Priority Map Research Articles

Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Frequent flooding has become a persistent issue in floodplain regions, causing significant disasters during each rainy season due to insufficient disaster management planning. This study proposes a methodology to prioritize flood susceptibility areas at the district level and identify suitable sites for flood shelters using a combination of machine learning algorithms and multi-criteria analysis, supported by geospatial technology. Flood shelter suitability mapping was conducted using the Analytical Hierarchy Process (AHP), while flood susceptibility zones were assessed using four different machine learning models: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. The integration of machine learning models with the AHP technique is vital in situations where conventional numerical models face challenges due to limited data, such as river discharge and water levels. The methodology includes a multicollinearity assessment to ensure the independence of selected flood-causing factors, information gain ratio to identify the most influential factors, Spearman's rho test to verify correlations between the machine learning models, and ROC-AUC along with statistical regression for validating the accuracy of the flood susceptibility maps. The findings indicate that the SVM algorithm, given its strong performance and effective training datasets, is recommended for areas with similar physical characteristics. The district-wise priority map generated from the weighted results of flood susceptibility assessments will be useful for flood management and mitigation strategies. Additionally, the study found that applying the AHP technique to determine flood shelter suitability, after assessing flood-prone areas, enhanced the efficiency of the flood management process. This research offers valuable insights for authorities to better address flooding and improve flood prevention and management efforts in floodplain regions, contributing to broader climate change adaptation strategies.

Multi-UAVs task allocation method based on MPSO-SA-DQN

Multi-UAVs play an important role in the battlefield. Although many methods are proposed to solve the Multi-UAV task allocation, there still existing the problems of complex time constraints and uncertain solution space. The reason is that multi-UAVs usually face changing environmental factors. Aiming at solving such problem, this paper proposes a multi-UAV task assignment method based on Deep Q-based evolutionary reinforcement learning algorithms (MPSO-SA-DQN). Specifically, this method builds a multi-agent training framework based on the deep evolutionary reinforcement learning mechanism and SA-DQN. Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. Based on task priority mapping, the MPSO-SA-DQN algorithm framework is proposed. As a result, multi-agents can optimize the execution state in real time in the environment interaction. Besides, it also has the ability to reach optimal state and maximum reward. According to the characteristics of multi-UAV global task assignment, this paper designs a priority state space autoencoder strategy and global task feature. A multi-UAVs tasks allocation and iterative optimization method based on MPSO-SA-DQN algorithm is proposed, so as to continuously optimize the task allocation scheme. The simulation results show that the multi-UAV task allocation method based on MPSO-SA-DQN can effectively solve the problem of uncertainty in the optimal solution space of task allocation. At the same time, the algorithm achieves faster convergence result, and a good prospect of promotion in the field of UAV swarm cooperative task planning.

Stimulus representations in neural priority maps are equally enhanced by attention independent of the number of attended locations

Stimulus representations in neural priority maps are equally enhanced by attention independent of the number of attended locations

The impact of predicted and unpredicted events on the spatial priority map: evidence from multivariate pattern analyses in EEG

The impact of predicted and unpredicted events on the spatial priority map: evidence from multivariate pattern analyses in EEG

Reward History and Statistical Learning Independently Impact Attention Search: An ERP Study.

Selection history is widely accepted as a vital source in attention control. Reward history indicates that a learned association captures attention even when the reward is no longer presented, while statistical learning indicates that a learned probability exerts its influence on attentional control (facilitation or inhibition). Existing research has shown that the effects of the reward history and statistical learning are additive, suggesting that these two components influence attention priority through different pathways. In the current study, leveraging the temporal resolution advantages of EEG, we explored whether these two components represent independent sources of attentional bias. The results revealed faster responses to the target at the high-probability location compared to low-probability locations. Both the target and distractor at high-probability locations elicited larger early Pd (50-150 ms) and Pd (150-250 ms) components. The reward distractor slowed the target search and elicited a larger N2pc (180-350 ms). Further, no interaction between statistical learning and the reward history was observed in RTs or N2pc. The different types of temporal progression in attention control indicate that statistical learning and the reward history independently modulate the attention priority map.

