Market Project Research Articles

Study on AI-Assisted Statistical Approach for Improving Stock Price Prediction Accuracy

Stock price (SP) prediction is crucial for financial decision-making, yet achieving excessive accuracy remains challenging due to market volatility. Current models frequently struggle with capturing the complexities of SP fluctuations, leading to significant prediction errors. This study aims to improve SP prediction accuracy through a unique technique that uses AI-assisted statistical techniques with the Redefined Spotted Hyena great-tuned Dynamic Gated Recurrent Unit (RSHDGRU). The dataset includes the closing costs of numerous stocks influenced through market demand, corporate performance, and economic situations. Pre-processing using Z-score normalization to standardize the statistics. The proposed RSH-DGRU model significantly outperforms traditional techniques, achieving a R-squared (R²) value of 0.9852, a Mean Absolute Error (MAE) of 15.624 and Root Mean Square Error (RMSE) of 20.321. These results reveal the effectiveness of the RSH-DGRU in minimizing prediction errors and accurately capturing the complexities of SP fluctuations. By evaluating its overall performance with present fashions, the RSH-DGRU technique showcases stronger predictive capabilities. Financial analysts and investors that have access to a strong instrument for more precise market projections make better-informed investment selections.

The Growing Trend of Cloud-Based Data Integration and Warehousing

This article examines the growing trend of cloud-based data integration and warehousing solutions in response to the exponential growth of data generation and the need for scalable, flexible analytics capabilities. It explores the key drivers behind cloud adoption, including data volume increases, demand for real-time insights, cost efficiencies, and advanced analytics requirements. The benefits of cloud solutions are discussed, such as scalability, cost-effectiveness, agility, and enhanced analytical capabilities. The article also addresses challenges like data security, vendor lock-in, and performance issues, providing best practices for successful implementation. Emerging trends in automated ETL processes, machine learning integration, and multi-cloud approaches are highlighted, showcasing the evolving landscape of cloud-based data management. Through analysis of market projections, case studies, and industry statistics, this comprehensive overview equips organizations with the knowledge to leverage cloud technologies to transform their data management capabilities and gain competitive advantages in the digital age.

استراتيجيات إدارة الأعمال الفعالة وتطبيقها في التخفيف من مخاطر مشاريع التطوير العقاري في المملكة العربية السعودية

The research intends to investigate efficient business management methods and their application in minimizing risks associated to real estate development projects in the Kingdom of Saudi Arabia. The questionnaires were delivered to a sample of 163 participants working in sectors connected to the study topic, and the data was examined using statistical analysis, which confirmed the reliability of the responses and the correctness of the findings. The findings highlight the essential significance that competent management methods play in decreasing the financial risks connected with real estate development projects. By implementing effective management practices, financial risks coming from market volatility, project delays, or cost overruns may be mitigated. The findings also revealed the need of good management methods in activating sustainable practices within these projects, since this contributes to the viability of projects in the long run by caring for the environment and resources. In addition, good management methods play a vital role in promoting transparency within real estate development projects, as they assist explain financial and administrative procedures, which adds to developing confidence among all parties engaged in the project. In conclusion, the research indicated that competent management is not only a tool for lowering risks, but also a vital aspect in assuring the sustainability and transparency of real estate developments in the Kingdom.

Development of an intelligent model for management of the requirements of marketing projects of the pharmaceutical industry

The object of research is the processes of managing the requirements of the marketing project using means of intelligent data analysis and artificial intelligence systems. Marketing projects include a large number of projects of various types, formats and scales. The work considers projects that are initiated at an existing pharmaceutical enterprise that produces drugs for the local market. Over a period of time, the company needs to introduce new dosage forms to the market. It is not about the development of new innovative drugs. Usually, these are new mixtures of known components, but according to the results of global research, adding new components to the formulation gives a new, additional effect. Also, it can be a new form, for example, a spray or syrup for children, etc. Nevertheless, in order to produce such a new product and bring it to the market, to make a profit, it is necessary to conduct a study of the local market in order to find analogues, consumer expectations and predict future cash flows from sales of new drugs. In order to form requirements for the project of creating and manufacturing a new medicinal form, the company initiates marketing projects. Such marketing projects are the subject of research. As a result of the marketing project of the analysis of the drug market, it is necessary to form the contours of a new product and the requirements for the project of creating this product – a new medicinal product. Collecting and forming requirements is not a trivial task. Therefore, in the work, it is proposed to apply means of intelligent data analysis, which will be implemented on artificial intelligence systems. As a result, an intelligent model for managing the requirements of marketing projects of the pharmaceutical industry was developed. The non-triviality of the task of requirements formation is based on the fact that it is multi-criteria. The desired requirements and, accordingly, the future product of the project depend on: the lack of a similar dosage form on the local market, the presence of theoretical and practical pharmacological developments in this direction, the technical and financial capabilities of the enterprise, the expectations of stakeholders and consumers of the product. The use of intelligent tools will allow to avoid a conflict of requirements and to develop a medicine that will not only meet the expectations of all participants of the local pharmaceutical market, but also provide profit to the developing company.

