Author: Nazar Habeeb Abbas
Data-Driven Climate Forecasting for Environmental Planning and Resource Management
ABSTRACT Background: Climate forecasting is a severe issue in environmental science, as the nonlinear interactions between the atmospheric variables and the growing complexity of the environmental data provided by current monitoring technologies create a significant challenge. Conventional statistical forecasting systems frequently have difficulty with the multidimensional nature of climate systems, especially when the environmental data are highly interdependent over time, and also when their features are heterogeneous. Recent advances in machine learning offer novel opportunities for analysis in the modeling of complex environmental processes, as well as enhancing the predictive performance of climate forecasting systems. Objective: This study aims to evaluate the application of machine learning algorithms in the prediction of climate variability and will also endeavor to establish whether better predictive models can be applied to enhance the accuracy of the forecasts than more traditional statistical methods. Methods: The research employs a quantitative predictive modeling framework based on environmental observations collected between 2010 and 2024, consisting of 1,260 climate records representing key atmospheric indicators including temperature, precipitation, humidity, solar radiation, wind speed, and evapotranspiration. Several predictive algorithms were implemented, including Random Forest, Gradient Boosting Machine, and Deep Neural Networks. Model performance was evaluated using statistical forecasting metrics such as Root Mean Squared Error, Mean Absolute Error, and the coefficient of determination. Results: The findings showed the presence of strong variability within key environmental predictors confirms that the dataset contains sufficient dynamic information for effective machine learning forecasting, and thermodynamic variables predominated predictive models of climate variability. Also, the findings proved that machine learning models are more efficient in comparison to conventional regression-based forecasting. Conclusion: The empirical results proved that machine learning algorithms are much more effective than the traditional regression-based models in terms of forecasting. Neural network architectures and ensemble learning methods were especially effective at the task of nonlinear modeling of the dependencies between environmental variables. Also, such findings suggest that predictive models that were trained with combined environmental data can be used to detect latent relationships in climatic data and provide more precise projections of atmospheric behavior. Finally, models of climate prediction that rely on data and can utilize large volumes of environmental data can thus help in making more efficient environmental planning and policy formulation.
Algorithmic Authority and Contractual Sovereignty: Legal and Social Implications of AI Negotiation Systems Across Jurisdictions.
Abstract The use of Artificial Intelligence (AI) in contract negotiation is stretching the limits of legal automation and raising important ethical and legal questions. The study evaluates five popular AI-driven contract platforms: LexAI, ClauseBot, LegalMind, JurisDraft, and SmartClause5, in five contract types (leasing, employment, procurement, IP licensing, NDAs). As part of a multi-dimensional approach, the research evaluates the performance of platforms in clause amendment, draft throughput, enforceability calibration, multi-party negotiation support and transparency. A series of refined equations and indices were developed to measure the operational and semantic reliability of each of the vehicles under negotiation stress. The results further demonstrate that systems with the more advanced natural language processing and adaptive semantic modeling technology, including LegalMind and LexAI, performed better than the rest in producing legally coherent and enforceable agreements in the shortest possible time frames. However, there were substantial differences in jurisdictional transferability, traceability, and bias sensitivity, highlighting ongoing shortcomings in existing AI frameworks. It’s yet another reminder of the need for human oversight and ethical control of automated legal decisions. The findings from this study suggest that although AI can produce significant efficiencies in contracting contexts, such technology should continue to be limited to hybrid model approaches involving machine-generated output and legal review. The results support the adoption of standard auditing procedures and indicate the relevance of aligning algorithmic power with de jure expectations. Future work is also needed to investigate enforcement outcome in the field, inter-cultural negotiation dynamics, and the development of AI legal agency.
