Search Results (76,551)

This study develops a three-state Markov framework to estimate the transition rates between normal, preclinical screen-detectable phase (PCDP), and clinical breast cancer using simulated data. Two exponential models are explored: a five-mode transition model and a six-mode transition model, the latter incorporating exact cancer case timings. Each model is analyzed both with and without covariates to evaluate their influence on breast cancer progression. Parameters are estimated utilizing maximum likelihood estimation and Bayesian models with Gibbs sampling to ensure robustness and methodological rigor. Additionally, a nonhomogeneous model based on the Weibull distribution is introduced to account for time-varying transition rates, providing a more dynamic perspective on disease progression. While the analysis is conducted with simulated data, the framework is adaptable to real-world datasets, offering valuable insights for refining screening policies and optimizing inter-screening intervals. Full article

Mangrove Swamp Rice is a unique labour-, time-, and knowledge-intensive farming system across West Africa requiring a complex water management through dikes, canals, and pipes. Cooperation is crucial in the socio-spatial and temporal organisation of water management, helping mitigate risks like saltwater intrusion, prolonged submersion, and drying of paddies, particularly intensified by climate change. We adopted a mixed-methods approach integrating grounded theory with case study design and multi-level analysis and employed several tools, such as social network analysis, a land tenure register, a digital elevation model, and a machine learning model. This research strategy allowed us to gain an in-depth understanding of the complex socio-ecological system in water management. Our study examined the benefits of cooperation for water infrastructure management, highlighting its critical role in routine monitoring and repair, specialised technical advice, and annual belt dike maintenance. Within the large array of factors influencing cooperation, key drivers, such as spatial proximity and farmers’ technical knowledge, labour ethics, and fame, emerged and are discussed. Our findings provide important insights into the social organisation of “traditional” societies and offer valuable guidance for development practitioners, emphasising the importance of involving reputed farmers for sustainable socio-technical adaptation. Full article

This paper presents an economic feasibility analysis for constructing a 100 MW floating solar power plant in Korea aimed at utilizing idle water spaces to mitigate geographical limitations for large-scale renewable energy projects. Employing both Discounted Cash Flow (DCF) analysis and Real Options Analysis (ROA), the study evaluates the economic viability and strategic flexibility of the project under future market uncertainties. The DCF method reveals an NPV of 11,863,000 USD and a B/C ratio of 1.1, indicating baseline feasibility. However, the ROA demonstrates the value of adaptive strategies, with real option values showing that plant expansion (to 200 MW) is economically advantageous under favorable conditions. This approach supports strategic decision-making aligned with Korea’s renewable energy targets. The study emphasizes the importance of incorporating market uncertainties into feasibility assessments and proposes detailed strategies for expanding renewable energy generation. Full article

This paper considers the single-machine scheduling problem of total tardiness minimization. Due to its computational intractability, exact approaches such as dynamic programming algorithms and branch-and-bound algorithms struggle to produce optimal solutions for large-scale instances in a reasonable time. The advent of Deep Q-Networks (DQNs) within the reinforcement learning paradigm could be a viable approach to transcending these limitations, offering a robust and adaptive approach. This study introduces a novel approach utilizing DQNs to model the complexities of job scheduling for minimizing tardiness through an informed selection utilizing look-ahead mechanisms of actions within a defined state space. The framework incorporates seven distinct reward-shaping strategies, among which the Minimum Estimated Future Tardiness strategy notably enhances the DQN model’s performance. Specifically, it achieves an average improvement of 14.33% over Earliest Due Date (EDD), 11.90% over Shortest Processing Time (SPT), 17.65% over Least Slack First (LSF), and 8.86% over Apparent Tardiness Cost (ATC). Conversely, the Number of Delayed Jobs strategy secures an average improvement of 11.56% over EDD, 9.10% over SPT, 15.01% over LSF, and 5.99% over ATC, all while requiring minimal computational resources. The results of a computational study demonstrate DQN’s impressive performance compared to traditional heuristics. This underscores the capacity of advanced machine learning techniques to improve industrial scheduling processes, potentially leading to decent operational efficiency. Full article