Interaction of spatial attention and the associated reward value of audiovisual objects

Reward value and selective attention both enhance the representation of sensory stimuli at the earliest stages of processing. It is still debated whether and how reward-driven and attentional mechanisms interact to influence perception. Here we ask whether the interaction between reward value and selective attention depends on the sensory modality through which the reward information is conveyed. Human participants first learned the reward value of uni-modal visual and auditory stimuli during a conditioning phase. Subsequently, they performed a target detection task on bimodal stimuli containing a previously rewarded stimulus in one, both, or neither of the modalities. Additionally, participants were required to focus their attention on one side and only report targets on the attended side. Our results showed a strong modulation of visual and auditory event-related potentials (ERPs) by spatial attention. We found no main effect of reward value but importantly we found an interaction effect as the strength of attentional modulation of the ERPs was significantly affected by the reward value. When reward effects were examined separately with respect to each modality, auditory value-driven modulation of attention was found to dominate the ERP effects whereas visual reward value on its own led to no effect, likely due to its interference with the target processing. These results inspire a two-stage model where first the salience of a high reward stimulus is enhanced on a local priority map specific to each sensory modality, and at a second stage reward value and top-down attentional mechanisms are integrated across sensory modalities to affect perception.

Radiomap Inpainting for Restricted Areas Based on Propagation Priority and Depth Map

Radiomap Inpainting for Restricted Areas Based on Propagation Priority and Depth Map

A Decision Support Evaluation Framework for Community-Based Collaborative Urban Regeneration Processes

The current debate on urban regeneration highlights the significance of social innovation and the reuse of cultural heritage to develop new economic and entrepreneurial models, creating value chains and benefits for communities. International and European directives, such as the European Agenda 2030 and the New European Bauhaus, advocate for inclusive and sustainable urbanization, encouraging participatory and circular urban regeneration processes. This study presents a decision support framework for community-based collaborative urban regeneration processes, focusing on the urban area of Reggio Calabria in Southern Italy. In this region, some cultural assets have been reactivated and made accessible, while others remain inactive, underutilized, or abandoned. The research aims to explore how to effectively enhance cultural heritage and promote urban regeneration through a collaborative network strategy. By employing multicriteria analysis, involving the selection and definition of specific criteria and indicators for each site, a priority map is created to facilitate the implementation of regeneration processes. The application of this approach in the urban center of Reggio Calabria demonstrated its effectiveness in revitalizing unused or abandoned cultural heritage, transforming it into a catalyst for urban vibrancy and vitality.

Network mapping of climate change priorities in USA: golden cut bipolar q-ROFSs

The purpose of this study is to identify affordable and clean energy-based climate change priorities in USA for the sustainable development. Five factors that can influence clean energy-based sustainable development are weighted with Multi step wise weight assessment ratio analysis approach. Furthermore, selected priorities for the climate change goal of sustainable development are evaluated. The novelty of this study is presenting affordable and clean energy-based climate change priorities for the sustainable development by considering an original fuzzy decision-making model based on M-SWARA and ELECTRE with bipolar q-rung orthopair fuzzy sets and golden cut. Because they include both membership, non-membership and hesitancy, it can be possible to perform more effective analysis. This issue helps to reach more reliable results. The main findings solve the problem that government support has the greatest weight with respect to the clean energy-based sustainable development.

Neural Mechanisms of Visual Search and Working Memory

Visual search is a useful experimental paradigm investigating various aspects of attention. For efficient search, participants must avoid revisiting previously viewed objects. Inhibitory tagging and inhibition of return are phenomena related to this process, but their neural mechanisms are yet to be elucidated. Recent studies have shown that the rate of revisit behavior during visual search correlates with working memory capacity. This suggests that top-down signals from the frontal executive system alter the priority map that guides attention and eye movements. With this in mind, we have developed a novel visual search paradigm with many identical distractors and an evaluation model that assesses multiple parameters of working memory. The model incorporated memory capacity, memory decay, and utility rate, and when applied to data obtained from experimental animals, these parameters could be reliably evaluated. Furthermore, using the behavioral paradigm and model, we found that systemic administration of subanesthetic dose of ketamine decreased utility rate and memory capacity, while nicotine administration increased utility rate. Since our behavioral paradigm does not require complex instruction, it can be applied to a variety of patients in future clinical studies.