The Evaluating the Performance of the Iraqi Stock Market Compared with Some Arab Financial Markets for the Period from 2010 – 2022
Abstract The research evaluates the performance of the Iraqi stock market compared with some Arab financial markets for the period from 2010 – 2022, through the use of some performance indicators. Some Arab financial markets were selected as a sample of research. He used the method of quantitative descriptive research to analyze the data obtained from those markets to reach the results of the research, and the research reached several conclusions and recommendations, the most important of which is the need to activate the performance of Iraqi stock market, through the benefit of the experiences of Arab financial markets with high-performance indicators. Keywords Performance Indicators, Iraq Stock Exchange, Arab Capital Markets https://doi.org/10.55562/jrucs.v53i1.569
Real options theory and its applications In Strategic Human Resource Management
Abstract Many Researchers refer to a firm’s human resources as an asset that can provide value and competitive advantage for it. However, the (SHRM) field has tended to ignore the fact that involves Human assets investment (including Human, Intellectual, Social, Emotional, and Spiritual Capital) have associated uncertainties and risks. The real options view provides a theoretical framework for how firms manage uncertainties associated with investments in real assets. The problematic of Theorizers in human resources options had been explained by analyzing some of the uncertainty cases related to the investment in human assets, and discussing how the organization manages these uncertainty cases through available options for HRM, which are capabilities generated by some practices of those resources. This research discusses these practices and has developed an options model for managing different types of uncertainties. Keywords: Human assets’ investment, Real Options Theory, Intellectual, Social, Emotional, and Spiritual Capital, Options of HRM. https://www.iasj.net/iasj/download/66fcd2ae0194b6a5
Approaches to customer profitability analysis – An analytical theoretical study
Abstract: The process of analyzing customer profitability has become one of the most important methods used in collecting the required information about customers. The main goal of conducting customer profitability analysis is to collect data on the costs incurred by organizations related to serving their customers, as well as to calculate the returns resulting from dealing with them. Therefore, there is a need to accurately determine the cost of each customer and the return associated with dealing with him. The aim of the current research is to address theoretical concepts related to customer profitability analysis, as well as to clarify the methods used by companies for the purpose of distinguishing between profitable and unprofitable customers. The research also aimed to clarify the need for the company to achieve a balance between the returns resulting from dealing with the customer and making him satisfied with the services provided to him. Keywords: Customer profitability, Cost of Customer, profit of Customer. https://www.iasj.net/iasj/download/96a05df5b206a950
Short-term demand forecasting methods for blood – An applied study at the National Center for Blood Transfusion
Abstract: The current research deals with short term forecasting of demand on Blood material, and its’ problem represented by increasing of forecast’ errors in The National Center for Blood Transfusion because using inappropriate method of forecasting by Centers’ management, represented with Naive Model. The importance of research represented by the great affect for forecasts accuracy on operational performance for health care organizations, and necessity of providing blood material with desired quantity and in suitable time. The literatures deal with subject of short term forecasting of demand with using the time series models in order to getting of accuracy results, because depending these models on data of last demand, that is being stable in short term. The aim of research is decreasing forecasting’ errors of demand on blood units & plasma for period (2005-2007) through using three seasonal quantitative models for forecasting, these are multiplicative, additional, and Winters models, and choosing model that performs lowest amount from measures of forecast’ errors, these are mean squired error, mean absolute deviation, and mean absolute percentage error. The results of the research showed achievement the multiplicative model which is used in forecasting of demand of blood units, and the additive model which is used for plasma, to lowest amount of forecast’ error, and the recommendation was using these models for forecasting of demand. Keywords: Short term forecasting of demand, Blood material, Forecast’ errors. https://jeasiq.uobaghdad.edu.iq/index.php/JEASIQ/article/view/1213/1108
Application of business management in the field of football with study of the case of Spanish clubs (Real Madrid) and (Barcelona)
Abstract Football has progressed from being a ritual and a celebration to becoming an amateur sport, a professional sport, and now, increasingly, a commercial sport. In the analysis of the football business model, the systemic approach should be adopted. If sport is regarded as one of the business sectors, then the application of business system methodology can be fully justified. The interest in creating a strong football business system calls for the search for ways to popularize the football business model and boost the economic potential of its participants. In the research, literature tends to ignore the business processes within the sports business. Besides, the systemic approach in the football business is usually limited to presenting the structural elements of the system and the superficial analysis of their importance. In this research, we seek to structure football business by using the business system perspective. The case study used here so explains the football business system, and this had been done by analyzing the financial and sports strategies of Real Madrid and Barcelona Clubs over the period (2000-2006) and exploring the different ways in which sports organizations can succeed, and how their success can be measured. The outcome of this research is providing the universal structure of the football business system that enables a more thorough conceptual analysis of the football business. Keywords: Business Management, Football Industry, Real Madrid, Barcelona. https://jeasiq.uobaghdad.edu.iq/index.php/JEASIQ/article/view/914/820
Application of Manufacturing Resources Planning System (MRP II) in the High Education Sector
Abstract The current study deals with the use of MRP II in planning for graduate study in the Department of Economics, College of Administration and Economics, al-Mustansiriyah University. This the study aims to study the possibility of qualifying the Systems of Planning and Control over Production for functioning in the Higher Education sector; and the ability to benefit from the reports submitted by these systems in making decisions relevant to the best utilization of the available resources. Such decisions may include determining the proper number of students who may be seated by the aforesaid department. When preparing the reports of MRP II, statistical techniques and percentages were used to utilize classrooms, laboratories, and teaching staff capacities. The most significant conclusion of the study is the ability to employing MRP II system in the field of higher education; and the inability to generalize bill of materials (bachelor’s degree in economics) over the other departments of the college or the other economics departments in other universities, because bill of materials varies according to the educational system being used in the relevant college, the curriculum, and other variable factors. The most significant recommendations, however, are represented by conducting more studies relevant to applying other systems of planning and control in the educational institutions, such as the system of Enterprise Resources Planning (ERP), to enhance the accuracy of the planning process in these institutions. Keywords Manufacturing Resources Planning System (MRP II), Planning and Control Systems of Production, the High Education Sector. https://www.iasj.net/iasj/download/1447fe0322e783fa
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