Search Results (473)

Semantically enriched trajectories have attracted growing interest in recent research, driven by the need for more expressive and context-aware movement data analysis. Two primary approaches have emerged for the storage and management of such data: moving object databases, which operate at the transactional or operational level, and trajectory data warehouses (TDWs), which support analytical processing within decision support systems. Conventional TDW methodologies typically model semantic aspects of trajectories by introducing new dimensions into the data warehouse schema. However, this approach often requires structural modifications to the schema in order to accommodate additional semantic attributes, potentially resulting in significant disruptions to the architecture and maintenance of the underlying decision support systems. To overcome this limitation, we propose a novel TDW model that supports dynamic and extensible integration of semantic aspects, without necessitating changes to the schema. This design enhances flexibility and promotes seamless adaptability to domain-specific requirements. To enable such extensibility, we propose an innovative approach to representing semantic trajectories by leveraging natural language processing (NLP) techniques. without relying on traditional spatiotemporal features. This enables the analysis of semantic movement patterns purely through textual context. Finally, we present a comprehensive framework that implements the proposed model in real-world application scenarios, demonstrating its practical extensibility. Full article

This study investigates the fire dynamics and structural response of steel-framed warehouse racking systems under various fire scenarios, emphasizing the critical importance of fire safety measures in mitigating structural damage. Through advanced computational simulations (Fire Dynamics Simulator) and thermomechanical analysis, this research reveals that fire intensity and progression are highly influenced by the ignition point and the stored material types, with maximum recorded temperatures reaching 720 °C and 970 °C in different scenarios. The results highlight the localization of significant strain and drift ratios in structural elements near the ignition zone, underscoring their vulnerability. This study demonstrates the rapid loss of load-bearing capacity in steel elements at elevated temperatures, leading to severe deformations and increased collapse risks. Key findings emphasize the necessity of strategically positioned sprinkler systems and the integration of passive fire protection measures, such as fire-resistant coatings, to enhance structural resilience. Performance-based fire design approaches, aligning with FEMA-356 criteria, offer realistic frameworks for improving the fire safety of warehouse structures. Full article

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling. Full article

In practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task Allocation (MRTA). Researchers have devised a range of methodologies to tackle MRTA problems, aiming to achieve optimal solutions, yet there remains room for further enhancements in this field. Among the complex challenges in MRTA, the identification of an optimal coalition formation (CF) solution stands out as one of the (Nondeterministic Polynomial) NP-hard problems. CF pertains to the effective coordination and grouping of agents or robots for efficient task execution, achieved through optimal task allocation. In this context, this paper delivers a succinct overview of dynamic task allocation and CF strategies. It conducts a comprehensive examination of diverse strategies employed for MRTA. The analysis encompasses the advantages, disadvantages, and comparative assessments of these strategies with a focus on CF. Furthermore, this study introduces a novel classification system for prominent task allocation methods and compares these methods with simulation analysis. The fidelity and effectiveness of the proposed CF approach are substantiated through comparative assessments and simulation studies. Full article

Digital twin technology has proven to be a transformative enabler for sustainable manufacturing by providing real-time virtual representations of physical assets and supply chain processes. This paper explores the integration of digital twins with agile supply chain strategies to improve the sustainability of manufacturing systems. By leveraging real-time data and advanced simulations, digital twins facilitate dynamic decision making, optimize resource utilization and reduce environmental impact. A case study is presented in which a digital twin is implemented with the aim of improving the responsiveness of agile supply chains and suggesting appropriate times for the delivery of components and the shipment of the final product, with the goal of minimizing the time components spend in warehouses. The analysis shows how digital twins improve clarity, adaptability and predictive capabilities, leading to greater efficiency and sustainability. The results show that the combination of digital twin technology and agile supply chain frameworks contributes significantly to resource optimization, emissions reduction and overall operational resilience. The proposed approach proves to be highly effective for various manufacturing environments, especially those that strive to balance efficiency and sustainability goals. Full article