Global change and disturbance ecology, including the risks and benefits of wildfires for humans, sustainability of ecosystems and biodiversity, is a current research topic in applied science. Fires and their impacts are often considered in the context of climate change, carbon dioxide emissions and air pollution. Despite a significant decline in wildfires at the global scale in recent decades (cf. Global Wildfire Information System (GWIS)), it is a widespread conviction that the burned area is increasing due to global warming. In an attempt to identify how this discrepancy has arisen, we analysed IPCC reports from 2018–2023 via text mining including word frequency analyses and compared considerations about wildfires and fire weather with findings from ecology and public information on the internet. Both a negativity bias and repetition bias were identified. Numerous examples of disasters and models indicating a global increase of wildfires are composed of alarming messages. Examples of decreasing wildfires and the global decline are much less frequently communicated. Important facts are ignored, especially in summaries for policymakers. Measured against fire-ecological conditions and benefits for the nature, alarming trends and risks due to climate change are exaggerated. We therefore call for a comprising and differentiated reflection of ecological conditions and processes in the future. Full article

Driven by the growing frequency of flood risks, this study focused on farming water resource management (FWRM) as an ecosystem-based solution. Despite its significance, there are limited studies investigating paddy farmers’ community-based adaptations (CBAs) for managing diverse farming water resources at a micro-spatial level, particularly within multidimensional communities. This study aims to bridge this gap and focuses on how community diversity and household characteristics impact farmers’ adaptation to different CBA methods. We conducted a household questionnaire survey in floodplain paddy farming communities in Kawasoti Municipality, Nepal, based on cultural, socioeconomic, and settlement diversity. The questionnaire was subjected to farmers’ CBAs for integrated FWRM and multiple structural and nonstructural adaptation measures for irrigation and rivers. The results showed that farmer participation varied across community groups. To understand the most important associated factors within community diversity and household attributes to adopt different water resources, the most adopted structural measures of water flow management (54%) from irrigation and buffer zone vegetation (54%) from rivers were analyzed. We used the Chi-squared Automatic Interaction Detector model, which suggests that water accessibility associated with community diversity, landholding, and water intake is important to improve farmers’ participation in irrigation management. However, for river management, community diversity, which relates to location in relation to a river and is associated with household income and farmland distance, is an important factor. Full article

Aiming at the problem of lateral deviation of large-scale long-endurance solar-powered UAVs relative to the runway during takeoff or landing, a UAV ground motion control structure based on the combination of engine differential and rudder was proposed. According to the structural characteristics of large-scale long-endurance solar-powered UAVs, a ground motion model of a three-point layout UAV including landing gear was established, and the ground rolling dynamics and modal characteristics were analyzed. In order to accurately correct the trajectory error, the outer loop designs a trajectory correction control law and gives the inner loop desired control instructions. In order to solve the problem of environmental disturbance and small heading damping, the inner loop adopts the adaptive back-stepping control method. The disturbance signal is estimated through the adaptive law and compensated into the control system to achieve balanced control of speed and rolling correction. Finally, medium-speed and high-speed sliding tests were designed to verify the rationality of the proposed control scheme and control structure, as well as the efficiency of the control law design method adopted. Full article

The navigation obstacle avoidance method based on deep reinforcement learning has stronger adaptability and better performance compared to traditional algorithms in complex unknown dynamic environments, and has been widely developed and applied. However, when using multimodal information input, deep reinforcement learning strategy networks extract features that differ significantly between simulated and real world environments, resulting in poor algorithm output strategies and difficulty in transferring models obtained from simulation training to actual environments. To address the aforementioned issues, this article utilizes image segmentation to narrow the gap in environmental features, integrates multimodal information, and designs a deep reinforcement learning multimodal local obstacle avoidance algorithm, MMSEG-PPO, based on proximal strategy optimization algorithms. The algorithm is then ported to practical environments for deployment and testing. The experiment shows that the algorithm proposed in this article reduces the gap between the simulation environment and the actual environment, and has better performance and generalization when transplanted to the real world environment. Full article

