Real-time Data Integration Research Articles

Developing a Dynamic Business Strategy: From Research and Goal Setting to Implementation and Evaluation

A dynamic company plan was essential for retaining competitiveness in today’s continuously shifting corporate climate. This review paper explored the effectiveness of combining continuous research, flexible goal formulation, agile execution, and continuing assessment to provide a strong framework for adjusting to market changes, technological breakthroughs, and competitive challenges. This review conducted a comprehensive evaluation of literature published using data from Scopus and Google Scholar. It employed thematic and meta-analytical methodologies to identify the key elements of dynamic strategy creation. The main discoveries consisted of the significance of using real-time data analytics to make well-informed decisions, the need for flexible goal-setting frameworks, and the advantages of Agile and Lean techniques in carrying out tasks. Furthermore, the implementation of ongoing evaluation and feedback processes was essential for improving tactics and maintaining a competitive edge. The review also highlighted problems such as the integration of real-time data, the establishment of flexible goals, and the promotion of organisational agility. Subsequent investigations prioritised examining the effects of developing technology and dynamic tactics on small and medium-sized firms (SMEs). This examination offered a thorough comprehension of how to manage and flourish in the current dynamic business environment by combining theoretical ideas and practical experiences. Keywords: Dynamic Business Strategy Continuous Research, Flexible Goal Formulation, Agile Execution Ongoing Assessment, Real-Time Data Analytics, Agile Methodologies Lean.

Facilitating Circular Transition in the Construction Industry: Optimizing a Prefabricated Construction Site Layout Using a Novel BIM-Integrated SLP-GA Model

Prefabricated construction is being developed as one of the pathways toward circularity in the construction industry. However, compared to traditional cast-in-place construction methods, the design of prefabricated construction site layouts presents unique challenges, such as managing the space for prefabricated components and lifting equipment, and coordinating the precise timing between off-site fabrication and on-site assembly. Existing research has primarily focused on traditional cast-in-place construction, leaving room for improvement in optimisation models for prefabricated site layouts. This study develops a BIM-based System Layout Planning-Genetic Algorithm (SLP-GA) model specifically tailored for prefabricated construction site optimisation. The proposed model improves the accuracy and visualisation of layout planning through BIM technology, enabling dynamic adjustments and real-time data integration. It also incorporates genetic algorithms to address complex multi-objective optimisation problems, avoiding local optima and overcoming the limitations of traditional SLP methods that rely on subjective judgements. Unlike previous studies that do not consider secondary handling, the optimisation objectives of this study focus on minimising material handling costs associated with secondary handling and maximising comprehensive relationships, including efficiency, safety and space utilisation. The application of this model in a case study shows a reduction in logistics costs of 8.58% and an improvement in comprehensive relationships of 11.61%, indicating significant improvements. This research advances optimisation methods for prefabricated construction site layouts, enriches optimisation objectives by considering secondary handling, and provides practical guidance for improving the efficiency and effectiveness of prefabricated construction projects.

Reservoir Operation based Machine Learning Models: Comprehensive Review for Limitations, Research Gap, and Possible Future Research Direction

The operation of dams and reservoirs is critical for water resource management, including flood control, irrigation, hydropower generation, and environmental conservation. Traditional optimization techniques like Dynamic Programming (DP), Linear Programming (LP), and Nonlinear Programming (NLP) have been foundational in managing these operations. However, they often fall short in addressing the complexities of modern water management challenges posed by climate variability and increasing water demands. Machine learning (ML) techniques have emerged as powerful tools to enhance the efficiency and accuracy of dam and reservoir operations. This paper provides a comprehensive review of various ML models, including Neural Networks, Genetic Algorithms, Decision Trees, and Ensemble Methods, highlighting their applications in predicting reservoir inflows, optimizing water release schedules, and improving flood risk management. Notably, ML models like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) have shown significant improvements in forecasting accuracy and operational decision-making. Despite these advancements, several limitations and research gaps persist, including the need for real-time data integration, adaptive learning mechanisms, and models that consider socio-economic and climatic factors. This review underscores the importance of addressing these gaps to develop more robust and generalizable ML models. Future research directions are suggested to focus on hybrid models combining ML with traditional optimization techniques, comprehensive validation across diverse conditions, and the integration of ecological and economic considerations. By systematically identifying and addressing these limitations, this research aims to pave the way for more effective and sustainable dam and reservoir management practices. Leading towards suggesting that enhancing real-time data integration and developing adaptive learning mechanisms are in order to improve model responsiveness.