A pathway to urban resilience: investigating green open space priority areas to mitigate the urban heat island phenomenon in Samarinda City, Indonesia

PurposeThe potential for massive economic growth exists in Samarinda City due to the intensification of activities in built-up areas. This suggests the potential for increased urban disease in the relocation of Indonesia’s new capital city to a location adjacent to Samarinda. One of the most striking impacts is the urban heat island (UHI). The increase in this phenomenon can be addressed effectively and efficiently through the provision and arrangement of appropriate vegetation-based actions. Therefore, this study aims to identify priority areas of green open space (GOS) based on UHI levels. In addition, this study also aims to present alternative mitigation measures to reduce the risk of disasters due to UHI.Design/methodology/approachA mixed-method approach was used in this research, involving an initial land surface temperature analysis to identify the UHI class. This analysis was complemented by quantitative spatial analyses, such as scoring, overlay and intersect methods, to determine the priority level class and the typology of GOS priority. A qualitative analysis was also conducted through data triangulation or comparison methods, such as examining existing land use, GOS priority maps and spatial plan policies.FindingsThe findings show that the total UHI area in Samarinda City was 6,936.4 ha in 2019 and is divided into three classifications. In Class 1, the UHI area is very dominant, reaching 87% of the total area. Meanwhile, the main results identified two priority classes of GOS in Samarinda, namely, the medium and high categories with an area of 960.43 ha and 113.57 ha, respectively. The results also showed that there were 17 typologies associated with five alternative mitigation measures: green industry, greening parking lots, improving urban green infrastructure and buildings, urban greening and mining restoration.Research limitations/implicationsSpecific to assessing UHI, image data were available only in medium spatial resolution, leading to a consequence of detailed accuracy. In addition, since the determination of mitigation considered local policies, the method should be used in other locations requiring adjustments to existing regulations, specifically those related to spatial planning.Originality/valueThis study makes a significant contribution to the understanding of the UHI phenomenon in Indonesia, especially in the urban areas of Kalimantan Island. In addition, the study presents new insights into alternative mitigation actions to reduce the risk of UHI. Innovatively, this study introduces a typology of regions associated with appropriate alternative mitigation actions, making it an important achievement for the first time in the context of this study.

Analisis Pengukuran Usability Aplikasi DAPODIK Kabupaten Karawang Dengan Metode Use Questionnaire dan IPA

Choosing an internet-based application as a communication tool is considered a smart decision due to its ability to deliver information or messages to users anywhere without being limited by region or location. An application created by an institution can provide important information about the institution's activities to users, while users can access detailed data through the application. Currently, the Dapodik application has been implemented in various schools in Kabupaten Karawang. However, the use of this application faces several problems, including frequent system errors that cause double transactions and require a long time to update data. Based on these problems, this study was conducted to determine the extent of the advantages and disadvantages of the Dapodik application. The research was conducted using the Use Questionnaire and Importance Performance Analysis methods using a questionnaire distributed to 50 application users using google form. The results of this study obtained with the Importance Performance Analysis method produce a priority map for improving the Dapodik application. The map is divided into quadrant I as the top priority from the user's perspective. In quadrant II, there are aspects of the Dapodik application which are areas of achievement or excellence that must be maintained because they have met user expectations, namely the appearance of the Dapodik application is easy to recognize, the Dapodik application is easy to operate and the color combination of the Dapodik application is pleasing to the eye and not boring.

