Production of biodiesel represents a way to attain economic growth by increasing and securing energy supply for the developing countries, and it can also create job opportunities and an attractive source for the farmers. The research aims to utilize the Citrullus Colocynthis (L.) plant as a valuable alternative for producing biodiesel. The attention was drawn to the Citrullus Colocynthis (L.) due to the plant having the advantage of needing less water compared to other plants. Also, the Citrullus colocynthis plant can be planted in different climate conditions, so it has a good impact on the production capacity. Furthermore, the use of p-p-toluene-4-sulfonic acid monohydrate (PTSA) as an acid catalyst for pretreatment of Citrullus Colocynthis (L.) is the first time to use the acid as a catalyst with Citrullus Colocynthis (L.) oil to produce biodiesel. Citrullus colocynthis (L.) plant seeds were collected from western and northern Sudan; the oil was extracted through screw press extraction using an oil extractor; and the Citrullus colocynthis oil (CCO) was treated to reduce the free fatty acid (FFA) contents before starting the transesterification reaction using 0.3% p-toluene-4-sulfonic acid monohydrate (PTSA) as an acid catalyst. FFA was reduced to 0.3%, and FFA conversion was 91.76%. The final biodiesel produced was found to be 98.51%. The physiochemical properties of biodiesel were flash point 228°C, measured by the Seta Multiflash Cleveland Flash Point Tester; kinematic viscosity at 40°C, 5.094 cSt, measured by the viscometer (Petro Test Instrument Model TV400); pour point, -30°C, measured by the Cloud and Pour Point Test Cabinet (Norma Lab Analysis Instrument Model P592-France); and density at 150°C, 0.878 gm/cm³, measured by the Digital Density Meter (Petro Instrument Model DMA4500). The results attained in this study conformed to the international standard specifications for biodiesel fuel. The results of this study show that the Citrullus Colocynthis (L.) oil has the potential for use as an industrial feedstock for biodiesel production. PTSA can be used as a catalyst in the pretreatment of CCO via esterification reaction, where it showed very high catalytic activity to reduce the free fatty acids (FFA) content in the CCO; also, it has less environmental impact due to its ease of recoverability and reusability.

The article substantiates the task of creating and programmatically implementing artificial consciousness (AS), its model and architecture. To solve the problem of creating an automated control system, a platform is proposed that includes ten levels, starting from the basic level of collecting and systematizing information about the outside world and ending with the upper level of human-coordinated impact on it and the level of decision-making. In conclusion, the most important, from the programmer's point of view, properties of the software product characterizing artificial consciousness are given and its model with a fragment of the program code is briefly described.

This study explores the integration of artificial intelligence (AI) into aerodynamic optimization processes for Unmanned Aerial Vehicle (UAV) prototypes in subsonic wind tunnel environments. Traditional aerodynamic testing, while reliable, often demands extensive manual parameter adjustments and prolonged experimental cycles. By incorporating AI-driven computational models, machine learning algorithms, and real-time data analytics, we demonstrate a more efficient approach to shape refinement, drag reduction, and stability enhancement. Our results show that AI-based optimization reduces testing time by up to 35% while improving lift-to-drag ratios and aerodynamic stability. The findings underscore the potential of AI to transform UAV design cycles, reduce costs, and accelerate the deployment of advanced aerial systems.

This study presents a novel approach to real-time wind tunnel data reduction by integrating a JR3 six-axis force-torque sensor with machine learning algorithms. Traditional aerodynamic testing often involves large volumes of raw data from force balances, which require extensive post-processing. This paper proposes a machine learning-based model that accelerates the data reduction pipeline, allowing for near-instantaneous derivation of aerodynamic coefficients from JR3 balance data. The framework includes a synchronized data acquisition module, signal preprocessing, a trained regression model, and an interactive visualization tool. Results show that the proposed system can achieve real-time performance while maintaining high accuracy, significantly reducing the computational and time costs associated with wind tunnel testing.

Critical infrastructures, such as transportation, healthcare, and energy systems, are becoming increasingly interconnected, creating an urgent need for secure and efficient data sharing between agencies. However, the complexity of inter-agency collaboration is heightened by significant challenges, including privacy concerns, regulatory constraints, and inherent security risks. To address these concerns, Federated Learning (FL), a machine learning technique that facilitates the collaborative training of models across decentralized data sources without the need to transfer sensitive data, has emerged as a highly promising solution. FL ensures that agencies can jointly leverage the power of data-driven insights while ensuring privacy preservation. This paper investigates the potential of federated learning as a means to enable secure, scalable data collaboration between agencies in critical infrastructure sectors. We propose a novel federated learning framework tailored specifically for these sectors, taking into account sector-specific data requirements, regulatory frameworks, and security needs. Additionally, we discuss the effectiveness, challenges, and limitations of the proposed framework, as well as explore its potential for future applications and advancements. This paper aims to contribute to the growing body of research on privacy-preserving machine learning solutions in high-stakes, sensitive environments.