Effect of Data Integration Techniques on Operational Efficiency in Manufacturing Industries in Iran

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.

Utilizing a Dashboard for Efficient Biodiversity Data Overview and Gap-Filling Effort

Taiwan Biodiversity Information Facility has been leading the effort to integrate and mobilize biodiversity data nationally. We recently accomplished the integration of local databases and successfully developed a data portal that efficiently links to the most comprehensive biodiversity database in Taiwan: the Taiwan Biodiversity Information Alliance Data Portal. However, we identified an emerging challenge following this development. Preliminary analyses have revealed several gaps in the current dataset, impeding robust data use. For instance, land data dominates over sea data, and bird records are more abundant than those of other taxonomic groups. In our endeavour to enhance national biodiversity data coverage, we are actively exploring methodologies to address these existing data gaps. Here, we broadly define data gap as the absence of required data or information for use. Our primary objective is to empower users to identify these gaps, thereby facilitating efficient, informed survey planning and eventually filling these gaps. For this purpose, we are evaluating the effectiveness of a user-friendly dashboard, tailored for biodiversity survey planning in Taiwan. We developed and deployed a R Shiny dashboard*1 that provides users with insightful data visualizations that highlight spatial, temporal, and taxonomical data overviews. It also offers customizable filtering options, such as by major taxon groups and habitat types, for easy viewing. Additionally, the dashboard includes a download function, so users can access spatial and taxon gap information in their survey planning efforts. After deploying the dashboard, we conducted user interviews to collect feedback aimed at refining its usability and effectiveness. In addition to helping survey planning, we anticipate that this tool will also aid data users in promptly assessing data integrity within the national biodiversity data portal, thereby enabling them to identify potential data gaps and biases that may affect subsequent use. Through this presentation, we aim to share our experience in developing an interactive dashboard tailored for public use. Preliminary results had indicated that the dashboard highlighted some visible data gaps and biases, particularly in underrepresented areas such as marine habitats and less-studied taxonomic groups. Additionally, user interviews revealed a demand for a feature that provides single species data overviews, suggesting a potential area for further development to enhance the tool's utility. Some challenges remain, such as the need to enhance data granularity and user interface usability. Future work will focus on incorporating more real-time data integration and expanding the range of customizable filters. We believe these efforts will further empower users in Taiwan to conduct more informed and efficient biodiversity surveys, ultimately supporting research and policy-making to address critical biodiversity data gaps.

Advancing Healthcare Research Through Live-Data Integration: Implementing IEEE 11073 and FHIR in Clinical Environment.

The efficient direct integration of real-time medical device data is a promising approach to improve patient care enabling a direct and eminent intervention. This study presents a comprehensive approach for integrating real-time medical device data into clinical environments using the HL7® FHIR® standards and IEEE 11073 Service-Oriented Device Connectivity (SDC). The study proposes a conceptual framework and an opensource proof-of-concept implementation for real-time data integration within the Medical Data Integration Center (MeDIC) at UKSH. Key components include a selective recording mechanism to mitigate storage issues and ensure accurate data capture. Our robust network architecture utilizes Kafka brokers for seamless data transfer in isolated networks. The study demonstrates the selective capturing of real-time data within a clinical setting to enable medical device data for a down-stream processing and analysis.