The SARS-CoV-2 virus infection, the most severe pandemic in recent human history, found healthcare systems around the world more or less unprepared. Adjusting to this challenge involved changes in the daily routines of healthcare systems, as well as the patients, once again highlighting the importance of primary care (family physician or general practitioner). In the context of the COVID-19 pandemic, the family doctor in Romania played a crucial role in patient management, rapidly adapting to the changes and challenges imposed by the state of emergency. Their involvement quickly evolved from in-person consultations to online assessments, as they took on responsibilities such as monitoring patients in isolation or quarantine and issuing necessary medical leaves. Moreover, family doctors were directly involved in the COVID-19 vaccination process, facing challenges related to access to scheduling platforms and limited resources of protective equipment. Although they were on the front line of the healthcare response, recognition through incentives or compensations came late and incompletely, and their efforts in combating the pandemic were often overlooked. Designating family doctors’ offices as public utility medical units (regardless of their organisational form) and supporting their activities through increased equipment and medical devices provided by local or central authorities are the keys to fighting for human lives in critical situations. Implementing clear and universal rules regarding the competencies (skills) and duties of family doctors, both in normal life situations and in exceptional circumstances, is of utmost importance. Little is known about the dedicated work and dedication of family physicians to their patients. Few studies have been carried out on the activity of the family doctor and their professional difficulties during the pandemic period. Some studies, on a small number of subjects, tried to evaluate the psychological adaptation of the family doctor to the new epidemiological situation. The aim of this narrative review is to highlight the difficulties to which family doctors had to adapt, comparing the data from the Romanian medical system with those discovered in the medical literature regarding family doctors from all over the world. Full article

The paper solves the problem of constructing step adjustment algorithms for a gradient method based on the principle of the steepest descent. The expansion of the step adjustment principle, its formalization and parameterization led the researchers to gradient-type methods with incomplete relaxation or over-relaxation. Such methods require only the gradient of the function to be calculated at the iteration. Optimization of the parameters of the step adaptation algorithms enables us to obtain methods that significantly exceed the steepest descent method in terms of convergence rate. In this paper, we present a universal step adjustment algorithm that does not require selecting optimal parameters. The algorithm is based on orthogonality of successive gradients and replacing complete relaxation with some degree of incomplete relaxation or over-relaxation. Its convergence rate corresponds to algorithms with optimization of the step adaptation algorithm parameters. In our experiments, on average, the proposed algorithm outperforms the steepest descent method by 2.7 times in the number of iterations. The advantage of the proposed methods is their operability under interference conditions. Our paper presents examples of solving test problems in which the interference values are uniformly distributed vectors in a ball with a radius 8 times greater than the gradient norm. Full article

The integration of 3D concrete printing (3DCP) into architectural design and production offers a solution to challenges in the construction industry. This technology presents benefits such as mass customization, waste reduction, and support for complex designs. However, its adoption in construction faces various limitations, including technical, logistical, and legal barriers. This study provides insights relevant to architecture, engineering, and construction practices, guiding future developments in the field. The methodology involves fabricating closed architectural units using 3DCP, emphasizing space-filling geometries and ensuring structural strength. Across three production trials, iterative improvements were made, revealing challenges and insights into design optimization and fabrication techniques. Prioritizing controlled filling of the unit’s internal volume ensures portability and ease of assembly. Leveraging 3D robotic concrete printing technology enables precise fabrication of closed units with controlled voids, enhancing speed and accuracy in production. Experimentation with varying unit sizes and internal support mechanisms, such as sand infill and central supports, enhances performance and viability, addressing geometric capabilities and fabrication efficiency. Among these strategies, sand filling has emerged as an effective solution for internal support as it reduces unit weight, simplifies fabrication, and maintains structural integrity. This approach highlights the potential of lightweight and adaptable modular constructions in the use of 3DCP technologies for architectural applications. Full article

Vehicle-to-Everything (V2X) communication is one of the essential technologies in 5G systems and will certainly play an important role in incoming 6G communications. Two modes of 5G New Radio V2X communication (NR-V2X) have been defined to standardize the direct exchange of messages between vehicles. This paper concentrates on Mode 1, in which message exchange takes place with the support of the cellular infrastructure. In this mode, each vehicle uses a fixed number of subchannels with pre-configured subchannel sizes to transmit packet messages. However, if the packet sizes vary in each transmission, some resource blocks (RBs) assigned to V2X links are wasted. This paper presents the results of investigations on more efficient use of resource blocks, intending to minimize their waste and limit the delay in resource selection. In this paper, two new algorithms for radio resource block assignment are proposed and analyzed. The algorithms are characterized by a lower waste of RBs and a shorter delay in resource assignment compared to current solutions. The first algorithm uses adjacent RBs, whereas the second one can assign non-adjacent RBs, resulting in an even lower waste of radio resources and a shorter delay in their assignment. The simulation results presented confirm the quality of the proposed algorithms. Full article

