Search Results (129)

Background: Gold mining is a critical part of the industry of Central Asia, contributing significantly to regional economic growth. However, gold production management faces numerous challenges, including adopting innovative technologies such as AI, using improved logistical equipment, resolving supply chain inefficiencies and disruptions, and incorporating modernized waste management and advancements in gold bar processing technologies. This study explores how advanced technologies and improved logistical processes can enhance efficiency and sustainability. Method: This paper examines gold production processes in Kyrgyzstan, a gold-producing country in Central Asia. The case study approach combines qualitative interviews with industry stakeholders and a system dynamics (SD) simulation model to compare current operations with a technology-based scenario. Results: The simulation model shows improved outcomes when innovative technologies are applied to ore processing, waste refinement, and gold bar production. The results also indicate an approximate twenty-five percent reduction in transport time, a thirty percent decrease in equipment downtime, a thirty percent reduction in emissions, and a fifteen percent increase in gold extraction when using artificial intelligence, smart logistics, and regional smelting. Conclusions: The study concludes with recommendations to modernize equipment, localize processing, and invest in digital logistics to support sustainable mining and improve operational performance in Kyrgyzstan’s gold sector. Full article

Logistics transports of various resources such as production materials, foods, and products support the operation of smart cities. The ability to trace the states of logistics transports requires an efficient storage and retrieval of the states of logistics transports and locations of logistics objects. However, the restriction of sharing states and locations of logistics objects across organizations makes it hard to deploy a centralized database for supporting traceability in a cross-organization logistics system. This paper proposes a semantic data model on Blockchain to represent a logistics process based on the Semantic Link Network model, where each semantic link represents a logistics transport of a logistics object between two organizations. A state representation model is designed to represent the states of a logistics transport with semantic links. It enables the locations of logistics objects to be derived from the link states. A mapping from the semantic links into the blockchain transactions is designed to enable the schema of semantic links and the states of semantic links to be published in blockchain transactions. To improve the efficiency of tracing a path of semantic links on a blockchain platform, an algorithm is designed to build shortcuts along the path of semantic links to enable a query on the path of a logistics object to reach the target in logarithmic steps on the blockchain platform. A reward–penalty policy is designed to allow participants to confirm the states of links on the blockchain. Analysis and simulation demonstrate the flexibility, effectiveness, and efficiency of the Semantic Link Network on immutable blockchain for implementing logistics traceability. Full article

This paper presents a novel system designed to enhance pedestrian safety in urban environments by utilizing real-time video analysis and machine learning techniques. With a focus on the bustling streets of Macao, known for its high pedestrian traffic and complex road conditions, the proposed model alerts drivers to the presence of pedestrians, significantly reducing the risk of accidents. Leveraging the You Only Look Once algorithm, this research demonstrates how timely alerts can be generated based on risk assessments derived from video footage. The model is rigorously tested against diverse driving scenarios, providing robust accuracy in detecting potential hazards. A comparative analysis of various machine learning algorithms, including Gradient Boosting and Logistic Regression, underscores the effectiveness and reliability of the system. The key finding of this research indicates that dataset refinement and enhanced feature differentiation could lead to improved model performance. Ultimately, this work seeks to contribute to the development of smart city initiatives that prioritize safety through advanced technological solutions. This approach exemplifies a vision for more responsive and responsible urban transport systems. Full article

The increasing disparity in global food distribution has amplified the urgency of addressing food waste and food insecurity, both of which exacerbate economic, environmental, and social inequalities. Traditional food bank models often struggle with logistical inefficiencies, limited accessibility, and a lack of transparency in food distribution, hindering their effectiveness in mitigating these challenges. This study proposes a novel Food Bank Network Redesign (FBNR) that leverages the Quito Metro system to create a decentralized food bank network, enhancing efficiency and equity in food redistribution by introducing strategically positioned donation lockers at metro stations for convenient drop-offs, with donations transported using spare metro capacity to designated stations for collection by charities, reducing reliance on dedicated transportation. To ensure transparency and operational efficiency, we integrate a blockchain-based traceability system with smart contracts, enabling secure, real-time tracking of donations to enhance stakeholder trust, prevent food loss, and ensure regulatory compliance. We develop a multi-objective optimization framework that balances food waste reduction, transportation cost minimization, and social impact maximization, supported by a mixed-integer linear programming (MIP) model to optimize donation allocation based on urban demand patterns. By combining decentralized logistics, blockchain-enhanced traceability, and advanced optimization techniques, this study offers a scalable and adaptable framework for urban food redistribution, improving food security in Quito while providing a replicable blueprint for cities worldwide seeking to implement circular and climate-resilient food supply chains. Full article