Efficient material handling is crucial in the logistics operations of modern salt warehouses, where Rail Guided Vehicles (RGVs) and Air Sorting Robots (ASRs) are often deployed to manage inbound and outbound tasks. However, as the number of tasks increases within a given period, conflicts and deadlocks between simultaneously operating RGVs and ASRs become more frequent, reducing efficiency and increasing energy consumption during transportation. To address this, the research frames the inbound and outbound problem as a task allocation issue for the RGV/ASR system with a finite buffer, and proposes a collision avoidance strategy and a zero-wait strategy for loaded machines to reallocate tasks. To improve computational efficiency, we introduce an adaptive multi-neighborhood hybrid search (AMHS) algorithm, which integrates a dual-sequence coding scheme and an elite solution initialization strategy. A dedicated global search operator is designed to expand the search landscape, while an adaptive local search operator, inspired by biological hormone regulation mechanisms, along with a perturbation strategy, is used to refine the local search. In a case study on packaged salt storage, the proposed AMHS algorithm reduced the total makespan by 30.1% compared to the original task queue. Additionally, in 15 randomized test scenarios, AMHS demonstrated superior performance over three benchmark algorithms—Genetic Algorithm (GA), Discrete Imperialist Competitive Algorithm (DICA), and Improved Whale Optimization Algorithm (IWOA)—achieving an average makespan reduction of 12.6% relative to GA. Full article

The construction industry faces challenges like project complexity, labor intensity, and dynamic changes, which demand effective knowledge management practices to attain competitiveness. Successful knowledge management implementation relies on management support, specifically budget allocations, to achieve knowledge-based competitiveness. This study aims to examine the dynamic effects of management support on competitiveness enhancement over time and suggest short- and long-term strategies for knowledge management practices. The study develops a system dynamics model comprising five key factors: knowledge storage, knowledge acquisition, knowledge dissemination, knowledge responsiveness, and knowledge utilization to simulate the effects of management support on knowledge management implementation and knowledge-based competitiveness over time. The simulation results reveal the fluctuations of the competitiveness scores through knowledge management implementation, which is decided by management. It is suggested that the efforts should be focused on improving the activities under the knowledge utilization and dissemination factors to raise the scores in a short period. To achieve sustainable knowledge-based competitiveness development, management must commit to enhancing human- and technology-related activities under the knowledge storage, acquisition, and responsiveness factors and provide adequate financial support to invest in knowledge management-related systems. Such activities as skill training, data warehouse systems, and stakeholder interconnection are crucial to maintaining knowledge management performance. This study provides valuable insights into the strategic planning of knowledge management practices through budget allocation from management to achieve long-term competitiveness. Full article

The article describes an Arduino-based, portable, remotely usable weather monitoring station capable of measuring temperature, relative humidity, pressure, and carbon monoxide (CO) concentration and transmitting the collected data to the Cloud through the mobile telephony network. The main modules of the station are as follows: a DHT11 sensor for temperature and relative humidity sensing, a BMP180 sensor for pressure monitoring (with temperature compensation), a MQ7 sensor for the monitoring of the CO concentration, an Arduino Uno board, a GSM SIM900 module, and a buzzer, which is activated when the temperature exceeds 35 °C. The station operates as follows: the Arduino Uno board gathers the data collected by the sensors and, by means of the GSM SIM900 module, it transmits the data to the Cloud by using the mobile telephony network as well as the ThingSpeak software which is an open-code IoT application that, among others, enables saving and recovering of sensing and monitoring data. The main novelty of this work is the combined use of the GSM network and the Cloud which enhances the portability and usability of the proposed system and enables remote collection of data in a straightforward way. Additional merits of the system are the easiness and the low cost of its development (owing to the easily available, low-cost hardware combined with an open-code software) as well as its modularity and scalability which allows its customization depending on specific application it is intended for. The system could be used for real-time, remote monitoring of essential environmental parameters in spaces such as farms, warehouses, rooms etc. Full article

