Resource Mapping Research Articles

Registration of sorghum backcross‐nested association mapping (BC‐NAM) families in a BTx623 or RTx436 background

AbstractTwo sorghum [Sorghum bicolor (L.) Moench] backcross‐nested association mapping (BC‐NAM) populations (Reg. no. MP‐5, NSL 546724 MAP) have been developed, composed of 30 families totaling 2189 BC1F4 lines using an elite maintainer (B) or restorer (R) line as the recurrent parent. The families were derived from unadapted founder lines that were backcrossed to BTx623 and/or RTx436 based on the fertility reaction score and phylogenetic analysis of the founder lines. These unadapted founder lines were selected based on agronomic fitness, breeder desirability, and genetic diversity and represent a range of races and working groups. The 30 families were developed from 27 founder parents—three of the founder parents were backcrossed to both recurrent parents to provide a genetic relationship across all 30 families. Each family consists of 45–100 BC1F4 lines selected for standard agronomic fitness, and each population was genotyped using genotype‐by‐sequencing. To identify families that possess (or segregate for) traits of interest, 13 lines from each population were randomly selected and phenotyped for a range of agronomic and stress tolerance traits. These BC‐NAM populations, which are adapted to US production regions and possess novel genetic diversity, have application in genetic research as a mapping resource for traits of interest and for sorghum breeding programs to diversify R and B line parental heterotic pools.

Civic Innovation in Portugal

Democracy in Portugal is challenged by the lack of awareness regarding decision-making processes, disappointment with quality of life expectations, and a general lack of trust in politics. Citizens' disenchantment with the performance of governors and politicians is reflected in low electoral participation and radicalisation in the public sphere. Despite this, there is a growing inclination towards participatory dynamics and solidarity initiatives. The 50th Portuguese anniversary of democracy in 2024 should draw attention to innovative civic engagement projects that enhance democratic maturity and citizens’ commitment.
 The present article aims to conceptualise citizen innovation, and analyse its practices in Portugal, typify models of civic laboratories, and discuss their effectiveness, focusing on results, particularly those that are managed as a bottom-linked model This study carried out a literature review and analysed four recent initiatives in Portugal. Structured in three parts, it discusses civic labs as settings for structural change and analysis of civic innovation in Portugal.
 The study emphasises equally the transformative potential of collaborative collective action between citizens and communities, particularly when it is mediated and geographically delimited. Citizen labs are seen as valuable spaces for mapping resources, combining knowledge, and experiencing social transformations with low risk. Regarding civic labs governance models, the bottom-linked approach, although with limitations, tends to reveal a strong potential when compared to top-down institutional or bottom-up community approaches.
 Cover page: The conceptual and operative dimensions of the Urban Civic Labs

Optimal Wind Power Plant Layout Using Ant Colony Optimization

The optimal layout of wind power plants is very important as the arrangement of wind turbine generators (WTGs) profoundly affects the overall energy output of the wind power plant. To address this important issue, this research investigates the best layout for WTGs in wind power plants with different terrain features across three locations in Thailand using the Ant Colony Optimization (ACO) algorithm. The objective functions of maximizing the net annual energy production (AEP) and minimizing the wake losses were used to achieve the optimal wind power plant layout. Using the MERRA-2 database, computational fluid dynamics (CFD) wind flow modeling was performed to create 10x10 km2 microscale wind resource maps of locations characterized by flat, semi-complex, and complex terrains to install wind power plants. The CFD wind flow modeling yielded wind speeds of 5.00 to 5.76, 4.21 to 8.90, and 3.10 to 4.45 m/s for the flat, semi-complex, and complex terrains, respectively, making them feasible for utility-scale wind power plants. WTGs of multiple blade diameters, ranging from 90 to 126 m, with a nominal capacity of 2.5 MW at 100 m above ground-level hub heights, were used in this study. The Gamesa G126-2.5MW WTG with a 126 m blade diameter produces the highest net AEP of 14.3, 76.1, and 38.9 GWh/yr for the three terrains. Hence, this WTG was used to perform an ACO-based optimization to improve the electricity production of the wind power plants. Such studies are important to improve the efficiency of wind power plants, thus extracting the maximum kinetic energy possible from the winds and improving the economic viability of wind power plants.

Mapping Evidence-Based Non-Opioid and Non-Pharmacological Pain Management Modalities Across Minnesota: The Non-Opioid Pain Alleviation Information Network Project.