The development of the supply chain’s outsourcing and globalization has intensified the requirement for sustainable transport. The evaluation of the transport providers’ maturity, which is crucial to all and any achievements in this context, is hampered by a myriad of factors ranging from the transport ecosystem complexity, self-contradictory and unverifiable data and the ceaseless march of modern technology. This study argues for an agile and smart approach to evaluate the maturity level of transport providers, particularly for high-risk areas like hazardous materials transport. Such models should include holistic analysis frameworks of all performance indicator measurement systems with their data collection methods and technology tools to be employed. The need to involve all stakeholders within the supply chain is said to require diverse collaboration. With regard to the solution, collaborative participation between transport providers and relevant institutions is vital to reduce the environmental impacts and improve the efficiency of the entire sector. Full article

This research examines the strategic integration of Artificial Intelligence (AI) into global net-zero emissions strategies, with a focus on both terrestrial and extraterrestrial sustainability. The objectives include quantifying AI’s impact on reducing greenhouse gas (GHG) emissions, improving energy efficiency, and optimizing resource utilization, a particularly critical but underexplored domain. A mixed-methods approach was employed, comprising a systematic literature review, a meta-analysis of quantitative data, and case study evaluations. Advanced mathematical models, including logistic growth and optimization equations, were applied to predict trends and assess the effectiveness of AI. The results reveal that AI-driven innovations achieve emissions reductions of 15–30% across energy, transportation, and manufacturing sectors, with predictive maintenance optimizing energy utilization by 20% and extending equipment lifespans. AI-enabled smart grids improved energy efficiency by 26.7%, surpassing the 20% benchmark in prior studies. Specific applications include optimized fuel usage and predictive modeling, which can cut emissions by up to 20%. Quantitative data demonstrated significant cost savings of 20% across sectors. Statistical tests confirmed results with p-values < 0.05, indicating strong significance. This study underscores AI’s transformative potential in achieving net-zero goals by extending sustainability frameworks. It provides actionable insights for policymakers, industry leaders, and researchers, advocating for the broader adoption of AI to address global environmental challenges. Full article

With the rapid acceleration of global urbanization and the advent of smart city initiatives, large metropolises confront the dual challenges of surging logistics demand and constrained surface transportation resources. Traditional surface logistics networks struggle to support sustainable urban development in high-density areas due to traffic congestion, high carbon emissions, and inefficient last-mile delivery. This paper addresses the layout optimization of a hub-and-spoke underground space logistics system (ULS) network for smart cities under stochastic scenarios by proposing an immune-inspired multi-objective particle swarm optimization (IS-MPSO) algorithm. By integrating a stochastic robust Capacity–Location–Allocation–Routing (CLAR) model, the approach concurrently minimizes construction costs, maximizes operational efficiency, and enhances underground corridor load rates while embedding probability density functions to capture multidimensional uncertainty parameters. Case studies in Beijing’s Fifth Ring area demonstrate that the IS-MPSO algorithm reduces the total objective function value from 9.8 million to 3.4 million within 500 iterations, achieving stable convergence in an average of 280 iterations. The optimized ULS network adopts a “ring–synapse” topology, elevating the underground corridor load rate to 59% and achieving a road freight alleviation rate (RFAR) of 98.1%, thereby shortening the last-mile delivery distance to 1.1 km. This research offers a decision-making paradigm that balances economic efficiency and robustness for the planning of underground logistics space in smart cities, contributing to the sustainable urban development of high-density regions and validating the algorithm’s effectiveness in large-scale combinatorial optimization problems. Full article

Background: The global climate crisis has intensified the demand for sustainable solutions, positioning Reverse Logistics (RL) as a critical strategy for minimizing environmental impacts. Simultaneously, Industry 4.0 technologies are transforming RL operations by enhancing their collection, transportation, storage, sorting, remanufacturing, recycling, and disposal processes. Understanding the roles of these technologies is essential for improving efficiency and sustainability. Methods: This study employs a systematic literature review, following the PRISMA methodology, to identify key Industry 4.0 technologies applicable to RL. Publications from Scopus and Web of Science were analyzed, leading to the development of a theoretical framework linking these technologies to RL activities. Results: The findings highlight the fact that technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, and Blockchain enhance RL by improving traceability, automation, and sustainability. Their application optimizes execution time, reduces operational costs, and mitigates environmental impacts. Conclusions: For the transportation and manufacturing sectors, integrating Industry 4.0 technologies into RL can streamline supply chains, enhance decision-making, and improve resource utilization. Smart tracking, predictive maintenance, and automated sorting systems reduce waste and improve operational resilience, reinforcing the transition toward a circular economy. By adopting these innovations, stakeholders can achieve economic and environmental benefits while ensuring regulatory compliance and long-term competitiveness. Full article

The optimization of asymmetric transportation problems is a critical challenge in modern logistics, where the complexity of the operational environment significantly influences efficiency. In first-mile and last-mile logistics operations, strategic optimization plays a crucial role in enhancing transportation efficiency. This article explores advanced optimization techniques that improve decision-making in such scenarios. By utilizing mathematical modeling and heuristic algorithms, transportation routes and schedules can be refined to minimize costs and enhance overall performance. The study demonstrates the potential of this approach through a case study focusing on asymmetric transportation problems using micromobility devices in an integrated first-mile/last-mile delivery network. Numerical results from optimization using heuristic solution methods show that the novel approach is suitable to optimize micromobility-based integrated first-mile and las-mile delivery tasks. We examine a network of eight restaurants located in downtown Miskolc, Hungary. To compare the optimized solution with a traditional one, we looked at the total distance in shuttle-based services, which was 121.65 km, with our solution covering 44.55% of the delivery. This led to a 19% improvement in the use of micromobility devices when demand and supply were synchronized. The findings indicate significant improvements in cost-effectiveness, delivery times, and resource utilization, highlighting the importance of structured optimization frameworks in complex logistics networks. Full article

Food delivery apps (FDAs) have emerged as transformative tools in the digital age, reshaping consumer behavior and urban mobility through their convenience and accessibility. This study explores the factors influencing the adoption of FDAs among university students in a secondary city in Thailand, framed within the broader context of smart mobility. This study employs an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, incorporating key constructs including performance expectancy, effort expectancy, social influence, facilitating conditions, and environmental concerns. Data were collected from 396 students at Mahasarakham University through a structured questionnaire and analyzed using structural equation modeling. The results reveal that effort expectancy, social influence, and environmental concerns significantly impact behavioral intention, while behavioral intention and facilitating conditions drive actual usage behavior. Environmental concerns emerged as a critical determinant, reflecting the growing alignment between consumer preferences and sustainability goals. The findings underscore the role of FDAs as key enablers of smart mobility, optimizing urban logistics, reducing transportation inefficiencies, and supporting sustainable city systems. By integrating environmental concerns into the UTAUT model, this study contributes to understanding technology adoption dynamics in secondary cities. Practical implications include promoting eco-friendly practices, enhancing digital infrastructure, and leveraging FDAs to foster sustainable and inclusive mobility ecosystems. Full article

Recently, the maritime logistics industry has been transitioning to smart logistics by leveraging such technologies as AI and IoT. In particular, maritime big data plays a significant role in providing various services, including ship operation monitoring and greenhouse gas emissions assessment, and is considered essential for delivering maritime logistics services. Marine big data comprise real-world data collected during ship operations, but it is susceptible to loss due to temporal and environmental constraints. To address this issue, an Emulator is proposed to generate supplemental data, including location data, data count, and average distance, using accumulated maritime transport data. This study proposes an Emulator that repetitively generates new data such as location data, data count, and average distance using maritime transport data accumulated up to now. The location data is generated using the cumulative distance and trigonometric ratios based on the location information of standard routes. The data count and average distance are calculated based on user-input parameters such as voyage time and data interval. The generated data is inserted into a database and monitored on a map in real time. Experiments were conducted using maritime transport route data, and the results validated the effectiveness of the Emulator. Full article

Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) for smart cities. Despite the clear necessity for sound-based diagnostic systems, the scarcity of specialized publicly available datasets presents a major challenge. This study addresses this gap by contributing in multiple dimensions. Firstly, it emphasizes the significance of sound-based diagnostics for real-time detection of faults through analyzing sounds directly generated by vehicles, such as engine or brake noises, and the classification of external emergency sounds, like sirens, relevant to vehicle safety. Secondly, this paper introduces a novel dataset encompassing vehicle fault sounds, emergency sirens, and environmental noises specifically curated to address the absence of such specialized datasets. A comprehensive framework is proposed, combining audio preprocessing, feature extraction (via Mel Spectrograms, MFCCs, and Chromatograms), and classification using 11 models. Evaluations using both compact (52 features) and expanded (126 features) representations show that several classes (e.g., Engine Misfire, Fuel Pump Cartridge Fault, Radiator Fan Failure) achieve near-perfect accuracy, though acoustically similar classes like Universal Joint Failure, Knocking, and Pre-ignition Problem remain challenging. Logistic Regression yielded the highest accuracy of 86.5% for the vehicle fault dataset (DB1) using compact features, while neural networks performed best for datasets DB2 and DB3, achieving 88.4% and 85.5%, respectively. In the second scenario, a Bayesian-Optimized Weighted Soft Voting with Feature Selection (BOWSVFS) approach is proposed, significantly enhancing accuracy to 91.04% for DB1, 88.85% for DB2, and 86.85% for DB3. These results highlight the effectiveness of the proposed methods in addressing key ITS limitations and enhancing accessibility for individuals with disabilities through auditory-based vehicle diagnostics and emergency recognition systems. Full article

Advancements in transport engineering and technology play a crucial role in improving multimodal transport systems and optimizing logistics operations. This study focuses on efficient material flow management in an industrial enterprise, directly supporting the goals of sustainable transport and innovative logistics strategies. The manufacturing plant in Veselí nad Lužnicí was selected as a case study because of the identified inefficiencies in its logistics processes and the availability of detailed operational data, allowing for an accurate analysis of material flows. The research identifies weaknesses in the current material flow and proposes the following two optimization solutions: replacing an external operator for semi-finished goods transport with in-house logistics and substituting external transport providers for finished goods transportation with an internally managed fleet. The proposed methodology introduces a novel integration of analytical tools, including checkerboard table analysis, cost modeling, and return-on-investment (ROI) assessment, to evaluate logistics efficiency and minimize material handling costs. This study demonstrates how optimized material flows, particularly using railway logistics, can contribute to cost-effective and sustainable supply chains. The research reflects current trends in transport system planning, emphasizing transport modeling, digital twin simulations, and smart railway technologies to enhance operational efficiency and resilience. The results provide practical recommendations for companies seeking to integrate rail transport into their logistics processes, contributing to broader objectives of environmental sustainability and digital transformation in the transport sector. Full article

In spite of its strong increase and relevant position in the evolution of international maritime routes, the global livestock trade is still a poorly treated topic in the maritime business domain of research. Aiming to cover this gap, the authors are focused on revealing the livestock logistics technology in intermodal transports, approaching both equipment reliability and operation flow design, applying the business processes modeling method to map the most relevant stages in animals’ port operation, transfer, and maritime transportation. This paper examines the intricate logistics of maritime livestock transportation through a case study on the Port of Midia, administrated by the Constanța Maritime Port Administration, one of Romania’s primary export hubs for livestock operations, using BPM software, seeking to identify the most important deficiencies and alternatives in improving the technical and technological effectiveness. Key findings indicate that improving ramp availability, automating document verification, and implementing RFID-based animal tracking systems could significantly enhance operational efficiency. By integrating workflow models, real-time monitoring, and simulation-based optimization, the study offers a comprehensive framework for streamlining multimodal livestock transportation. The implications extend to policymakers, port authorities, and logistics operators, emphasizing the necessity of digital transformation, regulatory harmonization, and technological integration in livestock maritime transportation. This research contributes to the expansion of intermodal transportation studies, providing practical recommendations for enhancing livestock logistics efficiency while ensuring compliance with European animal welfare regulations. The findings pave the way for further research into AI-driven risk assessments, smart logistics solutions, and sustainable livestock transportation alternatives. Full article

This study explores how digital technologies and data analytics can transform urban waste management in smart cities by addressing systemic inefficiencies. Integrating perspectives from the Resource-Based View, Socio-Technical Systems Theory, Circular Economy Theory, and Institutional Theory, the research examines sustainability, operational efficiency, and resilience in extended supply chains. A case study of Company A and its demand-side supply chain with Retailer B highlights key drivers of waste, including overstocking, inventory mismanagement, and inefficiencies in transportation and promotional activities. Using a mixed-methods approach, the study combines quantitative analysis of operational data with advanced statistical techniques and machine learning models. Key data sources include inventory records, sales forecasts, promotional activities, waste logs, and IoT sensor data collected over a two-year period. Machine learning techniques were employed to uncover complex, non-linear relationships between waste drivers and waste generation. A waste-type-specific emissions framework was used to assess environmental impacts, while IoT-enabled optimization algorithms helped improve logistics efficiency and reduce waste collection costs. Our findings indicate that the adoption of IoT and AI technologies significantly reduced waste by enhancing inventory control, optimizing transportation, and improving supply chain coordination. These digital innovations also align with circular economy principles by minimizing resource consumption and emissions, contributing to broader sustainability and resilience goals in urban environments. The study underscores the importance of integrating digital solutions into waste management strategies to foster more sustainable and efficient urban supply chains. While the research is particularly relevant to the food production and retail sectors, it also provides valuable insights for policymakers, urban planners, and supply chain stakeholders. By bridging theoretical frameworks with practical applications, this study demonstrates the potential of digital technologies to drive sustainability and resilience in smart cities. Full article