Companies have tried to innovate in their training processes to increase their productivity indicators, reduce equipment maintenance costs, and improve the work environment. The use of Augmented Reality (AR) has been one of the implemented strategies to upgrade training processes, since it optimizes, through User Interface (UI) Design, experiences designed for users (UX) that are focused on education and training contexts. This research describes the definition and implementation of an IT architecture based on the ISO/IEC/IEEE 42010 standard using the Zachman and Kruchten frameworks. The methodological proposal presents an architecture seen from a business perspective, taking into account the strategic and technological components of the organization under a strategic alignment approach. The result is a six-layer architecture: The Government Strategy Layer (1) that accounts for the strategic component; the Business Layer (2) that presents the business management perspective; the Information Layer (4) that defines the metrics system: efficiency through task time, effectiveness through tasks completed, and satisfaction with overall satisfaction. In the Data Layer (4), the data collected with the metrics are structured in an industrial scenario with a cylinder turning process on a Winston Lathe. The experiment was carried out with two groups of 272 participants. In the Systems and Applications Layer (5), two applications were designed: a web client and a mobile application with augmented reality, and finally, the Networks and Infrastructure Layer (6), which delivers the two functional applications. The architecture validation was carried out using the mobile application. The analysis of the results showed a significance value of less than 0.001 in the three indicators: efficiency, effectiveness, and satisfaction in the Levene test and Student’s t-test. To corroborate the results, a test of equality of means with the Mann–Whitney U was carried out, showing that the three indicators presented significantly different values in the two experimental groups of this study. Thus, the group trained with the application obtained better results in the three indicators. The proposed architecture is adaptable to other training contexts. Information, data, and systems and application layers allowed for the exchange of training processes so that the augmented reality application is updated according to the new requirements. Full article

Sustainable agriculture is pivotal to global food security and economic stability, with plant disease detection being a key challenge to ensuring healthy crop production. The early and accurate identification of plant diseases can significantly enhance agricultural practices, minimize crop losses, and reduce the environmental impacts. This paper presents an innovative approach to sustainable development by leveraging machine learning models to detect plant diseases, focusing on tomato crops—a vital and globally significant agricultural product. Advanced object detection models including YOLOv8 (minor and nano variants), Roboflow 3.0 (Fast), EfficientDetV2 (with EfficientNetB0 backbone), and Faster R-CNN (with ResNet50 backbone) were evaluated for their precision, efficiency, and suitability for mobile and field applications. YOLOv8 nano emerged as the optimal choice, offering a mean average precision (MAP) of 98.6% with minimal computational requirements, facilitating its integration into mobile applications for real-time support to farmers. This research underscores the potential of machine learning in advancing sustainable agriculture and highlights future opportunities to integrate these models with drone technology, Internet of Things (IoT)-based irrigation, and disease management systems. Expanding datasets and exploring alternative models could enhance this technology’s efficacy and adaptability to diverse agricultural contexts. Full article

To address the demands of efficient forest fire detection and suppression, an adaptive multiple UAV swarm collaborative firefighting strategy considering dynamic forest fire spread and resource constraints was proposed in this paper. The multiple UAV swarm adaptive information-driven collaborative search (MUSAIDCS) algorithm and the resource-limited firefighting model were established. A temperature change-driven adaptive step-length search strategy is proposed to improve the accuracy of the search and detection of fire spots. The critical water flow rate required for fire suppression is calculated to evaluate the firefight performance, and an emergency bidding algorithm is applied to enable multiple UAV swarms collaborative firefighting under limited resources, including different payloads per UAV and swarm number. The comparative simulations for four search strategies indicate that the MUSAIDCS search strategy can significantly reduce task completion time and improve firefighting efficiency compared with the other traditional search strategies. Increasing payload quantity per UAV and swarm number can further enhance task completion efficiency and firefighting effectiveness. This study demonstrates that a resource-constrained collaborative firefighting strategy enables the dynamic allocation of UAV swarm resources under limited conditions and then optimizes firefighting performance within constraints. Full article