Empowering and supporting frontline providers in the paediatric-adolescent HIV response: results from participatory priority-setting and group discussions in twenty-four sites in twelve high HIV-burden African countries

ABSTRACT Children and adolescents living with HIV in Africa experience poor outcomes across the HIV cascade of care. Paediatric and adolescent-friendly services are crucial to their well-being, and recent years have seen a call for urgent service improvements. While frontline health workers are responsible for these services, less attention has been given to their contextual realities, and what constitutes an enabling service delivery environment. We engaged participatory priority-setting and group discussions across twenty-four sites in twelve high HIV-burden African countries in November 2022 with 801 frontline paediatric-adolescent providers. Data were analysed using thematic analysis and priority mapping. We constructed a socio-ecological model for supportive and empowering service delivery environments for frontline paediatric-adolescent HIV services. Individual-level themes related to well-being, self-care and mental health. Interpersonal themes included supportive supervision/mentorship, teamwork and acknowledgement. At the organisational level, resources, physical infrastructure and confidential spaces were included. The community level included feeling appreciated and positive stakeholder relationships. The structural level included funding, discriminatory SRHR policies and guidelines. Results provide insight into priorities, challenges and needs of frontline providers in the paediatric-adolescent HIV response. Improving the well-being of HIV-affected children and adolescents requires greater investment and attention to creating more caring, supportive environments for their frontline providers.

Seeking shade equity: Tree canopy coverage in public schools in Austin, TX.

Tree canopies have dermatologic and environmental benefits, especially on school campuses. However, inequities likely exist, and tree planting initiatives may further exacerbate disparities. We sought to identify any relationship between tree canopy shade on public school campuses in Austin, TX and the socioeconomic makeup of the student population, as well as whether current initiatives appropriately address any inequities. In this cross-sectional study, we used ArcGIS, a publicly available geographic information system (GIS), to calculate the percentage of tree canopy on campuses within the Austin Independent School District (AISD) for the years 2006 and 2022. We compared this with the percentage of students eligible for free and reduced meals (FRM) at each school. The percentages of FRM-eligible students were also compared for "low priority" versus "high priority" neighborhoods, as assigned by Austin's Tree Priority Map. Among 112 schools analyzed, schools with minority FRM-eligible students had significantly more tree canopy compared to schools with majority FRM-eligible students (19.9% vs. 12.4%, p < .001). When comparing tree priorities, there was a significant difference between the percentage of FRM-eligible students in "low priority" schools compared to "high priority" schools (23.8% vs. 62.2%, p < .001). Additional work is needed to rectify inequities in tree canopy access for public school students. Designations such as those used in Austin's Tree Priority Map would likely help direct such efforts. We hope this study encourages future research with GIS by both dermatologists and other health care professionals to promote interdisciplinary work with urban planners.

Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

AbstractAttention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a history map that encodes learning effects and a goal-driven task control to prioritize visual features. The model works on coded features rather than image pixels which is common in many traditional saliency models. We test the model on a reaction time (RT) data from a psychophysical experiment. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms selection history effects on attention guidance. The model is able to capture individual differences between participants’ RTs and response probabilities per group. Moreover, we demonstrate that a model with a reduced set of maps performs worse, indicating that integrating history, saliency and task information are required for a quantitative description of human attention. Besides, we show that adding intertrial effect to the model (as another lingering bias) improves the model’s predictive performance.

Spatial transfer of object-based statistical learning

A large number of recent studies have demonstrated that efficient attentional selection depends to a large extent on the ability to extract regularities present in the environment. Through statistical learning, attentional selection is facilitated by directing attention to locations in space that were relevant in the past while suppressing locations that previously were distracting. The current study shows that we are not only able to learn to prioritize locations in space but also locations within objects independent of space. Participants learned that within a specific object, particular locations within the object were more likely to contain relevant information than other locations. The current results show that this learned prioritization was bound to the object as the learned bias to prioritize a specific location within the object stayed in place even when the object moved to a completely different location in space. We conclude that in addition to spatial attention prioritization of locations in space, it is also possible to learn to prioritize relevant locations within specific objects. The current findings have implications for the inferred spatial priority map of attentional weights as this map cannot be strictly retinotopically organized.