Objective: The Non-Opioid Pain Alleviation Information Network (NOPAINMN) project was designed to identify, consolidate, and map evidence-based non-opioid and non-pharmacological pain management complementary and integrative health (CIH) modalities for chronic pain management across Minnesota into a searchable and informational website (www.nopainmn.org ). Methods: The Academic Consortium for Integrative Medicine & Health's Pain Task Force White Paper identifying evidence-based research of non-pharmacological pain practice (NPPC) was reviewed and referenced. National and state certifying boards and accrediting organizations for NPPC modalities were accessed to identify Minnesota-based NPPC providers' name, business/health-system affiliation, address, contact information, and credentials. The NOPAINMN website displays these data in a consumer-facing website with searchable fields such as NPPC modality type, and location with varying distances. The website was β-tested by practitioners and stakeholders for optimization. Eight main NPPC modalities and their respective subcategories were identified and mapped: Acupuncture; Integrative Medical Care (Functional medicine consultation and Integrative medicine consultation); Massage Therapy; Mind-Body Therapies (Biofeedback, Clinical Hypnosis, Mindfulness-Based Stress Reduction, and Music Therapy); Movement Therapies (Tai Chi, qigong, and Yoga Therapy); Psychology (Cognitive Behavioral Therapy); Rehabilitative Therapies (Physical and Occupational Therapy); and Spinal Manipulation. Results: All information compiled resulted in 17,155 providers/practitioners. Physical Therapy had the greatest number of reported providers (n = 5224), followed by Occupational Therapy (n = 3792), Psychology (n = 3324), Chiropractic (n = 3033), Acupuncture (n = 591), and Massage Therapy (n = 544). The Resource Map included 56 major health systems, 686 facilities, 2651 solo or private group practices, and 14 academic training schools. With web-based cross-referencing, providers and facilities were affiliated and linked with health systems to produce an interconnected mapping system. β-Testing with patients found that the website was reported as relatively easy to use and informative. Conclusion: The website was created to assist individuals, health care providers, insurance carriers, and health care facilities in finding evidence-based information and resources on NPPC to guide, support, and proactively manage and engage chronic pain patients across Minnesota.

Global transcontinental power pools for low-carbon electricity

The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5–52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.

Critical care services in Bagmati province of Nepal: A cross sectional survey

Background This study aimed to assess the current status of critical care services in 13 districts of Bagmati Province in Nepal, with a focus on access, infrastructure, human resources, and intensive care unit (ICU) services. Methods A cross-sectional survey was conducted among healthcare workers employed in 87 mixed medical/surgical ICUs across Bagmati Province. Data were collected through structured questionnaires administered via face-to-face and telephone interviews. Descriptive analysis was used for data analysis, involving frequencies and percentages. Results A total of 123 ICUs were identified in the province, providing 1167 beds and 615 functioning ventilators. The average ICU bed availability per 100,000 population was 19, ranging from 3.6 in Makwanpur to 33.9 in Kathmandu. Out of 13 districts, 95% of beds were concentrated in just four districts, while six had no ICU facilities. Of the available facilities, 69.9% were owned by private entities. One-to-one nurse-to-ventilated bed ratio was maintained by 63.4% of ICUs during daytime, and 62.6% at nighttime. Furthermore, 74.8% of ICUs had consultants trained in critical care medicine. While essential equipment availability was higher in Bagmati province, gaps existed in the availability of oxygen plants and isolation rooms. Similarly, many ICUs offered continuous medical education and cardiopulmonary resuscitation (CPR) training, but improvements were necessary in clinical audits, antibiotic stewardship programs, and research engagement. Conclusions Disparities in critical care resources were evident across districts in Bagmati Province, highlighting the need for a balanced and decentralized approach to ensure equitable access to care. Although there were disparities, numerous ICUs were effectively carrying out multiple critical care procedures. This study suggests conducting a nationwide mapping of ICU resources, prioritizing infrastructure development, optimizing resource allocation, and establishing national protocols.

MFSCNN: APPENDING A MASKED BRANCH TO FAST-SCNN TO IMPROVE ROAD MARKING EXTRACTION ON SPARSE MLS POINT CLOUD-DERIVED IMAGES

Abstract. With the rise of self-driving cars, an increasing number of vehicles are equipped with low-cost light detection and ranging (LiDAR) sensors that could potentially serve as a massive mobile mapping resource, particularly for jobs that require multiple and frequent scanning, such as maintaining dynamic high-definition maps or digital twins. However, low-cost LiDAR sensors produce sparser point clouds during scanning which can make deep learning techniques for the automatic retrieval of features difficult like extracting road markings. In this work, we aim to improve the performance of a convolutional neural network (CNN) model for road marking extraction from sparse mobile LiDAR scanning (MLS) point cloud-derived images. We propose the modification of the Fast-SCNN model structure by adding a 2D convolution branch with masking in the feature fusion step: MFSCNN. To retain speed we only use MFSCNN to boost model training and still utilize Fast-SCNN for inference. Our results indicate potential, with a 4.6% increase in mean f1-score and an 8% decrease in uncertainty for the road marking class after multiple trials. Additionally, this research aims to support and increase research interest in lower-cost LiDARs for mobile mapping.