The impact of agricultural policy evolution on long‐run grain market projection

The impact of agricultural policy evolution on long‐run grain market projection

People – the forgotten chapter? From the EU’s neighbourhood policy to post-Brexit Ireland (north and south) – and lasting damage to the integrative capacity of the EU Internal Market project

People – the forgotten chapter? From the EU’s neighbourhood policy to post-Brexit Ireland (north and south) – and lasting damage to the integrative capacity of the EU Internal Market project

An Overview of Social Marketing in Türkiye

Background of the Country The Republic of Türkiye, is a transcontinental country located in East Thrace in south-eastern Europe and on the peninsula of Anatolia in southwestern Asia. With the collapse of the Ottoman Empire, which was an immense power in Eurasia and Africa from southeast Europe to western Asia and North Africa, and the proclamation of the Republic in 1923, the Turkish people gained the right to self-government for the first time in centuries. With an amendment to the Constitution of the Republic, Türkiye became governed by a new system in which the president has executive powers. Türkiye ranked 48th out of 191 countries in the 2022 Human Development Index. Primary social Problems in the Country Unemployment has been a major socio-economic problem in Türkiye. Other important social issues include the integration of irregular migrants into social life, education inequality, violence against women, and forest fires. Use of Social Marketing Although they have not been found to be sufficient to solve the fundamental social problems there are projects that have been carried out in Türkiye for many years. These projects are corporate social responsibility initiatives that focus on market research to solve a fundamental social problem, in a select target market in a bid to change behavior through effective communication. Academic Stakeholders and Activities The few social marketing projects carried out in Türkiye have been funded by marketing academics and have focused primarily on the development of environmentally friendly behaviors. Social marketing courses are included in the curricula of a small number of universities in Türkiye. It can be stated that the concept of social marketing in Türkiye is not clearly understood by practitioners. However, efforts to develop behavior change for the benefit of society seem to have started with charity and evolved into corporate social responsibility projects. Discussion and Conclusions Despite not being deemed sufficient to solve the fundamental social problems, there are projects that have been ongoing in Türkiye for many years. These projects are corporate social responsibility initiatives aimed at addressing crucial social issues. Social marketing has been used in Türkiye for four decades but still has not achieved its full potential. Because social marketing is a concept similar to the understanding of the philanthropy that existed in Turkish culture many centuries ago, it can easily be adapted to the Turkish market. It is recommended that the government of Türkiye use social marketing to promote behavioral practices that benefit society.

Enhancing IT Project Portfolio Management Through Dynamic Dashboard Visualization and Integrated Chatbot and Mathematical ML Model for Strategic Decision Making

This project paper presents an innovative approach to enhance IT Project Portfolio Management (IT PPM) using dynamic dashboard visualization and machine learning (ML) techniques. Effective IT PPM is essential for organizations to align IT projects with business objectives and optimize resource allocation. However, conventional methods often lack innovation and real-time visibility, leading to suboptimal decision-making and costly software investments. The objective of this project is to develop a dynamic dashboard visualization system integrated with a machine learning model derived from the Meta-Portfolio Method (MPM). The MPM leverages advanced analytics and historical project data to dynamically allocate resources and prioritize projects based on risk-return profiles. By incorporating the MPM into the dashboard, organizations gain real-time insights into project status, progress, and resource allocation, enabling informed decision-making and proactive portfolio optimization. The ML model derived from the MPM analyzes historical project data, including success rates, resource intensity, criticality, regulatory compliance, and portfolio diversification, to predict optimal project selection and resource allocation. By dynamically adapting to changing market conditions and project dynamics, the ML model facilitates strategic decision-making and enhances portfolio performance. Through the integration of dynamic dashboard visualization and ML-driven decision support, organizations can streamline IT PPM processes, improve project outcomes, and achieve strategic objectives effectively and efficiently. This innovative approach empowers organizations to navigate the complexities of IT project portfolios with agility, foresight, and confidence.