Building green infrastructure for mitigating urban flood risk in Beijing, China

Due to climate change and accelerated urbanization, urban flooding has an increasing impact on the sustainable development of metropolitan areas. Since the 1990s, green infrastructure has developed as an alternative and sustainable approach to the mitigation of flood disaster in urban areas. To improve the resilience of urban ecological flood control and build a green infrastructure network, we provide quantitative measures of flood risk assessment to generate a grid-scale (1 ×1 km) flood risk assessment results and analyze its spatiotemporal heterogeneity. Then, we propose a GIS-based multi-criteria assessment method to determine the priority areas of green infrastructure according to four criteria, generating a distribution map of green infrastructure priorities in Beijing at the township scale. From the perspective of urban flood risk mitigation, the research proposes a new method to determine the priority areas of green infrastructure, thus establishing suggestions for future green infrastructure construction in Beijing.

Primary driving factors of ecological environment system change based on directed weighted network illustrating with the Three-River Headwaters Region

The primary driving factors of ecological environment change have received significant attention. However, previous research methods for identifying the main drivers of ecological environment change have primarily relied on correlation analysis and regression analysis. While these methods can reveal co-occurrences, associations, and correlations among elemental characteristics, they often struggle to uncover the deep-seated interactions among elements within complex, unstable, nonlinear, and high-dimensional systems. To address this, we used the Three-River Headwaters Region as a case study and introduced a complex network model from the perspective of the ecological environment system to investigate the main driving factors of ecological environment change. In our analysis, we considered 12 factors related to the atmosphere, hydrology, vegetation, and soil, including evaporation, long-wave radiation, short-wave radiation, specific humidity, soil temperature, precipitation rate, soil water content, air temperature, air pressure, vegetation normalization index, wind speed, and natural surface runoff. Watersheds were selected as the fundamental units for constructing ecological environment datasets. We applied the Ensemble Empirical Mode Decomposition (EEMD) method and Hilbert-Huang Transform (HHT) to analyze causal relationships between time series pairs and constructed two directed weighted network models based on sub-catchments. The results showed that both network models yielded consistent conclusions, with the sparse network exhibiting higher efficiency. Radiation and temperature were identified as the primary driving factors of ecosystem change, and the water cycle was determined to be the ultimate manifestation of ecological system change throughout the Three-River Headwaters Region. Furthermore, based on node out-strength, we generated a vegetation protection priority map.

Global conservation priorities for wetlands and setting post-2025 targets

Wetland conservation is becoming increasingly important as wetland areas decline globally. However, no comprehensive global-scale mapping of wetland conservation priorities and targets has been published. This information is needed to extend the current protected area network and improve the conservation efficiency for wetlands. Here, we propose a cost-effective assessment model for wetland conservation by integrating wetland conservation value- and human impact-related indicators to identify global wetland conservation priorities. These priorities cover 28% of the potential global wetland distribution, and of that, only 44% is currently protected by existing protected areas. To protect more wetland conservation priorities, we propose three target-setting scenarios for protected area expansion that offer additional contributions of 9.40%, 42.40%, and 55.97%, respectively. These three global targets can be downscaled to the national level and used to update national wetland biodiversity conservation strategies and action plans under a harmonized legal and regulatory regime at different scales and jurisdictions.

Reforms and legislation in the field of education in Albania, October 1921- February 1924

The article aims to analyze the policies, issues, and priorities of the Ministry of Education in the Albanian state from October 1921 to February 1924. The analysis is based on the documentation of this institution found in the Central Archive of the Albanian State. The method used is primarily content-based text analysis. The documentation found was catalogued based on the topic addressed to create a map of the education priorities. During this period, two important laws were introduced to regulate the education system. Statistical summaries were also included to provide an overview of the status of teachers and the number, age, and gender of children attending schools. From the conclusions, it was found that despite short-lived governments and the challenges faced by a troubled and poor society, efforts were made to strengthen and improve the education system throughout the country. There are efforts to improve the quality of education by equalizing the school system in rural areas with that in urban areas, improving the curriculum and quality of instruction, encouraging the pursuit of education at a younger age, as is the initiative to open kindergartens, creating more opportunities, and drafting laws for education. Modernization of the system was also prioritized.