The action logic of the older adults about health-seeking in South Rural China

BackgroundThe Chinese government has invested significant resources to build many rural healthcare stations. However, in the face of convenient medical paths and accessible medical resources, the utilization rate of health services for older adults in rural areas is surprisingly low. This study explored why health-seeking behavior among older adults in rural China was not active.MethodsData were collected through participatory rural appraisal (PRA) with 108 participants in 12 villages in southern China. Daily schedule and social and resource mapping were employed to outline the range of activities and the routine of the older adults, as well as in-depth interviews to understand the logic of their healthcare choices. Data collected were analyzed by content analysis.ResultsThree themes were generated: (1) perceptions of health status (being healthy or sick): the rural older adults used the ability to handle routine chores as a measure of health status; (2) prioritization of solving symptoms over curing diseases: the older adults preferred the informal self-medication to cope with diseases, as long as there were no symptoms and no pain; (3) ‘unpredictable’ troubles: they tended to favor the ‘optimal’ solution of keeping their lives in order rather than the best medical treatment options.ConclusionThis study showed that the medical practices of the rural elderly were profoundly influenced by their perceptions of health and their life experiences. In the face of diseases, they tended to keep their lives in order, preferring self-treatment practices that address symptoms or selectively following medical advice rather than medical and science-based clinical solutions. In the future, the construction of rural health care should focus on changing the ‘inaccessibility’ of healthcare resources at the subjective level of the rural elderly and develop culturally adaptable health education.

METAMORPHISM OF ALS POINT DATA FOR MULTITUDE APPLICATION

Abstract. Technologically assisted decision-making in urban planning and governance is significant to envisage Sustainable Development Goal (SDG) 11, of developing sustainable cities and communities. In the current millennium, planners and decision-makers require knowledge-rich virtual models for managing the man-made and natural resources in the city. To effectively utilize the technological advancement for sustainable urban development, there is a need for expeditious entail towards accurate urban resource mapping, development of flexible monitoring and information extraction framework, and enticing visualization. Airborne LiDAR Scanning (ALS) is capable of producing very precise 3D geometric data over expansive urban areas in a timely and economically efficient manner. However, it is challenging to derive viable outcomes from the unstructured and voluminous point cloud. This work proposes an intermediary metamorphosed point cloud storage framework to enhance the utility of point clouds for multiple pragmatic applications. The proposed methodological approach transforms the unstructured, massive point cloud into an ontologically stored urban object collection to utilize the large-scale urban point cloud in decision-making. Further, the paper demonstrates the direct applicability of the metamorphosed point cloud storage framework for two specific applications related to sustainable urban development. Experiments carried out using the proposed framework on DALES benchmark dataset show promising results.

Youth Mental Health Crisis Response: A Multiple Methods Study to Map Resources and Identify Facilitators and Barriers to Service Access

Youth Mental Health Crisis Response: A Multiple Methods Study to Map Resources and Identify Facilitators and Barriers to Service Access

Use of the positive deviance approach for healthcare system service improvement: a scoping review protocol

IntroductionHealthcare systems are currently facing challenges in enhancing access and improving the quality of healthcare services around the world, and one of the innovative strategies that have been used to...

A learning automata based edge resource allocation approach for IoT-enabled smart cities

The development of the Internet of Things (IoT) technology is leading to a new era of smart applications such as smart transportation, buildings, and smart homes. Moreover, these applications act as the building blocks of IoT-enabled smart cities. The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing. However, there is a high computation latency due to the presence of a remote cloud server. Edge computing, which brings the computation close to the data source is introduced to overcome this problem. In an IoT-enabled smart city environment, one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint. An efficient resource allocation at the edge is helpful to address this issue. In this paper, an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem. First, we presented a three-layer network architecture for IoT-enabled smart cities. Then, we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem. Learning Automata (LA) is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping. An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