Powering Through the Turn: Finding Time for Concept Exploration Before Industry Stagnation

This study examines how the tension between exploration and exploitation affects early-stage development within the engineering teams of large corporations. Using survey data collected from over 900 system engineers and managers, it was observed that exploration decreased as an organization’s market growth declined, but dire market projections prompted a refocus on exploration. In addition, engineers routinely desire more concept exploration time than they perceive that they have available. The authors argue that engineering teams should more intentionally consider their innovation strategy, and that companies with stagnant market growth should invest in concept exploration before they get to a period of market decline.

Male contraceptive acceptability versus male acceptance of contraceptive responsibility.

Despite the projected impact of new male contraceptives, resources and investments directed at their development remain limited in part due to concerns that men would not actually use them. Now, more than 30 studies have been conducted over the last 30 years-regionally and internationally, within clinical trials, and across populations-examining men and women's attitudes towards new male contraceptive methods, all consistently demonstrating interest in and willingness to use new methods. Yet even these studies, inclusive of competitive contraceptive market projections, seem not to be convincing enough. Rather than study whether men would be willing to use male contraceptives, more resources should be devoted to developing the infrastructure and supporting the cultural changes needed to ensure that when new male contraceptives inevitably emerge, that they will be disseminated quickly and made readily accessible. Men's views on what their roles are in society, families, relationships, and pregnancy prevention are changing in ways that may impact what they consider to be acceptable contraceptive risks. As society moves toward more gender equitable beliefs, men's positive involvement in contraception might organically develop into an expected behavior. Interventions aimed at sensitizing men toward gender equitable beliefs may pay dividends in improving male contraceptive acceptability. The current lack of a reversible male contraceptive method prevents us from collecting data that might disprove presumptions that men would be unwilling to take on responsibility for pregnancy prevention. However, studies of men's involvement in (1) over-the-counter emergency contraception, (2) vasectomy, and (3) abortion offer case studies for men's increasing consciousness of opportunities for shared contraceptive responsibility, the structural and sociopolitical barriers that men face when trying to participate in family planning, and how these might translate into new male contraceptive interest and development.

Embodied carbon saving of reusing concrete elements in new buildings: A Swedish pilot study

Reusing the building elements is the highest possible level of circularity for buildings that must be demolished, potentially slowing down climate change. This study explores the embodied carbon reduction of construction of a pilot building with structural elements of reused concrete. The assessment focuses on applying different methodological approaches and discussing the upscaling opportunities of reusing concrete elements from a global warming potential perspective. The assessment shows large embodied carbon savings compared to conventional building practices like recycling the concrete and building with new low-carbon and prefabricated elements. Embodied carbon saving is also obvious when applying alternative system modelling, future market projection and different allocation approaches of the production emissions of the elements. Finally, the study emphasises the need for further research in evaluating the benefits of reusing structural concrete elements broadly, like including the deconstruction impact related to elements for reuse, to be able to draw general conclusions.

Mangrove-Based Carbon Market Projects: 15 Considerations for Engaging and Supporting Local Communities

Mangroves provide numerous ecological, social, and economic benefits that include carbon sequestration, habitat for biodiversity, food, recreation and leisure, income, and coastal resilience. In this regard, mangrove-based carbon market projects (MbCMP), involving mangrove conservation, protection, and restoration, are a nature-based solution (NbS) for climate change mitigation. Despite the proliferation of blue carbon projects, a highly publicized need for local community participation by developers, and existing project implementation standards, local communities are usually left out for several reasons, such as a lack of capacity to engage in business-to-business (B2B) market agreements and communication gaps. Local communities need to be engaged and supported at all stages of the MbCMP development process to enable them to protect their ecological, economic, and social interests as custodians of such a critical ecosystem. In this paper, we provided 15 strategic considerations and recommendations to engage and secure the interests of local communities in the growing mangrove carbon market trade. The 15 considerations are grouped into four recommendation categories: (i) project development and community engagement, (ii) capacity building and educational activities, (iii) transparency in resource allocation and distribution, and (iv) partnerships with local entities and long-term monitoring. We expect our study to increase local participation and community-level ecological, social, and economic benefits from MbCMP by incorporating equitable benefit-sharing mechanisms in a B2B conservation-agreement model.

