness, cutting temperatures, cutting pressures, vibrations, etc., which are the main quality responses. Surface roughness is one of the most precise quality criteria that affects how machined parts work. By choosing the appropriate cutting settings, it should be possible to get a greater surface finish and a longer service life for the machined parts. The link between changes in surface roughness caused by turning operations with respect to different machining settings is investigated in this study using the Taguchi method L9(33). The orthogonal array, signal-to-noise ratio, and analysis of variance are utilized to examine the performance characteristics when turning Inconel 718 bars with CCMT09T308N-SU coated cemented carbide insert tools. The optimal setting of cutting parameters to lessen surface roughness is chosen using the Taguchi method. Speed, feed rate, and cut depth are the three cutting parameters. Experimental results are given to illustrate the effectiveness of this strategy.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114588","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}
COVID-19 has affected the entire globe with its rapid spreading, causing a high transmission rate. A huge amount of people come in contact with this deadly virus, and early diagnosis of such kind of viruses may save many lives. This paper proposes an improved approach for detecting COVID-19 based on Long Short Term Memory (LSTM) and taking advantage of early clinical reports. To train the LSTM-based classifier for COVID-19 detection, various preprocessing techniques and word embeddings are employed. These techniques ensure the data is in a suitable format for the LSTM model. The proposed LSTM model is then compared against state-of-the-art ensemble models like Bagging and Random Forest, demonstrating its superior performance. The evaluation results showcase a testing accuracy of 87.15%, with a precision of 91% and a recall of 88%. These metrics indicate the effectiveness of the proposed LSTM model in accurately detecting COVID-19-positive cases. By leveraging early clinical reports and utilizing advanced deep learning techniques, our approach achieves significant improvements in COVID-19 detection compared to existing ensemble models.
{"title":"Bi-Directional LSTM-Based COVID-19 Detection Using Clinical Reports","authors":"Salah BOUKTIF, Akib Mohi Ud Din KHANDAY, Ali OUNI","doi":"10.55549/epstem.1365791","DOIUrl":"https://doi.org/10.55549/epstem.1365791","url":null,"abstract":"COVID-19 has affected the entire globe with its rapid spreading, causing a high transmission rate. A huge amount of people come in contact with this deadly virus, and early diagnosis of such kind of viruses may save many lives. This paper proposes an improved approach for detecting COVID-19 based on Long Short Term Memory (LSTM) and taking advantage of early clinical reports. To train the LSTM-based classifier for COVID-19 detection, various preprocessing techniques and word embeddings are employed. These techniques ensure the data is in a suitable format for the LSTM model. The proposed LSTM model is then compared against state-of-the-art ensemble models like Bagging and Random Forest, demonstrating its superior performance. The evaluation results showcase a testing accuracy of 87.15%, with a precision of 91% and a recall of 88%. These metrics indicate the effectiveness of the proposed LSTM model in accurately detecting COVID-19-positive cases. By leveraging early clinical reports and utilizing advanced deep learning techniques, our approach achieves significant improvements in COVID-19 detection compared to existing ensemble models.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114604","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}
Nabila GUECHTOULI, Noura KICHOU, Amal BOUZAHEUR, Celia ADJAL
Schiff base complexes are extensively studied because of their affinity, selectivity, and sensitivity to a wide variety of metals. They have been found to be very useful in catalysis, medicine as antibiotics, anti-inflammatory agents and also in industry as compounds with anti-corrosive properties. In this work, we will focus on the study of some Schiff base ligands and their complexes based on nickel and copper. An energetic, structural, spectral (IR, UV) and electronic study was carried out, using the density functional theory method. All the calculations have been made with density functional theory (DFT) using Becke's three parameters hybrid method and the Lee-Yang-Parr correlation functional (B3LYP) with LANL2DZ basis set for heavy metals and 6-31G** for all others atoms in gas phase using Gaussian 03 program package. We used the GaussView program to draw the optimized geometries and to visualize the the normal modes vibrations. The stability of the considered complexes has been studied in the basis of the binding energies. A study of reactivity indices will be highlighted in order to predict attack sites. The in-silico biological properties of compounds studied have been calculated and discussed. The theoretical results will be compared with the available experimental ones.
希夫碱配合物因其对多种金属的亲和力、选择性和敏感性而被广泛研究。人们发现它们在催化、医学上作为抗生素、抗炎剂以及在工业上作为具有防腐性能的化合物非常有用。在本研究中，我们将重点研究一些希夫碱配体及其基于镍和铜的配合物。采用密度泛函理论方法对其进行了能量、结构、光谱(红外、紫外)和电子学研究。所有的计算均采用密度泛函数理论(DFT)，采用Becke的三参数混合方法，采用li - yang - parr相关泛函数(B3LYP)，其中重金属为LANL2DZ基集，其余气相原子为6-31G**，采用高斯03程序包。我们使用GaussView程序绘制了优化后的几何图形，并可视化了正常模式的振动。在结合能的基础上研究了所考虑的配合物的稳定性。为了预测攻击地点，将重点研究反应性指数。计算并讨论了所研究化合物的硅生物特性。理论结果将与现有的实验结果进行比较。
{"title":"DFT Study of a Schiff Base Ligand and Its Nickel and Copper Complexes: Structure, Vibration, Chemical Reactivity and in Silico Biological","authors":"Nabila GUECHTOULI, Noura KICHOU, Amal BOUZAHEUR, Celia ADJAL","doi":"10.55549/epstem.1374930","DOIUrl":"https://doi.org/10.55549/epstem.1374930","url":null,"abstract":"Schiff base complexes are extensively studied because of their affinity, selectivity, and sensitivity to a wide variety of metals. They have been found to be very useful in catalysis, medicine as antibiotics, anti-inflammatory agents and also in industry as compounds with anti-corrosive properties. In this work, we will focus on the study of some Schiff base ligands and their complexes based on nickel and copper. An energetic, structural, spectral (IR, UV) and electronic study was carried out, using the density functional theory method. All the calculations have been made with density functional theory (DFT) using Becke's three parameters hybrid method and the Lee-Yang-Parr correlation functional (B3LYP) with LANL2DZ basis set for heavy metals and 6-31G** for all others atoms in gas phase using Gaussian 03 program package. We used the GaussView program to draw the optimized geometries and to visualize the the normal modes vibrations. The stability of the considered complexes has been studied in the basis of the binding energies. A study of reactivity indices will be highlighted in order to predict attack sites. The in-silico biological properties of compounds studied have been calculated and discussed. The theoretical results will be compared with the available experimental ones.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112506","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}
For the first time, a density functional theory (DFT) study was conducted on the structure of a well-known antibacterial agent namely potassium 2,4-Hexadienoate, in order to elucidate its vibrational, electronic and reactivity proprieties. Structure optimization was performed using three common hybrid functionals (DFT/ B3LYP-D3; DFT/ M05-2X and DFT/M06-2X) to identify the suitable functional. Geometric parameters, IR and UV-vis spectra were well reproduced when using DFT/M06-2X with 6-311(d)G+ basis set (R2 = 0.99913). The assimilation of IR frequencies has been achieved using potential energy distribution (PED)analysis at M06-2X/6-311(d) G + level. Time-dependent density functional theory (TD-DFT) and natural bond orbital (NBO) analysis were realized to identify the excited states of 2,4-Hexadienoate anion in the liquid phase, using the solute electron density solvation model (SMD). Moreover, reactive sites in the molecule were localized by molecular electrostatic potential (MEP) analysis. Highest Occupied Molecular Orbitals (HOMO), lowest Unoccupied Molecular Orbitals (LUMO) and energy gap (HOMO-LUMO gap), were used to calculate global reactivity descriptors (GRDs), according to the frontier molecular orbitals (FMO) theory, the resulting values were analyzed to explore the chemical reactivity of the molecule and elucidate the structure-activity relationship.
首次利用密度泛函理论(DFT)研究了著名抗菌剂2,4-己二烯酸钾的结构，以阐明其振动、电子和反应性特性。采用三种常见的混合泛函(DFT/ B3LYP-D3;DFT/ M05-2X和DFT/M06-2X)来识别合适的功能。DFT/M06-2X以6-311(d)G+基组(R2 = 0.99913)重现几何参数、红外光谱和紫外-可见光谱。利用M06-2X/6-311(d) G +能级的势能分布(PED)分析实现了红外频率的同化。利用溶质电子密度溶剂化模型(SMD)，实现了时间依赖密度泛函理论(TD-DFT)和自然键轨道(NBO)分析，确定了2,4-己二酸阴离子在液相中的激发态。此外，通过分子静电势(MEP)分析确定了分子中的活性位点。利用最高已占据分子轨道(HOMO)、最低未占据分子轨道(LUMO)和能隙(HOMO-LUMO)计算整体反应性描述子(GRDs)，并根据前沿分子轨道(FMO)理论对结果值进行分析，探讨分子的化学反应性，阐明构效关系。
{"title":"Comparative Experimental and Theoretical Study on the Structure of Potassium 2,4-Hexadienoate: Structure-Activity Relationship","authors":"Manel TAFERGUENNIT, Noura KICHOU, Zakia HANK","doi":"10.55549/epstem.1361714","DOIUrl":"https://doi.org/10.55549/epstem.1361714","url":null,"abstract":"For the first time, a density functional theory (DFT) study was conducted on the structure of a well-known antibacterial agent namely potassium 2,4-Hexadienoate, in order to elucidate its vibrational, electronic and reactivity proprieties. Structure optimization was performed using three common hybrid functionals (DFT/ B3LYP-D3; DFT/ M05-2X and DFT/M06-2X) to identify the suitable functional. Geometric parameters, IR and UV-vis spectra were well reproduced when using DFT/M06-2X with 6-311(d)G+ basis set (R2 = 0.99913). The assimilation of IR frequencies has been achieved using potential energy distribution (PED)analysis at M06-2X/6-311(d) G + level. Time-dependent density functional theory (TD-DFT) and natural bond orbital (NBO) analysis were realized to identify the excited states of 2,4-Hexadienoate anion in the liquid phase, using the solute electron density solvation model (SMD). Moreover, reactive sites in the molecule were localized by molecular electrostatic potential (MEP) analysis. Highest Occupied Molecular Orbitals (HOMO), lowest Unoccupied Molecular Orbitals (LUMO) and energy gap (HOMO-LUMO gap), were used to calculate global reactivity descriptors (GRDs), according to the frontier molecular orbitals (FMO) theory, the resulting values were analyzed to explore the chemical reactivity of the molecule and elucidate the structure-activity relationship.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112508","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}
Thi Minh Nhut VO, Chia-nan WANG, Fu-chiang YANG, Van Thanh Tien NGUYEN
The Software as a Service (SaaS) industry is expected to experience growth in the coming years for these reasons. These include the rising demand for affordable and adaptable software solutions, the popularity of cloud computing, and the increasing trend of work. In a study comparing the 11 SaaS providers using DEA SBM analysis Aspen Technology Inc, HubSpot Inc, Rapid 7 Inc, and Ring Central emerged as the most efficient company. These research findings offer a framework to assess the efficiency of leading SaaS providers. By adopting this approach, businesses across sectors can gain insights into market trends, identify opportunities that drive digital transformation efforts, and foster creativity. Individuals interested in this field can leverage this methodology to evaluate the success of leading SaaS companies.
{"title":"SaaS Model Assessment- A DEA Approach","authors":"Thi Minh Nhut VO, Chia-nan WANG, Fu-chiang YANG, Van Thanh Tien NGUYEN","doi":"10.55549/epstem.1363369","DOIUrl":"https://doi.org/10.55549/epstem.1363369","url":null,"abstract":"The Software as a Service (SaaS) industry is expected to experience growth in the coming years for these reasons. These include the rising demand for affordable and adaptable software solutions, the popularity of cloud computing, and the increasing trend of work. In a study comparing the 11 SaaS providers using DEA SBM analysis Aspen Technology Inc, HubSpot Inc, Rapid 7 Inc, and Ring Central emerged as the most efficient company. These research findings offer a framework to assess the efficiency of leading SaaS providers. By adopting this approach, businesses across sectors can gain insights into market trends, identify opportunities that drive digital transformation efforts, and foster creativity. Individuals interested in this field can leverage this methodology to evaluate the success of leading SaaS companies.","PeriodicalId":22384,"journal":{"name":"The Eurasia Proceedings of Science Technology Engineering and Mathematics","volume":"74 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112524","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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