Pansharpening of remote sensing images using dominant pixels

Pansharpening refers to the synthesis of super-resolution multispectral images (i.e., pansharpened images, PIs) designed to overcome the technical constraints of Earth Observation Satellites. A PI is synthesized by fusing the high-resolution chromatic information carried by the multispectral image with the high-resolution spatial information carried by the panchromatic image. This process generates precise and comprehensive data suitable for diverse applications, such as land cover mapping, urban planning, and natural resource management. Various histogram transformation techniques are utilized in pansharpening methods to improve the chromatic fidelity of PIs. However, many existing histogram transformation techniques are susceptible to chromatic distortions. In this paper, a novel pansharpening method named “Pansharpening Using Dominant Pixels” (PDP) is introduced. PDP employs a new method for histogram transformation that preserves chromatic information. Furthermore, PDP is structurally simple, easy to implement, fast, and capable of producing high-quality pansharpened images. Six Remote Sensing images and seventeen pansharpening methods were used in experiments to examine PDP’s success in generating pansharpened images. The experimental results demonstrate that PDP statistically outperforms the comparison methods, synthesizing PIs with high spectral and spatial fidelity.

Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization

SummaryA difficult problem in the service‐oriented computing paradigm is improving task scheduler policy or resource provisioning.In order to increase the performance of cloud applications, this article primarily focuses on tasks for resource mapping policy optimization. With the aim of reducing makespan and execution overhead and increasing the average resource utilization, we suggested an efficient independent task scheduler employing supervised neural networks in this paper. The suggested ANN‐based scheduler uses the status of the cloud environment and incoming tasks as inputs to determine the optimal computing resource for a given assignment as a result that assembles our goal. We proposed a novel algorithm in this paper that uses a hybrid methodology based on a swarm intelligence algorithm (PSO) in combination with a machine learning technique (ANN). PSO is used to prepare the train and test dataset for the neural network. Results clearly state that suggested work achieves significant improvement to considered algorithms in makespan (45%–55%), average VM utilization (15%–20%), and execution overhead(20%–30%).

Groundwater resource exploration and mapping methods: a review

Groundwater, a vital resource for various human activities and ecosystems, necessitates efficient management and sustainable utilisation. Groundwater potential zone mapping plays a pivotal role in identifying areas where groundwater resources are abundant, thereby aiding decision makers in optimal resource allocation. This review paper presents an in-depth analysis of diverse methods employed for groundwater potential zone mapping, offering a comprehensive overview of their strengths, weaknesses and recent advancements. The review covers traditional methods rooted in hydrogeological principles, as well as modern techniques that harness the power of geospatial technologies and machine learning. Furthermore, the paper explores the integration of remote sensing and geographic information systems for spatial data analysis, emphasising their role in enhancing the accuracy of potential zone mapping. In the context of recent advancements, the review sheds light on the emergence of hybrid methods that combine the strengths of multiple approaches, resulting in improved prediction accuracy and robustness. Challenges associated with each method, such as data quality, model complexity and interpretability, are critically examined, providing insights into the potential limitations and avenues for improvement. The review also emphasises the importance of validation and uncertainty assessment, ensuring the reliability of potential zone mapping results. Finally, this review paper serves as a comprehensive guide for researchers, practitioners and policymakers engaged in groundwater resource management. By offering a holistic understanding of the diverse methods available for groundwater potential zone mapping, this paper contributes to informed decision making and the advancement of sustainable groundwater-management practices.

Pukekauri farm – snapshots from a sustainability journey

Pukekauri Farm is a 295 ha mixed livestock hill country property in the western Bay of Plenty. Over the last 20+ years the owners have been on a development journey with a focus on the multiple dimensions of sustainability – economic, environmental, and social. This paper seeks to document that journey, with supporting resource, production, financial and environmental data to quantify progress. Some of the most useful tools have included high quality forage crops, deferred grazing, sheep genetics, accounting software and paddock-scale soil and water resource maps. Key lessons include the value of knowledge networks and modelling tools, the power of a farm plan for optimising land use and management, the importance of both profitability and grants to underwrite development investment, and the emergence of secondary benefits from decisions focused on system-wide improvement. Over 20 years, despite areduction in grazed area of 20%, meat production has increased by 16%, EBITR/ha has increased by 6%, modelled net greenhouse gas emissions have declined by 60% and modelled N leaching has declined by 29%. Overall, the farm is easier to manage and more pleasant to dwell in.