Evolving nature-based solutions for Australia’s Indigenous estate in 2024 – opportunities and challenges

Globally, there is growing interest and potential for investment in Nature-based Solutions (NbS) to protect, manage or restore ecosystems through incentive schemes including Payment for Ecosystem Services (PES), Nature Repair, Carbon Markets, Common Asset Trusts. Collectively, these NbS markets establish interchangeable mechanisms to help address biodiversity and climate crises, as well as socio-economic issues concerning many Indigenous Peoples and Local Communities (IPLCs). IPLCs manage and/or own almost 32% of the world’s land area through customary and community-based tenure arrangements. Since 2000 several PES schemes have been implemented in Australia. These schemes have been overwhelmingly implemented as public-good expenditure, with governments providing ~90% of total funding. Indigenous people have either the legal right to run or veto a carbon market or nature repair project over 57% of the Australian land mass, increasing to 63% in savanna and 85% in desert regions. Here we critically assess opportunities and challenges for Australia’s Indigenous estate associated with existing and emerging NbS market approaches, especially the Commonwealth’s current Savanna Fire Management (SFM) and Human Induced Regeneration methods, evolving Integrated Farm & Land Management (IFLM) and Blue Carbon methods, and proposed Nature Repair (NR) market. To date, Indigenous NbS interests have focused especially on SFM across northern Australia, with prospective opportunities especially for IFLM and NR markets. Most available schemes focus on remediation of degraded lands and seas, ignoring cost-effective investment opportunities to maintain habitats and ecosystems in less-degraded condition. Government-supported Common Asset Trusts can provide effective models for governance of stewardship schemes relevant to on-going care for relatively intact ecosystems. In Discussion we summarise key methodological, institutional, and policy opportunities and challenges for constructive Indigenous engagement with developing NbS markets. Our purpose is to provide an Indigenous land and sea management context to inform development of rapidly evolving NbS markets in Australia.

Formulation challenges associated with microbial biofertilizers in sustainable agriculture and paths forward

AbstractSustainable increase in agriculture productivity is confronted by over‐reliance and over‐use of synthetic chemical fertilizers. With a market projection of $5.02 billion by 2030, biofertilizers are gaining momentum as a supplement and, in some cases, as an alternative to chemical fertilizers. Biofertilizers can improve the nutritional supply to the plant and simultaneously can improve soil health, reduce greenhouse emissions, and hence directly contribute towards environmental sustainability. Plant growth‐promoting microbes (PGPMs) are particularly receiving significant attention as biofertilizers. They are widely known for their ability to improve plant growth via increasing nutrient availability and use efficiency. However, except for a few successful cases, the commercialization of PGPM‐based inoculants is still limited, mainly due to lack of field efficacy and consistency. Lack of effective formulation technologies that keep microbial inoculants viable during storage, transport and field application is considered one of the key factors that drive inconsistent efficacy of microbial biofertilizers. In this review, we identify current challenges associated with the application and formulation of microbial inoculants. We propose future paths, including advancement in formulation technologies that are potentially efficient, eco‐friendly and cost‐effective. We argue that to enhance the global adoption of biofertilizers, new innovations based on transdisciplinary approaches are indispensable. The emerging framework should encompass a robust quality control system at all stages. Additionally, the active partnership between the academic and industry stakeholders will pave the way for enhanced global adoption of microbial fertilizers.

Co-digestion of Sewage Sludge and Biowaste for Biogas Production, GHG Avoided Emissions, and Profitable Carbon Credit Development

In Estonia, where biowaste comprises a significant portion of municipal waste, efficient waste management strategies are essential. Co-digestion presents a promising avenue to enhance biogas yield and reduce greenhouse gas emissions. This study explores the potential of co-digesting sewage sludge and biowaste for biogas production, emphasizing carbon credit calculation and offset project development. Sewage sludge was effectively utilized as a co-feedstock for the co-digestion process, contributing to increased biogas production. In Narva City, 20,401.08037 m3 /year of biogas was produced in 2012, facilitating the generation of electricity from renewable sources and thus reducing GHG emissions, which facilitates the calculation of carbon credits. We developed a robust carbon offset project based on the biogas volume, meeting additionality criteria and demonstrating long-term benefits. The revenue potential from carbon credits ranged from 118 EUR to 41300 EUR, depending on market prices and project attributes. Moreover, the offset project and calculated carbon credits offer tangible benefits to sustainable waste management and the implementation of the circular economy. By valorizing sewage sludge and biowaste through anaerobic digestion, the project contributes to waste diversion from landfills, reduces methane emissions, and promotes renewable energy generation. This integrated approach aligns with principles of sustainable waste management and supports the transition towards a circular economy model. Our findings provide valuable insights for policymakers and stakeholders interested in leveraging anaerobic digestion for renewable energy production and carbon mitigation.