The increasing unpredictability of environmental events due to climate change has amplified the need for more resilient infrastructure and supply chains. Integrating climate-aware decision intelligence into planning processes can significantly improve the ability of organizations and industries to manage these risks effectively. This paper explores the crucial role of incorporating environmental risk assessments into infrastructure and supply chain planning. We propose a decision intelligence framework that combines real-time climate data, predictive modeling, and dynamic simulation techniques to inform decision-making. This approach aims to enhance the adaptability and sustainability of infrastructure and supply chains in response to climate-related challenges. The paper also reviews existing methodologies in environmental risk management and highlights case studies that demonstrate the practical application and success of such frameworks. By integrating predictive analytics and climate risk data, decision-makers can identify potential disruptions and make more informed decisions to mitigate these risks. The proposed solution not only improves resilience but also enables organizations to proactively adjust to changing environmental conditions, ensuring long-term operational stability. In this context, climate-aware decision intelligence becomes an essential tool for organizations seeking to future-proof their infrastructure and supply chain operations against the growing threat of climate change. This paper outlines the benefits and applications of the proposed framework and suggests future directions for research in this evolving field.

Financial analytics in emerging economies is evolving rapidly with the increasing deployment of Management Information Systems (MIS). These platforms allow businesses, governments, and financial institutions to integrate diverse financial data, generate real-time insights, and apply predictive models to support strategic decision-making. Emerging economies face unique challenges such as limited infrastructure, fragmented data flows, and insufficient digital literacy that often restrict the efficiency and transparency of financial ecosystems. MIS platforms provide a structured approach to overcoming these barriers by enabling automated reporting, reducing human error, and supporting more reliable financial forecasting. This paper investigates the role of MIS-driven financial analytics in advancing transparency, accountability, and sustainability in developing financial systems. Through a review of existing literature, we examine how MIS supports credit scoring, fraud detection, SME financing, and policy formulation. We also propose a methodology that integrates data collection, predictive modeling, and dashboard visualization to improve financial governance and investor confidence. While challenges related to cost, interoperability, and regulatory alignment persist, the broader implication is clear: MIS platforms can serve as foundational tools for inclusive and sustainable financial growth, positioning emerging economies to align with global financial standards.

The apparel and textile industry faces pervasive opacity along its global supply chains, with fragmented data across disparate systems, limited end-to-end provenance, and rising compliance and ethical concerns. We present a comprehensive framework called Blockchain and ERP-Integrated MIS (BE-IMIS) designed to deliver transparent, auditable, and efficient supply chains for apparel and textiles. BE-IMIS combines a permissioned blockchain layer for immutable provenance, an enterprise resource planning (ERP) core for transactional data, and a management information system (MIS) layer for analytics and decision support. The architecture supports GS1-compliant data exchange, IoT/ RFID-enabled traceability, and smart contracts to enforce business rules and certifications. We detail the reference architecture, data model, governance, integration strategy with ERP (e.g., SAP S/4HANA), and MIS tools, and provide an evaluation plan along with preliminary findings from a lab-based pilot. Our contributions include [1] a layered, interoperable architecture for ERP–MIS–blockchain integration in apparel supply chains, [2] a scalable data model and smart contracts for end-to-end provenance, [3] a practical integration blueprint leveraging industry standards, and [4] an evaluation framework to quantify improvements in traceability, data integrity, and audit readiness.

Industrial operations increasingly rely on Internet of Things (IoT) sensors to monitor machine health, process variables, and environmental conditions. This paper presents an end-to-end approach for deploying IoT-driven predictive maintenance dashboards that transform raw sensor streams into actionable maintenance decisions. We describe a scalable data architecture for real-time ingestion, processing, and storage; predictive models for remaining useful life (RUL) estimation and anomaly detection; a health-score framework that synthesizes multiple indicators; and a dashboard design that supports operators, maintenance planners, and line managers. A pilot deployment in a manufacturing setting demonstrates measurable improvements in asset uptime, reduced mean time to repair (MTTR), and more efficient maintenance scheduling. Key contributions include [1] an integrated IoT-to-dashboard framework bridging data science and operations, [2] a modular modeling approach combining time-series forecasting and anomaly detection with interpretable health scores, [3] a dashboard design guided by human factors and decision-support needs, and [4] practical guidelines for data governance, security, and deployment. The results indicate that well-designed predictive dashboards can shorten decision cycles, increase asset availability, and reduce maintenance costs while maintaining data quality and security.