Thematic tactile maps for accessible flood mitigation planning: design and evaluation

ABSTRACT Maps are frequently employed in the natural hazard mitigation planning (NHMP) process for analyze a community’s vulnerability to hazards and illustrating the character of potential hazards. But because the encoded information of these maps relies on visual access, blind or low-vision (B/LV) people who want to contribute to their community’s NHMP efforts are therefore effectively excluded from any stage of the process involving maps. In response, we investigate tactile flood maps as an accessible tool for NHMP. Our study proposes a workflow for creating thematic tactile flood maps using existing resources, methods, and design conventions, and then evaluates those thematic tactile maps to understand user confidence amongst B/LV users. This work contributes a proposed configuration of tactile mapping resources to be used for NHMP and evaluates user confidence while using those resources. Results suggest that B/LV users respond positively to using tactile maps in a high-stakes context such as NHMP, and that tactile maps can expand the number and diversity of people who are able to contribute to NHMP. To maximize contributions, we recommend that future tactile map research invest a greater amount of attention to developing resources for B/LV people to create, edit, and distribute maps themselves.

A53Simulation equipment - What do you have? What do you do with it?

Educators and workforce planners appreciate that clinical skills development and simulation-based learning are key strategic responses to safe and effective service provision and a sustainable workforce. However, some simulation-based experiences require simulators costing tens of thousands of pounds. In order to promote a ‘Once for Scotland’ approach, collaboration is essential to encourage sustainability by enabling sharing of equipment, training materials, processes, procedures and faculty between boards. Although there have been other scoping exercises [1], we believe that this the first to take place at a national level. This scoping exercise aimed to: Establish what equipment is available in the territorial health boards; Clarify the types of skills-based courses being run across NHS Scotland; Connect and encourage collaboration between simulation providers Information was gathered via Teams discussions and questionnaires. The responses were collated into Excel spreadsheets. These have been linked into an application to present the data in a more user-friendly manner online ( Interactive map of simulation resources in Scotland Responses reveal that: All health boards in Scotland have invested in simulation-based learning. There is pioneering work with the establishment of medical and surgical bootcamps as well as national pharmacy simulation. Additional work needs to be carried out to ensure that resources are invested wisely to areas of greatest need and that resources are shared within and between health boards. It is clear where abundant simulation activities are taking place, as well as where there are gaps in equipment and faculty. Distribution of the information gathered will reduce duplication of effort, increase collaboration and encourage the sharing of equipment between health and social care workers across Scotland. It is hoped that this scoping project will support the creation of new relationships between people, not just for their mutual benefit, but for the benefit of the people of Scotland. The results are a first step to providing a detailed inventory of the resources available to help ensure best value for money. We hope to see an increase in the sharing of equipment across Scotland similar to the use of the NES surgical cut suit which was purchased by the Scottish Centre for Simulation and Clinical Human Factors and recently used by the simulation team at NHS Lothian to provide a multi-professional immersive paediatric training session. The plan is to update this database on a regular basis and to widen the results to incorporate all health boards as well as universities. Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable.

Exodus of Lebanese doctors in times of crisis: a qualitative study.

Since 2019, Lebanon is experiencing an unprecedented exodus of doctors, seriously threatening the national health system, which is expected to continue without quick and effective solutions. Therefore, this study aimed to understand the factors that push Lebanese doctors to migrate and the factors that retain others in the country. Additionally, this study aims to propose solutions to preserve an adequate supply of medical care amidst the crisis. Qualitative semi-structured interviews and focus group discussions were conducted using pre-developed guides. Purposive and snowball sampling was adopted to recruit physicians who emigrated and physicians staying in Lebanon. Transcripts of interviews and focus groups were coded using Dedoose software and analyzed through a combination of inductive and deductive approaches. Emigration was found to be the result of numerous interconnected factors. The main drivers for emigration were declining income, career problems, reduced quality of care, unhealthy work environment, and the deteriorated political and socio-economic contexts leading to instability and insecurity. As for the retention factors, they included affective attachment and sense of belonging to the professional environment and the country, followed by recognition and valorization at work. Several recommendations were developed to maintain quality of care delivery, including reforms of the health system, development of focused human resource retention strategies based on resource mapping evidence, negotiations with recruiting institutions to endorse the code ethics ending unethical practices draining countries' human resources, provision of financial incentives to doctors, and the recognition and valorization of physicians. Other rapid interventions were suggested, such as short-term medical missions to mitigate shortages in certain specialties, telemedicine, adaptation of recruitment processes to compensate for resources shortages in certain specialties, and adoption of task-shifting approaches to alleviate the workload on overburdened specialists. The findings of this study shed the light on the different factors influencing migration while framing them in the Lebanese context. These findings and recommendations should inform stakeholders and policy makers about the interventions needed to restore the quality of care. The feasibility and sustainability of most formulated recommendations depend on several factors, with political and socio-economic security and stability being the most crucial ones.

Critical Minerals for a Carbon-Neutral Future

The Earth Mapping Resources Initiative is filling data gaps and fostering more holistic understanding of critical mineral resources in the United States using a novel mineral systems framework.