This study develops a calibrated multiscale simulation of three lightweight industrial warehouses located in Tecámac, Mexico, to evaluate the dual role of rooftop photovoltaic (PV) arrays as renewable energy generators and passive thermal modifiers. Dynamic energy models were developed using EnergyPlus via Ladybug Tools v. 1.8.0 and calibrated against 2021 real-world electricity billing data, following ASHRAE Guideline 14. Statistical analyses conducted in RStudio v2024.12.1 Build 563 confirmed significant passive cooling effects induced by PV integration, achieving up to 15.3 °C reductions in peak indoor operative temperatures and improving thermal comfort rates by approximately 10 percentage points. While operational energy savings were evident, the primary focus of this research was on the multiscale modeling of thermal performance enhancement in composite metallic-PV roofing systems under semi-arid climatic conditions. These results provide new insights into computational approaches for optimizing passive thermal performance in lightweight industrial envelopes. Full article

With the rapid development of e-commerce, warehousing and logistics systems are facing the dual challenges of increasing order processing demand and green and low-carbon transformation. Traditional manual and single-robot scheduling methods are not only limited in efficiency, but will also make it difficult to meet the strategic needs of sustainable development due to their high energy consumption and resource redundancy. Therefore, in order to respond to the sustainable development goals of green logistics and resource optimization, this paper replaces the traditional mobile handling robot in warehousing and logistics with a composite robot composed of a mobile chassis and a robotic arm, which reduces energy consumption and labor costs by reducing manual intervention and improving the level of automation. Based on the traditional contract net protocol framework, a distributed task allocation strategy optimization method based on an improved genetic algorithm is proposed. This framework achieves real-time optimization of the robot task list and enhances the rationality of the task allocation strategy. By combining the improved genetic algorithm with the contract net protocol, multi-robot multi-task allocation is realized. The experimental results show that the improvement strategy can effectively support the transformation of the warehousing and logistics system to a low-carbon and intelligent sustainable development mode while improving the rationality of task allocation. Full article

The contemporary challenge in remote sensing lies in the precise retrieval of increasingly abundant and high-resolution remotely sensed images (RS image) stored in expansive data warehouses. The heightened spatial and spectral resolutions, coupled with accelerated image acquisition rates, necessitate advanced tools for effective data management, retrieval, and exploitation. The classification of large-sized images at the pixel level generates substantial data, escalating the workload and search space for similarity measurement. Semantic-based image retrieval remains an open problem due to limitations in current artificial intelligence techniques. Furthermore, on-board storage constraints compel the application of numerous compression algorithms to reduce storage space, intensifying the difficulty of retrieving substantial, sensitive, and target-specific data. This research proposes an innovative hybrid approach to enhance the retrieval of remotely sensed images. The approach leverages multilevel classification and multiscale feature extraction strategies to enhance performance. The retrieval system comprises two primary phases: database building and retrieval. Initially, the proposed Multiscale Multiangle Mean-shift with Breaking Ties (MSMA-MSBT) algorithm selects informative unlabeled samples for hyperspectral and synthetic aperture radar images through an active learning strategy. Addressing the scaling and rotation variations in image capture, a flexible and dynamic algorithm, modified Deep Image Registration using Dynamic Inlier (IRDI), is introduced for image registration. Given the complexity of remote sensing images, feature extraction occurs at two levels. Low-level features are extracted using the modified Multiscale Multiangle Completed Local Binary Pattern (MSMA-CLBP) algorithm to capture local contexture features, while high-level features are obtained through a hybrid CNN structure combining pretrained networks (Alexnet, Caffenet, VGG-S, VGG-M, VGG-F, VGG-VDD-16, VGG-VDD-19) and a fully connected dense network. Fusion of low- and high-level features facilitates final class distinction, with soft thresholding mitigating misclassification issues. A region-based similarity measurement enhances matching percentages. Results, evaluated on high-resolution remote sensing datasets, demonstrate the effectiveness of the proposed method, outperforming traditional algorithms with an average accuracy of 86.66%. The hybrid retrieval system exhibits substantial improvements in classification accuracy, similarity measurement, and computational efficiency compared to state-of-the-art scene classification and retrieval methods. Full article

Multi-floored grain warehouses are widely used in China due to their efficient space utilization and high storage capacity. This study evaluates the seismic performance of such structures using a Composite Structure of Steel and Concrete (CSSC) system under various grain-loading conditions. A finite element model was developed in OpenSees based on actual loading scenarios, with both pushover and time history analyses conducted. Results show that the EEF condition (E = Empty, F = Full; top–middle–bottom = Empty–Empty–Full) leads to a 35.14% increase in peak base shear compared to the FEE condition (grain on the top floor only). Capacity spectrum analysis indicates that EEF provides higher initial stiffness and lower displacement across all performance points. Time history results reveal that configurations with lighter upper mass (EFF, EEE) are more prone to top-floor acceleration amplification, while FFF and FFE demonstrate more stable responses due to balanced mass distribution. The maximum inter-story drift consistently occurs at the second floor, with FFF and FFE showing the most significant deformation. All drift ratios meet code limits, confirming the safety and applicability of the CSSC system under various storage scenarios. Full article

This study presents an optimized design and vision-guided control strategy for a multi-functional robotic gripper integrated into an automatic loading system for warehouse environments. The system adopts a modular architecture, including standardized platforms, transport containers, four collaborative 6-DOF robotic arms, and a multi-sensor vision module. Methodologically, we first developed three gripper prototypes, selecting the optimal design (30° angle between the gripper and container side) through workspace and interference analysis. A deep vision-based recognition system, enhanced by an improved YOLOv5 algorithm and multi-feature fusion, was employed for real-time object detection and pose estimation. Kinematic modeling and seventh-order polynomial trajectory planning ensured smooth and precise robotic arm movements. Key results from simulations and experiments demonstrated a 95.72% success rate in twist lock operations, with a positioning accuracy of 1.2 mm. The system achieved a control cycle of 35 ms, ensuring efficiency compared with non-vision-based methods. Practical implications include enabling fully autonomous container handling in logistics, reducing labor costs, and enhancing operational safety. Limitations include dependency on fixed camera setups and sensitivity to extreme lighting conditions. Full article

Background: Long COVID can develop in individuals who have had COVID-19, regardless of the severity of their initial infection or the treatment they received. Several studies have examined the prevalence and manifestation of symptom phenotypes to comprehend the pathophysiological mechanisms associated with these symptoms. Numerous articles outlined specific approaches for multidisciplinary management and treatment of these patients, focusing primarily on those with mild acute illness. The various management models implemented focused on a patient-centered approach, where the specialists were positioned around the patient. On the other hand, the created pathways do not consider the possibility of symptom clusters when determining how to define diagnostic algorithms. Methods: This retrospective longitudinal study took place at the “Fondazione IRCCS Policlinico San Matteo”, Pavia, Italy (SMATTEO) and at the “Ospedale di Cremona”, ASST Cremona, Italy (CREMONA). Information was retrieved from the administrative data warehouse and from two dedicated registries. We included patients discharged with a diagnosis of severe COVID-19, systematically invited for a 3-month follow-up visit. Unsupervised machine learning was used to identify potential patient phenotypes. Results: Three hundred and eighty-two patients were included in these analyses. About one-third of patients were older than 65 years; a quarter were female; more than 80% of patients had multi-morbidities. Diagnoses related to the circulatory system were the most frequent, comprising 46% of cases, followed by endocrinopathies at 20%. PCA (principal component analysis) had no clustering tendency, which was comparable to the PCA plot of a random dataset. The unsupervised machine learning approach confirms these findings. Indeed, while dendrograms for the hierarchical clustering approach may visually indicate some clusters, this is not the case for the PAM method. Notably, most patients were concentrated in one cluster. Conclusions: The extreme heterogeneity of patients affected by post-acute sequelae of SARS-CoV-2 infection (PASC) has not allowed for the identification of specific symptom clusters with the most recent statistical techniques, thus preventing the generation of common diagnostic-therapeutic pathways. Full article

The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems. Full article