The increasing adoption of renewable energy sources, particularly solar power, has created a need for efficient integration into smart grids. This paper investigates the combination of Internet of Things (IoT) technology with solar energy systems, focusing on real-time monitoring and control to improve system performance and optimize energy utilization. A key component of this system is the bidirectional DC-DC converter, which facilitates smooth energy flow between solar power generation, storage, and the grid. This paper presents an innovative approach that integrates IoT-based monitoring systems with bidirectional DC-DC converters to enhance solar energy management in smart grids. The proposed system allows for continuous monitoring of energy production and consumption, ensuring optimal performance and seamless interaction between the solar energy system and the grid. The system’s performance is evaluated across three main metrics: energy efficiency, communication reliability, and scalability. Results from our evaluation show significant improvements in grid stability, energy management, and cost-effectiveness. The system’s ability to dynamically respond to energy fluctuations and provide real-time data for decision-making positions it as a promising solution for enhancing the performance of smart grids and optimizing solar energy usage. This paper highlights the potential of integrating IoT technologies and advanced power electronics for the future of renewable energy integration.

Enterprise business intelligence platform migration represents a critical organizational transformation requiring careful orchestration of technical, financial, and human factors. The comprehensive framework presented addresses the multifaceted challenges organizations face when transitioning from legacy reporting systems to modern analytics platforms. Strategic assessment begins with systematic usage evaluation and asset prioritization, enabling informed decisions about migration scope and resource allocation. Platform evaluation encompasses total cost of ownership considerations, feature compatibility assessment, and long-term scalability requirements. Technical validation ensures seamless transition through systematic documentation of data dependencies, function compatibility testing, and proof-of-concept development for complex reporting assets. The implementation framework emphasizes parallel system operation during migration phases, comprehensive user acceptance testing protocols, and iterative feedback incorporation. Change management strategies focus on stakeholder communication, training program development, and phased decommissioning processes that minimize operational disruption. Organizations implementing this systematic framework achieve successful platform transitions while maintaining data integrity, user adoption, and business continuity. The methodology provides practical guidance for managing complex enterprise migrations, addressing both immediate technical requirements and long-term organizational objectives through structured implementation phases.

In this study, zinc oxide nanoparticles were produced using a special method (co-precipitation), which had a hexagonal shape, in which zinc acetate serving as a starting material. The structure and optical characteristic were examined using technique X-ray diffraction (XRD), IR spectroscopy, and UV-Visible absorption spectroscopy. The XRD analysis confirm the formation of zinc oxide (ZnO) particles and via Scherrer equation lattice parameters and average crystallite size was find out. Furthermore, the dielectric properties like permittivity and dielectric loss of ZnO were analyzed by different frequencies and temperature to understand the potential of electronic application of Zinc Oxide.

Quantum-inspired nanobiotechnology re-conceptualizes conventional bioassays by utilizing the waveparticle duality in quantum physics. Rather than considering photons, electrons or excitations strictly as wave or particle, the aim is to develop assays that exploit both aspects of their duality for ultra-sensitive, specific and informative bioassay designs. Wave-oriented assays utilize interference and coherence to amplify signals from a single molecule, particle-centric assays quantitatively count discrete events, such as an electron tunnelling through a nanopore, or photons emitted from a quantum dot; and correlation-centric assays exploit entanglement and quantum correlations to surpass classical noise thresholds. In total, these forms of bioassays provide powerful designs for In Vitro diagnostics, live-cell imaging and translational medicine. In this narrative review, we provide an overview of wave-centric, particle-centric, and correlation-centric bioassays, clarify their advantages and disadvantages, and point out the potential to merge and expand with artificial intelligence (AI), hybrid nanodevices that incorporate nanobiotechnologies, and nanotheranostics. We also cover health and safety, biocompatibility, ethical, and regulatory considerations that need to be considered in order to transition quantum-inspired bioassays from the lab to the clinic. Ultimately, we will suggest a roadmap for the next decade of this rapidly developing field.