Search results for: optimization intelligence strategy

Authors: Zhao Tianxu, Cui Shumei

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This paper proposes a novel multiphase motor structure. The armature winding consists of several independent multiphase windings that have different rating rotate speed and power. Compared to conventional motor, the novel motor structure has more operation mode and fault tolerance mode, which makes it adapt to high-reliability requirement situation such as electric vehicle, aircraft and ship. Performance of novel motor structure varies with winding match. In order to find optimum control strategy, motor torque character, efficiency performance and fault tolerance ability under different operation mode are analyzed in this paper, and torque distribution strategy for efficiency optimization is proposed. Simulation analyze is taken and the result shows that proposed structure has the same efficiency on heavy load and higher efficiency on light load operation points, which expands high efficiency area of motor and cruise range of vehicle. The proposed structure can improve motor highest speed.

Keywords: multiphase motor, armature winding match, torque distribution strategy, efficiency

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Authors: Aelita Skarzauskiene, Monika Maciuliene

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In this paper, we conduct a systematic survey of urban communities in Lithuania to evaluate their potential to co-create collective intelligence or “civic intelligence” applying Digital Co-creation Index methodology that includes different socio-technological indicators. Civic intelligence is a form of collective intelligence that refers to the group’s capacity to perceive societal problems and to address them effectively. The research focuses on evaluation of diverse organizational designs that increase efficient collective performance. The current scientific project advanced the state of the art by evaluating the basic preconditions in the urban communities through which the collective intelligence is being co-created under the systemic manner. The research subject is the “bottom up” digital enabled urban platforms, initiated by Lithuanian public organizations, civic movements or business entities. The web-based monitoring results obtained by applying a social indices calculation methodology and Pearson correlation analysis provided the information about the potential and limits of the urban communities and what possible changes need to be implemented to overcome the limitations.

Keywords: computer supported collaboration, socio-technological system, collective intelligence, networked society

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Authors: Arun Prasad Jaganathan

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In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper introduces an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a hybrid distributed algorithm has been suggested for multi-objective dynamic flexible job shop scheduling problem. The proposed algorithm is high level, in which several algorithms search the space on different machines simultaneously also it is a hybrid algorithm that takes advantages of the artificial intelligence, evolutionary and optimization methods. Distribution is done at different levels and new approaches are used for design of the algorithm. Apache spark and Hadoop frameworks have been used for the distribution of the algorithm. The Pareto optimality approach is used for solving the multi-objective benchmarks. The suggested algorithm that is able to solve large-size problems in short times has been compared with the successful algorithms of the literature. The results prove high speed and efficiency of the algorithm.

Keywords: distributed algorithms, apache-spark, Hadoop, flexible dynamic job shop scheduling, multi-objective optimization

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Authors: Jiani Gao, Qingping Luo, Xinlei Fang

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In order to try to achieve better rights and interests for both doctors and patients in the new medical environment, the paper will focus on the renewal and development of medical buildings. In today's highly developed society, many factors have a profound guiding significance for the development of medical buildings. By doing social research, the paper has found that these factors come from all aspects. These factors include the optimization of traditional medical model, rapid alternation of medical technology and equipment, the reform of the social, medical security system, changes in the age structure of the population, the birth of intelligent medical care under the Internet, and the deepening of the concept of green sustainable building development, etc. The purpose of this paper is to capture sensitively these various factors that may affect the evolution of medical buildings in the context of the current era, and to put forward, by using an adaptable development strategy, some feasible suggestions on the design of medical buildings when facing these changes and challenges. Specifically speaking, the adaptable development strategy includes some basic principles and methods, such as using modular design, adopting scalable streamline, selecting a long-span structural system and using replaceable materials and components, etc.

Keywords: medical architecture, adaptable development, medical model, space design

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Authors: L. Alejandro Cárdenas, Fernando Herrera, David Nova, Juan Ballesteros

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This paper presents a methodology to optimize the maintenance of grid-connected photovoltaic systems, considering the cleaning and module replacement periods based on an asset management strategy. The methodology is based on the analysis of the energy production of the PV plant, the energy feed-in tariff, and the cost of cleaning and replacement of the PV modules, with the overall revenue received being the optimization variable. The methodology is evaluated as a case study of a 5.6 kWp solar PV plant located on the Bogotá campus of the Universidad Nacional de Colombia. The asset management strategy implemented consists of assessing the PV modules through visual inspection, energy performance analysis, pollution, and degradation. Within the visual inspection of the plant, the general condition of the modules and the structure is assessed, identifying dust deposition, visible fractures, and water accumulation on the bottom. The energy performance analysis is performed with the energy production reported by the monitoring systems and compared with the values estimated in the simulation. The pollution analysis is performed using the soiling rate due to dust accumulation, which can be modelled by a black box with an exponential function dependent on historical pollution values. The pollution rate is calculated with data collected from the energy generated during two years in a photovoltaic plant on the campus of the National University of Colombia. Additionally, the alternative of assessing the temperature degradation of the PV modules is evaluated by estimating the cell temperature with parameters such as ambient temperature and wind speed. The medium-term energy decrease of the PV modules is assessed with the asset management strategy by calculating the health index to determine the replacement period of the modules due to degradation. This study proposes a tool for decision making related to the maintenance of photovoltaic systems. The above, projecting the increase in the installation of solar photovoltaic systems in power systems associated with the commitments made in the Paris Agreement for the reduction of CO2 emissions. In the Colombian context, it is estimated that by 2030, 12% of the installed power capacity will be solar PV.

Keywords: asset management, PV module, optimization, maintenance

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Authors: Nerella Srinivas

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This study explores the significance of patent protection for artificial intelligence (AI) innovations in the pharmaceutical sector, emphasizing applications in drug discovery, personalized medicine, and clinical trial optimization. The challenges of patenting AI-driven inventions are outlined, focusing on the classification of algorithms as abstract ideas, meeting the non-obviousness standard, and issues around defining inventorship. The methodology includes examining case studies and existing patents, with an emphasis on how companies like Benevolent AI and Insilico Medicine have successfully secured patent rights. Findings demonstrate that a strategic approach to patent protection is essential, with particular attention to showcasing AI’s technical contributions to pharmaceutical advancements. Conclusively, the study underscores the critical role of understanding patent law and innovation strategies in leveraging intellectual property rights in the rapidly advancing field of AI-driven pharmaceuticals.

Keywords: artificial intelligence, pharmaceutical industry, patent protection, drug discovery, personalized medicine, clinical trials, intellectual property, non-obviousness

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Authors: Nabila Daly, Hamdi Ellouzi, Hela Ltifi

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In last year’s, several recent researches’ that focus on e-learning approaches having as goal to improve pedagogy and student’s academy level assessment. E-learning-related works have become an important research file nowadays due to several problems that make it impossible for students join classrooms, especially in last year’s. Among those problems, we note the current epidemic problems in the word case of Covid-19. For those reasons, several e-learning-related works based on Artificial Intelligence techniques are proposed to improve distant education targets. In the current paper, we will present a short survey of the most relevant e-learning based on Artificial Intelligence techniques giving birth to newly developed e-learning tools that rely on new technologies.

Keywords: artificial intelligence techniques, decision, e-learning, support system, survey

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Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

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As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: Kadiri Kayode Ibrahim, Kadiri Omowunmi

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The research study was concerned with an evaluation of the effect of marketing strategy on the performance of SMEs in Abuja. This was achieved, specifically, through the examination of the effect of disaggregated components of Marketing Strategy (Product, Price, Promotion, Placement and Process) on Sales Volume (as a proxy for performance). The study design was causal in nature, with the use of quantitative methods involving a cross-sectional survey carried out with the administration of a structured questionnaire. A multistage sample of 398 respondents was utilized to provide the primary data used in the study. Subsequently, path analysis was employed in processing the obtained data and testing formulated hypotheses. Findings from the study indicated that all modeled components of marketing strategy were positive and statistically significant determinants of performance among businesses in the zone. It was, therefore, recommended that SMEs invest in continuous product innovation and development that are in line with the needs and preferences of the target market, as well as adopt a dynamic pricing strategy that considers both cost factors and market conditions. It is, therefore, crucial that businesses in the zone adopt marker communication measures that would stimulate brand awareness and increase engagement, including the use of social media platforms and content marketing. Additionally, owner-managers should ensure that their products are readily available to their target customers through an emphasis on availability and accessibility measures. Furthermore, a commitment to consistent optimization of internal operations is crucial for improved productivity, reduced costs, and enhanced customer satisfaction, which in turn will positively impact their overall performance.

Keywords: product, price, promotion, placement

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Authors: Raghunandan Gurumurthy, Aricson Pereira, Sandeep Patil

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The escalating adoption of electric vehicles (EVs) across the globe has underscored the critical importance of advancing battery system technologies. This has catalyzed a shift towards the design and development of battery systems that not only exhibit higher energy efficiency but also boast enhanced thermal performance and sophisticated multi-material enclosures. A significant leap in this domain has been the incorporation of simulation-based design optimization for battery packs and Battery Management Systems (BMS), a move further enriched by integrating artificial intelligence/machine learning (AI/ML) approaches. These strategies are pivotal in refining the design, manufacturing, and operational processes for electric vehicles and energy storage systems. By leveraging AI/ML, stakeholders can now predict battery performance metrics—such as State of Health, State of Charge, and State of Power—with unprecedented accuracy. Furthermore, as Li-ion batteries (LIBs) become more prevalent in urban settings, the imperative for bolstering thermal and fire resilience has intensified. This has propelled Battery Thermal Management Systems (BTMs) to the forefront of energy storage research, highlighting the role of machine learning and AI not just as tools for enhanced safety management through accurate temperature forecasts and diagnostics but also as indispensable allies in the early detection and warning of potential battery fires.

Keywords: electric vehicles, battery thermal management, industrial engineering, machine learning, artificial intelligence, manufacturing

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Gunniga Anugkakul, Suwaree Yordchim

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The objectives were to study language learning strategies (LLSs) employed by Chinese students, and the frequency of LLSs they used, and examine the relationship between the use of LLSs and gender. The Strategy Inventory for Language Learning (SILL) by Oxford was administered to thirty-six Chinese students at Suan Sunandha Rajabhat University in Thailand. The data obtained was analyzed using descriptive statistics and chi-square tests. Three useful findings were found on the use of LLSs reported by Chinese students. First, Chinese students used overall LLSs at a high level. Second, among the six strategy groups, Chinese students employed compensation strategy most frequently and memory strategy least frequently. Third, the research results also revealed that gender had significant effect on Chinese Student’s use of overall LLSs.

Keywords: English language, language learning strategy, Chinese students, compensation strategy

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Authors: Muhammad Hamza

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This research is based on the effect of self-questioning strategy on reading comprehension of second language learners at medium level. This research is conducted to find out the effects of self-questioning strategy and how self-questioning strategy helps English learners to improve their reading comprehension. In this research study the researcher has analyzed that how much self-questioning is effective in the field of learning second language and how much it helps second language learners to improve their reading comprehension. For this purpose, the researcher has studied different reading strategies, analyzed, collected data from certificate level class at NUML, Peshawar campus and then found out the effects of self-questioning strategy on reading comprehension of ESL learners. The researcher has randomly selected the participants from certificate class. The data was analyzed through pre-test and post-test and then in the final stage the results of both tests were compared. After the pre-test and post-test, the result of both pre-test and post-test indicated that if the learners start to use self-questioning strategy before reading a text, while reading a text and after reading a particular text there’ll be improvement in comprehension level of ESL learners. The present research has addressed the benefits of self-questioning strategy by taking two tests (pre and post-test).After the result of post-test it is revealed that the use of the self-questioning strategy has a significant effect on the readers’ comprehension thus, they can improve their reading comprehension by using self-questioning strategy.

Keywords: strategy, self-questioning, comprehension, intermediate level ESL learner

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Authors: Zhenzhong Yang

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Herbal medicines are important supplements to chemical drugs and usually consist of a complex mixture of constituents. The current quality control strategy of herbal medicines is mainly based on chemical markers, which largely failed to owe to the markers, not reflecting the herbal medicines’ multiple mechanisms of action. Herein, a bioactive chemical markers based strategy was proposed and applied to the quality assessment and control of herbal medicines. This strategy mainly includes the comprehensive chemical characterization of herbal medicines, bioactive chemical markers identification, and related quantitative analysis methods development. As a proof-of-concept, this strategy was applied to a Panax notoginseng derived herbal medicine. The bioactive chemical markers based strategy offers a rational approach for quality assessment and control of herbal medicines.

Keywords: bioactive chemical markers, herbal medicines, quality assessment, quality control

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Authors: Abdul Razak Rahmat, Azizah Ahmad, Azman Ta’aa

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The deployment of Business Intelligence (BI) for organization at the beginning phase is very crucial. Results from the previous studies found that more than half of the BI project fails to meet the objective even though a lot money are spent. Based on that problem, the readiness level of BI for the organization is important to identify in order to reduce the risk before the actual BI project is implemented. In this paper, rigorous literature review on the aspect success factors such as Critical Success Factors (CSFs), Readiness Factors (RFs), Success Factors (SFs), are discussed by different authors. The paper also adopted a few models from previous study as a guide for the assessment of BI readiness. The expected finding from this research is the Business Intelligent Readiness Model (BiRM) as a guild before implement the BI system.

Keywords: business intelligence readiness model, business intelligence for higher learning, BI readiness factors, BI critical success factors(CSF)

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Authors: Lei Zhang, Tingting Xu, Jun He, Zhenyu Yan

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Email marketing is one of the most important segments of online marketing. It has been proved to be the most effective way to acquire and retain customers. The email content is vital to customers. Different customers may have different familiarity with a product, so a successful marketing strategy must personalize email content based on individual customers’ product affinity. In this study, we build our personalized email marketing strategy with three types of emails: nurture, promotion, and conversion. Each type of email has a different influence on customers. We investigate this difference by analyzing customers’ open rates, click rates and opt-out rates. Feature importance from response models is also analyzed. The goal of the marketing strategy is to improve the click rate on conversion-type emails. To build the personalized strategy, we formulate the problem as a reinforcement learning problem and adopt a Q-learning algorithm with variations. The simulation results show that our model-based strategy outperforms the current marketer’s strategy.

Keywords: email marketing, email content, reinforcement learning, machine learning, Q-learning

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Authors: Sang Ho Lee, Yountaik Leem

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This paper reviews Korean cases of smart cities through the analysis framework of strategic foci, characteristics and effects. Firstly, national strategies including c(cyber), e(electronic), u(ubiquitous) and s(smart) Korea strategies were considered from strategic angles. Secondly, the characteristics of smart cities in Korea were looked through the smart cities examples such as Seoul, Busan, Songdo and Sejong cities etc. from the views on the by STIM (Service, Technology, Infrastructure and Management) analysis. Finally, the effects of smart cities on socio-economies were investigated from industrial perspective using the input-output model and structural path analysis. Korean smart city strategies revealed that there were different kinds of strategic foci. c-Korea strategy focused on information and communications network building and user IT literacy. e-Korea strategy encouraged e-government and e-business through utilizing high-speed information and communications network. u-Korea strategy made ubiquitous service as well as integrated information and communication operations center. s-Korea strategy is propelling 4th industrial platform. Smart cities in Korea showed their own features and trends such as eco-intelligence, high efficiency and low cost oriented IoT, citizen sensored city, big data city. Smart city progress made new production chains fostering ICTs (Information Communication Technologies) and knowledge intermediate inputs to industries.

Keywords: Korean smart cities, Korean smart city strategies, STIM, smart service, infrastructure, technologies, management, effect of smart city

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Authors: David Lindström

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We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cycling parameters of the optimization method to get a balance between local and global search. We also discuss the eventual problem with Runge oscillations in the response surface.

Keywords: expensive function, infill sampling criterion, kriging, global optimization, response surface, Runge phenomenon

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Sazzad Hossain Talukder

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Survival in the competitive international market with unforeseen environmental contingencies has always been a concern of the firms that led to adopting different strategies to deal with different situations. Language strategy is considered to enhance the international performance of a firm by organizing language diversity and fostering communications within and outside the firm. Yet there is a lack of theoretical attention or model development on the role of language strategy on firm international survival. From resource dependence perspective, the adoption of language strategy and its relationship with firm survival are determined by the firm´s capability to prevent dependency concentration and/or increase relative power on the external environment. However, the impact of language strategy on firm survival is complex and multifaceted as the strategy influence firm performance indirectly through communication, coordination, learning and value creation. The evidence of various types of language strategies and different forms of firm survival also bring in complexities to understand the effects of a language strategy on the international survival of a firm. Based on language literatures and resource dependence logic, certain propositions are developed to conceptualize the relationship between language strategy and firm international survival in this conceptual paper. For the purpose of this paper, a conceptual model is proposed to examine how different kinds of language strategy foster reduction of resource dependency that lead to firm international survival in respond to local responsiveness and global integration. In this proposed model, it is theorized that language strategy has a positive relationship with the international survival of the firm, as the strategy is likely to reduce external resource dependency and increase the ability to continue independent operations both in short and long term.

Keywords: language strategy, language diversity, firm international survival, resource dependence logic

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Authors: Jong-Yeon Kim, Kwang-Bok Shin, Tae-Hwan Ko

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The purpose of this paper is to suggest a weight-reduction design method for the aluminum extrusion carbody structure of a double deck high-speed train using size optimization method. The size optimization method was used to optimize thicknesses of skin and rib of the aluminum extrusion for the carbody structure. Thicknesses of 1st underframe, 2nd underframe, solebar and roof frame were selected by design variables in order to conduct size optimization. The results of the size optimization analysis showed that the weight of the aluminum extrusion could be reduced by 0.61 tons (5.60%) compared to the weight of the original carbody structure.

Keywords: double deck high-speed train, size optimization, weigh-reduction, aluminum extrusion

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Authors: Syed Auon Raza, Tahir Mahmood, Syed Basit Ali Bukhari

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In power system properly coordinated protection scheme is designed to make sure that only the faulty part of the system will be isolated when abnormal operating condition of the system will reach. The complexity of the system as well as the increased user demand and the deregulated environment enforce the utilities to improve system reliability by using a properly coordinated protection scheme. This paper presents overview of over current relay coordination techniques. Different techniques such as Deterministic Techniques, Meta Heuristic Optimization techniques, Hybrid Optimization Techniques, and Trial and Error Optimization Techniques have been reviewed in terms of method of their implementation, operation modes, nature of distribution system, and finally their advantages as well as the disadvantages.

Keywords: distribution system, relay coordination, optimization, Plug Setting Multiplier (PSM)

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Authors: Issa M. Shehabat, Huda F. Y. Nimri

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This paper aims to study the relationship between knowledge management in its processes, including knowledge creation, knowledge sharing, knowledge organization, and knowledge application, and business intelligence tools, including OLAP, data mining, and data warehouse, and their impact on the decision-making process in the banking sector in Jordan. A total of 200 questionnaires were distributed to the sample of the study. The study hypotheses were tested using the statistical package SPSS. Study findings suggest that decision-making processes were positively related to knowledge management processes. Additionally, the components of business intelligence had a positive impact on decision-making. The study recommended conducting studies similar to this study in other sectors such as the industrial, telecommunications, and service sectors to contribute to enhancing understanding of the role of the knowledge management processes and business intelligence tools.

Keywords: business intelligence, knowledge management, decision making, Jordan, banking sector

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Alexandra Lipitakis, Evangelia A. E. C. Lipitakis

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In this article, a class of e-business strategy planning parameters are introduced and their effect on financial and non-financial performance of e-businesses and organizations is investigated. The relationships between these strategic planning parameters, i.e. Formality, Participation, Sophistication, Thoroughness, Synergy and Cooperation, Entropic Factor, Adaptivity, Uncertainty and Financial and Non-Financial Performance are examined and the directions of these relationships are given. A conceptual model has been constructed and quantitative research methods can be used to test the considered eight hypotheses. In the framework of e-business strategy planning this research study clearly demonstrates how strategic planning components have positive relationships with e-business strategy management and performance.

Keywords: e-business management, e-business model, e-business performance assessments, strategy management methodologies, strategy planning, quantitative methods

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Authors: Ali Rabah

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This research received no grant or financial support from any public, commercial, or none governmental agency. The author conducted this work as part of his normal research activities as a professor of Chemical Engineering at the University of Khartoum, Sudan. Abstract While fossil oil reserves have been receding, the demand for diesel and gasoline has been growing. In recent years, syngas of biomass origin has been emerging as a viable feedstock for Fischer-Tropsch (FT) synthesis, a process for manufacturing synthetic gasoline and diesel. This paper reports the optimization of syngas quality to match FT synthesis requirements. The optimization model maximizes the thermal efficiency under the constraint of H2/CO≥2.0 and operating conditions of equivalent ratio (0 ≤ ER ≤ 1.0), steam to biomass ratio (0 ≤ SB ≤ 5), and gasification temperature (500 °C ≤ Tg ≤ 1300 °C). The optimization model is executed using the optimization section of the Model Analysis Tools of the Aspen Plus simulator. The model is tested using eleven (11) types of MSW. The optimum operating conditions under which the objective function and the constraint are satisfied are ER=0, SB=0.66-1.22, and Tg=679 - 763°C. Under the optimum operating conditions, the syngas quality is H2=52.38 - 58.67-mole percent, LHV=12.55 - 17.15 MJ/kg, N2=0.38 - 2.33-mole percent, and H2/CO≥2.15. The generalized optimization model reported could be extended to any other type of biomass and coal. Keywords: MSW, Syngas, Optimization, Fischer-Tropsch.

Keywords: syngas, MSW, optimization, Fisher-Tropsh

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Authors: Mohamed Sharof Mostafa, Atefeh Ahmadi

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Most of traditional single modality psychotherapy and counselling approaches to clients’ problems are based on the application of one therapy in all sessions. Modern developments in these sciences focus on eclectic and integrative interventions to consider all dimensions of an issue and all characteristics of the clients. This paper presents and overview eclectic therapy and its pros and cons. In addition, multiple intelligence theory and its application in eclectic therapy approaches are mentioned.

Keywords: eclectic therapy, client, multiple intelligence theory, dimensions

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Authors: Peng Liu

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Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework.

Keywords: deep reinforcement learning, deep Q networks, portfolio optimization, multi-period optimization

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Authors: Alina Parincu, Giuseppe Empoli, Alexandru Capatina

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The purpose of this paper is to identify the most influential predictors of emotional intelligence skills, in the case of 20 business innovation coaches, on the co-creation of knowledge through coaching services delivered to innovative start-ups from Europe, funded through Horizon 2020 – SME Instrument. We considered the emotional intelligence skills (self-awareness, self-regulation, motivation, empathy and social skills) as antecedent conditions of the outcome: the quality of coaching services, perceived by the entrepreneurs who received funding within SME instrument, using fuzzy-sets qualitative comparative analysis (fsQCA) approach. The findings reveal that emotional intelligence skills, trained with neuro-management techniques, were associated with increased goal-focused business coaching skills.

Keywords: neuro-management, innovative start-ups, business coaching, fsQCA

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