Predictive modeling of combined cycle power plant performance using a digital twin-based neural ODE approach

This study presents a novel digital twin-based predictive modeling approach to enhance the operation, maintenance, and management of combined cycle power plants. The research aimed to accurately forecast the combined cycle power (CCP) output, a critical performance indicator, to support intelligent decision-making for plant operators and engineers. The proposed framework leverages a neural ordinary differential equation (NODE) model integrated within a digital twin architecture to capture the complex, nonlinear dynamics of combined cycle gas turbine unit (CCGU) system. The predictive model was trained and validated using multivariate time-series data collected from a CCGU, achieving high accuracy with an R2_score of 0.993, a mean absolute percentage error of 0.325 %, and a root mean squared error of 1.518. Importantly, the NODE model demonstrated superior forecasting capabilities compared to traditional machine learning techniques. The digital twin (DT) concept enables seamless real-time data integration, physics-based models, and advanced data analytics to facilitate comprehensive CCGU lifecycle management. This novel approach provides operators with reliable, real-time insights to optimize plant performance, reduce maintenance costs, and improve overall sustainability. The generalization of the NODE model to an independent CCGU unit validated its applicability across diverse power plant configurations, highlighting the originality and practical value of the research. The key contribution of this work is the development of a digital twin-based predictive modeling framework centered on the innovative use of neural ordinary differential equations to enhance the operation and maintenance of critical energy infrastructure. This research advances the state-of-the-art in intelligent asset management for the built environment.

An Innovative Approach for Mission Sharing and Route Planning of Swarm Unmanned Aerial Vehicles in Disaster Management

Fast and effective response in disaster situations is critical for the success of rescue operations. In this context, swarm Unmanned Aerial Vehicles (UAVs) play an important role in disaster response by rapidly scanning large areas and performing situation assessments. In this paper, we propose an innovative method for task allocation and route planning for swarm UAVs. By combining Genetic Algorithm (GA) and Ant Colony Optimization (ACO) techniques, this method aims to ensure the most efficient movement of UAVs. First, clusters are created using GA to determine the regions of the disaster area that need to be scanned. At this stage, factors such as the capacities of the UAVs, their flight times, and the breadth of their mission areas are taken into account. Each UAV is optimized to scan a specific area assigned to it. Once the clusters are formed, the routes of the UAVs within each cluster are determined by the Ant Colony Algorithm (ACA). The route planning is tested both on Google Maps and in a simulation environment. Google Maps is used to evaluate the accuracy and feasibility of route planning based on real-world conditions, while the simulation environment provides the opportunity to test the behavior of the UAVs and the effectiveness of the routes in a virtual setting. With real-time data integration, the UAVs' route planning can be updated instantly and quickly adapted to emergency situations.

An integrated cyberinfrastructure system for water quality resources in the Upper Mississippi River Basin

ABSTRACT The Upper Mississippi Information System (UMIS) is a cyberinfrastructure framework designed to support large-scale real-time water-quality data integration, analysis, and visualization for the Upper Mississippi River Basin (UMRB). UMIS is intended to directly address three of the Grand Challenges for Engineering including (1) understanding access to clean drinking water, (2) management of the nitrogen cycle, and (3) engineering the tools of scientific discovery. The UMIS is designed to provide significant immediate and long-term impacts including a central platform for data access, integration, discovery, and adoption of cyberinfrastructure tools and services. The UMIS demonstrates that public data aggregators and central repositories can provide important services to anyone interested in water-quality research or education. In addition, working across multiple scales (e.g., state, region, county, or watershed) allows researchers to understand broad and narrow effects of water-quality strategies. Exploration of data across these scales encourages the development of problem-based research questions that can eventually provide feedback to public policies.

Maximising the value of hyperspectral drill core scanning through real-time processing and analysis

Hyperspectral imaging is gaining widespread use in the resource sector, with applications in mineral exploration, geometallurgy and mine mapping. However, the sheer size of many hyperspectral datasets (>1 Tb) and associated correction, visualisation and analysis challenges can limit the integration of this technique into time-critical exploration and mining workflows. In this contribution, we propose and demonstrate a novel open-source workflow for rapidly processing hyperspectral data acquired on exploration drillcores. The resulting products are adaptable to the varied needs of geologists, geophysicists and geological engineers, facilitating better integration of hyperspectral data during decision making. These tools are applied to process hyperspectral data of 6.4 km of exploration drill cores from Stonepark (Ireland), Collinstown (Ireland) and Spremberg (Germany). The results are presented via an open-source web-viewing platform that we have developed to facilitate easy on and off-site access to hyperspectral data and its derivatives. We suggest that maximum value can be extracted from hyperspectral data if it is acquired shortly after drilling and processed on-site in real time, so that results can be quickly validated and used to inform time-critical decisions on sample selection, geological interpretation (logging) and drillhole continuation or termination. This timeliness and accessibility is key to ensure rapid data availability for decision makers during mineral exploration and exploitation. Finally, we discuss several remaining challenges that limit the real-time integration of hyperspectral drill core scanning data, and explore some opportunities that may arise as these rich datasets become more widely collected.

Natural Language Processing (NLP) for Sentiment Analysis in Social Media

Purpose: This study sought to analyze Natural Language Processing (NLP) for sentiment analysis in social media. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Natural Language Processing (NLP) for sentiment analysis in social media. Preliminary empirical review revealed that advanced computational techniques significantly advanced the understanding of sentiments across diverse social media platforms. Methodologies such as machine learning algorithms and deep learning models like CNNs and RNNs demonstrated robust capabilities in categorizing sentiments accurately and capturing contextual nuances such as sarcasm and slang. The research highlighted the interdisciplinary nature of NLP applications, integrating linguistics with computer science and social sciences to develop effective frameworks for analyzing large-scale social media data. These findings contributed to enhancing decision-making in marketing, politics, and public opinion research, pointing towards future directions in hybrid NLP models for improved sentiment analysis across different languages and cultural contexts. Unique Contribution to Theory, Practice and Policy: The Social Constructionism, Cognitive Linguistics and Discourse Analysis Theory may be used to anchor future studies on Natural Language Processing (NLP). The recommendations aimed to advance theoretical foundations by exploring deep learning models and nuanced sentiment lexicons. Practical applications were enhanced through the development of scalable NLP tools for real-time data processing and integration into social media platforms. Policy implications focused on establishing ethical guidelines for data privacy and bias mitigation in sentiment analysis algorithms. Cross-disciplinary collaboration fostered innovation by integrating insights from computer science, linguistics, psychology, and social sciences. Education initiatives and international collaborations were prioritized to build capacity and standardize methodologies globally, ensuring advancements in both research and practical deployment of sentiment analysis technologies.

Exploring the Use of Graph Neural Networks for Blockchain Transaction Analysis and Fraud Detection

The digital system is increasing day by day while various organizations are facing problems during transactions and false activities. This research is investigating fraud detection in blockchain transactions- data used to focus on Ethereum_network. To implement the layers of Graph-Convolutional Networks (GCNs) that remain in the study, they convert blockchain transactional data into a graph structure with nodes representing addresses and edges representing transactions. The methodology includes data collection with preprocessing and graph representation in the implementation of GCN layers to analyze and detect deceitful activities. The outcomes illustration of the GNN model achieves a high accuracy score and precision with recall and F1-score. The analyses effectively identify fraudulent transactions while minimizing false positives. This work demonstrates the probability of GNNs to enhance fraud detection in blockchain systems and recommends future research directions convoluted in real-time data integration and advanced neural-network architectures toward advancing the toughness and effectiveness of fraud-detection mechanisms in trendy decentralized financial ecosystems.

Digital twin-based modeling of natural gas leakage and dispersion in urban utility tunnels

Background Unexpected leakage accidents of the natural gas pipeline inside urban utility tunnels can pose great threats to public safety, property, and the environment. It highlights the modeling of natural gas leakage and dispersion dynamics, especially from a digital twin implementation perspective facilitating effective emergency response in a data-driven way. Methods In this study, a digital twin-based emergency response framework for gas leakage accidents in urban utility tunnels is proposed. Within this framework, the data-calibrated gas concentration prediction (DC-GCP) model is developed by integrating the Lattice Boltzmann Method (LBM) with data assimilation (DA) techniques. This combination enables accurate spatiotemporal predictions of gas concentrations, even with a prior or inaccurate gas leakage source term. Specifically, we develop a high-performance LBM-based gas concentration prediction model using the parallel programming language Taichi Lang. Based on this model, real-time integration of gas sensor data from utility tunnels is achieved through the DA algorithm. Therefore, the predicted results can be calibrated by the continuous data in the absence of complete source term information. Furthermore, a widely used twin experiment and statistical performance measures (SPMs) are used to evaluate and validate the effectiveness of the proposed approach. Results The results show that all SPMs progressively converge towards their ideal values as calibration progresses. And both the gas concentration predictions and the source term estimations can be calibrated effectively by the proposed approach, achieving a relative error of less than 5%. Conclusions This study helps for dynamic risk assessment and emergency response of natural gas leakage accidents, as well as facilitating the implementation of predictive digital twin in utility tunnels.

Conceptual Framework of Information Flow Synchronization Throughout the Building Lifecycle

The construction industry’s reliance on traditional methods and fragmented workflows results in significant information loss, inefficiencies, increased costs, and errors. This study addresses these issues by integrating comprehensive urban planning with building information modeling (BIM) to create a seamless information flow throughout the building lifecycle. We propose a holistic framework that synchronizes data from planning to demolition, incorporating national and municipal digital twins. An imperative literature review and analysis of international best practices were conducted to develop a conceptual framework aimed at improving data accuracy and interoperability. Our findings underscore the importance of adopting open standards such as Industry Foundation Classes (IFC) and CityGML for effective information exchange. By implementing an information model (IM)-based approach in urban planning and public sector permit processes, project timelines can be streamlined, and regulatory compliance enhanced. This study concludes that continuous, integrated information flow facilitates more efficient, cost-effective construction practices and improved decision-making. Furthermore, this research illustrates the potential of digital twin technology to revolutionize the construction industry by enabling real-time data integration and fostering stakeholder collaboration, ultimately offering a robust framework for practitioners, and significantly enhancing the efficiency and accuracy of construction processes.

A geospatial approach-based assessment of soil erosion impacts on the dams silting in the semi-arid region

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements in artificial intelligence and the availability of large, high-quality datasets. This review explores the current state of ML applications in hydrology, emphasizing the utilization of extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, and GRACE. These datasets provide critical data for modeling various hydrological parameters, including streamflow, precipitation, groundwater levels, and flood frequency, particularly in data-scarce regions. We discuss the type of ML methods used in hydrology and significant successes achieved through those ML models, highlighting their enhanced predictive accuracy and the integration of diverse data sources. The review also addresses the challenges inherent in hydrological ML applications, such as data heterogeneity, spatial and temporal inconsistencies, issues regarding downscaling the LSH, and the need for incorporating human activities. In addition to discussing the limitations, this article highlights the benefits of utilizing high-resolution datasets compared to traditional ones. Additionally, we examine the emerging trends and future directions, including the integration of real-time data and the quantification of uncertainties to improve model reliability. We also place a strong emphasis on incorporating citizen science and the IoT for data collection in hydrology. By synthesizing the latest research, this paper aims to guide future efforts in leveraging large datasets and ML techniques to advance hydrological science and enhance water resource management practices.

Enhancing Vehicle Routing Efficiency through Branch and Bound and Heuristic Methods

The Vehicle Routing Problem (VRP) is a critical challenge in logistics, impacting delivery efficiency and costs. Traditional VRP solutions often fail to address real-world dynamics such as fluctuating traffic conditions and varying customer demands. This research proposes a novel VRP model integrating real-time data to enhance route optimization. By combining the precision of the Branch and Bound (B&B) approach with the flexibility of heuristics like Genetic Algorithms and Simulated Annealing, the hybrid method dynamically adjusts routes based on live traffic and demand updates. The objective is to reduce operational costs and improve logistical performance. The hybrid model’s effectiveness is validated through comparative analysis with traditional VRP solutions, demonstrating significant improvements in cost reduction, fuel consumption, vehicle wear and tear, and customer satisfaction due to timely deliveries. These advancements highlight the potential of real-time data integration and advanced optimization techniques in providing robust solutions for modern logistics challenges. Future research should focus on incorporating more advanced data sources and testing the model in various real-world scenarios to further enhance its practicality and performance, ensuring businesses remain competitive in a dynamic market. This study underscores the importance of continuous innovation in VRP solutions to achieve sustainable, efficient, and customer-centric logistics operations.

Analysis of decentralized drone control model and interception trajectory calculation

Subject matter: This article is devoted to the study of applying the innovative Cascade DataHub method for optimizing the management of automated mobile systems, especially unmanned aerial vehicles. The work analyzes both theoretical and practical aspects of implementing this method across various application sectors. Goal: The objective of the study is to conduct a comprehensive analysis of contemporary models and methods for managing a group of drones, focusing on decentralized approaches. Additionally, the study aims to develop effective algorithms for optimizing the interception trajectory, with the goal of enhancing the accuracy and reliability of managing complex automated systems through increased real-time data integration. The research is directed towards identifying the potential advantages of this method in reducing system response times and improving decision-making accuracy. Tasks: The main tasks of the research include the development of comprehensive algorithms for rapid processing and analysis of large volumes of data from various sources, creating reliable communication protocols to ensure connection stability under extreme conditions. Another important task is the integration of these developments into practical applications, which will increase their effectiveness in real operational conditions. Methods: To achieve the set goals, advanced techniques of mathematical modeling, statistical analysis, machine learning, and deep learning are used. The application of these techniques ensures high accuracy and reliability of the management systems. Results: During the research, it was found that the Cascade DataHub method significantly reduces the response time of systems to commands, increases the accuracy of task execution, and decreases data loss during their transmission. The implementation of this method also contributes to a more efficient distribution of resources among automated units, which is critically important for missions requiring high coordination and time synchronization. Conclusions: A comprehensive analysis of contemporary models and methods for managing a group of drones with a focus on decentralized approaches has been conducted. Effective algorithms for optimizing the interception trajectory have been developed, aimed at enhancing the accuracy and reliability of managing complex automated systems through real-time data integration. The study revealed the potential advantages of the proposed method in reducing system response times and improving decision-making accuracy, contributing to the more efficient functioning of automated systems.

TRANSPORT SERVICE WEBSITE

Abstract: Transport is an awesome website that offers a wide range of shipping services. They specialize in transporting goods by ship, providing efficient and reliable solutions for both domestic and international shipping needs. Whether you need to transport large cargo or small packages, ShipEx has got you covered. Their website provides all the information you need about their services, including rates, tracking options, and customer reviews. It’s a convenient and trustworthy platform for all your shipping needs. Online transportation service has become a means of transportation development which able to attract people’s interest. This research explores the development and implementation of an intelligent transportation services website aimed at optimizing transportation services. Leveraging advanced algorithms, real-time data integration, and user-centric design, our platform facilitates seamless connections between various transportation modes, providing users with personalized, efficient, and sustainable goods transport options. Our findings highlight the positive impact of inclusive design on user satisfaction, accessibility, and overall usability, contributing to the broader discourse on creating equitable transportation solutions. This research explores the integration of augmented reality (AR) to improve user experience on transportation services websites. By overlaying real-time information onto the physical environment, our AR-enhanced platform provides users with intuitive navigation and enhanced situational awareness. We will ensure interactions and experience is top notch for the users. Index Term-: Management Processes, Transport Services, Strategic Functions, HTML, CSS, PHP.

Enhancing Multimodal Route Optimization in Maritime Transport: Integrating Real-Time Data and Professionalism

This research investigates methods to enhance multimodal route optimization in the maritime sector through the integration of real-time traffic data and professionalism standards. Conducted by researchers and lecturers at the Maritime Institute, the study involves 100 cadets predominantly studying multimodal transportation, logistics, and port management. The research utilises qualitative methods to explore cadets' perspectives on route optimization and professionalism in the industry. Findings reveal a recognition of the significance of real-time traffic data for informed routing decisions, along with an emphasis on compliance with international standards, training, and ethical considerations. Furthermore, cadets perceive machine learning algorithms as effective tools to address the complexities of route optimization. These insights contribute to advancing knowledge in transportation management and education, highlighting the importance of integrating real-time data and professionalism in enhancing route optimization practices.