The Comparison Between Random Forest and LSTM Models Based on the Gold Price Prediction

The gold price forecast is crucial for financial institutions, investors, mining firms, and associated enterprises. Precise price prediction is especially important in the gold market because of its volatility and unpredictability; this has important consequences for strategy formulation and decision making. In order to give practitioners a better prediction technique to increase the accuracy of market trading, the aim of this study is to assess the predictive effectiveness of the two models in predicting the gold market. The study forecasts gold prices using the Random Forest and Long Short-Term Memory (LSTM) models, comparing their performances using five assessment metrics. The end findings prove that the Random Forest method outperforms the LSTM model in comparison. In particular, the Random Forest model predicts more accurately and with less inaccuracy. The study finds that, while the Random Forest model is more accurate, both the LSTM model and the Random Forest model are appropriate techniques for gold market projections. To increase the precision and consistency of the predictions, future studies will keep refining the LSTM model and look for causes for its subpar performance.

The Quest for the Holy Grail Of 3D Printing: A Critical Review of Recycling in Polymer Powder Bed Fusion Additive Manufacturing.

The benefits of additive manufacturing (AM) are widely recognised, boosting the AM method's use in industry, while it is predicted AM will dominate the global manufacturing industry. Alas, 3D printing's growth is hindered by its sustainability. AM methods generate vast amounts of residuals considered as waste, which are disposed of. Additionally, the energy consumed, the materials used, and numerous other factors render AM unsustainable. This paper aims to bring forward all documented solutions in the literature. The spotlight is on potential solutions for the Powder Bed Fusion (PBF) AM, focusing on Selective Laser Sintering (SLS), as these are candidates for mass manufacturing by industry. Solutions are evaluated critically, to identify research gaps regarding the recyclability of residual material. Only then can AM dominate the manufacturing industry, which is extremely important since this is a milestone for our transition into sustainable manufacturing. This transition itself is a complex bottleneck on our quest for becoming a sustainable civilisation. Unlike previous reviews that primarily concentrate on specific AM recycling materials, this paper explores the state of the art in AM recycling processes, incorporating the latest market data and projections. By offering a holistic and forward-looking perspective on the evolution and potential of AM, this review serves as a valuable resource for researchers and industry professionals alike.

Quantitative Easing and Climate Change: A Case Study

In the context of increasing global climate change, this article takes an in-depth look at how central banks can use quantitative easing to help address this issue. Based on three research methods: Green Bond, SDGs Bond, Carbon Trading Market and Carbon Reduction Project, this paper analyzes the pros and cons of specific measures of quantitative easing in dealing with climate change. The study found that the above three types of quantitative easing policies can effectively guide funds to green industries, promote green economy and sustainable development, and thus mitigate the economic impact of climate change. This research not only provides a new perspective for central bank policy making, but also provides investors with new opportunities for green investment. This paper suggests that the central bank should increase its support for green projects when formulating quantitative easing policies, and encourage investors to actively participate in green investment to jointly promote the response to climate change.

Negotiating North-South relations: The World Bank’s Grain Marketing Project in revolutionary Ethiopia, 1974–1978

This article explores the driving reasons that shaped the World Bank policy toward Africa in the 1970s through the lens of the relationship between the World Bank, western countries, and revolutionary Ethiopia from 1974 to 1978, during the negotiations for the Grain Marketing project (GMP). The trajectory of the GMP reveals that the World Bank’s attitude was shaped by external and internal factors as well. The organization operated under pressure of major Western shareholders and associated business groups demanding compensation for the nationalization of their assets. The continuation of negotiations and the final approval of the GMP, however, were primarily shaped by the demands of the Ethiopian government and the internal confrontation between the advocates of macro-economic orthodoxy and the supporters of a flexible approach to development. According to the latter, the relationship with Addis Ababa was a critical test for the World Bank’s legitimacy in sub-Saharan Africa at a time when demands for a New International Economic Order challenged established patterns of North-South relations.

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market’s complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder’s effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry.