Search Results (1,041)

The detection of coils in reservoir areas is part of the environmental perception technology of unmanned cranes. In order to improve the perception ability of unmanned cranes to include environmental information in reservoir areas, a method of automatic detection of coils based on two-dimensional LiDAR dynamic scanning is proposed, which realizes the detection of the position and attitude of coils in reservoir areas. This algorithm realizes map reconstruction of 3D point cloud by fusing LiDAR point cloud data and the motion position information of intelligent cranes. Additionally, a processing method based on histogram statistical analysis and 3D normal curvature estimation is proposed to solve the problem of over-segmentation and under-segmentation in 3D point cloud segmentation. Finally, for segmented point cloud clusters, coil models are fitted by the RANSAC method to identify their position and attitude. The accuracy, recall, and F1 score of the detection model are all higher than 0.91, indicating that the model has a good recognition effect. Full article

In an era of exponential data growth, ensuring high data quality has become essential for effective, evidence-based decision making. This study presents a structured and comparative review of the field by integrating data classifications, quality dimensions, assessment methodologies, and modern software tools. Unlike earlier reviews that focus narrowly on individual aspects, this work synthesizes foundational concepts with formal frameworks, including the Findable, Accessible, Interoperable, and Reusable (FAIR) principles and the ISO/IEC 25000 series on software and data quality. It further examines well-established assessment models, such as Total Data Quality Management (TDQM), Data Warehouse Quality (DWQ), and High-Quality Data Management (HDQM), and critically evaluates commercial platforms in terms of functionality, AI integration, and adaptability. A key contribution lies in the development of conceptual mappings that link data quality dimensions with FAIR indicators and maturity levels, offering a practical reference model. The findings also identify gaps in current tools and approaches, particularly around cost-awareness, explainability, and process adaptability. By bridging theory and practice, the study contributes to the academic literature while offering actionable insights for building scalable, standards-aligned, and context-sensitive data quality management strategies. Full article

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

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article

Globalization has greatly changed the way logistics firms function, improving speed, accuracy, and efficiency in everything from logistic management to warehousing. Robotics and automation technologies driven by artificial intelligence improve warehouse operations’ efficiency and adaptability, allowing warehouses to easily manage a variety of items, packaging kinds, and order profiles. Nevertheless, more research is still needed to fully comprehend how automation has affected logistics and how it has evolved. In addition, to date, no scholarly work has provided a thorough analysis of particular automated logistic process automation strategies used by Lithuanian businesses. Although many of the assessments that are currently available in this field offer valuable insights, they are frequently overly broad. In order to tackle this problem, we conducted a methodical study that attempts to offer a strong and pertinent basis, focusing on the automation of logistics processes that are used in supply chain management together with artificial intelligence. This study’s objective was to examine conditions for increasing logistics automation processes in Lithuanian logistic companies. The novelty of this article is the consideration of the main factors influencing the automation of logistics processes, which include the key drivers of AI-powered warehouse automation processes to evaluate the real level of automation. Full article

This article presents a model for orchestrating data extraction, processing, and storage, addressing the challenges posed by diverse data sources and increasing data volumes. The proposed model includes three primary components: data production, data transfer, and data consumption and storage. Key architectures for data production are explored, such as modular designs and distributed processes, each with advantages and limitations regarding scalability, fault tolerance, and resource efficiency. A buffering module is introduced to enable temporary data storage, ensuring resilience and asynchronous processing. The data consumption module focuses on transforming and storing data in data warehouses while providing options for parallel and unified processing architectures to enhance efficiency. Additionally, a notification module demonstrates real-time alerts based on specific data events, integrating seamlessly with messaging platforms like Telegram. The model is designed to ensure adaptability, scalability, and robustness for modern data-driven applications, making it a versatile solution for effective data flow management. Full article

Background: With a high human immunodeficiency virus (HIV) adult prevalence, people living with HIV (PLHIV) in Botswana continue to experience a high burden of comorbid HIV and cancer. We sought to investigate the trends of acquired immunodeficiency syndrome (AIDS) defining cancers (ADCs), non-AIDS defining cancers (NADCs), and associated risk factors in PLHIV compared with those without HIV. Methods: We analyzed data from adults aged ≥18 years reported in Botswana National Cancer Registry and National Data Warehouse. The crude, age-standardized incidence rate (ASIR), standardized incidence ratios (SIRs) of cancers and time trends were computed. Risk factors were determined using the Cox-regression model. Results: Over a 30-year period, 27,726 cases of cancer were documented. Of these, 13,737 (49.5%) were PLHIV and 3505 (12.6%) were people without HIV and 10,484 (37.8%) had an unknown HIV status. Compared to the HIV-uninfected, the PLHIV had higher and increasing trends in the cancer incidence overall during the study period (from 44.2 to 1047.6 per 100,000; p-trend < 0.001) versus (from 1.4 to 27.2 per 100,000; p-trend < 0.001). The ASIRs also increased in PLHIV for overall ADCs, NADCs and other sub-types like cervical, lung, breast, and conjunctiva cancers (p-trend < 0.001). Further, PLHIV had elevated SIRs for cervical cancer, Kaposi sarcoma in males and some NADCs. The most common risk factors were HIV infection and female sex for ADCs incidence and advanced age and being HIV-uninfected for NADCs incidence. Conclusions: Increasing trends of ADCs and NADCs during ART expansion were observed among PLHIV compared to those without HIV highlighting a greater need for targeted effective prevention and screening strategies including the provision of access to timely HIV and cancer treatment. 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 recent years, the application of ergonomics to workplace safety monitoring has gained increasing interest from companies and public institutions, allowing for the evaluation of the potential impact that dangerous situations may have on workers during their routine activities. This study presents a method for real-time monitoring of human physiological and motor responses to simulated workplace hazards during virtual reality safety training. The setup allows for precise measurements of both physiological and postural parameters during simulated scenarios. Moreover, a representative case study involving the sudden arrival of a forklift in a warehouse is presented. Five healthy participants were exposed to this scenario, with changes in heart rate variability and trunk posture being captured. The results demonstrate the effectiveness of sensor-based monitoring in detecting stress responses and postural adaptations to hazardous stimuli. This approach provides a basis for understanding human responses in simulated hazardous environments and may help to optimize safety training aimed at increasing workers’ risk perception and improving overall workplace safety. Although based on a small sample, the findings provide preliminary insights into the feasibility of sensor-based monitoring during VR safety training. Full article

Warehouses are vital in linking production to consumption, often using a forward–reserve layout to balance picking efficiency and bulk storage. However, replenishing the forward area from reserve storage is prone to delays and congestion, especially during high-demand periods. This study investigates the strategic use of buffer areas—intermediate zones between forward and reserve locations—to enhance flexibility and reduce bottlenecks. Although buffer zones are common in practice, they often lack a structured decision-making framework. We address this gap by developing an optimization model that integrates demand forecasts to guide daily replenishment decisions. To handle the computational complexity arising from large state and action spaces, we implement an approximate dynamic programming (ADP) approach using certainty-equivalent control within a rolling-horizon framework. A real-world case study from an automotive spare parts warehouse demonstrates the model’s effectiveness. Results show that strategically integrating buffer zones with an ADP model significantly improves replenishment timing, reduces direct picking by up to 90%, minimizes congestion, and enhances overall flow of intra-warehouse inventory management. Full article

Acute neutrophil responses following myocardial infarction (MI) play a central role in remodeling, contributing to both repair and potential maladaptive responses. Although prior studies have investigated circulating immune cell indices at a single time point during hospitalization for MI, limited data exist on acute intra-individual changes in circulating immune profiles during evolving MI. We analyzed clinical measurements, such as the count and proportion of immune cell components in a serial complete blood count, with differential tests conducted for patients hospitalized with ST-elevation MI (STEMI) in various hospitals in the Northwestern Medicine system from 1 January 2002 to 1 August 2024. Patients with STEMI diagnosis, troponin peaks ≥ 5 ng/mL, and cell count and proportion data prior to the troponin peak and within 24 h after the troponin peak were included. Primary analyses investigated the associations between the troponin peak and peri-STEMI changes in immune cell subsets. Multivariable-adjusted Cox models were used to investigate associations between these peri-STEMI immune cell changes and mortality at 1 year and 3 years. Among the 694 STEMI patients meeting the inclusion criteria, a higher troponin peak was associated with a modest peri-MI increase in neutrophil proportion. Higher adjusted peri-STEMI increases in neutrophil count and proportion were strongly associated with mortality at one and three years [hazard ratio (HR) = 1.31 (95% confidence interval (CI) 1.15–1.49) and HR = 1.27 (95% CI 1.14–1.45) per 1000 cells/μL absolute neutrophil increase, respectively]. Individuals with higher STEMI-related neutrophil increases had higher mortality at one year and three years, independent of the extent of troponin elevation. Full article

South Africa’s bioeconomy strategy identifies bio-innovation as a key driver of economic growth and social development, with plant breeding playing a central role in improving food security through the development of high-yielding, resilient, and high-quality crops. However, consumer perceptions of recent advances, particularly new breeding techniques (NBTs), remain underexplored. This study examines South African consumer attitudes towards plant breeding innovations, using a mixed-methods approach. The initial focus group interviews informed the development of a structured quantitative survey examining familiarity, perceptions, and acceptance of plant breeding technologies. Consumer awareness of plant breeding principles was found to be limited, with 67–68% of respondents unfamiliar with both conventional and modern plant breeding procedures. Despite this information gap, consumers expressed conditional support for modern breeding techniques, especially when associated with actual benefits like increased nutritional value, environmental sustainability, and crop resilience. When favourable effects were outlined, support for general investment in modern breeding practices climbed from 45% to 74%. Consumer purchase decisions emphasised price, product quality, and convenience over manufacturing techniques, with sustainability ranked last among the assessed factors. Trust in the sources of food safety information varied greatly, with medical experts and scientists being ranked highly, while government sources were viewed more sceptically. The results further suggest that targeted education could improve customer confidence, as there is a significant positive association (R² = 0.938) between familiarity and acceptance. These findings emphasise the significance of open communication strategies and focused consumer education in increasing the adoption of plant breeding breakthroughs. The study offers useful insights for policymakers, researchers, and industry stakeholders working on engagement strategies to facilitate the ethical growth and application of agricultural biotechnology in support of food security and quality in South Africa. This study contributes to a better understanding of South African consumers’ perceptions of plant breeding innovations and food safety. The research findings offer valuable insights for policymakers, researchers, and industry stakeholders in developing effective engagement and communication strategies that address consumer concerns and promote the adoption of products derived from diverse plant breeding technologies. 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

This case study aims to combine the advantage of the additive manufacturing of sensors with a mixed reality (MR) app, developed in a lab-scale workshop, to safely monitor and control the temperature of parts. Namely, the measurements were carried out in real time via a 3D-printed graphene–PLA nanocomposite sensor and communicated wirelessly using a low-power microcontroller with the IoT capability, and then transferred to the user display in the MR. In order to investigate the performance of the proposed computer-mediated reality, a user experience experiment (n = 8) was conducted. Statistical analysis results showed that the system leads to faster (>2.2 times) and more accurate (>82%) temperature control and monitoring by the users, as compared to the conventional technique using a thermal camera. Using a Holistic Presence Questionnaire (HPQ) scale, the users’ experience/training was significantly improved, while they reported less fatigue by 50%. Full article